diff --git a/cmake/package.cmake b/cmake/package.cmake index 8ed2e7d448c..1534655a85d 100644 --- a/cmake/package.cmake +++ b/cmake/package.cmake @@ -90,7 +90,7 @@ install( ) install( - TARGETS mindspore_core + TARGETS mindspore_core mindspore_common DESTINATION ${INSTALL_LIB_DIR} COMPONENT mindspore ) diff --git a/cmake/package_mac.cmake b/cmake/package_mac.cmake index 0c0077ca295..4440c66ce87 100644 --- a/cmake/package_mac.cmake +++ b/cmake/package_mac.cmake @@ -67,7 +67,7 @@ install( ) install( - TARGETS mindspore_core + TARGETS mindspore_core mindspore_common DESTINATION ${INSTALL_LIB_DIR} COMPONENT mindspore ) diff --git a/cmake/package_tar.cmake b/cmake/package_tar.cmake index 189acb2110f..87754fc96d3 100644 --- a/cmake/package_tar.cmake +++ b/cmake/package_tar.cmake @@ -27,7 +27,7 @@ install( ) install( - TARGETS mindspore_core + TARGETS mindspore_core mindspore_common DESTINATION ${INSTALL_LIB_DIR} COMPONENT mindspore ) diff --git a/cmake/package_win.cmake b/cmake/package_win.cmake index f3fa5f0dad4..1a7486fec2e 100644 --- a/cmake/package_win.cmake +++ b/cmake/package_win.cmake @@ -74,7 +74,7 @@ install( ) install( - TARGETS mindspore_core + TARGETS mindspore_core mindspore_common DESTINATION ${INSTALL_LIB_DIR} COMPONENT mindspore ) diff --git a/mindspore/ccsrc/CMakeLists.txt b/mindspore/ccsrc/CMakeLists.txt index db1ed4d6a26..6eb808620a4 100644 --- a/mindspore/ccsrc/CMakeLists.txt +++ b/mindspore/ccsrc/CMakeLists.txt @@ -230,21 +230,21 @@ endif() ## make sub objects set(SUB_COMP - transform/graph_ir transform/express_ir frontend/optimizer frontend/parallel frontend/operator pipeline/jit pipeline/pynative - debug pybind_api utils profiler ps fl distributed + debug pybind_api + profiler ps fl distributed kernel - common common/mem_reuse backend/common/optimizer backend/common/pass backend/common/session backend/common/somas + common/graph_kernel backend/graph_compiler runtime/collective runtime/device @@ -278,6 +278,31 @@ endforeach() set_property(SOURCE ${SUB_OBJECTS_SRC} PROPERTY COMPILE_DEFINITIONS SUBMODULE_ID=mindspore::SubModuleId::SM_ME) add_library(mindspore STATIC ${SUB_OBJECTS_SRC}) +set(COMMON_SUB_COMP + transform/graph_ir + utils + common + ) + +foreach(_comp ${COMMON_SUB_COMP}) + add_subdirectory(${_comp}) + string(REPLACE "/" "_" sub ${_comp}) + if(TARGET _mindspore_${sub}_obj) + list(APPEND COMMON_SUB_OBJECTS_SRC $) + add_dependencies(_mindspore_${sub}_obj proto_input mindspore_core) + if(CMAKE_SYSTEM_NAME MATCHES "Windows") + target_compile_definitions(_mindspore_${sub}_obj PRIVATE COMMON_DLL) + endif() + endif() +endforeach() + +add_library(mindspore_common SHARED ${COMMON_SUB_OBJECTS_SRC}) +target_link_libraries(mindspore_common PRIVATE mindspore_core proto_input mindspore::protobuf) +set_target_properties(mindspore_common PROPERTIES INSTALL_RPATH $ORIGIN) +if(CMAKE_SYSTEM_NAME MATCHES "Windows") + target_link_libraries(mindspore_common PRIVATE mindspore::pybind11_module) +endif() + if(ENABLE_DEBUGGER) # debugger: link grpc if(CMAKE_SYSTEM_NAME MATCHES "Darwin") @@ -402,12 +427,14 @@ set_target_properties(_c_expression PROPERTIES INSTALL_RPATH ${MINDSPORE_RPATH}) if(CMAKE_SYSTEM_NAME MATCHES "Windows") target_link_libraries(mindspore PUBLIC mindspore::pybind11_module) - target_link_libraries(_c_expression PRIVATE -Wl,--whole-archive mindspore -Wl,--no-whole-archive mindspore_core) + target_link_libraries(_c_expression PRIVATE -Wl,--whole-archive mindspore -Wl,--no-whole-archive mindspore_core + mindspore_common) elseif(CMAKE_SYSTEM_NAME MATCHES "Darwin") target_link_libraries(mindspore PUBLIC proto_input mindspore::protobuf mindspore::event mindspore::event_pthreads mindspore::event_openssl mindspore::eigen mindspore::json) target_link_libraries(mindspore PUBLIC mindspore::event_core ps_cache) - target_link_libraries(_c_expression PRIVATE -Wl,-all_load mindspore proto_input -Wl,-noall_load mindspore_core) + target_link_libraries(_c_expression PRIVATE -Wl,-all_load mindspore proto_input -Wl,-noall_load mindspore_core + mindspore_common) target_link_libraries(_c_expression PRIVATE mindspore::pybind11_module) else() if(ENABLE_CPU AND NOT WIN32) @@ -419,7 +446,7 @@ else() endif() endif() target_link_libraries(_c_expression PRIVATE -Wl,--whole-archive mindspore proto_input -Wl,--no-whole-archive - mindspore_core) + mindspore_core mindspore_common) target_link_libraries(_c_expression PRIVATE mindspore::pybind11_module) endif() @@ -477,3 +504,4 @@ if(ENABLE_D) endif() add_subdirectory(cxx_api) +# include(${CMAKE_CURRENT_SOURCE_DIR}/sharedlib.cmake) diff --git a/mindspore/ccsrc/backend/common/optimizer/const_input_to_attr.cc b/mindspore/ccsrc/backend/common/optimizer/const_input_to_attr.cc index bc916c5a59d..e5c675adf28 100644 --- a/mindspore/ccsrc/backend/common/optimizer/const_input_to_attr.cc +++ b/mindspore/ccsrc/backend/common/optimizer/const_input_to_attr.cc @@ -17,7 +17,7 @@ #include #include "utils/anf_utils.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/log_adapter.h" #include "base/core_ops.h" diff --git a/mindspore/ccsrc/backend/common/optimizer/fusion_id_allocator.cc b/mindspore/ccsrc/backend/common/optimizer/fusion_id_allocator.cc index f2ebecefe62..a0bf04779d7 100644 --- a/mindspore/ccsrc/backend/common/optimizer/fusion_id_allocator.cc +++ b/mindspore/ccsrc/backend/common/optimizer/fusion_id_allocator.cc @@ -14,7 +14,7 @@ * limitations under the License. */ #include "backend/common/optimizer/fusion_id_allocator.h" -#include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -35,19 +35,19 @@ bool FusionIdAllocator::HasFusionIdAttr(const AnfNodePtr &node) const { return false; } auto cnode = node->cast(); - return AnfAlgo::HasNodeAttr(kAttrFusionId, cnode); + return common::AnfAlgo::HasNodeAttr(kAttrFusionId, cnode); } int64_t FusionIdAllocator::GetFusionId(const AnfNodePtr &node) { if (HasFusionIdAttr(node)) { - return AnfAlgo::GetNodeAttr(node, kAttrFusionId); + return common::AnfAlgo::GetNodeAttr(node, kAttrFusionId); } return -1; } void FusionIdAllocator::SetFusionId(const AnfNodePtr &node, int64_t id) { ValuePtr fusion_id_v = MakeValue(id); - AnfAlgo::SetNodeAttr(kAttrFusionId, fusion_id_v, node); + common::AnfAlgo::SetNodeAttr(kAttrFusionId, fusion_id_v, node); } } // namespace opt } // namespace mindspore diff --git a/mindspore/ccsrc/backend/common/optimizer/helper.cc b/mindspore/ccsrc/backend/common/optimizer/helper.cc index 51698b16f6d..ecf76b13146 100644 --- a/mindspore/ccsrc/backend/common/optimizer/helper.cc +++ b/mindspore/ccsrc/backend/common/optimizer/helper.cc @@ -22,14 +22,13 @@ #include #include #include "utils/hash_set.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "base/base_ref.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "base/core_ops.h" -#include "plugin/device/ascend/kernel/tbe/tbe_dynaminc_shape_util.h" -#include "frontend/operator/ops.h" #include "utils/ms_utils.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" #include "runtime/device/kernel_info.h" #include "utils/ms_context.h" #include "utils/trace_base.h" @@ -47,8 +46,8 @@ void UpdateDumpFlagAndDebugInfo(const CNodePtr &node, const std::vectorcast(); - if (AnfAlgo::HasNodeAttr(kAttrDump, orig_cnode)) { - AnfAlgo::CopyNodeAttr(kAttrDump, orig_cnode, node); + if (common::AnfAlgo::HasNodeAttr(kAttrDump, orig_cnode)) { + common::AnfAlgo::CopyNodeAttr(kAttrDump, orig_cnode, node); } orig_real_cnodes.push_back(orig_node); } @@ -155,7 +154,7 @@ CNodePtr CheckAnfNodeIfCNodeAndInputSize(const AnfNodePtr &node, size_t input_si void CheckCNodeInputSize(const CNodePtr &cnode, size_t input_tensor_size) { MS_EXCEPTION_IF_NULL(cnode); - auto real_input_tensor_num = AnfAlgo::GetInputTensorNum(cnode); + auto real_input_tensor_num = common::AnfAlgo::GetInputTensorNum(cnode); if (real_input_tensor_num != input_tensor_size) { MS_LOG(EXCEPTION) << "The input tensor size[" << real_input_tensor_num << "] of node [" + cnode->DebugString() + "] is not equal to " << input_tensor_size @@ -227,8 +226,9 @@ void CreateMultipleOutputsOfAnfNode(const FuncGraphPtr &func_graph, const AnfNod idx->set_abstract(abstract_scalar); auto tuple_getitem = func_graph->NewCNode({NewValueNode(prim::kPrimTupleGetItem), node, idx}); MS_EXCEPTION_IF_NULL(tuple_getitem); - AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(type_ptr, i)}, - {AnfAlgo::GetOutputInferShape(node, shape_ptr, i)}, tuple_getitem.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({common::AnfAlgo::GetOutputInferDataType(type_ptr, i)}, + {common::AnfAlgo::GetOutputInferShape(node, shape_ptr, i)}, + tuple_getitem.get()); (*outputs).push_back(tuple_getitem); } } @@ -295,54 +295,12 @@ tensor::TensorPtr CreateTupleTensor(const ValueTuplePtr &value_tuple) { return tensor; } -bool IsNopNode(const AnfNodePtr &node) { - auto context_ptr = MsContext::GetInstance(); - MS_EXCEPTION_IF_NULL(context_ptr); - auto target = GetCNodeTarget(node); - if (target == kCPUDevice) { - return false; - } - if (context_ptr->get_param(MS_CTX_DEVICE_TARGET) != kAscendDevice && - context_ptr->get_param(MS_CTX_DEVICE_TARGET) != kGPUDevice) { - return false; - } - - static mindspore::HashSet nop_nodes = {prim::kPrimReshape->name(), kExpandDimsOpName, - prim::kPrimSqueeze->name(), prim::kPrimFlatten->name(), - kFlattenGradOpName, prim::kPrimReformat->name()}; - if (node == nullptr || !node->isa()) { - return false; - } - CNodePtr cnode = node->cast(); - MS_EXCEPTION_IF_NULL(cnode); - if (cnode->inputs().empty()) { - return false; - } - auto input0 = cnode->input(0); - MS_EXCEPTION_IF_NULL(input0); - if (!input0->isa()) { - return false; - } - bool is_nop_node = false; - if (AnfAlgo::HasNodeAttr(kAttrNopOp, cnode)) { - is_nop_node = AnfAlgo::GetNodeAttr(cnode, kAttrNopOp); - } - if (nop_nodes.find(AnfAlgo::GetCNodeName(cnode)) == nop_nodes.end() && !is_nop_node) { - return false; - } - const size_t kNopNodeInputSize = 2; - if (cnode->size() != kNopNodeInputSize) { - return false; - } - return true; -} - bool IsAllNopNode(const session::KernelGraph *const graph) { MS_EXCEPTION_IF_NULL(graph); auto execution_order = graph->execution_order(); for (auto &cnode : execution_order) { MS_EXCEPTION_IF_NULL(cnode); - if (!IsNopNode(cnode)) { + if (!common::AnfAlgo::IsNopNode(cnode)) { return false; } } @@ -352,7 +310,7 @@ bool IsAllNopNode(const session::KernelGraph *const graph) { bool NeedHideNode(const std::vector &outputs, const AnfNodePtr &node, bool is_dynamic_graph) { MS_EXCEPTION_IF_NULL(node); // if node is not a nop node, keep it in execution order - if (!IsNopNode(node)) { + if (!common::AnfAlgo::IsNopNode(node)) { return false; } // if node is nop node and the graph is dynamic graph, check if the nop node is graph's output. @@ -378,7 +336,7 @@ void HideNopNode(session::KernelGraph *const graph) { for (auto &cnode : execution_order) { MS_EXCEPTION_IF_NULL(cnode); if (NeedHideNode(outputs, cnode, is_dynamic_graph)) { - AnfAlgo::SetNodeAttr(kAttrSkipNopOpAddr, MakeValue(true), cnode); + common::AnfAlgo::SetNodeAttr(kAttrSkipNopOpAddr, MakeValue(true), cnode); } else { new_nodes.push_back(cnode); } @@ -402,7 +360,7 @@ void RemoveNopNode(session::KernelGraph *const graph) { MS_EXCEPTION_IF_NULL(cnode); // ignore nop node itself if (NeedHideNode(outputs, cnode, is_dynamic_graph)) { - AnfAlgo::SetNodeAttr(kAttrSkipNopOpAddr, MakeValue(true), cnode); + common::AnfAlgo::SetNodeAttr(kAttrSkipNopOpAddr, MakeValue(true), cnode); continue; } // Replace the input which is nop node @@ -413,7 +371,7 @@ void RemoveNopNode(session::KernelGraph *const graph) { auto input = cnode->input(i); MS_EXCEPTION_IF_NULL(input); auto cinput = input->cast(); - if (cinput == nullptr || !IsNopNode(cinput)) { + if (cinput == nullptr || !common::AnfAlgo::IsNopNode(cinput)) { new_inputs.push_back(input); continue; } @@ -454,7 +412,7 @@ std::shared_ptr>> GetRealNodeUsedList(con } auto output_info_list = iter->second; for (const auto &output_info : output_info_list) { - auto cnode_name = AnfAlgo::GetCNodeName(output_info.first); + auto cnode_name = common::AnfAlgo::GetCNodeName(output_info.first); if ((cnode_name == prim::kPrimDepend->name() && output_info.second == kDependAttachNodeIndex) || (cnode_name == prim::kPrimUpdateState->name())) { continue; @@ -477,20 +435,20 @@ std::shared_ptr>> GetRealNodeUsedListByOu } auto output_info_list = iter->second; for (const auto &output_info : output_info_list) { - auto cnode_name = AnfAlgo::GetCNodeName(output_info.first); + auto cnode_name = common::AnfAlgo::GetCNodeName(output_info.first); if ((cnode_name == prim::kPrimDepend->name() && output_info.second == kDependAttachNodeIndex) || (cnode_name == prim::kPrimUpdateState->name())) { continue; } size_t used_output_index; if (cnode_name == prim::kPrimTupleGetItem->name()) { - used_output_index = AnfAlgo::GetTupleGetItemOutIndex(utils::cast(output_info.first)); - } else if (AnfAlgo::GetCNodeName(node) == prim::kPrimTupleGetItem->name()) { + used_output_index = common::AnfAlgo::GetTupleGetItemOutIndex(utils::cast(output_info.first)); + } else if (common::AnfAlgo::GetCNodeName(node) == prim::kPrimTupleGetItem->name()) { used_output_index = output_index; } else { - auto kernel_with_index = AnfAlgo::GetPrevNodeOutput(output_info.first, IntToSize(output_info.second - 1)); + auto kernel_with_index = common::AnfAlgo::GetPrevNodeOutput(output_info.first, IntToSize(output_info.second - 1)); if (kernel_with_index.first.get() != node.get()) { - MS_LOG(EXCEPTION) << "Get used node failed for op[" << AnfAlgo::GetCNodeName(node) << "]"; + MS_LOG(EXCEPTION) << "Get used node failed for op[" << common::AnfAlgo::GetCNodeName(node) << "]"; } used_output_index = kernel_with_index.second; } @@ -519,7 +477,7 @@ bool IsNotRealUsedByOthers(const FuncGraphPtr &graph, const AnfNodePtr &node) { } for (const auto &output : *output_node_list) { auto out_node = output.first; - auto name = AnfAlgo::GetCNodeName(out_node); + auto name = common::AnfAlgo::GetCNodeName(out_node); if (name == prim::kPrimDepend->name() || name == prim::kPrimMakeTuple->name() || name == prim::kPrimTupleGetItem->name() || name == prim::kPrimLoad->name()) { auto result = IsNotRealUsedByOthers(graph, out_node); @@ -731,7 +689,7 @@ AbstractBasePtrList RectifyAbstractFromRegAttr(const PrimitivePtr &primitive, if (!opt::ConstInputToAttrInfoRegistry::Instance().GetRegisterByOpName(primitive->name(), ®)) { return input_abstract; } - if (AnfAlgo::HasDynamicShapeFlag(primitive)) { + if (common::AnfAlgo::HasDynamicShapeFlag(primitive)) { return input_abstract; } auto ms_context = MsContext::GetInstance(); @@ -895,12 +853,12 @@ bool GetBoolAttr(const AnfNodePtr &node, const std::string &attr_name) { } auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - return AnfAlgo::HasNodeAttr(attr_name, cnode) && AnfAlgo::GetNodeAttr(node, attr_name); + return common::AnfAlgo::HasNodeAttr(attr_name, cnode) && common::AnfAlgo::GetNodeAttr(node, attr_name); } bool CheckSupportDataType(const AnfNodePtr &node, const std::set &supported_data_type_set) { MS_EXCEPTION_IF_NULL(node); - TypeId data_type = AnfAlgo::GetOutputInferDataType(node, 0); + TypeId data_type = common::AnfAlgo::GetOutputInferDataType(node, 0); if (supported_data_type_set.find(data_type) != supported_data_type_set.end()) { return true; } @@ -923,7 +881,7 @@ ValueNodePtr MakeValueNode(const ValueNodePtr &value_node) { kernel_build_info_builder->SetOutputsFormat(std::vector{kOpFormat_DEFAULT}); // set value node initial device data type = infer data type std::vector types; - size_t output_num = AnfAlgo::GetOutputTensorNum(value_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(value_node); for (size_t index = 0; index < output_num; ++index) { types.push_back(kTypeUnknown); } @@ -942,8 +900,8 @@ void TransferDependOrUpdateState(const CNodePtr &old_node, const FuncGraphPtr &g for (const auto &node_index : node_users) { AnfNodePtr output = node_index.first; MS_EXCEPTION_IF_NULL(output); - if (AnfAlgo::CheckPrimitiveType(output, prim::kPrimDepend) || - AnfAlgo::CheckPrimitiveType(output, prim::kPrimUpdateState)) { + if (common::AnfAlgo::CheckPrimitiveType(output, prim::kPrimDepend) || + common::AnfAlgo::CheckPrimitiveType(output, prim::kPrimUpdateState)) { auto depend = output->cast(); MS_EXCEPTION_IF_NULL(depend); manager->SetEdge(depend, node_index.second, new_node); @@ -987,16 +945,16 @@ kernel::KernelBuildInfoPtr GenerateKernelBuildInfo(const std::vector for (size_t idx = 0; idx < node_list.size(); ++idx) { auto cnode = utils::cast(node_list[idx]); MS_EXCEPTION_IF_NULL(cnode); - size_t input_num = AnfAlgo::GetInputTensorNum(cnode); + size_t input_num = common::AnfAlgo::GetInputTensorNum(cnode); for (size_t input_index = 0; input_index < input_num; ++input_index) { (void)inputs_device_format.emplace_back(kOpFormat_DEFAULT); - (void)inputs_device_type.emplace_back(AnfAlgo::GetPrevNodeOutputInferDataType(cnode, input_index)); + (void)inputs_device_type.emplace_back(common::AnfAlgo::GetPrevNodeOutputInferDataType(cnode, input_index)); } - size_t output_num = AnfAlgo::GetOutputTensorNum(cnode); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(cnode); for (size_t output_index = 0; output_index < output_num; ++output_index) { (void)outputs_device_format.emplace_back(kOpFormat_DEFAULT); - (void)outputs_device_type.emplace_back(AnfAlgo::GetOutputInferDataType(cnode, output_index)); - (void)outputs_shape.emplace_back(AnfAlgo::GetOutputInferShape(cnode, output_index)); + (void)outputs_device_type.emplace_back(common::AnfAlgo::GetOutputInferDataType(cnode, output_index)); + (void)outputs_shape.emplace_back(common::AnfAlgo::GetOutputInferShape(cnode, output_index)); } } builder.SetInputsFormat(inputs_device_format); @@ -1010,14 +968,14 @@ std::vector GetNodeOutputUsedNum(const session::KernelGraph &kernel_gra MS_EXCEPTION_IF_NULL(node); auto manager = kernel_graph.manager(); MS_EXCEPTION_IF_NULL(manager); - auto output_num = AnfAlgo::GetOutputTensorNum(node); + auto output_num = common::AnfAlgo::GetOutputTensorNum(node); std::vector output_used_num(output_num, 0); if (output_num == 1) { output_used_num[0] = SizeToLong(manager->node_users()[node].size()); } else { for (auto out_getitem : manager->node_users()[node]) { MS_EXCEPTION_IF_NULL(out_getitem.first); - if (!AnfAlgo::CheckPrimitiveType(out_getitem.first, prim::kPrimTupleGetItem)) { + if (!common::AnfAlgo::CheckPrimitiveType(out_getitem.first, prim::kPrimTupleGetItem)) { continue; } auto out_getitem_ptr = out_getitem.first->cast(); diff --git a/mindspore/ccsrc/backend/common/optimizer/helper.h b/mindspore/ccsrc/backend/common/optimizer/helper.h index 5058af6f9cd..4662b83bfb7 100644 --- a/mindspore/ccsrc/backend/common/optimizer/helper.h +++ b/mindspore/ccsrc/backend/common/optimizer/helper.h @@ -174,8 +174,6 @@ tensor::TensorPtr CreateTupleTensor(const ValueTuplePtr &value_tuple); bool IsAllNopNode(const session::KernelGraph *const graph); -bool IsNopNode(const AnfNodePtr &node); - void HideNopNode(session::KernelGraph *const graph); void RemoveNopNode(session::KernelGraph *const graph); diff --git a/mindspore/ccsrc/backend/common/optimizer/node_pass.cc b/mindspore/ccsrc/backend/common/optimizer/node_pass.cc index 6663935c05d..5b6b94e45bf 100644 --- a/mindspore/ccsrc/backend/common/optimizer/node_pass.cc +++ b/mindspore/ccsrc/backend/common/optimizer/node_pass.cc @@ -22,7 +22,8 @@ #include "ir/manager.h" #include "utils/hash_map.h" #include "utils/hash_set.h" -#include "backend/common/session/anf_runtime_algorithm.h" +#include "backend/common/session/kernel_graph.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -34,7 +35,7 @@ void AddOutputAndCallerToMap(const CNodePtr &cnode, mindspore::HashMapinputs(); - if (AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimSwitch)) { + if (common::AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimSwitch)) { auto partial_node = dyn_cast(inputs.at(kSwitchBranchIndex)); MS_EXCEPTION_IF_NULL(partial_node); const auto &partial_inputs = partial_node->inputs(); @@ -44,7 +45,7 @@ void AddOutputAndCallerToMap(const CNodePtr &cnode, mindspore::HashMap(partial_inputs.at(kPartialArgsIndex)); MS_EXCEPTION_IF_NULL(switch_subgraph); (*out_caller_map)[switch_subgraph->output()] = cnode; - } else if (AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimCall)) { + } else if (common::AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimCall)) { auto call_subgraph = GetValueNode(inputs.at(kCallArgsIndex)); MS_EXCEPTION_IF_NULL(call_subgraph); (*out_caller_map)[call_subgraph->output()] = cnode; @@ -85,7 +86,7 @@ bool NodePass::Run(const FuncGraphPtr &func_graph) { auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); auto end_label = kernel_graph->get_end_goto(); - if (cnode == end_label && AnfAlgo::GetCNodeName(cnode) == kLabelSwitchOpName) { + if (cnode == end_label && common::AnfAlgo::GetCNodeName(cnode) == kLabelSwitchOpName) { kernel_graph->set_end_goto(new_node->cast()); } } @@ -100,7 +101,7 @@ bool NodePass::Run(const FuncGraphPtr &func_graph) { (void)todo.emplace_back(const_func_graph->output(), const_func_graph); } } else if (new_node && new_node->isa()) { - if (AnfAlgo::IsGraphKernel(new_node)) { + if (common::AnfAlgo::IsGraphKernel(new_node)) { (void)todo.emplace_back(new_node, func_graph); } auto cnode = new_node->cast(); diff --git a/mindspore/ccsrc/backend/common/optimizer/optimizer.cc b/mindspore/ccsrc/backend/common/optimizer/optimizer.cc index a760d899a70..d10460de259 100644 --- a/mindspore/ccsrc/backend/common/optimizer/optimizer.cc +++ b/mindspore/ccsrc/backend/common/optimizer/optimizer.cc @@ -22,6 +22,7 @@ #include "backend/common/optimizer/pass_manager.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/manager.h" namespace mindspore { diff --git a/mindspore/ccsrc/backend/common/optimizer/pass_manager.cc b/mindspore/ccsrc/backend/common/optimizer/pass_manager.cc index a9654098cb0..e9bc34f85c0 100644 --- a/mindspore/ccsrc/backend/common/optimizer/pass_manager.cc +++ b/mindspore/ccsrc/backend/common/optimizer/pass_manager.cc @@ -19,11 +19,10 @@ #include #include #include "ir/anf.h" -#include "ir/func_graph.h" #include "ir/manager.h" #include "utils/ms_context.h" #include "debug/anf_ir_dump.h" -#include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -50,11 +49,11 @@ TypeId CacheManager::GetOutputType(const AnfNodePtr &node, size_t index) { } return kTypeUnknown; } - auto output_nums = AnfAlgo::GetOutputTensorNum(node); + auto output_nums = common::AnfAlgo::GetOutputTensorNum(node); std::map index_to_types; TypeId result = kTypeUnknown; for (size_t i = 0; i < output_nums; i++) { - auto output_type = AnfAlgo::GetOutputInferDataType(node, i); + auto output_type = common::AnfAlgo::GetOutputInferDataType(node, i); (void)index_to_types.emplace(i, output_type); if (index == i) { result = output_type; @@ -75,11 +74,11 @@ std::vector CacheManager::GetOutputShape(const AnfNodePtr &node, size_t } return {}; } - auto output_nums = AnfAlgo::GetOutputTensorNum(node); + auto output_nums = common::AnfAlgo::GetOutputTensorNum(node); std::map> index_to_shapes; std::vector result = {}; for (size_t i = 0; i < output_nums; i++) { - auto output_shape = AnfAlgo::GetOutputInferShape(node, i); + auto output_shape = common::AnfAlgo::GetOutputInferShape(node, i); (void)index_to_shapes.emplace(i, output_shape); if (index == i) { result = output_shape; diff --git a/mindspore/ccsrc/backend/common/pass/add_akg_kernel_attrs.cc b/mindspore/ccsrc/backend/common/pass/add_akg_kernel_attrs.cc index ebe47867906..49b008a3825 100644 --- a/mindspore/ccsrc/backend/common/pass/add_akg_kernel_attrs.cc +++ b/mindspore/ccsrc/backend/common/pass/add_akg_kernel_attrs.cc @@ -20,6 +20,7 @@ #include #include "base/core_ops.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -28,7 +29,7 @@ void ClonePrimitive(const AnfNodePtr &node) { // Several CNode may share a primitive pointer, so we clone the primitive before setting attr. auto cnode = node->cast(); if (cnode == nullptr) return; - auto prim_node = NewValueNode(AnfAlgo::GetCNodePrimitive(cnode)->Clone()); + auto prim_node = NewValueNode(common::AnfAlgo::GetCNodePrimitive(cnode)->Clone()); cnode->set_input(kAnfPrimitiveIndex, prim_node); } } // namespace @@ -38,20 +39,20 @@ void ProcessCast(const AnfNodePtr &node) { std::vector input_names = {"x", kAttrDstType}; std::vector output_names = {"output"}; ClonePrimitive(node); - AnfAlgo::SetNodeAttr(kAttrInputNames, MakeValue(input_names), node); - AnfAlgo::SetNodeAttr(kAttrOutputNames, MakeValue(output_names), node); + common::AnfAlgo::SetNodeAttr(kAttrInputNames, MakeValue(input_names), node); + common::AnfAlgo::SetNodeAttr(kAttrOutputNames, MakeValue(output_names), node); TypeId output_type = AnfAlgo::GetOutputDeviceDataType(node, 0); - AnfAlgo::SetNodeAttr(kAttrDstType, TypeIdToType(output_type), node); + common::AnfAlgo::SetNodeAttr(kAttrDstType, TypeIdToType(output_type), node); } void ProcessMatMul(const AnfNodePtr &node) { ClonePrimitive(node); TypeId output_type = AnfAlgo::GetOutputDeviceDataType(node, 0); - AnfAlgo::SetNodeAttr(kAttrDstType, TypeIdToType(output_type), node); + common::AnfAlgo::SetNodeAttr(kAttrDstType, TypeIdToType(output_type), node); auto left_format = AnfAlgo::GetInputFormat(node, 0); auto right_format = AnfAlgo::GetInputFormat(node, 1); - AnfAlgo::SetNodeAttr("left_format", MakeValue(left_format), node); - AnfAlgo::SetNodeAttr("right_format", MakeValue(right_format), node); + common::AnfAlgo::SetNodeAttr("left_format", MakeValue(left_format), node); + common::AnfAlgo::SetNodeAttr("right_format", MakeValue(right_format), node); } const AnfNodePtr AddAkgKernelAttrs::Process(const FuncGraphPtr &func_graph, const AnfNodePtr &node, diff --git a/mindspore/ccsrc/backend/common/pass/add_dynamic_shape_attr.cc b/mindspore/ccsrc/backend/common/pass/add_dynamic_shape_attr.cc index 8abf9c03c9f..dda4929ad72 100644 --- a/mindspore/ccsrc/backend/common/pass/add_dynamic_shape_attr.cc +++ b/mindspore/ccsrc/backend/common/pass/add_dynamic_shape_attr.cc @@ -16,9 +16,8 @@ #include "backend/common/pass/add_dynamic_shape_attr.h" #include "ir/anf.h" -#include "utils/convert_utils.h" #include "backend/common/optimizer/optimizer.h" -#include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -26,7 +25,7 @@ const AnfNodePtr AddDynamicShapeAttr::Process(const FuncGraphPtr &func_graph, co const EquivPtr &) const { MS_EXCEPTION_IF_NULL(func_graph); MS_EXCEPTION_IF_NULL(node); - if (AnfAlgo::IsDynamicShape(node)) { + if (common::AnfAlgo::IsDynamicShape(node)) { MS_LOG(DEBUG) << "Set Dynamic Shape Attr to Node:" << node->fullname_with_scope(); auto kernel_graph = func_graph->cast(); MS_EXCEPTION_IF_NULL(kernel_graph); diff --git a/mindspore/ccsrc/backend/common/pass/add_dynamic_shape_attr.h b/mindspore/ccsrc/backend/common/pass/add_dynamic_shape_attr.h index 60b35d34141..9930209fb55 100644 --- a/mindspore/ccsrc/backend/common/pass/add_dynamic_shape_attr.h +++ b/mindspore/ccsrc/backend/common/pass/add_dynamic_shape_attr.h @@ -18,7 +18,7 @@ #define MINDSPORE_ADD_DYNAMIC_SHAPE_ATTR_H #include #include "ir/anf.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" #include "backend/common/optimizer/optimizer.h" namespace mindspore { namespace opt { diff --git a/mindspore/ccsrc/backend/common/pass/add_training_attr.cc b/mindspore/ccsrc/backend/common/pass/add_training_attr.cc index 5d06d3443d3..5eb59105868 100644 --- a/mindspore/ccsrc/backend/common/pass/add_training_attr.cc +++ b/mindspore/ccsrc/backend/common/pass/add_training_attr.cc @@ -24,9 +24,7 @@ #include "utils/hash_set.h" #include "ir/graph_utils.h" #include "backend/common/optimizer/helper.h" -#include "backend/common/session/anf_runtime_algorithm.h" -#include "backend/common/session/kernel_graph.h" -#include "kernel/common_utils.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -38,12 +36,12 @@ bool CheckOP(const FuncGraphManagerPtr &manager, const AnfNodePtr &cnode, const for (const auto &node_index : manager->node_users()[cnode]) { auto output = node_index.first; MS_EXCEPTION_IF_NULL(output); - if (AnfAlgo::CheckPrimitiveType(output, prim::kPrimTupleGetItem)) { + if (common::AnfAlgo::CheckPrimitiveType(output, prim::kPrimTupleGetItem)) { if (CheckOP(manager, output, set)) { return true; } } else if (output->isa()) { - auto name = AnfAlgo::GetCNodeName(output); + auto name = common::AnfAlgo::GetCNodeName(output); if (set.find(name) != set.end()) { return true; } @@ -59,7 +57,7 @@ void AddAttrTraining(const FuncGraphPtr &func_graph, const CNodePtr &cnode) { if (manager->node_users().find(cnode) == manager->node_users().end()) { return; } - auto set = MarkOp[AnfAlgo::GetCNodeName(cnode)]; + auto set = MarkOp[common::AnfAlgo::GetCNodeName(cnode)]; if (CheckOP(manager, cnode, set)) { cnode->AddAttr(kAttrIsTraining, MakeValue(true)); } else { @@ -70,14 +68,14 @@ void AddAttrTraining(const FuncGraphPtr &func_graph, const CNodePtr &cnode) { const AnfNodePtr AddTrainingAttr::Process(const FuncGraphPtr &func_graph, const AnfNodePtr &node, const EquivPtr &) const { - if (node == nullptr || func_graph == nullptr || AnfAlgo::CheckPrimitiveType(node, prim::kPrimTupleGetItem) || - AnfAlgo::CheckPrimitiveType(node, prim::kPrimMakeTuple)) { + if (node == nullptr || func_graph == nullptr || common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimTupleGetItem) || + common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimMakeTuple)) { return nullptr; } if (!node->isa()) { return nullptr; } - auto name = AnfAlgo::GetCNodeName(node); + auto name = common::AnfAlgo::GetCNodeName(node); auto iter = MarkOp.find(name); if (iter == MarkOp.end()) { return nullptr; diff --git a/mindspore/ccsrc/backend/common/pass/add_training_attr.h b/mindspore/ccsrc/backend/common/pass/add_training_attr.h index d54f99a4df7..fb60b6f8104 100644 --- a/mindspore/ccsrc/backend/common/pass/add_training_attr.h +++ b/mindspore/ccsrc/backend/common/pass/add_training_attr.h @@ -19,7 +19,7 @@ #include #include "ir/anf.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" #include "backend/common/optimizer/optimizer.h" namespace mindspore { diff --git a/mindspore/ccsrc/backend/common/pass/adjust_depend_for_parallel_optimizer_recompute_all_gather.cc b/mindspore/ccsrc/backend/common/pass/adjust_depend_for_parallel_optimizer_recompute_all_gather.cc index 4542ed2b6e0..52061c6eb6c 100644 --- a/mindspore/ccsrc/backend/common/pass/adjust_depend_for_parallel_optimizer_recompute_all_gather.cc +++ b/mindspore/ccsrc/backend/common/pass/adjust_depend_for_parallel_optimizer_recompute_all_gather.cc @@ -16,7 +16,7 @@ #include "backend/common/pass/adjust_depend_for_parallel_optimizer_recompute_all_gather.h" #include -#include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -35,11 +35,12 @@ bool AdjustDependForParallelOptimizerRecomputeAllGather::Run(const FuncGraphPtr continue; } auto cnode = node->cast(); - if (!AnfAlgo::IsAllgather(cnode) || !AnfAlgo::IsFusion(cnode) || !AnfAlgo::IsFromParallelOptimizer(cnode)) { + if (!common::AnfAlgo::IsAllgather(cnode) || !common::AnfAlgo::IsFusion(cnode) || + !common::AnfAlgo::IsFromParallelOptimizer(cnode)) { continue; } - if (AnfAlgo::IsRecompute(cnode)) { - int64_t fusion_id = AnfAlgo::GetNodeAttr(cnode, kAttrFusion); + if (common::AnfAlgo::IsRecompute(cnode)) { + int64_t fusion_id = common::AnfAlgo::GetNodeAttr(cnode, kAttrFusion); if (std::find(parallel_optimizer_recompute_allgather_fusion_ids.begin(), parallel_optimizer_recompute_allgather_fusion_ids.end(), fusion_id) == parallel_optimizer_recompute_allgather_fusion_ids.end()) { @@ -52,10 +53,10 @@ bool AdjustDependForParallelOptimizerRecomputeAllGather::Run(const FuncGraphPtr parallel_optimizer_recompute_allgathers.push_back(node); } } else { - int64_t unrecompute_fusion_id = AnfAlgo::GetNodeAttr(cnode, kAttrFusion); + int64_t unrecompute_fusion_id = common::AnfAlgo::GetNodeAttr(cnode, kAttrFusion); unrecompute_max_fusion_id = std::max(unrecompute_fusion_id, unrecompute_max_fusion_id); - bool would_be_recomputed = - AnfAlgo::HasNodeAttr(kAttrRecompute, cnode) && AnfAlgo::GetNodeAttr(cnode, kAttrRecompute); + bool would_be_recomputed = common::AnfAlgo::HasNodeAttr(kAttrRecompute, cnode) && + common::AnfAlgo::GetNodeAttr(cnode, kAttrRecompute); auto [iter, inserted] = forward_allgather_recompute_value_in_fusion_group.emplace(unrecompute_fusion_id, would_be_recomputed); if (!inserted && iter->second != would_be_recomputed) { @@ -79,14 +80,14 @@ void AdjustDependForParallelOptimizerRecomputeAllGather::IncreaseAllgatherFusion if (recompute_min_fusion_id <= unrecompute_max_fusion_id) { MS_LOG(WARNING) << "Increase the duplicated allgather fusion id"; for (auto &adjust_node : parallel_optimizer_recompute_first_fusion_allgathers) { - int64_t current_fusion_id = AnfAlgo::GetNodeAttr(adjust_node, kAttrFusion); + int64_t current_fusion_id = common::AnfAlgo::GetNodeAttr(adjust_node, kAttrFusion); int64_t destination_fusion_id = current_fusion_id + unrecompute_max_fusion_id - recompute_min_fusion_id + 2; - AnfAlgo::SetNodeAttr(kAttrFusion, MakeValue(destination_fusion_id), adjust_node); + common::AnfAlgo::SetNodeAttr(kAttrFusion, MakeValue(destination_fusion_id), adjust_node); } for (auto &adjust_node : parallel_optimizer_recompute_allgathers) { - int64_t current_fusion_id = AnfAlgo::GetNodeAttr(adjust_node, kAttrFusion); + int64_t current_fusion_id = common::AnfAlgo::GetNodeAttr(adjust_node, kAttrFusion); int64_t destination_fusion_id = current_fusion_id + unrecompute_max_fusion_id - recompute_min_fusion_id + 2; - AnfAlgo::SetNodeAttr(kAttrFusion, MakeValue(destination_fusion_id), adjust_node); + common::AnfAlgo::SetNodeAttr(kAttrFusion, MakeValue(destination_fusion_id), adjust_node); } } } @@ -97,7 +98,7 @@ bool AdjustDependForParallelOptimizerRecomputeAllGather::AdjustAllgatherDepend( bool changed = false; for (auto &node : parallel_optimizer_recompute_allgathers) { auto cnode = node->cast(); - auto depend_node = AnfAlgo::GetInputNode(cnode, 0); + auto depend_node = common::AnfAlgo::GetInputNode(cnode, 0); if (IsPrimitiveCNode(depend_node, prim::kPrimDepend)) { auto depend_cnode = depend_node->cast(); AnfNodeIndexSet allgather_node_set = manager->node_users()[cnode]; @@ -108,17 +109,17 @@ bool AdjustDependForParallelOptimizerRecomputeAllGather::AdjustAllgatherDepend( continue; } std::vector inputs = {NewValueNode(std::make_shared(prim::kPrimDepend->name())), - allgather_next_node, AnfAlgo::GetInputNode(depend_cnode, 1)}; + allgather_next_node, common::AnfAlgo::GetInputNode(depend_cnode, 1)}; auto new_depend = graph->NewCNode(inputs); new_depend->set_abstract(depend_node->abstract()); - manager->SetEdge(node, 1, AnfAlgo::GetInputNode(depend_cnode, 0)); + manager->SetEdge(node, 1, common::AnfAlgo::GetInputNode(depend_cnode, 0)); (void)manager->Replace(allgather_next_node, new_depend); changed = true; } } else if (IsPrimitiveCNode(depend_node, prim::kPrimCast) && - IsPrimitiveCNode(AnfAlgo::GetInputNode(depend_node->cast(), 0), prim::kPrimDepend)) { + IsPrimitiveCNode(common::AnfAlgo::GetInputNode(depend_node->cast(), 0), prim::kPrimDepend)) { auto cast_cnode = depend_node->cast(); - auto cast_depend_node = AnfAlgo::GetInputNode(cast_cnode, 0); + auto cast_depend_node = common::AnfAlgo::GetInputNode(cast_cnode, 0); auto cast_depend_cnode = cast_depend_node->cast(); AnfNodeIndexSet allgather_node_set = manager->node_users()[cnode]; for (auto &node_pair : allgather_node_set) { @@ -128,10 +129,10 @@ bool AdjustDependForParallelOptimizerRecomputeAllGather::AdjustAllgatherDepend( continue; } std::vector inputs = {NewValueNode(std::make_shared(prim::kPrimDepend->name())), - allgather_next_node, AnfAlgo::GetInputNode(cast_depend_cnode, 1)}; + allgather_next_node, common::AnfAlgo::GetInputNode(cast_depend_cnode, 1)}; auto new_depend = graph->NewCNode(inputs); new_depend->set_abstract(cast_depend_node->abstract()); - manager->SetEdge(depend_node, 1, AnfAlgo::GetInputNode(cast_depend_cnode, 0)); + manager->SetEdge(depend_node, 1, common::AnfAlgo::GetInputNode(cast_depend_cnode, 0)); (void)manager->Replace(allgather_next_node, new_depend); changed = true; } diff --git a/mindspore/ccsrc/backend/common/pass/common_subexpression_elimination.cc b/mindspore/ccsrc/backend/common/pass/common_subexpression_elimination.cc index 3c9693ecaf8..f114ef84b77 100644 --- a/mindspore/ccsrc/backend/common/pass/common_subexpression_elimination.cc +++ b/mindspore/ccsrc/backend/common/pass/common_subexpression_elimination.cc @@ -16,10 +16,10 @@ #include "backend/common/pass/common_subexpression_elimination.h" #include -#include "backend/common/session/anf_runtime_algorithm.h" #include "runtime/device/kernel_info.h" #include "utils/flags.h" #include "utils/ms_context.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -28,15 +28,15 @@ bool HasSideEffectAttr(const AnfNodePtr &node) { MS_EXCEPTION_IF_NULL(node); auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - if (!AnfAlgo::HasNodeAttr(GRAPH_FLAG_SIDE_EFFECT, cnode)) { + if (!common::AnfAlgo::HasNodeAttr(GRAPH_FLAG_SIDE_EFFECT, cnode)) { return false; } - return AnfAlgo::GetNodeAttr(cnode, GRAPH_FLAG_SIDE_EFFECT); + return common::AnfAlgo::GetNodeAttr(cnode, GRAPH_FLAG_SIDE_EFFECT); } bool CheckIgnoreCase(const AnfNodePtr &node) { MS_EXCEPTION_IF_NULL(node); - if (AnfAlgo::GetCNodeName(node) != kTransDataOpName) { + if (common::AnfAlgo::GetCNodeName(node) != kTransDataOpName) { return false; } auto cnode = node->cast(); @@ -44,7 +44,7 @@ bool CheckIgnoreCase(const AnfNodePtr &node) { bool need_ignore = true; auto input_size = cnode->inputs().size() - 1; for (size_t k = 0; k < input_size; ++k) { - auto input = AnfAlgo::VisitKernelWithReturnType(AnfAlgo::GetInputNode(cnode, k), 0).first; + auto input = common::AnfAlgo::VisitKernelWithReturnType(common::AnfAlgo::GetInputNode(cnode, k), 0).first; if (input != nullptr && input->isa()) { need_ignore = false; break; @@ -58,7 +58,7 @@ bool BackendCSE::CheckEqualKernelBuildInfo(const AnfNodePtr &main, const AnfNode MS_EXCEPTION_IF_NULL(main); MS_EXCEPTION_IF_NULL(node); if (main->isa()) { - auto main_name = AnfAlgo::GetCNodeName(main); + auto main_name = common::AnfAlgo::GetCNodeName(main); if (main_name == prim::kPrimTensorMove->name() || main_name == prim::kPrimMemCpyAsync->name()) { return false; } diff --git a/mindspore/ccsrc/backend/common/pass/communication_op_fusion.cc b/mindspore/ccsrc/backend/common/pass/communication_op_fusion.cc index 4cd1484e5ec..8b641efc5d9 100644 --- a/mindspore/ccsrc/backend/common/pass/communication_op_fusion.cc +++ b/mindspore/ccsrc/backend/common/pass/communication_op_fusion.cc @@ -24,9 +24,10 @@ #include "base/core_ops.h" #include "runtime/device/kernel_info.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "kernel/kernel_build_info.h" #include "backend/common/optimizer/helper.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" namespace mindspore { namespace opt { @@ -49,29 +50,30 @@ kernel::KernelBuildInfoPtr GenerateKernelBuildInfo(const CommunicationOpInfo &co for (size_t idx = start_index; idx <= end_index; ++idx) { auto cnode = communication_op_info.communication_op_nodes[idx]; int64_t rank_size = 1; - if (AnfAlgo::HasNodeAttr(kAttrRankSize, cnode) && AnfAlgo::GetCNodeName(cnode) == kAllGatherOpName) { - rank_size = AnfAlgo::GetNodeAttr(cnode, kAttrRankSize); + if (common::AnfAlgo::HasNodeAttr(kAttrRankSize, cnode) && + common::AnfAlgo::GetCNodeName(cnode) == kAllGatherOpName) { + rank_size = common::AnfAlgo::GetNodeAttr(cnode, kAttrRankSize); } size_t rank_size_t = LongToSize(rank_size); if (rank_size_t == 0) { MS_LOG(EXCEPTION) << "Rank size should not be zero."; } MS_EXCEPTION_IF_NULL(cnode); - size_t input_num = AnfAlgo::GetInputTensorNum(cnode); + size_t input_num = common::AnfAlgo::GetInputTensorNum(cnode); for (size_t input_index = 0; input_index < input_num; ++input_index) { inputs_device_format.push_back(AnfAlgo::GetInputFormat(cnode, input_index)); inputs_device_type.push_back(AnfAlgo::GetInputDeviceDataType(cnode, input_index)); } for (size_t rank_index = 0; rank_index < rank_size_t; ++rank_index) { - size_t output_num = AnfAlgo::GetOutputTensorNum(cnode); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(cnode); for (size_t output_index = 0; output_index < output_num; ++output_index) { outputs_device_format.push_back(AnfAlgo::GetOutputFormat(cnode, output_index)); outputs_device_type.push_back(AnfAlgo::GetOutputDeviceDataType(cnode, output_index)); - std::vector shape = AnfAlgo::GetOutputInferShape(cnode, output_index); + std::vector shape = common::AnfAlgo::GetOutputInferShape(cnode, output_index); if (!shape.empty()) { shape[0] /= rank_size_t; } - outputs_shape.push_back(AnfAlgo::GetOutputInferShape(cnode, output_index)); + outputs_shape.push_back(common::AnfAlgo::GetOutputInferShape(cnode, output_index)); } } builder.SetFusionType(AnfAlgo::GetFusionType(cnode)); @@ -86,7 +88,7 @@ kernel::KernelBuildInfoPtr GenerateKernelBuildInfo(const CommunicationOpInfo &co } std::string GetFusionGroupKey(const AnfNodePtr &node) { - auto primitive = AnfAlgo::GetCNodePrimitive(node); + auto primitive = common::AnfAlgo::GetCNodePrimitive(node); MS_EXCEPTION_IF_NULL(primitive); ValuePtr attr_fusion = primitive->GetAttr(kAttrFusion); if (attr_fusion == nullptr) { @@ -106,7 +108,7 @@ std::string GetFusionGroupKey(const AnfNodePtr &node) { if (attr_op != nullptr) { op = GetValue(attr_op); } - auto dtype = AnfAlgo::GetPrevNodeOutputInferDataType(node, 0); + auto dtype = common::AnfAlgo::GetPrevNodeOutputInferDataType(node, 0); return group + op + std::to_string(fusion) + TypeIdLabel(dtype); } @@ -338,22 +340,23 @@ AnfNodePtr CommunicationOpFusion::CreateFusedCommunicationOp(const FuncGraphPtr auto final_node = communication_op_info.communication_op_nodes[end_index]; size_t node_num = end_index - start_index + 1; int64_t rank_size = 1; - if (AnfAlgo::HasNodeAttr(kAttrRankSize, final_node) && AnfAlgo::GetCNodeName(final_node) == kAllGatherOpName) { - rank_size = AnfAlgo::GetNodeAttr(final_node, kAttrRankSize); + if (common::AnfAlgo::HasNodeAttr(kAttrRankSize, final_node) && + common::AnfAlgo::GetCNodeName(final_node) == kAllGatherOpName) { + rank_size = common::AnfAlgo::GetNodeAttr(final_node, kAttrRankSize); } size_t rank_size_t = LongToSize(rank_size); if (rank_size_t == 0) { MS_LOG(EXCEPTION) << "Rank size should not be zero."; } size_t output_num = node_num * rank_size_t; - std::vector dtypes(output_num, AnfAlgo::GetOutputInferDataType(final_node, 0)); + std::vector dtypes(output_num, common::AnfAlgo::GetOutputInferDataType(final_node, 0)); std::vector> shapes; int64_t fusion_total_size = 0; for (size_t i = 0; i < rank_size_t; ++i) { for (size_t idx = start_index; idx <= end_index; ++idx) { auto input_node = communication_op_info.communication_op_nodes[idx]; MS_EXCEPTION_IF_NULL(input_node); - std::vector shape = AnfAlgo::GetOutputInferShape(input_node, 0); + std::vector shape = common::AnfAlgo::GetOutputInferShape(input_node, 0); if (!shape.empty()) { shape[0] /= rank_size_t; } @@ -368,32 +371,32 @@ AnfNodePtr CommunicationOpFusion::CreateFusedCommunicationOp(const FuncGraphPtr fusion_total_size += static_cast(tensor_size); } } - AnfAlgo::SetOutputInferTypeAndShape(dtypes, shapes, fused_node.get()); + common::AnfAlgo::SetOutputInferTypeAndShape(dtypes, shapes, fused_node.get()); auto kernel_build_info = GenerateKernelBuildInfo(communication_op_info, start_index, end_index); AnfAlgo::SetSelectKernelBuildInfo(kernel_build_info, fused_node.get()); const std::vector kHcclFusionAttrs = {kAttrFusion, kAttrGroup, kAttrGroupBack, kAttrSrTag, kAttrDestRank, kAttrSrcRank, kAttrDType, kAttrOp, kAttrRankSize}; for (const auto &attr : kHcclFusionAttrs) { - if (AnfAlgo::HasNodeAttr(attr, final_node)) { - AnfAlgo::CopyNodeAttr(attr, final_node, fused_node); + if (common::AnfAlgo::HasNodeAttr(attr, final_node)) { + common::AnfAlgo::CopyNodeAttr(attr, final_node, fused_node); } } - if (AnfAlgo::HasNodeAttr(kAttrShape, final_node)) { + if (common::AnfAlgo::HasNodeAttr(kAttrShape, final_node)) { std::vector fusion_total_shape{fusion_total_size}; - AnfAlgo::SetNodeAttr(kAttrShape, MakeValue(fusion_total_shape), fused_node); + common::AnfAlgo::SetNodeAttr(kAttrShape, MakeValue(fusion_total_shape), fused_node); } bool is_recompute = final_node->GetAttr(kAttrDuplicated) != nullptr && GetValue(final_node->GetAttr(kAttrDuplicated)); - if (AnfAlgo::GetCNodeName(final_node) == kAllGatherOpName && is_recompute) { + if (common::AnfAlgo::GetCNodeName(final_node) == kAllGatherOpName && is_recompute) { auto fused_cnode = fused_node->cast(); fused_cnode->AddAttr("duplicated", MakeValue(true)); auto fused_prim = GetCNodePrimitive(fused_cnode); auto final_node_prim = GetCNodePrimitive(final_node); fused_prim->set_instance_name(final_node_prim->instance_name()); } - if (AnfAlgo::HasNodeAttr(kAttrNotDelayFusion, final_node)) { - AnfAlgo::CopyNodeAttr(kAttrNotDelayFusion, final_node, fused_node); + if (common::AnfAlgo::HasNodeAttr(kAttrNotDelayFusion, final_node)) { + common::AnfAlgo::CopyNodeAttr(kAttrNotDelayFusion, final_node, fused_node); } return fused_node; } @@ -457,7 +460,7 @@ bool CommunicationOpFusion::Run(const FuncGraphPtr &func_graph) { mindspore::HashMap candidate_groups; std::vector node_list = TopoSort(func_graph->get_return()); for (auto &node : node_list) { - if (node != nullptr && node->isa() && AnfAlgo::GetCNodeName(node) == op_name_) { + if (node != nullptr && node->isa() && common::AnfAlgo::GetCNodeName(node) == op_name_) { std::string key = GetFusionGroupKey(node); if (key.empty()) { continue; @@ -479,11 +482,12 @@ bool CommunicationOpFusion::Run(const FuncGraphPtr &func_graph) { } auto first_node = it.second.communication_op_nodes[0]; TraceGuard guard(std::make_shared(first_node->debug_info())); - if (AnfAlgo::HasNodeAttr(kAttrIndex, first_node) && AnfAlgo::GetNodeAttr(first_node, kAttrIndex) > 0) { + if (common::AnfAlgo::HasNodeAttr(kAttrIndex, first_node) && + common::AnfAlgo::GetNodeAttr(first_node, kAttrIndex) > 0) { std::stable_sort(it.second.communication_op_nodes.begin(), it.second.communication_op_nodes.end(), [](const CNodePtr &a, const CNodePtr &b) { - return AnfAlgo::GetNodeAttr(a, kAttrIndex) < - AnfAlgo::GetNodeAttr(b, kAttrIndex); + return common::AnfAlgo::GetNodeAttr(a, kAttrIndex) < + common::AnfAlgo::GetNodeAttr(b, kAttrIndex); }); } size_t segment_num = 0; diff --git a/mindspore/ccsrc/backend/common/pass/communication_op_fusion.h b/mindspore/ccsrc/backend/common/pass/communication_op_fusion.h index eee1d6850bb..810ee30b36f 100644 --- a/mindspore/ccsrc/backend/common/pass/communication_op_fusion.h +++ b/mindspore/ccsrc/backend/common/pass/communication_op_fusion.h @@ -22,7 +22,7 @@ #include "backend/common/optimizer/pass.h" #include "ir/func_graph.h" #include "ir/anf.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace opt { diff --git a/mindspore/ccsrc/backend/common/pass/const_to_attr_strided_slice_grad.cc b/mindspore/ccsrc/backend/common/pass/const_to_attr_strided_slice_grad.cc index 5d50358c494..4f1b64e2980 100644 --- a/mindspore/ccsrc/backend/common/pass/const_to_attr_strided_slice_grad.cc +++ b/mindspore/ccsrc/backend/common/pass/const_to_attr_strided_slice_grad.cc @@ -17,12 +17,12 @@ #include #include #include -#include "backend/common/session/anf_runtime_algorithm.h" #include "ir/primitive.h" #include "utils/ms_context.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "abstract/abstract_value.h" #include "backend/common/optimizer/const_input_to_attr.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -92,13 +92,13 @@ bool CheckValues(const ValuePtrList &strides_values) { bool CheckAttrs(const CNodePtr &strided_slice_grad) { MS_EXCEPTION_IF_NULL(strided_slice_grad); - if (!AnfAlgo::HasNodeAttr(kAttrNewAxisMask, strided_slice_grad) || - !AnfAlgo::HasNodeAttr(kAttrShrinkAxisMask, strided_slice_grad)) { + if (!common::AnfAlgo::HasNodeAttr(kAttrNewAxisMask, strided_slice_grad) || + !common::AnfAlgo::HasNodeAttr(kAttrShrinkAxisMask, strided_slice_grad)) { MS_LOG(INFO) << "new_axis_mask or shrink_axis_mask not exist in cnode[" + strided_slice_grad->DebugString() + "]"; return false; } - auto new_axis_mask = AnfAlgo::GetNodeAttr(strided_slice_grad, kAttrNewAxisMask); - auto shrink_axis_mask = AnfAlgo::GetNodeAttr(strided_slice_grad, kAttrShrinkAxisMask); + auto new_axis_mask = common::AnfAlgo::GetNodeAttr(strided_slice_grad, kAttrNewAxisMask); + auto shrink_axis_mask = common::AnfAlgo::GetNodeAttr(strided_slice_grad, kAttrShrinkAxisMask); if (new_axis_mask != 0 || shrink_axis_mask != 0) { MS_LOG(INFO) << "new_axis_mask or shrink_axis_mask not equal 0"; return false; diff --git a/mindspore/ccsrc/backend/common/pass/conv_transpose_to_conv_bp.cc b/mindspore/ccsrc/backend/common/pass/conv_transpose_to_conv_bp.cc index 59b370fac41..4aacadbc91d 100644 --- a/mindspore/ccsrc/backend/common/pass/conv_transpose_to_conv_bp.cc +++ b/mindspore/ccsrc/backend/common/pass/conv_transpose_to_conv_bp.cc @@ -17,7 +17,7 @@ #include #include #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/trace_base.h" #include "backend/common/optimizer/helper.h" diff --git a/mindspore/ccsrc/backend/common/pass/convert_attr_to_unify_mindir.cc b/mindspore/ccsrc/backend/common/pass/convert_attr_to_unify_mindir.cc index 3d5abc71b20..328fc94b1bb 100644 --- a/mindspore/ccsrc/backend/common/pass/convert_attr_to_unify_mindir.cc +++ b/mindspore/ccsrc/backend/common/pass/convert_attr_to_unify_mindir.cc @@ -18,6 +18,7 @@ #include #include "utils/check_convert_utils.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "kernel/common_utils.h" namespace mindspore { diff --git a/mindspore/ccsrc/backend/common/pass/convert_const_input_to_attr.cc b/mindspore/ccsrc/backend/common/pass/convert_const_input_to_attr.cc index 20140cfd488..77c3728e1ca 100644 --- a/mindspore/ccsrc/backend/common/pass/convert_const_input_to_attr.cc +++ b/mindspore/ccsrc/backend/common/pass/convert_const_input_to_attr.cc @@ -15,11 +15,10 @@ */ #include "backend/common/pass/convert_const_input_to_attr.h" #include "backend/common/optimizer/const_input_to_attr.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/ms_context.h" #include "base/core_ops.h" -#include "backend/common/session/anf_runtime_algorithm.h" -#include "kernel/common_utils.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -32,49 +31,52 @@ const AnfNodePtr ConvertConstInputToAttr::Process(const FuncGraphPtr &, const An auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); ConstInputToAttrInfoRegister reg; - if (!ConstInputToAttrInfoRegistry::Instance().GetRegisterByOpName(AnfAlgo::GetCNodeName(cnode), ®)) { + if (!ConstInputToAttrInfoRegistry::Instance().GetRegisterByOpName(common::AnfAlgo::GetCNodeName(cnode), ®)) { return nullptr; } - if (AnfAlgo::GetCNodeName(cnode) == prim::kPrimEmbeddingLookup->name() || - AnfAlgo::GetCNodeName(cnode) == prim::kPrimEmbeddingLookupCommGrad->name()) { - if (!AnfAlgo::HasNodeAttr(kAttrPrimitiveTarget, cnode)) { + if (common::AnfAlgo::GetCNodeName(cnode) == prim::kPrimEmbeddingLookup->name() || + common::AnfAlgo::GetCNodeName(cnode) == prim::kPrimEmbeddingLookupCommGrad->name()) { + if (!common::AnfAlgo::HasNodeAttr(kAttrPrimitiveTarget, cnode)) { return nullptr; } } auto ms_context = MsContext::GetInstance(); MS_EXCEPTION_IF_NULL(ms_context); auto device = ms_context->get_param(MS_CTX_DEVICE_TARGET); - if (AnfAlgo::GetCNodeName(cnode) == prim::kPrimGatherD->name()) { + if (common::AnfAlgo::GetCNodeName(cnode) == prim::kPrimGatherD->name()) { if (device != kGPUDevice) { return nullptr; } } - if (AnfAlgo::IsDynamicShape(cnode)) { + if (common::AnfAlgo::IsDynamicShape(cnode)) { if (device == kGPUDevice) { - if (DynamicShapeConstInputToAttrGPU.find(AnfAlgo::GetCNodeName(cnode)) == DynamicShapeConstInputToAttrGPU.end()) { + if (DynamicShapeConstInputToAttrGPU.find(common::AnfAlgo::GetCNodeName(cnode)) == + DynamicShapeConstInputToAttrGPU.end()) { MS_LOG(INFO) << "current node is dynamic shape " << cnode->fullname_with_scope(); return nullptr; } } else if (device == kCPUDevice) { - if (DynamicShapeConstInputToAttrCPU.find(AnfAlgo::GetCNodeName(cnode)) == DynamicShapeConstInputToAttrCPU.end()) { + if (DynamicShapeConstInputToAttrCPU.find(common::AnfAlgo::GetCNodeName(cnode)) == + DynamicShapeConstInputToAttrCPU.end()) { MS_LOG(INFO) << "current node is dynamic shape " << cnode->fullname_with_scope(); return nullptr; } } else { - if (DynamicShapeConstInputToAttr.find(AnfAlgo::GetCNodeName(cnode)) == DynamicShapeConstInputToAttr.end()) { + if (DynamicShapeConstInputToAttr.find(common::AnfAlgo::GetCNodeName(cnode)) == + DynamicShapeConstInputToAttr.end()) { MS_LOG(INFO) << "current node is dynamic shape " << cnode->fullname_with_scope(); return nullptr; } } } if (device == kAscendDevice && - NeedConvertToValueNodeSet.find(AnfAlgo::GetCNodeName(cnode)) != NeedConvertToValueNodeSet.end() && - !AnfAlgo::HasNodeAttr(kAttrNeedConvertToValueNode, cnode)) { + NeedConvertToValueNodeSet.find(common::AnfAlgo::GetCNodeName(cnode)) != NeedConvertToValueNodeSet.end() && + !common::AnfAlgo::HasNodeAttr(kAttrNeedConvertToValueNode, cnode)) { auto input_attrs = reg.GetConstInputAttrInfo(); std::vector need_convert_to_constant; std::transform(input_attrs.begin(), input_attrs.end(), std::back_inserter(need_convert_to_constant), [](size_t i) { return i + 1; }); - AnfAlgo::SetNodeAttr(kAttrNeedConvertToValueNode, MakeValue(need_convert_to_constant), cnode); + common::AnfAlgo::SetNodeAttr(kAttrNeedConvertToValueNode, MakeValue(need_convert_to_constant), cnode); return nullptr; } diff --git a/mindspore/ccsrc/backend/common/pass/convert_const_input_to_tensor_input.cc b/mindspore/ccsrc/backend/common/pass/convert_const_input_to_tensor_input.cc index a60d09e4525..1d9245b03ee 100644 --- a/mindspore/ccsrc/backend/common/pass/convert_const_input_to_tensor_input.cc +++ b/mindspore/ccsrc/backend/common/pass/convert_const_input_to_tensor_input.cc @@ -22,6 +22,7 @@ #include "ir/graph_utils.h" #include "backend/common/optimizer/helper.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/session/kernel_graph.h" #include "kernel/common_utils.h" @@ -65,7 +66,7 @@ AnfNodePtr ConvertConstInputToTensorInput::ConstInputToTensorInput(const FuncGra MS_EXCEPTION_IF_NULL(func_graph); MS_EXCEPTION_IF_NULL(cnode); const std::set no_need_to_convert_nodes = {kStackOpName}; - auto node_type = AnfAlgo::GetCNodeName(cnode); + auto node_type = common::AnfAlgo::GetCNodeName(cnode); if (no_need_to_convert_nodes.find(node_type) != no_need_to_convert_nodes.end()) { return nullptr; } @@ -93,13 +94,13 @@ AnfNodePtr ConvertConstInputToTensorInput::ConstInputToTensorInput(const FuncGra MS_EXCEPTION_IF_NULL(func_graph); auto new_cnode = NewCNode(new_inputs, func_graph); MS_EXCEPTION_IF_NULL(new_cnode); - if (AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimDepend)) { + if (common::AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimDepend)) { new_cnode->set_abstract(new_inputs[1]->abstract()); } else { new_cnode->set_abstract(cnode->abstract()); } new_cnode->set_scope(cnode->scope()); - AnfAlgo::CopyNodeAttrs(cnode, new_cnode); + common::AnfAlgo::CopyNodeAttrs(cnode, new_cnode); if (kernel_graph != nullptr) { kernel_graph->FrontBackendlMapUpdate(cnode, new_cnode); } @@ -110,8 +111,8 @@ AnfNodePtr ConvertConstInputToTensorInput::ConstInputToTensorInput(const FuncGra const AnfNodePtr ConvertConstInputToTensorInput::Process(const FuncGraphPtr &func_graph, const AnfNodePtr &node, const EquivPtr &) const { - if (node == nullptr || func_graph == nullptr || AnfAlgo::CheckPrimitiveType(node, prim::kPrimTupleGetItem) || - AnfAlgo::CheckPrimitiveType(node, prim::kPrimMakeTuple)) { + if (node == nullptr || func_graph == nullptr || common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimTupleGetItem) || + common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimMakeTuple)) { return nullptr; } if (!node->isa()) { diff --git a/mindspore/ccsrc/backend/common/pass/convert_const_input_to_tensor_input.h b/mindspore/ccsrc/backend/common/pass/convert_const_input_to_tensor_input.h index 3169acfd341..37b947b97fe 100644 --- a/mindspore/ccsrc/backend/common/pass/convert_const_input_to_tensor_input.h +++ b/mindspore/ccsrc/backend/common/pass/convert_const_input_to_tensor_input.h @@ -18,7 +18,7 @@ #include #include "ir/anf.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" #include "backend/common/optimizer/optimizer.h" namespace mindspore { diff --git a/mindspore/ccsrc/backend/common/pass/convert_const_scalar_to_tensor.cc b/mindspore/ccsrc/backend/common/pass/convert_const_scalar_to_tensor.cc index 6130b3ee26e..04bfa38ae5a 100644 --- a/mindspore/ccsrc/backend/common/pass/convert_const_scalar_to_tensor.cc +++ b/mindspore/ccsrc/backend/common/pass/convert_const_scalar_to_tensor.cc @@ -16,9 +16,10 @@ #include "backend/common/pass/convert_const_scalar_to_tensor.h" #include #include -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" #include "backend/common/optimizer/helper.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/session/kernel_graph.h" namespace mindspore { @@ -57,7 +58,7 @@ AnfNodePtr CreateTensorInput(const KernelGraphPtr &kernel_graph, const AnfNodePt const AnfNodePtr ConvertConstScalarToTensor::Process(const FuncGraphPtr &func_graph, const AnfNodePtr &node, const EquivPtr &) const { - if (node == nullptr || func_graph == nullptr || AnfAlgo::CheckPrimitiveType(node, prim::kPrimTupleGetItem)) { + if (node == nullptr || func_graph == nullptr || common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimTupleGetItem)) { return nullptr; } // input is scalar, and link to graph return diff --git a/mindspore/ccsrc/backend/common/pass/convert_tuple_input_to_dynamic_input.cc b/mindspore/ccsrc/backend/common/pass/convert_tuple_input_to_dynamic_input.cc index fd2fcf5ff7d..debe83cd413 100644 --- a/mindspore/ccsrc/backend/common/pass/convert_tuple_input_to_dynamic_input.cc +++ b/mindspore/ccsrc/backend/common/pass/convert_tuple_input_to_dynamic_input.cc @@ -18,32 +18,29 @@ #include #include -#include "backend/common/session/anf_runtime_algorithm.h" #include "backend/common/optimizer/helper.h" -#include "backend/common/session/kernel_graph.h" -#include "kernel/common_utils.h" -#include "runtime/device/kernel_info.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { namespace { int64_t SplitTupleInputs(const FuncGraphPtr &graph, const AnfNodePtr &tuple_input, std::vector *plant_inputs) { - if (!AnfAlgo::IsTupleOutput(tuple_input)) { + if (!common::AnfAlgo::IsTupleOutput(tuple_input)) { auto abs = tuple_input->abstract(); MS_EXCEPTION_IF_NULL(abs); MS_LOG(WARNING) << "The Function only split the output type is tuple type but got" << abs->ToString(); return -1; } MS_EXCEPTION_IF_NULL(plant_inputs); - auto input_size = AnfAlgo::GetOutputTensorNum(tuple_input); - if (tuple_input->isa() && AnfAlgo::CheckPrimitiveType(tuple_input, prim::kPrimMakeTuple)) { + auto input_size = common::AnfAlgo::GetOutputTensorNum(tuple_input); + if (tuple_input->isa() && common::AnfAlgo::CheckPrimitiveType(tuple_input, prim::kPrimMakeTuple)) { auto make_tuple = tuple_input->cast(); MS_EXCEPTION_IF_NULL(make_tuple); - size_t tuple_input_num = AnfAlgo::GetInputTensorNum(make_tuple); + size_t tuple_input_num = common::AnfAlgo::GetInputTensorNum(make_tuple); for (size_t j = 0; j < tuple_input_num; ++j) { // using for graph kernel - auto dyn_input_node = AnfAlgo::GetInputNode(make_tuple, j); + auto dyn_input_node = common::AnfAlgo::GetInputNode(make_tuple, j); MS_EXCEPTION_IF_NULL(dyn_input_node); (void)plant_inputs->emplace_back(dyn_input_node); } @@ -59,18 +56,18 @@ int64_t SplitTupleInputs(const FuncGraphPtr &graph, const AnfNodePtr &tuple_inpu void ConvertMakeTupleInputToPlantInputs(const FuncGraphPtr &graph, const CNodePtr &cnode_ptr) { MS_EXCEPTION_IF_NULL(cnode_ptr); MS_EXCEPTION_IF_NULL(graph); - if (AnfAlgo::CheckPrimitiveType(cnode_ptr, prim::kPrimCall) || - AnfAlgo::CheckPrimitiveType(cnode_ptr, prim::kPrimPartial)) { + if (common::AnfAlgo::CheckPrimitiveType(cnode_ptr, prim::kPrimCall) || + common::AnfAlgo::CheckPrimitiveType(cnode_ptr, prim::kPrimPartial)) { return; } std::vector plant_inputs; std::vector dyn_input_sizes; - plant_inputs.push_back(AnfAlgo::GetCNodePrimitiveNode(cnode_ptr)); + plant_inputs.push_back(common::AnfAlgo::GetCNodePrimitiveNode(cnode_ptr)); size_t input_num = cnode_ptr->inputs().size() - 1; for (size_t i = 0; i < input_num; ++i) { - auto input_node = AnfAlgo::GetInputNode(cnode_ptr, i); + auto input_node = common::AnfAlgo::GetInputNode(cnode_ptr, i); MS_EXCEPTION_IF_NULL(input_node); - if (AnfAlgo::IsTupleOutput(input_node)) { + if (common::AnfAlgo::IsTupleOutput(input_node)) { (void)dyn_input_sizes.emplace_back(SplitTupleInputs(graph, input_node, &plant_inputs)); } else { dyn_input_sizes.push_back(-1); @@ -79,7 +76,7 @@ void ConvertMakeTupleInputToPlantInputs(const FuncGraphPtr &graph, const CNodePt } // If there is dynamic input, set the dyn_input_sizes as an attribute and update the inputs. if (std::any_of(dyn_input_sizes.begin(), dyn_input_sizes.end(), [](int64_t s) { return s >= 0; })) { - AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(dyn_input_sizes), cnode_ptr); + common::AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(dyn_input_sizes), cnode_ptr); cnode_ptr->set_inputs(plant_inputs); } } diff --git a/mindspore/ccsrc/backend/common/pass/convert_tuple_output_to_maketuple.cc b/mindspore/ccsrc/backend/common/pass/convert_tuple_output_to_maketuple.cc index c3ff2d43680..84c82c59b66 100644 --- a/mindspore/ccsrc/backend/common/pass/convert_tuple_output_to_maketuple.cc +++ b/mindspore/ccsrc/backend/common/pass/convert_tuple_output_to_maketuple.cc @@ -18,10 +18,9 @@ #include #include -#include "utils/hash_map.h" -#include "backend/common/session/anf_runtime_algorithm.h" #include "backend/common/optimizer/helper.h" #include "backend/common/session/kernel_graph.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -30,7 +29,7 @@ AnfNodePtr ConvertTupleInputToMakeTuple(const FuncGraphPtr &graph, const AnfNode MS_EXCEPTION_IF_NULL(tuple_anf); MS_EXCEPTION_IF_NULL(graph); - if (!AnfAlgo::IsTupleOutput(tuple_anf)) { + if (!common::AnfAlgo::IsTupleOutput(tuple_anf)) { return tuple_anf; } auto kernel_graph = graph->cast(); @@ -65,7 +64,7 @@ const AnfNodePtr ConvertTupleOutputToMaketuple::Process(const FuncGraphPtr &func auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); if (IsPrimitiveCNode(cnode, prim::kPrimTupleGetItem)) { - auto real_input = AnfAlgo::GetTupleGetItemRealInput(cnode); + auto real_input = common::AnfAlgo::GetTupleGetItemRealInput(cnode); MS_EXCEPTION_IF_NULL(real_input); if (!real_input->isa() && !real_input->isa()) { return nullptr; @@ -77,8 +76,8 @@ const AnfNodePtr ConvertTupleOutputToMaketuple::Process(const FuncGraphPtr &func bool cnode_input_changed = false; for (size_t i = 0; i < cnode->inputs().size(); ++i) { const auto &input = cnode->inputs()[i]; - if (input->Type() != nullptr && AnfUtils::IsRealKernel(input) && AnfAlgo::IsTupleOutput(input) && - !AnfAlgo::CheckPrimitiveType(input, prim::kPrimCall)) { + if (input->Type() != nullptr && AnfUtils::IsRealKernel(input) && common::AnfAlgo::IsTupleOutput(input) && + !common::AnfAlgo::CheckPrimitiveType(input, prim::kPrimCall)) { cnode->set_input(i, ConvertTupleInputToMakeTuple(func_graph, input)); cnode_input_changed = true; } diff --git a/mindspore/ccsrc/backend/common/pass/custom_op_const_input_to_attr.cc b/mindspore/ccsrc/backend/common/pass/custom_op_const_input_to_attr.cc index e3a13eb1813..d606fc1c002 100644 --- a/mindspore/ccsrc/backend/common/pass/custom_op_const_input_to_attr.cc +++ b/mindspore/ccsrc/backend/common/pass/custom_op_const_input_to_attr.cc @@ -19,7 +19,7 @@ #include "utils/hash_set.h" #include "backend/common/optimizer/const_input_to_attr.h" -#include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -36,7 +36,7 @@ const AnfNodePtr CustomOpConstInputToAttr::Process(const FuncGraphPtr &, const A if (!IsPrimitiveCNode(cnode, prim::kPrimCustom)) { return nullptr; } - auto primitive = AnfAlgo::GetCNodePrimitive(cnode); + auto primitive = common::AnfAlgo::GetCNodePrimitive(cnode); MS_EXCEPTION_IF_NULL(primitive); mindspore::HashSet attr_indices; GetCustomOpAttrIndex(primitive, &attr_indices); diff --git a/mindspore/ccsrc/backend/common/pass/custom_op_reg_info_to_attr.cc b/mindspore/ccsrc/backend/common/pass/custom_op_reg_info_to_attr.cc index 086c0e0e722..34c27f63534 100644 --- a/mindspore/ccsrc/backend/common/pass/custom_op_reg_info_to_attr.cc +++ b/mindspore/ccsrc/backend/common/pass/custom_op_reg_info_to_attr.cc @@ -20,9 +20,9 @@ #include #include -#include "backend/common/session/anf_runtime_algorithm.h" #include "kernel/oplib/opinfo.h" #include "kernel/oplib/oplib.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -87,10 +87,10 @@ void ParseAttrDefaultValue(const std::string &op_name, const std::string &attr_n void AddMissingAttrs(const CNodePtr &cnode, kernel::OpImplyType imply_type, const std::unordered_set &missing_attrs) { MS_EXCEPTION_IF_NULL(cnode); - auto primitive = AnfAlgo::GetCNodePrimitive(cnode); + auto primitive = common::AnfAlgo::GetCNodePrimitive(cnode); MS_EXCEPTION_IF_NULL(primitive); primitive = primitive->Clone(); - auto op_name = AnfAlgo::GetCNodeName(cnode); + auto op_name = common::AnfAlgo::GetCNodeName(cnode); auto op_info_ptr = mindspore::kernel::OpLib::FindOp(op_name, imply_type); MS_EXCEPTION_IF_NULL(op_info_ptr); auto all_attrs = op_info_ptr->attrs_ptr(); @@ -131,9 +131,9 @@ const AnfNodePtr CustomOpRegInfoToAttr::Process(const FuncGraphPtr &, const AnfN if (!IsPrimitiveCNode(cnode, prim::kPrimCustom)) { return nullptr; } - auto primitive = AnfAlgo::GetCNodePrimitive(cnode); + auto primitive = common::AnfAlgo::GetCNodePrimitive(cnode); MS_EXCEPTION_IF_NULL(primitive); - auto func_type = AnfAlgo::GetNodeAttr(cnode, kAttrFuncType); + auto func_type = common::AnfAlgo::GetNodeAttr(cnode, kAttrFuncType); // AKG/AICPU need to process attr, TBE will process later in the json creating phase. if (kCustomTypeAkg.find(func_type) == kCustomTypeAkg.end() || func_type == kCustomTypeAICPU) { return nullptr; diff --git a/mindspore/ccsrc/backend/common/pass/eliminate_func_data_type.cc b/mindspore/ccsrc/backend/common/pass/eliminate_func_data_type.cc index 331a1514daa..120f5aefc4c 100644 --- a/mindspore/ccsrc/backend/common/pass/eliminate_func_data_type.cc +++ b/mindspore/ccsrc/backend/common/pass/eliminate_func_data_type.cc @@ -18,9 +18,9 @@ #include #include #include "ir/anf.h" -#include "backend/common/session/anf_runtime_algorithm.h" #include "backend/common/session/kernel_graph.h" #include "backend/common/optimizer/helper.h" +#include "include/common/utils/anfalgo.h" namespace mindspore::opt { namespace { @@ -107,7 +107,7 @@ const AnfNodePtr EliminateFuncDataType::Process(const FuncGraphPtr &func_graph, if (abs->isa() && FuncDataTypeExistsInAbstractTuple(abs)) { RemoveInputFuncNodeForKernelGraph(kernel_graph, param); (void)tr.Replace(param, constant_); - } else if (AnfAlgo::GetOutputInferDataType(param, 0) == kObjectTypeFunction) { + } else if (common::AnfAlgo::GetOutputInferDataType(param, 0) == kObjectTypeFunction) { RemoveInputFuncNodeForKernelGraph(kernel_graph, param); (void)tr.Replace(param, constant_); } else { @@ -125,7 +125,7 @@ const AnfNodePtr EliminateFuncDataType::Process(const FuncGraphPtr &func_graph, if (abs->isa()) { auto abs_tuple = dyn_cast(abs); node->set_abstract(std::make_shared(EliminateFuncDataTypeForAbstractTuple(abs_tuple))); - } else if (AnfAlgo::GetOutputInferDataType(node, 0) == kObjectTypeFunction) { + } else if (common::AnfAlgo::GetOutputInferDataType(node, 0) == kObjectTypeFunction) { node->set_abstract(constant_abs_); } } diff --git a/mindspore/ccsrc/backend/common/pass/eliminate_redundant_op.cc b/mindspore/ccsrc/backend/common/pass/eliminate_redundant_op.cc index f987cd67c4c..04719b596ef 100644 --- a/mindspore/ccsrc/backend/common/pass/eliminate_redundant_op.cc +++ b/mindspore/ccsrc/backend/common/pass/eliminate_redundant_op.cc @@ -19,7 +19,8 @@ #include #include "utils/hash_map.h" #include "backend/common/session/anf_runtime_algorithm.h" -#include "utils/utils.h" +#include "include/common/utils/anfalgo.h" +#include "include/common/utils/utils.h" #include "backend/common/optimizer/helper.h" #include "base/core_ops.h" #include "kernel/common_utils.h" @@ -94,7 +95,7 @@ const AnfNodePtr EliminateRedundantOp::ProcessMatchedNodes(const FuncGraphPtr &f auto pass_size = pass_vector->size(); for (size_t idx = 1; idx <= pass_size - 1; ++idx) { auto nd = (*pass_vector)[idx].first; - if (AnfAlgo::CheckPrimitiveType(nd, prim::kPrimDepend)) { + if (common::AnfAlgo::CheckPrimitiveType(nd, prim::kPrimDepend)) { has_depend_node = true; } if (users[nd].size() > 1) { @@ -140,7 +141,7 @@ void EliminateRedundantOp::Init() { const AnfNodePtr EliminateRedundantOp::DoEliminate(const FuncGraphPtr &func_graph, const CNodePtr &cnode) const { // match the first name - auto name1 = AnfAlgo::GetCNodeName(cnode); + auto name1 = common::AnfAlgo::GetCNodeName(cnode); auto it = redundant_process_map_.find(name1); if (it == redundant_process_map_.end()) { return nullptr; @@ -152,7 +153,7 @@ const AnfNodePtr EliminateRedundantOp::DoEliminate(const FuncGraphPtr &func_grap return nullptr; } // match the second name - auto name2 = AnfAlgo::GetCNodeName(prev_cnode); + auto name2 = common::AnfAlgo::GetCNodeName(prev_cnode); if (name2 != it->second.first) { return nullptr; } diff --git a/mindspore/ccsrc/backend/common/pass/erase_visit_attr.cc b/mindspore/ccsrc/backend/common/pass/erase_visit_attr.cc index 055d7cb812b..afc1660a4e6 100644 --- a/mindspore/ccsrc/backend/common/pass/erase_visit_attr.cc +++ b/mindspore/ccsrc/backend/common/pass/erase_visit_attr.cc @@ -18,6 +18,7 @@ #include #include "kernel/common_utils.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/optimizer/helper.h" namespace mindspore { @@ -29,7 +30,7 @@ const BaseRef EraseVisitAttr::DefinePattern() const { } const AnfNodePtr EraseVisitAttr::Process(const FuncGraphPtr &, const AnfNodePtr &node, const EquivPtr &) const { - AnfAlgo::EraseNodeAttr(kAttrVisited, node); + common::AnfAlgo::EraseNodeAttr(kAttrVisited, node); return nullptr; } } // namespace opt diff --git a/mindspore/ccsrc/backend/common/pass/getitem_tuple.cc b/mindspore/ccsrc/backend/common/pass/getitem_tuple.cc index 764c35d5cdf..e4aa4fda93f 100644 --- a/mindspore/ccsrc/backend/common/pass/getitem_tuple.cc +++ b/mindspore/ccsrc/backend/common/pass/getitem_tuple.cc @@ -17,7 +17,7 @@ #include #include "base/core_ops.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace opt { diff --git a/mindspore/ccsrc/backend/common/pass/insert_assign_for_custom_op.cc b/mindspore/ccsrc/backend/common/pass/insert_assign_for_custom_op.cc index a7dbe2f63bc..db422dc83a9 100644 --- a/mindspore/ccsrc/backend/common/pass/insert_assign_for_custom_op.cc +++ b/mindspore/ccsrc/backend/common/pass/insert_assign_for_custom_op.cc @@ -20,7 +20,7 @@ #include #include #include "backend/common/optimizer/helper.h" -#include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -30,14 +30,14 @@ constexpr auto kCustomAttrInplaceAssignOutput = "inplace_assign_output"; // Used to find Custom op outputs' inplace assign index std::vector> GetHybridInplaceIndex(const CNodePtr &cnode) { - if (AnfAlgo::GetNodeAttr(cnode, kAttrFuncType) != kCustomTypeHybrid) { + if (common::AnfAlgo::GetNodeAttr(cnode, kAttrFuncType) != kCustomTypeHybrid) { return {}; } - if (!AnfAlgo::HasNodeAttr(kCustomAttrInplaceAssignOutput, cnode)) { + if (!common::AnfAlgo::HasNodeAttr(kCustomAttrInplaceAssignOutput, cnode)) { return {}; } - auto inplace_index_str = AnfAlgo::GetNodeAttr(cnode, kCustomAttrInplaceAssignOutput); + auto inplace_index_str = common::AnfAlgo::GetNodeAttr(cnode, kCustomAttrInplaceAssignOutput); std::regex delimiters(" "); std::vector index( std::sregex_token_iterator(inplace_index_str.begin(), inplace_index_str.end(), delimiters, -1), @@ -80,7 +80,7 @@ CNodePtr InsertAssign(const FuncGraphPtr &func_graph, const AnfNodePtr &src, con CNodePtr InsertAssignAfterCustom(const FuncGraphPtr &func_graph, const CNodePtr &cnode) { auto inplace_info = GetHybridInplaceIndex(cnode); if (inplace_info.size() != 1) return nullptr; - auto input_size = AnfAlgo::GetInputTensorNum(cnode); + auto input_size = common::AnfAlgo::GetInputTensorNum(cnode); if (auto i = LongToSize(inplace_info[0][kCustomInput]); i < input_size) { return InsertAssign(func_graph, cnode->input(i + 1), cnode); } else { @@ -102,7 +102,7 @@ CNodePtr InsertAssignAfterTupleGetItem(const FuncGraphPtr &func_graph, const CNo auto inplace_info = GetHybridInplaceIndex(real_input); for (auto index : inplace_info) { if (index[kCustomOutput] == gt_idx && index[kCustomInput] >= 0) { - auto custom_input_size = AnfAlgo::GetInputTensorNum(real_input); + auto custom_input_size = common::AnfAlgo::GetInputTensorNum(real_input); if (auto i = LongToSize(index[kCustomInput]); i < custom_input_size) { return InsertAssign(func_graph, real_input->input(i + 1), cnode); } diff --git a/mindspore/ccsrc/backend/common/pass/optimize_dependence.cc b/mindspore/ccsrc/backend/common/pass/optimize_dependence.cc index f399b22608a..349b1c80871 100644 --- a/mindspore/ccsrc/backend/common/pass/optimize_dependence.cc +++ b/mindspore/ccsrc/backend/common/pass/optimize_dependence.cc @@ -18,13 +18,12 @@ #include #include #include -#include #include "backend/common/optimizer/helper.h" #include "base/core_ops.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" +#include "include/common/utils/anfalgo.h" #include "utils/trace_base.h" #include "backend/common/session/kernel_graph.h" -#include "backend/common/session/anf_runtime_algorithm.h" namespace mindspore { namespace opt { @@ -52,15 +51,15 @@ CNodePtr CreateNewDependNode(const FuncGraphPtr &func_graph, const CNodePtr &cno CNodePtr CheckIsolatedVirtualNode(const CNodePtr &cnode) { MS_EXCEPTION_IF_NULL(cnode); - if (AnfAlgo::GetCNodeName(cnode) != prim::kPrimDepend->name() && - AnfAlgo::GetCNodeName(cnode) != prim::kPrimLoad->name()) { + if (common::AnfAlgo::GetCNodeName(cnode) != prim::kPrimDepend->name() && + common::AnfAlgo::GetCNodeName(cnode) != prim::kPrimLoad->name()) { return nullptr; } - auto virtual_input_op = AnfAlgo::GetInputNode(cnode, kIsolatedDependVirtualInputIndex); + auto virtual_input_op = common::AnfAlgo::GetInputNode(cnode, kIsolatedDependVirtualInputIndex); if (!HasAbstractMonad(virtual_input_op)) { return nullptr; } - auto real_input_op = AnfAlgo::GetInputNode(cnode, kIsolatedDependRealInputIndex); + auto real_input_op = common::AnfAlgo::GetInputNode(cnode, kIsolatedDependRealInputIndex); MS_EXCEPTION_IF_NULL(real_input_op); if (!real_input_op->isa()) { return nullptr; @@ -96,7 +95,7 @@ AnfNodePtr GetReplaceNode(const FuncGraphPtr &func_graph, const AnfNodePtr &node if (isolated_cnode != nullptr) { replace_cnode = isolated_cnode; } - string op_name = AnfAlgo::GetCNodeName(replace_cnode); + string op_name = common::AnfAlgo::GetCNodeName(replace_cnode); // Currently we only eliminate transdata or cast nodes. if (op_name != kTransDataOpName && op_name != prim::kPrimCast->name()) { return nullptr; @@ -115,14 +114,14 @@ AnfNodePtr GetReplaceNode(const FuncGraphPtr &func_graph, const AnfNodePtr &node AnfNodePtr ReplaceMakeTuple(const FuncGraphPtr &func_graph, const CNodePtr &cnode) { MS_EXCEPTION_IF_NULL(func_graph); MS_EXCEPTION_IF_NULL(cnode); - if (AnfAlgo::GetCNodeName(cnode) != prim::kPrimMakeTuple->name()) { + if (common::AnfAlgo::GetCNodeName(cnode) != prim::kPrimMakeTuple->name()) { return nullptr; } - std::vector new_make_tuple_inputs = {AnfAlgo::GetCNodePrimitiveNode(cnode)}; + std::vector new_make_tuple_inputs = {common::AnfAlgo::GetCNodePrimitiveNode(cnode)}; bool need_update = false; - size_t input_num = AnfAlgo::GetInputTensorNum(cnode); + size_t input_num = common::AnfAlgo::GetInputTensorNum(cnode); for (size_t index = 0; index < input_num; ++index) { - auto input = AnfAlgo::GetInputNode(cnode, index); + auto input = common::AnfAlgo::GetInputNode(cnode, index); AnfNodePtr replace_input = GetReplaceNode(func_graph, input); // If replace input is not null, it will be the input of the TransData or Cast. if (replace_input == nullptr) { @@ -166,9 +165,9 @@ std::vector SearchTransDataAndCast(const CNodePtr &cnode) { std::vector result; for (size_t i = 1; i < cnode->size(); ++i) { auto &input = cnode->input(i); - if (AnfAlgo::CheckPrimitiveType(input, prim::kPrimCast) || - AnfAlgo::CheckPrimitiveType(input, prim::kPrimTransData) || - AnfAlgo::CheckPrimitiveType(input, prim::kPrimMakeTuple)) { + if (common::AnfAlgo::CheckPrimitiveType(input, prim::kPrimCast) || + common::AnfAlgo::CheckPrimitiveType(input, prim::kPrimTransData) || + common::AnfAlgo::CheckPrimitiveType(input, prim::kPrimMakeTuple)) { (void)result.emplace_back(i); } } diff --git a/mindspore/ccsrc/backend/common/pass/optimize_updatestate.cc b/mindspore/ccsrc/backend/common/pass/optimize_updatestate.cc index d5c9f64ac51..f18958c2671 100644 --- a/mindspore/ccsrc/backend/common/pass/optimize_updatestate.cc +++ b/mindspore/ccsrc/backend/common/pass/optimize_updatestate.cc @@ -19,7 +19,7 @@ #include #include #include "base/core_ops.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace opt { diff --git a/mindspore/ccsrc/backend/common/pass/reduce_sum_optimizer.cc b/mindspore/ccsrc/backend/common/pass/reduce_sum_optimizer.cc index 72c95798000..dafc186c8ba 100644 --- a/mindspore/ccsrc/backend/common/pass/reduce_sum_optimizer.cc +++ b/mindspore/ccsrc/backend/common/pass/reduce_sum_optimizer.cc @@ -16,7 +16,7 @@ #include "backend/common/pass/reduce_sum_optimizer.h" #include -#include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -55,7 +55,7 @@ AnfNodePtr ReduceSumOptimizer::NewRankOp(const AnfNodePtr &cnode, const KernelGr std::vector rank_inputs; auto prim = std::make_shared(prim::kPrimRank->name()); rank_inputs.push_back(NewValueNode(prim)); - auto prev_node = AnfAlgo::GetPrevNodeOutput(cnode, 1); + auto prev_node = common::AnfAlgo::GetPrevNodeOutput(cnode, 1); rank_inputs.push_back(prev_node.first); auto rank_op = NewCNode(rank_inputs, kernel_graph); MS_EXCEPTION_IF_NULL(rank_op); @@ -99,7 +99,7 @@ AnfNodePtr ReduceSumOptimizer::InsertAssistNode(const CNodePtr &cnode, const Ker auto rank_op = NewRankOp(cnode, kernel_graph); // new range op auto range_op = NewRangeOp(rank_op, kernel_graph); - std::vector new_inputs = {AnfAlgo::GetCNodePrimitiveNode(cnode)}; + std::vector new_inputs = {common::AnfAlgo::GetCNodePrimitiveNode(cnode)}; new_inputs.push_back(cnode->input(1)); new_inputs.push_back(range_op); auto new_node = NewCNode(cnode, kernel_graph); @@ -118,7 +118,7 @@ AnfNodePtr ReduceSumOptimizer::NewAssistValueNode(const CNodePtr &cnode, const K // axis is a tuple ,maybe empty or contain a value less 0; auto axis_input = cnode->input(axis_input_index); if (IsValueNode(axis_input)) { - std::vector new_inputs = {AnfAlgo::GetCNodePrimitiveNode(cnode)}; + std::vector new_inputs = {common::AnfAlgo::GetCNodePrimitiveNode(cnode)}; new_inputs.push_back(cnode->input(1)); auto value_node = axis_input->cast(); MS_EXCEPTION_IF_NULL(value_node); @@ -167,16 +167,16 @@ const AnfNodePtr ReduceSumOptimizer::Process(const FuncGraphPtr &func_graph, con MS_EXCEPTION_IF_NULL(node); auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - auto op_name = AnfAlgo::GetCNodeName(cnode); + auto op_name = common::AnfAlgo::GetCNodeName(cnode); if (op_name != kReduceSumOpName) { MS_LOG(DEBUG) << "Current node is not: " << kReduceSumOpName << ", skip!"; return nullptr; } - if (!AnfAlgo::IsDynamicShape(cnode)) { + if (!common::AnfAlgo::IsDynamicShape(cnode)) { MS_LOG(DEBUG) << "Current node is not dynamic shape, skip!"; return nullptr; } - AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node); + common::AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node); auto kernel_graph = func_graph->cast>(); MS_EXCEPTION_IF_NULL(kernel_graph); if (AnfUtils::IsDimUnknown(cnode) && IsNeedComputeRank(cnode)) { diff --git a/mindspore/ccsrc/backend/common/pass/replace_node_by_proxy.cc b/mindspore/ccsrc/backend/common/pass/replace_node_by_proxy.cc index 78bb6496510..85215461527 100644 --- a/mindspore/ccsrc/backend/common/pass/replace_node_by_proxy.cc +++ b/mindspore/ccsrc/backend/common/pass/replace_node_by_proxy.cc @@ -18,6 +18,7 @@ #include #include "runtime/device/kernel_info.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "kernel/kernel_build_info.h" namespace mindspore { @@ -30,16 +31,16 @@ kernel::KernelBuildInfoPtr ReplaceNodeByProxy::GenerateKernelBuildInfo(const CNo std::vector outputs_device_type; std::vector> outputs_shape; kernel::KernelBuildInfo::KernelBuildInfoBuilder builder; - size_t input_num = AnfAlgo::GetInputTensorNum(cnode); + size_t input_num = common::AnfAlgo::GetInputTensorNum(cnode); for (size_t input_index = 0; input_index < input_num; ++input_index) { inputs_device_format.push_back(AnfAlgo::GetInputFormat(cnode, input_index)); inputs_device_type.push_back(AnfAlgo::GetInputDeviceDataType(cnode, input_index)); } - size_t output_num = AnfAlgo::GetOutputTensorNum(cnode); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(cnode); for (size_t output_index = 0; output_index < output_num; ++output_index) { outputs_device_format.push_back(AnfAlgo::GetOutputFormat(cnode, output_index)); outputs_device_type.push_back(AnfAlgo::GetOutputDeviceDataType(cnode, output_index)); - outputs_shape.push_back(AnfAlgo::GetOutputInferShape(cnode, output_index)); + outputs_shape.push_back(common::AnfAlgo::GetOutputInferShape(cnode, output_index)); } builder.SetFusionType(AnfAlgo::GetFusionType(cnode)); builder.SetProcessor(AnfAlgo::GetProcessor(cnode)); @@ -58,7 +59,7 @@ bool ReplaceNodeByProxy::Run(const FuncGraphPtr &func_graph) { MS_EXCEPTION_IF_NULL(manager); std::vector node_list = TopoSort(func_graph->get_return()); for (auto node : node_list) { - if (node != nullptr && node->isa() && AnfAlgo::GetCNodeName(node) == kEmbeddingLookupOpName) { + if (node != nullptr && node->isa() && common::AnfAlgo::GetCNodeName(node) == kEmbeddingLookupOpName) { TraceGuard guard(std::make_shared(node->debug_info())); auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); @@ -74,8 +75,8 @@ bool ReplaceNodeByProxy::Run(const FuncGraphPtr &func_graph) { proxy_node->set_kernel_info(kernel_info); AbstractBasePtrList abstract_list; - AnfAlgo::CopyNodeAttr(kAttrPsKey, cnode, proxy_node); - AnfAlgo::CopyNodeAttr("offset", cnode, proxy_node); + common::AnfAlgo::CopyNodeAttr(kAttrPsKey, cnode, proxy_node); + common::AnfAlgo::CopyNodeAttr("offset", cnode, proxy_node); abstract_list.push_back(cnode->abstract()); auto abstract_tuple = std::make_shared(abstract_list); MS_EXCEPTION_IF_NULL(abstract_tuple); diff --git a/mindspore/ccsrc/backend/common/pass/replace_node_by_proxy.h b/mindspore/ccsrc/backend/common/pass/replace_node_by_proxy.h index 92e77c6c078..0b1e1bbc923 100644 --- a/mindspore/ccsrc/backend/common/pass/replace_node_by_proxy.h +++ b/mindspore/ccsrc/backend/common/pass/replace_node_by_proxy.h @@ -22,7 +22,7 @@ #include "backend/common/optimizer/pass.h" #include "ir/func_graph.h" #include "ir/anf.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "kernel/kernel_build_info.h" namespace mindspore { diff --git a/mindspore/ccsrc/backend/common/pass/sparse_process.cc b/mindspore/ccsrc/backend/common/pass/sparse_process.cc index c48500467d1..3a391b1e5f1 100644 --- a/mindspore/ccsrc/backend/common/pass/sparse_process.cc +++ b/mindspore/ccsrc/backend/common/pass/sparse_process.cc @@ -18,11 +18,10 @@ #include #include "backend/common/pass/sparse_process.h" #include "ir/anf.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" #include "utils/anf_utils.h" #include "backend/common/optimizer/helper.h" -#include "backend/common/optimizer/optimizer.h" -#include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -102,7 +101,7 @@ bool SplitParameter(const AnfNodePtr &node, std::vector *new_inputs, bool SplitCNode(const AnfNodePtr &node, std::vector *new_inputs) { auto cnode = node->cast(); - auto sparse_prim = AnfAlgo::GetCNodePrimitive(cnode); + auto sparse_prim = common::AnfAlgo::GetCNodePrimitive(cnode); MS_EXCEPTION_IF_NULL(sparse_prim); // Currently, only MakeCSR and MakeTuple nodes can be split. if (make_sparse_set.count(sparse_prim->name()) <= 0 && sparse_prim->name().compare(prim::kPrimMakeTuple->name()) != 0) @@ -140,7 +139,7 @@ const AnfNodePtr SparseProcess::Process(const FuncGraphPtr &func_graph, const An } auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - auto prim = AnfAlgo::GetCNodePrimitive(cnode); + auto prim = common::AnfAlgo::GetCNodePrimitive(cnode); MS_EXCEPTION_IF_NULL(prim); std::string prim_name = prim->name(); auto kernel_graph = func_graph->cast>(); diff --git a/mindspore/ccsrc/backend/common/pass/sparse_process.h b/mindspore/ccsrc/backend/common/pass/sparse_process.h index a65d471ca7d..751c9f5b6d3 100644 --- a/mindspore/ccsrc/backend/common/pass/sparse_process.h +++ b/mindspore/ccsrc/backend/common/pass/sparse_process.h @@ -18,7 +18,7 @@ #include #include "ir/anf.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" #include "backend/common/optimizer/optimizer.h" namespace mindspore { diff --git a/mindspore/ccsrc/backend/common/session/anf_runtime_algorithm.cc b/mindspore/ccsrc/backend/common/session/anf_runtime_algorithm.cc index 9f825f91a53..aa6f88a1bf8 100644 --- a/mindspore/ccsrc/backend/common/session/anf_runtime_algorithm.cc +++ b/mindspore/ccsrc/backend/common/session/anf_runtime_algorithm.cc @@ -21,21 +21,21 @@ #include #include #include "ir/anf.h" -#include "ir/func_graph.h" #include "base/core_ops.h" -#include "utils/utils.h" #include "utils/shape_utils.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/utils.h" +#include "include/common/utils/parallel_context.h" +#include "include/common/utils/anfalgo.h" #include "runtime/device/kernel_info.h" #include "runtime/device/device_address.h" #include "backend/common/optimizer/helper.h" #include "kernel/kernel.h" #include "kernel/kernel_build_info.h" -#include "utils/ms_device_shape_transfer.h" -#include "abstract/param_validator.h" +#include "runtime/device/ms_device_shape_transfer.h" #include "pipeline/jit/static_analysis/static_analysis.h" #include "utils/trace_base.h" #include "utils/anf_utils.h" +#include "utils/ms_context.h" namespace mindspore { namespace session { @@ -48,80 +48,11 @@ using kernel::KernelMod; using kernel::KernelModPtr; namespace { constexpr size_t kNopNodeInputSize = 2; -constexpr size_t kNopNodeRealInputIndex = 1; constexpr size_t kReturnDataIndex = 1; constexpr size_t kSwitchTrueBranchIndex = 2; -constexpr size_t kPartialFuncGraphPos = 1; -constexpr size_t kSwitchLayerBranchPos = 2; -constexpr size_t kSwitchTrueBranchPos = 2; -constexpr size_t kMakeTupleInputStartPos = 1; -const std::set kNodeTupleOutSet = {prim::kMakeTuple, prim::kGetNext}; - -std::vector TransShapeToSizet(const abstract::ShapePtr &shape) { - MS_EXCEPTION_IF_NULL(shape); - std::vector shape_size_t; - if (AnfUtils::IsShapeDynamic(shape)) { - if (std::all_of(shape->max_shape().begin(), shape->max_shape().end(), [](int64_t s) { return s >= 0; })) { - std::transform(shape->max_shape().begin(), shape->max_shape().end(), std::back_inserter(shape_size_t), - LongToSize); - } else { - MS_LOG(EXCEPTION) << "Invalid Max Shape"; - } - } else { - std::transform(shape->shape().begin(), shape->shape().end(), std::back_inserter(shape_size_t), LongToSize); - } - return shape_size_t; -} enum class ShapeType { kMaxShape, kMinShape }; -void GetRealOutputRecursively(const AnfNodePtr &node, size_t output_index, - std::vector *inputs) { - MS_EXCEPTION_IF_NULL(node); - if (node->isa() || node->isa()) { - return inputs->push_back(std::make_pair(node, 0)); - } - - // Skip control node - if (AnfAlgo::CheckPrimitiveType(node, prim::kPrimDepend) || AnfAlgo::CheckPrimitiveType(node, prim::kPrimLoad) || - AnfAlgo::CheckPrimitiveType(node, prim::kPrimUpdateState)) { - return GetRealOutputRecursively(node->cast()->input(kRealInputIndexInDepend), 0, inputs); - } - - // Bypass TupleGetItem - if (AnfAlgo::CheckPrimitiveType(node, prim::kPrimTupleGetItem)) { - auto tuple_get_item = node->cast(); - MS_EXCEPTION_IF_NULL(tuple_get_item); - auto input = AnfAlgo::GetTupleGetItemRealInput(tuple_get_item); - auto index = AnfAlgo::GetTupleGetItemOutIndex(tuple_get_item); - - // Conceal MakeTuple + TupleGetItem pair. - if (AnfAlgo::CheckPrimitiveType(input, prim::kPrimMakeTuple)) { - auto make_tuple = input->cast(); - MS_EXCEPTION_IF_NULL(make_tuple); - auto real_input = AnfAlgo::GetInputNode(make_tuple, index); - return GetRealOutputRecursively(real_input, 0, inputs); - } - - // Skip TupleGetItem. - return GetRealOutputRecursively(input, index, inputs); - } - - // Flatten MakeTuple inputs. - if (AnfAlgo::CheckPrimitiveType(node, prim::kPrimMakeTuple)) { - auto make_tuple = node->cast(); - MS_EXCEPTION_IF_NULL(make_tuple); - size_t input_num = AnfAlgo::GetInputTensorNum(make_tuple); - for (size_t input_index = 0; input_index < input_num; ++input_index) { - auto input_node = AnfAlgo::GetInputNode(make_tuple, input_index); - GetRealOutputRecursively(input_node, 0, inputs); - } - return; - } - - return inputs->push_back(std::make_pair(node, output_index)); -} - // ops pair that dynamic input order is differ from the fixed shape ops // pair: ori_input, ori_input->real_input> static std::map, std::map>> spec_dynamic_node_list = { @@ -152,112 +83,6 @@ static std::map, std::mapname(), {{{0, 1}, {1, 2}, {2, 3}, {3, 4}, {4, 0}}, {{1, 0}, {2, 1}, {3, 2}, {4, 3}, {0, 4}}}}}; - -std::vector GetAllOutputWithIndexInner(const AnfNodePtr &node) { - std::vector ret; - std::vector ret_empty; - const PrimitiveSet expand_prims{ - prim::kPrimMakeTuple, - prim::kPrimMakeCSRTensor, - prim::kPrimMakeCOOTensor, - prim::kPrimMakeRowTensor, - }; - // The MakeTuple/MakeSparse node need expand and recurse. - if (IsOneOfPrimitiveCNode(node, expand_prims)) { - auto make_tuple = node->cast(); - MS_EXCEPTION_IF_NULL(make_tuple); - for (size_t i = 1; i < make_tuple->inputs().size(); i++) { - auto make_tuple_output = GetAllOutputWithIndexInner(make_tuple->input(i)); - (void)std::copy(make_tuple_output.begin(), make_tuple_output.end(), std::back_inserter(ret)); - } - return ret; - } - - // The depend node need get the real node. - if (AnfAlgo::CheckPrimitiveType(node, prim::kPrimDepend)) { - auto depend_node = node->cast(); - MS_EXCEPTION_IF_NULL(depend_node); - auto real_output = GetAllOutputWithIndexInner(depend_node->input(kRealInputIndexInDepend)); - (void)std::copy(real_output.begin(), real_output.end(), std::back_inserter(ret)); - return ret; - } - - // Value node need get all the elements. - if (node->isa()) { - auto value = node->cast()->value(); - MS_EXCEPTION_IF_NULL(value); - if (value->isa()) { - return ret; - } else if (value->isa()) { - auto value_tuple = value->cast(); - auto value_tuple_size = CountValueNum(value_tuple); - for (size_t i = 0; i < value_tuple_size; ++i) { - (void)ret.emplace_back(node, i); - } - } else { - (void)ret.emplace_back(node, 0); - } - return ret; - } - - size_t outputs_num = 1; - if (AnfUtils::IsRealCNodeKernel(node)) { - outputs_num = AnfAlgo::GetOutputTensorNum(node); - } - - // If the node is a call, the outputs num should get from the abstract. - if (AnfAlgo::IsCallNode(node) || AnfAlgo::CheckPrimitiveType(node, prim::kPrimTupleGetItem)) { - auto abstract = node->abstract(); - MS_EXCEPTION_IF_NULL(abstract); - outputs_num = AnfAlgo::GetOutputNumByAbstract(abstract); - } - - // The output may be the tuple of node, so need visit all the outputs of node. - for (size_t i = 0; i < outputs_num; ++i) { - // Maybe this scene: tupleGetItem + depend + makeTuple, can be done correctly in VisitKernelWithReturnType. - // The output may be updataState node for connecting dependencies between subgraphs. - auto output_with_index = - AnfAlgo::VisitKernelWithReturnType(node, i, false, {prim::kPrimMakeTuple, prim::kPrimUpdateState}); - MS_EXCEPTION_IF_NULL(output_with_index.first); - - // The MakeTuple/MakeSparse node need recurse. - if (IsOneOfPrimitiveCNode(output_with_index.first, expand_prims)) { - auto output_vector = GetAllOutputWithIndexInner(output_with_index.first); - (void)std::copy(output_vector.begin(), output_vector.end(), std::back_inserter(ret)); - continue; - } - - // The InitDataSetQueue node has no output. - if (AnfAlgo::CheckPrimitiveType(output_with_index.first, prim::kPrimInitDataSetQueue)) { - return ret_empty; - } - - MS_LOG(INFO) << "Output node: " << output_with_index.first->fullname_with_scope() - << " with output index: " << output_with_index.second; - ret.push_back(output_with_index); - } - return ret; -} - -bool IsNodeDynamicShape(const AnfNodePtr &node) { - MS_EXCEPTION_IF_NULL(node); - if (!node->isa()) { - MS_LOG(DEBUG) << "Node is not a cnode"; - return false; - } - auto cnode = node->cast(); - auto in_dynamic = AnfAlgo::IsNodeInputDynamicShape(cnode); - auto out_dynamic = AnfUtils::IsNodeOutputDynamicShape(cnode); - if (in_dynamic && !AnfAlgo::HasNodeAttr(kAttrInputIsDynamicShape, cnode)) { - AnfAlgo::SetNodeAttr(kAttrInputIsDynamicShape, MakeValue(true), cnode); - MS_LOG(DEBUG) << "Set Input Dynamic Shape Attr to Node:" << cnode->fullname_with_scope(); - } - if (out_dynamic && !AnfAlgo::HasNodeAttr(kAttrOutputIsDynamicShape, cnode)) { - AnfAlgo::SetNodeAttr(kAttrOutputIsDynamicShape, MakeValue(true), cnode); - MS_LOG(DEBUG) << "Set Output Dynamic Shape Attr to Node:" << cnode->fullname_with_scope(); - } - return in_dynamic || out_dynamic; -} } // namespace AnfNodePtr AnfRuntimeAlgorithm::MakeMonadValueNode(const KernelGraphPtr &kg) { @@ -300,352 +125,15 @@ void AnfRuntimeAlgorithm::KeepOrder(const KernelGraphPtr &kg, const AnfNodePtr & } } -AnfNodePtr AnfRuntimeAlgorithm::GetTupleGetItemRealInput(const CNodePtr &tuple_get_item) { - MS_EXCEPTION_IF_NULL(tuple_get_item); - if (tuple_get_item->size() != kTupleGetItemInputSize) { - MS_LOG(EXCEPTION) << "The node tuple_get_item must have 2 inputs!"; - } - return tuple_get_item->input(kRealInputNodeIndexInTupleGetItem); -} - -size_t AnfRuntimeAlgorithm::GetTupleGetItemOutIndex(const CNodePtr &tuple_get_item) { - MS_EXCEPTION_IF_NULL(tuple_get_item); - if (tuple_get_item->size() != kTupleGetItemInputSize) { - MS_LOG(EXCEPTION) << "The node tuple_get_item must have 2 inputs!"; - } - auto output_index_value_node = tuple_get_item->input(kInputNodeOutputIndexInTupleGetItem); - MS_EXCEPTION_IF_NULL(output_index_value_node); - auto value_node = output_index_value_node->cast(); - MS_EXCEPTION_IF_NULL(value_node); - return LongToSize(GetValue(value_node->value())); -} - -KernelWithIndex AnfRuntimeAlgorithm::VisitKernel(const AnfNodePtr &anf_node, size_t index) { - // this function was moved to AnfUtils. - return AnfUtils::VisitKernel(anf_node, index); -} - -KernelWithIndex AnfRuntimeAlgorithm::VisitKernelWithReturnType(const AnfNodePtr &anf_node, size_t index, - bool skip_nop_node, - const std::vector &return_types, - abstract::AbstractBasePtr *abstract) { - MS_EXCEPTION_IF_NULL(anf_node); - if (std::any_of(return_types.begin(), return_types.end(), [&anf_node](const PrimitivePtr &prim_type) -> bool { - return CheckPrimitiveType(anf_node, prim_type); - })) { - return KernelWithIndex(anf_node, index); - } - if (!anf_node->isa()) { - return KernelWithIndex(anf_node, index); - } - auto cnode = anf_node->cast(); - MS_EXCEPTION_IF_NULL(cnode); - if (CheckPrimitiveType(cnode, prim::kPrimTupleGetItem)) { - abstract::AbstractBasePtr abs = nullptr; - auto item_with_index_tmp = VisitKernelWithReturnType( - GetTupleGetItemRealInput(cnode), GetTupleGetItemOutIndex(cnode), skip_nop_node, return_types, &abs); - if (CheckPrimitiveType(item_with_index_tmp.first, prim::kPrimMakeTuple)) { - MS_EXCEPTION_IF_NULL(item_with_index_tmp.first); - auto make_tuple = item_with_index_tmp.first->cast(); - MS_EXCEPTION_IF_NULL(make_tuple); - const std::vector &make_tuple_inputs = make_tuple->inputs(); - size_t make_tuple_input_index = item_with_index_tmp.second + 1; - if (make_tuple_input_index >= make_tuple_inputs.size()) { - MS_LOG(EXCEPTION) << "Index[" << make_tuple_input_index << "] out of range[" << make_tuple_inputs.size() - << "]."; - } - return VisitKernelWithReturnType(make_tuple_inputs[make_tuple_input_index], index, skip_nop_node, return_types); - } - if (IsCallNode(item_with_index_tmp.first) || item_with_index_tmp.first->isa()) { - size_t real_index = item_with_index_tmp.second; - if (abs == nullptr) { - abs = item_with_index_tmp.first->abstract(); - real_index = 0; - } - MS_EXCEPTION_IF_NULL(abs); - if (abs->isa()) { - auto tuple_abstract = abs->cast(); - MS_EXCEPTION_IF_NULL(tuple_abstract); - auto sub_abstracts = tuple_abstract->elements(); - if (sub_abstracts.size() <= GetTupleGetItemOutIndex(cnode)) { - MS_LOG(EXCEPTION) << "Invalid index:" << GetTupleGetItemOutIndex(cnode) - << " for abstract:" << abs->ToString(); - } - for (size_t i = 0; i < GetTupleGetItemOutIndex(cnode); ++i) { - MS_EXCEPTION_IF_NULL(sub_abstracts[i]); - real_index += AnfAlgo::GetOutputNumByAbstract(sub_abstracts[i]); - } - if (abstract != nullptr) { - (*abstract) = sub_abstracts[GetTupleGetItemOutIndex(cnode)]; - MS_EXCEPTION_IF_NULL((*abstract)); - } else { - // In recursion of getitem node, the index of the first input of its real node is returned. - // When the recursion ends, the outermost index needs to be accumulated. - real_index += index; - } - return {item_with_index_tmp.first, real_index}; - } - } - return item_with_index_tmp; - } - if (AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimUpdateState)) { - return VisitKernelWithReturnType(cnode->input(kUpdateStateStateInput), index, skip_nop_node, return_types); - } - const PrimitiveSet follow_first_input_prims = {prim::kPrimDepend, prim::kPrimLoad}; - if (IsOneOfPrimitiveCNode(cnode, follow_first_input_prims)) { - return VisitKernelWithReturnType(cnode->input(kRealInputIndexInDepend), index, skip_nop_node, return_types); - } - if (opt::IsNopNode(cnode) && skip_nop_node) { - if (cnode->size() != kNopNodeInputSize) { - MS_LOG(EXCEPTION) << "Invalid nop node " << cnode->DebugString() << trace::DumpSourceLines(cnode); - } - return VisitKernelWithReturnType(cnode->input(kNopNodeRealInputIndex), 0, skip_nop_node, return_types); - } - return KernelWithIndex(anf_node, index); -} - -std::vector AnfRuntimeAlgorithm::GetAllOutput(const AnfNodePtr &node, - const std::vector &return_types) { - std::vector ret; - auto return_prim_type = return_types; - // if visited make_tuple should return back - return_prim_type.push_back(prim::kPrimMakeTuple); - auto item_with_index = AnfAlgo::VisitKernelWithReturnType(node, 0, false, return_prim_type); - if (AnfAlgo::CheckPrimitiveType(item_with_index.first, prim::kPrimMakeTuple)) { - MS_EXCEPTION_IF_NULL(item_with_index.first); - auto make_tuple = item_with_index.first->cast(); - MS_EXCEPTION_IF_NULL(make_tuple); - for (size_t i = 1; i < make_tuple->inputs().size(); i++) { - auto input_i_vector = GetAllOutput(make_tuple->input(i), return_types); - (void)std::copy(input_i_vector.begin(), input_i_vector.end(), std::back_inserter(ret)); - } - return ret; - } - ret.push_back(item_with_index.first); - return ret; -} - -size_t AnfRuntimeAlgorithm::GetOutputNumByAbstract(const AbstractBasePtr &node_abstract) { - MS_EXCEPTION_IF_NULL(node_abstract); - size_t result = 0; - if (node_abstract->isa()) { - auto csr_tensor_abstract = node_abstract->cast(); - MS_EXCEPTION_IF_NULL(csr_tensor_abstract); - result += GetOutputNumByAbstract(csr_tensor_abstract->indptr()); - result += GetOutputNumByAbstract(csr_tensor_abstract->indices()); - result += GetOutputNumByAbstract(csr_tensor_abstract->values()); - result += GetOutputNumByAbstract(csr_tensor_abstract->dense_shape()); - return result; - } - - if (node_abstract->isa()) { - auto coo_tensor_abstract = node_abstract->cast(); - MS_EXCEPTION_IF_NULL(coo_tensor_abstract); - result += GetOutputNumByAbstract(coo_tensor_abstract->dense_shape()); - result += GetOutputNumByAbstract(coo_tensor_abstract->indices()); - result += GetOutputNumByAbstract(coo_tensor_abstract->values()); - return result; - } - - if (!node_abstract->isa()) { - return 1; - } - - auto tuple_abstract = node_abstract->cast(); - MS_EXCEPTION_IF_NULL(tuple_abstract); - const auto &sub_abstracts = tuple_abstract->elements(); - for (const auto &sub_abstract : sub_abstracts) { - MS_EXCEPTION_IF_NULL(sub_abstract); - result += GetOutputNumByAbstract(sub_abstract); - } - return result; -} - -std::vector AnfRuntimeAlgorithm::GetAllOutputWithIndex(const AnfNodePtr &node) { - auto ret = GetAllOutputWithIndexInner(node); - std::map value_node_index; - - // Unify the output of the front and back end to the ValueTuple - for (auto &output_with_index : ret) { - auto value_node = output_with_index.first; - if (!value_node->isa()) { - continue; - } - if (value_node_index.find(value_node) == value_node_index.end() || - value_node_index[value_node] < output_with_index.second) { - value_node_index[value_node] = output_with_index.second; - } else { - value_node_index[value_node]++; - MS_LOG(INFO) << "Set output value node new index, value node: " << value_node->fullname_with_scope() - << ", original index: " << output_with_index.second - << ", new index:" << value_node_index[value_node]; - output_with_index.second = value_node_index[value_node]; - } - } - return ret; -} - -AnfNodePtr AnfRuntimeAlgorithm::GetCNodePrimitiveNode(const CNodePtr &node) { - MS_EXCEPTION_IF_NULL(node); - return node->input(kAnfPrimitiveIndex); -} - -PrimitivePtr AnfRuntimeAlgorithm::GetCNodePrimitive(const AnfNodePtr &node) { - MS_EXCEPTION_IF_NULL(node); - auto cnode = node->cast(); - MS_EXCEPTION_IF_NULL(cnode); - auto attr_input = GetCNodePrimitiveNode(cnode); - MS_EXCEPTION_IF_NULL(attr_input); - auto value_node = attr_input->cast(); - MS_EXCEPTION_IF_NULL(value_node); - auto value = value_node->value(); - MS_EXCEPTION_IF_NULL(value); - auto primitive = value->cast(); - return primitive; -} - -bool AnfRuntimeAlgorithm::CheckPrimitiveType(const AnfNodePtr &node, const PrimitivePtr &primitive_type) { - MS_EXCEPTION_IF_NULL(node); - if (!node->isa()) { - return false; - } - auto cnode = node->cast(); - MS_EXCEPTION_IF_NULL(cnode); - return IsPrimitive(cnode->input(kAnfPrimitiveIndex), primitive_type); -} - -FuncGraphPtr AnfRuntimeAlgorithm::GetCNodeFuncGraphPtr(const AnfNodePtr &node) { - MS_EXCEPTION_IF_NULL(node); - auto cnode = node->cast(); - MS_EXCEPTION_IF_NULL(cnode); - auto attr_input = cnode->input(kAnfPrimitiveIndex); - MS_EXCEPTION_IF_NULL(attr_input); - auto value_node = attr_input->cast(); - MS_EXCEPTION_IF_NULL(value_node); - auto value = value_node->value(); - MS_EXCEPTION_IF_NULL(value); - return value->cast(); -} - -std::string AnfRuntimeAlgorithm::GetCNodeName(const AnfNodePtr &node) { - // this function was moved to AnfUtils. - return AnfUtils::GetCNodeName(node); -} - -std::string AnfRuntimeAlgorithm::GetNodeDebugString(const AnfNodePtr &node) { - MS_EXCEPTION_IF_NULL(node); - return node->DebugString(); -} - -void AnfRuntimeAlgorithm::SetNodeAttr(const std::string &key, const ValuePtr &value, const AnfNodePtr &node) { - // this function was moved to AnfUtils. - return AnfUtils::SetNodeAttr(key, value, node); -} - -void AnfRuntimeAlgorithm::CopyNodeAttr(const std::string &key, const AnfNodePtr &from, const AnfNodePtr &to) { - CopyNodeAttr(key, key, from, to); -} - -void AnfRuntimeAlgorithm::CopyNodeAttr(const std::string &old_key, const std::string &new_key, const AnfNodePtr &from, - const AnfNodePtr &to) { - MS_EXCEPTION_IF_NULL(from); - MS_EXCEPTION_IF_NULL(to); - if (!from->isa() || !to->isa()) { - MS_LOG(EXCEPTION) << "Only cnode has attr, but this from_anf is " << from->DebugString() << " ,to_node is " - << to->DebugString() << trace::DumpSourceLines(from); - } - auto from_primitive = AnfAlgo::GetCNodePrimitive(from); - MS_EXCEPTION_IF_NULL(from_primitive); - auto to_primitive = AnfAlgo::GetCNodePrimitive(to); - MS_EXCEPTION_IF_NULL(to_primitive); - to_primitive->set_attr(new_key, from_primitive->GetAttr(old_key)); -} - -void AnfRuntimeAlgorithm::CopyNodeAttrs(const AnfNodePtr &from, const AnfNodePtr &to) { - MS_EXCEPTION_IF_NULL(from); - MS_EXCEPTION_IF_NULL(to); - if (!from->isa() || !to->isa()) { - MS_LOG(EXCEPTION) << "Only cnode has attr, but this from_anf is " << from->DebugString() << ",to_node is " - << from->DebugString() << trace::DumpSourceLines(from); - } - auto from_primitive = AnfAlgo::GetCNodePrimitive(from); - MS_EXCEPTION_IF_NULL(from_primitive); - auto to_primitive = AnfAlgo::GetCNodePrimitive(to); - MS_EXCEPTION_IF_NULL(to_primitive); - auto from_cnode = from->cast(); - auto to_cnode = to->cast(); - if (from_cnode->HasPrimalAttr(kAttrMicro)) { - to_cnode->AddPrimalAttr(kAttrMicro, from_cnode->GetPrimalAttr(kAttrMicro)); - } - (void)to_primitive->SetAttrs(from_primitive->attrs()); -} - -void AnfRuntimeAlgorithm::EraseNodeAttr(const std::string &key, const AnfNodePtr node) { - MS_EXCEPTION_IF_NULL(node); - if (!node->isa()) { - MS_LOG(EXCEPTION) << "Only cnode has attr, but this anf is " << node->DebugString() << trace::DumpSourceLines(node); - } - // single op cnode. - auto primitive = AnfAlgo::GetCNodePrimitive(node); - if (primitive != nullptr) { - primitive->EraseAttr(key); - return; - } - // graph kernel cnode. - auto fg = AnfAlgo::GetCNodeFuncGraphPtr(node); - MS_EXCEPTION_IF_NULL(fg); - fg->erase_flag(key); -} - -bool AnfRuntimeAlgorithm::HasNodeAttr(const std::string &key, const CNodePtr &node) { - MS_EXCEPTION_IF_NULL(node); - if (!node->isa()) { - MS_LOG(WARNING) << "Only cnode has attr, but this anf is " << node->DebugString(); - return false; - } - // call node's input0 is not a primitive. - if (!IsValueNode(node->cast()->input(0))) { - return false; - } - // single op cnode. - auto primitive = AnfAlgo::GetCNodePrimitive(node); - if (primitive != nullptr) { - return primitive->HasAttr(key); - } - // graph kernel cnode. - auto fg = AnfAlgo::GetCNodeFuncGraphPtr(node); - MS_EXCEPTION_IF_NULL(fg); - return fg->has_attr(key); -} - -size_t AnfRuntimeAlgorithm::GetInputNum(const CNodePtr &cnode) { - MS_EXCEPTION_IF_NULL(cnode); - size_t input_num = cnode->size(); - if (input_num == 0) { - MS_LOG(EXCEPTION) << "Cnode inputs size can't be zero." << trace::DumpSourceLines(cnode); - } - return input_num - 1; -} - -size_t AnfRuntimeAlgorithm::GetInputTensorNum(const AnfNodePtr &node) { - // this function was moved to AnfUtils. - return AnfUtils::GetInputTensorNum(node); -} - -size_t AnfRuntimeAlgorithm::GetOutputTensorNum(const AnfNodePtr &node) { - // this function was moved to AnfUtils. - return AnfUtils::GetOutputTensorNum(node); -} - size_t AnfRuntimeAlgorithm::GetOutputTensorMemSize(const AnfNodePtr &node, size_t output_index) { MS_EXCEPTION_IF_NULL(node); - if (output_index >= AnfAlgo::GetOutputTensorNum(node)) { + if (output_index >= common::AnfAlgo::GetOutputTensorNum(node)) { MS_EXCEPTION(ArgumentError) << "output index [" << output_index << "] large than the output size [" - << AnfAlgo::GetOutputTensorNum(node) << "] of node!"; + << common::AnfAlgo::GetOutputTensorNum(node) << "] of node!"; } TypeId output_type_id = AnfAlgo::GetOutputDeviceDataType(node, output_index); if (output_type_id == kTypeUnknown) { - output_type_id = AnfAlgo::GetOutputInferDataType(node, output_index); + output_type_id = common::AnfAlgo::GetOutputInferDataType(node, output_index); } size_t type_size = GetTypeByte(TypeIdToType(output_type_id)); std::vector shape = AnfAlgo::GetOutputDeviceShape(node, output_index); @@ -732,9 +220,9 @@ std::string AnfRuntimeAlgorithm::GetOriginDataFormat(const AnfNodePtr &node) { std::string AnfRuntimeAlgorithm::GetOutputFormat(const AnfNodePtr &node, size_t output_idx) { MS_EXCEPTION_IF_NULL(node); - if (output_idx > GetOutputTensorNum(node)) { + if (output_idx > common::AnfAlgo::GetOutputTensorNum(node)) { MS_LOG(EXCEPTION) << "Output index:" << output_idx - << " is out of the node output range :" << GetOutputTensorNum(node) << " #node [" + << " is out of the node output range :" << common::AnfAlgo::GetOutputTensorNum(node) << " #node [" << node->DebugString() << "]" << trace::DumpSourceLines(node); } if (!AnfUtils::IsRealKernel(node)) { @@ -754,10 +242,10 @@ std::string AnfRuntimeAlgorithm::GetOutputFormat(const AnfNodePtr &node, size_t std::string AnfRuntimeAlgorithm::GetInputFormat(const AnfNodePtr &node, size_t input_idx) { MS_EXCEPTION_IF_NULL(node); - if (input_idx > GetInputTensorNum(node)) { + if (input_idx > common::AnfAlgo::GetInputTensorNum(node)) { MS_LOG(EXCEPTION) << "Input index :" << input_idx - << " is out of the number node Input range :" << GetInputTensorNum(node) << "#node [" - << node->DebugString() << "]" << trace::DumpSourceLines(node); + << " is out of the number node Input range :" << common::AnfAlgo::GetInputTensorNum(node) + << "#node [" << node->DebugString() << "]" << trace::DumpSourceLines(node); } if (!AnfUtils::IsRealKernel(node)) { return GetPrevNodeOutputFormat(node, input_idx); @@ -774,105 +262,20 @@ std::string AnfRuntimeAlgorithm::GetInputFormat(const AnfNodePtr &node, size_t i return format; } -KernelWithIndex AnfRuntimeAlgorithm::GetPrevNodeOutput(const AnfNodePtr &anf_node, size_t input_idx, - bool skip_nop_node) { - MS_EXCEPTION_IF_NULL(anf_node); - if (!anf_node->isa()) { - MS_LOG(EXCEPTION) << anf_node->DebugString() << "anf_node is not CNode." << trace::DumpSourceLines(anf_node); - } - auto kernel_info = anf_node->kernel_info(); - if (kernel_info) { - auto runtime_cache = kernel_info->runtime_cache(); - if (runtime_cache.runtime_cache().is_valid()) { - auto output = runtime_cache.runtime_cache().get_prev_node_output(input_idx); - if (output.first != nullptr) { - return output; - } - } - } - KernelWithIndex res; - if (CheckPrimitiveType(anf_node, prim::kPrimTupleGetItem)) { - res = VisitKernelWithReturnType(anf_node, 0, skip_nop_node); - } else { - auto input_node = AnfAlgo::GetInputNode(anf_node->cast(), input_idx); - MS_EXCEPTION_IF_NULL(input_node); - res = VisitKernelWithReturnType(input_node, 0, skip_nop_node); - } - if (kernel_info) { - auto runtime_cache = kernel_info->runtime_cache(); - if (runtime_cache.runtime_cache().is_valid()) { - runtime_cache.runtime_cache().set_prev_node_output(input_idx, res); - } - } - return res; -} - std::string AnfRuntimeAlgorithm::GetPrevNodeOutputFormat(const AnfNodePtr &anf_node, size_t input_idx) { - KernelWithIndex kernel_with_index = AnfAlgo::GetPrevNodeOutput(anf_node, input_idx); + KernelWithIndex kernel_with_index = common::AnfAlgo::GetPrevNodeOutput(anf_node, input_idx); return AnfRuntimeAlgorithm::GetOutputFormat(kernel_with_index.first, kernel_with_index.second); } std::string AnfRuntimeAlgorithm::GetPrevNodeOutputReshapeType(const AnfNodePtr &node, size_t input_idx) { - KernelWithIndex kernel_with_index = AnfAlgo::GetPrevNodeOutput(node, input_idx); + KernelWithIndex kernel_with_index = common::AnfAlgo::GetPrevNodeOutput(node, input_idx); return GetOutputReshapeType(kernel_with_index.first, kernel_with_index.second); } -std::vector AnfRuntimeAlgorithm::GetOutputInferShape(const AnfNodePtr &node, - const abstract::BaseShapePtr &base_shape, - size_t output_idx) { - MS_EXCEPTION_IF_NULL(node); - MS_EXCEPTION_IF_NULL(base_shape); - if (base_shape->isa()) { - if (output_idx == 0) { - return TransShapeToSizet(base_shape->cast()); - } - MS_LOG(EXCEPTION) << "The node " << node->DebugString() << "is a single output node but got index [" << output_idx - << trace::DumpSourceLines(node); - } else if (base_shape->isa()) { - auto tuple_shape = base_shape->cast(); - MS_EXCEPTION_IF_NULL(tuple_shape); - if (output_idx >= tuple_shape->size()) { - MS_LOG(EXCEPTION) << "Output index " << output_idx << "is larger than output number " << tuple_shape->size() - << node->DebugString() << trace::DumpSourceLines(node); - } - auto b_shp = (*tuple_shape)[output_idx]; - if (b_shp->isa()) { - return TransShapeToSizet(b_shp->cast()); - } else if (b_shp->isa()) { - return std::vector(); - } else if (b_shp->isa()) { - // Usually there is no tuple in tuple for the shape of the kernel graph parameter, but there will be such a - // scenario when dump ir is in the compilation process, here return an empty shape so that dump ir can work - // normally. - MS_LOG(INFO) << "The output shape of node:" << node->DebugString() << " index:" << output_idx - << " is a TupleShape:" << base_shape->ToString(); - return std::vector(); - } else { - MS_LOG(EXCEPTION) << "The output type of ApplyKernel index:" << output_idx - << " should be a NoShape , ArrayShape or a TupleShape, but it is " << base_shape->ToString() - << "node :" << node->DebugString() << "." << trace::DumpSourceLines(node); - } - } else if (base_shape->isa()) { - return std::vector(); - } - MS_LOG(EXCEPTION) << "The output type of ApplyKernel should be a NoShape , ArrayShape or a TupleShape, but it is " - << base_shape->ToString() << " node : " << node->DebugString() << trace::DumpSourceLines(node); -} - -std::vector AnfRuntimeAlgorithm::GetOutputInferShape(const AnfNodePtr &node, size_t output_idx) { - MS_EXCEPTION_IF_NULL(node); - return GetOutputInferShape(node, node->Shape(), output_idx); -} - -std::vector AnfRuntimeAlgorithm::GetPrevNodeOutputInferShape(const AnfNodePtr &node, size_t input_idx) { - KernelWithIndex kernel_with_index = AnfAlgo::GetPrevNodeOutput(node, input_idx); - return AnfRuntimeAlgorithm::GetOutputInferShape(kernel_with_index.first, kernel_with_index.second); -} - std::vector AnfRuntimeAlgorithm::GetOutputDeviceShapeForTbeBuild(const AnfNodePtr &node, const size_t output_idx, const std::string &format) { - auto output_shape = GetOutputDetailShape(node, output_idx); + auto output_shape = common::AnfAlgo::GetOutputDetailShape(node, output_idx); std::vector infer_shape; if (output_shape->isa()) { auto shape_ptr = output_shape->cast(); @@ -893,7 +296,7 @@ std::vector AnfRuntimeAlgorithm::GetOutputDeviceShapeForTbeBuild(const std::vector AnfRuntimeAlgorithm::GetOutputDeviceShape(const AnfNodePtr &node, size_t output_idx) { auto format = GetOutputFormat(node, output_idx); - auto infer_shape = GetOutputInferShape(node, output_idx); + auto infer_shape = common::AnfAlgo::GetOutputInferShape(node, output_idx); if (infer_shape.empty()) { return infer_shape; } @@ -907,7 +310,7 @@ std::vector AnfRuntimeAlgorithm::GetOutputDeviceShape(const AnfNodePtr & std::vector AnfRuntimeAlgorithm::GetInputDeviceShapeForTbeBuild(const AnfNodePtr &node, const size_t input_idx, const std::string &format) { - auto output_shape = GetPrevNodeOutputDetailShape(node, input_idx); + auto output_shape = common::AnfAlgo::GetPrevNodeOutputDetailShape(node, input_idx); std::vector infer_shape; if (output_shape->isa()) { auto shape_ptr = output_shape->cast(); @@ -928,7 +331,7 @@ std::vector AnfRuntimeAlgorithm::GetInputDeviceShapeForTbeBuild(const A std::vector AnfRuntimeAlgorithm::GetInputDeviceShape(const AnfNodePtr &node, size_t input_idx) { auto format = GetInputFormat(node, input_idx); - auto infer_shape = GetPrevNodeOutputInferShape(node, input_idx); + auto infer_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node, input_idx); if (infer_shape.empty()) { return infer_shape; } @@ -942,10 +345,10 @@ std::vector AnfRuntimeAlgorithm::GetInputDeviceShape(const AnfNodePtr &n std::string AnfRuntimeAlgorithm::GetInputReshapeType(const AnfNodePtr &node, size_t input_idx) { MS_EXCEPTION_IF_NULL(node); - if (input_idx > GetInputTensorNum(node)) { + if (input_idx > common::AnfAlgo::GetInputTensorNum(node)) { MS_LOG(EXCEPTION) << "The index:" << input_idx - << " is out of range of the node's input size : " << GetInputTensorNum(node) << "#node[" - << node->DebugString() << "]" << trace::DumpSourceLines(node); + << " is out of range of the node's input size : " << common::AnfAlgo::GetInputTensorNum(node) + << "#node[" << node->DebugString() << "]" << trace::DumpSourceLines(node); } if (!AnfUtils::IsRealKernel(node)) { return GetPrevNodeOutputReshapeType(node, input_idx); @@ -962,9 +365,9 @@ std::string AnfRuntimeAlgorithm::GetInputReshapeType(const AnfNodePtr &node, siz std::string AnfRuntimeAlgorithm::GetOutputReshapeType(const AnfNodePtr &node, size_t output_idx) { MS_EXCEPTION_IF_NULL(node); - if (output_idx > GetOutputTensorNum(node)) { + if (output_idx > common::AnfAlgo::GetOutputTensorNum(node)) { MS_LOG(EXCEPTION) << "The index [" << output_idx << "] is out of range of the node's output size [ " - << GetOutputTensorNum(node) << "#node[ " << node->DebugString() << "]" + << common::AnfAlgo::GetOutputTensorNum(node) << "#node[ " << node->DebugString() << "]" << trace::DumpSourceLines(node); } if (!AnfUtils::IsRealKernel(node)) { @@ -980,53 +383,11 @@ std::string AnfRuntimeAlgorithm::GetOutputReshapeType(const AnfNodePtr &node, si return build_info->GetOutputReshapeType(output_idx); } -TypeId AnfRuntimeAlgorithm::GetOutputInferDataType(const TypePtr &type, size_t output_idx) { - auto type_ptr = type; - MS_EXCEPTION_IF_NULL(type_ptr); - if (type_ptr->isa()) { - auto tuple_ptr = type_ptr->cast(); - MS_EXCEPTION_IF_NULL(tuple_ptr); - if (output_idx >= tuple_ptr->size()) { - MS_LOG(EXCEPTION) << "Output index " << output_idx << " must be less than output number " << tuple_ptr->size(); - } - type_ptr = (*tuple_ptr)[output_idx]; - MS_EXCEPTION_IF_NULL(type_ptr); - } - - if (type_ptr->isa()) { - auto tensor_ptr = type_ptr->cast(); - MS_EXCEPTION_IF_NULL(tensor_ptr); - TypePtr elem = tensor_ptr->element(); - MS_EXCEPTION_IF_NULL(elem); - return elem->type_id(); - } - - if (type_ptr->isa()) { - auto tensor_ptr = type_ptr->cast(); - MS_EXCEPTION_IF_NULL(tensor_ptr); - TypePtr elem = tensor_ptr->element(); - MS_EXCEPTION_IF_NULL(elem); - return elem->type_id(); - } - - return type_ptr->type_id(); -} - -TypeId AnfRuntimeAlgorithm::GetOutputInferDataType(const AnfNodePtr &node, size_t output_idx) { - MS_EXCEPTION_IF_NULL(node); - return GetOutputInferDataType(node->Type(), output_idx); -} - -TypeId AnfRuntimeAlgorithm::GetPrevNodeOutputInferDataType(const AnfNodePtr &node, size_t input_idx) { - KernelWithIndex kernel_with_index = AnfAlgo::GetPrevNodeOutput(node, input_idx); - return AnfRuntimeAlgorithm::GetOutputInferDataType(kernel_with_index.first, kernel_with_index.second); -} - TypeId AnfRuntimeAlgorithm::GetOutputDeviceDataType(const AnfNodePtr &node, size_t output_idx) { MS_EXCEPTION_IF_NULL(node); - if (output_idx > GetOutputTensorNum(node)) { + if (output_idx > common::AnfAlgo::GetOutputTensorNum(node)) { MS_LOG(EXCEPTION) << "The index [" << output_idx << "] is out of range of the node's output size [ " - << GetOutputTensorNum(node) << "#node [ " << node->DebugString() << "]" + << common::AnfAlgo::GetOutputTensorNum(node) << "#node [ " << node->DebugString() << "]" << trace::DumpSourceLines(node); } if (!AnfUtils::IsRealKernel(node)) { @@ -1045,9 +406,9 @@ TypeId AnfRuntimeAlgorithm::GetOutputDeviceDataType(const AnfNodePtr &node, size TypeId AnfRuntimeAlgorithm::GetInputDeviceDataType(const AnfNodePtr &node, size_t input_idx) { MS_EXCEPTION_IF_NULL(node); - if (input_idx > GetInputTensorNum(node)) { + if (input_idx > common::AnfAlgo::GetInputTensorNum(node)) { MS_LOG(EXCEPTION) << "The index [" << input_idx << "] is out of range of the node's input size [ " - << GetInputTensorNum(node) << "#node [ " << node->DebugString() << "]" + << common::AnfAlgo::GetInputTensorNum(node) << "#node [ " << node->DebugString() << "]" << trace::DumpSourceLines(node); } if (!AnfUtils::IsRealKernel(node)) { @@ -1066,14 +427,14 @@ TypeId AnfRuntimeAlgorithm::GetInputDeviceDataType(const AnfNodePtr &node, size_ } TypeId AnfRuntimeAlgorithm::GetPrevNodeOutputDeviceDataType(const AnfNodePtr &anf_node, size_t input_idx) { - KernelWithIndex kernel_with_index = AnfAlgo::GetPrevNodeOutput(anf_node, input_idx); + KernelWithIndex kernel_with_index = common::AnfAlgo::GetPrevNodeOutput(anf_node, input_idx); return AnfRuntimeAlgorithm::GetOutputDeviceDataType(kernel_with_index.first, kernel_with_index.second); } // get output device addr of anf_node const DeviceAddress *AnfRuntimeAlgorithm::GetOutputAddr(const AnfNodePtr &node, size_t output_idx, bool skip_nop_node) { MS_EXCEPTION_IF_NULL(node); - if (opt::IsNopNode(node) && (skip_nop_node || IsNeedSkipNopOpAddr(node))) { + if (common::AnfAlgo::IsNopNode(node) && (skip_nop_node || common::AnfAlgo::IsNeedSkipNopOpAddr(node))) { auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); if (cnode->size() == kNopNodeInputSize) { @@ -1095,7 +456,7 @@ const DeviceAddress *AnfRuntimeAlgorithm::GetOutputAddr(const AnfNodePtr &node, DeviceAddressPtr AnfRuntimeAlgorithm::GetMutableOutputAddr(const AnfNodePtr &node, size_t output_idx, bool skip_nop_node) { MS_EXCEPTION_IF_NULL(node); - if (opt::IsNopNode(node) && (skip_nop_node || IsNeedSkipNopOpAddr(node))) { + if (common::AnfAlgo::IsNopNode(node) && (skip_nop_node || common::AnfAlgo::IsNeedSkipNopOpAddr(node))) { auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); if (cnode->inputs().size() == kNopNodeInputSize) { @@ -1118,11 +479,11 @@ DeviceAddressPtr AnfRuntimeAlgorithm::GetMutableOutputAddr(const AnfNodePtr &nod // get output device addr of anf_node bool AnfRuntimeAlgorithm::OutputAddrExist(const AnfNodePtr &node, size_t output_idx, bool skip_nop_node) { MS_EXCEPTION_IF_NULL(node); - if (opt::IsNopNode(node) && (skip_nop_node || IsNeedSkipNopOpAddr(node))) { + if (common::AnfAlgo::IsNopNode(node) && (skip_nop_node || common::AnfAlgo::IsNeedSkipNopOpAddr(node))) { auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); if (cnode->inputs().size() > 1) { - auto kernel_with_index = AnfAlgo::GetPrevNodeOutput(cnode, 0); + auto kernel_with_index = common::AnfAlgo::GetPrevNodeOutput(cnode, 0); return OutputAddrExist(kernel_with_index.first, kernel_with_index.second, skip_nop_node); } return false; @@ -1143,13 +504,13 @@ bool AnfRuntimeAlgorithm::WorkspaceAddrExist(const AnfNodePtr &node, size_t outp const DeviceAddress *AnfRuntimeAlgorithm::GetPrevNodeOutputAddr(const AnfNodePtr &anf_node, size_t input_idx, bool skip_nop_node) { - KernelWithIndex kernel_with_index = AnfAlgo::GetPrevNodeOutput(anf_node, input_idx); + KernelWithIndex kernel_with_index = common::AnfAlgo::GetPrevNodeOutput(anf_node, input_idx); return AnfRuntimeAlgorithm::GetOutputAddr(kernel_with_index.first, kernel_with_index.second, skip_nop_node); } DeviceAddressPtr AnfRuntimeAlgorithm::GetPrevNodeMutableOutputAddr(const AnfNodePtr &anf_node, size_t input_idx, bool skip_nop_node) { - KernelWithIndex kernel_with_index = AnfAlgo::GetPrevNodeOutput(anf_node, input_idx); + KernelWithIndex kernel_with_index = common::AnfAlgo::GetPrevNodeOutput(anf_node, input_idx); return AnfRuntimeAlgorithm::GetMutableOutputAddr(kernel_with_index.first, kernel_with_index.second, skip_nop_node); } @@ -1210,140 +571,6 @@ DeviceAddressPtr AnfRuntimeAlgorithm::GetMutableWorkspaceAddr(const AnfNodePtr & return addr; } -abstract::BaseShapePtr AnfRuntimeAlgorithm::GetOutputDetailShape(const AnfNodePtr &node, size_t output_idx) { - MS_EXCEPTION_IF_NULL(node); - auto base_shape = node->Shape(); - MS_EXCEPTION_IF_NULL(base_shape); - if (base_shape->isa()) { - if (output_idx == 0) { - return base_shape; - } - MS_LOG(EXCEPTION) << "The node " << node->DebugString() << "is a single output node but got index [" << output_idx - << "]." << trace::DumpSourceLines(node); - } else if (base_shape->isa()) { - auto tuple_shape = base_shape->cast(); - MS_EXCEPTION_IF_NULL(tuple_shape); - if (output_idx >= tuple_shape->size()) { - MS_LOG(EXCEPTION) << "Output index " << output_idx << "is larger than output number " << tuple_shape->size() - << " node:" << node->DebugString() << "." << trace::DumpSourceLines(node); - } - auto b_shp = (*tuple_shape)[output_idx]; - if (b_shp->isa() || b_shp->isa()) { - return b_shp; - } else { - MS_LOG(EXCEPTION) << "The output type of ApplyKernel index:" << output_idx - << " should be a NoShape , ArrayShape or a TupleShape, but it is " << base_shape->ToString() - << "node :" << node->DebugString() << "." << trace::DumpSourceLines(node); - } - } else if (base_shape->isa()) { - return base_shape; - } - MS_LOG(EXCEPTION) << "The output type of ApplyKernel should be a NoShape , ArrayShape or a TupleShape, but it is " - << base_shape->ToString() << " node : " << node->DebugString() << trace::DumpSourceLines(node); -} - -abstract::BaseShapePtr AnfRuntimeAlgorithm::GetPrevNodeOutputDetailShape(const AnfNodePtr &node, size_t input_idx) { - KernelWithIndex kernel_with_index = AnfAlgo::GetPrevNodeOutput(node, input_idx); - return AnfRuntimeAlgorithm::GetOutputDetailShape(kernel_with_index.first, kernel_with_index.second); -} - -// set infer shapes and types of anf node -void AnfRuntimeAlgorithm::SetOutputTypeAndDetailShape(const std::vector &types, - const std::vector &shapes, - AnfNode *node) { - MS_EXCEPTION_IF_NULL(node); - auto node_ptr = node->cast(); - MS_EXCEPTION_IF_NULL(node_ptr); - std::string node_name = ""; - if (node_ptr->isa()) { - node_name = GetCNodeName(node_ptr); - } - if (types.size() != shapes.size()) { - MS_LOG(EXCEPTION) << "Types size " << types.size() << "should be same with shapes size " << shapes.size() << "." - << trace::DumpSourceLines(node); - } - - auto tuple_node = kNodeTupleOutSet.find(node_name); - if (shapes.empty() && tuple_node == kNodeTupleOutSet.end()) { - node->set_abstract(std::make_shared()); - } else if (shapes.size() == 1 && tuple_node == kNodeTupleOutSet.end()) { - // single output handle - auto abstract = std::make_shared(TypeIdToType(types[0]), shapes[0]); - node->set_abstract(abstract); - } else { - // multiple output handle - std::vector abstract_list; - for (size_t i = 0; i < types.size(); ++i) { - auto abstract = std::make_shared(TypeIdToType(types[i]), shapes[i]); - abstract_list.emplace_back(abstract); - } - auto abstract_tuple = std::make_shared(abstract_list); - node->set_abstract(abstract_tuple); - } -} - -// set infer shapes and types of anf node -void AnfRuntimeAlgorithm::SetOutputInferTypeAndShape(const std::vector &types, - const std::vector> &shapes, AnfNode *node) { - MS_EXCEPTION_IF_NULL(node); - auto node_ptr = node->cast(); - std::string node_name = ""; - if (node_ptr->isa()) { - node_name = GetCNodeName(node_ptr); - } - MS_EXCEPTION_IF_NULL(node_ptr); - if (types.size() != shapes.size()) { - MS_LOG(EXCEPTION) << "Types size " << types.size() << "should be same with shapes size " << shapes.size() << "." - << trace::DumpSourceLines(node); - } - auto abstract_ptr = node_ptr->abstract(); - - auto tuple_node = kNodeTupleOutSet.find(node_name); - if (shapes.empty() && tuple_node == kNodeTupleOutSet.end()) { - node->set_abstract(std::make_shared()); - } else if (shapes.size() == 1 && tuple_node == kNodeTupleOutSet.end()) { - // single output handle - ShapeVector shape_int; - abstract::AbstractTensorPtr abstract = nullptr; - if (abstract_ptr != nullptr) { - auto max_shape0 = GetOutputMaxShape(node_ptr, 0); - auto min_shape0 = GetOutputMinShape(node_ptr, 0); - std::transform(shapes[0].begin(), shapes[0].end(), std::back_inserter(shape_int), SizeToLong); - abstract = std::make_shared(TypeIdToType(types[0]), - std::make_shared(shape_int, min_shape0, max_shape0)); - } else { - abstract = std::make_shared(TypeIdToType(types[0]), shape_int); - } - node->set_abstract(abstract); - } else { - // multiple output handle - std::vector abstract_list; - for (size_t i = 0; i < types.size(); ++i) { - ShapeVector shape_int; - abstract::AbstractTensorPtr abstract = nullptr; - if (abstract_ptr != nullptr) { - auto max_shape = GetOutputMaxShape(node_ptr, i); - auto min_shape = GetOutputMinShape(node_ptr, i); - std::transform(shapes[i].begin(), shapes[i].end(), std::back_inserter(shape_int), SizeToLong); - abstract = std::make_shared(TypeIdToType(types[i]), - std::make_shared(shape_int, min_shape, max_shape)); - } else { - abstract = - std::make_shared(TypeIdToType(types[i]), std::make_shared(shape_int)); - } - abstract_list.emplace_back(abstract); - } - auto abstract_tuple = std::make_shared(abstract_list); - node->set_abstract(abstract_tuple); - } -} -// copy an abstract of a node to another node -void AnfRuntimeAlgorithm::CopyAbstract(const AnfNodePtr &from_node, AnfNode *to_node) { - MS_EXCEPTION_IF_NULL(from_node); - MS_EXCEPTION_IF_NULL(to_node); - to_node->set_abstract(from_node->abstract()); -} - kernel::OpPattern AnfRuntimeAlgorithm::GetOpPattern(const AnfNodePtr &node) { MS_EXCEPTION_IF_NULL(node); auto kernel_info = dynamic_cast(node->kernel_info()); @@ -1448,64 +675,6 @@ void AnfRuntimeAlgorithm::SetKernelMod(const KernelModPtr &kernel_mod, AnfNode * kernel_info->set_kernel_mod(kernel_mod); } -bool AnfRuntimeAlgorithm::IsGraphKernel(const AnfNodePtr &node) { - // this function was moved to AnfUtils. - return AnfUtils::IsGraphKernel(node); -} - -bool AnfRuntimeAlgorithm::IsNodeInGraphKernel(const AnfNodePtr &node) { - // this function was moved to AnfUtils. - return AnfUtils::IsNodeInGraphKernel(node); -} - -AnfNodePtr AnfRuntimeAlgorithm::GetOutputOfGraphkernel(const KernelWithIndex &kernel_with_index) { - auto func_graph = GetCNodeFuncGraph(kernel_with_index.first); - if (func_graph == nullptr) { - return kernel_with_index.first; - } - auto output = func_graph->output(); - if (CheckPrimitiveType(output, prim::kPrimMakeTuple)) { - return output->cast()->input(kernel_with_index.second + 1); - } - return output; -} - -bool AnfRuntimeAlgorithm::IsParameterWeight(const ParameterPtr &node) { - MS_EXCEPTION_IF_NULL(node); - return node->has_default(); -} - -bool AnfRuntimeAlgorithm::IsLabelIndexInNode(const AnfNodePtr &node, size_t label_index) { - MS_EXCEPTION_IF_NULL(node); - if (!node->isa()) { - return false; - } - auto cnode = node->cast(); - MS_EXCEPTION_IF_NULL(cnode); - if (AnfAlgo::GetCNodeName(cnode) == kLabelGotoOpName && - (AnfAlgo::GetNodeAttr(cnode, kAttrLabelIndex) == label_index)) { - return true; - } else if (AnfAlgo::GetCNodeName(cnode) == kLabelSwitchOpName) { - auto label_list = AnfAlgo::GetNodeAttr>(cnode, kAttrLabelSwitchList); - if (std::find(label_list.begin(), label_list.end(), label_index) != label_list.end()) { - return true; - } - } - return false; -} - -bool AnfRuntimeAlgorithm::IsUpdateParameterKernel(const CNodePtr &node) { - MS_EXCEPTION_IF_NULL(node); - auto node_name = GetCNodeName(node); - if (HasNodeAttr(kAttrAsync, node) && GetNodeAttr(node, kAttrAsync)) { - return false; - } - if (kOptOperatorSet.find(node_name) == kOptOperatorSet.end() && node_name.find("Assign") == string::npos) { - return false; - } - return true; -} - void AnfRuntimeAlgorithm::SetStreamId(uint32_t stream_id, AnfNode *node) { MS_EXCEPTION_IF_NULL(node); auto kernel_info = dynamic_cast(node->kernel_info()); @@ -1548,27 +717,6 @@ uint32_t AnfRuntimeAlgorithm::GetGraphId(const AnfNode *node) { return kernel_info->graph_id(); } -bool AnfRuntimeAlgorithm::IsTupleOutput(const AnfNodePtr &anf) { - MS_EXCEPTION_IF_NULL(anf); - TypePtr type = anf->Type(); - if (type == nullptr) { - return false; - } - MS_EXCEPTION_IF_NULL(type); - return type->isa(); -} - -AnfNodePtr AnfRuntimeAlgorithm::GetInputNode(const CNodePtr &node, size_t index) { - MS_EXCEPTION_IF_NULL(node); - auto get_input_index = index + 1; - if (get_input_index >= node->inputs().size()) { - MS_LOG(EXCEPTION) << "Input index size " << get_input_index << "but the node input size just" - << node->inputs().size() << "." << trace::DumpSourceLines(node); - } - // input 0 is primitive node - return node->input(get_input_index); -} - bool AnfRuntimeAlgorithm::IsFeatureMapOutput(const AnfNodePtr &node) { MS_EXCEPTION_IF_NULL(node); if (node->isa()) { @@ -1597,9 +745,9 @@ bool AnfRuntimeAlgorithm::IsFeatureMapInput(const AnfNodePtr &node, size_t input size_t AnfRuntimeAlgorithm::GetRealInputIndex(const mindspore::AnfNodePtr &anf_node, const size_t cur_index) { MS_EXCEPTION_IF_NULL(anf_node); size_t ret = cur_index; - auto node_name = AnfAlgo::GetCNodeName(anf_node); + auto node_name = common::AnfAlgo::GetCNodeName(anf_node); if (AnfAlgo::GetKernelType(anf_node) == TBE_KERNEL) { - if (IsDynamicShape(anf_node)) { + if (common::AnfAlgo::IsDynamicShape(anf_node)) { auto find_dynamic = spec_dynamic_node_list.find(node_name); if (find_dynamic != spec_dynamic_node_list.end()) { auto dyn_index_converter = find_dynamic->second; @@ -1621,9 +769,9 @@ size_t AnfRuntimeAlgorithm::GetRealInputIndex(const mindspore::AnfNodePtr &anf_n size_t AnfRuntimeAlgorithm::GetOriginalInputIndex(const mindspore::AnfNodePtr &anf_node, const size_t cur_index) { MS_EXCEPTION_IF_NULL(anf_node); size_t ret = cur_index; - auto node_name = AnfAlgo::GetCNodeName(anf_node); + auto node_name = common::AnfAlgo::GetCNodeName(anf_node); if (AnfAlgo::GetKernelType(anf_node) == TBE_KERNEL) { - if (IsDynamicShape(anf_node)) { + if (common::AnfAlgo::IsDynamicShape(anf_node)) { auto find_dynamic = spec_dynamic_node_list.find(node_name); if (find_dynamic != spec_dynamic_node_list.end()) { auto dyn_index_converter = find_dynamic->second; @@ -1642,103 +790,11 @@ size_t AnfRuntimeAlgorithm::GetOriginalInputIndex(const mindspore::AnfNodePtr &a return ret; } -void AnfRuntimeAlgorithm::SetNodeInput(const CNodePtr &node, const AnfNodePtr &input_node, size_t index) { - MS_EXCEPTION_IF_NULL(node); - MS_EXCEPTION_IF_NULL(input_node); - node->set_input(index + 1, input_node); -} - -bool AnfRuntimeAlgorithm::IsInplaceNode(const mindspore::AnfNodePtr &kernel, const string &type) { - MS_EXCEPTION_IF_NULL(kernel); - auto primitive = AnfAlgo::GetCNodePrimitive(kernel); - if (!primitive) { - return false; - } - - auto inplace_attr = primitive->GetAttr(type); - if (inplace_attr == nullptr) { - return false; - } - - return true; -} - -bool AnfRuntimeAlgorithm::IsCommunicationOp(const AnfNodePtr &node) { - static const std::set kCommunicationOpNames = {kAllReduceOpName, kAllGatherOpName, kBroadcastOpName, - kReduceScatterOpName, kHcomSendOpName, kReceiveOpName, - kAllToAllVOpName}; - MS_EXCEPTION_IF_NULL(node); - if (!node->isa()) { - return false; - } - auto kernel_name = AnfAlgo::GetCNodeName(node); - return (kCommunicationOpNames.find(kernel_name) != kCommunicationOpNames.end()); -} - -bool AnfRuntimeAlgorithm::IsFusedCommunicationOp(const AnfNodePtr &node) { - if (!IsCommunicationOp(node)) { - return false; - } - auto primitive = AnfAlgo::GetCNodePrimitive(node); - MS_EXCEPTION_IF_NULL(primitive); - ValuePtr attr_fusion = primitive->GetAttr(kAttrFusion); - ValuePtr attr_not_delay_fusion = primitive->GetAttr(kAttrNotDelayFusion); - if (attr_fusion == nullptr) { - return false; - } - - auto fusion = GetValue(attr_fusion); - if (fusion == 0) { - return false; - } - if (attr_not_delay_fusion && GetValue(attr_not_delay_fusion)) { - return false; - } - return true; -} - -bool AnfRuntimeAlgorithm::IsGetNext(const NotNull &node) { - auto kernel_name = AnfAlgo::GetCNodeName(node); - return kernel_name == kGetNextOpName; -} - -bool AnfRuntimeAlgorithm::IsNeedSkipNopOpAddr(const AnfNodePtr &node) { - MS_EXCEPTION_IF_NULL(node); - if (!node->isa()) { - return false; - } - - auto primitive = AnfAlgo::GetCNodePrimitive(node); - if (primitive == nullptr) { - return false; - } - - auto skip_nop_op_addr_attr = primitive->GetAttr(kAttrSkipNopOpAddr); - if (skip_nop_op_addr_attr == nullptr) { - return false; - } - - return GetValue(skip_nop_op_addr_attr); -} - -FuncGraphPtr AnfRuntimeAlgorithm::GetValueNodeFuncGraph(const AnfNodePtr &node) { - MS_EXCEPTION_IF_NULL(node); - auto value_node = node->cast(); - if (value_node == nullptr) { - return nullptr; - } - auto value = value_node->value(); - if (value == nullptr) { - return nullptr; - } - auto func_graph = value->cast(); - return func_graph; -} - std::vector AnfRuntimeAlgorithm::GetCallSwitchKernelGraph(const CNodePtr &cnode) { MS_EXCEPTION_IF_NULL(cnode); - if (!(AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimCall) || AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimSwitch) || - AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimSwitchLayer))) { + if (!(common::AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimCall) || + common::AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimSwitch) || + common::AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimSwitchLayer))) { MS_LOG(EXCEPTION) << "Node: " << cnode->DebugString() << "is not a call or switch or switch_layer node." << trace::DumpSourceLines(cnode); } @@ -1759,7 +815,7 @@ std::vector AnfRuntimeAlgorithm::GetCallSwitchKernelGraph(const auto child_graph = graph_value->cast(); return child_graph; }; - if (AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimCall)) { + if (common::AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimCall)) { auto input1 = cnode->input(kPartialGraphIndex); MS_EXCEPTION_IF_NULL(input1); auto value_node = input1->cast(); @@ -1767,9 +823,9 @@ std::vector AnfRuntimeAlgorithm::GetCallSwitchKernelGraph(const auto kernel_graph = value_node->value(); MS_EXCEPTION_IF_NULL(kernel_graph); return {kernel_graph->cast()}; - } else if (AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimSwitch)) { + } else if (common::AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimSwitch)) { return {get_switch_kernel_graph(kSwitchTrueBranchIndex), get_switch_kernel_graph(kSwitchFalseBranchIndex)}; - } else if (AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimSwitchLayer)) { + } else if (common::AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimSwitchLayer)) { std::vector child_graphs; for (size_t idx = kSwitchLayerBranchesIndex; idx < cnode->inputs().size(); idx++) { auto child_graph = get_switch_kernel_graph(idx); @@ -1780,212 +836,27 @@ std::vector AnfRuntimeAlgorithm::GetCallSwitchKernelGraph(const return {}; } -bool AnfRuntimeAlgorithm::IsSwitchCall(const CNodePtr &call_node) { - MS_EXCEPTION_IF_NULL(call_node); - if (!CheckPrimitiveType(call_node, prim::kPrimCall)) { - MS_LOG(EXCEPTION) << "Call node should be a 'call', but is a " << call_node->DebugString() << "." - << trace::DumpSourceLines(call_node); - } - auto input1 = call_node->input(1); - MS_EXCEPTION_IF_NULL(input1); - if (input1->isa()) { - return false; - } else if (input1->isa() && AnfAlgo::CheckPrimitiveType(input1, prim::kPrimSwitch)) { - return true; - } - MS_LOG(EXCEPTION) << "Unexpected input1 of call node,input1:" << input1->DebugString() << "." - << trace::DumpSourceLines(call_node); -} - -bool AnfRuntimeAlgorithm::IsScalarInput(const CNodePtr &cnode, size_t index) { - auto shape = AnfAlgo::GetPrevNodeOutputInferShape(cnode, index); - if (shape.empty()) { - return true; - } - return shape.size() == kShape1dDims && shape[0] == 1; -} - -bool AnfRuntimeAlgorithm::IsScalarOutput(const CNodePtr &cnode, size_t index) { - auto shape = AnfAlgo::GetPrevNodeOutputInferShape(cnode, index); - if (shape.empty()) { - return true; - } - return shape.size() == kShape1dDims && shape[0] == 1; -} - -namespace { -void FindDelayExecPosition(const std::vector &nodes, size_t current_index, std::set *invalid_position, - std::map> *insert_nodes) { - MS_EXCEPTION_IF_NULL(invalid_position); - MS_EXCEPTION_IF_NULL(insert_nodes); - if (current_index >= nodes.size()) { - return; - } - auto &node = nodes[current_index]; - for (size_t j = current_index + 1; j < nodes.size(); ++j) { - auto &child = nodes[j]; - auto input_size = child->inputs().size() - 1; - for (size_t k = 0; k < input_size; ++k) { - auto kernel_index = AnfAlgo::VisitKernelWithReturnType(AnfAlgo::GetInputNode(child, k), 0, true); - if (kernel_index.first != node) { - continue; - } - if (AnfAlgo::GetCNodeName(child) == kApplyMomentumOpName) { - return; - } - (void)invalid_position->insert(current_index); - auto iter = insert_nodes->find(j); - if (iter != insert_nodes->end()) { - iter->second.emplace_back(node); - } else { - (*insert_nodes)[j] = {node}; - } - return; - } - } -} - -std::vector DelayExecNode(const std::vector &nodes, const std::string &node_name, bool only_seed) { - std::map> insert_nodes; - std::set invalid_position; - for (size_t i = 0; i < nodes.size(); ++i) { - auto &node = nodes[i]; - if (AnfAlgo::GetCNodeName(node) != node_name) { - continue; - } - if (only_seed) { - bool is_seed = true; - auto input_size = node->inputs().size() - 1; - for (size_t k = 0; k < input_size; ++k) { - auto input = AnfAlgo::VisitKernelWithReturnType(AnfAlgo::GetInputNode(node, k), 0, true).first; - if (input != nullptr && input->isa()) { - is_seed = false; - break; - } - } - if (!is_seed) { - continue; - } - } - FindDelayExecPosition(nodes, i, &invalid_position, &insert_nodes); - } - std::vector result; - for (size_t i = 0; i < nodes.size(); ++i) { - auto iter = insert_nodes.find(i); - if (iter != insert_nodes.end()) { - (void)result.insert(result.end(), iter->second.rbegin(), iter->second.rend()); - } - if (invalid_position.find(i) != invalid_position.end()) { - continue; - } - result.emplace_back(nodes[i]); - } - return result; -} -} // namespace - -void AnfRuntimeAlgorithm::ReorderExecList(NotNull *> node_list) { - std::vector result; - std::copy(node_list->begin(), node_list->end(), std::back_inserter(result)); - result = DelayExecNode(result, kTransDataOpName, true); - result = DelayExecNode(result, kCastOpName, true); - result = DelayExecNode(result, kAdamApplyOneWithDecayOpName, false); - result = DelayExecNode(result, kAdamApplyOneOpName, false); - if (parallel::ParallelContext::GetInstance()->pipeline_stage_split_num() > 1) { - result = DelayExecNode(result, kDropoutGenMaskOpName, true); - } - node_list->clear(); - std::copy(result.begin(), result.end(), std::back_inserter(*node_list)); -} - -void AnfRuntimeAlgorithm::ReorderPosteriorExecList(NotNull *> node_list) { - std::vector ordinary_node_list; - std::vector posterior_node_list; - - for (const auto &node : *node_list) { - MS_EXCEPTION_IF_NULL(node); - if (kPosteriorOperatorSet.find(AnfAlgo::GetCNodeName(node)) != kPosteriorOperatorSet.end()) { - posterior_node_list.emplace_back(node); - } else { - ordinary_node_list.emplace_back(node); - } - } - node_list->clear(); - std::copy(ordinary_node_list.begin(), ordinary_node_list.end(), std::back_inserter(*node_list)); - std::copy(posterior_node_list.begin(), posterior_node_list.end(), std::back_inserter(*node_list)); -} - -TypeId AnfRuntimeAlgorithm::GetCNodeOutputPrecision(const AnfNodePtr &node) { - MS_EXCEPTION_IF_NULL(node); - auto prim = AnfAlgo::GetCNodePrimitive(node); - if (prim == nullptr) { - return kTypeUnknown; - } - - TypeId except_type = kTypeUnknown; - if (prim->GetAttr(kAttrOutputPrecision) != nullptr) { - auto output_type_str = GetValue(prim->GetAttr(kAttrOutputPrecision)); - if (output_type_str == "float16") { - except_type = kNumberTypeFloat16; - } else if (output_type_str == "float32") { - except_type = kNumberTypeFloat32; - } else { - MS_LOG(EXCEPTION) << "The fix precision must be float16 or float32, but got " << output_type_str << "." - << trace::DumpSourceLines(node); - } - } - - return except_type; -} - -TypeId AnfRuntimeAlgorithm::GetPrevNodeOutputPrecision(const AnfNodePtr &node, size_t input_idx) { - MS_EXCEPTION_IF_NULL(node); - if (!node->isa()) { - MS_LOG(EXCEPTION) << node->DebugString() << ", input node is not CNode." << trace::DumpSourceLines(node); - } - auto cnode = node->cast(); - MS_EXCEPTION_IF_NULL(cnode); - if (input_idx + 1 >= cnode->inputs().size()) { - MS_LOG(EXCEPTION) << "Input index " << input_idx << " is larger than input number " << GetInputTensorNum(cnode) - << "." << trace::DumpSourceLines(node); - } - auto input_node = cnode->input(input_idx + 1); - MS_EXCEPTION_IF_NULL(input_node); - auto kernel_with_index = VisitKernel(input_node, 0); - if (!kernel_with_index.first->isa()) { - return kTypeUnknown; - } - return GetCNodeOutputPrecision(kernel_with_index.first); -} - -bool AnfRuntimeAlgorithm::IsCondControlKernel(const CNodePtr &node) { - MS_EXCEPTION_IF_NULL(node); - if (node->inputs().empty()) { - MS_LOG(EXCEPTION) << "Illegal null input of cnode." << trace::DumpSourceLines(node); - } - auto input = node->input(kAnfPrimitiveIndex); - return IsPrimitive(input, prim::kPrimLabelGoto) || IsPrimitive(input, prim::kPrimLabelSwitch); -} - bool AnfRuntimeAlgorithm::IsIndependentNode(const CNodePtr &node) { MS_EXCEPTION_IF_NULL(node); if (AnfAlgo::GetKernelType(node) != AICPU_KERNEL) { return false; } - if (AnfAlgo::GetCNodeName(node) == kGetNextOpName) { + if (common::AnfAlgo::GetCNodeName(node) == kGetNextOpName) { MS_LOG(INFO) << "GetNext should not be independent node"; return false; } // aicpu stack ops are not independent nodes. - if (AnfAlgo::GetCNodeName(node) == kStackInitOpName || AnfAlgo::GetCNodeName(node) == kStackDestroyOpName || - AnfAlgo::GetCNodeName(node) == kStackPopOpName || AnfAlgo::GetCNodeName(node) == kStackPushOpName) { + if (common::AnfAlgo::GetCNodeName(node) == kStackInitOpName || + common::AnfAlgo::GetCNodeName(node) == kStackDestroyOpName || + common::AnfAlgo::GetCNodeName(node) == kStackPopOpName || + common::AnfAlgo::GetCNodeName(node) == kStackPushOpName) { MS_LOG(INFO) << "AICPU stack ops should not be independent node"; return false; } - size_t input_nums = AnfAlgo::GetInputTensorNum(node); + size_t input_nums = common::AnfAlgo::GetInputTensorNum(node); if (input_nums == 0) { return true; } @@ -1999,175 +870,11 @@ bool AnfRuntimeAlgorithm::IsIndependentNode(const CNodePtr &node) { return true; } -bool AnfRuntimeAlgorithm::GetBooleanAttr(const AnfNodePtr &node, const std::string &attr) { - MS_EXCEPTION_IF_NULL(node); - if (!node->isa()) { - return false; - } - auto cnode = node->cast(); - MS_EXCEPTION_IF_NULL(cnode); - auto has_attr = AnfAlgo::HasNodeAttr(attr, cnode); - if (!has_attr) { - return false; - } - return AnfAlgo::GetNodeAttr(node, attr); -} - -std::optional AnfRuntimeAlgorithm::GetDumpFlag(const AnfNodePtr &node) { - MS_EXCEPTION_IF_NULL(node); - auto cnode = node->cast(); - if (cnode == nullptr || !AnfAlgo::HasNodeAttr(kAttrDump, cnode)) { - return {}; - } - return std::optional{AnfAlgo::GetNodeAttr(node, kAttrDump)}; -} - -bool AnfRuntimeAlgorithm::HasDynamicShapeFlag(const PrimitivePtr &prim) { - auto get_bool_attr = [](const PrimitivePtr &primitive, const std::string &attr_name) -> bool { - MS_EXCEPTION_IF_NULL(primitive); - if (!primitive->HasAttr(attr_name)) { - return false; - } - return GetValue(primitive->GetAttr(attr_name)); - }; - return get_bool_attr(prim, kAttrInputIsDynamicShape) || get_bool_attr(prim, kAttrOutputIsDynamicShape); -} - -bool AnfRuntimeAlgorithm::IsDynamicShape(const AnfNodePtr &node) { - MS_EXCEPTION_IF_NULL(node); - if (!node->isa()) { - MS_LOG(DEBUG) << "Node is not a cnode."; - return false; - } - auto cnode = node->cast(); - if ((!HasNodeAttr(kAttrInputIsDynamicShape, cnode)) && (!HasNodeAttr(kAttrOutputIsDynamicShape, cnode))) { - auto ret = IsNodeDynamicShape(node); - MS_LOG(DEBUG) << "The Node:" << node->fullname_with_scope() << " is dynamic shape or not:" << ret; - return ret; - } - return GetBooleanAttr(node, kAttrInputIsDynamicShape) || GetBooleanAttr(node, kAttrOutputIsDynamicShape); -} - -void AnfRuntimeAlgorithm::GetRealDynamicShape(const std::vector &shape, - NotNull *> dynamic_shape) { - for (auto size : shape) { - if (size == SIZE_MAX) { - dynamic_shape->push_back(-1); - } else { - dynamic_shape->push_back(SizeToLong(size)); - } - } -} - -std::vector GetShapeFromSequenceShape(const abstract::SequenceShapePtr &sequeue_shape_ptr, size_t index, - ShapeType type) { - MS_EXCEPTION_IF_NULL(sequeue_shape_ptr); - auto shape_list = sequeue_shape_ptr->shape(); - if (index >= shape_list.size()) { - MS_LOG(EXCEPTION) << "Output Index:" << index << " >= " << shape_list.size(); - } - - auto shape = shape_list[index]; - MS_EXCEPTION_IF_NULL(shape); - if (shape->isa()) { - auto shape_ptr = shape->cast(); - if (type == ShapeType::kMaxShape) { - return shape_ptr->max_shape().empty() ? shape_ptr->shape() : shape_ptr->max_shape(); - } else { - return shape_ptr->min_shape().empty() ? shape_ptr->shape() : shape_ptr->min_shape(); - } - } else { - MS_LOG(EXCEPTION) << "Invalid Shape Type In Shape List"; - } -} - -std::vector AnfRuntimeAlgorithm::GetInputMaxShape(const AnfNodePtr &anf_node, size_t index) { - auto input_node_with_index = AnfAlgo::GetPrevNodeOutput(anf_node, index); - return GetOutputMaxShape(input_node_with_index.first, input_node_with_index.second); -} - -std::vector AnfRuntimeAlgorithm::GetInputMinShape(const AnfNodePtr &anf_node, size_t index) { - auto input_node_with_index = AnfAlgo::GetPrevNodeOutput(anf_node, index); - return GetOutputMinShape(input_node_with_index.first, input_node_with_index.second); -} - -std::vector AnfRuntimeAlgorithm::GetOutputMaxShape(const AnfNodePtr &anf_node, size_t index) { - MS_EXCEPTION_IF_NULL(anf_node); - auto shape = anf_node->Shape(); - MS_EXCEPTION_IF_NULL(shape); - if (shape->isa()) { - auto shape_ptr = shape->cast(); - return shape_ptr->max_shape().empty() ? shape_ptr->shape() : shape_ptr->max_shape(); - } else if (shape->isa()) { - auto sequeue_shape_ptr = shape->cast(); - return GetShapeFromSequenceShape(sequeue_shape_ptr, index, ShapeType::kMaxShape); - } else if (shape->isa()) { - return {}; - } else { - MS_LOG(EXCEPTION) << "Invalid shape type." << trace::DumpSourceLines(anf_node); - } -} - -std::vector AnfRuntimeAlgorithm::GetOutputMinShape(const AnfNodePtr &anf_node, size_t index) { - MS_EXCEPTION_IF_NULL(anf_node); - auto shape = anf_node->Shape(); - MS_EXCEPTION_IF_NULL(shape); - if (shape->isa()) { - auto shape_ptr = shape->cast(); - return shape_ptr->min_shape().empty() ? shape_ptr->shape() : shape_ptr->min_shape(); - } else if (shape->isa()) { - auto sequeue_shape_ptr = shape->cast(); - return GetShapeFromSequenceShape(sequeue_shape_ptr, index, ShapeType::kMinShape); - } else if (shape->isa()) { - return {}; - } else { - MS_LOG(EXCEPTION) << "Invalid shape type." << trace::DumpSourceLines(anf_node); - } -} - -bool AnfRuntimeAlgorithm::IsNodeInputDynamicShape(const CNodePtr &anf_node_ptr) { - MS_EXCEPTION_IF_NULL(anf_node_ptr); - auto input_num = AnfAlgo::GetInputTensorNum(anf_node_ptr); - for (size_t i = 0; i < input_num; ++i) { - auto input_with_index = AnfAlgo::GetPrevNodeOutput(anf_node_ptr, i); - auto input = input_with_index.first; - auto index = input_with_index.second; - MS_EXCEPTION_IF_NULL(input); - auto base_shape = input->Shape(); - if (base_shape == nullptr) { - MS_LOG(INFO) << "Invalid shape ptr, node:" << input->fullname_with_scope(); - continue; - } - if (base_shape->isa()) { - if (AnfUtils::IsShapeDynamic(base_shape->cast())) { - return true; - } - } else if (base_shape->isa()) { - auto tuple_shape = base_shape->cast(); - MS_EXCEPTION_IF_NULL(tuple_shape); - - if (index >= tuple_shape->size()) { - MS_LOG(INFO) << "Node:" << anf_node_ptr->fullname_with_scope() << "Invalid index:" << index - << " and tuple_shape size:" << tuple_shape->size(); - continue; - } - auto b_shp = (*tuple_shape)[index]; - if (!b_shp->isa()) { - continue; - } - if (AnfUtils::IsShapeDynamic(b_shp->cast())) { - return true; - } - } - } - return false; -} - std::vector AnfRuntimeAlgorithm::GetInputRealDeviceShapeIfExist(const AnfNodePtr &anf_node, size_t index) { auto device_shape = GetInputDeviceShape(anf_node, index); // Initialize GPUKernel with max shape to fit 'InitDynamicOutputKernelRef()' for memory reuse. if (AnfUtils::IsShapeDynamic(device_shape)) { - auto max_shape = GetInputMaxShape(anf_node, index); + auto max_shape = common::AnfAlgo::GetInputMaxShape(anf_node, index); std::transform(max_shape.begin(), max_shape.end(), device_shape.begin(), IntToSize); auto format = GetInputFormat(anf_node, index); auto dtype = GetInputDeviceDataType(anf_node, index); @@ -2180,7 +887,7 @@ std::vector AnfRuntimeAlgorithm::GetOutputRealDeviceShapeIfExist(const A auto device_shape = GetOutputDeviceShape(anf_node, index); // Initialize GPUKernel with max shape to fit 'InitDynamicOutputKernelRef()' for memory reuse. if (AnfUtils::IsShapeDynamic(device_shape)) { - auto max_shape = GetOutputMaxShape(anf_node, index); + auto max_shape = common::AnfAlgo::GetOutputMaxShape(anf_node, index); std::transform(max_shape.begin(), max_shape.end(), device_shape.begin(), IntToSize); auto format = GetOutputFormat(anf_node, index); auto dtype = GetOutputDeviceDataType(anf_node, index); @@ -2204,92 +911,15 @@ AnfNodePtr AnfRuntimeAlgorithm::FetchFrontNodeByBackendNode(const AnfNodePtr &ba return front_node; } -void AnfRuntimeAlgorithm::GetAllVisitedCNode(const CNodePtr &anf_node, std::vector *used_kernels, - std::set *visited) { - MS_EXCEPTION_IF_NULL(anf_node); - MS_EXCEPTION_IF_NULL(used_kernels); - MS_EXCEPTION_IF_NULL(visited); - if (visited->find(anf_node) != visited->end()) { - MS_LOG(INFO) << "Node:" << anf_node->fullname_with_scope() << " has already been visited"; - return; - } - visited->insert(anf_node); - auto input_size = anf_node->inputs().size() - 1; - for (size_t i = 0; i < input_size; ++i) { - auto input = AnfAlgo::GetInputNode(anf_node, i); - if (!input->isa()) { - continue; - } - if (!AnfUtils::IsRealKernel(input) || opt::IsNopNode(input)) { - GetAllVisitedCNode(input->cast(), used_kernels, visited); - } else { - used_kernels->push_back(input); - } - } -} - -void AnfRuntimeAlgorithm::GetAllFatherRealNode(const AnfNodePtr &anf_node, std::vector *result, - std::set *visited) { - MS_EXCEPTION_IF_NULL(anf_node); - MS_EXCEPTION_IF_NULL(result); - MS_EXCEPTION_IF_NULL(visited); - if (visited->find(anf_node) != visited->end()) { - MS_LOG(INFO) << "Node:" << anf_node->fullname_with_scope() << " has already been visited"; - return; - } - visited->insert(anf_node); - if (AnfUtils::IsRealKernel(anf_node)) { - result->emplace_back(anf_node); - return; - } - if (!anf_node->isa()) { - return; - } - auto cnode = anf_node->cast(); - MS_EXCEPTION_IF_NULL(cnode); - if (cnode->inputs().empty()) { - MS_LOG(EXCEPTION) << "Illegal null input of cnode(%s)" << anf_node->DebugString() << "." - << trace::DumpSourceLines(cnode); - } - auto input0 = cnode->input(0); - if (IsPrimitive(input0, prim::kPrimMakeTuple)) { - for (size_t i = 1; i < cnode->inputs().size(); ++i) { - GetAllFatherRealNode(cnode->input(i), result, visited); - } - } else if (IsPrimitive(input0, prim::kPrimTupleGetItem)) { - if (cnode->inputs().size() != kTupleGetItemInputSize) { - MS_LOG(EXCEPTION) << "The node tuple_get_item must have 2 inputs!"; - } - GetAllFatherRealNode(cnode->input(kRealInputNodeIndexInTupleGetItem), result, visited); - } else if (IsPrimitive(input0, prim::kPrimDepend)) { - if (cnode->inputs().size() != kDependInputSize) { - MS_LOG(EXCEPTION) << "Depend node must have 2 inputs!" << trace::DumpSourceLines(cnode); - } - GetAllFatherRealNode(cnode->input(kRealInputIndexInDepend), result, visited); - GetAllFatherRealNode(cnode->input(kDependAttachNodeIndex), result, visited); - } -} - -bool AnfRuntimeAlgorithm::IsHostKernel(const CNodePtr &kernel_node) { - const std::set host_kernel = {prim::kPrimDynamicShape->name(), prim::kPrimReshape->name(), - prim::kPrimDynamicBroadcastGradientArgs->name(), - prim::kPrimTensorShape->name()}; - auto op_name = AnfAlgo::GetCNodeName(kernel_node); - if (host_kernel.find(op_name) == host_kernel.end()) { - return false; - } - return true; -} - namespace { // Host kernel with inputs on host bool SkipDataSync(const CNodePtr &node, const std::map &depend_tensors) { - if (!AnfAlgo::IsHostKernel(node)) { + if (!common::AnfAlgo::IsHostKernel(node)) { return false; } - auto input_size = AnfAlgo::GetInputTensorNum(node); + auto input_size = common::AnfAlgo::GetInputTensorNum(node); for (size_t i = 0; i < input_size; ++i) { - auto input_with_index = AnfAlgo::GetPrevNodeOutput(node, i); + auto input_with_index = common::AnfAlgo::GetPrevNodeOutput(node, i); auto real_input = input_with_index.first; auto iter_tensor = depend_tensors.find(i); if (iter_tensor != depend_tensors.end()) { @@ -2314,9 +944,9 @@ void AnfRuntimeAlgorithm::InferShape(const CNodePtr &node, std::map(inputs[0]); - auto input_size = AnfAlgo::GetInputTensorNum(node); + auto input_size = common::AnfAlgo::GetInputTensorNum(node); for (size_t i = 0; i < input_size; ++i) { - auto input_with_index = AnfAlgo::GetPrevNodeOutput(node, i); + auto input_with_index = common::AnfAlgo::GetPrevNodeOutput(node, i); auto real_input = input_with_index.first; MS_EXCEPTION_IF_NULL(real_input); auto cnode_input = node->input(i + 1); @@ -2336,7 +966,7 @@ void AnfRuntimeAlgorithm::InferShape(const CNodePtr &node, std::mapisa()) { real_abs->set_value(tensor_ptr); } else if (real_abs->isa()) { - auto tuple_get_item_index = AnfAlgo::GetTupleGetItemOutIndex(cnode_input->cast()); + auto tuple_get_item_index = common::AnfAlgo::GetTupleGetItemOutIndex(cnode_input->cast()); auto abstract_tuple = real_abs->cast(); MS_EXCEPTION_IF_NULL(abstract_tuple); auto tuple_elements = abstract_tuple->elements()[tuple_get_item_index]; @@ -2344,38 +974,12 @@ void AnfRuntimeAlgorithm::InferShape(const CNodePtr &node, std::mapset_abstract(eval_result); } -void AnfRuntimeAlgorithm::AddArgList(AbstractBasePtrList *args_spec_list, const AnfNodePtr &cnode_input, - const AnfNodePtr &real_input, size_t index) { - if (AnfAlgo::CheckPrimitiveType(cnode_input, prim::kPrimTupleGetItem)) { - // cppcheck-suppress unreadVariable - auto lock = AnfUtils::GetAbstractLock(real_input.get()); - auto base_shape = real_input->Shape(); - if (!base_shape->isa()) { - MS_LOG(EXCEPTION) << "Node input is a tuple_get_item but real input node shape is not a TupleShape. trace: " - << trace::DumpSourceLines(real_input); - } - auto abs = real_input->abstract()->Clone()->cast(); - MS_EXCEPTION_IF_NULL(abs); - auto tuple_get_item_indexk = AnfAlgo::GetTupleGetItemOutIndex(cnode_input->cast()); - auto abs_i = abs->elements()[tuple_get_item_indexk]; - (void)args_spec_list->emplace_back(abs_i); - } else if (cnode_input->isa() && AnfAlgo::GetCNodeName(cnode_input) == prim::kPrimReshape->name()) { - // cppcheck-suppress unreadVariable - auto lock = AnfUtils::GetAbstractLock(cnode_input.get()); - (void)args_spec_list->emplace_back(cnode_input->abstract()->Clone()); - } else { - // cppcheck-suppress unreadVariable - auto lock = AnfUtils::GetAbstractLock(real_input.get()); - (void)args_spec_list->emplace_back(real_input->abstract()->Clone()); - } -} - void AnfRuntimeAlgorithm::InsertMakeTupleForOutput(const NotNull &root_graph) { auto return_node = root_graph->get_return(); MS_EXCEPTION_IF_NULL(return_node); @@ -2387,78 +991,6 @@ void AnfRuntimeAlgorithm::InsertMakeTupleForOutput(const NotNull root_graph->set_output(make_tuple); } -AnfNodeIndexSet AnfRuntimeAlgorithm::GetUpdateStateUsers(const FuncGraphManagerPtr &manager, const AnfNodePtr &node) { - AnfNodeIndexSet update_states; - for (auto &user : manager->node_users()[node]) { - if (AnfAlgo::CheckPrimitiveType(user.first, prim::kPrimUpdateState)) { - update_states.insert(user); - } - } - return update_states; -} - -void AnfRuntimeAlgorithm::GetRealInputs(const AnfNodePtr &node, std::vector *inputs) { - size_t input_num = AnfAlgo::GetInputTensorNum(node); - for (size_t input_index = 0; input_index < input_num; ++input_index) { - auto input_node = AnfAlgo::GetInputNode(node->cast(), input_index); - GetRealOutputRecursively(input_node, 0, inputs); - } -} - -bool AnfRuntimeAlgorithm::IsTensorBroadcast(const std::vector &lhs, const std::vector &rhs) { - if (lhs.size() != rhs.size()) { - return true; - } - for (size_t i = 0; i < lhs.size(); i++) { - if (lhs[i] != rhs[i]) { - return true; - } - } - return false; -} - -bool AnfRuntimeAlgorithm::IsControlOpExecInBackend(const AnfNodePtr &node) { - if (!node->isa()) { - return false; - } - // Operators in set control_ops_exec_in_backend will be compiled into kernel graph, rather than be cut into single op - // and executed in VM. - static std::set control_ops_exec_in_backend = {kBpropCutOpName}; - return control_ops_exec_in_backend.find(AnfAlgo::GetCNodeName(node)) != control_ops_exec_in_backend.end(); -} - -bool AnfRuntimeAlgorithm::IsNodeInputContainMonad(const AnfNodePtr &node) { - auto input_size = GetInputTensorNum(node); - for (size_t i = 0; i < input_size; ++i) { - auto input_with_index = GetPrevNodeOutput(node, i); - if (HasAbstractMonad(input_with_index.first)) { - return true; - } - } - return false; -} - -bool AnfRuntimeAlgorithm::IsNonTaskOp(const CNodePtr &node) { - auto op_name = GetCNodeName(node); - return (op_name == kSplitOpName || op_name == kSplitVOpName) && AnfAlgo::HasNodeAttr(kAttrNonTask, node); -} - -bool AnfRuntimeAlgorithm::IsNoneInput(const AnfNodePtr &node, size_t index) { - auto op_name = GetCNodeName(node); - constexpr auto none_placeholder_index = 3; - if (op_name == kDynamicRNNOpName && index == none_placeholder_index) { - return true; - } - if (op_name == kDynamicGRUV2OpName) { - auto none_index = AnfAlgo::GetNodeAttr>(node, kAttrPlaceHolderIndex); - auto item = std::find(none_index.begin(), none_index.end(), index); - if (item != none_index.end()) { - return true; - } - } - return false; -} - void AnfRuntimeAlgorithm::CacheAddrForGraph(const KernelGraphPtr &kernel_graph) { MS_EXCEPTION_IF_NULL(kernel_graph); auto ms_context = MsContext::GetInstance(); @@ -2472,8 +1004,8 @@ void AnfRuntimeAlgorithm::CacheAddrForGraph(const KernelGraphPtr &kernel_graph) // Skip transpose kernel with "nop_op" attr which is not hidden or removed in PyNative infer scenario. Transpose // kernel, which is not supposed to be executed, is generated in TransDataSplit to support specific Transdata. // And hard code here should be removed after new Transdata programme is implemented in the foreseeable future. - if (HasNodeAttr(kAttrNopOp, kernel)) { - for (size_t idx = 0; idx < GetOutputTensorNum(kernel); idx += 1) { + if (common::AnfAlgo::HasNodeAttr(kAttrNopOp, kernel)) { + for (size_t idx = 0; idx < common::AnfAlgo::GetOutputTensorNum(kernel); idx += 1) { auto real_input = GetRealInputIndex(kernel, idx); auto device_address = GetPrevNodeMutableOutputAddr(kernel, real_input); SetOutputAddr(device_address, idx, kernel.get()); @@ -2482,7 +1014,7 @@ void AnfRuntimeAlgorithm::CacheAddrForGraph(const KernelGraphPtr &kernel_graph) } auto kernel_mod = GetKernelMod(kernel); MS_EXCEPTION_IF_NULL(kernel_mod); - if (GetCNodeName(kernel) == kAtomicAddrCleanOpName) { + if (common::AnfAlgo::GetCNodeName(kernel) == kAtomicAddrCleanOpName) { CacheAddrForAtomicClean(kernel, kernel_mod); continue; } @@ -2501,9 +1033,9 @@ void AnfRuntimeAlgorithm::CacheAddrForKernel(const AnfNodePtr &node, kernel::Ker auto ms_context = MsContext::GetInstance(); MS_EXCEPTION_IF_NULL(ms_context); auto skip_nop_node = (ms_context->get_param(MS_CTX_EXECUTION_MODE) != kPynativeMode); - size_t input_num = GetInputTensorNum(node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(node); for (size_t i = 0; i < input_num; ++i) { - if (IsNoneInput(node, i)) { + if (common::AnfAlgo::IsNoneInput(node, i)) { continue; } auto real_input = GetRealInputIndex(node, i); @@ -2551,11 +1083,11 @@ void AnfRuntimeAlgorithm::CacheAddrForAtomicClean(const AnfNodePtr &node, kernel MS_EXCEPTION_IF_NULL(cnode->inputs()[1]); auto pre_node = (cnode->inputs()[1])->cast(); // set clean output address - if (HasNodeAttr(kAttrAtomicOutputIndexs, pre_node)) { + if (common::AnfAlgo::HasNodeAttr(kAttrAtomicOutputIndexs, pre_node)) { #if defined(__APPLE__) auto clean_output_indexes = GetNodeAttr>(pre_node, kAttrAtomicOutputIndexs); #else - auto clean_output_indexes = GetNodeAttr>(pre_node, kAttrAtomicOutputIndexs); + auto clean_output_indexes = common::AnfAlgo::GetNodeAttr>(pre_node, kAttrAtomicOutputIndexs); #endif for (auto index : clean_output_indexes) { auto device_address = GetOutputAddr(pre_node, index); @@ -2569,11 +1101,12 @@ void AnfRuntimeAlgorithm::CacheAddrForAtomicClean(const AnfNodePtr &node, kernel MS_LOG(DEBUG) << "AtomicAddClean clean output size:" << clean_output_indexes.size(); } // set clean workspace address - if (HasNodeAttr(kAttrAtomicWorkspaceIndexs, pre_node)) { + if (common::AnfAlgo::HasNodeAttr(kAttrAtomicWorkspaceIndexs, pre_node)) { #if defined(__APPLE__) auto clean_workspaces_indexes = GetNodeAttr>(pre_node, kAttrAtomicWorkspaceIndexs); #else - auto clean_workspaces_indexes = GetNodeAttr>(pre_node, kAttrAtomicWorkspaceIndexs); + auto clean_workspaces_indexes = + common::AnfAlgo::GetNodeAttr>(pre_node, kAttrAtomicWorkspaceIndexs); #endif for (const auto &index : clean_workspaces_indexes) { auto device_address = GetWorkspaceAddr(pre_node, index); @@ -2609,25 +1142,13 @@ size_t OpRuntimeInfo::output_tensor_size(size_t index) const { return output_tensor_size_[index]; } -bool AnfRuntimeAlgorithm::IsCallNode(const AnfNodePtr &node) { - MS_EXCEPTION_IF_NULL(node); - if (!node->isa()) { - return false; - } - auto input0 = node->cast()->input(0); - if (IsValueNode(input0)) { - return false; - } - return true; -} - void AnfRuntimeAlgorithm::UpdateGraphValidRefPair(const KernelGraphPtr &graph) { MS_EXCEPTION_IF_NULL(graph); const auto &origin_ref_map = graph->GetRefMap(); std::map new_ref_map; for (const auto &node : graph->execution_order()) { MS_EXCEPTION_IF_NULL(node); - auto output_num = AnfAlgo::GetOutputTensorNum(node); + auto output_num = common::AnfAlgo::GetOutputTensorNum(node); if (output_num == 0) { MS_LOG(DEBUG) << "This kernel has no output size."; continue; @@ -2645,72 +1166,5 @@ void AnfRuntimeAlgorithm::UpdateGraphValidRefPair(const KernelGraphPtr &graph) { } graph->set_ref_out_in_map(new_ref_map); } - -int64_t AnfRuntimeAlgorithm::GetAttrGroups(const AnfNodePtr &node, size_t index) { - if (node == nullptr) { - return 1; - } - if (node->isa()) { - auto cnode = node->cast(); - if (AnfAlgo::HasNodeAttr(kAttrFracZGroup, cnode)) { - auto node_name = AnfAlgo::GetCNodeName(cnode); - if (node_name == kAllReduceOpName || node_name == kBroadcastOpName) { - // if index not exists in fracz_group_idx, return default value 1 - auto fz_group_idx = AnfAlgo::GetNodeAttr>(cnode, kAttrFracZGroupIdx); - int64_t out_index = SizeToLong(index); - auto fz_iter = std::find(std::begin(fz_group_idx), std::end(fz_group_idx), out_index); - if (fz_iter == std::end(fz_group_idx)) { - return 1; - } - } - return AnfAlgo::GetNodeAttr(cnode, kAttrFracZGroup); - } - } else if (node->isa()) { - auto param = node->cast(); - MS_EXCEPTION_IF_NULL(param); - return param->fracz_group(); - } - return 1; -} - -AnfNodePtr AnfRuntimeAlgorithm::GetTupleIndexes(const AnfNodePtr &node, std::vector *const index_stack) { - MS_EXCEPTION_IF_NULL(node); - MS_EXCEPTION_IF_NULL(index_stack); - - if (IsPrimitiveCNode(node, prim::kPrimTupleGetItem)) { - auto tuple_getitem = node->cast(); - MS_EXCEPTION_IF_NULL(tuple_getitem); - // Get cur index - auto output_index_value_node = tuple_getitem->input(kInputNodeOutputIndexInTupleGetItem); - MS_EXCEPTION_IF_NULL(output_index_value_node); - auto value_node = output_index_value_node->cast(); - MS_EXCEPTION_IF_NULL(value_node); - auto output_idx = LongToSize(GetValue(value_node->value())); - index_stack->push_back(output_idx); - auto real_input = tuple_getitem->input(kRealInputNodeIndexInTupleGetItem); - return GetTupleIndexes(real_input, index_stack); - } - if (IsPrimitiveCNode(node, prim::kPrimMakeTuple)) { - // If make_tuple in make_tuple, visit may start with inner tuple_getitem. - if (index_stack->empty()) { - MS_LOG(WARNING) << "Visit make tuple: " << node->DebugString() - << ", but index are empty, visit should not start with inner tuple_getitem."; - return nullptr; - } - auto make_tuple = node->cast(); - MS_EXCEPTION_IF_NULL(make_tuple); - auto output_idx = index_stack->back(); - index_stack->pop_back(); - return GetTupleIndexes(make_tuple->input(1 + output_idx), index_stack); - } - if (IsPrimitiveCNode(node, prim::kPrimDepend)) { - return GetTupleIndexes(node->cast()->input(kRealInputIndexInDepend), index_stack); - } - if (IsPrimitiveCNode(node, prim::kPrimLoad)) { - return GetTupleIndexes(node->cast()->input(1), index_stack); - } - MS_LOG(DEBUG) << "Get real node:" << node->DebugString(); - return node; -} } // namespace session } // namespace mindspore diff --git a/mindspore/ccsrc/backend/common/session/anf_runtime_algorithm.h b/mindspore/ccsrc/backend/common/session/anf_runtime_algorithm.h index e563149ca78..bd842419917 100644 --- a/mindspore/ccsrc/backend/common/session/anf_runtime_algorithm.h +++ b/mindspore/ccsrc/backend/common/session/anf_runtime_algorithm.h @@ -34,13 +34,12 @@ #include "kernel/kernel.h" #include "kernel/kernel_build_info.h" #include "base/core_ops.h" -#include "utils/contract.h" +#include "include/common/utils/contract.h" #include "utils/anf_utils.h" #include "backend/common/session/kernel_graph.h" namespace mindspore { namespace session { -using AnfVisitFuncion = std::function; using DeviceAddress = device::DeviceAddress; using DeviceAddressPtr = device::DeviceAddressPtr; using Address = kernel::Address; @@ -72,67 +71,6 @@ class AnfRuntimeAlgorithm { public: static AnfNodePtr MakeMonadValueNode(const KernelGraphPtr &kg); static void KeepOrder(const KernelGraphPtr &kg, const AnfNodePtr &former, const AnfNodePtr &latter); - // get real input node of tuple_get_item - static AnfNodePtr GetTupleGetItemRealInput(const CNodePtr &tuple_get_item); - static size_t GetTupleGetItemOutIndex(const CNodePtr &tuple_get_item); - // get input_anf_node's real kernel by recurse - static KernelWithIndex VisitKernel(const AnfNodePtr &input_anf_node, size_t output_index); - static KernelWithIndex VisitKernelWithReturnType( - const AnfNodePtr &input_anf_node, size_t output_index, bool skip_nop_node = false, - const std::vector &return_types = {prim::kPrimMakeTuple}, - abstract::AbstractBasePtr *abstract = nullptr); - static std::vector GetAllOutput(const AnfNodePtr &node, - const std::vector &return_types = {}); - static std::vector GetAllOutputWithIndex(const AnfNodePtr &node); - // get cnode primitive - static AnfNodePtr GetCNodePrimitiveNode(const CNodePtr &node); - static void SetNodeInput(const CNodePtr &node, const AnfNodePtr &input_node, size_t index); - static PrimitivePtr GetCNodePrimitive(const AnfNodePtr &node); - // check whether anf node is a node of 'primitive_type',such as make_tuple is a cnode of kPrimMakeTuple - static bool CheckPrimitiveType(const AnfNodePtr &node, const PrimitivePtr &primitive_type); - // get cnode primitive - static FuncGraphPtr GetCNodeFuncGraphPtr(const AnfNodePtr &node); - // get kernel_name of anf node - static std::string GetCNodeName(const AnfNodePtr &node); - // get detail info of anf node - static std::string GetNodeDebugString(const AnfNodePtr &node); - // get attr of anf node - template - static T GetNodeAttr(const AnfNodePtr &node, const std::string &key) { - MS_EXCEPTION_IF_NULL(node); - if (!node->isa()) { - std::string node_debug_log = node->DebugString(); - MS_LOG(EXCEPTION) << "Only cnode has attr, but this anf is " << node_debug_log.c_str(); - } - // single op cnode. - if (auto primitive = GetCNodePrimitive(node); primitive != nullptr) { - return GetValue(primitive->GetAttr(key)); - } - // graph kernel cnode. - auto fg = GetCNodeFuncGraphPtr(node); - MS_EXCEPTION_IF_NULL(fg); - return GetValue(fg->get_attr(key)); - } - static bool IsTupleOutput(const AnfNodePtr &anf); - // set attr of anf node - static void SetNodeAttr(const std::string &key, const ValuePtr &value, const AnfNodePtr &node); - // set attr of key from 'from' node to 'to' node - static void CopyNodeAttr(const std::string &key, const AnfNodePtr &from, const AnfNodePtr &to); - // set a new key for attr from 'from' node to 'to' node - static void CopyNodeAttr(const std::string &old_key, const std::string &new_key, const AnfNodePtr &from, - const AnfNodePtr &to); - // set all attrs from 'from' node to 'to' node - static void CopyNodeAttrs(const AnfNodePtr &from, const AnfNodePtr &to); - // check whether a cnode has the specified attr. - static bool HasNodeAttr(const std::string &key, const CNodePtr &node); - // delete attr of anf node - static void EraseNodeAttr(const std::string &key, AnfNodePtr node); - // get the num of inputs include monads for a cnode - static size_t GetInputNum(const CNodePtr &cnode); - // get the num of inputs exclude monads for real_kernel (which can be build and run in device) - static size_t GetInputTensorNum(const AnfNodePtr &node); - // get the num of output real_kernel(which can be build and run in device) - static size_t GetOutputTensorNum(const AnfNodePtr &node); // Get the memory size of output tensor of node. static size_t GetOutputTensorMemSize(const AnfNodePtr &node, size_t output_index); // get all outputs format select of anf node @@ -149,18 +87,10 @@ class AnfRuntimeAlgorithm { static std::string GetOutputFormat(const AnfNodePtr &node, size_t output_idx); // get input format select of anf node static std::string GetInputFormat(const AnfNodePtr &node, size_t input_idx); - // get prev node output width output index - static KernelWithIndex GetPrevNodeOutput(const AnfNodePtr &anf_node, size_t input_idx, bool skip_nop_node = false); // get output format from prev node,input_index is the input index of current node related to prev node static std::string GetPrevNodeOutputFormat(const AnfNodePtr &node, size_t input_idx); // get reshape_type of from the output of input node. static std::string GetPrevNodeOutputReshapeType(const AnfNodePtr &node, size_t input_idx); - // get output shapes inferred by ME from input nodes. - static std::vector GetOutputInferShape(const AnfNodePtr &node, size_t output_idx); - static std::vector GetOutputInferShape(const AnfNodePtr &node, const abstract::BaseShapePtr &base_shape, - size_t output_idx); - // get input shapes inferred by ME from input nodes. - static std::vector GetPrevNodeOutputInferShape(const AnfNodePtr &node, size_t input_idx); // get output shapes which will built and run in device static std::vector GetOutputDeviceShape(const AnfNodePtr &node, size_t output_idx); // get input shapes which will built and run in device @@ -175,11 +105,6 @@ class AnfRuntimeAlgorithm { static std::string GetInputReshapeType(const AnfNodePtr &node, size_t output_idx); // Get Output Padding Axis static std::string GetOutputReshapeType(const AnfNodePtr &node, size_t output_idx); - // get output data type inferred by ME of anf node - static TypeId GetOutputInferDataType(const AnfNodePtr &node, size_t output_idx); - static TypeId GetOutputInferDataType(const TypePtr &type_ptr, size_t output_idx); - // get output original data type from prev node,input_index is the input index of current node related to prev node - static TypeId GetPrevNodeOutputInferDataType(const AnfNodePtr &node, size_t input_idx); // get output select data type of anf node static TypeId GetOutputDeviceDataType(const AnfNodePtr &node, size_t output_idx); // get input select data type of anf node @@ -211,15 +136,6 @@ class AnfRuntimeAlgorithm { static DeviceAddress *GetWorkspaceAddr(const AnfNodePtr &node, size_t output_idx); // get workspace device mutable addr of anf_node static DeviceAddressPtr GetMutableWorkspaceAddr(const AnfNodePtr &node, size_t index); - // set infer shapes and types of anf node - static void SetOutputInferTypeAndShape(const std::vector &types, - const std::vector> &shapes, AnfNode *node); - // get and set output shape ptr - static abstract::BaseShapePtr GetOutputDetailShape(const AnfNodePtr &node, size_t output_idx); - static abstract::BaseShapePtr GetPrevNodeOutputDetailShape(const AnfNodePtr &node, size_t input_idx); - static void SetOutputTypeAndDetailShape(const std::vector &types, - const std::vector &shapes, AnfNode *node); - static void CopyAbstract(const AnfNodePtr &from_node, AnfNode *to_node); // get op pattern of the node static kernel::OpPattern GetOpPattern(const AnfNodePtr &node); // get KernelBuildType of node ,such as ATT,RT,FWK and so on @@ -239,18 +155,6 @@ class AnfRuntimeAlgorithm { static kernel::KernelMod *GetKernelMod(const AnfNodePtr &node); // set kernel mod static void SetKernelMod(const kernel::KernelModPtr &kernel_mod, AnfNode *node); - // checkout whether the anf node is a graph kernel. - static bool IsGraphKernel(const AnfNodePtr &node); - // checkout whether the anf node is an inner node of graph kernel. - static bool IsNodeInGraphKernel(const AnfNodePtr &node); - // get the real output of GraphKernel. - static AnfNodePtr GetOutputOfGraphkernel(const KernelWithIndex &kernel_with_index); - // check parameter is weight or data - static bool IsParameterWeight(const ParameterPtr &node); - // checkout whether the anf node is include the label_index. - static bool IsLabelIndexInNode(const AnfNodePtr &node, size_t label_index); - // Check whether the cnode update parameter - static bool IsUpdateParameterKernel(const CNodePtr &node); // set stream id of kernel,which will be set in stream assign and be used in stream generate static void SetStreamId(uint32_t stream_id, AnfNode *node); // get stream id @@ -263,7 +167,6 @@ class AnfRuntimeAlgorithm { static void SetGraphId(uint32_t graph_id, AnfNode *node); // get graph id static uint32_t GetGraphId(const AnfNode *node); - static AnfNodePtr GetInputNode(const CNodePtr &node, size_t index); // charge if the node's output is a feature map output static bool IsFeatureMapOutput(const AnfNodePtr &node); // charge if the node's input is from a feature map output @@ -272,103 +175,19 @@ class AnfRuntimeAlgorithm { static size_t GetRealInputIndex(const AnfNodePtr &anf_node, const size_t cur_index); // get me input index for some tbe ops which input order is different between me and tbe impl static size_t GetOriginalInputIndex(const AnfNodePtr &anf_node, const size_t cur_index); - static bool IsCommunicationOp(const AnfNodePtr &node); - static bool IsFusedCommunicationOp(const AnfNodePtr &node); - static bool IsInplaceNode(const AnfNodePtr &node, const string &type); - static bool IsGetNext(const NotNull &node); - static bool IsNeedSkipNopOpAddr(const AnfNodePtr &node); - static FuncGraphPtr GetValueNodeFuncGraph(const AnfNodePtr &node); static std::vector GetCallSwitchKernelGraph(const CNodePtr &cnode); - static bool IsSwitchCall(const CNodePtr &call_node); - static bool IsScalarInput(const CNodePtr &cnode, size_t index); - static bool IsScalarOutput(const CNodePtr &cnode, size_t index); - static void ReorderExecList(NotNull *> node_list); - static void ReorderPosteriorExecList(NotNull *> node_list); - // get fix output precision of cnode. - static TypeId GetCNodeOutputPrecision(const AnfNodePtr &node); - // get fix output precision from prev node, input_idx is the input index of current node related to prev node. - static TypeId GetPrevNodeOutputPrecision(const AnfNodePtr &node, size_t input_idx); - static bool IsNodeInputDynamicShape(const CNodePtr &anf_node_ptr); - static bool IsDynamicShape(const AnfNodePtr &node); - static bool HasDynamicShapeFlag(const PrimitivePtr &prim); - static bool IsCondControlKernel(const CNodePtr &node); static bool IsIndependentNode(const CNodePtr &node); - static bool GetBooleanAttr(const AnfNodePtr &node, const std::string &attr); - static std::optional GetDumpFlag(const AnfNodePtr &node); - static void GetRealDynamicShape(const std::vector &shape, NotNull *> dynamic_shape); - static std::vector GetInputMaxShape(const AnfNodePtr &anf_node, size_t index); - static std::vector GetInputMinShape(const AnfNodePtr &anf_node, size_t index); - static std::vector GetOutputMaxShape(const AnfNodePtr &anf_node, size_t index); - static std::vector GetOutputMinShape(const AnfNodePtr &anf_node, size_t index); - static bool IsHostKernel(const CNodePtr &node); static void InferShape(const CNodePtr &node, std::map *depend_tensors = nullptr); - // return true if use cnode_input's abstract, false if use real_input's abstract - static void AddArgList(AbstractBasePtrList *args_spec_list, const AnfNodePtr &cnode_input, - const AnfNodePtr &real_input, size_t index); static std::vector GetInputRealDeviceShapeIfExist(const AnfNodePtr &anf_node, size_t index); static std::vector GetOutputRealDeviceShapeIfExist(const AnfNodePtr &anf_node, size_t index); - // Find real input nodes. - static void GetAllFatherRealNode(const AnfNodePtr &anf_node, std::vector *result, - std::set *visited); - static void GetAllVisitedCNode(const CNodePtr &cnode, std::vector *used_kernels, - std::set *visited); static AnfNodePtr FetchFrontNodeByBackendNode(const AnfNodePtr &backend_node, const KernelGraph &graph); static void InsertMakeTupleForOutput(const NotNull &root_graph); - static AnfNodeIndexSet GetUpdateStateUsers(const FuncGraphManagerPtr &manager, const AnfNodePtr &node); - // Get node real inputs, skip `MakeTuple`, `TupleGetItem`, `Depend`, `Load`, `UpdateState` etc. - static void GetRealInputs(const AnfNodePtr &anf_node, std::vector *inputs); - // Check whether tensors need broadcast or not. - static bool IsTensorBroadcast(const std::vector &lhs, const std::vector &rhs); - // Calc tensor size in byte. - template - static size_t TensorSizeInByte(const std::vector &shape) { - size_t result = sizeof(T); - for (size_t i = 0; i < shape.size(); i++) { - result *= shape[i]; - } - return result; - } - - // Judge a control operator need be compiled into kernel graph rather than be cut into single op and - // executed in vm. For example, the operator "bprop_cut" will be compiled into kernel graph and be launch - // in backend in PyNative mode. - static bool IsControlOpExecInBackend(const AnfNodePtr &node); - - static bool IsNodeInputContainMonad(const AnfNodePtr &node); - // Check if node is non-task op. - static bool IsNonTaskOp(const CNodePtr &node); - // Check if node has none input after IR fusion. - static bool IsNoneInput(const AnfNodePtr &node, size_t index); // Save inputs/outputs/workspace address in kernel_mod. static void CacheAddrForGraph(const KernelGraphPtr &kernel_graph); static void CacheAddrForKernel(const AnfNodePtr &node, kernel::KernelMod *kernel_mod); static void CacheAddrForAtomicClean(const AnfNodePtr &node, kernel::KernelMod *kernel_mod); - // Check whether node is a call node, call nodes are those cnodes whose first input is not primitive node. - static bool IsCallNode(const AnfNodePtr &node); - // Get the output number according to abstract, when there is a tuple in abstract, it needs to get recursively. - static size_t GetOutputNumByAbstract(const AbstractBasePtr &node_abstract); - // Get attr groups - static int64_t GetAttrGroups(const AnfNodePtr &node, size_t index); - - static inline bool IsAllgather(const CNodePtr &cnode) { return GetCNodeName(cnode) == kAllGatherOpName; } - - static inline bool IsFusion(const CNodePtr &cnode) { - return HasNodeAttr(kAttrFusion, cnode) && GetNodeAttr(cnode, kAttrFusion) > 0; - } - - static inline bool IsFromParallelOptimizer(const CNodePtr &cnode) { - auto primitive = GetCNodePrimitive(cnode); - return (primitive != nullptr) && primitive->instance_name().find("parallel_optimizer") != std::string::npos; - } - - static inline bool IsRecompute(const CNodePtr &cnode) { - auto attr_dup = cnode->GetAttr(kAttrDuplicated); - return attr_dup != nullptr && GetValue(attr_dup); - } static void UpdateGraphValidRefPair(const KernelGraphPtr &graph); - // Get the real output node and indexes of get item, make tuple, depend, load. - static AnfNodePtr GetTupleIndexes(const AnfNodePtr &node, std::vector *index_stack); }; } // namespace session using AnfAlgo = session::AnfRuntimeAlgorithm; diff --git a/mindspore/ccsrc/backend/common/session/ascend_auto_monad.cc b/mindspore/ccsrc/backend/common/session/ascend_auto_monad.cc index b6fa12016b2..9a6a998860d 100644 --- a/mindspore/ccsrc/backend/common/session/ascend_auto_monad.cc +++ b/mindspore/ccsrc/backend/common/session/ascend_auto_monad.cc @@ -31,6 +31,7 @@ #include "debug/anf_ir_dump.h" #include "pipeline/jit/base.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "plugin/device/ascend/hal/device/kernel_select_ascend.h" namespace mindspore { @@ -121,14 +122,14 @@ void DumpExecuteOrder(const NotNull kg) { for (auto &cnode : kg->execution_order()) { MS_EXCEPTION_IF_NULL(cnode); if (IsPrimitiveCNode(cnode, prim::kPrimLabelSet)) { - fout << "L" << AnfAlgo::GetNodeAttr(cnode, kAttrLabelIndex) << ":\n"; + fout << "L" << common::AnfAlgo::GetNodeAttr(cnode, kAttrLabelIndex) << ":\n"; } fout << " [" << index << "], " << cnode->DebugString(); - if (AnfAlgo::HasNodeAttr(kAttrLabelIndex, cnode)) { - fout << " : L" << AnfAlgo::GetNodeAttr(cnode, kAttrLabelIndex); + if (common::AnfAlgo::HasNodeAttr(kAttrLabelIndex, cnode)) { + fout << " : L" << common::AnfAlgo::GetNodeAttr(cnode, kAttrLabelIndex); } - if (AnfAlgo::HasNodeAttr(kAttrLabelSwitchList, cnode)) { - auto labels = AnfAlgo::GetNodeAttr>(cnode, kAttrLabelSwitchList); + if (common::AnfAlgo::HasNodeAttr(kAttrLabelSwitchList, cnode)) { + auto labels = common::AnfAlgo::GetNodeAttr>(cnode, kAttrLabelSwitchList); fout << " : "; for (size_t i = 0; i < labels.size(); ++i) { fout << ((i > 0) ? ", L" : "L") << labels[i]; @@ -419,11 +420,11 @@ class CallInfoFinder { // Found a node with UMonad abstract, set it as the last monad. last_monad = node; call_info->return_monad_ = last_monad; - } else if (AnfAlgo::CheckPrimitiveType(node, prim::kPrimCall)) { + } else if (common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimCall)) { MakeCallSite(node->cast(), last_monad, call_info); call_info->return_monad_ = nullptr; - } else if (AnfAlgo::CheckPrimitiveType(node, prim::kPrimSwitch) || - AnfAlgo::CheckPrimitiveType(node, prim::kPrimSwitchLayer)) { + } else if (common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimSwitch) || + common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimSwitchLayer)) { MakeSwitchCallSite(node->cast(), last_monad, call_info); call_info->return_monad_ = nullptr; } @@ -731,8 +732,8 @@ class AscendAutoMonadConverter { kernel_graph_->SetExecOrderByDefault(); if (call_info_.recursive) { const auto &nodes = kernel_graph_->execution_order(); - AnfAlgo::SetNodeAttr(kAttrRecursiveStart, prim::kValueOne, *nodes.begin()); - AnfAlgo::SetNodeAttr(kAttrRecursiveEnd, prim::kValueOne, *nodes.rbegin()); + common::AnfAlgo::SetNodeAttr(kAttrRecursiveStart, prim::kValueOne, *nodes.begin()); + common::AnfAlgo::SetNodeAttr(kAttrRecursiveEnd, prim::kValueOne, *nodes.rbegin()); } for (auto &call_site : call_info_.call_sites) { if (need_stackops_ && call_site.recursive) { @@ -762,7 +763,7 @@ class AscendAutoMonadConverter { if (!AnfUtils::IsRealCNodeKernel(*iter)) { continue; } - if (AnfAlgo::CheckPrimitiveType(*iter, prim::kPrimLabelSet)) { + if (common::AnfAlgo::CheckPrimitiveType(*iter, prim::kPrimLabelSet)) { const auto &last_call_site = context->call_info_map[kg].call_sites.back(); for (auto &branch : last_call_site.callees) { if (memo.find(branch.graph) != memo.end()) { @@ -772,7 +773,7 @@ class AscendAutoMonadConverter { } break; } - AnfAlgo::SetNodeAttr(ITEREND, prim::kValueOne, *iter); + common::AnfAlgo::SetNodeAttr(ITEREND, prim::kValueOne, *iter); MS_LOG(INFO) << "Set profiling iter-end points: " << (*iter)->DebugString(); return; } @@ -792,16 +793,17 @@ class AscendAutoMonadConverter { if (!AnfUtils::IsRealCNodeKernel(*iter)) { continue; } - if (AnfAlgo::CheckPrimitiveType(*iter, prim::kPrimLabelGoto) && AnfAlgo::HasNodeAttr(kAttrReturn, *iter)) { + if (common::AnfAlgo::CheckPrimitiveType(*iter, prim::kPrimLabelGoto) && + common::AnfAlgo::HasNodeAttr(kAttrReturn, *iter)) { continue; } - if (AnfAlgo::CheckPrimitiveType(*iter, prim::kPrimLabelGoto) || - AnfAlgo::CheckPrimitiveType(*iter, prim::kPrimLabelSwitch) || - AnfAlgo::CheckPrimitiveType(*iter, prim::kPrimLabelSet)) { + if (common::AnfAlgo::CheckPrimitiveType(*iter, prim::kPrimLabelGoto) || + common::AnfAlgo::CheckPrimitiveType(*iter, prim::kPrimLabelSwitch) || + common::AnfAlgo::CheckPrimitiveType(*iter, prim::kPrimLabelSet)) { MS_LOG(INFO) << "this node is Labelxxxx, do not found iter end."; break; } - AnfAlgo::SetNodeAttr(ITEREND, prim::kValueOne, *iter); + common::AnfAlgo::SetNodeAttr(ITEREND, prim::kValueOne, *iter); MS_LOG(INFO) << "Set profiling iter-end points: " << (*iter)->DebugString(); return; } @@ -858,7 +860,7 @@ class AscendAutoMonadConverter { std::vector stack_pushs; bool find_call_point = false; for (auto &node : exec_order) { - auto node_name = AnfAlgo::GetCNodeName(node); + auto node_name = common::AnfAlgo::GetCNodeName(node); if (node == call_point) { find_call_point = true; continue; @@ -887,7 +889,7 @@ class AscendAutoMonadConverter { std::vector *stack_pushs) { MS_EXCEPTION_IF_NULL(node); uint32_t start_index = 1; - if (AnfAlgo::CheckPrimitiveType(node, prim::kPrimAssign)) { + if (common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimAssign)) { start_index = kInputIndex; } for (uint32_t i = start_index; i < node->inputs().size(); i++) { @@ -998,7 +1000,7 @@ class AscendAutoMonadConverter { // For Switch, we reverse the graphes and labels, so that the false branch // is the first one, since for kernel LabelSwitch, false is the first branch. - if (AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimSwitch)) { + if (common::AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimSwitch)) { std::reverse(graphes.begin(), graphes.end()); std::reverse(labels.begin(), labels.end()); } @@ -1007,7 +1009,7 @@ class AscendAutoMonadConverter { auto label_goto_switch = MakeLabelGotoSwitch(cnode, graphes, labels); call_site->conversion_cnode = label_goto_switch; if (call_site->recursive) { - AnfAlgo::SetNodeAttr(kAttrRecursive, prim::kValueOne, label_goto_switch); + common::AnfAlgo::SetNodeAttr(kAttrRecursive, prim::kValueOne, label_goto_switch); } // Setup return label and output if required. @@ -1082,15 +1084,15 @@ class AscendAutoMonadConverter { CNodePtr MakeLabelGotoSwitch(const CNodePtr &cnode, const std::vector &graphes, const std::vector &labels) { // Create LabelGoto or LabelSwitch according the cnode type. - const bool is_call = AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimCall); + const bool is_call = common::AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimCall); auto label_goto_switch = (is_call ? LabelGoto(labels.front()) : LabelSwitch(cnode->input(1), labels)); // Set child graph attribute for the LabelGoto or LabelSwitch node. SetChildGrapAttr(label_goto_switch, graphes); // Mark the label_switch node is for 'switch_layer' if it is. - if (AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimSwitchLayer)) { - AnfAlgo::SetNodeAttr(kAttrSwitchLayer, prim::kValueOne, label_goto_switch); + if (common::AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimSwitchLayer)) { + common::AnfAlgo::SetNodeAttr(kAttrSwitchLayer, prim::kValueOne, label_goto_switch); } return label_goto_switch; } @@ -1114,7 +1116,7 @@ class AscendAutoMonadConverter { // Insert label_goto for return. auto &return_point = return_points.front(); auto return_goto = LabelGoto(return_point.call_site->return_label); - AnfAlgo::SetNodeAttr(kAttrReturn, prim::kValueOne, return_goto); + common::AnfAlgo::SetNodeAttr(kAttrReturn, prim::kValueOne, return_goto); kernel_graph_->set_end_goto(return_goto); return; } @@ -1128,9 +1130,9 @@ class AscendAutoMonadConverter { auto &label_param = call_info_.label_param; MS_EXCEPTION_IF_NULL(label_param); auto return_switch = LabelSwitch(label_param, return_labels); - AnfAlgo::SetNodeAttr(kAttrReturn, prim::kValueOne, return_switch); + common::AnfAlgo::SetNodeAttr(kAttrReturn, prim::kValueOne, return_switch); if (!call_info_.recursive) { - AnfAlgo::SetNodeAttr(kAttrMultiCallEnd, prim::kValueOne, return_switch); + common::AnfAlgo::SetNodeAttr(kAttrMultiCallEnd, prim::kValueOne, return_switch); } kernel_graph_->set_end_goto(return_switch); context_.SetSubGraphMultiCall(true); @@ -1249,29 +1251,29 @@ class AscendAutoMonadConverter { // AissgnAll support tuple to tuple assign. AnfNodePtr AssignAll(const AnfNodePtr &target, const AnfNodePtr &source, bool link, bool keep, bool output) { - if (!AnfAlgo::CheckPrimitiveType(target, prim::kPrimMakeTuple)) { + if (!common::AnfAlgo::CheckPrimitiveType(target, prim::kPrimMakeTuple)) { // Assign single value. return Assign(target, source, link, keep, output); } // Assign tuple. - std::vector targets = AnfAlgo::GetAllOutput(target); - std::vector sources = AnfAlgo::GetAllOutput(source); + std::vector targets = common::AnfAlgo::GetAllOutput(target); + std::vector sources = common::AnfAlgo::GetAllOutput(source); if (targets.size() != sources.size()) { MS_LOG(EXCEPTION) << "Target size " << targets.size() << " != source size " << sources.size(); } AnfNodePtrList tuple_inputs; - auto source_item_with_index = AnfAlgo::VisitKernelWithReturnType(source, 0); + auto source_item_with_index = common::AnfAlgo::VisitKernelWithReturnType(source, 0); MS_EXCEPTION_IF_NULL(source_item_with_index.first); auto source_cnode = source_item_with_index.first->cast(); auto target_cnode = target->cast(); MS_EXCEPTION_IF_NULL(source_cnode); MS_EXCEPTION_IF_NULL(target_cnode); - if (!AnfAlgo::CheckPrimitiveType(source_cnode, prim::kPrimMakeTuple)) { + if (!common::AnfAlgo::CheckPrimitiveType(source_cnode, prim::kPrimMakeTuple)) { MS_LOG(WARNING) << "Source : " << source_cnode->DebugString() << " is not MakeTuple."; } tuple_inputs.emplace_back(NewValueNode(prim::kPrimMakeTuple)); for (size_t i = 1; i < target_cnode->inputs().size(); ++i) { - if (AnfAlgo::IsTupleOutput(target_cnode->input(i))) { + if (common::AnfAlgo::IsTupleOutput(target_cnode->input(i))) { tuple_inputs.emplace_back(AssignAll(target_cnode->input(i), source_cnode->input(i), link, keep, output)); } else { tuple_inputs.emplace_back(Assign(target_cnode->input(i), source_cnode->input(i), link, keep, output)); @@ -1354,7 +1356,7 @@ class AscendAutoMonadConverter { auto monad = GetMonad(); auto label_goto = NewPrimitive(prim::kPrimLabelGoto); auto cnode = kernel_graph_->NewCNode({label_goto, monad}); - AnfAlgo::SetNodeAttr(kAttrLabelIndex, MakeValue(label_id), cnode); + common::AnfAlgo::SetNodeAttr(kAttrLabelIndex, MakeValue(label_id), cnode); cnode->set_abstract(monad->abstract()); monad_ = cnode; return cnode; @@ -1365,7 +1367,7 @@ class AscendAutoMonadConverter { auto monad = GetMonad(); auto label_set = NewPrimitive(prim::kPrimLabelSet); auto cnode = kernel_graph_->NewCNode({label_set, monad}); - AnfAlgo::SetNodeAttr(kAttrLabelIndex, MakeValue(label_id), cnode); + common::AnfAlgo::SetNodeAttr(kAttrLabelIndex, MakeValue(label_id), cnode); cnode->set_abstract(monad->abstract()); monad_ = cnode; return cnode; @@ -1377,7 +1379,7 @@ class AscendAutoMonadConverter { auto label_switch = NewPrimitive(prim::kPrimLabelSwitch); auto cnode = kernel_graph_->NewCNode({label_switch, cond, monad}); auto label_list = MakeValue(labels); - AnfAlgo::SetNodeAttr(kAttrLabelSwitchList, label_list, cnode); + common::AnfAlgo::SetNodeAttr(kAttrLabelSwitchList, label_list, cnode); cnode->set_abstract(monad->abstract()); monad_ = cnode; return cnode; @@ -1385,7 +1387,7 @@ class AscendAutoMonadConverter { // Set child graph attribute for label_goto/label_switch node. void SetChildGrapAttr(const AnfNodePtr &node, const std::vector &graphs) { - AnfAlgo::SetNodeAttr(kAttrChildGraph, MakeValue(graphs), node); + common::AnfAlgo::SetNodeAttr(kAttrChildGraph, MakeValue(graphs), node); } // Make a StackInit node. @@ -1393,7 +1395,7 @@ class AscendAutoMonadConverter { auto monad = AnfAlgo::MakeMonadValueNode(kg); auto stack_init = NewPrimitive(prim::kPrimStackInit); auto cnode = kg->NewCNode({stack_init, monad}); - AnfAlgo::SetNodeAttr(kAttrIndex, MakeValue(0), cnode); + common::AnfAlgo::SetNodeAttr(kAttrIndex, MakeValue(0), cnode); cnode->set_abstract(monad->abstract()); return cnode; } @@ -1403,7 +1405,7 @@ class AscendAutoMonadConverter { auto monad = AnfAlgo::MakeMonadValueNode(kg); auto stack_destroy = NewPrimitive(prim::kPrimStackDestroy); auto cnode = kg->NewCNode({stack_destroy, monad}); - AnfAlgo::SetNodeAttr(kAttrIndex, MakeValue(0), cnode); + common::AnfAlgo::SetNodeAttr(kAttrIndex, MakeValue(0), cnode); cnode->set_abstract(monad->abstract()); return cnode; } @@ -1413,9 +1415,9 @@ class AscendAutoMonadConverter { auto monad = AnfAlgo::MakeMonadValueNode(kernel_graph_); auto stack_push = NewPrimitive(prim::kPrimStackPush); auto cnode = kernel_graph_->NewCNode({stack_push, input, monad}); - AnfAlgo::SetNodeAttr(kAttrIndex, MakeValue(0), cnode); + common::AnfAlgo::SetNodeAttr(kAttrIndex, MakeValue(0), cnode); auto op_name = std::to_string(kernel_graph_->graph_id()) + "_stack_push_" + std::to_string(name_index_++); - AnfAlgo::SetNodeAttr(kAttrStackOpName, MakeValue(op_name), cnode); + common::AnfAlgo::SetNodeAttr(kAttrStackOpName, MakeValue(op_name), cnode); cnode->set_abstract(monad->abstract()); return cnode; } @@ -1425,9 +1427,9 @@ class AscendAutoMonadConverter { auto monad = AnfAlgo::MakeMonadValueNode(kernel_graph_); auto stack_pop = NewPrimitive(prim::kPrimStackPop); auto cnode = kernel_graph_->NewCNode({stack_pop, monad}); - AnfAlgo::SetNodeAttr(kAttrIndex, MakeValue(0), cnode); + common::AnfAlgo::SetNodeAttr(kAttrIndex, MakeValue(0), cnode); auto op_name = std::to_string(kernel_graph_->graph_id()) + "_stack_pop_" + std::to_string(name_index_++); - AnfAlgo::SetNodeAttr(kAttrStackOpName, MakeValue(op_name), cnode); + common::AnfAlgo::SetNodeAttr(kAttrStackOpName, MakeValue(op_name), cnode); cnode->set_abstract(monad->abstract()); // need to refresh output's abstract(). return cnode; } @@ -1482,8 +1484,8 @@ class ExecuteOrderGenerator { uint32_t FindMaxLabelId(const std::vector &nodes) { uint32_t max_label = 0; for (auto &node : nodes) { - if (AnfAlgo::CheckPrimitiveType(node, prim::kPrimLabelSet)) { - auto label_id = AnfAlgo::GetNodeAttr(node, kAttrLabelIndex); + if (common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimLabelSet)) { + auto label_id = common::AnfAlgo::GetNodeAttr(node, kAttrLabelIndex); max_label = std::max(label_id, max_label); } } @@ -1493,7 +1495,7 @@ class ExecuteOrderGenerator { void HandleLabelSwitch(const AnfNodePtr &node, std::vector *labels, std::vector *switch_labels, std::multimap *labels_multimap) { bool is_new_labels = false; - auto label_list = AnfAlgo::GetNodeAttr>(node, kAttrLabelSwitchList); + auto label_list = common::AnfAlgo::GetNodeAttr>(node, kAttrLabelSwitchList); std::vector new_labels; new_labels.reserve(label_list.size()); for (auto label_id : label_list) { @@ -1511,19 +1513,19 @@ class ExecuteOrderGenerator { } (void)switch_labels->insert(switch_labels->end(), new_labels.begin(), new_labels.end()); if (is_new_labels) { - AnfAlgo::SetNodeAttr(kAttrLabelSwitchList, MakeValue(new_labels), node); + common::AnfAlgo::SetNodeAttr(kAttrLabelSwitchList, MakeValue(new_labels), node); } } void HandleLabelGoto(const AnfNodePtr &node, std::vector *labels, std::vector *switch_labels, std::multimap *labels_multimap) { - auto label_id = AnfAlgo::GetNodeAttr(node, kAttrLabelIndex); + auto label_id = common::AnfAlgo::GetNodeAttr(node, kAttrLabelIndex); auto iter = std::find(switch_labels->begin(), switch_labels->end(), label_id); if (iter == switch_labels->end()) { (void)labels->emplace_back(label_id); return; } - AnfAlgo::SetNodeAttr(kAttrLabelIndex, MakeValue(++max_label_), node); + common::AnfAlgo::SetNodeAttr(kAttrLabelIndex, MakeValue(++max_label_), node); (void)labels_multimap->emplace(*iter, max_label_); (void)labels->emplace_back(max_label_); } @@ -1536,11 +1538,11 @@ class ExecuteOrderGenerator { std::multimap labels_multimap; max_label_ = FindMaxLabelId(nodes); for (auto &node : nodes) { - if (AnfAlgo::CheckPrimitiveType(node, prim::kPrimLabelSwitch)) { + if (common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimLabelSwitch)) { HandleLabelSwitch(node, &labels, &switch_labels, &labels_multimap); continue; } - if (AnfAlgo::CheckPrimitiveType(node, prim::kPrimLabelGoto)) { + if (common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimLabelGoto)) { HandleLabelGoto(node, &labels, &switch_labels, &labels_multimap); continue; } @@ -1555,10 +1557,10 @@ class ExecuteOrderGenerator { auto old_label = labels.first; auto new_label = labels.second; auto iter = std::find_if(nodes->begin(), nodes->end(), [old_label](auto node) { - if (!AnfAlgo::CheckPrimitiveType(node, prim::kPrimLabelSet)) { + if (!common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimLabelSet)) { return false; } - auto label_id = AnfAlgo::GetNodeAttr(node, kAttrLabelIndex); + auto label_id = common::AnfAlgo::GetNodeAttr(node, kAttrLabelIndex); return label_id == old_label; }); if (iter == nodes->end()) { @@ -1566,8 +1568,8 @@ class ExecuteOrderGenerator { } auto label_set = NewValueNode(std::make_shared(prim::kPrimLabelSet->name())); auto cnode = graph_->NewCNode({label_set}); - AnfAlgo::CopyNodeAttrs(*iter, cnode); - AnfAlgo::SetNodeAttr(kAttrLabelIndex, MakeValue(new_label), cnode); + common::AnfAlgo::CopyNodeAttrs(*iter, cnode); + common::AnfAlgo::SetNodeAttr(kAttrLabelIndex, MakeValue(new_label), cnode); auto monad = graph_->NewValueNode(kUMonad->ToAbstract(), kUMonad); cnode->set_abstract(monad->abstract()); (void)device::ascend::SelectKernelInfo(cnode); @@ -1580,10 +1582,10 @@ class ExecuteOrderGenerator { execution_order->insert(execution_order->end(), order.begin(), order.end()); } - bool HasSubGraphs(const CNodePtr &cnode) { return (cnode && AnfAlgo::HasNodeAttr(kAttrChildGraph, cnode)); } + bool HasSubGraphs(const CNodePtr &cnode) { return (cnode && common::AnfAlgo::HasNodeAttr(kAttrChildGraph, cnode)); } std::vector GetSubGraphs(const CNodePtr &cnode) { - return AnfAlgo::GetNodeAttr>(cnode, kAttrChildGraph); + return common::AnfAlgo::GetNodeAttr>(cnode, kAttrChildGraph); } void EraseNodeFromExecOrder(const AnfNodePtr &node, const NotNull *> exec_order) { @@ -1612,7 +1614,7 @@ class ExecuteOrderGenerator { // and then append them to current execution order list. if (HasSubGraphs(cnode)) { auto sub_graphs = GetSubGraphs(cnode); - if (!AnfAlgo::HasNodeAttr(kAttrSwitchLayer, cnode)) { + if (!common::AnfAlgo::HasNodeAttr(kAttrSwitchLayer, cnode)) { // For Switch, we use reversed order to generate sub-graph's execution order, // because the true branch of LabelSwitch is the second one, but // we want to make true branch ahead of false branch in the generated @@ -1628,7 +1630,7 @@ class ExecuteOrderGenerator { AppendGraphOrder(&execution_order, sub_graph); } // Clear ChildGraph attribute after execute order generated. - AnfAlgo::EraseNodeAttr(kAttrChildGraph, cnode); + common::AnfAlgo::EraseNodeAttr(kAttrChildGraph, cnode); } } // Save generated execution order into the graph. @@ -1762,10 +1764,10 @@ class ExecuteOrderGenerator { return {p.first.first, {p.first.second, p.second.first, p.second.second}}; }); auto validate_ref_parameter = [](AnfNodePtr node) -> AnfNodePtr { - if (node->isa() && AnfAlgo::CheckPrimitiveType(node, prim::kPrimTransData)) { + if (node->isa() && common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimTransData)) { auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - auto first_input = AnfAlgo::VisitKernelWithReturnType(cnode->input(kFirstDataInputIndex), 0, true); + auto first_input = common::AnfAlgo::VisitKernelWithReturnType(cnode->input(kFirstDataInputIndex), 0, true); MS_EXCEPTION_IF_NULL(first_input.first); return first_input.first; } @@ -1794,7 +1796,7 @@ class ExecuteOrderGenerator { (void)refed_parameters.insert(validate_ref_parameter(std::get<1>(iter->second))); } for (auto &in : node->inputs()) { - auto visit_node = AnfAlgo::VisitKernelWithReturnType(in, 0).first; + auto visit_node = common::AnfAlgo::VisitKernelWithReturnType(in, 0).first; visit_node = validate_ref_parameter(visit_node); if (!visit_node->isa() || root_inputs.find(visit_node) != root_inputs.end()) { continue; @@ -1836,16 +1838,16 @@ class ExecuteOrderGenerator { for (auto iter = exec_order.begin(); iter != exec_order.end();) { auto &node = *iter; if (IsPrimitiveCNode(node, prim::kPrimLabelSwitch)) { - auto labels = AnfAlgo::GetNodeAttr>(node, kAttrLabelSwitchList); + auto labels = common::AnfAlgo::GetNodeAttr>(node, kAttrLabelSwitchList); for (auto label : labels) { label_used.insert(label); } } else if (IsPrimitiveCNode(node, prim::kPrimLabelGoto)) { - auto label = AnfAlgo::GetNodeAttr(node, kAttrLabelIndex); + auto label = common::AnfAlgo::GetNodeAttr(node, kAttrLabelIndex); auto next = std::next(iter); if (next != exec_order.end() && IsPrimitiveCNode(*next, prim::kPrimLabelSet)) { // The LabelGoto that jump to next node can be removed. - auto next_label = AnfAlgo::GetNodeAttr(*next, kAttrLabelIndex); + auto next_label = common::AnfAlgo::GetNodeAttr(*next, kAttrLabelIndex); if (next_label == label) { iter = exec_order.erase(iter); continue; @@ -1859,7 +1861,7 @@ class ExecuteOrderGenerator { for (auto iter = exec_order.begin(); iter != exec_order.end();) { auto &node = *iter; if (IsPrimitiveCNode(node, prim::kPrimLabelSet)) { - auto label = AnfAlgo::GetNodeAttr(node, kAttrLabelIndex); + auto label = common::AnfAlgo::GetNodeAttr(node, kAttrLabelIndex); if (label_used.find(label) == label_used.end()) { iter = exec_order.erase(iter); continue; diff --git a/mindspore/ccsrc/backend/common/session/ascend_inference_session.cc b/mindspore/ccsrc/backend/common/session/ascend_inference_session.cc index 09e5e73cc81..c5a24408adf 100644 --- a/mindspore/ccsrc/backend/common/session/ascend_inference_session.cc +++ b/mindspore/ccsrc/backend/common/session/ascend_inference_session.cc @@ -22,9 +22,10 @@ #include "ir/param_info.h" #include "runtime/device/kernel_runtime.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "utils/ms_utils.h" -#include "utils/ms_device_shape_transfer.h" -#include "utils/config_manager.h" +#include "runtime/device/ms_device_shape_transfer.h" +#include "include/common/utils/config_manager.h" namespace mindspore { namespace session { @@ -45,7 +46,7 @@ void AscendInferenceSession::LoadInputData(const std::shared_ptr &k MS_EXCEPTION_IF_NULL(pk_node); auto device_address = AnfAlgo::GetMutableOutputAddr(pk_node, 0); MS_EXCEPTION_IF_NULL(device_address); - if (!AnfAlgo::IsParameterWeight(pk_node)) { + if (!common::AnfAlgo::IsParameterWeight(pk_node)) { tensor = inputs[no_weight_input++]; if (!device_address->SyncHostToDevice(trans::GetRuntimePaddingShape(pk_node, 0), LongToSize(tensor->data().nbytes()), tensor->data_type(), tensor->data_c(), @@ -71,7 +72,7 @@ GraphId AscendInferenceSession::CompileGraphImpl(NotNull func_grap MS_EXCEPTION_IF_NULL(pk_node); auto device_address = AnfAlgo::GetMutableOutputAddr(pk_node, 0); MS_EXCEPTION_IF_NULL(device_address); - if (AnfAlgo::IsParameterWeight(pk_node)) { + if (common::AnfAlgo::IsParameterWeight(pk_node)) { const auto ¶m_value = pk_node->default_param(); MS_EXCEPTION_IF_NULL(param_value); auto tensor = std::dynamic_pointer_cast(param_value); @@ -101,7 +102,7 @@ bool AscendInferenceSession::CheckModelInputs(uint32_t graph_id, const std::vect continue; } auto parameter = kernel_graph_inputs[i]->cast(); - if (!AnfAlgo::IsParameterWeight(parameter)) { + if (!common::AnfAlgo::IsParameterWeight(parameter)) { paras.push_back(parameter); } } diff --git a/mindspore/ccsrc/backend/common/session/ascend_session.cc b/mindspore/ccsrc/backend/common/session/ascend_session.cc index a9fc9b7a3ef..67cd8eb1e1b 100644 --- a/mindspore/ccsrc/backend/common/session/ascend_session.cc +++ b/mindspore/ccsrc/backend/common/session/ascend_session.cc @@ -26,7 +26,7 @@ #include "base/base_ref_utils.h" #include "ir/tensor.h" #include "ir/anf.h" -#include "utils/ms_device_shape_transfer.h" +#include "runtime/device/ms_device_shape_transfer.h" #include "runtime/device/kernel_runtime.h" #include "plugin/device/ascend/hal/device/kernel_select_ascend.h" #include "plugin/device/ascend/hal/device/kernel_build_ascend.h" @@ -38,12 +38,13 @@ #include "runtime/device/kernel_adjust.h" #include "plugin/device/ascend/hal/device/ascend_stream_assign.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "utils/ms_utils.h" -#include "utils/utils.h" -#include "utils/context/graph_kernel_flags.h" +#include "include/common/utils/utils.h" +#include "include/common/utils/context/graph_kernel_flags.h" #include "backend/common/optimizer/helper.h" #include "runtime/device/kernel_runtime_manager.h" -#include "utils/config_manager.h" +#include "include/common/utils/config_manager.h" #ifndef ENABLE_SECURITY #include "debug/data_dump/dump_json_parser.h" #include "debug/data_dump/e2e_dump.h" @@ -179,7 +180,7 @@ void GenOpOutputStubTensor(const KernelGraphPtr &single_op_graph, const CNodePtr if (cnode_refcount.find(kernel_with_index) == cnode_refcount.end()) { continue; } - const auto &output_kernel_with_index = AnfAlgo::VisitKernel(output, 0); + const auto &output_kernel_with_index = common::AnfAlgo::VisitKernel(output, 0); const auto &output_node = output_kernel_with_index.first; const auto &output_index = output_kernel_with_index.second; auto out_abstract = output_node->abstract(); @@ -190,7 +191,7 @@ void GenOpOutputStubTensor(const KernelGraphPtr &single_op_graph, const CNodePtr } abstract::AbstractTensorPtr tensor_abstract = out_abstract->cast(); MS_EXCEPTION_IF_NULL(tensor_abstract); - const auto &infer_type = AnfAlgo::GetOutputInferDataType(output_node, output_index); + const auto &infer_type = common::AnfAlgo::GetOutputInferDataType(output_node, output_index); tensor::TensorPtr stub_output_tensor = std::make_shared(infer_type, tensor_abstract->shape()->shape(), nullptr); const auto &output_type = AnfAlgo::GetOutputDeviceDataType(output_node, output_index); @@ -255,7 +256,7 @@ bool TensorNeedSync(const std::shared_ptr &kernel_graph, const AnfN #endif auto input_param = parameter->cast(); MS_EXCEPTION_IF_NULL(input_param); - if (AnfAlgo::IsParameterWeight(input_param) || kernel_graph->IsUpdatedParameter(input_param)) { + if (common::AnfAlgo::IsParameterWeight(input_param) || kernel_graph->IsUpdatedParameter(input_param)) { tensor->set_device_address(device_address); } if (kernel_graph->IsUpdatedParameter(input_param)) { @@ -340,8 +341,8 @@ void AscendSession::LoadInputData(const std::shared_ptr &kernel_gra auto tensor_shape = tensor->shape(); std::vector shape_tmp; (void)std::transform(tensor_shape.begin(), tensor_shape.end(), std::back_inserter(shape_tmp), LongToSize); - AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(input_node, 0)}, {shape_tmp}, - input_node.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({common::AnfAlgo::GetOutputInferDataType(input_node, 0)}, {shape_tmp}, + input_node.get()); size = abstract::ShapeSize(shape_tmp) * abstract::TypeIdSize(tensor->data_type()); } if (AnfAlgo::OutputAddrExist(input_node, 0) && @@ -362,7 +363,7 @@ void AscendSession::LoadInputData(const std::shared_ptr &kernel_gra auto ms_context = MsContext::GetInstance(); MS_EXCEPTION_IF_NULL(ms_context); if (ms_context->get_param(MS_CTX_EXECUTION_MODE) == kPynativeMode || - AnfAlgo::IsParameterWeight(input_param) || kernel_graph->IsUpdatedParameter(input_param)) { + common::AnfAlgo::IsParameterWeight(input_param) || kernel_graph->IsUpdatedParameter(input_param)) { tensor->set_device_address(device_address); } if (kernel_graph->IsUpdatedParameter(input_param)) { @@ -771,7 +772,7 @@ void AscendSession::PrepareForOutputTensor(const KernelGraphPtr &graph, void StoreCNodePrimitive(const KernelGraphPtr &graph) { const auto &nodes = graph->execution_order(); for (auto &node : nodes) { - auto primitive = AnfAlgo::GetCNodePrimitive(node); + auto primitive = common::AnfAlgo::GetCNodePrimitive(node); MS_EXCEPTION_IF_NULL(primitive); auto new_primitive = std::make_shared(*primitive); node->set_input(kAnfPrimitiveIndex, NewValueNode(new_primitive)); @@ -908,7 +909,7 @@ void AscendSession::CacheCNodeOutputInfo(const KernelGraph &graph) const { std::vector formats; std::vector types; std::vector tensor_sizes; - auto output_num = AnfAlgo::GetOutputTensorNum(node); + auto output_num = common::AnfAlgo::GetOutputTensorNum(node); for (size_t i = 0; i < output_num; ++i) { std::string output_format = AnfAlgo::GetOutputFormat(node, i); auto output_type = AnfAlgo::GetOutputDeviceDataType(node, i); @@ -930,12 +931,12 @@ void AscendSession::CacheCNodeOutputInfo(const KernelGraph &graph) const { std::vector formats; std::vector types; std::vector tensor_sizes; - auto output_size = AnfAlgo::GetOutputTensorNum(input); + auto output_size = common::AnfAlgo::GetOutputTensorNum(input); for (size_t index = 0; index < output_size; index++) { auto format = AnfAlgo::GetOutputFormat(input, index); auto type_id = AnfAlgo::GetOutputDeviceDataType(input, index); if (type_id == kTypeUnknown) { - type_id = AnfAlgo::GetOutputInferDataType(input, index); + type_id = common::AnfAlgo::GetOutputInferDataType(input, index); } auto tensor_size = AnfAlgo::GetOutputTensorMemSize(input, index); formats.emplace_back(format); @@ -952,10 +953,10 @@ void AscendSession::GetOpInputStubTensors(const CNodePtr &cnode, const std::map< InputTensorInfo *input_tensor_info) { MS_EXCEPTION_IF_NULL(cnode); MS_EXCEPTION_IF_NULL(input_tensor_info); - const auto input_tensor_num = AnfAlgo::GetInputTensorNum(cnode); + const auto input_tensor_num = common::AnfAlgo::GetInputTensorNum(cnode); for (size_t i = 1; i <= input_tensor_num; i += 1) { const auto &input = cnode->input(i); - auto kernel_with_index = AnfAlgo::VisitKernel(input, 0); + auto kernel_with_index = common::AnfAlgo::VisitKernel(input, 0); auto real_input = kernel_with_index.first; MS_EXCEPTION_IF_NULL(real_input); tensor::TensorPtr tensor = nullptr; @@ -1109,7 +1110,7 @@ void AscendSession::AdjustKernel(const std::shared_ptr &kernel_grap MS_LOG(INFO) << "Status record: start adjust kernel. graph id: " << kernel_graph->graph_id(); opt::HideNopNode(kernel_graph.get()); auto execution_order = kernel_graph->execution_order(); - AnfAlgo::ReorderExecList(NOT_NULL(&execution_order)); + common::AnfAlgo::ReorderExecList(NOT_NULL(&execution_order)); kernel_graph->set_execution_order(execution_order); // Insert CLearZero op // prepare for next step from json get atomic info @@ -1170,7 +1171,7 @@ void AscendSession::BuildDynamicKernel(const std::shared_ptr &kerne MS_EXCEPTION_IF_NULL(kernel_graph); const auto &kernels = kernel_graph->execution_order(); auto iter = std::find_if(kernels.begin(), kernels.end(), [](const CNodePtr &kernel) { - return AnfAlgo::GetBooleanAttr(kernel, kAttrOutputIsDynamicShape); + return common::AnfAlgo::GetBooleanAttr(kernel, kAttrOutputIsDynamicShape); }); if (iter == kernels.end()) { return; @@ -1189,7 +1190,7 @@ static CNodePtr GetNextLabelSet(const std::vector &kernel_nodes, uint3 MS_LOG(EXCEPTION) << "there is no node after this node:" << kernel_nodes[index]->DebugString(); } auto kernel = kernel_nodes[index + 1]; - if (AnfAlgo::GetCNodeName(kernel) != kLabelSetOpName) { + if (common::AnfAlgo::GetCNodeName(kernel) != kLabelSetOpName) { MS_LOG(EXCEPTION) << "the node is not labelset follow labelgoto/labelswitch, node: " << kernel_nodes[index]->DebugString(); } @@ -1210,14 +1211,14 @@ static std::vector HandleRecursiveCall(const std::vector &ke } else { back->emplace_back(kernel_cnodes[i]); } - if (AnfAlgo::HasNodeAttr(kAttrRecursiveEnd, kernel_cnodes[i])) { + if (common::AnfAlgo::HasNodeAttr(kAttrRecursiveEnd, kernel_cnodes[i])) { *index = i; back->insert(back->end(), back_temp.begin(), back_temp.end()); return front; } - if (AnfAlgo::HasNodeAttr(kAttrRecursive, kernel_cnodes[i])) { + if (common::AnfAlgo::HasNodeAttr(kAttrRecursive, kernel_cnodes[i])) { back_flag = true; - if (!AnfAlgo::IsLabelIndexInNode(kernel_cnodes[i], back_label)) { + if (!common::AnfAlgo::IsLabelIndexInNode(kernel_cnodes[i], back_label)) { auto temp = HandleRecursiveCall(kernel_cnodes, back_label, &(++i), &back_temp); front.insert(front.end(), temp.begin(), temp.end()); } @@ -1236,11 +1237,11 @@ static void UnfoldRecursiveExecOrder(KernelGraph *kernel_graph) { std::vector mem_reuse_order; mem_reuse_order.reserve(kernel_cnodes.size()); for (uint32_t i = 0; i < kernel_cnodes.size(); i++) { - if (!AnfAlgo::HasNodeAttr(kAttrRecursiveStart, kernel_cnodes[i])) { + if (!common::AnfAlgo::HasNodeAttr(kAttrRecursiveStart, kernel_cnodes[i])) { mem_reuse_order.emplace_back(kernel_cnodes[i]); continue; } - auto label_id = AnfAlgo::GetNodeAttr(kernel_cnodes[i], kAttrLabelIndex); + auto label_id = common::AnfAlgo::GetNodeAttr(kernel_cnodes[i], kAttrLabelIndex); std::vector back; auto front = HandleRecursiveCall(kernel_cnodes, label_id, &i, &back); mem_reuse_order.insert(mem_reuse_order.end(), front.begin(), front.end()); @@ -1253,11 +1254,11 @@ static void GetSubGraphExecOrder(const KernelGraph *kernel_graph, uint32_t index std::vector *mem_reuse_order) { MS_EXCEPTION_IF_NULL(kernel_graph); MS_EXCEPTION_IF_NULL(mem_reuse_order); - auto label_id = AnfAlgo::GetNodeAttr(back_node, kAttrLabelIndex); + auto label_id = common::AnfAlgo::GetNodeAttr(back_node, kAttrLabelIndex); auto kernel_cnodes = kernel_graph->execution_order(); for (auto i = index; i < kernel_cnodes.size(); i++) { mem_reuse_order->emplace_back(kernel_cnodes[i]); - if (AnfAlgo::IsLabelIndexInNode(kernel_cnodes[i], label_id)) { + if (common::AnfAlgo::IsLabelIndexInNode(kernel_cnodes[i], label_id)) { return; } } @@ -1273,10 +1274,10 @@ void InitMemReuseExecOrder(KernelGraph *kernel_graph) { std::vector mem_reuse_order; for (uint32_t i = 0; i < kernel_cnodes.size(); i++) { mem_reuse_order.emplace_back(kernel_cnodes[i]); - if (AnfAlgo::CheckPrimitiveType(kernel_cnodes[i], prim::kPrimLabelSwitch) && - !AnfAlgo::HasNodeAttr(kAttrRecursive, kernel_cnodes[i]) && - !AnfAlgo::HasNodeAttr(kAttrReturn, kernel_cnodes[i])) { - auto label_list = AnfAlgo::GetNodeAttr>(kernel_cnodes[i], kAttrLabelSwitchList); + if (common::AnfAlgo::CheckPrimitiveType(kernel_cnodes[i], prim::kPrimLabelSwitch) && + !common::AnfAlgo::HasNodeAttr(kAttrRecursive, kernel_cnodes[i]) && + !common::AnfAlgo::HasNodeAttr(kAttrReturn, kernel_cnodes[i])) { + auto label_list = common::AnfAlgo::GetNodeAttr>(kernel_cnodes[i], kAttrLabelSwitchList); for (auto label_id : label_list) { if (label_id_index_map.find(label_id) == label_id_index_map.end()) { continue; @@ -1286,10 +1287,10 @@ void InitMemReuseExecOrder(KernelGraph *kernel_graph) { } continue; } - if (AnfAlgo::CheckPrimitiveType(kernel_cnodes[i], prim::kPrimLabelGoto) && - !AnfAlgo::HasNodeAttr(kAttrRecursive, kernel_cnodes[i]) && - !AnfAlgo::HasNodeAttr(kAttrReturn, kernel_cnodes[i])) { - auto label_id = AnfAlgo::GetNodeAttr(kernel_cnodes[i], kAttrLabelIndex); + if (common::AnfAlgo::CheckPrimitiveType(kernel_cnodes[i], prim::kPrimLabelGoto) && + !common::AnfAlgo::HasNodeAttr(kAttrRecursive, kernel_cnodes[i]) && + !common::AnfAlgo::HasNodeAttr(kAttrReturn, kernel_cnodes[i])) { + auto label_id = common::AnfAlgo::GetNodeAttr(kernel_cnodes[i], kAttrLabelIndex); if (label_id_index_map.find(label_id) == label_id_index_map.end()) { continue; } @@ -1297,9 +1298,9 @@ void InitMemReuseExecOrder(KernelGraph *kernel_graph) { GetSubGraphExecOrder(kernel_graph, label_id_index_map[label_id], back_node, &mem_reuse_order); continue; } - if (AnfAlgo::CheckPrimitiveType(kernel_cnodes[i], prim::kPrimLabelSet) && - !AnfAlgo::HasNodeAttr(kAttrRecursive, kernel_cnodes[i])) { - auto label_id = AnfAlgo::GetNodeAttr(kernel_cnodes[i], kAttrLabelIndex); + if (common::AnfAlgo::CheckPrimitiveType(kernel_cnodes[i], prim::kPrimLabelSet) && + !common::AnfAlgo::HasNodeAttr(kAttrRecursive, kernel_cnodes[i])) { + auto label_id = common::AnfAlgo::GetNodeAttr(kernel_cnodes[i], kAttrLabelIndex); if (label_id_index_map.find(label_id) != label_id_index_map.end()) { MS_LOG(EXCEPTION) << "Two labelsets with same label id."; } @@ -1584,8 +1585,8 @@ void AscendSession::IrFusionPass(const NotNull graph, NotNull &nodes) const { for (const auto &node : nodes) { auto status = device::ascend::SelectKernelInfo(node); - AnfAlgo::EraseNodeAttr(kAttrPynativeNextOpName, node); - AnfAlgo::EraseNodeAttr(kAttrPynativeNextIndex, node); + common::AnfAlgo::EraseNodeAttr(kAttrPynativeNextOpName, node); + common::AnfAlgo::EraseNodeAttr(kAttrPynativeNextIndex, node); if (status == device::ascend::kStatusRaisePrecision) { raise_precision_count_++; } else if (status == device::ascend::kStatusReducePrecision) { @@ -1830,8 +1831,8 @@ void AscendSession::UpdateOutputTensors(const VectorRef *outputs, tensor_device_addr_map_[tensor] = dst_device_address; } - if (AnfAlgo::IsDynamicShape(node)) { - const auto &updated_shape = AnfAlgo::GetOutputInferShape(node, output_index); + if (common::AnfAlgo::IsDynamicShape(node)) { + const auto &updated_shape = common::AnfAlgo::GetOutputInferShape(node, output_index); ShapeVector int_shape; (void)std::transform(updated_shape.begin(), updated_shape.end(), std::back_inserter(int_shape), SizeToInt); (void)tensor->set_shape(int_shape); diff --git a/mindspore/ccsrc/backend/common/session/cpu_session.cc b/mindspore/ccsrc/backend/common/session/cpu_session.cc index 2378afb42f7..d586ae8bd4b 100644 --- a/mindspore/ccsrc/backend/common/session/cpu_session.cc +++ b/mindspore/ccsrc/backend/common/session/cpu_session.cc @@ -21,8 +21,9 @@ #include "ir/anf.h" #include "utils/ms_utils.h" #include "utils/trace_base.h" -#include "utils/context/graph_kernel_flags.h" +#include "include/common/utils/context/graph_kernel_flags.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "runtime/device/kernel_runtime.h" #include "plugin/device/cpu/kernel/akg/akg_cpu_kernel_build.h" #include "plugin/device/cpu/kernel/cpu_kernel_factory.h" @@ -79,7 +80,9 @@ ParameterPtr CPUSession::CreateNewParameterFromParameter(const AnfNodePtr &anf, } // Remove after PS feature finish adapting push/pull in auto_monad. -void CPUSession::Reorder(std::vector *node_list) { AnfAlgo::ReorderPosteriorExecList(NOT_NULL(node_list)); } +void CPUSession::Reorder(std::vector *node_list) { + common::AnfAlgo::ReorderPosteriorExecList(NOT_NULL(node_list)); +} void CPUSession::Optimize(const std::shared_ptr &kernel_graph) { auto optimizer = std::make_shared(); @@ -178,7 +181,7 @@ void CPUSession::LoadInputData(const std::shared_ptr &kernel_graph, continue; } auto input_param = input_node->cast(); - if (AnfAlgo::IsParameterWeight(input_param) && !tensor->IsUpdatedByDevice()) { + if (common::AnfAlgo::IsParameterWeight(input_param) && !tensor->IsUpdatedByDevice()) { continue; } if (std::dynamic_pointer_cast(tensor_address)->DeviceType() != @@ -249,10 +252,10 @@ void CPUSession::SetOutputFlags(const VectorRef &base_ref) { void CPUSession::UpdateDynamicOutputShape(const std::map &tensor_to_node) { for (const auto &tensor_node : tensor_to_node) { - if (AnfAlgo::IsDynamicShape(tensor_node.second.first)) { + if (common::AnfAlgo::IsDynamicShape(tensor_node.second.first)) { const auto &kernel = tensor_node.second.first; const auto &output_index = tensor_node.second.second; - const auto &shape = AnfAlgo::GetOutputInferShape(kernel, output_index); + const auto &shape = common::AnfAlgo::GetOutputInferShape(kernel, output_index); std::vector refresh_shape; (void)std::copy(shape.begin(), shape.end(), std::back_inserter(refresh_shape)); MS_EXCEPTION_IF_NULL(tensor_node.first); @@ -314,7 +317,7 @@ void CPUSession::SetKernelInfo(const KernelGraph *kernel_graph) { namespace { void KernelNotSupportException(const AnfNodePtr &kernel_node) { - std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node); + std::string kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); std::stringstream operator_info; operator_info << "Operator[" << kernel_name << "] "; auto kernel_info = dynamic_cast(kernel_node->kernel_info()); @@ -362,7 +365,7 @@ void CPUSession::BuildKernel(const KernelGraph *kernel_graph) { std::vector akg_nodes; for (const auto &kernel_node : kernel_nodes) { MS_EXCEPTION_IF_NULL(kernel_node); - std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node); + std::string kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); MS_LOG(INFO) << "Cpu building operator[" << kernel_name << "]."; if (session::AnfRuntimeAlgorithm::GetKernelType(kernel_node) == KernelType::AKG_KERNEL) { if (!bin_map->initialized()) { diff --git a/mindspore/ccsrc/backend/common/session/executor.cc b/mindspore/ccsrc/backend/common/session/executor.cc index f383d106fd7..1e276256d7b 100644 --- a/mindspore/ccsrc/backend/common/session/executor.cc +++ b/mindspore/ccsrc/backend/common/session/executor.cc @@ -20,8 +20,8 @@ #include #include #include "runtime/device/kernel_runtime_manager.h" -#include "utils/comm_manager.h" -#include "utils/scoped_long_running.h" +#include "include/common/utils/comm_manager.h" +#include "include/common/utils/scoped_long_running.h" #include "pybind_api/ir/tensor_py.h" #if ((defined ENABLE_CPU) && (!defined _WIN32) && !defined(__APPLE__)) #include "ps/ps_cache/ps_cache_manager.h" diff --git a/mindspore/ccsrc/backend/common/session/executor.h b/mindspore/ccsrc/backend/common/session/executor.h index a9a59074726..069d36fe1b2 100644 --- a/mindspore/ccsrc/backend/common/session/executor.h +++ b/mindspore/ccsrc/backend/common/session/executor.h @@ -31,8 +31,8 @@ #include "ir/anf.h" #include "ir/tensor.h" #include "utils/any.h" -#include "utils/comm_manager.h" -#include "utils/contract.h" +#include "include/common/utils/comm_manager.h" +#include "include/common/utils/contract.h" namespace mindspore { namespace session { diff --git a/mindspore/ccsrc/backend/common/session/executor_manager.cc b/mindspore/ccsrc/backend/common/session/executor_manager.cc index 917e0425b3b..6637cd9551f 100644 --- a/mindspore/ccsrc/backend/common/session/executor_manager.cc +++ b/mindspore/ccsrc/backend/common/session/executor_manager.cc @@ -14,7 +14,7 @@ * limitations under the License. */ #include "backend/common/session/executor_manager.h" -#include "common/thread_pool.h" +#include "include/common/thread_pool.h" namespace mindspore { namespace session { std::shared_ptr ExecutorManager::GetExecutor(const std::string &device_name, uint32_t device_id) { diff --git a/mindspore/ccsrc/backend/common/session/gpu_inference_session.cc b/mindspore/ccsrc/backend/common/session/gpu_inference_session.cc index fcafde35011..ec4055f0a9a 100644 --- a/mindspore/ccsrc/backend/common/session/gpu_inference_session.cc +++ b/mindspore/ccsrc/backend/common/session/gpu_inference_session.cc @@ -21,9 +21,10 @@ #include "ir/param_info.h" #include "runtime/device/kernel_runtime.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "utils/ms_utils.h" -#include "utils/ms_device_shape_transfer.h" -#include "utils/config_manager.h" +#include "runtime/device/ms_device_shape_transfer.h" +#include "include/common/utils/config_manager.h" namespace mindspore { namespace session { @@ -44,7 +45,7 @@ void GpuInferenceSession::LoadInputData(const std::shared_ptr &kern MS_EXCEPTION_IF_NULL(pk_node); auto device_address = AnfAlgo::GetMutableOutputAddr(pk_node, 0); MS_EXCEPTION_IF_NULL(device_address); - if (!AnfAlgo::IsParameterWeight(pk_node)) { + if (!common::AnfAlgo::IsParameterWeight(pk_node)) { tensor = inputs[no_weight_input++]; if (!device_address->SyncHostToDevice(trans::GetRuntimePaddingShape(pk_node, 0), LongToSize(tensor->data().nbytes()), tensor->data_type(), @@ -70,7 +71,7 @@ GraphId GpuInferenceSession::CompileGraphImpl(NotNull func_graph) MS_EXCEPTION_IF_NULL(pk_node); auto device_address = AnfAlgo::GetMutableOutputAddr(pk_node, 0); MS_EXCEPTION_IF_NULL(device_address); - if (AnfAlgo::IsParameterWeight(pk_node)) { + if (common::AnfAlgo::IsParameterWeight(pk_node)) { const auto ¶m_value = pk_node->default_param(); MS_EXCEPTION_IF_NULL(param_value); auto tensor = std::dynamic_pointer_cast(param_value); @@ -100,7 +101,7 @@ bool GpuInferenceSession::CheckModelInputs(uint32_t graph_id, const std::vector< continue; } auto parameter = kernel_graph_inputs[i]->cast(); - if (!AnfAlgo::IsParameterWeight(parameter)) { + if (!common::AnfAlgo::IsParameterWeight(parameter)) { paras.push_back(parameter); } } diff --git a/mindspore/ccsrc/backend/common/session/gpu_session.cc b/mindspore/ccsrc/backend/common/session/gpu_session.cc index 401165c30b0..5fca8e5af6f 100644 --- a/mindspore/ccsrc/backend/common/session/gpu_session.cc +++ b/mindspore/ccsrc/backend/common/session/gpu_session.cc @@ -58,7 +58,7 @@ #include "backend/common/pass/getitem_tuple.h" #include "backend/common/pass/optimize_updatestate.h" #include "backend/common/pass/adjust_depend_for_parallel_optimizer_recompute_all_gather.h" -#include "utils/ms_device_shape_transfer.h" +#include "runtime/device/ms_device_shape_transfer.h" #include "debug/anf_ir_dump.h" #include "debug/dump_proto.h" #ifdef ENABLE_DEBUGGER @@ -80,10 +80,10 @@ #include "plugin/device/gpu/hal/device/gpu_bucket.h" #include "plugin/device/gpu/hal/device/gpu_device_address.h" #include "utils/ms_utils.h" -#include "utils/config_manager.h" +#include "include/common/utils/config_manager.h" #include "utils/ms_context.h" -#include "utils/context/graph_kernel_flags.h" -#include "utils/utils.h" +#include "include/common/utils/context/graph_kernel_flags.h" +#include "include/common/utils/utils.h" #include "abstract/utils.h" #if ENABLE_CPU && ENABLE_GPU #include "ps/util.h" @@ -313,8 +313,8 @@ size_t UpdateGraphInputAbstract(const AnfNodePtr input_node, const tensor::Tenso auto tensor_shape = tensor->shape(); std::vector shape_tmp; (void)std::transform(tensor_shape.begin(), tensor_shape.end(), std::back_inserter(shape_tmp), LongToSize); - AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(input_node, 0)}, {shape_tmp}, - input_node.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({common::AnfAlgo::GetOutputInferDataType(input_node, 0)}, {shape_tmp}, + input_node.get()); size = abstract::ShapeSize(shape_tmp) * abstract::TypeIdSize(tensor->data_type()); } return size; @@ -372,7 +372,7 @@ void GPUSession::LoadInputData(const std::shared_ptr &kernel_graph, MS_EXCEPTION_IF_NULL(device_address); bool need_sync = CheckIfNeedSync(tensor, device_address, pk_node); if (need_sync) { - if (AnfAlgo::IsParameterWeight(pk_node) || UpdatedByAssign(kernel_graph, input_node) || + if (common::AnfAlgo::IsParameterWeight(pk_node) || UpdatedByAssign(kernel_graph, input_node) || ms_context->get_param(MS_CTX_EXECUTION_MODE) == kPynativeMode) { tensor->set_device_address(device_address); } @@ -611,11 +611,11 @@ void GPUSession::UpdateOutputTensors(const VectorRef *outputs, #if ((defined ENABLE_CPU) && (!defined _WIN32)) ps_mode = ps::PSContext::instance()->is_ps_mode(); #endif - if (node->isa() && !AnfAlgo::IsCommunicationOp(node) && !ps_mode) { + if (node->isa() && !common::AnfAlgo::IsCommunicationOp(node) && !ps_mode) { auto new_address = std::make_shared(nullptr, address->GetSize()); // If a nop node is output, its previous node should be set. - if (opt::IsNopNode(node)) { - auto pre_node = AnfAlgo::GetPrevNodeOutput(node, 0, true); + if (common::AnfAlgo::IsNopNode(node)) { + auto pre_node = common::AnfAlgo::GetPrevNodeOutput(node, 0, true); if (!pre_node.first->isa()) { AnfAlgo::SetOutputAddr(new_address, pre_node.second, pre_node.first.get()); } @@ -632,8 +632,8 @@ void GPUSession::UpdateOutputTensors(const VectorRef *outputs, } } - if (AnfAlgo::IsDynamicShape(node)) { - const auto &updated_shape = AnfAlgo::GetOutputInferShape(node, output_index); + if (common::AnfAlgo::IsDynamicShape(node)) { + const auto &updated_shape = common::AnfAlgo::GetOutputInferShape(node, output_index); ShapeVector int_shape; std::transform(updated_shape.begin(), updated_shape.end(), std::back_inserter(int_shape), SizeToInt); tensor->set_shape(int_shape); diff --git a/mindspore/ccsrc/backend/common/session/kernel_build_client.h b/mindspore/ccsrc/backend/common/session/kernel_build_client.h index 28840fa34cf..059ad6fecc7 100644 --- a/mindspore/ccsrc/backend/common/session/kernel_build_client.h +++ b/mindspore/ccsrc/backend/common/session/kernel_build_client.h @@ -24,7 +24,7 @@ #include #include -#include "common/duplex_pipe.h" +#include "include/common/duplex_pipe.h" #include "utils/log_adapter.h" #include "utils/ms_context.h" diff --git a/mindspore/ccsrc/backend/common/session/kernel_graph.cc b/mindspore/ccsrc/backend/common/session/kernel_graph.cc index 23b87d2de00..253df3bebf9 100644 --- a/mindspore/ccsrc/backend/common/session/kernel_graph.cc +++ b/mindspore/ccsrc/backend/common/session/kernel_graph.cc @@ -21,9 +21,10 @@ #include "utils/hash_set.h" #include "base/core_ops.h" #include "ir/param_info.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/check_convert_utils.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "runtime/device/kernel_info.h" #include "kernel/kernel_build_info.h" #include "runtime/device/kernel_runtime_manager.h" @@ -53,11 +54,11 @@ void PushNoVisitedNode(const AnfNodePtr &node, std::queue *que, std::vector GetCallRealOutputs(const AnfNodePtr &call_node) { auto item_with_index = - AnfAlgo::VisitKernelWithReturnType(call_node, 0, false, {prim::kPrimTupleGetItem, prim::kPrimMakeTuple}); + common::AnfAlgo::VisitKernelWithReturnType(call_node, 0, false, {prim::kPrimTupleGetItem, prim::kPrimMakeTuple}); AnfNodePtr node = item_with_index.first; MS_EXCEPTION_IF_NULL(node); - if (AnfAlgo::CheckPrimitiveType(node, prim::kPrimMakeTuple)) { - auto outputs = AnfAlgo::GetAllOutput(node); + if (common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimMakeTuple)) { + auto outputs = common::AnfAlgo::GetAllOutput(node); std::set memo; std::vector new_output; for (auto &output : outputs) { @@ -67,11 +68,11 @@ std::vector GetCallRealOutputs(const AnfNodePtr &call_node) { memo.insert(output); new_output.push_back(output); } - if (new_output.size() == 1 && AnfAlgo::CheckPrimitiveType(new_output[0], prim::kPrimCall)) { + if (new_output.size() == 1 && common::AnfAlgo::CheckPrimitiveType(new_output[0], prim::kPrimCall)) { node = new_output[0]; } } - if (!AnfAlgo::CheckPrimitiveType(node, prim::kPrimCall)) { + if (!common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimCall)) { return {node}; } std::vector real_inputs; @@ -95,9 +96,9 @@ bool IsSameLabel(const CNodePtr &left, const CNodePtr &right) { if (!IsPrimitiveCNode(left, GetCNodePrimitive(right))) { return false; } - if (AnfAlgo::HasNodeAttr(kAttrLabelIndex, left) && AnfAlgo::HasNodeAttr(kAttrLabelIndex, right)) { - return AnfAlgo::GetNodeAttr(left, kAttrLabelIndex) == - AnfAlgo::GetNodeAttr(right, kAttrLabelIndex); + if (common::AnfAlgo::HasNodeAttr(kAttrLabelIndex, left) && common::AnfAlgo::HasNodeAttr(kAttrLabelIndex, right)) { + return common::AnfAlgo::GetNodeAttr(left, kAttrLabelIndex) == + common::AnfAlgo::GetNodeAttr(right, kAttrLabelIndex); } return false; } @@ -132,25 +133,25 @@ void SyncDeviceInfoToValueNode(const ValueNodePtr &value_node, std::vectorcast(); - if (AnfAlgo::HasNodeAttr(kAttrGroup, cnode)) { - return AnfAlgo::GetNodeAttr(cnode, kAttrGroup); + if (common::AnfAlgo::HasNodeAttr(kAttrGroup, cnode)) { + return common::AnfAlgo::GetNodeAttr(cnode, kAttrGroup); } return ""; } void SetInternalOutputAttr(const AnfNodePtr &node) { - if (!opt::IsNopNode(node)) { + if (!common::AnfAlgo::IsNopNode(node)) { return; } auto p = GetCNodePrimitive(node); if (p == nullptr) return; auto prim_node = NewValueNode(p->Clone()); node->cast()->set_input(kAnfPrimitiveIndex, prim_node); - AnfAlgo::SetNodeAttr(kAttrIsInternalOutputNopNode, MakeValue(true), node); + common::AnfAlgo::SetNodeAttr(kAttrIsInternalOutputNopNode, MakeValue(true), node); } bool NeedOptimize(const AnfNodePtr &node, const std::string &optimized_comm_group) { - bool is_fused_comm = AnfAlgo::IsFusedCommunicationOp(node); + bool is_fused_comm = common::AnfAlgo::IsFusedCommunicationOp(node); if (!is_fused_comm) { return false; } @@ -219,8 +220,8 @@ void KernelGraph::EnqueueReadyNodes(const AnfNodePtr &node, std::queuefind(next_node) == visited_nodes->end()) { (void)visited_nodes->insert(next_node); - bool is_comm_node = AnfAlgo::IsCommunicationOp(next_node); - if (AnfAlgo::CheckPrimitiveType(next_node, prim::kPrimLoad)) { + bool is_comm_node = common::AnfAlgo::IsCommunicationOp(next_node); + if (common::AnfAlgo::CheckPrimitiveType(next_node, prim::kPrimLoad)) { EnqueueReadyNodes(next_node, visit_queue, visited_nodes); } else if ((is_comm_node && comm_first) || (!is_comm_node && !comm_first)) { MS_LOG(DEBUG) << "Visit node:" << next_node->DebugString(); @@ -272,7 +273,7 @@ void KernelGraph::SetExecOrderByDefault() { execution_order_.push_back(node->cast()); } // delay execute comm ops that need optimize - bool is_comm = AnfAlgo::IsCommunicationOp(node); + bool is_comm = common::AnfAlgo::IsCommunicationOp(node); bool optimize_comm = NeedOptimize(node, optimized_comm_group); if (optimize_comm) { optimized_comm_group = GetNodeGroup(node); @@ -451,11 +452,11 @@ CNodePtr KernelGraph::NewCNode(const std::vector &inputs) { void KernelGraph::PostNewCNode(const CNodePtr &cnode) { MS_EXCEPTION_IF_NULL(cnode); cnode->set_abstract(std::make_shared()); - if (AnfAlgo::IsGraphKernel(cnode)) { + if (common::AnfAlgo::IsGraphKernel(cnode)) { CreateKernelInfoFromNewParameter(cnode); } - if (AnfAlgo::GetCNodeName(cnode) == prim::kPrimCast->name()) { - AnfAlgo::SetNodeAttr(kIsBackendCast, MakeValue(false), cnode); + if (common::AnfAlgo::GetCNodeName(cnode) == prim::kPrimCast->name()) { + common::AnfAlgo::SetNodeAttr(kIsBackendCast, MakeValue(false), cnode); } SetKernelInfoForNode(cnode); AnfAlgo::SetGraphId(graph_id_, cnode.get()); @@ -472,7 +473,7 @@ CNodePtr KernelGraph::NewCNodeWithInfos(const std::vector &inputs, c } void KernelGraph::CreateKernelInfoFromNewParameter(const CNodePtr &cnode) { - auto func_graph = AnfAlgo::GetCNodeFuncGraphPtr(cnode); + auto func_graph = common::AnfAlgo::GetCNodeFuncGraphPtr(cnode); MS_EXCEPTION_IF_NULL(func_graph); std::vector node_list; @@ -486,7 +487,7 @@ void KernelGraph::CreateKernelInfoFromNewParameter(const CNodePtr &cnode) { } auto anf_cnode = anf_node->cast(); MS_EXCEPTION_IF_NULL(anf_cnode); - size_t input_num = AnfAlgo::GetInputTensorNum(anf_cnode); + size_t input_num = common::AnfAlgo::GetInputTensorNum(anf_cnode); for (size_t i = 0; i < input_num; ++i) { auto input_node = anf_cnode->input(i + 1); MS_EXCEPTION_IF_NULL(input_node); @@ -507,14 +508,14 @@ void KernelGraph::CreateKernelInfoFromNewParameter(const CNodePtr &cnode) { } void KernelGraph::ResetAssignInputFeatureMapFlag(const CNodePtr &cnode) const { - if (kOpAssignKernelNameList.find(AnfAlgo::GetCNodeName(cnode)) == kOpAssignKernelNameList.end()) { + if (kOpAssignKernelNameList.find(common::AnfAlgo::GetCNodeName(cnode)) == kOpAssignKernelNameList.end()) { MS_LOG(EXCEPTION) << "Only supported to change the node [Assign , AssignSub, AssignAdd] node's input feature map " "flag but got the node :" << cnode->DebugString(); } - auto input_node = AnfAlgo::GetInputNode(cnode, 0); + auto input_node = common::AnfAlgo::GetInputNode(cnode, 0); MS_EXCEPTION_IF_NULL(input_node); - auto assign_value_node = AnfAlgo::GetInputNode(cnode, 1); + auto assign_value_node = common::AnfAlgo::GetInputNode(cnode, 1); if (AnfAlgo::IsFeatureMapOutput(input_node)) { return; } @@ -531,7 +532,7 @@ void KernelGraph::SetKernelInfoForNode(const AnfNodePtr &node) const { MS_EXCEPTION_IF_NULL(kernel_info); node->set_kernel_info(kernel_info); if (node->isa()) { - if (kOpAssignKernelNameList.find(AnfAlgo::GetCNodeName(node)) != kOpAssignKernelNameList.end()) { + if (kOpAssignKernelNameList.find(common::AnfAlgo::GetCNodeName(node)) != kOpAssignKernelNameList.end()) { ResetAssignInputFeatureMapFlag(node->cast()); } #if defined(__APPLE__) @@ -540,21 +541,21 @@ void KernelGraph::SetKernelInfoForNode(const AnfNodePtr &node) const { std::vector feature_map_input_indexs; #endif kernel_info->set_feature_map_flag(false); - size_t input_num = AnfAlgo::GetInputTensorNum(node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(node); for (size_t index = 0; index < input_num; ++index) { if (AnfAlgo::IsFeatureMapInput(node, index)) { kernel_info->set_feature_map_flag(true); feature_map_input_indexs.push_back(index); } } - if (AnfAlgo::GetInputTensorNum(node) == 0) { + if (common::AnfAlgo::GetInputTensorNum(node) == 0) { kernel_info->set_feature_map_flag(true); } if (AnfUtils::IsRealKernel(node)) { // if the node only has the primitive(such as getNext) or the node's input has a feature map input // then the node's output is a feature map output - AnfAlgo::SetNodeAttr(kIsFeatureMapOutput, MakeValue(kernel_info->is_feature_map()), node); - AnfAlgo::SetNodeAttr(kIsFeatureMapInputList, MakeValue(feature_map_input_indexs), node); + common::AnfAlgo::SetNodeAttr(kIsFeatureMapOutput, MakeValue(kernel_info->is_feature_map()), node); + common::AnfAlgo::SetNodeAttr(kIsFeatureMapInputList, MakeValue(feature_map_input_indexs), node); } return; } @@ -572,9 +573,9 @@ void KernelGraph::SetKernelInfoForNode(const AnfNodePtr &node) const { if (node->isa()) { auto parameter = node->cast(); MS_EXCEPTION_IF_NULL(parameter); - bool is_weight = AnfAlgo::IsParameterWeight(parameter); + bool is_weight = common::AnfAlgo::IsParameterWeight(parameter); kernel_info->set_feature_map_flag(!is_weight); - types.push_back(is_weight ? kTypeUnknown : AnfAlgo::GetOutputInferDataType(parameter, 0)); + types.push_back(is_weight ? kTypeUnknown : common::AnfAlgo::GetOutputInferDataType(parameter, 0)); } // set parameter initaial device data type kernel_build_info_builder->SetOutputsFormat(formats); @@ -599,7 +600,7 @@ ParameterPtr KernelGraph::NewParameter(const ParameterPtr ¶meter) { // if don't use default parameter = nullptr,it remarks create a new parameter from a old parameter if (parameter != nullptr) { new_parameter->set_name(parameter->name()); - if (AnfAlgo::IsParameterWeight(parameter)) { + if (common::AnfAlgo::IsParameterWeight(parameter)) { new_parameter->set_default_param(parameter->default_param()); } } @@ -712,9 +713,9 @@ AnfNodePtr KernelGraph::CreatTupleGetItemNode(const AnfNodePtr &node, size_t out AnfNodePtr tuple_getitem = NewCNode({mindspore::NewValueNode(prim::kPrimTupleGetItem), node, idx}); MS_EXCEPTION_IF_NULL(tuple_getitem); tuple_getitem->set_scope(node->scope()); - std::vector origin_shape = AnfAlgo::GetOutputInferShape(node, output_idx); - TypeId origin_type = AnfAlgo::GetOutputInferDataType(node, output_idx); - AnfAlgo::SetOutputInferTypeAndShape({origin_type}, {origin_shape}, tuple_getitem.get()); + std::vector origin_shape = common::AnfAlgo::GetOutputInferShape(node, output_idx); + TypeId origin_type = common::AnfAlgo::GetOutputInferDataType(node, output_idx); + common::AnfAlgo::SetOutputInferTypeAndShape({origin_type}, {origin_shape}, tuple_getitem.get()); return tuple_getitem; } @@ -723,20 +724,20 @@ AnfNodePtr KernelGraph::TransCNodeTuple(const CNodePtr &node) { std::vector types; std::vector> shapes; std::vector make_tuple_inputs_list = {mindspore::NewValueNode(prim::kPrimMakeTuple)}; - size_t output_num = AnfAlgo::GetOutputTensorNum(node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(node); for (size_t tuple_out_index = 0; tuple_out_index < output_num; ++tuple_out_index) { make_tuple_inputs_list.emplace_back(CreatTupleGetItemNode(node, tuple_out_index)); - types.push_back(AnfAlgo::GetOutputInferDataType(node, tuple_out_index)); - shapes.emplace_back(AnfAlgo::GetOutputInferShape(node, tuple_out_index)); + types.push_back(common::AnfAlgo::GetOutputInferDataType(node, tuple_out_index)); + shapes.emplace_back(common::AnfAlgo::GetOutputInferShape(node, tuple_out_index)); } auto make_tuple = NewCNode(std::move(make_tuple_inputs_list)); - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, make_tuple.get()); + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, make_tuple.get()); return make_tuple; } AnfNodePtr KernelGraph::TransTupleToMakeTuple(const AnfNodePtr &node) { MS_EXCEPTION_IF_NULL(node); - if (!AnfAlgo::IsTupleOutput(node)) { + if (!common::AnfAlgo::IsTupleOutput(node)) { return node; } if (node->isa()) { @@ -930,11 +931,11 @@ void KernelGraph::SetOutputNodeToTensor(const KernelMapTensor &node_to_tensor) { for (const auto &item : output_node_to_tensor_) { auto node = item.first.first; auto out_index = item.first.second; - if (!opt::IsNopNode(node)) { + if (!common::AnfAlgo::IsNopNode(node)) { continue; } - while (opt::IsNopNode(node)) { - const auto kernel_with_index = AnfAlgo::GetPrevNodeOutput(node, 0); + while (common::AnfAlgo::IsNopNode(node)) { + const auto kernel_with_index = common::AnfAlgo::GetPrevNodeOutput(node, 0); node = kernel_with_index.first; out_index = kernel_with_index.second; } @@ -1007,7 +1008,7 @@ std::vector KernelGraph::FindNodeByPrimitive(const PrimitivePtr &primi std::vector result; for (const auto &anf : execution_order_) { MS_EXCEPTION_IF_NULL(anf); - if (AnfAlgo::CheckPrimitiveType(anf, primitive) && AnfAlgo::GetGraphId(anf.get()) == graph_id_) { + if (common::AnfAlgo::CheckPrimitiveType(anf, primitive) && AnfAlgo::GetGraphId(anf.get()) == graph_id_) { result.push_back(anf->cast()); } } @@ -1019,7 +1020,7 @@ std::vector KernelGraph::FindNodeByPrimitive(const std::vectorcast()); } } @@ -1037,17 +1038,19 @@ void KernelGraph::PrintGraphExecuteOrder() const { MS_EXCEPTION_IF_NULL(cur_cnode_ptr); std::string event_str; - if (AnfAlgo::HasNodeAttr(kAttrEventId, cur_cnode_ptr)) { - event_str = ", event id[" + std::to_string(AnfAlgo::GetNodeAttr(cur_cnode_ptr, kAttrEventId)) + "]"; + if (common::AnfAlgo::HasNodeAttr(kAttrEventId, cur_cnode_ptr)) { + event_str = + ", event id[" + std::to_string(common::AnfAlgo::GetNodeAttr(cur_cnode_ptr, kAttrEventId)) + "]"; } std::string label_str; - if (AnfAlgo::HasNodeAttr(kAttrLabelIndex, cur_cnode_ptr)) { - label_str = ", label id[" + std::to_string(AnfAlgo::GetNodeAttr(cur_cnode_ptr, kAttrLabelIndex)) + "]"; + if (common::AnfAlgo::HasNodeAttr(kAttrLabelIndex, cur_cnode_ptr)) { + label_str = + ", label id[" + std::to_string(common::AnfAlgo::GetNodeAttr(cur_cnode_ptr, kAttrLabelIndex)) + "]"; } - if (AnfAlgo::HasNodeAttr(kAttrLabelSwitchList, cur_cnode_ptr)) { - auto label_list = AnfAlgo::GetNodeAttr>(cur_cnode_ptr, kAttrLabelSwitchList); + if (common::AnfAlgo::HasNodeAttr(kAttrLabelSwitchList, cur_cnode_ptr)) { + auto label_list = common::AnfAlgo::GetNodeAttr>(cur_cnode_ptr, kAttrLabelSwitchList); label_str = ", label id["; for (size_t j = 0; j < label_list.size(); ++j) { label_str += std::to_string(label_list[j]) + (j + 1 < label_list.size() ? ", " : "]"); @@ -1055,8 +1058,8 @@ void KernelGraph::PrintGraphExecuteOrder() const { } std::string active_stream_str; - if (AnfAlgo::HasNodeAttr(kAttrActiveStreamList, cur_cnode_ptr)) { - auto stream_list = AnfAlgo::GetNodeAttr>(cur_cnode_ptr, kAttrActiveStreamList); + if (common::AnfAlgo::HasNodeAttr(kAttrActiveStreamList, cur_cnode_ptr)) { + auto stream_list = common::AnfAlgo::GetNodeAttr>(cur_cnode_ptr, kAttrActiveStreamList); active_stream_str = ", active stream id["; for (size_t j = 0; j < stream_list.size(); ++j) { active_stream_str += std::to_string(stream_list[j]) + (j + 1 < stream_list.size() ? ", " : "]"); @@ -1064,8 +1067,9 @@ void KernelGraph::PrintGraphExecuteOrder() const { } std::string group_str; - if (AnfAlgo::GetKernelType(cur_cnode_ptr) == HCCL_KERNEL && AnfAlgo::HasNodeAttr(kAttrGroup, cur_cnode_ptr)) { - group_str = ", group[" + AnfAlgo::GetNodeAttr(cur_cnode_ptr, kAttrGroup) + "]"; + if (AnfAlgo::GetKernelType(cur_cnode_ptr) == HCCL_KERNEL && + common::AnfAlgo::HasNodeAttr(kAttrGroup, cur_cnode_ptr)) { + group_str = ", group[" + common::AnfAlgo::GetNodeAttr(cur_cnode_ptr, kAttrGroup) + "]"; } MS_LOG(INFO) << "Index[" << i << "], node name[" << cur_cnode_ptr->fullname_with_scope() << "], logic id[" @@ -1084,8 +1088,8 @@ void KernelGraph::AddInternalOutput(const AnfNodePtr &front_node, const AnfNodeP MS_LOG(INFO) << "Add internal node " << node->DebugString() << " with front node " << front_node->DebugString(); front_to_internal_outputs_map_[front_node] = node; SetInternalOutputAttr(node); - if (AnfAlgo::CheckPrimitiveType(front_node, prim::kPrimTupleGetItem)) { - output_idx = AnfAlgo::GetTupleGetItemOutIndex(front_node->cast()); + if (common::AnfAlgo::CheckPrimitiveType(front_node, prim::kPrimTupleGetItem)) { + output_idx = common::AnfAlgo::GetTupleGetItemOutIndex(front_node->cast()); } internal_outputs_to_front_map_[node][output_idx] = std::pair(front_node, unique_target); } @@ -1191,7 +1195,7 @@ void KernelGraph::CacheInternalParameterToFrontNode(const AnfNodePtr ¶meter, return; } - auto front_outputs = AnfAlgo::GetAllOutputWithIndex(front_node_with_index.first); + auto front_outputs = common::AnfAlgo::GetAllOutputWithIndex(front_node_with_index.first); AnfWithOutIndex new_front_node_with_index; if (front_node_with_index.second < front_outputs.size()) { new_front_node_with_index = front_outputs[front_node_with_index.second]; @@ -1234,7 +1238,7 @@ void KernelGraph::CacheGraphOutputToFrontNodeWithIndex(const std::vector backend_output_nodes; for (auto &backend_output : backend_outputs) { - auto temp_backend_outputs = AnfAlgo::GetAllOutputWithIndex(backend_output); + auto temp_backend_outputs = common::AnfAlgo::GetAllOutputWithIndex(backend_output); (void)backend_output_nodes.insert(backend_output_nodes.end(), temp_backend_outputs.begin(), temp_backend_outputs.end()); } @@ -1242,7 +1246,7 @@ void KernelGraph::CacheGraphOutputToFrontNodeWithIndex(const std::vector front_output_nodes; for (auto &front_output : front_outputs) { - auto temp_front_outputs = AnfAlgo::GetAllOutputWithIndex(front_output); + auto temp_front_outputs = common::AnfAlgo::GetAllOutputWithIndex(front_output); (void)front_output_nodes.insert(front_output_nodes.end(), temp_front_outputs.begin(), temp_front_outputs.end()); } @@ -1357,7 +1361,7 @@ void KernelGraph::RemoveNodeFromGraph(const AnfNodePtr &node) { void KernelGraph::UpdateGraphDynamicAttr() { for (const auto &cnode : execution_order_) { - if (AnfAlgo::IsDynamicShape(cnode)) { + if (common::AnfAlgo::IsDynamicShape(cnode)) { MS_LOG(INFO) << "Update Graph Dynamic Attr"; is_dynamic_shape_ = true; return; @@ -1369,7 +1373,7 @@ void KernelGraph::UpdateGraphDynamicAttr() { void KernelGraph::SetInputNodes() { input_nodes_.clear(); for (const auto &input_node : inputs()) { - auto params = AnfAlgo::GetAllOutput(input_node); + auto params = common::AnfAlgo::GetAllOutput(input_node); if (params.size() == 1) { FrontBackendlMapUpdate(input_node, params[0]); } else { @@ -1390,7 +1394,7 @@ void KernelGraph::SetInputNodes() { void KernelGraph::UpdateGraphAquireGilAttr() { for (const auto &cnode : execution_order_) { - if (AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimPyFunc)) { + if (common::AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimPyFunc)) { MS_LOG(INFO) << "The Graph require GIL. Graph id: " << graph_id_; is_need_gil_ = true; return; @@ -1402,12 +1406,12 @@ void KernelGraph::SetOptimizerFlag() { has_optimizer_ = false; for (const auto &cnode : execution_order_) { MS_EXCEPTION_IF_NULL(cnode); - if (!AnfAlgo::IsUpdateParameterKernel(cnode)) { + if (!common::AnfAlgo::IsUpdateParameterKernel(cnode)) { continue; } for (auto &input : cnode->inputs()) { MS_EXCEPTION_IF_NULL(input); - auto real_node = AnfAlgo::VisitKernel(input, 0).first; + auto real_node = common::AnfAlgo::VisitKernel(input, 0).first; MS_EXCEPTION_IF_NULL(real_node); if (!real_node->isa()) { continue; @@ -1431,7 +1435,7 @@ bool KernelGraph::IsDatasetGraph() const { return false; } for (const auto &node : nodes) { - auto node_name = AnfAlgo::GetCNodeName(node); + auto node_name = common::AnfAlgo::GetCNodeName(node); if (node_name == prim::kPrimInitDataSetQueue->name()) { return true; } @@ -1445,7 +1449,7 @@ bool KernelGraph::IsChildGraphResult(const AnfNodePtr &node) { std::vector child_graph_results; for (const auto &child_graph_result : child_graph_result_) { MS_EXCEPTION_IF_NULL(child_graph_result); - auto outputs = AnfAlgo::GetAllOutput(child_graph_result); + auto outputs = common::AnfAlgo::GetAllOutput(child_graph_result); (void)child_graph_results.insert(child_graph_results.end(), outputs.begin(), outputs.end()); } diff --git a/mindspore/ccsrc/backend/common/session/kernel_graph.h b/mindspore/ccsrc/backend/common/session/kernel_graph.h index b61fa566505..a4c1ad0a638 100644 --- a/mindspore/ccsrc/backend/common/session/kernel_graph.h +++ b/mindspore/ccsrc/backend/common/session/kernel_graph.h @@ -30,7 +30,7 @@ #include "ir/func_graph.h" #include "ir/anf.h" #include "ir/graph_utils.h" -#include "utils/contract.h" +#include "include/common/utils/contract.h" #include "runtime/device/kernel_info.h" namespace mindspore { diff --git a/mindspore/ccsrc/backend/common/session/pynative_task_manager.h b/mindspore/ccsrc/backend/common/session/pynative_task_manager.h index 75da147a413..f0b3fa1d553 100644 --- a/mindspore/ccsrc/backend/common/session/pynative_task_manager.h +++ b/mindspore/ccsrc/backend/common/session/pynative_task_manager.h @@ -25,6 +25,7 @@ #include #include "backend/common/session/kernel_graph.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace session { diff --git a/mindspore/ccsrc/backend/common/session/session_basic.cc b/mindspore/ccsrc/backend/common/session/session_basic.cc index 45e5410fbc6..76667a7729e 100644 --- a/mindspore/ccsrc/backend/common/session/session_basic.cc +++ b/mindspore/ccsrc/backend/common/session/session_basic.cc @@ -29,9 +29,10 @@ #include "kernel/common_utils.h" #include "base/core_ops.h" #include "base/base_ref_utils.h" -#include "utils/ms_device_shape_transfer.h" -#include "utils/config_manager.h" +#include "runtime/device/ms_device_shape_transfer.h" +#include "include/common/utils/config_manager.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/session/executor_manager.h" #include "backend/common/optimizer/common_backend_optimization.h" #include "backend/common/optimizer/helper.h" @@ -39,12 +40,12 @@ #include "utils/ms_utils.h" #include "ir/anf.h" #include "ir/func_graph_cloner.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "debug/anf_ir_dump.h" #include "debug/dump_proto.h" #include "utils/file_utils.h" #include "utils/trace_base.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" #if ((defined ENABLE_CPU) && (!defined _WIN32) && !defined(__APPLE__)) #include "ps/ps_cache/ps_cache_manager.h" #include "ps/constants.h" @@ -90,11 +91,11 @@ bool RecursiveCheck(const FuncGraphManagerPtr &manager, const std::pair 1 && - (AnfAlgo::CheckPrimitiveType(node, prim::kPrimDepend) || AnfAlgo::CheckPrimitiveType(node, prim::kPrimLoad))) { + if (kernel.second > 1 && (common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimDepend) || + common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimLoad))) { return false; } - if (AnfUtils::IsRealKernel(node) && !AnfAlgo::CheckPrimitiveType(node, prim::kPrimPartial)) { + if (AnfUtils::IsRealKernel(node) && !common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimPartial)) { return true; } (*idx) += 1; @@ -201,13 +202,13 @@ BaseRef CreateNodeOutputTensor(const session::KernelWithIndex &node_output_pair, } TypeId type_id = AnfAlgo::GetOutputDeviceDataType(node, output_index); if (type_id == kTypeUnknown) { - type_id = AnfAlgo::GetOutputInferDataType(node, output_index); + type_id = common::AnfAlgo::GetOutputInferDataType(node, output_index); } std::vector temp_shape; - auto shape = AnfAlgo::GetOutputInferShape(node, output_index); + auto shape = common::AnfAlgo::GetOutputInferShape(node, output_index); (void)std::copy(shape.begin(), shape.end(), std::back_inserter(temp_shape)); - if (AnfAlgo::IsDynamicShape(node)) { - auto max_shape = AnfAlgo::GetOutputMaxShape(node, output_index); + if (common::AnfAlgo::IsDynamicShape(node)) { + auto max_shape = common::AnfAlgo::GetOutputMaxShape(node, output_index); temp_shape = abstract::ShapeSize(max_shape) > abstract::ShapeSize(temp_shape) ? max_shape : temp_shape; } tensor::TensorPtr tensor; @@ -249,11 +250,11 @@ BaseRef CreateNodeOutputTensors(const AnfNodePtr &anf, const KernelGraphPtr &gra MS_EXCEPTION_IF_NULL(tensor_to_node); MS_EXCEPTION_IF_NULL(node_to_tensor); MS_LOG(DEBUG) << "Create tensor for output[" << anf->DebugString() << "]"; - auto item_with_index = AnfAlgo::VisitKernelWithReturnType(anf, 0); + auto item_with_index = common::AnfAlgo::VisitKernelWithReturnType(anf, 0); MS_EXCEPTION_IF_NULL(item_with_index.first); MS_LOG(DEBUG) << "Create tensor for output after visit:" << item_with_index.first->DebugString(); // special handle for maketuple - if (AnfAlgo::CheckPrimitiveType(item_with_index.first, prim::kPrimMakeTuple)) { + if (common::AnfAlgo::CheckPrimitiveType(item_with_index.first, prim::kPrimMakeTuple)) { auto cnode = item_with_index.first->cast(); MS_EXCEPTION_IF_NULL(cnode); VectorRef ret; @@ -264,7 +265,7 @@ BaseRef CreateNodeOutputTensors(const AnfNodePtr &anf, const KernelGraphPtr &gra return ret; } // if is graph return nothing ,the function should return a null anylist - size_t size = AnfAlgo::GetOutputTensorNum(item_with_index.first); + size_t size = common::AnfAlgo::GetOutputTensorNum(item_with_index.first); if (size == 0) { return VectorRef(); } @@ -341,7 +342,7 @@ ParameterPtr ConstructRunOpParameter(const std::shared_ptr &graph, auto device_address = std::dynamic_pointer_cast(input_tensor->device_address()); if (NeedDiscardTensorProperties(op_run_info.device_target, device_address)) { kernel_build_info_builder->SetOutputsFormat(std::vector{kOpFormat_DEFAULT}); - TypeId param_init_data_type = AnfAlgo::IsParameterWeight(param) ? kTypeUnknown : input_tensor->data_type(); + TypeId param_init_data_type = common::AnfAlgo::IsParameterWeight(param) ? kTypeUnknown : input_tensor->data_type(); kernel_build_info_builder->SetOutputsDeviceType(std::vector{param_init_data_type}); } else { kernel_build_info_builder->SetOutputsFormat(std::vector{device_address->format()}); @@ -439,11 +440,11 @@ BaseRef CreateNodeOutputPlaceholder(const AnfNodePtr &anf, const KernelGraphPtr MS_EXCEPTION_IF_NULL(anf); MS_EXCEPTION_IF_NULL(output_indexes); MS_LOG(DEBUG) << "Create placeholder for output[" << anf->DebugString() << "]"; - auto item_with_index = AnfAlgo::VisitKernelWithReturnType(anf, 0); + auto item_with_index = common::AnfAlgo::VisitKernelWithReturnType(anf, 0); MS_EXCEPTION_IF_NULL(item_with_index.first); MS_LOG(DEBUG) << "Create placeholder for output after visit:" << item_with_index.first->DebugString(); // special handle for maketuple - if (AnfAlgo::CheckPrimitiveType(item_with_index.first, prim::kPrimMakeTuple)) { + if (common::AnfAlgo::CheckPrimitiveType(item_with_index.first, prim::kPrimMakeTuple)) { auto cnode = item_with_index.first->cast(); MS_EXCEPTION_IF_NULL(cnode); VectorRef ret; @@ -456,7 +457,7 @@ BaseRef CreateNodeOutputPlaceholder(const AnfNodePtr &anf, const KernelGraphPtr return ret; } // if is graph return nothing ,the function should return a null anylist - size_t size = AnfAlgo::GetOutputTensorNum(item_with_index.first); + size_t size = common::AnfAlgo::GetOutputTensorNum(item_with_index.first); if (size == 0) { return VectorRef(); } @@ -466,17 +467,17 @@ BaseRef CreateNodeOutputPlaceholder(const AnfNodePtr &anf, const KernelGraphPtr void CheckInputTensorShape(const TensorPtr &tensor, const CNodePtr &kernel, size_t input_index) { MS_EXCEPTION_IF_NULL(tensor); const auto &tensor_shape = tensor->shape(); - const auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel, input_index); + const auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel, input_index); if (tensor_shape.size() != input_shape.size()) { MS_LOG(EXCEPTION) << "The input tensor's shape size: " << tensor_shape.size() << " is not equal to expected size: " << input_shape.size() << " for input[" << input_index - << "] of kernel: " << AnfAlgo::GetCNodeName(kernel) << trace::DumpSourceLines(kernel); + << "] of kernel: " << common::AnfAlgo::GetCNodeName(kernel) << trace::DumpSourceLines(kernel); } for (size_t i = 0; i < tensor_shape.size(); i++) { if (tensor_shape[i] < 0 || static_cast(tensor_shape[i]) != input_shape[i]) { MS_LOG(EXCEPTION) << "The input tensor's shape: " << tensor_shape << " is not equal to expected shape: " << input_shape << " for input[" << input_index - << "] of kernel: " << AnfAlgo::GetCNodeName(kernel) << trace::DumpSourceLines(kernel); + << "] of kernel: " << common::AnfAlgo::GetCNodeName(kernel) << trace::DumpSourceLines(kernel); } } } @@ -497,7 +498,7 @@ void SetReturnNode(const AnfNodePtr &node, KernelGraph *graph) { MS_EXCEPTION_IF_NULL(graph); MS_EXCEPTION_IF_NULL(node); - if (AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { + if (common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { constexpr auto kReturnInputIdx = 1; auto return_node = node->cast(); graph->set_return(return_node); @@ -507,7 +508,7 @@ void SetReturnNode(const AnfNodePtr &node, KernelGraph *graph) { // If return's input is value node, then the graph has no kernel, and the pass 'trans tuple to make_tuple' cannot // match this pattern because that pass begin with output node but return node. So we add transform value tuple // to make_tuple here. - if (AnfAlgo::IsTupleOutput(graph_output) && graph_output->isa()) { + if (common::AnfAlgo::IsTupleOutput(graph_output) && graph_output->isa()) { return_node->set_input(kReturnInputIdx, graph->TransTupleToMakeTuple(graph_output)); } } @@ -535,7 +536,7 @@ void GetNodeUsedList(const FuncGraphPtr &kernel_graph, const AnfNodePtr &node, auto node_users = iter->second; for (const auto &node_user : node_users) { - if (AnfAlgo::GetCNodeName(node_user.first) == prim::kPrimLoad->name()) { + if (common::AnfAlgo::GetCNodeName(node_user.first) == prim::kPrimLoad->name()) { GetNodeUsedList(kernel_graph, node_user.first, node_users_list); } else { node_users_list->push_back(node_user.first); @@ -611,10 +612,10 @@ void SessionBasic::InitInternalOutputParameter(const AnfNodePtr &out_node, const return; } size_t output_idx = 0; - if (AnfAlgo::CheckPrimitiveType(out_node, prim::kPrimTupleGetItem)) { - output_idx = AnfAlgo::GetTupleGetItemOutIndex(out_node->cast()); + if (common::AnfAlgo::CheckPrimitiveType(out_node, prim::kPrimTupleGetItem)) { + output_idx = common::AnfAlgo::GetTupleGetItemOutIndex(out_node->cast()); } - auto real_kernel = AnfAlgo::VisitKernel(ref_node, output_idx); + auto real_kernel = common::AnfAlgo::VisitKernel(ref_node, output_idx); auto ref_real_node = real_kernel.first; auto ref_real_node_index = real_kernel.second; if (ref_real_node->isa() && node_graph->IsUniqueTargetInternalOutput(ref_real_node, ref_real_node_index)) { @@ -623,7 +624,7 @@ void SessionBasic::InitInternalOutputParameter(const AnfNodePtr &out_node, const MS_LOG(INFO) << "No kernel info"; return; } - if (!opt::IsNopNode(ref_real_node) && !AnfAlgo::OutputAddrExist(ref_real_node, ref_real_node_index)) { + if (!common::AnfAlgo::IsNopNode(ref_real_node) && !AnfAlgo::OutputAddrExist(ref_real_node, ref_real_node_index)) { MS_LOG(INFO) << "No kernel address"; return; } @@ -648,11 +649,11 @@ AnfNodePtr SessionBasic::CreateParameterFromTuple(const AnfNodePtr &node, Kernel MS_EXCEPTION_IF_NULL(node); MS_EXCEPTION_IF_NULL(graph); auto new_parameter = graph->TransTupleToMakeTuple(graph->NewParameter(node->abstract())); - auto parameters = AnfAlgo::GetAllOutput(new_parameter); + auto parameters = common::AnfAlgo::GetAllOutput(new_parameter); std::vector pre_graph_out = {node}; // If a cnode is a call, it's input0 is a cnode too, so it doesn't have primitive if (!pre_graph_out.empty() && !AnfUtils::IsRealKernel(node)) { - pre_graph_out = AnfAlgo::GetAllOutput(node, {prim::kPrimTupleGetItem, prim::kPrimUpdateState}); + pre_graph_out = common::AnfAlgo::GetAllOutput(node, {prim::kPrimTupleGetItem, prim::kPrimUpdateState}); } for (size_t i = 0; i < parameters.size(); ++i) { @@ -673,7 +674,7 @@ AnfNodePtr SessionBasic::CreateParameterFromTuple(const AnfNodePtr &node, Kernel } size_t param_index = 0; for (const auto &out_node : pre_graph_out) { - size_t output_size = AnfAlgo::GetOutputTensorNum(out_node); + size_t output_size = common::AnfAlgo::GetOutputTensorNum(out_node); for (size_t i = 0; i < output_size; i++) { if (param_index >= parameters.size()) { MS_LOG(EXCEPTION) << "Parameters size:" << parameters.size() << "out of range.Node:" << node->DebugString() @@ -742,7 +743,7 @@ AnfNodePtr SessionBasic::CreateNewParameterFromCNode(const AnfNodePtr &anf, Kern MS_EXCEPTION_IF_NULL(graph); MS_LOG(INFO) << "Create a new parameter from cnode[" << anf->DebugString() << "]"; if (IsPrimitiveCNode(anf, prim::kPrimLoad)) { - auto input = AnfAlgo::GetInputNode(anf->cast(), 0); + auto input = common::AnfAlgo::GetInputNode(anf->cast(), 0); MS_EXCEPTION_IF_NULL(input); if (input->isa()) { auto new_param = CreateNewParameterFromParameter(input, graph); @@ -760,12 +761,12 @@ AnfNodePtr SessionBasic::CreateNewParameterFromCNode(const AnfNodePtr &anf, Kern void SessionBasic::GetCNodeInfo(const CNodePtr &cnode, std::vector *cnode_inputs) const { MS_EXCEPTION_IF_NULL(cnode); MS_EXCEPTION_IF_NULL(cnode_inputs); - auto prim = AnfAlgo::GetCNodePrimitive(cnode); + auto prim = common::AnfAlgo::GetCNodePrimitive(cnode); if (prim != nullptr) { // push attr to inputs[0] of new cnode cnode_inputs->push_back(std::make_shared(std::make_shared(*prim))); } else { - auto fg = AnfAlgo::GetCNodeFuncGraphPtr(cnode); + auto fg = common::AnfAlgo::GetCNodeFuncGraphPtr(cnode); MS_EXCEPTION_IF_NULL(fg); auto new_fg = BasicClone(fg); cnode_inputs->push_back(std::make_shared(new_fg)); @@ -842,7 +843,7 @@ CNodePtr SessionBasic::CreateSwitchInput(const CNodePtr &cnode, const AnfNodePtr MS_EXCEPTION_IF_NULL(graph); // switch input generalizes partial std::vector partial_inputs = {NewValueNode(std::make_shared(prim::kPrimPartial->name()))}; - if (AnfAlgo::CheckPrimitiveType(node_input, prim::kPrimPartial)) { + if (common::AnfAlgo::CheckPrimitiveType(node_input, prim::kPrimPartial)) { auto backend_node = graph->GetBackendAnfByFrontAnf(node_input); return backend_node->cast(); } else if (node_input->isa() && IsValueNode(node_input)) { @@ -883,7 +884,7 @@ std::vector SessionBasic::CreateCallSwitchInputs(const CNodePtr &cno for (size_t index = kSwitchTrueBranchIndex; index < switch_cnode->inputs().size(); index++) { auto node = switch_cnode->input(index); // there is real input in call, should put it to true and false branch in switch - if (AnfAlgo::CheckPrimitiveType(node, prim::kPrimPartial)) { + if (common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimPartial)) { auto partial_node = node->cast(); MS_EXCEPTION_IF_NULL(partial_node); std::vector partial_inputs = partial_node->inputs(); @@ -920,7 +921,7 @@ void SessionBasic::ProcessNodeRetFunc(const CNodePtr &cnode, KernelGraph *graph, // return node is a function std::vector call_inputs = { graph->NewValueNode(NewValueNode(std::make_shared(prim::kPrimCall->name())))}; - if (AnfAlgo::CheckPrimitiveType(return_input, prim::kPrimPartial)) { + if (common::AnfAlgo::CheckPrimitiveType(return_input, prim::kPrimPartial)) { auto return_input_cnode = return_input->cast(); auto partial_inputs = return_input_cnode->inputs(); call_inputs.insert(call_inputs.end(), partial_inputs.begin() + kFirstDataInputIndex, partial_inputs.end()); @@ -991,7 +992,7 @@ std::vector SessionBasic::CreateCallSwitchLayerInputs(const CNodePtr std::vector new_partial_inputs; KernelGraphPtr partial_kernel_graph; // switch_layer node input is partial cnode - if (AnfAlgo::CheckPrimitiveType(partial_idx, prim::kPrimPartial)) { + if (common::AnfAlgo::CheckPrimitiveType(partial_idx, prim::kPrimPartial)) { auto partial_node = partial_idx->cast(); MS_EXCEPTION_IF_NULL(partial_node); auto partial_input = partial_node->input(kFirstDataInputIndex); @@ -1007,7 +1008,7 @@ std::vector SessionBasic::CreateCallSwitchLayerInputs(const CNodePtr auto ret = partial_kernel_graph->get_return(); MS_EXCEPTION_IF_NULL(ret); auto return_input = ret->input(kFirstDataInputIndex); - if (AnfAlgo::CheckPrimitiveType(return_input, prim::kPrimPartial) || return_input->isa()) { + if (common::AnfAlgo::CheckPrimitiveType(return_input, prim::kPrimPartial) || return_input->isa()) { ProcessNodeRetFunc(cnode, partial_kernel_graph.get(), real_inputs); } // partial node add input args @@ -1042,7 +1043,7 @@ std::vector SessionBasic::CreateSwitchOrPartialNode(const CNodePtr & return {}; } // if the node is partial, insert the inputs of partial to the call - if (AnfAlgo::CheckPrimitiveType(cnode_input, prim::kPrimPartial)) { + if (common::AnfAlgo::CheckPrimitiveType(cnode_input, prim::kPrimPartial)) { auto partial_node = attr_input->cast(); MS_EXCEPTION_IF_NULL(partial_node); auto partial_inputs = partial_node->inputs(); @@ -1052,9 +1053,9 @@ std::vector SessionBasic::CreateSwitchOrPartialNode(const CNodePtr & return graph->GetBackendAnfByFrontAnf(node); }); return cnode_inputs; - } else if (AnfAlgo::CheckPrimitiveType(cnode_input, prim::kPrimSwitch)) { + } else if (common::AnfAlgo::CheckPrimitiveType(cnode_input, prim::kPrimSwitch)) { return CreateCallSwitchInputs(cnode, graph); - } else if (AnfAlgo::CheckPrimitiveType(cnode_input, prim::kPrimSwitchLayer)) { + } else if (common::AnfAlgo::CheckPrimitiveType(cnode_input, prim::kPrimSwitchLayer)) { return CreateCallSwitchLayerInputs(cnode, graph); } MS_LOG(ERROR) << "CNode:" << cnode->DebugString() << " input[0]" << cnode_input->DebugString() @@ -1068,8 +1069,8 @@ std::vector SessionBasic::CreateValueNode(const CNodePtr &cnode, Ker std::vector cnode_inputs; auto attr_input = cnode->input(kAnfPrimitiveIndex); MS_EXCEPTION_IF_NULL(attr_input); - if (AnfAlgo::IsGraphKernel(cnode)) { - auto fg = AnfAlgo::GetCNodeFuncGraphPtr(cnode); + if (common::AnfAlgo::IsGraphKernel(cnode)) { + auto fg = common::AnfAlgo::GetCNodeFuncGraphPtr(cnode); MS_EXCEPTION_IF_NULL(fg); auto new_fg = BasicClone(fg); cnode_inputs.push_back(std::make_shared(new_fg)); @@ -1092,7 +1093,7 @@ std::vector SessionBasic::CreateValueNode(const CNodePtr &cnode, Ker void SessionBasic::CreateCNodeInputs(const CNodePtr &cnode, KernelGraph *graph, std::vector *cnode_inputs) { MS_EXCEPTION_IF_NULL(cnode); MS_EXCEPTION_IF_NULL(graph); - if (AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimSwitch)) { + if (common::AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimSwitch)) { (void)cnode_inputs->emplace_back(graph->GetBackendAnfByFrontAnf(cnode->input(kFirstDataInputIndex))); for (size_t index = kSwitchTrueBranchIndex; index < cnode->inputs().size(); index++) { auto node_input = cnode->input(index); @@ -1135,7 +1136,7 @@ CNodePtr SessionBasic::CreateNewCNode(const CNodePtr &cnode, KernelGraph *graph) } } else { // get primitive of old node - auto prim = AnfAlgo::GetCNodePrimitive(cnode); + auto prim = common::AnfAlgo::GetCNodePrimitive(cnode); MS_EXCEPTION_IF_NULL(prim); // push attr to inputs[0] of new cnode cnode_inputs = {graph->NewValueNode(NewValueNode(std::make_shared(*prim)))}; @@ -1148,12 +1149,12 @@ CNodePtr SessionBasic::CreateNewCNode(const CNodePtr &cnode, KernelGraph *graph) if (new_cnode->inputs().size() > 1) { auto first_input = new_cnode->input(kFirstDataInputIndex); MS_EXCEPTION_IF_NULL(first_input); - if (AnfAlgo::CheckPrimitiveType(new_cnode, prim::kPrimCall) && - AnfAlgo::CheckPrimitiveType(first_input, prim::kPrimSwitch)) { + if (common::AnfAlgo::CheckPrimitiveType(new_cnode, prim::kPrimCall) && + common::AnfAlgo::CheckPrimitiveType(first_input, prim::kPrimSwitch)) { new_cnode = first_input->cast(); } - if (AnfAlgo::CheckPrimitiveType(new_cnode, prim::kPrimCall) && - AnfAlgo::CheckPrimitiveType(first_input, prim::kPrimSwitchLayer)) { + if (common::AnfAlgo::CheckPrimitiveType(new_cnode, prim::kPrimCall) && + common::AnfAlgo::CheckPrimitiveType(first_input, prim::kPrimSwitchLayer)) { auto abstract = cnode->abstract(); new_cnode = first_input->cast(); new_cnode->set_abstract(abstract); @@ -1167,7 +1168,7 @@ ValueNodePtr SessionBasic::CreateValueNodeKernelGraph(const AnfNodePtr &anf, Ker MS_EXCEPTION_IF_NULL(graph); auto value_node = anf->cast(); MS_EXCEPTION_IF_NULL(value_node); - auto sub_func_graph = AnfAlgo::GetValueNodeFuncGraph(anf); + auto sub_func_graph = common::AnfAlgo::GetValueNodeFuncGraph(anf); MS_EXCEPTION_IF_NULL(sub_func_graph); if (front_backend_graph_map_.find(sub_func_graph.get()) == front_backend_graph_map_.end()) { MS_LOG(EXCEPTION) << "FuncGraph: " << sub_func_graph->ToString() << " has not been transformed to KernelGraph."; @@ -1293,11 +1294,11 @@ void SessionBasic::SetInputNodeUsage(const KernelGraphPtr &graph, const FuncGrap GraphInfo SessionBasic::GetSingleOpGraphInfo(const CNodePtr &kernel, const std::vector &input_tensors) { MS_EXCEPTION_IF_NULL(kernel); - auto prim = AnfAlgo::GetCNodePrimitive(kernel); + auto prim = common::AnfAlgo::GetCNodePrimitive(kernel); MS_EXCEPTION_IF_NULL(prim); const AbstractBasePtr &abstract = kernel->abstract(); MS_EXCEPTION_IF_NULL(abstract); - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel); GraphInfo graph_info; // get input tensor info for (const auto &tensor : input_tensors) { @@ -1328,7 +1329,7 @@ GraphInfo SessionBasic::GetSingleOpGraphInfo(const CNodePtr &kernel, MS_EXCEPTION_IF_NULL(build_shape); (void)graph_info.append(build_shape->ToString() + "_"); for (size_t output_index = 0; output_index < output_num; output_index += 1) { - const auto output_type = AnfAlgo::GetOutputInferDataType(kernel, output_index); + const auto output_type = common::AnfAlgo::GetOutputInferDataType(kernel, output_index); (void)graph_info.append(std::to_string(output_type) + "_"); } graph_info.append(std::to_string(prim->id())); @@ -1339,7 +1340,7 @@ OpRunInfo SessionBasic::GetSingleOpRunInfo(const CNodePtr &cnode, const GraphInf const InputTensorInfo &tensor_info, GraphOutputInfo *const graph_output_info) { MS_EXCEPTION_IF_NULL(cnode); - auto primitive = AnfAlgo::GetCNodePrimitive(cnode); + auto primitive = common::AnfAlgo::GetCNodePrimitive(cnode); const auto &abstract = cnode->abstract(); if (abstract == nullptr) { MS_LOG(EXCEPTION) << "Abstract is nullptr, node = " << cnode->DebugString(); @@ -1375,7 +1376,7 @@ void SessionBasic::GetParameterIndex(const KernelGraph *graph, const std::vector MS_EXCEPTION_IF_NULL(parameter_index); size_t index = 0; for (const auto &input_node : graph->input_nodes()) { - auto params = AnfAlgo::GetAllOutput(input_node); + auto params = common::AnfAlgo::GetAllOutput(input_node); for (const auto ¶m : params) { if (index >= inputs.size()) { MS_LOG(EXCEPTION) << "Parameter size out of range. Parameter index: " << index @@ -1385,7 +1386,7 @@ void SessionBasic::GetParameterIndex(const KernelGraph *graph, const std::vector MS_EXCEPTION_IF_NULL(input); // Check shape of input and parameter const auto &input_shape = input->shape(); - const auto ¶m_shape = AnfAlgo::GetOutputInferShape(param, 0); + const auto ¶m_shape = common::AnfAlgo::GetOutputInferShape(param, 0); if (input_shape.size() != param_shape.size()) { MS_LOG(EXCEPTION) << "Shape size of input tensor(" << input_shape << ") and parameter(" << param_shape << ") are different, input index: " << index << ", parameter: " << param->DebugString(); @@ -1422,7 +1423,7 @@ void SessionBasic::GetRefCount(const KernelGraph *graph, std::mapexecution_order()) { for (size_t i = 1; i < kernel->inputs().size(); i += 1) { const auto &input = kernel->input(i); - auto kernel_with_index = AnfAlgo::VisitKernel(input, 0); + auto kernel_with_index = common::AnfAlgo::VisitKernel(input, 0); const auto &node = kernel_with_index.first; if (node->isa()) { (*ref_count)[kernel_with_index] += 1; @@ -1446,10 +1447,10 @@ void SessionBasic::GetForwardOpOutputRefCount(const KernelGraph *graph, const st const auto &forward_op_output_id = pynative::PynativeExecutor::GetInstance()->grad_executor()->forward_op_output_id(); MS_LOG(DEBUG) << "Total forward op out put size " << forward_op_output_id.size(); for (const auto &kernel : graph->execution_order()) { - const auto input_tensor_num = AnfAlgo::GetInputTensorNum(kernel); + const auto input_tensor_num = common::AnfAlgo::GetInputTensorNum(kernel); for (size_t i = 1; i <= input_tensor_num; ++i) { const auto &input = kernel->input(i); - auto kernel_with_index = AnfAlgo::VisitKernel(input, 0); + auto kernel_with_index = common::AnfAlgo::VisitKernel(input, 0); auto real_input = kernel_with_index.first; MS_EXCEPTION_IF_NULL(real_input); if (real_input->isa()) { @@ -1632,15 +1633,15 @@ void SessionBasic::GetOpInputTensors(const CNodePtr &cnode, InputTensorInfo *input_tensor_info) { MS_EXCEPTION_IF_NULL(cnode); MS_EXCEPTION_IF_NULL(input_tensor_info); - auto has_const_input_to_attr = AnfAlgo::HasNodeAttr(kAttrNeedConvertToValueNode, cnode); + auto has_const_input_to_attr = common::AnfAlgo::HasNodeAttr(kAttrNeedConvertToValueNode, cnode); std::vector const_input_attr_index = {}; if (has_const_input_to_attr) { - const_input_attr_index = AnfAlgo::GetNodeAttr>(cnode, kAttrNeedConvertToValueNode); + const_input_attr_index = common::AnfAlgo::GetNodeAttr>(cnode, kAttrNeedConvertToValueNode); } - const auto input_tensor_num = AnfAlgo::GetInputTensorNum(cnode); + const auto input_tensor_num = common::AnfAlgo::GetInputTensorNum(cnode); for (size_t i = 1; i <= input_tensor_num; i += 1) { const auto &input = cnode->input(i); - auto kernel_with_index = AnfAlgo::VisitKernel(input, 0); + auto kernel_with_index = common::AnfAlgo::VisitKernel(input, 0); auto real_input = kernel_with_index.first; MS_EXCEPTION_IF_NULL(real_input); tensor::TensorPtr tensor = nullptr; @@ -1661,7 +1662,7 @@ void SessionBasic::GetOpInputTensors(const CNodePtr &cnode, : kParameterDataTensorMask); } else if (real_input->isa()) { tensor = GetCNodeOutputTensor(kernel_with_index, op_output); - if (AnfAlgo::IsControlOpExecInBackend(real_input)) { + if (common::AnfAlgo::IsControlOpExecInBackend(real_input)) { CheckInputTensorShape(tensor, cnode, i - 1); } input_tensor_info->input_kernel.insert(kernel_with_index); @@ -1690,7 +1691,7 @@ tensor::TensorPtr SessionBasic::GetOpInputTensorByIndex(const CNodePtr &cnode, } const auto &input = cnode->input(input_index + 1); - auto kernel_with_index = AnfAlgo::VisitKernel(input, 0); + auto kernel_with_index = common::AnfAlgo::VisitKernel(input, 0); auto real_input = kernel_with_index.first; MS_EXCEPTION_IF_NULL(real_input); @@ -1698,7 +1699,7 @@ tensor::TensorPtr SessionBasic::GetOpInputTensorByIndex(const CNodePtr &cnode, return GetParameterOutputTensor(real_input, parameter_index, graph_inputs); } else if (real_input->isa()) { tensor::TensorPtr tensor = GetCNodeOutputTensor(kernel_with_index, op_output); - if (AnfAlgo::IsControlOpExecInBackend(real_input)) { + if (common::AnfAlgo::IsControlOpExecInBackend(real_input)) { CheckInputTensorShape(tensor, cnode, input_index); } input_tensor_info->input_kernel.insert(kernel_with_index); @@ -1765,7 +1766,7 @@ std::shared_ptr SessionBasic::ConstructKernelGraph(const FuncGraphP continue; } // Create child kernel graph according ValueNode - FuncGraphPtr child_graph = AnfAlgo::GetValueNodeFuncGraph(node); + FuncGraphPtr child_graph = common::AnfAlgo::GetValueNodeFuncGraph(node); if (front_backend_graph_map_.find(child_graph.get()) == front_backend_graph_map_.end()) { (void)ConstructKernelGraph(child_graph, all_out_graph, device_target); } @@ -1850,8 +1851,8 @@ void SessionBasic::UpdateOutputs(const std::shared_ptr &kernel_grap tensor->SetNeedWait(false); MS_LOG(DEBUG) << "Debug address: Output tensor obj " << tensor.get() << ", tensor id " << tensor->id() << ", device address " << tensor->device_address().get(); - if (AnfAlgo::IsDynamicShape(node)) { - const auto &updated_shape = AnfAlgo::GetOutputInferShape(node, output_index); + if (common::AnfAlgo::IsDynamicShape(node)) { + const auto &updated_shape = common::AnfAlgo::GetOutputInferShape(node, output_index); ShapeVector int_shape; (void)std::transform(updated_shape.begin(), updated_shape.end(), std::back_inserter(int_shape), SizeToInt); (void)tensor->set_shape(int_shape); @@ -1870,7 +1871,7 @@ void SessionBasic::UpdateOutputAbstract(const std::shared_ptr &kern const auto &kernels = kernel_graph->execution_order(); for (const auto &kernel : kernels) { MS_EXCEPTION_IF_NULL(kernel); - if (AnfAlgo::GetCNodeName(kernel) == op_run_info->op_name) { + if (common::AnfAlgo::GetCNodeName(kernel) == op_run_info->op_name) { op_run_info->abstract = kernel->abstract(); } } @@ -1945,8 +1946,8 @@ void SessionBasic::UpdateOutputTensors(const VectorRef *outputs, const auto &address = AnfAlgo::GetMutableOutputAddr(node, output_index); tensor->set_device_address(address); - if (AnfAlgo::IsDynamicShape(node)) { - const auto &updated_shape = AnfAlgo::GetOutputInferShape(node, output_index); + if (common::AnfAlgo::IsDynamicShape(node)) { + const auto &updated_shape = common::AnfAlgo::GetOutputInferShape(node, output_index); ShapeVector int_shape; (void)std::transform(updated_shape.begin(), updated_shape.end(), std::back_inserter(int_shape), SizeToInt); (void)tensor->set_shape(int_shape); @@ -1976,7 +1977,7 @@ void SessionBasic::GetModelInputsInfo(uint32_t graph_id, std::vectorcast(); - if (!AnfAlgo::IsParameterWeight(parameter)) { + if (!common::AnfAlgo::IsParameterWeight(parameter)) { vector input_shape; auto parameter_shape = AnfAlgo::GetOutputDeviceShape(parameter, 0); (void)std::transform(parameter_shape.begin(), parameter_shape.end(), std::back_inserter(input_shape), @@ -2042,7 +2043,7 @@ void SessionBasic::SetSummaryNodes(KernelGraph *graph) { } auto node = cnode->input(kSummaryGetItem); MS_EXCEPTION_IF_NULL(node); - auto item_with_index = AnfAlgo::VisitKernelWithReturnType(node, 0, false); + auto item_with_index = common::AnfAlgo::VisitKernelWithReturnType(node, 0, false); MS_EXCEPTION_IF_NULL(item_with_index.first); if (!AnfUtils::IsRealKernel(item_with_index.first)) { MS_LOG(EXCEPTION) << "Unexpected node:" << item_with_index.first->DebugString(); @@ -2078,8 +2079,8 @@ void SessionBasic::Summary(KernelGraph *graph) { auto node = output_item.second.first; size_t index = IntToSize(output_item.second.second); auto address = AnfAlgo::GetOutputAddr(node, index, false); - auto shape = AnfAlgo::GetOutputInferShape(node, index); - TypeId type_id = AnfAlgo::GetOutputInferDataType(node, index); + auto shape = common::AnfAlgo::GetOutputInferShape(node, index); + TypeId type_id = common::AnfAlgo::GetOutputInferDataType(node, index); std::vector temp_shape; (void)std::copy(shape.begin(), shape.end(), std::back_inserter(temp_shape)); tensor::TensorPtr tensor = std::make_shared(type_id, temp_shape); @@ -2121,8 +2122,8 @@ std::vector ExtendNodeUsers(const FuncGraphManagerPtr &front_func_gr auto &users = front_func_graph_manager->node_users()[front_node]; std::vector result; for (auto &user : users) { - if (AnfAlgo::CheckPrimitiveType(user.first, prim::kPrimDepend) || - AnfAlgo::CheckPrimitiveType(user.first, prim::kPrimLoad)) { + if (common::AnfAlgo::CheckPrimitiveType(user.first, prim::kPrimDepend) || + common::AnfAlgo::CheckPrimitiveType(user.first, prim::kPrimLoad)) { auto depend_cnode = user.first->cast(); if (depend_cnode == nullptr) { continue; @@ -2132,7 +2133,7 @@ std::vector ExtendNodeUsers(const FuncGraphManagerPtr &front_func_gr } auto res = ExtendNodeUsers(front_func_graph_manager, user.first); result.insert(result.end(), res.begin(), res.end()); - } else if (AnfAlgo::CheckPrimitiveType(user.first, prim::kPrimMakeTuple)) { + } else if (common::AnfAlgo::CheckPrimitiveType(user.first, prim::kPrimMakeTuple)) { auto res = ExtendNodeUsers(front_func_graph_manager, user.first); (void)result.insert(result.end(), res.begin(), res.end()); } else { @@ -2150,10 +2151,10 @@ AnfNodePtr GetSupportedInternalNode(const AnfNodePtr &front_node) { if (AnfUtils::IsRealKernel(front_node)) { return front_node; } - if (AnfAlgo::CheckPrimitiveType(front_node, prim::kPrimTupleGetItem)) { + if (common::AnfAlgo::CheckPrimitiveType(front_node, prim::kPrimTupleGetItem)) { return front_node; } - if (AnfAlgo::CheckPrimitiveType(front_node, prim::kPrimMakeTuple)) { + if (common::AnfAlgo::CheckPrimitiveType(front_node, prim::kPrimMakeTuple)) { auto cnode = front_node->cast(); MS_EXCEPTION_IF_NULL(cnode); auto &inputs = cnode->inputs(); @@ -2161,7 +2162,7 @@ AnfNodePtr GetSupportedInternalNode(const AnfNodePtr &front_node) { return GetSupportedInternalNode(inputs[1]); } } - if (AnfAlgo::CheckPrimitiveType(front_node, prim::kPrimDepend)) { + if (common::AnfAlgo::CheckPrimitiveType(front_node, prim::kPrimDepend)) { auto cnode = front_node->cast(); MS_EXCEPTION_IF_NULL(cnode); auto &inputs = cnode->inputs(); @@ -2239,8 +2240,8 @@ void SessionBasic::HandleInternalOutput(const AnfNodePtr &input_front_node, cons if (front_node == nullptr) { return; } - auto front_real_kernel_pair = AnfAlgo::VisitKernel(front_node, 0); - auto backend_real_kernel_pair = AnfAlgo::VisitKernel(backend_node, 0); + auto front_real_kernel_pair = common::AnfAlgo::VisitKernel(front_node, 0); + auto backend_real_kernel_pair = common::AnfAlgo::VisitKernel(backend_node, 0); auto backend_real_kernel = backend_real_kernel_pair.first; if (backend_real_kernel == nullptr || !backend_real_kernel->isa()) { return; @@ -2249,8 +2250,8 @@ void SessionBasic::HandleInternalOutput(const AnfNodePtr &input_front_node, cons std::string kernel_target = GetCNodeTarget(front_real_kernel); bool internal_output = CNodeFirstInputIsPrimitive(front_real_kernel); bool unique_target = true; - if (internal_output && opt::IsNopNode(front_real_kernel)) { - auto pre_node_pair = AnfAlgo::GetPrevNodeOutput(front_real_kernel, 0); + if (internal_output && common::AnfAlgo::IsNopNode(front_real_kernel)) { + auto pre_node_pair = common::AnfAlgo::GetPrevNodeOutput(front_real_kernel, 0); auto pre_node_target = GetCNodeTarget(pre_node_pair.first); if (pre_node_target != kernel_target) { unique_target = false; @@ -2259,7 +2260,7 @@ void SessionBasic::HandleInternalOutput(const AnfNodePtr &input_front_node, cons if (internal_output) { auto users = ExtendNodeUsers(front_func_graph_manager, front_node); for (auto &user : users) { - if (AnfAlgo::CheckPrimitiveType(user, prim::kPrimPartial) && kernel_target != kGPUDevice && + if (common::AnfAlgo::CheckPrimitiveType(user, prim::kPrimPartial) && kernel_target != kGPUDevice && !ExistGraphCaller(user)) { auto partial_target = AddPartialParametersMap(user); if (partial_target != kNoTarget && partial_target != kernel_target) { @@ -2267,7 +2268,7 @@ void SessionBasic::HandleInternalOutput(const AnfNodePtr &input_front_node, cons } continue; } - if (AnfAlgo::CheckPrimitiveType(user, prim::kPrimUpdateState)) { + if (common::AnfAlgo::CheckPrimitiveType(user, prim::kPrimUpdateState)) { continue; } if (IsUnusedInternlOutput(user)) { @@ -2324,16 +2325,16 @@ void SessionBasic::CreateOutputNode(const CNodePtr &cnode, const std::shared_ptr std::vector make_tuple_inputs; make_tuple_inputs.push_back(NewValueNode(prim::kPrimMakeTuple)); MS_EXCEPTION_IF_NULL(graph); - if (AnfRuntimeAlgorithm::GetOutputTensorNum(cnode) > 1) { - for (size_t output_index = 0; output_index < AnfRuntimeAlgorithm::GetOutputTensorNum(cnode); output_index++) { + if (common::AnfAlgo::GetOutputTensorNum(cnode) > 1) { + for (size_t output_index = 0; output_index < common::AnfAlgo::GetOutputTensorNum(cnode); output_index++) { auto idx = NewValueNode(SizeToLong(output_index)); MS_EXCEPTION_IF_NULL(idx); auto imm = std::make_shared(output_index); idx->set_abstract(std::make_shared(imm)); auto getitem = graph->NewCNode({NewValueNode(prim::kPrimTupleGetItem), cnode, idx}); - std::vector types = {AnfAlgo::GetOutputInferDataType(cnode, output_index)}; - std::vector> shapes = {AnfAlgo::GetOutputInferShape(cnode, output_index)}; - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, getitem.get()); + std::vector types = {common::AnfAlgo::GetOutputInferDataType(cnode, output_index)}; + std::vector> shapes = {common::AnfAlgo::GetOutputInferShape(cnode, output_index)}; + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, getitem.get()); make_tuple_inputs.push_back(getitem); } } else { @@ -2380,10 +2381,10 @@ std::shared_ptr SessionBasic::ConstructSingleOpGraph(const OpRunInf // set abstract,which include inferred shapes and types cnode->set_abstract(op_run_info.abstract); // get output dynamic shape info - AnfAlgo::SetNodeAttr(kAttrOutputIsDynamicShape, MakeValue(op_run_info.is_dynamic_shape), cnode); + common::AnfAlgo::SetNodeAttr(kAttrOutputIsDynamicShape, MakeValue(op_run_info.is_dynamic_shape), cnode); if (op_run_info.is_auto_mixed_precision) { - AnfAlgo::SetNodeAttr(kAttrPynativeNextOpName, MakeValue(op_run_info.next_op_name), cnode); - AnfAlgo::SetNodeAttr(kAttrPynativeNextIndex, MakeValue(op_run_info.next_input_index), cnode); + common::AnfAlgo::SetNodeAttr(kAttrPynativeNextOpName, MakeValue(op_run_info.next_op_name), cnode); + common::AnfAlgo::SetNodeAttr(kAttrPynativeNextIndex, MakeValue(op_run_info.next_input_index), cnode); } // set execution order std::vector exe_order = {cnode}; @@ -2420,8 +2421,8 @@ AnfNodePtr SessionBasic::FindPullNode(const AnfNodePtr &push_node, const std::ve for (auto &node : node_list) { if (node != nullptr && node->isa()) { for (auto input : node->cast()->inputs()) { - if (push_node == AnfAlgo::VisitKernel(input, 0).first) { - if (AnfAlgo::GetCNodeName(node) != kPullOpName) { + if (push_node == common::AnfAlgo::VisitKernel(input, 0).first) { + if (common::AnfAlgo::GetCNodeName(node) != kPullOpName) { MS_LOG(EXCEPTION) << "The edge between Push and Pull node is invalid."; } return node; @@ -2583,9 +2584,9 @@ void SessionBasic::EraseValueNodeTensor(const std::vector &tensors_mask bool SessionBasic::IsGetNextGraph(const std::shared_ptr &kernel_graph, std::string *channel_name) { MS_EXCEPTION_IF_NULL(kernel_graph); for (const auto &kernel_node : kernel_graph->execution_order()) { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); if (kernel_name == kGetNextOpName) { - auto prim = AnfAlgo::GetCNodePrimitive(kernel_node); + auto prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(prim); *channel_name = GetValue(prim->GetAttr("shared_name")); return true; @@ -2622,7 +2623,7 @@ std::vector SessionBasic::GetAllReduceSplitIndex() { } uint32_t GetBpropGraphGradsCount(const KernelGraphPtr &graph) { - return AnfAlgo::GetAllOutput(graph->output(), {prim::kPrimTupleGetItem}).size(); + return common::AnfAlgo::GetAllOutput(graph->output(), {prim::kPrimTupleGetItem}).size(); } void SetGraphBpropAttr(const KernelGraphPtr &graph) { @@ -2699,7 +2700,7 @@ void SessionBasic::InitAllBucket(const KernelGraphPtr &graph, const device::Devi auto parallel_context = parallel::ParallelContext::GetInstance(); MS_EXCEPTION_IF_NULL(parallel_context); auto parallel_mode = parallel_context->parallel_mode(); - if (!pynative_mode || parallel_mode != parallel::DATA_PARALLEL) { + if (!pynative_mode || parallel_mode != parallel::kDataParallel) { return; } SetGraphBpropAttr(graph); @@ -2741,7 +2742,7 @@ void SessionBasic::AddGradAddrToBucket(const GraphId &graph_id, const std::vecto auto parallel_context = parallel::ParallelContext::GetInstance(); MS_EXCEPTION_IF_NULL(parallel_context); auto parallel_mode = parallel_context->parallel_mode(); - if (parallel_mode != parallel::DATA_PARALLEL) { + if (parallel_mode != parallel::kDataParallel) { return; } @@ -2873,7 +2874,7 @@ void SessionBasic::InitPsWorker(const KernelGraphPtr &kernel_graph) { MS_EXCEPTION_IF_NULL(runtime_instance); auto context = runtime_instance->context(); const auto &kernels = kernel_graph->execution_order(); - if (kernels.size() > 0 && AnfAlgo::GetCNodeName(kernels[0]) == "InitDataSetQueue") { + if (kernels.size() > 0 && common::AnfAlgo::GetCNodeName(kernels[0]) == "InitDataSetQueue") { GetBatchElements(kernels[0]); ps::ps_cache_instance.Initialize(); } @@ -2886,8 +2887,8 @@ void SessionBasic::InitPsWorker(const KernelGraphPtr &kernel_graph) { } void SessionBasic::GetBatchElements(const AnfNodePtr &kernel_node) const { - auto shapes = AnfAlgo::GetNodeAttr>>(kernel_node, "shapes"); - auto types = AnfAlgo::GetNodeAttr>(kernel_node, "types"); + auto shapes = common::AnfAlgo::GetNodeAttr>>(kernel_node, "shapes"); + auto types = common::AnfAlgo::GetNodeAttr>(kernel_node, "types"); if (shapes.size() != types.size() || shapes.size() == 0 || types.size() == 0) { MS_LOG(EXCEPTION) << "Invalid shapes of op[InitDataSetQueue]: shapes size " << shapes.size() << ", types size " << types; @@ -2933,12 +2934,12 @@ void SessionBasic::AssignParamKey(const KernelGraphPtr &kernel_graph) { if (node != nullptr && node->isa()) { // Assign key for forward kernel EmbeddingLookup. // The key will be assigned to embedding table ande Push kernel as well. - if (AnfAlgo::GetCNodeName(node) == kEmbeddingLookupOpName) { + if (common::AnfAlgo::GetCNodeName(node) == kEmbeddingLookupOpName) { size_t embedding_table_idx = 0; - auto embedding_table = AnfAlgo::GetInputNode(node->cast(), embedding_table_idx); + auto embedding_table = common::AnfAlgo::GetInputNode(node->cast(), embedding_table_idx); size_t key = ps::Worker::GetInstance().SetParamKey(embedding_table->fullname_with_scope()); - AnfAlgo::SetNodeAttr(kAttrPsKey, MakeValue(key), node); - } else if (AnfAlgo::GetCNodeName(node) == kPushOpName) { + common::AnfAlgo::SetNodeAttr(kAttrPsKey, MakeValue(key), node); + } else if (common::AnfAlgo::GetCNodeName(node) == kPushOpName) { auto pull_node = FindPullNode(node, node_list); if (!pull_node) { MS_LOG(EXCEPTION) << "Assigning parameter key failed: can't find Pull node of the Push node."; @@ -2946,12 +2947,12 @@ void SessionBasic::AssignParamKey(const KernelGraphPtr &kernel_graph) { // Second input of Pull node is the trainable parameter. size_t parameter_index = 1; - auto parameter_node = AnfAlgo::GetInputNode(pull_node->cast(), parameter_index); + auto parameter_node = common::AnfAlgo::GetInputNode(pull_node->cast(), parameter_index); size_t key = ps::Worker::GetInstance().SetParamKey(parameter_node->fullname_with_scope()); - AnfAlgo::SetNodeAttr(kAttrPsKey, MakeValue(key), node); - AnfAlgo::SetNodeAttr(kAttrPsKey, MakeValue(key), pull_node); + common::AnfAlgo::SetNodeAttr(kAttrPsKey, MakeValue(key), node); + common::AnfAlgo::SetNodeAttr(kAttrPsKey, MakeValue(key), pull_node); - std::string optimizer_name = AnfAlgo::GetNodeAttr(node, kAttrOptimizerType); + std::string optimizer_name = common::AnfAlgo::GetNodeAttr(node, kAttrOptimizerType); ps::Worker::GetInstance().SetKeyOptimId(key, optimizer_name); } } diff --git a/mindspore/ccsrc/backend/common/session/session_basic.h b/mindspore/ccsrc/backend/common/session/session_basic.h index 5d571e7c4e6..a3ce8273e41 100644 --- a/mindspore/ccsrc/backend/common/session/session_basic.h +++ b/mindspore/ccsrc/backend/common/session/session_basic.h @@ -26,10 +26,11 @@ #include "backend/common/session/session_context.h" #include "backend/common/session/kernel_graph.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/anf.h" #include "ir/tensor.h" #include "utils/any.h" -#include "utils/contract.h" +#include "include/common/utils/contract.h" #include "runtime/device/kernel_info.h" #include "utils/ms_context.h" #include "runtime/device/bucket.h" diff --git a/mindspore/ccsrc/backend/common/session/single_kernel_graph.cc b/mindspore/ccsrc/backend/common/session/single_kernel_graph.cc index d3ba86caa2f..b77a236ba49 100644 --- a/mindspore/ccsrc/backend/common/session/single_kernel_graph.cc +++ b/mindspore/ccsrc/backend/common/session/single_kernel_graph.cc @@ -15,8 +15,8 @@ */ #include "backend/common/session/single_kernel_graph.h" -#include "backend/common/session/anf_runtime_algorithm.h" #include "utils/trace_base.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace session { @@ -44,14 +44,14 @@ std::shared_ptr SingleKernelGraph::ConstructKernelGraphBas auto cnode = graph->NewCNode(inputs); MS_EXCEPTION_IF_NULL(cnode); // get output dynamic shape info - AnfAlgo::SetNodeAttr(kAttrOutputIsDynamicShape, MakeValue(false), cnode); + common::AnfAlgo::SetNodeAttr(kAttrOutputIsDynamicShape, MakeValue(false), cnode); if (output_dtypes.size() != output_shapes.size()) { MS_LOG(EXCEPTION) << "The size of output_dtypes should be equal to size of output_shapes, but got output_dtypes size: " << output_dtypes.size() << ", output_shapes size: " << output_shapes.size() << ". The op name is: " << op_name << trace::DumpSourceLines(cnode); } - AnfAlgo::SetOutputInferTypeAndShape(output_dtypes, output_shapes, cnode.get()); + common::AnfAlgo::SetOutputInferTypeAndShape(output_dtypes, output_shapes, cnode.get()); // set execution order std::vector exe_order = {cnode}; graph->set_execution_order(exe_order); diff --git a/mindspore/ccsrc/backend/common/somas/somas.cc b/mindspore/ccsrc/backend/common/somas/somas.cc index b65d804bf62..e8c6f0010f4 100644 --- a/mindspore/ccsrc/backend/common/somas/somas.cc +++ b/mindspore/ccsrc/backend/common/somas/somas.cc @@ -36,7 +36,7 @@ #ifdef ENABLE_DUMP_IR #include "debug/rdr/running_data_recorder.h" #endif -#include "common/thread_pool.h" +#include "include/common/thread_pool.h" #ifndef ENABLE_SECURITY #include "profiler/device/ascend/memory_profiling.h" @@ -409,7 +409,7 @@ void Somas::InitSomasStreamAndNode(const session::KernelGraph *graph) { // Node NodeType type = kCommonNode; - if (AnfAlgo::IsCommunicationOp(kernel)) { + if (common::AnfAlgo::IsCommunicationOp(kernel)) { type = kCommunicationNode; } auto node = std::make_shared(node_index, type, stream); @@ -500,7 +500,7 @@ void Somas::InitSomasInputTensors(const session::KernelGraph *graph) { static const auto enable_fusion_clear = (common::GetEnv("ENV_FUSION_CLEAR") == "1"); auto kernel_cnodes = graph->execution_order(); for (const auto &kernel : kernel_cnodes) { - if (AnfAlgo::GetCNodeName(kernel) != kAtomicAddrCleanOpName) { + if (common::AnfAlgo::GetCNodeName(kernel) != kAtomicAddrCleanOpName) { InitCommonNodeInputs(is_all_nop_node, kernel); } else { InitAtomicCleanInputs(enable_fusion_clear, kernel); @@ -516,24 +516,24 @@ void Somas::InitCommonNodeInputs(bool is_all_nop_node, const CNodePtr &kernel) { MS_EXCEPTION_IF_NULL(stream); // Input Tensor - auto input_tensor_num = AnfAlgo::GetInputTensorNum(kernel); + auto input_tensor_num = common::AnfAlgo::GetInputTensorNum(kernel); size_t real_input_index = 0; for (size_t i = 0; i < input_tensor_num; i++) { auto input_node = kernel->input(i + 1); MS_EXCEPTION_IF_NULL(input_node); session::KernelWithIndex prenode_index; if (is_all_nop_node) { - prenode_index = AnfAlgo::VisitKernelWithReturnType(input_node, 0, false); + prenode_index = common::AnfAlgo::VisitKernelWithReturnType(input_node, 0, false); } else { - prenode_index = AnfAlgo::VisitKernelWithReturnType(input_node, 0, true); + prenode_index = common::AnfAlgo::VisitKernelWithReturnType(input_node, 0, true); } - if (AnfAlgo::CheckPrimitiveType(prenode_index.first, prim::kPrimMakeTuple)) { + if (common::AnfAlgo::CheckPrimitiveType(prenode_index.first, prim::kPrimMakeTuple)) { MS_LOG(EXCEPTION) << "Input node [" << input_node->DebugString() << "]'s input " << i << " is MakeTuple"; } MS_EXCEPTION_IF_NULL(prenode_index.first); if (!AnfUtils::IsRealCNodeKernel(prenode_index.first)) { - auto op_name = AnfAlgo::GetCNodeName(kernel); - TypeId input_origin_type = AnfAlgo::GetPrevNodeOutputInferDataType(kernel, i); + auto op_name = common::AnfAlgo::GetCNodeName(kernel); + TypeId input_origin_type = common::AnfAlgo::GetPrevNodeOutputInferDataType(kernel, i); if ((op_name == kDynamicRNNOpName || op_name == kDynamicGRUV2OpName) && input_origin_type == kMetaTypeNone) { continue; } @@ -588,7 +588,7 @@ void Somas::InitAtomicCleanInputs(bool enable_fusion_clear, const CNodePtr &kern auto stream = node->GetStream(); MS_EXCEPTION_IF_NULL(stream); - auto input_tensor_num = AnfAlgo::GetInputTensorNum(kernel); + auto input_tensor_num = common::AnfAlgo::GetInputTensorNum(kernel); for (size_t i = 0; i < input_tensor_num; i++) { MS_EXCEPTION_IF_NULL(kernel->inputs()[i + 1]); auto pre_node = kernel->input(i + 1)->cast(); @@ -600,8 +600,8 @@ void Somas::InitAtomicCleanInputs(bool enable_fusion_clear, const CNodePtr &kern auto pre_somas_node = iter->second.at(0); MS_EXCEPTION_IF_NULL(pre_somas_node); // set clean output tensors - if (AnfAlgo::HasNodeAttr(kAttrAtomicOutputIndexs, pre_node)) { - auto clean_output_indexs = AnfAlgo::GetNodeAttr>(pre_node, kAttrAtomicOutputIndexs); + if (common::AnfAlgo::HasNodeAttr(kAttrAtomicOutputIndexs, pre_node)) { + auto clean_output_indexs = common::AnfAlgo::GetNodeAttr>(pre_node, kAttrAtomicOutputIndexs); for (auto index : clean_output_indexs) { if (index > pre_somas_node->output_tensors_.size()) { MS_LOG(EXCEPTION) << "Output index " << index << " exceed input node [" << pre_node->fullname_with_scope() @@ -618,8 +618,9 @@ void Somas::InitAtomicCleanInputs(bool enable_fusion_clear, const CNodePtr &kern } } // set clean workspace tensors - if (AnfAlgo::HasNodeAttr(kAttrAtomicWorkspaceIndexs, pre_node)) { - auto clean_workspace_indexs = AnfAlgo::GetNodeAttr>(pre_node, kAttrAtomicWorkspaceIndexs); + if (common::AnfAlgo::HasNodeAttr(kAttrAtomicWorkspaceIndexs, pre_node)) { + auto clean_workspace_indexs = + common::AnfAlgo::GetNodeAttr>(pre_node, kAttrAtomicWorkspaceIndexs); for (const auto &index : clean_workspace_indexs) { if (index > pre_somas_node->output_tensors_.size()) { MS_LOG(EXCEPTION) << "Workspace index " << index << " exceed input node [" << pre_node->fullname_with_scope() @@ -645,7 +646,7 @@ void Somas::InitSomasEventInfos() { send_recv_map = device::ascend::AscendStreamAssign::GetInstance().get_event_map(); #endif for (auto &send_recv : send_recv_map) { - size_t event_id = AnfAlgo::GetNodeAttr(send_recv.first, kAttrEventId); + size_t event_id = common::AnfAlgo::GetNodeAttr(send_recv.first, kAttrEventId); event_map_[event_id] = std::make_pair(send_recv.first, send_recv.second); } @@ -748,7 +749,7 @@ void Somas::GetNextOutputProcess(const session::KernelGraph *graph) { auto kernel_cnodes = graph->execution_order(); size_t total_size = 0; for (const auto &kernel : kernel_cnodes) { - if (AnfAlgo::GetCNodeName(kernel) != kGetNextOpName) { + if (common::AnfAlgo::GetCNodeName(kernel) != kGetNextOpName) { continue; } auto iter = nodes_map_.find(kernel.get()); @@ -809,7 +810,7 @@ void Somas::SummaryInputProcess(const session::KernelGraph *graph) { for (auto &node_item : summary_nodes) { auto origin_node = node_item.second.first; size_t origin_index = IntToSize(node_item.second.second); - auto item_with_index = AnfAlgo::VisitKernelWithReturnType(origin_node, origin_index, true); + auto item_with_index = common::AnfAlgo::VisitKernelWithReturnType(origin_node, origin_index, true); auto node = item_with_index.first; size_t index = item_with_index.second; auto iter = nodes_map_.find(node.get()); @@ -895,8 +896,8 @@ void Somas::NonTaskSplitProcess(const session::KernelGraph *graph) { MS_EXCEPTION_IF_NULL(graph); auto kernel_cnodes = graph->execution_order(); for (const auto &kernel : kernel_cnodes) { - auto op_name = AnfAlgo::GetCNodeName(kernel); - if (AnfAlgo::IsNonTaskOp(kernel)) { + auto op_name = common::AnfAlgo::GetCNodeName(kernel); + if (common::AnfAlgo::IsNonTaskOp(kernel)) { std::vector refnode_input_output; auto node = nodes_map_[kernel.get()].at(0); MS_EXCEPTION_IF_NULL(node); @@ -1815,7 +1816,7 @@ uint8_t *Somas::GetNodeOutputPtr(const AnfNodePtr &node, size_t index) const { auto output_tensor = somas_node->output_tensors_[index]; ptr = mem_base_addr_ + output_tensor->offset_; } else { - MS_LOG(EXCEPTION) << "node [" << AnfAlgo::GetCNodeName(node) << "] don't exist in nodes_map"; + MS_LOG(EXCEPTION) << "node [" << common::AnfAlgo::GetCNodeName(node) << "] don't exist in nodes_map"; } return ptr; } diff --git a/mindspore/ccsrc/backend/common/somas/somas.h b/mindspore/ccsrc/backend/common/somas/somas.h index 658bb644014..758b07aec0b 100644 --- a/mindspore/ccsrc/backend/common/somas/somas.h +++ b/mindspore/ccsrc/backend/common/somas/somas.h @@ -31,6 +31,7 @@ #include "backend/common/somas/somas_stream.h" #include "backend/common/somas/somas_parameter.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/session/kernel_graph.h" namespace mindspore { diff --git a/mindspore/ccsrc/backend/common/somas/somas_solver_pre.cc b/mindspore/ccsrc/backend/common/somas/somas_solver_pre.cc index 7f34231bc97..a674ae7f492 100644 --- a/mindspore/ccsrc/backend/common/somas/somas_solver_pre.cc +++ b/mindspore/ccsrc/backend/common/somas/somas_solver_pre.cc @@ -19,7 +19,7 @@ #include #include #include -#include "common/thread_pool.h" +#include "include/common/thread_pool.h" #include "backend/common/somas/somas_solver_core.h" #include "backend/common/somas/somas_solver_pre.h" diff --git a/mindspore/ccsrc/backend/graph_compiler/backend.cc b/mindspore/ccsrc/backend/graph_compiler/backend.cc index 3fad75ea18d..eaf65c1853a 100644 --- a/mindspore/ccsrc/backend/graph_compiler/backend.cc +++ b/mindspore/ccsrc/backend/graph_compiler/backend.cc @@ -19,7 +19,7 @@ #include #include -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" #include "backend/graph_compiler/transform.h" #include "backend/common/session/session_factory.h" #include "runtime/op_builder/op_lazy_builder.h" @@ -30,15 +30,15 @@ #include "ir/anf.h" #include "pybind_api/ir/base_ref_py.h" #include "pybind_api/pybind_patch.h" -#include "utils/callbacks.h" -#include "utils/convert_utils.h" +#include "include/common/utils/callbacks.h" +#include "include/common/utils/convert_utils.h" #include "utils/log_adapter.h" #include "utils/ms_utils.h" #include "runtime/hardware/device_context_manager.h" #include "runtime/graph_scheduler/graph_compiler.h" -#include "utils/scoped_long_running.h" +#include "include/common/utils/scoped_long_running.h" #ifdef ENABLE_D -#include "utils/callbacks_ge.h" +#include "include/common/utils/callbacks_ge.h" #endif #ifdef ENABLE_DEBUGGER #include "debug/debugger/debugger.h" @@ -196,7 +196,7 @@ void UpdateOutputAbstract(const KernelGraphPtr &kernel_graph, OpRunInfo *op_run_ const auto &kernels = kernel_graph->execution_order(); for (const auto &kernel : kernels) { MS_EXCEPTION_IF_NULL(kernel); - if (AnfAlgo::GetCNodeName(kernel) == op_run_info->op_name) { + if (common::AnfAlgo::GetCNodeName(kernel) == op_run_info->op_name) { op_run_info->abstract = kernel->abstract(); } } @@ -206,9 +206,9 @@ TensorPtr CreateOutputTensor(const AnfNodePtr &output_node, size_t output_index) MS_EXCEPTION_IF_NULL(output_node); // Create host tensor, the output tensor should use the infer type, it will be handed correctly by tensor data sync // when infer type is not equal to device type. - auto type_id = AnfAlgo::GetOutputInferDataType(output_node, output_index); + auto type_id = common::AnfAlgo::GetOutputInferDataType(output_node, output_index); std::vector temp_shape; - const auto &shape = AnfAlgo::GetOutputInferShape(output_node, output_index); + const auto &shape = common::AnfAlgo::GetOutputInferShape(output_node, output_index); (void)std::copy(shape.begin(), shape.end(), std::back_inserter(temp_shape)); auto tensor = std::make_shared(type_id, temp_shape); tensor->set_padding_type(AnfAlgo::GetOutputReshapeType(output_node, output_index)); @@ -263,7 +263,7 @@ void UpdateInputDeviceAddress(const KernelGraphPtr &graph) { MS_EXCEPTION_IF_NULL(graph); for (const auto &node : graph->input_nodes()) { MS_EXCEPTION_IF_NULL(node); - if (node->isa() && (!AnfAlgo::IsParameterWeight(node->cast()))) { + if (node->isa() && (!common::AnfAlgo::IsParameterWeight(node->cast()))) { AnfAlgo::SetOutputAddr(nullptr, 0, node.get()); } } @@ -277,7 +277,7 @@ std::vector GetRealValueNodeTensorFromGraph( } const auto &node = graph->execution_order().back(); - auto input_num = AnfAlgo::GetInputTensorNum(node); + auto input_num = common::AnfAlgo::GetInputTensorNum(node); // No value node in graph if (input_num == tensors_without_value_node.size()) { return new_input_tensors; @@ -287,7 +287,7 @@ std::vector GetRealValueNodeTensorFromGraph( std::map value_node_pos; for (size_t i = 0; i < input_num; ++i) { - auto input = AnfAlgo::GetInputNode(node, i); + auto input = common::AnfAlgo::GetInputNode(node, i); MS_EXCEPTION_IF_NULL(input); if (input->isa()) { auto value_node = input->cast(); @@ -453,7 +453,7 @@ const ActorInfo &MindRTBackend::CompileGraphs(const FuncGraphPtr &func_graph) { ms_execution_mode_ = context_ptr->get_param(MS_CTX_EXECUTION_MODE); real_execution_mode_ = ms_execution_mode_; auto parallel_mode = parallel::ParallelContext::GetInstance()->parallel_mode(); - auto is_parallel = (parallel_mode == parallel::SEMI_AUTO_PARALLEL || parallel_mode == parallel::AUTO_PARALLEL); + auto is_parallel = (parallel_mode == parallel::kSemiAutoParallel || parallel_mode == parallel::kAutoParallel); // Run in GRAPH_MODE if the func_graph is ms_function or the func_graph contain multi-subgraph. if (ms_execution_mode_ == kPynativeMode && @@ -573,8 +573,8 @@ void MindRTBackend::CompileGraph(const GraphSegmentPtr &segment) { MS_EXCEPTION_IF_NULL(cut_node); MS_LOG(INFO) << "Compile cut segment, the cut node: " << cut_node->DebugString(); control_nodes_.push_back(cut_node); - if (AnfAlgo::IsCallNode(cut_node) || AnfAlgo::CheckPrimitiveType(cut_node, prim::kPrimSwitch) || - AnfAlgo::CheckPrimitiveType(cut_node, prim::kPrimSwitchLayer)) { + if (common::AnfAlgo::IsCallNode(cut_node) || common::AnfAlgo::CheckPrimitiveType(cut_node, prim::kPrimSwitch) || + common::AnfAlgo::CheckPrimitiveType(cut_node, prim::kPrimSwitchLayer)) { const auto &func_graph = cut_node->func_graph(); MS_EXCEPTION_IF_NULL(func_graph); (void)func_graph_to_kernel_graph_ids_[func_graph].emplace_back(std::vector()); @@ -629,7 +629,7 @@ void GetControlOpInput(const std::shared_ptr &graph_compiler, con continue; } // Hook single-input or single-output. - auto real_input = AnfAlgo::VisitKernel(input_node, 0).first; + auto real_input = common::AnfAlgo::VisitKernel(input_node, 0).first; MS_EXCEPTION_IF_NULL(real_input); if (!real_input->isa()) { auto tensor = graph_compiler->GetSingleOpInputTensorByIndex(backend_cnode, op_output_map, parameter_index, @@ -870,7 +870,7 @@ void MindRTBackend::RunGraphBySingleOp(const std::vector &graphs for (const auto &kernel : graph->execution_order()) { InputTensorInfo input_tensor_info; VectorRef op_outputs; - if (!AnfAlgo::IsControlOpExecInBackend(kernel)) { + if (!common::AnfAlgo::IsControlOpExecInBackend(kernel)) { OpRunInfo op_run_info; GraphInfo graph_info; graph_compiler_->GetSingleOpInputTensors(kernel, op_output_map, parameter_index, inputs[graph_index], @@ -893,7 +893,7 @@ void MindRTBackend::RunGraphBySingleOp(const std::vector &graphs graph_compiler_->RecoverGraphOutput(kernel, op_outputs, cnode_ref_count, &op_output_map, &graph_output_info); // Save grad node to Bucket - if (graph->is_bprop() && (!AnfAlgo::IsControlOpExecInBackend(kernel)) && !kernel->is_parallel()) { + if (graph->is_bprop() && (!common::AnfAlgo::IsControlOpExecInBackend(kernel)) && !kernel->is_parallel()) { graph_compiler_->AddGradAddrToBucket(graph->graph_id(), graph_output_info.graph_output_tensors); } } @@ -1002,7 +1002,7 @@ BaseRef MindRTBackend::ConstructOutputByAbstract(const abstract::AbstractBasePtr MS_EXCEPTION_IF_NULL(abstract); MS_EXCEPTION_IF_NULL(output_position); - size_t outputs_num = AnfAlgo::GetOutputNumByAbstract(abstract); + size_t outputs_num = common::AnfAlgo::GetOutputNumByAbstract(abstract); if (*output_position + outputs_num > output_tensors.size()) { MS_LOG(EXCEPTION) << "The output position is out of range: " << *output_position << " need:" << outputs_num << " total:" << output_tensors.size(); @@ -1070,14 +1070,14 @@ void MindRTBackend::ConstructOutputs(const AnfNodePtr &output_node, } // The depend node need get the real node. - if (AnfAlgo::CheckPrimitiveType(output_node, prim::kPrimDepend)) { + if (common::AnfAlgo::CheckPrimitiveType(output_node, prim::kPrimDepend)) { auto depend_node = output_node->cast(); MS_EXCEPTION_IF_NULL(depend_node); ConstructOutputs(depend_node->input(kRealInputIndexInDepend), output_tensors, output_position, outputs); return; } - auto outputs_num = AnfAlgo::GetOutputTensorNum(output_node); + auto outputs_num = common::AnfAlgo::GetOutputTensorNum(output_node); // The value node uses the value to be output, to avoid the host memory of value free due to value node destruction. if (output_node->isa()) { auto value = output_node->cast()->value(); @@ -1093,7 +1093,7 @@ void MindRTBackend::ConstructOutputs(const AnfNodePtr &output_node, return; } - if (AnfAlgo::IsCallNode(output_node)) { + if (common::AnfAlgo::IsCallNode(output_node)) { auto abstract = output_node->abstract(); MS_EXCEPTION_IF_NULL(abstract); outputs->emplace_back(ConstructOutputByAbstract(abstract, output_tensors, output_position)); @@ -1176,9 +1176,9 @@ std::unique_ptr MindRTBackend::ConstructGraphCompilerInfo(con runtime::KernelMapPosition outputs_order; const auto &root_output = - AnfAlgo::VisitKernelWithReturnType(root_graph->output(), 0, false, {prim::kPrimTupleGetItem}).first; + common::AnfAlgo::VisitKernelWithReturnType(root_graph->output(), 0, false, {prim::kPrimTupleGetItem}).first; size_t position = 0; - auto outputs = AnfAlgo::GetAllOutputWithIndex(root_output); + auto outputs = common::AnfAlgo::GetAllOutputWithIndex(root_output); size_t outputs_num = outputs.size(); for (const auto &output : outputs) { if (outputs_order.count(output) == 0) { @@ -1209,7 +1209,7 @@ std::unique_ptr MindRTBackend::ConstructGraphCompilerInfo( (void)graphs.emplace_back(graph); (void)device_contexts.emplace_back(graph_info_to_context.second); - auto outputs = AnfAlgo::GetAllOutputWithIndex(graph->output()); + auto outputs = common::AnfAlgo::GetAllOutputWithIndex(graph->output()); for (const auto &output : outputs) { if (outputs_order.count(output) == 0) { outputs_order[output] = {position++}; @@ -1276,7 +1276,7 @@ void MindRTBackend::RunSingleOpGraph(const KernelGraphPtr &graph, const OpRunInf const auto &kernels = graph->execution_order(); for (const auto &kernel : kernels) { MS_EXCEPTION_IF_NULL(kernel); - if (kOpCacheBlackList.find(AnfAlgo::GetCNodeName(kernel)) != kOpCacheBlackList.end()) { + if (kOpCacheBlackList.find(common::AnfAlgo::GetCNodeName(kernel)) != kOpCacheBlackList.end()) { auto kernel_mod = AnfAlgo::GetKernelMod(kernel); if (kernel_mod) { kernel_mod->ReleaseResource(); @@ -1485,7 +1485,7 @@ void MindRTBackend::UpdateOutput(const std::vector &ou MS_EXCEPTION_IF_NULL(outputs); for (auto &item_with_index : output_nodes) { MS_EXCEPTION_IF_NULL(item_with_index.first); - if (AnfAlgo::GetOutputTensorNum(item_with_index.first) == 0) { + if (common::AnfAlgo::GetOutputTensorNum(item_with_index.first) == 0) { continue; } auto output_tensor = CreateOutputTensor(item_with_index.first, item_with_index.second); diff --git a/mindspore/ccsrc/backend/graph_compiler/backend.h b/mindspore/ccsrc/backend/graph_compiler/backend.h index 7760d010338..f5c26027b87 100644 --- a/mindspore/ccsrc/backend/graph_compiler/backend.h +++ b/mindspore/ccsrc/backend/graph_compiler/backend.h @@ -25,7 +25,7 @@ #include #include "utils/hash_map.h" -#include "utils/contract.h" +#include "include/common/utils/contract.h" #include "ir/anf.h" #include "backend/graph_compiler/segment_runner.h" #include "backend/graph_compiler/graph_partition.h" diff --git a/mindspore/ccsrc/backend/graph_compiler/graph_partition.cc b/mindspore/ccsrc/backend/graph_compiler/graph_partition.cc index b56074e3c33..ebb75ab4e1a 100644 --- a/mindspore/ccsrc/backend/graph_compiler/graph_partition.cc +++ b/mindspore/ccsrc/backend/graph_compiler/graph_partition.cc @@ -24,12 +24,12 @@ #include #include #include "base/core_ops.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/ms_context.h" #include "ps/ps_context.h" #include "utils/anf_utils.h" #ifdef ENABLE_D -#include "transform/graph_ir/convert.h" +#include "include/transform/graph_ir/convert.h" #endif namespace mindspore { const char kMsConvert[] = "ms"; diff --git a/mindspore/ccsrc/backend/graph_compiler/segment_runner.cc b/mindspore/ccsrc/backend/graph_compiler/segment_runner.cc index 9b83dc064ad..2b146f37af6 100644 --- a/mindspore/ccsrc/backend/graph_compiler/segment_runner.cc +++ b/mindspore/ccsrc/backend/graph_compiler/segment_runner.cc @@ -29,7 +29,7 @@ #include "utils/hash_map.h" #include "utils/hash_set.h" #include "utils/log_adapter.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "ir/manager.h" #include "ir/func_graph_cloner.h" #include "frontend/operator/ops.h" diff --git a/mindspore/ccsrc/backend/graph_compiler/transform.cc b/mindspore/ccsrc/backend/graph_compiler/transform.cc index 77b2cca5a1b..be2e0f19a48 100644 --- a/mindspore/ccsrc/backend/graph_compiler/transform.cc +++ b/mindspore/ccsrc/backend/graph_compiler/transform.cc @@ -26,7 +26,7 @@ #include "abstract/abstract_value.h" #ifdef ENABLE_D -#include "transform/graph_ir/convert.h" +#include "include/transform/graph_ir/convert.h" #endif #include "ir/graph_utils.h" #include "utils/ms_context.h" diff --git a/mindspore/ccsrc/backend/graph_compiler/vmimpl.cc b/mindspore/ccsrc/backend/graph_compiler/vmimpl.cc index b1f6c22ba74..bcafedabff7 100644 --- a/mindspore/ccsrc/backend/graph_compiler/vmimpl.cc +++ b/mindspore/ccsrc/backend/graph_compiler/vmimpl.cc @@ -26,8 +26,8 @@ #include "frontend/operator/ops.h" #include "ir/manager.h" #include "ir/func_graph_cloner.h" -#include "utils/convert_utils.h" -#include "utils/primitive_utils.h" +#include "include/common/utils/convert_utils.h" +#include "include/common/utils/primitive_utils.h" namespace mindspore { namespace compile { diff --git a/mindspore/ccsrc/common/CMakeLists.txt b/mindspore/ccsrc/common/CMakeLists.txt index 068e25a55dc..90a0ef62716 100644 --- a/mindspore/ccsrc/common/CMakeLists.txt +++ b/mindspore/ccsrc/common/CMakeLists.txt @@ -10,17 +10,6 @@ else() ) endif() -if(ENABLE_AKG AND ${CMAKE_SYSTEM_NAME} MATCHES "Linux") - file(GLOB_RECURSE _GK_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} - "graph_kernel/*.cc" - ) - file(GLOB_RECURSE _GK_LITE_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} - "graph_kernel/lite_adapter/*.cc" - ) - list(REMOVE_ITEM _GK_SRC_LIST ${_GK_LITE_LIST}) - list(APPEND _COMMON_ALL_SRC_FILES ${_GK_SRC_LIST}) -endif() - set_property(SOURCE ${_COMMON_ALL_SRC_FILES} PROPERTY COMPILE_DEFINITIONS SUBMODULE_ID=mindspore::SubModuleId::SM_COMMON) add_library(_mindspore_common_obj OBJECT ${_COMMON_ALL_SRC_FILES}) diff --git a/mindspore/ccsrc/common/duplex_pipe.cc b/mindspore/ccsrc/common/duplex_pipe.cc index b4c817d4b51..084bf205598 100644 --- a/mindspore/ccsrc/common/duplex_pipe.cc +++ b/mindspore/ccsrc/common/duplex_pipe.cc @@ -14,7 +14,7 @@ * limitations under the License. */ -#include "common/duplex_pipe.h" +#include "include/common/duplex_pipe.h" #include #include diff --git a/mindspore/ccsrc/common/duplex_pipe_win.cc b/mindspore/ccsrc/common/duplex_pipe_win.cc index 4374298a2d5..676bbdb7798 100644 --- a/mindspore/ccsrc/common/duplex_pipe_win.cc +++ b/mindspore/ccsrc/common/duplex_pipe_win.cc @@ -14,7 +14,7 @@ * limitations under the License. */ -#include "common/duplex_pipe.h" +#include "include/common/duplex_pipe.h" namespace mindspore { int DuplexPipe::Open(const std::initializer_list &arg_list, bool append_fds) { diff --git a/mindspore/ccsrc/common/graph_kernel/CMakeLists.txt b/mindspore/ccsrc/common/graph_kernel/CMakeLists.txt new file mode 100644 index 00000000000..b8a3bcb27cb --- /dev/null +++ b/mindspore/ccsrc/common/graph_kernel/CMakeLists.txt @@ -0,0 +1,12 @@ +if(ENABLE_AKG AND ${CMAKE_SYSTEM_NAME} MATCHES "Linux") + file(GLOB_RECURSE _GK_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} + "*.cc" + ) + file(GLOB_RECURSE _GK_LITE_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} + "lite_adapter/*.cc" + ) + list(REMOVE_ITEM _GK_SRC_LIST ${_GK_LITE_LIST}) + list(APPEND _GRAPH_KERNEL_SRC_FILES ${_GK_SRC_LIST}) + + add_library(_mindspore_common_graph_kernel_obj OBJECT ${_GRAPH_KERNEL_SRC_FILES}) +endif() diff --git a/mindspore/ccsrc/common/graph_kernel/adapter/callback_impl.cc b/mindspore/ccsrc/common/graph_kernel/adapter/callback_impl.cc index ebd8d305087..43d80d37344 100644 --- a/mindspore/ccsrc/common/graph_kernel/adapter/callback_impl.cc +++ b/mindspore/ccsrc/common/graph_kernel/adapter/callback_impl.cc @@ -22,6 +22,7 @@ #include #include "utils/ms_context.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "kernel/common_utils.h" #include "common/graph_kernel/adapter/fake_abstract_shape.h" #if ENABLE_D @@ -50,14 +51,14 @@ ShapeVector CallbackImpl::GetOutputShape(const AnfNodePtr &node, size_t i) { } ShapeVector CallbackImpl::GetInputInferShape(const AnfNodePtr &node, size_t i) { - auto vec = AnfAlgo::GetPrevNodeOutputInferShape(node, i); + auto vec = common::AnfAlgo::GetPrevNodeOutputInferShape(node, i); ShapeVector ret; (void)std::transform(vec.begin(), vec.end(), std::back_inserter(ret), SizeToLong); return ret; } ShapeVector CallbackImpl::GetOutputInferShape(const AnfNodePtr &node, size_t i) { - auto vec = AnfAlgo::GetOutputInferShape(node, i); + auto vec = common::AnfAlgo::GetOutputInferShape(node, i); ShapeVector ret; (void)std::transform(vec.begin(), vec.end(), std::back_inserter(ret), SizeToLong); return ret; @@ -70,11 +71,11 @@ TypeId CallbackImpl::GetOutputType(const AnfNodePtr &node, size_t i) { } TypeId CallbackImpl::GetInputInferType(const AnfNodePtr &node, size_t i) { - return AnfAlgo::GetPrevNodeOutputInferDataType(node, i); + return common::AnfAlgo::GetPrevNodeOutputInferDataType(node, i); } TypeId CallbackImpl::GetOutputInferType(const AnfNodePtr &node, size_t i) { - return AnfAlgo::GetOutputInferDataType(node, i); + return common::AnfAlgo::GetOutputInferDataType(node, i); } std::string CallbackImpl::GetInputFormat(const AnfNodePtr &node, size_t i) { return AnfAlgo::GetInputFormat(node, i); } @@ -135,7 +136,7 @@ void CallbackImpl::SetGraphKernelNodeKernelInfo(const AnfNodePtr &node) { outputs.push_back(fg->output()); } for (size_t i = 0; i < outputs.size(); ++i) { - auto kernel_with_index = AnfAlgo::VisitKernel(outputs[i], 0); + auto kernel_with_index = common::AnfAlgo::VisitKernel(outputs[i], 0); graph_output_format.push_back(AnfAlgo::GetOutputFormat(kernel_with_index.first, kernel_with_index.second)); graph_output_type.push_back(AnfAlgo::GetOutputDeviceDataType(kernel_with_index.first, kernel_with_index.second)); } @@ -160,7 +161,7 @@ void CallbackImpl::SetBasicNodeKernelInfo(const AnfNodePtr &node, const std::vec if (cnode != nullptr) { auto &inputs = cnode->inputs(); for (size_t i = 1; i < inputs.size(); ++i) { - auto kernel_with_index = AnfAlgo::VisitKernel(inputs[i], 0); + auto kernel_with_index = common::AnfAlgo::VisitKernel(inputs[i], 0); auto input_format = AnfAlgo::GetOutputFormat(kernel_with_index.first, kernel_with_index.second); input_formats.push_back(input_format); auto input_type = AnfAlgo::GetOutputDeviceDataType(kernel_with_index.first, kernel_with_index.second); diff --git a/mindspore/ccsrc/common/graph_kernel/adapter/fake_abstract_shape.cc b/mindspore/ccsrc/common/graph_kernel/adapter/fake_abstract_shape.cc index 3c5c46e043a..c6e72150758 100644 --- a/mindspore/ccsrc/common/graph_kernel/adapter/fake_abstract_shape.cc +++ b/mindspore/ccsrc/common/graph_kernel/adapter/fake_abstract_shape.cc @@ -15,7 +15,7 @@ */ #include "common/graph_kernel/adapter/fake_abstract_shape.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore::graphkernel { namespace { diff --git a/mindspore/ccsrc/common/graph_kernel/adapter/graph_kernel_expander_with_py.cc b/mindspore/ccsrc/common/graph_kernel/adapter/graph_kernel_expander_with_py.cc index f03a50af001..5c2cc36fb3f 100644 --- a/mindspore/ccsrc/common/graph_kernel/adapter/graph_kernel_expander_with_py.cc +++ b/mindspore/ccsrc/common/graph_kernel/adapter/graph_kernel_expander_with_py.cc @@ -22,14 +22,15 @@ #include #include "utils/ms_context.h" -#include "utils/context/graph_kernel_flags.h" +#include "include/common/utils/context/graph_kernel_flags.h" #include "kernel/akg/akg_kernel_json_generator.h" #include "common/graph_kernel/graph_kernel_helper.h" #include "common/graph_kernel/split_umonad.h" #include "common/graph_kernel/substitute_dropout.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "mindspore/core/ir/graph_utils.h" -#include "pipeline/jit/parse/python_adapter.h" +#include "include/common/utils/python_adapter.h" #include "pybind_api/ir/primitive_py.h" namespace mindspore::graphkernel { @@ -58,7 +59,7 @@ FuncGraphPtr PyExpander::CreateExpandFuncGraph(const CNodePtr &node) { // call graph kernel ops generator. MS_LOG(DEBUG) << "CallPyFn: [" << kGetGraphKernelOpExpander << "] with input json:\n" << node_desc_str; - auto ret = parse::python_adapter::CallPyFn(kGraphKernelModule, kGetGraphKernelOpExpander, node_desc_str); + auto ret = python_adapter::CallPyFn(kGraphKernelModule, kGetGraphKernelOpExpander, node_desc_str); // parse result. if (py::isinstance(ret)) { MS_LOG(ERROR) << "CallPyFn: [" << kGetGraphKernelOpExpander << "] return invalid result, input json:\n" @@ -151,7 +152,7 @@ ExpanderPtr GraphKernelComplexExpander::GetExpander(const AnfNodePtr &) { bool ComplexOpExpander::ExpandJsonInfo(const AnfNodePtr &node, nlohmann::json *kernel_json) { auto cnode = node->cast(); if (!PyExpander::ExpandJsonInfo(cnode, kernel_json)) return false; - (*kernel_json)["name"] = std::string("C") + AnfAlgo::GetCNodeName(cnode); + (*kernel_json)["name"] = std::string("C") + common::AnfAlgo::GetCNodeName(cnode); return true; } bool GraphKernelComplexExpander::Run(const FuncGraphPtr &func_graph) { return DoExpand(func_graph); } diff --git a/mindspore/ccsrc/common/graph_kernel/adapter/graph_kernel_optimization.cc b/mindspore/ccsrc/common/graph_kernel/adapter/graph_kernel_optimization.cc index ad32792345e..6b04527a18a 100644 --- a/mindspore/ccsrc/common/graph_kernel/adapter/graph_kernel_optimization.cc +++ b/mindspore/ccsrc/common/graph_kernel/adapter/graph_kernel_optimization.cc @@ -21,7 +21,7 @@ #include "ir/func_graph.h" #include "utils/ms_context.h" -#include "utils/context/graph_kernel_flags.h" +#include "include/common/utils/context/graph_kernel_flags.h" #include "common/graph_kernel/add_atomic_clean.h" #include "common/graph_kernel/add_stitch_atomic_clean_gpu.h" #include "common/graph_kernel/arithmetic_simplify.h" diff --git a/mindspore/ccsrc/common/graph_kernel/adapter/graph_kernel_pass_manager.h b/mindspore/ccsrc/common/graph_kernel/adapter/graph_kernel_pass_manager.h index 190f23c9164..e66a54c3af2 100644 --- a/mindspore/ccsrc/common/graph_kernel/adapter/graph_kernel_pass_manager.h +++ b/mindspore/ccsrc/common/graph_kernel/adapter/graph_kernel_pass_manager.h @@ -21,7 +21,7 @@ #include #include -#include "utils/context/graph_kernel_flags.h" +#include "include/common/utils/context/graph_kernel_flags.h" #include "backend/common/optimizer/pass_manager.h" namespace mindspore::graphkernel { diff --git a/mindspore/ccsrc/common/graph_kernel/adapter/graph_kernel_splitter_with_py.cc b/mindspore/ccsrc/common/graph_kernel/adapter/graph_kernel_splitter_with_py.cc index 4ed74b08dc1..a7eed1a06f7 100644 --- a/mindspore/ccsrc/common/graph_kernel/adapter/graph_kernel_splitter_with_py.cc +++ b/mindspore/ccsrc/common/graph_kernel/adapter/graph_kernel_splitter_with_py.cc @@ -23,12 +23,11 @@ #include #include "utils/hash_map.h" #include "utils/ms_context.h" -#include "pipeline/jit/parse/python_adapter.h" +#include "include/common/utils/python_adapter.h" #include "kernel/akg/akg_kernel_json_generator.h" #include "kernel/common_utils.h" #include "common/graph_kernel/graph_kernel_helper.h" -#include "backend/common/session/anf_runtime_algorithm.h" -#include "utils/context/graph_kernel_flags.h" +#include "include/common/utils/context/graph_kernel_flags.h" namespace mindspore::graphkernel { class CostModelSplitSchemer : public SplitSchemer { @@ -71,7 +70,7 @@ class CostModelSplitSchemer : public SplitSchemer { auto flags_str = CollectSplitFlags(); MS_LOG(DEBUG) << "CallPyFn: [" << kGraphKernelSplitFunc << "] with input json: " << json_desc_str << ". flag: " << flags_str; - auto ret = parse::python_adapter::CallPyFn(kGraphKernelModule, kGraphKernelSplitFunc, json_desc_str, flags_str); + auto ret = python_adapter::CallPyFn(kGraphKernelModule, kGraphKernelSplitFunc, json_desc_str, flags_str); if (py::isinstance(ret)) { MS_LOG(ERROR) << "CallPyFn: [" << kGraphKernelSplitFunc << "] return invalid result. input json:\n" << json_desc_str << ". flag: " << flags_str; @@ -180,7 +179,7 @@ class CostModelSplitSchemer : public SplitSchemer { return; } // assign the make_tuple node to a new group. - if (AnfAlgo::CheckPrimitiveType(output, prim::kPrimMakeTuple)) { + if (common::AnfAlgo::CheckPrimitiveType(output, prim::kPrimMakeTuple)) { auto group_id = split_plan_.size(); split_plan_.emplace_back(AnfNodePtrList{output, ret_node}); need_inline_.emplace_back(1); diff --git a/mindspore/ccsrc/common/graph_kernel/add_atomic_clean.cc b/mindspore/ccsrc/common/graph_kernel/add_atomic_clean.cc index a57100e4356..1cc158d0bfa 100644 --- a/mindspore/ccsrc/common/graph_kernel/add_atomic_clean.cc +++ b/mindspore/ccsrc/common/graph_kernel/add_atomic_clean.cc @@ -26,12 +26,11 @@ #include #include "base/core_ops.h" #include "ir/tensor.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/log_adapter.h" #include "kernel/kernel.h" #include "common/graph_kernel/graph_kernel_helper.h" #include "common/graph_kernel/core/graph_kernel_utils.h" -#include "backend/common/session/anf_runtime_algorithm.h" #include "backend/common/session/kernel_graph.h" #include "debug/anf_ir_dump.h" #include "kernel/common_utils.h" @@ -41,7 +40,7 @@ namespace { auto constexpr NUMBER_COND_FOR_FILTER_INPLACE = 2; std::set GetUniqReduceAxes(const AnfNodePtr &node, bool is_ascend = false) { if (!IsPrimitiveCNode(node, prim::kPrimReduceSum)) { - MS_LOG(EXCEPTION) << "Expect ReduceSum node, but got " << AnfAlgo::GetCNodeName(node); + MS_LOG(EXCEPTION) << "Expect ReduceSum node, but got " << common::AnfAlgo::GetCNodeName(node); } auto input = node->cast()->input(kFirstDataInputIndex); @@ -110,7 +109,7 @@ bool AtomicAddChecker::FindCandidate(const AnfNodePtr &anf_node) { atomic_add_infos_.clear(); auto node = anf_node->cast(); MS_EXCEPTION_IF_NULL(node); - auto sub_graph = AnfAlgo::GetCNodeFuncGraphPtr(node); + auto sub_graph = common::AnfAlgo::GetCNodeFuncGraphPtr(node); auto mng_sub = sub_graph->manager(); if (mng_sub == nullptr) { mng_sub = Manage(sub_graph, false); @@ -176,7 +175,7 @@ bool AtomicAddChecker::CanActivateAtomicAdd(const AnfNodePtr &anf_node) { } bool AtomicAddChecker::Check(const AnfNodePtr &node) { - return (AnfAlgo::IsGraphKernel(node) && CanActivateAtomicAdd(node)); + return (common::AnfAlgo::IsGraphKernel(node) && CanActivateAtomicAdd(node)); } bool AtomicAddCheckerGPU::SuitableForAtomicAdd(const AnfNodePtr &node) { @@ -277,7 +276,7 @@ void AtomicCleanInsertter::CorrectKernelBuildInfo( for (const auto &clean_info : clean_infos) { auto &new_input = clean_info.second; - auto kernel_with_index = AnfAlgo::VisitKernel(new_input, 0); + auto kernel_with_index = common::AnfAlgo::VisitKernel(new_input, 0); new_inputs_format.push_back(AnfAlgo::GetOutputFormat(kernel_with_index.first, kernel_with_index.second)); new_inputs_type.push_back(AnfAlgo::GetOutputDeviceDataType(kernel_with_index.first, kernel_with_index.second)); } @@ -390,7 +389,7 @@ void AtomicCleanInsertter::CorrectAbstract( void AtomicCleanInsertter::ProcessOriginCNode( const AnfNodePtr &composite_node, const std::vector> &info_and_broadcast_to_nodes) { - auto sub_graph = AnfAlgo::GetCNodeFuncGraphPtr(composite_node); + auto sub_graph = common::AnfAlgo::GetCNodeFuncGraphPtr(composite_node); auto mng_sub = sub_graph->manager(); if (mng_sub == nullptr) { mng_sub = Manage(sub_graph, false); diff --git a/mindspore/ccsrc/common/graph_kernel/add_stitch_atomic_clean_gpu.cc b/mindspore/ccsrc/common/graph_kernel/add_stitch_atomic_clean_gpu.cc index 70adf28a409..4f01a8be107 100644 --- a/mindspore/ccsrc/common/graph_kernel/add_stitch_atomic_clean_gpu.cc +++ b/mindspore/ccsrc/common/graph_kernel/add_stitch_atomic_clean_gpu.cc @@ -20,13 +20,12 @@ #include #include "base/core_ops.h" #include "ir/tensor.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/log_adapter.h" #include "kernel/kernel.h" #include "kernel/common_utils.h" #include "common/graph_kernel/graph_kernel_helper.h" #include "common/graph_kernel/core/graph_kernel_utils.h" -#include "backend/common/session/anf_runtime_algorithm.h" #include "backend/common/session/kernel_graph.h" namespace mindspore::graphkernel { @@ -49,7 +48,7 @@ void StitchAtomicCleanInsertter::CorrectKernelBuildInfo( new_outputs_type.push_back(origin_outputs_type[i]); } - auto kernel_with_index = AnfAlgo::VisitKernel(clean_infos[0].second, 0); + auto kernel_with_index = common::AnfAlgo::VisitKernel(clean_infos[0].second, 0); new_inputs_format.push_back(AnfAlgo::GetOutputFormat(kernel_with_index.first, kernel_with_index.second)); new_inputs_type.push_back(AnfAlgo::GetOutputDeviceDataType(kernel_with_index.first, kernel_with_index.second)); @@ -88,7 +87,7 @@ CNodePtr StitchAtomicCleanInsertter::CreateInplaceAssignNode(const FuncGraphPtr CreateCNode({NewValueNode(prim::kPrimInplaceAssign), new_parameter, out_node, out_node}, sub_graph, {.format = GetFormat(out_node), .shape = GetShape(out_node), .type = GetType(out_node)}); SetNodeAttrSafely("fake_output", MakeValue(true), inplace_assign_node); - AnfAlgo::EraseNodeAttr(kAttrStitch, out_node); + common::AnfAlgo::EraseNodeAttr(kAttrStitch, out_node); SetNodeAttrSafely(kAttrStitch, MakeValue("common"), inplace_assign_node); return inplace_assign_node; } @@ -96,7 +95,7 @@ CNodePtr StitchAtomicCleanInsertter::CreateInplaceAssignNode(const FuncGraphPtr void StitchAtomicCleanInsertter::ProcessOriginCNode( const AnfNodePtr &composite_node, const std::vector> &info_and_broadcast_to_nodes) { - auto sub_graph = AnfAlgo::GetCNodeFuncGraphPtr(composite_node); + auto sub_graph = common::AnfAlgo::GetCNodeFuncGraphPtr(composite_node); auto mng_sub = sub_graph->manager(); if (mng_sub == nullptr) { mng_sub = Manage(sub_graph, false); @@ -147,7 +146,7 @@ std::vector> StitchAtomicCleanInsertter::FindInnerCNo const CNodePtr &target) const { auto node = inner_node->cast(); MS_EXCEPTION_IF_NULL(node); - auto sub_graph = AnfAlgo::GetCNodeFuncGraphPtr(node); + auto sub_graph = common::AnfAlgo::GetCNodeFuncGraphPtr(node); auto mng_sub = sub_graph->manager(); if (mng_sub == nullptr) { mng_sub = Manage(sub_graph, false); @@ -161,15 +160,16 @@ std::vector> StitchAtomicCleanInsertter::FindInnerCNo } std::pair StitchAtomicCleanInsertter::IsStitchWithAtomic(const AnfNodePtr &anf_node) { - if (!AnfAlgo::IsGraphKernel(anf_node)) return {false, AtomicAddInfo()}; + if (!common::AnfAlgo::IsGraphKernel(anf_node)) return {false, AtomicAddInfo()}; auto node = anf_node->cast(); MS_EXCEPTION_IF_NULL(node); - auto sub_graph = AnfAlgo::GetCNodeFuncGraphPtr(node); + auto sub_graph = common::AnfAlgo::GetCNodeFuncGraphPtr(node); AnfNodePtrList kernel_nodes; kernel::GetValidKernelNodes(sub_graph, &kernel_nodes); for (auto &n : kernel_nodes) { - if (AnfAlgo::HasNodeAttr(kAttrStitch, n->cast()) && - AnfAlgo::GetNodeAttr(n, kAttrStitch) == "atomic" && IsPrimitiveCNode(n, prim::kPrimReduceSum)) { + if (common::AnfAlgo::HasNodeAttr(kAttrStitch, n->cast()) && + common::AnfAlgo::GetNodeAttr(n, kAttrStitch) == "atomic" && + IsPrimitiveCNode(n, prim::kPrimReduceSum)) { MS_LOG(INFO) << "GOT STITCH WITH ATOMIC!!!"; AtomicAddInfo info; info.atomic_add_node = n->cast(); diff --git a/mindspore/ccsrc/common/graph_kernel/arithmetic_simplify.cc b/mindspore/ccsrc/common/graph_kernel/arithmetic_simplify.cc index 340a418b86a..e5a87526738 100644 --- a/mindspore/ccsrc/common/graph_kernel/arithmetic_simplify.cc +++ b/mindspore/ccsrc/common/graph_kernel/arithmetic_simplify.cc @@ -28,8 +28,9 @@ #include "common/graph_kernel/core/graph_builder.h" #include "common/graph_kernel/core/graph_kernel_utils.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/anf.h" -#include "utils/context/graph_kernel_flags.h" +#include "include/common/utils/context/graph_kernel_flags.h" namespace mindspore::graphkernel { // operator which follows commutative rules @@ -634,8 +635,8 @@ bool ArithmeticSimplify::Run(const FuncGraphPtr &func_graph) { bool do_simplify = false; expressions_map_ = GetExpressions(); for (auto node : func_graph->GetOrderedCnodes()) { - if (AnfAlgo::IsGraphKernel(node)) { - auto sub_graph = AnfAlgo::GetCNodeFuncGraphPtr(node); + if (common::AnfAlgo::IsGraphKernel(node)) { + auto sub_graph = common::AnfAlgo::GetCNodeFuncGraphPtr(node); inner::LiteGraphPtr lg = GkUtils::AnfGraph2LiteGraph(sub_graph); bool find_pattern = true; bool change_anf_graph = false; diff --git a/mindspore/ccsrc/common/graph_kernel/axis_normalizer.cc b/mindspore/ccsrc/common/graph_kernel/axis_normalizer.cc index 3aba2c64518..86ca018c100 100644 --- a/mindspore/ccsrc/common/graph_kernel/axis_normalizer.cc +++ b/mindspore/ccsrc/common/graph_kernel/axis_normalizer.cc @@ -20,6 +20,7 @@ #include "ir/scalar.h" #include "common/graph_kernel/graph_kernel_helper.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore::graphkernel { int64_t AxisNormalizer::NormAxis(int64_t x, size_t rank) const { return x >= 0 ? x : x + static_cast(rank); } @@ -83,8 +84,8 @@ bool AxisNormalizer::Run(const FuncGraphPtr &func_graph) { bool changed = false; auto todos = TopoSort(func_graph->get_return()); for (auto node : todos) { - if (AnfAlgo::IsGraphKernel(node)) { - auto sub_func_graph = AnfAlgo::GetCNodeFuncGraphPtr(node); + if (common::AnfAlgo::IsGraphKernel(node)) { + auto sub_func_graph = common::AnfAlgo::GetCNodeFuncGraphPtr(node); changed = Process(sub_func_graph) || changed; } } diff --git a/mindspore/ccsrc/common/graph_kernel/cast_matmul_fusion.cc b/mindspore/ccsrc/common/graph_kernel/cast_matmul_fusion.cc index a83cf5e7ef3..1dfdac1134a 100644 --- a/mindspore/ccsrc/common/graph_kernel/cast_matmul_fusion.cc +++ b/mindspore/ccsrc/common/graph_kernel/cast_matmul_fusion.cc @@ -18,6 +18,7 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "kernel/common_utils.h" #include "common/graph_kernel/graph_kernel_helper.h" @@ -98,8 +99,8 @@ bool CastMatmulFusion::Run(const FuncGraphPtr &func_graph) { auto changed = false; auto nodes = TopoSort(func_graph->get_return()); for (auto node : nodes) { - if (!AnfAlgo::IsGraphKernel(node)) continue; - auto graph_kernel_fg = AnfAlgo::GetCNodeFuncGraphPtr(node); + if (!common::AnfAlgo::IsGraphKernel(node)) continue; + auto graph_kernel_fg = common::AnfAlgo::GetCNodeFuncGraphPtr(node); MS_EXCEPTION_IF_NULL(graph_kernel_fg); changed = DoFuse(graph_kernel_fg) || changed; } diff --git a/mindspore/ccsrc/common/graph_kernel/core/graph_builder.cc b/mindspore/ccsrc/common/graph_kernel/core/graph_builder.cc index 898f9a8eb5b..f6ae0c2d1ea 100644 --- a/mindspore/ccsrc/common/graph_kernel/core/graph_builder.cc +++ b/mindspore/ccsrc/common/graph_kernel/core/graph_builder.cc @@ -24,7 +24,7 @@ #include "base/core_ops.h" #include "ir/func_graph.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/anf_utils.h" #include "utils/ordered_set.h" #include "common/graph_kernel/core/graph_kernel_callback.h" diff --git a/mindspore/ccsrc/common/graph_kernel/core/graph_kernel_cluster.cc b/mindspore/ccsrc/common/graph_kernel/core/graph_kernel_cluster.cc index a766dc340d0..957e880a31f 100644 --- a/mindspore/ccsrc/common/graph_kernel/core/graph_kernel_cluster.cc +++ b/mindspore/ccsrc/common/graph_kernel/core/graph_kernel_cluster.cc @@ -27,7 +27,7 @@ #include "utils/anf_utils.h" #include "utils/ms_context.h" #include "utils/file_utils.h" -#include "utils/context/graph_kernel_flags.h" +#include "include/common/utils/context/graph_kernel_flags.h" #include "backend/common/pass/getitem_tuple.h" #include "common/graph_kernel/core/graph_kernel_callback.h" #include "common/graph_kernel/core/graph_kernel_utils.h" diff --git a/mindspore/ccsrc/common/graph_kernel/core/graph_kernel_expander.cc b/mindspore/ccsrc/common/graph_kernel/core/graph_kernel_expander.cc index 47182fe94bf..fa1b426cb27 100644 --- a/mindspore/ccsrc/common/graph_kernel/core/graph_kernel_expander.cc +++ b/mindspore/ccsrc/common/graph_kernel/core/graph_kernel_expander.cc @@ -24,7 +24,7 @@ #include "ir/graph_utils.h" #include "utils/anf_utils.h" #include "utils/ms_context.h" -#include "utils/context/graph_kernel_flags.h" +#include "include/common/utils/context/graph_kernel_flags.h" #include "common/graph_kernel/core/graph_builder.h" #include "common/graph_kernel/core/graph_kernel_callback.h" #include "common/graph_kernel/core/graph_kernel_utils.h" diff --git a/mindspore/ccsrc/common/graph_kernel/core/graph_kernel_splitter.cc b/mindspore/ccsrc/common/graph_kernel/core/graph_kernel_splitter.cc index 5a1208865bc..bdfdf626a51 100644 --- a/mindspore/ccsrc/common/graph_kernel/core/graph_kernel_splitter.cc +++ b/mindspore/ccsrc/common/graph_kernel/core/graph_kernel_splitter.cc @@ -25,7 +25,7 @@ #include "utils/anf_utils.h" #include "utils/hash_map.h" #include "utils/hash_set.h" -#include "utils/context/graph_kernel_flags.h" +#include "include/common/utils/context/graph_kernel_flags.h" #include "kernel/akg/akg_kernel_json_generator.h" #include "common/graph_kernel/core/graph_kernel_callback.h" #include "common/graph_kernel/core/graph_kernel_utils.h" diff --git a/mindspore/ccsrc/common/graph_kernel/core/graph_kernel_utils.cc b/mindspore/ccsrc/common/graph_kernel/core/graph_kernel_utils.cc index e83f8b90855..f1518f98110 100644 --- a/mindspore/ccsrc/common/graph_kernel/core/graph_kernel_utils.cc +++ b/mindspore/ccsrc/common/graph_kernel/core/graph_kernel_utils.cc @@ -21,7 +21,7 @@ #include #include "base/core_ops.h" #include "utils/anf_utils.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "common/graph_kernel/core/graph_kernel_callback.h" namespace mindspore::graphkernel { diff --git a/mindspore/ccsrc/common/graph_kernel/decrease_compute_precision.cc b/mindspore/ccsrc/common/graph_kernel/decrease_compute_precision.cc index 822bcc8638f..824f0d21818 100644 --- a/mindspore/ccsrc/common/graph_kernel/decrease_compute_precision.cc +++ b/mindspore/ccsrc/common/graph_kernel/decrease_compute_precision.cc @@ -23,6 +23,7 @@ #include "backend/common/optimizer/helper.h" #include "plugin/device/ascend/optimizer/ascend_helper.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "common/graph_kernel/graph_kernel_helper.h" #include "common/graph_kernel/core/graph_kernel_utils.h" #include "runtime/device/kernel_info.h" @@ -52,16 +53,16 @@ CNodePtr AddCastCNode(const FuncGraphPtr &func_graph, const AnfNodePtr &input, c cast->set_kernel_info(kernel_info); } AnfAlgo::SetSelectKernelBuildInfo(builder.Build(), cast.get()); - AnfAlgo::SetOutputInferTypeAndShape({origin_type}, {origin_shape}, cast.get()); - AnfAlgo::SetNodeAttr(kAttrDstType, TypeIdToType(output_type), cast); - AnfAlgo::SetNodeAttr(kIsBackendCast, MakeValue(true), cast); - AnfAlgo::SetNodeAttr(kAttrDatadumpOriginalNames, MakeValue>({}), cast); + common::AnfAlgo::SetOutputInferTypeAndShape({origin_type}, {origin_shape}, cast.get()); + common::AnfAlgo::SetNodeAttr(kAttrDstType, TypeIdToType(output_type), cast); + common::AnfAlgo::SetNodeAttr(kIsBackendCast, MakeValue(true), cast); + common::AnfAlgo::SetNodeAttr(kAttrDatadumpOriginalNames, MakeValue>({}), cast); return cast; } // Update Output Abatract and BuildInfo as Input Changed void UpdateOutputInfo(const AnfNodePtr &cnode) { - if (!AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimMakeTuple)) { + if (!common::AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimMakeTuple)) { ShapeVector out_shape = GetShape(cnode); auto abs_shape_ptr = std::make_shared(abstract::Shape(out_shape)); auto abstract = std::make_shared(TypeIdToType(TypeId::kNumberTypeFloat16), abs_shape_ptr); @@ -81,13 +82,13 @@ void UpdateOutputInfo(const AnfNodePtr &cnode) { CNodePtr InsertCastForGraphKernel(const FuncGraphPtr &func_graph, const CNodePtr &cnode) { MS_EXCEPTION_IF_NULL(cnode); auto mng = func_graph->manager(); - size_t in_num = AnfAlgo::GetInputNum(cnode); // include monads. + size_t in_num = common::AnfAlgo::GetInputNum(cnode); // include monads. for (size_t input_index = 0; input_index < in_num; ++input_index) { - auto cur_input = AnfAlgo::GetInputNode(cnode, input_index); + auto cur_input = common::AnfAlgo::GetInputNode(cnode, input_index); if (HasAbstractMonad(cur_input)) { continue; } - auto prev_node = AnfAlgo::GetPrevNodeOutput(cnode, input_index); + auto prev_node = common::AnfAlgo::GetPrevNodeOutput(cnode, input_index); auto in_node = prev_node.first; auto in_index = prev_node.second; auto ori_shape = AnfAlgo::GetOutputDeviceShape(in_node, in_index); @@ -115,7 +116,7 @@ CNodePtr InsertCastForGraphKernel(const FuncGraphPtr &func_graph, const CNodePtr auto abstract = std::make_shared(TypeIdToType(TypeId::kNumberTypeFloat16), abs_shape_ptr); cast->set_abstract(abstract); - AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), cast); + common::AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), cast); (void)mng->Replace(cur_input, cast); } } @@ -136,7 +137,7 @@ bool DecreaseComputePrecision::Process(const FuncGraphPtr &func_graph) { bool changed = false; // Cast Down CNODES for (auto node : todos) { - if (node->isa() && !AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { + if (node->isa() && !common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { auto cnode = node->cast(); if (IsPrimitiveCNode(cnode, prim::kPrimCast)) { if (AnfAlgo::GetOutputDeviceDataType(cnode->input(1), 0) == kNumberTypeFloat16) { @@ -197,9 +198,9 @@ bool DecreaseComputePrecision::Process(const FuncGraphPtr &func_graph) { }; std::vector new_inputs; - if (AnfAlgo::CheckPrimitiveType(old_output, prim::kPrimMakeTuple)) { + if (common::AnfAlgo::CheckPrimitiveType(old_output, prim::kPrimMakeTuple)) { (void)new_inputs.emplace_back(NewValueNode(prim::kPrimMakeTuple)); - auto all_out = AnfAlgo::GetAllOutput(old_output); + auto all_out = common::AnfAlgo::GetAllOutput(old_output); for (const auto &out : all_out) { auto c_out = out->cast(); if (c_out) { @@ -229,7 +230,7 @@ bool IsCastUnAware(const FuncGraphPtr &func_graph) { auto todos = TopoSort(func_graph->get_return()); for (auto node : todos) { if (node->isa()) { - if (std::find(cast_aware_list.begin(), cast_aware_list.end(), AnfAlgo::GetCNodePrimitive(node)) != + if (std::find(cast_aware_list.begin(), cast_aware_list.end(), common::AnfAlgo::GetCNodePrimitive(node)) != cast_aware_list.end()) { return false; } @@ -251,8 +252,8 @@ bool DecreaseComputePrecision::Run(const FuncGraphPtr &func_graph) { auto todos = TopoSort(func_graph->get_return()); bool changed = false; for (const auto &node : todos) { - if (AnfAlgo::IsGraphKernel(node)) { - auto sub_func_graph = AnfAlgo::GetCNodeFuncGraphPtr(node); + if (common::AnfAlgo::IsGraphKernel(node)) { + auto sub_func_graph = common::AnfAlgo::GetCNodeFuncGraphPtr(node); MS_ERROR_IF_NULL(sub_func_graph); if (IsCastUnAware(sub_func_graph)) { changed = Process(sub_func_graph) || changed; diff --git a/mindspore/ccsrc/common/graph_kernel/decrease_transfer_precision.cc b/mindspore/ccsrc/common/graph_kernel/decrease_transfer_precision.cc index 41eb2b84072..9e337c1328d 100644 --- a/mindspore/ccsrc/common/graph_kernel/decrease_transfer_precision.cc +++ b/mindspore/ccsrc/common/graph_kernel/decrease_transfer_precision.cc @@ -20,10 +20,8 @@ #include #include #include "base/core_ops.h" -#include "utils/utils.h" -#include "backend/common/optimizer/helper.h" +#include "include/common/utils/utils.h" #include "common/graph_kernel/graph_kernel_helper.h" -#include "backend/common/session/anf_runtime_algorithm.h" #include "ir/tensor.h" #include "ir/manager.h" #include "kernel/kernel_build_info.h" @@ -41,15 +39,15 @@ int64_t ObtainGetItemIndex(const AnfNodePtr &getitem) { } bool IsPreNodeReduce(const FuncGraphPtr &, const AnfNodePtr &node, bool is_tuple_out, size_t index) { - auto gk_graph = AnfAlgo::GetCNodeFuncGraphPtr(node); + auto gk_graph = common::AnfAlgo::GetCNodeFuncGraphPtr(node); MS_EXCEPTION_IF_NULL(gk_graph); if (is_tuple_out) { auto tuple_output = gk_graph->output()->cast(); - if (AnfAlgo::GetCNodeName(tuple_output) != prim::kPrimMakeTuple->name()) { - MS_LOG(EXCEPTION) << "Expect MakeTuple node, but got " << AnfAlgo::GetCNodeName(tuple_output); + if (common::AnfAlgo::GetCNodeName(tuple_output) != prim::kPrimMakeTuple->name()) { + MS_LOG(EXCEPTION) << "Expect MakeTuple node, but got " << common::AnfAlgo::GetCNodeName(tuple_output); } auto input_node = tuple_output->input(index + 1); - if (AnfAlgo::GetCNodeName(input_node) == prim::kPrimReduceSum->name()) { + if (common::AnfAlgo::GetCNodeName(input_node) == prim::kPrimReduceSum->name()) { return true; } } @@ -57,17 +55,17 @@ bool IsPreNodeReduce(const FuncGraphPtr &, const AnfNodePtr &node, bool is_tuple } size_t GetGraphKernelSize(const AnfNodePtr &node) { - auto gk_graph = AnfAlgo::GetCNodeFuncGraphPtr(node); + auto gk_graph = common::AnfAlgo::GetCNodeFuncGraphPtr(node); MS_EXCEPTION_IF_NULL(gk_graph); return gk_graph->GetOrderedCnodes().size(); } bool IsCandidateNode(const AnfNodePtr &node) { - bool is_gk = AnfAlgo::IsGraphKernel(node); + bool is_gk = common::AnfAlgo::IsGraphKernel(node); if (is_gk) { auto num = GetGraphKernelSize(node); if (num > GK_MIN_SIZE) { - auto sub_graph = AnfAlgo::GetCNodeFuncGraphPtr(node); + auto sub_graph = common::AnfAlgo::GetCNodeFuncGraphPtr(node); auto graph_name = GetValue(sub_graph->get_attr(FUNC_GRAPH_ATTR_GRAPH_KERNEL)); if (graph_name.find("atomic") == std::string::npos) { return true; @@ -144,7 +142,7 @@ bool DecreaseTransferPrecision::Run(const FuncGraphPtr &func_graph) { bool DecreaseTransferPrecision::ProcessFather(const FuncGraphPtr &, const AnfNodePtr &node, bool is_tuple_out, size_t index) { - auto gk_graph = AnfAlgo::GetCNodeFuncGraphPtr(node); + auto gk_graph = common::AnfAlgo::GetCNodeFuncGraphPtr(node); MS_EXCEPTION_IF_NULL(gk_graph); auto mng = gk_graph->manager(); MS_EXCEPTION_IF_NULL(mng); @@ -179,7 +177,7 @@ bool DecreaseTransferPrecision::ProcessFather(const FuncGraphPtr &, const AnfNod if (!is_tuple_out) { auto old_output = gk_graph->output()->cast(); MS_EXCEPTION_IF_NULL(old_output); - if (AnfAlgo::GetCNodeName(old_output) == prim::kPrimCast->name() && + if (common::AnfAlgo::GetCNodeName(old_output) == prim::kPrimCast->name() && AnfAlgo::GetInputDeviceDataType(old_output, 0) == kNumberTypeFloat16 && AnfAlgo::GetOutputDeviceDataType(old_output, 0) == kNumberTypeFloat32) { auto real_output = old_output->input(1); @@ -200,8 +198,8 @@ bool DecreaseTransferPrecision::ProcessFather(const FuncGraphPtr &, const AnfNod } else { // cast for graph kernel with make tuple output auto tuple_output = gk_graph->output()->cast(); - if (AnfAlgo::GetCNodeName(tuple_output) != prim::kPrimMakeTuple->name()) { - MS_LOG(EXCEPTION) << "Expect MakeTuple node, but got " << AnfAlgo::GetCNodeName(tuple_output); + if (common::AnfAlgo::GetCNodeName(tuple_output) != prim::kPrimMakeTuple->name()) { + MS_LOG(EXCEPTION) << "Expect MakeTuple node, but got " << common::AnfAlgo::GetCNodeName(tuple_output); } auto input_node = tuple_output->input(index + 1); auto cnode = func_add_cast_fp16(input_node); @@ -234,7 +232,7 @@ bool DecreaseTransferPrecision::ProcessFather(const FuncGraphPtr &, const AnfNod } bool DecreaseTransferPrecision::ProcessSon(const FuncGraphPtr &, const AnfNodePtr &node, size_t index) { - auto gk_graph = AnfAlgo::GetCNodeFuncGraphPtr(node); + auto gk_graph = common::AnfAlgo::GetCNodeFuncGraphPtr(node); MS_EXCEPTION_IF_NULL(gk_graph); auto mng = gk_graph->manager(); MS_EXCEPTION_IF_NULL(mng); diff --git a/mindspore/ccsrc/common/graph_kernel/depend_elimination.cc b/mindspore/ccsrc/common/graph_kernel/depend_elimination.cc index 53d1c5c6981..dde8baa5fa8 100644 --- a/mindspore/ccsrc/common/graph_kernel/depend_elimination.cc +++ b/mindspore/ccsrc/common/graph_kernel/depend_elimination.cc @@ -15,7 +15,7 @@ */ #include "common/graph_kernel/depend_elimination.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore::graphkernel { bool DependElimination::Run(const FuncGraphPtr &func_graph) { diff --git a/mindspore/ccsrc/common/graph_kernel/graph_kernel_cse.cc b/mindspore/ccsrc/common/graph_kernel/graph_kernel_cse.cc index f4b77673920..777821db46e 100644 --- a/mindspore/ccsrc/common/graph_kernel/graph_kernel_cse.cc +++ b/mindspore/ccsrc/common/graph_kernel/graph_kernel_cse.cc @@ -19,17 +19,16 @@ #include #include #include -#include #include -#include "backend/common/session/anf_runtime_algorithm.h" #include "runtime/device/kernel_info.h" #include "utils/ms_utils.h" +#include "include/common/utils/anfalgo.h" namespace mindspore::graphkernel { namespace { bool IsCNodePrimitveEqual(const CNodePtr &main, const CNodePtr &node, const std::vector &black_list) { - auto main_primitive = AnfAlgo::GetCNodePrimitive(main); - auto node_primitive = AnfAlgo::GetCNodePrimitive(node); + auto main_primitive = common::AnfAlgo::GetCNodePrimitive(main); + auto node_primitive = common::AnfAlgo::GetCNodePrimitive(node); if (main_primitive != nullptr && node_primitive != nullptr) { // Some ops such as Reshape is not real op, cse these type will not get gain. And for ops fusion, keep these op // alone can prevent some redundant output case (input -> reshape -> output). @@ -62,7 +61,7 @@ bool GraphKernelBackendCSE::CheckEqualKernelBuildInfo(const AnfNodePtr &main, co MS_EXCEPTION_IF_NULL(main); MS_EXCEPTION_IF_NULL(node); - if (!AnfAlgo::IsNodeInGraphKernel(main)) { + if (!common::AnfAlgo::IsNodeInGraphKernel(main)) { return BackendCSE::CheckEqualKernelBuildInfo(main, node); } @@ -98,7 +97,7 @@ bool GraphKernelBackendCSE::CheckEqualCnodeInputs(const AnfNodePtr &main, const auto c_node = node->cast(); MS_EXCEPTION_IF_NULL(c_node); - if (!AnfAlgo::IsNodeInGraphKernel(c_main)) { + if (!common::AnfAlgo::IsNodeInGraphKernel(c_main)) { return BackendCSE::CheckEqualCnodeInputs(main, node); } diff --git a/mindspore/ccsrc/common/graph_kernel/graph_kernel_helper.cc b/mindspore/ccsrc/common/graph_kernel/graph_kernel_helper.cc index b41d3edb7a6..8751c0f23e1 100644 --- a/mindspore/ccsrc/common/graph_kernel/graph_kernel_helper.cc +++ b/mindspore/ccsrc/common/graph_kernel/graph_kernel_helper.cc @@ -27,13 +27,14 @@ #include "kernel/akg/akg_kernel_json_decoder.h" #include "kernel/kernel.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "common/graph_kernel/adapter/fake_abstract_shape.h" #include "common/graph_kernel/core/graph_builder.h" #include "ir/func_graph_cloner.h" #include "ir/func_graph.h" -#include "pipeline/jit/parse/python_adapter.h" +#include "include/common/utils/python_adapter.h" #include "pipeline/jit/action.h" -#include "utils/context/graph_kernel_flags.h" +#include "include/common/utils/context/graph_kernel_flags.h" namespace mindspore::graphkernel { namespace { @@ -47,7 +48,7 @@ bool IsMakeTupleOut(const AnfNodePtr &out, AnfNodePtrList *real_outs) { return true; } - if (auto fg = AnfAlgo::GetCNodeFuncGraphPtr(out); fg != nullptr) { + if (auto fg = common::AnfAlgo::GetCNodeFuncGraphPtr(out); fg != nullptr) { auto fg_out = fg->output(); if (IsPrimitiveCNode(fg_out, prim::kPrimMakeTuple)) { auto inputs = fg_out->cast()->inputs(); @@ -75,7 +76,7 @@ bool GenJson(const AnfNodePtrList &op_nodes, const std::pair graph_output_format; std::vector graph_output_type; for (size_t i = 0; i < inputs.size(); ++i) { - auto kernel_with_index = AnfAlgo::VisitKernel(inputs[i], 0); + auto kernel_with_index = common::AnfAlgo::VisitKernel(inputs[i], 0); if (kernel_with_index.first->isa()) { auto tensor = GetValueNode(kernel_with_index.first); MS_EXCEPTION_IF_NULL(tensor); @@ -153,14 +154,14 @@ void SetNewKernelInfo(const AnfNodePtr &new_node, const FuncGraphPtr &fg, const AnfAlgo::SetSelectKernelBuildInfo(para_info_builder.Build(), fg->parameters()[i].get()); } auto new_outputs = outputs; - if (outputs.size() == 1 && AnfAlgo::IsGraphKernel(outputs[0])) { + if (outputs.size() == 1 && common::AnfAlgo::IsGraphKernel(outputs[0])) { std::vector real_outs; if (IsMakeTupleOut(outputs[0], &real_outs)) { new_outputs = real_outs; } } for (size_t i = 0; i < new_outputs.size(); ++i) { - auto kernel_with_index = AnfAlgo::VisitKernel(new_outputs[i], 0); + auto kernel_with_index = common::AnfAlgo::VisitKernel(new_outputs[i], 0); auto output_format = AnfAlgo::GetOutputFormat(kernel_with_index.first, kernel_with_index.second); auto output_type = AnfAlgo::GetOutputDeviceDataType(kernel_with_index.first, kernel_with_index.second); graph_output_format.push_back(output_format); @@ -185,13 +186,13 @@ bool AnfToJsonDesc(const AnfNodePtrList &nodes, const DumpOption &dump_option, n MS_LOG(ERROR) << "Input nodes is empty."; return false; } - bool has_graph_kernel = std::any_of(nodes.begin(), nodes.end(), AnfAlgo::IsGraphKernel); + bool has_graph_kernel = std::any_of(nodes.begin(), nodes.end(), common::AnfAlgo::IsGraphKernel); bool is_single_graph_kernel = has_graph_kernel && nodes.size() == 1; FuncGraphPtr fg; AnfNodePtrList op_nodes, inputs, outputs; if (is_single_graph_kernel) { - fg = AnfAlgo::GetCNodeFuncGraphPtr(nodes[0]); + fg = common::AnfAlgo::GetCNodeFuncGraphPtr(nodes[0]); kernel::GetValidKernelNodes(fg, &op_nodes, &inputs, &outputs); } else if (!has_graph_kernel) { std::tie(fg, inputs, outputs) = BuildGraphFromNodes(nodes); @@ -215,8 +216,8 @@ bool AnfToJsonDesc(const AnfNodePtrList &nodes, const DumpOption &dump_option, n FuncGraphPtr fg; - if (nodes.size() == 1 && AnfAlgo::IsGraphKernel(nodes[0])) { - fg = AnfAlgo::GetCNodeFuncGraphPtr(nodes[0]); + if (nodes.size() == 1 && common::AnfAlgo::IsGraphKernel(nodes[0])) { + fg = common::AnfAlgo::GetCNodeFuncGraphPtr(nodes[0]); } else { std::tie(fg, std::ignore, std::ignore) = BuildSingleGraphFromNodes(nodes); } @@ -385,7 +386,7 @@ CNodePtr CreateCNode(const std::vector &inputs, const FuncGraphPtr & std::vector input_formats; std::vector input_types; for (size_t i = 1; i < inputs.size(); ++i) { - auto kernel_with_index = AnfAlgo::VisitKernel(inputs[i], 0); + auto kernel_with_index = common::AnfAlgo::VisitKernel(inputs[i], 0); auto input_format = AnfAlgo::GetOutputFormat(kernel_with_index.first, kernel_with_index.second); input_formats.push_back(input_format); auto input_type = AnfAlgo::GetOutputDeviceDataType(kernel_with_index.first, kernel_with_index.second); @@ -416,12 +417,12 @@ void SetNodeAttrSafely(const std::string &key, const ValuePtr &value, const AnfN if (cnode == nullptr) { return; } - AnfNodePtrList new_inputs = {NewValueNode(AnfAlgo::GetCNodePrimitive(cnode)->Clone())}; + AnfNodePtrList new_inputs = {NewValueNode(common::AnfAlgo::GetCNodePrimitive(cnode)->Clone())}; auto inputs = cnode->inputs(); new_inputs.insert(new_inputs.end(), inputs.begin() + 1, inputs.end()); cnode->set_inputs(new_inputs); // Set attr secondly. - AnfAlgo::SetNodeAttr(key, value, node); + common::AnfAlgo::SetNodeAttr(key, value, node); } } // namespace mindspore::graphkernel diff --git a/mindspore/ccsrc/common/graph_kernel/graph_kernel_helper.h b/mindspore/ccsrc/common/graph_kernel/graph_kernel_helper.h index dc5b23adbe8..32acc9dc66f 100644 --- a/mindspore/ccsrc/common/graph_kernel/graph_kernel_helper.h +++ b/mindspore/ccsrc/common/graph_kernel/graph_kernel_helper.h @@ -28,6 +28,7 @@ #include "ir/func_graph.h" #include "ir/primitive.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/session/kernel_graph.h" #include "kernel/akg/akg_kernel_json_generator.h" #include diff --git a/mindspore/ccsrc/common/graph_kernel/graph_kernel_recompute.cc b/mindspore/ccsrc/common/graph_kernel/graph_kernel_recompute.cc index f90ec27115d..df6457398f6 100644 --- a/mindspore/ccsrc/common/graph_kernel/graph_kernel_recompute.cc +++ b/mindspore/ccsrc/common/graph_kernel/graph_kernel_recompute.cc @@ -147,7 +147,7 @@ OrderedSet GetLongTermNodes(const AnfNodePtrList &nodes, const AnfNo const FuncGraphManagerPtr &mng) { OrderedSet long_term_nodes; for (auto node : nodes) { - auto real_node = AnfAlgo::VisitKernelWithReturnType(node, 0).first; + auto real_node = common::AnfAlgo::VisitKernelWithReturnType(node, 0).first; // Parameter or value have long term tensors. if (!utils::isa(real_node)) { (void)long_term_nodes.insert(node); @@ -225,10 +225,10 @@ AnfNodePtrList AutoRecompute::Filter(const AnfNodePtr &source_node, const AnfNod AnfNodePtrList check_inputs; if (IsPrimitiveCNode(end_node->cast()->input(IntToSize(edge_pos)), prim::kPrimTupleGetItem)) { auto out_index = GetSourceLinkOutPos(end_node, edge_pos); - auto sub_graph = AnfAlgo::GetCNodeFuncGraphPtr(source_node); + auto sub_graph = common::AnfAlgo::GetCNodeFuncGraphPtr(source_node); auto out = sub_graph->output(); if (!IsPrimitiveCNode(out, prim::kPrimMakeTuple)) { - MS_LOG(EXCEPTION) << "Expect MakeTuple node, but got " << AnfAlgo::GetCNodeName(out); + MS_LOG(EXCEPTION) << "Expect MakeTuple node, but got " << common::AnfAlgo::GetCNodeName(out); } // Find subgraph's input according to edge node. @@ -345,7 +345,7 @@ OutPosLinkList AutoRecompute::JudegeTargetAndCaptureSource(const AnfNodePtr &nod // Direct users include long term users and short term users. // If the short term user is graph kernel composite node, it may be absorb and reduce the local peak memory. for (auto user : direct_users) { - if (long_term_users.count(user) == 0 && AnfAlgo::IsGraphKernel(user)) { + if (long_term_users.count(user) == 0 && common::AnfAlgo::IsGraphKernel(user)) { (void)target_link_infos.emplace_back(user, user_edge_pos[user], EdgeLifeTimeType::ShortTerm); } } @@ -441,7 +441,7 @@ void AutoRecompute::FindCandidates(const FuncGraphPtr &func_graph) { // 2. Memory variety between split out and origin more than threshold: // `Size(gs_direct_outs_to_gt) - filter(gs_inputs, its) > threshold`. for (auto node : topo_nodes) { - if (!AnfAlgo::IsGraphKernel(node)) { + if (!common::AnfAlgo::IsGraphKernel(node)) { continue; } auto target_graphs = JudegeTargetAndCaptureSource(node, mng); @@ -544,7 +544,7 @@ void AutoRecompute::RecomputeCandidatesLog(const std::vector &candida std::pair GraphKernelRecompute::CloneGraph(const CNodePtr &source_graph, const AnfNodePtrList &recompute_edges) { MS_EXCEPTION_IF_NULL(source_graph); - auto gs = AnfAlgo::GetCNodeFuncGraphPtr(source_graph); + auto gs = common::AnfAlgo::GetCNodeFuncGraphPtr(source_graph); MS_EXCEPTION_IF_NULL(gs); AnfNodePtrList inputs(source_graph->inputs().begin() + 1, source_graph->inputs().end()); auto new_funcgraph = BasicClone(gs); @@ -576,7 +576,7 @@ void GraphKernelRecompute::LinkIntoTargetFuncGraph( const Candidate &candidate, const FuncGraphPtr &cloned_func, const AnfNodePtrList &cloned_inputs, const std::function(const Candidate &, const AnfNodePtr &)> &edge_match_func) { auto cloned_nodes = TopoSort(cloned_func->get_return()); - auto gt = AnfAlgo::GetCNodeFuncGraphPtr(candidate.target_graph); + auto gt = common::AnfAlgo::GetCNodeFuncGraphPtr(candidate.target_graph); MS_EXCEPTION_IF_NULL(gt); auto mng = gt->manager(); if (mng == nullptr) { @@ -639,7 +639,7 @@ void GraphKernelRecompute::Process(const Candidate &candidate) { std::function(const Candidate &, const AnfNodePtr &)> edge_match_func; if (candidate.recompute_edges.empty()) { // single output, clone the whole source_graph. - auto gs = AnfAlgo::GetCNodeFuncGraphPtr(candidate.source_graph); + auto gs = common::AnfAlgo::GetCNodeFuncGraphPtr(candidate.source_graph); MS_EXCEPTION_IF_NULL(gs); new_funcgraph = BasicClone(gs); auto source_cnode = candidate.source_graph->cast(); @@ -669,7 +669,7 @@ void GraphKernelRecompute::Process(const Candidate &candidate) { new_funcgraph->set_manager(mng); } - if (AnfAlgo::IsGraphKernel(candidate.target_graph)) { + if (common::AnfAlgo::IsGraphKernel(candidate.target_graph)) { // the target graph is a GraphKernel, push the new_funcgraph into the target graph. LinkIntoTargetFuncGraph(candidate, new_funcgraph, inputs, edge_match_func); } else { @@ -690,7 +690,7 @@ bool GraphKernelRecompute::Run(const FuncGraphPtr &func_graph) { auto mng = func_graph->manager(); MS_EXCEPTION_IF_NULL(mng); for (auto &c : candidates_) { - if (!AnfAlgo::IsGraphKernel(c.target_graph)) { + if (!common::AnfAlgo::IsGraphKernel(c.target_graph)) { continue; } std::ostringstream oss; diff --git a/mindspore/ccsrc/common/graph_kernel/graph_kernel_recompute.h b/mindspore/ccsrc/common/graph_kernel/graph_kernel_recompute.h index b3e014024c0..4b44405f003 100644 --- a/mindspore/ccsrc/common/graph_kernel/graph_kernel_recompute.h +++ b/mindspore/ccsrc/common/graph_kernel/graph_kernel_recompute.h @@ -22,7 +22,7 @@ #include #include #include -#include "utils/context/graph_kernel_flags.h" +#include "include/common/utils/context/graph_kernel_flags.h" #include "backend/common/optimizer/pass.h" #include "ir/func_graph.h" diff --git a/mindspore/ccsrc/common/graph_kernel/insert_pad.cc b/mindspore/ccsrc/common/graph_kernel/insert_pad.cc index b4d8783fe81..7e9afba9dfd 100644 --- a/mindspore/ccsrc/common/graph_kernel/insert_pad.cc +++ b/mindspore/ccsrc/common/graph_kernel/insert_pad.cc @@ -18,7 +18,7 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" -#include "kernel/common_utils.h" +#include "include/common/utils/anfalgo.h" #include "common/graph_kernel/graph_kernel_helper.h" namespace mindspore { @@ -108,7 +108,7 @@ bool IsAkgMatMul(size_t K, size_t M, size_t N) { // Return ture if (K, M, N) need pad std::tuple NeedPad(const CNodePtr &matmul, vec *pad_shape_a, vec *pad_shape_b, vec *unpad_shape, vec *tail_shape_a, vec *tail_shape_b, vec *tail_shape_unpad) { - auto mm_attrs = AnfAlgo::GetCNodePrimitive(matmul)->attrs(); + auto mm_attrs = common::AnfAlgo::GetCNodePrimitive(matmul)->attrs(); if (mm_attrs.count("transpose_a") == 0 || mm_attrs.count("transpose_b") == 0) { MS_LOG(ERROR) << "attrs transpose_a and transpose_b need to be set in node " << matmul->fullname_with_scope(); return std::tuple(false, false, false); @@ -164,7 +164,7 @@ void InsertPad(const CNodePtr &matmul, const FuncGraphPtr &func_graph, const Fun SetNodeAttrSafely("head", MakeValue(head), pad_cnode); SetNodeAttrSafely("tail", MakeValue(tail), pad_cnode); SetNodeAttrSafely("pad_val", MakeValue(std::make_shared(0)), pad_cnode); - std::vector pad_type = {AnfAlgo::GetPrevNodeOutputInferDataType(matmul, 0)}; + std::vector pad_type = {common::AnfAlgo::GetPrevNodeOutputInferDataType(matmul, 0)}; ShapeVector abs_shape; (void)abs_shape.insert(abs_shape.begin(), pad_shape.begin(), pad_shape.end()); @@ -194,7 +194,7 @@ void InsertUnpad(const CNodePtr &matmul, const FuncGraphPtr &func_graph, const F ShapeVector tail; (void)tail.insert(tail.begin(), tail_shape.begin(), tail_shape.end()); SetNodeAttrSafely("tail", MakeValue(tail), unpad_cnode); - std::vector unpad_type = {AnfAlgo::GetOutputInferDataType(matmul, 0)}; + std::vector unpad_type = {common::AnfAlgo::GetOutputInferDataType(matmul, 0)}; ShapeVector abs_shape; (void)abs_shape.insert(abs_shape.begin(), unpad_shape.begin(), unpad_shape.end()); @@ -221,7 +221,7 @@ void UpdateMatmulInfo(const AnfNodePtr &matmul_node, const vec &unpad_shape, con abs_shape.push_back(unpad_shape[i] + tail_shape[i]); } auto abs_shape_ptr = std::make_shared(abstract::Shape(abs_shape)); - TypeId abs_type = AnfAlgo::GetOutputInferDataType(matmul_node, 0); + TypeId abs_type = common::AnfAlgo::GetOutputInferDataType(matmul_node, 0); auto abstract = std::make_shared(TypeIdToType(abs_type), abs_shape_ptr); matmul_node->set_abstract(abstract); @@ -240,7 +240,7 @@ bool InsertPadUnpad(const FuncGraphPtr &func_graph) { auto todos = TopoSort(func_graph->get_return()); bool changed = false; for (const auto &n : todos) { - if (!AnfAlgo::CheckPrimitiveType(n, prim::kPrimMatMul)) continue; + if (!common::AnfAlgo::CheckPrimitiveType(n, prim::kPrimMatMul)) continue; auto mm_cnode = n->cast(); vec pad_shape_a, pad_shape_b, tail_shape_a, tail_shape_b, tail_shape_unpad, unpad_shape; bool pad_K{false}, pad_M{false}, pad_N{false}; @@ -283,8 +283,8 @@ bool InsertPadOps::Run(const FuncGraphPtr &func_graph) { auto changed = false; auto nodes = TopoSort(func_graph->get_return()); for (auto node : nodes) { - if (!AnfAlgo::IsGraphKernel(node)) continue; - auto graph_kernel_fg = AnfAlgo::GetCNodeFuncGraphPtr(node); + if (!common::AnfAlgo::IsGraphKernel(node)) continue; + auto graph_kernel_fg = common::AnfAlgo::GetCNodeFuncGraphPtr(node); MS_EXCEPTION_IF_NULL(graph_kernel_fg); changed = InsertPadUnpad(graph_kernel_fg) || changed; } diff --git a/mindspore/ccsrc/common/graph_kernel/lite_adapter/callback_impl.cc b/mindspore/ccsrc/common/graph_kernel/lite_adapter/callback_impl.cc index df5cea06f00..1d9750d52a4 100644 --- a/mindspore/ccsrc/common/graph_kernel/lite_adapter/callback_impl.cc +++ b/mindspore/ccsrc/common/graph_kernel/lite_adapter/callback_impl.cc @@ -23,7 +23,7 @@ #include "base/core_ops.h" #include "ir/dtype.h" #include "utils/anf_utils.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore::graphkernel { // register the callback object diff --git a/mindspore/ccsrc/common/graph_kernel/lite_adapter/graph_kernel_optimization.cc b/mindspore/ccsrc/common/graph_kernel/lite_adapter/graph_kernel_optimization.cc index 45067a449ca..dbdfc3386bb 100644 --- a/mindspore/ccsrc/common/graph_kernel/lite_adapter/graph_kernel_optimization.cc +++ b/mindspore/ccsrc/common/graph_kernel/lite_adapter/graph_kernel_optimization.cc @@ -20,7 +20,7 @@ #include #include "ir/func_graph.h" -#include "utils/context/graph_kernel_flags.h" +#include "include/common/utils/context/graph_kernel_flags.h" #include "backend/common/pass/getitem_tuple.h" #include "common/graph_kernel/core/graph_kernel_cluster.h" #include "common/graph_kernel/core/graph_kernel_expander.h" diff --git a/mindspore/ccsrc/common/graph_kernel/lite_adapter/graph_kernel_pass_manager.h b/mindspore/ccsrc/common/graph_kernel/lite_adapter/graph_kernel_pass_manager.h index 1c6b623ca54..fe123c466ab 100644 --- a/mindspore/ccsrc/common/graph_kernel/lite_adapter/graph_kernel_pass_manager.h +++ b/mindspore/ccsrc/common/graph_kernel/lite_adapter/graph_kernel_pass_manager.h @@ -21,7 +21,7 @@ #include #include -#include "utils/context/graph_kernel_flags.h" +#include "include/common/utils/context/graph_kernel_flags.h" #include "backend/common/optimizer/pass_manager.h" namespace mindspore::graphkernel { diff --git a/mindspore/ccsrc/common/graph_kernel/model/node.h b/mindspore/ccsrc/common/graph_kernel/model/node.h index bc354c0a687..6f19bbe0b41 100644 --- a/mindspore/ccsrc/common/graph_kernel/model/node.h +++ b/mindspore/ccsrc/common/graph_kernel/model/node.h @@ -26,7 +26,7 @@ #include "ir/tensor.h" #include "utils/hash_map.h" #include "utils/shape_utils.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore::graphkernel::inner { enum class NType { diff --git a/mindspore/ccsrc/common/graph_kernel/optimize_assign.cc b/mindspore/ccsrc/common/graph_kernel/optimize_assign.cc index 7ab561a9e95..5840d668f39 100644 --- a/mindspore/ccsrc/common/graph_kernel/optimize_assign.cc +++ b/mindspore/ccsrc/common/graph_kernel/optimize_assign.cc @@ -23,6 +23,7 @@ #include "base/core_ops.h" #include "backend/common/optimizer/helper.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "common/graph_kernel/graph_kernel_helper.h" namespace mindspore::graphkernel { @@ -37,7 +38,7 @@ namespace { */ std::map FindAssignAndOutputVal(const CNodePtr &fg_cnode) { // Check output includes assign - auto func_graph = AnfAlgo::GetCNodeFuncGraphPtr(fg_cnode); + auto func_graph = common::AnfAlgo::GetCNodeFuncGraphPtr(fg_cnode); MS_EXCEPTION_IF_NULL(func_graph); auto out_cnode = func_graph->output()->cast(); MS_EXCEPTION_IF_NULL(out_cnode); @@ -73,7 +74,7 @@ std::map FindAssignAndOutputVal(const CNodePtr &fg_cnode) { } bool HasPathToParamUser(const AnfNodePtr &gk_node, const AnfNodePtr ¶m_user, const AnfNodePtr &getitem) { - auto mng = AnfAlgo::GetCNodeFuncGraphPtr(gk_node)->manager(); + auto mng = common::AnfAlgo::GetCNodeFuncGraphPtr(gk_node)->manager(); MS_EXCEPTION_IF_NULL(mng); bool result = false; auto IncludeUser = [&result, &gk_node, &getitem](const AnfNodePtr &node) { @@ -145,7 +146,7 @@ bool RepalceOutputByParameter(const FuncGraphPtr &func_graph) { bool changed = false; for (const auto &n : todos) { - if (!AnfAlgo::IsGraphKernel(n)) continue; + if (!common::AnfAlgo::IsGraphKernel(n)) continue; auto cnode = n->cast(); auto replaceable_nodes = FindAssignAndOutputVal(cnode); if (replaceable_nodes.empty()) continue; @@ -163,7 +164,7 @@ bool ReplaceAssignByInplaceAssignInGraphkernel(const FuncGraphPtr &func_graph) { auto todos = TopoSort(func_graph->get_return()); bool changed = false; for (const auto &n : todos) { - if (!AnfAlgo::CheckPrimitiveType(n, prim::kPrimAssign)) continue; + if (!common::AnfAlgo::CheckPrimitiveType(n, prim::kPrimAssign)) continue; changed = true; auto cnode = n->cast(); AnfNodePtrList inputs = {NewValueNode(prim::kPrimInplaceAssign), cnode->input(1), cnode->input(2), cnode->input(2)}; @@ -192,8 +193,8 @@ bool RepalceAssignByInplaceAssign(const FuncGraphPtr &func_graph) { auto changed = false; for (const auto &n : todos) { - if (!AnfAlgo::IsGraphKernel(n)) continue; - auto graph_kernel_fg = AnfAlgo::GetCNodeFuncGraphPtr(n); + if (!common::AnfAlgo::IsGraphKernel(n)) continue; + auto graph_kernel_fg = common::AnfAlgo::GetCNodeFuncGraphPtr(n); MS_EXCEPTION_IF_NULL(graph_kernel_fg); changed = ReplaceAssignByInplaceAssignInGraphkernel(graph_kernel_fg) || changed; } diff --git a/mindspore/ccsrc/common/graph_kernel/parallel_cost_model.cc b/mindspore/ccsrc/common/graph_kernel/parallel_cost_model.cc index 8c588f20bbc..63bc8f1f4d7 100644 --- a/mindspore/ccsrc/common/graph_kernel/parallel_cost_model.cc +++ b/mindspore/ccsrc/common/graph_kernel/parallel_cost_model.cc @@ -19,7 +19,7 @@ #include #include "kernel/akg/akg_kernel_json_generator.h" #include "common/graph_kernel/graph_kernel_helper.h" -#include "pipeline/jit/parse/python_adapter.h" +#include "include/common/utils/python_adapter.h" namespace mindspore::graphkernel { std::string CommonDimInfo::ToString() { @@ -37,7 +37,7 @@ int ParallelCostModel::GetNodeCalAmount(const AnfNodePtr &node) const { } auto json_desc_str = json_desc.dump(); - auto ret = parse::python_adapter::CallPyFn(kGraphKernelModule, kGraphKernelGetNodeCalAmount, json_desc_str); + auto ret = python_adapter::CallPyFn(kGraphKernelModule, kGraphKernelGetNodeCalAmount, json_desc_str); if (py::isinstance(ret)) { MS_LOG(EXCEPTION) << "CallPyFn: [" << kGraphKernelSplitFunc << "] return invalid result. input json:\n" << json_desc_str; @@ -57,7 +57,7 @@ std::tuple, int, FusionInfoPtr> ParallelCostModel::CalFu } auto json_desc_str = json_desc.dump(); - auto ret = parse::python_adapter::CallPyFn(kGraphKernelModule, kGraphKernelEstimateOps, json_desc_str); + auto ret = python_adapter::CallPyFn(kGraphKernelModule, kGraphKernelEstimateOps, json_desc_str); if (py::isinstance(ret)) { MS_LOG(EXCEPTION) << "CallPyFn: [" << kGraphKernelSplitFunc << "] return invalid result. input json:\n" << json_desc_str; diff --git a/mindspore/ccsrc/common/graph_kernel/parallel_cost_model.h b/mindspore/ccsrc/common/graph_kernel/parallel_cost_model.h index 90f85d36d6d..39b5299f7a3 100644 --- a/mindspore/ccsrc/common/graph_kernel/parallel_cost_model.h +++ b/mindspore/ccsrc/common/graph_kernel/parallel_cost_model.h @@ -26,10 +26,11 @@ #include "base/base.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/optimizer/optimizer.h" #include "common/graph_kernel/parallel_cost_model.h" #include "backend/common/session/kernel_graph.h" -#include "pipeline/jit/parse/python_adapter.h" +#include "include/common/utils/python_adapter.h" #include "utils/ms_context.h" namespace mindspore::graphkernel { diff --git a/mindspore/ccsrc/common/graph_kernel/parallel_fusion.cc b/mindspore/ccsrc/common/graph_kernel/parallel_fusion.cc index 5916df14198..f3c3a342d49 100644 --- a/mindspore/ccsrc/common/graph_kernel/parallel_fusion.cc +++ b/mindspore/ccsrc/common/graph_kernel/parallel_fusion.cc @@ -21,7 +21,7 @@ #include #include #include -#include "utils/context/graph_kernel_flags.h" +#include "include/common/utils/context/graph_kernel_flags.h" #include "kernel/kernel.h" #include "common/graph_kernel/graph_kernel_helper.h" #include "kernel/common_utils.h" @@ -270,7 +270,7 @@ std::tuple GetIn bool WhiteOpsFilter(const AnfNodePtr &node) { std::vector whiteable_ops = {}; // Not special for now. - return session::AnfRuntimeAlgorithm::IsGraphKernel(node) || IsOneOf(node, whiteable_ops); + return common::AnfAlgo::IsGraphKernel(node) || IsOneOf(node, whiteable_ops); } bool Unfavorable(const AnfNodePtr &node) { @@ -279,7 +279,7 @@ bool Unfavorable(const AnfNodePtr &node) { MS_EXCEPTION_IF_NULL(cnode); auto input = cnode->input(kAnfPrimitiveIndex); if (!IsValueNode(input)) { - return AnfAlgo::HasNodeAttr(kAttrStitch, cnode); + return common::AnfAlgo::HasNodeAttr(kAttrStitch, cnode); } auto func_graph = GetValueNode(input); @@ -289,7 +289,7 @@ bool Unfavorable(const AnfNodePtr &node) { for (auto sub_node : sub_nodes) { auto sub_cnode = sub_node->cast(); MS_EXCEPTION_IF_NULL(sub_cnode); - if (AnfAlgo::HasNodeAttr(kAttrStitch, sub_cnode)) { + if (common::AnfAlgo::HasNodeAttr(kAttrStitch, sub_cnode)) { return true; } } @@ -385,7 +385,7 @@ std::string DumpNode(const AnfNodePtr &node) { auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); std::stringstream buf; - buf << (AnfAlgo::IsGraphKernel(cnode) ? "[graph]" : "[primitive]") << cnode->fullname_with_scope() << "|" + buf << (common::AnfAlgo::IsGraphKernel(cnode) ? "[graph]" : "[primitive]") << cnode->fullname_with_scope() << "|" << cnode->ToString(); return buf.str(); } @@ -431,8 +431,8 @@ inline bool ParameterLimit(const AnfNodePtrList &nodes) { // The number of inputs and outputs for a valid kernel should be less than cuda's limit. size_t para_count = 0; for (const auto &node : nodes) { - para_count += AnfAlgo::GetInputTensorNum(node); - para_count += AnfAlgo::GetOutputTensorNum(node); + para_count += common::AnfAlgo::GetInputTensorNum(node); + para_count += common::AnfAlgo::GetOutputTensorNum(node); } res = para_count <= CUDA_PARA_LIMIT; } break; @@ -709,7 +709,7 @@ void ParallelOpFusion::SetFusedParallelOpAttrToReturnNode(const ParallelInfo &pa for (size_t i = 0; i < parallel_info.GetSize(); ++i) { const auto &fuse_nodes = parallel_info.nodes(); std::vector info = {i, std::dynamic_pointer_cast(parallel_info.dims()[i])->dim_info()}; - if (!AnfAlgo::IsGraphKernel(fuse_nodes[i])) { + if (!common::AnfAlgo::IsGraphKernel(fuse_nodes[i])) { attach_node = fuse_nodes[i]; SetNodeAttrSafely(kAttrParallelDimInfo, MakeValue>(info), fuse_nodes[i]); } else { @@ -734,11 +734,11 @@ void ParallelOpFusion::SetFusedParallelOpAttrToReturnNode(const ParallelInfo &pa void ParallelOpFusion::SetFusionInfoAttrToNode(const AnfNodePtr &node, const ParallelInfo ¶llel_info) { auto fusion_type = parallel_info.fusion_info()->FusionType(); - AnfAlgo::SetNodeAttr(kAttrParallelFusionType, MakeValue(fusion_type), node); + common::AnfAlgo::SetNodeAttr(kAttrParallelFusionType, MakeValue(fusion_type), node); if (parallel_info.fusion_info()->ExistTypeInfo()) { if (auto pipeline_fusion = std::dynamic_pointer_cast(parallel_info.fusion_info())) { - AnfAlgo::SetNodeAttr(kAttrParallelTypeInfo, - MakeValue>>(pipeline_fusion->PipelineIds()), node); + common::AnfAlgo::SetNodeAttr(kAttrParallelTypeInfo, + MakeValue>>(pipeline_fusion->PipelineIds()), node); } } } @@ -755,7 +755,7 @@ bool ParallelOpFusion::CreateParallelOpSubGraphs(const std::vector changed = true; SetFusedParallelOpAttrToReturnNode(parallel_infos[i]); auto sg_node = ReplaceNodesWithGraphKernelNode(fuse_nodes, kernel_graph, "parallel"); - AnfAlgo::SetNodeAttr(kAttrCompositeType, MakeValue("parallel_fusion"), sg_node); + common::AnfAlgo::SetNodeAttr(kAttrCompositeType, MakeValue("parallel_fusion"), sg_node); DumpParallelFusionDetail(fuse_nodes, sg_node); } diff --git a/mindspore/ccsrc/common/graph_kernel/parallel_fusion.h b/mindspore/ccsrc/common/graph_kernel/parallel_fusion.h index 76e4bee023b..9324c04516d 100644 --- a/mindspore/ccsrc/common/graph_kernel/parallel_fusion.h +++ b/mindspore/ccsrc/common/graph_kernel/parallel_fusion.h @@ -27,6 +27,7 @@ #include "base/base.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/optimizer/optimizer.h" #include "common/graph_kernel/parallel_cost_model.h" #include "backend/common/session/kernel_graph.h" diff --git a/mindspore/ccsrc/common/graph_kernel/raise_reduction_precision.cc b/mindspore/ccsrc/common/graph_kernel/raise_reduction_precision.cc index 0444a7bee5d..bdd3664abcc 100644 --- a/mindspore/ccsrc/common/graph_kernel/raise_reduction_precision.cc +++ b/mindspore/ccsrc/common/graph_kernel/raise_reduction_precision.cc @@ -17,11 +17,12 @@ #include #include "base/core_ops.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/optimizer/helper.h" #include "common/graph_kernel/core/graph_kernel_utils.h" #include "common/graph_kernel/graph_kernel_helper.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/tensor.h" #include "kernel/kernel_build_info.h" #include "kernel/common_utils.h" @@ -39,7 +40,7 @@ AnfNodePtr RaiseReductionPrecision::CreateCast(const AnfNodePtr &input, const Ty AnfNodePtrList inputs = {NewValueNode(prim::kPrimCast), input}; auto cnode = CreateCNode(inputs, func_graph, {.format = format, .shape = GetShape(input), .type = dst_type}); SetNodeAttrSafely(kAttrDstType, dst_type, cnode); - AnfAlgo::SetNodeAttr(kIsBackendCast, MakeValue(true), cnode); + common::AnfAlgo::SetNodeAttr(kIsBackendCast, MakeValue(true), cnode); return cnode; } @@ -115,8 +116,8 @@ bool RaiseReductionPrecision::Run(const FuncGraphPtr &func_graph) { bool changed = false; auto todos = TopoSort(func_graph->get_return()); for (const auto &node : todos) { - if (AnfAlgo::IsGraphKernel(node)) { - auto sub_func_graph = AnfAlgo::GetCNodeFuncGraphPtr(node); + if (common::AnfAlgo::IsGraphKernel(node)) { + auto sub_func_graph = common::AnfAlgo::GetCNodeFuncGraphPtr(node); MS_ERROR_IF_NULL(sub_func_graph); changed = Process(sub_func_graph) || changed; } diff --git a/mindspore/ccsrc/common/graph_kernel/reduce_fake_out_mem.cc b/mindspore/ccsrc/common/graph_kernel/reduce_fake_out_mem.cc index c13274de319..09380072804 100644 --- a/mindspore/ccsrc/common/graph_kernel/reduce_fake_out_mem.cc +++ b/mindspore/ccsrc/common/graph_kernel/reduce_fake_out_mem.cc @@ -59,10 +59,10 @@ bool ReduceFakeOutMem::Run(const FuncGraphPtr &func_graph) { auto todos = TopoSort(func_graph->get_return()); for (auto node : todos) { - if (!AnfAlgo::IsGraphKernel(node)) { + if (!common::AnfAlgo::IsGraphKernel(node)) { continue; } - auto sub_graph = AnfAlgo::GetCNodeFuncGraphPtr(node); + auto sub_graph = common::AnfAlgo::GetCNodeFuncGraphPtr(node); MS_EXCEPTION_IF_NULL(sub_graph); AnfNodePtrList output_list; @@ -72,7 +72,8 @@ bool ReduceFakeOutMem::Run(const FuncGraphPtr &func_graph) { auto &out = output_list[i]; auto out_cnode = out->cast(); MS_EXCEPTION_IF_NULL(out_cnode); - if (AnfAlgo::HasNodeAttr(kFakeOut, out_cnode) && AnfAlgo::GetNodeAttr(out_cnode, kFakeOut)) { + if (common::AnfAlgo::HasNodeAttr(kFakeOut, out_cnode) && + common::AnfAlgo::GetNodeAttr(out_cnode, kFakeOut)) { (void)fake_real_indices.insert(i); } } diff --git a/mindspore/ccsrc/common/graph_kernel/reduce_fake_out_mem.h b/mindspore/ccsrc/common/graph_kernel/reduce_fake_out_mem.h index 0ee2e9b3152..5c45fdff3e1 100644 --- a/mindspore/ccsrc/common/graph_kernel/reduce_fake_out_mem.h +++ b/mindspore/ccsrc/common/graph_kernel/reduce_fake_out_mem.h @@ -18,7 +18,7 @@ #include #include -#include "utils/context/graph_kernel_flags.h" +#include "include/common/utils/context/graph_kernel_flags.h" #include "backend/common/optimizer/pass.h" #include "common/graph_kernel/add_atomic_clean.h" #include "ir/func_graph.h" diff --git a/mindspore/ccsrc/common/graph_kernel/reorder_ops.cc b/mindspore/ccsrc/common/graph_kernel/reorder_ops.cc index b50ff54c813..1a50772f0ed 100644 --- a/mindspore/ccsrc/common/graph_kernel/reorder_ops.cc +++ b/mindspore/ccsrc/common/graph_kernel/reorder_ops.cc @@ -20,9 +20,10 @@ #include #include "utils/hash_set.h" #include "base/core_ops.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/log_adapter.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "debug/anf_ir_dump.h" #include "common/graph_kernel/core/graph_kernel_utils.h" @@ -43,7 +44,7 @@ enum CastType { CAST_UP, CAST_DOWN, CAST_OTHER }; CastType GetCastType(const CNodePtr &node) { MS_EXCEPTION_IF_NULL(node); if (!IsPrimitiveCNode(node, prim::kPrimCast)) { - MS_LOG(EXCEPTION) << "Expect Cast node, but got " << AnfAlgo::GetCNodeName(node); + MS_LOG(EXCEPTION) << "Expect Cast node, but got " << common::AnfAlgo::GetCNodeName(node); } TypeId input_type = AnfAlgo::GetInputDeviceDataType(node, 0); TypeId output_type = AnfAlgo::GetOutputDeviceDataType(node, 0); @@ -89,8 +90,8 @@ void SetNodeInfo(const CNodePtr &orig_node, const CNodePtr &new_node, const Node MS_EXCEPTION_IF_NULL(orig_node); MS_EXCEPTION_IF_NULL(new_node); - auto node_name = AnfAlgo::GetCNodeName(new_node); - auto orig_node_name = AnfAlgo::GetCNodeName(orig_node); + auto node_name = common::AnfAlgo::GetCNodeName(new_node); + auto orig_node_name = common::AnfAlgo::GetCNodeName(orig_node); if (orig_node_name != node_name) { MS_LOG(EXCEPTION) << "Can not process on different nodes " << orig_node_name << " and " << node_name; } @@ -100,7 +101,7 @@ void SetNodeInfo(const CNodePtr &orig_node, const CNodePtr &new_node, const Node MS_LOG(EXCEPTION) << "Can not set empty output type of new node from " << orig_node->fullname_with_scope(); } if (node_name == "Cast") { - auto node_input = AnfAlgo::GetInputNode(new_node, 0); + auto node_input = common::AnfAlgo::GetInputNode(new_node, 0); MS_EXCEPTION_IF_NULL(node_input); MS_EXCEPTION_IF_NULL(node_input->abstract()); new_abstract = std::make_shared(TypeIdToType(node_io_info.outputs_type[0]), @@ -114,7 +115,7 @@ void SetNodeInfo(const CNodePtr &orig_node, const CNodePtr &new_node, const Node // Set abstract info new_node->set_abstract(new_abstract); // Set attrs - AnfAlgo::CopyNodeAttrs(orig_node, new_node); + common::AnfAlgo::CopyNodeAttrs(orig_node, new_node); // Set kernel build info new_node->set_kernel_info(std::make_shared()); kernel::KernelBuildInfo::KernelBuildInfoBuilder info_builder; @@ -213,7 +214,7 @@ bool ReorderOps::ReorderTypeInsensitiveCastDown(const FuncGraphPtr &func_graph, auto small_type = AnfAlgo::GetOutputDeviceDataType(node, 0); auto pattern_output_format = AnfAlgo::GetOutputFormat(node, 0); - auto node_input = AnfAlgo::GetInputNode(node, 0); + auto node_input = common::AnfAlgo::GetInputNode(node, 0); auto type_insens_node = node_input->cast(); // Limitation: // Find type insensitive node before cast node. @@ -232,7 +233,7 @@ bool ReorderOps::ReorderTypeInsensitiveCastDown(const FuncGraphPtr &func_graph, std::vector new_cast_nodes; for (const auto &index : op_input_indexes) { auto new_cast_node = func_graph->NewCNode({NewValueNode(std::make_shared(prim::kPrimCast->name())), - AnfAlgo::GetInputNode(type_insens_node, index)}); + common::AnfAlgo::GetInputNode(type_insens_node, index)}); NodeIOInfo cast_io_info; cast_io_info.inputs_format.push_back(AnfAlgo::GetInputFormat(type_insens_node, index)); cast_io_info.outputs_format = cast_io_info.inputs_format; @@ -270,13 +271,13 @@ bool ReorderOps::ReorderCastUpTypeInsensitive(const FuncGraphPtr &func_graph, co std::vector cast_input_nodes; auto op_input_indexes = GetOpDataInputIndexes(node); for (const auto &index : op_input_indexes) { - auto node_input = AnfAlgo::GetInputNode(node, index); + auto node_input = common::AnfAlgo::GetInputNode(node, index); auto cast_node = node_input->cast(); if (cast_node != nullptr && IsPrimitiveCNode(cast_node, prim::kPrimCast) && GetCastType(cast_node) == CAST_UP && AnfAlgo::GetInputFormat(node, 0) == AnfAlgo::GetOutputFormat(node, 0) && mng->node_users()[cast_node].size() == 1) { cast_nodes.push_back(cast_node); - cast_input_nodes.push_back(AnfAlgo::GetInputNode(cast_node, 0)); + cast_input_nodes.push_back(common::AnfAlgo::GetInputNode(cast_node, 0)); } } if (cast_nodes.empty() || cast_nodes.size() != op_input_indexes.size()) { @@ -356,8 +357,8 @@ bool ReorderOps::Run(const FuncGraphPtr &func_graph) { continue; } - if (AnfAlgo::IsGraphKernel(node)) { - auto sub_func_graph = AnfAlgo::GetCNodeFuncGraphPtr(node); + if (common::AnfAlgo::IsGraphKernel(node)) { + auto sub_func_graph = common::AnfAlgo::GetCNodeFuncGraphPtr(node); bool need_traverse = true; while (need_traverse) { need_traverse = ReorderCastTypeInsensitive(sub_func_graph); diff --git a/mindspore/ccsrc/common/graph_kernel/rewrite_output_shape.cc b/mindspore/ccsrc/common/graph_kernel/rewrite_output_shape.cc index 0e5dfd1f6a0..bfd6fc62d14 100644 --- a/mindspore/ccsrc/common/graph_kernel/rewrite_output_shape.cc +++ b/mindspore/ccsrc/common/graph_kernel/rewrite_output_shape.cc @@ -20,6 +20,7 @@ #include "ir/scalar.h" #include "common/graph_kernel/graph_kernel_helper.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore::graphkernel { bool SaveOutputShape::Run(const FuncGraphPtr &func_graph) { @@ -101,7 +102,7 @@ bool RewriteOutputShape::Run(const FuncGraphPtr &func_graph) { Process(output->input(i), 0, abs_tuple->elements()[i - 1]); } // remove the MakeTuple node if it was added by SaveOutputShape - auto prim = AnfAlgo::GetCNodePrimitive(output); + auto prim = common::AnfAlgo::GetCNodePrimitive(output); MS_EXCEPTION_IF_NULL(prim); if (prim->HasAttr("graph_kernel")) { auto mng = func_graph->manager(); diff --git a/mindspore/ccsrc/common/graph_kernel/split_umonad.cc b/mindspore/ccsrc/common/graph_kernel/split_umonad.cc index c617f15e14f..2ab5119354c 100644 --- a/mindspore/ccsrc/common/graph_kernel/split_umonad.cc +++ b/mindspore/ccsrc/common/graph_kernel/split_umonad.cc @@ -21,7 +21,7 @@ #include #include "base/core_ops.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "runtime/device/kernel_info.h" #include "backend/common/optimizer/helper.h" diff --git a/mindspore/ccsrc/common/graph_kernel/substitute_dropout.cc b/mindspore/ccsrc/common/graph_kernel/substitute_dropout.cc index 048918feeeb..a7c920c502a 100644 --- a/mindspore/ccsrc/common/graph_kernel/substitute_dropout.cc +++ b/mindspore/ccsrc/common/graph_kernel/substitute_dropout.cc @@ -21,10 +21,11 @@ #include #include "base/core_ops.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/optimizer/helper.h" #include "common/graph_kernel/graph_kernel_helper.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/tensor.h" #include "kernel/kernel_build_info.h" #include "runtime/device/kernel_info.h" @@ -66,7 +67,7 @@ AnfNodePtr DropoutExpander::PreProcess(const FuncGraphPtr &func_graph, const Anf uniform_real_input[1]->set_kernel_info(std::make_shared()); auto uniform_real_node = func_graph->NewCNode(uniform_real_input); SetNodeAttrSafely("seed", MakeValue(seed), uniform_real_node); - AnfAlgo::SetNodeAttr("seed2", MakeValue(static_cast(0)), uniform_real_node); + common::AnfAlgo::SetNodeAttr("seed2", MakeValue(static_cast(0)), uniform_real_node); uniform_real_node->set_abstract(std::make_shared(kFloat32, shape_i64)); // Set kernel_info for uniform_real node auto uniform_real_kernel_info_builder = std::make_shared(); @@ -81,7 +82,7 @@ AnfNodePtr DropoutExpander::PreProcess(const FuncGraphPtr &func_graph, const Anf // Create a GKDropout node with uniform_real as its second input. AnfNodePtrList gkdropout_inputs = {NewValueNode(prim::kPrimGkDropout), cnode->input(1), uniform_real_node}; auto new_dropout_node = func_graph->NewCNode(gkdropout_inputs); - SetNodeAttrSafely("keep_prob", MakeValue(AnfAlgo::GetNodeAttr(cnode, "keep_prob")), new_dropout_node); + SetNodeAttrSafely("keep_prob", MakeValue(common::AnfAlgo::GetNodeAttr(cnode, "keep_prob")), new_dropout_node); // the output info is unchanged. new_dropout_node->set_abstract(node->abstract()); auto old_kernel_info = AnfAlgo::GetSelectKernelBuildInfo(node); diff --git a/mindspore/ccsrc/common/graph_kernel/transform_op_optimizer.cc b/mindspore/ccsrc/common/graph_kernel/transform_op_optimizer.cc index 89cb90638f4..d109b82160e 100644 --- a/mindspore/ccsrc/common/graph_kernel/transform_op_optimizer.cc +++ b/mindspore/ccsrc/common/graph_kernel/transform_op_optimizer.cc @@ -28,6 +28,7 @@ #include "debug/common.h" #include "kernel/common_utils.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "common/graph_kernel/graph_kernel_helper.h" #include "common/graph_kernel/model/lite_graph.h" #include "common/graph_kernel/model/op_register.h" @@ -439,8 +440,8 @@ bool TransformOpOptimizer::Run(const FuncGraphPtr &kernel_graph) { auto todos = TopoSort(kernel_graph->get_return()); bool changed = false; for (auto node : todos) { - if (!AnfAlgo::IsGraphKernel(node)) continue; - auto sub_func_graph = AnfAlgo::GetCNodeFuncGraphPtr(node); + if (!common::AnfAlgo::IsGraphKernel(node)) continue; + auto sub_func_graph = common::AnfAlgo::GetCNodeFuncGraphPtr(node); auto litegraph = GkUtils::AnfGraph2LiteGraph(sub_func_graph); if (Process(litegraph)) { changed = true; diff --git a/mindspore/ccsrc/common/graph_kernel/tsa_atomic_add_to_first_tensor.cc b/mindspore/ccsrc/common/graph_kernel/tsa_atomic_add_to_first_tensor.cc index f5ffc5b8b06..d6cd44c063b 100644 --- a/mindspore/ccsrc/common/graph_kernel/tsa_atomic_add_to_first_tensor.cc +++ b/mindspore/ccsrc/common/graph_kernel/tsa_atomic_add_to_first_tensor.cc @@ -21,9 +21,10 @@ #include #include "base/core_ops.h" #include "ir/tensor.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/log_adapter.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/session/kernel_graph.h" #include "kernel/kernel.h" #include "kernel/common_utils.h" @@ -58,7 +59,7 @@ std::pair TsaAtomicAddToFirstTensor::FindTsaFirstRealInputIn const AnfNodePtr &node) { auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - auto sub_graph = AnfAlgo::GetCNodeFuncGraphPtr(cnode); + auto sub_graph = common::AnfAlgo::GetCNodeFuncGraphPtr(cnode); auto mng_sub = sub_graph->manager(); if (mng_sub == nullptr) { mng_sub = Manage(sub_graph, false); @@ -105,7 +106,7 @@ std::pair TsaAtomicAddToFirstTensor::GetOrCreateNewTsaFirstN // Create composite op's sub-graph. auto new_sub_graph = std::make_shared(); auto parameter = new_sub_graph->add_parameter(); - auto kernel_with_index = AnfAlgo::VisitKernel(tsa_first_input.first, 0); + auto kernel_with_index = common::AnfAlgo::VisitKernel(tsa_first_input.first, 0); parameter->set_abstract(GetOutputAbstract(kernel_with_index.first, kernel_with_index.second)); parameter->set_kernel_info(std::make_shared()); std::string parameter_format; @@ -178,7 +179,7 @@ void TsaAtomicAddToFirstTensor::CorrectKernelBuildInfo( for (const auto &outer_info : outer_infos) { auto &modified_input = std::get<1>(outer_info); auto tsa_first_input_index = std::get<2>(outer_info); - auto kernel_with_index = AnfAlgo::VisitKernel(modified_input, 0); + auto kernel_with_index = common::AnfAlgo::VisitKernel(modified_input, 0); modified_inputs_format[tsa_first_input_index] = AnfAlgo::GetOutputFormat(kernel_with_index.first, kernel_with_index.second); modified_inputs_type[tsa_first_input_index] = @@ -199,7 +200,7 @@ void TsaAtomicAddToFirstTensor::CorrectKernelBuildInfo( void TsaAtomicAddToFirstTensor::ProcessOriginCNode( const AnfNodePtr &composite_node, const std::vector> &outer_nodes) { - auto sub_graph = AnfAlgo::GetCNodeFuncGraphPtr(composite_node); + auto sub_graph = common::AnfAlgo::GetCNodeFuncGraphPtr(composite_node); auto mng_sub = sub_graph->manager(); if (mng_sub == nullptr) { mng_sub = Manage(sub_graph, false); diff --git a/mindspore/ccsrc/common/graph_kernel/uss_atomic_add.cc b/mindspore/ccsrc/common/graph_kernel/uss_atomic_add.cc index c3d836b86ee..59b4299e112 100644 --- a/mindspore/ccsrc/common/graph_kernel/uss_atomic_add.cc +++ b/mindspore/ccsrc/common/graph_kernel/uss_atomic_add.cc @@ -18,7 +18,7 @@ #include #include "base/core_ops.h" #include "ir/tensor.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "kernel/kernel.h" #include "kernel/common_utils.h" #include "common/graph_kernel/graph_kernel_helper.h" diff --git a/mindspore/ccsrc/common/graph_kernel/value_graph_binder.cc b/mindspore/ccsrc/common/graph_kernel/value_graph_binder.cc index b4dde800d1a..bef003e4393 100644 --- a/mindspore/ccsrc/common/graph_kernel/value_graph_binder.cc +++ b/mindspore/ccsrc/common/graph_kernel/value_graph_binder.cc @@ -14,10 +14,8 @@ * limitations under the License. */ #include "common/graph_kernel/value_graph_binder.h" - -#include "frontend/optimizer/irpass.h" #include "backend/common/session/anf_runtime_algorithm.h" -#include "kernel/common_utils.h" +#include "include/common/utils/anfalgo.h" #include "common/graph_kernel/graph_kernel_helper.h" namespace mindspore::graphkernel { diff --git a/mindspore/ccsrc/common/mem_reuse/mem_dynamic_allocator.cc b/mindspore/ccsrc/common/mem_reuse/mem_dynamic_allocator.cc index 0e542d7f7e9..15ed69a8525 100644 --- a/mindspore/ccsrc/common/mem_reuse/mem_dynamic_allocator.cc +++ b/mindspore/ccsrc/common/mem_reuse/mem_dynamic_allocator.cc @@ -17,7 +17,7 @@ #include "common/mem_reuse/mem_dynamic_allocator.h" #include #include "utils/ms_utils.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" #include "utils/log_adapter.h" #include "utils/ms_context.h" diff --git a/mindspore/ccsrc/common/mem_reuse/mem_reuse.cc b/mindspore/ccsrc/common/mem_reuse/mem_reuse.cc index 8ffab8b1c3a..5612d50584d 100644 --- a/mindspore/ccsrc/common/mem_reuse/mem_reuse.cc +++ b/mindspore/ccsrc/common/mem_reuse/mem_reuse.cc @@ -18,7 +18,7 @@ #include #include #include -#include "utils/context/graph_kernel_flags.h" +#include "include/common/utils/context/graph_kernel_flags.h" #include "common/mem_reuse/mem_reuse_checker.h" #include "backend/common/optimizer/helper.h" @@ -48,7 +48,7 @@ bool MemReuseUtil::InitDynamicOutputKernelRef() { if (iter == kernel_output_refs_.end()) { auto output_sizes = kernel_mod->GetOutputSizeList(); KernelRefCountPtrList kernel_refs; - bool is_comm_op = AnfAlgo::IsCommunicationOp(kernel_cnode); + bool is_comm_op = common::AnfAlgo::IsCommunicationOp(kernel_cnode); size_t output_index = 0; for (auto size : output_sizes) { total_dy_size_ += size; @@ -182,8 +182,8 @@ void MemReuseUtil::SetInputMap(const CNodePtr &kernel, KernelDef *kernel_def_ptr MS_EXCEPTION_IF_NULL(kernel); MS_EXCEPTION_IF_NULL(kernel_def_ptr); auto key = kernel.get(); - bool is_comm_op = AnfAlgo::IsCommunicationOp(kernel); - size_t input_tensor_num = AnfAlgo::GetInputTensorNum(kernel); + bool is_comm_op = common::AnfAlgo::IsCommunicationOp(kernel); + size_t input_tensor_num = common::AnfAlgo::GetInputTensorNum(kernel); for (size_t i = 0; i < input_tensor_num; ++i) { auto ref_ptr = GetKernelInputRef(kernel, i); if (ref_ptr != nullptr) { @@ -262,9 +262,9 @@ KernelRefCountPtr MemReuseUtil::GetRef(const AnfNodePtr &node, size_t output_idx } KernelRefCountPtr MemReuseUtil::GetKernelInputRef(const CNodePtr &kernel, size_t input_idx) { - if (input_idx >= AnfAlgo::GetInputTensorNum(kernel)) { + if (input_idx >= common::AnfAlgo::GetInputTensorNum(kernel)) { MS_LOG(EXCEPTION) << "Input index " << input_idx << " is larger than input number " - << AnfAlgo::GetInputTensorNum(kernel); + << common::AnfAlgo::GetInputTensorNum(kernel); } auto input_node = kernel->input(input_idx + 1); // Graph may be all nop nodes and not remove nop node, so this can not skip nop node. @@ -287,7 +287,7 @@ void MemReuseUtil::SetKernelDefMap() { auto kernel_cnodes = graph_->execution_order(); for (auto &kernel : kernel_cnodes) { KernelDefPtr kernel_def_ptr = std::make_shared(); - kernel_def_ptr->set_kernel_name(AnfAlgo::GetCNodeName(kernel)); + kernel_def_ptr->set_kernel_name(common::AnfAlgo::GetCNodeName(kernel)); kernel_def_ptr->set_scope_full_name(kernel->fullname_with_scope()); kernel_def_ptr->set_stream_id(AnfAlgo::GetStreamId(kernel)); SetInputMap(kernel, kernel_def_ptr.get()); @@ -296,7 +296,7 @@ void MemReuseUtil::SetKernelDefMap() { auto key = kernel.get(); kernel_def_ptr->set_input_refs(kernel_def_ptr->inputs_[key]); kernel_def_ptr->set_output_refs(kernel_def_ptr->outputs_[key]); - if (AnfAlgo::IsCommunicationOp(kernel)) { + if (common::AnfAlgo::IsCommunicationOp(kernel)) { kernel_def_ptr->type_ = kCommunicationNode; } else { kernel_def_ptr->type_ = kCommonNode; @@ -317,20 +317,20 @@ void MemReuseUtil::SetKernelDefInputs() { MS_LOG(EXCEPTION) << "kernel [" << kernel->fullname_with_scope() << "] is not init."; } auto kernel_def = iter->second; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel); for (size_t i = 0; i < input_num; ++i) { auto ref_ptr = GetKernelInputRef(kernel, i); if (ref_ptr != nullptr) { // set the inputs of this kernel_def - auto input_node = AnfAlgo::GetInputNode(kernel, i); + auto input_node = common::AnfAlgo::GetInputNode(kernel, i); // Graph may be all nop nodes and not remove nop node, so this can not skip nop node. session::KernelWithIndex input; if (is_all_nop_node_) { // The graph does not remove the nop node. - input = AnfAlgo::VisitKernelWithReturnType(input_node, 0, false); + input = common::AnfAlgo::VisitKernelWithReturnType(input_node, 0, false); } else { // The graph removes the nop node. - input = AnfAlgo::VisitKernelWithReturnType(input_node, 0, true); + input = common::AnfAlgo::VisitKernelWithReturnType(input_node, 0, true); } if (IsPrimitive(input.first, prim::kPrimMakeTuple)) { MS_LOG(EXCEPTION) << "Input node [" << input_node->DebugString() << "]'s input " << i << " is MakeTuple"; @@ -415,15 +415,15 @@ void MemReuseUtil::SetRefNodesInputRefCount() { } void MemReuseUtil::SetGraphOutputRefCount() { - auto nodes = AnfAlgo::GetAllOutput(graph_->output(), {prim::kPrimTupleGetItem}); + auto nodes = common::AnfAlgo::GetAllOutput(graph_->output(), {prim::kPrimTupleGetItem}); for (const auto &node : nodes) { session::KernelWithIndex kernel_input; if (is_all_nop_node_) { // The graph does not remove the nop node. - kernel_input = AnfAlgo::VisitKernelWithReturnType(node, 0, false); + kernel_input = common::AnfAlgo::VisitKernelWithReturnType(node, 0, false); } else { // The graph removes the nop node. - kernel_input = AnfAlgo::VisitKernelWithReturnType(node, 0, true); + kernel_input = common::AnfAlgo::VisitKernelWithReturnType(node, 0, true); } MS_EXCEPTION_IF_NULL(kernel_input.first); if (!kernel_input.first->isa() || !AnfUtils::IsRealKernel(kernel_input.first)) { @@ -482,7 +482,7 @@ uint8_t *MemReuseUtil::GetNodeOutputPtr(const AnfNodePtr &node, size_t index) co auto output_ref = iter->second[index]; ptr = mem_base_ + output_ref->offset_; } else { - MS_LOG(EXCEPTION) << "node [" << AnfAlgo::GetCNodeName(node) << "] don't exist in kernel_output_refs"; + MS_LOG(EXCEPTION) << "node [" << common::AnfAlgo::GetCNodeName(node) << "] don't exist in kernel_output_refs"; } return ptr; } @@ -503,15 +503,15 @@ uint8_t *MemReuseUtil::GetNodeWorkSpacePtr(const AnfNodePtr &node, size_t index) session::KernelWithIndex MemReuseUtil::VisitKernelWithReturnType(const AnfNodePtr &node, size_t i, bool skip_nop_node) { if (!enable_visit_kernel_cache_ || i != 0) { - return AnfAlgo::VisitKernelWithReturnType(node, i, skip_nop_node); + return common::AnfAlgo::VisitKernelWithReturnType(node, i, skip_nop_node); } auto &cache = skip_nop_node ? visit_kernel_with_return_type_in0pos_cache_ : visit_kernel_with_return_type_in0pos_skip_nop_cache_; mindspore::HashMap::iterator tag_iter; if (auto iter = cache.find(node); iter == cache.end()) { - auto tmp_item = - std::pair{node, AnfAlgo::VisitKernelWithReturnType(node, i, skip_nop_node)}; + auto tmp_item = std::pair{ + node, common::AnfAlgo::VisitKernelWithReturnType(node, i, skip_nop_node)}; tag_iter = cache.emplace(tmp_item).first; } else { tag_iter = iter; diff --git a/mindspore/ccsrc/common/mem_reuse/mem_reuse.h b/mindspore/ccsrc/common/mem_reuse/mem_reuse.h index c8f88d9c468..ed19a18768a 100644 --- a/mindspore/ccsrc/common/mem_reuse/mem_reuse.h +++ b/mindspore/ccsrc/common/mem_reuse/mem_reuse.h @@ -22,6 +22,7 @@ #include "utils/hash_map.h" #include "common/mem_reuse/kernel_refcount.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/session/kernel_graph.h" #include "plugin/device/ascend/kernel/tbe/tbe_utils.h" #include "utils/ms_context.h" diff --git a/mindspore/ccsrc/common/mem_reuse/mem_reuse_checker.cc b/mindspore/ccsrc/common/mem_reuse/mem_reuse_checker.cc index 682c27bc912..586a2660b35 100644 --- a/mindspore/ccsrc/common/mem_reuse/mem_reuse_checker.cc +++ b/mindspore/ccsrc/common/mem_reuse/mem_reuse_checker.cc @@ -26,12 +26,12 @@ MemReuseChecker &MemReuseChecker::GetInstance() { } void MemReuseChecker::CheckSignalOps(const CNodePtr &c_node) { - std::string node_name = AnfAlgo::GetCNodeName(c_node); + std::string node_name = common::AnfAlgo::GetCNodeName(c_node); if (node_name == kSendOpName || node_name == kRecvOpName) { MS_LOG(INFO) << "MemReuseChecker check op_name of Send or Send"; // get op's info && check - MS_LOG(INFO) << "op: " << node_name << " in_num: " << AnfAlgo::GetInputTensorNum(c_node) - << " out_num: " << AnfAlgo::GetOutputTensorNum(c_node); + MS_LOG(INFO) << "op: " << node_name << " in_num: " << common::AnfAlgo::GetInputTensorNum(c_node) + << " out_num: " << common::AnfAlgo::GetOutputTensorNum(c_node); } } @@ -44,7 +44,7 @@ void MemReuseChecker::CheckWorkSpace(const std::vector &max_list) { void MemReuseChecker::CheckOutRef(const KernelRefs &kernel_refs, const CNodePtr &c_node, size_t output_idx) { auto key = c_node.get(); auto iter = kernel_refs.find(key); - auto node_name = AnfAlgo::GetCNodeName(c_node); + auto node_name = common::AnfAlgo::GetCNodeName(c_node); if (iter == kernel_refs.end()) { MS_LOG(EXCEPTION) << "kernel [" << node_name << "] has no output tensor, node: " << c_node->DebugString() << " output index: " << output_idx; @@ -63,12 +63,12 @@ int64_t MemReuseChecker::CalculOriInput(const KernelGraph *graph) const { if (!item->isa()) { continue; } - auto output_size = AnfAlgo::GetOutputTensorNum(item); + auto output_size = common::AnfAlgo::GetOutputTensorNum(item); for (size_t index = 0; index < output_size; index++) { TypeId ou_type = AnfAlgo::GetOutputDeviceDataType(item, index); // parameter has not init by a cnode if (ou_type == kTypeUnknown) { - ou_type = AnfAlgo::GetOutputInferDataType(item, index); + ou_type = common::AnfAlgo::GetOutputInferDataType(item, index); } size_t type_size = GetTypeByte(TypeIdToType(ou_type)); std::vector shape = AnfAlgo::GetOutputDeviceShape(item, index); @@ -211,12 +211,12 @@ bool MemReuseChecker::CheckGraphOutputAssigned(const session::KernelGraph *graph // set real graph output node to be special who's refcount equal kMaxRefCount for (const auto &output : graph->outputs()) { MS_EXCEPTION_IF_NULL(output); - size_t input_num = AnfAlgo::GetInputTensorNum(output); + size_t input_num = common::AnfAlgo::GetInputTensorNum(output); for (size_t i = 0; i < input_num; ++i) { if (output->isa()) { auto cnode = output->cast(); auto input_node = cnode->input(i + 1); - auto kernel_input_with_idx = AnfAlgo::VisitKernel(input_node, 0); + auto kernel_input_with_idx = common::AnfAlgo::VisitKernel(input_node, 0); auto kernel_input = kernel_input_with_idx.first; MS_EXCEPTION_IF_NULL(kernel_input); auto kernel_mod = AnfAlgo::GetKernelMod(kernel_input); @@ -362,7 +362,7 @@ void MemReuseChecker::CheckNormalIR(const session::KernelGraph *graph) { const auto &cnodes = graph->execution_order(); for (const auto &node : cnodes) { std::vector curr_ous; - size_t output_num = AnfAlgo::GetOutputTensorNum(node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(node); for (size_t i = 0; i < output_num; ++i) { auto it = AnfAlgo::GetOutputAddr(node, i); MS_EXCEPTION_IF_NULL(it); @@ -373,17 +373,17 @@ void MemReuseChecker::CheckNormalIR(const session::KernelGraph *graph) { } (void)node_ous_.emplace(node.get(), curr_ous); std::vector curr_ins; - size_t input_num = AnfAlgo::GetInputTensorNum(node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(node); for (size_t i = 0; i < input_num; ++i) { if (i + 1 >= node->inputs().size()) { MS_LOG(EXCEPTION) << "Input index: " << i - << " is larger than input number: " << AnfAlgo::GetInputTensorNum(node) + << " is larger than input number: " << common::AnfAlgo::GetInputTensorNum(node) << trace::DumpSourceLines(node); } auto real_input_index = AnfAlgo::GetRealInputIndex(node, i); auto input = node->input(real_input_index + 1); MS_EXCEPTION_IF_NULL(input); - auto kernel_with_index = AnfAlgo::VisitKernel(input, 0); + auto kernel_with_index = common::AnfAlgo::VisitKernel(input, 0); if (kernel_with_index.first->isa()) { continue; } diff --git a/mindspore/ccsrc/common/mem_reuse/mem_reuse_checker.h b/mindspore/ccsrc/common/mem_reuse/mem_reuse_checker.h index f454bd8018b..629f508c6f4 100644 --- a/mindspore/ccsrc/common/mem_reuse/mem_reuse_checker.h +++ b/mindspore/ccsrc/common/mem_reuse/mem_reuse_checker.h @@ -24,6 +24,7 @@ #include #include "mindspore/core/ir/anf.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "common/mem_reuse/mem_reuse.h" #include "kernel/common_utils.h" namespace mindspore { diff --git a/mindspore/ccsrc/common/mem_reuse/mem_swap_manager.cc b/mindspore/ccsrc/common/mem_reuse/mem_swap_manager.cc index d0f24ce7f55..26511d31f48 100644 --- a/mindspore/ccsrc/common/mem_reuse/mem_swap_manager.cc +++ b/mindspore/ccsrc/common/mem_reuse/mem_swap_manager.cc @@ -17,6 +17,7 @@ #include "common/mem_reuse/mem_swap_manager.h" #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/optimizer/helper.h" #include "runtime/device/kernel_runtime_manager.h" @@ -37,7 +38,7 @@ bool MemSwapManager::Init(const mindspore::session::KernelGraph *kernel_graph) { MS_EXCEPTION_IF_NULL(kernel_mod); auto output_sizes = kernel_mod->GetOutputSizeList(); - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel); for (size_t output_idx = 0; output_idx < output_num; ++output_idx) { TensorInfo tensor_info = {output_sizes[output_idx], kernel, output_idx}; ordered_tensors_.push_back(tensor_info); @@ -175,7 +176,7 @@ std::vector> MemSwapManager::CheckDistanceBetweenKerne bool MemSwapManager::IsCommunicationRelevantOp(const AnfNodePtr &kernel) const { MS_EXCEPTION_IF_NULL(kernel); - if (AnfAlgo::IsCommunicationOp(kernel)) { + if (common::AnfAlgo::IsCommunicationOp(kernel)) { return true; } @@ -189,14 +190,15 @@ bool MemSwapManager::IsCommunicationRelevantOp(const AnfNodePtr &kernel) const { AnfNodeIndexSet node_set = iter->second; adjacent_with_communication_op = std::any_of( node_set.begin(), node_set.end(), - [](const std::pair &node_pair) { return AnfAlgo::IsCommunicationOp(node_pair.first); }); + [](const std::pair &node_pair) { return common::AnfAlgo::IsCommunicationOp(node_pair.first); }); } return adjacent_with_communication_op; } bool MemSwapManager::IsInplaceRelevantOp(const TensorInfo &tensor) { MS_EXCEPTION_IF_NULL(tensor.kernel_); - if (AnfAlgo::IsInplaceNode(tensor.kernel_, "inplace_algo") || AnfAlgo::IsInplaceNode(tensor.kernel_, "skip")) { + if (common::AnfAlgo::IsInplaceNode(tensor.kernel_, "inplace_algo") || + common::AnfAlgo::IsInplaceNode(tensor.kernel_, "skip")) { return true; } @@ -207,11 +209,11 @@ bool MemSwapManager::IsInplaceRelevantOp(const TensorInfo &tensor) { auto users = user_map.find(tensor.kernel_); for (const auto &user : users->second) { - if (!AnfAlgo::IsInplaceNode(user.first, "aggregate")) { + if (!common::AnfAlgo::IsInplaceNode(user.first, "aggregate")) { continue; } - auto kernel_with_index = AnfAlgo::GetPrevNodeOutput(user.first, IntToSize(user.second)); + auto kernel_with_index = common::AnfAlgo::GetPrevNodeOutput(user.first, IntToSize(user.second)); if (tensor.output_idx_ == kernel_with_index.second) { MS_LOG(INFO) << " [inplace optimizer] tensor: " << tensor.kernel_->DebugString() << "output idx: " << tensor.output_idx_ << " used by aggregate node: " << user.first->DebugString(); @@ -239,15 +241,16 @@ void MemSwapManager::SaveUserKernelTopoOrder() { continue; } - if (opt::IsNopNode(user_kernel)) { + if (common::AnfAlgo::IsNopNode(user_kernel)) { continue; } size_t user_kernel_topo_sort = SearchKernelExecutionInfo(user_kernel).topo_order_; - auto kernel_with_index = AnfAlgo::GetPrevNodeOutput(user_kernel, IntToSize(node_pair.second - 1)); + auto kernel_with_index = common::AnfAlgo::GetPrevNodeOutput(user_kernel, IntToSize(node_pair.second - 1)); auto &output_idx = kernel_with_index.second; if (kernel_with_index.first.get() != kernel.get()) { - MS_LOG(EXCEPTION) << "Save user kernel topo order failed for op[" << AnfAlgo::GetCNodeName(kernel) << "]"; + MS_LOG(EXCEPTION) << "Save user kernel topo order failed for op[" << common::AnfAlgo::GetCNodeName(kernel) + << "]"; } kernel_exec_info.node_users_map_[output_idx].push_back(user_kernel_topo_sort); } @@ -294,7 +297,7 @@ void MemSwapManager::AddSwapInfo() { MemSwapInfo mem_swap_in_info = {SwapKind::kHostToDevice, kernel_exec_info.topo_order_, output_idx, swap_out_order}; if (swap_in_order <= swap_out_order) { - MS_LOG(EXCEPTION) << "Select swap in point failed for op[" << AnfAlgo::GetCNodeName(kernel) << "]"; + MS_LOG(EXCEPTION) << "Select swap in point failed for op[" << common::AnfAlgo::GetCNodeName(kernel) << "]"; } AddKernelMemSwapInfo(execution_order_[swap_in_order], mem_swap_in_info); } @@ -417,7 +420,7 @@ void MemSwapManager::CacheCurSwapInfoSet(const AnfNodePtr &kernel) { void MemSwapManager::AddFirstTimeMovePos(const AnfNodePtr &kernel, size_t index, bool first_time) { auto iter = kernel_first_move_cache_map_.find(kernel.get()); if (iter == kernel_first_move_cache_map_.end()) { - MS_LOG(EXCEPTION) << "Can not find first time move pos info of op[" << AnfAlgo::GetCNodeName(kernel) << "]"; + MS_LOG(EXCEPTION) << "Can not find first time move pos info of op[" << common::AnfAlgo::GetCNodeName(kernel) << "]"; } auto &first_move_list = iter->second; if (index >= first_move_list.size()) { @@ -429,7 +432,7 @@ void MemSwapManager::AddFirstTimeMovePos(const AnfNodePtr &kernel, size_t index, bool MemSwapManager::QueryFirstTimeMovePos(const AnfNodePtr &kernel, size_t index) const { auto iter = kernel_first_move_cache_map_.find(kernel.get()); if (iter == kernel_first_move_cache_map_.end()) { - MS_LOG(EXCEPTION) << "Can not find first time move pos info of op[" << AnfAlgo::GetCNodeName(kernel) << "]"; + MS_LOG(EXCEPTION) << "Can not find first time move pos info of op[" << common::AnfAlgo::GetCNodeName(kernel) << "]"; } const auto &first_move_list = iter->second; if (index >= first_move_list.size()) { @@ -475,7 +478,7 @@ KernelExecutionInfo &MemSwapManager::SearchKernelExecutionInfo(const AnfNodePtr MS_EXCEPTION_IF_NULL(kernel); auto iter = kernel_execution_info_.find(kernel.get()); if (iter == kernel_execution_info_.end()) { - MS_LOG(EXCEPTION) << "Can not find execution info of op[" << AnfAlgo::GetCNodeName(kernel) << "]"; + MS_LOG(EXCEPTION) << "Can not find execution info of op[" << common::AnfAlgo::GetCNodeName(kernel) << "]"; } return const_cast(iter->second); } @@ -511,7 +514,8 @@ void MemSwapManager::RemoveKernelMemSwapInfo(const AnfNodePtr &kernel, const Mem if (mem_swap_info.swap_kind_ == SwapKind::kHostToDevice) { auto map_iter = mem_swap_info_map_.find(kernel.get()); if (map_iter == mem_swap_info_map_.end()) { - MS_LOG(EXCEPTION) << "Can not find memory swap information of op[" << AnfAlgo::GetCNodeName(kernel) << "]"; + MS_LOG(EXCEPTION) << "Can not find memory swap information of op[" << common::AnfAlgo::GetCNodeName(kernel) + << "]"; } MemSwapInfoSet &mem_swap_info_set = map_iter->second; @@ -570,14 +574,14 @@ const PerformPair &MemSwapManager::QueryKernelSwapPerform(const AnfNodePtr &kern MS_EXCEPTION_IF_NULL(kernel); auto iter_kernel = kernel_swap_perform_.find(kernel.get()); if (iter_kernel == kernel_swap_perform_.end()) { - MS_LOG(EXCEPTION) << "Can not find swap performance data of op[" << AnfAlgo::GetCNodeName(kernel) << "]"; + MS_LOG(EXCEPTION) << "Can not find swap performance data of op[" << common::AnfAlgo::GetCNodeName(kernel) << "]"; } auto &perform_map = iter_kernel->second; auto iter_output = perform_map.find(output_idx); if (iter_output == perform_map.end()) { MS_LOG(EXCEPTION) << "Can not find swap performance data of output[" << output_idx << "] of op[" - << AnfAlgo::GetCNodeName(kernel) << "]"; + << common::AnfAlgo::GetCNodeName(kernel) << "]"; } return iter_output->second; } @@ -586,7 +590,7 @@ const MemSwapInfoSet &MemSwapManager::QueryKernelMemSwapInfo(const AnfNodePtr &k MS_EXCEPTION_IF_NULL(kernel); auto iter = mem_swap_info_map_.find(kernel.get()); if (iter == mem_swap_info_map_.end()) { - MS_LOG(EXCEPTION) << "Can not find memory swap information of op[" << AnfAlgo::GetCNodeName(kernel) << "]"; + MS_LOG(EXCEPTION) << "Can not find memory swap information of op[" << common::AnfAlgo::GetCNodeName(kernel) << "]"; } return iter->second; } @@ -611,7 +615,7 @@ const HostAddress &MemSwapManager::QueryKernelHostAddr(const AnfNodePtr &kernel, auto &host_addrs = kernel_exec_info.host_addrs_; auto iter = host_addrs.find(output_idx); if (iter == host_addrs.end()) { - MS_LOG(EXCEPTION) << "Can not find host address of op[" << AnfAlgo::GetCNodeName(kernel) << "]"; + MS_LOG(EXCEPTION) << "Can not find host address of op[" << common::AnfAlgo::GetCNodeName(kernel) << "]"; } return (iter->second).first; } @@ -621,7 +625,7 @@ void MemSwapManager::AddKernelHostAddrIsDirty(const AnfNodePtr &kernel, size_t o auto &host_addrs = kernel_exec_info.host_addrs_; auto iter = host_addrs.find(output_idx); if (iter == host_addrs.end()) { - MS_LOG(EXCEPTION) << "Can not find host memory dirty info of op[" << AnfAlgo::GetCNodeName(kernel) << "]"; + MS_LOG(EXCEPTION) << "Can not find host memory dirty info of op[" << common::AnfAlgo::GetCNodeName(kernel) << "]"; } (iter->second).second = dirty; } @@ -631,7 +635,7 @@ bool MemSwapManager::QueryKernelHostAddrIsDirty(const AnfNodePtr &kernel, size_t auto &host_addrs = kernel_exec_info.host_addrs_; auto iter = host_addrs.find(output_idx); if (iter == host_addrs.end()) { - MS_LOG(EXCEPTION) << "Can not find host memory dirty info of op[" << AnfAlgo::GetCNodeName(kernel) << "]"; + MS_LOG(EXCEPTION) << "Can not find host memory dirty info of op[" << common::AnfAlgo::GetCNodeName(kernel) << "]"; } return (iter->second).second; } @@ -686,18 +690,18 @@ void MemSwapManager::DumpSwapInfo() const { } auto &kernel_exec_info = SearchKernelExecutionInfo(kernel); MS_LOG(WARNING) << "Trigger kernel topo order[" << kernel_exec_info.topo_order_ << "] , op name[" - << AnfAlgo::GetCNodeName(kernel) << "]"; + << common::AnfAlgo::GetCNodeName(kernel) << "]"; const MemSwapInfoSet &mem_swap_info_set = QueryKernelMemSwapInfo(kernel); for (auto &mem_swap_info : mem_swap_info_set) { if (mem_swap_info.swap_kind_ == SwapKind::kDeviceToHost) { MS_LOG(WARNING) << " Swap Out Task: swapped kernel topo order[" << mem_swap_info.topo_order_ << "], op name[" - << AnfAlgo::GetCNodeName(QueryKernelByTopoOrder(mem_swap_info.topo_order_)) << "], output idx[" - << mem_swap_info.output_idx_ << "]"; + << common::AnfAlgo::GetCNodeName(QueryKernelByTopoOrder(mem_swap_info.topo_order_)) + << "], output idx[" << mem_swap_info.output_idx_ << "]"; } else { MS_LOG(WARNING) << " Swap In Task: swapped kernel topo order[" << mem_swap_info.topo_order_ << "], op name[" - << AnfAlgo::GetCNodeName(QueryKernelByTopoOrder(mem_swap_info.topo_order_)) << "], output idx[" - << mem_swap_info.output_idx_ << "]"; + << common::AnfAlgo::GetCNodeName(QueryKernelByTopoOrder(mem_swap_info.topo_order_)) + << "], output idx[" << mem_swap_info.output_idx_ << "]"; } } } @@ -708,7 +712,7 @@ void MemSwapManager::DumpUserNodes() const { const auto &kernel_exec_info = SearchKernelExecutionInfo(kernel); const auto &node_users_map = kernel_exec_info.node_users_map_; MS_LOG(WARNING) << "Kernel topo order[" << kernel_exec_info.topo_order_ << "], op name[" - << AnfAlgo::GetCNodeName(kernel) << "]"; + << common::AnfAlgo::GetCNodeName(kernel) << "]"; if (node_users_map.empty()) { MS_LOG(WARNING) << " Kernel does not own any user node"; } @@ -718,7 +722,8 @@ void MemSwapManager::DumpUserNodes() const { auto &node_users = item.second; for (auto &order : node_users) { MS_LOG(WARNING) << " Output index[" << output_idx << "] tensor is used by kernel[" - << AnfAlgo::GetCNodeName(QueryKernelByTopoOrder(order)) << "], topo order[" << order << "]"; + << common::AnfAlgo::GetCNodeName(QueryKernelByTopoOrder(order)) << "], topo order[" << order + << "]"; } } } diff --git a/mindspore/ccsrc/common/thread_pool.cc b/mindspore/ccsrc/common/thread_pool.cc index 25a9543dc34..054a9741be3 100644 --- a/mindspore/ccsrc/common/thread_pool.cc +++ b/mindspore/ccsrc/common/thread_pool.cc @@ -14,7 +14,7 @@ * limitations under the License. */ -#include "common/thread_pool.h" +#include "include/common/thread_pool.h" #include #include #include "utils/log_adapter.h" diff --git a/mindspore/ccsrc/cxx_api/CMakeLists.txt b/mindspore/ccsrc/cxx_api/CMakeLists.txt index a6ebc6f5e6a..d5dcc07a5d5 100644 --- a/mindspore/ccsrc/cxx_api/CMakeLists.txt +++ b/mindspore/ccsrc/cxx_api/CMakeLists.txt @@ -82,8 +82,7 @@ if(BUILD_LITE) target_link_libraries(mindspore_shared_lib PRIVATE $) add_dependencies(mindspore_shared_lib _mindspore_transform_graph_ir_obj) elseif(ENABLE_ACL AND NOT ENABLE_D) - target_link_libraries(mindspore_shared_lib PRIVATE $ - $) + target_link_libraries(mindspore_shared_lib PRIVATE $) endif() if(NOT BUILD_LITE) set_target_properties(mindspore_shared_lib PROPERTIES OUTPUT_NAME mindspore) @@ -91,13 +90,14 @@ endif() if(ENABLE_D OR ENABLE_GPU) target_link_libraries(mindspore_shared_lib PRIVATE -Wl,--as-needed ${PYTHON_LIBRARIES} ${SECUREC_LIBRARY} - -Wl,--whole-archive mindspore -Wl,--no-whole-archive mindspore_core proto_input mindspore::protobuf) + -Wl,--whole-archive mindspore -Wl,--no-whole-archive mindspore_core mindspore_common proto_input + mindspore::protobuf) else() if(BUILD_LITE) target_link_libraries(mindspore_shared_lib PRIVATE ${SECUREC_LIBRARY}) else() target_link_libraries(mindspore_shared_lib PRIVATE ${PY3_LIBG} ${SECUREC_LIBRARY} - mindspore mindspore_core proto_input mindspore::protobuf) + mindspore mindspore_core mindspore_common proto_input mindspore::protobuf) endif() endif() diff --git a/mindspore/ccsrc/cxx_api/factory.h b/mindspore/ccsrc/cxx_api/factory.h index db0be3be2ea..fd227495e68 100644 --- a/mindspore/ccsrc/cxx_api/factory.h +++ b/mindspore/ccsrc/cxx_api/factory.h @@ -21,7 +21,7 @@ #include #include #include -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { inline enum DeviceType g_device_target = kInvalidDeviceType; diff --git a/mindspore/ccsrc/cxx_api/graph/acl/model_process.cc b/mindspore/ccsrc/cxx_api/graph/acl/model_process.cc index 09cac8212d7..22192514b67 100644 --- a/mindspore/ccsrc/cxx_api/graph/acl/model_process.cc +++ b/mindspore/ccsrc/cxx_api/graph/acl/model_process.cc @@ -18,7 +18,7 @@ #include #include #include -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "mindspore/core/utils/convert_utils_base.h" namespace mindspore { diff --git a/mindspore/ccsrc/cxx_api/graph/ascend/ascend_graph_impl.cc b/mindspore/ccsrc/cxx_api/graph/ascend/ascend_graph_impl.cc index f8bd8127ddc..650a940f95f 100644 --- a/mindspore/ccsrc/cxx_api/graph/ascend/ascend_graph_impl.cc +++ b/mindspore/ccsrc/cxx_api/graph/ascend/ascend_graph_impl.cc @@ -20,7 +20,7 @@ #include "cxx_api/akg_kernel_register.h" #include "cxx_api/acl_utils.h" #include "utils/log_adapter.h" -#include "utils/context/context_extends.h" +#include "runtime/device/context_extends.h" #include "mindspore/core/base/base_ref_utils.h" #include "backend/common/session/session_factory.h" #include "backend/common/session/executor_manager.h" diff --git a/mindspore/ccsrc/cxx_api/graph/graph_impl.h b/mindspore/ccsrc/cxx_api/graph/graph_impl.h index 626d64b23aa..6db8cb9baac 100644 --- a/mindspore/ccsrc/cxx_api/graph/graph_impl.h +++ b/mindspore/ccsrc/cxx_api/graph/graph_impl.h @@ -24,7 +24,7 @@ #include "include/api/cell.h" #include "include/api/graph.h" #include "cxx_api/graph/graph_data.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { class GraphCell::GraphImpl { diff --git a/mindspore/ccsrc/cxx_api/model/acl/acl_vm/acl_multi_graph_session.cc b/mindspore/ccsrc/cxx_api/model/acl/acl_vm/acl_multi_graph_session.cc index 2032009f241..c7d9412af08 100644 --- a/mindspore/ccsrc/cxx_api/model/acl/acl_vm/acl_multi_graph_session.cc +++ b/mindspore/ccsrc/cxx_api/model/acl/acl_vm/acl_multi_graph_session.cc @@ -101,10 +101,10 @@ VectorRef MultiGraphAclSession::ConstructOutputRef(GraphId graph_id, std::deque< VectorRef outs; auto out_nodes = kernel_graphs_[graph_id]->outputs(); for (auto &out : out_nodes) { - auto item_with_index = AnfAlgo::VisitKernelWithReturnType( + auto item_with_index = common::AnfAlgo::VisitKernelWithReturnType( out, 0, false, std::vector{prim::kPrimMakeTuple, prim::kPrimUpdateState, prim::kPrimStateSetItem}); auto &anf_node = item_with_index.first; - if (AnfAlgo::CheckPrimitiveType(anf_node, prim::kPrimMakeTuple)) { + if (common::AnfAlgo::CheckPrimitiveType(anf_node, prim::kPrimMakeTuple)) { auto cnode = anf_node->cast(); MS_EXCEPTION_IF_NULL(cnode); outs.emplace_back(ConstructOutputRefByTupleNode(cnode, out_tensors)); @@ -131,11 +131,11 @@ VectorRef MultiGraphAclSession::ConstructOutputRefByTupleNode(const CNodePtr &tu MS_EXCEPTION_IF_NULL(out_tensors); VectorRef outs; for (size_t i = 1; i < tuple_node->inputs().size(); ++i) { - auto item_with_index = AnfAlgo::VisitKernelWithReturnType( + auto item_with_index = common::AnfAlgo::VisitKernelWithReturnType( tuple_node->input(i), 0, false, std::vector{prim::kPrimMakeTuple, prim::kPrimUpdateState, prim::kPrimStateSetItem}); auto &anf_node = item_with_index.first; - if (AnfAlgo::CheckPrimitiveType(anf_node, prim::kPrimMakeTuple)) { + if (common::AnfAlgo::CheckPrimitiveType(anf_node, prim::kPrimMakeTuple)) { auto cnode = anf_node->cast(); MS_EXCEPTION_IF_NULL(cnode); outs.emplace_back(ConstructOutputRefByTupleNode(cnode, out_tensors)); diff --git a/mindspore/ccsrc/cxx_api/model/acl/model_converter.cc b/mindspore/ccsrc/cxx_api/model/acl/model_converter.cc index d6ca06f6b0a..b21f6a230ff 100644 --- a/mindspore/ccsrc/cxx_api/model/acl/model_converter.cc +++ b/mindspore/ccsrc/cxx_api/model/acl/model_converter.cc @@ -16,8 +16,8 @@ #include "cxx_api/model/acl/model_converter.h" #include -#include "transform/graph_ir/convert.h" -#include "transform/graph_ir/graph_runner.h" +#include "include/transform/graph_ir/convert.h" +#include "include/transform/graph_ir/graph_runner.h" #include "mindspore/core/utils/ms_context.h" #include "include/api/serialization.h" #include "graph/model.h" diff --git a/mindspore/ccsrc/cxx_api/model/acl/model_converter.h b/mindspore/ccsrc/cxx_api/model/acl/model_converter.h index dfd54fbded4..e4a6723f49a 100644 --- a/mindspore/ccsrc/cxx_api/model/acl/model_converter.h +++ b/mindspore/ccsrc/cxx_api/model/acl/model_converter.h @@ -23,7 +23,7 @@ #include "include/api/types.h" #include "include/api/status.h" #include "mindspore/core/ir/func_graph.h" -#include "transform/graph_ir/types.h" +#include "include/transform/graph_ir/types.h" #include "external/ge/ge_ir_build.h" #include "cxx_api/model/acl/acl_model_options.h" diff --git a/mindspore/ccsrc/cxx_api/model/model.cc b/mindspore/ccsrc/cxx_api/model/model.cc index afa3e5f51cd..0c3c03c77f3 100644 --- a/mindspore/ccsrc/cxx_api/model/model.cc +++ b/mindspore/ccsrc/cxx_api/model/model.cc @@ -17,7 +17,7 @@ #include "include/api/context.h" #include "cxx_api/model/model_impl.h" #include "cxx_api/factory.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { Status Model::Build(GraphCell graph_cell, const std::shared_ptr &model_context, diff --git a/mindspore/ccsrc/cxx_api/model/model_impl.h b/mindspore/ccsrc/cxx_api/model/model_impl.h index 2711b9515a6..306377bacb2 100644 --- a/mindspore/ccsrc/cxx_api/model/model_impl.h +++ b/mindspore/ccsrc/cxx_api/model/model_impl.h @@ -25,7 +25,7 @@ #include "include/api/model.h" #include "include/api/graph.h" #include "cxx_api/graph/graph_data.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "ir/func_graph.h" namespace mindspore { diff --git a/mindspore/ccsrc/debug/anf_ir_dump.cc b/mindspore/ccsrc/debug/anf_ir_dump.cc index 463e1febc1a..55df35c1c44 100644 --- a/mindspore/ccsrc/debug/anf_ir_dump.cc +++ b/mindspore/ccsrc/debug/anf_ir_dump.cc @@ -26,6 +26,7 @@ #include "runtime/device/kernel_info.h" #include "ir/graph_utils.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "frontend/parallel/ops_info/operator_info.h" #include "pipeline/jit/base.h" #include "debug/trace.h" @@ -184,7 +185,7 @@ void DumpKernelInfo(const CNodePtr &node, const std::shared_ptr } gsub->buffer << " : ("; - size_t input_num = AnfAlgo::GetInputTensorNum(node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(node); for (size_t i = 0; i < input_num; ++i) { if (i != 0) { gsub->buffer << ", "; @@ -195,7 +196,7 @@ void DumpKernelInfo(const CNodePtr &node, const std::shared_ptr PrintKernelFormatAndType(gsub->buffer, format, type, shape); } gsub->buffer << ") -> ("; - size_t output_num = AnfAlgo::GetOutputTensorNum(node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(node); for (size_t i = 0; i < output_num; ++i) { if (i != 0) { gsub->buffer << ", "; @@ -238,7 +239,7 @@ int32_t DumpParams(const FuncGraphPtr &graph, std::ostringstream &buffer, Ordere auto format = AnfAlgo::GetOutputFormat(p, 0); auto shape = AnfAlgo::GetOutputDeviceShape(p, 0); PrintKernelFormatAndType(buffer, format, type, shape); - buffer << " : IsWeight:" << std::boolalpha << AnfAlgo::IsParameterWeight(parameter_ptr); + buffer << " : IsWeight:" << std::boolalpha << common::AnfAlgo::IsParameterWeight(parameter_ptr); } buffer << std::endl; diff --git a/mindspore/ccsrc/debug/anf_ir_utils.cc b/mindspore/ccsrc/debug/anf_ir_utils.cc index 3d4621a2cff..bf2de134fad 100644 --- a/mindspore/ccsrc/debug/anf_ir_utils.cc +++ b/mindspore/ccsrc/debug/anf_ir_utils.cc @@ -27,13 +27,13 @@ #include "ir/meta_func_graph.h" #include "ir/param_info.h" #include "pybind_api/ir/tensor_py.h" -#include "pipeline/jit/parse/python_adapter.h" +#include "include/common/utils/python_adapter.h" #include "pipeline/jit/parse/resolve.h" #include "frontend/operator/composite/composite.h" #include "frontend/operator/composite/map.h" #include "utils/ordered_map.h" #include "utils/ordered_set.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/shape_utils.h" #include "debug/trace.h" #include "utils/label.h" diff --git a/mindspore/ccsrc/debug/anf_ir_utils.h b/mindspore/ccsrc/debug/anf_ir_utils.h index 1fd45e319eb..3bab837b295 100644 --- a/mindspore/ccsrc/debug/anf_ir_utils.h +++ b/mindspore/ccsrc/debug/anf_ir_utils.h @@ -29,13 +29,13 @@ #include "ir/anf.h" #include "ir/func_graph.h" #include "ir/meta_func_graph.h" -#include "pipeline/jit/parse/python_adapter.h" +#include "include/common/utils/python_adapter.h" #include "pipeline/jit/parse/resolve.h" #include "frontend/operator/composite/composite.h" #include "utils/symbolic.h" #include "utils/ordered_map.h" #include "utils/ordered_set.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { diff --git a/mindspore/ccsrc/debug/common.cc b/mindspore/ccsrc/debug/common.cc index 7276a511ab6..c473666a3c6 100644 --- a/mindspore/ccsrc/debug/common.cc +++ b/mindspore/ccsrc/debug/common.cc @@ -24,7 +24,7 @@ #include "utils/system/file_system.h" #include "utils/log_adapter.h" #include "utils/file_utils.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { std::optional Common::CreatePrefixPath(const std::string &input_path, const bool support_relative_path) { diff --git a/mindspore/ccsrc/debug/common.h b/mindspore/ccsrc/debug/common.h index 5dafd097d0e..78e7b53e002 100644 --- a/mindspore/ccsrc/debug/common.h +++ b/mindspore/ccsrc/debug/common.h @@ -19,9 +19,9 @@ #include #include -#include "utils/contract.h" +#include "include/common/utils/contract.h" #include "utils/ms_context.h" -#include "utils/comm_manager.h" +#include "include/common/utils/comm_manager.h" #include "utils/system/base.h" namespace mindspore { @@ -49,7 +49,7 @@ class Common { private: static bool IsEveryFilenameValid(const std::string &path, size_t length_limit, const std::string &error_message); - static string GetUserDefineCachePath(); + static std::string GetUserDefineCachePath(); }; inline std::string GetSaveGraphsPathName(const std::string &file_name, const std::string &save_path = "") { diff --git a/mindspore/ccsrc/debug/data_dump/cpu_e2e_dump.cc b/mindspore/ccsrc/debug/data_dump/cpu_e2e_dump.cc index 41781535a1b..3c76ad77d3b 100644 --- a/mindspore/ccsrc/debug/data_dump/cpu_e2e_dump.cc +++ b/mindspore/ccsrc/debug/data_dump/cpu_e2e_dump.cc @@ -17,6 +17,7 @@ #include "debug/data_dump/cpu_e2e_dump.h" #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "debug/anf_ir_utils.h" #include "debug/common.h" @@ -84,9 +85,9 @@ void CPUE2eDump::DumpCNodeOutputs(const CNodePtr &node, const std::string &dump_ void CPUE2eDump::DumpInputImpl(const CNodePtr &node, const std::string &dump_path, std::string *kernel_name) { MS_EXCEPTION_IF_NULL(node); GetFileKernelName(NOT_NULL(kernel_name)); - auto input_size = AnfAlgo::GetInputTensorNum(node); + auto input_size = common::AnfAlgo::GetInputTensorNum(node); for (size_t j = 0; j < input_size; ++j) { - auto kernel_with_index = AnfAlgo::GetPrevNodeOutput(node, j); + auto kernel_with_index = common::AnfAlgo::GetPrevNodeOutput(node, j); auto input = kernel_with_index.first; auto index = kernel_with_index.second; if (!AnfAlgo::OutputAddrExist(input, index)) { @@ -95,8 +96,8 @@ void CPUE2eDump::DumpInputImpl(const CNodePtr &node, const std::string &dump_pat auto addr = AnfAlgo::GetOutputAddr(input, index); ShapeVector int_shapes; GetDumpIntShape(input, index, NOT_NULL(&int_shapes)); - auto type = AnfAlgo::GetOutputInferDataType(input, index); - std::string op_type = AnfAlgo::GetCNodeName(node); + auto type = common::AnfAlgo::GetOutputInferDataType(input, index); + std::string op_type = common::AnfAlgo::GetCNodeName(node); std::string op_name = GetOpNameWithoutScope(*kernel_name); uint64_t timestamp = GetTimeStamp(); const uint32_t kTaskId = 0; @@ -111,7 +112,7 @@ void CPUE2eDump::DumpInputImpl(const CNodePtr &node, const std::string &dump_pat void CPUE2eDump::DumpOutputImpl(const CNodePtr &node, const std::string &dump_path, std::string *kernel_name) { MS_EXCEPTION_IF_NULL(node); GetFileKernelName(NOT_NULL(kernel_name)); - auto output_size = AnfAlgo::GetOutputTensorNum(node); + auto output_size = common::AnfAlgo::GetOutputTensorNum(node); for (size_t j = 0; j < output_size; ++j) { if (!AnfAlgo::OutputAddrExist(node, j)) { continue; @@ -120,8 +121,8 @@ void CPUE2eDump::DumpOutputImpl(const CNodePtr &node, const std::string &dump_pa MS_EXCEPTION_IF_NULL(addr); ShapeVector int_shapes; GetDumpIntShape(node, j, NOT_NULL(&int_shapes)); - auto type = AnfAlgo::GetOutputInferDataType(node, j); - std::string op_type = AnfAlgo::GetCNodeName(node); + auto type = common::AnfAlgo::GetOutputInferDataType(node, j); + std::string op_type = common::AnfAlgo::GetCNodeName(node); std::string op_name = GetOpNameWithoutScope(*kernel_name); const uint32_t kTaskId = 0; const uint32_t kStreamId = 0; @@ -167,7 +168,7 @@ void CPUE2eDump::DumpSingleAnfNode(const AnfNodePtr &anf_node, const size_t outp } ShapeVector int_shapes; GetDumpIntShape(anf_node, output_index, NOT_NULL(&int_shapes)); - auto type = AnfAlgo::GetOutputInferDataType(anf_node, output_index); + auto type = common::AnfAlgo::GetOutputInferDataType(anf_node, output_index); uint64_t timestamp = GetTimeStamp(); const uint32_t kTaskId = 0; diff --git a/mindspore/ccsrc/debug/data_dump/dump_json_parser.cc b/mindspore/ccsrc/debug/data_dump/dump_json_parser.cc index 811631c338f..b12d8679ee3 100644 --- a/mindspore/ccsrc/debug/data_dump/dump_json_parser.cc +++ b/mindspore/ccsrc/debug/data_dump/dump_json_parser.cc @@ -20,9 +20,10 @@ #include "utils/ms_context.h" #include "utils/convert_utils_base.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "debug/data_dump/npy_header.h" #include "debug/anf_ir_utils.h" -#include "utils/comm_manager.h" +#include "include/common/utils/comm_manager.h" namespace { constexpr auto kCommonDumpSettings = "common_dump_settings"; @@ -735,7 +736,7 @@ void DumpJsonParser::GetCellDumpFlag(const session::KernelGraph &kernel_graph) { } for (const auto &kernel : kernel_graph.execution_order()) { MS_EXCEPTION_IF_NULL(kernel); - auto dump_flag = AnfAlgo::GetDumpFlag(kernel); + auto dump_flag = common::AnfAlgo::GetDumpFlag(kernel); if (dump_flag.has_value() && dump_flag.value().compare("true") == 0) { MS_LOG(DEBUG) << "Dump flag is true for " << GetKernelNodeName(kernel); cell_dump_kernels_.push_back(GetKernelNodeName(kernel)); @@ -757,9 +758,9 @@ void DumpJsonParser::UpdateNeedDumpKernels(const session::KernelGraph &kernel_gr MS_EXCEPTION_IF_NULL(kernel); if (AnfAlgo::GetKernelType(kernel) == HCCL_KERNEL && DumpJsonParser::GetInstance().NeedDump(GetKernelNodeName(kernel))) { - auto input_size = AnfAlgo::GetInputTensorNum(kernel); + auto input_size = common::AnfAlgo::GetInputTensorNum(kernel); for (size_t i = 0; i < input_size; ++i) { - auto input_with_index = AnfAlgo::GetPrevNodeOutput(kernel, i); + auto input_with_index = common::AnfAlgo::GetPrevNodeOutput(kernel, i); auto input = input_with_index.first; MS_EXCEPTION_IF_NULL(input); if (input->isa()) { diff --git a/mindspore/ccsrc/debug/data_dump/dump_utils.cc b/mindspore/ccsrc/debug/data_dump/dump_utils.cc index 8310dec59fd..9ce99393bbb 100644 --- a/mindspore/ccsrc/debug/data_dump/dump_utils.cc +++ b/mindspore/ccsrc/debug/data_dump/dump_utils.cc @@ -18,13 +18,14 @@ #include #include -#include "utils/ms_device_shape_transfer.h" +#include "runtime/device/ms_device_shape_transfer.h" #include "utils/ms_context.h" #include "debug/anf_ir_utils.h" #include "debug/data_dump/dump_json_parser.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "runtime/device/kernel_runtime_manager.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "debug/common.h" #include "runtime/graph_scheduler/device_tensor_store.h" @@ -106,7 +107,7 @@ const DeviceTensorPtr GetParameterInfo(const AnfNodePtr &node, NotNullGetNodeIndex().first; MS_EXCEPTION_IF_NULL(ref_node); GetDumpIntShape(ref_node, PARAMETER_OUTPUT_INDEX, int_shapes, trans_flag); - *host_type = AnfAlgo::GetOutputInferDataType(ref_node, PARAMETER_OUTPUT_INDEX); + *host_type = common::AnfAlgo::GetOutputInferDataType(ref_node, PARAMETER_OUTPUT_INDEX); *device_type = AnfAlgo::GetOutputDeviceDataType(ref_node, PARAMETER_OUTPUT_INDEX); return device_addr; } diff --git a/mindspore/ccsrc/debug/data_dump/e2e_dump.cc b/mindspore/ccsrc/debug/data_dump/e2e_dump.cc index d9e33e41978..9a59e5ac3aa 100644 --- a/mindspore/ccsrc/debug/data_dump/e2e_dump.cc +++ b/mindspore/ccsrc/debug/data_dump/e2e_dump.cc @@ -25,13 +25,14 @@ #include #include #include "debug/data_dump/dump_json_parser.h" -#include "utils/ms_device_shape_transfer.h" +#include "runtime/device/ms_device_shape_transfer.h" #include "debug/anf_ir_utils.h" #include "debug/common.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "utils/ms_context.h" #include "runtime/device/kernel_runtime_manager.h" -#include "utils/config_manager.h" +#include "include/common/utils/config_manager.h" #include "utils/file_utils.h" #include "debug/data_dump/tensor_stat_dump.h" #include "abstract/utils.h" @@ -164,7 +165,7 @@ void E2eDump::DumpOutputImpl(const CNodePtr &node, bool trans_flag, const std::s std::string *kernel_name, const Debugger *debugger) { MS_EXCEPTION_IF_NULL(node); GetFileKernelName(NOT_NULL(kernel_name)); - auto output_size = AnfAlgo::GetOutputTensorNum(node); + auto output_size = common::AnfAlgo::GetOutputTensorNum(node); for (size_t j = 0; j < output_size; ++j) { if (!AnfAlgo::OutputAddrExist(node, j)) { continue; @@ -173,9 +174,9 @@ void E2eDump::DumpOutputImpl(const CNodePtr &node, bool trans_flag, const std::s MS_EXCEPTION_IF_NULL(addr); ShapeVector int_shapes; GetDumpIntShape(node, j, NOT_NULL(&int_shapes), trans_flag); - auto type = AnfAlgo::GetOutputInferDataType(node, j); + auto type = common::AnfAlgo::GetOutputInferDataType(node, j); auto device_type = AnfAlgo::GetOutputDeviceDataType(node, j); - std::string op_type = AnfAlgo::GetCNodeName(node); + std::string op_type = common::AnfAlgo::GetCNodeName(node); std::string op_name = GetOpNameWithoutScope(*kernel_name); uint32_t task_id = 0; uint32_t stream_id = 0; @@ -253,9 +254,9 @@ void E2eDump::DumpInputImpl(const CNodePtr &node, bool trans_flag, const std::st std::string *kernel_name, const Debugger *debugger, const KernelLaunchInfo *launch_info) { MS_EXCEPTION_IF_NULL(node); GetFileKernelName(NOT_NULL(kernel_name)); - auto input_size = AnfAlgo::GetInputTensorNum(node); + auto input_size = common::AnfAlgo::GetInputTensorNum(node); for (size_t j = 0; j < input_size; ++j) { - auto kernel_with_index = AnfAlgo::GetPrevNodeOutput(node, j); + auto kernel_with_index = common::AnfAlgo::GetPrevNodeOutput(node, j); auto input = kernel_with_index.first; auto index = kernel_with_index.second; if (!AnfAlgo::OutputAddrExist(input, index)) { @@ -274,9 +275,9 @@ void E2eDump::DumpInputImpl(const CNodePtr &node, bool trans_flag, const std::st } ShapeVector int_shapes; GetDumpIntShape(input, index, NOT_NULL(&int_shapes), trans_flag); - auto type = AnfAlgo::GetOutputInferDataType(input, index); + auto type = common::AnfAlgo::GetOutputInferDataType(input, index); auto device_type = AnfAlgo::GetOutputDeviceDataType(input, index); - std::string op_type = AnfAlgo::GetCNodeName(node); + std::string op_type = common::AnfAlgo::GetCNodeName(node); std::string op_name = GetOpNameWithoutScope(*kernel_name); uint64_t timestamp = GetTimeStamp(); uint32_t task_id = 0; @@ -335,7 +336,7 @@ void E2eDump::DumpSingleAnfNode(const AnfNodePtr &anf_node, const size_t output_ MS_EXCEPTION_IF_NULL(addr); ShapeVector int_shapes; GetDumpIntShape(anf_node, output_index, NOT_NULL(&int_shapes), trans_flag); - auto type = AnfAlgo::GetOutputInferDataType(anf_node, output_index); + auto type = common::AnfAlgo::GetOutputInferDataType(anf_node, output_index); auto device_type = AnfAlgo::GetOutputDeviceDataType(anf_node, output_index); uint64_t timestamp = GetTimeStamp(); uint32_t task_id = 0; diff --git a/mindspore/ccsrc/debug/debug_services.cc b/mindspore/ccsrc/debug/debug_services.cc index fb29367ac44..d7f5d8a889d 100644 --- a/mindspore/ccsrc/debug/debug_services.cc +++ b/mindspore/ccsrc/debug/debug_services.cc @@ -33,6 +33,7 @@ #include "debug/debugger/debugger.h" #include "debug/anf_ir_utils.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #endif #include "nlohmann/json.hpp" #include "debug/debugger/tensor_summary.h" @@ -1738,7 +1739,7 @@ bool DebugServices::IsWatchPoint(const std::string &kernel_name, const CNodePtr bool DebugServices::IsWatchPointNodeInput(const std::string &w_name, const CNodePtr &kernel) const { if (kernel != nullptr && w_name.length() > 0) { - auto input_size = AnfAlgo::GetInputTensorNum(kernel); + auto input_size = common::AnfAlgo::GetInputTensorNum(kernel); for (size_t j = 0; j < input_size; ++j) { auto input_kernel = kernel->input(j + 1); std::string input_kernel_name = GetKernelNodeName(input_kernel); @@ -1823,7 +1824,7 @@ void DebugServices::ResetLoadedTensors() { std::vector> DebugServices::GetNodeTensor(const CNodePtr &kernel) { MS_EXCEPTION_IF_NULL(kernel); std::vector> result; - auto output_size = AnfAlgo::GetOutputTensorNum(kernel); + auto output_size = common::AnfAlgo::GetOutputTensorNum(kernel); auto kernel_name = GetKernelNodeName(kernel); for (size_t j = 0; j < output_size; ++j) { auto tensor_name_with_slot = kernel_name + ":" + std::to_string(j); diff --git a/mindspore/ccsrc/debug/debugger/debugger.cc b/mindspore/ccsrc/debug/debugger/debugger.cc index 311ef879124..6c2ad1cdb80 100644 --- a/mindspore/ccsrc/debug/debugger/debugger.cc +++ b/mindspore/ccsrc/debug/debugger/debugger.cc @@ -29,12 +29,13 @@ #include "debug/data_dump/dump_json_parser.h" #include "pipeline/jit/pipeline.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "runtime/device/kernel_runtime_manager.h" #include "runtime/device/kernel_runtime.h" #include "debug/data_dump/e2e_dump.h" -#include "utils/config_manager.h" +#include "include/common/utils/config_manager.h" #include "debug/env_config_parser.h" -#include "utils/comm_manager.h" +#include "include/common/utils/comm_manager.h" #include "runtime/hardware/device_context_manager.h" #include "debug/anf_ir_dump.h" #include "debug/anf_ir_utils.h" @@ -737,7 +738,7 @@ void Debugger::CheckDatasetGraph() { // check if there is GetNext or InitDataSetQueue node const auto &nodes = graph_ptr_->execution_order(); for (const auto &node : nodes) { - auto node_name = AnfAlgo::GetCNodeName(node); + auto node_name = common::AnfAlgo::GetCNodeName(node); MS_LOG(INFO) << "node: " << GetKernelNodeName(node); if (node_name == "GetNext" || node_name == "InitDataSetQueue") { MS_LOG(INFO) << "Not enabling debugger for graph " << graph_ptr_->graph_id() << ": found dataset graph node " @@ -1515,7 +1516,7 @@ void Debugger::LoadSingleAnfnode(const AnfNodePtr &anf_node, const size_t output // When MindRT is used, only ValueNodes and ParameterWeights can be loaded from device to host if (MsContext::GetInstance()->get_param(MS_CTX_ENABLE_MINDRT)) { if (!anf_node->isa() && - !(anf_node->isa() && AnfAlgo::IsParameterWeight(anf_node->cast()))) { + !(anf_node->isa() && common::AnfAlgo::IsParameterWeight(anf_node->cast()))) { return; } } @@ -1529,7 +1530,7 @@ void Debugger::LoadSingleAnfnode(const AnfNodePtr &anf_node, const size_t output } auto addr = AnfAlgo::GetOutputAddr(anf_node, output_index); MS_EXCEPTION_IF_NULL(addr); - auto type = AnfAlgo::GetOutputInferDataType(anf_node, output_index); + auto type = common::AnfAlgo::GetOutputInferDataType(anf_node, output_index); if (!IsTypeDebuggerSupported(type)) { return; } @@ -1684,7 +1685,7 @@ void Debugger::LoadGraphOutputs() { for (const auto &node : apply_kernels) { MS_EXCEPTION_IF_NULL(node); std::string kernel_name = GetKernelNodeName(node); - auto output_size = AnfAlgo::GetOutputTensorNum(node); + auto output_size = common::AnfAlgo::GetOutputTensorNum(node); if (partial_memory_) { if (!debug_services_->IsWatchPoint(kernel_name, node)) { continue; @@ -1697,7 +1698,7 @@ void Debugger::LoadGraphOutputs() { } auto addr = AnfAlgo::GetOutputAddr(node, j); MS_EXCEPTION_IF_NULL(addr); - auto type = AnfAlgo::GetOutputInferDataType(node, j); + auto type = common::AnfAlgo::GetOutputInferDataType(node, j); if (!IsTypeDebuggerSupported(type)) { continue; } diff --git a/mindspore/ccsrc/debug/debugger/debugger.h b/mindspore/ccsrc/debug/debugger/debugger.h index f180929ef19..7c088f39bd2 100644 --- a/mindspore/ccsrc/debug/debugger/debugger.h +++ b/mindspore/ccsrc/debug/debugger/debugger.h @@ -25,7 +25,7 @@ #include "backend/common/session/kernel_graph.h" #include "debug/debugger/grpc_client.h" #include "debug/debug_services.h" -#include "utils/ms_device_shape_transfer.h" +#include "runtime/device/ms_device_shape_transfer.h" #ifdef ENABLE_D #include "debug/dump_data_builder.h" #endif diff --git a/mindspore/ccsrc/debug/debugger/debugger_utils.cc b/mindspore/ccsrc/debug/debugger/debugger_utils.cc index 9525b2859b1..aebc5c8ec26 100644 --- a/mindspore/ccsrc/debug/debugger/debugger_utils.cc +++ b/mindspore/ccsrc/debug/debugger/debugger_utils.cc @@ -27,9 +27,10 @@ #include "debug/dump_data_builder.h" #endif #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "kernel/kernel.h" #include "debug/data_dump/e2e_dump.h" -#include "utils/config_manager.h" +#include "include/common/utils/config_manager.h" #include "backend/common/session/session_basic.h" constexpr int kFailure = 1; @@ -73,12 +74,12 @@ void LoadInputs(const CNodePtr &cnode, const KernelLaunchInfo *launch_info, uint const DeviceContext *device_context) { // get inputs auto kernel_inputs = launch_info->inputs_; - auto input_size = AnfAlgo::GetInputTensorNum(cnode); + auto input_size = common::AnfAlgo::GetInputTensorNum(cnode); for (size_t j = 0; j < input_size; ++j) { auto input_kernel = cnode->input(j + 1); std::string input_kernel_name = GetKernelNodeName(input_kernel); auto addr = kernel_inputs[j]; - auto type = AnfAlgo::GetOutputInferDataType(input_kernel, PARAMETER_OUTPUT_INDEX); + auto type = common::AnfAlgo::GetOutputInferDataType(input_kernel, PARAMETER_OUTPUT_INDEX); // For example, this happens with the Depend op if (type == kMetaTypeNone) { continue; @@ -107,14 +108,14 @@ void LoadOutputs(const CNodePtr &cnode, const KernelLaunchInfo *launch_info, uin uint32_t root_graph_id, const DeviceContext *device_context) { // get outputs auto kernel_outputs = launch_info->outputs_; - auto output_size = AnfAlgo::GetOutputTensorNum(cnode); - auto node_name = AnfAlgo::GetCNodeName(cnode); + auto output_size = common::AnfAlgo::GetOutputTensorNum(cnode); + auto node_name = common::AnfAlgo::GetCNodeName(cnode); std::string kernel_name = GetKernelNodeName(cnode); std::vector real_outputs = CheckRealOutput(node_name, output_size); for (size_t j : real_outputs) { auto addr = kernel_outputs[j]; - auto type = AnfAlgo::GetOutputInferDataType(cnode, j); + auto type = common::AnfAlgo::GetOutputInferDataType(cnode, j); // For example, this happens with the Depend op if (type == kMetaTypeNone) { continue; @@ -209,7 +210,7 @@ void ReadDataAndDump(const CNodePtr &cnode, const KernelLaunchInfo *launch_info, } if (IsDeviceTargetGPU()) { // check if the node is last kernel - bool last_kernel = !AnfAlgo::IsInplaceNode(cnode, "skip"); + bool last_kernel = !common::AnfAlgo::IsInplaceNode(cnode, "skip"); debugger->PostExecuteNode(cnode, last_kernel); } } diff --git a/mindspore/ccsrc/debug/draw.cc b/mindspore/ccsrc/debug/draw.cc index 0ae3b3cc248..915412abe4a 100644 --- a/mindspore/ccsrc/debug/draw.cc +++ b/mindspore/ccsrc/debug/draw.cc @@ -20,14 +20,14 @@ #include #include #include -#include #include "ir/meta_func_graph.h" #include "ir/param_info.h" #include "ir/primitive.h" #include "ir/graph_utils.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "frontend/operator/composite/composite.h" +#include "frontend/parallel/ops_info/operator_info.h" #include "pipeline/jit/parse/resolve.h" #include "ir/tensor.h" diff --git a/mindspore/ccsrc/debug/dump_proto.cc b/mindspore/ccsrc/debug/dump_proto.cc index 2d025802c93..427d0f5c6a3 100644 --- a/mindspore/ccsrc/debug/dump_proto.cc +++ b/mindspore/ccsrc/debug/dump_proto.cc @@ -27,9 +27,10 @@ #include "ir/graph_utils.h" #include "utils/ms_context.h" #include "utils/symbolic.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "pipeline/jit/base.h" #include "utils/anf_utils.h" +#include "mindspore/ccsrc/frontend/parallel/ops_info/operator_info.h" namespace mindspore { class ProtoExporter { diff --git a/mindspore/ccsrc/debug/rdr/base_recorder.cc b/mindspore/ccsrc/debug/rdr/base_recorder.cc index 7a12e7686e4..827b6a40e67 100644 --- a/mindspore/ccsrc/debug/rdr/base_recorder.cc +++ b/mindspore/ccsrc/debug/rdr/base_recorder.cc @@ -15,8 +15,8 @@ */ #include "debug/rdr/base_recorder.h" #include "debug/common.h" -#include "utils/utils.h" -#include "utils/comm_manager.h" +#include "include/common/utils/utils.h" +#include "include/common/utils/comm_manager.h" namespace mindspore { std::optional BaseRecorder::GetFileRealPath(const std::string &suffix) const { diff --git a/mindspore/ccsrc/debug/rdr/graph_exec_order_recorder.cc b/mindspore/ccsrc/debug/rdr/graph_exec_order_recorder.cc index 810a44ef336..6326bd6405c 100644 --- a/mindspore/ccsrc/debug/rdr/graph_exec_order_recorder.cc +++ b/mindspore/ccsrc/debug/rdr/graph_exec_order_recorder.cc @@ -18,7 +18,8 @@ #include "mindspore/core/ir/anf.h" #include "mindspore/core/utils/log_adapter.h" #include "backend/common/session/anf_runtime_algorithm.h" -#include "utils/utils.h" +#include "include/common/utils/anfalgo.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace { diff --git a/mindspore/ccsrc/debug/rdr/stream_exec_order_recorder.cc b/mindspore/ccsrc/debug/rdr/stream_exec_order_recorder.cc index f254c74746d..71a42a93981 100644 --- a/mindspore/ccsrc/debug/rdr/stream_exec_order_recorder.cc +++ b/mindspore/ccsrc/debug/rdr/stream_exec_order_recorder.cc @@ -19,7 +19,8 @@ #include "mindspore/core/ir/anf.h" #include "mindspore/core/utils/log_adapter.h" #include "backend/common/session/anf_runtime_algorithm.h" -#include "utils/utils.h" +#include "include/common/utils/anfalgo.h" +#include "include/common/utils/utils.h" namespace mindspore { std::string Vector2String(const std::vector &v) { diff --git a/mindspore/ccsrc/debug/rdr/stream_exec_order_recorder.h b/mindspore/ccsrc/debug/rdr/stream_exec_order_recorder.h index d9957178239..68662bfe905 100644 --- a/mindspore/ccsrc/debug/rdr/stream_exec_order_recorder.h +++ b/mindspore/ccsrc/debug/rdr/stream_exec_order_recorder.h @@ -21,6 +21,7 @@ #include #include "nlohmann/json.hpp" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "debug/rdr/base_recorder.h" using json = nlohmann::json; @@ -64,24 +65,24 @@ class StreamExecOrderRecorder : public BaseRecorder { ExecNode(i, cur_cnode_ptr->fullname_with_scope(), AnfAlgo::GetStreamDistinctionLabel(cur_cnode_ptr.get()), AnfAlgo::GetStreamId(cur_cnode_ptr), cur_cnode_ptr->DebugString()); - if (AnfAlgo::HasNodeAttr(kAttrEventId, cur_cnode_ptr)) { - exec_node.SetEventId(AnfAlgo::GetNodeAttr(cur_cnode_ptr, kAttrEventId)); + if (common::AnfAlgo::HasNodeAttr(kAttrEventId, cur_cnode_ptr)) { + exec_node.SetEventId(common::AnfAlgo::GetNodeAttr(cur_cnode_ptr, kAttrEventId)); } - if (AnfAlgo::HasNodeAttr(kAttrLabelIndex, cur_cnode_ptr)) { - exec_node.SetLabelId(AnfAlgo::GetNodeAttr(cur_cnode_ptr, kAttrLabelIndex)); + if (common::AnfAlgo::HasNodeAttr(kAttrLabelIndex, cur_cnode_ptr)) { + exec_node.SetLabelId(common::AnfAlgo::GetNodeAttr(cur_cnode_ptr, kAttrLabelIndex)); } - if (AnfAlgo::HasNodeAttr(kAttrLabelSwitchList, cur_cnode_ptr)) { - auto label_list = AnfAlgo::GetNodeAttr>(cur_cnode_ptr, kAttrLabelSwitchList); + if (common::AnfAlgo::HasNodeAttr(kAttrLabelSwitchList, cur_cnode_ptr)) { + auto label_list = common::AnfAlgo::GetNodeAttr>(cur_cnode_ptr, kAttrLabelSwitchList); for (size_t j = 0; j < label_list.size(); ++j) { exec_node.SetLabelId(label_list[j]); } } std::string active_stream_str; - if (AnfAlgo::HasNodeAttr(kAttrActiveStreamList, cur_cnode_ptr)) { - auto stream_list = AnfAlgo::GetNodeAttr>(cur_cnode_ptr, kAttrActiveStreamList); + if (common::AnfAlgo::HasNodeAttr(kAttrActiveStreamList, cur_cnode_ptr)) { + auto stream_list = common::AnfAlgo::GetNodeAttr>(cur_cnode_ptr, kAttrActiveStreamList); for (size_t j = 0; j < stream_list.size(); ++j) { exec_node.SetActiveStreamId(stream_list[j]); } diff --git a/mindspore/ccsrc/debug/rdr/string_recorder.cc b/mindspore/ccsrc/debug/rdr/string_recorder.cc index 983d952e05d..1e9421a8a4a 100644 --- a/mindspore/ccsrc/debug/rdr/string_recorder.cc +++ b/mindspore/ccsrc/debug/rdr/string_recorder.cc @@ -17,7 +17,7 @@ #include #include #include "debug/common.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "mindspore/core/utils/log_adapter.h" namespace mindspore { diff --git a/mindspore/ccsrc/debug/trace.cc b/mindspore/ccsrc/debug/trace.cc index 1880765fdfb..50568ce1e50 100644 --- a/mindspore/ccsrc/debug/trace.cc +++ b/mindspore/ccsrc/debug/trace.cc @@ -38,7 +38,7 @@ #include "pipeline/jit/static_analysis/async_eval_result.h" #include "pipeline/jit/base.h" #include "utils/log_adapter.h" -#include "utils/comm_manager.h" +#include "include/common/utils/comm_manager.h" #include "abstract/abstract_value.h" #include "utils/file_utils.h" diff --git a/mindspore/ccsrc/distributed/persistent/storage/block.cc b/mindspore/ccsrc/distributed/persistent/storage/block.cc index 45255012cec..dc20f63ba18 100644 --- a/mindspore/ccsrc/distributed/persistent/storage/block.cc +++ b/mindspore/ccsrc/distributed/persistent/storage/block.cc @@ -17,7 +17,7 @@ #include "distributed/persistent/storage/block.h" #include "utils/system/sha256.h" #include "utils/log_adapter.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace distributed { diff --git a/mindspore/ccsrc/distributed/persistent/storage/file_io_utils.cc b/mindspore/ccsrc/distributed/persistent/storage/file_io_utils.cc index f1e2f402b1a..4bb7605a0e0 100644 --- a/mindspore/ccsrc/distributed/persistent/storage/file_io_utils.cc +++ b/mindspore/ccsrc/distributed/persistent/storage/file_io_utils.cc @@ -20,7 +20,7 @@ #include #include -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/convert_utils_base.h" #include "utils/log_adapter.h" diff --git a/mindspore/ccsrc/distributed/persistent/storage/json_utils.cc b/mindspore/ccsrc/distributed/persistent/storage/json_utils.cc index 8eea692b175..6ac1734e649 100644 --- a/mindspore/ccsrc/distributed/persistent/storage/json_utils.cc +++ b/mindspore/ccsrc/distributed/persistent/storage/json_utils.cc @@ -16,7 +16,7 @@ #include "distributed/persistent/storage/json_utils.h" #include "distributed/persistent/storage/file_io_utils.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace distributed { diff --git a/mindspore/ccsrc/distributed/persistent/storage/json_utils.h b/mindspore/ccsrc/distributed/persistent/storage/json_utils.h index 7883604be3e..74ef95ba094 100644 --- a/mindspore/ccsrc/distributed/persistent/storage/json_utils.h +++ b/mindspore/ccsrc/distributed/persistent/storage/json_utils.h @@ -20,7 +20,7 @@ #include #include -#include "utils/json_operation_utils.h" +#include "include/common/utils/json_operation_utils.h" #include "nlohmann/json.hpp" namespace mindspore { diff --git a/mindspore/ccsrc/distributed/persistent/storage/local_file.cc b/mindspore/ccsrc/distributed/persistent/storage/local_file.cc index 968e437f401..d58f79c55f3 100644 --- a/mindspore/ccsrc/distributed/persistent/storage/local_file.cc +++ b/mindspore/ccsrc/distributed/persistent/storage/local_file.cc @@ -26,7 +26,7 @@ #include "utils/convert_utils_base.h" #include "utils/file_utils.h" #include "utils/log_adapter.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "distributed/persistent/storage/constants.h" namespace mindspore { diff --git a/mindspore/ccsrc/fl/server/common.h b/mindspore/ccsrc/fl/server/common.h index bfd9e8c3065..b76a0bbb58c 100644 --- a/mindspore/ccsrc/fl/server/common.h +++ b/mindspore/ccsrc/fl/server/common.h @@ -26,7 +26,7 @@ #include "proto/ps.pb.h" #include "proto/fl.pb.h" #include "ir/anf.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "ir/dtype/type_id.h" #include "plugin/device/cpu/kernel/cpu_kernel.h" #include "schema/fl_job_generated.h" @@ -265,8 +265,8 @@ inline size_t GetTypeIdByte(const TypeId &type) { inline AddressPtr GenerateParameterNodeAddrPtr(const CNodePtr &kernel_node, size_t param_idx) { MS_ERROR_IF_NULL_W_RET_VAL(kernel_node, nullptr); - auto param_node = - AnfAlgo::VisitKernelWithReturnType(AnfAlgo::GetInputNode(kernel_node, param_idx), 0).first->cast(); + auto param_node = common::AnfAlgo::VisitKernelWithReturnType(common::AnfAlgo::GetInputNode(kernel_node, param_idx), 0) + .first->cast(); MS_ERROR_IF_NULL_W_RET_VAL(param_node, nullptr); auto param_tensor = param_node->default_param()->cast(); MS_ERROR_IF_NULL_W_RET_VAL(param_tensor, nullptr); diff --git a/mindspore/ccsrc/fl/server/executor.cc b/mindspore/ccsrc/fl/server/executor.cc index 42057616e61..42470e01625 100644 --- a/mindspore/ccsrc/fl/server/executor.cc +++ b/mindspore/ccsrc/fl/server/executor.cc @@ -213,13 +213,14 @@ bool Executor::unmasked() const { std::string Executor::GetTrainableParamName(const CNodePtr &cnode) { MS_EXCEPTION_IF_NULL(cnode); - std::string cnode_name = AnfAlgo::GetCNodeName(cnode); + std::string cnode_name = common::AnfAlgo::GetCNodeName(cnode); if (kNameToIdxMap.count(cnode_name) == 0) { return ""; } const OptimParamNameToIndex &index_info = kNameToIdxMap.at(cnode_name); size_t weight_idx = index_info.at("inputs").at(kWeight); - AnfNodePtr weight_node = AnfAlgo::VisitKernelWithReturnType(AnfAlgo::GetInputNode(cnode, weight_idx), 0).first; + AnfNodePtr weight_node = + common::AnfAlgo::VisitKernelWithReturnType(common::AnfAlgo::GetInputNode(cnode, weight_idx), 0).first; MS_EXCEPTION_IF_NULL(weight_node); if (!weight_node->isa()) { MS_LOG(EXCEPTION) << weight_idx << " input of " << cnode_name << " is not a Parameter."; diff --git a/mindspore/ccsrc/fl/server/kernel/aggregation_kernel_factory.cc b/mindspore/ccsrc/fl/server/kernel/aggregation_kernel_factory.cc index fdcf78d4757..0bee4d8da60 100644 --- a/mindspore/ccsrc/fl/server/kernel/aggregation_kernel_factory.cc +++ b/mindspore/ccsrc/fl/server/kernel/aggregation_kernel_factory.cc @@ -23,7 +23,7 @@ namespace server { namespace kernel { bool AggregationKernelFactory::Matched(const ParamsInfo ¶ms_info, const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - std::string cnode_name = AnfAlgo::GetCNodeName(kernel_node); + std::string cnode_name = common::AnfAlgo::GetCNodeName(kernel_node); if (kNameToIdxMap.count(cnode_name) == 0) { MS_LOG(ERROR) << "Can't find index info for kernel " << cnode_name; return false; @@ -40,7 +40,7 @@ bool AggregationKernelFactory::Matched(const ParamsInfo ¶ms_info, const CNod continue; } size_t input_idx = input_name_to_idx.at(name); - TypeId kernel_node_input_type = AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, input_idx); + TypeId kernel_node_input_type = common::AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, input_idx); TypeId registered_input_type = one_input_name_type.second; if (registered_input_type != kernel_node_input_type) { return false; @@ -58,7 +58,7 @@ bool AggregationKernelFactory::Matched(const ParamsInfo ¶ms_info, const CNod continue; } size_t output_idx = output_name_to_idx.at(name); - TypeId kernel_node_output_type = AnfAlgo::GetOutputInferDataType(kernel_node, output_idx); + TypeId kernel_node_output_type = common::AnfAlgo::GetOutputInferDataType(kernel_node, output_idx); TypeId registered_output_type = one_output_name_type.second; if (registered_output_type != kernel_node_output_type) { return false; diff --git a/mindspore/ccsrc/fl/server/kernel/fed_avg_kernel.h b/mindspore/ccsrc/fl/server/kernel/fed_avg_kernel.h index 68f5a8f58a7..4e07bde8291 100644 --- a/mindspore/ccsrc/fl/server/kernel/fed_avg_kernel.h +++ b/mindspore/ccsrc/fl/server/kernel/fed_avg_kernel.h @@ -54,14 +54,14 @@ class FedAvgKernel : public AggregationKernelMod { void InitKernel(const CNodePtr &kernel_node) override { MS_EXCEPTION_IF_NULL(kernel_node); - std::string cnode_name = AnfAlgo::GetCNodeName(kernel_node); + std::string cnode_name = common::AnfAlgo::GetCNodeName(kernel_node); if (kNameToIdxMap.count(cnode_name) == 0 || kNameToIdxMap.at(cnode_name).count("inputs") == 0 || kNameToIdxMap.at(cnode_name).at("inputs").count("weight") == 0) { MS_LOG(EXCEPTION) << "Can't find index info of weight for kernel " << cnode_name; return; } cnode_weight_idx_ = kNameToIdxMap.at(cnode_name).at("inputs").at("weight"); - std::vector weight_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, cnode_weight_idx_); + std::vector weight_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, cnode_weight_idx_); size_t weight_size = std::accumulate(weight_shape.begin(), weight_shape.end(), sizeof(T), std::multiplies()); size_t new_weight_size = weight_size; @@ -72,7 +72,8 @@ class FedAvgKernel : public AggregationKernelMod { input_size_list_.push_back(sizeof(size_t)); auto weight_node = - AnfAlgo::VisitKernelWithReturnType(AnfAlgo::GetInputNode(kernel_node, cnode_weight_idx_), 0).first; + common::AnfAlgo::VisitKernelWithReturnType(common::AnfAlgo::GetInputNode(kernel_node, cnode_weight_idx_), 0) + .first; MS_EXCEPTION_IF_NULL(weight_node); name_ = cnode_name + "." + weight_node->fullname_with_scope(); first_cnt_handler_ = [&](std::shared_ptr) { diff --git a/mindspore/ccsrc/fl/server/kernel/optimizer_kernel.h b/mindspore/ccsrc/fl/server/kernel/optimizer_kernel.h index c9c55709ba2..88e078fd29f 100644 --- a/mindspore/ccsrc/fl/server/kernel/optimizer_kernel.h +++ b/mindspore/ccsrc/fl/server/kernel/optimizer_kernel.h @@ -67,17 +67,17 @@ class OptimizerKernelMod : public NativeCpuKernelMod { void InitServerKernelInputOutputSize(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); size_t type_size = sizeof(float); for (size_t input_index = 0; input_index < input_num; ++input_index) { - std::vector shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, input_index); + std::vector shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, input_index); size_t tensor_size = shape.empty() ? type_size : std::accumulate(shape.begin(), shape.end(), type_size, std::multiplies()); input_size_list_.emplace_back(tensor_size); } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); for (size_t output_index = 0; output_index < output_num; ++output_index) { - std::vector shape = AnfAlgo::GetOutputInferShape(kernel_node, output_index); + std::vector shape = common::AnfAlgo::GetOutputInferShape(kernel_node, output_index); size_t tensor_size = shape.empty() ? type_size : std::accumulate(shape.begin(), shape.end(), type_size, std::multiplies()); output_size_list_.emplace_back(tensor_size); diff --git a/mindspore/ccsrc/fl/server/parameter_aggregator.cc b/mindspore/ccsrc/fl/server/parameter_aggregator.cc index 993be972319..9d3b01ccca5 100644 --- a/mindspore/ccsrc/fl/server/parameter_aggregator.cc +++ b/mindspore/ccsrc/fl/server/parameter_aggregator.cc @@ -35,7 +35,7 @@ bool ParameterAggregator::Init(const CNodePtr &cnode, size_t threshold_count) { // required_pull_count_ normally used in parameter server training mode. required_pull_count_ = threshold_count; - MS_LOG(INFO) << "Start initializing kernels for " << AnfAlgo::GetCNodeName(cnode); + MS_LOG(INFO) << "Start initializing kernels for " << common::AnfAlgo::GetCNodeName(cnode); if (!InitAggregationKernels(cnode)) { MS_LOG(EXCEPTION) << "Initializing aggregation kernels failed."; return false; @@ -161,14 +161,16 @@ bool ParameterAggregator::requires_aggr() const { return requires_aggr_; } bool ParameterAggregator::InitAggregationKernels(const CNodePtr &cnode) { MS_EXCEPTION_IF_NULL(cnode); if (!JudgeRequiredAggr(cnode)) { - MS_LOG(WARNING) << "Aggregation for weight of kernel " << AnfAlgo::GetCNodeName(cnode) << " is not required."; + MS_LOG(WARNING) << "Aggregation for weight of kernel " << common::AnfAlgo::GetCNodeName(cnode) + << " is not required."; } std::vector aggr_kernel_names = SelectAggregationAlgorithm(cnode); for (const std::string &name : aggr_kernel_names) { auto aggr_kernel = kernel::AggregationKernelFactory::GetInstance().Create(name, cnode); if (aggr_kernel == nullptr) { - MS_LOG(EXCEPTION) << "Fail to create aggregation kernel " << name << " for " << AnfAlgo::GetCNodeName(cnode); + MS_LOG(EXCEPTION) << "Fail to create aggregation kernel " << name << " for " + << common::AnfAlgo::GetCNodeName(cnode); return false; } @@ -232,8 +234,8 @@ bool ParameterAggregator::AssignMemory(const K server_kernel, const CNodePtr &cn // Reusing memory of the kernel node means the memory of the input is already assigned by the front end, which // is to say, the input node is a parameter node. size_t index = reuse_kernel_node_inputs_info.at(name); - MS_LOG(INFO) << "Try to reuse memory of kernel node " << AnfAlgo::GetCNodeName(cnode) << " for parameter " << name - << ", kernel node index " << index; + MS_LOG(INFO) << "Try to reuse memory of kernel node " << common::AnfAlgo::GetCNodeName(cnode) << " for parameter " + << name << ", kernel node index " << index; AddressPtr input_addr = GenerateParameterNodeAddrPtr(cnode, index); MS_EXCEPTION_IF_NULL(input_addr); memory_register->RegisterAddressPtr(name, input_addr); @@ -289,14 +291,15 @@ std::vector ParameterAggregator::SelectAggregationAlgorithm(const C bool ParameterAggregator::JudgeRequiredAggr(const CNodePtr &cnode) { MS_EXCEPTION_IF_NULL(cnode); - std::string cnode_name = AnfAlgo::GetCNodeName(cnode); + std::string cnode_name = common::AnfAlgo::GetCNodeName(cnode); if (kNameToIdxMap.count(cnode_name) == 0 || kNameToIdxMap.at(cnode_name).count("inputs") == 0 || kNameToIdxMap.at(cnode_name).at("inputs").count("weight") == 0) { MS_LOG(EXCEPTION) << "Can't find index info of weight for kernel " << cnode_name; return false; } size_t cnode_weight_idx = kNameToIdxMap.at(cnode_name).at("inputs").at("weight"); - auto weight_node = AnfAlgo::VisitKernelWithReturnType(AnfAlgo::GetInputNode(cnode, cnode_weight_idx), 0).first; + auto weight_node = + common::AnfAlgo::VisitKernelWithReturnType(common::AnfAlgo::GetInputNode(cnode, cnode_weight_idx), 0).first; MS_EXCEPTION_IF_NULL(weight_node); if (!weight_node->isa()) { diff --git a/mindspore/ccsrc/frontend/operator/cc_implementations.cc b/mindspore/ccsrc/frontend/operator/cc_implementations.cc index 7ead7240180..6b86479637a 100644 --- a/mindspore/ccsrc/frontend/operator/cc_implementations.cc +++ b/mindspore/ccsrc/frontend/operator/cc_implementations.cc @@ -20,7 +20,7 @@ #include #include #include "utils/log_adapter.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" #include "utils/ms_utils.h" namespace mindspore { diff --git a/mindspore/ccsrc/frontend/operator/composite/composite.cc b/mindspore/ccsrc/frontend/operator/composite/composite.cc index 9c403c7f99c..e2c19dc4d13 100644 --- a/mindspore/ccsrc/frontend/operator/composite/composite.cc +++ b/mindspore/ccsrc/frontend/operator/composite/composite.cc @@ -33,7 +33,7 @@ #include "ir/signature.h" #include "debug/trace.h" #include "utils/ms_context.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { // namespace to support composite operators definition diff --git a/mindspore/ccsrc/frontend/operator/composite/composite.h b/mindspore/ccsrc/frontend/operator/composite/composite.h index b65952a3b1a..8b4aadffc16 100644 --- a/mindspore/ccsrc/frontend/operator/composite/composite.h +++ b/mindspore/ccsrc/frontend/operator/composite/composite.h @@ -37,6 +37,7 @@ #include "utils/any.h" #include "ir/dtype.h" #include "ir/meta_func_graph.h" +#include "base/core_ops.h" namespace mindspore { // namespace to support composite operators definition diff --git a/mindspore/ccsrc/frontend/operator/ops_extends.cc b/mindspore/ccsrc/frontend/operator/ops_extends.cc index 87fd1721090..e7a82b321b4 100755 --- a/mindspore/ccsrc/frontend/operator/ops_extends.cc +++ b/mindspore/ccsrc/frontend/operator/ops_extends.cc @@ -16,13 +16,14 @@ #include "frontend/operator/ops.h" #include -#include "pipeline/jit/parse/python_adapter.h" +#include "include/common/utils/python_adapter.h" +#include "pipeline/jit/parse/data_converter.h" namespace mindspore { // namespace to support primitive operators namespace prim { ValuePtr GetPythonOps(const std::string &op_name, const std::string &module_name, bool use_signature) { - py::object obj = parse::python_adapter::GetPyFn(module_name, op_name); + py::object obj = python_adapter::GetPyFn(module_name, op_name); ValuePtr node = nullptr; bool succ = parse::ConvertData(obj, &node, use_signature); if (!succ) { diff --git a/mindspore/ccsrc/frontend/operator/ops_front_infer_function.cc b/mindspore/ccsrc/frontend/operator/ops_front_infer_function.cc index f664d8ec7e5..c026e168ad0 100644 --- a/mindspore/ccsrc/frontend/operator/ops_front_infer_function.cc +++ b/mindspore/ccsrc/frontend/operator/ops_front_infer_function.cc @@ -28,8 +28,8 @@ #include "pybind_api/ir/tensor_py.h" #include "frontend/operator/ops.h" #include "abstract/infer_functions.h" -#include "utils/convert_utils_py.h" -#include "utils/utils.h" +#include "include/common/utils/convert_utils_py.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace abstract { diff --git a/mindspore/ccsrc/frontend/optimizer/ad/dfunctor.h b/mindspore/ccsrc/frontend/optimizer/ad/dfunctor.h index ed0ef863130..80d1c5a4fa9 100644 --- a/mindspore/ccsrc/frontend/optimizer/ad/dfunctor.h +++ b/mindspore/ccsrc/frontend/optimizer/ad/dfunctor.h @@ -35,7 +35,7 @@ #include "frontend/optimizer/ad/pynative_dfunctor.h" #include "frontend/operator/ops.h" #include "debug/trace.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace ad { diff --git a/mindspore/ccsrc/frontend/optimizer/ad/kprim.cc b/mindspore/ccsrc/frontend/optimizer/ad/kprim.cc index c02664cb66b..697d300cd71 100644 --- a/mindspore/ccsrc/frontend/optimizer/ad/kprim.cc +++ b/mindspore/ccsrc/frontend/optimizer/ad/kprim.cc @@ -33,9 +33,9 @@ #include "frontend/optimizer/ad/dfunctor.h" #include "frontend/operator/ops.h" #include "frontend/operator/composite/composite.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/symbolic.h" -#include "utils/primitive_utils.h" +#include "include/common/utils/primitive_utils.h" #include "utils/ms_context.h" #include "utils/info.h" #include "debug/trace.h" @@ -441,7 +441,7 @@ FuncGraphPtr KPrim::GetPossibleBprop(const PrimitivePtr &prim) { FuncGraphPtr KPrim::GetFprop(const PrimitivePtr &prim) { static const std::string ad_module = "mindspore.ops._grad.grad_implementations"; std::string func_name = "_fprop_" + prim->name(); - py::function fn = parse::python_adapter::GetPyFn(ad_module, func_name); + py::function fn = python_adapter::GetPyFn(ad_module, func_name); auto func_graph = parse::ParsePythonCode(fn); MS_EXCEPTION_IF_NULL(func_graph); return BasicClone(func_graph); diff --git a/mindspore/ccsrc/frontend/optimizer/cse.cc b/mindspore/ccsrc/frontend/optimizer/cse.cc index 2a6fc3181f6..d80f40d4e7e 100644 --- a/mindspore/ccsrc/frontend/optimizer/cse.cc +++ b/mindspore/ccsrc/frontend/optimizer/cse.cc @@ -24,7 +24,7 @@ #include "utils/hash_map.h" #include "abstract/abstract_function.h" #include "utils/flags.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/anf_utils.h" namespace mindspore { diff --git a/mindspore/ccsrc/frontend/optimizer/environ_conversion.cc b/mindspore/ccsrc/frontend/optimizer/environ_conversion.cc index 1416c98797a..943c6f0dbf1 100644 --- a/mindspore/ccsrc/frontend/optimizer/environ_conversion.cc +++ b/mindspore/ccsrc/frontend/optimizer/environ_conversion.cc @@ -23,7 +23,7 @@ #include "utils/hash_map.h" #include "abstract/abstract_function.h" #include "utils/flags.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/anf_utils.h" #include "utils/symbolic.h" diff --git a/mindspore/ccsrc/frontend/optimizer/irpass/branch_culling.cc b/mindspore/ccsrc/frontend/optimizer/irpass/branch_culling.cc index 445db797f8d..e50f8dc76f8 100644 --- a/mindspore/ccsrc/frontend/optimizer/irpass/branch_culling.cc +++ b/mindspore/ccsrc/frontend/optimizer/irpass/branch_culling.cc @@ -22,6 +22,7 @@ #include "utils/hash_map.h" #include "ir/func_graph.h" #include "frontend/operator/ops.h" +#include "include/common/utils/convert_utils.h" namespace mindspore { namespace opt { diff --git a/mindspore/ccsrc/frontend/optimizer/irpass/cast_eliminate.cc b/mindspore/ccsrc/frontend/optimizer/irpass/cast_eliminate.cc index df1886cf69d..501fe3e8bc4 100644 --- a/mindspore/ccsrc/frontend/optimizer/irpass/cast_eliminate.cc +++ b/mindspore/ccsrc/frontend/optimizer/irpass/cast_eliminate.cc @@ -20,7 +20,7 @@ #include "frontend/operator/ops.h" #include "ir/func_graph.h" #include "pipeline/jit/parse/data_converter.h" -#include "pipeline/jit/parse/python_adapter.h" +#include "include/common/utils/python_adapter.h" namespace mindspore { namespace opt { @@ -92,7 +92,7 @@ AnfNodePtr TwoCastEliminater::operator()(const OptimizerPtr &, const AnfNodePtr AnfVisitor::Match(prim::kPrimCast, {IsCNode, IsNode})(node); if (x_ != nullptr && t_ != nullptr) { - auto cast_op = parse::python_adapter::GetPyFn("mindspore.ops.operations", "Cast")(); + auto cast_op = python_adapter::GetPyFn("mindspore.ops.operations", "Cast")(); ValuePtr cast = parse::data_converter::PyDataToValue(cast_op); auto cnode = NewCNode({NewValueNode(cast), x_, t_}, node->func_graph()); cnode->set_abstract(node->abstract()); diff --git a/mindspore/ccsrc/frontend/optimizer/irpass/inline.h b/mindspore/ccsrc/frontend/optimizer/irpass/inline.h index 46b17e7c9e2..bedc0fc4e08 100644 --- a/mindspore/ccsrc/frontend/optimizer/irpass/inline.h +++ b/mindspore/ccsrc/frontend/optimizer/irpass/inline.h @@ -23,7 +23,7 @@ #include "utils/hash_map.h" #include "frontend/optimizer/irpass.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" #include "frontend/optimizer/optimizer.h" #include "frontend/optimizer/anf_visitor.h" #include "ir/func_graph.h" @@ -31,7 +31,7 @@ #include "ir/tensor.h" #include "frontend/operator/ops.h" #include "abstract/abstract_value.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace opt { diff --git a/mindspore/ccsrc/frontend/optimizer/irpass/item_tuple_or_list_eliminate.h b/mindspore/ccsrc/frontend/optimizer/irpass/item_tuple_or_list_eliminate.h index 753acec8ef1..83dda5fa6e4 100644 --- a/mindspore/ccsrc/frontend/optimizer/irpass/item_tuple_or_list_eliminate.h +++ b/mindspore/ccsrc/frontend/optimizer/irpass/item_tuple_or_list_eliminate.h @@ -27,7 +27,7 @@ #include "frontend/operator/ops.h" #include "frontend/optimizer/irpass.h" #include "frontend/optimizer/optimizer.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace opt { diff --git a/mindspore/ccsrc/frontend/optimizer/irpass/recompute_prepare.h b/mindspore/ccsrc/frontend/optimizer/irpass/recompute_prepare.h index a4b30e044d9..57b1ec49419 100644 --- a/mindspore/ccsrc/frontend/optimizer/irpass/recompute_prepare.h +++ b/mindspore/ccsrc/frontend/optimizer/irpass/recompute_prepare.h @@ -21,7 +21,7 @@ #include "frontend/optimizer/irpass.h" #include "frontend/optimizer/optimizer.h" #include "frontend/optimizer/anf_visitor.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" #include "ir/func_graph.h" namespace mindspore { @@ -54,8 +54,8 @@ class SetCellOutputNoRecompute : public AnfVisitor { for (const auto &real_output : real_outputs) { // Set the attr of cnode in case of shared primitives. real_output->AddAttr(kAttrRecompute, MakeValue(false)); - if (parallel::ParallelContext::GetInstance()->parallel_mode() == parallel::SEMI_AUTO_PARALLEL || - parallel::ParallelContext::GetInstance()->parallel_mode() == parallel::AUTO_PARALLEL) { + if (parallel::ParallelContext::GetInstance()->parallel_mode() == parallel::kSemiAutoParallel || + parallel::ParallelContext::GetInstance()->parallel_mode() == parallel::kAutoParallel) { auto prim = GetCNodePrimitive(real_output); if (prim->HasAttr(kAttrSliceActivation) && GetValue(prim->GetAttr(kAttrSliceActivation))) { real_output->AddAttr(kAttrSliceActivation, MakeValue(true)); diff --git a/mindspore/ccsrc/frontend/optimizer/irpass/special_op_eliminate.h b/mindspore/ccsrc/frontend/optimizer/irpass/special_op_eliminate.h index 066e5508cb9..44b2c11b253 100644 --- a/mindspore/ccsrc/frontend/optimizer/irpass/special_op_eliminate.h +++ b/mindspore/ccsrc/frontend/optimizer/irpass/special_op_eliminate.h @@ -30,8 +30,8 @@ #include "frontend/optimizer/irpass.h" #include "frontend/optimizer/irpass/prim_eliminate.h" #include "frontend/optimizer/optimizer.h" -#include "utils/comm_manager.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/comm_manager.h" +#include "include/common/utils/parallel_context.h" #include "pipeline/jit/parse/resolve.h" #include "frontend/parallel/step_parallel.h" #include "utils/tensor_construct_utils.h" @@ -655,8 +655,8 @@ class AllReduceConstElim : public OptimizerCaller { auto pattern = PPrimitive(prim::kPrimAllReduce, x); // If AllReduce takes constant value as input and values across devices are all the same(ensured by parallel mode) if (pattern.TryCapture(node) && IsVNode(x.GetNode(node)) && - (pattern.GetFuncGraph()->has_flag(parallel::AUTO_PARALLEL) || - pattern.GetFuncGraph()->has_flag(parallel::SEMI_AUTO_PARALLEL))) { + (pattern.GetFuncGraph()->has_flag(parallel::kAutoParallel) || + pattern.GetFuncGraph()->has_flag(parallel::kSemiAutoParallel))) { auto cur_func_graph = pattern.GetFuncGraph(); // If reduce operation is sum, then multiply constant by number of devices, otherwise just return the constant auto prim_cnode = pattern.GetOriginalNode(); diff --git a/mindspore/ccsrc/frontend/optimizer/irpass/symbol_resolver.h b/mindspore/ccsrc/frontend/optimizer/irpass/symbol_resolver.h index d61f99a4ae9..3919effc757 100644 --- a/mindspore/ccsrc/frontend/optimizer/irpass/symbol_resolver.h +++ b/mindspore/ccsrc/frontend/optimizer/irpass/symbol_resolver.h @@ -27,7 +27,7 @@ #include "frontend/operator/ops.h" #include "ir/pattern_matcher.h" #include "pipeline/jit/parse/data_converter.h" -#include "pipeline/jit/parse/python_adapter.h" +#include "include/common/utils/python_adapter.h" #include "pipeline/jit/parse/parse_base.h" namespace mindspore { diff --git a/mindspore/ccsrc/frontend/optimizer/recompute.cc b/mindspore/ccsrc/frontend/optimizer/recompute.cc index e5692c7440e..9947437a38b 100644 --- a/mindspore/ccsrc/frontend/optimizer/recompute.cc +++ b/mindspore/ccsrc/frontend/optimizer/recompute.cc @@ -25,7 +25,7 @@ #include "utils/hash_set.h" #include "ir/func_graph.h" #include "mindspore/core/base/core_ops.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace opt { diff --git a/mindspore/ccsrc/frontend/optimizer/slice_activation_in_recompute.cc b/mindspore/ccsrc/frontend/optimizer/slice_activation_in_recompute.cc index 142f158667a..4ca2fe85b33 100644 --- a/mindspore/ccsrc/frontend/optimizer/slice_activation_in_recompute.cc +++ b/mindspore/ccsrc/frontend/optimizer/slice_activation_in_recompute.cc @@ -23,7 +23,7 @@ #include #include #include "mindspore/core/base/core_ops.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "frontend/parallel/tensor_layout/construct_operator.h" #include "frontend/parallel/step_parallel.h" @@ -217,8 +217,8 @@ void SpreadRecomputeDepend(const FuncGraphManagerPtr &manager, const std::vector } // namespace void SliceRecomputedActivationNodes(const FuncGraphPtr &graph) { - if (parallel::ParallelContext::GetInstance()->parallel_mode() != parallel::SEMI_AUTO_PARALLEL && - parallel::ParallelContext::GetInstance()->parallel_mode() != parallel::AUTO_PARALLEL) { + if (parallel::ParallelContext::GetInstance()->parallel_mode() != parallel::kSemiAutoParallel && + parallel::ParallelContext::GetInstance()->parallel_mode() != parallel::kAutoParallel) { return; } MS_EXCEPTION_IF_NULL(graph); diff --git a/mindspore/ccsrc/frontend/parallel/allreduce_fusion/step_allreduce_fusion.cc b/mindspore/ccsrc/frontend/parallel/allreduce_fusion/step_allreduce_fusion.cc index 37b0ac9bd1e..66acb29f496 100644 --- a/mindspore/ccsrc/frontend/parallel/allreduce_fusion/step_allreduce_fusion.cc +++ b/mindspore/ccsrc/frontend/parallel/allreduce_fusion/step_allreduce_fusion.cc @@ -19,7 +19,7 @@ #include #include "frontend/optimizer/optimizer.h" #include "frontend/parallel/allreduce_fusion/allreduce_fusion.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" #include "frontend/parallel/graph_util/graph_info.h" #include "frontend/parallel/status.h" #include "frontend/parallel/step_parallel.h" @@ -39,7 +39,7 @@ bool StepAllreduceFusion(const FuncGraphPtr &root, const opt::OptimizerPtr &opti // assume no change to graph bool changes = false; // control whether use model_parallel mode - if (!root->has_flag(AUTO_PARALLEL) || ((parallel_mode != AUTO_PARALLEL) && (parallel_mode != SEMI_AUTO_PARALLEL)) || + if (!root->has_flag(kAutoParallel) || ((parallel_mode != kAutoParallel) && (parallel_mode != kSemiAutoParallel)) || ((!enable_all_reduce_fusion) && (!enable_all_gather_fusion) && (!enable_reduce_scatter_fusion)) || (root->has_flag(ALLREDUCE_FUSION_RUN_ONCE_ONLY)) || graph_set.size() < 1) { return changes; @@ -66,8 +66,8 @@ bool StepAllreduceFusion(const FuncGraphPtr &root, const opt::OptimizerPtr &opti MS_EXCEPTION_IF_NULL(ret); AllCommFusion allcomm_fusion; - vector comm_ops = {ALL_REDUCE, ALL_GATHER, REDUCE_SCATTER}; - vector fusionlist = {enable_all_reduce_fusion, enable_all_gather_fusion, enable_reduce_scatter_fusion}; + std::vector comm_ops = {ALL_REDUCE, ALL_GATHER, REDUCE_SCATTER}; + std::vector fusionlist = {enable_all_reduce_fusion, enable_all_gather_fusion, enable_reduce_scatter_fusion}; for (size_t i = 0; i < comm_ops.size(); i++) { if (fusionlist[i]) { if (allcomm_fusion.ProcessCommOpsFusion(ret, comm_ops[i]) != SUCCESS) { diff --git a/mindspore/ccsrc/frontend/parallel/device_manager.h b/mindspore/ccsrc/frontend/parallel/device_manager.h index e0f6254ab06..7c40cbdfdc2 100644 --- a/mindspore/ccsrc/frontend/parallel/device_manager.h +++ b/mindspore/ccsrc/frontend/parallel/device_manager.h @@ -30,7 +30,7 @@ #include "frontend/parallel/group_manager.h" #include "frontend/parallel/status.h" #include "frontend/parallel/strategy.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" #include "utils/ms_utils.h" namespace mindspore { diff --git a/mindspore/ccsrc/frontend/parallel/device_matrix.h b/mindspore/ccsrc/frontend/parallel/device_matrix.h index 9f2648de810..d756de844ea 100644 --- a/mindspore/ccsrc/frontend/parallel/device_matrix.h +++ b/mindspore/ccsrc/frontend/parallel/device_matrix.h @@ -22,7 +22,7 @@ #include #include "frontend/parallel/status.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/frontend/parallel/graph_util/generate_graph.cc b/mindspore/ccsrc/frontend/parallel/graph_util/generate_graph.cc index 01815d8e20c..d9e3b204cc4 100644 --- a/mindspore/ccsrc/frontend/parallel/graph_util/generate_graph.cc +++ b/mindspore/ccsrc/frontend/parallel/graph_util/generate_graph.cc @@ -20,9 +20,10 @@ #include #include -#include "pipeline/jit/parse/python_adapter.h" -#include "utils/convert_utils_py.h" +#include "include/common/utils/python_adapter.h" +#include "include/common/utils/convert_utils_py.h" #include "frontend/parallel/graph_util/node_info.h" +#include "mindspore/ccsrc/pipeline/jit/parse/parse_base.h" using mindspore::tensor::Tensor; @@ -61,7 +62,7 @@ ValuePtr CreateOpInstance(const OperatorAttrs &attrs, const OperatorName &op_nam (void)std::transform(attrs.begin(), attrs.end(), std::back_inserter(arg_list), [](const Attr &attr) { return ValueToPyData(attr.second); }); py::object obj = - parse::python_adapter::CallPyFn(GET_OP_FUNCTION_PATH, GET_OP_FUNCTION, op_name, op_path, instance_name, arg_list); + python_adapter::CallPyFn(GET_OP_FUNCTION_PATH, GET_OP_FUNCTION, op_name, op_path, instance_name, arg_list); ValuePtr op_instance = nullptr; bool succ = parse::ConvertData(obj, &op_instance); if (!succ) { diff --git a/mindspore/ccsrc/frontend/parallel/graph_util/graph_info.cc b/mindspore/ccsrc/frontend/parallel/graph_util/graph_info.cc index 394dd584d87..ec07c31b2b4 100644 --- a/mindspore/ccsrc/frontend/parallel/graph_util/graph_info.cc +++ b/mindspore/ccsrc/frontend/parallel/graph_util/graph_info.cc @@ -22,6 +22,7 @@ #include "utils/ms_context.h" #include "ir/graph_utils.h" #include "pipeline/jit/pipeline.h" +#include "frontend/parallel/ops_info/ops_utils.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/frontend/parallel/graph_util/graph_splitter.cc b/mindspore/ccsrc/frontend/parallel/graph_util/graph_splitter.cc index a2978f9ce5c..c2de04853d5 100644 --- a/mindspore/ccsrc/frontend/parallel/graph_util/graph_splitter.cc +++ b/mindspore/ccsrc/frontend/parallel/graph_util/graph_splitter.cc @@ -22,10 +22,10 @@ #include #include #include -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "base/core_ops.h" #include "mindspore/core/utils/ms_context.h" -#include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace parallel { @@ -341,15 +341,15 @@ void GraphSplitter::SetSendNodeAttr(const AnfNodePtr &send_node, const AnfNodePt // These attributes are the inter-process edge information. std::vector dst_ranks = {node_labels_[send_to_node].rank_id}; - AnfAlgo::SetNodeAttr(kAttrSendDstRanks, MakeValue(dst_ranks), send_node); + common::AnfAlgo::SetNodeAttr(kAttrSendDstRanks, MakeValue(dst_ranks), send_node); std::vector dst_roles = {node_labels_[send_to_node].ms_role}; - AnfAlgo::SetNodeAttr(kAttrSendDstRoles, MakeValue(dst_roles), send_node); + common::AnfAlgo::SetNodeAttr(kAttrSendDstRoles, MakeValue(dst_roles), send_node); - AnfAlgo::SetNodeAttr(kAttrSendSrcNodeName, MakeValue(from_node_name), send_node); - AnfAlgo::SetNodeAttr(kAttrSendDstNodeName, MakeValue(to_node_name), send_node); + common::AnfAlgo::SetNodeAttr(kAttrSendSrcNodeName, MakeValue(from_node_name), send_node); + common::AnfAlgo::SetNodeAttr(kAttrSendDstNodeName, MakeValue(to_node_name), send_node); // Set send node to CPU for now. - AnfAlgo::SetNodeAttr(kAttrPrimitiveTarget, MakeValue(kCPUDevice), send_node); + common::AnfAlgo::SetNodeAttr(kAttrPrimitiveTarget, MakeValue(kCPUDevice), send_node); } void GraphSplitter::SetRecvNodeAttr(const AnfNodePtr &recv_node, const AnfNodePtr &recv_from_node, @@ -366,15 +366,15 @@ void GraphSplitter::SetRecvNodeAttr(const AnfNodePtr &recv_node, const AnfNodePt // These attributes are the inter-process edge information. std::vector src_ranks = {node_labels_[recv_from_node].rank_id}; - AnfAlgo::SetNodeAttr(kAttrRecvSrcRanks, MakeValue(src_ranks), recv_node); + common::AnfAlgo::SetNodeAttr(kAttrRecvSrcRanks, MakeValue(src_ranks), recv_node); std::vector src_roles = {node_labels_[recv_from_node].ms_role}; - AnfAlgo::SetNodeAttr(kAttrRecvSrcRoles, MakeValue(src_roles), recv_node); + common::AnfAlgo::SetNodeAttr(kAttrRecvSrcRoles, MakeValue(src_roles), recv_node); - AnfAlgo::SetNodeAttr(kAttrRecvSrcNodeName, MakeValue(from_node_name), recv_node); - AnfAlgo::SetNodeAttr(kAttrRecvDstNodeName, MakeValue(to_node_name), recv_node); + common::AnfAlgo::SetNodeAttr(kAttrRecvSrcNodeName, MakeValue(from_node_name), recv_node); + common::AnfAlgo::SetNodeAttr(kAttrRecvDstNodeName, MakeValue(to_node_name), recv_node); // Set recv node to CPU for now. - AnfAlgo::SetNodeAttr(kAttrPrimitiveTarget, MakeValue(kCPUDevice), recv_node); + common::AnfAlgo::SetNodeAttr(kAttrPrimitiveTarget, MakeValue(kCPUDevice), recv_node); } std::vector GraphSplitter::FindInterProcessInDegree(const std::vector &nodes, diff --git a/mindspore/ccsrc/frontend/parallel/graph_util/node_info.cc b/mindspore/ccsrc/frontend/parallel/graph_util/node_info.cc index 0603dd4b0bc..687d14cb2ed 100644 --- a/mindspore/ccsrc/frontend/parallel/graph_util/node_info.cc +++ b/mindspore/ccsrc/frontend/parallel/graph_util/node_info.cc @@ -21,7 +21,7 @@ #include "base/core_ops.h" #include "ir/param_info.h" #include "ir/meta_tensor.h" -#include "pipeline/jit/parse/python_adapter.h" +#include "include/common/utils/python_adapter.h" #include "frontend/parallel/ops_info/ops_utils.h" #include "frontend/parallel/step_parallel.h" #include "frontend/parallel/step_parallel_utils.h" diff --git a/mindspore/ccsrc/frontend/parallel/graph_util/pipeline_split_utils.cc b/mindspore/ccsrc/frontend/parallel/graph_util/pipeline_split_utils.cc index bfbec3459c9..29a8d3acc43 100644 --- a/mindspore/ccsrc/frontend/parallel/graph_util/pipeline_split_utils.cc +++ b/mindspore/ccsrc/frontend/parallel/graph_util/pipeline_split_utils.cc @@ -25,7 +25,7 @@ #include "ir/value.h" #include "frontend/parallel/ops_info/ops_utils.h" #include "frontend/parallel/device_manager.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" #include "frontend/parallel/step_parallel.h" #include "frontend/parallel/graph_util/node_info.h" #include "utils/parallel_node_check.h" @@ -454,7 +454,7 @@ PipelinePair Deduplicate(const std::vector &node_vector, const FuncG auto manager = root->manager(); for (int64_t i = 0; i <= micro_max; ++i) { temp_vec.clear(); - if (!root->has_flag(TRAINING)) { + if (!root->has_flag(kTraining)) { temp_vec = node_vector; } else { for (auto &node : node_vector) { @@ -744,7 +744,7 @@ void Reorder(const FuncGraphPtr &root) { GetBorderNode(&forward_start, &forward_end, &backward_start, &backward_end, &forward_params, &backward_params, &allreduce_params, root); int64_t micro_max = 0; - if (root->has_flag(TRAINING)) { + if (root->has_flag(kTraining)) { auto forward_end_cnode = forward_end.back()->cast(); auto micro_size = forward_end_cnode->GetPrimalAttr(MICRO); MS_EXCEPTION_IF_NULL(micro_size); diff --git a/mindspore/ccsrc/frontend/parallel/group_manager.cc b/mindspore/ccsrc/frontend/parallel/group_manager.cc index 71c98406e37..0d138ac79f1 100644 --- a/mindspore/ccsrc/frontend/parallel/group_manager.cc +++ b/mindspore/ccsrc/frontend/parallel/group_manager.cc @@ -24,7 +24,7 @@ #include "frontend/parallel/parallel_stub/executor_manager_stub.h" #endif #include "frontend/parallel/device_manager.h" -#include "utils/comm_manager.h" +#include "include/common/utils/comm_manager.h" #include "utils/ms_context.h" namespace mindspore { @@ -188,7 +188,7 @@ Status GroupManager::CreateGroup(const std::string &group_name, const std::vecto (void)group->Init(group_name, devices); groups_[group_name] = *group; - vector ranks; + std::vector ranks; (void)std::transform(std::begin(devices), std::end(devices), std::back_inserter(ranks), [](const Device dev) { return (uint32_t)dev.rank(); }); // Create group through the executor diff --git a/mindspore/ccsrc/frontend/parallel/ops_info/crop_and_resize_info.cc b/mindspore/ccsrc/frontend/parallel/ops_info/crop_and_resize_info.cc index 1e35f02594b..cc5a22d15f3 100644 --- a/mindspore/ccsrc/frontend/parallel/ops_info/crop_and_resize_info.cc +++ b/mindspore/ccsrc/frontend/parallel/ops_info/crop_and_resize_info.cc @@ -20,7 +20,7 @@ #include "frontend/parallel/ops_info/crop_and_resize_info.h" #include "frontend/parallel/graph_util/generate_graph.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/frontend/parallel/ops_info/gather_info.cc b/mindspore/ccsrc/frontend/parallel/ops_info/gather_info.cc index 7ffdc48f696..29d10781447 100644 --- a/mindspore/ccsrc/frontend/parallel/ops_info/gather_info.cc +++ b/mindspore/ccsrc/frontend/parallel/ops_info/gather_info.cc @@ -24,7 +24,7 @@ #include "frontend/parallel/device_matrix.h" #include "frontend/parallel/graph_util/generate_graph.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" #if ((defined ENABLE_CPU) && (!defined _WIN32) && !defined(__APPLE__)) #include "ps/ps_cache/ps_cache_manager.h" #include "utils/ms_context.h" diff --git a/mindspore/ccsrc/frontend/parallel/ops_info/get_next_info.cc b/mindspore/ccsrc/frontend/parallel/ops_info/get_next_info.cc index 04cab5cfb23..0825400420f 100644 --- a/mindspore/ccsrc/frontend/parallel/ops_info/get_next_info.cc +++ b/mindspore/ccsrc/frontend/parallel/ops_info/get_next_info.cc @@ -24,7 +24,7 @@ #include "ir/value.h" #include "frontend/parallel/device_matrix.h" #include "frontend/parallel/strategy.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" #include "frontend/parallel/tensor_layout/tensor_redistribution.h" namespace mindspore { diff --git a/mindspore/ccsrc/frontend/parallel/ops_info/operator_info.cc b/mindspore/ccsrc/frontend/parallel/ops_info/operator_info.cc index 55642ee616d..30c9331faa5 100644 --- a/mindspore/ccsrc/frontend/parallel/ops_info/operator_info.cc +++ b/mindspore/ccsrc/frontend/parallel/ops_info/operator_info.cc @@ -28,7 +28,7 @@ #include "ir/value.h" #include "frontend/parallel/auto_parallel/edge_costmodel.h" #include "frontend/parallel/auto_parallel/graph_costmodel.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" #include "utils/log_adapter.h" namespace mindspore { diff --git a/mindspore/ccsrc/frontend/parallel/ops_info/reluv2_info.cc b/mindspore/ccsrc/frontend/parallel/ops_info/reluv2_info.cc index d4913bdafbf..3919d2f293e 100644 --- a/mindspore/ccsrc/frontend/parallel/ops_info/reluv2_info.cc +++ b/mindspore/ccsrc/frontend/parallel/ops_info/reluv2_info.cc @@ -26,7 +26,7 @@ #include "frontend/parallel/device_matrix.h" #include "ir/value.h" #include "frontend/parallel/auto_parallel/costmodel.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" #include "frontend/parallel/strategy.h" namespace mindspore { diff --git a/mindspore/ccsrc/frontend/parallel/ops_info/reshape_info.cc b/mindspore/ccsrc/frontend/parallel/ops_info/reshape_info.cc index 31dba98f642..68c4fb475dd 100644 --- a/mindspore/ccsrc/frontend/parallel/ops_info/reshape_info.cc +++ b/mindspore/ccsrc/frontend/parallel/ops_info/reshape_info.cc @@ -24,7 +24,7 @@ #include "frontend/parallel/device_matrix.h" #include "frontend/parallel/step_parallel.h" #include "frontend/parallel/auto_parallel/graph_costmodel.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" #include "utils/log_adapter.h" namespace mindspore { diff --git a/mindspore/ccsrc/frontend/parallel/ops_info/roi_align_info.cc b/mindspore/ccsrc/frontend/parallel/ops_info/roi_align_info.cc index 4705a7c8cb3..86e56690e03 100644 --- a/mindspore/ccsrc/frontend/parallel/ops_info/roi_align_info.cc +++ b/mindspore/ccsrc/frontend/parallel/ops_info/roi_align_info.cc @@ -19,7 +19,7 @@ #include "frontend/parallel/ops_info/roi_align_info.h" #include "frontend/parallel/graph_util/generate_graph.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/frontend/parallel/ops_info/split_info.cc b/mindspore/ccsrc/frontend/parallel/ops_info/split_info.cc index 54d1ccd0613..0cf99d2f321 100644 --- a/mindspore/ccsrc/frontend/parallel/ops_info/split_info.cc +++ b/mindspore/ccsrc/frontend/parallel/ops_info/split_info.cc @@ -23,7 +23,7 @@ #include "frontend/parallel/device_matrix.h" #include "frontend/parallel/strategy.h" #include "frontend/parallel/tensor_layout/tensor_redistribution.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" #include "pipeline/jit/resource.h" namespace mindspore { diff --git a/mindspore/ccsrc/frontend/parallel/ops_info/transpose_info.cc b/mindspore/ccsrc/frontend/parallel/ops_info/transpose_info.cc index 5e9943cbb7e..6762a314069 100644 --- a/mindspore/ccsrc/frontend/parallel/ops_info/transpose_info.cc +++ b/mindspore/ccsrc/frontend/parallel/ops_info/transpose_info.cc @@ -22,7 +22,7 @@ #include "frontend/parallel/device_manager.h" #include "frontend/parallel/device_matrix.h" #include "frontend/parallel/step_parallel.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" #include "utils/log_adapter.h" namespace mindspore { diff --git a/mindspore/ccsrc/frontend/parallel/ops_info/uniform_candidate_sampler_info.cc b/mindspore/ccsrc/frontend/parallel/ops_info/uniform_candidate_sampler_info.cc index c9b89bb30ee..c3b722dc146 100644 --- a/mindspore/ccsrc/frontend/parallel/ops_info/uniform_candidate_sampler_info.cc +++ b/mindspore/ccsrc/frontend/parallel/ops_info/uniform_candidate_sampler_info.cc @@ -25,7 +25,7 @@ #include "frontend/parallel/strategy.h" #include "frontend/parallel/tensor_layout/tensor_redistribution.h" #include "frontend/parallel/graph_util/generate_graph.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" #include "pipeline/jit/resource.h" namespace mindspore { diff --git a/mindspore/ccsrc/frontend/parallel/ops_info/unique_info.cc b/mindspore/ccsrc/frontend/parallel/ops_info/unique_info.cc index 2d6219a89aa..d2ccf93fc33 100644 --- a/mindspore/ccsrc/frontend/parallel/ops_info/unique_info.cc +++ b/mindspore/ccsrc/frontend/parallel/ops_info/unique_info.cc @@ -26,7 +26,7 @@ #include "frontend/parallel/device_matrix.h" #include "frontend/parallel/graph_util/generate_graph.h" #include "frontend/parallel/strategy.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" #include "frontend/parallel/tensor_layout/tensor_redistribution.h" #if ((defined ENABLE_CPU) && (!defined _WIN32) && (!defined(__APPLE__))) #include "ps/ps_cache/ps_cache_manager.h" diff --git a/mindspore/ccsrc/frontend/parallel/ops_info/virtual_dataset_info.cc b/mindspore/ccsrc/frontend/parallel/ops_info/virtual_dataset_info.cc index 6142f289c4b..e293d48b0a4 100644 --- a/mindspore/ccsrc/frontend/parallel/ops_info/virtual_dataset_info.cc +++ b/mindspore/ccsrc/frontend/parallel/ops_info/virtual_dataset_info.cc @@ -25,7 +25,7 @@ #include "frontend/parallel/device_manager.h" #include "frontend/parallel/device_matrix.h" #include "frontend/parallel/step_parallel.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" #include "utils/log_adapter.h" namespace mindspore { diff --git a/mindspore/ccsrc/frontend/parallel/ops_info/virtual_output_info.cc b/mindspore/ccsrc/frontend/parallel/ops_info/virtual_output_info.cc index 73543a0851e..0dfa822b1f6 100644 --- a/mindspore/ccsrc/frontend/parallel/ops_info/virtual_output_info.cc +++ b/mindspore/ccsrc/frontend/parallel/ops_info/virtual_output_info.cc @@ -23,7 +23,7 @@ #include "frontend/parallel/device_manager.h" #include "frontend/parallel/device_matrix.h" #include "frontend/parallel/step_parallel.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" #include "utils/log_adapter.h" namespace mindspore { diff --git a/mindspore/ccsrc/frontend/parallel/parallel_optimizer/opt_param_mgr.cc b/mindspore/ccsrc/frontend/parallel/parallel_optimizer/opt_param_mgr.cc index 3c0a24bd459..c18e52025ad 100644 --- a/mindspore/ccsrc/frontend/parallel/parallel_optimizer/opt_param_mgr.cc +++ b/mindspore/ccsrc/frontend/parallel/parallel_optimizer/opt_param_mgr.cc @@ -21,7 +21,7 @@ #include #include #include "frontend/parallel/ops_info/operator_info.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" #include "ir/dtype/type_id.h" namespace mindspore { diff --git a/mindspore/ccsrc/frontend/parallel/parameter_manager.cc b/mindspore/ccsrc/frontend/parallel/parameter_manager.cc index 3e8841d6234..1780e843d43 100644 --- a/mindspore/ccsrc/frontend/parallel/parameter_manager.cc +++ b/mindspore/ccsrc/frontend/parallel/parameter_manager.cc @@ -31,7 +31,7 @@ #include "base/core_ops.h" #include "frontend/operator/ops.h" #include "frontend/optimizer/optimizer.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" #include "frontend/parallel/device_manager.h" #include "frontend/parallel/graph_util/generate_graph.h" #include "frontend/parallel/graph_util/graph_info.h" @@ -41,7 +41,7 @@ #include "ir/param_info.h" #include "ir/tensor.h" #include "utils/trace_base.h" -#include "utils/comm_manager.h" +#include "include/common/utils/comm_manager.h" #include "utils/ms_context.h" #include "utils/symbolic.h" #include "mindspore/core/utils/parallel_node_check.h" @@ -380,7 +380,7 @@ static void InsertFullySplitParamGradAccu(const std::pair &node void HandleFullySplitParameters(const FuncGraphPtr &root) { int64_t grad_accumulation_step = ParallelContext::GetInstance()->grad_accumulation_step(); - if ((grad_accumulation_step <= 1) || root->has_flag(ACCUMULATION)) { + if ((grad_accumulation_step <= 1) || root->has_flag(kAccumulation)) { return; } diff --git a/mindspore/ccsrc/frontend/parallel/pipeline_transformer/pipeline_transformer.cc b/mindspore/ccsrc/frontend/parallel/pipeline_transformer/pipeline_transformer.cc index f2de7a7cf11..71e02d25bcf 100644 --- a/mindspore/ccsrc/frontend/parallel/pipeline_transformer/pipeline_transformer.cc +++ b/mindspore/ccsrc/frontend/parallel/pipeline_transformer/pipeline_transformer.cc @@ -26,7 +26,7 @@ #include "frontend/parallel/ops_info/ops_utils.h" #include "frontend/parallel/group_manager.h" #include "frontend/parallel/parameter_manager.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" #include "frontend/parallel/step_parallel.h" #include "frontend/parallel/node_check.h" #include "frontend/parallel/graph_util/node_info.h" @@ -35,7 +35,7 @@ #include "ir/anf.h" #include "ir/graph_utils.h" #include "base/core_ops.h" -#include "utils/comm_manager.h" +#include "include/common/utils/comm_manager.h" #include "utils/ms_context.h" #include "mindspore/core/utils/parallel_node_check.h" @@ -57,7 +57,7 @@ static bool IsInWhiteList(const CNodePtr &cnode) { } void PipelineTransformer::MainGraph() { - if (!root_->has_flag(TRAINING)) { + if (!root_->has_flag(kTraining)) { main_graph_ = root_; return; } @@ -147,7 +147,7 @@ bool PipelineTransformer::LabelParameterStart(const FuncGraphPtr &graph, const C } void PipelineTransformer::LabelMicroBatch() { - if (!root_->has_flag(TRAINING)) { + if (!root_->has_flag(kTraining)) { return; } MS_EXCEPTION_IF_NULL(main_graph_); @@ -194,7 +194,7 @@ void PipelineTransformer::Coloring() { while (need_coloring) { need_coloring = false; for (auto &fg : manager_->func_graphs()) { - if (fg == root_ && root_->has_flag(TRAINING)) { + if (fg == root_ && root_->has_flag(kTraining)) { continue; } auto value_nodes = fg->value_nodes(); @@ -869,7 +869,7 @@ std::pair, std::vector> PipelineTransformer: std::vector all_nodes = DeepScopedGraphSearch(ret); std::reverse(all_nodes.begin(), all_nodes.end()); auto stage_num = g_device_manager->stage_num(); - if (root_->has_flag(TRAINING) && (stage_num > micro_size_)) { + if (root_->has_flag(kTraining) && (stage_num > micro_size_)) { MS_LOG(EXCEPTION) << "MicroBatch size: " << micro_size_ << " can't less than stage num: " << stage_num; } for (auto &node : all_nodes) { @@ -896,7 +896,7 @@ void PipelineTransformer::CutGraph() { if (IsLastStage()) { return; } - if (send_ops.empty() && !root_->has_flag(TRAINING)) { + if (send_ops.empty() && !root_->has_flag(kTraining)) { return; } (void)make_tuple_inputs.insert(make_tuple_inputs.end(), send_ops.begin(), send_ops.end()); diff --git a/mindspore/ccsrc/frontend/parallel/step_auto_parallel.cc b/mindspore/ccsrc/frontend/parallel/step_auto_parallel.cc index eb04615d343..ff63f1ba0cf 100644 --- a/mindspore/ccsrc/frontend/parallel/step_auto_parallel.cc +++ b/mindspore/ccsrc/frontend/parallel/step_auto_parallel.cc @@ -37,7 +37,7 @@ #include "frontend/parallel/auto_parallel/rec_core/rec_generate_strategy.h" #include "frontend/parallel/auto_parallel/rec_core/rec_parse_graph.h" #include "frontend/parallel/auto_parallel/rec_core/rec_partition.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" #include "frontend/parallel/graph_util/node_info.h" #include "frontend/parallel/graph_util/graph_info.h" #include "frontend/parallel/ops_info/reshape_info.h" @@ -66,7 +66,7 @@ bool StepAutoParallel(const FuncGraphPtr &root, const opt::OptimizerPtr &) { // assume no change to graph bool changes = false; // control whether use model_parallel mode - if (!root->has_flag(AUTO_PARALLEL) || (parallel_mode != AUTO_PARALLEL) || + if (!root->has_flag(kAutoParallel) || (parallel_mode != kAutoParallel) || root->has_flag(AUTO_PARALLEL_RUN_ONCE_ONLY)) { return changes; } @@ -103,12 +103,12 @@ bool StepAutoParallel(const FuncGraphPtr &root, const opt::OptimizerPtr &) { } // search parallelization strategy - if ((strategy_search_mode == DYNAMIC_PROGRAMMING) || (strategy_search_mode == SHARDING_PROPAGATION)) { + if ((strategy_search_mode == kDynamicProgramming) || (strategy_search_mode == kShardingPropagation)) { if (ParallelStrategySearch(all_nodes, root) != SUCCESS) { MS_LOG(EXCEPTION) << "Auto-parallel strategy search failed when using " << strategy_search_mode << " searching mode"; } - } else if (strategy_search_mode == RECURSIVE_PROGRAMMING) { + } else if (strategy_search_mode == kRecursiveProgramming) { if (ParallelStrategyRecSearch(all_nodes, root) != SUCCESS) { MS_LOG(EXCEPTION) << "Auto-parallel strategy search failed when using RP searching mode"; } @@ -376,7 +376,7 @@ OperatorInfoPtr CreateTheOperatorInfo(const PrimitivePtr &prim, const CNodePtr & // BatchParallelInfo operator operator_info->ComputeBatchSplitFlagList(); Status retGenStra; - if (AttrFound(attrs, STRATEGY_GEN_MODE) && GetValue(attrs[STRATEGY_GEN_MODE]) == DATA_PARALLEL) { + if (AttrFound(attrs, STRATEGY_GEN_MODE) && GetValue(attrs[STRATEGY_GEN_MODE]) == kDataParallel) { MS_LOG(INFO) << "generating batch parallel strategy..."; StrategyPtr strategyPtr = parallel::GenerateBatchParallelStrategy(operator_info, prim); retGenStra = operator_info->SetCostUnderStrategy(strategyPtr); @@ -392,7 +392,7 @@ OperatorInfoPtr CreateTheOperatorInfo(const PrimitivePtr &prim, const CNodePtr & return nullptr; } - bool use_sp_and_dataset = ((ParallelContext::GetInstance()->strategy_search_mode() == SHARDING_PROPAGATION) || + bool use_sp_and_dataset = ((ParallelContext::GetInstance()->strategy_search_mode() == kShardingPropagation) || (ParallelContext::GetInstance()->sharding_propagation())) && (operator_info->name().find(VIRTUAL_DATA_SET_INFO) != std::string::npos); if (use_sp_and_dataset) { @@ -459,7 +459,7 @@ Status ConstructCostGraphNodesByUniqueId(const std::vector &all_node ValueNodePtr prim_anf_node = cnode->input(0)->cast(); if (!IsAutoParallelCareNode(cnode)) { // Needed by rec_parser - if (ParallelContext::GetInstance()->strategy_search_mode() == RECURSIVE_PROGRAMMING) { + if (ParallelContext::GetInstance()->strategy_search_mode() == kRecursiveProgramming) { auto prev_cnode = GetInternalOperatorInfo(cnode, prim_anf_node); if (prev_cnode != nullptr) { entire_costgraph->add_tuple_getitem(std::make_pair(cnode->UniqueId(), prev_cnode->UniqueId())); @@ -580,7 +580,7 @@ Status ConstructCostGraphNodesByUniqueIdTC(const std::vector &all_no ValueNodePtr prim_anf_node = cnode->input(0)->cast(); if (!IsAutoParallelCareNode(cnode)) { // Needed by rec_parser - if (ParallelContext::GetInstance()->strategy_search_mode() == RECURSIVE_PROGRAMMING) { + if (ParallelContext::GetInstance()->strategy_search_mode() == kRecursiveProgramming) { auto prev_cnode = GetInternalOperatorInfo(cnode, prim_anf_node); if (prev_cnode != nullptr) { entire_costgraph->add_tuple_getitem(std::make_pair(cnode->UniqueId(), prev_cnode->UniqueId())); @@ -690,7 +690,7 @@ void CreateEdgeBetweenTwoOps(const OperatorInfoPtr &prev_op_info, const Operator node_op_info->AddPrevEdge(edge_ptr); prev_op_info->AddSuccEdge(edge_ptr); entire_costgraph->AddEdge(prev_op_info, node_op_info, edge_ptr); - bool use_sp = (ParallelContext::GetInstance()->strategy_search_mode() == SHARDING_PROPAGATION) || + bool use_sp = (ParallelContext::GetInstance()->strategy_search_mode() == kShardingPropagation) || (ParallelContext::GetInstance()->sharding_propagation()); if (use_sp && (prev_prim->name() == CAST) && (configured_stra_ops_.find(node_op_info) != configured_stra_ops_.end())) { @@ -1054,7 +1054,7 @@ Status ParallelStrategySearch(const std::vector &all_nodes, const Fu } // Step 4: run the strategy searching algorithm - bool use_sp = (ParallelContext::GetInstance()->strategy_search_mode() == SHARDING_PROPAGATION) || + bool use_sp = (ParallelContext::GetInstance()->strategy_search_mode() == kShardingPropagation) || (ParallelContext::GetInstance()->sharding_propagation()); if (use_sp) { entire_costgraph->StrategyPropagate(configured_stra_ops_); @@ -1213,7 +1213,7 @@ Status ParallelStrategyRecSearch(const std::vector &all_nodes, const } bool is_training = true; - if (!root->has_flag(TRAINING)) { + if (!root->has_flag(kTraining)) { is_training = false; } GenerateStrategy(graph, ops, eli_list, input_tensor_names, index_list, is_training, shared_tensors_ops); diff --git a/mindspore/ccsrc/frontend/parallel/step_parallel.cc b/mindspore/ccsrc/frontend/parallel/step_parallel.cc index 052424d596f..4f0a9d72d32 100644 --- a/mindspore/ccsrc/frontend/parallel/step_parallel.cc +++ b/mindspore/ccsrc/frontend/parallel/step_parallel.cc @@ -32,7 +32,7 @@ #include "frontend/operator/ops.h" #include "frontend/optimizer/optimizer.h" #include "frontend/parallel/auto_parallel/graph_costmodel.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" #include "frontend/parallel/device_manager.h" #include "frontend/parallel/dynamic_creator.h" #include "frontend/parallel/graph_util/generate_graph.h" @@ -45,7 +45,7 @@ #include "ir/param_info.h" #include "ir/tensor.h" #include "utils/trace_base.h" -#include "utils/comm_manager.h" +#include "include/common/utils/comm_manager.h" #include "utils/ms_context.h" #include "utils/symbolic.h" #include "mindspore/core/utils/parallel_node_check.h" @@ -196,7 +196,7 @@ std::vector CreateMirrorInput(const FuncGraphPtr &root, const Operat SetCommunicationOpGroupLabel(new_node_input); // gradient accumulation if (grad_accumulation_step > 1) { - bool add_accu = root->has_flag(ACCUMULATION); + bool add_accu = root->has_flag(kAccumulation); // MiniStep need to do mirror at each micro step as we use the gradient accumulation sharding, SetMiniStepOpDoMirrorLabel(new_node_input, !add_accu, !add_accu); } @@ -955,8 +955,8 @@ FuncGraphPtr PynativeParallelGraph(const FuncGraphPtr &root, const std::vector &all_nodes) { - vector last_forward_node_ids; - vector last_indexs; + std::vector last_forward_node_ids; + std::vector last_indexs; auto real_graph = PynativeParallelGraph(root, all_nodes); FindLastNodesUniqueId(real_graph, &last_forward_node_ids, &last_indexs); MS_LOG(INFO) << "there are " << last_forward_node_ids.size() << " output nodes in eval/predict"; @@ -1587,7 +1587,7 @@ static void InsertAllGatherOp(const FuncGraphPtr &root, const std::string &group AddCommOpMirrorFlag(allgather, !grad_accumulation_shard); } else if (op_name == MINI_STEP_ALL_GATHER) { // We need to manually set the add_accu to be false if it's father node is MirrorMiniStep - bool add_accu = root->has_flag(ACCUMULATION); + bool add_accu = root->has_flag(kAccumulation); bool is_with_mirror = opt_shard_mirror_group.size() > 1; AddCommOpAddAccuFlag(allgather, !add_accu && !is_with_mirror); AddCommOpMirrorFlag(allgather, grad_accumulation_shard || !add_accu); @@ -1826,7 +1826,8 @@ void SetVirtualDatasetStrategy(const CNodePtr &node) { } // find previous parallel care node's next node. -bool FindPreNodes(const AnfNodePtr &node, vector *unique_ids, vector *indexes, size_t curr_depth) { +bool FindPreNodes(const AnfNodePtr &node, std::vector *unique_ids, std::vector *indexes, + size_t curr_depth) { if (curr_depth > MAX_RECURSIVE_DEPTH) { MS_LOG(WARNING) << "When find the previous node, exceeded the maximum recursion depth: " << MAX_RECURSIVE_DEPTH; return false; @@ -2536,7 +2537,7 @@ void StepSplitSens(const std::pair &sens_loss_pair) { bool IsPynativeParallel() { auto parallel_mode = ParallelContext::GetInstance()->parallel_mode(); auto execution_mode = MsContext::GetInstance()->get_param(MS_CTX_EXECUTION_MODE); - return (execution_mode == kPynativeMode) && (parallel_mode == SEMI_AUTO_PARALLEL || parallel_mode == AUTO_PARALLEL); + return (execution_mode == kPynativeMode) && (parallel_mode == kSemiAutoParallel || parallel_mode == kAutoParallel); } // Sens node satisfies the following conditions: cnode(sens)-->cnode(tuple_getitem)-->cnode-->cnode(J) @@ -3019,8 +3020,8 @@ Status ParallelInit() { stages.push_back(device_num / split_stage_num); } - if ((split_stage_num > 1) && (parallel_mode != SEMI_AUTO_PARALLEL)) { - MS_LOG(ERROR) << "To enable the pipeline parallel, please set the parallel mode to " << SEMI_AUTO_PARALLEL; + if ((split_stage_num > 1) && (parallel_mode != kSemiAutoParallel)) { + MS_LOG(ERROR) << "To enable the pipeline parallel, please set the parallel mode to " << kSemiAutoParallel; return FAILED; } @@ -3081,7 +3082,7 @@ bool CreateGroupsByCkptFile(const std::string &file) { void ReorderForPipelineSplit(const FuncGraphPtr &root, const FuncGraphManagerPtr &manager, int64_t pipeline_stages) { if (!root->has_flag(BACKWARD) && pipeline_stages > 1) { root->set_flag(BACKWARD, true); - if (root->has_flag(TRAINING)) { + if (root->has_flag(kTraining)) { Reorder(root); } else { ReorderForPredict(root, manager); @@ -3096,12 +3097,12 @@ bool IsInsertVirtualOutput(const FuncGraphPtr &root) { int64_t per_stage_device_num = comm_info.device_num / split_stage_num; int64_t current_stage = comm_info.global_rank / per_stage_device_num; MS_LOG(INFO) << "The current stage is: " << current_stage; - if (!root->has_flag(TRAINING) && !ParallelContext::GetInstance()->dataset_strategy().empty()) { + if (!root->has_flag(kTraining) && !ParallelContext::GetInstance()->dataset_strategy().empty()) { MS_LOG(WARNING) << "In eval/predict net, the output parallel strategy would not follow " "the input parallel strategy when using context.set_auto_parallel_context(dataset_strategy)" " to configure the input strategy."; } - return ((!root->has_flag(TRAINING) && ParallelContext::GetInstance()->dataset_strategy().empty() && + return ((!root->has_flag(kTraining) && ParallelContext::GetInstance()->dataset_strategy().empty() && current_stage == split_stage_num - 1) || IsPynativeParallel()); } @@ -3123,7 +3124,7 @@ static void HandleGroupInfo(const FuncGraphPtr &root) { static void HandleDataParallel() { std::string parallel_mode = ParallelContext::GetInstance()->parallel_mode(); - if (parallel_mode == DATA_PARALLEL) { + if (parallel_mode == kDataParallel) { auto group_info_save_path = common::GetEnv("GROUP_INFO_FILE"); if (!group_info_save_path.empty()) { std::vector>> group_info; @@ -3324,7 +3325,7 @@ bool StepParallel(const FuncGraphPtr &root, const opt::OptimizerPtr &optimizer) // assume no change to graph bool changes = false; // control whether use model_parallel mode - if (!root->has_flag(AUTO_PARALLEL) || ((parallel_mode != AUTO_PARALLEL) && (parallel_mode != SEMI_AUTO_PARALLEL)) || + if (!root->has_flag(kAutoParallel) || ((parallel_mode != kAutoParallel) && (parallel_mode != kSemiAutoParallel)) || (root->has_flag(SEMI_AUTO_PARALLEL_RUN_ONCE_ONLY))) { if (!root->has_flag(CHECK_SET_STRATEGY_VALID_ONCE_ONLY)) { MS_LOG(WARNING) << "Strategies would be ignored in " << parallel_mode @@ -3345,7 +3346,7 @@ bool StepParallel(const FuncGraphPtr &root, const opt::OptimizerPtr &optimizer) MS_EXCEPTION_IF_NULL(ret); std::vector all_nodes = DeepScopedGraphSearch(ret); std::reverse(all_nodes.begin(), all_nodes.end()); - if (parallel_mode != AUTO_PARALLEL) { + if (parallel_mode != kAutoParallel) { TOTAL_OPS = 0; if (pipeline_stages <= 1 && ParallelInit() != SUCCESS) { MS_LOG(EXCEPTION) << "Parallel init failed"; diff --git a/mindspore/ccsrc/frontend/parallel/step_parallel_utils.cc b/mindspore/ccsrc/frontend/parallel/step_parallel_utils.cc index 9ee531977ef..e1bfdfa9966 100644 --- a/mindspore/ccsrc/frontend/parallel/step_parallel_utils.cc +++ b/mindspore/ccsrc/frontend/parallel/step_parallel_utils.cc @@ -31,7 +31,7 @@ #include "base/core_ops.h" #include "frontend/operator/ops.h" #include "frontend/optimizer/optimizer.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" #include "frontend/parallel/device_manager.h" #include "frontend/parallel/graph_util/generate_graph.h" #include "frontend/parallel/graph_util/graph_info.h" @@ -42,7 +42,7 @@ #include "ir/param_info.h" #include "ir/tensor.h" #include "utils/trace_base.h" -#include "utils/comm_manager.h" +#include "include/common/utils/comm_manager.h" #include "utils/ms_context.h" #include "utils/symbolic.h" #include "mindspore/core/utils/parallel_node_check.h" @@ -466,7 +466,7 @@ TypePtr FindChildCastWithFP32ToFP16(const CNodePtr &cnode_ptr, const NodeUsersMa // Return the new cast node with pre_node as the inputs. AnfNodePtr CreateFP16Cast(const CNodePtr &node, const AnfNodePtr &pre_node, const TypePtr &compute_node_type) { const char kOpsFunctionModelName[] = "mindspore.ops.functional"; - static py::object cast_prim = parse::python_adapter::GetPyFn(kOpsFunctionModelName, "cast"); + static py::object cast_prim = python_adapter::GetPyFn(kOpsFunctionModelName, "cast"); const auto &adapter = py::cast(cast_prim); MS_EXCEPTION_IF_NULL(adapter); MS_EXCEPTION_IF_NULL(compute_node_type); diff --git a/mindspore/ccsrc/frontend/parallel/strategy_checkpoint/parallel_strategy_checkpoint.cc b/mindspore/ccsrc/frontend/parallel/strategy_checkpoint/parallel_strategy_checkpoint.cc index dc43fb25bce..30147e17628 100644 --- a/mindspore/ccsrc/frontend/parallel/strategy_checkpoint/parallel_strategy_checkpoint.cc +++ b/mindspore/ccsrc/frontend/parallel/strategy_checkpoint/parallel_strategy_checkpoint.cc @@ -19,8 +19,9 @@ #include #include +#include "include/common/utils/utils.h" #include "utils/ms_utils.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" #include "utils/log_adapter.h" #include "debug/common.h" #include "proto/node_strategy.pb.h" diff --git a/mindspore/ccsrc/frontend/parallel/strategy_checkpoint/parallel_strategy_checkpoint.h b/mindspore/ccsrc/frontend/parallel/strategy_checkpoint/parallel_strategy_checkpoint.h index b59a9531e02..847de30e63d 100644 --- a/mindspore/ccsrc/frontend/parallel/strategy_checkpoint/parallel_strategy_checkpoint.h +++ b/mindspore/ccsrc/frontend/parallel/strategy_checkpoint/parallel_strategy_checkpoint.h @@ -24,7 +24,7 @@ #include "utils/hash_map.h" #include "frontend/parallel/ops_info/ops_utils.h" #include "frontend/parallel/strategy.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" #include "frontend/parallel/tensor_layout/tensor_layout.h" #include "frontend/parallel/tensor_layout/tensor_info.h" diff --git a/mindspore/ccsrc/frontend/parallel/tensor_layout/arrangement.cc b/mindspore/ccsrc/frontend/parallel/tensor_layout/arrangement.cc index e6cf0ed732b..6e1ef8ef503 100644 --- a/mindspore/ccsrc/frontend/parallel/tensor_layout/arrangement.cc +++ b/mindspore/ccsrc/frontend/parallel/tensor_layout/arrangement.cc @@ -20,7 +20,7 @@ #include "utils/ms_utils.h" #include "frontend/parallel/status.h" #include "frontend/parallel/tensor_layout/shape_util.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" #include "utils/log_adapter.h" namespace mindspore { diff --git a/mindspore/ccsrc/frontend/parallel/tensor_layout/map.cc b/mindspore/ccsrc/frontend/parallel/tensor_layout/map.cc index 728944f1964..47d7a2b81fb 100644 --- a/mindspore/ccsrc/frontend/parallel/tensor_layout/map.cc +++ b/mindspore/ccsrc/frontend/parallel/tensor_layout/map.cc @@ -20,7 +20,7 @@ #include "utils/ms_utils.h" #include "frontend/parallel/status.h" #include "frontend/parallel/tensor_layout/shape_util.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" #include "utils/log_adapter.h" namespace mindspore { diff --git a/mindspore/ccsrc/frontend/parallel/tensor_layout/redistribution_operator_infer.cc b/mindspore/ccsrc/frontend/parallel/tensor_layout/redistribution_operator_infer.cc index ef7bfad927d..d5d78284dba 100644 --- a/mindspore/ccsrc/frontend/parallel/tensor_layout/redistribution_operator_infer.cc +++ b/mindspore/ccsrc/frontend/parallel/tensor_layout/redistribution_operator_infer.cc @@ -19,7 +19,7 @@ #include #include "frontend/parallel/device_manager.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/frontend/parallel/tensor_layout/redistribution_operator_infer.h b/mindspore/ccsrc/frontend/parallel/tensor_layout/redistribution_operator_infer.h index 89101bbabf3..2480b0910d5 100644 --- a/mindspore/ccsrc/frontend/parallel/tensor_layout/redistribution_operator_infer.h +++ b/mindspore/ccsrc/frontend/parallel/tensor_layout/redistribution_operator_infer.h @@ -25,7 +25,7 @@ #include "utils/hash_map.h" #include "frontend/parallel/tensor_layout/construct_operator.h" #include "frontend/parallel/tensor_layout/redistribution_layout_transfer.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" namespace mindspore { namespace parallel { using DeviceArrangement = Shape; diff --git a/mindspore/ccsrc/frontend/parallel/tensor_layout/tensor_layout.cc b/mindspore/ccsrc/frontend/parallel/tensor_layout/tensor_layout.cc index 29dab27e81b..e0434e0e805 100644 --- a/mindspore/ccsrc/frontend/parallel/tensor_layout/tensor_layout.cc +++ b/mindspore/ccsrc/frontend/parallel/tensor_layout/tensor_layout.cc @@ -21,7 +21,7 @@ #include "ir/value.h" #include "frontend/parallel/device_matrix.h" #include "frontend/parallel/status.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" #include "frontend/parallel/tensor_layout/shape_util.h" #include "utils/log_adapter.h" diff --git a/mindspore/ccsrc/frontend/parallel/tensor_layout/tensor_layout.h b/mindspore/ccsrc/frontend/parallel/tensor_layout/tensor_layout.h index a9e2da6c69f..0adcea17738 100644 --- a/mindspore/ccsrc/frontend/parallel/tensor_layout/tensor_layout.h +++ b/mindspore/ccsrc/frontend/parallel/tensor_layout/tensor_layout.h @@ -28,7 +28,7 @@ #include "frontend/parallel/status.h" #include "frontend/parallel/tensor_layout/arrangement.h" #include "frontend/parallel/tensor_layout/map.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/common/duplex_pipe.h b/mindspore/ccsrc/include/common/duplex_pipe.h similarity index 92% rename from mindspore/ccsrc/common/duplex_pipe.h rename to mindspore/ccsrc/include/common/duplex_pipe.h index 3615a73e326..84ec3a24a4c 100644 --- a/mindspore/ccsrc/common/duplex_pipe.h +++ b/mindspore/ccsrc/include/common/duplex_pipe.h @@ -14,8 +14,8 @@ * limitations under the License. */ -#ifndef MINDSPORE_CCSRC_COMMON_DUPLEX_PIPE_H_ -#define MINDSPORE_CCSRC_COMMON_DUPLEX_PIPE_H_ +#ifndef MINDSPORE_CCSRC_INCLUDE_COMMON_DUPLEX_PIPE_H_ +#define MINDSPORE_CCSRC_INCLUDE_COMMON_DUPLEX_PIPE_H_ #include #include @@ -25,6 +25,8 @@ #include #include "utils/log_adapter.h" +#include "include/common/visible.h" + #define DP_DEBUG MS_LOG(DEBUG) << "[DuplexPipe] " #define DP_INFO MS_LOG(INFO) << "[DuplexPipe] " #define DP_ERROR MS_LOG(ERROR) << "[DuplexPipe] " @@ -33,7 +35,7 @@ namespace mindspore { // A tool to run a command as child process and build a duplex pipe between them. // Similar to 'popen()', but use duplex not simplex pipe, more like 'socketpair'. -class DuplexPipe : public std::enable_shared_from_this { +class COMMON_EXPORT DuplexPipe : public std::enable_shared_from_this { public: constexpr inline static int kBufferSize = 4096; constexpr inline static unsigned int kTimeOutSeconds = 5; @@ -103,7 +105,7 @@ class DuplexPipe : public std::enable_shared_from_this { int remote_stdin_; int remote_stdout_; - class SignalHandler { + class COMMON_EXPORT SignalHandler { public: SignalHandler(const std::weak_ptr &dp, pid_t *pid); ~SignalHandler(); @@ -131,4 +133,4 @@ class DuplexPipe : public std::enable_shared_from_this { pid_t pid_; }; } // namespace mindspore -#endif // MINDSPORE_CCSRC_COMMON_DUPLEX_PIPE_H_ +#endif // MINDSPORE_CCSRC_INCLUDE_COMMON_DUPLEX_PIPE_H_ diff --git a/mindspore/ccsrc/common/thread_pool.h b/mindspore/ccsrc/include/common/thread_pool.h similarity index 85% rename from mindspore/ccsrc/common/thread_pool.h rename to mindspore/ccsrc/include/common/thread_pool.h index 233ea3254c7..dbe261f395e 100644 --- a/mindspore/ccsrc/common/thread_pool.h +++ b/mindspore/ccsrc/include/common/thread_pool.h @@ -1,5 +1,5 @@ /** - * Copyright 2020 Huawei Technologies Co., Ltd + * Copyright 2020-2022 Huawei Technologies Co., Ltd * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -14,8 +14,8 @@ * limitations under the License. */ -#ifndef MINDSPORE_CCSRC_COMMON_THREAD_POOL_H_ -#define MINDSPORE_CCSRC_COMMON_THREAD_POOL_H_ +#ifndef MINDSPORE_CCSRC_INCLUDE_COMMON_THREAD_POOL_H_ +#define MINDSPORE_CCSRC_INCLUDE_COMMON_THREAD_POOL_H_ #include #include @@ -29,6 +29,7 @@ #include #include #include "utils/log_adapter.h" +#include "include/common/visible.h" namespace mindspore { namespace common { @@ -41,7 +42,7 @@ struct ThreadContext { const Task *task{nullptr}; }; -class ThreadPool { +class COMMON_EXPORT ThreadPool { public: ~ThreadPool(); ThreadPool(const ThreadPool &) = delete; @@ -64,4 +65,4 @@ class ThreadPool { } // namespace common } // namespace mindspore -#endif // MINDSPORE_CCSRC_COMMON_THREAD_POOL_H_ +#endif // MINDSPORE_CCSRC_INCLUDE_COMMON_THREAD_POOL_H_ diff --git a/mindspore/ccsrc/include/common/utils/anfalgo.h b/mindspore/ccsrc/include/common/utils/anfalgo.h new file mode 100644 index 00000000000..c1a73a7c3ba --- /dev/null +++ b/mindspore/ccsrc/include/common/utils/anfalgo.h @@ -0,0 +1,232 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_ANFALGO_H +#define MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_ANFALGO_H + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include "ir/anf.h" +#include "ir/func_graph.h" +#include "ir/dtype.h" +#include "base/base.h" +#include "ir/primitive.h" +#include "ir/kernel_info_dev.h" +#include "base/core_ops.h" +#include "include/common/utils/contract.h" +#include "utils/anf_utils.h" +#include "include/common/utils/utils.h" +#include "include/common/visible.h" + +namespace mindspore { +namespace common { +using KernelWithIndex = std::pair; + +class COMMON_EXPORT AnfAlgo { + public: + // get real input node of tuple_get_item + static AnfNodePtr GetTupleGetItemRealInput(const CNodePtr &tuple_get_item); + static size_t GetTupleGetItemOutIndex(const CNodePtr &tuple_get_item); + // get input_anf_node's real kernel by recurse + static KernelWithIndex VisitKernel(const AnfNodePtr &input_anf_node, size_t output_index); + static KernelWithIndex VisitKernelWithReturnType( + const AnfNodePtr &input_anf_node, size_t output_index, bool skip_nop_node = false, + const std::vector &return_types = {prim::kPrimMakeTuple}, + abstract::AbstractBasePtr *abstract = nullptr); + static std::vector GetAllOutput(const AnfNodePtr &node, + const std::vector &return_types = {}); + static std::vector GetAllOutputWithIndex(const AnfNodePtr &node); + // get cnode primitive + static AnfNodePtr GetCNodePrimitiveNode(const CNodePtr &node); + static void SetNodeInput(const CNodePtr &node, const AnfNodePtr &input_node, size_t index); + static PrimitivePtr GetCNodePrimitive(const AnfNodePtr &node); + // check whether anf node is a node of 'primitive_type',such as make_tuple is a cnode of kPrimMakeTuple + static bool CheckPrimitiveType(const AnfNodePtr &node, const PrimitivePtr &primitive_type); + // get cnode primitive + static FuncGraphPtr GetCNodeFuncGraphPtr(const AnfNodePtr &node); + // get kernel_name of anf node + static std::string GetCNodeName(const AnfNodePtr &node); + // get detail info of anf node + static std::string GetNodeDebugString(const AnfNodePtr &node); + // get attr of anf node + template + static T GetNodeAttr(const AnfNodePtr &node, const std::string &key) { + MS_EXCEPTION_IF_NULL(node); + if (!node->isa()) { + std::string node_debug_log = node->DebugString(); + MS_LOG(EXCEPTION) << "Only cnode has attr, but this anf is " << node_debug_log.c_str(); + } + // single op cnode. + if (auto primitive = GetCNodePrimitive(node); primitive != nullptr) { + return GetValue(primitive->GetAttr(key)); + } + // graph kernel cnode. + auto fg = GetCNodeFuncGraphPtr(node); + MS_EXCEPTION_IF_NULL(fg); + return GetValue(fg->get_attr(key)); + } + static bool IsTupleOutput(const AnfNodePtr &anf); + // set attr of anf node + static void SetNodeAttr(const std::string &key, const ValuePtr &value, const AnfNodePtr &node); + // set attr of key from 'from' node to 'to' node + static void CopyNodeAttr(const std::string &key, const AnfNodePtr &from, const AnfNodePtr &to); + // set a new key for attr from 'from' node to 'to' node + static void CopyNodeAttr(const std::string &old_key, const std::string &new_key, const AnfNodePtr &from, + const AnfNodePtr &to); + // set all attrs from 'from' node to 'to' node + static void CopyNodeAttrs(const AnfNodePtr &from, const AnfNodePtr &to); + // check whether a cnode has the specified attr. + static bool HasNodeAttr(const std::string &key, const CNodePtr &node); + // delete attr of anf node + static void EraseNodeAttr(const std::string &key, AnfNodePtr node); + // get the num of inputs include monads for a cnode + static size_t GetInputNum(const CNodePtr &cnode); + // get the num of inputs exclude monads for real_kernel (which can be build and run in device) + static size_t GetInputTensorNum(const AnfNodePtr &node); + // get the num of output real_kernel(which can be build and run in device) + static size_t GetOutputTensorNum(const AnfNodePtr &node); + // get prev node output width output index + static KernelWithIndex GetPrevNodeOutput(const AnfNodePtr &anf_node, size_t input_idx, bool skip_nop_node = false); + // get output shapes inferred by ME from input nodes. + static std::vector GetOutputInferShape(const AnfNodePtr &node, size_t output_idx); + static std::vector GetOutputInferShape(const AnfNodePtr &node, const abstract::BaseShapePtr &base_shape, + size_t output_idx); + // get input shapes inferred by ME from input nodes. + static std::vector GetPrevNodeOutputInferShape(const AnfNodePtr &node, size_t input_idx); + // get output data type inferred by ME of anf node + static TypeId GetOutputInferDataType(const AnfNodePtr &node, size_t output_idx); + static TypeId GetOutputInferDataType(const TypePtr &type_ptr, size_t output_idx); + // get output original data type from prev node,input_index is the input index of current node related to prev node + static TypeId GetPrevNodeOutputInferDataType(const AnfNodePtr &node, size_t input_idx); + // set infer shapes and types of anf node + static void SetOutputInferTypeAndShape(const std::vector &types, + const std::vector> &shapes, AnfNode *node); + // get and set output shape ptr + static abstract::BaseShapePtr GetOutputDetailShape(const AnfNodePtr &node, size_t output_idx); + static abstract::BaseShapePtr GetPrevNodeOutputDetailShape(const AnfNodePtr &node, size_t input_idx); + static void SetOutputTypeAndDetailShape(const std::vector &types, + const std::vector &shapes, AnfNode *node); + static void CopyAbstract(const AnfNodePtr &from_node, AnfNode *to_node); + // checkout whether the anf node is a graph kernel. + static bool IsGraphKernel(const AnfNodePtr &node); + // checkout whether the anf node is an inner node of graph kernel. + static bool IsNodeInGraphKernel(const AnfNodePtr &node); + // get the real output of GraphKernel. + static AnfNodePtr GetOutputOfGraphkernel(const KernelWithIndex &kernel_with_index); + // check parameter is weight or data + static bool IsParameterWeight(const ParameterPtr &node); + // checkout whether the anf node is include the label_index. + static bool IsLabelIndexInNode(const AnfNodePtr &node, size_t label_index); + // Check whether the cnode update parameter + static bool IsUpdateParameterKernel(const CNodePtr &node); + static AnfNodePtr GetInputNode(const CNodePtr &node, size_t index); + static bool IsCommunicationOp(const AnfNodePtr &node); + static bool IsFusedCommunicationOp(const AnfNodePtr &node); + static bool IsInplaceNode(const AnfNodePtr &node, const string &type); + static bool IsGetNext(const NotNull &node); + static bool IsNeedSkipNopOpAddr(const AnfNodePtr &node); + static FuncGraphPtr GetValueNodeFuncGraph(const AnfNodePtr &node); + static bool IsSwitchCall(const CNodePtr &call_node); + static bool IsScalarInput(const CNodePtr &cnode, size_t index); + static bool IsScalarOutput(const CNodePtr &cnode, size_t index); + static void ReorderExecList(NotNull *> node_list); + static void ReorderPosteriorExecList(NotNull *> node_list); + // get fix output precision of cnode. + static TypeId GetCNodeOutputPrecision(const AnfNodePtr &node); + // get fix output precision from prev node, input_idx is the input index of current node related to prev node. + static TypeId GetPrevNodeOutputPrecision(const AnfNodePtr &node, size_t input_idx); + static bool IsNodeInputDynamicShape(const CNodePtr &anf_node_ptr); + static bool IsDynamicShape(const AnfNodePtr &node); + static bool HasDynamicShapeFlag(const PrimitivePtr &prim); + static bool IsCondControlKernel(const CNodePtr &node); + static bool GetBooleanAttr(const AnfNodePtr &node, const std::string &attr); + static std::optional GetDumpFlag(const AnfNodePtr &node); + static void GetRealDynamicShape(const std::vector &shape, NotNull *> dynamic_shape); + static std::vector GetInputMaxShape(const AnfNodePtr &anf_node, size_t index); + static std::vector GetInputMinShape(const AnfNodePtr &anf_node, size_t index); + static std::vector GetOutputMaxShape(const AnfNodePtr &anf_node, size_t index); + static std::vector GetOutputMinShape(const AnfNodePtr &anf_node, size_t index); + static bool IsHostKernel(const CNodePtr &node); + // return true if use cnode_input's abstract, false if use real_input's abstract + static void AddArgList(AbstractBasePtrList *args_spec_list, const AnfNodePtr &cnode_input, + const AnfNodePtr &real_input, size_t index); + // Find real input nodes. + static void GetAllFatherRealNode(const AnfNodePtr &anf_node, std::vector *result, + std::set *visited); + static void GetAllVisitedCNode(const CNodePtr &cnode, std::vector *used_kernels, + std::set *visited); + static AnfNodeIndexSet GetUpdateStateUsers(const FuncGraphManagerPtr &manager, const AnfNodePtr &node); + // Get node real inputs, skip `MakeTuple`, `TupleGetItem`, `Depend`, `Load`, `UpdateState` etc. + static void GetRealInputs(const AnfNodePtr &anf_node, std::vector *inputs); + // Check whether tensors need broadcast or not. + static bool IsTensorBroadcast(const std::vector &lhs, const std::vector &rhs); + // Calc tensor size in byte. + template + static size_t TensorSizeInByte(const std::vector &shape) { + size_t result = sizeof(T); + for (size_t i = 0; i < shape.size(); i++) { + result *= shape[i]; + } + return result; + } + + // Judge a control operator need be compiled into kernel graph rather than be cut into single op and + // executed in vm. For example, the operator "bprop_cut" will be compiled into kernel graph and be launch + // in backend in PyNative mode. + static bool IsControlOpExecInBackend(const AnfNodePtr &node); + + static bool IsNodeInputContainMonad(const AnfNodePtr &node); + // Check if node is non-task op. + static bool IsNonTaskOp(const CNodePtr &node); + // Check if node has none input after IR fusion. + static bool IsNoneInput(const AnfNodePtr &node, size_t index); + // Check whether node is a call node, call nodes are those cnodes whose first input is not primitive node. + static bool IsCallNode(const AnfNodePtr &node); + // Get the output number according to abstract, when there is a tuple in abstract, it needs to get recursively. + static size_t GetOutputNumByAbstract(const AbstractBasePtr &node_abstract); + // Get attr groups + static int64_t GetAttrGroups(const AnfNodePtr &node, size_t index); + + static inline bool IsAllgather(const CNodePtr &cnode) { return GetCNodeName(cnode) == kAllGatherOpName; } + + static inline bool IsFusion(const CNodePtr &cnode) { + return HasNodeAttr(kAttrFusion, cnode) && GetNodeAttr(cnode, kAttrFusion) > 0; + } + + static inline bool IsFromParallelOptimizer(const CNodePtr &cnode) { + auto primitive = GetCNodePrimitive(cnode); + return (primitive != nullptr) && primitive->instance_name().find("parallel_optimizer") != std::string::npos; + } + + static inline bool IsRecompute(const CNodePtr &cnode) { + auto attr_dup = cnode->GetAttr(kAttrDuplicated); + return attr_dup != nullptr && GetValue(attr_dup); + } + + // Get the real output node and indexes of get item, make tuple, depend, load. + static AnfNodePtr GetTupleIndexes(const AnfNodePtr &node, std::vector *index_stack); + static bool IsNopNode(const AnfNodePtr &node); +}; +} // namespace common +} // namespace mindspore +#endif // MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_ANFALGO_H diff --git a/mindspore/ccsrc/utils/callbacks.h b/mindspore/ccsrc/include/common/utils/callbacks.h similarity index 79% rename from mindspore/ccsrc/utils/callbacks.h rename to mindspore/ccsrc/include/common/utils/callbacks.h index 38cfd1ad414..47764bf344b 100644 --- a/mindspore/ccsrc/utils/callbacks.h +++ b/mindspore/ccsrc/include/common/utils/callbacks.h @@ -1,5 +1,5 @@ /** - * Copyright 2019-2020 Huawei Technologies Co., Ltd + * Copyright 2019-2022 Huawei Technologies Co., Ltd * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -13,14 +13,15 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -#ifndef MINDSPORE_CCSRC_UTILS_CALLBACKS_H_ -#define MINDSPORE_CCSRC_UTILS_CALLBACKS_H_ +#ifndef MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_CALLBACKS_H_ +#define MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_CALLBACKS_H_ #include #include #include #include #include "ir/tensor.h" +#include "include/common/visible.h" namespace mindspore { namespace callbacks { @@ -41,9 +42,8 @@ const int kCallbackFalied = 1; bool GetParameterShape(const FuncGraphPtr &anf_graph, const std::string ¶m_name, const std::shared_ptr> &shape); -uint32_t SummarySaveCallback(uint32_t, const std::map &); - +COMMON_EXPORT uint32_t SummarySaveCallback(uint32_t, const std::map &); } // namespace callbacks } // namespace mindspore -#endif // MINDSPORE_CCSRC_UTILS_CALLBACKS_H_ +#endif // MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_CALLBACKS_H_ diff --git a/mindspore/ccsrc/utils/callbacks_ge.h b/mindspore/ccsrc/include/common/utils/callbacks_ge.h similarity index 66% rename from mindspore/ccsrc/utils/callbacks_ge.h rename to mindspore/ccsrc/include/common/utils/callbacks_ge.h index 75b65c817bc..e742ba93b1e 100644 --- a/mindspore/ccsrc/utils/callbacks_ge.h +++ b/mindspore/ccsrc/include/common/utils/callbacks_ge.h @@ -1,5 +1,5 @@ /** - * Copyright 2020 Huawei Technologies Co., Ltd + * Copyright 2020-2022 Huawei Technologies Co., Ltd * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -13,25 +13,26 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -#ifndef MINDSPORE_CCSRC_UTILS_CALLBACKS_GE_H_ -#define MINDSPORE_CCSRC_UTILS_CALLBACKS_GE_H_ +#ifndef MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_CALLBACKS_GE_H_ +#define MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_CALLBACKS_GE_H_ #ifdef ENABLE_D #include #include #include #include -#include "transform/graph_ir/types.h" -#include "transform/graph_ir/util.h" +#include "include/transform/graph_ir/types.h" +#include "include/transform/graph_ir/util.h" #include "ir/tensor.h" +#include "include/common/visible.h" namespace mindspore { namespace callbacks { using mindspore::tensor::TensorPtr; uint32_t CheckpointSaveCallback(uint32_t, const std::map &); -uint32_t SummarySaveCallback(uint32_t, const std::map &); +COMMON_EXPORT uint32_t SummarySaveCallback(uint32_t, const std::map &); } // namespace callbacks } // namespace mindspore #endif -#endif // MINDSPORE_CCSRC_UTILS_CALLBACKS_GE_H_ +#endif // MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_CALLBACKS_GE_H_ diff --git a/mindspore/ccsrc/include/common/utils/comm_manager.h b/mindspore/ccsrc/include/common/utils/comm_manager.h new file mode 100644 index 00000000000..c0c3983667e --- /dev/null +++ b/mindspore/ccsrc/include/common/utils/comm_manager.h @@ -0,0 +1,56 @@ +/** + * Copyright 2019-2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_COMM_MANAGER_H +#define MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_COMM_MANAGER_H + +#include +#include +#include +#include +#include +#include "utils/log_adapter.h" +#include "include/common/visible.h" + +namespace mindspore { +class COMMON_EXPORT CommManager { + public: + static CommManager &GetInstance() noexcept; + static bool Register(const std::string &name, const std::shared_ptr &instance); + + CommManager(const CommManager &) = delete; + virtual ~CommManager() = default; + + virtual bool CreateGroupSync(const std::string &group, const std::vector &rank_id_list) const = 0; + virtual bool DestroyGroup(const std::string &group) const = 0; + virtual bool GetRankID(const std::string &group, unsigned int *rank_id) const = 0; + virtual bool GetRankSize(const std::string &group, unsigned int *rank_size) const = 0; + virtual uint32_t GetRank() = 0; + + protected: + explicit CommManager(std::string backend) : backend_(std::move(backend)) {} + + std::string backend_; +}; + +COMMON_EXPORT uint32_t GetRank(); + +COMMON_EXPORT bool IsStandAlone(); + +#define COMM_MANAGER_REG(NAME, INSTANCE) \ + static bool g_CommManager_##NAME##_reg_result = mindspore::CommManager::Register(NAME, INSTANCE) +} // namespace mindspore +#endif // MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_COMM_MANAGER_H diff --git a/mindspore/ccsrc/utils/config_manager.h b/mindspore/ccsrc/include/common/utils/config_manager.h similarity index 94% rename from mindspore/ccsrc/utils/config_manager.h rename to mindspore/ccsrc/include/common/utils/config_manager.h index a5bbf3084bd..73ad90cc874 100644 --- a/mindspore/ccsrc/utils/config_manager.h +++ b/mindspore/ccsrc/include/common/utils/config_manager.h @@ -14,8 +14,8 @@ * limitations under the License. */ -#ifndef MINDSPORE_CCSRC_UTILS_CONFIG_MANAGER_H_ -#define MINDSPORE_CCSRC_UTILS_CONFIG_MANAGER_H_ +#ifndef MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_CONFIG_MANAGER_H_ +#define MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_CONFIG_MANAGER_H_ #include #include @@ -25,6 +25,7 @@ #include #include "utils/overload.h" +#include "include/common/visible.h" namespace mindspore { @@ -70,7 +71,7 @@ class DatasetGraphParam { std::vector input_indexes_; }; -class ConfigManager { +class COMMON_EXPORT ConfigManager { public: ConfigManager(const ConfigManager &) = delete; ConfigManager &operator=(const ConfigManager &) = delete; @@ -134,4 +135,4 @@ class ConfigManager { } // namespace mindspore -#endif // MINDSPORE_CCSRC_UTILS_CONFIG_MANAGER_H_ +#endif // MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_CONFIG_MANAGER_H_ diff --git a/mindspore/ccsrc/utils/context/graph_kernel_flags.h b/mindspore/ccsrc/include/common/utils/context/graph_kernel_flags.h similarity index 92% rename from mindspore/ccsrc/utils/context/graph_kernel_flags.h rename to mindspore/ccsrc/include/common/utils/context/graph_kernel_flags.h index afdcab17f88..921229a615c 100644 --- a/mindspore/ccsrc/utils/context/graph_kernel_flags.h +++ b/mindspore/ccsrc/include/common/utils/context/graph_kernel_flags.h @@ -14,14 +14,15 @@ * limitations under the License. */ -#ifndef MINDSPORE_CCSRC_UTILS_GRAPH_KERNEL_FLAGS_H -#define MINDSPORE_CCSRC_UTILS_GRAPH_KERNEL_FLAGS_H +#ifndef MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_GRAPH_KERNEL_FLAGS_H +#define MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_GRAPH_KERNEL_FLAGS_H #include #include #include #include #include +#include "include/common/visible.h" namespace mindspore::graphkernel { constexpr unsigned int OptLevel_0 = 0; // Disabled @@ -34,17 +35,9 @@ constexpr unsigned int OpLevel_0 = 0; constexpr unsigned int OpLevel_1 = 1; constexpr unsigned int OpLevel_MAX = 2; -class GraphKernelFlags { +class COMMON_EXPORT GraphKernelFlags { public: - static const GraphKernelFlags &GetInstance() { - static std::unique_ptr flags(nullptr); - auto contexts = GetGraphKernelContext(); - if (flags == nullptr || contexts.first != flags->flags_cache_ || contexts.second != flags->enable_graph_kernel_) { - flags.reset(new GraphKernelFlags(contexts.first, contexts.second)); - flags->Refresh(); - } - return *flags; - } + static const GraphKernelFlags &GetInstance(); // Dump all flags to json-format string std::string DumpAllFlags() const; @@ -229,4 +222,4 @@ class GraphKernelFlags { bool enable_graph_kernel_; }; } // namespace mindspore::graphkernel -#endif // MINDSPORE_CCSRC_UTILS_GRAPH_KERNEL_FLAGS_H +#endif // MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_GRAPH_KERNEL_FLAGS_H diff --git a/mindspore/ccsrc/utils/contract.h b/mindspore/ccsrc/include/common/utils/contract.h similarity index 93% rename from mindspore/ccsrc/utils/contract.h rename to mindspore/ccsrc/include/common/utils/contract.h index 2b0a93abec5..1e641ccb874 100644 --- a/mindspore/ccsrc/utils/contract.h +++ b/mindspore/ccsrc/include/common/utils/contract.h @@ -1,5 +1,5 @@ /** - * Copyright 2019 Huawei Technologies Co., Ltd + * Copyright 2019-2022 Huawei Technologies Co., Ltd * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -14,8 +14,8 @@ * limitations under the License. */ -#ifndef MINDSPORE_CCSRC_UTILS_CONTRACT_H -#define MINDSPORE_CCSRC_UTILS_CONTRACT_H +#ifndef MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_CONTRACT_H +#define MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_CONTRACT_H #include #include #include @@ -105,4 +105,4 @@ using NotNull = Ensures; #define ENSURE(_v, _rule) Ensures(_v, {{__FILE__, __LINE__, __FUNCTION__}, #_v}) #define NOT_NULL(_v) ENSURE(_v, NotNullRule) } // namespace mindspore -#endif // MINDSPORE_CCSRC_UTILS_CONTRACT_H +#endif // MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_CONTRACT_H diff --git a/mindspore/ccsrc/utils/convert_utils.h b/mindspore/ccsrc/include/common/utils/convert_utils.h similarity index 77% rename from mindspore/ccsrc/utils/convert_utils.h rename to mindspore/ccsrc/include/common/utils/convert_utils.h index 7c55e360657..ef4f7d9e680 100644 --- a/mindspore/ccsrc/utils/convert_utils.h +++ b/mindspore/ccsrc/include/common/utils/convert_utils.h @@ -1,5 +1,5 @@ /** - * Copyright 2019-2021 Huawei Technologies Co., Ltd + * Copyright 2019-2022 Huawei Technologies Co., Ltd * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -14,8 +14,8 @@ * limitations under the License. */ -#ifndef MINDSPORE_CCSRC_UTILS_CONVERT_UTILS_H_ -#define MINDSPORE_CCSRC_UTILS_CONVERT_UTILS_H_ +#ifndef MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_CONVERT_UTILS_H_ +#define MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_CONVERT_UTILS_H_ #include #include @@ -33,6 +33,7 @@ #include "base/base.h" #include "ir/anf.h" #include "ir/func_graph.h" +#include "include/common/visible.h" namespace mindspore { namespace tensor { @@ -40,9 +41,9 @@ class Tensor; using TensorPtr = std::shared_ptr; } // namespace tensor -bool BaseRefToBool(const BaseRef &in, bool *out); -bool BaseRefToInt(const ValuePtr &v, int64_t *value); -bool ValueToBool(const ValuePtr &in, bool *out); +COMMON_EXPORT bool BaseRefToBool(const BaseRef &in, bool *out); +COMMON_EXPORT bool BaseRefToInt(const ValuePtr &v, int64_t *value); +COMMON_EXPORT bool ValueToBool(const ValuePtr &in, bool *out); // Isomorphism struct PairHasher { @@ -59,10 +60,10 @@ enum EquivState { kNotEquiv = 0, kEquiv = 1, kPending = 2 }; using FuncGraphPairMapEquiv = mindspore::HashMap, EquivState, PairHasher>; using NodeMapEquiv = mindspore::HashMap; -bool Isomorphic(const FuncGraphPtr &g1, const FuncGraphPtr &g2, FuncGraphPairMapEquiv *equiv_func_graph, - NodeMapEquiv *equiv_node); +COMMON_EXPORT bool Isomorphic(const FuncGraphPtr &g1, const FuncGraphPtr &g2, FuncGraphPairMapEquiv *equiv_func_graph, + NodeMapEquiv *equiv_node); -tensor::TensorPtr ScalarToTensor(const ScalarPtr &scalar); +COMMON_EXPORT tensor::TensorPtr ScalarToTensor(const ScalarPtr &scalar); template std::vector TensorValueToVector(const tensor::TensorPtr &tensor) { @@ -76,9 +77,9 @@ std::vector TensorValueToVector(const tensor::TensorPtr &tensor) { return value; } -void TensorValueToTensor(const ValuePtr &value, std::vector *tensors); +COMMON_EXPORT void TensorValueToTensor(const ValuePtr &value, std::vector *tensors); -size_t CountValueNum(const ValueTuplePtr &value_tuple); +COMMON_EXPORT size_t CountValueNum(const ValueTuplePtr &value_tuple); // sparse_attr_map converts CNode{kPrimSparseGetAttr, SparseTensor} // to CNode{kPrimTupleGetItem, SparseTensor, int64_t(index)}, used @@ -102,7 +103,7 @@ const mindspore::HashSet sparse_op_set = {{prim::kSparseTensorDense {prim::kCSRGather}, {prim::kCSR2COO}}; -bool IsCustomCSROP(const AnfNodePtr &cnode); +COMMON_EXPORT bool IsCustomCSROP(const AnfNodePtr &cnode); } // namespace mindspore -#endif // MINDSPORE_CCSRC_UTILS_CONVERT_UTILS_H_ +#endif // MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_CONVERT_UTILS_H_ diff --git a/mindspore/ccsrc/utils/convert_utils_py.h b/mindspore/ccsrc/include/common/utils/convert_utils_py.h similarity index 55% rename from mindspore/ccsrc/utils/convert_utils_py.h rename to mindspore/ccsrc/include/common/utils/convert_utils_py.h index c53f5ead4a3..af9adeecbea 100644 --- a/mindspore/ccsrc/utils/convert_utils_py.h +++ b/mindspore/ccsrc/include/common/utils/convert_utils_py.h @@ -1,5 +1,5 @@ /** - * Copyright 2019-2020 Huawei Technologies Co., Ltd + * Copyright 2019-2022 Huawei Technologies Co., Ltd * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -14,8 +14,8 @@ * limitations under the License. */ -#ifndef MINDSPORE_CCSRC_UTILS_CONVERT_UTILS_PY_H_ -#define MINDSPORE_CCSRC_UTILS_CONVERT_UTILS_PY_H_ +#ifndef MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_CONVERT_UTILS_PY_H_ +#define MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_CONVERT_UTILS_PY_H_ #include @@ -25,20 +25,18 @@ #include "base/base_ref.h" #include "base/base.h" #include "ir/anf.h" +#include "include/common/visible.h" namespace py = pybind11; namespace mindspore { py::object AnyToPyData(const Any &value); -py::object BaseRefToPyData(const BaseRef &value); -py::object BaseRefToPyData(const BaseRef &value, const AbstractBasePtr &output); -py::object ValueToPyData(const ValuePtr &value); +COMMON_EXPORT py::object BaseRefToPyData(const BaseRef &value); +COMMON_EXPORT py::object BaseRefToPyData(const BaseRef &value, const AbstractBasePtr &output); +COMMON_EXPORT py::object ValueToPyData(const ValuePtr &value); -bool IsGraphOutputValueNodeOrParameter(const AnfNodePtr &output, const py::tuple &args, - const std::shared_ptr &ret_val); - -AbstractBasePtr MakePyInferRes2Abstract(const py::object &output); -void SetValueRange(const AbstractBasePtr &tensor, const py::object &output); +COMMON_EXPORT bool IsGraphOutputValueNodeOrParameter(const AnfNodePtr &output, const py::tuple &args, + const std::shared_ptr &ret_val); } // namespace mindspore -#endif // MINDSPORE_CCSRC_UTILS_CONVERT_UTILS_PY_H_ +#endif // MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_CONVERT_UTILS_PY_H_ diff --git a/mindspore/ccsrc/utils/json_operation_utils.h b/mindspore/ccsrc/include/common/utils/json_operation_utils.h similarity index 78% rename from mindspore/ccsrc/utils/json_operation_utils.h rename to mindspore/ccsrc/include/common/utils/json_operation_utils.h index 6487ec2755b..01aeb2086ec 100644 --- a/mindspore/ccsrc/utils/json_operation_utils.h +++ b/mindspore/ccsrc/include/common/utils/json_operation_utils.h @@ -1,5 +1,5 @@ /** - * Copyright 2021 Huawei Technologies Co., Ltd + * Copyright 2021-2022 Huawei Technologies Co., Ltd * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -14,12 +14,13 @@ * limitations under the License. */ -#ifndef MINDSPORE_JSON_OPERATION_UTILS_H -#define MINDSPORE_JSON_OPERATION_UTILS_H +#ifndef MINDSPORE_INCLUDE_COMMON_UTILS_JSON_OPERATION_UTILS_H +#define MINDSPORE_INCLUDE_COMMON_UTILS_JSON_OPERATION_UTILS_H #include #include #include #include "ir/dtype.h" +#include "include/common/visible.h" namespace mindspore { @@ -40,7 +41,7 @@ T GetJsonValue(const nlohmann::json &json, const std::string &key) { } } -bool ParseJson(const std::string &str, nlohmann::json *des_json); +COMMON_EXPORT bool ParseJson(const std::string &str, nlohmann::json *des_json); } // namespace mindspore -#endif // MINDSPORE_JSON_OPERATION_UTILS_H +#endif // MINDSPORE_INCLUDE_COMMON_UTILS_JSON_OPERATION_UTILS_H diff --git a/mindspore/ccsrc/utils/mpi/mpi_config.h b/mindspore/ccsrc/include/common/utils/mpi/mpi_config.h similarity index 79% rename from mindspore/ccsrc/utils/mpi/mpi_config.h rename to mindspore/ccsrc/include/common/utils/mpi/mpi_config.h index 044e7677629..2021caa66a1 100644 --- a/mindspore/ccsrc/utils/mpi/mpi_config.h +++ b/mindspore/ccsrc/include/common/utils/mpi/mpi_config.h @@ -14,13 +14,14 @@ * limitations under the License. */ -#ifndef MINDSPORE_CCSRC_UTILS_MPI_MS_CONTEXT_H_ -#define MINDSPORE_CCSRC_UTILS_MPI_MS_CONTEXT_H_ +#ifndef MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_MPI_MS_CONTEXT_H_ +#define MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_MPI_MS_CONTEXT_H_ + #include -#include "utils/log_adapter.h" +#include "include/common/visible.h" namespace mindspore { -class MpiConfig { +class COMMON_EXPORT MpiConfig { public: ~MpiConfig() = default; MpiConfig(const MpiConfig &) = delete; @@ -34,9 +35,8 @@ class MpiConfig { private: MpiConfig() : enable_mpi_(false) {} - static std::shared_ptr instance_; bool enable_mpi_; }; } // namespace mindspore -#endif // MINDSPORE_CCSRC_UTILS_MPI_MS_CONTEXT_H_ +#endif // MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_MPI_MS_CONTEXT_H_ diff --git a/mindspore/ccsrc/frontend/parallel/context.h b/mindspore/ccsrc/include/common/utils/parallel_context.h similarity index 85% rename from mindspore/ccsrc/frontend/parallel/context.h rename to mindspore/ccsrc/include/common/utils/parallel_context.h index d432d354ce0..77a75620939 100644 --- a/mindspore/ccsrc/frontend/parallel/context.h +++ b/mindspore/ccsrc/include/common/utils/parallel_context.h @@ -14,8 +14,8 @@ * limitations under the License. */ -#ifndef MINDSPORE_CCSRC_FRONTEND_PARALLEL_CONTEXT_H_ -#define MINDSPORE_CCSRC_FRONTEND_PARALLEL_CONTEXT_H_ +#ifndef MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_PARALLEL_CONTEXT_H_ +#define MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_PARALLEL_CONTEXT_H_ #include #include @@ -24,47 +24,44 @@ #include #include "abstract/abstract_value.h" -#include "frontend/parallel/ops_info/ops_utils.h" -#include "frontend/parallel/status.h" #include "ir/anf.h" #include "ir/func_graph.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" #include "utils/info.h" -#include "pipeline/jit/pipeline.h" +#include "include/common/visible.h" -namespace mindspore { -namespace parallel { -constexpr char STAND_ALONE[] = "stand_alone"; -constexpr char DATA_PARALLEL[] = "data_parallel"; -constexpr char HYBRID_PARALLEL[] = "hybrid_parallel"; -constexpr char AUTO_PARALLEL[] = "auto_parallel"; -constexpr char SEMI_AUTO_PARALLEL[] = "semi_auto_parallel"; +namespace mindspore::parallel { +constexpr char kStandalone[] = "stand_alone"; +constexpr char kDataParallel[] = "data_parallel"; +constexpr char kHybridParallel[] = "hybrid_parallel"; +constexpr char kAutoParallel[] = "auto_parallel"; +constexpr char kSemiAutoParallel[] = "semi_auto_parallel"; -constexpr char DYNAMIC_PROGRAMMING[] = "dynamic_programming"; -constexpr char RECURSIVE_PROGRAMMING[] = "recursive_programming"; -constexpr char SHARDING_PROPAGATION[] = "sharding_propagation"; +constexpr char kDynamicProgramming[] = "dynamic_programming"; +constexpr char kRecursiveProgramming[] = "recursive_programming"; +constexpr char kShardingPropagation[] = "sharding_propagation"; -constexpr char TRAINING[] = "training"; -constexpr char ACCUMULATION[] = "accumulation"; +constexpr char kTraining[] = "training"; +constexpr char kAccumulation[] = "accumulation"; -constexpr char ALL_GROUP_PARALLEL[] = "all_group_parallel"; -constexpr char SAME_SERVER_GROUP_PARALLEL[] = "same_server_group_parallel"; -constexpr char NO_GROUP_PARALLEL[] = "no_group_parallel"; +constexpr char kAllGroupParallel[] = "all_group_parallel"; +constexpr char kSameServerGroupParallel[] = "same_server_group_parallel"; +constexpr char kNoGroupParallel[] = "no_group_parallel"; -constexpr char IS_FIRST_ITERATION[] = "is_first_iteration"; +constexpr char kIsFirstIteration[] = "is_first_iteration"; -constexpr char FUSION_AUTO[] = "auto"; -constexpr char FUSION_SIZE[] = "size"; -constexpr char FUSION_INDEX[] = "index"; -constexpr int64_t FUSUION_THRESHOLD = 64; -class ParallelContext { +constexpr char kFusionAuto[] = "auto"; +constexpr char kFusionSize[] = "size"; +constexpr char kFusionIndex[] = "index"; +constexpr int64_t kFusionThreshold = 64; + +class COMMON_EXPORT ParallelContext { public: + static std::shared_ptr GetInstance(); ~ParallelContext() = default; ParallelContext(const ParallelContext &) = delete; ParallelContext &operator=(const ParallelContext &) = delete; - static std::shared_ptr GetInstance(); - void set_gradients_mean(bool gradients_mean); bool gradients_mean() const { return gradients_mean_; } @@ -182,7 +179,7 @@ class ParallelContext { private: ParallelContext(); - static std::shared_ptr inst_context_; + bool gradients_mean_; bool full_batch_; bool gradient_fp32_sync_; @@ -224,8 +221,6 @@ class ParallelContext { bool sharding_propagation_; std::string fusion_mode_; }; +} // namespace mindspore::parallel -} // namespace parallel -} // namespace mindspore - -#endif // MINDSPORE_CCSRC_FRONTEND_PARALLEL_CONTEXT_H_ +#endif // MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_PARALLEL_CONTEXT_H_ diff --git a/mindspore/ccsrc/include/common/utils/primitive_utils.h b/mindspore/ccsrc/include/common/utils/primitive_utils.h new file mode 100644 index 00000000000..f15e63bcb85 --- /dev/null +++ b/mindspore/ccsrc/include/common/utils/primitive_utils.h @@ -0,0 +1,44 @@ +/** + * Copyright 2020-2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_PRIMITIVE_UTILS_H_ +#define MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_PRIMITIVE_UTILS_H_ + +#include +#include "pybind11/pybind11.h" +#include "base/base_ref.h" +#include "include/common/utils/convert_utils.h" +#include "include/common/utils/utils.h" + +namespace py = pybind11; + +namespace mindspore { +COMMON_EXPORT py::function GetBpropFunctionByObj(const py::object &obj); + +COMMON_EXPORT py::function GetBpropFunction(const std::string &name); + +COMMON_EXPORT py::function GetComputeFunction(const std::string &name); + +COMMON_EXPORT BaseRef RunComputeFunction(const PrimitivePtr &prim, const VectorRef &args); + +COMMON_EXPORT py::function GetComputeFunctionWithoutPyObj(const std::string &name); + +COMMON_EXPORT BaseRef RunComputeFunctionWithoutPyObj(const PrimitivePtr &prim, const VectorRef &args); + +COMMON_EXPORT py::tuple ConvertDatatoPyTuple(const VectorRef &args); +} // namespace mindspore + +#endif // MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_PRIMITIVE_UTILS_H_ diff --git a/mindspore/ccsrc/pipeline/jit/parse/python_adapter.h b/mindspore/ccsrc/include/common/utils/python_adapter.h similarity index 66% rename from mindspore/ccsrc/pipeline/jit/parse/python_adapter.h rename to mindspore/ccsrc/include/common/utils/python_adapter.h index d2cabdb2d8d..050dafad07e 100644 --- a/mindspore/ccsrc/pipeline/jit/parse/python_adapter.h +++ b/mindspore/ccsrc/include/common/utils/python_adapter.h @@ -1,5 +1,5 @@ /** - * Copyright 2019 Huawei Technologies Co., Ltd + * Copyright 2019-2022 Huawei Technologies Co., Ltd * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -14,8 +14,8 @@ * limitations under the License. */ -#ifndef MINDSPORE_CCSRC_PIPELINE_JIT_PARSE_PYTHON_ADAPTER_H_ -#define MINDSPORE_CCSRC_PIPELINE_JIT_PARSE_PYTHON_ADAPTER_H_ +#ifndef MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_PYTHON_ADAPTER_H_ +#define MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_PYTHON_ADAPTER_H_ #include #include #include @@ -24,15 +24,15 @@ #include "pybind11/pybind11.h" #include "pybind11/stl.h" -#include "pipeline/jit/parse/parse_base.h" #include "utils/log_adapter.h" +#include "include/common/visible.h" +namespace py = pybind11; namespace mindspore { -namespace parse { // A utility to call python interface namespace python_adapter { -py::module GetPyModule(const std::string &module); -py::object GetPyObjAttr(const py::object &obj, const std::string &attr); +COMMON_EXPORT py::module GetPyModule(const std::string &module); +COMMON_EXPORT py::object GetPyObjAttr(const py::object &obj, const std::string &attr); template py::object CallPyObjMethod(const py::object &obj, const std::string &method, T... args) { if (!method.empty() && !py::isinstance(obj)) { @@ -51,15 +51,15 @@ py::object CallPyModFn(const py::module &mod, const std::string &function, T... } // turn off the signature when ut use parser to construct a graph. -void set_use_signature_in_resolve(bool use_signature) noexcept; -bool UseSignatureInResolve(); +COMMON_EXPORT void set_use_signature_in_resolve(bool use_signature) noexcept; +COMMON_EXPORT bool UseSignatureInResolve(); -std::shared_ptr set_python_scoped(); -void ResetPythonScope(); -bool IsPythonEnv(); -void SetPythonPath(const std::string &path); -void set_python_env_flag(bool python_env) noexcept; -py::object GetPyFn(const std::string &module, const std::string &name); +COMMON_EXPORT std::shared_ptr set_python_scoped(); +COMMON_EXPORT void ResetPythonScope(); +COMMON_EXPORT bool IsPythonEnv(); +COMMON_EXPORT void SetPythonPath(const std::string &path); +COMMON_EXPORT void set_python_env_flag(bool python_env) noexcept; +COMMON_EXPORT py::object GetPyFn(const std::string &module, const std::string &name); // Call the python function template py::object CallPyFn(const std::string &module, const std::string &name, T... args) { @@ -72,7 +72,5 @@ py::object CallPyFn(const std::string &module, const std::string &name, T... arg return py::none(); } } // namespace python_adapter -} // namespace parse } // namespace mindspore - -#endif // MINDSPORE_CCSRC_PIPELINE_JIT_PARSE_PYTHON_ADAPTER_H_ +#endif // MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_PYTHON_ADAPTER_H_ diff --git a/mindspore/ccsrc/utils/runtime_error_codes.h b/mindspore/ccsrc/include/common/utils/runtime_error_codes.h similarity index 63% rename from mindspore/ccsrc/utils/runtime_error_codes.h rename to mindspore/ccsrc/include/common/utils/runtime_error_codes.h index 5753d26c675..e49ddd66cd3 100644 --- a/mindspore/ccsrc/utils/runtime_error_codes.h +++ b/mindspore/ccsrc/include/common/utils/runtime_error_codes.h @@ -1,5 +1,5 @@ /** - * Copyright 2021 Huawei Technologies Co., Ltd + * Copyright 2021-2022 Huawei Technologies Co., Ltd * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -14,11 +14,13 @@ * limitations under the License. */ -#ifndef MINDSPORE_CCSRC_UTILS_RUNTIME_ERROR_CODES_H_ -#define MINDSPORE_CCSRC_UTILS_RUNTIME_ERROR_CODES_H_ +#ifndef MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_RUNTIME_ERROR_CODES_H_ +#define MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_RUNTIME_ERROR_CODES_H_ #include +#include "include/common/visible.h" + namespace mindspore { -std::string GetErrorMsg(uint32_t rt_error_code); +COMMON_EXPORT std::string GetErrorMsg(uint32_t rt_error_code); } // namespace mindspore -#endif // MINDSPORE_CCSRC_UTILS_RUNTIME_ERROR_CODES_H_ +#endif // MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_RUNTIME_ERROR_CODES_H_ diff --git a/mindspore/ccsrc/utils/scoped_long_running.h b/mindspore/ccsrc/include/common/utils/scoped_long_running.h similarity index 64% rename from mindspore/ccsrc/utils/scoped_long_running.h rename to mindspore/ccsrc/include/common/utils/scoped_long_running.h index d81592d7d89..d68ec08547a 100644 --- a/mindspore/ccsrc/utils/scoped_long_running.h +++ b/mindspore/ccsrc/include/common/utils/scoped_long_running.h @@ -1,5 +1,5 @@ /** - * Copyright 2020 Huawei Technologies Co., Ltd + * Copyright 2020-2022 Huawei Technologies Co., Ltd * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -14,15 +14,16 @@ * limitations under the License. */ -#ifndef MINDSPORE_CCSRC_UTILS_SCOPED_LONG_RUNNING_H_ -#define MINDSPORE_CCSRC_UTILS_SCOPED_LONG_RUNNING_H_ +#ifndef MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_SCOPED_LONG_RUNNING_H_ +#define MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_SCOPED_LONG_RUNNING_H_ #include #include +#include "include/common/visible.h" namespace mindspore { // Base Class for scoped long running code. -// Enter() should release some global resoure, like Python GIL; +// Enter() should release some global resource, like Python GIL; // Leave() should acquire the same global resource released. class ScopedLongRunningHook { public: @@ -35,26 +36,14 @@ using ScopedLongRunningHookPtr = std::unique_ptr; // Before calling into long-running code, construct this RAII class to release global resource // like Python GIL. -class ScopedLongRunning { +class COMMON_EXPORT ScopedLongRunning { public: - ScopedLongRunning() { - if (hook_ != nullptr) { - hook_->Enter(); - } - } - ~ScopedLongRunning() { - if (hook_ != nullptr) { - hook_->Leave(); - } - } - static void SetHook(ScopedLongRunningHookPtr hook) { - if (hook_ == nullptr) { - hook_ = std::move(hook); - } - } + ScopedLongRunning(); + ~ScopedLongRunning(); + static void SetHook(ScopedLongRunningHookPtr hook); private: - static ScopedLongRunningHookPtr hook_; + inline static ScopedLongRunningHookPtr hook_ = nullptr; }; } // namespace mindspore -#endif // MINDSPORE_CCSRC_UTILS_SCOPED_LONG_RUNNING_H_ +#endif // MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_SCOPED_LONG_RUNNING_H_ diff --git a/mindspore/ccsrc/utils/signal_util.h b/mindspore/ccsrc/include/common/utils/signal_util.h similarity index 74% rename from mindspore/ccsrc/utils/signal_util.h rename to mindspore/ccsrc/include/common/utils/signal_util.h index 6dd3a1f8907..7579d900a02 100644 --- a/mindspore/ccsrc/utils/signal_util.h +++ b/mindspore/ccsrc/include/common/utils/signal_util.h @@ -1,5 +1,5 @@ /** - * Copyright 2020 Huawei Technologies Co., Ltd + * Copyright 2020-2022 Huawei Technologies Co., Ltd * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -13,14 +13,15 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -#ifndef MINDSPORE_CCSRC_UTILS_SIGNAL_UTIL_H_ -#define MINDSPORE_CCSRC_UTILS_SIGNAL_UTIL_H_ +#ifndef MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_SIGNAL_UTIL_H_ +#define MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_SIGNAL_UTIL_H_ #include +#include "include/common/visible.h" namespace mindspore { typedef void (*IntHandlerFunc)(int, siginfo_t *, void *); -class SignalGuard { +class COMMON_EXPORT SignalGuard { public: explicit SignalGuard(IntHandlerFunc func); ~SignalGuard(); @@ -31,4 +32,4 @@ class SignalGuard { struct sigaction int_action; }; } // namespace mindspore -#endif // MINDSPORE_CCSRC_UTILS_SIGNAL_UTIL_H_ +#endif // MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_SIGNAL_UTIL_H_ diff --git a/mindspore/ccsrc/utils/summary/event_writer.h b/mindspore/ccsrc/include/common/utils/summary/event_writer.h similarity index 75% rename from mindspore/ccsrc/utils/summary/event_writer.h rename to mindspore/ccsrc/include/common/utils/summary/event_writer.h index c9dda19b58d..542a17cd7dc 100644 --- a/mindspore/ccsrc/utils/summary/event_writer.h +++ b/mindspore/ccsrc/include/common/utils/summary/event_writer.h @@ -1,5 +1,5 @@ /** - * Copyright 2019 Huawei Technologies Co., Ltd + * Copyright 2019-2022 Huawei Technologies Co., Ltd * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -14,29 +14,23 @@ * limitations under the License. */ -#ifndef SUMMARY_EVENT_WRITER_H_ -#define SUMMARY_EVENT_WRITER_H_ +#ifndef MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_SUMMARY_EVENT_WRITER_H_ +#define MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_SUMMARY_EVENT_WRITER_H_ #include #include #include "pybind11/pybind11.h" #include "securec/include/securec.h" -#include "utils/system/base.h" -#include "utils/system/file_system.h" -#include "utils/system/crc32c.h" -#include "utils/system/env.h" +#include "include/common/visible.h" namespace mindspore { +namespace system { +class FileSystem; +class WriteFile; +} // namespace system namespace summary { -namespace py = pybind11; -using string = std::string; -using Env = system::Env; -using WriteFile = system::WriteFile; -using WriteFilePtr = std::shared_ptr; -using FileSystem = system::FileSystem; - -class EventWriter { +class COMMON_EXPORT EventWriter { public: // The file name = path + file_name explicit EventWriter(const std::string &file_full_name); @@ -75,13 +69,13 @@ class EventWriter { void CloseFile() noexcept; // True: valid / False: closed bool status_ = false; - std::shared_ptr fs_; + std::shared_ptr fs_; std::string filename_; - WriteFilePtr event_file_; + std::shared_ptr event_file_; int32_t events_write_count_ = 0; }; } // namespace summary } // namespace mindspore -#endif // SUMMARY_EVENT_WRITER_H_ +#endif // MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_SUMMARY_EVENT_WRITER_H_ diff --git a/mindspore/ccsrc/utils/tensorprint_utils.h b/mindspore/ccsrc/include/common/utils/tensorprint_utils.h similarity index 75% rename from mindspore/ccsrc/utils/tensorprint_utils.h rename to mindspore/ccsrc/include/common/utils/tensorprint_utils.h index 2e06bdb3632..6024e8e0fd0 100644 --- a/mindspore/ccsrc/utils/tensorprint_utils.h +++ b/mindspore/ccsrc/include/common/utils/tensorprint_utils.h @@ -1,5 +1,5 @@ /** - * Copyright 2020 Huawei Technologies Co., Ltd + * Copyright 2020-2022 Huawei Technologies Co., Ltd * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -14,8 +14,8 @@ * limitations under the License. */ -#ifndef MINDSPORE_CCSRC_UTILS_TENSORPRINT_UTILS_H_ -#define MINDSPORE_CCSRC_UTILS_TENSORPRINT_UTILS_H_ +#ifndef MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_TENSORPRINT_UTILS_H_ +#define MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_TENSORPRINT_UTILS_H_ #include #include @@ -26,9 +26,10 @@ #include "tdt/data_common.h" #include "tdt/tdt_host_interface.h" #include "proto/print.pb.h" +#include "include/common/visible.h" namespace mindspore { -class TensorPrint { +class COMMON_EXPORT TensorPrint { public: explicit TensorPrint(const std::string &path, const acltdtChannelHandle *acl_handle) : print_file_path_(path), acl_handle_(acl_handle) {} @@ -41,4 +42,4 @@ class TensorPrint { }; } // namespace mindspore #endif -#endif // MINDSPORE_CCSRC_UTILS_TENSOR_PRINT_H_ +#endif // MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_TENSORPRINT_UTILS_H_ diff --git a/mindspore/ccsrc/utils/utils.h b/mindspore/ccsrc/include/common/utils/utils.h similarity index 99% rename from mindspore/ccsrc/utils/utils.h rename to mindspore/ccsrc/include/common/utils/utils.h index 7c3efef2053..68eb8ff9567 100644 --- a/mindspore/ccsrc/utils/utils.h +++ b/mindspore/ccsrc/include/common/utils/utils.h @@ -14,8 +14,8 @@ * limitations under the License. */ -#ifndef MINDSPORE_CCSRC_UTILS_UTILS_H_ -#define MINDSPORE_CCSRC_UTILS_UTILS_H_ +#ifndef MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_UTILS_H_ +#define MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_UTILS_H_ #include #include @@ -835,4 +835,4 @@ static inline uint64_t GetCurrentUSec() { MS_LOG(INFO) << #stage << " called " << count_##stage << " times, costs " << total_##stage << " usec."; \ } while (0) } // namespace mindspore -#endif // MINDSPORE_CCSRC_UTILS_UTILS_H_ +#endif // MINDSPORE_CCSRC_INCLUDE_COMMON_UTILS_UTILS_H_ diff --git a/mindspore/ccsrc/pybind_api/ir/base_ref_py.cc b/mindspore/ccsrc/include/common/visible.h similarity index 50% rename from mindspore/ccsrc/pybind_api/ir/base_ref_py.cc rename to mindspore/ccsrc/include/common/visible.h index 0828d324da9..07af2b8bf11 100644 --- a/mindspore/ccsrc/pybind_api/ir/base_ref_py.cc +++ b/mindspore/ccsrc/include/common/visible.h @@ -1,5 +1,5 @@ /** - * Copyright 2020 Huawei Technologies Co., Ltd + * Copyright 2022 Huawei Technologies Co., Ltd * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -14,15 +14,18 @@ * limitations under the License. */ -#include "pybind_api/ir/base_ref_py.h" +#ifndef MINDSPORE_CCSRC_INCLUDE_COMMON_VISIBLE_H_ +#define MINDSPORE_CCSRC_INCLUDE_COMMON_VISIBLE_H_ -namespace mindspore { -bool PyObjectRef::operator==(const BaseRef &other) const { - if (!utils::isa(other)) { - return false; - } - return *this == utils::cast(other); -} - -bool PyObjectRef::operator==(const PyObjectRef &other) const { return object_.is(other.object_); } -} // namespace mindspore +#if (defined(_WIN32) || defined(__WIN32__) || defined(WIN32) || defined(__CYGWIN__)) +#ifdef COMMON_DLL +#define COMMON_EXPORT __declspec(dllexport) +#else +#define COMMON_EXPORT __declspec(dllimport) +#endif +#define COMMON_LOCAL +#else +#define COMMON_EXPORT __attribute__((visibility("default"))) +#define COMMON_LOCAL __attribute__((visibility("hidden"))) +#endif +#endif // MINDSPORE_CCSRC_INCLUDE_COMMON_VISIBLE_H_ diff --git a/mindspore/ccsrc/transform/graph_ir/convert.h b/mindspore/ccsrc/include/transform/graph_ir/convert.h similarity index 94% rename from mindspore/ccsrc/transform/graph_ir/convert.h rename to mindspore/ccsrc/include/transform/graph_ir/convert.h index da27d465bff..62a89812b7f 100644 --- a/mindspore/ccsrc/transform/graph_ir/convert.h +++ b/mindspore/ccsrc/include/transform/graph_ir/convert.h @@ -14,8 +14,8 @@ * limitations under the License. */ -#ifndef MINDSPORE_CCSRC_TRANSFORM_GRAPH_IR_CONVERT_H_ -#define MINDSPORE_CCSRC_TRANSFORM_GRAPH_IR_CONVERT_H_ +#ifndef MINDSPORE_CCSRC_INCLUDE_TRANSFORM_GRAPH_IR_CONVERT_H_ +#define MINDSPORE_CCSRC_INCLUDE_TRANSFORM_GRAPH_IR_CONVERT_H_ #define DRAW_GE_GRAPH @@ -30,29 +30,28 @@ #include #include -#include "utils/config_manager.h" +#include "include/common/utils/config_manager.h" #include "utils/hash_map.h" #include "utils/ms_context.h" #include "ir/anf.h" #include "ir/func_graph.h" -#include "transform/graph_ir/util.h" +#include "include/transform/graph_ir/util.h" #include "ir/tensor.h" -#include "transform/graph_ir/df_graph_manager.h" -#include "transform/graph_ir/op_adapter.h" +#include "include/transform/graph_ir/df_graph_manager.h" #include "graph/operator_reg.h" -#ifdef OPEN_SOURCE -#include "ge/client/ge_api.h" -#else #include "external/ge/ge_api.h" -#endif #include "graph/tensor.h" #include "ops/hcom_ops.h" +#include "include/common/visible.h" namespace mindspore { namespace transform { +class BaseOpAdapter; using TensorOrderMap = std::map>; using HcomBroadcast = ge::op::HcomBroadcast; -class DfGraphConvertor { +using OpAdapterPtr = std::shared_ptr; + +class COMMON_EXPORT DfGraphConvertor { public: explicit DfGraphConvertor(const AnfGraphPtr &anf_graph) : anf_graph_(anf_graph) { MS_EXCEPTION_IF_NULL(anf_graph); @@ -227,4 +226,4 @@ class DfGraphConvertor { } // namespace transform } // namespace mindspore -#endif // MINDSPORE_CCSRC_TRANSFORM_GRAPH_IR_CONVERT_H_ +#endif // MINDSPORE_CCSRC_INCLUDE_TRANSFORM_GRAPH_IR_CONVERT_H_ diff --git a/mindspore/ccsrc/transform/graph_ir/df_graph_manager.h b/mindspore/ccsrc/include/transform/graph_ir/df_graph_manager.h similarity index 88% rename from mindspore/ccsrc/transform/graph_ir/df_graph_manager.h rename to mindspore/ccsrc/include/transform/graph_ir/df_graph_manager.h index 0a24845f837..68547e32b1b 100644 --- a/mindspore/ccsrc/transform/graph_ir/df_graph_manager.h +++ b/mindspore/ccsrc/include/transform/graph_ir/df_graph_manager.h @@ -13,8 +13,8 @@ * limitations under the License. */ -#ifndef MINDSPORE_CCSRC_TRANSFORM_GRAPH_IR_DF_GRAPH_MANAGER_H_ -#define MINDSPORE_CCSRC_TRANSFORM_GRAPH_IR_DF_GRAPH_MANAGER_H_ +#ifndef MINDSPORE_CCSRC_INCLUDE_TRANSFORM_GRAPH_IR_DF_GRAPH_MANAGER_H_ +#define MINDSPORE_CCSRC_INCLUDE_TRANSFORM_GRAPH_IR_DF_GRAPH_MANAGER_H_ #include #include @@ -23,8 +23,9 @@ #include #include #include -#include "transform/graph_ir/types.h" +#include "include/transform/graph_ir/types.h" #include "ir/anf.h" +#include "include/common/visible.h" namespace mindspore { const char BROADCAST_GRAPH_NAME[] = "broadcast_subgraph"; @@ -46,7 +47,7 @@ struct DfGraphWrapper { using DfGraphWrapperPtr = std::shared_ptr; -class DfGraphManager { +class COMMON_EXPORT DfGraphManager { public: ~DfGraphManager(); void ClearGraph() noexcept; @@ -83,4 +84,4 @@ class DfGraphManager { } // namespace transform } // namespace mindspore -#endif // MINDSPORE_CCSRC_TRANSFORM_GRAPH_IR_DF_GRAPH_MANAGER_H_ +#endif // MINDSPORE_CCSRC_INCLUDE_TRANSFORM_GRAPH_IR_DF_GRAPH_MANAGER_H_ diff --git a/mindspore/ccsrc/transform/graph_ir/graph_builder.h b/mindspore/ccsrc/include/transform/graph_ir/graph_builder.h similarity index 64% rename from mindspore/ccsrc/transform/graph_ir/graph_builder.h rename to mindspore/ccsrc/include/transform/graph_ir/graph_builder.h index 1262625673a..9467f466c63 100644 --- a/mindspore/ccsrc/transform/graph_ir/graph_builder.h +++ b/mindspore/ccsrc/include/transform/graph_ir/graph_builder.h @@ -14,21 +14,22 @@ * limitations under the License. */ -#ifndef MINDSPORE_CCSRC_TRANSFORM_GRAPH_IR_GRAPH_BUILDER_H_ -#define MINDSPORE_CCSRC_TRANSFORM_GRAPH_IR_GRAPH_BUILDER_H_ +#ifndef MINDSPORE_CCSRC_INCLUDE_TRANSFORM_GRAPH_IR_GRAPH_BUILDER_H_ +#define MINDSPORE_CCSRC_INCLUDE_TRANSFORM_GRAPH_IR_GRAPH_BUILDER_H_ #include #include #include #include #include -#include "transform/graph_ir/types.h" -#include "transform/graph_ir/convert.h" +#include "include/transform/graph_ir/types.h" +#include "include/transform/graph_ir/convert.h" +#include "include/common/visible.h" namespace mindspore { namespace transform { -Status BuildDatasetGraph(const DatasetGraphParam ¶m, const std::string &phase = "dataset"); +COMMON_EXPORT Status BuildDatasetGraph(const DatasetGraphParam ¶m, const std::string &phase = "dataset"); } // namespace transform } // namespace mindspore -#endif // MINDSPORE_CCSRC_TRANSFORM_GRAPH_IR_GRAPH_BUILDER_H_ +#endif // MINDSPORE_CCSRC_INCLUDE_TRANSFORM_GRAPH_IR_GRAPH_BUILDER_H_ diff --git a/mindspore/ccsrc/transform/graph_ir/graph_runner.h b/mindspore/ccsrc/include/transform/graph_ir/graph_runner.h similarity index 77% rename from mindspore/ccsrc/transform/graph_ir/graph_runner.h rename to mindspore/ccsrc/include/transform/graph_ir/graph_runner.h index 55f8beddae7..d1b6d2d073f 100644 --- a/mindspore/ccsrc/transform/graph_ir/graph_runner.h +++ b/mindspore/ccsrc/include/transform/graph_ir/graph_runner.h @@ -1,5 +1,5 @@ /** - * Copyright 2019-2020 Huawei Technologies Co., Ltd + * Copyright 2019-2022 Huawei Technologies Co., Ltd * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -14,8 +14,8 @@ * limitations under the License. */ -#ifndef MINDSPORE_CCSRC_TRANSFORM_GRAPH_IR_GRAPH_RUNNER_H_ -#define MINDSPORE_CCSRC_TRANSFORM_GRAPH_IR_GRAPH_RUNNER_H_ +#ifndef MINDSPORE_CCSRC_INCLUDE_TRANSFORM_GRAPH_IR_GRAPH_RUNNER_H_ +#define MINDSPORE_CCSRC_INCLUDE_TRANSFORM_GRAPH_IR_GRAPH_RUNNER_H_ #include #include @@ -23,10 +23,11 @@ #include #include -#include "transform/graph_ir/types.h" -#include "transform/graph_ir/util.h" +#include "include/transform/graph_ir/types.h" +#include "include/transform/graph_ir/util.h" +#include "include/transform/graph_ir/df_graph_manager.h" +#include "include/common/visible.h" #include "ir/tensor.h" -#include "transform/graph_ir/df_graph_manager.h" namespace mindspore { namespace transform { @@ -44,7 +45,7 @@ struct RunOptions { std::string name; }; -class GraphRunner { +class COMMON_EXPORT GraphRunner { public: explicit GraphRunner(const GraphRunnerOptions &options); ~GraphRunner() { sess_ = nullptr; } @@ -60,4 +61,4 @@ class GraphRunner { } // namespace transform } // namespace mindspore -#endif // MINDSPORE_CCSRC_TRANSFORM_GRAPH_IR_GRAPH_RUNNER_H_ +#endif // MINDSPORE_CCSRC_INCLUDE_TRANSFORM_GRAPH_IR_GRAPH_RUNNER_H_ diff --git a/mindspore/ccsrc/transform/graph_ir/op_adapter_map.h b/mindspore/ccsrc/include/transform/graph_ir/op_adapter_map.h similarity index 97% rename from mindspore/ccsrc/transform/graph_ir/op_adapter_map.h rename to mindspore/ccsrc/include/transform/graph_ir/op_adapter_map.h index cf25169059d..d357938e1ba 100644 --- a/mindspore/ccsrc/transform/graph_ir/op_adapter_map.h +++ b/mindspore/ccsrc/include/transform/graph_ir/op_adapter_map.h @@ -14,12 +14,13 @@ * limitations under the License. */ -#ifndef MINDSPORE_CCSRC_TRANSFORM_GRAPH_IR_OP_ADAPTER_MAP_H_ -#define MINDSPORE_CCSRC_TRANSFORM_GRAPH_IR_OP_ADAPTER_MAP_H_ +#ifndef MINDSPORE_CCSRC_INCLUDE_TRANSFORM_GRAPH_IR_OP_ADAPTER_MAP_H_ +#define MINDSPORE_CCSRC_INCLUDE_TRANSFORM_GRAPH_IR_OP_ADAPTER_MAP_H_ #include +#include #include "utils/hash_map.h" -#include "transform/graph_ir/op_adapter_desc.h" +#include "include/common/visible.h" namespace mindspore { namespace transform { @@ -347,13 +348,12 @@ constexpr const char kNameTensorArray[] = "TensorArray"; constexpr const char kNameTensorArrayWrite[] = "TensorArrayWrite"; constexpr const char kNameTensorArrayGather[] = "TensorArrayGather"; -class OpAdapterMap { - public: - static mindspore::HashMap &get(); +class OpAdapterDesc; - private: - static mindspore::HashMap adpt_map_; +class COMMON_EXPORT OpAdapterMap { + public: + static mindspore::HashMap> &get(); }; } // namespace transform } // namespace mindspore -#endif // MINDSPORE_CCSRC_TRANSFORM_GRAPH_IR_OP_ADAPTER_MAP_H_ +#endif // MINDSPORE_CCSRC_INCLUDE_TRANSFORM_GRAPH_IR_OP_ADAPTER_MAP_H_ diff --git a/mindspore/ccsrc/transform/graph_ir/types.h b/mindspore/ccsrc/include/transform/graph_ir/types.h similarity index 75% rename from mindspore/ccsrc/transform/graph_ir/types.h rename to mindspore/ccsrc/include/transform/graph_ir/types.h index b91c0899c94..05fed8fe3c5 100644 --- a/mindspore/ccsrc/transform/graph_ir/types.h +++ b/mindspore/ccsrc/include/transform/graph_ir/types.h @@ -14,8 +14,8 @@ * limitations under the License. */ -#ifndef MINDSPORE_CCSRC_TRANSFORM_GRAPH_IR_TYPES_H_ -#define MINDSPORE_CCSRC_TRANSFORM_GRAPH_IR_TYPES_H_ +#ifndef MINDSPORE_CCSRC_INCLUDE_TRANSFORM_GRAPH_IR_TYPES_H_ +#define MINDSPORE_CCSRC_INCLUDE_TRANSFORM_GRAPH_IR_TYPES_H_ #include #include @@ -27,11 +27,8 @@ #include "ir/tensor.h" #include "graph/tensor.h" -#ifdef OPEN_SOURCE -#include "ge/client/ge_api.h" -#else #include "external/ge/ge_api.h" -#endif + using GeTensor = ge::Tensor; namespace mindspore { @@ -53,7 +50,20 @@ using OperatorPtr = std::shared_ptr; using DfGraph = ge::Graph; using DfGraphPtr = std::shared_ptr; using TensorMap = mindspore::HashMap>; + +struct OutHandler { + OperatorPtr op; + std::string out; + AnfNodePtr node; + OutHandler() : op(nullptr), out(""), node(nullptr) {} + OutHandler(const OperatorPtr &op, const std::string out, const AnfNodePtr &node = nullptr) + : op(op), out(out), node(node) {} +}; + +struct ControlEdge { + OperatorPtr src_op; + OperatorPtr dest_op; +}; } // namespace transform } // namespace mindspore - -#endif // MINDSPORE_CCSRC_TRANSFORM_GRAPH_IR_TYPES_H_ +#endif // MINDSPORE_CCSRC_INCLUDE_TRANSFORM_GRAPH_IR_TYPES_H_ diff --git a/mindspore/ccsrc/transform/graph_ir/util.h b/mindspore/ccsrc/include/transform/graph_ir/util.h similarity index 96% rename from mindspore/ccsrc/transform/graph_ir/util.h rename to mindspore/ccsrc/include/transform/graph_ir/util.h index 6333e2f99ff..6372a5cfc47 100644 --- a/mindspore/ccsrc/transform/graph_ir/util.h +++ b/mindspore/ccsrc/include/transform/graph_ir/util.h @@ -14,8 +14,8 @@ * limitations under the License. */ -#ifndef MINDSPORE_CCSRC_TRANSFORM_GRAPH_IR_UTIL_H_ -#define MINDSPORE_CCSRC_TRANSFORM_GRAPH_IR_UTIL_H_ +#ifndef MINDSPORE_CCSRC_INCLUDE_TRANSFORM_GRAPH_IR_UTIL_H_ +#define MINDSPORE_CCSRC_INCLUDE_TRANSFORM_GRAPH_IR_UTIL_H_ #include #include @@ -25,13 +25,14 @@ #include "ir/anf.h" #include "ir/dtype.h" #include "ir/tensor.h" -#include "transform/graph_ir/types.h" +#include "include/transform/graph_ir/types.h" #include "graph/tensor.h" #include "utils/shape_utils.h" +#include "include/common/visible.h" namespace mindspore { namespace transform { -class TransformUtil { +class COMMON_EXPORT TransformUtil { public: /* * Parameters: @@ -237,4 +238,4 @@ class TransformUtil { } // namespace transform } // namespace mindspore -#endif // MINDSPORE_CCSRC_TRANSFORM_GRAPH_IR_UTIL_H_ +#endif // MINDSPORE_CCSRC_INCLUDE_TRANSFORM_GRAPH_IR_UTIL_H_ diff --git a/mindspore/ccsrc/kernel/akg/akg_kernel_build.cc b/mindspore/ccsrc/kernel/akg/akg_kernel_build.cc index b32637e3373..054faa80f8c 100644 --- a/mindspore/ccsrc/kernel/akg/akg_kernel_build.cc +++ b/mindspore/ccsrc/kernel/akg/akg_kernel_build.cc @@ -34,10 +34,11 @@ #include "nlohmann/json.hpp" #include "ir/dtype.h" #include "ir/func_graph.h" -#include "utils/context/graph_kernel_flags.h" +#include "include/common/utils/context/graph_kernel_flags.h" #include "kernel/common_utils.h" #include "kernel/akg/akg_kernel_json_generator.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace kernel { @@ -587,8 +588,8 @@ bool AkgKernelBuilder::AkgKernelParallelBuild(const std::vector &anf MS_EXCEPTION_IF_NULL(cnode); bool is_custom_node = IsPrimitiveCNode(cnode, prim::kPrimCustom) || IsCustomCSROP(cnode); // Graph kernel node and Custom node need to generate composite json - if (AnfAlgo::IsGraphKernel(cnode) || is_custom_node) { - FuncGraphPtr func_graph = is_custom_node ? cnode->func_graph() : AnfAlgo::GetCNodeFuncGraphPtr(cnode); + if (common::AnfAlgo::IsGraphKernel(cnode) || is_custom_node) { + FuncGraphPtr func_graph = is_custom_node ? cnode->func_graph() : common::AnfAlgo::GetCNodeFuncGraphPtr(cnode); MS_EXCEPTION_IF_NULL(func_graph); auto mng = func_graph->manager(); if (mng == nullptr) { diff --git a/mindspore/ccsrc/kernel/akg/akg_kernel_json_decoder.cc b/mindspore/ccsrc/kernel/akg/akg_kernel_json_decoder.cc index acef63e7186..e9fd8682720 100644 --- a/mindspore/ccsrc/kernel/akg/akg_kernel_json_decoder.cc +++ b/mindspore/ccsrc/kernel/akg/akg_kernel_json_decoder.cc @@ -20,6 +20,7 @@ #include "kernel/common_utils.h" #include "common/graph_kernel/adapter/fake_abstract_shape.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "debug/anf_ir_dump.h" #include "frontend/operator/ops.h" #include "ir/anf.h" @@ -29,11 +30,11 @@ #include "ir/manager.h" #include "ir/meta_tensor.h" #include "pipeline/jit/parse/data_converter.h" -#include "pipeline/jit/parse/python_adapter.h" +#include "include/common/utils/python_adapter.h" #include "runtime/device/kernel_info.h" -#include "utils/convert_utils.h" -#include "utils/convert_utils_py.h" -#include "utils/utils.h" +#include "include/common/utils/convert_utils.h" +#include "include/common/utils/convert_utils_py.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace kernel { @@ -190,10 +191,10 @@ class CNodeDecoder { // then the node's output is a feature map output const auto &inputs = cnode_->inputs(); for (size_t index = 1; index < inputs.size(); ++index) { - auto node = AnfAlgo::VisitKernel(inputs[index], 0); + auto node = common::AnfAlgo::VisitKernel(inputs[index], 0); if ((node.first)->isa()) { auto parameter = (node.first)->cast(); - bool is_weight = AnfAlgo::IsParameterWeight(parameter); + bool is_weight = common::AnfAlgo::IsParameterWeight(parameter); kernel_info->set_feature_map_flag(!is_weight); if (!is_weight) { feature_map_input_indexs.push_back(index - 1); @@ -203,15 +204,15 @@ class CNodeDecoder { feature_map_input_indexs.push_back(index - 1); } } - if (AnfAlgo::GetCNodeName(cnode_) == prim::kPrimCast->name()) { - AnfAlgo::SetNodeAttr(kIsBackendCast, MakeValue(false), cnode_); + if (common::AnfAlgo::GetCNodeName(cnode_) == prim::kPrimCast->name()) { + common::AnfAlgo::SetNodeAttr(kIsBackendCast, MakeValue(false), cnode_); } if (inputs.size() == 1 || !feature_map_input_indexs.empty()) { kernel_info->set_feature_map_flag(true); } if (AnfUtils::IsRealCNodeKernel(cnode_)) { - AnfAlgo::SetNodeAttr(kIsFeatureMapOutput, MakeValue(kernel_info->is_feature_map()), cnode_); - AnfAlgo::SetNodeAttr(kIsFeatureMapInputList, MakeValue(feature_map_input_indexs), cnode_); + common::AnfAlgo::SetNodeAttr(kIsFeatureMapOutput, MakeValue(kernel_info->is_feature_map()), cnode_); + common::AnfAlgo::SetNodeAttr(kIsFeatureMapInputList, MakeValue(feature_map_input_indexs), cnode_); } cnode_->set_kernel_info(kernel_info); // create kernel_build_info. diff --git a/mindspore/ccsrc/kernel/akg/akg_kernel_json_generator.h b/mindspore/ccsrc/kernel/akg/akg_kernel_json_generator.h index 2ede440a359..8b1ce9d2273 100644 --- a/mindspore/ccsrc/kernel/akg/akg_kernel_json_generator.h +++ b/mindspore/ccsrc/kernel/akg/akg_kernel_json_generator.h @@ -25,7 +25,7 @@ #include "nlohmann/json.hpp" #include "kernel/oplib/opinfo.h" #include "common/graph_kernel/core/graph_kernel_callback.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" namespace mindspore::graphkernel { using kernel::OpAttrPtr; diff --git a/mindspore/ccsrc/kernel/akg/akg_kernel_metadata.cc b/mindspore/ccsrc/kernel/akg/akg_kernel_metadata.cc index e0c074409a3..df3dd9adf2a 100644 --- a/mindspore/ccsrc/kernel/akg/akg_kernel_metadata.cc +++ b/mindspore/ccsrc/kernel/akg/akg_kernel_metadata.cc @@ -16,6 +16,7 @@ #include "kernel/akg/akg_kernel_metadata.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "kernel/oplib/oplib.h" #include "kernel/common_utils.h" @@ -26,7 +27,7 @@ void AkgMetadataInfo(const CNodePtr &kernel_node, MS_EXCEPTION_IF_NULL(kernel_node); MS_EXCEPTION_IF_NULL(kernel_info_list); - std::string op_name = AnfAlgo::GetCNodeName(kernel_node); + std::string op_name = common::AnfAlgo::GetCNodeName(kernel_node); for (size_t i = 0; i < support_devices.size(); i++) { auto op_info_ptr = mindspore::kernel::OpLib::FindOp(op_name, OpImplyType::kAKG); if (op_info_ptr == nullptr) { diff --git a/mindspore/ccsrc/kernel/common_utils.cc b/mindspore/ccsrc/kernel/common_utils.cc index 9c9f287fecf..2ff7cd2627b 100644 --- a/mindspore/ccsrc/kernel/common_utils.cc +++ b/mindspore/ccsrc/kernel/common_utils.cc @@ -25,6 +25,7 @@ #include #include "nlohmann/json.hpp" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "utils/file_utils.h" #include "utils/ms_utils.h" #include "ir/manager.h" @@ -418,14 +419,14 @@ bool ParseMetadata(const CNodePtr &kernel_node, const std::shared_ptr> *const kernel_info_list) { MS_EXCEPTION_IF_NULL(kernel_node); MS_EXCEPTION_IF_NULL(kernel_info_list); - size_t real_input_num = AnfAlgo::GetInputTensorNum(kernel_node); - size_t real_output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t real_input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); + size_t real_output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); std::vector> inputs = op_info_ptr->inputs_ptr(); std::vector> outputs = op_info_ptr->outputs_ptr(); std::vector dyn_input_sizes; - auto primitive = AnfAlgo::GetCNodePrimitive(kernel_node); + auto primitive = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(primitive); - auto op_name = AnfAlgo::GetCNodeName(kernel_node); + auto op_name = common::AnfAlgo::GetCNodeName(kernel_node); if (primitive->GetAttr("dyn_input_sizes") != nullptr) { dyn_input_sizes = GetValue>(primitive->GetAttr("dyn_input_sizes")); } @@ -560,7 +561,7 @@ std::vector> GetOutputIndex(const std::vectorinputs().size(); ++input_idx) { auto input_node = cnode->input(input_idx); MS_EXCEPTION_IF_NULL(input_node); - if (input_node->isa() && AnfAlgo::GetInputTensorNum(input_node) == 0) { + if (input_node->isa() && common::AnfAlgo::GetInputTensorNum(input_node) == 0) { continue; } - output_list->push_back(AnfAlgo::VisitKernel(input_node, 0).first); + output_list->push_back(common::AnfAlgo::VisitKernel(input_node, 0).first); } } else { // single output. - output_list->push_back(AnfAlgo::VisitKernel(func_output, 0).first); + output_list->push_back(common::AnfAlgo::VisitKernel(func_output, 0).first); } } else { // single output. - output_list->push_back(AnfAlgo::VisitKernel(func_output, 0).first); + output_list->push_back(common::AnfAlgo::VisitKernel(func_output, 0).first); } } @@ -643,20 +644,20 @@ bool IsWeightBoundary(const AnfNodePtr &node) { if (node->isa()) { return true; } - if (node->isa() && AnfAlgo::IsParameterWeight(node->cast())) { + if (node->isa() && common::AnfAlgo::IsParameterWeight(node->cast())) { return true; } return false; } std::vector GetReduceAttrAxis(const CNodePtr &cnode) { - if (AnfAlgo::GetInputTensorNum(cnode) != 1 || AnfAlgo::GetOutputTensorNum(cnode) != 1) { + if (common::AnfAlgo::GetInputTensorNum(cnode) != 1 || common::AnfAlgo::GetOutputTensorNum(cnode) != 1) { MS_LOG(EXCEPTION) << "The reduce node [" << cnode->DebugString() << "] is not single input or single output." << trace::DumpSourceLines(cnode); } std::vector axis; - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(cnode, 0); - auto primitive = AnfAlgo::GetCNodePrimitive(cnode); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(cnode, 0); + auto primitive = common::AnfAlgo::GetCNodePrimitive(cnode); MS_EXCEPTION_IF_NULL(primitive); auto axis_attr = primitive->GetAttr(kAxis); if (axis_attr == nullptr) { @@ -676,7 +677,7 @@ std::vector GetReduceAttrAxis(const CNodePtr &cnode) { (void)axis.emplace_back(elem); } } - AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(axis), cnode); + common::AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(axis), cnode); return axis; } @@ -687,7 +688,7 @@ void FillEmptyDims(const CNodePtr &kernel_node, std::vector *begin, std std::vector &_stride = *stride; std::vector &_input_shape = *input_shape; if (_begin.size() != _end.size() || _begin.size() != _stride.size() || _begin.size() > _input_shape.size()) { - MS_LOG(EXCEPTION) << "For '" << AnfAlgo::GetCNodeName(kernel_node) + MS_LOG(EXCEPTION) << "For '" << common::AnfAlgo::GetCNodeName(kernel_node) << "', the length of 'begin', 'stride' and 'end' should be equal " "and less than or equal to the dimension of 'input_x', but got the length of 'begin': " << _begin.size() << ", the length of 'stride': " << _stride.size() @@ -736,7 +737,7 @@ std::vector Dec2Bin(const int64_t &mask) { void ComputeBeginMask(const CNodePtr &kernel_node, std::vector *begin, const std::vector &stride, const std::vector &input_shape) { std::vector &_begin = *begin; - auto begin_mask_int = AnfAlgo::GetNodeAttr(kernel_node, kAttrBeginMask); + auto begin_mask_int = common::AnfAlgo::GetNodeAttr(kernel_node, kAttrBeginMask); auto begin_mask = Dec2Bin(begin_mask_int); for (size_t i = 0; i < begin_mask.size(); i++) { if (i < kStridedSliceMaxDims && begin_mask[i]) { @@ -748,7 +749,7 @@ void ComputeBeginMask(const CNodePtr &kernel_node, std::vector *begin, void ComputeEndMask(const CNodePtr &kernel_node, std::vector *end, const std::vector &stride, const std::vector &input_shape) { std::vector &_end = *end; - auto end_mask_int = AnfAlgo::GetNodeAttr(kernel_node, kAttrEndMask); + auto end_mask_int = common::AnfAlgo::GetNodeAttr(kernel_node, kAttrEndMask); auto end_mask = Dec2Bin(end_mask_int); for (size_t j = 0; j < end_mask.size(); j++) { if (j < kStridedSliceMaxDims && end_mask[j]) { @@ -762,7 +763,7 @@ void ComputeEllipsisMask(const CNodePtr &kernel_node, std::vector *begi std::vector &_begin = *begin; std::vector &_end = *end; std::vector &_stride = *stride; - auto ellipsis_mask_int = AnfAlgo::GetNodeAttr(kernel_node, kAttrEllipsisMask); + auto ellipsis_mask_int = common::AnfAlgo::GetNodeAttr(kernel_node, kAttrEllipsisMask); auto ellipsis_mask = Dec2Bin(ellipsis_mask_int); for (size_t k = 0; k < ellipsis_mask.size(); k++) { if (k < kStridedSliceMaxDims && ellipsis_mask[k]) { @@ -778,7 +779,7 @@ void ComputNewAxisMask(const CNodePtr &kernel_node, std::vector *begin, std::vector &_begin = *begin; std::vector &_end = *end; std::vector &_stride = *stride; - auto new_axis_mask_int = AnfAlgo::GetNodeAttr(kernel_node, kAttrNewAxisMask); + auto new_axis_mask_int = common::AnfAlgo::GetNodeAttr(kernel_node, kAttrNewAxisMask); auto new_axis_mask = Dec2Bin(new_axis_mask_int); for (size_t l = 0; l < new_axis_mask.size(); l++) { if (l < kStridedSliceMaxDims && new_axis_mask[l]) { @@ -793,7 +794,7 @@ void ComputShrinkAxisMask(const CNodePtr &kernel_node, const std::vector *stride) { std::vector &_end = *end; std::vector &_stride = *stride; - auto shrink_axis_mask_int = AnfAlgo::GetNodeAttr(kernel_node, kAttrShrinkAxisMask); + auto shrink_axis_mask_int = common::AnfAlgo::GetNodeAttr(kernel_node, kAttrShrinkAxisMask); auto shrink_axis_mask = Dec2Bin(shrink_axis_mask_int); for (size_t m = 0; m < shrink_axis_mask.size(); m++) { if (m < kStridedSliceMaxDims && shrink_axis_mask[m]) { diff --git a/mindspore/ccsrc/kernel/common_utils.h b/mindspore/ccsrc/kernel/common_utils.h index 9531a82194f..de250243369 100644 --- a/mindspore/ccsrc/kernel/common_utils.h +++ b/mindspore/ccsrc/kernel/common_utils.h @@ -27,7 +27,7 @@ #include #include #include -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "kernel/kernel.h" #include "kernel/oplib/opinfo.h" #include "kernel/kernel_build_info.h" diff --git a/mindspore/ccsrc/kernel/environ_manager.cc b/mindspore/ccsrc/kernel/environ_manager.cc index 61aa73e44e2..5dcf38a28ac 100644 --- a/mindspore/ccsrc/kernel/environ_manager.cc +++ b/mindspore/ccsrc/kernel/environ_manager.cc @@ -65,7 +65,7 @@ void EnvironMgr::Clear() { bool EnvironMgr::CheckEnvInput(const CNodePtr &kernel_node) const { MS_EXCEPTION_IF_NULL(kernel_node); // Check the value type attr. - auto value_type_attr = TypeId(AnfAlgo::GetNodeAttr(kernel_node, kEnvValueTypeAttr)); + auto value_type_attr = TypeId(common::AnfAlgo::GetNodeAttr(kernel_node, kEnvValueTypeAttr)); if ((value_type_attr != kObjectTypeTensorType) && (value_type_attr != kObjectTypeEnvType)) { MS_LOG(ERROR) << "The value type is not supported: " << value_type_attr << ", kernel: " << kernel_node->fullname_with_scope(); diff --git a/mindspore/ccsrc/kernel/kash/kernel_pack.cc b/mindspore/ccsrc/kernel/kash/kernel_pack.cc index db259c40c71..df9640bf5e6 100644 --- a/mindspore/ccsrc/kernel/kash/kernel_pack.cc +++ b/mindspore/ccsrc/kernel/kash/kernel_pack.cc @@ -20,7 +20,7 @@ #include "nlohmann/json.hpp" #include "securec/include/securec.h" #include "utils/log_adapter.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" #include "utils/system/sha256.h" #include "kernel/common_utils.h" namespace mindspore { diff --git a/mindspore/ccsrc/kernel/kernel.cc b/mindspore/ccsrc/kernel/kernel.cc index c373b18bcb7..7e893f07859 100644 --- a/mindspore/ccsrc/kernel/kernel.cc +++ b/mindspore/ccsrc/kernel/kernel.cc @@ -18,11 +18,11 @@ #include #include -#include #include "utils/ms_context.h" #include "utils/anf_utils.h" -#include "utils/ms_device_shape_transfer.h" +#include "runtime/device/ms_device_shape_transfer.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/optimizer/helper.h" namespace mindspore { @@ -56,21 +56,21 @@ void KernelMod::InferShape() { MS_EXCEPTION_IF_NULL(context); AbstractBasePtrList args_spec_list; auto primitive = GetValueNode(inputs[0]); - auto input_size = AnfAlgo::GetInputTensorNum(cnode); + auto input_size = common::AnfAlgo::GetInputTensorNum(cnode); for (size_t i = 0; i < input_size; i++) { - auto input_node_with_index = AnfAlgo::GetPrevNodeOutput(cnode, i); + auto input_node_with_index = common::AnfAlgo::GetPrevNodeOutput(cnode, i); auto real_input = input_node_with_index.first; MS_EXCEPTION_IF_NULL(real_input); auto cnode_input = cnode->input(i + 1); MS_EXCEPTION_IF_NULL(cnode_input); InferShapeForNopNode(&real_input); if (depend_list_.find(i) != depend_list_.end()) { - auto pre_node_with_index = AnfAlgo::GetPrevNodeOutput(cnode, i); + auto pre_node_with_index = common::AnfAlgo::GetPrevNodeOutput(cnode, i); bool skip_nop_node = !context->get_param(MS_CTX_ENABLE_MINDRT); auto output_addr = AnfAlgo::GetPrevNodeMutableOutputAddr(cnode, i, skip_nop_node); std::vector shapes = trans::GetRuntimePaddingShape(pre_node_with_index.first, pre_node_with_index.second); - auto host_type = AnfAlgo::GetOutputInferDataType(pre_node_with_index.first, pre_node_with_index.second); + auto host_type = common::AnfAlgo::GetOutputInferDataType(pre_node_with_index.first, pre_node_with_index.second); auto out_tensor = std::make_shared(host_type, shapes); MS_EXCEPTION_IF_NULL(out_tensor); // The second parameter must be false, otherwise the device address cannot be released and allocated, and the @@ -88,14 +88,14 @@ void KernelMod::InferShape() { if (real_abs->isa()) { real_abs->set_value(out_tensor); } else if (real_abs->isa()) { - auto tuple_get_item_index = AnfAlgo::GetTupleGetItemOutIndex(cnode_input->cast()); + auto tuple_get_item_index = common::AnfAlgo::GetTupleGetItemOutIndex(cnode_input->cast()); auto abstract_tuple = real_abs->cast(); MS_EXCEPTION_IF_NULL(abstract_tuple); auto tuple_elements = abstract_tuple->elements()[tuple_get_item_index]; tuple_elements->set_value(out_tensor); } } - AnfAlgo::AddArgList(&args_spec_list, cnode_input, real_input, i); + common::AnfAlgo::AddArgList(&args_spec_list, cnode_input, real_input, i); } auto eval_result = opt::CppInferShape(primitive, args_spec_list); cnode->set_abstract(eval_result); @@ -103,10 +103,10 @@ void KernelMod::InferShape() { bool KernelMod::InferShapeForDefiniteOutputNode(const CNodePtr &cnode) { MS_EXCEPTION_IF_NULL(cnode); - if (!AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimShape)) { + if (!common::AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimShape)) { return false; } - auto input_size = AnfAlgo::GetInputTensorNum(cnode); + auto input_size = common::AnfAlgo::GetInputTensorNum(cnode); if (input_size != 1) { MS_LOG(EXCEPTION) << "Node only has one input: " << cnode->fullname_with_scope(); } @@ -127,7 +127,7 @@ bool KernelMod::InferShapeForDefiniteOutputNode(const CNodePtr &cnode) { void KernelMod::InferShapeForNopNode(AnfNodePtr *input_node) { MS_EXCEPTION_IF_NULL(*input_node); - if (!opt::IsNopNode(*input_node) || !AnfAlgo::IsDynamicShape(*input_node)) { + if (!common::AnfAlgo::IsNopNode(*input_node) || !common::AnfAlgo::IsDynamicShape(*input_node)) { MS_LOG(INFO) << "Input node is not a nop node, no need infer."; return; } @@ -137,10 +137,10 @@ void KernelMod::InferShapeForNopNode(AnfNodePtr *input_node) { /*lint -e716*/ while (true) { - auto input_node_with_idx = AnfAlgo::GetPrevNodeOutput(*input_node, 0); + auto input_node_with_idx = common::AnfAlgo::GetPrevNodeOutput(*input_node, 0); auto in_node = input_node_with_idx.first; MS_EXCEPTION_IF_NULL(in_node); - if (opt::IsNopNode(in_node)) { + if (common::AnfAlgo::IsNopNode(in_node)) { nop_road.push(in_node); *input_node = in_node; } else { diff --git a/mindspore/ccsrc/kernel/kernel.h b/mindspore/ccsrc/kernel/kernel.h index b506b3236df..f26b76ed40b 100644 --- a/mindspore/ccsrc/kernel/kernel.h +++ b/mindspore/ccsrc/kernel/kernel.h @@ -23,7 +23,7 @@ #include "nlohmann/json.hpp" #include "ir/anf.h" #include "ir/dtype.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "ir/tensor.h" #include "abstract/dshape.h" #include "utils/log_adapter.h" diff --git a/mindspore/ccsrc/kernel/kernel_query.cc b/mindspore/ccsrc/kernel/kernel_query.cc index 3caa0a41954..09e55e4ed89 100644 --- a/mindspore/ccsrc/kernel/kernel_query.cc +++ b/mindspore/ccsrc/kernel/kernel_query.cc @@ -23,6 +23,7 @@ #include "plugin/device/ascend/kernel/tbe/tbe_kernel_select/tbe_kernel_select.h" #include "kernel/akg/akg_kernel_metadata.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "utils/ms_context.h" #include "utils/trace_base.h" @@ -36,8 +37,8 @@ void FilterInvalidKernelInfo(const CNodePtr &kernel_node, return; } MS_EXCEPTION_IF_NULL(kernel_node); - size_t output_tensor_num = AnfAlgo::GetOutputTensorNum(kernel_node); - size_t input_tensor_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t output_tensor_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); + size_t input_tensor_num = common::AnfAlgo::GetInputTensorNum(kernel_node); std::vector> filtered_list; (void)std::copy_if( kernel_info_list->begin(), kernel_info_list->end(), std::back_inserter(filtered_list), @@ -70,7 +71,7 @@ void FilterInvalidKernelInfo(const CNodePtr &kernel_node, bool SelectAicpuReshapeInTaskSink(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - if (AnfAlgo::GetCNodeName(kernel_node) != "Reshape") { + if (common::AnfAlgo::GetCNodeName(kernel_node) != "Reshape") { return false; } const size_t AicpuReshapeSize = 2; diff --git a/mindspore/ccsrc/minddata/dataset/engine/serdes.cc b/mindspore/ccsrc/minddata/dataset/engine/serdes.cc index a37de09b817..08ac35693c9 100644 --- a/mindspore/ccsrc/minddata/dataset/engine/serdes.cc +++ b/mindspore/ccsrc/minddata/dataset/engine/serdes.cc @@ -19,7 +19,7 @@ #include "minddata/dataset/core/pybind_support.h" #include "utils/file_utils.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace dataset { diff --git a/mindspore/ccsrc/minddata/dataset/text/sentence_piece_vocab.cc b/mindspore/ccsrc/minddata/dataset/text/sentence_piece_vocab.cc index 9afd792bd24..f2b9345043b 100644 --- a/mindspore/ccsrc/minddata/dataset/text/sentence_piece_vocab.cc +++ b/mindspore/ccsrc/minddata/dataset/text/sentence_piece_vocab.cc @@ -22,7 +22,7 @@ #include "utils/file_utils.h" #include "utils/ms_utils.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "minddata/dataset/util/path.h" namespace mindspore { diff --git a/mindspore/ccsrc/pipeline/jit/action.cc b/mindspore/ccsrc/pipeline/jit/action.cc index d7abd71e6a2..deae792c72f 100644 --- a/mindspore/ccsrc/pipeline/jit/action.cc +++ b/mindspore/ccsrc/pipeline/jit/action.cc @@ -26,11 +26,12 @@ #include "ir/func_graph_cloner.h" #include "ir/param_info.h" #include "ir/cell.h" -#include "parse/python_adapter.h" +#include "include/common/utils/python_adapter.h" #include "abstract/abstract_value.h" #include "frontend/parallel/costmodel_context.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" #include "frontend/parallel/graph_util/graph_splitter.h" +#include "pipeline/jit/pipeline.h" #include "pipeline/jit/pass.h" #include "pipeline/jit/parse/parse_base.h" #include "pipeline/jit/parse/data_converter.h" @@ -91,7 +92,7 @@ bool IsDynamicShapeGraph(const FuncGraphPtr &func_graph) { MS_EXCEPTION_IF_NULL(func_graph); std::vector node_list = TopoSort(func_graph->get_return()); return std::any_of(node_list.begin(), node_list.end(), - [](const AnfNodePtr &node) { return AnfAlgo::IsDynamicShape(node); }); + [](const AnfNodePtr &node) { return common::AnfAlgo::IsDynamicShape(node); }); } // Disable mindRT in the heterogeneous scenario + dynamic_shape scenario. @@ -107,7 +108,7 @@ void DisableMindRT(const ResourcePtr &res) { auto parallel_context = parallel::ParallelContext::GetInstance(); MS_EXCEPTION_IF_NULL(parallel_context); auto parallel_mode = parallel_context->parallel_mode(); - bool is_parallel_mode = parallel_mode == parallel::SEMI_AUTO_PARALLEL || parallel_mode == parallel::AUTO_PARALLEL; + bool is_parallel_mode = parallel_mode == parallel::kSemiAutoParallel || parallel_mode == parallel::kAutoParallel; bool enable_old_runtime = (common::GetEnv("MS_DEV_ENABLE_CLOSURE") == "0"); bool use_old_vm_for_dynamic_shape = func_graph->exist_multi_target() && IsDynamicShapeGraph(func_graph); bool use_old_vm_for_control_parallel = @@ -377,8 +378,8 @@ bool ParseAction(const ResourcePtr &res) { py::module path = py::module::import("os.path"); std::string dir = path.attr("dirname")(py::globals()["__file__"]).cast(); - parse::python_adapter::set_python_env_flag(true); - parse::python_adapter::SetPythonPath(dir); + python_adapter::set_python_env_flag(true); + python_adapter::SetPythonPath(dir); ValuePtr converted_ret = nullptr; bool converted = parse::ConvertData(input, &converted_ret, true); diff --git a/mindspore/ccsrc/pipeline/jit/compile_cache_manager.cc b/mindspore/ccsrc/pipeline/jit/compile_cache_manager.cc index bcc8660c5ce..c6d74ce3d9a 100644 --- a/mindspore/ccsrc/pipeline/jit/compile_cache_manager.cc +++ b/mindspore/ccsrc/pipeline/jit/compile_cache_manager.cc @@ -19,12 +19,14 @@ #include #include #include +#include #include "pipeline/jit/parse/data_converter.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" #include "debug/common.h" #include "debug/anf_ir_dump.h" #include "debug/dump_proto.h" #include "utils/system/sha256.h" +#include "include/common/utils/utils.h" #if ((defined ENABLE_CPU) && (!defined _WIN32)) #include "ps/ps_context.h" @@ -240,7 +242,7 @@ FuncGraphPtr CompileCacheManager::GetCachedFuncGraph(const FuncGraphManagerPtr & // Determine whether to load parallel information. std::string parallel_mode = parallel::ParallelContext::GetInstance()->parallel_mode(); bool has_parallel_info = false; - if ((parallel_mode == parallel::AUTO_PARALLEL) || (parallel_mode == parallel::SEMI_AUTO_PARALLEL)) { + if ((parallel_mode == parallel::kAutoParallel) || (parallel_mode == parallel::kSemiAutoParallel)) { has_parallel_info = true; } // Load the compilation cache file. diff --git a/mindspore/ccsrc/pipeline/jit/init.cc b/mindspore/ccsrc/pipeline/jit/init.cc index 39c8dfb77ec..e6bca32290c 100644 --- a/mindspore/ccsrc/pipeline/jit/init.cc +++ b/mindspore/ccsrc/pipeline/jit/init.cc @@ -21,14 +21,14 @@ #include "pipeline/pynative/pynative_execute.h" #include "utils/symbolic.h" #include "pybind_api/api_register.h" -#include "pipeline/jit/parse/python_adapter.h" +#include "include/common/utils/python_adapter.h" #ifndef ENABLE_SECURITY -#include "utils/summary/event_writer.h" +#include "include/common/utils/summary/event_writer.h" #endif -#include "utils/config_manager.h" -#include "utils/mpi/mpi_config.h" +#include "include/common/utils/config_manager.h" +#include "include/common/utils/mpi/mpi_config.h" #include "utils/ms_utils.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" #include "frontend/parallel/costmodel_context.h" #include "frontend/optimizer/ad/dfunctor.h" #ifdef ENABLE_GPU_COLLECTIVE @@ -341,7 +341,7 @@ PYBIND11_MODULE(_c_expression, m) { MS_LOG(INFO) << "End release dataset handles."; #endif // only in case that c++ calling python interface, ClearResAtexit should be called. - if (mindspore::parse::python_adapter::IsPythonEnv()) { + if (mindspore::python_adapter::IsPythonEnv()) { mindspore::pipeline::ClearResAtexit(); } }}); diff --git a/mindspore/ccsrc/pipeline/jit/parse/data_converter.cc b/mindspore/ccsrc/pipeline/jit/parse/data_converter.cc index 2faf5fb9fd4..87f7272478d 100644 --- a/mindspore/ccsrc/pipeline/jit/parse/data_converter.cc +++ b/mindspore/ccsrc/pipeline/jit/parse/data_converter.cc @@ -23,14 +23,14 @@ #include #include "utils/hash_map.h" #include "pipeline/jit/parse/resolve.h" -#include "pipeline/jit/parse/python_adapter.h" +#include "include/common/utils/python_adapter.h" #include "frontend/operator/ops.h" #include "frontend/operator/composite/composite.h" #include "ir/func_graph_cloner.h" #include "ir/cell.h" #include "utils/symbolic.h" #include "utils/ms_context.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace parse { diff --git a/mindspore/ccsrc/pipeline/jit/parse/data_converter.h b/mindspore/ccsrc/pipeline/jit/parse/data_converter.h index 8543b0c5301..9c7828d1fb3 100644 --- a/mindspore/ccsrc/pipeline/jit/parse/data_converter.h +++ b/mindspore/ccsrc/pipeline/jit/parse/data_converter.h @@ -25,7 +25,7 @@ #include #include "utils/hash_map.h" #include "pipeline/jit/parse/parse_base.h" -#include "pipeline/jit/parse/python_adapter.h" +#include "include/common/utils/python_adapter.h" #include "utils/log_adapter.h" namespace mindspore { diff --git a/mindspore/ccsrc/pipeline/jit/parse/function_block.cc b/mindspore/ccsrc/pipeline/jit/parse/function_block.cc index 988d99b5dee..a3950d0bd97 100644 --- a/mindspore/ccsrc/pipeline/jit/parse/function_block.cc +++ b/mindspore/ccsrc/pipeline/jit/parse/function_block.cc @@ -29,7 +29,7 @@ #include "frontend/operator/ops.h" #include "utils/info.h" #include "debug/trace.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace py = pybind11; diff --git a/mindspore/ccsrc/pipeline/jit/parse/parse.cc b/mindspore/ccsrc/pipeline/jit/parse/parse.cc index 8e09fdbd622..3851ae79611 100644 --- a/mindspore/ccsrc/pipeline/jit/parse/parse.cc +++ b/mindspore/ccsrc/pipeline/jit/parse/parse.cc @@ -34,7 +34,7 @@ #include "utils/interpret_node_recorder.h" #include "debug/trace.h" #include "mindspore/core/ir/cell.h" -#include "mindspore/ccsrc/utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace parse { diff --git a/mindspore/ccsrc/pipeline/jit/parse/parse.h b/mindspore/ccsrc/pipeline/jit/parse/parse.h index b9733c36aff..a8bbf553751 100644 --- a/mindspore/ccsrc/pipeline/jit/parse/parse.h +++ b/mindspore/ccsrc/pipeline/jit/parse/parse.h @@ -30,7 +30,7 @@ #include "utils/misc.h" #include "ir/anf.h" #include "pipeline/jit/parse/parse_base.h" -#include "pipeline/jit/parse/python_adapter.h" +#include "include/common/utils/python_adapter.h" #include "pipeline/jit/parse/function_block.h" namespace mindspore { diff --git a/mindspore/ccsrc/pipeline/jit/parse/parse_dynamic.cc b/mindspore/ccsrc/pipeline/jit/parse/parse_dynamic.cc index 92625adc832..a97cbb11daa 100644 --- a/mindspore/ccsrc/pipeline/jit/parse/parse_dynamic.cc +++ b/mindspore/ccsrc/pipeline/jit/parse/parse_dynamic.cc @@ -65,11 +65,11 @@ void DynamicParser::ParseInputArgs(const std::shared_ptr &ast, const py::object &node) { MS_LOG(DEBUG) << "Parse if/while expr"; - py::object test_node = parse::python_adapter::GetPyObjAttr(node, parse::NAMED_PRIMITIVE_TEST); + py::object test_node = python_adapter::GetPyObjAttr(node, parse::NAMED_PRIMITIVE_TEST); const auto &node_name = ParseNodeName(ast, test_node, parse::AST_MAIN_TYPE_EXPR); if (node_name == parse::NAMED_PRIMITIVE_COMPARE) { - py::object left_node = parse::python_adapter::GetPyObjAttr(test_node, parse::NAMED_PRIMITIVE_LEFT); - py::list comparators_node = parse::python_adapter::GetPyObjAttr(test_node, parse::NAMED_PRIMITIVE_COMPARATORS); + py::object left_node = python_adapter::GetPyObjAttr(test_node, parse::NAMED_PRIMITIVE_LEFT); + py::list comparators_node = python_adapter::GetPyObjAttr(test_node, parse::NAMED_PRIMITIVE_COMPARATORS); if (comparators_node.empty()) { MS_LOG(DEBUG) << "Get comparators node failed!"; return false; @@ -78,12 +78,12 @@ bool DynamicParser::ParseIfWhileExprNode(const std::shared_ptr self.b and changed self.a or self.b if (left == parse::NAMED_PRIMITIVE_ATTRIBUTE && right == parse::NAMED_PRIMITIVE_ATTRIBUTE) { - auto left_value = parse::python_adapter::GetPyObjAttr(left_node, parse::NAMED_PRIMITIVE_VALUE); + auto left_value = python_adapter::GetPyObjAttr(left_node, parse::NAMED_PRIMITIVE_VALUE); std::string left_variable; if (py::hasattr(left_node, "attr") && py::hasattr(left_value, "id")) { left_variable = py::cast(left_value.attr("id")) + py::cast(left_node.attr("attr")); } - auto right_value = parse::python_adapter::GetPyObjAttr(comparators_node[0], parse::NAMED_PRIMITIVE_VALUE); + auto right_value = python_adapter::GetPyObjAttr(comparators_node[0], parse::NAMED_PRIMITIVE_VALUE); std::string right_variable; if (py::hasattr(comparators_node[0], "attr") && py::hasattr(right_value, "id")) { right_variable = @@ -93,7 +93,7 @@ bool DynamicParser::ParseIfWhileExprNode(const std::shared_ptr &ast, const py::object &node) { MS_LOG(DEBUG) << "Parse assign expr"; - py::object value_node = parse::python_adapter::GetPyObjAttr(node, parse::NAMED_PRIMITIVE_VALUE); + py::object value_node = python_adapter::GetPyObjAttr(node, parse::NAMED_PRIMITIVE_VALUE); const auto &node_name = ParseNodeName(ast, value_node, parse::AST_MAIN_TYPE_EXPR); if (node_name == parse::NAMED_PRIMITIVE_CALL) { - py::object func_node = parse::python_adapter::GetPyObjAttr(value_node, parse::NAMED_PRIMITIVE_FUNC); + py::object func_node = python_adapter::GetPyObjAttr(value_node, parse::NAMED_PRIMITIVE_FUNC); const auto &func_name = ParseNodeName(ast, func_node, parse::AST_MAIN_TYPE_EXPR); if (func_name == parse::NAMED_PRIMITIVE_SUBSCRIPT) { - py::object slice_node = parse::python_adapter::GetPyObjAttr(func_node, parse::NAMED_PRIMITIVE_SLICE); - py::object value_in_slice_node = parse::python_adapter::GetPyObjAttr(slice_node, parse::NAMED_PRIMITIVE_VALUE); + py::object slice_node = python_adapter::GetPyObjAttr(func_node, parse::NAMED_PRIMITIVE_SLICE); + py::object value_in_slice_node = python_adapter::GetPyObjAttr(slice_node, parse::NAMED_PRIMITIVE_VALUE); if (py::isinstance(value_in_slice_node)) { MS_LOG(DEBUG) << "Parse value node is none!"; return false; @@ -147,12 +147,12 @@ bool DynamicParser::ParseAugAssignExprNode(const std::shared_ptr(target_node)) { MS_LOG(DEBUG) << "Parse target node is none!"; return ret; } - py::object value_node = parse::python_adapter::GetPyObjAttr(target_node, parse::NAMED_PRIMITIVE_VALUE); + py::object value_node = python_adapter::GetPyObjAttr(target_node, parse::NAMED_PRIMITIVE_VALUE); if (py::isinstance(value_node)) { MS_LOG(DEBUG) << "Parse value node is none!"; return ret; @@ -170,12 +170,12 @@ bool DynamicParser::ParseAugAssignExprNode(const std::shared_ptr &ast, const py::object &node) { MS_LOG(DEBUG) << "Parse for expr"; - py::object body_node = parse::python_adapter::GetPyObjAttr(node, parse::NAMED_PRIMITIVE_BODY); + py::object body_node = python_adapter::GetPyObjAttr(node, parse::NAMED_PRIMITIVE_BODY); if (py::isinstance(body_node)) { MS_LOG(DEBUG) << "Parse body of for expression is none!"; return false; } - py::int_ pcount = parse::python_adapter::CallPyObjMethod(body_node, parse::PYTHON_GET_METHOD_LEN); + py::int_ pcount = python_adapter::CallPyObjMethod(body_node, parse::PYTHON_GET_METHOD_LEN); size_t count = LongToSize(pcount); MS_LOG(DEBUG) << "The for nodes count in body is " << count; for (size_t i = 0; i < count; ++i) { @@ -191,12 +191,12 @@ bool DynamicParser::ParseForExprNode(const std::shared_ptr &ast, const py::object &fn_node, const std::vector &compare_prim) { MS_EXCEPTION_IF_NULL(ast); - py::object func_obj = parse::python_adapter::GetPyObjAttr(fn_node, parse::NAMED_PRIMITIVE_BODY); + py::object func_obj = python_adapter::GetPyObjAttr(fn_node, parse::NAMED_PRIMITIVE_BODY); if (py::isinstance(func_obj)) { MS_LOG(DEBUG) << "Parse body of cell is none!"; return false; } - py::int_ pcount = parse::python_adapter::CallPyObjMethod(func_obj, parse::PYTHON_GET_METHOD_LEN); + py::int_ pcount = python_adapter::CallPyObjMethod(func_obj, parse::PYTHON_GET_METHOD_LEN); size_t count = IntToSize(pcount); MS_LOG(DEBUG) << "The nodes count in body is " << count; bool ret = false; diff --git a/mindspore/ccsrc/pipeline/jit/parse/resolve.cc b/mindspore/ccsrc/pipeline/jit/parse/resolve.cc index f9b9ebc2eaf..d4e29df4a79 100644 --- a/mindspore/ccsrc/pipeline/jit/parse/resolve.cc +++ b/mindspore/ccsrc/pipeline/jit/parse/resolve.cc @@ -24,7 +24,7 @@ #include "ir/param_info.h" #include "pipeline/jit/parse/data_converter.h" #include "pipeline/jit/parse/parse.h" -#include "pipeline/jit/parse/python_adapter.h" +#include "include/common/utils/python_adapter.h" #include "utils/any.h" #include "frontend/operator/ops.h" #include "frontend/optimizer/opt.h" @@ -75,7 +75,7 @@ abstract::AbstractBasePtr ClassType::ToAbstract() { namespace { std::string GetPyObjId(const py::object &obj) { - py::object out = parse::python_adapter::CallPyFn(parse::PYTHON_MOD_PARSE_MODULE, parse::PYTHON_MOD_GET_OBJ_ID, obj); + py::object out = python_adapter::CallPyFn(parse::PYTHON_MOD_PARSE_MODULE, parse::PYTHON_MOD_GET_OBJ_ID, obj); if (py::isinstance(out)) { MS_LOG(EXCEPTION) << "Get pyobj failed"; } @@ -217,7 +217,7 @@ bool ResolveObjectToNode(const FuncGraphPtr &func_graph, const py::object &obj, output = NewCNode(std::move(args), func_graph); } else { ValuePtr convert_result = nullptr; - bool converted = ConvertData(obj, &convert_result, parse::python_adapter::UseSignatureInResolve()); + bool converted = ConvertData(obj, &convert_result, python_adapter::UseSignatureInResolve()); if (!converted) { MS_LOG(ERROR) << "Convert data failed"; return false; @@ -345,7 +345,7 @@ py::object GetObjectFromSequence(const NameSpacePtr &name_space, const SymbolPtr const std::string fn = PYTHON_MOD_GET_ITEM_FROM_SEQUENCE; const std::string module = "mindspore._extends.parse.parser"; auto index = imm_value->value(); - py::object item_obj = parse::python_adapter::GetPyFn(module, fn)(obj, py::int_(index)); + py::object item_obj = python_adapter::GetPyFn(module, fn)(obj, py::int_(index)); return item_obj; } @@ -440,7 +440,7 @@ AnfNodePtr ResolveCellWithAttr(const FuncGraphManagerPtr &manager, const py::obj const std::string fn = PYTHON_MOD_GET_MEMBER_NAMESPACE_SYMBOL; const std::string module = "mindspore._extends.parse.parser"; - py::object namespace_obj = parse::python_adapter::GetPyFn(module, fn)(obj); + py::object namespace_obj = python_adapter::GetPyFn(module, fn)(obj); auto new_namespace = std::make_shared(RESOLVE_NAMESPACE_NAME_CLASS_MEMBER, namespace_obj); std::string attr_as_string = GetValueNode(attr)->value(); auto new_symbol = std::make_shared(attr_as_string); @@ -487,7 +487,7 @@ bool ResolveFuncGraph(const FuncGraphPtr &func_graph, const pipeline::ResourceBa opt::OptimizerPtr opt_resolve = opt::Optimizer::MakeOptimizer("opt_resolve", res, GetOptResolvePasses(irpass), false, false, false); - (void)parse::python_adapter::set_python_scoped(); + (void)python_adapter::set_python_scoped(); MS_EXCEPTION_IF_NULL(opt_resolve); (void)opt_resolve->step(func_graph, use_profile); diff --git a/mindspore/ccsrc/pipeline/jit/parse/resolve.h b/mindspore/ccsrc/pipeline/jit/parse/resolve.h index 7445a2562c5..f6f8cd371be 100644 --- a/mindspore/ccsrc/pipeline/jit/parse/resolve.h +++ b/mindspore/ccsrc/pipeline/jit/parse/resolve.h @@ -23,7 +23,7 @@ #include "ir/anf.h" #include "ir/manager.h" -#include "pipeline/jit/parse/python_adapter.h" +#include "include/common/utils/python_adapter.h" #include "pipeline/jit/parse/parse_base.h" #include "abstract/abstract_value.h" #include "utils/log_adapter.h" diff --git a/mindspore/ccsrc/pipeline/jit/pass.cc b/mindspore/ccsrc/pipeline/jit/pass.cc index ded40a3c073..74a0a54e092 100644 --- a/mindspore/ccsrc/pipeline/jit/pass.cc +++ b/mindspore/ccsrc/pipeline/jit/pass.cc @@ -34,7 +34,7 @@ #include "frontend/optimizer/irpass.h" #include "frontend/optimizer/graph_transform.h" #include "frontend/optimizer/auto_monad_eliminate.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" #include "frontend/parallel/step_parallel.h" #include "frontend/parallel/step_auto_parallel.h" #include "frontend/parallel/cache_embedding/cache_embedding.h" @@ -229,7 +229,7 @@ bool parallel_mode() { } #endif std::string parallel_mode = parallel::ParallelContext::GetInstance()->parallel_mode(); - return (parallel_mode == parallel::AUTO_PARALLEL) || (parallel_mode == parallel::SEMI_AUTO_PARALLEL); + return (parallel_mode == parallel::kAutoParallel) || (parallel_mode == parallel::kSemiAutoParallel); } void AddParallelRenormalize(OptPassGroupMap *map_a) { diff --git a/mindspore/ccsrc/pipeline/jit/pipeline.cc b/mindspore/ccsrc/pipeline/jit/pipeline.cc index 894f0b24b09..88d5b2773b0 100644 --- a/mindspore/ccsrc/pipeline/jit/pipeline.cc +++ b/mindspore/ccsrc/pipeline/jit/pipeline.cc @@ -36,17 +36,17 @@ #include "frontend/optimizer/py_pass_manager.h" #include "frontend/optimizer/ad/dfunctor.h" #include "frontend/optimizer/ad/prim_bprop_optimizer.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" #include "frontend/parallel/graph_util/get_parallel_info.h" #include "frontend/parallel/auto_parallel/graph_costmodel.h" -#include "utils/config_manager.h" -#include "utils/convert_utils.h" -#include "utils/convert_utils_py.h" -#include "utils/context/context_extends.h" +#include "include/common/utils/config_manager.h" +#include "include/common/utils/convert_utils.h" +#include "include/common/utils/convert_utils_py.h" +#include "runtime/device/context_extends.h" #include "utils/shape_utils.h" #include "utils/info.h" #include "utils/crypto.h" -#include "utils/comm_manager.h" +#include "include/common/utils/comm_manager.h" #include "utils/interpret_node_recorder.h" #include "debug/anf_ir_dump.h" #include "debug/dump_proto.h" @@ -79,9 +79,9 @@ #ifdef ENABLE_D #include "pipeline/jit/pipeline_ge.h" -#include "transform/graph_ir/convert.h" -#include "transform/graph_ir/df_graph_manager.h" -#include "transform/graph_ir/op_adapter_map.h" +#include "include/transform/graph_ir/convert.h" +#include "include/transform/graph_ir/df_graph_manager.h" +#include "include/transform/graph_ir/op_adapter_map.h" #include "plugin/device/ascend/hal/device/profiling/profiling_manager.h" #include "plugin/device/ascend/hal/device/distribute/ascend_collective.h" #endif @@ -672,8 +672,8 @@ void GraphExecutorPy::SaveCompiledGraph(const std::string &phase) { MS_LOG(INFO) << "Save compiled func graph(" << func_graph->ToString() << ") phase(" << phase << ")!"; info_[phase]->func_graph = func_graph; - if ((func_graph != nullptr) && func_graph->has_flag(parallel::AUTO_PARALLEL) && - ((parallel_mode == parallel::AUTO_PARALLEL) || (parallel_mode == parallel::SEMI_AUTO_PARALLEL))) { + if ((func_graph != nullptr) && func_graph->has_flag(parallel::kAutoParallel) && + ((parallel_mode == parallel::kAutoParallel) || (parallel_mode == parallel::kSemiAutoParallel))) { MS_LOG(DEBUG) << "Save model parallel parameter layout graph!"; auto res = info_[phase]->resource; // When using frontend compile cache, model parallel parameter layout graph is not saved. @@ -1313,7 +1313,7 @@ bool InitExecDatasetVm(const std::string &queue_name, int64_t size, int64_t batc } #endif MS_LOG(INFO) << "Start InitDataSet Entry"; - mindspore::parse::python_adapter::set_python_env_flag(true); + mindspore::python_adapter::set_python_env_flag(true); ShapeVector int_input_indexes; (void)std::transform(input_indexes.begin(), input_indexes.end(), std::back_inserter(int_input_indexes), [](int64_t item) { return static_cast(item); }); @@ -1420,7 +1420,7 @@ void InitHccl() { } #endif - mindspore::parse::python_adapter::set_python_env_flag(true); + mindspore::python_adapter::set_python_env_flag(true); uint32_t device_id = ms_context->get_param(MS_CTX_DEVICE_ID); #if ENABLE_D bool task_sink = true; @@ -1526,7 +1526,7 @@ void ReleaseGeTsd() { void InitPipeline() { // set python env flag RecordInitStatus(); - mindspore::parse::python_adapter::set_python_env_flag(true); + mindspore::python_adapter::set_python_env_flag(true); // open tsd before ge initialize auto ms_context = MsContext::GetInstance(); MS_EXCEPTION_IF_NULL(ms_context); @@ -1641,7 +1641,7 @@ void ClearResAtexit() { ReleaseGeTsd(); MS_LOG(INFO) << "Start clear python_adapter..."; - parse::python_adapter::ResetPythonScope(); + python_adapter::ResetPythonScope(); MS_LOG(INFO) << "End clear python_adapter."; MS_LOG(INFO) << "Start clear AnalysisResultCacheMgr..."; diff --git a/mindspore/ccsrc/pipeline/jit/pipeline.h b/mindspore/ccsrc/pipeline/jit/pipeline.h index 82b996b3c8f..fc74fdc465a 100644 --- a/mindspore/ccsrc/pipeline/jit/pipeline.h +++ b/mindspore/ccsrc/pipeline/jit/pipeline.h @@ -34,6 +34,7 @@ #include "backend/graph_compiler/segment_runner.h" #include "backend/graph_compiler/transform.h" #include "pipeline/jit/base.h" +#include "frontend/parallel/strategy.h" namespace mindspore { extern const char kMsConvert[]; diff --git a/mindspore/ccsrc/pipeline/jit/pipeline_ge.cc b/mindspore/ccsrc/pipeline/jit/pipeline_ge.cc index f25a9ffdf2b..cea77984cad 100644 --- a/mindspore/ccsrc/pipeline/jit/pipeline_ge.cc +++ b/mindspore/ccsrc/pipeline/jit/pipeline_ge.cc @@ -24,13 +24,14 @@ #include "utils/hash_map.h" #include "debug/anf_ir_dump.h" #include "ir/tensor.h" -#include "transform/graph_ir/convert.h" -#include "transform/graph_ir/df_graph_manager.h" -#include "transform/graph_ir/graph_builder.h" -#include "transform/graph_ir/graph_runner.h" +#include "include/transform/graph_ir/convert.h" +#include "include/transform/graph_ir/df_graph_manager.h" +#include "include/transform/graph_ir/graph_builder.h" +#include "include/transform/graph_ir/graph_runner.h" #include "debug/draw.h" #include "abstract/abstract_value.h" -#include "utils/convert_utils_py.h" +#include "include/common/utils/convert_utils_py.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace pipeline { diff --git a/mindspore/ccsrc/pipeline/jit/pipeline_split.cc b/mindspore/ccsrc/pipeline/jit/pipeline_split.cc index cb758609226..83f0d8231e9 100644 --- a/mindspore/ccsrc/pipeline/jit/pipeline_split.cc +++ b/mindspore/ccsrc/pipeline/jit/pipeline_split.cc @@ -21,8 +21,8 @@ #include #include "pipeline/jit/pipeline_split.h" #include "utils/ms_context.h" -#include "utils/comm_manager.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/comm_manager.h" +#include "include/common/utils/parallel_context.h" #include "frontend/parallel/pipeline_transformer/pipeline_transformer.h" #include "frontend/parallel/step_parallel.h" #if ((defined ENABLE_CPU) && (!defined _WIN32) && !defined(__APPLE__)) @@ -139,7 +139,7 @@ static std::set FindForwardGraph(const FuncGraphPtr &root, const s } auto expect_prim = GetValueNode(cnode->input(0)); FuncGraphPtr fun_graph = nullptr; - if (!root->has_flag(mindspore::parallel::TRAINING)) { + if (!root->has_flag(mindspore::parallel::kTraining)) { graph_sets.insert(root); } if (expect_prim->name() == mindspore::parallel::J || expect_prim->name() == mindspore::parallel::SHARD) { @@ -199,7 +199,7 @@ void GenerateDefaultStrategy(const ValueNodePtr &axes, const std::vectorisa()) { - auto node_size = AnfAlgo::GetOutputInferShape(node, 0).size(); + auto node_size = common::AnfAlgo::GetOutputInferShape(node, 0).size(); std::vector current_d_strategy(node_size, 1); if (node_size >= 1) { current_d_strategy[0] = device_num; @@ -338,7 +338,7 @@ void SetOutputLayout(const FuncGraphPtr &func_graph, const AnfNodePtr &out_axes, for (size_t i = 0; i < output_nodes.size(); ++i) { auto node = output_nodes[i]; - auto output_shape = AnfAlgo::GetOutputInferShape(node, 0); + auto output_shape = common::AnfAlgo::GetOutputInferShape(node, 0); if (output_shape.size() != output_strategy[i].size()) { MS_LOG(EXCEPTION) << "Output dimension: " << output_shape.size() << " is not equal to out_axes dimension: " << output_strategy[i].size() << " at index " << i; @@ -383,7 +383,7 @@ void SetInputLayout(const FuncGraphPtr &func_graph, const AnfNodePtr &in_axes, c if (parameter->cast()->name() == "u" || parameter->cast()->name() == "io") { continue; } - auto output_shape = AnfAlgo::GetOutputInferShape(parameter, 0); + auto output_shape = common::AnfAlgo::GetOutputInferShape(parameter, 0); if (output_shape.size() != input_strategy[i].size()) { MS_LOG(EXCEPTION) << "Input dimension: " << output_shape.size() << " is not equal to in_axes dimension: " << input_strategy[i].size() << " at index " << i; @@ -457,7 +457,7 @@ bool PipelineSplit(const ResourcePtr &res) { #endif MS_EXCEPTION_IF_NULL(res); auto parallel_mode = parallel::ParallelContext::GetInstance()->parallel_mode(); - if (parallel_mode != parallel::SEMI_AUTO_PARALLEL && parallel_mode != parallel::AUTO_PARALLEL) { + if (parallel_mode != parallel::kSemiAutoParallel && parallel_mode != parallel::kAutoParallel) { MS_LOG(INFO) << "Only auto_parallel and semi_auto_parallel support pipeline split."; return true; } @@ -469,7 +469,7 @@ bool PipelineSplit(const ResourcePtr &res) { std::vector all_nodes = DeepScopedGraphSearch(ret); auto execution_mode = MsContext::GetInstance()->get_param(MS_CTX_EXECUTION_MODE); if ((execution_mode == kPynativeMode) && - (parallel_mode == parallel::SEMI_AUTO_PARALLEL || parallel_mode == parallel::AUTO_PARALLEL)) { + (parallel_mode == parallel::kSemiAutoParallel || parallel_mode == parallel::kAutoParallel)) { if (!parallel::ParallelContext::GetInstance()->device_num_is_set()) { MS_LOG(EXCEPTION) << "device_num must be set when use shard function"; } @@ -528,7 +528,7 @@ bool PipelineSplit(const ResourcePtr &res) { // step4: Cut Graph transformer->CutGraph(); // step5: Handle Sens - if (root->has_flag(parallel::TRAINING)) { + if (root->has_flag(parallel::kTraining)) { transformer->CoverSensShape(); } // step6: Elim Graph stages and no used parameter diff --git a/mindspore/ccsrc/pipeline/jit/remove_value_node_dup.cc b/mindspore/ccsrc/pipeline/jit/remove_value_node_dup.cc index c47d565dc61..3e5e7f80300 100644 --- a/mindspore/ccsrc/pipeline/jit/remove_value_node_dup.cc +++ b/mindspore/ccsrc/pipeline/jit/remove_value_node_dup.cc @@ -23,7 +23,7 @@ #include "frontend/optimizer/cse.h" #include "utils/log_adapter.h" #include "utils/hashing.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" namespace mindspore { namespace pipeline { diff --git a/mindspore/ccsrc/pipeline/jit/resource.cc b/mindspore/ccsrc/pipeline/jit/resource.cc index 2ee8d268e0a..9e1cfefa33a 100644 --- a/mindspore/ccsrc/pipeline/jit/resource.cc +++ b/mindspore/ccsrc/pipeline/jit/resource.cc @@ -23,7 +23,7 @@ #include "pipeline/jit/parse/data_converter.h" #include "frontend/operator/ops.h" #include "frontend/optimizer/ad/dfunctor.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" namespace mindspore { // namespace to support opmap definition @@ -348,8 +348,8 @@ void Resource::GetCompileCacheResource(const py::list &compile_cache_dep_files, void Resource::CacheFuncGraph() const { FuncGraphPtr layout_fg = nullptr; std::string parallel_mode = parallel::ParallelContext::GetInstance()->parallel_mode(); - if (func_graph_->has_flag(parallel::AUTO_PARALLEL) && - ((parallel_mode == parallel::AUTO_PARALLEL) || (parallel_mode == parallel::SEMI_AUTO_PARALLEL))) { + if (func_graph_->has_flag(parallel::kAutoParallel) && + ((parallel_mode == parallel::kAutoParallel) || (parallel_mode == parallel::kSemiAutoParallel))) { layout_fg = GetResult(kStepParallelGraph).cast(); } compile_cache_manager_->CacheFuncGraph(func_graph_, layout_fg); diff --git a/mindspore/ccsrc/pipeline/jit/static_analysis/async_eval_result.cc b/mindspore/ccsrc/pipeline/jit/static_analysis/async_eval_result.cc index 3a6136edde3..7f3301122b6 100644 --- a/mindspore/ccsrc/pipeline/jit/static_analysis/async_eval_result.cc +++ b/mindspore/ccsrc/pipeline/jit/static_analysis/async_eval_result.cc @@ -19,7 +19,7 @@ #include "utils/symbolic.h" #include "debug/common.h" #include "pipeline/jit/base.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace abstract { diff --git a/mindspore/ccsrc/pipeline/jit/static_analysis/auto_monad.cc b/mindspore/ccsrc/pipeline/jit/static_analysis/auto_monad.cc index 99ef067f2e8..78f6dee7949 100644 --- a/mindspore/ccsrc/pipeline/jit/static_analysis/auto_monad.cc +++ b/mindspore/ccsrc/pipeline/jit/static_analysis/auto_monad.cc @@ -25,7 +25,7 @@ #include "frontend/operator/ops.h" #include "frontend/operator/composite/multitype_funcgraph.h" #include "utils/flags.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/hash_map.h" #include "utils/hash_set.h" #include "utils/ordered_map.h" diff --git a/mindspore/ccsrc/pipeline/jit/static_analysis/order_enforce.cc b/mindspore/ccsrc/pipeline/jit/static_analysis/order_enforce.cc index 65a4fa0fb9d..bb3c9a66f69 100644 --- a/mindspore/ccsrc/pipeline/jit/static_analysis/order_enforce.cc +++ b/mindspore/ccsrc/pipeline/jit/static_analysis/order_enforce.cc @@ -22,7 +22,7 @@ #include #include "utils/hash_map.h" #include "utils/hash_set.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "base/core_ops.h" namespace mindspore::pipeline { diff --git a/mindspore/ccsrc/pipeline/jit/static_analysis/prim.cc b/mindspore/ccsrc/pipeline/jit/static_analysis/prim.cc index b1ab74428b6..8b20730ba1a 100644 --- a/mindspore/ccsrc/pipeline/jit/static_analysis/prim.cc +++ b/mindspore/ccsrc/pipeline/jit/static_analysis/prim.cc @@ -36,8 +36,8 @@ #include "pipeline/jit/resource.h" #include "pipeline/jit/parse/resolve.h" #include "pipeline/jit/pipeline.h" -#include "utils/convert_utils.h" -#include "utils/convert_utils_py.h" +#include "include/common/utils/convert_utils.h" +#include "include/common/utils/convert_utils_py.h" #include "utils/ms_context.h" #include "pipeline/jit/parse/data_converter.h" #include "abstract/primitive_infer_map.h" @@ -637,6 +637,150 @@ void CheckCustomPrimOutputInferResult(const PrimitivePtr &prim, const AbstractBa } } +void SetValueRange(const AbstractBasePtr &tensor, const py::object &output) { + if (output.is_none()) { + return; + } + py::object obj_min = + output.contains(py::str(ATTR_MIN_VALUE)) ? (py::object)output[ATTR_MIN_VALUE] : (py::object)py::none(); + py::object obj_max = + output.contains(py::str(ATTR_MAX_VALUE)) ? (py::object)output[ATTR_MAX_VALUE] : (py::object)py::none(); + + if (!obj_min.is_none() && !obj_max.is_none()) { + bool converted = true; + ValuePtr min_value = nullptr; + ValuePtr max_value = nullptr; + converted = parse::ConvertData(obj_min, &min_value); + if (!converted) { + MS_LOG(EXCEPTION) << "Convert shape min value data failed"; + } + converted = parse::ConvertData(obj_max, &max_value); + if (!converted) { + MS_LOG(EXCEPTION) << "Convert shape max value data failed"; + } + auto abs_tensor = dyn_cast(tensor); + abs_tensor->set_value_range(min_value, max_value); + } +} + +static bool IsMonadType(const py::object &type_obj) { + if (py::isinstance(type_obj)) { + auto type = type_obj.cast(); + return type->isa(); + } + return false; +} + +AbstractBasePtr ToMonadAbstract(const py::object &type_obj) { + if (py::isinstance(type_obj)) { + auto type = type_obj.cast(); + if (!type->isa()) { + MS_LOG(EXCEPTION) << "Not a monad type object: " << py::str(type_obj); + } + return abstract::MakeMonadAbstract(type->cast()); + } + MS_LOG(EXCEPTION) << "Not a type object: " << py::str(type_obj); +} + +py::object GetPyAbsItemOfTupleOut(const py::object &output, const size_t index) { + auto out_dict = output.cast(); + auto type_obj = out_dict[ATTR_DTYPE]; + auto shape_obj = out_dict[ATTR_SHAPE]; + auto out_item = py::dict(); + auto shape_tuple = shape_obj.cast(); + auto typeid_tuple = type_obj.cast(); + out_item[ATTR_DTYPE] = typeid_tuple[index]; + out_item[ATTR_SHAPE] = shape_tuple[index]; + if (output.contains(py::str(ATTR_MIN_SHAPE))) { + out_item[ATTR_MIN_SHAPE] = output[ATTR_MIN_SHAPE].cast()[index]; + } + if (output.contains(py::str(ATTR_MAX_SHAPE))) { + out_item[ATTR_MAX_SHAPE] = output[ATTR_MAX_SHAPE].cast()[index]; + } + out_item[ATTR_VALUE] = py::none(); + return out_item; +} + +AbstractBasePtr MakePyInferRes2AbstractTensor(const py::object &shape_obj, const py::object &type_obj, + const py::object &output) { + auto ret_vec = shape_obj.cast(); + auto ret_dtype = type_obj.cast(); + ShapeVector min_shape_vec; + ShapeVector max_shape_vec; + + if (!output.is_none()) { + py::object min_shape = + output.contains(py::str(ATTR_MIN_SHAPE)) ? (py::object)output[ATTR_MIN_SHAPE] : (py::object)py::none(); + py::object max_shape = + output.contains(py::str(ATTR_MAX_SHAPE)) ? (py::object)output[ATTR_MAX_SHAPE] : (py::object)py::none(); + if (!min_shape.is_none()) { + min_shape_vec = min_shape.cast(); + } + if (!max_shape.is_none()) { + max_shape_vec = max_shape.cast(); + } + } + + auto ret_shape = std::make_shared(ret_vec, min_shape_vec, max_shape_vec); + AbstractBasePtr tensor = MakeAbstractTensor(ret_shape, ret_dtype); + + SetValueRange(tensor, output); + return tensor; +} + +AbstractBasePtr MakePyInferRes2Abstract(const py::object &output) { + auto out_dict = output.cast(); + auto type_obj = out_dict[ATTR_DTYPE]; + auto shape_obj = out_dict[ATTR_SHAPE]; + if ((py::isinstance(shape_obj) || py::isinstance(shape_obj)) && py::isinstance(type_obj)) { + auto ret_vec = shape_obj.cast(); + auto ret_dtype = type_obj.cast(); + MS_EXCEPTION_IF_NULL(ret_dtype); + // if the size of shape list is empty, return an scalar abstract + if (ret_vec.empty() && (!ret_dtype->isa())) { + abstract::AbstractScalarPtr abs_scalar = std::make_shared(kAnyValue, ret_dtype); + return abs_scalar; + } + return MakePyInferRes2AbstractTensor(shape_obj, type_obj, output); + } else if (py::isinstance(shape_obj) && py::isinstance(type_obj)) { + auto typeid_tuple = type_obj.cast(); + AbstractBasePtrList ptr_list; + for (size_t it = 0; it < typeid_tuple.size(); ++it) { + auto output_it = GetPyAbsItemOfTupleOut(output, it); + auto tensor_it = MakePyInferRes2Abstract(output_it); + ptr_list.push_back(tensor_it); + } + auto tuple = std::make_shared(ptr_list); + return tuple; + } else if (py::isinstance(shape_obj) && py::isinstance(type_obj)) { + auto typeid_list = type_obj.cast(); + AbstractBasePtrList ptr_list; + for (size_t it = 0; it < typeid_list.size(); ++it) { + auto output_it = GetPyAbsItemOfTupleOut(output, it); + auto tensor_it = MakePyInferRes2Abstract(output_it); + ptr_list.push_back(tensor_it); + } + auto list = std::make_shared(ptr_list); + return list; + } else if (shape_obj.is_none() && type_obj.is_none()) { + // AbstractNone indicates there is no output for this CNode node. + auto abstract_none = std::make_shared(); + return abstract_none; + } else if (IsMonadType(type_obj)) { + // Return monad abstract if it is monad type. + return ToMonadAbstract(type_obj); + } else { + // When sparse enabled, the undetermined might be raised and eliminated in opt passes + auto context = MsContext::GetInstance(); + MS_EXCEPTION_IF_NULL(context); + bool enable_sparse = context->get_param(MS_CTX_ENABLE_SPARSE); + if (enable_sparse) { + return std::make_shared(); + } + MS_LOG(EXCEPTION) << "Python evaluator return invalid shape or type. " << (std::string)py::str(type_obj); + } +} + AbstractBasePtr PyInferRes2Abstract(const PrimitivePyPtr &prim_py, const py::dict &output) { // Convert to AbstractValue based on type and shape if (output[ATTR_VALUE].is_none()) { diff --git a/mindspore/ccsrc/pipeline/jit/static_analysis/program_specialize.cc b/mindspore/ccsrc/pipeline/jit/static_analysis/program_specialize.cc index 3f0856e8f8c..8c3592cc7c8 100644 --- a/mindspore/ccsrc/pipeline/jit/static_analysis/program_specialize.cc +++ b/mindspore/ccsrc/pipeline/jit/static_analysis/program_specialize.cc @@ -25,7 +25,7 @@ #include "frontend/operator/composite/do_signature.h" #include "abstract/abstract_function.h" #include "abstract/utils.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "ir/graph_utils.h" #include "utils/log_adapter.h" #include "debug/trace.h" diff --git a/mindspore/ccsrc/pipeline/pynative/pynative_execute.cc b/mindspore/ccsrc/pipeline/pynative/pynative_execute.cc index 79224a0c7cf..04d22e1e4ba 100644 --- a/mindspore/ccsrc/pipeline/pynative/pynative_execute.cc +++ b/mindspore/ccsrc/pipeline/pynative/pynative_execute.cc @@ -34,18 +34,18 @@ #include "ir/cell.h" #include "ir/tensor.h" #include "utils/any.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/ms_context.h" #include "utils/check_convert_utils.h" -#include "utils/context/context_extends.h" -#include "utils/config_manager.h" -#include "utils/convert_utils_py.h" -#include "utils/scoped_long_running.h" +#include "runtime/device/context_extends.h" +#include "include/common/utils/config_manager.h" +#include "include/common/utils/convert_utils_py.h" +#include "include/common/utils/scoped_long_running.h" #include "frontend/optimizer/ad/grad.h" #include "frontend/optimizer/ad/prim_bprop_optimizer.h" #include "frontend/operator/ops.h" #include "frontend/operator/composite/do_signature.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" #include "pipeline/jit/action.h" #include "pipeline/jit/pass.h" #include "pipeline/jit/parse/data_converter.h" @@ -136,7 +136,7 @@ inline ValuePtr PyObjToValue(const py::object &obj) { } std::string GetPyObjId(const py::handle &obj) { - py::object out = parse::python_adapter::CallPyFn(parse::PYTHON_MOD_PARSE_MODULE, parse::PYTHON_MOD_GET_OBJ_ID, obj); + py::object out = python_adapter::CallPyFn(parse::PYTHON_MOD_PARSE_MODULE, parse::PYTHON_MOD_GET_OBJ_ID, obj); if (py::isinstance(out)) { MS_LOG(EXCEPTION) << "Get pyobj failed"; } @@ -725,7 +725,7 @@ void RunReplace(const CNodePtr &added_make_tuple, const std::vectorAddValueNode(output_vnode); MS_LOG(DEBUG) << "Original output value node: " << output_vnode << " info: " << output_vnode->ToString(); - size_t output_num = AnfAlgo::GetOutputTensorNum(cnode); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(cnode); if (index + output_num > total_output_tensors.size()) { MS_LOG(EXCEPTION) << "The size of total_output_tensors: " << total_output_tensors.size() << ", but the current index: " << index << ", output num: " << output_num; @@ -1285,7 +1285,7 @@ void ForwardExecutor::GetOpOutput(const OpExecInfoPtr &op_exec_info, py::object ForwardExecutor::DoAutoCast(const py::object &arg, const TypeId &type_id, const std::string &op_name, size_t index) { - static py::object cast_prim = parse::python_adapter::GetPyFn(kOpsFunctionModelName, "cast"); + static py::object cast_prim = python_adapter::GetPyFn(kOpsFunctionModelName, "cast"); const auto &op_exec_info = std::make_shared(); op_exec_info->op_name = prim::kPrimCast->name(); const auto &adapter = py::cast(cast_prim); @@ -1504,7 +1504,7 @@ AnfNodePtr GradExecutor::GetInput(const py::object &obj, bool op_mask) { if (op_mask) { MS_LOG(DEBUG) << "Cell parameters(weights)"; // get the parameter name from parameter object - auto name_attr = parse::python_adapter::GetPyObjAttr(obj, "name"); + auto name_attr = python_adapter::GetPyObjAttr(obj, "name"); if (py::isinstance(name_attr)) { MS_LOG(EXCEPTION) << "Parameter object should have name attribute"; } @@ -2020,7 +2020,7 @@ py::tuple ForwardExecutor::RunOpWithInitBackendPolicy(const OpExecInfoPtr &op_ex MsBackendPolicy ForwardExecutor::GetBackendPolicy(const OpExecInfoPtr &op_exec_info) { MS_EXCEPTION_IF_NULL(op_exec_info); MS_LOG(DEBUG) << "RunOp start, op name is: " << op_exec_info->op_name; - parse::python_adapter::set_python_env_flag(true); + python_adapter::set_python_env_flag(true); auto ms_context = MsContext::GetInstance(); MS_EXCEPTION_IF_NULL(ms_context); @@ -2599,7 +2599,7 @@ void GradExecutor::DoGradForCustomBprop(const py::object &cell, const py::object return; } MS_LOG(DEBUG) << "Do grad for custom bprop"; - size_t par_number = py::tuple(parse::python_adapter::CallPyObjMethod(cell, "get_parameters")).size(); + size_t par_number = py::tuple(python_adapter::CallPyObjMethod(cell, "get_parameters")).size(); if (par_number > 0) { MS_LOG(EXCEPTION) << "When user defines the net bprop, the 'Parameter' data type is not supported in the net."; } @@ -2740,7 +2740,7 @@ void GradExecutor::GradNetInner(py::object *ret, const prim::GradOperationPtr &g resource->SetResult(pipeline::kBackend, compile::CreateBackend()); MS_LOG(DEBUG) << "Start task emit action"; auto parallel_mode = parallel::ParallelContext::GetInstance()->parallel_mode(); - if (parallel_mode == parallel::SEMI_AUTO_PARALLEL || parallel_mode == parallel::AUTO_PARALLEL) { + if (parallel_mode == parallel::kSemiAutoParallel || parallel_mode == parallel::kAutoParallel) { MarkMsFunctionNodes(resource); } TaskEmitAction(resource); @@ -2784,7 +2784,7 @@ std::vector GradExecutor::GetWeightsArgs(const py::object &weights, w_args.emplace_back(para_node); continue; } - const auto &name_attr = parse::python_adapter::GetPyObjAttr(param, "name"); + const auto &name_attr = python_adapter::GetPyObjAttr(param, "name"); if (py::isinstance(name_attr)) { MS_LOG(EXCEPTION) << "Parameter object should have name attribute"; } @@ -3292,7 +3292,7 @@ py::object GradExecutor::GradMsFunction(const py::object &out, const py::args &a MS_EXCEPTION_IF_NULL(grad_graph); GradMsFunctionInner(phase, out, args, ms_func_graph, grad_graph); auto parallel_mode = parallel::ParallelContext::GetInstance()->parallel_mode(); - if (parallel_mode == parallel::SEMI_AUTO_PARALLEL || parallel_mode == parallel::AUTO_PARALLEL) { + if (parallel_mode == parallel::kSemiAutoParallel || parallel_mode == parallel::kAutoParallel) { for (auto ¶meter : ms_func_graph->parameters()) { auto param = parameter->cast(); if (param->has_default()) { diff --git a/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_comm_manager.cc b/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_comm_manager.cc new file mode 100644 index 00000000000..faef6ec84b6 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_comm_manager.cc @@ -0,0 +1,138 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "include/common/utils/comm_manager.h" +#include "plugin/device/ascend/hal/hccl_adapter/hccl_adapter.h" +#include "hccl/hcom.h" +#include "plugin/device/ascend/hal/device/distribute/ascend_collective.h" +#include "utils/ms_context.h" +#include "include/common/utils/parallel_context.h" + +namespace mindspore { +namespace { +constexpr char kHcclWorldGroup[] = "hccl_world_group"; + +#define HCCL_RUN_CHECK(op_name, group, op) \ + do { \ + auto hccl_result = (op); \ + if (hccl_result != 0) { \ + MS_LOG(ERROR) << op_name << " failed: #" << group << "#"; \ + return false; \ + } \ + } while (0) + +#define HCCL_GROUP_CHECK_EMPTY(group) \ + do { \ + if (group.length() == 0) { \ + MS_LOG(ERROR) << "The length of group name should not be 0"; \ + return false; \ + } \ + } while (0) + +#define HCCL_GROUP_CHECK_IS_WORLD(group) \ + do { \ + if (group == "hccl_world_group") { \ + MS_LOG(ERROR) << "The group name should not be hccl_world_group"; \ + return false; \ + } \ + } while (0) + +class AscendCommManager : public CommManager { + public: + AscendCommManager() : CommManager("hccl") {} + ~AscendCommManager() override = default; + + bool CreateGroupSync(const string &group, const std::vector &rank_id_list) const override { + auto rank_size = rank_id_list.size(); + HCCL_GROUP_CHECK_EMPTY(group); + HCCL_GROUP_CHECK_IS_WORLD(group); + auto context_ptr = MsContext::GetInstance(); + MS_EXCEPTION_IF_NULL(context_ptr); + bool is_task_sink = context_ptr->get_param(MS_CTX_ENABLE_TASK_SINK); + auto mode = context_ptr->get_param(MS_CTX_EXECUTION_MODE); + if (!is_task_sink && mode == kGraphMode) { + HcclCollectiveGroup::instance().CreateCommGroup(group, rank_id_list); + } else { + HCCL_RUN_CHECK(string("create communicate group"), group, + hccl::HcclAdapter::GetInstance().HcclCreateGroup(group, UlongToUint(rank_size), + std::vector(rank_id_list).data())); + } + return true; + } + + bool GetRankID(const string &group, unsigned int *rank_id) const override { + auto context = MsContext::GetInstance(); + MS_EXCEPTION_IF_NULL(context); + if (context->get_param(MS_CTX_EXECUTION_MODE) == kGraphMode) { + HCCL_GROUP_CHECK_EMPTY(group); + if (!context->get_param(MS_CTX_ENABLE_TASK_SINK)) { + *rank_id = static_cast(HcclCollectiveGroup::instance().GetRankId(group)); + } else { + HCCL_RUN_CHECK(string("get rank_id"), group, hccl::HcclAdapter::GetInstance().HcclGetRankId(group, rank_id)); + } + } else { + HCCL_RUN_CHECK(string("get rank_id"), group, hccl::HcclAdapter::GetInstance().HcclGetRankId(rank_id)); + } + return true; + } + + bool GetRankSize(const string &group, unsigned int *rank_size) const override { + auto context = MsContext::GetInstance(); + MS_EXCEPTION_IF_NULL(context); + if (context->get_param(MS_CTX_EXECUTION_MODE) == kGraphMode) { + HCCL_GROUP_CHECK_EMPTY(group); + if (!context->get_param(MS_CTX_ENABLE_TASK_SINK)) { + *rank_size = static_cast(HcclCollectiveGroup::instance().GetRankSize(group)); + } else { + HCCL_RUN_CHECK(string("get rank size"), group, + hccl::HcclAdapter::GetInstance().HcclGetRankSize(group, rank_size)); + } + } else { + HCCL_RUN_CHECK(string("get rank size"), group, hccl::HcclAdapter::GetInstance().HcclGetRankSize(rank_size)); + } + return true; + } + + bool DestroyGroup(const string &group) const override { + HCCL_GROUP_CHECK_EMPTY(group); + HCCL_GROUP_CHECK_IS_WORLD(group); + HCCL_RUN_CHECK(string("destroy communicate group"), group, + hccl::HcclAdapter::GetInstance().HcclDestroyGroup(group)); + return true; + } + + uint32_t GetRank() override { + uint32_t rank_id = 0; + auto ms_context = MsContext::GetInstance(); + MS_EXCEPTION_IF_NULL(ms_context); + auto parallel_context = parallel::ParallelContext::GetInstance(); + MS_EXCEPTION_IF_NULL(parallel_context); + if (parallel_context->parallel_mode() != parallel::kStandalone) { + // Check HCCL inited. + if (!hccl::HcclAdapter::GetInstance().Inited()) { + MS_LOG(DEBUG) << "HCCL not inited, return rank_id = 0"; + return rank_id; + } + if (!GetRankID(kHcclWorldGroup, &rank_id)) { + MS_LOG(EXCEPTION) << "Get rank id failed."; + } + } + return rank_id; + } +}; +COMM_MANAGER_REG(kAscendDevice, std::make_shared()); +} // namespace +} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_device_address.cc b/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_device_address.cc index 500db458429..738ebf04d63 100644 --- a/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_device_address.cc +++ b/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_device_address.cc @@ -32,8 +32,8 @@ #include "ir/dtype/type.h" #include "ir/tensor.h" #include "abstract/utils.h" -#include "utils/utils.h" -#include "utils/ms_device_shape_transfer.h" +#include "include/common/utils/utils.h" +#include "runtime/device/ms_device_shape_transfer.h" #ifndef ENABLE_SECURITY #include "debug/data_dump/dump_json_parser.h" #endif diff --git a/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_kernel_runtime.cc b/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_kernel_runtime.cc index d448840092d..041dc125d48 100644 --- a/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_kernel_runtime.cc +++ b/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_kernel_runtime.cc @@ -20,13 +20,13 @@ #include #include #include -#include "utils/signal_util.h" +#include "include/common/utils/signal_util.h" #include "plugin/device/ascend/hal/device/ascend_device_address.h" #include "plugin/device/ascend/hal/device/distribute/ascend_collective.h" #include "utils/ms_context.h" -#include "utils/context/context_extends.h" -#include "utils/mpi/mpi_config.h" -#include "utils/ms_device_shape_transfer.h" +#include "runtime/device/context_extends.h" +#include "include/common/utils/mpi/mpi_config.h" +#include "runtime/device/ms_device_shape_transfer.h" #include "runtime/rt.h" #include "acl/acl_rt.h" #include "plugin/device/ascend/hal/device/ascend_stream_manager.h" @@ -34,6 +34,7 @@ #include "plugin/device/ascend/hal/device/ge_runtime/model_runner.h" #include "plugin/device/ascend/hal/device/tasksink/task_generator.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/session/kernel_build_client.h" #include "plugin/device/ascend/kernel/aicpu/aicpu_kernel_load.h" #ifndef ENABLE_SECURITY @@ -51,15 +52,15 @@ #include "graphengine/inc/external/acl/error_codes/rt_error_codes.h" #include "common/util/error_manager/error_manager.h" #include "debug/anf_ir_dump.h" -#include "frontend/parallel/context.h" -#include "utils/comm_manager.h" -#include "utils/runtime_error_codes.h" +#include "include/common/utils/parallel_context.h" +#include "include/common/utils/comm_manager.h" +#include "include/common/utils/runtime_error_codes.h" #ifdef MEM_REUSE_DEBUG #include "common/mem_reuse/mem_reuse_checker.h" #include "debug/env_config_parser.h" #endif #include "plugin/device/ascend/hal/device/executor/hccl_dynamic_kernel.h" -#include "utils/config_manager.h" +#include "include/common/utils/config_manager.h" #include "plugin/device/ascend/hal/hccl_adapter/hccl_adapter.h" #ifdef ENABLE_TDTQUE #include "minddata/dataset/engine/tdt/tdt_handle.h" @@ -114,7 +115,7 @@ void IntHandler(int, siginfo_t *, void *) { (void)kill(this_pid, SIGTERM); } -void AscendEnableDynamicRuntimeCache(const KernelGraph *graph) { +void AscendEnableDynamicRuntimeCache(const session::KernelGraph *graph) { const auto &node_list = graph->TopoSort(graph->get_return()); for (auto &node : node_list) { auto kernel_info = node->kernel_info(); @@ -446,7 +447,8 @@ bool AscendKernelRuntime::GenDynamicKernel(const session::KernelGraph &graph) { MS_EXCEPTION_IF_NULL(kernel_mod); auto dynamic_kernel = kernel_mod->GenDynamicKernel(cnode, stream_); if (dynamic_kernel == nullptr) { - MS_LOG(EXCEPTION) << "Dynamic shape is not supported with the operator [" << AnfAlgo::GetCNodeName(cnode) << "]."; + MS_LOG(EXCEPTION) << "Dynamic shape is not supported with the operator [" << common::AnfAlgo::GetCNodeName(cnode) + << "]."; } dynamic_kernel->Initialize(); dynamic_kernels.emplace_back(dynamic_kernel); @@ -672,7 +674,7 @@ std::string AscendKernelRuntime::GetDumpPath() { uint32_t rank_id = 0; auto inst = parallel::ParallelContext::GetInstance(); MS_EXCEPTION_IF_NULL(inst); - if (inst->parallel_mode() != parallel::STAND_ALONE) { + if (inst->parallel_mode() != parallel::kStandalone) { if (!CommManager::GetInstance().GetRankID(kHcclWorldGroup, &rank_id)) { MS_LOG(WARNING) << "Get rank id failed, now using the default value 0."; } @@ -784,7 +786,7 @@ void AscendKernelRuntime::GetShadowBackendNodeMap(const session::KernelGraph &gr auto front_node = AnfAlgo::FetchFrontNodeByBackendNode(node, graph); for (auto &knode : input_nodes) { if (knode == node) break; - if (!AnfAlgo::IsTupleOutput(front_node) && front_node != nullptr && + if (!common::AnfAlgo::IsTupleOutput(front_node) && front_node != nullptr && front_node == AnfAlgo::FetchFrontNodeByBackendNode(knode, graph)) { shadow_backend_node_map->emplace(node, knode); break; @@ -842,7 +844,7 @@ void AscendKernelRuntime::GenKernelEvents(const session::KernelGraph &graph) { std::vector stream_hit(stream_num, false); std::vector used_kernels; std::set visited_kernels; - AnfAlgo::GetAllVisitedCNode(kernel, &used_kernels, &visited_kernels); + common::AnfAlgo::GetAllVisitedCNode(kernel, &used_kernels, &visited_kernels); bool found_depend = false; for (int k = SizeToInt(i) - 1; k >= 0; --k) { auto pre_cnode = kernels[IntToSize(k)]; @@ -897,7 +899,7 @@ void AscendKernelRuntime::GenKernelEventsForMindRT(const session::KernelGraph &g auto wait_stream = stream_id_map_[curr_stream_id]; std::vector used_kernels; std::set visited_kernels; - AnfAlgo::GetAllVisitedCNode(kernel, &used_kernels, &visited_kernels); + common::AnfAlgo::GetAllVisitedCNode(kernel, &used_kernels, &visited_kernels); bool found_depend = false; std::set record_nodes; // set events for nodes and its input: [input_node_stream, node_stream] @@ -978,7 +980,8 @@ void AscendKernelRuntime::ProcessBoundaryEvent( MS_EXCEPTION_IF_NULL(child); auto input_size = child->inputs().size() - 1; for (size_t k = 0; k < input_size; ++k) { - auto kernel_index = AnfAlgo::VisitKernelWithReturnType(AnfAlgo::GetInputNode(child, k), 0, true); + auto kernel_index = + common::AnfAlgo::VisitKernelWithReturnType(common::AnfAlgo::GetInputNode(child, k), 0, true); if (kernel_index.first == kernel) { found_nearest_child = true; break; diff --git a/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_label_assign.cc b/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_label_assign.cc index e344edc0d08..099defcfd4e 100644 --- a/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_label_assign.cc +++ b/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_label_assign.cc @@ -19,6 +19,7 @@ #include #include "plugin/device/ascend/hal/device/ascend_label_assign.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace device { @@ -27,7 +28,7 @@ static constexpr uint32_t kLabelGotoLabelId = 1; static constexpr uint32_t kLabelSwitchLabelId = 2; static void UpdateLabelGoto(NotNull node) { - if (AnfAlgo::HasNodeAttr(kAttrLabelIndex, node)) { + if (common::AnfAlgo::HasNodeAttr(kAttrLabelIndex, node)) { return; } if (node->size() <= kLabelGotoLabelId) { @@ -35,14 +36,14 @@ static void UpdateLabelGoto(NotNull node) { } auto input = node->input(kLabelGotoLabelId); - uint32_t goto_label_id = AnfAlgo::GetNodeAttr(input, kAttrLabelIndex); - AnfAlgo::SetNodeAttr(kAttrLabelIndex, MakeValue(goto_label_id), node.get()); + uint32_t goto_label_id = common::AnfAlgo::GetNodeAttr(input, kAttrLabelIndex); + common::AnfAlgo::SetNodeAttr(kAttrLabelIndex, MakeValue(goto_label_id), node.get()); MS_LOG(INFO) << "Node " << node->DebugString() << " goto label id " << goto_label_id; node->set_inputs({node->input(0)}); } static void UpdateLabelSwitch(NotNull node) { - if (AnfAlgo::HasNodeAttr(kAttrLabelIndex, node)) { + if (common::AnfAlgo::HasNodeAttr(kAttrLabelIndex, node)) { return; } if (node->size() <= kLabelGotoLabelId) { @@ -52,15 +53,15 @@ static void UpdateLabelSwitch(NotNull node) { for (size_t i = kLabelSwitchLabelId; i < node->size(); ++i) { auto input = node->input(i); MS_EXCEPTION_IF_NULL(input); - if (!input->isa() || AnfAlgo::GetCNodeName(input) != kLabelSetOpName) { + if (!input->isa() || common::AnfAlgo::GetCNodeName(input) != kLabelSetOpName) { break; } - uint32_t goto_label_id = AnfAlgo::GetNodeAttr(input, kAttrLabelIndex); + uint32_t goto_label_id = common::AnfAlgo::GetNodeAttr(input, kAttrLabelIndex); label_list.push_back(goto_label_id); MS_LOG(INFO) << "Switch " << node->DebugString() << " case " << i - kLabelSwitchLabelId << ": id " << goto_label_id; } - AnfAlgo::SetNodeAttr(kAttrLabelSwitchList, MakeValue>(label_list), node.get()); + common::AnfAlgo::SetNodeAttr(kAttrLabelSwitchList, MakeValue>(label_list), node.get()); node->set_inputs({node->input(kAnfPrimitiveIndex), node->input(kFirstDataInputIndex)}); } @@ -82,9 +83,9 @@ static void AssignLabelForLabelSet(NotNull auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - std::string node_name = AnfAlgo::GetCNodeName(node); - if (node_name == kLabelSetOpName && !AnfAlgo::HasNodeAttr(kAttrLabelIndex, cnode)) { - AnfAlgo::SetNodeAttr(kAttrLabelIndex, MakeValue(*label_id), node); + std::string node_name = common::AnfAlgo::GetCNodeName(node); + if (node_name == kLabelSetOpName && !common::AnfAlgo::HasNodeAttr(kAttrLabelIndex, cnode)) { + common::AnfAlgo::SetNodeAttr(kAttrLabelIndex, MakeValue(*label_id), node); MS_LOG(INFO) << "Node " << node->DebugString() << " assign label id " << *label_id; ++(*label_id); } diff --git a/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_label_assign.h b/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_label_assign.h index ac1ab4733f0..844b5f47fa5 100644 --- a/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_label_assign.h +++ b/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_label_assign.h @@ -20,7 +20,7 @@ #include #include #include "backend/common/session/kernel_graph.h" -#include "utils/contract.h" +#include "include/common/utils/contract.h" namespace mindspore { namespace device { diff --git a/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_launch_atomic_clean.cc b/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_launch_atomic_clean.cc index cc96b47710a..bbcfad40ec8 100644 --- a/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_launch_atomic_clean.cc +++ b/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_launch_atomic_clean.cc @@ -18,6 +18,7 @@ #include "abstract/utils.h" #include "backend/common/session/single_kernel_graph.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "debug/anf_ir_dump.h" namespace mindspore::device::ascend { @@ -109,7 +110,7 @@ void AscendLaunchAtomicClean::ConstructKernelGraphAndSetAttr() { AnfAlgo::SetSelectKernelBuildInfo(builder->Build(), clean_node.get()); // set attr std::vector clean_size = {total_size_}; - AnfAlgo::SetNodeAttr(kAttrAtomicAddMemSize, MakeValue(clean_size), clean_node); + common::AnfAlgo::SetNodeAttr(kAttrAtomicAddMemSize, MakeValue(clean_size), clean_node); } } } // namespace mindspore::device::ascend diff --git a/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_launch_mul.cc b/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_launch_mul.cc index 5371e6ffdba..107c80d82ce 100644 --- a/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_launch_mul.cc +++ b/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_launch_mul.cc @@ -19,7 +19,7 @@ #include "runtime/mem.h" #include "acl/acl_rt.h" #include "backend/common/session/single_kernel_graph.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" namespace mindspore::device::ascend { void AscendLaunchMul::FreeDeviceMem(void *addr) { AscendLaunchKernel::FreeDeviceMem(addr); } diff --git a/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_launch_transdata.cc b/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_launch_transdata.cc index d482318156c..2ab9a465649 100644 --- a/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_launch_transdata.cc +++ b/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_launch_transdata.cc @@ -20,6 +20,7 @@ #include "abstract/utils.h" #include "backend/common/session/single_kernel_graph.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore::device::ascend { void AscendLaunchTransData::FreeDeviceMem(void *addr) { AscendLaunchKernel::FreeDeviceMem(addr); } @@ -98,7 +99,7 @@ void AscendLaunchTransData::ConstructKernelGraphAndSetAttr() { if (!transdata_graph_->execution_order().empty()) { auto transdata_node = transdata_graph_->execution_order()[0]; // set output infer type and shape - AnfAlgo::SetOutputInferTypeAndShape({dtype_}, {shape_}, transdata_node.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({dtype_}, {shape_}, transdata_node.get()); // set build info auto builder = std::make_shared(); builder->SetKernelType(KernelType::TBE_KERNEL); @@ -111,8 +112,8 @@ void AscendLaunchTransData::ConstructKernelGraphAndSetAttr() { builder->SetOutputsFormat(outputs_format); AnfAlgo::SetSelectKernelBuildInfo(builder->Build(), transdata_node.get()); // set attr - AnfAlgo::SetNodeAttr(kAttrSrcFormat, MakeValue(src_format_), transdata_node); - AnfAlgo::SetNodeAttr(kAttrDstFormat, MakeValue(dst_format_), transdata_node); + common::AnfAlgo::SetNodeAttr(kAttrSrcFormat, MakeValue(src_format_), transdata_node); + common::AnfAlgo::SetNodeAttr(kAttrDstFormat, MakeValue(dst_format_), transdata_node); } } } // namespace mindspore::device::ascend diff --git a/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_stream_assign.cc b/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_stream_assign.cc index e8e7bbce426..d39e5c32c0d 100644 --- a/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_stream_assign.cc +++ b/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_stream_assign.cc @@ -22,14 +22,15 @@ #include "ir/manager.h" #include "utils/ms_context.h" #include "utils/ms_utils.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" #include "frontend/parallel/device_manager.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "runtime/device/kernel_adjust.h" #include "plugin/device/ascend/hal/device/ascend_stream_manager.h" #include "backend/common/optimizer/helper.h" #include "kernel/oplib/oplib.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #ifdef ENABLE_DUMP_IR #include "debug/rdr/running_data_recorder.h" @@ -66,11 +67,11 @@ bool IsSameServer(const std::vector &rank_ids) { string DoGetHcomGroup(const string &original_group) { string communi_parallel_mode = parallel::ParallelContext::GetInstance()->communi_parallel_mode(); - if (communi_parallel_mode == parallel::ALL_GROUP_PARALLEL) { + if (communi_parallel_mode == parallel::kAllGroupParallel) { return original_group; } - if (communi_parallel_mode == parallel::NO_GROUP_PARALLEL) { + if (communi_parallel_mode == parallel::kNoGroupParallel) { return kDefaultGroup; } @@ -95,11 +96,11 @@ string DoGetHcomGroup(const string &original_group) { string GetHcomGroup(const CNodePtr &cnode) { MS_EXCEPTION_IF_NULL(cnode); - if (!AnfAlgo::HasNodeAttr(kAttrGroup, cnode)) { + if (!common::AnfAlgo::HasNodeAttr(kAttrGroup, cnode)) { MS_LOG_EXCEPTION << "Hcom node " << cnode->fullname_with_scope() << " has no group attribute."; } - auto group_name = AnfAlgo::GetNodeAttr(cnode, kAttrGroup); + auto group_name = common::AnfAlgo::GetNodeAttr(cnode, kAttrGroup); auto new_group = DoGetHcomGroup(group_name); MS_LOG_INFO << "hcom node: " << cnode->fullname_with_scope() << ", old group: " << group_name << ", new group: " << new_group; @@ -109,7 +110,7 @@ string GetHcomGroup(const CNodePtr &cnode) { uint32_t GetHcomTaskNum(const CNodePtr &cnode) { MS_EXCEPTION_IF_NULL(cnode); - if (!AnfAlgo::HasNodeAttr(kAttrGroup, cnode)) { + if (!common::AnfAlgo::HasNodeAttr(kAttrGroup, cnode)) { MS_LOG_EXCEPTION << "Hcom node " << cnode->fullname_with_scope() << " has no group attribute."; } @@ -118,14 +119,14 @@ uint32_t GetHcomTaskNum(const CNodePtr &cnode) { return kTaskNumPerHcomNode; } - auto node_name = AnfAlgo::GetCNodeName(cnode); + auto node_name = common::AnfAlgo::GetCNodeName(cnode); if (node_name == kHcomSendOpName || node_name == kReceiveOpName) { return kTaskNumPerHcomSendRecvNode; } MS_EXCEPTION_IF_NULL(parallel::ParallelContext::GetInstance()); auto device_num = parallel::ParallelContext::GetInstance()->device_num(); - auto group_name = AnfAlgo::GetNodeAttr(cnode, kAttrGroup); + auto group_name = common::AnfAlgo::GetNodeAttr(cnode, kAttrGroup); auto group_info = parallel::g_device_manager->group_info(); for (const auto &info : group_info) { if (info.first != group_name) { @@ -157,11 +158,11 @@ CNodePtr GetHcomAndOverflowMarker(const NotNull &graph_ptr, vect for (size_t i = 0; i < cnode_ptr_list.size(); ++i) { auto cur_cnode_ptr = cnode_ptr_list[i]; MS_EXCEPTION_IF_NULL(cur_cnode_ptr); - if (AnfAlgo::GetCNodeName(cur_cnode_ptr) == kNPUGetFloatStatusOpName) { + if (common::AnfAlgo::GetCNodeName(cur_cnode_ptr) == kNPUGetFloatStatusOpName) { overflow_marker = cur_cnode_ptr; } else if (AnfAlgo::GetKernelType(cur_cnode_ptr) == HCCL_KERNEL) { hcom_nodes->emplace_back(cur_cnode_ptr); - } else if (i > 0 && AnfAlgo::GetCNodeName(cnode_ptr_list[i - 1]) == kAtomicAddrCleanOpName) { + } else if (i > 0 && common::AnfAlgo::GetCNodeName(cnode_ptr_list[i - 1]) == kAtomicAddrCleanOpName) { auto graph_id = AnfAlgo::GetGraphId(cur_cnode_ptr.get()); AnfAlgo::SetGraphId(graph_id, cnode_ptr_list[i - 1].get()); } @@ -171,7 +172,7 @@ CNodePtr GetHcomAndOverflowMarker(const NotNull &graph_ptr, vect bool HasRefNodes(const vector &moved_backward_cnodes) { for (auto &cnode : moved_backward_cnodes) { - std::string op_name = AnfAlgo::GetCNodeName(cnode); + std::string op_name = common::AnfAlgo::GetCNodeName(cnode); auto op_info = mindspore::kernel::OpLib::FindOp(op_name, kernel::kTBE); if (op_info != nullptr && op_info->is_ref()) { MS_LOG(INFO) << "Find RefNode: " << op_name << ", full name: " << cnode->fullname_with_scope(); @@ -206,7 +207,7 @@ StreamActiveKind GetStreamKind(uint32_t cur_stream_id, uint32_t pre_stream_id, u void SetNodeStreamIDAttr(const NotNull &graph_ptr) { auto exec_orders = graph_ptr->execution_order(); for (auto node : exec_orders) { - AnfAlgo::SetNodeAttr(kAttrStreamID, MakeValue(AnfAlgo::GetStreamId(node)), node); + common::AnfAlgo::SetNodeAttr(kAttrStreamID, MakeValue(AnfAlgo::GetStreamId(node)), node); } } } // namespace @@ -246,8 +247,8 @@ void AscendStreamAssign::AssignStreamForNonTaskSink(const std::vector group_stream_id_map_[kHcclWorldGroup] = kWorldGroupStreamIndex; for (size_t i = 0; i < kernels.size(); ++i) { auto &node = kernels[i]; - if (AnfAlgo::IsCommunicationOp(node)) { - auto group = AnfAlgo::GetNodeAttr(node, kAttrGroup); + if (common::AnfAlgo::IsCommunicationOp(node)) { + auto group = common::AnfAlgo::GetNodeAttr(node, kAttrGroup); auto iter = group_stream_id_map_.find(group); if (iter == group_stream_id_map_.end()) { auto id = SizeToUint(group_stream_id_map_.size()) + kWorldGroupStreamIndex; @@ -265,7 +266,7 @@ void AscendStreamAssign::AssignStreamForNonTaskSink(const std::vector } } for (size_t i = 1; i < kernels.size(); ++i) { - if (AnfAlgo::GetCNodeName(kernels[i - 1]) == kAtomicAddrCleanOpName) { + if (common::AnfAlgo::GetCNodeName(kernels[i - 1]) == kAtomicAddrCleanOpName) { auto stream_id = AnfAlgo::GetStreamId(kernels[i]); AnfAlgo::SetStreamId(stream_id, kernels[i - 1].get()); } @@ -434,8 +435,9 @@ void AscendStreamAssign::CheckScenario(const NotNull &graph_ptr, return; } - auto label_switch_pos = find_if(last_grad_hcom_pos, cnode_ptr_list.end(), - [](CNodePtr &node) -> bool { return AnfAlgo::GetCNodeName(node) == "LabelSwitch"; }); + auto label_switch_pos = find_if(last_grad_hcom_pos, cnode_ptr_list.end(), [](CNodePtr &node) -> bool { + return common::AnfAlgo::GetCNodeName(node) == "LabelSwitch"; + }); if (label_switch_pos == cnode_ptr_list.end()) { MS_LOG(INFO) << "No branches after getting overflow status, no optimization needed"; last_grad_and_status->clear(); @@ -468,7 +470,7 @@ CNodePtr AscendStreamAssign::GetCNodesNeededMoved(vector *moved_backwa auto it = float_status_pos; while (AnfAlgo::GetGraphId((*it).get()) == graph_id && it < cnode_ptr_list.end()) { - if (AnfAlgo::GetCNodeName(*it) == kAtomicAddrCleanOpName) { + if (common::AnfAlgo::GetCNodeName(*it) == kAtomicAddrCleanOpName) { it++; continue; } @@ -481,12 +483,12 @@ CNodePtr AscendStreamAssign::GetCNodesNeededMoved(vector *moved_backwa } } if (is_independent) { - if (AnfAlgo::GetCNodeName(*(it - 1)) == kAtomicAddrCleanOpName) { + if (common::AnfAlgo::GetCNodeName(*(it - 1)) == kAtomicAddrCleanOpName) { moved_forward_cnodes->emplace_back(*(it - 1)); } moved_forward_cnodes->emplace_back(*it); } else { - if (AnfAlgo::GetCNodeName(*(it - 1)) == kAtomicAddrCleanOpName) { + if (common::AnfAlgo::GetCNodeName(*(it - 1)) == kAtomicAddrCleanOpName) { moved_backward_cnodes->emplace_back(*(it - 1)); } moved_backward_cnodes->emplace_back(*it); @@ -545,8 +547,9 @@ CNodePtr AscendStreamAssign::GetTargetOutputNode(const vector &moved_b bool AscendStreamAssign::FinetuneSubgraphExecOrder(vector *cnodes) { MS_EXCEPTION_IF_NULL(cnodes); - auto hcom_pos = find_if(cnodes->begin(), cnodes->end(), - [](CNodePtr &node_ptr) -> bool { return AnfAlgo::GetCNodeName(node_ptr) == "AllReduce"; }); + auto hcom_pos = find_if(cnodes->begin(), cnodes->end(), [](CNodePtr &node_ptr) -> bool { + return common::AnfAlgo::GetCNodeName(node_ptr) == "AllReduce"; + }); if (hcom_pos == cnodes->end()) { return false; } @@ -556,7 +559,7 @@ bool AscendStreamAssign::FinetuneSubgraphExecOrder(vector *cnodes) { cnodes->clear(); vector atomic_addr_clean; for (auto iter = ori_cnodes.begin(); iter < ori_cnodes.end(); ++iter) { - if (AnfAlgo::GetCNodeName(*iter) == kAtomicAddrCleanOpName) { + if (common::AnfAlgo::GetCNodeName(*iter) == kAtomicAddrCleanOpName) { atomic_addr_clean.emplace_back(*iter); continue; } @@ -569,8 +572,9 @@ bool AscendStreamAssign::FinetuneSubgraphExecOrder(vector *cnodes) { } if (last_input_pos == cnodes->end()) { auto hcom_it = find(cnodes->begin(), cnodes->end(), hcom_ptr); - if (hcom_it == cnodes->end() || AnfAlgo::GetCNodeName(*iter) == kLabelGotoOpName || - AnfAlgo::GetCNodeName(*iter) == kLabelSetOpName || AnfAlgo::GetCNodeName(*iter) == kLabelSwitchOpName) { + if (hcom_it == cnodes->end() || common::AnfAlgo::GetCNodeName(*iter) == kLabelGotoOpName || + common::AnfAlgo::GetCNodeName(*iter) == kLabelSetOpName || + common::AnfAlgo::GetCNodeName(*iter) == kLabelSwitchOpName) { cnodes->emplace_back(*iter); } else { cnodes->insert(hcom_it, *iter); @@ -628,7 +632,7 @@ void AscendStreamAssign::TrailingTimeOptimizationByReorder(const NotNull subgraph_cnodes; while (pos < cnode_ptr_list.end() && AnfAlgo::GetGraphId((*pos).get()) == subgraph_id) { - if (AnfAlgo::GetCNodeName(*pos) == kLabelGotoOpName) { + if (common::AnfAlgo::GetCNodeName(*pos) == kLabelGotoOpName) { break; } if (*pos != first_output_ptr) { @@ -885,7 +889,7 @@ void AscendStreamAssign::UpdateAtomicAddrCleanStreamId(const NotNull 0 && AnfAlgo::GetCNodeName(cnode_ptr_list[i - 1]) == kAtomicAddrCleanOpName) { + if (i > 0 && common::AnfAlgo::GetCNodeName(cnode_ptr_list[i - 1]) == kAtomicAddrCleanOpName) { AnfAlgo::SetStreamId(AnfAlgo::GetStreamId(cur_cnode_ptr), cnode_ptr_list[i - 1].get()); } } @@ -990,10 +994,11 @@ void AscendStreamAssign::ActiveOtherGraphParallel(const NotNull // 2.set active stream ids std::vector active_index_list; std::copy(it->second.begin(), it->second.end(), std::back_inserter(active_index_list)); - AnfAlgo::SetNodeAttr(kAttrActiveStreamList, MakeValue>(active_index_list), active_ptr); + common::AnfAlgo::SetNodeAttr(kAttrActiveStreamList, MakeValue>(active_index_list), + active_ptr); // find position for insert streamactive - if (AnfAlgo::GetCNodeName(cur_cnode_ptr) == kLabelSetOpName) { + if (common::AnfAlgo::GetCNodeName(cur_cnode_ptr) == kLabelSetOpName) { update_stream_list.emplace_back(cur_cnode_ptr); update_stream_list.emplace_back(active_ptr); } else { @@ -1012,23 +1017,23 @@ void AscendStreamAssign::ActiveRootGraphHcom(const NotNull &grap auto exe_orders = graph_ptr->execution_order(); for (size_t i = 0; i < exe_orders.size(); i++) { CNodePtr cur_cnode_ptr = exe_orders[i]; - if (AnfAlgo::GetCNodeName(cur_cnode_ptr) != kStreamSwitchOpName) { + if (common::AnfAlgo::GetCNodeName(cur_cnode_ptr) != kStreamSwitchOpName) { update_cnode_list.emplace_back(cur_cnode_ptr); continue; } - if (!AnfAlgo::HasNodeAttr(kAttrStreamSwitchKind, cur_cnode_ptr)) { + if (!common::AnfAlgo::HasNodeAttr(kAttrStreamSwitchKind, cur_cnode_ptr)) { update_cnode_list.emplace_back(cur_cnode_ptr); continue; } - auto kind = AnfAlgo::GetNodeAttr(cur_cnode_ptr, kAttrStreamSwitchKind); + auto kind = common::AnfAlgo::GetNodeAttr(cur_cnode_ptr, kAttrStreamSwitchKind); if (kind != kFpBpStreamSwitch) { update_cnode_list.emplace_back(cur_cnode_ptr); continue; } - auto true_stream_id = AnfAlgo::GetNodeAttr(cur_cnode_ptr, kAttrTrueBranchStream); + auto true_stream_id = common::AnfAlgo::GetNodeAttr(cur_cnode_ptr, kAttrTrueBranchStream); MS_LOG(INFO) << "FpBpStreamswtich stream id:" << AnfAlgo::GetStreamId(cur_cnode_ptr) << "; true branch stream id:" << true_stream_id; CNodePtr active_ptr = KernelAdjust::GetInstance().CreateStreamActiveOp(graph_ptr); @@ -1036,7 +1041,7 @@ void AscendStreamAssign::ActiveRootGraphHcom(const NotNull &grap vector active_ids; // active hcom stream std::copy(hcom_streams.begin(), hcom_streams.end(), std::back_inserter(active_ids)); - AnfAlgo::SetNodeAttr(kAttrActiveStreamList, MakeValue>(active_ids), active_ptr); + common::AnfAlgo::SetNodeAttr(kAttrActiveStreamList, MakeValue>(active_ids), active_ptr); update_cnode_list.emplace_back(cur_cnode_ptr); update_cnode_list.emplace_back(active_ptr); std::copy(exe_orders.begin() + i + 1, exe_orders.end(), std::back_inserter(update_cnode_list)); @@ -1054,17 +1059,17 @@ void AscendStreamAssign::ActiveRootGraphIndependent(const NotNullexecution_order(); for (size_t i = 0; i < exe_orders.size(); i++) { CNodePtr cur_cnode_ptr = exe_orders[i]; - if (AnfAlgo::GetCNodeName(cur_cnode_ptr) != kStreamSwitchOpName) { + if (common::AnfAlgo::GetCNodeName(cur_cnode_ptr) != kStreamSwitchOpName) { update_cnode_list.emplace_back(cur_cnode_ptr); continue; } - if (!AnfAlgo::HasNodeAttr(kAttrStreamSwitchKind, cur_cnode_ptr)) { + if (!common::AnfAlgo::HasNodeAttr(kAttrStreamSwitchKind, cur_cnode_ptr)) { update_cnode_list.emplace_back(cur_cnode_ptr); continue; } - auto kind = AnfAlgo::GetNodeAttr(cur_cnode_ptr, kAttrStreamSwitchKind); + auto kind = common::AnfAlgo::GetNodeAttr(cur_cnode_ptr, kAttrStreamSwitchKind); if (kind != kIndependentStreamSwitch) { update_cnode_list.emplace_back(cur_cnode_ptr); continue; @@ -1072,7 +1077,7 @@ void AscendStreamAssign::ActiveRootGraphIndependent(const NotNull(first_independent_stream), cur_cnode_ptr); + common::AnfAlgo::SetNodeAttr(kAttrTrueBranchStream, MakeValue(first_independent_stream), cur_cnode_ptr); update_cnode_list.emplace_back(cur_cnode_ptr); std::copy(exe_orders.begin() + i + 1, exe_orders.end(), std::back_inserter(update_cnode_list)); break; @@ -1113,9 +1118,10 @@ void AscendStreamAssign::InsertStreamActiveForCommon(const NotNull active_index_list{cur_stream_id}; - AnfAlgo::SetNodeAttr(kAttrActiveStreamList, MakeValue>(active_index_list), active_ptr); + common::AnfAlgo::SetNodeAttr(kAttrActiveStreamList, MakeValue>(active_index_list), + active_ptr); if (i > 0) { - auto pre_node = AnfAlgo::GetCNodeName(cnode_ptr_list[i - 1]); + auto pre_node = common::AnfAlgo::GetCNodeName(cnode_ptr_list[i - 1]); if (pre_node == kLabelSwitchOpName || pre_node == kLabelGotoOpName) { update_cnode_list.insert(update_cnode_list.end() - 1, active_ptr); AnfAlgo::SetStreamId(cur_stream_id, cnode_ptr_list[i - 1].get()); @@ -1124,7 +1130,7 @@ void AscendStreamAssign::InsertStreamActiveForCommon(const NotNull active_index_list{*(it + 1)}; - AnfAlgo::SetNodeAttr(kAttrActiveStreamList, MakeValue>(active_index_list), active_ptr); + common::AnfAlgo::SetNodeAttr(kAttrActiveStreamList, MakeValue>(active_index_list), + active_ptr); update_cnode_list.emplace_back(active_ptr); node_num = 0; } @@ -1205,16 +1212,16 @@ void AscendStreamAssign::GetProcessedStream(const NotNull &graph auto cur_cnode_ptr = cnode_ptr_list[i]; uint32_t cur_stream_id = AnfAlgo::GetStreamId(cur_cnode_ptr); - if (AnfAlgo::GetCNodeName(cur_cnode_ptr) == kStreamSwitchOpName) { - if (AnfAlgo::HasNodeAttr(kAttrTrueBranchStream, cur_cnode_ptr)) { - auto true_stream_id = AnfAlgo::GetNodeAttr(cur_cnode_ptr, kAttrTrueBranchStream); + if (common::AnfAlgo::GetCNodeName(cur_cnode_ptr) == kStreamSwitchOpName) { + if (common::AnfAlgo::HasNodeAttr(kAttrTrueBranchStream, cur_cnode_ptr)) { + auto true_stream_id = common::AnfAlgo::GetNodeAttr(cur_cnode_ptr, kAttrTrueBranchStream); processed_streams_.emplace(true_stream_id); } - if (!AnfAlgo::HasNodeAttr(kStreamNeedActivedFirst, cur_cnode_ptr)) { + if (!common::AnfAlgo::HasNodeAttr(kStreamNeedActivedFirst, cur_cnode_ptr)) { continue; } - auto need_active = AnfAlgo::GetNodeAttr(cur_cnode_ptr, kStreamNeedActivedFirst); + auto need_active = common::AnfAlgo::GetNodeAttr(cur_cnode_ptr, kStreamNeedActivedFirst); if (need_active) { processed_streams_.emplace(cur_stream_id); } @@ -1226,20 +1233,20 @@ void AscendStreamAssign::GetProcessedStream(const NotNull &graph } bool AscendStreamAssign::CheckStreamSwitch(const CNodePtr &switch_ptr) { - if (!AnfAlgo::HasNodeAttr(kStreamNeedActivedFirst, switch_ptr)) { + if (!common::AnfAlgo::HasNodeAttr(kStreamNeedActivedFirst, switch_ptr)) { return false; } - auto need_active = AnfAlgo::GetNodeAttr(switch_ptr, kStreamNeedActivedFirst); + auto need_active = common::AnfAlgo::GetNodeAttr(switch_ptr, kStreamNeedActivedFirst); if (!need_active) { return false; } - if (!AnfAlgo::HasNodeAttr(kAttrStreamSwitchKind, switch_ptr)) { + if (!common::AnfAlgo::HasNodeAttr(kAttrStreamSwitchKind, switch_ptr)) { return false; } - auto kind = AnfAlgo::GetNodeAttr(switch_ptr, kAttrStreamSwitchKind); + auto kind = common::AnfAlgo::GetNodeAttr(switch_ptr, kAttrStreamSwitchKind); if (kind == kEosStreamSwitch || kind == kGetNextStreamSwitch) { return false; } @@ -1258,12 +1265,12 @@ bool AscendStreamAssign::IsProcessedStream(uint32_t stream_id) { bool AscendStreamAssign::IsAllOutGraphOut(const KernelGraphPtr &graph, const CNodePtr &cnode) { MS_EXCEPTION_IF_NULL(graph); MS_EXCEPTION_IF_NULL(cnode); - auto cnode_out_num = AnfAlgo::GetOutputTensorNum(cnode); - auto nodes = AnfAlgo::GetAllOutput(graph->output(), {prim::kPrimTupleGetItem}); + auto cnode_out_num = common::AnfAlgo::GetOutputTensorNum(cnode); + auto nodes = common::AnfAlgo::GetAllOutput(graph->output(), {prim::kPrimTupleGetItem}); std::set output_index_set; // Assign Communicate Op Memory firstly. for (const auto &node : nodes) { - auto item_with_index = AnfAlgo::VisitKernelWithReturnType(node, 0, true); + auto item_with_index = common::AnfAlgo::VisitKernelWithReturnType(node, 0, true); MS_EXCEPTION_IF_NULL(item_with_index.first); if (!item_with_index.first->isa() || !AnfUtils::IsRealKernel(item_with_index.first)) { continue; @@ -1281,7 +1288,7 @@ bool AscendStreamAssign::IsAllOutGraphOut(const KernelGraphPtr &graph, const CNo vector::iterator AscendStreamAssign::FindGraphEnd(vector::iterator begin, vector::iterator end) { while (begin != end) { - if (AnfAlgo::HasNodeAttr(kAttrFpBpEnd, *begin)) { + if (common::AnfAlgo::HasNodeAttr(kAttrFpBpEnd, *begin)) { MS_LOG(INFO) << "FpBp end op is " << (*begin)->fullname_with_scope(); return begin; } @@ -1304,8 +1311,8 @@ bool AscendStreamAssign::ExistStreamSendAfterLastHcomNode(const NotNullexecution_order(); for (int64_t i = cnodes.size() - 1; i >= 0; i--) { if (AnfAlgo::GetGraphId(cnodes[i].get()) == graph_id && IsHcom(cnodes[i])) { - return (AnfAlgo::GetCNodeName(cnodes[i]) == kSendOpName) || - ((i < SizeToLong(cnodes.size() - 1)) && AnfAlgo::GetCNodeName(cnodes[i + 1]) == kSendOpName); + return (common::AnfAlgo::GetCNodeName(cnodes[i]) == kSendOpName) || + ((i < SizeToLong(cnodes.size() - 1)) && common::AnfAlgo::GetCNodeName(cnodes[i + 1]) == kSendOpName); } } MS_LOG(INFO) << "There is no hcom nodes in graph " << graph_id << ", root graph: " << graph_ptr->graph_id(); @@ -1340,7 +1347,7 @@ void AscendStreamAssign::GraphLoopSync(const NotNull &root_graph if (AnfAlgo::GetGraphId((*iter).get()) != graph_id) { continue; } - auto node_name = AnfAlgo::GetCNodeName(*iter); + auto node_name = common::AnfAlgo::GetCNodeName(*iter); auto cnode = (*iter)->cast(); MS_EXCEPTION_IF_NULL(cnode); CNodePtr recv_cnode = CreateRecvApplyKernel(root_graph, cur_event_id, AnfAlgo::GetStreamId(cnode)); @@ -1348,7 +1355,7 @@ void AscendStreamAssign::GraphLoopSync(const NotNull &root_graph MS_LOG(INFO) << "Insert StreamRecv " << cur_event_id << " before node: " << (*iter)->fullname_with_scope(); iter = cnodes.insert(iter, recv_cnode); break; - } else if ((iter < cnodes.end() - 1) && AnfAlgo::GetCNodeName(*(iter + 1)) == kLabelSetOpName) { + } else if ((iter < cnodes.end() - 1) && common::AnfAlgo::GetCNodeName(*(iter + 1)) == kLabelSetOpName) { MS_LOG(INFO) << "Insert StreamRecv " << cur_event_id << "after node: " << (*iter)->fullname_with_scope(); iter = cnodes.insert(iter + 1, recv_cnode); break; @@ -1451,23 +1458,23 @@ void AscendStreamAssign::InsertEventHcomDependCommonBak(const NotNull inputs_cnode = GetLastInputCnode(graph_ptr, cur_cnode_ptr); if (inputs_cnode.empty()) { cnodes.emplace_back(cur_cnode_ptr); - MS_LOG(WARNING) << "Hcom op:" << AnfAlgo::GetCNodeName(cur_cnode_ptr) << " can't find inputs nodes"; + MS_LOG(WARNING) << "Hcom op:" << common::AnfAlgo::GetCNodeName(cur_cnode_ptr) << " can't find inputs nodes"; continue; } - MS_LOG(INFO) << "Current hcom:" << AnfAlgo::GetCNodeName(cur_cnode_ptr) + MS_LOG(INFO) << "Current hcom:" << common::AnfAlgo::GetCNodeName(cur_cnode_ptr) << "; inputs cnode size:" << inputs_cnode.size(); for (size_t j = 0; j < inputs_cnode.size(); j++) { auto &cur_input = inputs_cnode.at(j); - MS_LOG(INFO) << "The index:" << j << " input, name:" << AnfAlgo::GetCNodeName(cur_input); + MS_LOG(INFO) << "The index:" << j << " input, name:" << common::AnfAlgo::GetCNodeName(cur_input); uint32_t cur_event_id = resource_manager.ApplyNewEvent(); auto pre_stream_id = AnfAlgo::GetStreamId(cur_input); auto send = CreateSendApplyKernel(graph_ptr, cur_event_id, pre_stream_id); auto it = std::find(cnodes.begin(), cnodes.end(), cur_input); if (it == cnodes.end()) { - MS_LOG_EXCEPTION << "Hcom:" << AnfAlgo::GetCNodeName(cur_cnode_ptr) - << " can't find input node:" << AnfAlgo::GetCNodeName(cur_input); + MS_LOG_EXCEPTION << "Hcom:" << common::AnfAlgo::GetCNodeName(cur_cnode_ptr) + << " can't find input node:" << common::AnfAlgo::GetCNodeName(cur_input); } cnodes.insert(it + 1, send); uint32_t cur_stream_id = AnfAlgo::GetStreamId(cur_cnode_ptr); @@ -1495,7 +1502,7 @@ vector AscendStreamAssign::GetLastInputCnode(const NotNull AscendStreamAssign::GetInputKernels(const CNodePtr &cnode) { queue nop_nodes; auto inputs = cnode->inputs(); for (size_t i = 1; i < inputs.size(); i++) { - auto real_input = AnfAlgo::VisitKernel(inputs[i], 0); + auto real_input = common::AnfAlgo::VisitKernel(inputs[i], 0); auto node = real_input.first; MS_EXCEPTION_IF_NULL(node); - if (opt::IsNopNode(node)) { + if (common::AnfAlgo::IsNopNode(node)) { nop_nodes.push(node->cast()); while (!nop_nodes.empty()) { auto cur_node = nop_nodes.front(); nop_nodes.pop(); auto new_inputs = cur_node->inputs(); for (size_t j = 1; j < new_inputs.size(); j++) { - auto new_real_input = AnfAlgo::VisitKernel(new_inputs[j], 0); + auto new_real_input = common::AnfAlgo::VisitKernel(new_inputs[j], 0); auto new_node = new_real_input.first; MS_EXCEPTION_IF_NULL(new_node); - if (opt::IsNopNode(new_node)) { + if (common::AnfAlgo::IsNopNode(new_node)) { nop_nodes.push(new_node->cast()); } else if (new_node->isa()) { input_cnodes.emplace_back(new_node->cast()); @@ -1619,7 +1626,7 @@ std::vector>> AscendStreamAssign::GetStreamID uint32_t cur_stream_id = AnfAlgo::GetStreamId(cur_cnode); auto group_name = GetHcomGroup(cur_cnode); auto cur_graph_id = AnfAlgo::GetGraphId(cur_cnode.get()); - MS_LOG(INFO) << "Hcom node name:" << AnfAlgo::GetCNodeName(cur_cnode) << "; group:" << group_name + MS_LOG(INFO) << "Hcom node name:" << common::AnfAlgo::GetCNodeName(cur_cnode) << "; group:" << group_name << "; stream id:" << cur_stream_id; if (group_name != group || cur_graph_id != graph_id) { continue; @@ -1831,22 +1838,22 @@ void AscendStreamAssign::GetIndependentMaxTarget(const NotNull & for (size_t m = 1; m < inputs.size(); m++) { auto input = inputs[m]; MS_EXCEPTION_IF_NULL(input); - if (opt::IsNopNode(input)) { + if (common::AnfAlgo::IsNopNode(input)) { auto cnode = input->cast(); auto new_inputs = cnode->inputs(); for (size_t k = 1; k < new_inputs.size(); k++) { - auto new_real_input = AnfAlgo::VisitKernel(new_inputs[k], 0); + auto new_real_input = common::AnfAlgo::VisitKernel(new_inputs[k], 0); if (key == new_real_input.first.get()) { - MS_LOG(DEBUG) << "Nop node find max target op:" << AnfAlgo::GetCNodeName(cur_node); + MS_LOG(DEBUG) << "Nop node find max target op:" << common::AnfAlgo::GetCNodeName(cur_node); independent_targets_.emplace(target_node.get()); flag = true; break; } } } else { - auto real_input = AnfAlgo::VisitKernel(input, 0); + auto real_input = common::AnfAlgo::VisitKernel(input, 0); if (key == real_input.first.get()) { - MS_LOG(DEBUG) << "Find max target op:" << AnfAlgo::GetCNodeName(cur_node); + MS_LOG(DEBUG) << "Find max target op:" << common::AnfAlgo::GetCNodeName(cur_node); independent_targets_.emplace(target_node.get()); flag = true; } @@ -1893,11 +1900,11 @@ uint32_t AscendStreamAssign::GetMaxIndexTarget(const NotNull &gr uint32_t AscendStreamAssign::GetIndependentStreamSwitchStreamId(const NotNull &graph_ptr) { auto &exe_orders = graph_ptr->execution_order(); for (const auto &item : exe_orders) { - if (AnfAlgo::GetCNodeName(item) == kStreamSwitchOpName) { - if (!AnfAlgo::HasNodeAttr(kAttrStreamSwitchKind, item)) { + if (common::AnfAlgo::GetCNodeName(item) == kStreamSwitchOpName) { + if (!common::AnfAlgo::HasNodeAttr(kAttrStreamSwitchKind, item)) { continue; } - auto kind = AnfAlgo::GetNodeAttr(item, kAttrStreamSwitchKind); + auto kind = common::AnfAlgo::GetNodeAttr(item, kAttrStreamSwitchKind); if (kind == kIndependentStreamSwitch) { return AnfAlgo::GetStreamId(item); } @@ -1929,7 +1936,7 @@ void AscendStreamAssign::InsertCtrlForIndependentParallel(const NotNull active_index_list{independent_switch_stream}; - AnfAlgo::SetNodeAttr(kAttrActiveStreamList, MakeValue>(active_index_list), active_ptr); + common::AnfAlgo::SetNodeAttr(kAttrActiveStreamList, MakeValue>(active_index_list), active_ptr); std::vector update_cnode_list; std::copy(exe_orders.begin(), exe_orders.begin() + max_index + 1, std::back_inserter(update_cnode_list)); @@ -1947,11 +1954,11 @@ void AscendStreamAssign::GetNeedActiveStreams(const NotNull &gra for (size_t i = 0; i < cnode_ptr_list.size(); ++i) { cur_cnode_ptr = cnode_ptr_list[i]; MS_EXCEPTION_IF_NULL(cur_cnode_ptr); - if (!AnfAlgo::HasNodeAttr(kStreamNeedActivedFirst, cur_cnode_ptr)) { + if (!common::AnfAlgo::HasNodeAttr(kStreamNeedActivedFirst, cur_cnode_ptr)) { continue; } - auto need_active = AnfAlgo::GetNodeAttr(cur_cnode_ptr, kStreamNeedActivedFirst); + auto need_active = common::AnfAlgo::GetNodeAttr(cur_cnode_ptr, kStreamNeedActivedFirst); if (need_active) { auto stream_id = AnfAlgo::GetStreamId(cur_cnode_ptr); MS_LOG(INFO) << "Stream id:" << stream_id << " is need activated at first"; @@ -2007,7 +2014,7 @@ void AscendStreamAssign::CheckStreamAssign(const NotNull &graph_ MS_EXCEPTION_IF_NULL(cur_cnode_ptr); uint32_t stream_id = AnfAlgo::GetStreamId(cur_cnode_ptr); if (stream_id == kInvalidStreamId) { - MS_LOG(EXCEPTION) << "Node:" << AnfAlgo::GetCNodeName(cur_cnode_ptr) << "had not been assigned stream"; + MS_LOG(EXCEPTION) << "Node:" << common::AnfAlgo::GetCNodeName(cur_cnode_ptr) << "had not been assigned stream"; } (void)streams.emplace(stream_id); @@ -2041,9 +2048,9 @@ void AscendStreamAssign::CheckEventAssign(const NotNull &graph_p for (size_t i = 0; i < cnode_ptr_list.size(); ++i) { CNodePtr cur_cnode_ptr = cnode_ptr_list[i]; MS_EXCEPTION_IF_NULL(cur_cnode_ptr); - auto name = AnfAlgo::GetCNodeName(cur_cnode_ptr); + auto name = common::AnfAlgo::GetCNodeName(cur_cnode_ptr); if (name == kSendOpName || name == kRecvOpName) { - uint32_t event_id = AnfAlgo::GetNodeAttr(cur_cnode_ptr, kAttrEventId); + uint32_t event_id = common::AnfAlgo::GetNodeAttr(cur_cnode_ptr, kAttrEventId); if (event_id > max_event_id) { max_event_id = event_id; } @@ -2074,8 +2081,8 @@ void AscendStreamAssign::CheckEventAssign(const NotNull &graph_p MS_LOG(EXCEPTION) << "Send/recv should be in pair and share one event id, invalid event id is:" << item.first << ", event size is:" << item.second.size(); } - auto first_name = AnfAlgo::GetCNodeName(item.second[0]); - auto second_name = AnfAlgo::GetCNodeName(item.second[1]); + auto first_name = common::AnfAlgo::GetCNodeName(item.second[0]); + auto second_name = common::AnfAlgo::GetCNodeName(item.second[1]); if (!(first_name == kSendOpName && second_name == kRecvOpName)) { MS_LOG(EXCEPTION) << "Send should be before recv, invalid event id is:" << item.first; } @@ -2096,7 +2103,7 @@ CNodePtr AscendStreamAssign::CreateSendApplyKernel(const NotNull kernel::KernelBuildInfo::KernelBuildInfoBuilder selected_kernel_builder; selected_kernel_builder.SetKernelType(KernelType::RT_KERNEL); AnfAlgo::SetSelectKernelBuildInfo(selected_kernel_builder.Build(), send_node_ptr.get()); - AnfAlgo::SetNodeAttr(kAttrEventId, MakeValue(event_id), send_node_ptr); + common::AnfAlgo::SetNodeAttr(kAttrEventId, MakeValue(event_id), send_node_ptr); auto abstract_none = std::make_shared(); MS_EXCEPTION_IF_NULL(abstract_none); send_node_ptr->set_abstract(abstract_none); @@ -2116,7 +2123,7 @@ CNodePtr AscendStreamAssign::CreateRecvApplyKernel(const NotNull kernel::KernelBuildInfo::KernelBuildInfoBuilder selected_kernel_builder; selected_kernel_builder.SetKernelType(KernelType::RT_KERNEL); AnfAlgo::SetSelectKernelBuildInfo(selected_kernel_builder.Build(), recv_node_ptr.get()); - AnfAlgo::SetNodeAttr(kAttrEventId, MakeValue(event_id), recv_node_ptr); + common::AnfAlgo::SetNodeAttr(kAttrEventId, MakeValue(event_id), recv_node_ptr); AnfAlgo::SetStreamId(stream_id, recv_node_ptr.get()); auto abstract_none = std::make_shared(); MS_EXCEPTION_IF_NULL(abstract_none); @@ -2130,12 +2137,12 @@ bool AscendStreamAssign::IsNopNodeTarget(const AnfNodePtr &nop_node, const CNode auto cnode = nop_node->cast(); auto new_inputs = cnode->inputs(); for (size_t i = 1; i < new_inputs.size(); i++) { - if (opt::IsNopNode(new_inputs[i])) { + if (common::AnfAlgo::IsNopNode(new_inputs[i])) { if (IsNopNodeTarget(new_inputs[i], target_node, cur_node, exclude_hcom)) { return true; } } else { - auto new_real_input = AnfAlgo::VisitKernel(new_inputs[i], 0); + auto new_real_input = common::AnfAlgo::VisitKernel(new_inputs[i], 0); if (target_node == new_real_input.first) { if (!(exclude_hcom && IsHcom(cur_node))) { return true; @@ -2154,12 +2161,12 @@ vector::iterator AscendStreamAssign::FindTargetOp(vector::it for (size_t i = 1; i < inputs.size(); i++) { auto input = inputs[i]; MS_EXCEPTION_IF_NULL(input); - if (opt::IsNopNode(input)) { + if (common::AnfAlgo::IsNopNode(input)) { if (IsNopNodeTarget(input, node, *begin, exclude_hcom)) { return begin; } } else { - auto real_input = AnfAlgo::VisitKernel(input, 0); + auto real_input = common::AnfAlgo::VisitKernel(input, 0); if (node == real_input.first) { if (!(exclude_hcom && IsHcom(*begin))) { MS_LOG(DEBUG) << "Nop node find target op[" << (*begin)->DebugString() << "]"; @@ -2306,7 +2313,7 @@ void AscendStreamAssign::FindStreamRelations(const NotNull &grap auto exe_orders = graph_ptr->execution_order(); for (size_t i = 0; i < exe_orders.size(); i++) { auto cur_cnode = exe_orders[i]; - auto name = AnfAlgo::GetCNodeName(cur_cnode); + auto name = common::AnfAlgo::GetCNodeName(cur_cnode); if (name != kStreamSwitchOpName && name != kStreamActiveOpName) { continue; } @@ -2325,7 +2332,7 @@ void AscendStreamAssign::FindStreamRelations(const NotNull &grap void AscendStreamAssign::GetStreamSwitchStreamRelation(const CNodePtr &node_ptr) { MS_EXCEPTION_IF_NULL(node_ptr); auto cur_stream_id = AnfAlgo::GetStreamId(node_ptr); - auto true_stream_id = AnfAlgo::GetNodeAttr(node_ptr, kAttrTrueBranchStream); + auto true_stream_id = common::AnfAlgo::GetNodeAttr(node_ptr, kAttrTrueBranchStream); if (true_stream_id <= cur_stream_id) { MS_LOG(ERROR) << "StreamSwitch self stream id " << cur_stream_id << " is greater than true branch stream id:" << true_stream_id; @@ -2355,7 +2362,7 @@ void AscendStreamAssign::GetStreamActiveStreamRelation(const NotNull>(cur_cnode, kAttrActiveStreamList); + auto active_list = common::AnfAlgo::GetNodeAttr>(cur_cnode, kAttrActiveStreamList); if (kind == kHead) { uint32_t active_current_stream_id = GetStreamByActivedStream(cur_stream_id); if (active_current_stream_id == kInvalidStreamId) { @@ -2413,8 +2420,8 @@ StreamActiveKind AscendStreamAssign::GetStreamActiveKind(const NotNull= 0; i--) { auto cnode = exe_orders[IntToSize(i)]; - auto name = AnfAlgo::GetCNodeName(cnode); + auto name = common::AnfAlgo::GetCNodeName(cnode); if (name == kSendOpName || name == kRecvOpName) { continue; } @@ -2451,7 +2458,7 @@ StreamActiveKind AscendStreamAssign::GetStreamActiveKind(const NotNull &graph // find all event info for (size_t i = 0; i < exe_orders.size(); i++) { auto cur_cnode = exe_orders[i]; - auto name = AnfAlgo::GetCNodeName(cur_cnode); + auto name = common::AnfAlgo::GetCNodeName(cur_cnode); if (name == kSendOpName) { event_map_[cur_cnode] = {}; } if (name == kRecvOpName) { - auto recv_event_id = AnfAlgo::GetNodeAttr(cur_cnode, kAttrEventId); + auto recv_event_id = common::AnfAlgo::GetNodeAttr(cur_cnode, kAttrEventId); for (auto &item : event_map_) { - auto send_event_id = AnfAlgo::GetNodeAttr(item.first, kAttrEventId); + auto send_event_id = common::AnfAlgo::GetNodeAttr(item.first, kAttrEventId); if (recv_event_id == send_event_id) { item.second = cur_cnode; break; @@ -2582,7 +2589,7 @@ void AscendStreamAssign::FindEventRelations(const NotNull &graph MS_LOG(INFO) << "Satisfied event info"; for (const auto &item : event_map_) { - MS_LOG(INFO) << "Event_id:" << AnfAlgo::GetNodeAttr(item.first, kAttrEventId); + MS_LOG(INFO) << "Event_id:" << common::AnfAlgo::GetNodeAttr(item.first, kAttrEventId); } } @@ -2594,7 +2601,7 @@ void AscendStreamAssign::AdjustAtomicAddrCleanOrder(const NotNull &graph_ptr, const CNodePt auto iter = std::find(exec_order.begin(), exec_order.end(), do_mask_cnode); for (; iter != exec_order.end(); iter++) { auto cnode = *iter; - if ((AnfAlgo::GetCNodeName(cnode) != kDropoutGenMaskOpName && - AnfAlgo::GetCNodeName(cnode) != kDropoutGenMaskV3OpName) || + if ((common::AnfAlgo::GetCNodeName(cnode) != kDropoutGenMaskOpName && + common::AnfAlgo::GetCNodeName(cnode) != kDropoutGenMaskV3OpName) || !cnode->HasPrimalAttr(kAttrMicro)) { continue; } @@ -2640,8 +2647,8 @@ void AscendStreamAssign::InsertEventForMicroBatchIndependent(const NotNullexecution_order(); for (auto &cnode : exec_order) { - if (AnfAlgo::GetCNodeName(cnode) != kDropoutDoMaskOpName && - AnfAlgo::GetCNodeName(cnode) != kDropoutDoMaskV3OpName) { + if (common::AnfAlgo::GetCNodeName(cnode) != kDropoutDoMaskOpName && + common::AnfAlgo::GetCNodeName(cnode) != kDropoutDoMaskV3OpName) { continue; } if (!cnode->HasPrimalAttr(kAttrMicro)) { @@ -2676,7 +2683,7 @@ void AscendStreamAssign::InsertEventForMicroBatchIndependent(const NotNullPrintGraphExecuteOrder(); std::vector new_exec_order; for (auto &cnode : exec_order) { - auto cnode_name = AnfAlgo::GetCNodeName(cnode); + auto cnode_name = common::AnfAlgo::GetCNodeName(cnode); if (cnode_name == kDropoutDoMaskOpName || cnode_name == kDropoutDoMaskV3OpName) { auto send_iter = node_send_map.find(cnode); if (send_iter != node_send_map.end()) { diff --git a/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_stream_assign.h b/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_stream_assign.h index 6c4e4da1d08..1df99cfc088 100644 --- a/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_stream_assign.h +++ b/mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_stream_assign.h @@ -31,7 +31,7 @@ #include "runtime/rt_model.h" #include "runtime/stream.h" #include "backend/common/session/kernel_graph.h" -#include "utils/contract.h" +#include "include/common/utils/contract.h" namespace mindspore { namespace device { diff --git a/mindspore/ccsrc/plugin/device/ascend/hal/device/distribute/ascend_collective.h b/mindspore/ccsrc/plugin/device/ascend/hal/device/distribute/ascend_collective.h index d8e17afaf8b..75d653b0f8f 100644 --- a/mindspore/ccsrc/plugin/device/ascend/hal/device/distribute/ascend_collective.h +++ b/mindspore/ccsrc/plugin/device/ascend/hal/device/distribute/ascend_collective.h @@ -22,7 +22,7 @@ #include #include #include "hccl/hccl_types.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/dlopen_macro.h" namespace mindspore { namespace device { diff --git a/mindspore/ccsrc/plugin/device/ascend/hal/device/dump/data_dumper.cc b/mindspore/ccsrc/plugin/device/ascend/hal/device/dump/data_dumper.cc index e3137f311e7..72b19fb8e22 100644 --- a/mindspore/ccsrc/plugin/device/ascend/hal/device/dump/data_dumper.cc +++ b/mindspore/ccsrc/plugin/device/ascend/hal/device/dump/data_dumper.cc @@ -22,6 +22,7 @@ #include #include "utility" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "utils/convert_utils_base.h" #include "runtime/dev.h" #include "runtime/mem.h" @@ -30,7 +31,7 @@ #include "runtime/rt_model.h" #include "plugin/device/ascend/hal/device/ge_types_convert.h" #include "proto/op_mapping_info.pb.h" -#include "utils/comm_manager.h" +#include "include/common/utils/comm_manager.h" #include "utils/ms_context.h" #ifndef ENABLE_SECURITY #include "debug/data_dump/dump_json_parser.h" @@ -82,9 +83,9 @@ void DataDumper::GetNeedDumpKernelList(NotNull * MS_EXCEPTION_IF_NULL(kernel); if (AnfAlgo::GetKernelType(kernel) == HCCL_KERNEL && DumpJsonParser::GetInstance().NeedDump(kernel->fullname_with_scope())) { - auto input_size = AnfAlgo::GetInputTensorNum(kernel); + auto input_size = common::AnfAlgo::GetInputTensorNum(kernel); for (size_t i = 0; i < input_size; ++i) { - auto input_with_index = AnfAlgo::GetPrevNodeOutput(kernel, i); + auto input_with_index = common::AnfAlgo::GetPrevNodeOutput(kernel, i); auto input = input_with_index.first; MS_EXCEPTION_IF_NULL(input); if (input->isa()) { @@ -262,7 +263,7 @@ void DataDumper::ConstructDumpTask(NotNull kernel, NotNullset_end_graph(false); auto iter = runtime_info_map_.find(kernel->UniqueName()); if (iter == runtime_info_map_.end()) { - if (AnfAlgo::IsNonTaskOp(kernel.get())) { + if (common::AnfAlgo::IsNonTaskOp(kernel.get())) { MS_LOG(INFO) << "[DataDump] kernel [" << kernel->UniqueName() << "] is a non-task node, skip dump."; return; } @@ -280,7 +281,7 @@ void DataDumper::ConstructDumpTask(NotNull kernel, NotNullset_stream_id(stream_id); MS_EXCEPTION_IF_NULL(dump_task->mutable_op()); dump_task->mutable_op()->set_op_name(kernel->fullname_with_scope()); - dump_task->mutable_op()->set_op_type(AnfAlgo::GetCNodeName(kernel.get())); + dump_task->mutable_op()->set_op_type(common::AnfAlgo::GetCNodeName(kernel.get())); #ifndef ENABLE_SECURITY DumpKernelOutput(kernel, args, dump_task); @@ -425,14 +426,14 @@ void DataDumper::DumpKernelOutput(const CNodePtr &kernel, void *args, NotNullfullname_with_scope(); - auto input_size = AnfAlgo::GetInputTensorNum(kernel); - auto output_size = AnfAlgo::GetOutputTensorNum(kernel); + auto input_size = common::AnfAlgo::GetInputTensorNum(kernel); + auto output_size = common::AnfAlgo::GetOutputTensorNum(kernel); uint64_t offset = sizeof(void *) * input_size; for (size_t i = 0; i < output_size; ++i) { auto data_type = AnfAlgo::GetOutputDeviceDataType(kernel, i); auto output_format = AnfAlgo::GetOutputFormat(kernel, i); auto output_shape = AnfAlgo::GetOutputDeviceShape(kernel, i); - auto output_origin_shape = AnfAlgo::GetOutputInferShape(kernel, i); + auto output_origin_shape = common::AnfAlgo::GetOutputInferShape(kernel, i); aicpu::dump::Output output; output.set_data_type(GeTypesConvert::GetGeDataType(data_type)); @@ -459,29 +460,29 @@ void DataDumper::DumpKernelInput(const CNodePtr &kernel, void *args, NotNullfullname_with_scope(); return; } MS_LOG(INFO) << "[DataDump] DumpKernelInput start. Kernel:" << kernel->fullname_with_scope(); - auto input_size = AnfAlgo::GetInputTensorNum(kernel); + auto input_size = common::AnfAlgo::GetInputTensorNum(kernel); uint64_t offset = 0; for (size_t i = 0; i < input_size; ++i) { - if (AnfAlgo::IsNoneInput(kernel, i)) { + if (common::AnfAlgo::IsNoneInput(kernel, i)) { continue; } aicpu::dump::Input input; - auto input_node_with_index = AnfAlgo::GetPrevNodeOutput(kernel, i); + auto input_node_with_index = common::AnfAlgo::GetPrevNodeOutput(kernel, i); auto input_node = input_node_with_index.first; auto input_index = input_node_with_index.second; std::string output_format = AnfAlgo::GetOutputFormat(input_node, input_index); auto output_type = AnfAlgo::GetOutputDeviceDataType(input_node, input_index); if (output_type == kTypeUnknown) { MS_LOG(WARNING) << "[DataDump] It is not suggested to use a lonely weight parameter as the output of graph"; - output_type = AnfAlgo::GetOutputInferDataType(input_node, input_index); + output_type = common::AnfAlgo::GetOutputInferDataType(input_node, input_index); } auto output_shape = AnfAlgo::GetOutputDeviceShape(input_node, input_index); - auto output_origin_shape = AnfAlgo::GetOutputInferShape(input_node, input_index); + auto output_origin_shape = common::AnfAlgo::GetOutputInferShape(input_node, input_index); input.set_data_type(GeTypesConvert::GetGeDataType(output_type)); input.set_format(GeTypesConvert::GetGeFormat(output_format, output_shape.size())); diff --git a/mindspore/ccsrc/plugin/device/ascend/hal/device/executor/ai_core_dynamic_kernel.cc b/mindspore/ccsrc/plugin/device/ascend/hal/device/executor/ai_core_dynamic_kernel.cc index 13a767d6536..3da6c12ed26 100644 --- a/mindspore/ccsrc/plugin/device/ascend/hal/device/executor/ai_core_dynamic_kernel.cc +++ b/mindspore/ccsrc/plugin/device/ascend/hal/device/executor/ai_core_dynamic_kernel.cc @@ -27,7 +27,7 @@ #include "runtime/mem.h" #include "pipeline/jit/static_analysis/static_analysis.h" #include "plugin/device/ascend/hal/device/executor/tiling/op_tiling_adapter.h" -#include "utils/ms_device_shape_transfer.h" +#include "runtime/device/ms_device_shape_transfer.h" #include "plugin/device/ascend/kernel/tbe/tbe_utils.h" #include "acl/acl_rt.h" @@ -46,7 +46,7 @@ AiCoreDynamicKernel::~AiCoreDynamicKernel() { bool AiCoreDynamicKernel::NeedSkipExecute(const CNodePtr &cnode) { // Skip run ReduceSum when axis is a Empty Tensor MS_EXCEPTION_IF_NULL(cnode); - auto op_name = AnfAlgo::GetCNodeName(cnode); + auto op_name = common::AnfAlgo::GetCNodeName(cnode); if (op_name != "ReduceSum") { return false; } @@ -84,8 +84,8 @@ void AiCoreDynamicKernel::Execute() { if (status != RT_ERROR_NONE) { MS_LOG(EXCEPTION) << "rtMemcpyAsync failed for " << node_info; } - std::vector dtypes{AnfAlgo::GetOutputInferDataType(cnode, 0)}; - AnfAlgo::SetOutputInferTypeAndShape(dtypes, {AnfAlgo::GetInputDeviceShape(cnode, 0)}, cnode.get()); + std::vector dtypes{common::AnfAlgo::GetOutputInferDataType(cnode, 0)}; + common::AnfAlgo::SetOutputInferTypeAndShape(dtypes, {AnfAlgo::GetInputDeviceShape(cnode, 0)}, cnode.get()); MS_LOG(INFO) << "Execute node:" << cnode->fullname_with_scope() << " success."; return; @@ -111,7 +111,7 @@ void AiCoreDynamicKernel::Execute() { void AiCoreDynamicKernel::ParseCompileJson() { auto cnode = cnode_ptr_.lock(); MS_EXCEPTION_IF_NULL(cnode); - if (!AnfAlgo::IsDynamicShape(cnode)) { + if (!common::AnfAlgo::IsDynamicShape(cnode)) { return; } bool get_flag = true; diff --git a/mindspore/ccsrc/plugin/device/ascend/hal/device/executor/ai_cpu_dynamic_kernel.cc b/mindspore/ccsrc/plugin/device/ascend/hal/device/executor/ai_cpu_dynamic_kernel.cc index 4fecc8bb41e..085df484403 100644 --- a/mindspore/ccsrc/plugin/device/ascend/hal/device/executor/ai_cpu_dynamic_kernel.cc +++ b/mindspore/ccsrc/plugin/device/ascend/hal/device/executor/ai_cpu_dynamic_kernel.cc @@ -20,8 +20,9 @@ #include "runtime/mem.h" #include "acl/acl_rt.h" #include "runtime/kernel.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "plugin/device/ascend/kernel/aicpu/aicpu_util.h" namespace mindspore { @@ -71,11 +72,11 @@ void AiCpuDynamicKernel::Initialize() { MS_LOG(INFO) << "Initialize node:" << cnode->fullname_with_scope(); DynamicKernel::Initialize(); - input_num_ = AnfAlgo::GetInputTensorNum(cnode); - output_num_ = AnfAlgo::GetOutputTensorNum(cnode); + input_num_ = common::AnfAlgo::GetInputTensorNum(cnode); + output_num_ = common::AnfAlgo::GetOutputTensorNum(cnode); UnknowShapeOpType shape_type = UnknowShapeOpType::DEPEND_IN_SHAPE; - auto op_name = AnfAlgo::GetCNodeName(cnode); + auto op_name = common::AnfAlgo::GetCNodeName(cnode); if (kComputeDepend.find(op_name) != kComputeDepend.end()) { shape_type = UnknowShapeOpType::DEPEND_COMPUTE; } @@ -167,7 +168,7 @@ bool AiCpuDynamicKernel::UpdateExtInfo() { } } - if (AnfAlgo::IsDynamicShape(cnode) && unknow_type_ != DEPEND_COMPUTE) { + if (common::AnfAlgo::IsDynamicShape(cnode) && unknow_type_ != DEPEND_COMPUTE) { for (size_t i = 0; i < output_num_; ++i) { if (!ext_info_handler_->UpdateOutputShapeAndType(i, NOT_NULL(cnode))) { MS_LOG(ERROR) << "Update output shape failed, cnode:" << cnode->fullname_with_scope() << " output:" << i; @@ -214,7 +215,7 @@ bool AiCpuDynamicKernel::UpdateOutputShapeFromExtInfo() { shapes.emplace_back(size_t_shape); } - AnfAlgo::SetOutputInferTypeAndShape(type_ids, shapes, cnode_ptr_.lock().get()); + common::AnfAlgo::SetOutputInferTypeAndShape(type_ids, shapes, cnode_ptr_.lock().get()); return true; } @@ -228,7 +229,7 @@ void AiCpuDynamicKernel::PostExecute() { if (RT_ERROR_NONE != rtStreamSynchronize(stream_)) { MS_LOG(EXCEPTION) << "Call runtime rtStreamSynchronize failed. Op name: " << cnode->fullname_with_scope(); } - if (AnfAlgo::IsDynamicShape(cnode)) { + if (common::AnfAlgo::IsDynamicShape(cnode)) { MS_LOG(INFO) << "Update aicpu kernel output shape from ext_info. Op name: " << cnode->fullname_with_scope(); UpdateOutputShapeFromExtInfo(); } diff --git a/mindspore/ccsrc/plugin/device/ascend/hal/device/executor/aicpu_ext_info_handle.cc b/mindspore/ccsrc/plugin/device/ascend/hal/device/executor/aicpu_ext_info_handle.cc index a90aa04f41e..2fbadb62009 100644 --- a/mindspore/ccsrc/plugin/device/ascend/hal/device/executor/aicpu_ext_info_handle.cc +++ b/mindspore/ccsrc/plugin/device/ascend/hal/device/executor/aicpu_ext_info_handle.cc @@ -17,6 +17,7 @@ #include "plugin/device/ascend/hal/device/executor/aicpu_ext_info_handle.h" #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "plugin/device/ascend/kernel/aicpu/aicpu_util.h" namespace mindspore { @@ -171,7 +172,7 @@ bool AicpuExtInfoHandler::UpdateOutputShapeAndType(uint32_t output_index, const } auto shape = AnfAlgo::GetOutputDeviceShape(anf_node, output_index); - auto max_shape = AnfAlgo::GetOutputMaxShape(anf_node, output_index); + auto max_shape = common::AnfAlgo::GetOutputMaxShape(anf_node, output_index); if (shape.size() != max_shape.size()) { MS_LOG(ERROR) << "shape size [" << shape.size() << "] != max_shape size [" << max_shape.size() << "], node: " << node_name_; diff --git a/mindspore/ccsrc/plugin/device/ascend/hal/device/executor/aicpu_ext_info_handle.h b/mindspore/ccsrc/plugin/device/ascend/hal/device/executor/aicpu_ext_info_handle.h index e71a6e129ba..e5b1217909f 100644 --- a/mindspore/ccsrc/plugin/device/ascend/hal/device/executor/aicpu_ext_info_handle.h +++ b/mindspore/ccsrc/plugin/device/ascend/hal/device/executor/aicpu_ext_info_handle.h @@ -22,7 +22,7 @@ #include #include #include "plugin/device/ascend/kernel/aicpu/aicpu_util.h" -#include "utils/contract.h" +#include "include/common/utils/contract.h" namespace mindspore { namespace device { diff --git a/mindspore/ccsrc/plugin/device/ascend/hal/device/executor/tiling/op_tiling_adapter.cc b/mindspore/ccsrc/plugin/device/ascend/hal/device/executor/tiling/op_tiling_adapter.cc index 6fa4a5a367f..79d1d7c401b 100644 --- a/mindspore/ccsrc/plugin/device/ascend/hal/device/executor/tiling/op_tiling_adapter.cc +++ b/mindspore/ccsrc/plugin/device/ascend/hal/device/executor/tiling/op_tiling_adapter.cc @@ -19,8 +19,9 @@ #include "plugin/device/ascend/kernel/tbe/tbe_kernel_build.h" #include "plugin/device/ascend/kernel/tbe/tbe_dynaminc_shape_util.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "plugin/device/ascend/hal/device/ge_types_convert.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "external/graph/tensor.h" #include "external/register/op_tiling_registry.h" #include "graph/utils/graph_utils.h" @@ -81,14 +82,14 @@ std::string OpTilingCalculateAdapter::GetInputName(const CNodePtr &node, size_t void OpTilingCalculateAdapter::ConvertInputShapeAndType(const CNodePtr &node, ge::OpDescPtr *op_desc) { MS_EXCEPTION_IF_NULL(node); MS_EXCEPTION_IF_NULL(*op_desc); - auto input_size = AnfAlgo::GetInputTensorNum(node); + auto input_size = common::AnfAlgo::GetInputTensorNum(node); for (size_t i = 0; i < input_size; i++) { // ms info auto real_index = AnfAlgo::GetRealInputIndex(node, i); - auto input_node_with_idx = AnfAlgo::GetPrevNodeOutput(node, real_index); + auto input_node_with_idx = common::AnfAlgo::GetPrevNodeOutput(node, real_index); auto input_node = input_node_with_idx.first; auto input_index = input_node_with_idx.second; - auto ms_ori_shape = AnfAlgo::GetOutputInferShape(input_node, input_index); + auto ms_ori_shape = common::AnfAlgo::GetOutputInferShape(input_node, input_index); auto ms_shape = AnfAlgo::GetOutputDeviceShape(input_node, input_index); auto ms_format = AnfAlgo::GetOutputFormat(input_node, input_index); auto ms_dtype = AnfAlgo::GetOutputDeviceDataType(input_node, input_index); @@ -117,10 +118,10 @@ void OpTilingCalculateAdapter::ConvertInputShapeAndType(const CNodePtr &node, ge void OpTilingCalculateAdapter::ConvertOutputShapeAndType(const CNodePtr &node, ge::OpDescPtr *op_desc) { MS_EXCEPTION_IF_NULL(node); MS_EXCEPTION_IF_NULL(*op_desc); - auto output_size = AnfAlgo::GetOutputTensorNum(node); + auto output_size = common::AnfAlgo::GetOutputTensorNum(node); for (size_t i = 0; i < output_size; i++) { auto ms_shape = AnfAlgo::GetOutputDeviceShape(node, i); - auto ms_ori_shape = AnfAlgo::GetOutputInferShape(node, i); + auto ms_ori_shape = common::AnfAlgo::GetOutputInferShape(node, i); auto ms_format = AnfAlgo::GetOutputFormat(node, i); auto ms_dtype = AnfAlgo::GetOutputDeviceDataType(node, i); @@ -160,7 +161,7 @@ ge::NodePtr OpTilingCalculateAdapter::NewConstantOp(const CNodePtr &node, const MS_EXCEPTION_IF_NULL(tensor_data); ge::OpDescPtr op_desc = std::make_shared(name, CONSTANTOP); auto ms_format = AnfAlgo::GetPrevNodeOutputFormat(node, index); - auto ms_ori_shape = AnfAlgo::GetPrevNodeOutputInferShape(node, index); + auto ms_ori_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node, index); auto ms_dtype = AnfAlgo::GetPrevNodeOutputDeviceDataType(node, index); auto ms_shape = AnfAlgo::GetInputDeviceShape(node, index); @@ -197,12 +198,12 @@ std::vector> OpTilingCalculateAdapter::Conv MS_LOG(INFO) << "The node " << op_name_ << " has no infer depend "; return {}; } - auto has_input_name_attr = AnfAlgo::HasNodeAttr("input_names", node); + auto has_input_name_attr = common::AnfAlgo::HasNodeAttr("input_names", node); if (!has_input_name_attr) { MS_LOG(EXCEPTION) << "Node should has attr: input_names. " << node->fullname_with_scope(); } - auto input_names_attr = AnfAlgo ::GetNodeAttr>(node, "input_names"); + auto input_names_attr = common::AnfAlgo::GetNodeAttr>(node, "input_names"); std::vector op_infer_depends; std::vector> constant_ops; for (auto index : depends_list_me) { @@ -242,7 +243,7 @@ void OpTilingCalculateAdapter::InitOpIoName(const CNodePtr &node) { MS_LOG(INFO) << "Get the every input name of " << op_name_; auto op_info_ptr = mindspore::kernel::tbe::TbeDynamicShapeUtil::FindOp(op_name_, node); MS_EXCEPTION_IF_NULL(op_info_ptr); - auto primitive = AnfAlgo::GetCNodePrimitive(node); + auto primitive = common::AnfAlgo::GetCNodePrimitive(node); MS_EXCEPTION_IF_NULL(primitive); auto inputs_ptr = op_info_ptr->inputs_ptr(); size_t dynamic_input_index = 0; @@ -279,7 +280,7 @@ ge::Operator OpTilingCalculateAdapter::AnfNodeToGeNodeAdapter( const CNodePtr &node, ge::ComputeGraphPtr *ge_graph, const std::map &depend_tensor_map, const std::string &op_compile_info) { MS_EXCEPTION_IF_NULL(node); - op_name_ = AnfAlgo::GetCNodeName(node); + op_name_ = common::AnfAlgo::GetCNodeName(node); MS_LOG(INFO) << "Convert anf node :" << op_name_ << " to ge node."; op_compile_info_ = op_compile_info; auto op_type = GetRealOpType(op_name_); diff --git a/mindspore/ccsrc/plugin/device/ascend/hal/device/ge_types_convert.h b/mindspore/ccsrc/plugin/device/ascend/hal/device/ge_types_convert.h index 5a24f85d2b9..fce4583c9f3 100644 --- a/mindspore/ccsrc/plugin/device/ascend/hal/device/ge_types_convert.h +++ b/mindspore/ccsrc/plugin/device/ascend/hal/device/ge_types_convert.h @@ -21,7 +21,7 @@ #include #include "proto/ge_dtype.pb.h" #include "ir/dtype/type_id.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "external/graph/types.h" namespace mindspore { diff --git a/mindspore/ccsrc/plugin/device/ascend/hal/device/kernel_build_ascend.cc b/mindspore/ccsrc/plugin/device/ascend/hal/device/kernel_build_ascend.cc index 56f59e1aa40..38d660ac122 100644 --- a/mindspore/ccsrc/plugin/device/ascend/hal/device/kernel_build_ascend.cc +++ b/mindspore/ccsrc/plugin/device/ascend/hal/device/kernel_build_ascend.cc @@ -129,15 +129,15 @@ bool IsAtomicNode(const CNodePtr &kernel_node) { if (parameters_indexes.empty()) { return false; } - if (AnfAlgo::IsDynamicShape(kernel_node)) { + if (common::AnfAlgo::IsDynamicShape(kernel_node)) { if (parameters_indexes.at(0) == 1) { (void)parameters_indexes.erase(parameters_indexes.begin()); } else { parameters_indexes.pop_back(); } } - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); size_t workspace_num = kernel_mod->GetWorkspaceSizeList().size(); size_t param_num = parameters_indexes.size(); size_t total_num = input_num + output_num + workspace_num; @@ -159,8 +159,8 @@ bool IsAtomicNode(const CNodePtr &kernel_node) { } // process output std::vector output_indexes = {}; - if (AnfAlgo::HasNodeAttr(kAttrAtomicOutputIndexs, kernel_node)) { - output_indexes = AnfAlgo::GetNodeAttr>(kernel_node, kAttrAtomicOutputIndexs); + if (common::AnfAlgo::HasNodeAttr(kAttrAtomicOutputIndexs, kernel_node)) { + output_indexes = common::AnfAlgo::GetNodeAttr>(kernel_node, kAttrAtomicOutputIndexs); } for (size_t i = 0; i < output_num; ++i) { @@ -174,7 +174,7 @@ bool IsAtomicNode(const CNodePtr &kernel_node) { if (!output_indexes.empty()) { std::set s(output_indexes.begin(), output_indexes.end()); output_indexes.assign(s.begin(), s.end()); - AnfAlgo::SetNodeAttr(kAttrAtomicOutputIndexs, MakeValue(output_indexes), kernel_node); + common::AnfAlgo::SetNodeAttr(kAttrAtomicOutputIndexs, MakeValue(output_indexes), kernel_node); } // process workspace std::vector workspace_indexes = {}; @@ -186,7 +186,7 @@ bool IsAtomicNode(const CNodePtr &kernel_node) { } } if (!workspace_indexes.empty()) { - AnfAlgo::SetNodeAttr(kAttrAtomicWorkspaceIndexs, MakeValue(workspace_indexes), kernel_node); + common::AnfAlgo::SetNodeAttr(kAttrAtomicWorkspaceIndexs, MakeValue(workspace_indexes), kernel_node); } return !(workspace_indexes.empty() && output_indexes.empty()); } @@ -210,8 +210,8 @@ std::vector GetClearSize(const CNodePtr &pre_node) { std::vector clean_size_list; constexpr size_t kAlignBytes = 32 - 1; // clean output - if (AnfAlgo::HasNodeAttr(kAttrAtomicOutputIndexs, pre_node)) { - auto output_indexes = AnfAlgo::GetNodeAttr>(pre_node, kAttrAtomicOutputIndexs); + if (common::AnfAlgo::HasNodeAttr(kAttrAtomicOutputIndexs, pre_node)) { + auto output_indexes = common::AnfAlgo::GetNodeAttr>(pre_node, kAttrAtomicOutputIndexs); auto output_men_size = kernel_mod->GetOutputSizeList(); for (auto index : output_indexes) { auto clean_item = (output_men_size.at(index) + kMemAlignSize + kAlignBytes) / kMemAlignSize * kMemAlignSize; @@ -219,8 +219,8 @@ std::vector GetClearSize(const CNodePtr &pre_node) { } } // clean workspace - if (AnfAlgo::HasNodeAttr(kAttrAtomicWorkspaceIndexs, pre_node)) { - auto workspace_indexes = AnfAlgo::GetNodeAttr>(pre_node, kAttrAtomicWorkspaceIndexs); + if (common::AnfAlgo::HasNodeAttr(kAttrAtomicWorkspaceIndexs, pre_node)) { + auto workspace_indexes = common::AnfAlgo::GetNodeAttr>(pre_node, kAttrAtomicWorkspaceIndexs); auto workspace_men_sizes = kernel_mod->GetWorkspaceSizeList(); for (const auto &index : workspace_indexes) { auto clean_item = (workspace_men_sizes.at(index) + kMemAlignSize + kAlignBytes) / kMemAlignSize * kMemAlignSize; @@ -260,11 +260,11 @@ void InsertFusionAtomicOp(const CNodePtr &first_clear_node, const std::vectorset_kernel_info(kernel_info); AbstractBasePtr abstract = std::make_shared(); MS_EXCEPTION_IF_NULL(abstract); - AnfAlgo::SetNodeAttr("input_names", MakeValue(std::vector({"x"})), clear_zero); + common::AnfAlgo::SetNodeAttr("input_names", MakeValue(std::vector({"x"})), clear_zero); SelectKernelInfo(clear_zero); // set the distinction label of clear same with anf AnfAlgo::SetStreamDistinctionLabel(AnfAlgo::GetStreamDistinctionLabel(node.get()), clear_zero.get()); MS_LOG(DEBUG) << "The AtomicClean currently does not support dynamic shape."; - AnfAlgo::SetNodeAttr(kAttrInputIsDynamicShape, MakeValue(false), clear_zero); - AnfAlgo::SetNodeAttr(kAttrOutputIsDynamicShape, MakeValue(false), clear_zero); + common::AnfAlgo::SetNodeAttr(kAttrInputIsDynamicShape, MakeValue(false), clear_zero); + common::AnfAlgo::SetNodeAttr(kAttrOutputIsDynamicShape, MakeValue(false), clear_zero); (*clean_ops)[node].emplace_back(clear_zero); } } @@ -319,9 +319,10 @@ void ProcessAtomicFusion(const std::vector &kernels, CleanOpsMap *clea CNodePtr first_node = nullptr; for (const auto &anf_node : kernels) { MS_EXCEPTION_IF_NULL(anf_node); - std::string apply_function_name = AnfAlgo::GetCNodeName(anf_node); + std::string apply_function_name = common::AnfAlgo::GetCNodeName(anf_node); SpecialAkgOps(apply_function_name, anf_node, clean_ops); - if (AnfAlgo::HasNodeAttr(kAttrNeedAtomic, anf_node) && AnfAlgo::GetNodeAttr(anf_node, kAttrNeedAtomic)) { + if (common::AnfAlgo::HasNodeAttr(kAttrNeedAtomic, anf_node) && + common::AnfAlgo::GetNodeAttr(anf_node, kAttrNeedAtomic)) { auto clean_sizes = GetClearSize(anf_node); if (!clean_sizes.empty()) { auto clean_total_num = clean_size_list.size() + clean_sizes.size(); @@ -358,9 +359,10 @@ void InsertAtomicOps(const std::vector &kernels, CleanOpsMap *clean_op } // single for (const auto &node : kernels) { - std::string apply_function_name = AnfAlgo::GetCNodeName(node); + std::string apply_function_name = common::AnfAlgo::GetCNodeName(node); SpecialAkgOps(apply_function_name, node, clean_ops); - if (AnfAlgo::HasNodeAttr(kAttrNeedAtomic, node) && AnfAlgo::GetNodeAttr(node, kAttrNeedAtomic)) { + if (common::AnfAlgo::HasNodeAttr(kAttrNeedAtomic, node) && + common::AnfAlgo::GetNodeAttr(node, kAttrNeedAtomic)) { InsertAtomicOpForNormalOp(node, clean_ops); } } @@ -370,19 +372,19 @@ std::map> GetCommunicationOpInputInfo(const std: std::map> comm_input_info_map; for (auto &kernel : kernels) { MS_EXCEPTION_IF_NULL(kernel); - auto input_num = AnfAlgo::GetInputTensorNum(kernel); - if (mindspore::session::AnfRuntimeAlgorithm::IsCommunicationOp(kernel)) { + auto input_num = common::AnfAlgo::GetInputTensorNum(kernel); + if (common::AnfAlgo::IsCommunicationOp(kernel)) { for (size_t i = 0; i < input_num; i++) { auto input_node = kernel->input(i + 1); - auto kernel_input = AnfAlgo::VisitKernelWithReturnType(input_node, 0, true); + auto kernel_input = common::AnfAlgo::VisitKernelWithReturnType(input_node, 0, true); MS_EXCEPTION_IF_NULL(kernel_input.first); if (!kernel_input.first->isa()) { continue; } auto cnode = kernel_input.first->cast(); MS_EXCEPTION_IF_NULL(cnode); - if (AnfAlgo::IsCommunicationOp(cnode) || AnfAlgo::IsIndependentNode(cnode) || - AnfAlgo::GetCNodeName(cnode) == kGetNextOpName) { + if (common::AnfAlgo::IsCommunicationOp(cnode) || AnfAlgo::IsIndependentNode(cnode) || + common::AnfAlgo::GetCNodeName(cnode) == kGetNextOpName) { // no need to add atomic for communication or independent or get_next op's output MS_LOG(INFO) << "No need insert atomic clean for op " << cnode->fullname_with_scope() << "'s output"; continue; @@ -417,16 +419,16 @@ void TagNeedInsertAtomicAttr(const std::vector &nodes) { for (const auto &anf_node : nodes) { if (comm_input_info_map.find(anf_node) != comm_input_info_map.end()) { auto indexes = comm_input_info_map[anf_node]; - if (AnfAlgo::HasNodeAttr(kAttrAtomicOutputIndexs, anf_node)) { - auto output_indexes = AnfAlgo::GetNodeAttr>(anf_node, kAttrAtomicOutputIndexs); + if (common::AnfAlgo::HasNodeAttr(kAttrAtomicOutputIndexs, anf_node)) { + auto output_indexes = common::AnfAlgo::GetNodeAttr>(anf_node, kAttrAtomicOutputIndexs); std::copy(indexes.begin(), indexes.end(), std::back_inserter(output_indexes)); std::set tmp(output_indexes.begin(), output_indexes.end()); indexes.assign(tmp.begin(), tmp.end()); } - AnfAlgo::SetNodeAttr(kAttrAtomicOutputIndexs, MakeValue(indexes), anf_node); - AnfAlgo::SetNodeAttr(kAttrNeedAtomic, MakeValue(true), anf_node); + common::AnfAlgo::SetNodeAttr(kAttrAtomicOutputIndexs, MakeValue(indexes), anf_node); + common::AnfAlgo::SetNodeAttr(kAttrNeedAtomic, MakeValue(true), anf_node); } else if (AnfAlgo::GetKernelType(anf_node) == KernelType::TBE_KERNEL && IsAtomicNode(anf_node)) { - AnfAlgo::SetNodeAttr(kAttrNeedAtomic, MakeValue(true), anf_node); + common::AnfAlgo::SetNodeAttr(kAttrNeedAtomic, MakeValue(true), anf_node); } } } diff --git a/mindspore/ccsrc/plugin/device/ascend/hal/device/kernel_select_ascend.cc b/mindspore/ccsrc/plugin/device/ascend/hal/device/kernel_select_ascend.cc index 406bccec5e5..6d51eb79584 100644 --- a/mindspore/ccsrc/plugin/device/ascend/hal/device/kernel_select_ascend.cc +++ b/mindspore/ccsrc/plugin/device/ascend/hal/device/kernel_select_ascend.cc @@ -31,13 +31,14 @@ #include "plugin/device/ascend/kernel/aicpu/aicpu_attr_to_input_registry.h" #include "backend/common/optimizer/helper.h" #include "backend/common/session/anf_runtime_algorithm.h" -#include "utils/ms_device_shape_transfer.h" +#include "include/common/utils/anfalgo.h" +#include "runtime/device/ms_device_shape_transfer.h" #include "debug/anf_ir_dump.h" #include "frontend/operator/ops.h" #include "utils/ms_context.h" #include "utils/ms_utils.h" #include "utils/trace_base.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" namespace mindspore { namespace device { @@ -63,14 +64,15 @@ bool MatchInferOutputDataType(const CNodePtr &cnode, const kernel::KernelBuildIn MS_EXCEPTION_IF_NULL(cnode); // Check input data type for (size_t input_index = 0; input_index < kernel_build_info.GetInputNum(); ++input_index) { - TypeId input_origin_type = AnfAlgo::GetPrevNodeOutputInferDataType(cnode, input_index); + TypeId input_origin_type = common::AnfAlgo::GetPrevNodeOutputInferDataType(cnode, input_index); if (kernel_build_info.GetInputDeviceType(input_index) != input_origin_type) { return false; } } // Check output data type for (size_t output_index = 0; output_index < kernel_build_info.GetOutputNum(); ++output_index) { - if (kernel_build_info.GetOutputDeviceType(output_index) != AnfAlgo::GetOutputInferDataType(cnode, output_index)) { + if (kernel_build_info.GetOutputDeviceType(output_index) != + common::AnfAlgo::GetOutputInferDataType(cnode, output_index)) { return false; } } @@ -84,7 +86,7 @@ string GetPriorityMatchFormat(const CNodePtr &cnode) { bool is_init = false; bool need_change_nd = false; bool is_5d_input = false; - size_t input_num = AnfAlgo::GetInputTensorNum(cnode); + size_t input_num = common::AnfAlgo::GetInputTensorNum(cnode); for (size_t index = 0; index < input_num; ++index) { auto pre_output_format = AnfAlgo::GetPrevNodeOutputFormat(cnode, index); if (AnfAlgo::IsFeatureMapInput(cnode, index) && @@ -96,7 +98,7 @@ string GetPriorityMatchFormat(const CNodePtr &cnode) { if (priority_matched_format != pre_output_format && pre_output_format != kOpFormat_DEFAULT) { priority_matched_format = kOpFormat_DEFAULT; } - auto input_shape_size = AnfAlgo::GetPrevNodeOutputInferShape(cnode, index).size(); + auto input_shape_size = common::AnfAlgo::GetPrevNodeOutputInferShape(cnode, index).size(); if (input_shape_size == k5dSize) { is_5d_input = true; } @@ -108,7 +110,7 @@ string GetPriorityMatchFormat(const CNodePtr &cnode) { if (is_5d_input && priority_matched_format != kOpFormat_FRAC_NZ) { priority_matched_format = kOpFormat_NDC1HWC0; } - AnfAlgo::SetNodeAttr(kPriChoosenFormat, MakeValue(priority_matched_format), cnode); + common::AnfAlgo::SetNodeAttr(kPriChoosenFormat, MakeValue(priority_matched_format), cnode); return priority_matched_format; } @@ -145,9 +147,10 @@ void UpdateCurMatchCounts(const kernel::KernelBuildInfo &kernel_build_info, cons MS_LOG(EXCEPTION) << "Out of range cur_kernel info_match_counts " << MATCH_COUNT_PRIORITY_END; } auto pri_match_format = GetPriorityMatchFormat(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); for (size_t input_index = 0; input_index < input_num; ++input_index) { - auto input_anf_node = AnfAlgo::VisitKernelWithReturnType(AnfAlgo::GetInputNode(kernel_node, input_index), 0).first; + auto input_anf_node = + common::AnfAlgo::VisitKernelWithReturnType(common::AnfAlgo::GetInputNode(kernel_node, input_index), 0).first; MS_EXCEPTION_IF_NULL(input_anf_node); // we do not take ValueNode into consideration in graph kernel. auto base_score = AnfAlgo::IsFeatureMapInput(kernel_node, input_index) ? kFeatureMapBaseScore : kWeightInitScore; @@ -158,7 +161,7 @@ void UpdateCurMatchCounts(const kernel::KernelBuildInfo &kernel_build_info, cons (*cur_kernelinfo_match_counts)[MATCH_FORMAT_COUNT] += base_score; } // we match output fix precision first. - auto prev_device_type = AnfAlgo::GetPrevNodeOutputPrecision(kernel_node, input_index); + auto prev_device_type = common::AnfAlgo::GetPrevNodeOutputPrecision(kernel_node, input_index); if (prev_device_type == kTypeUnknown) { prev_device_type = AnfAlgo::GetPrevNodeOutputDeviceDataType(kernel_node, input_index); } @@ -174,11 +177,11 @@ void UpdateCurMatchCounts(const kernel::KernelBuildInfo &kernel_build_info, cons } } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); for (size_t output_index = 0; output_index < output_num; ++output_index) { // cal count of same output dtype between abstract and kernel info if (kernel_build_info.GetOutputDeviceType(output_index) == - AnfAlgo::GetOutputInferDataType(kernel_node, output_index)) { + common::AnfAlgo::GetOutputInferDataType(kernel_node, output_index)) { (*cur_kernelinfo_match_counts)[MATCH_OUTPUT_DTYPE_COUNT] += 1; } if (kernel_build_info.GetOutputFormat(output_index) == pri_match_format) { @@ -261,7 +264,7 @@ bool TagRaiseReduce(const std::shared_ptr &kernel_build MS_EXCEPTION_IF_NULL(cnode); MS_EXCEPTION_IF_NULL(kernel_build_info); for (size_t input_index = 0; input_index < kernel_build_info->GetInputNum(); ++input_index) { - auto in_dtype = AnfAlgo::GetPrevNodeOutputInferDataType(cnode, input_index); + auto in_dtype = common::AnfAlgo::GetPrevNodeOutputInferDataType(cnode, input_index); auto device_dtype = kernel_build_info->GetInputDeviceType(input_index); if (device_dtype == kNumberTypeFloat || device_dtype == kNumberTypeFloat32) { device_dtype = kNumberTypeFloat32; @@ -272,7 +275,7 @@ bool TagRaiseReduce(const std::shared_ptr &kernel_build } for (size_t output_index = 0; output_index < kernel_build_info->GetOutputNum(); ++output_index) { - auto in_dtype = AnfAlgo::GetOutputInferDataType(cnode, output_index); + auto in_dtype = common::AnfAlgo::GetOutputInferDataType(cnode, output_index); auto device_dtype = kernel_build_info->GetOutputDeviceType(output_index); if (device_dtype == kNumberTypeFloat || device_dtype == kNumberTypeFloat32) { device_dtype = kNumberTypeFloat32; @@ -323,7 +326,7 @@ std::vector> FilterRaisedOrReducePrecis *precision_reduce = true; } if (int64_reduce) { - auto node_name = AnfAlgo::GetCNodeName(cnode); + auto node_name = common::AnfAlgo::GetCNodeName(cnode); MS_LOG(WARNING) << "Operator:[" << node_name << "] don't support int64, reduce precision from int64 to int32."; } return filtered_kernel_info_list; @@ -331,13 +334,13 @@ std::vector> FilterRaisedOrReducePrecis void SetCastAndWeightFormat(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - if (!AnfAlgo::HasNodeAttr(kAttrPynativeNextIndex, kernel_node) || - !AnfAlgo::HasNodeAttr(kAttrPynativeNextOpName, kernel_node)) { + if (!common::AnfAlgo::HasNodeAttr(kAttrPynativeNextIndex, kernel_node) || + !common::AnfAlgo::HasNodeAttr(kAttrPynativeNextOpName, kernel_node)) { MS_LOG(EXCEPTION) << "The node [" << kernel_node->DebugString() << "] attr of " << kAttrPynativeNextIndex << " or " << kAttrPynativeNextOpName << " has not been set yet!" << trace::DumpSourceLines(kernel_node); } - auto next_index = AnfAlgo::GetNodeAttr(kernel_node, kAttrPynativeNextIndex); - auto next_op_name = AnfAlgo::GetNodeAttr(kernel_node, kAttrPynativeNextOpName); + auto next_index = common::AnfAlgo::GetNodeAttr(kernel_node, kAttrPynativeNextIndex); + auto next_op_name = common::AnfAlgo::GetNodeAttr(kernel_node, kAttrPynativeNextOpName); auto iter = kNextOpFormatList.find(next_op_name); if (iter == kNextOpFormatList.end()) { MS_LOG(INFO) << "The op name " << next_op_name << "has not been set in the next op map "; @@ -347,7 +350,7 @@ void SetCastAndWeightFormat(const CNodePtr &kernel_node) { MS_LOG(EXCEPTION) << "Next input index " << next_index << "is out of range in the next op map max size is " << iter->second.size() << trace::DumpSourceLines(kernel_node); } - if (AnfAlgo::GetCNodeName(kernel_node) != prim::kPrimCast->name()) { + if (common::AnfAlgo::GetCNodeName(kernel_node) != prim::kPrimCast->name()) { MS_LOG(INFO) << "Only supported to change the node Cast's build info!!!"; return; } @@ -384,7 +387,7 @@ void SetWeightFormat(const AnfNodePtr &real_input_node, std::vector outp auto builder = std::make_shared(); MS_EXCEPTION_IF_NULL(builder); // we set special device info of a input tensor. - auto op_info = kernel::tbe::TbeDynamicShapeUtil::FindOp(AnfAlgo::GetCNodeName(kernel_node), kernel_node); + auto op_info = kernel::tbe::TbeDynamicShapeUtil::FindOp(common::AnfAlgo::GetCNodeName(kernel_node), kernel_node); if (op_info != nullptr) { force_fresh = op_info->is_ref() || force_fresh; } @@ -404,7 +407,7 @@ void SetWeightFormat(const AnfNodePtr &real_input_node, std::vector outp } if (AnfAlgo::GetOutputDeviceDataType(real_input_node, 0) == kTypeUnknown || force_fresh) { builder->SetOutputsFormat(output_format); - std::vector output_type = {AnfAlgo::GetOutputInferDataType(real_input_node, 0)}; + std::vector output_type = {common::AnfAlgo::GetOutputInferDataType(real_input_node, 0)}; builder->SetOutputsDeviceType(output_type); AnfAlgo::SetSelectKernelBuildInfo(builder->Build(), real_input_node.get()); } @@ -420,7 +423,7 @@ bool RefreshCastAndParamWeightFormat(const AnfNodePtr &input_node, const string } auto cast_node = input_node->cast(); MS_EXCEPTION_IF_NULL(cast_node); - if (AnfAlgo::GetCNodeName(cast_node) != prim::kPrimCast->name()) { + if (common::AnfAlgo::GetCNodeName(cast_node) != prim::kPrimCast->name()) { return true; } if (AnfAlgo::IsFeatureMapOutput(cast_node)) { @@ -435,7 +438,7 @@ bool RefreshCastAndParamWeightFormat(const AnfNodePtr &input_node, const string info_builder->SetInputsFormat({format}); info_builder->SetOutputsFormat({format}); AnfAlgo::SetSelectKernelBuildInfo(info_builder->Build(), cast_node.get()); - auto cast_input_node = AnfAlgo::VisitKernel(AnfAlgo::GetInputNode(cast_node, 0), 0); + auto cast_input_node = common::AnfAlgo::VisitKernel(common::AnfAlgo::GetInputNode(cast_node, 0), 0); SetWeightFormat(cast_input_node.first, {format}, cast_node, 0, true); return true; } @@ -443,13 +446,13 @@ bool RefreshCastAndParamWeightFormat(const AnfNodePtr &input_node, const string TypeId GetInputDeviceType(const CNodePtr &kernel_node, size_t input_idx) { MS_EXCEPTION_IF_NULL(kernel_node); TypeId type = kTypeUnknown; - auto input_node = AnfAlgo::GetPrevNodeOutput(kernel_node, input_idx).first; + auto input_node = common::AnfAlgo::GetPrevNodeOutput(kernel_node, input_idx).first; MS_EXCEPTION_IF_NULL(input_node); auto kernel_info = dynamic_cast(input_node->kernel_info()); if (kernel_info != nullptr && kernel_info->select_kernel_build_info() != nullptr) { type = AnfAlgo::GetPrevNodeOutputDeviceDataType(kernel_node, input_idx); } else { - type = AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, input_idx); + type = common::AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, input_idx); } return type; } @@ -457,7 +460,7 @@ TypeId GetInputDeviceType(const CNodePtr &kernel_node, size_t input_idx) { void GetInputsDeviceType(const CNodePtr &kernel_node, std::vector *input_types) { MS_EXCEPTION_IF_NULL(kernel_node); MS_EXCEPTION_IF_NULL(input_types); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); for (size_t i = 0; i < input_num; ++i) { auto type = GetInputDeviceType(kernel_node, i); input_types->emplace_back(type); @@ -489,9 +492,9 @@ string InferOutputFormat(const CNodePtr &kernel_node, const std::vector(kernel_node, kAttrFuncType); + auto func_type = common::AnfAlgo::GetNodeAttr(kernel_node, kAttrFuncType); if (func_type == kCustomTypeTbe) { *kernel_type = KernelType::TBE_KERNEL; } else if (kCustomTypeAkg.find(func_type) != kCustomTypeAkg.end()) { @@ -530,7 +533,7 @@ KernelSelectStatus SelectCustomKernelInfo(const CNodePtr &kernel_node, KernelTyp std::vector inputs_device_type; std::vector inputs_format; GetInputsDeviceType(kernel_node, &inputs_device_type); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); std::unordered_set all_input_formats; for (size_t i = 0; i < input_num; ++i) { auto format = AnfAlgo::GetPrevNodeOutputFormat(kernel_node, i); @@ -548,9 +551,9 @@ KernelSelectStatus SelectCustomKernelInfo(const CNodePtr &kernel_node, KernelTyp std::vector outputs_format; auto output_infer_format = InferOutputFormat(kernel_node, inputs_format); MS_LOG(INFO) << "Outputs of " << op_name << " will use same inferred format: " << output_infer_format; - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); for (size_t i = 0; i < output_num; ++i) { - outputs_device_type.push_back(AnfAlgo::GetOutputInferDataType(kernel_node, i)); + outputs_device_type.push_back(common::AnfAlgo::GetOutputInferDataType(kernel_node, i)); outputs_format.push_back(output_infer_format); } builder->SetOutputsDeviceType(outputs_device_type); @@ -597,7 +600,7 @@ void FillNoneInKernelInfo(const CNodePtr &kernel_node, std::vectorGetOutputNum(); ++i) { auto type = build_info->GetOutputDeviceType(i); if (type == TypeId::kMetaTypeNone) { - type = AnfAlgo::GetOutputInferDataType(kernel_node, i); + type = common::AnfAlgo::GetOutputInferDataType(kernel_node, i); } outputs_device_type.push_back(type); auto format = build_info->GetOutputFormat(i); @@ -613,20 +616,20 @@ void FillNoneInKernelInfo(const CNodePtr &kernel_node, std::vector(*selected_kernel_info); MS_EXCEPTION_IF_NULL(builder); - builder->SetInputsFormat(AnfAlgo::GetNodeAttr>(kernel_node, kAttrFixedInputFormat)); - builder->SetOutputsFormat(AnfAlgo::GetNodeAttr>(kernel_node, kAttrFixedOutputFormat)); + builder->SetInputsFormat(common::AnfAlgo::GetNodeAttr>(kernel_node, kAttrFixedInputFormat)); + builder->SetOutputsFormat(common::AnfAlgo::GetNodeAttr>(kernel_node, kAttrFixedOutputFormat)); *selected_kernel_info = builder->Build(); MS_LOG(INFO) << "Current node: " << kernel_node->fullname_with_scope() << " selected kernel build info after reset fixed format: " << (*selected_kernel_info)->ToString(); - AnfAlgo::EraseNodeAttr(kAttrFixedInputFormat, kernel_node); - AnfAlgo::EraseNodeAttr(kAttrFixedOutputFormat, kernel_node); + common::AnfAlgo::EraseNodeAttr(kAttrFixedInputFormat, kernel_node); + common::AnfAlgo::EraseNodeAttr(kAttrFixedOutputFormat, kernel_node); } } // namespace @@ -634,18 +637,19 @@ void SetTensorDeviceInfo(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); auto selected_kernel_info = AnfAlgo::GetSelectKernelBuildInfo(kernel_node); MS_EXCEPTION_IF_NULL(selected_kernel_info); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); for (size_t input_index = 0; input_index < input_num; ++input_index) { - auto input_kernel_node = AnfAlgo::GetInputNode(kernel_node, input_index); + auto input_kernel_node = common::AnfAlgo::GetInputNode(kernel_node, input_index); MS_EXCEPTION_IF_NULL(input_kernel_node); - auto input_with_index = AnfAlgo::VisitKernelWithReturnType(input_kernel_node, 0); + auto input_with_index = common::AnfAlgo::VisitKernelWithReturnType(input_kernel_node, 0); MS_EXCEPTION_IF_NULL(input_with_index.first); auto real_input_node = input_with_index.first; MS_EXCEPTION_IF_NULL(real_input_node); if (RefreshCastAndParamWeightFormat(real_input_node, selected_kernel_info->GetInputFormat(input_index))) { continue; } - if (real_input_node->isa() && !AnfAlgo::IsParameterWeight(real_input_node->cast())) { + if (real_input_node->isa() && + !common::AnfAlgo::IsParameterWeight(real_input_node->cast())) { continue; } auto refresh_format = selected_kernel_info->GetInputFormat(input_index); @@ -687,7 +691,7 @@ KernelSelectStatus SetMatchedKernelInfo(const CNodePtr &kernel_node, ResetPreFixedFormat(kernel_node, &selected_kernel_info); AnfAlgo::SetSelectKernelBuildInfo(selected_kernel_info, kernel_node.get()); // Set format and data type for input tensor. - if (AnfAlgo::HasNodeAttr(kAttrPynativeNextOpName, kernel_node)) { + if (common::AnfAlgo::HasNodeAttr(kAttrPynativeNextOpName, kernel_node)) { SetCastAndWeightFormat(kernel_node); } SetTensorDeviceInfo(kernel_node); @@ -699,7 +703,7 @@ void ConvertAttrToInput(const CNodePtr &kernel_node, std::vectorcast(); MS_EXCEPTION_IF_NULL(kernel_graph); - auto primitive = AnfAlgo::GetCNodePrimitive(kernel_node); + auto primitive = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(primitive); std::ostringstream buf; @@ -804,7 +808,7 @@ KernelSelectStatus SelectKernelInfo(const CNodePtr &kernel_node, KernelType kern std::vector> aicpu_kernel_info_list; std::ostringstream aicore_in_out_info, aicpu_in_out_info; MS_EXCEPTION_IF_NULL(kernel_node); - if (AnfAlgo::IsGraphKernel(kernel_node)) { + if (common::AnfAlgo::IsGraphKernel(kernel_node)) { auto func_graph = GetValueNode(kernel_node->input(kAnfPrimitiveIndex)); MS_EXCEPTION_IF_NULL(func_graph); SelectGraphKernelInfo(kernel_node, func_graph); @@ -834,12 +838,13 @@ KernelSelectStatus SelectKernelInfo(const CNodePtr &kernel_node, KernelType kern MS_LOG(DEBUG) << "The node [" << kernel_node->fullname_with_scope() << "] cannot find valid TBE kernel info, try to get ai_cpu kernel info"; std::vector> attr_to_input_infos; - if (kernel::GetAicpuOpAttrToInputInfo(kernel_node, &attr_to_input_infos) && !AnfAlgo::IsDynamicShape(kernel_node)) { + if (kernel::GetAicpuOpAttrToInputInfo(kernel_node, &attr_to_input_infos) && + !common::AnfAlgo::IsDynamicShape(kernel_node)) { ConvertAttrToInput(kernel_node, &attr_to_input_infos); } kernel::AICPUQuery(kernel_node, &aicpu_kernel_info_list); select_status = SetMatchedKernelInfo(kernel_node, aicpu_kernel_info_list); - AnfAlgo::SetNodeAttr(kAttrIsAiCpuKernel, MakeValue(true), kernel_node); + common::AnfAlgo::SetNodeAttr(kAttrIsAiCpuKernel, MakeValue(true), kernel_node); } // The kernel info can not find in ai_cpu kernel lists and ai_core kernel lists if (select_status == kNoMatched) { @@ -856,7 +861,7 @@ void SetKernelInfo(const CNodePtr &kernel_node, KernelType kernel_type) { auto kernel_build_info = kernel_info->select_kernel_build_info(); MS_EXCEPTION_IF_NULL(kernel_build_info); - if (AnfAlgo::IsGraphKernel(kernel_node)) { + if (common::AnfAlgo::IsGraphKernel(kernel_node)) { return; } diff --git a/mindspore/ccsrc/plugin/device/ascend/hal/device/kernel_select_graph_kernel.cc b/mindspore/ccsrc/plugin/device/ascend/hal/device/kernel_select_graph_kernel.cc index 4e4f47566c4..8a74614bf5d 100644 --- a/mindspore/ccsrc/plugin/device/ascend/hal/device/kernel_select_graph_kernel.cc +++ b/mindspore/ccsrc/plugin/device/ascend/hal/device/kernel_select_graph_kernel.cc @@ -16,6 +16,7 @@ #include "plugin/device/ascend/hal/device/kernel_select_ascend.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "runtime/device/kernel_info.h" #include "ir/func_graph.h" #include "kernel/common_utils.h" @@ -39,7 +40,7 @@ bool cmp_format_num(const std::pair &a, const std::pair frac_nz_axis = DefaultToFracNZAxis(infer_shape, default_axis); - AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue>(frac_nz_axis), cnode); - auto output_shape = AnfAlgo::GetOutputInferShape(cnode, 0); + common::AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue>(frac_nz_axis), cnode); + auto output_shape = common::AnfAlgo::GetOutputInferShape(cnode, 0); if (output_shape.size() == 1) { - AnfAlgo::SetNodeAttr(kAttrOutputDefault, MakeValue(true), cnode); + common::AnfAlgo::SetNodeAttr(kAttrOutputDefault, MakeValue(true), cnode); } } } @@ -197,9 +198,9 @@ void GetDefaultFormat(const CNodePtr &kernel_node, std::string *default_format, MS_EXCEPTION_IF_NULL(default_format); MS_EXCEPTION_IF_NULL(use_same_format); std::unordered_map all_input_formats; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); for (size_t i = 0; i < input_num; ++i) { - auto input_kernel_node = AnfAlgo::VisitKernel(kernel_node->input(i + 1), 0).first; + auto input_kernel_node = common::AnfAlgo::VisitKernel(kernel_node->input(i + 1), 0).first; MS_EXCEPTION_IF_NULL(input_kernel_node); if (!input_kernel_node->isa()) { ++all_input_formats[AnfAlgo::GetPrevNodeOutputFormat(kernel_node, i)]; @@ -227,13 +228,13 @@ void GetDefaultFormat(const CNodePtr &kernel_node, std::string *default_format, } for (size_t i = 0; i < input_num; ++i) { - auto input_kernel_node = AnfAlgo::VisitKernel(kernel_node->input(i + 1), 0).first; + auto input_kernel_node = common::AnfAlgo::VisitKernel(kernel_node->input(i + 1), 0).first; MS_EXCEPTION_IF_NULL(input_kernel_node); if (!input_kernel_node->isa() || AnfAlgo::GetOutputDeviceDataType(input_kernel_node, 0) != kTypeUnknown) { continue; } - auto weight_infer_shape = AnfAlgo::GetOutputInferShape(input_kernel_node, 0); + auto weight_infer_shape = common::AnfAlgo::GetOutputInferShape(input_kernel_node, 0); if (weight_infer_shape.size() < kShape2dDims && *default_format == kOpFormat_FRAC_NZ) { *default_format = kOpFormat_DEFAULT; *use_same_format = true; @@ -249,14 +250,14 @@ void UpdateInputsKernelInfo(const CNodePtr &kernel_node, const std::vectorinput(i + 1), 0).first; + auto input_kernel_node = common::AnfAlgo::VisitKernel(kernel_node->input(i + 1), 0).first; MS_EXCEPTION_IF_NULL(input_kernel_node); if (use_same_format) { bool can_convert = true; if (default_format == kOpFormat_FRAC_NZ) { - auto infer_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, i); + auto infer_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, i); if (!CanConvertDefaultShapeToNZ(infer_shape)) { MS_LOG(WARNING) << "Shape can't be converted to frac nz shape, so use default format instead"; can_convert = false; @@ -289,7 +290,7 @@ void UpdateInputsKernelInfo(const CNodePtr &kernel_node, const std::vectorpush_back(default_format); - graph_input_type->push_back(AnfAlgo::GetOutputInferDataType(input_kernel_node, 0)); + graph_input_type->push_back(common::AnfAlgo::GetOutputInferDataType(input_kernel_node, 0)); } for (size_t i = 0; i < input_num; ++i) { @@ -320,7 +321,7 @@ void UpdateEquivFormat(const std::vector &node_list, const FuncGraph UpdateFracNZReduceOp(cnode); // If ReduceSum's output is 1d and not Default format, convert it to Default format auto out_format = AnfAlgo::GetOutputFormat(cnode, 0); - if (out_format == kOpFormat_DEFAULT || !AnfAlgo::HasNodeAttr(kAttrOutputDefault, cnode)) { + if (out_format == kOpFormat_DEFAULT || !common::AnfAlgo::HasNodeAttr(kAttrOutputDefault, cnode)) { continue; } // Insert EquivFormat node, then select kernel info again @@ -328,9 +329,9 @@ void UpdateEquivFormat(const std::vector &node_list, const FuncGraph trans_inputs.push_back(NewValueNode(prim::kPrimEquivFormat)); trans_inputs.push_back(cnode); CNodePtr trans_node = func_graph->NewCNode(trans_inputs); - AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetPrevNodeOutputInferDataType(cnode, 0)}, - {AnfAlgo::GetOutputInferShape(cnode, 0)}, trans_node.get()); - AnfAlgo::SetNodeAttr(kAttrInputNames, MakeValue>({"x"}), trans_node); + common::AnfAlgo::SetOutputInferTypeAndShape({common::AnfAlgo::GetPrevNodeOutputInferDataType(cnode, 0)}, + {common::AnfAlgo::GetOutputInferShape(cnode, 0)}, trans_node.get()); + common::AnfAlgo::SetNodeAttr(kAttrInputNames, MakeValue>({"x"}), trans_node); if (trans_node->kernel_info() == nullptr) { trans_node->set_kernel_info(std::make_shared()); @@ -350,7 +351,7 @@ void CheckFormatsAndDtypes(const CNodePtr &kernel_node, const std::vectorsize() != input_num || graph_input_type->size() != input_num || need_update->size() != input_num) { MS_LOG(EXCEPTION) << "Graph input format size is not equal to input num of cnode[" << kernel_node->DebugString() @@ -382,7 +383,7 @@ void CheckFormatsAndDtypes(const CNodePtr &kernel_node, const std::vectorDebugString() << " of [" << kernel_node->DebugString() << "] selected different dtype. we use default: " << TypeIdLabel(default_dtype); @@ -398,7 +399,7 @@ void UpdateFormatsAndDtypes(const CNodePtr &kernel_node, const std::vector &graph_input_type) { MS_EXCEPTION_IF_NULL(kernel_node); // update graph input format and dtype use inner ops. - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (graph_input_format.size() != input_num || graph_input_type.size() != input_num || need_update.size() != input_num) { MS_LOG(EXCEPTION) << "Graph input format size is not equal to input num of cnode[" << kernel_node->DebugString() @@ -429,7 +430,7 @@ void UpdateFormatsAndDtypes(const CNodePtr &kernel_node, const std::vectorcast(); MS_EXCEPTION_IF_NULL(cnode); kernel::KernelBuildInfo::KernelBuildInfoBuilder builder; - size_t cnode_input_num = AnfAlgo::GetInputTensorNum(cnode); + size_t cnode_input_num = common::AnfAlgo::GetInputTensorNum(cnode); for (size_t j = 0; j < cnode_input_num; ++j) { auto input_node = cnode->input(j + 1); MS_EXCEPTION_IF_NULL(input_node); @@ -456,7 +457,7 @@ void SetGraphKernelInfo(const CNodePtr &kernel_node, const std::vectorisa()) { - output_type = AnfAlgo::GetCNodeOutputPrecision(output.first); + output_type = common::AnfAlgo::GetCNodeOutputPrecision(output.first); } if (output_type == kTypeUnknown) { output_type = AnfAlgo::GetOutputDeviceDataType(output.first, output.second); @@ -515,7 +516,7 @@ void SelectGraphKernelInfo(const CNodePtr &kernel_node, const FuncGraphPtr &func kernel::GetValidKernelNodes(func_graph, &node_list, &input_list, &output_list); // update graph input format and dtype use inner ops. - std::vector need_update(AnfAlgo::GetInputTensorNum(kernel_node), false); + std::vector need_update(common::AnfAlgo::GetInputTensorNum(kernel_node), false); CheckFormatsAndDtypes(kernel_node, input_list, mng, default_format, &graph_input_format, &graph_input_type, &need_update); UpdateFormatsAndDtypes(kernel_node, node_list, input_list, need_update, graph_input_format, graph_input_type); diff --git a/mindspore/ccsrc/plugin/device/ascend/hal/device/profiling/profiling_manager.cc b/mindspore/ccsrc/plugin/device/ascend/hal/device/profiling/profiling_manager.cc index 93ece3513f8..c21b75091e2 100644 --- a/mindspore/ccsrc/plugin/device/ascend/hal/device/profiling/profiling_manager.cc +++ b/mindspore/ccsrc/plugin/device/ascend/hal/device/profiling/profiling_manager.cc @@ -23,7 +23,7 @@ #include "utils/log_adapter.h" #include "utils/ms_context.h" #include "utils/ms_utils.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" #include "runtime/base.h" #include #include "plugin/device/ascend/hal/device/profiling/profiling_utils.h" diff --git a/mindspore/ccsrc/plugin/device/ascend/hal/device/profiling/profiling_manager.h b/mindspore/ccsrc/plugin/device/ascend/hal/device/profiling/profiling_manager.h index 0c937881d68..d4906577149 100644 --- a/mindspore/ccsrc/plugin/device/ascend/hal/device/profiling/profiling_manager.h +++ b/mindspore/ccsrc/plugin/device/ascend/hal/device/profiling/profiling_manager.h @@ -21,7 +21,7 @@ #include #include #include -#include "utils/contract.h" +#include "include/common/utils/contract.h" #include "utils/ms_context.h" #include "toolchain/prof_callback.h" #include "toolchain/prof_acl_api.h" diff --git a/mindspore/ccsrc/plugin/device/ascend/hal/device/profiling/profiling_reporter.cc b/mindspore/ccsrc/plugin/device/ascend/hal/device/profiling/profiling_reporter.cc index e9bbe924394..4ee0ae41107 100644 --- a/mindspore/ccsrc/plugin/device/ascend/hal/device/profiling/profiling_reporter.cc +++ b/mindspore/ccsrc/plugin/device/ascend/hal/device/profiling/profiling_reporter.cc @@ -18,7 +18,7 @@ #include "plugin/device/ascend/hal/device/profiling/profiling_reporter.h" #include "kernel/kernel.h" #include "kernel/ascend_kernel_mod.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace device { @@ -182,7 +182,7 @@ void ProfilingReporter::ReportTask(const CNodePtr &node, const uint32_t stream_i MsprofGeProfTaskData task_info{}; task_info.taskType = static_cast(KernelType2TaskTypeEnum[kernel_type]); (void)SetAlternativeValue(task_info.opName, MSPROF_MIX_DATA_STRING_LEN, node->fullname_with_scope(), device_id_); - (void)SetAlternativeValue(task_info.opType, MSPROF_GE_OP_TYPE_LEN, AnfAlgo::GetCNodeName(node), device_id_); + (void)SetAlternativeValue(task_info.opType, MSPROF_GE_OP_TYPE_LEN, common::AnfAlgo::GetCNodeName(node), device_id_); // Note: Currently, the profiler supports only static shapes. task_info.shapeType = static_cast(MSPROF_GE_SHAPE_TYPE_STATIC); task_info.blockDims = GetBlockDim(node); @@ -202,9 +202,9 @@ void ProfilingReporter::ReportNode(const CNodePtr &node, uint32_t stream_id, uin size_t total_size = 0; if (tensor_type == MSPROF_GE_TENSOR_TYPE_INPUT) { - total_size = AnfAlgo::GetInputTensorNum(node); + total_size = common::AnfAlgo::GetInputTensorNum(node); } else { - total_size = AnfAlgo::GetOutputTensorNum(node); + total_size = common::AnfAlgo::GetOutputTensorNum(node); } const size_t batch_size = total_size / MSPROF_GE_TENSOR_DATA_NUM; @@ -253,7 +253,7 @@ void ProfilingReporter::BuildTensorData(MsprofGeTensorData &tensor_data, const C std::vector shape; string data_format; if (tensor_type == MSPROF_GE_TENSOR_TYPE_INPUT) { - auto input_node_with_index = AnfAlgo::GetPrevNodeOutput(node, index); + auto input_node_with_index = common::AnfAlgo::GetPrevNodeOutput(node, index); auto input_node = input_node_with_index.first; auto input_index = input_node_with_index.second; shape = AnfAlgo::GetOutputDeviceShape(input_node, input_index); diff --git a/mindspore/ccsrc/plugin/device/ascend/hal/device/profiling/profiling_reporter.h b/mindspore/ccsrc/plugin/device/ascend/hal/device/profiling/profiling_reporter.h index 28db42d1af4..2773a6a79c5 100644 --- a/mindspore/ccsrc/plugin/device/ascend/hal/device/profiling/profiling_reporter.h +++ b/mindspore/ccsrc/plugin/device/ascend/hal/device/profiling/profiling_reporter.h @@ -24,6 +24,7 @@ #include "securec/include/securec.h" #include "utils/log_adapter.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "plugin/device/ascend/hal/device/profiling/profiling_manager.h" #include "toolchain/prof_common.h" #include "toolchain/prof_reporter.h" diff --git a/mindspore/ccsrc/plugin/device/ascend/hal/device/profiling/profiling_utils.cc b/mindspore/ccsrc/plugin/device/ascend/hal/device/profiling/profiling_utils.cc index a7db0a9e128..1bef04b5472 100644 --- a/mindspore/ccsrc/plugin/device/ascend/hal/device/profiling/profiling_utils.cc +++ b/mindspore/ccsrc/plugin/device/ascend/hal/device/profiling/profiling_utils.cc @@ -19,8 +19,9 @@ #include "kernel/kernel.h" #include "plugin/device/ascend/hal/device/profiling/profiling_manager.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "utils/ms_utils.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/ms_context.h" #include "nlohmann/json.hpp" #include "base/core_ops.h" @@ -81,7 +82,7 @@ ProfilingTraceInfo ProfilingUtils::GenerateProfilingTrace(const session::KernelG void ProfilingUtils::GetTraceHccl(const session::KernelGraph &kernel_graph, NotNull profiling_trace) { for (const auto &node : kernel_graph.execution_order()) { - if (AnfAlgo::IsCommunicationOp(node)) { + if (common::AnfAlgo::IsCommunicationOp(node)) { MS_EXCEPTION_IF_NULL(node); profiling_trace->trace_custom_node.insert(node->fullname_with_scope()); MS_LOG(INFO) << "Profiling graph:" << kernel_graph.graph_id() << " Get hccl node:" << node->fullname_with_scope(); @@ -128,12 +129,12 @@ void ProfilingUtils::GetCNodeOutputRealNode(const std::string &node_name, const for (const auto &cnode : kernel_graph.execution_order()) { MS_EXCEPTION_IF_NULL(cnode); for (const auto &input : cnode->inputs()) { - auto prev_cnode = AnfAlgo::VisitKernel(input, 0); + auto prev_cnode = common::AnfAlgo::VisitKernel(input, 0); MS_EXCEPTION_IF_NULL(prev_cnode.first); if (!prev_cnode.first->isa()) { continue; } - if (AnfAlgo::GetCNodeName(prev_cnode.first) == node_name) { + if (common::AnfAlgo::GetCNodeName(prev_cnode.first) == node_name) { getnext_outputs->insert(cnode->fullname_with_scope()); MS_LOG(INFO) << "Profiling graph:" << kernel_graph.graph_id() << " Find GetNext Output CNode:" << cnode->fullname_with_scope(); @@ -164,12 +165,12 @@ void ProfilingUtils::GetTraceBpEnd(const session::KernelGraph &kernel_graph, con auto &execution_orders = kernel_graph.execution_order(); auto iter = execution_orders.rbegin(); while (iter != execution_orders.rend()) { - if (AnfAlgo::IsCommunicationOp(*iter)) { + if (common::AnfAlgo::IsCommunicationOp(*iter)) { // store communication op input nodes' name std::set ar_input_node_names; - size_t input_num = AnfAlgo::GetInputTensorNum(*iter); + size_t input_num = common::AnfAlgo::GetInputTensorNum(*iter); for (size_t i = 0; i < input_num; ++i) { - auto input_node_with_index = AnfAlgo::GetPrevNodeOutput(*iter, i); + auto input_node_with_index = common::AnfAlgo::GetPrevNodeOutput(*iter, i); auto input_node = input_node_with_index.first; MS_EXCEPTION_IF_NULL(input_node); ar_input_node_names.insert(input_node->fullname_with_scope()); @@ -204,7 +205,7 @@ std::string ProfilingUtils::GetGraphLastKernelName(const session::KernelGraph &k for (auto iter = execution_order.rbegin(); iter != execution_order.rend(); ++iter) { MS_EXCEPTION_IF_NULL(*iter); if (AnfAlgo::GetKernelType(*iter) == TBE_KERNEL || AnfAlgo::GetKernelType(*iter) == AKG_KERNEL || - AnfAlgo::IsCommunicationOp(*iter)) { + common::AnfAlgo::IsCommunicationOp(*iter)) { last_tbe_kernel_name = (*iter)->fullname_with_scope(); break; } @@ -220,7 +221,7 @@ void ProfilingUtils::GetTraceIterEnd(const session::KernelGraph &kernel_graph, P // Find last execute node in control flow auto &execution_orders = kernel_graph.execution_order(); for (auto &node : execution_orders) { - if (AnfAlgo::HasNodeAttr(kAttrProfilingIterEnd, node)) { + if (common::AnfAlgo::HasNodeAttr(kAttrProfilingIterEnd, node)) { MS_LOG(INFO) << "Profiling graph:" << kernel_graph.graph_id() << " Found PROFILING_ITER_END:" << node->fullname_with_scope(); trace_info->trace_iter_end.insert(node->fullname_with_scope()); @@ -261,9 +262,9 @@ NotNull ProfilingUtils::CreateProfilingCNode(const ProfilingContent &p ValuePtr notify_value = MakeValue(profiling_content.notify); ValuePtr trace_id_value = MakeValue(profiling_content.profiler_trace_id); ValuePtr flags_value = MakeValue(profiling_content.flags); - AnfAlgo::SetNodeAttr(ProfilingUtils::kNotify, notify_value, cnode_ptr); - AnfAlgo::SetNodeAttr(ProfilingUtils::kProfilerTraceId, trace_id_value, cnode_ptr); - AnfAlgo::SetNodeAttr(ProfilingUtils::kFlags, flags_value, cnode_ptr); + common::AnfAlgo::SetNodeAttr(ProfilingUtils::kNotify, notify_value, cnode_ptr); + common::AnfAlgo::SetNodeAttr(ProfilingUtils::kProfilerTraceId, trace_id_value, cnode_ptr); + common::AnfAlgo::SetNodeAttr(ProfilingUtils::kFlags, flags_value, cnode_ptr); return NOT_NULL(cnode_ptr); } diff --git a/mindspore/ccsrc/plugin/device/ascend/hal/device/profiling/profiling_utils.h b/mindspore/ccsrc/plugin/device/ascend/hal/device/profiling/profiling_utils.h index e6f0238b211..560af81ba4a 100644 --- a/mindspore/ccsrc/plugin/device/ascend/hal/device/profiling/profiling_utils.h +++ b/mindspore/ccsrc/plugin/device/ascend/hal/device/profiling/profiling_utils.h @@ -23,7 +23,7 @@ #include #include #include "backend/common/session/kernel_graph.h" -#include "utils/contract.h" +#include "include/common/utils/contract.h" #include "plugin/device/ascend/hal/device/profiling/profiling_reporter.h" namespace mindspore { diff --git a/mindspore/ccsrc/plugin/device/ascend/hal/device/tasksink/task_generator.cc b/mindspore/ccsrc/plugin/device/ascend/hal/device/tasksink/task_generator.cc index 290ce70a289..def8287f2c5 100644 --- a/mindspore/ccsrc/plugin/device/ascend/hal/device/tasksink/task_generator.cc +++ b/mindspore/ccsrc/plugin/device/ascend/hal/device/tasksink/task_generator.cc @@ -18,8 +18,9 @@ #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "kernel/task_stream.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/ms_utils.h" #ifndef ENABLE_SECURITY #include "plugin/device/ascend/hal/device/profiling/profiling_utils.h" @@ -64,8 +65,8 @@ void TaskGenerator::LaunchAddrCleanAkgKernel(const CNodePtr &anf_node_ptr, Addre MS_EXCEPTION_IF_NULL(kernel_inputs); // akg process // set atomic clean addr - if (AnfAlgo::HasNodeAttr(kAttrAtomicOutputIndexs, anf_node_ptr)) { - auto clean_output_indexs = AnfAlgo::GetNodeAttr>(anf_node_ptr, kAttrAtomicOutputIndexs); + if (common::AnfAlgo::HasNodeAttr(kAttrAtomicOutputIndexs, anf_node_ptr)) { + auto clean_output_indexs = common::AnfAlgo::GetNodeAttr>(anf_node_ptr, kAttrAtomicOutputIndexs); auto graph = anf_node_ptr->func_graph(); MS_EXCEPTION_IF_NULL(graph); auto manager = graph->manager(); @@ -104,13 +105,13 @@ void TaskGenerator::LaunchAddrCleanKernel(const CNodePtr &anf_node_ptr, AddressP return; } // tbe process - auto input_tensor_num = AnfAlgo::GetInputTensorNum(anf_node_ptr); + auto input_tensor_num = common::AnfAlgo::GetInputTensorNum(anf_node_ptr); for (size_t i = 0; i < input_tensor_num; i++) { // set clean output addr MS_EXCEPTION_IF_NULL(anf_node_ptr->inputs()[i + 1]); auto pre_node = anf_node_ptr->input(i + 1)->cast(); - if (AnfAlgo::HasNodeAttr(kAttrAtomicOutputIndexs, pre_node)) { - auto clean_output_indexs = AnfAlgo::GetNodeAttr>(pre_node, kAttrAtomicOutputIndexs); + if (common::AnfAlgo::HasNodeAttr(kAttrAtomicOutputIndexs, pre_node)) { + auto clean_output_indexs = common::AnfAlgo::GetNodeAttr>(pre_node, kAttrAtomicOutputIndexs); for (auto index : clean_output_indexs) { auto device_address = AnfAlgo::GetOutputAddr(pre_node, index); kernel::AddressPtr input = std::make_shared(); @@ -124,8 +125,9 @@ void TaskGenerator::LaunchAddrCleanKernel(const CNodePtr &anf_node_ptr, AddressP MS_LOG(DEBUG) << "AtomicAddClean clean output size:" << clean_output_indexs.size(); } // set clean workspace address - if (AnfAlgo::HasNodeAttr(kAttrAtomicWorkspaceIndexs, pre_node)) { - auto clean_workspace_indexs = AnfAlgo::GetNodeAttr>(pre_node, kAttrAtomicWorkspaceIndexs); + if (common::AnfAlgo::HasNodeAttr(kAttrAtomicWorkspaceIndexs, pre_node)) { + auto clean_workspace_indexs = + common::AnfAlgo::GetNodeAttr>(pre_node, kAttrAtomicWorkspaceIndexs); for (const auto &index : clean_workspace_indexs) { auto device_address = AnfAlgo::GetWorkspaceAddr(pre_node, index); kernel::AddressPtr workspace = std::make_shared(); @@ -139,7 +141,7 @@ void TaskGenerator::LaunchAddrCleanKernel(const CNodePtr &anf_node_ptr, AddressP MS_LOG(DEBUG) << "AtomicAddClean clean workspace size:" << clean_workspace_indexs.size(); } } - auto clear_mems = AnfAlgo::GetNodeAttr>(anf_node_ptr, kAttrAtomicAddMemSize); + auto clear_mems = common::AnfAlgo::GetNodeAttr>(anf_node_ptr, kAttrAtomicAddMemSize); if (kernel_inputs->size() != clear_mems.size()) { MS_LOG(EXCEPTION) << "AtomicAddClean kernel inputs size not equal clear memory size, kernel inputs size:" << kernel_inputs->size() << ",clean mem size" << clear_mems.size(); @@ -157,9 +159,9 @@ bool TaskGenerator::LaunchKernel(const CNodePtr &anf_node_ptr, uint32_t stream_i MS_EXCEPTION_IF_NULL(kernel_mod); kernel_mod->set_unique_name(anf_node_ptr->UniqueName()); kernel_mod->set_fullname(anf_node_ptr->fullname_with_scope()); - kernel_mod->set_is_monad(AnfAlgo::IsNodeInputContainMonad(anf_node_ptr) && HasAbstractMonad(anf_node_ptr)); - auto op_name = AnfAlgo::GetCNodeName(anf_node_ptr); - if (AnfAlgo::IsNonTaskOp(anf_node_ptr)) { + kernel_mod->set_is_monad(common::AnfAlgo::IsNodeInputContainMonad(anf_node_ptr) && HasAbstractMonad(anf_node_ptr)); + auto op_name = common::AnfAlgo::GetCNodeName(anf_node_ptr); + if (common::AnfAlgo::IsNonTaskOp(anf_node_ptr)) { MS_LOG(INFO) << "Skip task generation for NonTask op " << anf_node_ptr->fullname_with_scope(); auto debug_info = std::make_shared(); MS_EXCEPTION_IF_NULL(debug_info); @@ -170,9 +172,9 @@ bool TaskGenerator::LaunchKernel(const CNodePtr &anf_node_ptr, uint32_t stream_i } if (op_name != kAtomicAddrCleanOpName) { - size_t input_num = AnfAlgo::GetInputTensorNum(anf_node_ptr); + size_t input_num = common::AnfAlgo::GetInputTensorNum(anf_node_ptr); for (size_t i = 0; i < input_num; ++i) { - if (AnfAlgo::IsNoneInput(anf_node_ptr, i)) { + if (common::AnfAlgo::IsNoneInput(anf_node_ptr, i)) { continue; } auto real_input_index = AnfAlgo::GetRealInputIndex(anf_node_ptr, i); @@ -182,10 +184,10 @@ bool TaskGenerator::LaunchKernel(const CNodePtr &anf_node_ptr, uint32_t stream_i input->addr = device_address->ptr_; input->size = device_address->size_; - auto prenode_with_index = AnfAlgo::GetPrevNodeOutput(anf_node_ptr, i); + auto prenode_with_index = common::AnfAlgo::GetPrevNodeOutput(anf_node_ptr, i); MS_EXCEPTION_IF_NULL(prenode_with_index.first); if (AnfUtils::IsRealCNodeKernel(prenode_with_index.first)) { - if (AnfAlgo::IsNonTaskOp(prenode_with_index.first->cast())) { + if (common::AnfAlgo::IsNonTaskOp(prenode_with_index.first->cast())) { // use memory offset to implement NonTask Type Split op // when op A -> split(NonTask) -> op B, op B's input addr is split's input0's addr + offset // offset is split's output index * split's output size @@ -202,7 +204,7 @@ bool TaskGenerator::LaunchKernel(const CNodePtr &anf_node_ptr, uint32_t stream_i // No kernel output if output of the cnode is monad, such as LabelSwitch. if (!HasAbstractMonad(anf_node_ptr)) { - size_t output_num = AnfAlgo::GetOutputTensorNum(anf_node_ptr); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(anf_node_ptr); for (size_t i = 0; i < output_num; ++i) { auto it = AnfAlgo::GetOutputAddr(anf_node_ptr, i, false); AddressPtr output = std::make_shared
(); diff --git a/mindspore/ccsrc/plugin/device/ascend/hal/hardware/ascend_device_context.cc b/mindspore/ccsrc/plugin/device/ascend/hal/hardware/ascend_device_context.cc index 95ebf0e1bfc..66336b36701 100644 --- a/mindspore/ccsrc/plugin/device/ascend/hal/hardware/ascend_device_context.cc +++ b/mindspore/ccsrc/plugin/device/ascend/hal/hardware/ascend_device_context.cc @@ -22,8 +22,8 @@ #include "runtime/dev.h" #include "plugin/device/ascend/optimizer/ascend_backend_optimization.h" #include "common/graph_kernel/adapter/graph_kernel_optimization.h" -#include "utils/context/graph_kernel_flags.h" -#include "utils/utils.h" +#include "include/common/utils/context/graph_kernel_flags.h" +#include "include/common/utils/utils.h" #include "plugin/device/ascend/hal/device/kernel_select_ascend.h" #include "runtime/device/kernel_adjust.h" #include "runtime/device/memory_manager.h" @@ -83,7 +83,7 @@ CNodePtr GetNextLabelSet(const std::vector &kernel_nodes, uint32_t ind MS_LOG(EXCEPTION) << "there is no node after this node:" << kernel_nodes[index]->DebugString(); } auto kernel = kernel_nodes[index + 1]; - if (AnfAlgo::GetCNodeName(kernel) != kLabelSetOpName) { + if (common::AnfAlgo::GetCNodeName(kernel) != kLabelSetOpName) { MS_LOG(EXCEPTION) << "the node is not labelset follow labelgoto/labelswitch, node: " << kernel_nodes[index]->DebugString(); } @@ -104,14 +104,14 @@ std::vector HandleRecursiveCall(const std::vector &kernel_cn } else { back->emplace_back(kernel_cnodes[i]); } - if (AnfAlgo::HasNodeAttr(kAttrRecursiveEnd, kernel_cnodes[i])) { + if (common::AnfAlgo::HasNodeAttr(kAttrRecursiveEnd, kernel_cnodes[i])) { *index = i; back->insert(back->end(), back_temp.begin(), back_temp.end()); return front; } - if (AnfAlgo::HasNodeAttr(kAttrRecursive, kernel_cnodes[i])) { + if (common::AnfAlgo::HasNodeAttr(kAttrRecursive, kernel_cnodes[i])) { back_flag = true; - if (!AnfAlgo::IsLabelIndexInNode(kernel_cnodes[i], back_label)) { + if (!common::AnfAlgo::IsLabelIndexInNode(kernel_cnodes[i], back_label)) { auto temp = HandleRecursiveCall(kernel_cnodes, back_label, &(++i), &back_temp); front.insert(front.end(), temp.begin(), temp.end()); } @@ -130,11 +130,11 @@ void UnfoldRecursiveExecOrder(KernelGraph *kernel_graph) { std::vector mem_reuse_order; mem_reuse_order.reserve(kernel_cnodes.size()); for (uint32_t i = 0; i < kernel_cnodes.size(); i++) { - if (!AnfAlgo::HasNodeAttr(kAttrRecursiveStart, kernel_cnodes[i])) { + if (!common::AnfAlgo::HasNodeAttr(kAttrRecursiveStart, kernel_cnodes[i])) { mem_reuse_order.emplace_back(kernel_cnodes[i]); continue; } - auto label_id = AnfAlgo::GetNodeAttr(kernel_cnodes[i], kAttrLabelIndex); + auto label_id = common::AnfAlgo::GetNodeAttr(kernel_cnodes[i], kAttrLabelIndex); std::vector back; auto front = HandleRecursiveCall(kernel_cnodes, label_id, &i, &back); mem_reuse_order.insert(mem_reuse_order.end(), front.begin(), front.end()); @@ -147,11 +147,11 @@ void GetSubGraphExecOrder(const KernelGraph *kernel_graph, uint32_t index, const std::vector *mem_reuse_order) { MS_EXCEPTION_IF_NULL(kernel_graph); MS_EXCEPTION_IF_NULL(mem_reuse_order); - auto label_id = AnfAlgo::GetNodeAttr(back_node, kAttrLabelIndex); + auto label_id = common::AnfAlgo::GetNodeAttr(back_node, kAttrLabelIndex); auto kernel_cnodes = kernel_graph->execution_order(); for (auto i = index; i < kernel_cnodes.size(); i++) { mem_reuse_order->emplace_back(kernel_cnodes[i]); - if (AnfAlgo::IsLabelIndexInNode(kernel_cnodes[i], label_id)) { + if (common::AnfAlgo::IsLabelIndexInNode(kernel_cnodes[i], label_id)) { return; } } @@ -167,10 +167,10 @@ void InitMemReuseExecOrder(KernelGraph *kernel_graph) { std::vector mem_reuse_order; for (uint32_t i = 0; i < kernel_cnodes.size(); i++) { mem_reuse_order.emplace_back(kernel_cnodes[i]); - if (AnfAlgo::CheckPrimitiveType(kernel_cnodes[i], prim::kPrimLabelSwitch) && - !AnfAlgo::HasNodeAttr(kAttrRecursive, kernel_cnodes[i]) && - !AnfAlgo::HasNodeAttr(kAttrReturn, kernel_cnodes[i])) { - auto label_list = AnfAlgo::GetNodeAttr>(kernel_cnodes[i], kAttrLabelSwitchList); + if (common::AnfAlgo::CheckPrimitiveType(kernel_cnodes[i], prim::kPrimLabelSwitch) && + !common::AnfAlgo::HasNodeAttr(kAttrRecursive, kernel_cnodes[i]) && + !common::AnfAlgo::HasNodeAttr(kAttrReturn, kernel_cnodes[i])) { + auto label_list = common::AnfAlgo::GetNodeAttr>(kernel_cnodes[i], kAttrLabelSwitchList); for (auto label_id : label_list) { if (label_id_index_map.find(label_id) == label_id_index_map.end()) { continue; @@ -180,10 +180,10 @@ void InitMemReuseExecOrder(KernelGraph *kernel_graph) { } continue; } - if (AnfAlgo::CheckPrimitiveType(kernel_cnodes[i], prim::kPrimLabelGoto) && - !AnfAlgo::HasNodeAttr(kAttrRecursive, kernel_cnodes[i]) && - !AnfAlgo::HasNodeAttr(kAttrReturn, kernel_cnodes[i])) { - auto label_id = AnfAlgo::GetNodeAttr(kernel_cnodes[i], kAttrLabelIndex); + if (common::AnfAlgo::CheckPrimitiveType(kernel_cnodes[i], prim::kPrimLabelGoto) && + !common::AnfAlgo::HasNodeAttr(kAttrRecursive, kernel_cnodes[i]) && + !common::AnfAlgo::HasNodeAttr(kAttrReturn, kernel_cnodes[i])) { + auto label_id = common::AnfAlgo::GetNodeAttr(kernel_cnodes[i], kAttrLabelIndex); if (label_id_index_map.find(label_id) == label_id_index_map.end()) { continue; } @@ -191,9 +191,9 @@ void InitMemReuseExecOrder(KernelGraph *kernel_graph) { GetSubGraphExecOrder(kernel_graph, label_id_index_map[label_id], back_node, &mem_reuse_order); continue; } - if (AnfAlgo::CheckPrimitiveType(kernel_cnodes[i], prim::kPrimLabelSet) && - !AnfAlgo::HasNodeAttr(kAttrRecursive, kernel_cnodes[i])) { - auto label_id = AnfAlgo::GetNodeAttr(kernel_cnodes[i], kAttrLabelIndex); + if (common::AnfAlgo::CheckPrimitiveType(kernel_cnodes[i], prim::kPrimLabelSet) && + !common::AnfAlgo::HasNodeAttr(kAttrRecursive, kernel_cnodes[i])) { + auto label_id = common::AnfAlgo::GetNodeAttr(kernel_cnodes[i], kAttrLabelIndex); if (label_id_index_map.find(label_id) != label_id_index_map.end()) { MS_LOG(EXCEPTION) << "Two labelsets with same label id."; } @@ -415,7 +415,7 @@ void AscendDeviceContext::PreprocessBeforeRunGraph(const KernelGraphPtr &graph) // TODO(dsj): for ms_function running in graph_mode. should be delete later const std::vector &kernels = graph->execution_order(); for (const auto &kernel : kernels) { - AnfAlgo::SetNodeAttr(kAttrMSFunction, MakeValue(true), kernel); + common::AnfAlgo::SetNodeAttr(kAttrMSFunction, MakeValue(true), kernel); } PROF_END(preprocess_before_run_graph); @@ -424,21 +424,21 @@ void AscendDeviceContext::PreprocessBeforeRunGraph(const KernelGraphPtr &graph) void AscendDeviceContext::AssignOutputNopNodeDeviceAddress(const KernelGraphPtr &graph) const { MS_EXCEPTION_IF_NULL(graph); - auto outputs = AnfAlgo::GetAllOutput(graph->output(), {prim::kPrimTupleGetItem}); + auto outputs = common::AnfAlgo::GetAllOutput(graph->output(), {prim::kPrimTupleGetItem}); for (auto output : outputs) { if (!output->isa() || !AnfUtils::IsRealKernel(output)) { continue; } - if (!opt::IsNopNode(output)) { + if (!common::AnfAlgo::IsNopNode(output)) { continue; } - if (!AnfAlgo::IsNeedSkipNopOpAddr(output)) { + if (!common::AnfAlgo::IsNeedSkipNopOpAddr(output)) { continue; } - size_t input_num = AnfAlgo::GetInputTensorNum(output); + size_t input_num = common::AnfAlgo::GetInputTensorNum(output); if (input_num != 1) { MS_LOG(WARNING) << "The input number of nop node :" << output->fullname_with_scope() << " is " << input_num << ", not equal 1"; @@ -456,7 +456,7 @@ void AscendDeviceContext::AssignOutputNopNodeDeviceAddress(const KernelGraphPtr device_address->set_is_ptr_persisted(true); device_address->set_host_shape(trans::GetRuntimePaddingShape(output, 0)); AnfAlgo::SetOutputAddr(device_address, 0, output.get()); - AnfAlgo::SetNodeAttr(kAttrSkipNopOpAddr, MakeValue(false), output); + common::AnfAlgo::SetNodeAttr(kAttrSkipNopOpAddr, MakeValue(false), output); MS_LOG(INFO) << "Assign device address to output nop node " << output->fullname_with_scope(); } } @@ -676,18 +676,18 @@ void AscendDeviceContext::PreprocessBeforeRunSingleOpGraph(const KernelGraphPtr for (const auto &node : nodes) { // Remove placeholder - auto op_name = AnfAlgo::GetCNodeName(node); + auto op_name = common::AnfAlgo::GetCNodeName(node); static const std::set place_holder_nodes = {kDynamicRNNOpName, kDynamicGRUV2OpName}; auto iter = place_holder_nodes.find(op_name); if (iter != place_holder_nodes.end()) { - auto none_index = AnfAlgo::GetNodeAttr>(node, kAttrPlaceHolderIndex); + auto none_index = common::AnfAlgo::GetNodeAttr>(node, kAttrPlaceHolderIndex); // Remove seq_length - auto input_num = AnfAlgo::GetInputTensorNum(node); - std::vector new_inputs = {AnfAlgo::GetCNodePrimitiveNode(node)}; + auto input_num = common::AnfAlgo::GetInputTensorNum(node); + std::vector new_inputs = {common::AnfAlgo::GetCNodePrimitiveNode(node)}; for (size_t i = 0; i < input_num; ++i) { auto item = std::find(none_index.begin(), none_index.end(), i); if (item == none_index.end()) { - auto input_node = AnfAlgo::GetInputNode(node, i); + auto input_node = common::AnfAlgo::GetInputNode(node, i); new_inputs.emplace_back(input_node); } } @@ -695,7 +695,7 @@ void AscendDeviceContext::PreprocessBeforeRunSingleOpGraph(const KernelGraphPtr } // Save the nop_op that needs to be memcpy - if (op_name == prim::kPrimTranspose->name() && AnfAlgo::HasNodeAttr(kAttrNopOp, node)) { + if (op_name == prim::kPrimTranspose->name() && common::AnfAlgo::HasNodeAttr(kAttrNopOp, node)) { nop_op_to_memcpy_.insert(node); } } @@ -782,7 +782,7 @@ void *AscendDeviceContext::GetKernelStream(const CNodePtr &node) const { bool AscendDeviceContext::GetKernelRealInputs(const CNodePtr &kernel, const vector &inputs, std::vector *real_inputs) const { - auto input_num = AnfAlgo::GetInputTensorNum(kernel); + auto input_num = common::AnfAlgo::GetInputTensorNum(kernel); if (input_num != inputs.size()) { MS_LOG(ERROR) << "Input num is " << input_num << " but input address num is " << inputs.size(); return false; @@ -818,7 +818,7 @@ bool AscendDeviceContext::LaunchKernel(const CNodePtr &kernel, const vector locker(launch_mutex_); // launch atomic clean if (!LaunchAtomicClean(kernel, workspace, outputs)) { @@ -833,7 +833,7 @@ bool AscendDeviceContext::LaunchKernel(const CNodePtr &kernel, const vectorfullname_with_scope(); // TODO(dsj): for ms_function running in graph_mode. should be delete later - if (is_dynamic_shape && !(AnfAlgo::GetBooleanAttr(kernel, kAttrMSFunction))) { + if (is_dynamic_shape && !(common::AnfAlgo::GetBooleanAttr(kernel, kAttrMSFunction))) { kernel::AscendKernelMod *ascend_kernel = dynamic_cast(kernel_mod); MS_EXCEPTION_IF_NULL(ascend_kernel); ascend_kernel->InitDynamicKernel(kernel, GetKernelStream(kernel)); @@ -875,8 +875,8 @@ bool AscendDeviceContext::LaunchAtomicClean(const CNodePtr &node, const std::vec if (atomic_node->inputs().size() != kAtomicCleanInputSize) { MS_LOG(EXCEPTION) << "Atomic Addr clean Node Input nodes not equal 2."; } - if (AnfAlgo::HasNodeAttr(kAttrAtomicOutputIndexs, node)) { - auto clean_output_indexes = AnfAlgo::GetNodeAttr>(node, kAttrAtomicOutputIndexs); + if (common::AnfAlgo::HasNodeAttr(kAttrAtomicOutputIndexs, node)) { + auto clean_output_indexes = common::AnfAlgo::GetNodeAttr>(node, kAttrAtomicOutputIndexs); for (auto output_index : clean_output_indexes) { if (output_index >= outputs.size()) { MS_LOG(EXCEPTION) << "Invalid output_index:" << output_index << " except less than " << outputs.size(); @@ -886,8 +886,8 @@ bool AscendDeviceContext::LaunchAtomicClean(const CNodePtr &node, const std::vec } // The workspace addr need to clean - if (AnfAlgo::HasNodeAttr(kAttrAtomicWorkspaceIndexs, node)) { - auto clean_workspace_indexes = AnfAlgo::GetNodeAttr>(node, kAttrAtomicWorkspaceIndexs); + if (common::AnfAlgo::HasNodeAttr(kAttrAtomicWorkspaceIndexs, node)) { + auto clean_workspace_indexes = common::AnfAlgo::GetNodeAttr>(node, kAttrAtomicWorkspaceIndexs); for (auto workspace_index : clean_workspace_indexes) { if (workspace_index >= workspace.size()) { MS_LOG(EXCEPTION) << "Invalid workspace_index:" << workspace_index << " except less than " << workspace.size(); diff --git a/mindspore/ccsrc/plugin/device/ascend/hal/hardware/ascend_graph_optimization.cc b/mindspore/ccsrc/plugin/device/ascend/hal/hardware/ascend_graph_optimization.cc index 229d5093a9a..03213f24d4f 100644 --- a/mindspore/ccsrc/plugin/device/ascend/hal/hardware/ascend_graph_optimization.cc +++ b/mindspore/ccsrc/plugin/device/ascend/hal/hardware/ascend_graph_optimization.cc @@ -21,7 +21,7 @@ #include "plugin/device/ascend/optimizer/ascend_backend_optimization.h" #include "common/graph_kernel/adapter/graph_kernel_optimization.h" #include "backend/common/session/ascend_auto_monad.h" -#include "utils/context/graph_kernel_flags.h" +#include "include/common/utils/context/graph_kernel_flags.h" #include "plugin/device/ascend/hal/device/kernel_select_ascend.h" #include "runtime/device/kernel_adjust.h" @@ -128,7 +128,7 @@ void AscendGraphOptimization::OptimizeExecutionOrder(const KernelGraphPtr &graph } auto execution_order = graph->execution_order(); - AnfAlgo::ReorderExecList(NOT_NULL(&execution_order)); + common::AnfAlgo::ReorderExecList(NOT_NULL(&execution_order)); graph->set_execution_order(execution_order); device::KernelAdjust::GetInstance().InsertOverflowCheckOperations(NOT_NULL(graph)); @@ -306,8 +306,8 @@ void AscendGraphOptimization::UnifyMindIR(const KernelGraphPtr &graph) { void AscendGraphOptimization::SetOperatorInfo(const std::vector &nodes) { for (const auto &node : nodes) { auto status = device::ascend::SelectKernelInfo(node); - AnfAlgo::EraseNodeAttr(kAttrPynativeNextOpName, node); - AnfAlgo::EraseNodeAttr(kAttrPynativeNextIndex, node); + common::AnfAlgo::EraseNodeAttr(kAttrPynativeNextOpName, node); + common::AnfAlgo::EraseNodeAttr(kAttrPynativeNextIndex, node); if (status == device::ascend::kStatusRaisePrecision) { raise_precision_count_++; } else if (status == device::ascend::kStatusReducePrecision) { diff --git a/mindspore/ccsrc/plugin/device/ascend/hal/hccl_adapter/all_to_all_v_calc_param.cc b/mindspore/ccsrc/plugin/device/ascend/hal/hccl_adapter/all_to_all_v_calc_param.cc index b9d9fea8e00..2e011390435 100644 --- a/mindspore/ccsrc/plugin/device/ascend/hal/hccl_adapter/all_to_all_v_calc_param.cc +++ b/mindspore/ccsrc/plugin/device/ascend/hal/hccl_adapter/all_to_all_v_calc_param.cc @@ -18,7 +18,8 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" -#include "transform/graph_ir/util.h" +#include "include/common/utils/anfalgo.h" +#include "include/transform/graph_ir/util.h" #include "runtime/device/memory_manager.h" namespace mindspore::hccl { @@ -44,15 +45,15 @@ AllToAllvCalcParam::AllToAllvCalcParam(const CNodeWeakPtr &cnode, uint32_t rank_ void AllToAllvCalcParam::CalcOpParam() { CNodePtr cnode = node_.lock(); MS_EXCEPTION_IF_NULL(cnode); - size_t input_num = AnfAlgo::GetInputTensorNum(cnode); + size_t input_num = common::AnfAlgo::GetInputTensorNum(cnode); // ignore send empty input - if (AnfAlgo::HasNodeAttr(kAttrNeedDropInput, cnode)) { - bool need_drop_input = AnfAlgo::GetNodeAttr(cnode, kAttrNeedDropInput); + if (common::AnfAlgo::HasNodeAttr(kAttrNeedDropInput, cnode)) { + bool need_drop_input = common::AnfAlgo::GetNodeAttr(cnode, kAttrNeedDropInput); if (need_drop_input) { input_num = 0; } } - size_t output_num = AnfAlgo::GetOutputTensorNum(cnode); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(cnode); std::vector input_aligned_mem_size(input_num); std::vector output_aligned_mem_size(output_num); std::vector input_real_mem_size(input_num); @@ -88,7 +89,7 @@ void AllToAllvCalcParam::CalcMemOffset(const std::vector &mem_sizes, con std::vector *displs) { CNodePtr cnode = node_.lock(); MS_EXCEPTION_IF_NULL(cnode); - auto rank_ids = AnfAlgo::GetNodeAttr>(cnode, rank_ids_attr); + auto rank_ids = common::AnfAlgo::GetNodeAttr>(cnode, rank_ids_attr); if (mem_sizes.size() != rank_ids.size() || real_sizes.size() != rank_ids.size()) { MS_LOG(EXCEPTION) << "Invalid addr num " << mem_sizes.size() << " and " << real_sizes.size() << " must be equal to rank ids size " << rank_ids.size(); diff --git a/mindspore/ccsrc/plugin/device/ascend/hal/hccl_adapter/converter.cc b/mindspore/ccsrc/plugin/device/ascend/hal/hccl_adapter/converter.cc index 352e3a1f883..42e11e863fa 100644 --- a/mindspore/ccsrc/plugin/device/ascend/hal/hccl_adapter/converter.cc +++ b/mindspore/ccsrc/plugin/device/ascend/hal/hccl_adapter/converter.cc @@ -23,9 +23,10 @@ #include "graph/debug/ge_attr_define.h" #undef google #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "utils/log_adapter.h" #include "mindspore/core/base/core_ops.h" -#include "transform/graph_ir/util.h" +#include "include/transform/graph_ir/util.h" #include "plugin/device/ascend/hal/hccl_adapter/all_to_all_v_calc_param.h" namespace mindspore::hccl { @@ -90,13 +91,13 @@ static T ConvertAttr(const CNodePtr &cnode, const ge::OpDescPtr &ge_op, const st const std::string &ge_attr_name) { MS_EXCEPTION_IF_NULL(cnode); MS_EXCEPTION_IF_NULL(ge_op); - if (!AnfAlgo::HasNodeAttr(anf_attr_name, cnode)) { + if (!common::AnfAlgo::HasNodeAttr(anf_attr_name, cnode)) { MS_LOG(INFO) << "Node " << cnode->DebugString() << " has no attr " << anf_attr_name << ", skip."; return T(); } bool ret; - auto attr = AnfAlgo::GetNodeAttr(cnode, anf_attr_name); + auto attr = common::AnfAlgo::GetNodeAttr(cnode, anf_attr_name); if constexpr (IsString::value) { ret = ge::AttrUtils::SetStr(*ge_op, ge_attr_name, attr); } else if constexpr (IsVector::value) { @@ -172,8 +173,8 @@ std::tuple GenerateStubGeNode(const AnfNodePtr ge::OpDescPtr op_desc = std::make_shared(kStubDataStructureName, ge_node_name); MS_EXCEPTION_IF_NULL(op_desc); - size_t input_num = AnfAlgo::GetInputTensorNum(cnode); - size_t output_num = AnfAlgo::GetOutputTensorNum(cnode); + size_t input_num = common::AnfAlgo::GetInputTensorNum(cnode); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(cnode); for (size_t i = 0; i < input_num; ++i) { std::vector ge_shape; auto ms_shape = AnfAlgo::GetInputDeviceShape(cnode, i); diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/aicpu/aicpu_attr_to_input_registry.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/aicpu/aicpu_attr_to_input_registry.cc index 6ba0a345d1a..762a9a1d11a 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/aicpu/aicpu_attr_to_input_registry.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/aicpu/aicpu_attr_to_input_registry.cc @@ -17,6 +17,7 @@ #include "plugin/device/ascend/kernel/aicpu/aicpu_attr_to_input_registry.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "base/core_ops.h" namespace mindspore { @@ -32,7 +33,7 @@ std::map>> AicpuOpAttrToInputMap = {prim::kPrimOneHot->name(), {{"depth", 1}}}}; bool GetAicpuOpAttrToInputInfo(const CNodePtr &kernel_node, std::vector> *info) { - std::string op_name = AnfAlgo::GetCNodeName(kernel_node); + std::string op_name = common::AnfAlgo::GetCNodeName(kernel_node); if (AicpuOpAttrToInputMap.find(op_name) == AicpuOpAttrToInputMap.end()) { return false; } else { diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/aicpu/aicpu_kernel_build.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/aicpu/aicpu_kernel_build.cc index b14ad89b6c1..d88d10d468b 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/aicpu/aicpu_kernel_build.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/aicpu/aicpu_kernel_build.cc @@ -22,7 +22,7 @@ #include #include #include -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "runtime/device/kernel_runtime.h" #include "plugin/device/ascend/kernel/aicpu/aicpu_kernel_mod.h" #include "plugin/device/ascend/kernel/aicpu/dynamic_aicpu_kernel_mod.h" @@ -31,6 +31,7 @@ #include "proto/attr.pb.h" #include "proto/node_def.pb.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "plugin/device/ascend/kernel/aicpu/aicpu_util.h" #include "plugin/device/ascend/kernel/aicpu/aicpu_kernel_load.h" #include "backend/common/session/kernel_graph.h" @@ -81,8 +82,8 @@ bool SetIOSize(const std::shared_ptr &anf_node, const std::shared_ptr input_size_list; std::vector output_size_list; - size_t input_num = AnfAlgo::GetInputTensorNum(anf_node); - size_t output_num = AnfAlgo::GetOutputTensorNum(anf_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(anf_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(anf_node); if (!SetIOIputSize(anf_node, input_num, &input_size_list)) { return false; @@ -146,7 +147,7 @@ void ParseAttrValue(const std::string &type, const std::string &attr_name, const void SetNodeAttr(const std::shared_ptr &anf_node, mindspore::NodeDef *proto) { MS_EXCEPTION_IF_NULL(anf_node); MS_EXCEPTION_IF_NULL(proto); - std::string op_name = AnfAlgo::GetCNodeName(anf_node); + std::string op_name = common::AnfAlgo::GetCNodeName(anf_node); if (op_name == kInitDataSetQueue) { op_name = kInitData; } @@ -157,7 +158,7 @@ void SetNodeAttr(const std::shared_ptr &anf_node, mindspore::NodeDef *p auto op_info_ptr = mindspore::kernel::OpLib::FindOp(op_name, OpImplyType::kAICPU); MS_EXCEPTION_IF_NULL(op_info_ptr); auto attrs_ptr = op_info_ptr->attrs_ptr(); - auto primitive = AnfAlgo::GetCNodePrimitive(anf_node); + auto primitive = common::AnfAlgo::GetCNodePrimitive(anf_node); MS_EXCEPTION_IF_NULL(primitive); ::google::protobuf::Map<::std::string, ::mindspore::AttrValue> *node_attr = proto->mutable_attrs(); for (const auto &attr_ptr : attrs_ptr) { @@ -181,15 +182,15 @@ void SetNodeAttr(const std::shared_ptr &anf_node, mindspore::NodeDef *p void SetNodeInputs(const std::shared_ptr &anf_node, mindspore::NodeDef *proto) { MS_EXCEPTION_IF_NULL(proto); MS_EXCEPTION_IF_NULL(anf_node); - size_t input_num = AnfAlgo::GetInputTensorNum(anf_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(anf_node); if (input_num == 0) { - MS_LOG(INFO) << "Node [" << AnfAlgo::GetCNodeName(anf_node) << "] does not have input."; + MS_LOG(INFO) << "Node [" << common::AnfAlgo::GetCNodeName(anf_node) << "] does not have input."; return; } std::vector input_size_list; if (!SetIOIputSize(anf_node, input_num, &input_size_list)) { - MS_LOG(ERROR) << "Node [" << AnfAlgo::GetCNodeName(anf_node) << "] get input size list failed."; + MS_LOG(ERROR) << "Node [" << common::AnfAlgo::GetCNodeName(anf_node) << "] get input size list failed."; return; } @@ -229,12 +230,12 @@ void SetNodeInputs(const std::shared_ptr &anf_node, mindspore::NodeDef void SetNodeOutputs(const std::shared_ptr &anf_node, mindspore::NodeDef *proto) { MS_EXCEPTION_IF_NULL(proto); MS_EXCEPTION_IF_NULL(anf_node); - size_t output_num = AnfAlgo::GetOutputTensorNum(anf_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(anf_node); if (output_num == 1 && HasAbstractMonad(anf_node)) { output_num = 0; } if (output_num == 0) { - MS_LOG(INFO) << "Node [" << AnfAlgo::GetCNodeName(anf_node) << "] does not have output. "; + MS_LOG(INFO) << "Node [" << common::AnfAlgo::GetCNodeName(anf_node) << "] does not have output. "; return; } @@ -254,7 +255,7 @@ void SetNodeOutputs(const std::shared_ptr &anf_node, mindspore::NodeDef int64_t data_size = 1; if (!GetShapeSize(output_shape, TypeIdToType(output_type), &data_size)) { - MS_LOG(ERROR) << "Node [" << AnfAlgo::GetCNodeName(anf_node) << "] get output size failed for output " + MS_LOG(ERROR) << "Node [" << common::AnfAlgo::GetCNodeName(anf_node) << "] get output size failed for output " << output_index; return; } @@ -268,7 +269,7 @@ void SetNodeOutputs(const std::shared_ptr &anf_node, mindspore::NodeDef void SetNodedefProto(const std::shared_ptr &anf_node, mindspore::NodeDef *proto) { MS_EXCEPTION_IF_NULL(anf_node); MS_EXCEPTION_IF_NULL(proto); - std::string op_name = AnfAlgo::GetCNodeName(anf_node); + std::string op_name = common::AnfAlgo::GetCNodeName(anf_node); if (op_name == kInitDataSetQueue) { op_name = kInitData; } @@ -385,14 +386,14 @@ void CreateExtInfo(const std::shared_ptr &anf_node, const std::shared_p return; } - if (!AnfAlgo::IsDynamicShape(anf_node)) { + if (!common::AnfAlgo::IsDynamicShape(anf_node)) { return; } uint64_t ext_info_head_len = kExtInfoHeadSize; std::string ext_info; - size_t input_num = AnfAlgo::GetInputTensorNum(anf_node); - size_t output_num = AnfAlgo::GetOutputTensorNum(anf_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(anf_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(anf_node); // 1.addr:unknown shape type uint64_t ext_info_len = ext_info.size(); @@ -409,7 +410,7 @@ void CreateExtInfo(const std::shared_ptr &anf_node, const std::shared_p char *ext_info_buf = ext_info.data(); UnknowShapeOpType shape_type = UnknowShapeOpType::DEPEND_IN_SHAPE; - auto op_name = AnfAlgo::GetCNodeName(anf_node); + auto op_name = common::AnfAlgo::GetCNodeName(anf_node); if (kComputeDepend.find(op_name) != kComputeDepend.end()) { shape_type = UnknowShapeOpType::DEPEND_COMPUTE; } @@ -424,12 +425,12 @@ void CreateExtInfo(const std::shared_ptr &anf_node, const std::shared_p KernelModPtr AicpuOpBuild(const std::shared_ptr &anf_node) { MS_EXCEPTION_IF_NULL(anf_node); - std::string op_name = AnfAlgo::GetCNodeName(anf_node); + std::string op_name = common::AnfAlgo::GetCNodeName(anf_node); if (op_name == kInitDataSetQueue) { op_name = kInitData; } std::shared_ptr kernel_mod_ptr; - if (AnfAlgo::IsDynamicShape(anf_node)) { + if (common::AnfAlgo::IsDynamicShape(anf_node)) { kernel_mod_ptr = std::make_shared(anf_node); } else { kernel_mod_ptr = std::make_shared(anf_node); diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/aicpu/aicpu_kernel_load.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/aicpu/aicpu_kernel_load.cc index 6bca6e57bc6..02ed9bfa376 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/aicpu/aicpu_kernel_load.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/aicpu/aicpu_kernel_load.cc @@ -23,9 +23,10 @@ #include "runtime/kernel.h" #include "runtime/mem.h" #include "runtime/context.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/file_utils.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace kernel { @@ -155,12 +156,12 @@ bool AicpuOpKernelLoad::LoadAicpuKernelSo(const AnfNodePtr &node, MS_EXCEPTION_IF_NULL(kernel_mod_ptr); CNodePtr cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - if (!AnfAlgo::HasNodeAttr(kAttrCustAicpu, cnode)) { + if (!common::AnfAlgo::HasNodeAttr(kAttrCustAicpu, cnode)) { MS_LOG(INFO) << "Current aicpu ops:" << cnode->fullname_with_scope() << " isn't a custom ops."; return true; } - std::string so_name = "lib" + AnfAlgo::GetNodeAttr(cnode, kAttrCustAicpu) + ".so"; + std::string so_name = "lib" + common::AnfAlgo::GetNodeAttr(cnode, kAttrCustAicpu) + ".so"; if (so_name == kLibAicpuKernelSoName || so_name == kLibCpuKernelSoName) { MS_LOG(INFO) << "Aicpu so:" << so_name << " is default so."; return true; diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/aicpu/aicpu_kernel_metadata.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/aicpu/aicpu_kernel_metadata.cc index e48911bd548..53b6b1b6682 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/aicpu/aicpu_kernel_metadata.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/aicpu/aicpu_kernel_metadata.cc @@ -21,6 +21,7 @@ #include "kernel/common_utils.h" #include "plugin/device/ascend/kernel/aicpu/aicpu_util.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace kernel { @@ -28,7 +29,7 @@ void AicpuMetadataInfo(const CNodePtr &kernel_node, std::vector inputs_format{}; std::vector inputs_type{}; - auto op_name = AnfAlgo::GetCNodeName(kernel_node); + auto op_name = common::AnfAlgo::GetCNodeName(kernel_node); if (kDynamicInputOps.find(op_name) != kDynamicInputOps.end()) { - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); for (size_t input_index = 0; input_index < input_num; ++input_index) { inputs_format.emplace_back(kOpFormat_DEFAULT); - (void)inputs_type.emplace_back(AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, input_index)); + (void)inputs_type.emplace_back(common::AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, input_index)); } } std::vector outputs_format; std::vector outputs_type; - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); for (size_t output_index = 0; output_index < output_num; ++output_index) { outputs_format.emplace_back(kOpFormat_DEFAULT); - (void)outputs_type.emplace_back(AnfAlgo::GetOutputInferDataType(kernel_node, output_index)); + (void)outputs_type.emplace_back(common::AnfAlgo::GetOutputInferDataType(kernel_node, output_index)); } auto builder = KernelBuildInfo::KernelBuildInfoBuilder(); builder.SetInputsFormat(inputs_format); diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/aicpu/aicpu_kernel_mod.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/aicpu/aicpu_kernel_mod.cc index e104931d5fe..4880701f4de 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/aicpu/aicpu_kernel_mod.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/aicpu/aicpu_kernel_mod.cc @@ -23,7 +23,7 @@ #include "runtime/mem.h" #include "runtime/rt.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" #include "plugin/device/ascend/kernel/aicpu/aicpu_util.h" #include "utils/ms_context.h" #include "plugin/device/ascend/hal/device/executor/ai_cpu_dynamic_kernel.h" diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/aicpu/aicpu_util.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/aicpu/aicpu_util.cc index 5dfa45f00b3..660bb80aad1 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/aicpu/aicpu_util.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/aicpu/aicpu_util.cc @@ -16,7 +16,7 @@ #include "plugin/device/ascend/kernel/aicpu/aicpu_util.h" #include "proto/types.pb.h" #include "runtime/mem.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" namespace mindspore { namespace kernel { diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/aicpu/dynamic_aicpu_kernel_mod.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/aicpu/dynamic_aicpu_kernel_mod.cc index 2b4d18fffd4..c04e9d501db 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/aicpu/dynamic_aicpu_kernel_mod.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/aicpu/dynamic_aicpu_kernel_mod.cc @@ -22,14 +22,15 @@ #include #include "runtime/mem.h" #include "acl/acl_rt.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" #include "plugin/device/ascend/kernel/aicpu/aicpu_util.h" #include "plugin/device/ascend/hal/device/ascend_memory_manager.h" #include "utils/ms_context.h" #include "runtime/device/kernel_runtime.h" #include "runtime/kernel.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace kernel { @@ -37,7 +38,7 @@ DynamicAicpuOpKernelMod::DynamicAicpuOpKernelMod(const AnfNodePtr &anf_node_ptr) unknow_type_ = device::ascend::UnknowShapeOpType::DEPEND_IN_SHAPE; auto cnode = anf_node_ptr->cast(); if (cnode != nullptr) { - auto op_name = AnfAlgo::GetCNodeName(cnode); + auto op_name = common::AnfAlgo::GetCNodeName(cnode); if (kComputeDepend.find(op_name) != kComputeDepend.end()) { unknow_type_ = device::ascend::UnknowShapeOpType::DEPEND_COMPUTE; } @@ -54,7 +55,7 @@ DynamicAicpuOpKernelMod::~DynamicAicpuOpKernelMod() { void DynamicAicpuOpKernelMod::InferOp() { auto node = anf_node_.lock(); MS_EXCEPTION_IF_NULL(node); - if (!AnfAlgo::IsDynamicShape(node)) { + if (!common::AnfAlgo::IsDynamicShape(node)) { MS_LOG(EXCEPTION) << "The node is not dynamic shape."; } KernelMod::InferShape(); @@ -65,13 +66,13 @@ void DynamicAicpuOpKernelMod::InitOp() { MS_EXCEPTION_IF_NULL(node); auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - if (!AnfAlgo::IsDynamicShape(cnode)) { + if (!common::AnfAlgo::IsDynamicShape(cnode)) { MS_LOG(EXCEPTION) << "The node is not dynamic shape: " << cnode->fullname_with_scope(); } MS_LOG(INFO) << "UpdateExtInfo of " << cnode->fullname_with_scope() << " start"; - auto input_num = AnfAlgo::GetInputTensorNum(cnode); - auto output_num = AnfAlgo::GetOutputTensorNum(cnode); + auto input_num = common::AnfAlgo::GetInputTensorNum(cnode); + auto output_num = common::AnfAlgo::GetOutputTensorNum(cnode); if (input_num == 0 && output_num == 0) { MS_LOG(INFO) << "Node:" << cnode->fullname_with_scope() << " no need to update output shape"; return; @@ -138,7 +139,7 @@ bool DynamicAicpuOpKernelMod::Launch(const std::vector &inputs, cons MS_LOG(INFO) << "Start launch of node: " << cnode->fullname_with_scope(); // is dynamic shape - if (!AnfAlgo::IsDynamicShape(cnode)) { + if (!common::AnfAlgo::IsDynamicShape(cnode)) { MS_LOG(EXCEPTION) << "The cnode is not dynamic shape:" << cnode->fullname_with_scope(); } @@ -188,7 +189,7 @@ void DynamicAicpuOpKernelMod::UpdateOp() { MS_EXCEPTION_IF_NULL(cnode); MS_LOG(INFO) << "Aicpu " << cnode->fullname_with_scope() << " PostExecute"; // is dynamic shape - if (!AnfAlgo::IsDynamicShape(cnode)) { + if (!common::AnfAlgo::IsDynamicShape(cnode)) { MS_LOG(EXCEPTION) << "The cnode is not dynamic shape:" << cnode->fullname_with_scope(); } @@ -214,7 +215,7 @@ bool DynamicAicpuOpKernelMod::UpdateOutputShapeFromExtInfo(const CNodePtr &cnode std::vector type_ids; std::vector> shapes; - auto output_num = AnfAlgo::GetOutputTensorNum(cnode); + auto output_num = common::AnfAlgo::GetOutputTensorNum(cnode); for (size_t i = 0; i < output_num; ++i) { std::vector shape; TypeId type_id; @@ -225,7 +226,7 @@ bool DynamicAicpuOpKernelMod::UpdateOutputShapeFromExtInfo(const CNodePtr &cnode shapes.emplace_back(size_t_shape); } - AnfAlgo::SetOutputInferTypeAndShape(type_ids, shapes, cnode.get()); + common::AnfAlgo::SetOutputInferTypeAndShape(type_ids, shapes, cnode.get()); return true; } } // namespace kernel diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/akg/akg_ascend_kernel_build.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/akg/akg_ascend_kernel_build.cc index 321bf8958cc..4d1c4e0ecbd 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/akg/akg_ascend_kernel_build.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/akg/akg_ascend_kernel_build.cc @@ -22,6 +22,7 @@ #include "plugin/device/ascend/kernel/tbe/tbe_utils.h" #include "plugin/device/ascend/kernel/akg/akg_ascend_kernel_mod.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace kernel { diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/akg/akg_ascend_kernel_mod.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/akg/akg_ascend_kernel_mod.cc index f88946f001e..34875ad2efd 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/akg/akg_ascend_kernel_mod.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/akg/akg_ascend_kernel_mod.cc @@ -21,8 +21,8 @@ #include #include "runtime/rt.h" #include "utils/log_adapter.h" -#include "utils/convert_utils.h" -#include "utils/runtime_error_codes.h" +#include "include/common/utils/convert_utils.h" +#include "include/common/utils/runtime_error_codes.h" namespace mindspore { namespace kernel { diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/hccl/hccl_kernel.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/hccl/hccl_kernel.cc index b8a2d50ec13..dcf10c99248 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/hccl/hccl_kernel.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/hccl/hccl_kernel.cc @@ -18,7 +18,8 @@ #include #include "backend/common/session/anf_runtime_algorithm.h" -#include "utils/utils.h" +#include "include/common/utils/anfalgo.h" +#include "include/common/utils/utils.h" #include "utils/ms_context.h" #include "runtime/device/kernel_runtime.h" #include "plugin/device/ascend/hal/device/executor/hccl_dynamic_kernel.h" @@ -87,7 +88,7 @@ HcclKernel::~HcclKernel() { bool HcclKernel::Init(const AnfNodePtr &anf_node) { MS_EXCEPTION_IF_NULL(anf_node); - op_name_ = AnfAlgo::GetCNodeName(anf_node); + op_name_ = common::AnfAlgo::GetCNodeName(anf_node); if (op_name_ == kHcomSend) { if (!HcomUtil::GetHcomDestRank(anf_node, &dest_rank_)) { MS_LOG(ERROR) << "GetHcomDestRank fail!"; @@ -175,25 +176,25 @@ const std::vector &HcclKernel::GetOutputSizeList() const { } auto cnode = anf_node->cast(); MS_EXCEPTION_IF_NULL(cnode); - auto op_name = AnfAlgo::GetCNodeName(cnode); + auto op_name = common::AnfAlgo::GetCNodeName(cnode); int64_t rank_size = 1; - if (AnfAlgo::HasNodeAttr(kAttrRankSize, cnode)) { - rank_size = AnfAlgo::GetNodeAttr(cnode, kAttrRankSize); + if (common::AnfAlgo::HasNodeAttr(kAttrRankSize, cnode)) { + rank_size = common::AnfAlgo::GetNodeAttr(cnode, kAttrRankSize); } int64_t fusion = 0; - if (AnfAlgo::HasNodeAttr(kAttrFusion, cnode)) { - fusion = AnfAlgo::GetNodeAttr(cnode, kAttrFusion); + if (common::AnfAlgo::HasNodeAttr(kAttrFusion, cnode)) { + fusion = common::AnfAlgo::GetNodeAttr(cnode, kAttrFusion); } if (hccl_data_type_list_.size() != hccl_kernel_input_shape_list_.size()) { MS_LOG(EXCEPTION) << "Invalid data type size " << hccl_data_type_list_.size() << " diff shape size " << hccl_kernel_input_shape_list_.size(); } ulong loop_size = hccl_data_type_list_.size(); - if (AnfAlgo::GetInputTensorNum(anf_node) > 1 && op_name == kAllGatherOpName && fusion >= 1) { + if (common::AnfAlgo::GetInputTensorNum(anf_node) > 1 && op_name == kAllGatherOpName && fusion >= 1) { loop_size *= static_cast(rank_size); } if (op_name == kReduceScatterOpName && fusion >= 1) { - loop_size = AnfAlgo::GetOutputTensorNum(anf_node); + loop_size = common::AnfAlgo::GetOutputTensorNum(anf_node); } for (ulong i = 0; i < loop_size; ++i) { if (!HcomUtil::GetHcclOpSize(hccl_data_type_list_[0], hccl_kernel_output_shape_list_[i], &size)) { @@ -225,7 +226,7 @@ std::vector HcclKernel::GenTask(const std::vector &inpu if (!anf_node) { MS_LOG(EXCEPTION) << "anf_node pointer is expired."; } - std::string hccl_type = AnfAlgo::GetCNodeName(anf_node); + std::string hccl_type = common::AnfAlgo::GetCNodeName(anf_node); if (hccl_type == kReceive) { if (outputs.empty()) { MS_LOG(EXCEPTION) << "Outputs is empty"; @@ -288,7 +289,7 @@ device::DynamicKernelPtr HcclKernel::GenDynamicKernel(const CNodePtr &cnode_ptr, KernelLaunchInfo kernel_launch_info; device::KernelRuntime::GenLaunchArgs(*this, cnode_ptr, &kernel_launch_info); - std::string hccl_type = MsOpNameToHcomOpType(AnfAlgo::GetCNodeName(anf_node_.lock())); + std::string hccl_type = MsOpNameToHcomOpType(common::AnfAlgo::GetCNodeName(anf_node_.lock())); if (kernel_launch_info.inputs_.empty()) { MS_LOG(EXCEPTION) << "Hccl kernel input is empty"; @@ -308,7 +309,7 @@ device::DynamicKernelPtr HcclKernel::GenDynamicKernel(const CNodePtr &cnode_ptr, } void HcclKernel::InferOp() { - if (AnfAlgo::IsDynamicShape(anf_node_.lock())) { + if (common::AnfAlgo::IsDynamicShape(anf_node_.lock())) { KernelMod::InferShape(); } } @@ -335,7 +336,7 @@ bool HcclKernel::Launch(const std::vector &inputs, const std::vector MS_EXCEPTION_IF_NULL(stream_ptr); MS_LOG(INFO) << "Start Execute: " << cnode->DebugString(); - std::string hccl_type = MsOpNameToHcomOpType(AnfAlgo::GetCNodeName(anf_node_.lock())); + std::string hccl_type = MsOpNameToHcomOpType(common::AnfAlgo::GetCNodeName(anf_node_.lock())); HcclDataType data_type = hccl_data_type_list_[0]; ::HcomOperation op_info; @@ -374,7 +375,7 @@ void HcclKernel::InitOp() { auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - if (!AnfAlgo::IsDynamicShape(cnode)) { + if (!common::AnfAlgo::IsDynamicShape(cnode)) { MS_LOG(DEBUG) << "The node is not dynamic shape: " << cnode->fullname_with_scope(); return; } diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/hccl/hccl_kernel_build.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/hccl/hccl_kernel_build.cc index 26578211f2e..d54157a43c3 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/hccl/hccl_kernel_build.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/hccl/hccl_kernel_build.cc @@ -20,12 +20,13 @@ #include #include "plugin/device/ascend/kernel/hccl/hccl_kernel.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace kernel { KernelModPtr HcclOpBuild(const AnfNodePtr &anf_node) { MS_EXCEPTION_IF_NULL(anf_node); - std::string opname = AnfAlgo::GetCNodeName(anf_node); + std::string opname = common::AnfAlgo::GetCNodeName(anf_node); MS_LOG(INFO) << "Hccl op [" << opname << "]"; auto kerPtr = HcclKernelFactory::Get(opname); if (kerPtr == nullptr) { diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/hccl/hccl_kernel_metadata.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/hccl/hccl_kernel_metadata.cc index dbe8da95873..7584fb8344a 100755 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/hccl/hccl_kernel_metadata.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/hccl/hccl_kernel_metadata.cc @@ -18,11 +18,12 @@ #include #include #include -#include "utils/ms_device_shape_transfer.h" -#include "utils/utils.h" +#include "runtime/device/ms_device_shape_transfer.h" +#include "include/common/utils/utils.h" #include "plugin/device/ascend/kernel/hccl/hcom_util.h" #include "backend/common/session/anf_runtime_algorithm.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/anfalgo.h" +#include "include/common/utils/parallel_context.h" namespace mindspore { namespace kernel { @@ -33,7 +34,7 @@ std::string GetKernelFormat(const CNodePtr &kernel_node, size_t index) { static const std::set kReduceNoSupportedSet = {kOpFormat_FRAC_Z, kOpFormat_FRACTAL_Z_C04, kOpFormat_C1HWNCoC0}; MS_EXCEPTION_IF_NULL(kernel_node); - auto op_name = AnfAlgo::GetCNodeName(kernel_node); + auto op_name = common::AnfAlgo::GetCNodeName(kernel_node); auto parallel_context_instance = parallel::ParallelContext::GetInstance(); MS_EXCEPTION_IF_NULL(parallel_context_instance); if (parallel_context_instance->enable_parallel_optimizer() && op_name == kBroadcast) { @@ -46,7 +47,7 @@ std::string GetKernelFormat(const CNodePtr &kernel_node, size_t index) { if (op_name != kReduceScatter && op_name != kAllGatherOpName) { return format; } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, index); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, index); if (op_name == kAllGatherOpName && input_shape.size() <= kShape4dDims) { auto pad_shape = trans::PaddingShapeTo4dDefault(input_shape); if (pad_shape[N_nchw] % kCubeSize != 0 || pad_shape[C_nchw] % kCubeSize != 0) { @@ -67,7 +68,7 @@ void HcclMetadataInfo(const CNodePtr &kernel_node, std::vector inputs_format{}; std::vector inputs_type{}; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); for (size_t input_index = 0; input_index < input_num; ++input_index) { (void)inputs_format.emplace_back(GetKernelFormat(kernel_node, input_index)); inputs_type.push_back(type); } std::vector outputs_format; std::vector outputs_type; - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); for (size_t output_index = 0; output_index < output_num; ++output_index) { outputs_format.emplace_back(GetKernelFormat(kernel_node, output_index)); if (op_name == kReceive) { diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/hccl/hcom_all_to_all.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/hccl/hcom_all_to_all.cc index 1ea867b5e35..3a177842013 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/hccl/hcom_all_to_all.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/hccl/hcom_all_to_all.cc @@ -17,6 +17,7 @@ #include "plugin/device/ascend/hal/hccl_adapter/hccl_adapter.h" #include "plugin/device/ascend/hal/device/ge_runtime/task_info.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore::kernel { HcomAllToAllKernel::HcomAllToAllKernel() {} @@ -35,12 +36,12 @@ bool HcomAllToAllKernel::Init(const AnfNodePtr &anf_node) { } auto cnode = anf_node->cast(); MS_EXCEPTION_IF_NULL(cnode); - if (AnfAlgo::HasNodeAttr(kAttrNeedDropInput, cnode)) { - need_drop_input_ = AnfAlgo::GetNodeAttr(cnode, kAttrNeedDropInput); + if (common::AnfAlgo::HasNodeAttr(kAttrNeedDropInput, cnode)) { + need_drop_input_ = common::AnfAlgo::GetNodeAttr(cnode, kAttrNeedDropInput); } if (hccl_data_type_list_.empty()) { - auto recv_type = AnfAlgo::GetNodeAttr(anf_node, kAttrRecvType); + auto recv_type = common::AnfAlgo::GetNodeAttr(anf_node, kAttrRecvType); MS_EXCEPTION_IF_NULL(recv_type); data_type_ = HcomUtil::ConvertHcclType(recv_type->type_id()); } else { diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/hccl/hcom_util.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/hccl/hcom_util.cc index 7c90bf55e4e..0e296dc68da 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/hccl/hcom_util.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/hccl/hcom_util.cc @@ -18,8 +18,9 @@ #include #include "kernel/common_utils.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "utils/ms_context.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/trace_base.h" namespace mindspore { @@ -34,7 +35,7 @@ bool IsPyNativeMode() { bool HcomUtil::GetKernelInputShape(const AnfNodePtr &anf_node, vector> *hccl_kernel_intput_shape_list) { MS_EXCEPTION_IF_NULL(anf_node); MS_EXCEPTION_IF_NULL(hccl_kernel_intput_shape_list); - size_t input_num = AnfAlgo::GetInputTensorNum(anf_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(anf_node); for (size_t i = 0; i < input_num; ++i) { std::vector shape_i = AnfAlgo::GetInputDeviceShape(anf_node, i); hccl_kernel_intput_shape_list->emplace_back(shape_i); @@ -46,7 +47,7 @@ bool HcomUtil::GetKernelInputShape(const AnfNodePtr &anf_node, vector> *hccl_kernel_output_shape_list) { MS_EXCEPTION_IF_NULL(anf_node); MS_EXCEPTION_IF_NULL(hccl_kernel_output_shape_list); - size_t output_num = AnfAlgo::GetOutputTensorNum(anf_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(anf_node); for (size_t i = 0; i < output_num; ++i) { std::vector shape_i = AnfAlgo::GetOutputDeviceShape(anf_node, i); hccl_kernel_output_shape_list->emplace_back(shape_i); @@ -66,10 +67,10 @@ bool HcomUtil::GetKernelOutputShape(const AnfNodePtr &anf_node, vector *data_type_list) { MS_EXCEPTION_IF_NULL(anf_node); MS_EXCEPTION_IF_NULL(data_type_list); - size_t tensor_num = AnfAlgo::GetInputTensorNum(anf_node); - auto op_name = AnfAlgo::GetCNodeName(anf_node); + size_t tensor_num = common::AnfAlgo::GetInputTensorNum(anf_node); + auto op_name = common::AnfAlgo::GetCNodeName(anf_node); if (op_name == kReceiveOpName) { - tensor_num = AnfAlgo::GetOutputTensorNum(anf_node); + tensor_num = common::AnfAlgo::GetOutputTensorNum(anf_node); } for (size_t i = 0; i < tensor_num; ++i) { TypeId type_ptr; @@ -129,11 +130,11 @@ bool HcomUtil::GetHcomCount(const AnfNodePtr &anf_node, const vectorcast(); MS_EXCEPTION_IF_NULL(cnode); - if (AnfAlgo::GetCNodeName(anf_node) == kReceiveOpName) { - size = AnfAlgo::GetOutputTensorNum(anf_node); + if (common::AnfAlgo::GetCNodeName(anf_node) == kReceiveOpName) { + size = common::AnfAlgo::GetOutputTensorNum(anf_node); } for (size_t i = 0; i < size; ++i) { if (!GetHcomTypeSize(data_type_list[i], &type_size)) { @@ -145,9 +146,9 @@ bool HcomUtil::GetHcomCount(const AnfNodePtr &anf_node, const vectorGetAttr(kAttrRankSize) != nullptr) { rank_size = GetValue(primitive->GetAttr(kAttrRankSize)); @@ -156,15 +157,17 @@ bool HcomUtil::GetHcomCount(const AnfNodePtr &anf_node, const vector(anf_node, kAttrFusion)) { + if (common::AnfAlgo::HasNodeAttr(kAttrFusion, cnode) && + common::AnfAlgo::GetNodeAttr(anf_node, kAttrFusion)) { actual_input_size = (input_size + align_size - 1 + filled_size) / align_size * align_size; } block_size = static_cast(actual_input_size / LongToSize(rank_size)); total_size = total_size + block_size; } else { - if (AnfAlgo::GetCNodeName(anf_node) == kAllGatherOpName) { - if (AnfAlgo::HasNodeAttr(kAttrFusion, cnode) && AnfAlgo::GetNodeAttr(anf_node, kAttrFusion) && - AnfAlgo::GetInputTensorNum(anf_node) > 1) { + if (common::AnfAlgo::GetCNodeName(anf_node) == kAllGatherOpName) { + if (common::AnfAlgo::HasNodeAttr(kAttrFusion, cnode) && + common::AnfAlgo::GetNodeAttr(anf_node, kAttrFusion) && + common::AnfAlgo::GetInputTensorNum(anf_node) > 1) { block_size = (input_size + align_size - 1 + filled_size) / align_size * align_size; } else { block_size = input_size; @@ -188,7 +191,7 @@ bool HcomUtil::GetHcomCount(const AnfNodePtr &anf_node, const vectorGetAttr(kAttrOp) == nullptr) { MS_LOG(ERROR) << "Get HCOM_ATTR_REDUCE_TYPE fail, not support!"; @@ -213,7 +216,7 @@ bool HcomUtil::GetHcomOperationType(const AnfNodePtr &anf_node, HcclReduceOp *op bool HcomUtil::GetHcomRootId(const AnfNodePtr &anf_node, uint32_t *root_id) { MS_EXCEPTION_IF_NULL(anf_node); MS_EXCEPTION_IF_NULL(root_id); - auto primitive = AnfAlgo::GetCNodePrimitive(anf_node); + auto primitive = common::AnfAlgo::GetCNodePrimitive(anf_node); MS_EXCEPTION_IF_NULL(primitive); if (primitive->GetAttr(kAttrRootRank) != nullptr) { *root_id = (uint32_t)GetValue(primitive->GetAttr(kAttrRootRank)); @@ -227,7 +230,7 @@ bool HcomUtil::GetHcomRootId(const AnfNodePtr &anf_node, uint32_t *root_id) { bool HcomUtil::GetHcomSrcRank(const AnfNodePtr &anf_node, uint32_t *src_rank) { MS_EXCEPTION_IF_NULL(anf_node); MS_EXCEPTION_IF_NULL(src_rank); - auto primitive = AnfAlgo::GetCNodePrimitive(anf_node); + auto primitive = common::AnfAlgo::GetCNodePrimitive(anf_node); MS_EXCEPTION_IF_NULL(primitive); if (primitive->GetAttr("src_rank") != nullptr) { *src_rank = static_cast(GetValue(primitive->GetAttr("src_rank"))); @@ -241,7 +244,7 @@ bool HcomUtil::GetHcomSrcRank(const AnfNodePtr &anf_node, uint32_t *src_rank) { bool HcomUtil::GetHcomDestRank(const AnfNodePtr &anf_node, uint32_t *dest_rank) { MS_EXCEPTION_IF_NULL(anf_node); MS_EXCEPTION_IF_NULL(dest_rank); - auto primitive = AnfAlgo::GetCNodePrimitive(anf_node); + auto primitive = common::AnfAlgo::GetCNodePrimitive(anf_node); MS_EXCEPTION_IF_NULL(primitive); if (primitive->GetAttr("dest_rank") != nullptr) { *dest_rank = static_cast(GetValue(primitive->GetAttr("dest_rank"))); @@ -255,7 +258,7 @@ bool HcomUtil::GetHcomDestRank(const AnfNodePtr &anf_node, uint32_t *dest_rank) bool HcomUtil::GetHcomReceiveType(const AnfNodePtr &anf_node, TypeId *receive_type) { MS_EXCEPTION_IF_NULL(anf_node); MS_EXCEPTION_IF_NULL(receive_type); - auto primitive = AnfAlgo::GetCNodePrimitive(anf_node); + auto primitive = common::AnfAlgo::GetCNodePrimitive(anf_node); MS_EXCEPTION_IF_NULL(primitive); if (primitive->GetAttr("dtype") != nullptr) { *receive_type = GetValue(primitive->GetAttr("dtype"))->type_id(); @@ -267,7 +270,7 @@ bool HcomUtil::GetHcomReceiveType(const AnfNodePtr &anf_node, TypeId *receive_ty } void HcomUtil::GetHcomGroup(NotNull anf_node, NotNull group) { - auto primitive = AnfAlgo::GetCNodePrimitive(anf_node); + auto primitive = common::AnfAlgo::GetCNodePrimitive(anf_node); MS_EXCEPTION_IF_NULL(primitive); auto attr = primitive->GetAttr(kAttrGroup); if (attr != nullptr) { diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/hccl/hcom_util.h b/mindspore/ccsrc/plugin/device/ascend/kernel/hccl/hcom_util.h index 915554e110d..a68631ea90b 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/hccl/hcom_util.h +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/hccl/hcom_util.h @@ -23,7 +23,7 @@ #include #include "ir/dtype.h" #include "hccl/base.h" -#include "utils/contract.h" +#include "include/common/utils/contract.h" #include "hccl/hccl_types.h" namespace mindspore { diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/host/dynamic_broadcast_gradient_args_kernel.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/host/dynamic_broadcast_gradient_args_kernel.cc index 37f58faad8e..105bbbb4670 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/host/dynamic_broadcast_gradient_args_kernel.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/host/dynamic_broadcast_gradient_args_kernel.cc @@ -16,6 +16,7 @@ #include "plugin/device/ascend/kernel/host/dynamic_broadcast_gradient_args_kernel.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "plugin/device/ascend/hal/device/ascend_kernel_runtime.h" #include "utils/trace_base.h" @@ -123,8 +124,8 @@ std::vector> CalculateOutput(const std::vector GetInputShape(const CNodePtr &cnode, size_t index) { auto address_x = AnfAlgo::GetPrevNodeMutableOutputAddr(cnode, index); - auto shape_x = AnfAlgo::GetPrevNodeOutputInferShape(cnode, index); - auto type_x = AnfAlgo::GetOutputInferDataType(cnode, index); + auto shape_x = common::AnfAlgo::GetPrevNodeOutputInferShape(cnode, index); + auto type_x = common::AnfAlgo::GetOutputInferDataType(cnode, index); if (type_x != TypeId::kNumberTypeInt64) { MS_LOG(EXCEPTION) << "Input x type must be int64, but got " << type_x << trace::DumpSourceLines(cnode); } @@ -193,7 +194,7 @@ void DynamicBroadcastGradientArgsKernel::Execute() { MS_LOG(INFO) << "Execute DynamicBroadcastGradientArgsKernel Start"; auto cnode = cnode_ptr_.lock(); MS_EXCEPTION_IF_NULL(cnode); - auto input_num = AnfAlgo::GetInputTensorNum(cnode); + auto input_num = common::AnfAlgo::GetInputTensorNum(cnode); if (input_num != kInputNum) { MS_LOG(EXCEPTION) << "Invalid input num, should be " << kInputNum << ", but got " << input_num << trace::DumpSourceLines(cnode); @@ -210,7 +211,7 @@ void DynamicBroadcastGradientArgsKernel::Execute() { std::vector r0_shp{r0_size}; std::vector r1_shp{r1_size}; auto output_type = TypeId::kNumberTypeInt64; - AnfAlgo::SetOutputInferTypeAndShape({output_type, output_type}, {r0_shp, r1_shp}, cnode.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({output_type, output_type}, {r0_shp, r1_shp}, cnode.get()); MS_LOG(INFO) << "Execute DynamicBroadcastGradientArgsKernel End"; } diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/host/dynamic_shape_kernel.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/host/dynamic_shape_kernel.cc index 41ad56bf682..7b7054bc4aa 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/host/dynamic_shape_kernel.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/host/dynamic_shape_kernel.cc @@ -16,6 +16,7 @@ #include "plugin/device/ascend/kernel/host/dynamic_shape_kernel.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "utils/trace_base.h" #include "plugin/device/ascend/hal/device/ascend_kernel_runtime.h" #include "runtime/mem.h" @@ -26,13 +27,13 @@ void TensorShapeKernel::Execute() { MS_LOG(INFO) << "Execute TensorShapeKernel Start"; auto cnode = cnode_ptr_.lock(); MS_EXCEPTION_IF_NULL(cnode); - auto input_num = AnfAlgo::GetInputTensorNum(cnode); + auto input_num = common::AnfAlgo::GetInputTensorNum(cnode); if (input_num != 1) { MS_LOG(EXCEPTION) << "Op [" << cnode->DebugString() << "] has invalid input num, should be 1, but got " << input_num << trace::DumpSourceLines(cnode); } - auto prev_output_shape = AnfAlgo::GetPrevNodeOutputInferShape(cnode, 0); + auto prev_output_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(cnode, 0); std::vector output_shape = {SizeToLong(prev_output_shape.size())}; auto output_type = TypeId::kNumberTypeInt64; @@ -75,13 +76,13 @@ void TensorShapeKernel::Execute(const std::vector &inputs, const std MS_LOG(INFO) << "Execute TensorShapeKernel Start"; auto cnode = cnode_ptr_.lock(); MS_EXCEPTION_IF_NULL(cnode); - auto input_num = AnfAlgo::GetInputTensorNum(cnode); + auto input_num = common::AnfAlgo::GetInputTensorNum(cnode); if (input_num != 1) { MS_LOG(EXCEPTION) << "Op [" << cnode->DebugString() << "] has invalid input num, should be 1, but got " << input_num << trace::DumpSourceLines(cnode); } - auto prev_output_shape = AnfAlgo::GetPrevNodeOutputInferShape(cnode, 0); + auto prev_output_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(cnode, 0); std::vector output_shape = {SizeToLong(prev_output_shape.size())}; auto output_type = TypeId::kNumberTypeInt64; diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/host/host_kernel_build.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/host/host_kernel_build.cc index a4ec9a2c82a..8a1b2cfc6cb 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/host/host_kernel_build.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/host/host_kernel_build.cc @@ -18,6 +18,7 @@ #include "runtime/device/kernel_runtime.h" #include "plugin/device/ascend/kernel/host/host_kernel_mod.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/session/kernel_graph.h" #include "kernel/common_utils.h" @@ -25,7 +26,7 @@ namespace mindspore { namespace kernel { KernelModPtr HostOpBuild(const std::shared_ptr &anf_node) { MS_EXCEPTION_IF_NULL(anf_node); - std::string opname = AnfAlgo::GetCNodeName(anf_node); + std::string opname = common::AnfAlgo::GetCNodeName(anf_node); MS_LOG(INFO) << "Host op [" << opname << "]"; auto kerPtr = HostKernelFactory::Get(opname); if (kerPtr == nullptr) { diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/host/host_kernel_metadata.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/host/host_kernel_metadata.cc index 108fc35cfde..1af823ab256 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/host/host_kernel_metadata.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/host/host_kernel_metadata.cc @@ -20,6 +20,7 @@ #include "kernel/oplib/oplib.h" #include "kernel/common_utils.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "base/core_ops.h" namespace mindspore { @@ -29,23 +30,23 @@ void HostMetadataInfo(const CNodePtr &kernel_node, std::vectorDebugString() << "]"; return; } std::vector inputs_format{}; std::vector inputs_type{}; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); for (size_t input_index = 0; input_index < input_num; ++input_index) { inputs_format.emplace_back(kOpFormat_DEFAULT); - inputs_type.push_back(AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, input_index)); + inputs_type.push_back(common::AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, input_index)); } std::vector outputs_format; std::vector outputs_type; - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); for (size_t output_index = 0; output_index < output_num; ++output_index) { outputs_format.emplace_back(kOpFormat_DEFAULT); - outputs_type.push_back(AnfAlgo::GetOutputInferDataType(kernel_node, output_index)); + outputs_type.push_back(common::AnfAlgo::GetOutputInferDataType(kernel_node, output_index)); } auto builder = KernelBuildInfo::KernelBuildInfoBuilder(); builder.SetInputsFormat(inputs_format); diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/host/host_kernel_mod.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/host/host_kernel_mod.cc index e601ee93ab3..731578536ce 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/host/host_kernel_mod.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/host/host_kernel_mod.cc @@ -44,8 +44,8 @@ HostKernelFactory &HostKernelFactory::Get() { bool HostKernelMod::Init(const AnfNodePtr &anf_node) { MS_EXCEPTION_IF_NULL(anf_node); - size_t input_num = AnfAlgo::GetInputTensorNum(anf_node); - size_t output_num = AnfAlgo::GetOutputTensorNum(anf_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(anf_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(anf_node); for (size_t i = 0; i < input_num; i++) { std::vector shape_i = AnfAlgo::GetInputDeviceShape(anf_node, i); @@ -78,7 +78,7 @@ bool HostKernelMod::Launch(const std::vector &, const std::vector #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "abstract/utils.h" #include "runtime/device/kernel_runtime_manager.h" #include "utils/check_convert_utils.h" @@ -32,13 +33,13 @@ constexpr size_t kInputNum = 2; std::vector GetInputValue(const CNodePtr &cnode, size_t index) { auto address_x = AnfAlgo::GetPrevNodeMutableOutputAddr(cnode, index); - auto shape_x = AnfAlgo::GetPrevNodeOutputInferShape(cnode, index); + auto shape_x = common::AnfAlgo::GetPrevNodeOutputInferShape(cnode, index); if (shape_x.size() != 1) { MS_LOG(EXCEPTION) << "Input" << index << " must be [1-D], but got " << shape_x.size() << "-D." << trace::DumpSourceLines(cnode); } - session::KernelWithIndex kernel_with_index = AnfAlgo::GetPrevNodeOutput(cnode, index); - auto type_x = AnfAlgo::GetOutputInferDataType(kernel_with_index.first, kernel_with_index.second); + session::KernelWithIndex kernel_with_index = common::AnfAlgo::GetPrevNodeOutput(cnode, index); + auto type_x = common::AnfAlgo::GetOutputInferDataType(kernel_with_index.first, kernel_with_index.second); if (type_x != TypeId::kNumberTypeInt64 && type_x != TypeId::kNumberTypeInt32) { MS_LOG(EXCEPTION) << "Input x type must be int64 or int32, but got " << TypeIdToType(type_x) << trace::DumpSourceLines(cnode); @@ -68,20 +69,20 @@ std::vector GetInputValue(const CNodePtr &cnode, size_t index) { } int64_t GetArrProd(const CNodePtr &cnode) { - auto shape_x = AnfAlgo::GetPrevNodeOutputInferShape(cnode, 0); + auto shape_x = common::AnfAlgo::GetPrevNodeOutputInferShape(cnode, 0); auto arr_prod = std::accumulate(shape_x.begin(), shape_x.end(), static_cast(1), std::multiplies()); return arr_prod; } std::vector GetOutputShapes(const CNodePtr &cnode) { std::vector output_shapes; - auto input_num = AnfAlgo::GetInputTensorNum(cnode); + auto input_num = common::AnfAlgo::GetInputTensorNum(cnode); if (input_num != kInputNum) { MS_LOG(DEBUG) << "Reshape has one input"; - auto prim = AnfAlgo::GetCNodePrimitive(cnode); + auto prim = common::AnfAlgo::GetCNodePrimitive(cnode); ValuePtr sh = prim->GetAttr("shape"); if (sh == nullptr) { - auto un_output_shapes = AnfAlgo::GetOutputInferShape(cnode, 0); + auto un_output_shapes = common::AnfAlgo::GetOutputInferShape(cnode, 0); (void)std::transform(std::begin(un_output_shapes), std::end(un_output_shapes), std::back_inserter(output_shapes), [](const uint64_t &i) -> int64_t { return static_cast(i); }); } else if (sh->isa()) { @@ -134,7 +135,7 @@ void ReshapeKernel::Execute() { MS_EXCEPTION_IF_NULL(address_x); std::vector output_shapes = GetOutputShapes(cnode); - auto type_x = AnfAlgo::GetOutputInferDataType(cnode, 0); + auto type_x = common::AnfAlgo::GetOutputInferDataType(cnode, 0); size_t input_size_byte = LongToSize(GetArrProd(cnode)) * abstract::TypeIdSize(type_x); // At execute reshape is noOpNode as all shapes are known so set skipNoOpNode false @@ -170,7 +171,7 @@ void ReshapeKernel::Execute(const std::vector &inputs, const std::ve auto output_addr = outputs[0]->addr; MS_EXCEPTION_IF_NULL(output_addr); - auto type_x = AnfAlgo::GetOutputInferDataType(cnode, 0); + auto type_x = common::AnfAlgo::GetOutputInferDataType(cnode, 0); size_t input_size_byte = LongToSize(GetArrProd(cnode)) * abstract::TypeIdSize(type_x); // cppcheck-suppress unreadVariable diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/rts/label_goto.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/rts/label_goto.cc index f316f2202d7..81bd705113c 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/rts/label_goto.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/rts/label_goto.cc @@ -18,6 +18,7 @@ #include "runtime/stream.h" #include "plugin/device/ascend/hal/device/ge_runtime/task_info.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" using mindspore::ge::model_runner::LabelGotoTaskInfo; using LabelGotoTaskInfoPtr = std::shared_ptr; @@ -32,10 +33,10 @@ bool LabelGotoKernel::Init(const AnfNodePtr &anf_node) { MS_EXCEPTION_IF_NULL(anf_node); MS_LOG(INFO) << "LabelGotoKernel init"; auto cnode = anf_node->cast(); - if (!AnfAlgo::HasNodeAttr(kAttrLabelIndex, cnode)) { + if (!common::AnfAlgo::HasNodeAttr(kAttrLabelIndex, cnode)) { MS_LOG(EXCEPTION) << "LabelGotoKernel has no attr label_index"; } - auto primitive = AnfAlgo::GetCNodePrimitive(anf_node); + auto primitive = common::AnfAlgo::GetCNodePrimitive(anf_node); MS_EXCEPTION_IF_NULL(primitive); label_ = GetValue(primitive->GetAttr(kAttrLabelIndex)); MS_LOG(INFO) << "LabelGotoKernel get attr label:" << label_; diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/rts/label_set.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/rts/label_set.cc index 28270694619..275ce7b60eb 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/rts/label_set.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/rts/label_set.cc @@ -18,6 +18,7 @@ #include "runtime/stream.h" #include "plugin/device/ascend/hal/device/ge_runtime/task_info.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" using mindspore::ge::model_runner::LabelSetTaskInfo; using LabelSetTaskInfoPtr = std::shared_ptr; @@ -32,10 +33,10 @@ bool LabelSetKernel::Init(const AnfNodePtr &anf_node) { MS_EXCEPTION_IF_NULL(anf_node); MS_LOG(INFO) << "LabelSetKernel init"; auto cnode = anf_node->cast(); - if (!AnfAlgo::HasNodeAttr(kAttrLabelIndex, cnode)) { + if (!common::AnfAlgo::HasNodeAttr(kAttrLabelIndex, cnode)) { MS_LOG(EXCEPTION) << "LabelSetKernel has no attr label_index"; } - auto primitive = AnfAlgo::GetCNodePrimitive(anf_node); + auto primitive = common::AnfAlgo::GetCNodePrimitive(anf_node); MS_EXCEPTION_IF_NULL(primitive); label_ = GetValue(primitive->GetAttr(kAttrLabelIndex)); MS_LOG(INFO) << "LabelSetKernel get attr label:" << label_; diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/rts/label_switch.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/rts/label_switch.cc index e8233c63976..f3f616e8f81 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/rts/label_switch.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/rts/label_switch.cc @@ -19,6 +19,7 @@ #include "runtime/stream.h" #include "plugin/device/ascend/hal/device/ge_runtime/task_info.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" using mindspore::ge::model_runner::LabelSwitchTaskInfo; using LabelSwitchTaskInfoPtr = std::shared_ptr; @@ -37,10 +38,10 @@ bool LabelSwitchKernel::Init(const AnfNodePtr &anf_node) { MS_EXCEPTION_IF_NULL(anf_node); MS_LOG(INFO) << "LabelSwitchKernel init"; auto cnode = anf_node->cast(); - if (!AnfAlgo::HasNodeAttr(kAttrLabelSwitchList, cnode)) { + if (!common::AnfAlgo::HasNodeAttr(kAttrLabelSwitchList, cnode)) { MS_LOG(EXCEPTION) << "LabelSwitchKernel has no attr label_switch_list"; } - auto primitive = AnfAlgo::GetCNodePrimitive(anf_node); + auto primitive = common::AnfAlgo::GetCNodePrimitive(anf_node); MS_EXCEPTION_IF_NULL(primitive); label_list_ = GetValue>(primitive->GetAttr(kAttrLabelSwitchList)); label_size_ = SizeToUint(label_list_.size()); diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/rts/memcpy_async.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/rts/memcpy_async.cc index 1c913db26be..77143e03d92 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/rts/memcpy_async.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/rts/memcpy_async.cc @@ -21,7 +21,8 @@ #include "runtime/mem.h" #include "acl/acl_rt.h" #include "backend/common/session/anf_runtime_algorithm.h" -#include "utils/ms_device_shape_transfer.h" +#include "include/common/utils/anfalgo.h" +#include "runtime/device/ms_device_shape_transfer.h" #include "utils/ms_context.h" #include "runtime/device/kernel_runtime.h" #include "plugin/device/ascend/hal/device/executor/rts/memcpy_rts_dynamic_kernel.h" @@ -80,7 +81,7 @@ bool MemCpyAsyncKernel::Init(const mindspore::AnfNodePtr &anf_node) { void MemCpyAsyncKernel::GetInputOutputDataType(const AnfNodePtr &anf_node) { MS_EXCEPTION_IF_NULL(anf_node); - size_t input_size = AnfAlgo::GetInputTensorNum(anf_node); + size_t input_size = common::AnfAlgo::GetInputTensorNum(anf_node); if (input_size != 1) { MS_LOG(EXCEPTION) << "MemCpyAsync input size is not 1, got " << input_size; } @@ -89,7 +90,7 @@ void MemCpyAsyncKernel::GetInputOutputDataType(const AnfNodePtr &anf_node) { void MemCpyAsyncKernel::GetInputOutputTotalCount(const AnfNodePtr &anf_node) { MS_EXCEPTION_IF_NULL(anf_node); - size_t input_size = AnfAlgo::GetInputTensorNum(anf_node); + size_t input_size = common::AnfAlgo::GetInputTensorNum(anf_node); if (input_size != 1) { MS_LOG(EXCEPTION) << "MemCpyAsync input size is not 1, got " << input_size; } diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/rts/profiling_kernel_mod.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/rts/profiling_kernel_mod.cc index f00e679ad22..7c964fabf22 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/rts/profiling_kernel_mod.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/rts/profiling_kernel_mod.cc @@ -19,6 +19,7 @@ #include "plugin/device/ascend/hal/device/ge_runtime/task_info.h" #include "plugin/device/ascend/hal/device/profiling/profiling_utils.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "plugin/device/ascend/hal/device/executor/rts/profiling_rts_dynamic_kernel.h" using ProfilerTraceTaskInfo = mindspore::ge::model_runner::ProfilerTraceTaskInfo; @@ -29,7 +30,7 @@ namespace mindspore { namespace kernel { bool ProfilingKernelMod::Init(const AnfNodePtr &anf_node) { MS_LOG(INFO) << "[profiling] init profiling kernel mod"; - auto primitive = AnfAlgo::GetCNodePrimitive(anf_node); + auto primitive = common::AnfAlgo::GetCNodePrimitive(anf_node); MS_EXCEPTION_IF_NULL(primitive); ValuePtr notify_ptr = primitive->GetAttr(ProfilingUtils::kNotify); diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/rts/recv.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/rts/recv.cc index 31105fb3dc2..7126700b223 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/rts/recv.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/rts/recv.cc @@ -19,6 +19,7 @@ #include "utils/ms_context.h" #include "plugin/device/ascend/hal/device/ge_runtime/task_info.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace kernel { @@ -31,9 +32,9 @@ RecvKernel::~RecvKernel() {} bool RecvKernel::Init(const AnfNodePtr &anf_node) { MS_EXCEPTION_IF_NULL(anf_node); - auto primitive = AnfAlgo::GetCNodePrimitive(anf_node); + auto primitive = common::AnfAlgo::GetCNodePrimitive(anf_node); MS_EXCEPTION_IF_NULL(primitive); - if (!AnfAlgo::HasNodeAttr(kAttrEventId, anf_node->cast())) { + if (!common::AnfAlgo::HasNodeAttr(kAttrEventId, anf_node->cast())) { MS_LOG(EXCEPTION) << "RecvKernel has no attr kAttrEventId"; } event_id_ = GetValue(primitive->GetAttr(kAttrEventId)); diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/rts/rt_kernel_build.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/rts/rt_kernel_build.cc index 1ca43c398b9..bda1a219703 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/rts/rt_kernel_build.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/rts/rt_kernel_build.cc @@ -20,12 +20,13 @@ #include #include "plugin/device/ascend/kernel/rts/rt_kernel.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace kernel { KernelModPtr RtOpBuild(const AnfNodePtr &anf_node) { MS_EXCEPTION_IF_NULL(anf_node); - std::string op_name = AnfAlgo::GetCNodeName(anf_node); + std::string op_name = common::AnfAlgo::GetCNodeName(anf_node); (void)std::transform(op_name.begin(), op_name.end(), op_name.begin(), ::tolower); MS_LOG(INFO) << "Op Name(tolower)[" << op_name << "]"; auto ker_ptr = RtKernelFactory::Create(op_name); diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/rts/rt_kernel_info.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/rts/rt_kernel_info.cc index 4612beb8fbd..da0483cb4af 100755 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/rts/rt_kernel_info.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/rts/rt_kernel_info.cc @@ -17,10 +17,11 @@ #include "plugin/device/ascend/kernel/rts/rt_kernel_info.h" #include #include -#include "utils/convert_utils.h" -#include "utils/utils.h" +#include "include/common/utils/convert_utils.h" +#include "include/common/utils/utils.h" #include "utils/ms_utils.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace kernel { @@ -54,7 +55,7 @@ void GetRtKelInfo(const CNodePtr &kernel_node, std::vector> *kernel_info_list) { MS_EXCEPTION_IF_NULL(kernel_info_list); MS_EXCEPTION_IF_NULL(kernel_node); - std::string opNameLower = AnfAlgo::GetCNodeName(kernel_node); + std::string opNameLower = common::AnfAlgo::GetCNodeName(kernel_node); (void)std::transform(opNameLower.begin(), opNameLower.end(), opNameLower.begin(), ::tolower); auto ker_desc_ptr = RtKerDescFactory::Create(opNameLower); @@ -63,16 +64,16 @@ void GetRtKelInfo(const CNodePtr &kernel_node, return; } // if can't find kernel info in kernel info database, use the default kernel info - auto node_name = AnfAlgo::GetCNodeName(kernel_node); + auto node_name = common::AnfAlgo::GetCNodeName(kernel_node); if (IsDefaultKernelInfo(node_name)) { auto kernel_build_info_builder = std::make_shared(); // set input infos - auto input_num = AnfAlgo::GetInputTensorNum(kernel_node); + auto input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); MS_EXCEPTION_IF_NULL(kernel_build_info_builder); kernel_build_info_builder->SetInputsFormat(std::vector(input_num, kOpFormat_DEFAULT)); std::vector input_types = {}; for (size_t i = 0; i < input_num; i++) { - input_types.push_back(AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, i)); + input_types.push_back(common::AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, i)); } kernel_build_info_builder->SetInputsDeviceType(input_types); // Kernel ops in while-list such as 'LabelSet' always return UMonad. diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/rts/rt_kernel_info.h b/mindspore/ccsrc/plugin/device/ascend/kernel/rts/rt_kernel_info.h index db9fdf6e3e1..27bd26156b3 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/rts/rt_kernel_info.h +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/rts/rt_kernel_info.h @@ -28,7 +28,7 @@ #include "ir/dtype.h" #include "kernel/kernel_build_info.h" #include "kernel/kernel.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace kernel { diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/rts/send.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/rts/send.cc index 336d4400e2c..2bfbc064576 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/rts/send.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/rts/send.cc @@ -18,6 +18,7 @@ #include "runtime/event.h" #include "plugin/device/ascend/hal/device/ge_runtime/task_info.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" using mindspore::ge::model_runner::EventRecordTaskInfo; using EventRecordTaskInfoPtr = std::shared_ptr; @@ -30,9 +31,9 @@ SendKernel::~SendKernel() {} bool SendKernel::Init(const AnfNodePtr &anf_node) { MS_EXCEPTION_IF_NULL(anf_node); - auto primitive = AnfAlgo::GetCNodePrimitive(anf_node); + auto primitive = common::AnfAlgo::GetCNodePrimitive(anf_node); MS_EXCEPTION_IF_NULL(primitive); - if (!AnfAlgo::HasNodeAttr(kAttrEventId, anf_node->cast())) { + if (!common::AnfAlgo::HasNodeAttr(kAttrEventId, anf_node->cast())) { MS_LOG(EXCEPTION) << "SendKernel has no attr kAttrEventId"; } event_id_ = GetValue(primitive->GetAttr(kAttrEventId)); diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/rts/stream_active.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/rts/stream_active.cc index 9d67a163b3b..f7a4ef610b8 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/rts/stream_active.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/rts/stream_active.cc @@ -18,6 +18,7 @@ #include "runtime/stream.h" #include "plugin/device/ascend/hal/device/ge_runtime/task_info.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" using mindspore::ge::model_runner::StreamActiveTaskInfo; using StreamActiveTaskInfoPtr = std::shared_ptr; @@ -31,9 +32,9 @@ StreamActiveKernel::~StreamActiveKernel() {} bool StreamActiveKernel::Init(const AnfNodePtr &anf_node) { MS_EXCEPTION_IF_NULL(anf_node); MS_LOG(INFO) << "stream active op init start"; - auto primitive = AnfAlgo::GetCNodePrimitive(anf_node); + auto primitive = common::AnfAlgo::GetCNodePrimitive(anf_node); MS_EXCEPTION_IF_NULL(primitive); - if (!AnfAlgo::HasNodeAttr(kAttrActiveStreamList, anf_node->cast())) { + if (!common::AnfAlgo::HasNodeAttr(kAttrActiveStreamList, anf_node->cast())) { MS_LOG(EXCEPTION) << "StreamActiveKernel " << anf_node->DebugString() << "has no attr kAttrActiveStreamList"; } active_streams_index_ = GetValue>(primitive->GetAttr(kAttrActiveStreamList)); diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/rts/stream_switch.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/rts/stream_switch.cc index accaf4bcf83..c7a1a37e04f 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/rts/stream_switch.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/rts/stream_switch.cc @@ -20,6 +20,7 @@ #include "runtime/stream.h" #include "plugin/device/ascend/hal/device/ge_runtime/task_info.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" using mindspore::ge::model_runner::StreamSwitchTaskInfo; using StreamSwitchTaskInfoPtr = std::shared_ptr; @@ -39,17 +40,17 @@ StreamSwitchKernel::~StreamSwitchKernel() {} bool StreamSwitchKernel::Init(const AnfNodePtr &anf_node) { MS_EXCEPTION_IF_NULL(anf_node); MS_LOG(INFO) << "stream switch op init start"; - auto primitive = AnfAlgo::GetCNodePrimitive(anf_node); + auto primitive = common::AnfAlgo::GetCNodePrimitive(anf_node); MS_EXCEPTION_IF_NULL(primitive); - if (!AnfAlgo::HasNodeAttr(kAttrSwitchCondition, anf_node->cast())) { + if (!common::AnfAlgo::HasNodeAttr(kAttrSwitchCondition, anf_node->cast())) { MS_LOG(EXCEPTION) << "StreamSwitchKernel has no attr kAttrSwitchCondition"; } cond_ = tagRtCondition(GetValue(primitive->GetAttr(kAttrSwitchCondition))); - if (!AnfAlgo::HasNodeAttr(kAttrTrueBranchStream, anf_node->cast())) { + if (!common::AnfAlgo::HasNodeAttr(kAttrTrueBranchStream, anf_node->cast())) { MS_LOG(EXCEPTION) << "StreamSwitchKernel has no attr kAttrTrueBranchStream"; } true_stream_index_ = GetValue(primitive->GetAttr(kAttrTrueBranchStream)); - if (!AnfAlgo::HasNodeAttr(kAttrDataType, anf_node->cast())) { + if (!common::AnfAlgo::HasNodeAttr(kAttrDataType, anf_node->cast())) { MS_LOG(EXCEPTION) << "StreamSwitchKernel has no attr kAttrDataType"; } data_type_ = tagRtSwitchDataType(GetValue(primitive->GetAttr(kAttrDataType))); diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/rts/tensor_copy_slices.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/rts/tensor_copy_slices.cc index 72381d50481..86820fabeb3 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/rts/tensor_copy_slices.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/rts/tensor_copy_slices.cc @@ -21,6 +21,7 @@ #include #include "abstract/utils.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "kernel/common_utils.h" #include "runtime/mem.h" #include "acl/acl_rt.h" @@ -73,9 +74,9 @@ bool TensorCopySlices::Init(const mindspore::AnfNodePtr &anf_node) { GetInputOutputInfo(anf_node); GetInputOutputTotalCount(anf_node); - auto begin = AnfAlgo::GetNodeAttr>(anf_node, kAttrBegin); - auto end = AnfAlgo::GetNodeAttr>(anf_node, kAttrEnd); - auto strides = AnfAlgo::GetNodeAttr>(anf_node, kAttrStrides); + auto begin = common::AnfAlgo::GetNodeAttr>(anf_node, kAttrBegin); + auto end = common::AnfAlgo::GetNodeAttr>(anf_node, kAttrEnd); + auto strides = common::AnfAlgo::GetNodeAttr>(anf_node, kAttrStrides); CheckSliceValid(begin, end, strides, input_shape_); auto dim_offset = CalDimOffset(input_shape_); @@ -86,7 +87,7 @@ bool TensorCopySlices::Init(const mindspore::AnfNodePtr &anf_node) { void TensorCopySlices::GetInputOutputInfo(const AnfNodePtr &anf_node) { MS_EXCEPTION_IF_NULL(anf_node); - size_t input_size = AnfAlgo::GetInputTensorNum(anf_node); + size_t input_size = common::AnfAlgo::GetInputTensorNum(anf_node); if (input_size != 2) { MS_LOG(EXCEPTION) << "TensorCopySlices input size is not 2, got " << input_size; } @@ -98,8 +99,8 @@ void TensorCopySlices::GetInputOutputInfo(const AnfNodePtr &anf_node) { << " output_type_id_:" << output_type_id_; } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(anf_node, 0); - auto update_shape = AnfAlgo::GetPrevNodeOutputInferShape(anf_node, 1); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(anf_node, 0); + auto update_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(anf_node, 1); auto output_shape = AnfAlgo::GetOutputDeviceShape(anf_node, 0); CastShapeSizeToLong(input_shape, &input_shape_); CastShapeSizeToLong(update_shape, &update_shape_); @@ -112,7 +113,7 @@ void *TensorCopySlices::VoidPointerOffset(void *ptr, size_t offset) const { void TensorCopySlices::GetInputOutputTotalCount(const AnfNodePtr &anf_node) { MS_EXCEPTION_IF_NULL(anf_node); - size_t input_size = AnfAlgo::GetInputTensorNum(anf_node); + size_t input_size = common::AnfAlgo::GetInputTensorNum(anf_node); if (input_size != kTensorCopySlicesInputSize) { MS_LOG(EXCEPTION) << "TensorCopySlices input size is not 2"; } diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/dynamic_tbe_kernel_mod.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/dynamic_tbe_kernel_mod.cc index 75a508f45f6..0cb0b72e336 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/dynamic_tbe_kernel_mod.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/dynamic_tbe_kernel_mod.cc @@ -21,6 +21,7 @@ #include "acl/acl_rt.h" #include "utils/ms_context.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "runtime/device/kernel_runtime.h" #include "backend/common/optimizer/helper.h" #include "framework/common/debug/log.h" @@ -32,8 +33,8 @@ #include "pipeline/jit/static_analysis/static_analysis.h" #include "plugin/device/ascend/hal/device/executor/tiling/op_tiling_adapter.h" #include "plugin/device/ascend/hal/device/ascend_memory_manager.h" -#include "utils/ms_device_shape_transfer.h" -#include "utils/utils.h" +#include "runtime/device/ms_device_shape_transfer.h" +#include "include/common/utils/utils.h" #include "register/op_tiling.h" #include "nlohmann/json.hpp" @@ -60,15 +61,15 @@ DynamicTbeKernelMod::~DynamicTbeKernelMod() { } void DynamicTbeKernelMod::InferOp() { - if (AnfAlgo::IsDynamicShape(anf_node_.lock())) { + if (common::AnfAlgo::IsDynamicShape(anf_node_.lock())) { auto node = anf_node_.lock(); MS_EXCEPTION_IF_NULL(node); auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); need_skip_execute_ = NeedSkipExecute(cnode); if (need_skip_execute_) { - std::vector dtypes{AnfAlgo::GetOutputInferDataType(cnode, 0)}; - AnfAlgo::SetOutputInferTypeAndShape(dtypes, {AnfAlgo::GetInputDeviceShape(cnode, 0)}, cnode.get()); + std::vector dtypes{common::AnfAlgo::GetOutputInferDataType(cnode, 0)}; + common::AnfAlgo::SetOutputInferTypeAndShape(dtypes, {AnfAlgo::GetInputDeviceShape(cnode, 0)}, cnode.get()); } else { KernelMod::InferShape(); } @@ -87,7 +88,7 @@ void DynamicTbeKernelMod::InitOp() { auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - if (!AnfAlgo::IsDynamicShape(cnode)) { + if (!common::AnfAlgo::IsDynamicShape(cnode)) { MS_LOG(EXCEPTION) << "The node is not dynamic shape: " << cnode->fullname_with_scope(); } @@ -193,7 +194,7 @@ bool DynamicTbeKernelMod::CopyTilingToDevice(void *stream_ptr) { bool DynamicTbeKernelMod::NeedSkipExecute(const CNodePtr &cnode) { // Skip run ReduceSum when axis is a Empty Tensor MS_EXCEPTION_IF_NULL(cnode); - auto op_name = AnfAlgo::GetCNodeName(cnode); + auto op_name = common::AnfAlgo::GetCNodeName(cnode); if (op_name != kReduceSumOpName) { return false; } @@ -237,7 +238,7 @@ bool DynamicTbeKernelMod::Launch(const std::vector &inputs, const st MS_EXCEPTION_IF_NULL(cnode); // is dynamic shape - if (!AnfAlgo::IsDynamicShape(cnode)) { + if (!common::AnfAlgo::IsDynamicShape(cnode)) { MS_LOG(EXCEPTION) << "The cnode is not dynamic shape:" << cnode->fullname_with_scope(); } diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_adapter.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_adapter.cc index 5791d4431a0..09856f3efc3 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_adapter.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_adapter.cc @@ -25,13 +25,14 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "kernel/oplib/opinfo.h" #include "frontend/parallel/ops_info/ops_utils.h" #include "plugin/device/ascend/kernel/tbe/tbe_dynaminc_shape_util.h" #include "plugin/device/ascend/kernel/tbe/tbe_json/tbe_json_utils.h" -#include "utils/json_operation_utils.h" +#include "include/common/utils/json_operation_utils.h" #include "utils/ms_context.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace kernel { @@ -78,7 +79,7 @@ std::unordered_set TbeAdapter::input_order_adjusted_ops_ = {kConv2D bool TbeAdapter::IsSpecialFusionComputeNode(const std::vector &compute_nodes) { auto result = std::find_if(compute_nodes.begin(), compute_nodes.end(), [](const auto &it) { - auto op_name = AnfAlgo::GetCNodeName(it); + auto op_name = common::AnfAlgo::GetCNodeName(it); return (op_name == kConv2DBackpropInputOpName || op_name == kConv2DOpName); }); return result != compute_nodes.end(); @@ -159,7 +160,7 @@ bool TbeAdapter::GetSpecDataInput(const FusionScopeInfo &fusion_scope_info, MS_EXCEPTION_IF_NULL(compute_node); std::vector layer = {}; std::vector reorder_layer = {}; - auto op_name = AnfAlgo::GetCNodeName(compute_node); + auto op_name = common::AnfAlgo::GetCNodeName(compute_node); auto ccompute_node = compute_node->cast(); if (ccompute_node == nullptr) { MS_LOG(WARNING) << "Fusion error: fusion compute node must be cnode, but the node is " @@ -186,14 +187,14 @@ bool TbeAdapter::IsPlaceHolderInput(const AnfNodePtr &node, const OpIOInfoPtr &i MS_EXCEPTION_IF_NULL(node); MS_EXCEPTION_IF_NULL(input_ptr); static std::set node_set = {kDynamicRNNOpName, kDynamicGRUV2OpName}; - auto cnode_name = AnfAlgo::GetCNodeName(node); + auto cnode_name = common::AnfAlgo::GetCNodeName(node); if (node_set.find(cnode_name) == node_set.end()) { return false; } auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - if (AnfAlgo::HasNodeAttr(kAttrPlaceHolderIndex, cnode)) { - auto none_index = AnfAlgo::GetNodeAttr>(node, kAttrPlaceHolderIndex); + if (common::AnfAlgo::HasNodeAttr(kAttrPlaceHolderIndex, cnode)) { + auto none_index = common::AnfAlgo::GetNodeAttr>(node, kAttrPlaceHolderIndex); return find(none_index.begin(), none_index.end(), input_ptr->index()) != none_index.end(); } else { MS_LOG(EXCEPTION) << "Cnode: " << cnode_name << " doesn't has attribute placeholder_index."; @@ -203,7 +204,7 @@ void TbeAdapter::CastAttrJsonPrePass(const AnfNodePtr &anf_node, std::vectorsize() != 1) { @@ -227,7 +228,7 @@ void TbeAdapter::CastAttrJsonPrePass(const AnfNodePtr &anf_node, std::vector dst_type_map{{0, "float32"}, {1, "float16"}, {2, "int8"}, {3, "int32"}, @@ -243,7 +244,7 @@ void TbeAdapter::CastAttrJsonPost(const AnfNodePtr &anf_node, nlohmann::json *at void TbeAdapter::LayerNormAttrJsonPost(const AnfNodePtr &anf_node, nlohmann::json *attrs_json) { MS_EXCEPTION_IF_NULL(anf_node); MS_EXCEPTION_IF_NULL(attrs_json); - if (AnfAlgo::GetCNodeName(anf_node) == parallel::LAYER_NORM) { + if (common::AnfAlgo::GetCNodeName(anf_node) == parallel::LAYER_NORM) { nlohmann::json new_attrs_json; for (auto &json_item : *attrs_json) { if (GetJsonValue(json_item, kJName) == kAttrEpsilon) { diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_adapter.h b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_adapter.h index 34c79926381..7be96d53cbf 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_adapter.h +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_adapter.h @@ -27,6 +27,7 @@ #include "kernel/oplib/opinfo.h" #include "kernel/kernel_fusion.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" // Note: This file is mainly used to adapt the ME front-end operator description and // the TBE back-end operator implementation difference namespace mindspore { @@ -71,7 +72,7 @@ class TbeAdapter { if (DynamicInputAdjusted(anf_node, inputs_list, inputs_json)) { return; } - auto op_name = AnfAlgo::GetCNodeName(anf_node); + auto op_name = common::AnfAlgo::GetCNodeName(anf_node); if (input_order_adjusted_ops_.find(op_name) == input_order_adjusted_ops_.end()) { (void)std::copy(inputs_list.begin(), inputs_list.end(), std::back_inserter((*inputs_json))); } else { @@ -124,10 +125,10 @@ class TbeAdapter { template static bool DynamicInputAdjusted(const std::shared_ptr &anf_node, std::vector const &inputs_list, std::vector *inputs_json) { - if (!AnfAlgo::IsDynamicShape(anf_node)) { + if (!common::AnfAlgo::IsDynamicShape(anf_node)) { return false; } - auto op_name = AnfAlgo::GetCNodeName(anf_node); + auto op_name = common::AnfAlgo::GetCNodeName(anf_node); if (op_name == kConv2DBackpropInputOpName) { // process dynamic Conv2DBackpropInput, tbe kernel input is x, input_size and dout inputs_json->push_back(inputs_list[kIndex2]); diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_convert_utils.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_convert_utils.cc index 13aed53406d..3222b643911 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_convert_utils.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_convert_utils.cc @@ -21,6 +21,7 @@ #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "utils/ms_utils.h" namespace mindspore { diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_dynaminc_shape_util.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_dynaminc_shape_util.cc index 9450e34999d..8a22fca8cdb 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_dynaminc_shape_util.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_dynaminc_shape_util.cc @@ -22,23 +22,24 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace kernel { namespace tbe { bool TbeDynamicShapeUtil::IsDynamicShapeNode(const CNodePtr &cnode) { MS_EXCEPTION_IF_NULL(cnode); - auto input_num = AnfAlgo ::GetInputTensorNum(cnode); + auto input_num = common::AnfAlgo::GetInputTensorNum(cnode); for (size_t i = 0; i < input_num; ++i) { - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(cnode, i); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(cnode, i); if (std::any_of(input_shape.begin(), input_shape.end(), [](const size_t &dim) { return dim < 0; })) { MS_LOG(INFO) << "Node(" << cnode->fullname_with_scope() << ") is dynamic shape node."; return true; } } - auto output_num = AnfAlgo ::GetOutputTensorNum(cnode); + auto output_num = common::AnfAlgo::GetOutputTensorNum(cnode); for (size_t i = 0; i < output_num; ++i) { - auto output_shape = AnfAlgo::GetOutputInferShape(cnode, i); + auto output_shape = common::AnfAlgo::GetOutputInferShape(cnode, i); if (std::any_of(output_shape.begin(), output_shape.end(), [](const size_t &dim) { return dim < 0; })) { MS_LOG(INFO) << "Node(" << cnode->fullname_with_scope() << ") is dynamic shape node."; return true; @@ -69,7 +70,7 @@ bool TbeDynamicShapeUtil::GetDynamicShapeAttr(const AnfNodePtr &anf_node) { bool TbeDynamicShapeUtil::GetDynamicShapeAttr(const CNodePtr &cnode) { MS_EXCEPTION_IF_NULL(cnode); - auto is_dynamic_shape = AnfAlgo::IsDynamicShape(cnode); + auto is_dynamic_shape = common::AnfAlgo::IsDynamicShape(cnode); return is_dynamic_shape; } @@ -98,8 +99,8 @@ RangePair TbeDynamicShapeUtil::GetInputDynamicRange(const AnfNodePtr &anf_node, kernel_info->select_kernel_build_info() == nullptr ? def_format : AnfAlgo::GetInputFormat(anf_node, index); auto data_type = kernel_info->select_kernel_build_info() == nullptr ? type : AnfAlgo::GetInputDeviceDataType(anf_node, index); - auto input_range_min = AnfAlgo::GetInputMinShape(anf_node, index); - auto input_range_max = AnfAlgo::GetInputMaxShape(anf_node, index); + auto input_range_min = common::AnfAlgo::GetInputMinShape(anf_node, index); + auto input_range_max = common::AnfAlgo::GetInputMaxShape(anf_node, index); if (input_range_min.size() != input_range_max.size()) { MS_EXCEPTION(ArgumentError) << "Input range size is not equal, min size: " << input_range_min.size() << "max size: " << input_range_max.size(); @@ -125,8 +126,8 @@ RangePair TbeDynamicShapeUtil::GetOutputDynamicRange(const AnfNodePtr &anf_node, kernel_info->select_kernel_build_info() == nullptr ? def_format : AnfAlgo::GetOutputFormat(anf_node, index); auto data_type = kernel_info->select_kernel_build_info() == nullptr ? type : AnfAlgo::GetOutputDeviceDataType(anf_node, index); - auto output_range_min = AnfAlgo::GetOutputMinShape(anf_node, index); - auto output_range_max = AnfAlgo::GetOutputMaxShape(anf_node, index); + auto output_range_min = common::AnfAlgo::GetOutputMinShape(anf_node, index); + auto output_range_max = common::AnfAlgo::GetOutputMaxShape(anf_node, index); if (output_range_min.size() != output_range_max.size()) { MS_EXCEPTION(ArgumentError) << "Onput range size is not equal, min size: " << output_range_min.size() << "max size: " << output_range_max.size(); diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_dynaminc_shape_util.h b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_dynaminc_shape_util.h index adc93728695..117a5731d0c 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_dynaminc_shape_util.h +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_dynaminc_shape_util.h @@ -21,7 +21,7 @@ #include #include #include -#include "utils/ms_device_shape_transfer.h" +#include "runtime/device/ms_device_shape_transfer.h" #include "mindspore/core/ir/anf.h" #include "kernel/oplib/oplib.h" namespace mindspore { diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_json/fusion_tbe_json_creator.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_json/fusion_tbe_json_creator.cc index 972fc4402b8..c5b4c152698 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_json/fusion_tbe_json_creator.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_json/fusion_tbe_json_creator.cc @@ -20,12 +20,13 @@ #include #include "base/core_ops.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "plugin/device/ascend/kernel/tbe/tbe_adapter.h" #include "plugin/device/ascend/kernel/tbe/tbe_convert_utils.h" #include "plugin/device/ascend/kernel/tbe/tbe_dynaminc_shape_util.h" #include "plugin/device/ascend/kernel/tbe/tbe_utils.h" #include "runtime/dev.h" -#include "utils/json_operation_utils.h" +#include "include/common/utils/json_operation_utils.h" #include "plugin/device/ascend/kernel/tbe/tbe_json/tbe_json_utils.h" namespace mindspore::kernel { @@ -157,7 +158,7 @@ AnfNodePtr FusionBuildTbeJsonCreator::GetInputCNode(const AnfNodePtr &node, cons auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); for (size_t i = 1; i < cnode->inputs().size(); i++) { - auto kernel_idx = AnfAlgo::VisitKernel(cnode->input(i), 0); + auto kernel_idx = common::AnfAlgo::VisitKernel(cnode->input(i), 0); auto full_name = kernel_idx.first->fullname_with_scope(); std::string desc_name = kernel_idx.second > 0 ? (full_name + "_" + std::to_string(kernel_idx.second)) : full_name; if (input_name == desc_name) { @@ -171,10 +172,10 @@ bool FusionBuildTbeJsonCreator::GenInputsJson(const AnfNodePtr &anf_node, nlohma MS_EXCEPTION_IF_NULL(anf_node); MS_EXCEPTION_IF_NULL(compute_json); std::vector input_desc_list_tmp = {}; - auto op_name = AnfAlgo::GetCNodeName(anf_node); + auto op_name = common::AnfAlgo::GetCNodeName(anf_node); auto cnode = anf_node->cast(); MS_EXCEPTION_IF_NULL(cnode); - bool is_dynamic_input = AnfAlgo::HasNodeAttr(kAttrDynInputSizes, cnode); + bool is_dynamic_input = common::AnfAlgo::HasNodeAttr(kAttrDynInputSizes, cnode); if (is_dynamic_input) { MS_LOG(INFO) << op_name << " has dynamic input."; if (!CheckDynamicInput(cnode)) { @@ -187,7 +188,7 @@ bool FusionBuildTbeJsonCreator::GenInputsJson(const AnfNodePtr &anf_node, nlohma if (HasAbstractMonad(input)) { continue; } - auto kernel_idx = AnfAlgo::VisitKernel(input, 0); + auto kernel_idx = common::AnfAlgo::VisitKernel(input, 0); nlohmann::json input_desc; GenDescJson(kernel_idx.first, kernel_idx.second, kernel_idx.second, &input_desc); GenInputConstValue(anf_node, i - 1, &input_desc); @@ -221,12 +222,13 @@ bool FusionBuildTbeJsonCreator::GenInputsJson(const AnfNodePtr &anf_node, nlohma bool FusionBuildTbeJsonCreator::CheckDynamicInput(const CNodePtr &cnode) { MS_EXCEPTION_IF_NULL(cnode); - if (!AnfAlgo::HasNodeAttr(kAttrDynInputSizes, cnode)) { - MS_LOG(WARNING) << "Fusion Error: cnode [ " << AnfAlgo::GetCNodeName(cnode) << "] has not attr dyn_input_sizes."; + if (!common::AnfAlgo::HasNodeAttr(kAttrDynInputSizes, cnode)) { + MS_LOG(WARNING) << "Fusion Error: cnode [ " << common::AnfAlgo::GetCNodeName(cnode) + << "] has not attr dyn_input_sizes."; return false; } // for dynamic input number, dyn_input_sizes has the info of dynamic input num for each input. - auto dyn_input_sizes = AnfAlgo::GetNodeAttr>(cnode, kAttrDynInputSizes); + auto dyn_input_sizes = common::AnfAlgo::GetNodeAttr>(cnode, kAttrDynInputSizes); if (dyn_input_sizes.size() != 1) { MS_LOG(WARNING) << "Fusion Error: fusion build not support dynamic input size > 1"; return false; @@ -243,14 +245,14 @@ bool FusionBuildTbeJsonCreator::CheckDynamicInput(const CNodePtr &cnode) { bool FusionBuildTbeJsonCreator::GenOutputsJson(const AnfNodePtr &anf_node, nlohmann::json *compute_json) { MS_EXCEPTION_IF_NULL(anf_node); MS_EXCEPTION_IF_NULL(compute_json); - auto output_size = AnfAlgo::GetOutputTensorNum(anf_node); + auto output_size = common::AnfAlgo::GetOutputTensorNum(anf_node); auto cnode = anf_node->cast(); MS_EXCEPTION_IF_NULL(cnode); std::vector output_desc_list; - if (AnfAlgo::HasNodeAttr(kAttrOutputUsedNum, cnode)) { - auto output_used_nums = AnfAlgo::GetNodeAttr>(anf_node, kAttrOutputUsedNum); + if (common::AnfAlgo::HasNodeAttr(kAttrOutputUsedNum, cnode)) { + auto output_used_nums = common::AnfAlgo::GetNodeAttr>(anf_node, kAttrOutputUsedNum); if (output_used_nums.size() != output_size) { - MS_LOG(WARNING) << "Fusion Error: [" << AnfAlgo::GetCNodeName(anf_node) << " ]'s output tensor num(" + MS_LOG(WARNING) << "Fusion Error: [" << common::AnfAlgo::GetCNodeName(anf_node) << " ]'s output tensor num(" << output_size << ")" << " is not match output used num(" << output_used_nums.size() << ")"; return false; diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_json/single_tbe_json_creator.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_json/single_tbe_json_creator.cc index ad002e6e7f9..f0a9cff54a2 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_json/single_tbe_json_creator.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_json/single_tbe_json_creator.cc @@ -19,13 +19,14 @@ #include #include "frontend/parallel/ops_info/ops_utils.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "plugin/device/ascend/kernel/tbe/tbe_adapter.h" #include "plugin/device/ascend/kernel/tbe/tbe_convert_utils.h" #include "plugin/device/ascend/kernel/tbe/tbe_dynaminc_shape_util.h" #include "plugin/device/ascend/kernel/tbe/tbe_utils.h" #include "runtime/dev.h" #include "utils/ms_utils.h" -#include "utils/json_operation_utils.h" +#include "include/common/utils/json_operation_utils.h" #include "plugin/device/ascend/kernel/tbe/tbe_json/tbe_json_utils.h" namespace mindspore::kernel { @@ -33,7 +34,7 @@ using mindspore::kernel::tbe::TbeAdapter; bool SingleTbeJsonCreator::GenJson(const AnfNodePtr &anf_node, nlohmann::json *kernel_json) { MS_EXCEPTION_IF_NULL(anf_node); MS_EXCEPTION_IF_NULL(kernel_json); - auto op_name = AnfAlgo::GetCNodeName(anf_node); + auto op_name = common::AnfAlgo::GetCNodeName(anf_node); MS_LOG(DEBUG) << "Start, node [ " << op_name << " ]."; nlohmann::json soc_info_json = kernel::tbe::TbeUtils::GenSocInfo(); std::vector op_list; @@ -59,7 +60,7 @@ bool SingleTbeJsonCreator::GenOpListJson(const AnfNodePtr &anf_node, std::vector MS_LOG(DEBUG) << "Start."; nlohmann::json compute_json; if (!GenComputeJson(anf_node, &compute_json)) { - MS_LOG(ERROR) << "Anf Node [" << AnfAlgo::GetCNodeName(anf_node) << "] generate compute json failed"; + MS_LOG(ERROR) << "Anf Node [" << common::AnfAlgo::GetCNodeName(anf_node) << "] generate compute json failed"; return false; } GenDataJson(anf_node, compute_json, op_list_json); @@ -73,7 +74,7 @@ void SingleTbeJsonCreator::GenDataJson(const AnfNodePtr &anf_node, const nlohman MS_EXCEPTION_IF_NULL(anf_node); MS_EXCEPTION_IF_NULL(op_list_json); MS_LOG(DEBUG) << "Start"; - auto op_name = AnfAlgo::GetCNodeName(anf_node); + auto op_name = common::AnfAlgo::GetCNodeName(anf_node); auto op_info_ptr = mindspore::kernel::tbe::TbeDynamicShapeUtil::FindOp(op_name, anf_node); auto inputs_ptr = op_info_ptr->inputs_ptr(); auto inputs_json = GetJsonValue>(compute_json, kJInputDesc); @@ -102,7 +103,7 @@ bool SingleTbeJsonCreator::GenInputsJson(const AnfNodePtr &anf_node, nlohmann::j MS_EXCEPTION_IF_NULL(anf_node); MS_EXCEPTION_IF_NULL(compute_json); MS_LOG(DEBUG) << "Start."; - auto op_name = AnfAlgo::GetCNodeName(anf_node); + auto op_name = common::AnfAlgo::GetCNodeName(anf_node); auto op_info_ptr = mindspore::kernel::tbe::TbeDynamicShapeUtil::FindOp(op_name, anf_node); MS_EXCEPTION_IF_NULL(op_info_ptr); std::vector inputs_ptr = op_info_ptr->inputs_ptr(); @@ -118,7 +119,7 @@ bool SingleTbeJsonCreator::GenInputsJson(const AnfNodePtr &anf_node, nlohmann::j } std::vector inputs_desc; - size_t real_input_num = AnfAlgo::GetInputTensorNum(anf_node); + size_t real_input_num = common::AnfAlgo::GetInputTensorNum(anf_node); for (size_t i = 0; i < real_input_num; i++) { nlohmann::json input_desc; GenInputDescJson(anf_node, i, &input_desc); @@ -127,7 +128,7 @@ bool SingleTbeJsonCreator::GenInputsJson(const AnfNodePtr &anf_node, nlohmann::j size_t need_input_num = std::accumulate(inputs_tensor_num.begin(), inputs_tensor_num.end(), static_cast(0)); // gen optional desc - for (size_t i = AnfAlgo::GetInputTensorNum(anf_node); i < need_input_num; i++) { + for (size_t i = common::AnfAlgo::GetInputTensorNum(anf_node); i < need_input_num; i++) { nlohmann::json input_desc; input_desc[kJValid] = false; input_desc[kJShape] = kJNull; @@ -242,7 +243,7 @@ bool SingleTbeJsonCreator::GenOutputsJson(const AnfNodePtr &anf_node, nlohmann:: MS_EXCEPTION_IF_NULL(anf_node); MS_EXCEPTION_IF_NULL(compute_json); MS_LOG(DEBUG) << "Start."; - auto op_name = AnfAlgo::GetCNodeName(anf_node); + auto op_name = common::AnfAlgo::GetCNodeName(anf_node); auto op_info_ptr = mindspore::kernel::tbe::TbeDynamicShapeUtil::FindOp(op_name, anf_node); MS_EXCEPTION_IF_NULL(op_info_ptr); std::vector outputs_tensor_num; @@ -253,7 +254,7 @@ bool SingleTbeJsonCreator::GenOutputsJson(const AnfNodePtr &anf_node, nlohmann:: size_t sum_outputs_num = std::accumulate(outputs_tensor_num.begin(), outputs_tensor_num.end(), static_cast(0)); - size_t real_output_num = AnfAlgo::GetOutputTensorNum(anf_node); + size_t real_output_num = common::AnfAlgo::GetOutputTensorNum(anf_node); std::vector outputs_desc; for (size_t i = 0; i < real_output_num; i++) { nlohmann::json output_desc; @@ -342,7 +343,7 @@ void SelectTbeJsonCreator::GenDescJson(const AnfNodePtr &anf_node, size_t node_o auto def_format = TbeJsonUtils::IsNeedChangeDefaultFormat(anf_node) ? kOpFormat_NCDHW : kOpFormat_NCHW; auto format = def_format; - (*output_desc)[kJDataType] = tbe::TypeIdToString(AnfAlgo::GetOutputInferDataType(anf_node, node_out_idx)); + (*output_desc)[kJDataType] = tbe::TypeIdToString(common::AnfAlgo::GetOutputInferDataType(anf_node, node_out_idx)); (*output_desc)[kJDtype] = GetJsonValue(*output_desc, kJDataType); (*output_desc)[kJFormat] = format; (*output_desc)[kJOriFormat] = def_format; @@ -363,7 +364,7 @@ void SelectTbeJsonCreator::GenInputDescJson(const AnfNodePtr &anf_node, size_t r auto def_format = TbeJsonUtils::IsNeedChangeDefaultFormat(anf_node) ? kOpFormat_NCDHW : kOpFormat_NCHW; auto format = def_format; - auto d_type = AnfAlgo::GetPrevNodeOutputInferDataType(anf_node, real_input_index); + auto d_type = common::AnfAlgo::GetPrevNodeOutputInferDataType(anf_node, real_input_index); (*input_desc)[kJDtype] = tbe::TypeIdToString(d_type); (*input_desc)[kJDataType] = GetJsonValue(*input_desc, kJDtype); (*input_desc)[kJOriShape] = ori_shape; diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_json/tbe_json_creator.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_json/tbe_json_creator.cc index 8cddecea94e..80e287707bf 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_json/tbe_json_creator.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_json/tbe_json_creator.cc @@ -20,6 +20,7 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "kernel/common_utils.h" #include "plugin/device/ascend/kernel/tbe/tbe_adapter.h" #include "plugin/device/ascend/kernel/tbe/tbe_convert_utils.h" @@ -27,8 +28,8 @@ #include "utils/ms_context.h" #include "runtime/dev.h" #include "utils/ms_utils.h" -#include "utils/json_operation_utils.h" -#include "utils/convert_utils.h" +#include "include/common/utils/json_operation_utils.h" +#include "include/common/utils/convert_utils.h" #include "plugin/device/ascend/kernel/tbe/tbe_json/tbe_json_utils.h" namespace mindspore::kernel { @@ -303,8 +304,8 @@ void TbeJsonCreator::GenAttrsJson(const AnfNodePtr &anf_node, const OpInfoPtr &o MS_LOG(EXCEPTION) << "PreProcessing node attr error, node: " << anf_node->fullname_with_scope(); } - std::string op_name = AnfAlgo::GetCNodeName(anf_node); - auto primitive = AnfAlgo::GetCNodePrimitive(anf_node); + std::string op_name = common::AnfAlgo::GetCNodeName(anf_node); + auto primitive = common::AnfAlgo::GetCNodePrimitive(anf_node); MS_EXCEPTION_IF_NULL(primitive); for (const auto &attr_ptr : attrs_ptr) { std::string attr_name = attr_ptr->name(); @@ -346,7 +347,7 @@ void TbeJsonCreator::GenAttrsDescJson(const AnfNodePtr &anf_node, nlohmann::json MS_EXCEPTION_IF_NULL(compute_json); auto cnode = anf_node->cast(); MS_EXCEPTION_IF_NULL(cnode); - auto op_name = AnfAlgo::GetCNodeName(cnode); + auto op_name = common::AnfAlgo::GetCNodeName(cnode); auto op_info_ptr = tbe::TbeDynamicShapeUtil::FindOp(op_name, cnode); nlohmann::json attrs_json; GenAttrsJson(cnode, op_info_ptr, &attrs_json); @@ -370,7 +371,7 @@ void TbeJsonCreator::GenComputeCommonJson(const AnfNodePtr &anf_node, nlohmann:: MS_EXCEPTION_IF_NULL(compute_json); auto cnode = anf_node->cast(); MS_EXCEPTION_IF_NULL(cnode); - auto op_name = AnfAlgo::GetCNodeName(cnode); + auto op_name = common::AnfAlgo::GetCNodeName(cnode); auto op_info_ptr = tbe::TbeDynamicShapeUtil::FindOp(op_name, cnode); auto func_name = op_info_ptr->kernel_name(); (*compute_json)[kJFuncName] = func_name; @@ -511,8 +512,8 @@ void TbeJsonCreator::GenInputConstValue(const AnfNodePtr &anf_node, size_t real_ auto cnode = anf_node->cast(); MS_EXCEPTION_IF_NULL(cnode); auto input_node = cnode->inputs()[real_input_index + 1]; - if (AnfAlgo::CheckPrimitiveType(input_node, prim::kPrimDepend)) { - input_node = AnfAlgo::VisitKernel(input_node, 0).first; + if (common::AnfAlgo::CheckPrimitiveType(input_node, prim::kPrimDepend)) { + input_node = common::AnfAlgo::VisitKernel(input_node, 0).first; } MS_EXCEPTION_IF_NULL(input_node); if (input_node->isa()) { diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_json/tbe_json_utils.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_json/tbe_json_utils.cc index 1a6dd8c792d..fcb7ca9f7ce 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_json/tbe_json_utils.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_json/tbe_json_utils.cc @@ -17,17 +17,18 @@ #include "plugin/device/ascend/kernel/tbe/tbe_json/tbe_json_utils.h" #include "base/core_ops.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "plugin/device/ascend/kernel/tbe/tbe_convert_utils.h" #include "plugin/device/ascend/kernel/tbe/tbe_dynaminc_shape_util.h" #include "runtime/dev.h" -#include "utils/json_operation_utils.h" +#include "include/common/utils/json_operation_utils.h" namespace mindspore::kernel { bool TbeJsonUtils::GetInputsRealNum(const AnfNodePtr &anf_node, const std::vector &inputs_ptr, std::vector *inputs_num) { MS_EXCEPTION_IF_NULL(anf_node); MS_EXCEPTION_IF_NULL(inputs_num); - auto primitive = AnfAlgo::GetCNodePrimitive(anf_node); + auto primitive = common::AnfAlgo::GetCNodePrimitive(anf_node); // for dynamic input number, dyn_input_sizes has the info of dynamic input num for each input. auto dyn_input_sizes_ptr = primitive->GetAttr(kAttrDynInputSizes); std::vector dyn_input_sizes = (dyn_input_sizes_ptr != nullptr) @@ -53,13 +54,13 @@ bool TbeJsonUtils::GetInputsRealNum(const AnfNodePtr &anf_node, const std::vecto bool TbeJsonUtils::GetOutputsRealNum(const AnfNodePtr &anf_node, const std::vector &outputs_ptr, std::vector *outputs_num) { MS_EXCEPTION_IF_NULL(anf_node); - size_t real_output_num = AnfAlgo::GetOutputTensorNum(anf_node); + size_t real_output_num = common::AnfAlgo::GetOutputTensorNum(anf_node); for (const auto &output_ptr : outputs_ptr) { if (output_ptr->param_type() == kJParamDynamic) { if (outputs_ptr.size() > 1) { - MS_LOG(ERROR) << "Dynamic output is unsupported multi output, node [ " << AnfAlgo::GetCNodeName(anf_node) - << " ] has " << outputs_ptr.size() << "outputs, however one of the outputs param_type is " - << output_ptr->param_type(); + MS_LOG(ERROR) << "Dynamic output is unsupported multi output, node [ " + << common::AnfAlgo::GetCNodeName(anf_node) << " ] has " << outputs_ptr.size() + << "outputs, however one of the outputs param_type is " << output_ptr->param_type(); return false; } outputs_num->emplace_back(real_output_num); @@ -72,13 +73,13 @@ bool TbeJsonUtils::GetOutputsRealNum(const AnfNodePtr &anf_node, const std::vect bool TbeJsonUtils::IsNeedChangeDefaultFormat(const AnfNodePtr &anf_node) { MS_EXCEPTION_IF_NULL(anf_node); - return anf_node->isa() && AnfAlgo::HasNodeAttr(kAttrFormat, anf_node->cast()) && - AnfAlgo::GetNodeAttr(anf_node, kAttrFormat) == kOpFormat_NCDHW; + return anf_node->isa() && common::AnfAlgo::HasNodeAttr(kAttrFormat, anf_node->cast()) && + common::AnfAlgo::GetNodeAttr(anf_node, kAttrFormat) == kOpFormat_NCDHW; } std::vector TbeJsonUtils::GetInputOriShapeForTbeBuild(const AnfNodePtr &anf_node, size_t real_idx) { MS_EXCEPTION_IF_NULL(anf_node); - session::KernelWithIndex kernel_with_index = AnfAlgo::GetPrevNodeOutput(anf_node, real_idx); + session::KernelWithIndex kernel_with_index = common::AnfAlgo::GetPrevNodeOutput(anf_node, real_idx); return GetOutputOriShapeForTbeBuild(kernel_with_index.first, kernel_with_index.second); } @@ -96,7 +97,7 @@ std::vector TbeJsonUtils::GetInputDeviceShapeForTbeBuild(const AnfNodeP std::vector TbeJsonUtils::GetOutputOriShapeForTbeBuild(const AnfNodePtr &anf_node, size_t real_idx) { MS_EXCEPTION_IF_NULL(anf_node); std::vector shape; - auto out_shape = AnfAlgo::GetOutputDetailShape(anf_node, real_idx); + auto out_shape = common::AnfAlgo::GetOutputDetailShape(anf_node, real_idx); MS_EXCEPTION_IF_NULL(out_shape); if (out_shape->isa()) { auto shape_ptr = out_shape->cast(); diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_json/tbe_json_utils.h b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_json/tbe_json_utils.h index ff61d95eb63..da3d9582d93 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_json/tbe_json_utils.h +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_json/tbe_json_utils.h @@ -24,6 +24,7 @@ #include #include "base/core_ops.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "plugin/device/ascend/kernel/tbe/tbe_convert_utils.h" #include "plugin/device/ascend/kernel/tbe/tbe_dynaminc_shape_util.h" #include "runtime/dev.h" diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_build.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_build.cc index a01d1dd0bf3..7731dc2cd1a 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_build.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_build.cc @@ -19,6 +19,7 @@ #include #include "base/core_ops.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "plugin/device/ascend/kernel/tbe/tbe_convert_utils.h" #include "utils/ms_context.h" #include "runtime/dev.h" @@ -177,7 +178,7 @@ bool TbeKernelBuild::CalOutputSize(const nlohmann::json &fusion_op_list, MS_EXCEPTION_IF_NULL(output_size_list); // cal output size for malloc for (const auto &output_node : output_nodes) { - auto kernel_idx = AnfAlgo::VisitKernel(output_node, 0); + auto kernel_idx = common::AnfAlgo::VisitKernel(output_node, 0); auto real_node = kernel_idx.first; size_t real_idx = kernel_idx.second; auto full_name = real_node->fullname_with_scope(); diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc index a4f3f0c58d2..6a1afb17b3b 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc @@ -33,6 +33,7 @@ #include "plugin/device/ascend/kernel/tbe/dynamic_tbe_kernel_mod.h" #include "plugin/device/ascend/kernel/tbe/tbe_convert_utils.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/session/kernel_build_client.h" #include "common/util/error_manager/error_manager.h" #include "debug/anf_ir_dump.h" @@ -40,8 +41,8 @@ #include "utils/ms_context.h" #include "utils/ms_utils.h" #include "utils/trace_base.h" -#include "utils/utils.h" -#include "utils/json_operation_utils.h" +#include "include/common/utils/utils.h" +#include "include/common/utils/json_operation_utils.h" namespace mindspore { namespace kernel { @@ -553,7 +554,7 @@ void TbeKernelCompileManager::GenKernelMod(const std::vector &node_lis } auto kernel_info_json = kernel_pack->kernel_json_info(); std::shared_ptr kernel_mod_ptr; - if (AnfAlgo::IsDynamicShape(node)) { + if (common::AnfAlgo::IsDynamicShape(node)) { kernel_mod_ptr = std::make_shared(kernel_pack, node); } else { kernel_mod_ptr = std::make_shared(kernel_pack, node); @@ -660,7 +661,7 @@ void TbeKernelCompileManager::DistributePreBuildTask(const std::vector pre_build_single_processed_kernels_.insert(json_name); JsonAssemble(kPreCompile, kernel_json, &build_json); auto task_id = GetJsonValue(build_json, kJobId); - auto is_dynamic = AnfAlgo::IsDynamicShape(node); + auto is_dynamic = common::AnfAlgo::IsDynamicShape(node); SaveTaskInfo(is_dynamic, build_json, json_name, full_name, task_id, INT64_MAX); // save pair for exception print and get node trace @@ -689,7 +690,8 @@ void TbeKernelCompileManager::DistributeCompileTask(const std::vector MS_EXCEPTION_IF_NULL(node); kernel_json.clear(); build_json.clear(); - if (AnfAlgo::HasNodeAttr(kAttrIsUBFusionOp, node) && AnfAlgo::GetNodeAttr(node, kAttrIsUBFusionOp)) { + if (common::AnfAlgo::HasNodeAttr(kAttrIsUBFusionOp, node) && + common::AnfAlgo::GetNodeAttr(node, kAttrIsUBFusionOp)) { // skip fusion op, if node has the attr: kAttrIsUBFusionOp, means already done fusion compile, can not do single // op compile continue; @@ -701,8 +703,8 @@ void TbeKernelCompileManager::DistributeCompileTask(const std::vector auto json_name = json_creator->GetJsonName(); auto full_name = node->fullname_with_scope(); full_name_to_json_name_[full_name] = json_name; - if (AnfAlgo::IsDynamicShape(node)) { - AnfAlgo::SetNodeAttr(kAttrJsonFileName, MakeValue(json_name), node); + if (common::AnfAlgo::IsDynamicShape(node)) { + common::AnfAlgo::SetNodeAttr(kAttrJsonFileName, MakeValue(json_name), node); } // save all task io size info for gen kernel mod SaveIOSizeInfo(kernel_json, json_name); @@ -720,7 +722,7 @@ void TbeKernelCompileManager::DistributeCompileTask(const std::vector auto build_str = build_json.dump(indent); TbeUtils::SaveJsonInfo(json_name, build_str); auto task_id = GetJsonValue(build_json, kJobId); - auto is_dynamic = AnfAlgo::IsDynamicShape(node); + auto is_dynamic = common::AnfAlgo::IsDynamicShape(node); SaveTaskInfo(is_dynamic, build_json, json_name, full_name, task_id, INT64_MAX); // save pair for exception print and get node trace diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_mod.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_mod.cc index 9172870aa1a..ea598d24ef9 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_mod.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_mod.cc @@ -130,7 +130,7 @@ std::vector TbeKernelMod::GenTask(const std::vector &in device::DynamicKernelPtr TbeKernelMod::GenDynamicKernel(const CNodePtr &cnode_ptr, void *stream_ptr) { KernelLaunchInfo kernel_launch_info; device::KernelRuntime::GenLaunchArgs(*this, cnode_ptr, &kernel_launch_info); - auto dynamic_flag = AnfAlgo::IsDynamicShape(cnode_ptr); + auto dynamic_flag = common::AnfAlgo::IsDynamicShape(cnode_ptr); // Get para_size from json auto kernel_json_info = kernel_pack_->kernel_json_info(); diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_select/common_utils.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_select/common_utils.cc index b5088d10587..2d88891a0dd 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_select/common_utils.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_select/common_utils.cc @@ -18,15 +18,16 @@ #include #include #include "base/base.h" -#include "utils/ms_device_shape_transfer.h" +#include "runtime/device/ms_device_shape_transfer.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace kernel { bool HostCheck::CheckValidDeviceShape(const AnfNodePtr &node) { - size_t real_input_num = AnfAlgo::GetInputTensorNum(node); + size_t real_input_num = common::AnfAlgo::GetInputTensorNum(node); for (size_t i = 0; i < real_input_num; i++) { - session::KernelWithIndex kernel_with_index = AnfAlgo::GetPrevNodeOutput(node, i); + session::KernelWithIndex kernel_with_index = common::AnfAlgo::GetPrevNodeOutput(node, i); auto format = AnfAlgo::GetInputFormat(node, i); if (!CheckValidOutputDeviceShape(kernel_with_index.first, kernel_with_index.second, format)) { MS_LOG(WARNING) << "TBE Host check input device shape failed, node:" << node->fullname_with_scope() @@ -35,7 +36,7 @@ bool HostCheck::CheckValidDeviceShape(const AnfNodePtr &node) { } } - size_t real_output_num = AnfAlgo::GetOutputTensorNum(node); + size_t real_output_num = common::AnfAlgo::GetOutputTensorNum(node); for (size_t i = 0; i < real_output_num; i++) { auto format = AnfAlgo::GetOutputFormat(node, i); if (!CheckValidOutputDeviceShape(node, i, format)) { @@ -49,7 +50,7 @@ bool HostCheck::CheckValidDeviceShape(const AnfNodePtr &node) { std::vector HostCheck::GetFinalInferShape(const AnfNodePtr &node, const size_t output_idx, const std::string &format) { - auto output_shape = AnfAlgo::GetOutputDetailShape(node, output_idx); + auto output_shape = common::AnfAlgo::GetOutputDetailShape(node, output_idx); std::vector infer_shape; if (output_shape->isa()) { auto shape_ptr = output_shape->cast(); diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_select/common_utils.h b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_select/common_utils.h index 1c096d801f8..305f67de083 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_select/common_utils.h +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_select/common_utils.h @@ -18,6 +18,7 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace kernel { diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_select/tbe_kernel_broadcast_selecter.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_select/tbe_kernel_broadcast_selecter.cc index e2b9c95febd..abed16377f5 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_select/tbe_kernel_broadcast_selecter.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_select/tbe_kernel_broadcast_selecter.cc @@ -14,9 +14,10 @@ * limitations under the License. */ #include "plugin/device/ascend/kernel/tbe/tbe_kernel_select/tbe_kernel_broadcast_selecter.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/trace_base.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "plugin/device/ascend/kernel/tbe/tbe_kernel_select/common_utils.h" namespace mindspore { @@ -34,34 +35,34 @@ bool TbeKernelBroadCastSelecter::GetShapeInfo(SupportFormat *support_format) { output_num_ = 0; input_shapes_.clear(); output_shapes_.clear(); - if (AnfAlgo::HasNodeAttr(kAttrDynInputSizes, cnode_ptr_)) { - auto dynamic_size_vec = AnfAlgo::GetNodeAttr>(cnode_ptr_, kAttrDynInputSizes); + if (common::AnfAlgo::HasNodeAttr(kAttrDynInputSizes, cnode_ptr_)) { + auto dynamic_size_vec = common::AnfAlgo::GetNodeAttr>(cnode_ptr_, kAttrDynInputSizes); constexpr int64_t DYNAMIC_INPUT_NUM = 2; if (dynamic_size_vec.empty()) { - MS_LOG(EXCEPTION) << "Node [" << AnfAlgo::GetCNodeName(cnode_ptr_) << "]'s attr [dyn_input_sizes] is empty" - << trace::DumpSourceLines(cnode_ptr_); + MS_LOG(EXCEPTION) << "Node [" << common::AnfAlgo::GetCNodeName(cnode_ptr_) + << "]'s attr [dyn_input_sizes] is empty" << trace::DumpSourceLines(cnode_ptr_); } if (dynamic_size_vec[0] < DYNAMIC_INPUT_NUM) { - MS_LOG(EXCEPTION) << "Node [" << AnfAlgo::GetCNodeName(cnode_ptr_) + MS_LOG(EXCEPTION) << "Node [" << common::AnfAlgo::GetCNodeName(cnode_ptr_) << "]'s attr [dyn_input_sizes] value less than " << DYNAMIC_INPUT_NUM << trace::DumpSourceLines(cnode_ptr_); } - auto dynamic_input_shape0_ = AnfAlgo::GetPrevNodeOutputInferShape(cnode_ptr_, kInputIndex_0); + auto dynamic_input_shape0_ = common::AnfAlgo::GetPrevNodeOutputInferShape(cnode_ptr_, kInputIndex_0); PadScalarShape(&dynamic_input_shape0_); input_shapes_.emplace_back(dynamic_input_shape0_); input_num_ = 1; } else { - input_num_ = AnfAlgo::GetInputTensorNum(cnode_ptr_); + input_num_ = common::AnfAlgo::GetInputTensorNum(cnode_ptr_); for (size_t i = 0; i < input_num_; ++i) { - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(cnode_ptr_, i); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(cnode_ptr_, i); PadScalarShape(&input_shape); input_shapes_.emplace_back(input_shape); } } - output_num_ = AnfAlgo::GetOutputTensorNum(cnode_ptr_); + output_num_ = common::AnfAlgo::GetOutputTensorNum(cnode_ptr_); for (size_t i = 0; i < output_num_; ++i) { - auto output = AnfAlgo::GetOutputInferShape(cnode_ptr_, i); + auto output = common::AnfAlgo::GetOutputInferShape(cnode_ptr_, i); PadScalarShape(&output); output_shapes_.emplace_back(output); } diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_select/tbe_kernel_reduce_selecter.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_select/tbe_kernel_reduce_selecter.cc index d19f333161c..cc171d6197a 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_select/tbe_kernel_reduce_selecter.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_select/tbe_kernel_reduce_selecter.cc @@ -17,8 +17,9 @@ #include "plugin/device/ascend/kernel/tbe/tbe_kernel_select/tbe_kernel_reduce_selecter.h" #include #include -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "plugin/device/ascend/kernel/tbe/tbe_kernel_select/common_utils.h" #include "kernel/common_utils.h" @@ -35,16 +36,16 @@ bool TbeKernelReduceSelecter::GetShapeInfo(SupportFormat *support_format) { input_shape_.clear(); output_shape_.clear(); axis_.clear(); - auto input_num = AnfAlgo::GetInputTensorNum(cnode_ptr_); - auto output_num = AnfAlgo::GetOutputTensorNum(cnode_ptr_); + auto input_num = common::AnfAlgo::GetInputTensorNum(cnode_ptr_); + auto output_num = common::AnfAlgo::GetOutputTensorNum(cnode_ptr_); if (input_num != 1 || output_num != 1) { MS_LOG(EXCEPTION) << "Reduce operator only support one input/output, input num: " << input_num << ", output num: " << output_num; } // get input/output shape - input_shape_ = AnfAlgo::GetPrevNodeOutputInferShape(cnode_ptr_, kInputIndex_0); + input_shape_ = common::AnfAlgo::GetPrevNodeOutputInferShape(cnode_ptr_, kInputIndex_0); PadScalarShape(&input_shape_); - output_shape_ = AnfAlgo::GetOutputInferShape(cnode_ptr_, kOutputIndex_0); + output_shape_ = common::AnfAlgo::GetOutputInferShape(cnode_ptr_, kOutputIndex_0); PadScalarShape(&output_shape_); // get keep dim attr GetReduceAttrKeepDim(); @@ -133,12 +134,12 @@ bool TbeKernelReduceSelecter::IsFracZAndC1HWNCoC0Common(const std::string &forma } void TbeKernelReduceSelecter::GetReduceAttrKeepDim() { - if (!AnfAlgo::HasNodeAttr(kAttrKeepDims, cnode_ptr_)) { + if (!common::AnfAlgo::HasNodeAttr(kAttrKeepDims, cnode_ptr_)) { MS_LOG(INFO) << "This node doesn't have keep_attr."; keep_dims_ = false; return; } - keep_dims_ = AnfAlgo::GetNodeAttr(cnode_ptr_, kAttrKeepDims); + keep_dims_ = common::AnfAlgo::GetNodeAttr(cnode_ptr_, kAttrKeepDims); } void TbeKernelReduceSelecter::AssignSupportFormat(const std::string &support_format_str, diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_select/tbe_kernel_select.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_select/tbe_kernel_select.cc index 853549c686c..5f3383c93f7 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_select/tbe_kernel_select.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_select/tbe_kernel_select.cc @@ -32,10 +32,11 @@ #include "plugin/device/ascend/kernel/tbe/tbe_kernel_select/tbe_property_checker.h" #include "backend/common/optimizer/helper.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/session/kernel_build_client.h" #include "nlohmann/json.hpp" #include "utils/convert_utils_base.h" -#include "utils/json_operation_utils.h" +#include "include/common/utils/json_operation_utils.h" namespace mindspore::kernel { constexpr auto kName = "name"; @@ -57,7 +58,7 @@ TbeKernelSelect::TbeKernelSelect(CNodePtr kernel_node, std::vectorfullname_with_scope(); auto op_info_ptr = tbe::TbeDynamicShapeUtil::FindOp(node_name_, cnode_ptr_); @@ -92,12 +93,13 @@ void TbeKernelSelect::TbeMetadataInfoEx() { void TbeKernelSelect::GetCommonPatternKernelInfo(const OpInfo &op_info) { auto dyn_input_sizes = GetNodeDynamicInputs(); // get real input/output num - size_t real_input_tensor_num = AnfAlgo::GetInputTensorNum(cnode_ptr_); + size_t real_input_tensor_num = common::AnfAlgo::GetInputTensorNum(cnode_ptr_); const auto inputs_info = op_info.inputs_ptr(); - size_t real_output_tensor_num = AnfAlgo::GetOutputTensorNum(cnode_ptr_); + size_t real_output_tensor_num = common::AnfAlgo::GetOutputTensorNum(cnode_ptr_); const auto outputs_info = op_info.outputs_ptr(); if (inputs_info.empty() && outputs_info.empty()) { - MS_LOG(EXCEPTION) << AnfAlgo::GetCNodeName(cnode_ptr_) << "'s op info input & output is null, please check."; + MS_LOG(EXCEPTION) << common::AnfAlgo::GetCNodeName(cnode_ptr_) + << "'s op info input & output is null, please check."; } // create kernel build info from opinfo size_t kernel_build_info_num = @@ -218,7 +220,7 @@ bool TbeKernelSelect::FilterInVaildShape(const KernelBuildInfoIter &kernel_build auto iter_num = is_dynamic_input && !kernel_build_info_inputs_format.empty() ? 1 : kernel_build_info_inputs_format.size(); for (size_t i = 0; i < iter_num; ++i) { - auto shape = AnfAlgo::GetPrevNodeOutputInferShape(cnode_ptr_, i); + auto shape = common::AnfAlgo::GetPrevNodeOutputInferShape(cnode_ptr_, i); const auto &format = kernel_build_info_inputs_format.at(i); if (!IsShapeMatchFormat(shape, format)) { return false; @@ -226,7 +228,7 @@ bool TbeKernelSelect::FilterInVaildShape(const KernelBuildInfoIter &kernel_build } const auto &kernel_build_info_outputs_format = (*kernel_build_info_iter)->GetAllOutputFormats(); for (size_t j = 0; j < kernel_build_info_outputs_format.size(); ++j) { - auto shape = AnfAlgo::GetOutputInferShape(cnode_ptr_, j); + auto shape = common::AnfAlgo::GetOutputInferShape(cnode_ptr_, j); const auto &format = kernel_build_info_outputs_format[j]; if (!IsShapeMatchFormat(shape, format)) { return false; @@ -287,7 +289,7 @@ void TbeKernelSelect::SetTbeBuildCommonInfo(const mindspore::kernel::OpInfo &op_ std::vector TbeKernelSelect::GetNodeDynamicInputs() { // get dynamic inputs - auto primitive = AnfAlgo::GetCNodePrimitive(cnode_ptr_); + auto primitive = common::AnfAlgo::GetCNodePrimitive(cnode_ptr_); MS_EXCEPTION_IF_NULL(primitive); std::vector dyn_input_sizes; if (primitive->HasAttr(kAttrDynInputSizes)) { diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_select/tbe_property_checker.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_select/tbe_property_checker.cc index e83add597b2..b3c09ef5b8b 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_select/tbe_property_checker.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_select/tbe_property_checker.cc @@ -18,6 +18,7 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "frontend/parallel/ops_info/ops_utils.h" #include "utils/trace_base.h" @@ -35,15 +36,15 @@ constexpr char kAttrShrinkAxisMask[] = "shrink_axis_mask"; static bool CheckStridedSlice(const CNodePtr &cnode) { // check stride[-1] != 1 - if (AnfAlgo::HasNodeAttr(kAttrStrides, cnode)) { - auto strides = AnfAlgo::GetNodeAttr>(cnode, kAttrStrides); + if (common::AnfAlgo::HasNodeAttr(kAttrStrides, cnode)) { + auto strides = common::AnfAlgo::GetNodeAttr>(cnode, kAttrStrides); if (!strides.empty() && strides[strides.size() - 1] != 1) { return false; } } // check reduction on the last dimension - if (GetCNodeFuncName(cnode) == kStridedSliceOpName && AnfAlgo::HasNodeAttr(kAttrShrinkAxisMask, cnode)) { - auto shrink_axis_mask = static_cast(AnfAlgo::GetNodeAttr(cnode, kAttrShrinkAxisMask)); + if (GetCNodeFuncName(cnode) == kStridedSliceOpName && common::AnfAlgo::HasNodeAttr(kAttrShrinkAxisMask, cnode)) { + auto shrink_axis_mask = static_cast(common::AnfAlgo::GetNodeAttr(cnode, kAttrShrinkAxisMask)); AnfNodePtr input = cnode->input(1); int input_dims = 0; MS_EXCEPTION_IF_NULL(input); @@ -76,8 +77,8 @@ static bool CheckStridedSlice(const CNodePtr &cnode) { } static bool CheckTopK(const CNodePtr &cnode) { - if (AnfAlgo::HasNodeAttr(kAttrSorted, cnode)) { - auto sorted = AnfAlgo::GetNodeAttr(cnode, kAttrSorted); + if (common::AnfAlgo::HasNodeAttr(kAttrSorted, cnode)) { + auto sorted = common::AnfAlgo::GetNodeAttr(cnode, kAttrSorted); return sorted; } MS_LOG(EXCEPTION) << "For 'TopK', it should be have attribute 'sorted'." << trace::DumpSourceLines(cnode); @@ -87,7 +88,7 @@ bool TbePropertyChecker::CheckTbeProperties(const mindspore::CNodePtr &cnode) { MS_EXCEPTION_IF_NULL(cnode); static std::map tbe_property_checker = { {kStridedSliceOpName, CheckStridedSlice}, {kStridedSliceGradOpName, CheckStridedSlice}, {kTopKOpName, CheckTopK}}; - auto cnode_type = AnfAlgo::GetCNodeName(cnode); + auto cnode_type = common::AnfAlgo::GetCNodeName(cnode); auto find_iter = tbe_property_checker.find(cnode_type); if (find_iter != tbe_property_checker.end()) { return find_iter->second(cnode); diff --git a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_utils.cc b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_utils.cc index 91f24a8ae7a..649d1f25408 100644 --- a/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_utils.cc +++ b/mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_utils.cc @@ -26,18 +26,19 @@ #include #include "runtime/kernel.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/ms_utils.h" #include "utils/ms_context.h" #include "ir/dtype/type.h" #include "runtime/dev.h" #include "plugin/device/ascend/hal/device/lic_manager.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "plugin/device/ascend/kernel/tbe/tbe_convert_utils.h" #include "plugin/device/ascend/kernel/tbe/tbe_json/tbe_json_creator.h" #include "plugin/device/ascend/kernel/tbe/tbe_json/single_tbe_json_creator.h" #include "securec/include/securec.h" -#include "utils/json_operation_utils.h" +#include "include/common/utils/json_operation_utils.h" #include "mindspore/ccsrc/debug/common.h" #include "kernel/common_utils.h" @@ -383,8 +384,8 @@ void TbeUtils::GetCompileInfo(const AnfNodePtr &node, std::string *compile_info, MS_EXCEPTION_IF_NULL(node); MS_LOG(INFO) << "Get compile info from json file start. [" << node->fullname_with_scope() << "]"; std::string json_name; - if (AnfAlgo::HasNodeAttr(kAttrJsonFileName, node->cast())) { - json_name = AnfAlgo::GetNodeAttr(node, kAttrJsonFileName); + if (common::AnfAlgo::HasNodeAttr(kAttrJsonFileName, node->cast())) { + json_name = common::AnfAlgo::GetNodeAttr(node, kAttrJsonFileName); } else { auto json_creator = std::make_shared(); MS_EXCEPTION_IF_NULL(json_creator); diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ascend_backend_optimization.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ascend_backend_optimization.cc index bd3f55664b0..6dbb9842d5d 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ascend_backend_optimization.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ascend_backend_optimization.cc @@ -160,8 +160,8 @@ #include "backend/common/pass/adjust_depend_for_parallel_optimizer_recompute_all_gather.h" #include "plugin/device/ascend/kernel/tbe/tbe_kernel_compile.h" #include "utils/ms_context.h" -#include "utils/config_manager.h" -#include "utils/context/graph_kernel_flags.h" +#include "include/common/utils/config_manager.h" +#include "include/common/utils/context/graph_kernel_flags.h" #include "debug/anf_ir_dump.h" #include "debug/dump_proto.h" #ifdef ENABLE_DUMP_IR diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ascend_helper.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ascend_helper.cc index ece15cdbbd7..6bf416a8a70 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ascend_helper.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ascend_helper.cc @@ -16,16 +16,17 @@ #include "plugin/device/ascend/optimizer/ascend_helper.h" #include -#include "utils/ms_device_shape_transfer.h" +#include "runtime/device/ms_device_shape_transfer.h" #include "utils/ms_utils.h" #include "utils/check_convert_utils.h" #include "backend/common/optimizer/helper.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "runtime/device/kernel_info.h" #include "kernel/oplib/oplib.h" #include "kernel/common_utils.h" #include "base/core_ops.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/session/kernel_graph.h" #include "utils/ms_context.h" #include "utils/trace_base.h" @@ -46,9 +47,10 @@ AnfNodePtr CreateReshapeNode(const FuncGraphPtr &func_graph, const AnfNodePtr &i trans_inputs.emplace_back(input_node); auto reshape = NewCNode(trans_inputs, func_graph, {orig_node}); MS_EXCEPTION_IF_NULL(reshape); - AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(input_node, 0)}, {dst_shape}, reshape.get()); - AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), reshape); - AnfAlgo::SetNodeAttr(kAttrShape, MakeValue(dst_shape), reshape); + common::AnfAlgo::SetOutputInferTypeAndShape({common::AnfAlgo::GetOutputInferDataType(input_node, 0)}, {dst_shape}, + reshape.get()); + common::AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), reshape); + common::AnfAlgo::SetNodeAttr(kAttrShape, MakeValue(dst_shape), reshape); reshape->set_scope(input_node->scope()); kernel_select->SelectKernel(reshape); return reshape; @@ -56,33 +58,34 @@ AnfNodePtr CreateReshapeNode(const FuncGraphPtr &func_graph, const AnfNodePtr &i void SetTransNodeAttr(const CNodePtr &trans_node) { MS_EXCEPTION_IF_NULL(trans_node); - auto trans_opname = AnfAlgo::GetCNodeName(trans_node); + auto trans_opname = common::AnfAlgo::GetCNodeName(trans_node); if (trans_opname == kTransDataOpName || trans_opname == kTransDataRNNOpName) { std::string input_format = AnfAlgo::GetInputFormat(trans_node, 0); std::string output_format = AnfAlgo::GetOutputFormat(trans_node, 0); if (input_format == kOpFormat_DEFAULT) { - input_format = AnfAlgo::GetCNodeName(trans_node) == kTransDataOpName ? kOpFormat_NCHW : kOpFormat_ND; + input_format = common::AnfAlgo::GetCNodeName(trans_node) == kTransDataOpName ? kOpFormat_NCHW : kOpFormat_ND; } if (output_format == kOpFormat_DEFAULT) { - output_format = AnfAlgo::GetCNodeName(trans_node) == kTransDataOpName ? kOpFormat_NCHW : kOpFormat_ND; + output_format = common::AnfAlgo::GetCNodeName(trans_node) == kTransDataOpName ? kOpFormat_NCHW : kOpFormat_ND; } - AnfAlgo::SetNodeAttr(kAttrSrcFormat, MakeValue(input_format), trans_node); - AnfAlgo::SetNodeAttr(kAttrDstFormat, MakeValue(output_format), trans_node); + common::AnfAlgo::SetNodeAttr(kAttrSrcFormat, MakeValue(input_format), trans_node); + common::AnfAlgo::SetNodeAttr(kAttrDstFormat, MakeValue(output_format), trans_node); } } void ReFreshInferShape(const AnfNodePtr &trans_node, const AnfNodePtr &node) { MS_EXCEPTION_IF_NULL(trans_node); MS_EXCEPTION_IF_NULL(node); - auto real_input_node = AnfAlgo::VisitKernelWithReturnType(node, 0).first; + auto real_input_node = common::AnfAlgo::VisitKernelWithReturnType(node, 0).first; if (!real_input_node->isa()) { return; } - auto op_name = AnfAlgo::GetCNodeName(real_input_node); - if (op_name == kBasicLSTMCellWeightGradOpName && AnfAlgo::GetCNodeName(trans_node) == prim::kPrimReshape->name()) { - auto shape = AnfAlgo::GetPrevNodeOutputInferShape(trans_node, 0); - auto type = AnfAlgo::GetPrevNodeOutputInferDataType(trans_node, 0); - AnfAlgo::SetOutputInferTypeAndShape({type}, {{shape[0], shape[1]}}, node.get()); + auto op_name = common::AnfAlgo::GetCNodeName(real_input_node); + if (op_name == kBasicLSTMCellWeightGradOpName && + common::AnfAlgo::GetCNodeName(trans_node) == prim::kPrimReshape->name()) { + auto shape = common::AnfAlgo::GetPrevNodeOutputInferShape(trans_node, 0); + auto type = common::AnfAlgo::GetPrevNodeOutputInferDataType(trans_node, 0); + common::AnfAlgo::SetOutputInferTypeAndShape({type}, {{shape[0], shape[1]}}, node.get()); } } @@ -98,12 +101,12 @@ void SetGroupAttr(const ParameterPtr ¶m, const AnfNodePtr &out_trans, const if (fz_group > 1) { if (out_trans->isa()) { // if has transdata after parameter - AnfAlgo::SetNodeAttr(kAttrGroups, MakeValue(fz_group), out_trans); - AnfAlgo::SetNodeAttr(kAttrFracZGroup, MakeValue(fz_group), out_trans); + common::AnfAlgo::SetNodeAttr(kAttrGroups, MakeValue(fz_group), out_trans); + common::AnfAlgo::SetNodeAttr(kAttrFracZGroup, MakeValue(fz_group), out_trans); } if (dest_format == kOpFormat_FRAC_Z) { - AnfAlgo::SetNodeAttr(kAttrGroups, MakeValue(fz_group), in_trans); - AnfAlgo::SetNodeAttr(kAttrFracZGroup, MakeValue(fz_group), in_trans); + common::AnfAlgo::SetNodeAttr(kAttrGroups, MakeValue(fz_group), in_trans); + common::AnfAlgo::SetNodeAttr(kAttrFracZGroup, MakeValue(fz_group), in_trans); } } } @@ -112,20 +115,20 @@ AnfNodePtr GetTransInputNodePtr(const FuncGraphPtr &func_graph, const CNodePtr & const KernelSelectPtr &kernel_select) { MS_EXCEPTION_IF_NULL(node); MS_EXCEPTION_IF_NULL(func_graph); - auto input_node = AnfAlgo::GetInputNode(node, index); + auto input_node = common::AnfAlgo::GetInputNode(node, index); if (HasAbstractMonad(input_node)) { // No transfer for monad inputs. return input_node; } - auto node_with_index = AnfAlgo::VisitKernel(input_node, 0); + auto node_with_index = common::AnfAlgo::VisitKernel(input_node, 0); MS_EXCEPTION_IF_NULL(node_with_index.first); auto real_input = node_with_index.first; if (real_input->isa() || real_input->isa()) { input_node = InsertTransOpForOutput(func_graph, input_node, input_node, kernel_select); MS_EXCEPTION_IF_NULL(input_node); - AnfAlgo::SetNodeInput(node, input_node, index); + common::AnfAlgo::SetNodeInput(node, input_node, index); } - std::vector origin_shape = AnfAlgo::GetPrevNodeOutputInferShape(node, index); + std::vector origin_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node, index); std::string dest_format = AnfAlgo::GetInputFormat(node, index); if (NeedInsertTransData(origin_shape, dest_format)) { MS_LOG(DEBUG) << node->DebugString() << "Insert transdata " << AnfAlgo::GetInputFormat(node, index) @@ -144,7 +147,7 @@ AnfNodePtr InsertTransOpForSingleOutput(const FuncGraphPtr &func_graph, const An MS_EXCEPTION_IF_NULL(node); MS_EXCEPTION_IF_NULL(func_graph); std::string output_format = AnfAlgo::GetOutputFormat(node, 0); - std::vector origin_shape = AnfAlgo::GetOutputInferShape(node, 0); + std::vector origin_shape = common::AnfAlgo::GetOutputInferShape(node, 0); if (output_format == kOpFormat_NC1KHKWHWC0) { MS_LOG(EXCEPTION) << "Got the hw format " << output_format << "when insert the transdata node " << node->DebugString() << trace::DumpSourceLines(node); @@ -162,7 +165,7 @@ AnfNodePtr InsertTransOpForMultipleOutput(const FuncGraphPtr &func_graph, const MS_EXCEPTION_IF_NULL(node); auto manager = func_graph->manager(); MS_EXCEPTION_IF_NULL(manager); - auto update_states = AnfAlgo::GetUpdateStateUsers(manager, orig_node); + auto update_states = common::AnfAlgo::GetUpdateStateUsers(manager, orig_node); for (auto &update_state : update_states) { manager->SetEdge(update_state.first, update_state.second, node); } @@ -171,7 +174,7 @@ AnfNodePtr InsertTransOpForMultipleOutput(const FuncGraphPtr &func_graph, const } std::vector make_tuple_inputs = {NewValueNode(prim::kPrimMakeTuple)}; auto kernel_graph = func_graph->cast(); - size_t out_num = AnfAlgo::GetOutputTensorNum(node); + size_t out_num = common::AnfAlgo::GetOutputTensorNum(node); for (size_t output_idx = 0; output_idx < out_num; ++output_idx) { std::string output_format = AnfAlgo::GetOutputFormat(node, output_idx); if (output_format == kOpFormat_NC1KHKWHWC0) { @@ -179,7 +182,7 @@ AnfNodePtr InsertTransOpForMultipleOutput(const FuncGraphPtr &func_graph, const << node->DebugString() << trace::DumpSourceLines(node); } auto tuple_getitem = CreatTupleGetItemNode(func_graph, node, output_idx); - std::vector origin_shape = AnfAlgo::GetOutputInferShape(node, output_idx); + std::vector origin_shape = common::AnfAlgo::GetOutputInferShape(node, output_idx); if (NeedInsertTransData(origin_shape, output_format)) { auto trans_op = AddTransOpNodeToGraph(func_graph, tuple_getitem, kernel_select, 0, false); if (kernel_graph != nullptr && kernel_graph->IsInternalOutput(node, output_idx)) { @@ -203,20 +206,20 @@ AnfNodePtr AddTransOpNodeToGraph(const FuncGraphPtr &func_graph, const AnfNodePt MS_EXCEPTION_IF_NULL(node); // Init std::string default_format = kOpFormat_DEFAULT; - AnfNodePtr input_node = is_insert_input ? AnfAlgo::GetInputNode(node->cast(), insert_index) : node; + AnfNodePtr input_node = is_insert_input ? common::AnfAlgo::GetInputNode(node->cast(), insert_index) : node; std::string input_format = is_insert_input ? default_format : AnfAlgo::GetOutputFormat(node, insert_index); std::string dst_format = is_insert_input ? AnfAlgo::GetInputFormat(node, insert_index) : default_format; std::string padding_axis = is_insert_input ? AnfAlgo::GetInputReshapeType(node, insert_index) : AnfAlgo::GetOutputReshapeType(node, insert_index); - auto input_node_out_shape = is_insert_input ? AnfAlgo::GetPrevNodeOutputInferShape(node, insert_index) - : AnfAlgo::GetOutputInferShape(input_node, insert_index); + auto input_node_out_shape = is_insert_input ? common::AnfAlgo::GetPrevNodeOutputInferShape(node, insert_index) + : common::AnfAlgo::GetOutputInferShape(input_node, insert_index); std::string spec_format = is_insert_input ? dst_format : input_format; bool need_padding = trans::IsNeedPadding(spec_format, input_node_out_shape.size()); std::string trans_opname = (spec_format == kOpFormat_FRACTAL_ZN_RNN || spec_format == kOpFormat_ND_RNN_BIAS) ? prim::kPrimTransDataRNN->name() : prim::kPrimTransData->name(); auto orig_node = node; - if (!is_insert_input && node->isa() && AnfAlgo::GetCNodeName(node) == prim::kTupleGetItem) { + if (!is_insert_input && node->isa() && common::AnfAlgo::GetCNodeName(node) == prim::kTupleGetItem) { auto cnode = node->cast(); orig_node = cnode->input(kRealInputNodeIndexInTupleGetItem); } @@ -242,13 +245,13 @@ AnfNodePtr AddTransOpNodeToGraph(const FuncGraphPtr &func_graph, const AnfNodePt trans_node = reshape_node; } if (trans_opname == prim::kPrimTransDataRNN->name()) { - AnfAlgo::CopyNodeAttr(kAttrHiddenSize, node, trans_data); - AnfAlgo::CopyNodeAttr(kAttrInputSize, node, trans_data); + common::AnfAlgo::CopyNodeAttr(kAttrHiddenSize, node, trans_data); + common::AnfAlgo::CopyNodeAttr(kAttrInputSize, node, trans_data); } if (spec_format == kOpFormat_FRAC_Z && orig_node->isa() && - AnfAlgo::HasNodeAttr(kAttrFracZGroup, orig_node->cast())) { - AnfAlgo::CopyNodeAttr(kAttrGroups, orig_node, trans_data); - AnfAlgo::CopyNodeAttr(kAttrFracZGroup, orig_node, trans_data); + common::AnfAlgo::HasNodeAttr(kAttrFracZGroup, orig_node->cast())) { + common::AnfAlgo::CopyNodeAttr(kAttrGroups, orig_node, trans_data); + common::AnfAlgo::CopyNodeAttr(kAttrFracZGroup, orig_node, trans_data); } // refresh the transdata's format to ori format & dst format RefreshKernelBuildInfo(input_format, dst_format, trans_data, padding_axis); @@ -285,9 +288,9 @@ CNodePtr NewTransOpNode(const FuncGraphPtr &func_graph, const AnfNodePtr &input, MS_EXCEPTION_IF_NULL(kernel_select); CNodePtr trans_node = NewCNode({NewValueNode(std::make_shared(op_name)), input}, func_graph, {orig_node}); MS_EXCEPTION_IF_NULL(trans_node); - auto infer_type = AnfAlgo::GetOutputInferDataType(input, 0); + auto infer_type = common::AnfAlgo::GetOutputInferDataType(input, 0); - auto out_shape_base = AnfAlgo::GetOutputDetailShape(input, 0); + auto out_shape_base = common::AnfAlgo::GetOutputDetailShape(input, 0); MS_EXCEPTION_IF_NULL(out_shape_base); ShapeVector out_shape; ShapeVector out_shape_min; @@ -317,9 +320,9 @@ CNodePtr NewTransOpNode(const FuncGraphPtr &func_graph, const AnfNodePtr &input, } else { pad_shape_ptr = std::make_shared(pad_shape); } - AnfAlgo::SetOutputTypeAndDetailShape({infer_type}, {pad_shape_ptr}, trans_node.get()); + common::AnfAlgo::SetOutputTypeAndDetailShape({infer_type}, {pad_shape_ptr}, trans_node.get()); } else { - AnfAlgo::SetOutputTypeAndDetailShape({infer_type}, {out_shape_base}, trans_node.get()); + common::AnfAlgo::SetOutputTypeAndDetailShape({infer_type}, {out_shape_base}, trans_node.get()); } // special handle for ut if (trans_node->kernel_info() == nullptr) { @@ -327,14 +330,14 @@ CNodePtr NewTransOpNode(const FuncGraphPtr &func_graph, const AnfNodePtr &input, trans_node->set_kernel_info(kernel_info); } if (op_name == prim::kPrimTranspose->name()) { - AnfAlgo::SetNodeAttr(kAttrPerm, MakeValue(perm), trans_node); + common::AnfAlgo::SetNodeAttr(kAttrPerm, MakeValue(perm), trans_node); } if (is_dynamic_shape) { - AnfAlgo::SetNodeAttr(kAttrInputIsDynamicShape, MakeValue(true), trans_node); - AnfAlgo::SetNodeAttr(kAttrOutputIsDynamicShape, MakeValue(true), trans_node); + common::AnfAlgo::SetNodeAttr(kAttrInputIsDynamicShape, MakeValue(true), trans_node); + common::AnfAlgo::SetNodeAttr(kAttrOutputIsDynamicShape, MakeValue(true), trans_node); } - AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), trans_node); - AnfAlgo::SetNodeAttr(kAttrDatadumpOriginalNames, MakeValue>({}), trans_node); + common::AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), trans_node); + common::AnfAlgo::SetNodeAttr(kAttrDatadumpOriginalNames, MakeValue>({}), trans_node); trans_node->set_scope(input->scope()); kernel_select->SelectKernel(trans_node); return trans_node; @@ -372,26 +375,26 @@ CNodePtr AddCastOpNodeToGraph(const FuncGraphPtr &func_graph, const AnfNodePtr & cast->set_kernel_info(kernel_info); } if (origin_shape->IsDynamic()) { - AnfAlgo::SetNodeAttr(kAttrInputIsDynamicShape, MakeValue(true), cast); - AnfAlgo::SetNodeAttr(kAttrOutputIsDynamicShape, MakeValue(true), cast); + common::AnfAlgo::SetNodeAttr(kAttrInputIsDynamicShape, MakeValue(true), cast); + common::AnfAlgo::SetNodeAttr(kAttrOutputIsDynamicShape, MakeValue(true), cast); } - AnfAlgo::SetNodeAttr("dst_type", TypeIdToType(origin_type), cast); + common::AnfAlgo::SetNodeAttr("dst_type", TypeIdToType(origin_type), cast); AnfAlgo::SetSelectKernelBuildInfo(builder.Build(), cast.get()); - AnfAlgo::SetOutputTypeAndDetailShape({origin_type}, {origin_shape}, cast.get()); - AnfAlgo::SetNodeAttr(kIsBackendCast, MakeValue(true), cast); - AnfAlgo::SetNodeAttr(kAttrDatadumpOriginalNames, MakeValue>({}), cast); + common::AnfAlgo::SetOutputTypeAndDetailShape({origin_type}, {origin_shape}, cast.get()); + common::AnfAlgo::SetNodeAttr(kIsBackendCast, MakeValue(true), cast); + common::AnfAlgo::SetNodeAttr(kAttrDatadumpOriginalNames, MakeValue>({}), cast); return cast; } AnfNodePtr InsertTransOpForOutput(const FuncGraphPtr &func_graph, const AnfNodePtr &orig_node, const AnfNodePtr &node, const KernelSelectPtr &kernel_select) { - size_t outputs_num = AnfAlgo::GetOutputTensorNum(node); + size_t outputs_num = common::AnfAlgo::GetOutputTensorNum(node); if (outputs_num == 0) { return node; } auto kernel_graph = func_graph->cast(); // Single output - if (outputs_num == 1 && (!AnfAlgo::IsTupleOutput(node))) { + if (outputs_num == 1 && (!common::AnfAlgo::IsTupleOutput(node))) { auto new_node = InsertTransOpForSingleOutput(func_graph, node, kernel_select); if (kernel_graph != nullptr && kernel_graph->IsInternalOutput(node, 0)) { kernel_graph->ReplaceInternalOutput(node, new_node); @@ -407,8 +410,8 @@ AnfNodePtr InsertTransOpForInput(const FuncGraphPtr &func_graph, const AnfNodePt MS_EXCEPTION_IF_NULL(node); auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - std::vector new_inputs = {AnfAlgo::GetCNodePrimitiveNode(cnode)}; - size_t in_num = AnfAlgo::GetInputNum(cnode); // include monads. + std::vector new_inputs = {common::AnfAlgo::GetCNodePrimitiveNode(cnode)}; + size_t in_num = common::AnfAlgo::GetInputNum(cnode); // include monads. for (size_t input_index = 0; input_index < in_num; ++input_index) { // Monad inputs keep unchanged from GetTransInputNodePtr(). AnfNodePtr input_node = GetTransInputNodePtr(func_graph, cnode, input_index, kernel_select); @@ -431,35 +434,36 @@ AnfNodePtr InsertTransOpForInput(const FuncGraphPtr &func_graph, const AnfNodePt CNodePtr InsertCastForInput(const FuncGraphPtr &func_graph, const CNodePtr &cnode) { MS_EXCEPTION_IF_NULL(cnode); MS_EXCEPTION_IF_NULL(func_graph); - std::vector new_inputs = {AnfAlgo::GetCNodePrimitiveNode(cnode)}; - size_t in_num = AnfAlgo::GetInputNum(cnode); // include monads. + std::vector new_inputs = {common::AnfAlgo::GetCNodePrimitiveNode(cnode)}; + size_t in_num = common::AnfAlgo::GetInputNum(cnode); // include monads. for (size_t input_index = 0; input_index < in_num; ++input_index) { - auto cur_input = AnfAlgo::GetInputNode(cnode, input_index); + auto cur_input = common::AnfAlgo::GetInputNode(cnode, input_index); MS_EXCEPTION_IF_NULL(cur_input); if (HasAbstractMonad(cur_input)) { // No cast for monad inputs. new_inputs.push_back(cur_input); continue; } - auto prev_node = AnfAlgo::GetPrevNodeOutput(cnode, input_index); - const auto infer_type = AnfAlgo::GetOutputInferDataType(prev_node.first, prev_node.second); + auto prev_node = common::AnfAlgo::GetPrevNodeOutput(cnode, input_index); + const auto infer_type = common::AnfAlgo::GetOutputInferDataType(prev_node.first, prev_node.second); TypeId origin_type(kTypeUnknown); - auto kernel_with_index = AnfAlgo::VisitKernelWithReturnType(cur_input, 0); + auto kernel_with_index = common::AnfAlgo::VisitKernelWithReturnType(cur_input, 0); auto real_input_node = kernel_with_index.first; MS_EXCEPTION_IF_NULL(real_input_node); if (kernel::IsWeightBoundary(real_input_node)) { // weight - origin_type = AnfAlgo::GetPrevNodeOutputPrecision(cnode, input_index); + origin_type = common::AnfAlgo::GetPrevNodeOutputPrecision(cnode, input_index); if (origin_type == kTypeUnknown) { origin_type = AnfAlgo::GetOutputDeviceDataType(prev_node.first, prev_node.second); } } else { // feature map - origin_type = AnfAlgo::GetOutputInferDataType(prev_node.first, prev_node.second); + origin_type = common::AnfAlgo::GetOutputInferDataType(prev_node.first, prev_node.second); } const std::string dev_fmt = AnfAlgo::GetInputFormat(cnode, input_index); - const abstract::BaseShapePtr origin_shape = AnfAlgo::GetOutputDetailShape(prev_node.first, prev_node.second); + const abstract::BaseShapePtr origin_shape = + common::AnfAlgo::GetOutputDetailShape(prev_node.first, prev_node.second); // In graph kernel, we check parameter, // the eliminate pass will not eliminate this case, so we just do not insert the no used cast. if (TypeId device_type = AnfAlgo::GetInputDeviceDataType(cnode, input_index); origin_type != device_type) { @@ -467,7 +471,7 @@ CNodePtr InsertCastForInput(const FuncGraphPtr &func_graph, const CNodePtr &cnod AddCastOpNodeToGraph(func_graph, cur_input, cnode, dev_fmt, origin_type, device_type, origin_shape, infer_type); MS_EXCEPTION_IF_NULL(cast); cast->set_scope(cnode->scope()); - AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), cast); + common::AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), cast); new_inputs.push_back(cast); } else { new_inputs.push_back(cur_input); @@ -494,7 +498,7 @@ AnfNodePtr CreateTensorMoveOp(const FuncGraphPtr &graph, const AnfNodePtr &node) MS_EXCEPTION_IF_NULL(new_node); new_node->set_abstract(node->abstract()); new_node->set_scope(node->scope()); - AnfAlgo::SetNodeAttr(kAttrDatadumpOriginalNames, MakeValue>({}), new_node); + common::AnfAlgo::SetNodeAttr(kAttrDatadumpOriginalNames, MakeValue>({}), new_node); return new_node; } } // namespace opt diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ascend_helper.h b/mindspore/ccsrc/plugin/device/ascend/optimizer/ascend_helper.h index 504c745f61e..962dd4a40fe 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ascend_helper.h +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ascend_helper.h @@ -24,6 +24,7 @@ #include "kernel/kernel_query.h" #include "kernel/oplib/oplib.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "plugin/device/ascend/kernel/tbe/tbe_dynaminc_shape_util.h" #include "plugin/device/ascend/kernel/tbe/tbe_kernel_select/tbe_kernel_select.h" @@ -65,7 +66,7 @@ class KernelQuery { if (!node->isa()) { return false; } - auto op_info = mindspore::kernel::tbe::TbeDynamicShapeUtil::FindOp(AnfAlgo::GetCNodeName(node), node); + auto op_info = mindspore::kernel::tbe::TbeDynamicShapeUtil::FindOp(common::AnfAlgo::GetCNodeName(node), node); if (op_info != nullptr) { return op_info->is_ref(); } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/batchmatmul_dropoutdomaskv3_fusion_pass.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/batchmatmul_dropoutdomaskv3_fusion_pass.cc index ebb78eba4db..9cb8f46203f 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/batchmatmul_dropoutdomaskv3_fusion_pass.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/batchmatmul_dropoutdomaskv3_fusion_pass.cc @@ -17,6 +17,7 @@ #include "kernel/kernel_fusion.h" #include "debug/anf_ir_dump.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "base/core_ops.h" #include "utils/ms_context.h" #include "backend/common/optimizer/fusion_id_allocator.h" @@ -29,7 +30,7 @@ void BatchMatmulDropoutDoMaskV3FusionPass::MatchBatchMatmulDropoutDoMaskV3( MS_EXCEPTION_IF_NULL(candidate_fusion); auto batch_matmul = cnode->input(1); MS_EXCEPTION_IF_NULL(batch_matmul); - if (batch_matmul->isa() && AnfAlgo::CheckPrimitiveType(batch_matmul, prim::kPrimBatchMatMul)) { + if (batch_matmul->isa() && common::AnfAlgo::CheckPrimitiveType(batch_matmul, prim::kPrimBatchMatMul)) { mindspore::HashSet record{cnode, batch_matmul}; candidate_fusion->push_back(record); SetRecordFusionId(record); @@ -42,13 +43,13 @@ void BatchMatmulDropoutDoMaskV3FusionPass::MatchSingleFusionPattern(const sessio const auto &node_list = TopoSort(kernel_graph.get_return()); for (auto &node : node_list) { if (!AnfUtils::IsRealCNodeKernel(node) || fusion_id_allocator->HasFusionIdAttr(node) || - AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { + common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { continue; } auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - if (AnfAlgo::GetCNodeName(cnode) == kDropoutDoMaskV3OpName) { + if (common::AnfAlgo::GetCNodeName(cnode) == kDropoutDoMaskV3OpName) { MatchBatchMatmulDropoutDoMaskV3(cnode, kernel_graph, candidate_fusion); } } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/batchmatmul_fusedmuladd_fusion_pass.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/batchmatmul_fusedmuladd_fusion_pass.cc index fca0f5b5419..0bc87f70902 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/batchmatmul_fusedmuladd_fusion_pass.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/batchmatmul_fusedmuladd_fusion_pass.cc @@ -16,6 +16,7 @@ #include "plugin/device/ascend/optimizer/buffer_fusion/batchmatmul_fusedmuladd_fusion_pass.h" #include "kernel/kernel_fusion.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "base/core_ops.h" #include "utils/ms_context.h" #include "backend/common/optimizer/fusion_id_allocator.h" @@ -29,7 +30,7 @@ void BatchMatmulFusedMulAddFusionPass::MatchBatchMatmulFusedMulAdd(const CNodePt MS_EXCEPTION_IF_NULL(candidate_fusion); auto batch_matmul = cnode->input(kIndex2); MS_EXCEPTION_IF_NULL(batch_matmul); - if (batch_matmul->isa() && AnfAlgo::CheckPrimitiveType(batch_matmul, prim::kPrimBatchMatMul)) { + if (batch_matmul->isa() && common::AnfAlgo::CheckPrimitiveType(batch_matmul, prim::kPrimBatchMatMul)) { mindspore::HashSet record{cnode, batch_matmul}; candidate_fusion->push_back(record); SetRecordFusionId(record); @@ -43,13 +44,13 @@ void BatchMatmulFusedMulAddFusionPass::MatchSingleFusionPattern(const session::K for (auto &node : node_list) { MS_EXCEPTION_IF_NULL(node); if (!AnfUtils::IsRealCNodeKernel(node) || fusion_id_allocator->HasFusionIdAttr(node) || - AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { + common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { continue; } auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - if (AnfAlgo::GetCNodeName(cnode) == kFusedMulAddOpName) { + if (common::AnfAlgo::GetCNodeName(cnode) == kFusedMulAddOpName) { MatchBatchMatmulFusedMulAdd(cnode, kernel_graph, candidate_fusion); } } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/bnupdate_eltwise_eltwise_fusion_pass.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/bnupdate_eltwise_eltwise_fusion_pass.cc index 35b5ba6b1f7..f6e195f83ed 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/bnupdate_eltwise_eltwise_fusion_pass.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/bnupdate_eltwise_eltwise_fusion_pass.cc @@ -18,6 +18,7 @@ #include "kernel/kernel_fusion.h" #include "debug/anf_ir_dump.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "base/core_ops.h" #include "utils/ms_context.h" #include "backend/common/optimizer/fusion_id_allocator.h" @@ -33,17 +34,17 @@ void BnupdateEltwiseEltwiseFusionPass::MatchBnupdateAddRelu(const CNodePtr &cnod MS_EXCEPTION_IF_NULL(relu_input); auto add = relu_input->cast(); MS_EXCEPTION_IF_NULL(add); - if (AnfAlgo::GetInputTensorNum(add) != (ELTWISE_DOUBLE_IN_INPUT_SIZE - 1)) { + if (common::AnfAlgo::GetInputTensorNum(add) != (ELTWISE_DOUBLE_IN_INPUT_SIZE - 1)) { return; } auto tuple_getitem = add->input(kIndex1); MS_EXCEPTION_IF_NULL(tuple_getitem); - if (tuple_getitem->isa() && AnfAlgo::GetCNodeName(tuple_getitem) == prim::kPrimTupleGetItem->name()) { + if (tuple_getitem->isa() && common::AnfAlgo::GetCNodeName(tuple_getitem) == prim::kPrimTupleGetItem->name()) { auto getitem = tuple_getitem->cast(); MS_EXCEPTION_IF_NULL(getitem); auto bnupdate = getitem->input(kRealInputNodeIndexInTupleGetItem); MS_EXCEPTION_IF_NULL(bnupdate); - if (bnupdate->isa() && AnfAlgo::GetCNodeName(bnupdate) == kBNTrainingUpdateOpName && + if (bnupdate->isa() && common::AnfAlgo::GetCNodeName(bnupdate) == kBNTrainingUpdateOpName && GetNodeOutputTotalUsedNum(kernel_graph, bnupdate) == kBNTrainingUpdateOutputUsedTotalNum) { mindspore::HashSet record{cnode, relu_input, bnupdate}; candidate_fusion->push_back(record); @@ -58,18 +59,18 @@ void BnupdateEltwiseEltwiseFusionPass::MatchSingleFusionPattern(const session::K const auto &node_list = TopoSort(kernel_graph.get_return()); for (auto &node : node_list) { if (!AnfUtils::IsRealCNodeKernel(node) || fusion_id_allocator->HasFusionIdAttr(node) || - AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { + common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { continue; } auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); if (AnfAlgo::GetKernelType(cnode) == KernelType::TBE_KERNEL && AnfAlgo::GetFusionType(cnode) == kernel::FusionType::ELEMWISE && - AnfAlgo::GetOutputTensorNum(cnode) == ELTWISE_DOUBLE_OUTPUT_SIZE && - AnfAlgo::GetInputTensorNum(cnode) == (ELTWISE_INPUT_SIZE - 1)) { + common::AnfAlgo::GetOutputTensorNum(cnode) == ELTWISE_DOUBLE_OUTPUT_SIZE && + common::AnfAlgo::GetInputTensorNum(cnode) == (ELTWISE_INPUT_SIZE - 1)) { auto eltwise_input = cnode->input(kIndex1); MS_EXCEPTION_IF_NULL(eltwise_input); - if (eltwise_input->isa() && AnfAlgo::CheckPrimitiveType(eltwise_input, prim::kPrimAdd)) { + if (eltwise_input->isa() && common::AnfAlgo::CheckPrimitiveType(eltwise_input, prim::kPrimAdd)) { MatchBnupdateAddRelu(cnode, eltwise_input, kernel_graph, candidate_fusion); } } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/bnupdate_eltwise_fusion_pass.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/bnupdate_eltwise_fusion_pass.cc index 741be809c8e..8c8a3f2e5fb 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/bnupdate_eltwise_fusion_pass.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/bnupdate_eltwise_fusion_pass.cc @@ -16,6 +16,7 @@ #include "plugin/device/ascend/optimizer/buffer_fusion/bnupdate_eltwise_fusion_pass.h" #include "kernel/kernel_fusion.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "base/core_ops.h" #include "utils/ms_context.h" #include "backend/common/optimizer/fusion_id_allocator.h" @@ -33,7 +34,7 @@ void BnupdateEltwiseFusionPass::MatchBnupdateDoubleOutputEltwise(const CNodePtr MS_EXCEPTION_IF_NULL(getitem); auto bnupdate = getitem->input(kIndex1); MS_EXCEPTION_IF_NULL(bnupdate); - if (bnupdate->isa() && AnfAlgo::GetCNodeName(bnupdate) == kBNTrainingUpdateOpName && + if (bnupdate->isa() && common::AnfAlgo::GetCNodeName(bnupdate) == kBNTrainingUpdateOpName && GetNodeOutputTotalUsedNum(kernel_graph, bnupdate) == kBNTrainingUpdateOutputUsedTotalNum) { mindspore::HashSet record{cnode, bnupdate}; candidate_fusion->push_back(record); @@ -47,16 +48,16 @@ void BnupdateEltwiseFusionPass::MatchSingleFusionPattern(const session::KernelGr const auto &node_list = TopoSort(kernel_graph.get_return()); for (auto &node : node_list) { if (!AnfUtils::IsRealCNodeKernel(node) || fusion_id_allocator->HasFusionIdAttr(node) || - AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { + common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { continue; } auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); if (AnfAlgo::GetKernelType(cnode) == KernelType::TBE_KERNEL && AnfAlgo::GetFusionType(cnode) == kernel::FusionType::ELEMWISE && - AnfAlgo::GetOutputTensorNum(cnode) == ELTWISE_DOUBLE_OUTPUT_SIZE) { + common::AnfAlgo::GetOutputTensorNum(cnode) == ELTWISE_DOUBLE_OUTPUT_SIZE) { auto eltwise_input = cnode->input(1); - if (eltwise_input->isa() && AnfAlgo::CheckPrimitiveType(eltwise_input, prim::kPrimTupleGetItem)) { + if (eltwise_input->isa() && common::AnfAlgo::CheckPrimitiveType(eltwise_input, prim::kPrimTupleGetItem)) { MatchBnupdateDoubleOutputEltwise(cnode, eltwise_input, kernel_graph, candidate_fusion); } } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/conv2dbackprop_eltwise_eltwise_fusion_pass.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/conv2dbackprop_eltwise_eltwise_fusion_pass.cc index 1e3e4a598e3..5395eff4c27 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/conv2dbackprop_eltwise_eltwise_fusion_pass.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/conv2dbackprop_eltwise_eltwise_fusion_pass.cc @@ -16,6 +16,7 @@ #include "plugin/device/ascend/optimizer/buffer_fusion/conv2dbackprop_eltwise_eltwise_fusion_pass.h" #include "kernel/kernel_fusion.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "base/core_ops.h" #include "utils/ms_context.h" #include "backend/common/optimizer/fusion_id_allocator.h" @@ -29,7 +30,8 @@ void Conv2DBackpropEltwiseEltwiseFusionPass::MatchConv2DBackpropInputEltwiseEltw mindspore::HashSet record{cnode}; auto eltwise_input = cnode->input(kIndex1); MS_EXCEPTION_IF_NULL(eltwise_input); - if (CheckDoubleInEltWiseNode(kernel_graph, eltwise_input) && AnfAlgo::GetCNodeName(eltwise_input) == kAddNOpName) { + if (CheckDoubleInEltWiseNode(kernel_graph, eltwise_input) && + common::AnfAlgo::GetCNodeName(eltwise_input) == kAddNOpName) { (void)record.insert(eltwise_input); } else { return; @@ -43,7 +45,7 @@ void Conv2DBackpropEltwiseEltwiseFusionPass::MatchConv2DBackpropInputEltwiseEltw if (!double_in_eltwise_input->isa() || !AnfUtils::IsRealCNodeKernel(double_in_eltwise_input)) { return; } - if (AnfAlgo::CheckPrimitiveType(double_in_eltwise_input, prim::kPrimConv2DBackpropInput) && + if (common::AnfAlgo::CheckPrimitiveType(double_in_eltwise_input, prim::kPrimConv2DBackpropInput) && !fusion_id_allocator->HasFusionIdAttr(double_in_eltwise_input)) { (void)record.insert(double_in_eltwise_input); candidate_fusion->push_back(record); @@ -54,7 +56,7 @@ void Conv2DBackpropEltwiseEltwiseFusionPass::MatchConv2DBackpropInputEltwiseEltw if (!double_in_eltwise_input_1->isa() || !AnfUtils::IsRealCNodeKernel(double_in_eltwise_input_1)) { return; } - if (AnfAlgo::CheckPrimitiveType(double_in_eltwise_input_1, prim::kPrimConv2DBackpropInput) && + if (common::AnfAlgo::CheckPrimitiveType(double_in_eltwise_input_1, prim::kPrimConv2DBackpropInput) && !fusion_id_allocator->HasFusionIdAttr(double_in_eltwise_input_1)) { (void)record.insert(double_in_eltwise_input_1); candidate_fusion->push_back(record); @@ -69,14 +71,14 @@ void Conv2DBackpropEltwiseEltwiseFusionPass::MatchSingleFusionPattern(const sess const auto &node_list = TopoSort(kernel_graph.get_return()); for (auto &node : node_list) { if (!AnfUtils::IsRealCNodeKernel(node) || fusion_id_allocator->HasFusionIdAttr(node) || - AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { + common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { continue; } auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); if (AnfAlgo::GetKernelType(cnode) == KernelType::TBE_KERNEL && AnfAlgo::GetFusionType(cnode) == kernel::FusionType::ELEMWISE && - AnfAlgo::GetCNodeName(cnode) == kReluGradV2OpName) { + common::AnfAlgo::GetCNodeName(cnode) == kReluGradV2OpName) { MatchConv2DBackpropInputEltwiseEltwise(cnode, kernel_graph, candidate_fusion); } } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/conv2dbackprop_eltwise_fusion_pass.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/conv2dbackprop_eltwise_fusion_pass.cc index 0adfea789a7..6dbd350db57 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/conv2dbackprop_eltwise_fusion_pass.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/conv2dbackprop_eltwise_fusion_pass.cc @@ -16,6 +16,7 @@ #include "plugin/device/ascend/optimizer/buffer_fusion/conv2dbackprop_eltwise_fusion_pass.h" #include "kernel/kernel_fusion.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "base/core_ops.h" #include "utils/ms_context.h" #include "backend/common/optimizer/fusion_id_allocator.h" @@ -34,7 +35,7 @@ void Conv2DBackpropEltwiseFusionPass::MatchConv2DBackpropInputEltwise(const CNod fusion_id_allocator->HasFusionIdAttr(eltwise_input)) { return; } - if (AnfAlgo::CheckPrimitiveType(eltwise_input, prim::kPrimConv2DBackpropInput)) { + if (common::AnfAlgo::CheckPrimitiveType(eltwise_input, prim::kPrimConv2DBackpropInput)) { (void)record.insert(eltwise_input); candidate_fusion->push_back(record); SetRecordFusionId(record); @@ -47,12 +48,12 @@ void Conv2DBackpropEltwiseFusionPass::MatchSingleFusionPattern(const session::Ke const auto &node_list = TopoSort(kernel_graph.get_return()); for (auto &node : node_list) { if (!AnfUtils::IsRealCNodeKernel(node) || fusion_id_allocator->HasFusionIdAttr(node) || - AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { + common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { continue; } auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - if (AnfAlgo::GetCNodeName(cnode) == kReluGradV2OpName) { + if (common::AnfAlgo::GetCNodeName(cnode) == kReluGradV2OpName) { MatchConv2DBackpropInputEltwise(cnode, kernel_graph, candidate_fusion); } } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/conv_bnreduce_fusion_pass.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/conv_bnreduce_fusion_pass.cc index c7eb808d520..ee7eae49433 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/conv_bnreduce_fusion_pass.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/conv_bnreduce_fusion_pass.cc @@ -17,6 +17,7 @@ #include "kernel/kernel_fusion.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "base/core_ops.h" #include "utils/ms_context.h" #include "backend/common/optimizer/fusion_id_allocator.h" @@ -30,7 +31,7 @@ void ConvBnReduceFusionPass::MatchConvBnreduce(const CNodePtr &cnode, const sess MS_EXCEPTION_IF_NULL(candidate_fusion); auto conv = cnode->input(kIndex1); MS_EXCEPTION_IF_NULL(conv); - if (conv->isa() && AnfAlgo::GetCNodeName(conv) == prim::kPrimConv2D->name() && + if (conv->isa() && common::AnfAlgo::GetCNodeName(conv) == prim::kPrimConv2D->name() && GetNodeOutputTotalUsedNum(kernel_graph, conv) == kConvOutputUsedTotalNum) { mindspore::HashSet record{cnode, conv}; candidate_fusion->push_back(record); @@ -44,12 +45,12 @@ void ConvBnReduceFusionPass::MatchSingleFusionPattern(const session::KernelGraph const auto &node_list = TopoSort(kernel_graph.get_return()); for (auto &node : node_list) { if (!AnfUtils::IsRealCNodeKernel(node) || fusion_id_allocator->HasFusionIdAttr(node) || - AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { + common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { continue; } auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - if (AnfAlgo::GetCNodeName(cnode) == kBNTrainingReduceOpName) { + if (common::AnfAlgo::GetCNodeName(cnode) == kBNTrainingReduceOpName) { MatchConvBnreduce(cnode, kernel_graph, candidate_fusion); } } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/conv_double_in_fusion_pass.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/conv_double_in_fusion_pass.cc index ef2782ed6c4..04e6e0640b0 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/conv_double_in_fusion_pass.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/conv_double_in_fusion_pass.cc @@ -16,6 +16,7 @@ #include "plugin/device/ascend/optimizer/buffer_fusion/conv_double_in_fusion_pass.h" #include "kernel/kernel_fusion.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "base/core_ops.h" #include "utils/ms_context.h" #include "backend/common/optimizer/fusion_id_allocator.h" @@ -56,14 +57,14 @@ void ConvDoubleInFusionPass::MatchSingleFusionPattern(const session::KernelGraph const auto &node_list = TopoSort(kernel_graph.get_return()); for (auto &node : node_list) { if (!AnfUtils::IsRealCNodeKernel(node) || fusion_id_allocator->HasFusionIdAttr(node) || - AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { + common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { continue; } auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); if (AnfAlgo::GetKernelType(cnode) == KernelType::TBE_KERNEL && AnfAlgo::GetFusionType(cnode) == kernel::FusionType::ELEMWISE && cnode->inputs().size() == ELTWISE_INPUT_SIZE && - !AnfAlgo::CheckPrimitiveType(node, prim::kPrimReluV2)) { + !common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimReluV2)) { MatchConvDoubleInEltwise(cnode, kernel_graph, candidate_fusion); } } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/conv_single_in_fusion_pass.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/conv_single_in_fusion_pass.cc index a9c5e86614f..c08497817a0 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/conv_single_in_fusion_pass.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/conv_single_in_fusion_pass.cc @@ -18,6 +18,7 @@ #include "kernel/kernel_fusion.h" #include "debug/anf_ir_dump.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "base/core_ops.h" #include "utils/ms_context.h" #include "backend/common/optimizer/fusion_id_allocator.h" @@ -58,7 +59,7 @@ void ConvSingleInFusionPass::MatchSingleFusionPattern(const session::KernelGraph const auto &node_list = TopoSort(kernel_graph.get_return()); for (auto &node : node_list) { if (!AnfUtils::IsRealCNodeKernel(node) || fusion_id_allocator->HasFusionIdAttr(node) || - AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { + common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { continue; } auto cnode = node->cast(); diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/depthwiseconv_eltwise_fusion_pass.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/depthwiseconv_eltwise_fusion_pass.cc index 4b40eec319c..62b8d19f5ce 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/depthwiseconv_eltwise_fusion_pass.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/depthwiseconv_eltwise_fusion_pass.cc @@ -17,6 +17,7 @@ #include "kernel/kernel_fusion.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "base/core_ops.h" #include "utils/ms_context.h" #include "backend/common/optimizer/fusion_id_allocator.h" @@ -55,7 +56,7 @@ void DepthwiseConvEltwiseFusionPass::MatchSingleFusionPattern(const session::Ker const auto &node_list = TopoSort(kernel_graph.get_return()); for (auto &node : node_list) { if (!AnfUtils::IsRealCNodeKernel(node) || fusion_id_allocator->HasFusionIdAttr(node) || - AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { + common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { continue; } auto cnode = node->cast(); @@ -63,10 +64,11 @@ void DepthwiseConvEltwiseFusionPass::MatchSingleFusionPattern(const session::Ker if (AnfAlgo::GetKernelType(cnode) == KernelType::TBE_KERNEL && AnfAlgo::GetFusionType(cnode) == kernel::FusionType::ELEMWISE) { auto eltwise_input = cnode->input(kIndex1); - if (eltwise_input->isa() && AnfAlgo::CheckPrimitiveType(eltwise_input, prim::kPrimDepthwiseConv2dNative)) { + if (eltwise_input->isa() && + common::AnfAlgo::CheckPrimitiveType(eltwise_input, prim::kPrimDepthwiseConv2dNative)) { MatchDepthwiseConvRelu(cnode, kernel_graph, candidate_fusion, true); } - } else if (AnfAlgo::GetCNodeName(cnode) == prim::kPrimDepthwiseConv2dNative->name()) { + } else if (common::AnfAlgo::GetCNodeName(cnode) == prim::kPrimDepthwiseConv2dNative->name()) { MatchDepthwiseConvRelu(cnode, kernel_graph, candidate_fusion, false); } } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/eltwise_fusion_pass.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/eltwise_fusion_pass.cc index 4d8abc0bd29..1092e4bd824 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/eltwise_fusion_pass.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/eltwise_fusion_pass.cc @@ -17,6 +17,7 @@ #include #include "kernel/kernel_fusion.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "base/core_ops.h" #include "utils/ms_context.h" #include "backend/common/optimizer/fusion_id_allocator.h" @@ -57,7 +58,7 @@ void EltwiseFusionPass::MatchSingleFusionPattern(const session::KernelGraph &ker for (auto &node : node_list) { MS_EXCEPTION_IF_NULL(node); if (!AnfUtils::IsRealCNodeKernel(node) || fusion_id_allocator->HasFusionIdAttr(node) || - AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { + common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { continue; } auto cnode = node->cast(); diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/fusion_base_pass.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/fusion_base_pass.cc index 434e88be4dc..1ef1b9ee717 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/fusion_base_pass.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/fusion_base_pass.cc @@ -18,6 +18,7 @@ #include "utils/ms_context.h" #include "backend/common/optimizer/fusion_id_allocator.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -74,7 +75,7 @@ size_t FusionBasePass::GetNotUpdateStateUserNums(const session::KernelGraph &ker size_t not_updatestate_users = 0; for (auto &user : user_nodes) { auto user_node = user.first; - if (!AnfAlgo::CheckPrimitiveType(user_node, prim::kPrimUpdateState)) { + if (!common::AnfAlgo::CheckPrimitiveType(user_node, prim::kPrimUpdateState)) { not_updatestate_users++; } } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/matmul_confusiontranspose_fusion_pass.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/matmul_confusiontranspose_fusion_pass.cc index 5eea9a95794..1313cdcc2b7 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/matmul_confusiontranspose_fusion_pass.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/matmul_confusiontranspose_fusion_pass.cc @@ -16,6 +16,7 @@ #include "plugin/device/ascend/optimizer/buffer_fusion/matmul_confusiontranspose_fusion_pass.h" #include "kernel/kernel_fusion.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "base/core_ops.h" #include "utils/ms_context.h" #include "backend/common/optimizer/fusion_id_allocator.h" @@ -29,8 +30,8 @@ void MatmulConfusionTranposeFusionPass::MatchMatmulConfusionTranpose(const CNode MS_EXCEPTION_IF_NULL(candidate_fusion); auto matmul = cnode->input(kIndex1); MS_EXCEPTION_IF_NULL(matmul); - if (matmul->isa() && (AnfAlgo::CheckPrimitiveType(matmul, prim::kPrimMatMul) || - AnfAlgo::CheckPrimitiveType(matmul, prim::kPrimBatchMatMul))) { + if (matmul->isa() && (common::AnfAlgo::CheckPrimitiveType(matmul, prim::kPrimMatMul) || + common::AnfAlgo::CheckPrimitiveType(matmul, prim::kPrimBatchMatMul))) { mindspore::HashSet record{cnode, matmul}; candidate_fusion->push_back(record); SetRecordFusionId(record); @@ -43,13 +44,13 @@ void MatmulConfusionTranposeFusionPass::MatchSingleFusionPattern(const session:: const auto &node_list = TopoSort(kernel_graph.get_return()); for (auto &node : node_list) { if (!AnfUtils::IsRealCNodeKernel(node) || fusion_id_allocator->HasFusionIdAttr(node) || - AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { + common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { continue; } auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - if (AnfAlgo::GetCNodeName(cnode) == kConfusionTransposeDOpName) { + if (common::AnfAlgo::GetCNodeName(cnode) == kConfusionTransposeDOpName) { MatchMatmulConfusionTranpose(cnode, kernel_graph, candidate_fusion); } } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/matmul_dropoutdomaskv3_add_fusion_pass.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/matmul_dropoutdomaskv3_add_fusion_pass.cc index 5e13e78974c..bf7c3821ece 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/matmul_dropoutdomaskv3_add_fusion_pass.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/matmul_dropoutdomaskv3_add_fusion_pass.cc @@ -17,6 +17,7 @@ #include "kernel/kernel_fusion.h" #include "debug/anf_ir_dump.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "base/core_ops.h" #include "utils/ms_context.h" #include "backend/common/optimizer/fusion_id_allocator.h" @@ -30,14 +31,14 @@ void MatmulDropoutDoMaskV3AddFusionPass::MatchMatmulDropoutDoMaskV3Add(const CNo MS_EXCEPTION_IF_NULL(candidate_fusion); auto add_input = cnode->input(2); MS_EXCEPTION_IF_NULL(add_input); - if (!add_input->isa() || !AnfAlgo::CheckPrimitiveType(add_input, prim::kPrimDropoutDoMaskV3)) { + if (!add_input->isa() || !common::AnfAlgo::CheckPrimitiveType(add_input, prim::kPrimDropoutDoMaskV3)) { return; } auto dropout_do_mask_v3 = add_input->cast(); MS_EXCEPTION_IF_NULL(dropout_do_mask_v3); auto matmul = dropout_do_mask_v3->input(1); MS_EXCEPTION_IF_NULL(matmul); - if (!matmul->isa() || !AnfAlgo::CheckPrimitiveType(matmul, prim::kPrimMatMul)) { + if (!matmul->isa() || !common::AnfAlgo::CheckPrimitiveType(matmul, prim::kPrimMatMul)) { return; } mindspore::HashSet record{cnode, dropout_do_mask_v3, matmul}; @@ -51,13 +52,13 @@ void MatmulDropoutDoMaskV3AddFusionPass::MatchSingleFusionPattern(const session: const auto &node_list = TopoSort(kernel_graph.get_return()); for (auto &node : node_list) { if (!AnfUtils::IsRealCNodeKernel(node) || fusion_id_allocator->HasFusionIdAttr(node) || - AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { + common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { continue; } auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - if (AnfAlgo::GetCNodeName(cnode) == kAddOpName) { + if (common::AnfAlgo::GetCNodeName(cnode) == kAddOpName) { MatchMatmulDropoutDoMaskV3Add(cnode, kernel_graph, candidate_fusion); } } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/matmul_eltwise_fusion_pass.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/matmul_eltwise_fusion_pass.cc index b37624424ec..6b7970c3531 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/matmul_eltwise_fusion_pass.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/matmul_eltwise_fusion_pass.cc @@ -16,8 +16,9 @@ #include "plugin/device/ascend/optimizer/buffer_fusion/matmul_eltwise_fusion_pass.h" #include "kernel/kernel_fusion.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "base/core_ops.h" -#include "utils/context/graph_kernel_flags.h" +#include "include/common/utils/context/graph_kernel_flags.h" #include "backend/common/optimizer/fusion_id_allocator.h" namespace mindspore { @@ -42,14 +43,14 @@ void MatmulEltwiseFusionPass::MatchSingleFusionPattern(const session::KernelGrap const auto &node_list = TopoSort(kernel_graph.get_return()); for (auto &node : node_list) { if (!AnfUtils::IsRealCNodeKernel(node) || fusion_id_allocator->HasFusionIdAttr(node) || - AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { + common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { continue; } auto cnode = node->cast(); if (graphkernel::GraphKernelFlags::GetInstance().IsEnableGraphKernel()) { if (AnfAlgo::GetKernelType(cnode) == KernelType::TBE_KERNEL && AnfAlgo::GetFusionType(cnode) == kernel::FusionType::ELEMWISE && - AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimAddN)) { + common::AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimAddN)) { continue; } } @@ -57,10 +58,10 @@ void MatmulEltwiseFusionPass::MatchSingleFusionPattern(const session::KernelGrap MS_EXCEPTION_IF_NULL(cnode); if (AnfAlgo::GetKernelType(cnode) == KernelType::TBE_KERNEL && AnfAlgo::GetFusionType(cnode) == kernel::FusionType::ELEMWISE && - AnfAlgo::GetOutputTensorNum(cnode) == ELTWISE_SINGLE_OUTPUT_SIZE) { + common::AnfAlgo::GetOutputTensorNum(cnode) == ELTWISE_SINGLE_OUTPUT_SIZE) { auto eltwise_input = cnode->input(kIndex1); MS_EXCEPTION_IF_NULL(eltwise_input); - if (eltwise_input->isa() && AnfAlgo::CheckPrimitiveType(eltwise_input, prim::kPrimMatMul)) { + if (eltwise_input->isa() && common::AnfAlgo::CheckPrimitiveType(eltwise_input, prim::kPrimMatMul)) { MatchMatmulEltwise(cnode, eltwise_input, kernel_graph, candidate_fusion); } } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/multi_output_fusion_pass.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/multi_output_fusion_pass.cc index b3cfbcdfa51..58e4cf08a24 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/multi_output_fusion_pass.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/multi_output_fusion_pass.cc @@ -17,6 +17,7 @@ #include #include "kernel/kernel_fusion.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "base/core_ops.h" #include "utils/ms_context.h" #include "backend/common/optimizer/fusion_id_allocator.h" @@ -61,7 +62,7 @@ void MultiOutputFusionPass::MatchSingleFusionPattern(const session::KernelGraph std::reverse(node_list.begin(), node_list.end()); for (auto &node : node_list) { if (!AnfUtils::IsRealCNodeKernel(node) || fusion_id_allocator->HasFusionIdAttr(node) || - AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { + common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { continue; } auto cnode = node->cast(); diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/reduce_eltwise_fusion_pass.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/reduce_eltwise_fusion_pass.cc index d81f778bc4a..d1efb6e6a49 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/reduce_eltwise_fusion_pass.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/reduce_eltwise_fusion_pass.cc @@ -18,6 +18,7 @@ #include "kernel/kernel_fusion.h" #include "debug/anf_ir_dump.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "base/core_ops.h" #include "utils/ms_context.h" #include "backend/common/optimizer/fusion_id_allocator.h" @@ -74,7 +75,7 @@ void ReduceEltwiseFusionPass::MatchSingleFusionPattern(const session::KernelGrap std::reverse(node_list.begin(), node_list.end()); for (auto &node : node_list) { if (!AnfUtils::IsRealCNodeKernel(node) || fusion_id_allocator->HasFusionIdAttr(node) || - AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { + common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { continue; } auto cnode = node->cast(); diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/segment_eltwise_fusion_pass.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/segment_eltwise_fusion_pass.cc index 5bc9e9c45b4..55cc382888e 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/segment_eltwise_fusion_pass.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/segment_eltwise_fusion_pass.cc @@ -17,6 +17,7 @@ #include #include "kernel/kernel_fusion.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "base/core_ops.h" #include "utils/ms_context.h" #include "backend/common/optimizer/fusion_id_allocator.h" @@ -71,7 +72,7 @@ void SegmentEltwiseFusionPass::MatchSingleFusionPattern(const session::KernelGra std::reverse(node_list.begin(), node_list.end()); for (auto &node : node_list) { if (!AnfUtils::IsRealCNodeKernel(node) || fusion_id_allocator->HasFusionIdAttr(node) || - AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { + common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { continue; } auto cnode = node->cast(); diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/stridedread_conv_stridedwrite_fusion_pass.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/stridedread_conv_stridedwrite_fusion_pass.cc index 669c6e9f948..c357351e896 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/stridedread_conv_stridedwrite_fusion_pass.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/stridedread_conv_stridedwrite_fusion_pass.cc @@ -16,6 +16,7 @@ #include "plugin/device/ascend/optimizer/buffer_fusion/stridedread_conv_stridedwrite_fusion_pass.h" #include "kernel/kernel_fusion.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "base/core_ops.h" #include "utils/ms_context.h" #include "backend/common/optimizer/fusion_id_allocator.h" @@ -53,7 +54,7 @@ void StridedReadConvStridedWriteFusionPass::MatchStridedReadConvStridedWrite(con fusion_id_allocator->HasFusionIdAttr(conv_input)) { return; } - if (AnfAlgo::GetCNodeName(conv_input) == kStridedReadOpName) { + if (common::AnfAlgo::GetCNodeName(conv_input) == kStridedReadOpName) { (void)record.insert(conv_input); candidate_fusion->push_back(record); SetRecordFusionId(record); @@ -67,12 +68,12 @@ void StridedReadConvStridedWriteFusionPass::MatchSingleFusionPattern(const sessi const auto &node_list = TopoSort(kernel_graph.get_return()); for (auto &node : node_list) { if (!AnfUtils::IsRealCNodeKernel(node) || fusion_id_allocator->HasFusionIdAttr(node) || - AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { + common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { continue; } auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - if (AnfAlgo::GetCNodeName(cnode) == kStridedWriteOpName) { + if (common::AnfAlgo::GetCNodeName(cnode) == kStridedWriteOpName) { MatchStridedReadConvStridedWrite(cnode, kernel_graph, candidate_fusion); } } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/ub_pattern_fusion.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/ub_pattern_fusion.cc index 8eb8ab0d2f3..1b4434ac0aa 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/ub_pattern_fusion.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/ub_pattern_fusion.cc @@ -64,7 +64,7 @@ CNodePtr CreateFusionOp(const std::vector &inputs_list, const std::v MS_EXCEPTION_IF_NULL(kernel_graph); std::string fusion_op_name = "FusionOp"; for (auto &node : anf_nodes) { - fusion_op_name += '_' + AnfAlgo::GetCNodeName(node); + fusion_op_name += '_' + common::AnfAlgo::GetCNodeName(node); } auto fusion_op = std::make_shared(fusion_op_name); MS_EXCEPTION_IF_NULL(fusion_op); @@ -85,12 +85,12 @@ CNodePtr CreateFusionOp(const std::vector &inputs_list, const std::v for (auto &node : anf_nodes) { MS_EXCEPTION_IF_NULL(node); auto cnode = node->cast(); - if (AnfAlgo::HasNodeAttr(kAttrFracZGroup, cnode)) { - auto fracz_group = AnfAlgo::GetNodeAttr(node, kAttrFracZGroup); + if (common::AnfAlgo::HasNodeAttr(kAttrFracZGroup, cnode)) { + auto fracz_group = common::AnfAlgo::GetNodeAttr(node, kAttrFracZGroup); fusion_op->set_attr(kAttrFracZGroup, MakeValue(fracz_group)); } - if (AnfAlgo::HasNodeAttr(kAttrDump, cnode)) { - auto dump_flag = AnfAlgo::GetNodeAttr(node, kAttrDump); + if (common::AnfAlgo::HasNodeAttr(kAttrDump, cnode)) { + auto dump_flag = common::AnfAlgo::GetNodeAttr(node, kAttrDump); fusion_op->set_attr(kAttrDump, MakeValue(dump_flag)); } } @@ -115,7 +115,7 @@ kernel::KernelBuildInfoPtr CreateFusionOpKernelInfo(const std::vector inputs_format; std::vector inputs_data_type; for (const auto &input : inputs_list) { - auto real_input = AnfAlgo::VisitKernel(input, 0); + auto real_input = common::AnfAlgo::VisitKernel(input, 0); (void)inputs_format.emplace_back(AnfAlgo::GetOutputFormat(real_input.first, real_input.second)); (void)inputs_data_type.emplace_back(AnfAlgo::GetOutputDeviceDataType(real_input.first, real_input.second)); } @@ -123,7 +123,7 @@ kernel::KernelBuildInfoPtr CreateFusionOpKernelInfo(const std::vector outputs_format; std::vector outputs_data_type; for (const auto &output : outputs_list) { - if (AnfAlgo::GetCNodeName(output) == prim::kPrimTupleGetItem->name()) { + if (common::AnfAlgo::GetCNodeName(output) == prim::kPrimTupleGetItem->name()) { auto tuple_getitem = output->cast(); MS_EXCEPTION_IF_NULL(tuple_getitem); (void)outputs_format.emplace_back(AnfAlgo::GetOutputFormat( @@ -160,9 +160,9 @@ AnfNodePtr CreateTupleGetItem(const AnfNodePtr &buffer_fusion_kernel, session::K tuple_getitem_inputs_list.push_back(idx); auto tuple_item = kernel_graph->NewCNode(tuple_getitem_inputs_list); MS_EXCEPTION_IF_NULL(tuple_item); - AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(buffer_fusion_kernel, output_index)}, - {AnfAlgo::GetOutputInferShape(buffer_fusion_kernel, output_index)}, - tuple_item.get()); + common::AnfAlgo::SetOutputInferTypeAndShape( + {common::AnfAlgo::GetOutputInferDataType(buffer_fusion_kernel, output_index)}, + {common::AnfAlgo::GetOutputInferShape(buffer_fusion_kernel, output_index)}, tuple_item.get()); return tuple_item; } @@ -217,8 +217,8 @@ void GetFusionScopeComputeNodeList(session::KernelGraph *kernel_graph, continue; } auto cnode = node->cast(); - if (AnfUtils::IsRealCNodeKernel(cnode) && AnfAlgo::HasNodeAttr(kOpAttrFusionId, cnode)) { - auto fusion_id = AnfAlgo::GetNodeAttr(cnode, kOpAttrFusionId); + if (AnfUtils::IsRealCNodeKernel(cnode) && common::AnfAlgo::HasNodeAttr(kOpAttrFusionId, cnode)) { + auto fusion_id = common::AnfAlgo::GetNodeAttr(cnode, kOpAttrFusionId); (*buffer_fusion_infos)[fusion_id].anf_nodes.push_back(cnode); } } @@ -240,7 +240,7 @@ void GetFusionScopeInputNodeList(const session::KernelGraph &kernel_graph, MS_EXCEPTION_IF_NULL(cnode); size_t old_input_num = fusion_info.inputs_list.size(); for (size_t idx = 1; idx < cnode->inputs().size(); ++idx) { - auto real_input = AnfAlgo::VisitKernel(cnode->input(idx), 0); + auto real_input = common::AnfAlgo::VisitKernel(cnode->input(idx), 0); if (std::find(fusion_info.anf_nodes.begin(), fusion_info.anf_nodes.end(), real_input.first) == fusion_info.anf_nodes.end()) { if (!HasAbstractMonad(cnode->input(idx))) { @@ -306,12 +306,12 @@ void GetFusionScopeOutputNodeList(session::KernelGraph *kernel_graph, for (size_t node_idx = 0; node_idx < fusion_info.anf_nodes.size(); ++node_idx) { const auto &node = fusion_info.anf_nodes[node_idx]; size_t old_output_num = fusion_info.outputs_list.size(); - if (AnfAlgo::GetOutputTensorNum(node) == 1) { + if (common::AnfAlgo::GetOutputTensorNum(node) == 1) { auto use_nodes = manager->node_users()[node]; for (auto use_node : use_nodes) { // Do not think of updatestate as real output, // Ensuring normal fusion requires eliminating the node of the updatestate - if (AnfAlgo::CheckPrimitiveType(use_node.first, prim::kPrimUpdateState)) { + if (common::AnfAlgo::CheckPrimitiveType(use_node.first, prim::kPrimUpdateState)) { auto new_updatestate = RemoveNodeFromUpdateState(kernel_graph, node, use_node.first); (void)manager->Replace(use_node.first, new_updatestate); continue; @@ -327,12 +327,12 @@ void GetFusionScopeOutputNodeList(session::KernelGraph *kernel_graph, std::vector tuple_getitem_nodes; auto users = manager->node_users()[node]; for (auto &user : users) { - if (AnfAlgo::CheckPrimitiveType(user.first, prim::kPrimUpdateState)) { + if (common::AnfAlgo::CheckPrimitiveType(user.first, prim::kPrimUpdateState)) { auto new_updatestate = RemoveNodeFromUpdateState(kernel_graph, node, user.first); (void)manager->Replace(user.first, new_updatestate); continue; } - if (AnfAlgo::CheckPrimitiveType(user.first, prim::kPrimTupleGetItem)) { + if (common::AnfAlgo::CheckPrimitiveType(user.first, prim::kPrimTupleGetItem)) { (void)tuple_getitem_nodes.emplace_back(user.first); } } @@ -372,7 +372,7 @@ void SetOutputUsedNumAttr(const session::KernelGraph &kernel_graph, for (auto iter = fusion_nodes.begin(); iter != fusion_nodes.end() - 1; ++iter) { auto node = *iter; auto output_used_num = GetNodeOutputUsedNum(kernel_graph, node); - AnfAlgo::SetNodeAttr(kAttrOutputUsedNum, MakeValue(output_used_num), node); + common::AnfAlgo::SetNodeAttr(kAttrOutputUsedNum, MakeValue(output_used_num), node); } } } @@ -385,8 +385,8 @@ void SetFusionOpRefInfos(session::KernelGraph *kernel_graph, const std::vectorisa() && AnfAlgo::GetCNodeName(output) == prim::kPrimTupleGetItem->name()) { - auto real_output = AnfAlgo::VisitKernel(output, 0); + if (output->isa() && common::AnfAlgo::GetCNodeName(output) == prim::kPrimTupleGetItem->name()) { + auto real_output = common::AnfAlgo::VisitKernel(output, 0); auto output_cnode = output->cast(); MS_EXCEPTION_IF_NULL(output_cnode); auto input2 = output_cnode->input(kIndex2); @@ -428,7 +428,7 @@ bool CheckCircle(const session::KernelGraph &kernel_graph, const BufferFusionInf bool has_circle = false; for (auto &inp : fusion_info.inputs_list) { MS_EXCEPTION_IF_NULL(inp); - if (!inp->isa() || AnfAlgo::CheckPrimitiveType(inp, prim::kPrimLoad)) { + if (!inp->isa() || common::AnfAlgo::CheckPrimitiveType(inp, prim::kPrimLoad)) { continue; } @@ -517,18 +517,18 @@ bool UbPatternFusion::ReplaceFusionOp(mindspore::HashMap types; std::vector> shapes; for (const auto &out_node : buffer_fusion_info.outputs_list) { - size_t out_num = AnfAlgo::GetOutputTensorNum(out_node); + size_t out_num = common::AnfAlgo::GetOutputTensorNum(out_node); for (size_t idx = 0; idx < out_num; ++idx) { - (void)types.emplace_back(AnfAlgo::GetOutputInferDataType(out_node, idx)); - (void)shapes.emplace_back(AnfAlgo::GetOutputInferShape(out_node, idx)); + (void)types.emplace_back(common::AnfAlgo::GetOutputInferDataType(out_node, idx)); + (void)shapes.emplace_back(common::AnfAlgo::GetOutputInferShape(out_node, idx)); } } if (types.empty() || shapes.empty()) { MS_LOG(WARNING) << "The outputs_list of buffer_fusion_info is empty."; return false; } - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, buffer_fusion.get()); - AnfAlgo::SetNodeAttr(kAttrIsUBFusionOp, MakeValue(true), buffer_fusion); + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, buffer_fusion.get()); + common::AnfAlgo::SetNodeAttr(kAttrIsUBFusionOp, MakeValue(true), buffer_fusion); SetFusionOpRefInfos(kernel_graph, buffer_fusion_info.outputs_list, buffer_fusion); ReplaceOldNode(buffer_fusion_infos, fusion_id, buffer_fusion, kernel_graph); return true; @@ -543,7 +543,7 @@ bool UbPatternFusion::RunPass(const FuncGraphPtr &graph) { // clear fusion_id attr for (auto &node : graph->nodes()) { if (node != nullptr && node->isa()) { - AnfAlgo::EraseNodeAttr(kAttrFusionId, node); + common::AnfAlgo::EraseNodeAttr(kAttrFusionId, node); } } return changed; diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/dynamic_shape/ascend_dynamic_shape_helper.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/dynamic_shape/ascend_dynamic_shape_helper.cc index 335c274a8b5..11c68cf7634 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/dynamic_shape/ascend_dynamic_shape_helper.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/dynamic_shape/ascend_dynamic_shape_helper.cc @@ -18,7 +18,8 @@ #include #include "backend/common/session/anf_runtime_algorithm.h" -#include "utils/utils.h" +#include "include/common/utils/anfalgo.h" +#include "include/common/utils/utils.h" #include "utils/anf_utils.h" namespace mindspore { @@ -52,7 +53,7 @@ bool IsDynamicOp(const BaseRef &n) { CNodePtr cnode = utils::cast(n); MS_EXCEPTION_IF_NULL(cnode); - auto op_name = AnfAlgo::GetCNodeName(cnode); + auto op_name = common::AnfAlgo::GetCNodeName(cnode); return kComputeDepend.find(op_name) != kComputeDepend.end(); } @@ -67,7 +68,7 @@ bool IsInheritedDynamicOp(const BaseRef &n) { CNodePtr cnode = utils::cast(n); MS_EXCEPTION_IF_NULL(cnode); - return AnfAlgo::IsNodeInputDynamicShape(cnode) || AnfUtils::IsNodeOutputDynamicShape(cnode); + return common::AnfAlgo::IsNodeInputDynamicShape(cnode) || AnfUtils::IsNodeOutputDynamicShape(cnode); } AnfNodePtr GenInferNode(const AnfNodePtr &node, bool fake_flag) { diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/dynamic_shape/convert_dynamic_op.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/dynamic_shape/convert_dynamic_op.cc index 33113c5c478..420517288a2 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/dynamic_shape/convert_dynamic_op.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/dynamic_shape/convert_dynamic_op.cc @@ -18,6 +18,7 @@ #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/optimizer/helper.h" #include "plugin/device/ascend/optimizer/dynamic_shape/ascend_dynamic_shape_helper.h" diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/dynamic_shape/convert_general_op.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/dynamic_shape/convert_general_op.cc index 70390847610..732191b406e 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/dynamic_shape/convert_general_op.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/dynamic_shape/convert_general_op.cc @@ -18,6 +18,7 @@ #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/optimizer/helper.h" #include "plugin/device/ascend/optimizer/dynamic_shape/ascend_dynamic_shape_helper.h" diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/dynamic_shape/convert_inherited_dynamic_op.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/dynamic_shape/convert_inherited_dynamic_op.cc index fd68061c14d..9d40d3b35b2 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/dynamic_shape/convert_inherited_dynamic_op.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/dynamic_shape/convert_inherited_dynamic_op.cc @@ -18,6 +18,7 @@ #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/optimizer/helper.h" #include "plugin/device/ascend/optimizer/dynamic_shape/ascend_dynamic_shape_helper.h" diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/dynamic_shape/link_custom_op.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/dynamic_shape/link_custom_op.cc index 7200c0f0ef8..c1ccbef5599 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/dynamic_shape/link_custom_op.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/dynamic_shape/link_custom_op.cc @@ -19,6 +19,7 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/optimizer/helper.h" #include "plugin/device/ascend/optimizer/dynamic_shape/ascend_dynamic_shape_helper.h" @@ -86,9 +87,9 @@ bool LinkInputOp(const FuncGraphPtr &g, const CNodePtr &cnode, AnfNodePtrList *d if (custom_nodes.infer_node == nullptr) { return changed; } - size_t input_num = AnfAlgo::GetInputNum(cnode); + size_t input_num = common::AnfAlgo::GetInputNum(cnode); for (size_t i = 0; i < input_num; ++i) { - auto prev = AnfAlgo::GetPrevNodeOutput(cnode, i); + auto prev = common::AnfAlgo::GetPrevNodeOutput(cnode, i); const auto &prev_node = prev.first; if (prev_node == nullptr || !CustomActorNodeManager::Instance().IsRegistered(prev_node)) { continue; @@ -124,7 +125,7 @@ bool LinkDependSync(const FuncGraphPtr &g, const CNodePtr &cnode, AnfNodePtrList } for (auto depend_index : dynamic_shape_depends) { - auto prev = AnfAlgo::GetPrevNodeOutput(cnode, depend_index); + auto prev = common::AnfAlgo::GetPrevNodeOutput(cnode, depend_index); const auto &prev_node = prev.first; if (prev_node == nullptr || !CustomActorNodeManager::Instance().IsRegistered(prev_node)) { continue; diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/add_attr_for_3d_graph.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/add_attr_for_3d_graph.cc index 7d2198629f0..485929e21be 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/add_attr_for_3d_graph.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/add_attr_for_3d_graph.cc @@ -29,11 +29,11 @@ const AnfNodePtr AddIoFormatAttrFor3DGraph::Process(const FuncGraphPtr &func_gra MS_EXCEPTION_IF_NULL(node); MS_EXCEPTION_IF_NULL(func_graph); if (AnfUtils::IsRealKernel(node)) { - AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node); + common::AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node); auto formats = AnfAlgo::GetAllOutputFormats(node); if (std::any_of(formats.begin(), formats.end(), [](const std::string &format) { return k3DFormatSet.find(format) != k3DFormatSet.end(); })) { - AnfAlgo::SetNodeAttr(kAttrFormat, MakeValue(kOpFormat_NCDHW), node); + common::AnfAlgo::SetNodeAttr(kAttrFormat, MakeValue(kOpFormat_NCDHW), node); } return node; } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/add_placeholder_for_dynamic_gru.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/add_placeholder_for_dynamic_gru.cc index dfad263651b..47614eb071f 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/add_placeholder_for_dynamic_gru.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/add_placeholder_for_dynamic_gru.cc @@ -17,7 +17,8 @@ #include "plugin/device/ascend/optimizer/enhancer/add_placeholder_for_dynamic_gru.h" #include "backend/common/optimizer/helper.h" #include "backend/common/session/anf_runtime_algorithm.h" -#include "utils/utils.h" +#include "include/common/utils/anfalgo.h" +#include "include/common/utils/utils.h" #include "abstract/abstract_value.h" #include "base/core_ops.h" @@ -35,20 +36,20 @@ const AnfNodePtr InsertPlaceholderForDynamicGRUV2::Process(const FuncGraphPtr &f MS_EXCEPTION_IF_NULL(node); auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - auto op_name = AnfAlgo::GetCNodeName(cnode); + auto op_name = common::AnfAlgo::GetCNodeName(cnode); if (op_name != kDynamicGRUV2OpName) { return nullptr; } - AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node); + common::AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node); auto kernel_graph = func_graph->cast>(); MS_EXCEPTION_IF_NULL(kernel_graph); - size_t input_num = AnfAlgo::GetInputTensorNum(node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(node); if (input_num == 0) { return nullptr; } - std::vector new_inputs = {AnfAlgo::GetCNodePrimitiveNode(cnode)}; - auto none_index = AnfAlgo::GetNodeAttr>(cnode, kAttrPlaceHolderIndex); + std::vector new_inputs = {common::AnfAlgo::GetCNodePrimitiveNode(cnode)}; + auto none_index = common::AnfAlgo::GetNodeAttr>(cnode, kAttrPlaceHolderIndex); size_t real_input_index = 0; for (size_t in_idx = 0; in_idx < input_num + none_index.size(); in_idx++) { auto item = find(none_index.begin(), none_index.end(), in_idx); @@ -61,7 +62,7 @@ const AnfNodePtr InsertPlaceholderForDynamicGRUV2::Process(const FuncGraphPtr &f kernel_graph->AddValueNodeToGraph(new_node); new_inputs.push_back(new_node); } else { - auto input_node = AnfAlgo::GetInputNode(cnode, real_input_index); + auto input_node = common::AnfAlgo::GetInputNode(cnode, real_input_index); new_inputs.push_back(input_node); real_input_index++; } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/add_placeholder_for_dynamic_rnn.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/add_placeholder_for_dynamic_rnn.cc index a3a715a50d3..c7f08581df7 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/add_placeholder_for_dynamic_rnn.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/add_placeholder_for_dynamic_rnn.cc @@ -19,7 +19,8 @@ #include #include "backend/common/optimizer/helper.h" #include "backend/common/session/anf_runtime_algorithm.h" -#include "utils/utils.h" +#include "include/common/utils/anfalgo.h" +#include "include/common/utils/utils.h" #include "abstract/abstract_value.h" #include "base/core_ops.h" @@ -38,21 +39,21 @@ const AnfNodePtr InsertPlaceholderForDynamicRNN::Process(const FuncGraphPtr &fun MS_EXCEPTION_IF_NULL(node); auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - auto op_name = AnfAlgo::GetCNodeName(cnode); + auto op_name = common::AnfAlgo::GetCNodeName(cnode); if (op_name != kDynamicRNNOpName) { return nullptr; } - AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node); + common::AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node); auto kernel_graph = func_graph->cast>(); MS_EXCEPTION_IF_NULL(kernel_graph); - size_t input_num = AnfAlgo::GetInputTensorNum(node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(node); if (input_num == 0) { return nullptr; } - std::vector new_inputs = {AnfAlgo::GetCNodePrimitiveNode(cnode)}; + std::vector new_inputs = {common::AnfAlgo::GetCNodePrimitiveNode(cnode)}; for (size_t in_idx = 0; in_idx < input_num; in_idx++) { - auto input_node = AnfAlgo::GetInputNode(cnode, in_idx); + auto input_node = common::AnfAlgo::GetInputNode(cnode, in_idx); if (in_idx == kInsertIdx) { auto value = std::make_shared(); auto value_node = NewValueNode(value); diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/concat_outputs_for_all_gather.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/concat_outputs_for_all_gather.cc index c1144de5f9f..5c4f6bc93d9 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/concat_outputs_for_all_gather.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/concat_outputs_for_all_gather.cc @@ -17,6 +17,7 @@ #include "plugin/device/ascend/optimizer/enhancer/concat_outputs_for_all_gather.h" #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore::opt { namespace { @@ -28,14 +29,14 @@ OutputInfo GetNodeOutputInfo(const AnfNodePtr &node) { std::vector output_device_dtype; auto type_ptr = node->Type(); auto shape_ptr = node->Shape(); - size_t output_num = AnfAlgo::GetOutputTensorNum(node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(node); auto kernel_info = dynamic_cast(node->kernel_info()); MS_EXCEPTION_IF_NULL(kernel_info); auto build_info = kernel_info->select_kernel_build_info(); MS_EXCEPTION_IF_NULL(build_info); for (size_t i = 0; i < output_num; i++) { - (void)output_infer_dtype.emplace_back(AnfAlgo::GetOutputInferDataType(type_ptr, i)); - (void)output_infer_shape.emplace_back(AnfAlgo::GetOutputInferShape(node, shape_ptr, i)); + (void)output_infer_dtype.emplace_back(common::AnfAlgo::GetOutputInferDataType(type_ptr, i)); + (void)output_infer_shape.emplace_back(common::AnfAlgo::GetOutputInferShape(node, shape_ptr, i)); (void)output_format.emplace_back(build_info->GetOutputFormat(i)); (void)output_device_dtype.emplace_back(build_info->GetOutputDeviceType(i)); } @@ -51,7 +52,7 @@ kernel::KernelBuildInfoPtr GenerateKernelBuildInfo(const AnfNodePtr &concat, con std::vector inputs_device_type; std::vector outputs_device_type; kernel::KernelBuildInfo::KernelBuildInfoBuilder builder; - size_t concat_input_num = AnfAlgo::GetInputTensorNum(concat); + size_t concat_input_num = common::AnfAlgo::GetInputTensorNum(concat); for (size_t i = 0; i < concat_input_num; ++i) { size_t input_index = allgather_input_idx + i * allgather_input_num; (void)inputs_device_format.emplace_back(std::get(allgather_output_info)[input_index]); @@ -91,7 +92,7 @@ AnfNodePtr ConcatOutputsForAllGather::InsertConcatForOutput(const FuncGraphPtr & int64_t rank_size) const { MS_EXCEPTION_IF_NULL(func_graph); std::vector make_tuple_inputs{NewValueNode(std::make_shared(prim::kPrimMakeTuple->name()))}; - size_t inputs_size = AnfAlgo::GetInputTensorNum(node); + size_t inputs_size = common::AnfAlgo::GetInputTensorNum(node); for (size_t i = 0; i < inputs_size; ++i) { std::vector concat_inputs{NewValueNode(std::make_shared(prim::kPrimConcat->name()))}; for (size_t j = 0, idx = i; j < LongToSize(rank_size); ++j, idx += inputs_size) { @@ -104,11 +105,11 @@ AnfNodePtr ConcatOutputsForAllGather::InsertConcatForOutput(const FuncGraphPtr & const auto &shape = std::get<1>(output_info)[i]; std::vector> shapes = {shape}; shapes[0][0] *= LongToSize(rank_size); - AnfAlgo::SetOutputInferTypeAndShape(dtypes, shapes, concat.get()); - AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(static_cast(0)), concat); - AnfAlgo::SetNodeAttr(kAttrInputNums, MakeValue(rank_size), concat); + common::AnfAlgo::SetOutputInferTypeAndShape(dtypes, shapes, concat.get()); + common::AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(static_cast(0)), concat); + common::AnfAlgo::SetNodeAttr(kAttrInputNums, MakeValue(rank_size), concat); std::vector dyn_input_size{rank_size}; - AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(dyn_input_size), concat); + common::AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(dyn_input_size), concat); auto kernel_build_info = GenerateKernelBuildInfo(concat, output_info, inputs_size, i); AnfAlgo::SetSelectKernelBuildInfo(kernel_build_info, concat.get()); auto kernel_graph = func_graph->cast(); @@ -134,21 +135,21 @@ const AnfNodePtr ConcatOutputsForAllGather::Process(const FuncGraphPtr &func_gra MS_EXCEPTION_IF_NULL(node); auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - if (!AnfAlgo::HasNodeAttr(kAttrFusion, cnode) || !AnfAlgo::HasNodeAttr(kAttrRankSize, cnode)) { + if (!common::AnfAlgo::HasNodeAttr(kAttrFusion, cnode) || !common::AnfAlgo::HasNodeAttr(kAttrRankSize, cnode)) { return nullptr; } - auto fusion = AnfAlgo::GetNodeAttr(cnode, kAttrFusion); + auto fusion = common::AnfAlgo::GetNodeAttr(cnode, kAttrFusion); if (fusion <= 0) { return nullptr; } - if (AnfAlgo::HasNodeAttr("fused", cnode) || AnfAlgo::GetInputTensorNum(node) == 1) { + if (common::AnfAlgo::HasNodeAttr("fused", cnode) || common::AnfAlgo::GetInputTensorNum(node) == 1) { return nullptr; } - AnfAlgo::SetNodeAttr("fused", MakeValue(true), node); - auto rank_size = AnfAlgo::GetNodeAttr(node, kAttrRankSize); + common::AnfAlgo::SetNodeAttr("fused", MakeValue(true), node); + auto rank_size = common::AnfAlgo::GetNodeAttr(node, kAttrRankSize); std::vector new_outputs; OutputInfo output_info = GetNodeOutputInfo(node); - size_t output_num = AnfAlgo::GetOutputTensorNum(node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(node); for (size_t i = 0; i < output_num; ++i) { int64_t temp = SizeToLong(i); auto idx = NewValueNode(temp); @@ -158,8 +159,8 @@ const AnfNodePtr ConcatOutputsForAllGather::Process(const FuncGraphPtr &func_gra idx->set_abstract(abstract_scalar); auto tuple_getitem = func_graph->NewCNode({NewValueNode(prim::kPrimTupleGetItem), node, idx}); MS_EXCEPTION_IF_NULL(tuple_getitem); - AnfAlgo::SetOutputInferTypeAndShape({std::get<0>(output_info)[i]}, {std::get<1>(output_info)[i]}, - tuple_getitem.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({std::get<0>(output_info)[i]}, {std::get<1>(output_info)[i]}, + tuple_getitem.get()); (void)new_outputs.emplace_back(std::move(tuple_getitem)); } return InsertConcatForOutput(func_graph, node, output_info, new_outputs, rank_size); diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/getnext_tensor_move_elimination.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/getnext_tensor_move_elimination.cc index 8a4bf2c79c8..eeac2cd4f02 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/getnext_tensor_move_elimination.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/getnext_tensor_move_elimination.cc @@ -17,6 +17,7 @@ #include "plugin/device/ascend/optimizer/enhancer/getnext_tensor_move_elimination.h" #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "frontend/optimizer/opt.h" namespace mindspore::opt { @@ -42,7 +43,7 @@ const AnfNodePtr GetnextTensorMoveElimination::Process(const FuncGraphPtr &graph } // 1. tensor move has attr kAttrLabelForInsertStreamActive - if (!AnfAlgo::HasNodeAttr(kAttrLabelForInsertStreamActive, tensor_move_node)) { + if (!common::AnfAlgo::HasNodeAttr(kAttrLabelForInsertStreamActive, tensor_move_node)) { MS_LOG(DEBUG) << "node has no label_for_insert_stream_active attr"; return nullptr; } @@ -64,15 +65,15 @@ const AnfNodePtr GetnextTensorMoveElimination::Process(const FuncGraphPtr &graph for (auto &item : next_nodes) { MS_EXCEPTION_IF_NULL(item.first); auto next_node = item.first->cast(); - if (opt::IsNopNode(next_node)) { + if (common::AnfAlgo::IsNopNode(next_node)) { return nullptr; } - if (AnfAlgo::IsCommunicationOp(next_node)) { + if (common::AnfAlgo::IsCommunicationOp(next_node)) { return nullptr; } - auto graph_outputs = AnfAlgo::GetAllOutput(graph->output(), {prim::kPrimTupleGetItem}); + auto graph_outputs = common::AnfAlgo::GetAllOutput(graph->output(), {prim::kPrimTupleGetItem}); auto iter = std::find(graph_outputs.begin(), graph_outputs.end(), next_node); if (iter != graph_outputs.end()) { return nullptr; @@ -83,7 +84,7 @@ const AnfNodePtr GetnextTensorMoveElimination::Process(const FuncGraphPtr &graph return nullptr; } // add attr label_for_insert_stream_active for next_node - AnfAlgo::SetNodeAttr(kAttrLabelForInsertStreamActive, MakeValue(true), next_node); + common::AnfAlgo::SetNodeAttr(kAttrLabelForInsertStreamActive, MakeValue(true), next_node); } return tensor_move_node->input(1); diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/insert_depend_for_all_gather.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/insert_depend_for_all_gather.cc index 5271de3254e..db1fa246c7b 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/insert_depend_for_all_gather.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/insert_depend_for_all_gather.cc @@ -16,8 +16,9 @@ #include #include "plugin/device/ascend/optimizer/enhancer/insert_depend_for_all_gather.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -33,9 +34,9 @@ bool InsertDependForAllGather::Run(const FuncGraphPtr &graph) { } auto cnode = node->cast(); bool is_recompute = cnode->GetAttr(kAttrDuplicated) != nullptr && GetValue(cnode->GetAttr(kAttrDuplicated)); - if (AnfAlgo::GetCNodeName(cnode) == kAllGatherOpName && AnfAlgo::HasNodeAttr(kAttrFusion, cnode) && - AnfAlgo::GetNodeAttr(cnode, kAttrFusion) > 0 && !is_recompute) { - all_gather_node[AnfAlgo::GetNodeAttr(cnode, kAttrFusion)] = node; + if (common::AnfAlgo::GetCNodeName(cnode) == kAllGatherOpName && common::AnfAlgo::HasNodeAttr(kAttrFusion, cnode) && + common::AnfAlgo::GetNodeAttr(cnode, kAttrFusion) > 0 && !is_recompute) { + all_gather_node[common::AnfAlgo::GetNodeAttr(cnode, kAttrFusion)] = node; } } auto iter = all_gather_node.begin(); @@ -45,10 +46,10 @@ bool InsertDependForAllGather::Run(const FuncGraphPtr &graph) { MS_EXCEPTION_IF_NULL(next_node); auto next_cnode = next_node->cast(); std::vector inputs = {NewValueNode(std::make_shared(prim::kPrimDepend->name())), - AnfAlgo::GetInputNode(next_cnode, 0), current_node}; + common::AnfAlgo::GetInputNode(next_cnode, 0), current_node}; auto new_input = graph->NewCNode(inputs); - new_input->set_abstract(AnfAlgo::GetInputNode(next_cnode, 0)->abstract()); - AnfAlgo::SetNodeInput(next_cnode, new_input, 0); + new_input->set_abstract(common::AnfAlgo::GetInputNode(next_cnode, 0)->abstract()); + common::AnfAlgo::SetNodeInput(next_cnode, new_input, 0); changed = true; } return changed; diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/insert_pad_for_nms_with_mask.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/insert_pad_for_nms_with_mask.cc index e0bf2f699be..ab3b2cc2d51 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/insert_pad_for_nms_with_mask.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/insert_pad_for_nms_with_mask.cc @@ -18,7 +18,8 @@ #include #include "backend/common/optimizer/helper.h" #include "backend/common/session/anf_runtime_algorithm.h" -#include "utils/utils.h" +#include "include/common/utils/anfalgo.h" +#include "include/common/utils/utils.h" #include "base/core_ops.h" namespace mindspore { @@ -43,7 +44,7 @@ AnfNodePtr InsertPadForNMSWithMask::InsertPadToGraph(const FuncGraphPtr &func_gr new_pad_inputs.push_back(input); CNodePtr pad = NewCNode(new_pad_inputs, func_graph); MS_EXCEPTION_IF_NULL(pad); - AnfAlgo::SetOutputInferTypeAndShape({origin_type}, {origin_shape}, pad.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({origin_type}, {origin_shape}, pad.get()); return pad; } @@ -51,21 +52,21 @@ const AnfNodePtr InsertPadForNMSWithMask::Process(const FuncGraphPtr &func_graph const EquivPtr &) const { MS_EXCEPTION_IF_NULL(func_graph); MS_EXCEPTION_IF_NULL(node); - if (AnfAlgo::IsDynamicShape(node)) { + if (common::AnfAlgo::IsDynamicShape(node)) { return nullptr; } auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - size_t input_num = AnfAlgo::GetInputTensorNum(node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(node); if (input_num == 0) { return nullptr; } - std::vector new_inputs = {AnfAlgo::GetCNodePrimitiveNode(cnode)}; + std::vector new_inputs = {common::AnfAlgo::GetCNodePrimitiveNode(cnode)}; for (size_t input_idx = 0; input_idx < input_num; input_idx++) { - auto cur_input = AnfAlgo::GetInputNode(cnode, input_idx); - auto origin_type = AnfAlgo::GetPrevNodeOutputInferDataType(cnode, input_idx); - auto origin_shape = AnfAlgo::GetPrevNodeOutputInferShape(cnode, input_idx); + auto cur_input = common::AnfAlgo::GetInputNode(cnode, input_idx); + auto origin_type = common::AnfAlgo::GetPrevNodeOutputInferDataType(cnode, input_idx); + auto origin_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(cnode, input_idx); if (!(origin_shape.size() == kShapeSize && origin_shape[1] == kShapeValue5)) { return nullptr; } @@ -73,7 +74,7 @@ const AnfNodePtr InsertPadForNMSWithMask::Process(const FuncGraphPtr &func_graph auto pad = InsertPadToGraph(func_graph, cur_input, origin_type, origin_shape); MS_EXCEPTION_IF_NULL(pad); pad->set_scope(cnode->scope()); - AnfAlgo::SetNodeAttr("paddings", MakeValue(std::vector>{{0, 0}, {0, 3}}), pad); + common::AnfAlgo::SetNodeAttr("paddings", MakeValue(std::vector>{{0, 0}, {0, 3}}), pad); new_inputs.push_back(pad); } auto kernel_graph = func_graph->cast>(); diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/insert_tensor_move_for_cascade.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/insert_tensor_move_for_cascade.cc index 50446a88f76..3f9160c7960 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/insert_tensor_move_for_cascade.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/insert_tensor_move_for_cascade.cc @@ -15,8 +15,9 @@ */ #include "plugin/device/ascend/optimizer/enhancer/insert_tensor_move_for_cascade.h" #include -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "frontend/optimizer/opt.h" #include "plugin/device/ascend/optimizer/ascend_helper.h" #include "utils/trace_base.h" @@ -28,14 +29,14 @@ bool IsPartOutputsOfHcclOp(const AnfNodePtr &node, const CNodePtr &cur_hccl, con MS_EXCEPTION_IF_NULL(node); MS_EXCEPTION_IF_NULL(cur_hccl); MS_EXCEPTION_IF_NULL(graph); - if (!AnfAlgo::CheckPrimitiveType(node, prim::kPrimTupleGetItem)) { + if (!common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimTupleGetItem)) { return false; } auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); auto prev_node = cnode->input(kRealInputNodeIndexInTupleGetItem); MS_EXCEPTION_IF_NULL(prev_node); - if (!AnfAlgo::IsCommunicationOp(prev_node)) { + if (!common::AnfAlgo::IsCommunicationOp(prev_node)) { return false; } auto prev_hccl_op = prev_node->cast(); @@ -82,7 +83,7 @@ AnfNodePtr InsertTensorMoveForCascade::InsertTensorMove(const FuncGraphPtr &grap if (tensor_move == nullptr) { MS_LOG(EXCEPTION) << "Create tensor_move op failed." << trace::DumpSourceLines(hccl_node); } - if (AnfAlgo::IsDynamicShape(input)) { + if (common::AnfAlgo::IsDynamicShape(input)) { MS_LOG(DEBUG) << "The tenser move op has dynamic shape attr."; } auto kernel_info = std::make_shared(); @@ -111,7 +112,7 @@ const AnfNodePtr InsertTensorMoveForCascade::Process(const FuncGraphPtr &func_gr return nullptr; } auto cnode = node->cast(); - if (!AnfAlgo::IsCommunicationOp(node)) { + if (!common::AnfAlgo::IsCommunicationOp(node)) { return nullptr; } return InsertTensorMove(func_graph, cnode); diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/insert_tensor_move_for_getnext.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/insert_tensor_move_for_getnext.cc index c64ae7750f5..ffe119014e2 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/insert_tensor_move_for_getnext.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/insert_tensor_move_for_getnext.cc @@ -19,6 +19,7 @@ #include "plugin/device/ascend/optimizer/ascend_helper.h" #include "backend/common/optimizer/helper.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -27,7 +28,7 @@ AnfNodePtr InsertTensorMoveForGetNextOutputs(const FuncGraphPtr &func_graph, con return nullptr; } - size_t output_num = AnfAlgo::GetOutputTensorNum(node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(node); if (output_num == 0) { MS_LOG(DEBUG) << "Output number is zero, no need to insert tensor_move!"; return node; @@ -43,10 +44,10 @@ AnfNodePtr InsertTensorMoveForGetNextOutputs(const FuncGraphPtr &func_graph, con if (new_node == nullptr) { MS_LOG(EXCEPTION) << "Create tensor move op failed!"; } - if (AnfAlgo::IsDynamicShape(tuple_get_item)) { + if (common::AnfAlgo::IsDynamicShape(tuple_get_item)) { MS_LOG(DEBUG) << "The tenser move op has dynamic shape attr."; } - AnfAlgo::SetNodeAttr(kAttrLabelForInsertStreamActive, MakeValue(true), new_node); + common::AnfAlgo::SetNodeAttr(kAttrLabelForInsertStreamActive, MakeValue(true), new_node); make_tuple_inputs.push_back(new_node); } AnfNodePtr make_tuple = func_graph->NewCNode(make_tuple_inputs); @@ -67,11 +68,11 @@ const AnfNodePtr InsertTensorMoveForGetNext::Process(const FuncGraphPtr &func_gr } auto cnode = node->cast(); - if (AnfAlgo::HasNodeAttr(kAttrVisited, cnode)) { + if (common::AnfAlgo::HasNodeAttr(kAttrVisited, cnode)) { MS_LOG(DEBUG) << "Node op_name[" << kGetNextOpName << "] has visited."; return nullptr; } - AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), cnode); + common::AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), cnode); return InsertTensorMoveForGetNextOutputs(func_graph, cnode); } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/insert_tensor_move_for_hccl_op.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/insert_tensor_move_for_hccl_op.cc index b8e53461e4e..16def414104 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/insert_tensor_move_for_hccl_op.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/insert_tensor_move_for_hccl_op.cc @@ -17,8 +17,9 @@ #include #include #include -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "frontend/optimizer/opt.h" #include "plugin/device/ascend/optimizer/ascend_helper.h" #include "utils/trace_base.h" @@ -31,7 +32,7 @@ const std::set kNeedInsertTensorMoveOpSet = {kLambNextMVOpName, kLa bool IsParameterOrValueNode(const AnfNodePtr &node) { MS_EXCEPTION_IF_NULL(node); - auto kernel_with_index = AnfAlgo::VisitKernelWithReturnType(node, 0, true); + auto kernel_with_index = common::AnfAlgo::VisitKernelWithReturnType(node, 0, true); auto real_node = kernel_with_index.first; MS_EXCEPTION_IF_NULL(real_node); if (real_node->isa()) { @@ -78,7 +79,7 @@ bool InsertTensorMoveForHcclOp::NeedInsertTensorMove(const FuncGraphPtr &graph, return false; } // visited nop node if exist. - auto kernel_with_index = AnfAlgo::VisitKernelWithReturnType(input, 0, false); + auto kernel_with_index = common::AnfAlgo::VisitKernelWithReturnType(input, 0, false); auto real_input = kernel_with_index.first; // when input is a parameter or is a value node if (IsParameterOrValueNode(real_input)) { @@ -90,7 +91,7 @@ bool InsertTensorMoveForHcclOp::NeedInsertTensorMove(const FuncGraphPtr &graph, } // when input is some special cnodes: kLambNextMVOpName, kLambNextMVWithDecayOpName, kLambUpdateWithLROpName, // kGetNextOpName - if (kNeedInsertTensorMoveOpSet.find(AnfAlgo::GetCNodeName(real_input)) != kNeedInsertTensorMoveOpSet.end()) { + if (kNeedInsertTensorMoveOpSet.find(common::AnfAlgo::GetCNodeName(real_input)) != kNeedInsertTensorMoveOpSet.end()) { return true; } // example1: NodeA --> Allreduce @@ -103,7 +104,7 @@ bool InsertTensorMoveForHcclOp::NeedInsertTensorMove(const FuncGraphPtr &graph, if (IsNodeOutPutUsedByOtherRealKernel(graph, input, input_idx, cur_node)) { return true; } - if (opt::IsNopNode(real_input)) { + if (common::AnfAlgo::IsNopNode(real_input)) { auto cnode = real_input->cast(); MS_EXCEPTION_IF_NULL(cnode); return NeedInsertTensorMove(graph, cnode->input(1), input_idx, cur_node); @@ -123,7 +124,7 @@ void InsertTensorMoveForHcclOp::InsertTensorMove(const FuncGraphPtr &graph, cons if (tensor_move == nullptr) { MS_LOG(EXCEPTION) << "Create tensor_move op failed."; } - if (input->isa() && AnfAlgo::IsDynamicShape(input)) { + if (input->isa() && common::AnfAlgo::IsDynamicShape(input)) { MS_LOG(DEBUG) << "The tenser move op has dynamic shape attr."; } new_inputs.push_back(tensor_move); @@ -153,7 +154,7 @@ const AnfNodePtr InsertTensorMoveForHcclOp::Process(const FuncGraphPtr &func_gra if (func_graph == nullptr || node == nullptr || !node->isa()) { return nullptr; } - if (!AnfAlgo::IsCommunicationOp(node)) { + if (!common::AnfAlgo::IsCommunicationOp(node)) { return nullptr; } InsertTensorMove(func_graph, node->cast()); diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/split_inputs_for_reduce_scatter.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/split_inputs_for_reduce_scatter.cc index aaecb007462..6b8bec37f76 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/split_inputs_for_reduce_scatter.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/split_inputs_for_reduce_scatter.cc @@ -16,6 +16,7 @@ #include "plugin/device/ascend/optimizer/enhancer/split_inputs_for_reduce_scatter.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -23,30 +24,30 @@ std::vector SplitInputsForReduceScatter::InsertSplitForInput(const F const CNodePtr &node, int64_t rank_size) const { MS_EXCEPTION_IF_NULL(func_graph); - size_t inputs_size = AnfAlgo::GetInputTensorNum(node); + size_t inputs_size = common::AnfAlgo::GetInputTensorNum(node); std::vector split_outputs; size_t rank_size_t = LongToSize(rank_size); for (size_t i = 0; i < inputs_size; i++) { std::vector split_inputs{NewValueNode(std::make_shared(prim::kPrimSplitV->name()))}; - split_inputs.push_back(AnfAlgo::GetInputNode(node, i)); + split_inputs.push_back(common::AnfAlgo::GetInputNode(node, i)); auto split = NewCNode(split_inputs, func_graph); MS_EXCEPTION_IF_NULL(split); - std::vector dtypes(rank_size, AnfAlgo::GetPrevNodeOutputInferDataType(node, i)); + std::vector dtypes(rank_size, common::AnfAlgo::GetPrevNodeOutputInferDataType(node, i)); std::vector> shapes; std::vector size_splits; for (size_t j = 0; j < rank_size_t; j++) { - std::vector output_node_shape = AnfAlgo::GetPrevNodeOutputInferShape(node, i); + std::vector output_node_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node, i); output_node_shape[0] /= rank_size_t; shapes.push_back(output_node_shape); size_splits.push_back(output_node_shape[0]); } - AnfAlgo::SetOutputInferTypeAndShape(dtypes, shapes, split.get()); - AnfAlgo::SetNodeAttr("split_dim", MakeValue(0L), split); - AnfAlgo::SetNodeAttr("num_split", MakeValue(rank_size), split); - AnfAlgo::SetNodeAttr("size_splits", MakeValue(size_splits), split); + common::AnfAlgo::SetOutputInferTypeAndShape(dtypes, shapes, split.get()); + common::AnfAlgo::SetNodeAttr("split_dim", MakeValue(0L), split); + common::AnfAlgo::SetNodeAttr("num_split", MakeValue(rank_size), split); + common::AnfAlgo::SetNodeAttr("size_splits", MakeValue(size_splits), split); kernel_select_->SelectKernel(split); std::vector new_outputs; - CreateMultipleOutputsOfAnfNode(func_graph, split, AnfAlgo::GetOutputTensorNum(split), &new_outputs); + CreateMultipleOutputsOfAnfNode(func_graph, split, common::AnfAlgo::GetOutputTensorNum(split), &new_outputs); for (size_t j = 0; j < new_outputs.size(); j++) { split_outputs.push_back(new_outputs[j]); } @@ -59,7 +60,7 @@ AnfNodePtr SplitInputsForReduceScatter::RearrangeInputsForReduceScatter(const Fu const std::vector &inputs, int64_t rank_size) const { MS_EXCEPTION_IF_NULL(func_graph); - size_t inputs_size = AnfAlgo::GetInputTensorNum(node); + size_t inputs_size = common::AnfAlgo::GetInputTensorNum(node); std::vector reduce_scatter_inputs{ NewValueNode(std::make_shared(prim::kPrimReduceScatter->name()))}; size_t rank_size_t = LongToSize(rank_size); @@ -71,8 +72,8 @@ AnfNodePtr SplitInputsForReduceScatter::RearrangeInputsForReduceScatter(const Fu auto reduce_scatter = NewCNode(reduce_scatter_inputs, func_graph); MS_EXCEPTION_IF_NULL(reduce_scatter); reduce_scatter->set_abstract(node->abstract()); - AnfAlgo::CopyNodeAttrs(node, reduce_scatter); - AnfAlgo::SetNodeAttr(kAttrFusion, MakeValue(1L), reduce_scatter); + common::AnfAlgo::CopyNodeAttrs(node, reduce_scatter); + common::AnfAlgo::SetNodeAttr(kAttrFusion, MakeValue(1L), reduce_scatter); kernel_select_->SelectKernel(reduce_scatter); return reduce_scatter; } @@ -89,23 +90,23 @@ const AnfNodePtr SplitInputsForReduceScatter::Process(const FuncGraphPtr &func_g auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - if (AnfAlgo::GetInputTensorNum(node) == 1) { - AnfAlgo::SetNodeAttr(kAttrFusion, MakeValue(0L), node); + if (common::AnfAlgo::GetInputTensorNum(node) == 1) { + common::AnfAlgo::SetNodeAttr(kAttrFusion, MakeValue(0L), node); return nullptr; } - if (!AnfAlgo::HasNodeAttr(kAttrFusion, cnode) || !AnfAlgo::HasNodeAttr(kAttrRankSize, cnode)) { + if (!common::AnfAlgo::HasNodeAttr(kAttrFusion, cnode) || !common::AnfAlgo::HasNodeAttr(kAttrRankSize, cnode)) { return nullptr; } - auto fusion = AnfAlgo::GetNodeAttr(cnode, kAttrFusion); + auto fusion = common::AnfAlgo::GetNodeAttr(cnode, kAttrFusion); if (fusion <= 0) { return nullptr; } - if (AnfAlgo::HasNodeAttr("Fused", cnode)) { + if (common::AnfAlgo::HasNodeAttr("Fused", cnode)) { return nullptr; } - AnfAlgo::SetNodeAttr("Fused", MakeValue(true), node); - auto rank_size = AnfAlgo::GetNodeAttr(node, kAttrRankSize); + common::AnfAlgo::SetNodeAttr("Fused", MakeValue(true), node); + auto rank_size = common::AnfAlgo::GetNodeAttr(node, kAttrRankSize); std::vector split_outputs = InsertSplitForInput(func_graph, cnode, rank_size); return RearrangeInputsForReduceScatter(func_graph, node, split_outputs, rank_size); } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/split_n_optimizer.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/split_n_optimizer.cc index 0aa91ed87b2..77610d4ba9c 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/split_n_optimizer.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/split_n_optimizer.cc @@ -20,7 +20,8 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" -#include "utils/utils.h" +#include "include/common/utils/anfalgo.h" +#include "include/common/utils/utils.h" #include "base/core_ops.h" #include "backend/common/optimizer/helper.h" @@ -92,9 +93,9 @@ bool InputCheck(const AnfNodePtr &node) { MS_EXCEPTION_IF_NULL(node); auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - auto in_nums = AnfAlgo::GetInputTensorNum(node); + auto in_nums = common::AnfAlgo::GetInputTensorNum(node); for (size_t i = 0; i < in_nums; i++) { - auto in_node = VisitSplitKernel(AnfAlgo::GetInputNode(cnode, i), 0).first; + auto in_node = VisitSplitKernel(common::AnfAlgo::GetInputNode(cnode, i), 0).first; MS_EXCEPTION_IF_NULL(in_node); if (in_node->isa() || in_node->isa()) { MS_LOG(INFO) << "Input is a Parameter or ValueNode, can not optimizer."; @@ -103,8 +104,8 @@ bool InputCheck(const AnfNodePtr &node) { if (in_node->isa()) { auto in_cnode = in_node->cast(); MS_EXCEPTION_IF_NULL(in_cnode); - auto in_node_name = AnfAlgo::GetCNodeName(in_cnode); - auto trans_input = AnfAlgo::VisitKernel(in_node, 0).first; + auto in_node_name = common::AnfAlgo::GetCNodeName(in_cnode); + auto trans_input = common::AnfAlgo::VisitKernel(in_node, 0).first; MS_EXCEPTION_IF_NULL(trans_input); if (in_node_name == kTransDataOpName && (trans_input->isa() || trans_input->isa())) { MS_LOG(INFO) << "Data->TransData->split, can not optimizer."; @@ -113,8 +114,9 @@ bool InputCheck(const AnfNodePtr &node) { if (in_node_name == prim::kPrimDepend->name() || in_node_name == prim::kPrimLoad->name()) { return false; } - if ((AnfAlgo::HasNodeAttr("non_task", in_cnode) && AnfAlgo::GetNodeAttr(in_node, "non_task")) || - opt::IsNopNode(in_cnode)) { + if ((common::AnfAlgo::HasNodeAttr("non_task", in_cnode) && + common::AnfAlgo::GetNodeAttr(in_node, "non_task")) || + common::AnfAlgo::IsNopNode(in_cnode)) { MS_LOG(INFO) << "Input is nop node or has non_task attr, can not optimizer."; return false; } @@ -141,16 +143,16 @@ bool OutputCheck(const FuncGraphPtr &func_graph, const AnfNodePtr &node) { MS_LOG(INFO) << "Next node is not a AICore node, can not optimizer."; return false; } - if (func_graph->output() == item || AnfAlgo::CheckPrimitiveType(item, prim::kPrimReturn)) { + if (func_graph->output() == item || common::AnfAlgo::CheckPrimitiveType(item, prim::kPrimReturn)) { MS_LOG(INFO) << "Next node is graph output or return, can not optimizer."; return false; } - auto op_name = AnfAlgo ::GetCNodeName(item); - if (InvalidOps.find(op_name) != InvalidOps.end() || AnfAlgo::IsCommunicationOp(item)) { + auto op_name = common::AnfAlgo::GetCNodeName(item); + if (InvalidOps.find(op_name) != InvalidOps.end() || common::AnfAlgo::IsCommunicationOp(item)) { MS_LOG(INFO) << "Next node is " << item->fullname_with_scope() << ", not a invalid node, can not optimizer."; return false; } - if (!AnfAlgo::GetOutputTensorNum(item)) { + if (!common::AnfAlgo::GetOutputTensorNum(item)) { MS_LOG(INFO) << "Next node has no output, can not optimizer."; return false; } @@ -163,17 +165,17 @@ bool NeedSkip(const FuncGraphPtr &func_graph, const AnfNodePtr &node) { FuncGraphManagerPtr func_manager = func_graph->manager(); MS_EXCEPTION_IF_NULL(func_manager); int64_t split_dim = -1; - auto op_name = AnfAlgo::GetCNodeName(node); + auto op_name = common::AnfAlgo::GetCNodeName(node); if (op_name == prim::kPrimSplit->name()) { - split_dim = AnfAlgo::GetNodeAttr(node, kAttrAxis); + split_dim = common::AnfAlgo::GetNodeAttr(node, kAttrAxis); } else if (op_name == prim::kPrimSplitV->name()) { - split_dim = AnfAlgo::GetNodeAttr(node, kAttrSplitDim); + split_dim = common::AnfAlgo::GetNodeAttr(node, kAttrSplitDim); } if (split_dim != 0) { MS_LOG(INFO) << "Split_dim is not 0, can not optimizer."; return true; } - if (AnfAlgo::IsDynamicShape(node)) { + if (common::AnfAlgo::IsDynamicShape(node)) { MS_LOG(INFO) << "Split is dynamic shape, can not optimizer."; return true; } @@ -202,14 +204,14 @@ const AnfNodePtr SplitOpOptimizer::Process(const FuncGraphPtr &func_graph, const if (!AnfUtils::IsRealCNodeKernel(node)) { return nullptr; } - AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node); - auto op_name = AnfAlgo::GetCNodeName(node); + common::AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node); + auto op_name = common::AnfAlgo::GetCNodeName(node); if (op_name != prim::kPrimSplit->name() && op_name != prim::kPrimSplitV->name()) { return nullptr; } if (!NeedSkip(func_graph, node)) { - AnfAlgo::SetNodeAttr("non_task", MakeValue(true), node); + common::AnfAlgo::SetNodeAttr("non_task", MakeValue(true), node); return node; } return nullptr; diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/change_axis_of_reduce_kernel.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/change_axis_of_reduce_kernel.cc index ee573e88c69..acfe098a93e 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/change_axis_of_reduce_kernel.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/change_axis_of_reduce_kernel.cc @@ -20,9 +20,10 @@ #include #include -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/trace_base.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "kernel/common_utils.h" namespace mindspore { @@ -44,7 +45,7 @@ void SafeCheckFunction(const CNodePtr &cnode, const std::vector &reduce MS_LOG(EXCEPTION) << "The node " << cnode->DebugString() << "'s reduce axis got a empty vector" << trace::DumpSourceLines(cnode); } - if (AnfAlgo::GetInputTensorNum(cnode) != 1 || AnfAlgo::GetOutputTensorNum(cnode) != 1) { + if (common::AnfAlgo::GetInputTensorNum(cnode) != 1 || common::AnfAlgo::GetOutputTensorNum(cnode) != 1) { MS_LOG(EXCEPTION) << "The kind of reduce node [" << cnode->DebugString() << "] is not single input or single output." << trace::DumpSourceLines(cnode); } @@ -57,14 +58,14 @@ void SafeCheckFunction(const CNodePtr &cnode, const std::vector &reduce void DynamicAttrUpdate(const AnfNodePtr &node) { MS_EXCEPTION_IF_NULL(node); - auto primitive = AnfAlgo::GetCNodePrimitive(node); + auto primitive = common::AnfAlgo::GetCNodePrimitive(node); MS_EXCEPTION_IF_NULL(primitive); auto input_names_ptr = primitive->GetAttr(kAttrInputNames); MS_EXCEPTION_IF_NULL(input_names_ptr); auto input_names_vec = GetValue>(input_names_ptr); const size_t axes_index = 1; input_names_vec[axes_index] = kAttrAxes; - AnfAlgo::SetNodeAttr(kAttrInputNames, MakeValue(input_names_vec), node); + common::AnfAlgo::SetNodeAttr(kAttrInputNames, MakeValue(input_names_vec), node); } void ConvertReduceAttrFraczAnd6HD(const CNodePtr &cnode) { @@ -90,7 +91,7 @@ void ConvertReduceAttrFraczAnd6HD(const CNodePtr &cnode) { << " but the format is not supported this reduce axis"; } } - AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(convert_axis), cnode); + common::AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(convert_axis), cnode); } } // namespace @@ -110,13 +111,13 @@ const AnfNodePtr ChangeAxisOfReduceKernel::Process(const FuncGraphPtr &, const A } auto convert_map = kReduceConvertMap.find(AnfAlgo::GetInputFormat(node, 0)); if (convert_map == kReduceConvertMap.end()) { - if (AnfAlgo::IsDynamicShape(node)) { + if (common::AnfAlgo::IsDynamicShape(node)) { DynamicAttrUpdate(node); } return nullptr; } convert_map->second(node->cast()); - if (AnfAlgo::IsDynamicShape(node)) { + if (common::AnfAlgo::IsDynamicShape(node)) { DynamicAttrUpdate(node); } return nullptr; diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/check_consistency.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/check_consistency.cc index 868f6092748..c4fe41512a4 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/check_consistency.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/check_consistency.cc @@ -19,9 +19,10 @@ #include #include -#include "utils/utils.h" -#include "utils/ms_device_shape_transfer.h" +#include "include/common/utils/utils.h" +#include "runtime/device/ms_device_shape_transfer.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "kernel/common_utils.h" namespace mindspore { @@ -30,14 +31,14 @@ namespace { bool CheckFormatForConsistency(const CNodePtr &node, const size_t input_index) { MS_EXCEPTION_IF_NULL(node); // get prior node's device output format - auto prev_node = AnfAlgo::GetPrevNodeOutput(node, input_index); + auto prev_node = common::AnfAlgo::GetPrevNodeOutput(node, input_index); string pre_output_format = AnfAlgo::GetOutputFormat(prev_node.first, prev_node.second); string selected_input_format = AnfAlgo::GetInputFormat(node, input_index); if (pre_output_format == selected_input_format) { if (selected_input_format == kOpFormat_FRAC_Z && (prev_node.first->isa() || prev_node.first->isa())) { - auto pre_groups = AnfAlgo::GetAttrGroups(prev_node.first, prev_node.second); - auto cur_groups = AnfAlgo::GetAttrGroups(node, input_index); + auto pre_groups = common::AnfAlgo::GetAttrGroups(prev_node.first, prev_node.second); + auto cur_groups = common::AnfAlgo::GetAttrGroups(node, input_index); if (pre_groups != cur_groups) { MS_LOG(ERROR) << "Found inconsistent format! input format " << input_index << ": " << pre_output_format << " with groups " << pre_groups << ", selected input format: " << selected_input_format @@ -47,7 +48,7 @@ bool CheckFormatForConsistency(const CNodePtr &node, const size_t input_index) { } return true; } - auto input_origin_shape = AnfAlgo::GetOutputInferShape(prev_node.first, prev_node.second); + auto input_origin_shape = common::AnfAlgo::GetOutputInferShape(prev_node.first, prev_node.second); if (pre_output_format == kOpFormat_DEFAULT || selected_input_format == kOpFormat_DEFAULT) { string checking_format = (pre_output_format == kOpFormat_DEFAULT) ? selected_input_format : pre_output_format; // when input shape size is 1D, default format and NC1HWC0 are compatible @@ -92,10 +93,10 @@ const AnfNodePtr CheckConsistency::Process(const FuncGraphPtr &, const AnfNodePt CNodePtr cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - size_t in_num = AnfAlgo::GetInputTensorNum(cnode); + size_t in_num = common::AnfAlgo::GetInputTensorNum(cnode); for (size_t i = 0; i < in_num; ++i) { if (!CheckFormatForConsistency(cnode, i) || !CheckDataTypeForConsistency(cnode, i)) { - MS_LOG(EXCEPTION) << "Found inconsistent format or data type! Op: " << AnfAlgo::GetCNodeName(cnode) << "[" + MS_LOG(EXCEPTION) << "Found inconsistent format or data type! Op: " << common::AnfAlgo::GetCNodeName(cnode) << "[" << cnode->DebugString() << "], fullname: " << node->fullname_with_scope(); } } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/convert_cast_format.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/convert_cast_format.cc index 7d36a87f463..0b1d4de8192 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/convert_cast_format.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/convert_cast_format.cc @@ -22,6 +22,7 @@ #include "utils/hash_map.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/optimizer/helper.h" namespace mindspore { namespace opt { @@ -36,15 +37,16 @@ const AnfNodePtr ConvertCastFormat::Process(const FuncGraphPtr &func_graph, cons if (node == nullptr || !node->isa() || !AnfUtils::IsRealCNodeKernel(node)) { return nullptr; } - auto node_name = AnfAlgo::GetCNodeName(node); + auto node_name = common::AnfAlgo::GetCNodeName(node); if (node_name == prim::kPrimCast->name()) { return nullptr; } auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - size_t input_num = AnfAlgo::GetInputTensorNum(cnode); + size_t input_num = common::AnfAlgo::GetInputTensorNum(cnode); for (size_t input_index = 0; input_index < input_num; ++input_index) { - auto input_node = AnfAlgo::VisitKernelWithReturnType(AnfAlgo::GetInputNode(cnode, input_index), 0).first; + auto input_node = + common::AnfAlgo::VisitKernelWithReturnType(common::AnfAlgo::GetInputNode(cnode, input_index), 0).first; MS_EXCEPTION_IF_NULL(input_node); if (!input_node->isa()) { continue; @@ -69,14 +71,15 @@ void ConvertCastFormat::SetCastFormat(const CNodePtr &cast_node, const string &f void ConvertCastFormat::ChangeCastFormat(const CNodePtr &cast_node, const FuncGraphPtr &func_graph) const { MS_EXCEPTION_IF_NULL(cast_node); MS_EXCEPTION_IF_NULL(func_graph); - auto input_node_name = AnfAlgo::GetCNodeName(cast_node); + auto input_node_name = common::AnfAlgo::GetCNodeName(cast_node); if (input_node_name != prim::kPrimCast->name()) { return; } - if (AnfAlgo::HasNodeAttr(kAttrVisited, cast_node) && AnfAlgo::GetNodeAttr(cast_node, kAttrVisited)) { + if (common::AnfAlgo::HasNodeAttr(kAttrVisited, cast_node) && + common::AnfAlgo::GetNodeAttr(cast_node, kAttrVisited)) { return; } - AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), cast_node); + common::AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), cast_node); auto used_cast_node_list = GetRealNodeUsedList(func_graph, cast_node); MS_EXCEPTION_IF_NULL(used_cast_node_list); mindspore::HashMap format_counter = CalculateFormat(used_cast_node_list, cast_node); @@ -119,10 +122,11 @@ mindspore::HashMap ConvertCastFormat::CalculateFormat( MS_EXCEPTION_IF_NULL(node_info.first); auto cast_out_node = node_info.first->cast(); MS_EXCEPTION_IF_NULL(cast_out_node); - size_t input_num = AnfAlgo::GetInputTensorNum(cast_out_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(cast_out_node); for (size_t index = 0; index < input_num; ++index) { - if (AnfAlgo::VisitKernelWithReturnType(AnfAlgo::GetInputNode(cast_out_node->cast(), index), 0).first != - cast_node) { + if (common::AnfAlgo::VisitKernelWithReturnType( + common::AnfAlgo::GetInputNode(cast_out_node->cast(), index), 0) + .first != cast_node) { continue; } auto format = AnfAlgo::GetInputFormat(cast_out_node, index); diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/convert_unsupported_transnode_to_aicpu.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/convert_unsupported_transnode_to_aicpu.cc index 8c6aa55ef9e..7c6775baf3c 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/convert_unsupported_transnode_to_aicpu.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/convert_unsupported_transnode_to_aicpu.cc @@ -17,6 +17,7 @@ #include "plugin/device/ascend/optimizer/format_type/convert_unsupported_transnode_to_aicpu.h" #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "kernel/kernel_build_info.h" namespace mindspore { @@ -33,7 +34,7 @@ const AnfNodePtr ConvertUnSupportNodeToAICPU::Process(const mindspore::FuncGraph if (node == nullptr || !node->isa()) { return nullptr; } - auto node_name = AnfAlgo::GetCNodeName(node); + auto node_name = common::AnfAlgo::GetCNodeName(node); if (node_name != prim::kPrimTransData->name() && node_name != prim::kPrimCast->name()) { return nullptr; } @@ -45,7 +46,7 @@ const AnfNodePtr ConvertUnSupportNodeToAICPU::Process(const mindspore::FuncGraph MS_EXCEPTION_IF_NULL(builder); builder->SetKernelType(AICPU_KERNEL); AnfAlgo::SetSelectKernelBuildInfo(builder->Build(), node.get()); - AnfAlgo::SetNodeAttr(kAttrIsAiCpuKernel, MakeValue(true), node); + common::AnfAlgo::SetNodeAttr(kAttrIsAiCpuKernel, MakeValue(true), node); } else { MS_LOG(EXCEPTION) << "Kernel " << kernel_builder_info->ToString() << "is not supported in AiCPU & AiCore : node [" << node->DebugString() << "]" << trace::DumpSourceLines(node); diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/deal_ref_and_split_unsupported_transdata.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/deal_ref_and_split_unsupported_transdata.cc index b836fb3fb3e..5e53867ed3d 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/deal_ref_and_split_unsupported_transdata.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/deal_ref_and_split_unsupported_transdata.cc @@ -21,20 +21,21 @@ #include #include "kernel/oplib/oplib.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/session/kernel_graph.h" #include "backend/common/optimizer/helper.h" namespace mindspore { namespace opt { session::KernelWithIndex DealRefAndSpiltUnSupportedTransdata::FindRefOriginNode(const AnfNodePtr &node) const { - session::KernelWithIndex kernel_with_index = AnfAlgo::VisitKernel(node, 0); + session::KernelWithIndex kernel_with_index = common::AnfAlgo::VisitKernel(node, 0); AnfNodePtr cur_node = kernel_with_index.first; size_t cur_out_index = kernel_with_index.second; MS_EXCEPTION_IF_NULL(cur_node); if (cur_node->isa()) { auto cnode = cur_node->cast(); MS_EXCEPTION_IF_NULL(cnode); - std::string op_name = AnfAlgo::GetCNodeName(cnode); + std::string op_name = common::AnfAlgo::GetCNodeName(cnode); auto op_info = mindspore::kernel::tbe::TbeDynamicShapeUtil::FindOp(op_name, cnode); // deal ref op if (op_info != nullptr && op_info->is_ref()) { @@ -52,7 +53,7 @@ session::KernelWithIndex DealRefAndSpiltUnSupportedTransdata::FindRefOriginNode( // deal special (trans,cast,reshape) op and nop-node if (op_name == prim::kPrimCast->name() || op_name == prim::kPrimTranspose->name() || - op_name == prim::kPrimReshape->name() || op_name == kTransDataOpName || opt::IsNopNode(cnode)) { + op_name == prim::kPrimReshape->name() || op_name == kTransDataOpName || common::AnfAlgo::IsNopNode(cnode)) { AnfNodePtr next_node = cnode->input(1); return FindRefOriginNode(next_node); } @@ -68,7 +69,8 @@ void DealRefAndSpiltUnSupportedTransdata::AddRefNodePairToKernelGraph(const Func auto kernel_graph = func_graph->cast(); MS_EXCEPTION_IF_NULL(kernel_graph); session::AnfWithOutIndex final_pair = std::make_pair(cnode, output_index); - session::KernelWithIndex kernel_with_index = AnfAlgo::VisitKernel(AnfAlgo::GetInputNode(cnode, input_index), 0); + session::KernelWithIndex kernel_with_index = + common::AnfAlgo::VisitKernel(common::AnfAlgo::GetInputNode(cnode, input_index), 0); kernel_graph->AddRefCorrespondPairs(final_pair, kernel_with_index); } @@ -103,7 +105,7 @@ CNodePtr DealRefAndSpiltUnSupportedTransdata::AddAdditionalToRefOutput(const Fun CNodePtr final_node = (get_item == nullptr ? cnode : get_item); bool need_refresh_ref_addr = false; size_t final_index = output_index; - AnfNodePtr input_node = AnfAlgo::GetInputNode(cnode, input_index); + AnfNodePtr input_node = common::AnfAlgo::GetInputNode(cnode, input_index); session::KernelWithIndex origin_pair = FindRefOriginNode(input_node); MS_EXCEPTION_IF_NULL(origin_pair.first); if (!origin_pair.first->isa()) { @@ -115,8 +117,8 @@ CNodePtr DealRefAndSpiltUnSupportedTransdata::AddAdditionalToRefOutput(const Fun auto origin_type = AnfAlgo::GetOutputDeviceDataType(origin_pair.first, origin_pair.second); auto cur_format = AnfAlgo::GetOutputFormat(cnode, output_index); auto cur_type = AnfAlgo::GetOutputDeviceDataType(cnode, output_index); - auto cur_shape = AnfAlgo::GetOutputInferShape(cnode, output_index); - auto detail_shape = AnfAlgo::GetOutputDetailShape(cnode, output_index); + auto cur_shape = common::AnfAlgo::GetOutputInferShape(cnode, output_index); + auto detail_shape = common::AnfAlgo::GetOutputDetailShape(cnode, output_index); // insert trans if (origin_format != cur_format && cur_shape.size() > 1) { auto kernel_select = std::make_shared(); @@ -169,7 +171,7 @@ CNodePtr DealRefAndSpiltUnSupportedTransdata::DealRefForMultipleOutput( auto manager = func_graph->manager(); MS_EXCEPTION_IF_NULL(manager); auto cnode = orig_cnode; - auto update_states = AnfAlgo::GetUpdateStateUsers(manager, orig_cnode); + auto update_states = common::AnfAlgo::GetUpdateStateUsers(manager, orig_cnode); if (!update_states.empty()) { auto kernel_graph = func_graph->cast(); MS_EXCEPTION_IF_NULL(kernel_graph); @@ -185,7 +187,7 @@ CNodePtr DealRefAndSpiltUnSupportedTransdata::DealRefForMultipleOutput( std::vector make_tuple_inputs; AbstractBasePtrList abstract_list; make_tuple_inputs.emplace_back(NewValueNode(prim::kPrimMakeTuple)); - size_t output_num = AnfAlgo::GetOutputTensorNum(cnode); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(cnode); for (size_t output_index = 0; output_index < output_num; ++output_index) { CNodePtr final_node = CreatTupleGetItemNode(func_graph, cnode, output_index); // deal with ref output @@ -228,10 +230,10 @@ const BaseRef DealRefAndSpiltUnSupportedTransdata::DefinePattern() const { void DealRefAndSpiltUnSupportedTransdata::DealBroadCastAsRef(const FuncGraphPtr &func_graph, const CNodePtr &cnode) const { - if (AnfAlgo::GetCNodeName(cnode) == kBroadcastOpName) { - auto input_size = AnfAlgo::GetInputTensorNum(cnode); + if (common::AnfAlgo::GetCNodeName(cnode) == kBroadcastOpName) { + auto input_size = common::AnfAlgo::GetInputTensorNum(cnode); for (size_t i = 0; i < input_size; ++i) { - auto input_node_with_index = AnfAlgo::GetPrevNodeOutput(cnode, i, true); + auto input_node_with_index = common::AnfAlgo::GetPrevNodeOutput(cnode, i, true); auto input_node = input_node_with_index.first; MS_EXCEPTION_IF_NULL(input_node); MS_LOG(INFO) << "origin node:" << input_node->fullname_with_scope(); @@ -245,7 +247,7 @@ const AnfNodePtr DealRefAndSpiltUnSupportedTransdata::Process(const FuncGraphPtr if (node == nullptr || !node->isa()) { return nullptr; } - AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node); + common::AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node); auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); if (!AnfUtils::IsRealCNodeKernel(cnode)) { @@ -254,7 +256,7 @@ const AnfNodePtr DealRefAndSpiltUnSupportedTransdata::Process(const FuncGraphPtr DealBroadCastAsRef(graph, cnode); - auto op_name = AnfAlgo::GetCNodeName(cnode); + auto op_name = common::AnfAlgo::GetCNodeName(cnode); auto op_info = mindspore::kernel::tbe::TbeDynamicShapeUtil::FindOp(op_name, cnode); if (op_info == nullptr || !op_info->is_ref()) { return nullptr; @@ -304,7 +306,7 @@ CNodePtr DealRefAndSpiltUnSupportedTransdata::SplitTransdataIfNotSupported(const RefreshKernelBuildInfo(kOpFormat_DEFAULT, AnfAlgo::GetOutputFormat(next_trans_node, 0), next_trans_node); if (IsFormatInvaild(cnode)) { auto after_split_node = DoSplit(func_graph, cnode); - AnfAlgo::SetNodeInput(next_trans_node, after_split_node, 0); + common::AnfAlgo::SetNodeInput(next_trans_node, after_split_node, 0); } if (IsFormatInvaild(next_trans_node)) { return DoSplit(func_graph, next_trans_node); diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/dynamic_rnn_grad_reformat.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/dynamic_rnn_grad_reformat.cc index d429c8eaf64..5998c749168 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/dynamic_rnn_grad_reformat.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/dynamic_rnn_grad_reformat.cc @@ -36,11 +36,11 @@ const AnfNodePtr DynamicRNNGradReformat::Process(const FuncGraphPtr &func_graph, MS_EXCEPTION_IF_NULL(split_v); auto matmul = CheckAnfNodeIfCNodeAndInputSize(split_v->input(1), kMatMulInputTensorNum); MS_EXCEPTION_IF_NULL(matmul); - auto input_node_with_idx = AnfAlgo::GetPrevNodeOutput(matmul, 0); + auto input_node_with_idx = common::AnfAlgo::GetPrevNodeOutput(matmul, 0); auto input_node = input_node_with_idx.first; MS_EXCEPTION_IF_NULL(input_node); if (!(input_node->isa() && - AnfAlgo::GetCNodeName(input_node->cast()) == kBasicLSTMCellCStateGradV2OpName)) { + common::AnfAlgo::GetCNodeName(input_node->cast()) == kBasicLSTMCellCStateGradV2OpName)) { return nullptr; } @@ -56,7 +56,7 @@ const AnfNodePtr DynamicRNNGradReformat::Process(const FuncGraphPtr &func_graph, matmul_new_builder->SetFusionType(matmul_kernel_build_info->fusion_type()); matmul_new_builder->SetProcessor(matmul_kernel_build_info->processor()); AnfAlgo::SetSelectKernelBuildInfo(matmul_new_builder->Build(), matmul.get()); - AnfAlgo::SetNodeAttr("insert_backend", MakeValue(true), matmul); + common::AnfAlgo::SetNodeAttr("insert_backend", MakeValue(true), matmul); // reformat split_v auto split_kernel_build_info = AnfAlgo::GetSelectKernelBuildInfo(split_v); @@ -70,7 +70,7 @@ const AnfNodePtr DynamicRNNGradReformat::Process(const FuncGraphPtr &func_graph, split_new_builder->SetFusionType(split_kernel_build_info->fusion_type()); split_new_builder->SetProcessor(split_kernel_build_info->processor()); AnfAlgo::SetSelectKernelBuildInfo(split_new_builder->Build(), split_v.get()); - AnfAlgo::SetNodeAttr("insert_backend", MakeValue(true), split_v); + common::AnfAlgo::SetNodeAttr("insert_backend", MakeValue(true), split_v); return split_v; } } // namespace opt diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/insert_cast.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/insert_cast.cc index 02bf0eb0252..d8241cf0acb 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/insert_cast.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/insert_cast.cc @@ -23,8 +23,9 @@ #include "backend/common/optimizer/helper.h" #include "kernel/kernel_build_info.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/session/kernel_graph.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "kernel/common_utils.h" namespace mindspore { @@ -36,7 +37,7 @@ AnfNodePtr InsertCastForMultipleOutput(const FuncGraphPtr &func_graph, const CNo MS_EXCEPTION_IF_NULL(cnode); auto manager = func_graph->manager(); MS_EXCEPTION_IF_NULL(manager); - auto update_states = AnfAlgo::GetUpdateStateUsers(manager, orig_cnode); + auto update_states = common::AnfAlgo::GetUpdateStateUsers(manager, orig_cnode); for (auto &update_state : update_states) { manager->SetEdge(update_state.first, update_state.second, cnode); } @@ -47,11 +48,11 @@ AnfNodePtr InsertCastForMultipleOutput(const FuncGraphPtr &func_graph, const CNo AbstractBasePtrList abstract_list; make_tuple_inputs.emplace_back(NewValueNode(prim::kPrimMakeTuple)); auto kernel_graph = func_graph->cast(); - size_t out_num = AnfAlgo::GetOutputTensorNum(cnode); + size_t out_num = common::AnfAlgo::GetOutputTensorNum(cnode); for (size_t output_idx = 0; output_idx < out_num; ++output_idx) { AnfNodePtr replace_node = nullptr; - const auto origin_shape = AnfAlgo::GetOutputDetailShape(cnode, output_idx); - const auto origin_type = AnfAlgo::GetOutputInferDataType(cnode, output_idx); + const auto origin_shape = common::AnfAlgo::GetOutputDetailShape(cnode, output_idx); + const auto origin_type = common::AnfAlgo::GetOutputInferDataType(cnode, output_idx); auto idx = NewValueNode(SizeToLong(output_idx)); MS_EXCEPTION_IF_NULL(idx); auto imm = std::make_shared(output_idx); @@ -69,7 +70,7 @@ AnfNodePtr InsertCastForMultipleOutput(const FuncGraphPtr &func_graph, const CNo origin_shape, origin_type, AnfAlgo::GetOutputReshapeType(getitem, 0)); MS_EXCEPTION_IF_NULL(replace_node); replace_node->set_scope(cnode->scope()); - AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), replace_node); + common::AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), replace_node); if (kernel_graph != nullptr && kernel_graph->IsInternalOutput(cnode, output_idx)) { kernel_graph->ReplaceInternalOutput(cnode, replace_node, output_idx, 0); } @@ -88,7 +89,7 @@ AnfNodePtr InsertCastForMultipleOutput(const FuncGraphPtr &func_graph, const CNo AnfNodePtr InsertCastForOutput(const FuncGraphPtr &func_graph, const CNodePtr &orig_cnode, const CNodePtr &cnode) { MS_EXCEPTION_IF_NULL(func_graph); MS_EXCEPTION_IF_NULL(cnode); - if (AnfAlgo::GetOutputTensorNum(cnode) == 0) { + if (common::AnfAlgo::GetOutputTensorNum(cnode) == 0) { return cnode; } MS_EXCEPTION_IF_NULL(cnode->Type()); @@ -96,8 +97,8 @@ AnfNodePtr InsertCastForOutput(const FuncGraphPtr &func_graph, const CNodePtr &o // Single output if (!cnode->Type()->isa()) { const std::string dev_fmt = AnfAlgo::GetOutputFormat(cnode, 0); - const abstract::BaseShapePtr origin_shape = AnfAlgo::GetOutputDetailShape(cnode, 0); - const TypeId origin_type = AnfAlgo::GetOutputInferDataType(cnode, 0); + const abstract::BaseShapePtr origin_shape = common::AnfAlgo::GetOutputDetailShape(cnode, 0); + const TypeId origin_type = common::AnfAlgo::GetOutputInferDataType(cnode, 0); const TypeId device_type = AnfAlgo::GetOutputDeviceDataType(cnode, 0); AnfNodePtr replace_node = cnode; if (origin_type != device_type) { @@ -105,7 +106,7 @@ AnfNodePtr InsertCastForOutput(const FuncGraphPtr &func_graph, const CNodePtr &o origin_shape, origin_type, AnfAlgo::GetOutputReshapeType(cnode, 0)); MS_EXCEPTION_IF_NULL(replace_node); replace_node->set_scope(cnode->scope()); - AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), replace_node); + common::AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), replace_node); if (kernel_graph != nullptr && kernel_graph->IsInternalOutput(cnode, 0)) { kernel_graph->ReplaceInternalOutput(cnode, replace_node); } @@ -128,7 +129,7 @@ const AnfNodePtr InsertCast::Process(const FuncGraphPtr &func_graph, const AnfNo if (!AnfUtils::IsRealCNodeKernel(node) || func_graph == nullptr) { return nullptr; } - AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node); + common::AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node); // process input CNodePtr cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/insert_trans_op.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/insert_trans_op.cc index 01802fc47b1..161c849966e 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/insert_trans_op.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/insert_trans_op.cc @@ -16,10 +16,11 @@ #include "plugin/device/ascend/optimizer/format_type/insert_trans_op.h" #include -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/optimizer/helper.h" #include "plugin/device/ascend/optimizer/ascend_helper.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "utils/ms_context.h" namespace mindspore { @@ -35,7 +36,7 @@ const AnfNodePtr InsertTransOp::Process(const FuncGraphPtr &func_graph, const An if (node == nullptr || !AnfUtils::IsRealKernel(node)) { return nullptr; } - AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node); + common::AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node); MS_LOG(DEBUG) << "process op: " << node->DebugString(); AnfNodePtr new_node = InsertTransOpForInput(func_graph, node, kernel_select_); auto kernel_graph = func_graph->cast>(); diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/insert_transdata_for_runop.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/insert_transdata_for_runop.cc index a3cf40fc8f5..58208c88994 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/insert_transdata_for_runop.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/insert_transdata_for_runop.cc @@ -15,9 +15,10 @@ */ #include "plugin/device/ascend/optimizer/format_type/insert_transdata_for_runop.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "plugin/device/ascend/optimizer/ascend_helper.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -28,15 +29,15 @@ bool RunOpInsertTransData::InsertTransdataForOutput(const FuncGraphPtr &graph) { auto output = graph->output(); MS_EXCEPTION_IF_NULL(output); auto cnode = output->cast(); - auto inputs_num = AnfAlgo::GetInputNum(cnode); - if (AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimMakeTuple)) { + auto inputs_num = common::AnfAlgo::GetInputNum(cnode); + if (common::AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimMakeTuple)) { for (size_t index = 0; index < inputs_num; index++) { auto format = AnfAlgo::GetPrevNodeOutputFormat(cnode, index); if (format == kOpFormat_ND_RNN_BIAS || format == kOpFormat_FRACTAL_ZN_RNN) { - auto cur_cnode_with_index = AnfAlgo::GetPrevNodeOutput(cnode, index, false); + auto cur_cnode_with_index = common::AnfAlgo::GetPrevNodeOutput(cnode, index, false); auto trans_node = AddTransOpNodeToGraph(graph, cur_cnode_with_index.first, kernel_select_, cur_cnode_with_index.second, false); - AnfAlgo::SetNodeInput(cnode, trans_node, index); + common::AnfAlgo::SetNodeInput(cnode, trans_node, index); has_changed = true; } } @@ -65,24 +66,24 @@ bool RunOpInsertTransData::Run(const FuncGraphPtr &graph) { continue; } auto cnode = node->cast(); - size_t input_num = AnfAlgo::GetInputTensorNum(cnode); + size_t input_num = common::AnfAlgo::GetInputTensorNum(cnode); for (size_t index = 0; index < input_num; ++index) { auto prev_input_format = AnfAlgo::GetPrevNodeOutputFormat(cnode, index); - auto prev_node_out_infer_shape = AnfAlgo::GetPrevNodeOutputInferShape(cnode, index); + auto prev_node_out_infer_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(cnode, index); auto input_format = AnfAlgo::GetInputFormat(cnode, index); - auto input_node = AnfAlgo::GetInputNode(cnode, index); + auto input_node = common::AnfAlgo::GetInputNode(cnode, index); // convert the format of node's input node to default if (kCommonFormatSet.find(prev_input_format) == kCommonFormatSet.end() && (prev_node_out_infer_shape.size() > 1 || prev_input_format == kOpFormat_ND_RNN_BIAS)) { auto trans_node = AddTransOpNodeToGraph(graph, input_node, kernel_select_, 0, false); - AnfAlgo::SetNodeInput(cnode, trans_node, index); + common::AnfAlgo::SetNodeInput(cnode, trans_node, index); has_changed = true; } // convert node's output format if (kCommonFormatSet.find(input_format) == kCommonFormatSet.end() && (prev_node_out_infer_shape.size() > 1 || input_format == kOpFormat_ND_RNN_BIAS)) { auto trans_node = AddTransOpNodeToGraph(graph, cnode, kernel_select_, index, true); - AnfAlgo::SetNodeInput(cnode, trans_node, index); + common::AnfAlgo::SetNodeInput(cnode, trans_node, index); has_changed = true; } } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/insert_transpose_for_basiclstm_op.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/insert_transpose_for_basiclstm_op.cc index 4821cfc53b9..470a3c7728d 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/insert_transpose_for_basiclstm_op.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/insert_transpose_for_basiclstm_op.cc @@ -17,9 +17,10 @@ #include "plugin/device/ascend/optimizer/format_type/insert_transpose_for_basiclstm_op.h" #include #include -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "plugin/device/ascend/optimizer/ascend_helper.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "runtime/device/kernel_info.h" #include "kernel/oplib/oplib.h" #include "utils/ms_context.h" @@ -43,15 +44,15 @@ CNodePtr Insert(const FuncGraphPtr &func_graph, const CNodePtr &cnode, const std transpose_inputs.push_back(NewValueNode(prim)); if (op_name == kBasicLSTMCellInputGradOpName) { - auto origin_type = AnfAlgo::GetPrevNodeOutputInferDataType(cnode, 1); - auto origin_shape = AnfAlgo::GetPrevNodeOutputInferShape(cnode, 1); + auto origin_type = common::AnfAlgo::GetPrevNodeOutputInferDataType(cnode, 1); + auto origin_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(cnode, 1); auto dst_shape = {origin_shape[1], origin_shape[0]}; - transpose_inputs.push_back(AnfAlgo::GetInputNode(cnode, 1)); + transpose_inputs.push_back(common::AnfAlgo::GetInputNode(cnode, 1)); CNodePtr transpose = func_graph->NewCNode(transpose_inputs); MS_EXCEPTION_IF_NULL(transpose); - AnfAlgo::SetOutputInferTypeAndShape({origin_type}, {dst_shape}, transpose.get()); - AnfAlgo::SetNodeAttr(kAttrPerm, MakeValue(std::vector{1, 0}), transpose); - AnfAlgo::SetNodeInput(cnode, transpose, 1); + common::AnfAlgo::SetOutputInferTypeAndShape({origin_type}, {dst_shape}, transpose.get()); + common::AnfAlgo::SetNodeAttr(kAttrPerm, MakeValue(std::vector{1, 0}), transpose); + common::AnfAlgo::SetNodeInput(cnode, transpose, 1); if (kernel_graph == nullptr) { new_node = std::make_shared(*cnode); } else { @@ -59,18 +60,18 @@ CNodePtr Insert(const FuncGraphPtr &func_graph, const CNodePtr &cnode, const std } } else if (op_name == kBasicLSTMCellWeightGradOpName) { std::vector make_tuple_inputs = {NewValueNode(prim::kPrimMakeTuple)}; - size_t out_num = AnfAlgo::GetOutputTensorNum(cnode); + size_t out_num = common::AnfAlgo::GetOutputTensorNum(cnode); for (size_t output_idx = 0; output_idx < out_num; output_idx++) { auto tuple_getitem = CreatTupleGetItemNode(func_graph, cnode, output_idx); - auto origin_shape = AnfAlgo::GetOutputInferShape(cnode, output_idx); + auto origin_shape = common::AnfAlgo::GetOutputInferShape(cnode, output_idx); if (origin_shape.size() > 1 && output_idx == 0) { - auto dtype = AnfAlgo::GetOutputInferDataType(cnode, output_idx); + auto dtype = common::AnfAlgo::GetOutputInferDataType(cnode, output_idx); auto dst_shape = {origin_shape[0], origin_shape[1]}; transpose_inputs.push_back(tuple_getitem); CNodePtr transpose = func_graph->NewCNode(transpose_inputs); MS_EXCEPTION_IF_NULL(transpose); - AnfAlgo::SetOutputInferTypeAndShape({dtype}, {dst_shape}, transpose.get()); - AnfAlgo::SetNodeAttr(kAttrPerm, MakeValue(std::vector{1, 0}), transpose); + common::AnfAlgo::SetOutputInferTypeAndShape({dtype}, {dst_shape}, transpose.get()); + common::AnfAlgo::SetNodeAttr(kAttrPerm, MakeValue(std::vector{1, 0}), transpose); make_tuple_inputs.push_back(transpose); } else { make_tuple_inputs.push_back(tuple_getitem); @@ -85,10 +86,10 @@ const AnfNodePtr InsertTranspose::Process(const FuncGraphPtr &func_graph, const const EquivPtr &) const { MS_EXCEPTION_IF_NULL(func_graph); MS_EXCEPTION_IF_NULL(node); - AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node); + common::AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node); auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - auto op_name = AnfAlgo::GetCNodeName(cnode); + auto op_name = common::AnfAlgo::GetCNodeName(cnode); CNodePtr new_node = nullptr; if (op_name == kBasicLSTMCellInputGradOpName || op_name == kBasicLSTMCellWeightGradOpName) { new_node = Insert(func_graph, cnode, op_name); diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/insert_transpose_for_dynamic_gru_v2.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/insert_transpose_for_dynamic_gru_v2.cc index 2e21cd095bd..25804ba47b2 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/insert_transpose_for_dynamic_gru_v2.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/insert_transpose_for_dynamic_gru_v2.cc @@ -17,9 +17,10 @@ #include "plugin/device/ascend/optimizer/format_type/insert_transpose_for_dyanmic_gru_v2.h" #include #include -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "plugin/device/ascend/optimizer/ascend_helper.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "runtime/device/kernel_info.h" #include "kernel/oplib/oplib.h" #include "utils/ms_context.h" @@ -49,18 +50,18 @@ CNodePtr Insert(const FuncGraphPtr &func_graph, const CNodePtr &cnode) { AnfNodePtr new_node = nullptr; AnfNodePtr new_transdata_node = nullptr; AnfNodePtr new_transpose_node = nullptr; - AnfNodePtr transdata_node = AnfAlgo::GetInputNode(cnode, index); + AnfNodePtr transdata_node = common::AnfAlgo::GetInputNode(cnode, index); auto input_format = AnfAlgo::GetInputFormat(transdata_node, 0); auto output_format = AnfAlgo::GetOutputFormat(transdata_node, 0); auto padding_axis = AnfAlgo::GetOutputReshapeType(transdata_node, 0); KernelSelectPtr kernel_select = std::make_shared(); // trans default to hwcn new_transpose_node = - NewTransOpNode(func_graph, AnfAlgo::GetInputNode(transdata_node->cast(), 0), cnode, kernel_select, - false, prim::kPrimTranspose->name(), std::vector{2, 3, 1, 0}); + NewTransOpNode(func_graph, common::AnfAlgo::GetInputNode(transdata_node->cast(), 0), cnode, + kernel_select, false, prim::kPrimTranspose->name(), std::vector{2, 3, 1, 0}); MS_EXCEPTION_IF_NULL(new_transpose_node); // This Transpose operator is only to change the shape, but does not expect to change the data arrangement! - AnfAlgo::SetNodeAttr(kAttrNopOp, MakeValue(true), new_transpose_node); + common::AnfAlgo::SetNodeAttr(kAttrNopOp, MakeValue(true), new_transpose_node); RefreshKernelBuildInfo(input_format, kOpFormat_HWCN, new_transpose_node); // trans hwcn to output_format new_transdata_node = @@ -85,10 +86,10 @@ const AnfNodePtr InsertTransposeForDynamicGRUV2::Process(const FuncGraphPtr &fun const EquivPtr &) const { MS_EXCEPTION_IF_NULL(func_graph); MS_EXCEPTION_IF_NULL(node); - AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node); + common::AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node); auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - auto op_name = AnfAlgo::GetCNodeName(cnode); + auto op_name = common::AnfAlgo::GetCNodeName(cnode); CNodePtr new_node = nullptr; if (op_name == kDynamicGRUV2OpName) { new_node = Insert(func_graph, cnode); diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/merge_cast_to_op.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/merge_cast_to_op.cc index 0ea579e180c..80acb856e11 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/merge_cast_to_op.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/merge_cast_to_op.cc @@ -22,7 +22,8 @@ #include #include "backend/common/session/anf_runtime_algorithm.h" -#include "utils/utils.h" +#include "include/common/utils/anfalgo.h" +#include "include/common/utils/utils.h" #include "utils/trace_base.h" #include "base/core_ops.h" @@ -73,7 +74,7 @@ bool GetNextNodeAndCastIndex(const FuncGraphPtr &graph, const AnfNodePtr &node, bool CheckInputs(const CNodePtr &node, const std::shared_ptr &kernel_info) { MS_EXCEPTION_IF_NULL(kernel_info); - if (AnfAlgo::GetInputTensorNum(node) != kernel_info->GetInputNum()) { + if (common::AnfAlgo::GetInputTensorNum(node) != kernel_info->GetInputNum()) { return false; } @@ -89,7 +90,7 @@ bool CheckInputs(const CNodePtr &node, const std::shared_ptr &kernel_info, const size_t idx) { MS_EXCEPTION_IF_NULL(kernel_info); - if (AnfAlgo::GetOutputTensorNum(node) != kernel_info->GetOutputNum()) { + if (common::AnfAlgo::GetOutputTensorNum(node) != kernel_info->GetOutputNum()) { return false; } for (size_t index = 0; index < kernel_info->GetOutputNum(); ++index) { @@ -113,7 +114,7 @@ bool CheckIndexOutput(const CNodePtr &node, const std::shared_ptrGetOutputDeviceType(index)) { return false; } - if (AnfAlgo::GetOutputInferShape(node, 0).size() == kInferShapeSize && + if (common::AnfAlgo::GetOutputInferShape(node, 0).size() == kInferShapeSize && AnfAlgo::GetOutputFormat(node, 0) == kOpFormat_NCHW && kernel_info->GetOutputFormat(index) == kOpFormat_DEFAULT) { return true; } @@ -123,22 +124,22 @@ bool CheckIndexOutput(const CNodePtr &node, const std::shared_ptr shapes; std::vector types; - size_t output_num = AnfAlgo::GetOutputTensorNum(cnode); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(cnode); for (size_t index = 0; index < output_num; ++index) { if (cast_index == index) { (void)shapes.emplace_back(cast_shape); (void)types.emplace_back(cast_dtype); continue; } - (void)shapes.emplace_back(AnfAlgo::GetOutputDetailShape(cnode, index)); - (void)types.emplace_back(AnfAlgo::GetOutputInferDataType(cnode, index)); + (void)shapes.emplace_back(common::AnfAlgo::GetOutputDetailShape(cnode, index)); + (void)types.emplace_back(common::AnfAlgo::GetOutputInferDataType(cnode, index)); } - AnfAlgo::SetOutputTypeAndDetailShape(types, shapes, cnode.get()); - auto prim_op = AnfAlgo::GetCNodePrimitive(cnode); + common::AnfAlgo::SetOutputTypeAndDetailShape(types, shapes, cnode.get()); + auto prim_op = common::AnfAlgo::GetCNodePrimitive(cnode); if (prim_op != nullptr) { (void)prim_op->AddAttr("cast_type", TypeIdToType(cast_dtype)); } @@ -157,7 +158,7 @@ AnfNodePtr MergeCastToNextOp(const FuncGraphPtr &graph, const CNodePtr &node, co return nullptr; } auto next_cnode = next_node->cast(); - auto next_op_name = AnfAlgo::GetCNodeName(next_cnode); + auto next_op_name = common::AnfAlgo::GetCNodeName(next_cnode); if (next_op_name == prim::kPrimSend->name() || next_op_name == kStackPushOpName) { return nullptr; } @@ -179,8 +180,9 @@ AnfNodePtr MergeCastToNextOp(const FuncGraphPtr &graph, const CNodePtr &node, co << "ori kernel info" << ori_kernel_info->ToString() << "alternative kernel info" << (*alternative_kernel_info)->ToString(); AnfAlgo::SetSelectKernelBuildInfo(*alternative_kernel_info, next_cnode.get()); - if (AnfAlgo::GetInputTensorNum(node) < kCastInputTensorNum) { - MS_LOG(EXCEPTION) << "Op[" << node->DebugString() << "] has wrong input num:" << AnfAlgo::GetInputTensorNum(node) + if (common::AnfAlgo::GetInputTensorNum(node) < kCastInputTensorNum) { + MS_LOG(EXCEPTION) << "Op[" << node->DebugString() + << "] has wrong input num:" << common::AnfAlgo::GetInputTensorNum(node) << ", should be not less than " << kCastInputTensorNum << trace::DumpSourceLines(node); } return node->input(1); @@ -192,7 +194,7 @@ bool GetPriorOp(const AnfNodePtr &x_node, CNodePtr *prior_op, bool *single_outpu auto x_cnode = x_node->cast(); *prior_op = x_cnode; // when x_node is tuple_getitem - if (AnfAlgo::GetCNodeName(x_node) == prim::kPrimTupleGetItem->name()) { + if (common::AnfAlgo::GetCNodeName(x_node) == prim::kPrimTupleGetItem->name()) { CheckCNodeInputSize(x_cnode, kTupleGetItemInputTensorNum); MS_EXCEPTION_IF_NULL(output_idx); AnfNodePtr input1 = x_cnode->input(1); @@ -232,8 +234,8 @@ AnfNodePtr MergeCastToPriorOp(const FuncGraphPtr &graph, const CNodePtr &cur_nod MS_EXCEPTION_IF_NULL(prior_op); std::vector> kernel_info_list; - if (AnfAlgo::GetCNodeName(prior_op) == prim::kPrimReceive->name() || - AnfAlgo::GetCNodeName(prior_op) == kStackPopOpName) { + if (common::AnfAlgo::GetCNodeName(prior_op) == prim::kPrimReceive->name() || + common::AnfAlgo::GetCNodeName(prior_op) == kStackPopOpName) { return nullptr; } kernel_query->Query(prior_op, &kernel_info_list); @@ -257,11 +259,11 @@ AnfNodePtr MergeCastToPriorOp(const FuncGraphPtr &graph, const CNodePtr &cur_nod MS_EXCEPTION_IF_NULL(x_node); ChangeNodeInferInfo(x_node->cast(), cur_node, 0); } - auto prior_name = AnfAlgo::GetCNodeName(prior_op); + auto prior_name = common::AnfAlgo::GetCNodeName(prior_op); if (prior_name == kFive2FourOpName) { - AnfAlgo::CopyNodeAttr("dst_type", "dstType", cur_node, prior_op); + common::AnfAlgo::CopyNodeAttr("dst_type", "dstType", cur_node, prior_op); } else if (prior_name == kFour2FiveOpName) { - AnfAlgo::CopyNodeAttr("dst_type", cur_node, prior_op); + common::AnfAlgo::CopyNodeAttr("dst_type", cur_node, prior_op); } return single_output ? prior_op : x_node; } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/rectify_do_mask_kernel_info.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/rectify_do_mask_kernel_info.cc index 1b10f443c4b..d1dba89d08b 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/rectify_do_mask_kernel_info.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/rectify_do_mask_kernel_info.cc @@ -17,8 +17,9 @@ #include "plugin/device/ascend/optimizer/format_type/rectify_do_mask_kernel_info.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "kernel/kernel_build_info.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/ms_context.h" #include "backend/common/optimizer/helper.h" @@ -41,14 +42,14 @@ const AnfNodePtr RectifyDoMaskKernelInfo::Process(const FuncGraphPtr &graph, con if (ms_context->get_param(MS_CTX_EXECUTION_MODE) == kPynativeMode) { return RectifyKernelInfoInPynativeProcess(node); } - if (AnfAlgo::GetCNodeName(cnode) != prim::kPrimDropoutGenMask->name()) { + if (common::AnfAlgo::GetCNodeName(cnode) != prim::kPrimDropoutGenMask->name()) { return nullptr; } std::vector do_mask_node_list; auto gen_mask_output_nodes = GetRealNodeUsedList(graph, cnode); MS_EXCEPTION_IF_NULL(gen_mask_output_nodes); for (const auto &output_node : *gen_mask_output_nodes) { - if (AnfAlgo::GetCNodeName(output_node.first) == prim::kPrimDropoutDoMask->name()) { + if (common::AnfAlgo::GetCNodeName(output_node.first) == prim::kPrimDropoutDoMask->name()) { MS_EXCEPTION_IF_NULL(output_node.first); auto output_cnode = output_node.first->cast(); do_mask_node_list.push_back(output_cnode); @@ -127,7 +128,7 @@ AnfNodePtr RectifyDoMaskKernelInfo::RectifyKernelInfoInPynativeProcess(const Anf if (cnode == nullptr) { return nullptr; } - if (AnfAlgo::GetCNodeName(cnode) != prim::kPrimDropoutDoMask->name()) { + if (common::AnfAlgo::GetCNodeName(cnode) != prim::kPrimDropoutDoMask->name()) { return nullptr; } auto do_mask_input_format = AnfAlgo::GetInputFormat(node, 0); @@ -158,8 +159,8 @@ void RectifyDoMaskKernelInfo::ReSelecChildNodeKernelInfo(const CNodePtr &cnode, if ((*new_build_info) != (*ori_build_info)) { ReSelecChildNodeKernelInfo(out_node, graph); } - } else if (AnfAlgo::GetCNodeName(out_node) == prim::kPrimTupleGetItem->name() || - AnfAlgo::GetCNodeName(out_node) == prim::kPrimDepend->name()) { + } else if (common::AnfAlgo::GetCNodeName(out_node) == prim::kPrimTupleGetItem->name() || + common::AnfAlgo::GetCNodeName(out_node) == prim::kPrimDepend->name()) { ReSelecChildNodeKernelInfo(out_node, graph); } else { MS_LOG(INFO) << "Reselected the node " << cnode->DebugString() << " failed"; diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/remove_internal_output.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/remove_internal_output.cc index 6c030d18401..308ba5de1e4 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/remove_internal_output.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/remove_internal_output.cc @@ -17,6 +17,7 @@ #include "plugin/device/ascend/optimizer/format_type/remove_internal_output.h" #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -32,7 +33,7 @@ bool UsedForOutputOnly(const FuncGraphPtr &func_graph, const AnfNodePtr &node) { } const auto &node_set = iter->second; for (const auto &node_index : node_set) { - if (!AnfAlgo::CheckPrimitiveType(node_index.first, prim::kPrimMakeTuple)) { + if (!common::AnfAlgo::CheckPrimitiveType(node_index.first, prim::kPrimMakeTuple)) { return false; } } @@ -54,7 +55,8 @@ const AnfNodePtr RemoveInternalOutput::Process(const FuncGraphPtr &func_graph, c const EquivPtr &) const { MS_EXCEPTION_IF_NULL(func_graph); MS_EXCEPTION_IF_NULL(node); - if (AnfAlgo::CheckPrimitiveType(node, prim::kPrimCast) && !AnfAlgo::GetBooleanAttr(node, kIsBackendCast)) { + if (common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimCast) && + !common::AnfAlgo::GetBooleanAttr(node, kIsBackendCast)) { return nullptr; } auto kernel_graph = func_graph->cast(); @@ -71,13 +73,13 @@ const AnfNodePtr RemoveInternalOutput::Process(const FuncGraphPtr &func_graph, c MS_EXCEPTION_IF_NULL(cnode); CheckCNodeInputSize(cnode, kTransOpInputTensorNum); auto input_node = cnode->input(1); - if (!AnfAlgo::CheckPrimitiveType(input_node, prim::kPrimTupleGetItem)) { + if (!common::AnfAlgo::CheckPrimitiveType(input_node, prim::kPrimTupleGetItem)) { kernel_graph->ReplaceInternalOutput(node, input_node); } else { auto tuple_getitem = input_node->cast(); MS_EXCEPTION_IF_NULL(tuple_getitem); - size_t idx = AnfAlgo::GetTupleGetItemOutIndex(tuple_getitem); - AnfNodePtr real_input_node = AnfAlgo::GetTupleGetItemRealInput(tuple_getitem); + size_t idx = common::AnfAlgo::GetTupleGetItemOutIndex(tuple_getitem); + AnfNodePtr real_input_node = common::AnfAlgo::GetTupleGetItemRealInput(tuple_getitem); kernel_graph->ReplaceInternalOutput(node, real_input_node, 0, idx); } return input_node; diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/set_fracz_group_attr.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/set_fracz_group_attr.cc index dd8a383ce7b..e2bfbee060a 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/set_fracz_group_attr.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/set_fracz_group_attr.cc @@ -19,7 +19,8 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" -#include "utils/utils.h" +#include "include/common/utils/anfalgo.h" +#include "include/common/utils/utils.h" #include "runtime/device/kernel_info.h" #include "backend/common/optimizer/helper.h" @@ -36,7 +37,7 @@ const std::set kInOutOperatorSet = {kAllReduceOpName, kBroadcastOpN AnfNodePtr GetOutputItem(const FuncGraphManagerPtr &manager, const CNodePtr &cnode, int64_t groups, const size_t index = 0) { - if (AnfAlgo::GetOutputTensorNum(cnode) == 1) { + if (common::AnfAlgo::GetOutputTensorNum(cnode) == 1) { return cnode; } std::vector depend_nodes{cnode}; @@ -44,14 +45,14 @@ AnfNodePtr GetOutputItem(const FuncGraphManagerPtr &manager, const CNodePtr &cno auto node = depend_nodes.back(); depend_nodes.pop_back(); for (auto node_index : manager->node_users()[node]) { - if (AnfAlgo::CheckPrimitiveType(node_index.first, prim::kPrimDepend) && node_index.second == 1) { + if (common::AnfAlgo::CheckPrimitiveType(node_index.first, prim::kPrimDepend) && node_index.second == 1) { (void)depend_nodes.emplace_back(node_index.first); - } else if (AnfAlgo::CheckPrimitiveType(node_index.first, prim::kPrimTupleGetItem)) { + } else if (common::AnfAlgo::CheckPrimitiveType(node_index.first, prim::kPrimTupleGetItem)) { auto getitem_cnode = node_index.first->cast(); MS_EXCEPTION_IF_NULL(getitem_cnode); - auto out_index = AnfAlgo::GetTupleGetItemOutIndex(getitem_cnode); + auto out_index = common::AnfAlgo::GetTupleGetItemOutIndex(getitem_cnode); if (out_index == index) { - AnfAlgo::SetNodeAttr(kAttrFracZGroup, MakeValue(groups), getitem_cnode); + common::AnfAlgo::SetNodeAttr(kAttrFracZGroup, MakeValue(groups), getitem_cnode); return getitem_cnode; } } @@ -72,30 +73,30 @@ bool HasFraczGroupAttrAndSet(const AnfNodePtr &node, size_t index, int64_t group } if (node->isa()) { auto cnode = node->cast(); - auto node_name = AnfAlgo::GetCNodeName(cnode); + auto node_name = common::AnfAlgo::GetCNodeName(cnode); if (node_name == kDependName && index != 0) { return true; } if (kInOutOperatorSet.find(node_name) != kInOutOperatorSet.end()) { auto index_l = SizeToLong(index); - if (AnfAlgo::HasNodeAttr(kAttrFracZGroupIdx, cnode)) { - auto fz_group_idx = AnfAlgo::GetNodeAttr>(cnode, kAttrFracZGroupIdx); + if (common::AnfAlgo::HasNodeAttr(kAttrFracZGroupIdx, cnode)) { + auto fz_group_idx = common::AnfAlgo::GetNodeAttr>(cnode, kAttrFracZGroupIdx); if (std::find(fz_group_idx.begin(), fz_group_idx.end(), index_l) != fz_group_idx.end()) { return true; } fz_group_idx.push_back(index_l); - AnfAlgo::SetNodeAttr(kAttrFracZGroupIdx, MakeValue(fz_group_idx), cnode); + common::AnfAlgo::SetNodeAttr(kAttrFracZGroupIdx, MakeValue(fz_group_idx), cnode); return false; } else { - AnfAlgo::SetNodeAttr(kAttrFracZGroupIdx, MakeValue(std::vector{index_l}), cnode); + common::AnfAlgo::SetNodeAttr(kAttrFracZGroupIdx, MakeValue(std::vector{index_l}), cnode); } } - if (AnfAlgo::HasNodeAttr(kAttrFracZGroup, cnode)) { + if (common::AnfAlgo::HasNodeAttr(kAttrFracZGroup, cnode)) { return true; } - AnfAlgo::SetNodeAttr(kAttrFracZGroup, MakeValue(groups), cnode); + common::AnfAlgo::SetNodeAttr(kAttrFracZGroup, MakeValue(groups), cnode); if (node_name == kTransDataOpName) { - AnfAlgo::SetNodeAttr(kAttrGroups, MakeValue(groups), cnode); + common::AnfAlgo::SetNodeAttr(kAttrGroups, MakeValue(groups), cnode); } return false; } @@ -104,9 +105,9 @@ bool HasFraczGroupAttrAndSet(const AnfNodePtr &node, size_t index, int64_t group std::vector GetCNodeNeighborFraczNodes(const FuncGraphManagerPtr &manager, const CNodePtr &cnode, size_t index, int64_t groups) { - auto node_name = AnfAlgo::GetCNodeName(cnode); - auto input_num = AnfAlgo::GetInputTensorNum(cnode); - auto output_num = AnfAlgo::GetOutputTensorNum(cnode); + auto node_name = common::AnfAlgo::GetCNodeName(cnode); + auto input_num = common::AnfAlgo::GetInputTensorNum(cnode); + auto output_num = common::AnfAlgo::GetOutputTensorNum(cnode); auto node_user = manager->node_users(); std::vector ret; if (node_name == kDependName || node_name == kLoadName) { @@ -120,9 +121,9 @@ std::vector GetCNodeNeighborFraczNodes(const FuncGraphManagerPt if (AnfAlgo::GetInputFormat(cnode, i) == kOpFormat_FRAC_Z) { auto input = cnode->input(i + 1); if (node_name == kTupleGetItemName) { - auto item_index = AnfAlgo::GetTupleGetItemOutIndex(cnode); - while (input->isa() && AnfAlgo::GetCNodeName(input) == kDependName) { - AnfAlgo::SetNodeAttr(kAttrFracZGroup, MakeValue(groups), input); + auto item_index = common::AnfAlgo::GetTupleGetItemOutIndex(cnode); + while (input->isa() && common::AnfAlgo::GetCNodeName(input) == kDependName) { + common::AnfAlgo::SetNodeAttr(kAttrFracZGroup, MakeValue(groups), input); input = input->cast()->input(1); } (void)ret.emplace_back(input, item_index); @@ -161,7 +162,7 @@ std::vector GetNeighborFraczNodes(const FuncGraphManagerPtr &ma return ret; } auto cnode = node->cast(); - auto node_name = AnfAlgo::GetCNodeName(cnode); + auto node_name = common::AnfAlgo::GetCNodeName(cnode); if (node_name == kUpdateStateName || node_name == kTransDataOpName) { return ret; } else if (kInOutOperatorSet.find(node_name) != kInOutOperatorSet.end()) { @@ -182,7 +183,7 @@ std::vector GetNeighborFraczNodes(const FuncGraphManagerPtr &ma bool SetAttrFraczGroup(const FuncGraphPtr &func_graph, const CNodePtr &cnode) { MS_EXCEPTION_IF_NULL(func_graph); MS_EXCEPTION_IF_NULL(cnode); - auto groups = AnfAlgo::GetNodeAttr(cnode, kAttrGroups); + auto groups = common::AnfAlgo::GetNodeAttr(cnode, kAttrGroups); if (groups == 1) { return false; } @@ -243,7 +244,7 @@ bool SetFraczGroupAttr::Run(const FuncGraphPtr &func_graph) { if (cnode == nullptr) { continue; } - auto node_name = AnfAlgo::GetCNodeName(cnode); + auto node_name = common::AnfAlgo::GetCNodeName(cnode); if (node_name == kConv2DOpName || node_name == kConv2DBackpropInputOpName || node_name == kConv2DBackpropFilterOpName) { changed = SetAttrFraczGroup(func_graph, cnode) || changed; diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/trans_op_format_refine.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/trans_op_format_refine.cc index 6ae5d6cc601..54838314c4b 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/trans_op_format_refine.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/format_type/trans_op_format_refine.cc @@ -17,6 +17,7 @@ #include "plugin/device/ascend/optimizer/format_type/trans_op_format_refine.h" #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -30,8 +31,8 @@ const AnfNodePtr TransOpFormatRefine::Process(const FuncGraphPtr &func_graph, co const EquivPtr &) const { MS_EXCEPTION_IF_NULL(func_graph); MS_EXCEPTION_IF_NULL(node); - AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node); - auto op_name = AnfAlgo::GetCNodeName(node); + common::AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node); + auto op_name = common::AnfAlgo::GetCNodeName(node); if (op_name == kTransDataOpName) { auto in_format = AnfAlgo::GetInputFormat(node, 0); auto out_format = AnfAlgo::GetOutputFormat(node, 0); @@ -42,13 +43,13 @@ const AnfNodePtr TransOpFormatRefine::Process(const FuncGraphPtr &func_graph, co builder->SetInputsFormat({kOpFormat_NCDHW}); builder->SetOutputsFormat({out_format}); AnfAlgo::SetSelectKernelBuildInfo(builder->Build(), node.get()); - AnfAlgo::SetNodeAttr(kAttrSrcFormat, MakeValue(kOpFormat_NCDHW), node); + common::AnfAlgo::SetNodeAttr(kAttrSrcFormat, MakeValue(kOpFormat_NCDHW), node); } if (out_format == kOpFormat_DEFAULT && k3DFormatSet.find(in_format) != k3DFormatSet.end()) { builder->SetInputsFormat({in_format}); builder->SetOutputsFormat({kOpFormat_NCDHW}); AnfAlgo::SetSelectKernelBuildInfo(builder->Build(), node.get()); - AnfAlgo::SetNodeAttr(kAttrDstFormat, MakeValue(kOpFormat_NCDHW), node); + common::AnfAlgo::SetNodeAttr(kAttrDstFormat, MakeValue(kOpFormat_NCDHW), node); } } return node; diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/addn_fission.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/addn_fission.cc index efc720ceed5..8de3eb31f76 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/addn_fission.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/addn_fission.cc @@ -17,6 +17,7 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -32,9 +33,9 @@ AnfNodePtr AddnFission::CreateNewAddn(const FuncGraphPtr &func_graph, const CNod MS_EXCEPTION_IF_NULL(new_addn); new_addn->set_scope(origin_addn_cnode->scope()); new_addn->set_abstract(origin_addn_cnode->abstract()); - AnfAlgo::SetNodeAttr(kAttrN, MakeValue(SizeToLong(offset)), new_addn); + common::AnfAlgo::SetNodeAttr(kAttrN, MakeValue(SizeToLong(offset)), new_addn); std::vector dyn_input_sizes{SizeToLong(offset)}; - AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(dyn_input_sizes), new_addn); + common::AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(dyn_input_sizes), new_addn); return new_addn; } @@ -70,9 +71,9 @@ const AnfNodePtr AddnFission::Process(const FuncGraphPtr &func_graph, const AnfN MS_EXCEPTION_IF_NULL(base_addn); base_addn->set_scope(new_cnode->scope()); base_addn->set_abstract(new_cnode->abstract()); - AnfAlgo::SetNodeAttr(kAttrN, MakeValue(SizeToLong(base_addn_inputs.size() - 1)), base_addn); + common::AnfAlgo::SetNodeAttr(kAttrN, MakeValue(SizeToLong(base_addn_inputs.size() - 1)), base_addn); std::vector dyn_input_sizes{SizeToLong(base_addn_inputs.size() - 1)}; - AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(dyn_input_sizes), base_addn); + common::AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(dyn_input_sizes), base_addn); new_cnode = base_addn; origin_input_size = base_addn->inputs().size() - 1; } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/batch_norm_bert_fission.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/batch_norm_bert_fission.cc index 530a42e605d..139fd1b4aa4 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/batch_norm_bert_fission.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/batch_norm_bert_fission.cc @@ -18,6 +18,7 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/optimizer/helper.h" #include "utils/trace_base.h" @@ -81,7 +82,7 @@ AnfNodePtr BatchNormBertFission::CreateBNTrainingReduce(const FuncGraphPtr &func auto abstract_tuple = std::make_shared(abstract_list); bn_training_reduce->set_abstract(abstract_tuple); bn_training_reduce->set_scope(bn->scope()); - AnfAlgo::CopyNodeAttrs(bn, bn_training_reduce); + common::AnfAlgo::CopyNodeAttrs(bn, bn_training_reduce); return bn_training_reduce; } @@ -122,7 +123,7 @@ AnfNodePtr BatchNormBertFission::CreateBNTrainingUpdateV2( auto abstract_tuple = std::make_shared(abstract_list); bn_training_update_v2->set_abstract(abstract_tuple); bn_training_update_v2->set_scope(bn->scope()); - AnfAlgo::CopyNodeAttrs(bn, bn_training_update_v2); + common::AnfAlgo::CopyNodeAttrs(bn, bn_training_update_v2); return bn_training_update_v2; } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/batch_norm_grad_infer_fission.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/batch_norm_grad_infer_fission.cc index c7987202429..94e81bab5e9 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/batch_norm_grad_infer_fission.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/batch_norm_grad_infer_fission.cc @@ -17,6 +17,7 @@ #include #include "backend/common/optimizer/helper.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "utils/trace_base.h" namespace mindspore { @@ -89,7 +90,7 @@ AnfNodePtr BatchNormGradInferFission::CreateBNInferGrad(const FuncGraphPtr &func << trace::DumpSourceLines(bn_grad); } bn_infer_grad->set_abstract(bn_grad_abstract_tuple->elements()[0]); - AnfAlgo::CopyNodeAttr(kAttrEpsilon, bn_grad, bn_infer_grad); + common::AnfAlgo::CopyNodeAttr(kAttrEpsilon, bn_grad, bn_infer_grad); bn_infer_grad->set_scope(bn_grad->scope()); return bn_infer_grad; } @@ -138,7 +139,7 @@ AnfNodePtr BatchNormGradInferFission::CreateBNTrainingUpdateGrad(const FuncGraph bn_grad_abstract_tuple->elements()[kIndex2]}; auto abstract_tuple = std::make_shared(abstract_list); bn_training_update_grad->set_abstract(abstract_tuple); - AnfAlgo::CopyNodeAttr(kAttrEpsilon, bn_grad, bn_training_update_grad); + common::AnfAlgo::CopyNodeAttr(kAttrEpsilon, bn_grad, bn_training_update_grad); bn_training_update_grad->set_scope(bn_grad->scope()); return bn_training_update_grad; } @@ -152,11 +153,11 @@ const AnfNodePtr BatchNormGradInferFission::Process(const FuncGraphPtr &func_gra const EquivPtr &equiv) const { MS_EXCEPTION_IF_NULL(func_graph); MS_EXCEPTION_IF_NULL(node); - if (!AnfAlgo::HasNodeAttr(kAttrIsTraining, node->cast())) { + if (!common::AnfAlgo::HasNodeAttr(kAttrIsTraining, node->cast())) { MS_LOG(DEBUG) << "The BatchNormGrad " << node->DebugString() << " has no is_training attr, should not be changed"; return nullptr; } - if (AnfAlgo::GetNodeAttr(node, kAttrIsTraining)) { + if (common::AnfAlgo::GetNodeAttr(node, kAttrIsTraining)) { MS_LOG(DEBUG) << "The is_training attr value of " << node->DebugString() << " is true, no need change"; return nullptr; } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/batch_norm_grad_split.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/batch_norm_grad_split.cc index 85d9d3239ec..d0cb365f1e8 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/batch_norm_grad_split.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/batch_norm_grad_split.cc @@ -18,11 +18,12 @@ #include #include -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/ms_context.h" #include "backend/common/optimizer/helper.h" #include "runtime/device/kernel_info.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "utils/trace_base.h" namespace mindspore { @@ -41,11 +42,13 @@ void BatchNormGradSplit::CreateOutputsOfUpdateGrad(const FuncGraphPtr &graph, co bn_update_grad->set_kernel_info(std::make_shared()); bn_update_grad->set_scope(bn_grad_node->scope()); - auto types = {AnfAlgo::GetOutputInferDataType(bn_grad_node, 1), AnfAlgo::GetOutputInferDataType(bn_grad_node, 2)}; - auto shapes = {AnfAlgo::GetOutputInferShape(bn_grad_node, 1), AnfAlgo::GetOutputInferShape(bn_grad_node, 2)}; - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, bn_update_grad.get()); + auto types = {common::AnfAlgo::GetOutputInferDataType(bn_grad_node, 1), + common::AnfAlgo::GetOutputInferDataType(bn_grad_node, 2)}; + auto shapes = {common::AnfAlgo::GetOutputInferShape(bn_grad_node, 1), + common::AnfAlgo::GetOutputInferShape(bn_grad_node, 2)}; + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, bn_update_grad.get()); - AnfAlgo::CopyNodeAttr(kAttrEpsilon, bn_grad_node, bn_update_grad); + common::AnfAlgo::CopyNodeAttr(kAttrEpsilon, bn_grad_node, bn_update_grad); CreateMultipleOutputsOfAnfNode(graph, bn_update_grad, kBNTrainingUpdateGradOutputNum, bn_update_grad_outputs); } @@ -75,11 +78,11 @@ void BatchNormGradSplit::CreateOutputsOfReduceGrad(const FuncGraphPtr &graph, co bn_reduce_grad->set_kernel_info(std::make_shared()); bn_reduce_grad->set_scope(bn_grad_node->scope()); - auto types = {AnfAlgo::GetOutputInferDataType(bn_grad_node, 0)}; - auto shapes = {AnfAlgo::GetOutputInferShape(bn_grad_node, 0)}; - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, bn_reduce_grad.get()); + auto types = {common::AnfAlgo::GetOutputInferDataType(bn_grad_node, 0)}; + auto shapes = {common::AnfAlgo::GetOutputInferShape(bn_grad_node, 0)}; + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, bn_reduce_grad.get()); - AnfAlgo::CopyNodeAttr(kAttrEpsilon, bn_grad_node, bn_reduce_grad); + common::AnfAlgo::CopyNodeAttr(kAttrEpsilon, bn_grad_node, bn_reduce_grad); (*bn_reduce_grad_outputs).push_back(bn_reduce_grad); } @@ -95,13 +98,13 @@ const AnfNodePtr BatchNormGradSplit::Process(const FuncGraphPtr &func_graph, con MS_EXCEPTION_IF_NULL(func_graph); auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - auto primitive = AnfAlgo::GetCNodePrimitive(cnode); + auto primitive = common::AnfAlgo::GetCNodePrimitive(cnode); MS_EXCEPTION_IF_NULL(primitive); if (!primitive->HasAttr(kAttrIsTraining)) { MS_LOG(INFO) << "Op BatchNormGrad must have attrs of is_training" << trace::DumpSourceLines(node); return nullptr; } - if (!AnfAlgo::GetNodeAttr(cnode, kAttrIsTraining)) { + if (!common::AnfAlgo::GetNodeAttr(cnode, kAttrIsTraining)) { MS_LOG(INFO) << "is_training must be true"; return nullptr; } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/bce_with_logits_loss_fission.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/bce_with_logits_loss_fission.cc index c4ec5f355d2..ccff3bbe526 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/bce_with_logits_loss_fission.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/bce_with_logits_loss_fission.cc @@ -18,10 +18,11 @@ #include #include #include -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/ms_context.h" #include "backend/common/optimizer/helper.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "utils/trace_base.h" namespace mindspore { @@ -38,12 +39,12 @@ AnfNodePtr BCEWithLogitsLossFission::AddReduceNode(const FuncGraphPtr &func_grap CNodePtr new_cnode = NewCNode(new_simoid_inputs, func_graph); MS_EXCEPTION_IF_NULL(new_cnode); auto predict_input = cnode->inputs()[kIndex1]; - auto new_node_dtype = {AnfAlgo::GetOutputInferDataType(predict_input, 0)}; - auto new_node_shape = {AnfAlgo::GetOutputInferShape(predict_input, 0)}; - AnfAlgo::SetOutputInferTypeAndShape(new_node_dtype, new_node_shape, new_cnode.get()); + auto new_node_dtype = {common::AnfAlgo::GetOutputInferDataType(predict_input, 0)}; + auto new_node_shape = {common::AnfAlgo::GetOutputInferShape(predict_input, 0)}; + common::AnfAlgo::SetOutputInferTypeAndShape(new_node_dtype, new_node_shape, new_cnode.get()); // Add reduce node - string reduction = AnfAlgo::GetNodeAttr(node, kAttrReduction); + string reduction = common::AnfAlgo::GetNodeAttr(node, kAttrReduction); MS_LOG(INFO) << "Create reduce node, reduction attr is: " << reduction; std::vector reduce_inputs; if (reduction == "sum") { @@ -56,15 +57,15 @@ AnfNodePtr BCEWithLogitsLossFission::AddReduceNode(const FuncGraphPtr &func_grap } auto reduce_node = NewCNode(reduce_inputs, func_graph); MS_EXCEPTION_IF_NULL(reduce_node); - auto type = AnfAlgo::GetOutputInferDataType(node, 0); + auto type = common::AnfAlgo::GetOutputInferDataType(node, 0); if (type == kNumberTypeFloat16) { type = kNumberTypeFloat32; } - auto shape = {AnfAlgo::GetOutputInferShape(node, 0)}; - AnfAlgo::SetOutputInferTypeAndShape({type}, shape, reduce_node.get()); - AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(std::vector{}), reduce_node); - AnfAlgo::SetNodeAttr("keep_dims", MakeValue(false), reduce_node); - AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), reduce_node); + auto shape = {common::AnfAlgo::GetOutputInferShape(node, 0)}; + common::AnfAlgo::SetOutputInferTypeAndShape({type}, shape, reduce_node.get()); + common::AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(std::vector{}), reduce_node); + common::AnfAlgo::SetNodeAttr("keep_dims", MakeValue(false), reduce_node); + common::AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), reduce_node); reduce_node->set_scope(cnode->scope()); return reduce_node; } @@ -84,11 +85,11 @@ const AnfNodePtr BCEWithLogitsLossFission::Process(const FuncGraphPtr &func_grap if (GetBoolAttr(cnode, kAttrVisited)) { return nullptr; } - AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node); + common::AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node); if (cnode->inputs().size() == 0) { return nullptr; } - if (!AnfAlgo::HasNodeAttr("reduction", cnode)) { + if (!common::AnfAlgo::HasNodeAttr("reduction", cnode)) { MS_LOG(INFO) << "Has no reduction attr."; return nullptr; } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/bn_grad_split.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/bn_grad_split.cc index 1ce870e1a2b..52df0860c0f 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/bn_grad_split.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/bn_grad_split.cc @@ -19,11 +19,12 @@ #include #include "plugin/device/ascend/optimizer/ir_fission/bn_split.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/ms_context.h" #include "backend/common/optimizer/helper.h" #include "runtime/device/kernel_info.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "utils/trace_base.h" namespace mindspore { @@ -42,18 +43,19 @@ void BnGradSplit::CreateOutputsOfUpdateGrad(const FuncGraphPtr &graph, const CNo bn_update_grad->set_kernel_info(std::make_shared()); bn_update_grad->set_scope(bn_grad_node->scope()); - auto types = {AnfAlgo::GetOutputInferDataType(bn_grad_node, 1), AnfAlgo::GetOutputInferDataType(bn_grad_node, 2)}; - auto shapes = {AnfAlgo::GetOutputDetailShape(bn_grad_node, 1), AnfAlgo::GetOutputDetailShape(bn_grad_node, 2)}; - AnfAlgo::SetOutputTypeAndDetailShape(types, shapes, bn_update_grad.get()); - - AnfAlgo::CopyNodeAttr(kAttrEpsilon, bn_grad_node, bn_update_grad); - if (AnfAlgo::HasNodeAttr(kAttrFormat, bn_grad_node)) { - AnfAlgo::CopyNodeAttr(kAttrFormat, bn_grad_node, bn_update_grad); + auto types = {common::AnfAlgo::GetOutputInferDataType(bn_grad_node, 1), + common::AnfAlgo::GetOutputInferDataType(bn_grad_node, 2)}; + auto shapes = {common::AnfAlgo::GetOutputDetailShape(bn_grad_node, 1), + common::AnfAlgo::GetOutputDetailShape(bn_grad_node, 2)}; + common::AnfAlgo::SetOutputTypeAndDetailShape(types, shapes, bn_update_grad.get()); + common::AnfAlgo::CopyNodeAttr(kAttrEpsilon, bn_grad_node, bn_update_grad); + if (common::AnfAlgo::HasNodeAttr(kAttrFormat, bn_grad_node)) { + common::AnfAlgo::CopyNodeAttr(kAttrFormat, bn_grad_node, bn_update_grad); } else { - AnfAlgo::SetNodeAttr(kAttrFormat, MakeValue(kOpFormat_NCHW), bn_update_grad); + common::AnfAlgo::SetNodeAttr(kAttrFormat, MakeValue(kOpFormat_NCHW), bn_update_grad); } if (is_dynamic) { - AnfAlgo::SetNodeAttr(kAttrInputIsDynamicShape, MakeValue(true), bn_update_grad); + common::AnfAlgo::SetNodeAttr(kAttrInputIsDynamicShape, MakeValue(true), bn_update_grad); } CreateMultipleOutputsOfAnfNode(graph, bn_update_grad, kBNTrainingUpdateGradOutputNum, bn_update_grad_outputs); } @@ -83,26 +85,25 @@ void BnGradSplit::CreateOutputsOfReduceGrad(const FuncGraphPtr &graph, const CNo bn_reduce_grad->set_kernel_info(std::make_shared()); bn_reduce_grad->set_scope(bn_grad_node->scope()); - auto types = {AnfAlgo::GetOutputInferDataType(bn_grad_node, 0)}; - auto shapes = {AnfAlgo::GetOutputDetailShape(bn_grad_node, 0)}; - AnfAlgo::SetOutputTypeAndDetailShape(types, shapes, bn_reduce_grad.get()); - - AnfAlgo::CopyNodeAttr(kAttrEpsilon, bn_grad_node, bn_reduce_grad); - if (AnfAlgo::HasNodeAttr(kAttrFormat, bn_grad_node)) { - AnfAlgo::CopyNodeAttr(kAttrFormat, bn_grad_node, bn_reduce_grad); + auto types = {common::AnfAlgo::GetOutputInferDataType(bn_grad_node, 0)}; + auto shapes = {common::AnfAlgo::GetOutputDetailShape(bn_grad_node, 0)}; + common::AnfAlgo::SetOutputTypeAndDetailShape(types, shapes, bn_reduce_grad.get()); + common::AnfAlgo::CopyNodeAttr(kAttrEpsilon, bn_grad_node, bn_reduce_grad); + if (common::AnfAlgo::HasNodeAttr(kAttrFormat, bn_grad_node)) { + common::AnfAlgo::CopyNodeAttr(kAttrFormat, bn_grad_node, bn_reduce_grad); } else { - AnfAlgo::SetNodeAttr(kAttrFormat, MakeValue(kOpFormat_NCHW), bn_reduce_grad); + common::AnfAlgo::SetNodeAttr(kAttrFormat, MakeValue(kOpFormat_NCHW), bn_reduce_grad); } if (is_dynamic) { - AnfAlgo::SetNodeAttr(kAttrInputIsDynamicShape, MakeValue(true), bn_reduce_grad); - AnfAlgo::SetNodeAttr(kAttrOutputIsDynamicShape, MakeValue(true), bn_reduce_grad); + common::AnfAlgo::SetNodeAttr(kAttrInputIsDynamicShape, MakeValue(true), bn_reduce_grad); + common::AnfAlgo::SetNodeAttr(kAttrOutputIsDynamicShape, MakeValue(true), bn_reduce_grad); } (*bn_reduce_grad_outputs).push_back(bn_reduce_grad); } CNodePtr BnGradSplit::BNGradSplitForTBE(const FuncGraphPtr &func_graph, const CNodePtr &cnode) const { MS_EXCEPTION_IF_NULL(func_graph); - bool is_dynamic = AnfAlgo::IsDynamicShape(cnode); + bool is_dynamic = common::AnfAlgo::IsDynamicShape(cnode); std::vector bn_update_grad_outputs; CreateOutputsOfUpdateGrad(func_graph, cnode, &bn_update_grad_outputs, is_dynamic); if (bn_update_grad_outputs.size() != kBNTrainingUpdateGradOutputNum) { @@ -129,7 +130,7 @@ CNodePtr SyncBnGradSplit::SyncBNGradSplitForTBE(const FuncGraphPtr &func_graph, MS_EXCEPTION_IF_NULL(cnode); std::vector bn_update_grad_outputs; - bool is_dynamic = AnfAlgo::IsDynamicShape(cnode); + bool is_dynamic = common::AnfAlgo::IsDynamicShape(cnode); CreateOutputsOfUpdateGrad(func_graph, cnode, &bn_update_grad_outputs, is_dynamic); if (bn_update_grad_outputs.size() != kBNTrainingUpdateGradOutputNum) { MS_LOG(EXCEPTION) << "Outputs of bn_update_grad has wrong size, should be " << kBNTrainingUpdateGradOutputNum diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/bn_split.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/bn_split.cc index 57b3c02b7e3..4553a53ea23 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/bn_split.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/bn_split.cc @@ -20,11 +20,12 @@ #include #include -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/ms_context.h" #include "backend/common/optimizer/helper.h" #include "runtime/device/kernel_info.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "utils/trace_base.h" namespace mindspore { @@ -41,7 +42,7 @@ bool BnSplit::CreateOutputsOfBNTrainingReduce(const FuncGraphPtr &graph, const C bool is_dynamic) const { MS_EXCEPTION_IF_NULL(graph); MS_EXCEPTION_IF_NULL(bn_cnode); - if (AnfAlgo::GetInputTensorNum(bn_cnode) != kBnInputTensorNum) { + if (common::AnfAlgo::GetInputTensorNum(bn_cnode) != kBnInputTensorNum) { MS_LOG(INFO) << "BatchNorm's input size less than " << kBnInputTensorNum << ". " << bn_cnode->DebugString(); return false; } @@ -53,14 +54,16 @@ bool BnSplit::CreateOutputsOfBNTrainingReduce(const FuncGraphPtr &graph, const C auto kernel_info = std::make_shared(); MS_EXCEPTION_IF_NULL(kernel_info); bn_training_reduce->set_kernel_info(kernel_info); - auto types = {AnfAlgo::GetOutputInferDataType(bn_cnode, 1), AnfAlgo::GetOutputInferDataType(bn_cnode, 1)}; - auto shapes = {AnfAlgo::GetOutputDetailShape(bn_cnode, 1), AnfAlgo::GetOutputDetailShape(bn_cnode, 1)}; - AnfAlgo::SetOutputTypeAndDetailShape(types, shapes, bn_training_reduce.get()); + auto types = {common::AnfAlgo::GetOutputInferDataType(bn_cnode, 1), + common::AnfAlgo::GetOutputInferDataType(bn_cnode, 1)}; + auto shapes = {common::AnfAlgo::GetOutputDetailShape(bn_cnode, 1), + common::AnfAlgo::GetOutputDetailShape(bn_cnode, 1)}; + common::AnfAlgo::SetOutputTypeAndDetailShape(types, shapes, bn_training_reduce.get()); bn_training_reduce->set_scope(bn_cnode->scope()); if (is_dynamic) { - AnfAlgo::SetNodeAttr(kAttrInputIsDynamicShape, MakeValue(true), bn_training_reduce); + common::AnfAlgo::SetNodeAttr(kAttrInputIsDynamicShape, MakeValue(true), bn_training_reduce); } - AnfAlgo::CopyNodeAttrs(bn_cnode, bn_training_reduce); + common::AnfAlgo::CopyNodeAttrs(bn_cnode, bn_training_reduce); CreateMultipleOutputsOfAnfNode(graph, bn_training_reduce, kBNTrainingReduceOutputNum, bn_training_reduce_outputs); return true; @@ -92,19 +95,19 @@ AnfNodePtr BnSplit::CreateOutputsOfBNTrainingUpdate(const FuncGraphPtr &graph, c bn_training_update->set_kernel_info(kernel_info); bn_training_update->set_abstract(bn_cnode->abstract()); bn_training_update->set_scope(bn_cnode->scope()); - auto factor = AnfAlgo::GetNodeAttr(bn_cnode, kAttrMomentum); - AnfAlgo::SetNodeAttr(kAttrFactor, MakeValue(factor), bn_training_update); + auto factor = common::AnfAlgo::GetNodeAttr(bn_cnode, kAttrMomentum); + common::AnfAlgo::SetNodeAttr(kAttrFactor, MakeValue(factor), bn_training_update); if (is_dynamic) { - AnfAlgo::SetNodeAttr(kAttrInputIsDynamicShape, MakeValue(true), bn_training_update); - AnfAlgo::SetNodeAttr(kAttrOutputIsDynamicShape, MakeValue(true), bn_training_update); + common::AnfAlgo::SetNodeAttr(kAttrInputIsDynamicShape, MakeValue(true), bn_training_update); + common::AnfAlgo::SetNodeAttr(kAttrOutputIsDynamicShape, MakeValue(true), bn_training_update); } - AnfAlgo::CopyNodeAttr(kAttrEpsilon, bn_cnode, bn_training_update); - if (AnfAlgo::HasNodeAttr(kAttrFormat, bn_cnode)) { - AnfAlgo::CopyNodeAttr(kAttrFormat, bn_cnode, bn_training_update); + common::AnfAlgo::CopyNodeAttr(kAttrEpsilon, bn_cnode, bn_training_update); + if (common::AnfAlgo::HasNodeAttr(kAttrFormat, bn_cnode)) { + common::AnfAlgo::CopyNodeAttr(kAttrFormat, bn_cnode, bn_training_update); } else { - AnfAlgo::SetNodeAttr(kAttrFormat, MakeValue(kOpFormat_NCHW), bn_training_update); + common::AnfAlgo::SetNodeAttr(kAttrFormat, MakeValue(kOpFormat_NCHW), bn_training_update); } - AnfAlgo::SetNodeAttr(kAttrIsRef, MakeValue(true), bn_training_update); + common::AnfAlgo::SetNodeAttr(kAttrIsRef, MakeValue(true), bn_training_update); return bn_training_update; } @@ -114,8 +117,8 @@ AnfNodePtr BnSplit::SplitBatchNormForTBE(const FuncGraphPtr &func_graph, const A auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - bool is_dynamic = AnfAlgo::IsDynamicShape(cnode); - if (AnfAlgo::GetInputTensorNum(cnode) < kBnInputTensorNum) { + bool is_dynamic = common::AnfAlgo::IsDynamicShape(cnode); + if (common::AnfAlgo::GetInputTensorNum(cnode) < kBnInputTensorNum) { MS_LOG(INFO) << "Op[" << cnode->DebugString() << "] has less input than " << kBnInputTensorNum << " inputs."; return nullptr; } @@ -139,8 +142,8 @@ AnfNodePtr SyncBnSplit::SyncBNSplitForTBE(const FuncGraphPtr &func_graph, const auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - bool is_dynamic = AnfAlgo::IsDynamicShape(cnode); - if (AnfAlgo::GetInputTensorNum(cnode) < kBnInputTensorNum) { + bool is_dynamic = common::AnfAlgo::IsDynamicShape(cnode); + if (common::AnfAlgo::GetInputTensorNum(cnode) < kBnInputTensorNum) { MS_LOG(INFO) << "Op[" << cnode->DebugString() << "] has less input than " << kBnInputTensorNum << " inputs."; return nullptr; } @@ -167,11 +170,11 @@ AnfNodePtr SyncBnSplit::SyncBNSplitForTBE(const FuncGraphPtr &func_graph, const AnfNodePtr CreateValueNodeOfDeviceNumReciprocal(const FuncGraphPtr &graph, const CNodePtr &sync_bn_cnode) { MS_EXCEPTION_IF_NULL(graph); MS_EXCEPTION_IF_NULL(sync_bn_cnode); - if (!AnfAlgo::HasNodeAttr(kDeviceNum, sync_bn_cnode)) { + if (!common::AnfAlgo::HasNodeAttr(kDeviceNum, sync_bn_cnode)) { MS_LOG(EXCEPTION) << "The node [" << sync_bn_cnode->DebugString() << "] does not have attr device_num." << trace::DumpSourceLines(sync_bn_cnode); } - auto device_num = AnfAlgo::GetNodeAttr(sync_bn_cnode, kDeviceNum); + auto device_num = common::AnfAlgo::GetNodeAttr(sync_bn_cnode, kDeviceNum); if (device_num == 0) { MS_LOG(EXCEPTION) << "The device_num attr of node [" << sync_bn_cnode->DebugString() << "] should not be 0." << trace::DumpSourceLines(sync_bn_cnode); @@ -199,10 +202,11 @@ AnfNodePtr CreateValueNodeOfDeviceNumReciprocal(const FuncGraphPtr &graph, const AnfNodePtr InsertCast(const FuncGraphPtr &graph, const AnfNodePtr &input, const TypeId dst_type) { MS_EXCEPTION_IF_NULL(graph); MS_EXCEPTION_IF_NULL(input); - if (AnfAlgo::GetOutputInferDataType(input, 0) != dst_type) { + if (common::AnfAlgo::GetOutputInferDataType(input, 0) != dst_type) { AnfNodePtr cast = graph->NewCNode({NewValueNode(std::make_shared(kCastOpName)), input}); - AnfAlgo::SetOutputInferTypeAndShape({dst_type}, {AnfAlgo::GetOutputInferShape(input, 0)}, cast.get()); - AnfAlgo::SetNodeAttr(kIsBackendCast, MakeValue(true), cast); + common::AnfAlgo::SetOutputInferTypeAndShape({dst_type}, {common::AnfAlgo::GetOutputInferShape(input, 0)}, + cast.get()); + common::AnfAlgo::SetNodeAttr(kIsBackendCast, MakeValue(true), cast); cast->set_scope(input->scope()); return cast; } @@ -227,8 +231,8 @@ AnfNodePtr CreateAllReduceAndMul(const FuncGraphPtr &graph, const AnfNodePtr &al MS_EXCEPTION_IF_NULL(allreduce); allreduce->set_abstract(input_node->abstract()); allreduce->set_scope(allreduce_input->scope()); - AnfAlgo::SetNodeAttr(kAttrOp, MakeValue(kReduceOpSum), allreduce); - AnfAlgo::CopyNodeAttr(kAttrGroup, sync_bn_cnode, allreduce); + common::AnfAlgo::SetNodeAttr(kAttrOp, MakeValue(kReduceOpSum), allreduce); + common::AnfAlgo::CopyNodeAttr(kAttrGroup, sync_bn_cnode, allreduce); // use SyncBatchNorm's opid as AllReduce's fusion attr auto sync_bn_opname = sync_bn_cnode->fullname_with_scope(); auto opid_pos = sync_bn_opname.rfind("-op"); @@ -242,7 +246,7 @@ AnfNodePtr CreateAllReduceAndMul(const FuncGraphPtr &graph, const AnfNodePtr &al if (opid < kFusionNumThreshold) { opid = opid - kFusionNumThreshold + std::numeric_limits::max(); } - AnfAlgo::SetNodeAttr(kAttrFusion, MakeValue(opid), allreduce); + common::AnfAlgo::SetNodeAttr(kAttrFusion, MakeValue(opid), allreduce); // create Mul auto device_num_reciprocal_vnode = CreateValueNodeOfDeviceNumReciprocal(graph, sync_bn_cnode); @@ -255,7 +259,7 @@ AnfNodePtr CreateAllReduceAndMul(const FuncGraphPtr &graph, const AnfNodePtr &al // Cast output to origin datatype to reduce the number of cast node. // Should be removed if BNTrainingReduce/BNTrainingUpdateGrad op support fp16 output. - return InsertCast(graph, mul, AnfAlgo::GetOutputInferDataType(allreduce_input, 0)); + return InsertCast(graph, mul, common::AnfAlgo::GetOutputInferDataType(allreduce_input, 0)); } const BaseRef BnSplit::DefinePattern() const { diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/broadcastto_fission.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/broadcastto_fission.cc index 7872f3febc0..6ac0a4ff81f 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/broadcastto_fission.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/broadcastto_fission.cc @@ -19,6 +19,7 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -29,16 +30,16 @@ CNodePtr AddCastNode(const FuncGraphPtr &func_graph, const TypeId dst_type, cons std::vector shape; if (fir_flag) { new_cast_inputs.emplace_back(input_node->inputs()[kIndex1]); - shape = AnfAlgo::GetOutputInferShape(input_node->inputs()[kIndex1], 0); + shape = common::AnfAlgo::GetOutputInferShape(input_node->inputs()[kIndex1], 0); } else { new_cast_inputs.emplace_back(input_node); - shape = AnfAlgo::GetOutputInferShape(input_node, 0); + shape = common::AnfAlgo::GetOutputInferShape(input_node, 0); } CNodePtr new_cast = NewCNode(new_cast_inputs, func_graph); new_cast->set_scope(input_node->scope()); new_cast->set_abstract(input_node->abstract()); - AnfAlgo::SetNodeAttr(kAttrDstType, MakeValue(static_cast(dst_type)), new_cast); - AnfAlgo::SetOutputInferTypeAndShape({dst_type}, {shape}, new_cast.get()); + common::AnfAlgo::SetNodeAttr(kAttrDstType, MakeValue(static_cast(dst_type)), new_cast); + common::AnfAlgo::SetOutputInferTypeAndShape({dst_type}, {shape}, new_cast.get()); return new_cast; } } // namespace @@ -52,7 +53,7 @@ CNodePtr AddBroadCastToNode(const FuncGraphPtr &func_graph, const CNodePtr &inpu const std::vector &broad_shape) { MS_EXCEPTION_IF_NULL(func_graph); MS_EXCEPTION_IF_NULL(input_node); - auto input_type = AnfAlgo::GetOutputInferDataType(input_node, 0); + auto input_type = common::AnfAlgo::GetOutputInferDataType(input_node, 0); std::vector broadcastto_inputs = { NewValueNode(std::make_shared(prim::kPrimBroadcastTo->name()))}; broadcastto_inputs.emplace_back(input_node); @@ -61,19 +62,19 @@ CNodePtr AddBroadCastToNode(const FuncGraphPtr &func_graph, const CNodePtr &inpu broadcastto_node->set_abstract(input_node->abstract()); std::vector out_shape; std::transform(broad_shape.begin(), broad_shape.end(), std::back_inserter(out_shape), SizeToLong); - AnfAlgo::SetNodeAttr(kAttrShape, MakeValue>(broad_shape), broadcastto_node); - AnfAlgo::SetOutputInferTypeAndShape({input_type}, {out_shape}, broadcastto_node.get()); + common::AnfAlgo::SetNodeAttr(kAttrShape, MakeValue>(broad_shape), broadcastto_node); + common::AnfAlgo::SetOutputInferTypeAndShape({input_type}, {out_shape}, broadcastto_node.get()); return broadcastto_node; } const AnfNodePtr BroadcasttoFission::Process(const FuncGraphPtr &graph, const AnfNodePtr &node, const EquivPtr &) const { - auto input_type = AnfAlgo::GetOutputInferDataType(node, 0); + auto input_type = common::AnfAlgo::GetOutputInferDataType(node, 0); if (input_type != kNumberTypeBool) { return nullptr; } auto cnode = node->cast(); - auto broad_shape = AnfAlgo::GetNodeAttr>(cnode, kAttrShape); + auto broad_shape = common::AnfAlgo::GetNodeAttr>(cnode, kAttrShape); auto cast_to_node = AddCastNode(graph, kNumberTypeInt8, cnode, true); auto broadcastto_node = AddBroadCastToNode(graph, cast_to_node, broad_shape); auto out_node = AddCastNode(graph, kNumberTypeBool, broadcastto_node, false); diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/cdist_fission.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/cdist_fission.cc index aecdfe2bd16..c9f8859d0ec 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/cdist_fission.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/cdist_fission.cc @@ -18,6 +18,7 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -62,21 +63,21 @@ AnfNodePtr AddBroadCastToNode(const FuncGraphPtr &func_graph, const AnfNodePtr & std::vector expand_dims_inputs = { NewValueNode(std::make_shared(prim::kPrimExpandDims->name())), input_node}; auto expand_dims = pass.NewCNode(expand_dims_inputs, func_graph); - auto dtype = AnfAlgo::GetOutputInferDataType(input_node, 0); - auto expand_shape = AnfAlgo::GetOutputInferShape(input_node, 0); + auto dtype = common::AnfAlgo::GetOutputInferDataType(input_node, 0); + auto expand_shape = common::AnfAlgo::GetOutputInferShape(input_node, 0); (void)expand_shape.insert(expand_shape.end() + dim, 1); - AnfAlgo::SetOutputInferTypeAndShape({dtype}, {expand_shape}, expand_dims.get()); - AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(dim), expand_dims); - AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), expand_dims); + common::AnfAlgo::SetOutputInferTypeAndShape({dtype}, {expand_shape}, expand_dims.get()); + common::AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(dim), expand_dims); + common::AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), expand_dims); // Add BroadCastTo Node std::vector broadcast_to_inputs = { NewValueNode(std::make_shared(prim::kPrimBroadcastTo->name())), expand_dims}; auto broadcast_to = pass.NewCNode(broadcast_to_inputs, func_graph); - AnfAlgo::SetOutputInferTypeAndShape({dtype}, {need_shape}, broadcast_to.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({dtype}, {need_shape}, broadcast_to.get()); std::vector shape; (void)std::transform(need_shape.begin(), need_shape.end(), std::back_inserter(shape), LongToSize); - AnfAlgo::SetNodeAttr(kAttrShape, MakeValue(shape), broadcast_to); - AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), broadcast_to); + common::AnfAlgo::SetNodeAttr(kAttrShape, MakeValue(shape), broadcast_to); + common::AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), broadcast_to); return broadcast_to; } } // namespace @@ -100,15 +101,15 @@ const AnfNodePtr CdistFission::Process(const FuncGraphPtr &graph, const AnfNodeP if (GetBoolAttr(cdist_cnode, kAttrVisited)) { return nullptr; } - AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node); + common::AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node); MS_EXCEPTION_IF_NULL(cdist_cnode); if (cdist_cnode->size() != kCdistInputNum + 1) { MS_LOG(INFO) << "The node " << cdist_cnode->DebugString() << " is not equal to " << cdist_cnode << " inputs"; return nullptr; } const auto &cdist_inputs = cdist_cnode->inputs(); - auto x_shape = AnfAlgo::GetOutputInferShape(cdist_inputs[kDim1], 0); - auto y_shape = AnfAlgo::GetOutputInferShape(cdist_inputs[kDim2], 0); + auto x_shape = common::AnfAlgo::GetOutputInferShape(cdist_inputs[kDim1], 0); + auto y_shape = common::AnfAlgo::GetOutputInferShape(cdist_inputs[kDim2], 0); auto broadcast_to_shape = CalCdistBroadCastShape(x_shape, y_shape); auto broadcast_input_x = AddBroadCastToNode(graph, cdist_inputs[kDim1], kInputXDimP, broadcast_to_shape, *this); auto broadcast_input_y = AddBroadCastToNode(graph, cdist_inputs[kDim2], kInputYDimR, broadcast_to_shape, *this); @@ -118,7 +119,7 @@ const AnfNodePtr CdistFission::Process(const FuncGraphPtr &graph, const AnfNodeP MS_EXCEPTION_IF_NULL(new_cnode); new_cnode->set_abstract(cdist_cnode->abstract()); new_cnode->set_scope(cdist_cnode->scope()); - AnfAlgo::CopyNodeAttrs(cdist_cnode, new_cnode); + common::AnfAlgo::CopyNodeAttrs(cdist_cnode, new_cnode); return new_cnode; } @@ -130,15 +131,15 @@ const AnfNodePtr CdistGradFission::Process(const FuncGraphPtr &graph, const AnfN if (GetBoolAttr(cdist_grad_cnode, kAttrVisited)) { return nullptr; } - AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node); + common::AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node); if (cdist_grad_cnode->size() != kCdistGradInputNum + 1) { MS_LOG(INFO) << "The node " << cdist_grad_cnode->DebugString() << " is not equal to " << cdist_grad_cnode << " inputs"; return nullptr; } const auto &cdist_grad_inputs = cdist_grad_cnode->inputs(); - auto x_shape = AnfAlgo::GetOutputInferShape(cdist_grad_inputs[kDim2], 0); - auto y_shape = AnfAlgo::GetOutputInferShape(cdist_grad_inputs[kDim3], 0); + auto x_shape = common::AnfAlgo::GetOutputInferShape(cdist_grad_inputs[kDim2], 0); + auto y_shape = common::AnfAlgo::GetOutputInferShape(cdist_grad_inputs[kDim3], 0); auto broadcast_to_shape = CalCdistBroadCastShape(x_shape, y_shape); auto broadcast_grad = AddBroadCastToNode(graph, cdist_grad_inputs[kDim1], 0, broadcast_to_shape, *this); auto broadcast_input_x = AddBroadCastToNode(graph, cdist_grad_inputs[kDim2], kInputXDimP, broadcast_to_shape, *this); @@ -150,7 +151,7 @@ const AnfNodePtr CdistGradFission::Process(const FuncGraphPtr &graph, const AnfN MS_EXCEPTION_IF_NULL(new_cnode); new_cnode->set_abstract(cdist_grad_cnode->abstract()); new_cnode->set_scope(cdist_grad_cnode->scope()); - AnfAlgo::CopyNodeAttrs(cdist_grad_cnode, new_cnode); + common::AnfAlgo::CopyNodeAttrs(cdist_grad_cnode, new_cnode); return new_cnode; } } // namespace opt diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/concat_fission.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/concat_fission.cc index f80eb967fcf..994fb3206b4 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/concat_fission.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/concat_fission.cc @@ -17,6 +17,7 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "utils/trace_base.h" namespace mindspore { @@ -33,23 +34,23 @@ AnfNodePtr ConcatFission::CreateNewConcat(const FuncGraphPtr &func_graph, const MS_EXCEPTION_IF_NULL(new_concat); new_concat->set_scope(origin_concat_cnode->scope()); // Set attrs - if (AnfAlgo::HasNodeAttr(kAttrAxis, origin_concat_cnode)) { - AnfAlgo::CopyNodeAttr(kAttrAxis, origin_concat_cnode, new_concat); + if (common::AnfAlgo::HasNodeAttr(kAttrAxis, origin_concat_cnode)) { + common::AnfAlgo::CopyNodeAttr(kAttrAxis, origin_concat_cnode, new_concat); } - if (AnfAlgo::HasNodeAttr(kAttrT, origin_concat_cnode)) { - AnfAlgo::CopyNodeAttr(kAttrT, origin_concat_cnode, new_concat); + if (common::AnfAlgo::HasNodeAttr(kAttrT, origin_concat_cnode)) { + common::AnfAlgo::CopyNodeAttr(kAttrT, origin_concat_cnode, new_concat); } - AnfAlgo::SetNodeAttr(kAttrN, MakeValue(SizeToLong(offset)), new_concat); - AnfAlgo::SetNodeAttr(kAttrInputNums, MakeValue(SizeToLong(offset)), new_concat); + common::AnfAlgo::SetNodeAttr(kAttrN, MakeValue(SizeToLong(offset)), new_concat); + common::AnfAlgo::SetNodeAttr(kAttrInputNums, MakeValue(SizeToLong(offset)), new_concat); std::vector dyn_input_sizes{SizeToLong(offset)}; - AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(dyn_input_sizes), new_concat); + common::AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(dyn_input_sizes), new_concat); // infer shape - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(origin_concat_cnode, 0); - auto axis_from_attr = AnfAlgo::GetNodeAttr(origin_concat_cnode, kAttrAxis); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(origin_concat_cnode, 0); + auto axis_from_attr = common::AnfAlgo::GetNodeAttr(origin_concat_cnode, kAttrAxis); if (axis_from_attr < 0) { axis_from_attr += SizeToLong(input_shape.size()); } - auto output_shape = AnfAlgo::GetOutputInferShape(origin_concat_cnode, 0); + auto output_shape = common::AnfAlgo::GetOutputInferShape(origin_concat_cnode, 0); if (axis_from_attr < 0 || axis_from_attr >= SizeToLong(output_shape.size()) || axis_from_attr >= SizeToLong(input_shape.size())) { MS_LOG(EXCEPTION) << "The concat_dim value " << axis_from_attr << "is out of range" @@ -58,11 +59,11 @@ AnfNodePtr ConcatFission::CreateNewConcat(const FuncGraphPtr &func_graph, const auto axis = LongToSize(axis_from_attr); output_shape[axis] = 0; for (size_t i = begin_index; i < begin_index + offset; ++i) { - input_shape = AnfAlgo::GetPrevNodeOutputInferShape(origin_concat_cnode, i - 1); + input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(origin_concat_cnode, i - 1); output_shape[axis] += input_shape[axis]; } - AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(origin_concat_cnode, 0)}, {output_shape}, - new_concat.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({common::AnfAlgo::GetOutputInferDataType(origin_concat_cnode, 0)}, + {output_shape}, new_concat.get()); return new_concat; } @@ -75,7 +76,7 @@ const AnfNodePtr ConcatFission::Process(const FuncGraphPtr &func_graph, const An const EquivPtr &) const { MS_EXCEPTION_IF_NULL(func_graph); MS_EXCEPTION_IF_NULL(node); - if (AnfAlgo::IsDynamicShape(node)) { + if (common::AnfAlgo::IsDynamicShape(node)) { return nullptr; } auto cnode = node->cast(); @@ -103,17 +104,17 @@ const AnfNodePtr ConcatFission::Process(const FuncGraphPtr &func_graph, const An base_concat->set_scope(new_cnode->scope()); base_concat->set_abstract(new_cnode->abstract()); // Set attrs - if (AnfAlgo::HasNodeAttr(kAttrAxis, new_cnode)) { - AnfAlgo::CopyNodeAttr(kAttrAxis, new_cnode, base_concat); + if (common::AnfAlgo::HasNodeAttr(kAttrAxis, new_cnode)) { + common::AnfAlgo::CopyNodeAttr(kAttrAxis, new_cnode, base_concat); } - if (AnfAlgo::HasNodeAttr(kAttrT, new_cnode)) { - AnfAlgo::CopyNodeAttr(kAttrT, new_cnode, base_concat); + if (common::AnfAlgo::HasNodeAttr(kAttrT, new_cnode)) { + common::AnfAlgo::CopyNodeAttr(kAttrT, new_cnode, base_concat); } - AnfAlgo::SetNodeAttr(kAttrN, MakeValue(SizeToLong(base_concat_inputs.size() - 1)), base_concat); - AnfAlgo::SetNodeAttr(kAttrInputNums, MakeValue(SizeToLong(base_concat_inputs.size() - 1)), base_concat); + common::AnfAlgo::SetNodeAttr(kAttrN, MakeValue(SizeToLong(base_concat_inputs.size() - 1)), base_concat); + common::AnfAlgo::SetNodeAttr(kAttrInputNums, MakeValue(SizeToLong(base_concat_inputs.size() - 1)), base_concat); std::vector dyn_input_sizes{SizeToLong(base_concat_inputs.size() - 1)}; - AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(dyn_input_sizes), base_concat); + common::AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(dyn_input_sizes), base_concat); new_cnode = base_concat; origin_input_size = base_concat->inputs().size() - 1; diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/diag_fission.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/diag_fission.cc index 05fbd826a26..ccb79e0970f 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/diag_fission.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/diag_fission.cc @@ -17,6 +17,7 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "frontend/optimizer/opt.h" #include "backend/common/optimizer/helper.h" @@ -46,7 +47,7 @@ ValueNodePtr DiagFission::CreateAssistNode(const FuncGraphPtr &func_graph, const dims = dims * ori_shape[i]; } (void)output_shape.insert(output_shape.end(), ori_shape.begin(), ori_shape.end()); - auto type = AnfAlgo::GetOutputInferDataType(node, 0); + auto type = common::AnfAlgo::GetOutputInferDataType(node, 0); std::vector assist_shape; std::transform(output_shape.begin(), output_shape.end(), std::back_inserter(assist_shape), SizeToLong); tensor::TensorPtr tensor = std::make_shared(type, assist_shape); @@ -68,7 +69,7 @@ ValueNodePtr DiagFission::CreateAssistNode(const FuncGraphPtr &func_graph, const MS_EXCEPTION_IF_NULL(kernel_graph); auto assist_value_node = kernel_graph->NewValueNode(x_abstract, tensor); kernel_graph->AddValueNodeToGraph(assist_value_node); - AnfAlgo::SetOutputInferTypeAndShape({type}, {output_shape}, assist_value_node.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({type}, {output_shape}, assist_value_node.get()); return assist_value_node; } @@ -88,7 +89,7 @@ const AnfNodePtr DiagFission::Process(const FuncGraphPtr &graph, const AnfNodePt MS_LOG(INFO) << "The node " << diag_cnode->DebugString() << " is not equal to " << kDiagInputNum << " inputs"; return nullptr; } - auto input_shape = AnfAlgo::GetOutputInferShape(diag_cnode->inputs()[kIndex1], 0); + auto input_shape = common::AnfAlgo::GetOutputInferShape(diag_cnode->inputs()[kIndex1], 0); if (input_shape.size() > kDiagInputMaxDim) { MS_EXCEPTION(ValueError) << "For Diag, rank of input should be less than 5, but got: " << input_shape.size(); } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/diag_part_fission.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/diag_part_fission.cc index 5bf1363df1c..68e0fcba771 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/diag_part_fission.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/diag_part_fission.cc @@ -17,6 +17,7 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore::opt { const BaseRef DiagPartFission::DefinePattern() const { @@ -37,8 +38,8 @@ const AnfNodePtr DiagPartFission::Process(const FuncGraphPtr &func_graph, const << " inputs"; return nullptr; } - auto out_shape = AnfAlgo::GetOutputInferShape(node, 0); - auto input_shape = AnfAlgo::GetOutputInferShape(diag_part_cnode->inputs()[kIndex1], 0); + auto out_shape = common::AnfAlgo::GetOutputInferShape(node, 0); + auto input_shape = common::AnfAlgo::GetOutputInferShape(diag_part_cnode->inputs()[kIndex1], 0); constexpr size_t kDiagPartInputMaxDim = 8; if (input_shape.size() > kDiagPartInputMaxDim) { MS_EXCEPTION(ValueError) << "For DiagPart, rank of input should be 2, 4, 6 or 8, but got: " << input_shape.size(); diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/dynamic_gru_v2_grad_fission.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/dynamic_gru_v2_grad_fission.cc index b0205429ff6..1c461dedd12 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/dynamic_gru_v2_grad_fission.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/dynamic_gru_v2_grad_fission.cc @@ -20,6 +20,7 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/optimizer/helper.h" namespace mindspore { @@ -87,7 +88,7 @@ AnfNodePtr DynamicGRUV2GradFission::CreateGRUV2HiddenGradCellNode(const FuncGrap } (void)gru_v2_hidden_grad_cell_inputs.emplace_back(dynamic_gru_v2_grad_inputs[input_index["dy"]]); auto input_dh = dynamic_gru_v2_grad_inputs[input_index["dh"]]; - dh_dtype = AnfAlgo::GetOutputInferDataType(input_dh, 0); + dh_dtype = common::AnfAlgo::GetOutputInferDataType(input_dh, 0); if (cur_t == 0) { (void)gru_v2_hidden_grad_cell_inputs.emplace_back(input_dh); } else { @@ -101,13 +102,13 @@ AnfNodePtr DynamicGRUV2GradFission::CreateGRUV2HiddenGradCellNode(const FuncGrap auto gru_v2_hidden_grad_cell_op = NewCNode(gru_v2_hidden_grad_cell_inputs, func_graph); std::vector dh_prev_shape = - AnfAlgo::GetOutputInferShape(dynamic_gru_grad_outputs[output_index["dh_prev"]], 0); + common::AnfAlgo::GetOutputInferShape(dynamic_gru_grad_outputs[output_index["dh_prev"]], 0); std::vector dgate_h_shape = {1, batch_size, kGateNum * hidden_size}; std::vector dnt_x_shape = {1, batch_size, hidden_size}; - AnfAlgo::SetOutputInferTypeAndShape({dh_dtype, dh_dtype, dh_dtype}, {dh_prev_shape, dgate_h_shape, dnt_x_shape}, - gru_v2_hidden_grad_cell_op.get()); - AnfAlgo::SetNodeAttr("t_state", MakeValue(SizeToLong(cur_t)), gru_v2_hidden_grad_cell_op); - AnfAlgo::SetNodeAttr("gate_order", MakeValue(gate_order), gru_v2_hidden_grad_cell_op); + common::AnfAlgo::SetOutputInferTypeAndShape( + {dh_dtype, dh_dtype, dh_dtype}, {dh_prev_shape, dgate_h_shape, dnt_x_shape}, gru_v2_hidden_grad_cell_op.get()); + common::AnfAlgo::SetNodeAttr("t_state", MakeValue(SizeToLong(cur_t)), gru_v2_hidden_grad_cell_op); + common::AnfAlgo::SetNodeAttr("gate_order", MakeValue(gate_order), gru_v2_hidden_grad_cell_op); return gru_v2_hidden_grad_cell_op; } @@ -117,8 +118,8 @@ void DynamicGRUV2GradFission::AddTLoopNode(const FuncGraphPtr &func_graph, const MS_EXCEPTION_IF_NULL(dynamic_gru_v2_grad_cnode); MS_EXCEPTION_IF_NULL(result_nodes); std::string gate_order = "rzh"; - if (AnfAlgo::HasNodeAttr("gate_order", dynamic_gru_v2_grad_cnode)) { - gate_order = AnfAlgo::GetNodeAttr(dynamic_gru_v2_grad_cnode, "gate_order"); + if (common::AnfAlgo::HasNodeAttr("gate_order", dynamic_gru_v2_grad_cnode)) { + gate_order = common::AnfAlgo::GetNodeAttr(dynamic_gru_v2_grad_cnode, "gate_order"); } std::vector gru_hidden_grad_cells; std::vector matmul_nodes; @@ -141,16 +142,16 @@ void DynamicGRUV2GradFission::AddTLoopNode(const FuncGraphPtr &func_graph, const std::vector reshape_inputs = {NewValueNode(std::make_shared(prim::kPrimReshape->name())), weight_hidden}; auto reshape = NewCNode(reshape_inputs, func_graph); - auto reshape_out_shape = {IntToSize(1), AnfAlgo::GetOutputInferShape(weight_hidden, 0)[0], - AnfAlgo::GetOutputInferShape(weight_hidden, 0)[1]}; - AnfAlgo::SetOutputInferTypeAndShape({dh_dtype}, {reshape_out_shape}, reshape.get()); + auto reshape_out_shape = {IntToSize(1), common::AnfAlgo::GetOutputInferShape(weight_hidden, 0)[0], + common::AnfAlgo::GetOutputInferShape(weight_hidden, 0)[1]}; + common::AnfAlgo::SetOutputInferTypeAndShape({dh_dtype}, {reshape_out_shape}, reshape.get()); (void)matmul_inputs.emplace_back(reshape); auto matmul_node = NewCNode(matmul_inputs, func_graph); MS_EXCEPTION_IF_NULL(matmul_node); std::vector out_shape = {1, batch_size, hidden_size}; - AnfAlgo::SetOutputInferTypeAndShape({dh_dtype}, {out_shape}, matmul_node.get()); - AnfAlgo::SetNodeAttr("transpose_x1", MakeValue(false), matmul_node); - AnfAlgo::SetNodeAttr("transpose_x2", MakeValue(true), matmul_node); + common::AnfAlgo::SetOutputInferTypeAndShape({dh_dtype}, {out_shape}, matmul_node.get()); + common::AnfAlgo::SetNodeAttr("transpose_x1", MakeValue(false), matmul_node); + common::AnfAlgo::SetNodeAttr("transpose_x2", MakeValue(true), matmul_node); last_hidden_grad_node = gru_hidden_grad_cell_node; last_matmul_node = matmul_node; @@ -179,13 +180,13 @@ AnfNodePtr DynamicGRUV2GradFission::AddTConcatNode(const FuncGraphPtr &func_grap (void)concat_inputs.emplace_back(gru_hidden_grad_node_outputs[concat_output_index]); } auto concat_t_node = NewCNode(concat_inputs, func_graph); - auto out_dims = AnfAlgo::GetOutputInferShape(gru_hidden_grad_nodes[kIndex0], concat_output_index); + auto out_dims = common::AnfAlgo::GetOutputInferShape(gru_hidden_grad_nodes[kIndex0], concat_output_index); std::vector concat_output_shape = {t_size, out_dims[kDim1], out_dims[kDim2]}; - auto out_type = AnfAlgo::GetOutputInferDataType(gru_hidden_grad_nodes[kIndex0], concat_output_index); - AnfAlgo::SetOutputInferTypeAndShape({out_type}, {concat_output_shape}, concat_t_node.get()); - AnfAlgo::SetNodeAttr(kAttrN, MakeValue(SizeToLong(t_size)), concat_t_node); - AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(std::vector{SizeToLong(t_size)}), concat_t_node); - AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(static_cast(0)), concat_t_node); + auto out_type = common::AnfAlgo::GetOutputInferDataType(gru_hidden_grad_nodes[kIndex0], concat_output_index); + common::AnfAlgo::SetOutputInferTypeAndShape({out_type}, {concat_output_shape}, concat_t_node.get()); + common::AnfAlgo::SetNodeAttr(kAttrN, MakeValue(SizeToLong(t_size)), concat_t_node); + common::AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(std::vector{SizeToLong(t_size)}), concat_t_node); + common::AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(static_cast(0)), concat_t_node); return concat_t_node; } @@ -226,36 +227,37 @@ AnfNodePtr DynamicGRUV2GradFission::AddHSplitNode(const FuncGraphPtr &func_graph input_h}; auto split_v = NewCNode(splitv_input, func_graph); // Set infer data type and shape - auto dtypes = {AnfAlgo::GetOutputInferDataType(input_h, 0), AnfAlgo::GetOutputInferDataType(input_h, 0)}; + auto dtypes = {common::AnfAlgo::GetOutputInferDataType(input_h, 0), + common::AnfAlgo::GetOutputInferDataType(input_h, 0)}; std::vector output1_shape = {t_size - 1, batch_size, hidden_size}; std::vector output2_shape = {1, batch_size, hidden_size}; std::vector split_list = {SizeToLong(t_size - 1), 1}; std::vector> shapes = {output1_shape, output2_shape}; - AnfAlgo::SetOutputInferTypeAndShape(dtypes, shapes, split_v.get()); + common::AnfAlgo::SetOutputInferTypeAndShape(dtypes, shapes, split_v.get()); // Set attr - AnfAlgo::SetNodeAttr(kAttrSplitDim, MakeValue(SizeToLong(0)), split_v); - AnfAlgo::SetNodeAttr(kAttrNumSplit, MakeValue(SizeToLong(kSplitVOutputNum)), split_v); - AnfAlgo::SetNodeAttr(kAttrSizeSplits, MakeValue(split_list), split_v); - AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), split_v); + common::AnfAlgo::SetNodeAttr(kAttrSplitDim, MakeValue(SizeToLong(0)), split_v); + common::AnfAlgo::SetNodeAttr(kAttrNumSplit, MakeValue(SizeToLong(kSplitVOutputNum)), split_v); + common::AnfAlgo::SetNodeAttr(kAttrSizeSplits, MakeValue(split_list), split_v); + common::AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), split_v); return split_v; } AnfNodePtr DynamicGRUV2GradFission::CreateHReshape(const FuncGraphPtr &graph, const AnfNodePtr &node) const { MS_EXCEPTION_IF_NULL(graph); MS_EXCEPTION_IF_NULL(node); - auto ori_shape = AnfAlgo::GetOutputInferShape(node, 0); + auto ori_shape = common::AnfAlgo::GetOutputInferShape(node, 0); std::vector> shape_tmp; if (ori_shape.size() == k3Dims) { shape_tmp = {ori_shape}; } else { shape_tmp = {{IntToSize(1), ori_shape[kDim0], ori_shape[kDim1]}}; } - auto ori_dtype = {AnfAlgo::GetOutputInferDataType(node, 0)}; + auto ori_dtype = {common::AnfAlgo::GetOutputInferDataType(node, 0)}; // reshape std::vector reshape_input = {NewValueNode(std::make_shared(prim::kPrimReshape->name())), node}; auto reshape = NewCNode(reshape_input, graph); - AnfAlgo::SetOutputInferTypeAndShape(ori_dtype, shape_tmp, reshape.get()); - AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), reshape); + common::AnfAlgo::SetOutputInferTypeAndShape(ori_dtype, shape_tmp, reshape.get()); + common::AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), reshape); return reshape; } @@ -279,13 +281,13 @@ AnfNodePtr DynamicGRUV2GradFission::AddHConcatNode(const FuncGraphPtr &func_grap auto concat = NewCNode(concat_inputs, func_graph); // Set infer data type and shape std::vector output_shape = {t_size, batch_size, hidden_size}; - AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(init_h_reshape, 0)}, {output_shape}, - concat.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({common::AnfAlgo::GetOutputInferDataType(init_h_reshape, 0)}, + {output_shape}, concat.get()); // Set attr - AnfAlgo::SetNodeAttr(kAttrN, MakeValue(SizeToLong(kConcatNum)), concat); - AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(std::vector{kConcatNum}), concat); - AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(SizeToLong(0)), concat); - AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), concat); + common::AnfAlgo::SetNodeAttr(kAttrN, MakeValue(SizeToLong(kConcatNum)), concat); + common::AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(std::vector{kConcatNum}), concat); + common::AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(SizeToLong(0)), concat); + common::AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), concat); return concat; } @@ -306,10 +308,10 @@ AnfNodePtr DynamicGRUV2GradFission::AddDwhMatmulNode(const FuncGraphPtr &func_gr } auto batch_matmul = NewCNode(matmul_inputs, func_graph); std::vector shape = {t_size, hidden_size, kGateNum * hidden_size}; - AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat16}, {shape}, batch_matmul.get()); - AnfAlgo::SetNodeAttr("transpose_x1", MakeValue(true), batch_matmul); - AnfAlgo::SetNodeAttr("transpose_x2", MakeValue(false), batch_matmul); - AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), batch_matmul); + common::AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat16}, {shape}, batch_matmul.get()); + common::AnfAlgo::SetNodeAttr("transpose_x1", MakeValue(true), batch_matmul); + common::AnfAlgo::SetNodeAttr("transpose_x2", MakeValue(false), batch_matmul); + common::AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), batch_matmul); return batch_matmul; } @@ -326,16 +328,17 @@ AnfNodePtr DynamicGRUV2GradFission::CreateDgateHSplitVDNode(const FuncGraphPtr & (void)splitvd_input.emplace_back(dgate_h); } auto split_vd = NewCNode(splitvd_input, func_graph); - auto dtypes = {AnfAlgo::GetOutputInferDataType(dgate_h, 0), AnfAlgo::GetOutputInferDataType(dgate_h, 0)}; + auto dtypes = {common::AnfAlgo::GetOutputInferDataType(dgate_h, 0), + common::AnfAlgo::GetOutputInferDataType(dgate_h, 0)}; std::vector shape = {t_size, batch_size, hidden_size << 1}; std::vector shape2 = {t_size, batch_size, hidden_size}; std::vector> shapes = {shape, shape2}; - AnfAlgo::SetOutputInferTypeAndShape(dtypes, shapes, split_vd.get()); - AnfAlgo::SetNodeAttr("split_dim", MakeValue(SizeToLong(kDim2)), split_vd); - AnfAlgo::SetNodeAttr("num_split", MakeValue(SizeToLong(kSplitVOutputNum)), split_vd); + common::AnfAlgo::SetOutputInferTypeAndShape(dtypes, shapes, split_vd.get()); + common::AnfAlgo::SetNodeAttr("split_dim", MakeValue(SizeToLong(kDim2)), split_vd); + common::AnfAlgo::SetNodeAttr("num_split", MakeValue(SizeToLong(kSplitVOutputNum)), split_vd); std::vector size_splits = {SizeToLong(hidden_size << 1), SizeToLong(hidden_size)}; - AnfAlgo::SetNodeAttr("size_splits", MakeValue(size_splits), split_vd); - AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), split_vd); + common::AnfAlgo::SetNodeAttr("size_splits", MakeValue(size_splits), split_vd); + common::AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), split_vd); return split_vd; } @@ -357,12 +360,12 @@ AnfNodePtr DynamicGRUV2GradFission::CreateDgateXConcatDNode(const FuncGraphPtr & } auto concat_op = NewCNode(concat_inputs, func_graph); std::vector shape = {t_size, batch_size, kGateNum * hidden_size}; - auto types = {AnfAlgo::GetOutputInferDataType(dnt_x, 0)}; - AnfAlgo::SetOutputInferTypeAndShape(types, {shape}, concat_op.get()); - AnfAlgo::SetNodeAttr(kAttrN, MakeValue(SizeToLong(kConcatNum)), concat_op); - AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(std::vector{kConcatNum}), concat_op); - AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(SizeToLong(kDim2)), concat_op); - AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), concat_op); + auto types = {common::AnfAlgo::GetOutputInferDataType(dnt_x, 0)}; + common::AnfAlgo::SetOutputInferTypeAndShape(types, {shape}, concat_op.get()); + common::AnfAlgo::SetNodeAttr(kAttrN, MakeValue(SizeToLong(kConcatNum)), concat_op); + common::AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(std::vector{kConcatNum}), concat_op); + common::AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(SizeToLong(kDim2)), concat_op); + common::AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), concat_op); return concat_op; } @@ -377,10 +380,10 @@ AnfNodePtr DynamicGRUV2GradFission::CreateDwxBatchMatMul(const FuncGraphPtr &gra auto batch_matmul = NewCNode(matmul_inputs, graph); MS_EXCEPTION_IF_NULL(batch_matmul); std::vector shape = {t_size, input_size, kGateNum * hidden_size}; - AnfAlgo::SetOutputInferTypeAndShape({dh_dtype}, {shape}, batch_matmul.get()); - AnfAlgo::SetNodeAttr("transpose_x1", MakeValue(true), batch_matmul); - AnfAlgo::SetNodeAttr("transpose_x2", MakeValue(false), batch_matmul); - AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), batch_matmul); + common::AnfAlgo::SetOutputInferTypeAndShape({dh_dtype}, {shape}, batch_matmul.get()); + common::AnfAlgo::SetNodeAttr("transpose_x1", MakeValue(true), batch_matmul); + common::AnfAlgo::SetNodeAttr("transpose_x2", MakeValue(false), batch_matmul); + common::AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), batch_matmul); return batch_matmul; } @@ -394,11 +397,11 @@ AnfNodePtr DynamicGRUV2GradFission::CreateDxtBatchMatMul(const FuncGraphPtr &fun dgate_concat, weight_input}; auto batch_matmul = NewCNode(matmul_inputs, func_graph); MS_EXCEPTION_IF_NULL(batch_matmul); - AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(dx, 0)}, {AnfAlgo::GetOutputInferShape(dx, 0)}, - batch_matmul.get()); - AnfAlgo::SetNodeAttr("transpose_x1", MakeValue(false), batch_matmul); - AnfAlgo::SetNodeAttr("transpose_x2", MakeValue(true), batch_matmul); - AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), batch_matmul); + common::AnfAlgo::SetOutputInferTypeAndShape({common::AnfAlgo::GetOutputInferDataType(dx, 0)}, + {common::AnfAlgo::GetOutputInferShape(dx, 0)}, batch_matmul.get()); + common::AnfAlgo::SetNodeAttr("transpose_x1", MakeValue(false), batch_matmul); + common::AnfAlgo::SetNodeAttr("transpose_x2", MakeValue(true), batch_matmul); + common::AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), batch_matmul); return batch_matmul; } @@ -409,11 +412,11 @@ AnfNodePtr DynamicGRUV2GradFission::CreateWBroadcastToDNode(const FuncGraphPtr & std::vector braodcast_to_input = {NewValueNode(std::make_shared(kBroadcastToOpName)), node}; auto broadcast_to_d = NewCNode(braodcast_to_input, graph); std::vector shape = {t_size, input_size, kGateNum * hidden_size}; - auto type = {AnfAlgo::GetOutputInferDataType(node, 0)}; - AnfAlgo::SetOutputInferTypeAndShape(type, {shape}, broadcast_to_d.get()); + auto type = {common::AnfAlgo::GetOutputInferDataType(node, 0)}; + common::AnfAlgo::SetOutputInferTypeAndShape(type, {shape}, broadcast_to_d.get()); std::vector attr_shape = {SizeToLong(t_size), SizeToLong(input_size), SizeToLong(kGateNum * hidden_size)}; - AnfAlgo::SetNodeAttr(kAttrShape, MakeValue(attr_shape), broadcast_to_d); - AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), broadcast_to_d); + common::AnfAlgo::SetNodeAttr(kAttrShape, MakeValue(attr_shape), broadcast_to_d); + common::AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), broadcast_to_d); return broadcast_to_d; } @@ -426,12 +429,12 @@ AnfNodePtr DynamicGRUV2GradFission::CreateDwReduceSumDNode(const FuncGraphPtr &g std::vector reducesum_inputs = {NewValueNode(std::make_shared(prim::kPrimReduceSum->name())), matmul}; auto reduce_sumd = NewCNode(reducesum_inputs, graph); - auto types = {AnfAlgo::GetOutputInferDataType(gru_grad, 0)}; - auto shapes = {AnfAlgo::GetOutputInferShape(gru_grad, 0)}; - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, reduce_sumd.get()); - AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(std::vector{0}), reduce_sumd); - AnfAlgo::SetNodeAttr("keep_dims", MakeValue(false), reduce_sumd); - AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), reduce_sumd); + auto types = {common::AnfAlgo::GetOutputInferDataType(gru_grad, 0)}; + auto shapes = {common::AnfAlgo::GetOutputInferShape(gru_grad, 0)}; + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, reduce_sumd.get()); + common::AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(std::vector{0}), reduce_sumd); + common::AnfAlgo::SetNodeAttr("keep_dims", MakeValue(false), reduce_sumd); + common::AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), reduce_sumd); return reduce_sumd; } @@ -446,11 +449,11 @@ AnfNodePtr DynamicGRUV2GradFission::CreateDbReduceSumDNode(const FuncGraphPtr &g auto reduce_sumd = NewCNode(reducesum_inputs, graph); MS_EXCEPTION_IF_NULL(reduce_sumd); std::vector shape = {kGateNum * hidden_size}; - auto types = {AnfAlgo::GetOutputInferDataType(node2, 0)}; - AnfAlgo::SetOutputInferTypeAndShape(types, {shape}, reduce_sumd.get()); - AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(std::vector{0, 1}), reduce_sumd); - AnfAlgo::SetNodeAttr("keep_dims", MakeValue(false), reduce_sumd); - AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), reduce_sumd); + auto types = {common::AnfAlgo::GetOutputInferDataType(node2, 0)}; + common::AnfAlgo::SetOutputInferTypeAndShape(types, {shape}, reduce_sumd.get()); + common::AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(std::vector{0, 1}), reduce_sumd); + common::AnfAlgo::SetNodeAttr("keep_dims", MakeValue(false), reduce_sumd); + common::AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), reduce_sumd); return reduce_sumd; } @@ -470,7 +473,7 @@ const AnfNodePtr DynamicGRUV2GradFission::Process(const FuncGraphPtr &func_graph << kDynamicGRUV2GradInputNum << " inputs"; return nullptr; } - if (AnfAlgo::IsDynamicShape(node)) { + if (common::AnfAlgo::IsDynamicShape(node)) { MS_LOG(INFO) << "DynamicGRUV2Grad is dynamic shape, can not do fission."; return nullptr; } @@ -481,10 +484,10 @@ const AnfNodePtr DynamicGRUV2GradFission::Process(const FuncGraphPtr &func_graph CreateMultipleOutputsOfAnfNode(func_graph, dynamic_gru_v2_grad_cnode, kDynamicGRUV2GradOutputNum, &gru_grad_outputs); auto input_h = ori_inputs[input_index["h"]]; auto input_x = ori_inputs[input_index["x"]]; - t_size = AnfAlgo::GetOutputInferShape(input_h, 0)[kDim0]; - batch_size = AnfAlgo::GetOutputInferShape(input_h, 0)[kDim1]; - hidden_size = AnfAlgo::GetOutputInferShape(input_h, 0)[kDim2]; - input_size = AnfAlgo::GetOutputInferShape(input_x, 0)[kDim2]; + t_size = common::AnfAlgo::GetOutputInferShape(input_h, 0)[kDim0]; + batch_size = common::AnfAlgo::GetOutputInferShape(input_h, 0)[kDim1]; + hidden_size = common::AnfAlgo::GetOutputInferShape(input_h, 0)[kDim2]; + input_size = common::AnfAlgo::GetOutputInferShape(input_x, 0)[kDim2]; MS_LOG(INFO) << "For DynamicGRUV2Grad op, t_size: " << t_size << ", batch_size: " << batch_size << ", hidden_size: " << hidden_size << ", input_size: " << input_size; // add GRUHiddenGrad {dhPrevNode, dgateHConcatTNode, dntXConcatTNode} diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/dynamic_rnn_grad_fission_v2.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/dynamic_rnn_grad_fission_v2.cc index 83ed237eae1..20c7c0fc8dd 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/dynamic_rnn_grad_fission_v2.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/dynamic_rnn_grad_fission_v2.cc @@ -19,6 +19,7 @@ #include #include "backend/common/session/kernel_graph.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "plugin/device/ascend/optimizer/ascend_helper.h" #include "utils/trace_base.h" #include "utils/tensor_construct_utils.h" @@ -43,8 +44,8 @@ void SetAttrInputAndHiddenSize(const FuncGraphPtr &func_graph, const CNodePtr &d MS_EXCEPTION_IF_NULL(input); // set for input while (input->isa()) { - AnfAlgo::SetNodeAttr(kAttrInputSize, MakeValue(input_size), input); - AnfAlgo::SetNodeAttr(kAttrHiddenSize, MakeValue(hidden_size), input); + common::AnfAlgo::SetNodeAttr(kAttrInputSize, MakeValue(input_size), input); + common::AnfAlgo::SetNodeAttr(kAttrHiddenSize, MakeValue(hidden_size), input); auto input_cnode = input->cast(); input = input_cnode->input(kIndex1); } @@ -58,10 +59,10 @@ void SetAttrInputAndHiddenSize(const FuncGraphPtr &func_graph, const CNodePtr &d auto manager = func_graph->manager(); MS_EXCEPTION_IF_NULL(manager); for (auto getitem_index : manager->node_users()[dynamic_rnn_grad_cnode]) { - if (AnfAlgo::CheckPrimitiveType(getitem_index.first, prim::kPrimTupleGetItem)) { + if (common::AnfAlgo::CheckPrimitiveType(getitem_index.first, prim::kPrimTupleGetItem)) { for (auto node_index : manager->node_users()[getitem_index.first]) { - AnfAlgo::SetNodeAttr(kAttrInputSize, MakeValue(input_size), node_index.first); - AnfAlgo::SetNodeAttr(kAttrHiddenSize, MakeValue(hidden_size), node_index.first); + common::AnfAlgo::SetNodeAttr(kAttrInputSize, MakeValue(input_size), node_index.first); + common::AnfAlgo::SetNodeAttr(kAttrHiddenSize, MakeValue(hidden_size), node_index.first); } } } @@ -78,7 +79,7 @@ void DynamicRnnGradFissionV2::CreateTLoopNode(const FuncGraphPtr &func_graph, co std::vector matmul_nodes; std::vector split_nodes; // Get the size of t - auto input_i_shape = AnfAlgo::GetOutputInferShape(dynamic_rnn_grad_cnode->input(kIndex12), 0); + auto input_i_shape = common::AnfAlgo::GetOutputInferShape(dynamic_rnn_grad_cnode->input(kIndex12), 0); for (size_t i = 0; i < specs.t_size; ++i) { // Create basic_lstm_cell_c_state_grad @@ -89,10 +90,10 @@ void DynamicRnnGradFissionV2::CreateTLoopNode(const FuncGraphPtr &func_graph, co std::vector output0_dims{specs.batch_size, kDimMultiNum * specs.hidden_nz_size * kCubeSize}; // batch_size, hidden_size std::vector output1_dims{input_i_shape[kDim1], input_i_shape[kDim2]}; - AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat16, kNumberTypeFloat32}, {output0_dims, output1_dims}, - basic_lstm_cell_c_state_grad.get()); - AnfAlgo::SetNodeAttr("forget_bias", MakeValue(1.0f), basic_lstm_cell_c_state_grad); - AnfAlgo::SetNodeAttr("activation", MakeValue("Tanh"), basic_lstm_cell_c_state_grad); + common::AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat16, kNumberTypeFloat32}, {output0_dims, output1_dims}, + basic_lstm_cell_c_state_grad.get()); + common::AnfAlgo::SetNodeAttr("forget_bias", MakeValue(1.0f), basic_lstm_cell_c_state_grad); + common::AnfAlgo::SetNodeAttr("activation", MakeValue("Tanh"), basic_lstm_cell_c_state_grad); // Create matmul std::vector matmul_inputs; @@ -102,20 +103,20 @@ void DynamicRnnGradFissionV2::CreateTLoopNode(const FuncGraphPtr &func_graph, co matmul_inputs.push_back(NewValueNode(std::make_shared(prim::kPrimMatMul->name()))); } auto matmul = NewCNode(matmul_inputs, func_graph); - AnfAlgo::SetOutputInferTypeAndShape( + common::AnfAlgo::SetOutputInferTypeAndShape( {kNumberTypeFloat32}, {{IntToSize(1), specs.batch_size, specs.input_size + specs.hidden_size}}, matmul.get()); - AnfAlgo::SetNodeAttr("transpose_x1", MakeValue(false), matmul); - AnfAlgo::SetNodeAttr("transpose_x2", MakeValue(true), matmul); + common::AnfAlgo::SetNodeAttr("transpose_x1", MakeValue(false), matmul); + common::AnfAlgo::SetNodeAttr("transpose_x2", MakeValue(true), matmul); if (specs.shape_need_align) { - AnfAlgo::SetNodeAttr(kAttrFixedInputFormat, - MakeValue(std::vector{kOpFormat_FRAC_NZ, kOpFormat_FRACTAL_ZN_RNN}), matmul); - AnfAlgo::SetNodeAttr(kAttrFixedOutputFormat, MakeValue(std::vector{kOpFormat_FRAC_NZ}), matmul); - AnfAlgo::SetNodeAttr(kAttrInputSize, MakeValue(SizeToLong(specs.input_size)), matmul); - AnfAlgo::SetNodeAttr(kAttrHiddenSize, MakeValue(SizeToLong(specs.hidden_size)), matmul); + common::AnfAlgo::SetNodeAttr(kAttrFixedInputFormat, + MakeValue(std::vector{kOpFormat_FRAC_NZ, kOpFormat_FRACTAL_ZN_RNN}), matmul); + common::AnfAlgo::SetNodeAttr(kAttrFixedOutputFormat, MakeValue(std::vector{kOpFormat_FRAC_NZ}), matmul); + common::AnfAlgo::SetNodeAttr(kAttrInputSize, MakeValue(SizeToLong(specs.input_size)), matmul); + common::AnfAlgo::SetNodeAttr(kAttrHiddenSize, MakeValue(SizeToLong(specs.hidden_size)), matmul); std::vector output_shape = {1, specs.input_nz_size + specs.hidden_nz_size, specs.batch_nz_size, kCubeSize, kCubeSize}; - AnfAlgo::SetNodeAttr(kAttrFixedOutputDeviceShape, - MakeValue(std::vector>{Convert2Long(output_shape)}), matmul); + common::AnfAlgo::SetNodeAttr(kAttrFixedOutputDeviceShape, + MakeValue(std::vector>{Convert2Long(output_shape)}), matmul); } // Create split @@ -123,22 +124,22 @@ void DynamicRnnGradFissionV2::CreateTLoopNode(const FuncGraphPtr &func_graph, co auto split_v = NewCNode(splitv_input, func_graph); std::vector split_v_output0_shape{IntToSize(1), specs.batch_size, specs.input_size}; std::vector split_v_output1_shape{IntToSize(1), specs.batch_size, specs.hidden_size}; - AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat32, kNumberTypeFloat32}, - {split_v_output0_shape, split_v_output1_shape}, split_v.get()); - AnfAlgo::SetNodeAttr(kAttrSizeSplits, - MakeValue(std::vector{SizeToLong(specs.input_nz_size * kCubeSize), - SizeToLong(specs.hidden_nz_size * kCubeSize)}), - split_v); - AnfAlgo::SetNodeAttr(kAttrSplitDim, MakeValue(static_cast(kAttrSplitDimValue)), split_v); - AnfAlgo::SetNodeAttr(kAttrNumSplit, MakeValue(static_cast(kAttrNumSplitValue)), split_v); + common::AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat32, kNumberTypeFloat32}, + {split_v_output0_shape, split_v_output1_shape}, split_v.get()); + common::AnfAlgo::SetNodeAttr(kAttrSizeSplits, + MakeValue(std::vector{SizeToLong(specs.input_nz_size * kCubeSize), + SizeToLong(specs.hidden_nz_size * kCubeSize)}), + split_v); + common::AnfAlgo::SetNodeAttr(kAttrSplitDim, MakeValue(static_cast(kAttrSplitDimValue)), split_v); + common::AnfAlgo::SetNodeAttr(kAttrNumSplit, MakeValue(static_cast(kAttrNumSplitValue)), split_v); if (specs.shape_need_align) { - AnfAlgo::SetNodeAttr(kAttrFixedInputFormat, MakeValue(std::vector{kOpFormat_FRAC_NZ}), split_v); - AnfAlgo::SetNodeAttr(kAttrFixedOutputFormat, MakeValue(std::vector{kOpFormat_FRAC_NZ, kOpFormat_FRAC_NZ}), - split_v); + common::AnfAlgo::SetNodeAttr(kAttrFixedInputFormat, MakeValue(std::vector{kOpFormat_FRAC_NZ}), split_v); + common::AnfAlgo::SetNodeAttr(kAttrFixedOutputFormat, + MakeValue(std::vector{kOpFormat_FRAC_NZ, kOpFormat_FRAC_NZ}), split_v); std::vector input_shape = {1, specs.input_nz_size + specs.hidden_nz_size, specs.batch_nz_size, kCubeSize, kCubeSize}; - AnfAlgo::SetNodeAttr(kAttrFixedInputDeviceShape, - MakeValue(std::vector>{Convert2Long(input_shape)}), split_v); + common::AnfAlgo::SetNodeAttr(kAttrFixedInputDeviceShape, + MakeValue(std::vector>{Convert2Long(input_shape)}), split_v); } basic_lstm_cell_c_state_grad_nodes.emplace_back(basic_lstm_cell_c_state_grad); @@ -157,10 +158,10 @@ AnfNodePtr DynamicRnnGradFissionV2::CreateLSTMSPlitV(const FuncGraphPtr &func_gr std::vector lstm_split_input = {NewValueNode(std::make_shared(prim::kPrimSplitV->name())), input}; auto lstm_split = NewCNode(lstm_split_input, func_graph); - AnfAlgo::SetOutputInferTypeAndShape(split_types, split_shapes, lstm_split.get()); - AnfAlgo::SetNodeAttr(kAttrSizeSplits, MakeValue(size_split), lstm_split); - AnfAlgo::SetNodeAttr(kAttrSplitDim, MakeValue(static_cast(0)), lstm_split); - AnfAlgo::SetNodeAttr(kAttrNumSplit, MakeValue(SizeToLong(num_split_x)), lstm_split); + common::AnfAlgo::SetOutputInferTypeAndShape(split_types, split_shapes, lstm_split.get()); + common::AnfAlgo::SetNodeAttr(kAttrSizeSplits, MakeValue(size_split), lstm_split); + common::AnfAlgo::SetNodeAttr(kAttrSplitDim, MakeValue(static_cast(0)), lstm_split); + common::AnfAlgo::SetNodeAttr(kAttrNumSplit, MakeValue(SizeToLong(num_split_x)), lstm_split); return lstm_split; } @@ -195,10 +196,10 @@ void DynamicRnnGradFissionV2::CreateTLoopNodeWithEdge(const FuncGraphPtr &func_g dynamic_rnn_grad_cnode->input(kIndex6)}; auto reshape = NewCNode(reshape_inputs, func_graph); auto reshape_out_shape = {IntToSize(1), - AnfAlgo::GetOutputInferShape(dynamic_rnn_grad_cnode->input(kIndex6), 0)[0], - AnfAlgo::GetOutputInferShape(dynamic_rnn_grad_cnode->input(kIndex6), 0)[1]}; - auto reshape_out_dtype = AnfAlgo::GetOutputInferDataType(dynamic_rnn_grad_cnode->input(kIndex6), 0); - AnfAlgo::SetOutputInferTypeAndShape({reshape_out_dtype}, {reshape_out_shape}, reshape.get()); + common::AnfAlgo::GetOutputInferShape(dynamic_rnn_grad_cnode->input(kIndex6), 0)[0], + common::AnfAlgo::GetOutputInferShape(dynamic_rnn_grad_cnode->input(kIndex6), 0)[1]}; + auto reshape_out_dtype = common::AnfAlgo::GetOutputInferDataType(dynamic_rnn_grad_cnode->input(kIndex6), 0); + common::AnfAlgo::SetOutputInferTypeAndShape({reshape_out_dtype}, {reshape_out_shape}, reshape.get()); (void)basic_lstm_cell_c_state_grad_inputs.emplace_back(reshape); } else { (void)basic_lstm_cell_c_state_grad_inputs.emplace_back(lstm_split_c_outputs[idx - 1]); @@ -218,7 +219,7 @@ void DynamicRnnGradFissionV2::CreateTLoopNodeWithEdge(const FuncGraphPtr &func_g (void)basic_lstm_cell_c_state_grad_inputs.emplace_back(lstm_split_tanh_outputs[idx]); auto basic_lstm_cell_c_state_grad = NewCNode(basic_lstm_cell_c_state_grad_inputs, func_graph); basic_lstm_cell_c_state_grad->set_abstract(basic_lstm_cell_c_state_grad_nodes[i]->abstract()); - AnfAlgo::CopyNodeAttrs(basic_lstm_cell_c_state_grad_nodes[i], basic_lstm_cell_c_state_grad); + common::AnfAlgo::CopyNodeAttrs(basic_lstm_cell_c_state_grad_nodes[i], basic_lstm_cell_c_state_grad); // Create outputs for current basic_lstm_cell_c_state_grad node std::vector basic_lstm_cell_c_state_grad_outputs; CreateMultipleOutputsOfAnfNode(func_graph, basic_lstm_cell_c_state_grad, kBasicCellOutputNum, @@ -232,13 +233,13 @@ void DynamicRnnGradFissionV2::CreateTLoopNodeWithEdge(const FuncGraphPtr &func_g dynamic_rnn_grad_cnode->input(kIndex2)}; auto matmul = NewCNode(matmul_inputs, func_graph); matmul->set_abstract(matmul_nodes[i]->abstract()); - AnfAlgo::CopyNodeAttrs(matmul_nodes[i], matmul); + common::AnfAlgo::CopyNodeAttrs(matmul_nodes[i], matmul); // Create splitv std::vector splitv_input = {NewValueNode(std::make_shared(kSplitVOpName)), matmul}; auto split_v = NewCNode(splitv_input, func_graph); split_v->set_abstract(split_nodes[i]->abstract()); - AnfAlgo::CopyNodeAttrs(split_nodes[i], split_v); + common::AnfAlgo::CopyNodeAttrs(split_nodes[i], split_v); // Create outputs for current split node std::vector split_outputs; @@ -250,7 +251,8 @@ void DynamicRnnGradFissionV2::CreateTLoopNodeWithEdge(const FuncGraphPtr &func_g if (specs.batch_size % kCubeSize == 0 && !specs.shape_need_align) { lstm_gage_concat_input[idx + 1] = basic_lstm_cell_c_state_grad_outputs[0]; } else { - auto basic_lstm_cell_output_0_shape = AnfAlgo::GetOutputInferShape(basic_lstm_cell_c_state_grad_outputs[0], 0); + auto basic_lstm_cell_output_0_shape = + common::AnfAlgo::GetOutputInferShape(basic_lstm_cell_c_state_grad_outputs[0], 0); std::vector temp_shape; if (basic_lstm_cell_output_0_shape.size() == kBasicLstmCStateGradOutput0DimNum) { temp_shape = basic_lstm_cell_output_0_shape; @@ -260,8 +262,9 @@ void DynamicRnnGradFissionV2::CreateTLoopNodeWithEdge(const FuncGraphPtr &func_g std::vector reshape_input = {NewValueNode(std::make_shared(prim::kPrimReshape->name())), basic_lstm_cell_c_state_grad_outputs[0]}; auto reshape = NewCNode(reshape_input, func_graph); - AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(basic_lstm_cell_c_state_grad_outputs[0], 0)}, - {temp_shape}, reshape.get()); + common::AnfAlgo::SetOutputInferTypeAndShape( + {common::AnfAlgo::GetOutputInferDataType(basic_lstm_cell_c_state_grad_outputs[0], 0)}, {temp_shape}, + reshape.get()); lstm_gage_concat_input[idx + 1] = reshape; } } @@ -278,11 +281,11 @@ AnfNodePtr DynamicRnnGradFissionV2::AddLSTMInputGradNode(const FuncGraphPtr &fun std::vector> result_nodes; CreateTLoopNode(func_graph, dynamic_rnn_grad_cnode, specs, &result_nodes); - auto origin_input5_shape = AnfAlgo::GetOutputInferShape(dynamic_rnn_grad_cnode->input(kIndex6), 0); + auto origin_input5_shape = common::AnfAlgo::GetOutputInferShape(dynamic_rnn_grad_cnode->input(kIndex6), 0); std::vector split_c_dims{IntToSize(1), origin_input5_shape[0], origin_input5_shape[1]}; auto origin_input7 = dynamic_rnn_grad_cnode->input(kIndex8); - size_t num_split_x = AnfAlgo::GetOutputInferShape(origin_input7, 0)[0]; + size_t num_split_x = common::AnfAlgo::GetOutputInferShape(origin_input7, 0)[0]; std::vector> split_shapes; std::vector split_types; std::vector size_split; @@ -358,11 +361,12 @@ AnfNodePtr DynamicRnnGradFissionV2::AddLSTMInputGradNode(const FuncGraphPtr &fun if (specs.t_size != 1) { // Create lstm_x_concat auto lstm_x_concat = NewCNode(lstm_x_concat_input, func_graph); - AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat32}, {AnfAlgo::GetOutputInferShape(dynamic_rnn_grad_cnode, 2)}, - lstm_x_concat.get()); - AnfAlgo::SetNodeAttr(kAttrN, MakeValue(SizeToLong(num_split_x)), lstm_x_concat); - AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(std::vector{SizeToLong(num_split_x)}), lstm_x_concat); - AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(static_cast(0)), lstm_x_concat); + common::AnfAlgo::SetOutputInferTypeAndShape( + {kNumberTypeFloat32}, {common::AnfAlgo::GetOutputInferShape(dynamic_rnn_grad_cnode, 2)}, lstm_x_concat.get()); + common::AnfAlgo::SetNodeAttr(kAttrN, MakeValue(SizeToLong(num_split_x)), lstm_x_concat); + common::AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(std::vector{SizeToLong(num_split_x)}), + lstm_x_concat); + common::AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(static_cast(0)), lstm_x_concat); // Create lstm_gage_concat auto lstm_gage_concat = NewCNode(lstm_gage_concat_input, func_graph); @@ -372,11 +376,11 @@ AnfNodePtr DynamicRnnGradFissionV2::AddLSTMInputGradNode(const FuncGraphPtr &fun } else { gage_concat_shape = {specs.t_size, specs.batch_size, kDimMultiNum * specs.hidden_nz_size * kCubeSize}; } - AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat16}, {gage_concat_shape}, lstm_gage_concat.get()); - AnfAlgo::SetNodeAttr(kAttrN, MakeValue(SizeToLong(num_split_x)), lstm_gage_concat); - AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(std::vector{SizeToLong(num_split_x)}), - lstm_gage_concat); - AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(SizeToLong(0)), lstm_gage_concat); + common::AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat16}, {gage_concat_shape}, lstm_gage_concat.get()); + common::AnfAlgo::SetNodeAttr(kAttrN, MakeValue(SizeToLong(num_split_x)), lstm_gage_concat); + common::AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(std::vector{SizeToLong(num_split_x)}), + lstm_gage_concat); + common::AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(SizeToLong(0)), lstm_gage_concat); outputs->emplace_back(lstm_x_concat); outputs->emplace_back(pre_split_outputs[1]); @@ -396,14 +400,14 @@ AnfNodePtr DynamicRnnGradFissionV2::CreateSplitV(const FuncGraphPtr &func_graph, MS_EXCEPTION_IF_NULL(dynamic_rnn_grad_cnode); // Create node auto origin_input6 = dynamic_rnn_grad_cnode->input(kIndex7); - auto origin_input6_dtype = AnfAlgo::GetOutputInferDataType(origin_input6, 0); - auto origin_input6_shape = AnfAlgo::GetOutputInferShape(origin_input6, 0); + auto origin_input6_dtype = common::AnfAlgo::GetOutputInferDataType(origin_input6, 0); + auto origin_input6_shape = common::AnfAlgo::GetOutputInferShape(origin_input6, 0); std::vector splitv_input = {NewValueNode(std::make_shared(prim::kPrimSplitV->name()))}; if (specs.batch_size % kCubeSize == 0 && !specs.shape_need_align) { std::vector reshape_input = {NewValueNode(std::make_shared(kReshapeOpName)), origin_input6}; auto reshape = NewCNode(reshape_input, func_graph); std::vector shape = {origin_input6_shape[kDim0] * origin_input6_shape[kDim1], origin_input6_shape[kDim2]}; - AnfAlgo::SetOutputInferTypeAndShape({origin_input6_dtype}, {shape}, reshape.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({origin_input6_dtype}, {shape}, reshape.get()); splitv_input.push_back(reshape); } else { splitv_input.push_back(origin_input6); @@ -420,10 +424,10 @@ AnfNodePtr DynamicRnnGradFissionV2::CreateSplitV(const FuncGraphPtr &func_graph, } auto dtypes = {origin_input6_dtype, origin_input6_dtype}; std::vector> shapes = {shape1, shape2}; - AnfAlgo::SetOutputInferTypeAndShape(dtypes, shapes, split_v.get()); + common::AnfAlgo::SetOutputInferTypeAndShape(dtypes, shapes, split_v.get()); // Set attr - AnfAlgo::SetNodeAttr(kAttrSplitDim, MakeValue(SizeToLong(0)), split_v); - AnfAlgo::SetNodeAttr(kAttrNumSplit, MakeValue(SizeToLong(kAttrNumSplitValue)), split_v); + common::AnfAlgo::SetNodeAttr(kAttrSplitDim, MakeValue(SizeToLong(0)), split_v); + common::AnfAlgo::SetNodeAttr(kAttrNumSplit, MakeValue(SizeToLong(kAttrNumSplitValue)), split_v); std::vector size_splits; if (specs.batch_size % kCubeSize == 0 && !specs.shape_need_align) { size_splits = {SizeToLong((origin_input6_shape[kDim0] - 1) * origin_input6_shape[kDim1]), @@ -431,8 +435,8 @@ AnfNodePtr DynamicRnnGradFissionV2::CreateSplitV(const FuncGraphPtr &func_graph, } else { size_splits = {SizeToLong(origin_input6_shape[kDim0] - 1), 1}; } - AnfAlgo::SetNodeAttr(kAttrSizeSplits, MakeValue(size_splits), split_v); - AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), split_v); + common::AnfAlgo::SetNodeAttr(kAttrSizeSplits, MakeValue(size_splits), split_v); + common::AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), split_v); return split_v; } @@ -450,7 +454,7 @@ AnfNodePtr DynamicRnnGradFissionV2::CreateHConcat(const FuncGraphPtr &func_graph << trace::DumpSourceLines(dynamic_rnn_grad_cnode); } auto origin_input4 = dynamic_rnn_grad_cnode->input(kIndex5); - auto origin_input4_shape = AnfAlgo::GetOutputInferShape(origin_input4, 0); + auto origin_input4_shape = common::AnfAlgo::GetOutputInferShape(origin_input4, 0); // Create reshape to change shape std::vector shape_tmp; if (specs.batch_size % kCubeSize == 0 && !specs.shape_need_align) { @@ -466,12 +470,13 @@ AnfNodePtr DynamicRnnGradFissionV2::CreateHConcat(const FuncGraphPtr &func_graph std::vector reshape_input = {NewValueNode(std::make_shared(prim::kPrimReshape->name())), origin_input4}; auto reshape = NewCNode(reshape_input, func_graph); - AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(origin_input4, 0)}, {shape_tmp}, reshape.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({common::AnfAlgo::GetOutputInferDataType(origin_input4, 0)}, {shape_tmp}, + reshape.get()); std::vector concat_inputs = {NewValueNode(std::make_shared(prim::kPrimConcat->name())), reshape, splitv_outputs[0]}; auto concat = NewCNode(concat_inputs, func_graph); // Set infer data type and shape - auto splitv_output0_shape = AnfAlgo::GetOutputInferShape(splitv, 0); + auto splitv_output0_shape = common::AnfAlgo::GetOutputInferShape(splitv, 0); std::vector shape; if (specs.batch_size % kCubeSize == 0 && !specs.shape_need_align) { shape = {splitv_output0_shape[0] + origin_input4_shape[0], origin_input4_shape[1]}; @@ -479,12 +484,13 @@ AnfNodePtr DynamicRnnGradFissionV2::CreateHConcat(const FuncGraphPtr &func_graph // t_size - 1 + 1, batch_size, hidden_size shape = {splitv_output0_shape[0] + 1, origin_input4_shape[0], origin_input4_shape[1]}; } - AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(origin_input4, 0)}, {shape}, concat.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({common::AnfAlgo::GetOutputInferDataType(origin_input4, 0)}, {shape}, + concat.get()); // Set attr - AnfAlgo::SetNodeAttr(kAttrN, MakeValue(SizeToLong(kAttrNValue)), concat); - AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(std::vector{kAttrDynInputSizesValue}), concat); - AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(SizeToLong(0)), concat); - AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), concat); + common::AnfAlgo::SetNodeAttr(kAttrN, MakeValue(SizeToLong(kAttrNValue)), concat); + common::AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(std::vector{kAttrDynInputSizesValue}), concat); + common::AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(SizeToLong(0)), concat); + common::AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), concat); return concat; } @@ -494,14 +500,14 @@ AnfNodePtr DynamicRnnGradFissionV2::CreateConcat(const FuncGraphPtr &func_graph, MS_EXCEPTION_IF_NULL(dynamic_rnn_grad_cnode); // Create node auto origin_input0 = dynamic_rnn_grad_cnode->input(1); - auto origin_input0_dtype = AnfAlgo::GetOutputInferDataType(origin_input0, 0); - auto origin_input0_shape = AnfAlgo::GetOutputInferShape(origin_input0, 0); + auto origin_input0_dtype = common::AnfAlgo::GetOutputInferDataType(origin_input0, 0); + auto origin_input0_shape = common::AnfAlgo::GetOutputInferShape(origin_input0, 0); std::vector concat_inputs = {NewValueNode(std::make_shared(prim::kPrimConcat->name()))}; if (specs.batch_size % kCubeSize == 0 && !specs.shape_need_align) { std::vector reshape_input = {NewValueNode(std::make_shared(kReshapeOpName)), origin_input0}; auto reshape = NewCNode(reshape_input, func_graph); std::vector shape = {origin_input0_shape[kDim0] * origin_input0_shape[kDim1], origin_input0_shape[kDim2]}; - AnfAlgo::SetOutputInferTypeAndShape({origin_input0_dtype}, {shape}, reshape.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({origin_input0_dtype}, {shape}, reshape.get()); // t_size * batch_size, input_size concat_inputs.push_back(reshape); } else { @@ -511,7 +517,7 @@ AnfNodePtr DynamicRnnGradFissionV2::CreateConcat(const FuncGraphPtr &func_graph, concat_inputs.push_back(h_concat); auto concat = NewCNode(concat_inputs, func_graph); // Set infer data type and shape - auto h_concat_output_shape = AnfAlgo::GetOutputInferShape(h_concat, 0); + auto h_concat_output_shape = common::AnfAlgo::GetOutputInferShape(h_concat, 0); std::vector shape; if (specs.batch_size % kCubeSize == 0 && !specs.shape_need_align) { shape = {origin_input0_shape[kDim0] * origin_input0_shape[kDim1], @@ -521,25 +527,25 @@ AnfNodePtr DynamicRnnGradFissionV2::CreateConcat(const FuncGraphPtr &func_graph, shape = {origin_input0_shape[kDim0], origin_input0_shape[kDim1], origin_input0_shape[kDim2] + h_concat_output_shape[kDim2]}; } - AnfAlgo::SetOutputInferTypeAndShape({origin_input0_dtype}, {shape}, concat.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({origin_input0_dtype}, {shape}, concat.get()); // Set attr - AnfAlgo::SetNodeAttr(kAttrN, MakeValue(SizeToLong(kAttrNValue)), concat); - AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(std::vector{kAttrDynInputSizesValue}), concat); + common::AnfAlgo::SetNodeAttr(kAttrN, MakeValue(SizeToLong(kAttrNValue)), concat); + common::AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(std::vector{kAttrDynInputSizesValue}), concat); if (specs.batch_size % kCubeSize == 0 && !specs.shape_need_align) { - AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(1), concat); + common::AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(1), concat); } else { - AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(SizeToLong(kAttrAxis2Value)), concat); + common::AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(SizeToLong(kAttrAxis2Value)), concat); } if (specs.shape_need_align) { - AnfAlgo::SetNodeAttr(kAttrFixedInputFormat, MakeValue(std::vector{kOpFormat_FRAC_NZ, kOpFormat_FRAC_NZ}), - concat); - AnfAlgo::SetNodeAttr(kAttrFixedOutputFormat, MakeValue(std::vector{kOpFormat_FRAC_NZ}), concat); + common::AnfAlgo::SetNodeAttr(kAttrFixedInputFormat, + MakeValue(std::vector{kOpFormat_FRAC_NZ, kOpFormat_FRAC_NZ}), concat); + common::AnfAlgo::SetNodeAttr(kAttrFixedOutputFormat, MakeValue(std::vector{kOpFormat_FRAC_NZ}), concat); std::vector out_shape = {specs.t_size, specs.input_nz_size + specs.hidden_nz_size, specs.batch_nz_size, kCubeSize, kCubeSize}; - AnfAlgo::SetNodeAttr(kAttrFixedOutputDeviceShape, - MakeValue(std::vector>{Convert2Long(out_shape)}), concat); + common::AnfAlgo::SetNodeAttr(kAttrFixedOutputDeviceShape, + MakeValue(std::vector>{Convert2Long(out_shape)}), concat); } - AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), concat); + common::AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), concat); return concat; } @@ -551,13 +557,13 @@ AnfNodePtr DynamicRnnGradFissionV2::CreateConcatNodeT1(const FuncGraphPtr &func_ // Create node std::vector concat_inputs = {NewValueNode(std::make_shared(prim::kPrimConcat->name()))}; auto origin_input0 = dynamic_rnn_grad_cnode->input(kIndex1); - auto origin_input0_dtype = AnfAlgo::GetOutputInferDataType(origin_input0, 0); - auto origin_input0_shape = AnfAlgo::GetOutputInferShape(origin_input0, 0); + auto origin_input0_dtype = common::AnfAlgo::GetOutputInferDataType(origin_input0, 0); + auto origin_input0_shape = common::AnfAlgo::GetOutputInferShape(origin_input0, 0); if (specs.batch_size % kCubeSize == 0 && !specs.shape_need_align) { std::vector reshape_inputs = {NewValueNode(std::make_shared(kReshapeOpName)), origin_input0}; auto reshape_in0 = NewCNode(reshape_inputs, func_graph); std::vector shape = {origin_input0_shape[kDim0] * origin_input0_shape[kDim1], origin_input0_shape[kDim2]}; - AnfAlgo::SetOutputInferTypeAndShape({origin_input0_dtype}, {shape}, reshape_in0.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({origin_input0_dtype}, {shape}, reshape_in0.get()); // t_size * batch_size, input_size (t_size = 1) concat_inputs.push_back(reshape_in0); } else { @@ -566,7 +572,7 @@ AnfNodePtr DynamicRnnGradFissionV2::CreateConcatNodeT1(const FuncGraphPtr &func_ } auto origin_input4 = dynamic_rnn_grad_cnode->input(kIndex5); - auto origin_input4_shape = AnfAlgo::GetOutputInferShape(origin_input4, 0); + auto origin_input4_shape = common::AnfAlgo::GetOutputInferShape(origin_input4, 0); std::vector shape_tmp; if (specs.batch_size % kCubeSize == 0 && !specs.shape_need_align) { shape_tmp = {origin_input4_shape[0], origin_input4_shape[1]}; @@ -581,8 +587,8 @@ AnfNodePtr DynamicRnnGradFissionV2::CreateConcatNodeT1(const FuncGraphPtr &func_ std::vector reshape_input = {NewValueNode(std::make_shared(prim::kPrimReshape->name())), origin_input4}; auto reshape_in4 = NewCNode(reshape_input, func_graph); - AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(origin_input4, 0)}, {shape_tmp}, - reshape_in4.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({common::AnfAlgo::GetOutputInferDataType(origin_input4, 0)}, {shape_tmp}, + reshape_in4.get()); concat_inputs.push_back(reshape_in4); auto concat = NewCNode(concat_inputs, func_graph); // Set infer data type and shape @@ -593,25 +599,25 @@ AnfNodePtr DynamicRnnGradFissionV2::CreateConcatNodeT1(const FuncGraphPtr &func_ // t_size, batch_size, input_size + hidden_size (t_size = 1) shape = {origin_input0_shape[kDim0], origin_input0_shape[kDim1], origin_input0_shape[kDim2] + shape_tmp[kDim2]}; } - AnfAlgo::SetOutputInferTypeAndShape({origin_input0_dtype}, {shape}, concat.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({origin_input0_dtype}, {shape}, concat.get()); // Set attr - AnfAlgo::SetNodeAttr(kAttrN, MakeValue(SizeToLong(kAttrNValue)), concat); - AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(std::vector{kAttrDynInputSizesValue}), concat); + common::AnfAlgo::SetNodeAttr(kAttrN, MakeValue(SizeToLong(kAttrNValue)), concat); + common::AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(std::vector{kAttrDynInputSizesValue}), concat); if (specs.batch_size % kCubeSize == 0 && !specs.shape_need_align) { - AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(1), concat); + common::AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(1), concat); } else { - AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(SizeToLong(kAttrAxis2Value)), concat); + common::AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(SizeToLong(kAttrAxis2Value)), concat); } if (specs.shape_need_align) { - AnfAlgo::SetNodeAttr(kAttrFixedInputFormat, MakeValue(std::vector{kOpFormat_FRAC_NZ, kOpFormat_FRAC_NZ}), - concat); - AnfAlgo::SetNodeAttr(kAttrFixedOutputFormat, MakeValue(std::vector{kOpFormat_FRAC_NZ}), concat); + common::AnfAlgo::SetNodeAttr(kAttrFixedInputFormat, + MakeValue(std::vector{kOpFormat_FRAC_NZ, kOpFormat_FRAC_NZ}), concat); + common::AnfAlgo::SetNodeAttr(kAttrFixedOutputFormat, MakeValue(std::vector{kOpFormat_FRAC_NZ}), concat); std::vector out_shape = {specs.t_size, specs.input_nz_size + specs.hidden_nz_size, specs.batch_nz_size, kCubeSize, kCubeSize}; - AnfAlgo::SetNodeAttr(kAttrFixedOutputDeviceShape, - MakeValue(std::vector>{Convert2Long(out_shape)}), concat); + common::AnfAlgo::SetNodeAttr(kAttrFixedOutputDeviceShape, + MakeValue(std::vector>{Convert2Long(out_shape)}), concat); } - AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), concat); + common::AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), concat); return concat; } @@ -625,8 +631,8 @@ AnfNodePtr DynamicRnnGradFissionV2::CreateMatMulNode(const FuncGraphPtr &func_gr lstm_input_grad}; auto matmul = NewCNode(matmul_inputs, func_graph); // Set infer data type and shape - auto concat_shape = AnfAlgo::GetOutputInferShape(concat, 0); - auto lstm_input_grad_shape = AnfAlgo::GetOutputInferShape(lstm_input_grad, 0); + auto concat_shape = common::AnfAlgo::GetOutputInferShape(concat, 0); + auto lstm_input_grad_shape = common::AnfAlgo::GetOutputInferShape(lstm_input_grad, 0); std::vector shape; if (specs.batch_size % kCubeSize == 0 && !specs.shape_need_align) { // t_size * (input_size + hidden_size), 4 * hidden_size @@ -635,26 +641,26 @@ AnfNodePtr DynamicRnnGradFissionV2::CreateMatMulNode(const FuncGraphPtr &func_gr // t_size, input_size + hidden_size, 4 * hidden_nz_size * 16 shape = {concat_shape[kDim0], concat_shape[kDim2], lstm_input_grad_shape[kDim2]}; } - AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat16}, {shape}, matmul.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat16}, {shape}, matmul.get()); // Set attr - AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), matmul); - AnfAlgo::SetNodeAttr("transpose_x1", MakeValue(true), matmul); - AnfAlgo::SetNodeAttr("transpose_x2", MakeValue(false), matmul); + common::AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), matmul); + common::AnfAlgo::SetNodeAttr("transpose_x1", MakeValue(true), matmul); + common::AnfAlgo::SetNodeAttr("transpose_x2", MakeValue(false), matmul); if (specs.shape_need_align) { - AnfAlgo::SetNodeAttr(kAttrFixedInputFormat, MakeValue(std::vector{kOpFormat_FRAC_NZ, kOpFormat_FRAC_NZ}), - matmul); - AnfAlgo::SetNodeAttr(kAttrFixedOutputFormat, MakeValue(std::vector{kOpFormat_FRAC_NZ}), matmul); + common::AnfAlgo::SetNodeAttr(kAttrFixedInputFormat, + MakeValue(std::vector{kOpFormat_FRAC_NZ, kOpFormat_FRAC_NZ}), matmul); + common::AnfAlgo::SetNodeAttr(kAttrFixedOutputFormat, MakeValue(std::vector{kOpFormat_FRAC_NZ}), matmul); std::vector in0_shape = {specs.t_size, specs.input_nz_size + specs.hidden_nz_size, specs.batch_nz_size, kCubeSize, kCubeSize}; std::vector in1_shape = {specs.t_size, kDimMultiNum * specs.hidden_nz_size, specs.batch_nz_size, kCubeSize, kCubeSize}; std::vector out_shape = {specs.t_size, kDimMultiNum * specs.hidden_nz_size, specs.input_nz_size + specs.hidden_nz_size, kCubeSize, kCubeSize}; - AnfAlgo::SetNodeAttr(kAttrFixedInputDeviceShape, - MakeValue(std::vector>{Convert2Long(in0_shape), Convert2Long(in1_shape)}), - matmul); - AnfAlgo::SetNodeAttr(kAttrFixedOutputDeviceShape, - MakeValue(std::vector>{Convert2Long(out_shape)}), matmul); + common::AnfAlgo::SetNodeAttr( + kAttrFixedInputDeviceShape, + MakeValue(std::vector>{Convert2Long(in0_shape), Convert2Long(in1_shape)}), matmul); + common::AnfAlgo::SetNodeAttr(kAttrFixedOutputDeviceShape, + MakeValue(std::vector>{Convert2Long(out_shape)}), matmul); } return matmul; } @@ -669,18 +675,18 @@ AnfNodePtr DynamicRnnGradFissionV2::CreateMatMulNode2(const FuncGraphPtr &func_g lstm_input_grad}; auto matmul = NewCNode(matmul_inputs, func_graph); // Set infer data type and shape - auto lstm_input_grad_shape = AnfAlgo::GetOutputInferShape(lstm_input_grad, 0); + auto lstm_input_grad_shape = common::AnfAlgo::GetOutputInferShape(lstm_input_grad, 0); std::vector out_shape; if (specs.batch_size % kCubeSize == 0 && !specs.shape_need_align) { out_shape = {IntToSize(1), lstm_input_grad_shape[kDim1]}; } else { out_shape = {lstm_input_grad_shape[kDim0], IntToSize(1), lstm_input_grad_shape[kDim2]}; } - AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat16}, {out_shape}, matmul.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat16}, {out_shape}, matmul.get()); // Set attr - AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), matmul); - AnfAlgo::SetNodeAttr("transpose_x1", MakeValue(false), matmul); - AnfAlgo::SetNodeAttr("transpose_x2", MakeValue(false), matmul); + common::AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), matmul); + common::AnfAlgo::SetNodeAttr("transpose_x1", MakeValue(false), matmul); + common::AnfAlgo::SetNodeAttr("transpose_x2", MakeValue(false), matmul); return matmul; } @@ -691,17 +697,18 @@ CNodePtr DynamicRnnGradFissionV2::CreateTranspose(const FuncGraphPtr &func_graph dw_reduce_sum}; auto transpose = NewCNode(transpose_inputs, func_graph); std::vector out_shape = {specs.input_size + specs.hidden_size, kDimMultiNum * specs.hidden_size}; - AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(dw_reduce_sum, 0)}, {out_shape}, - transpose.get()); - AnfAlgo::SetNodeAttr(kAttrPerm, MakeValue(std::vector{1, 0, 2, 3}), transpose); - AnfAlgo::SetNodeAttr(kAttrInputSize, MakeValue(SizeToLong(specs.input_size)), transpose); - AnfAlgo::SetNodeAttr(kAttrHiddenSize, MakeValue(SizeToLong(specs.hidden_size)), transpose); - AnfAlgo::SetNodeAttr(kAttrFixedInputFormat, MakeValue(std::vector{kOpFormat_FRAC_NZ}), transpose); - AnfAlgo::SetNodeAttr(kAttrFixedOutputFormat, MakeValue(std::vector{kOpFormat_FRACTAL_ZN_RNN}), transpose); + common::AnfAlgo::SetOutputInferTypeAndShape({common::AnfAlgo::GetOutputInferDataType(dw_reduce_sum, 0)}, {out_shape}, + transpose.get()); + common::AnfAlgo::SetNodeAttr(kAttrPerm, MakeValue(std::vector{1, 0, 2, 3}), transpose); + common::AnfAlgo::SetNodeAttr(kAttrInputSize, MakeValue(SizeToLong(specs.input_size)), transpose); + common::AnfAlgo::SetNodeAttr(kAttrHiddenSize, MakeValue(SizeToLong(specs.hidden_size)), transpose); + common::AnfAlgo::SetNodeAttr(kAttrFixedInputFormat, MakeValue(std::vector{kOpFormat_FRAC_NZ}), transpose); + common::AnfAlgo::SetNodeAttr(kAttrFixedOutputFormat, MakeValue(std::vector{kOpFormat_FRACTAL_ZN_RNN}), + transpose); std::vector in_shape = {kDimMultiNum * specs.hidden_nz_size, specs.input_nz_size + specs.hidden_nz_size, kCubeSize, kCubeSize}; - AnfAlgo::SetNodeAttr(kAttrFixedInputDeviceShape, MakeValue(std::vector>{Convert2Long(in_shape)}), - transpose); + common::AnfAlgo::SetNodeAttr(kAttrFixedInputDeviceShape, + MakeValue(std::vector>{Convert2Long(in_shape)}), transpose); return transpose; } @@ -716,21 +723,21 @@ AnfNodePtr DynamicRnnGradFissionV2::CreateDwReduceSum(const FuncGraphPtr &func_g // Set infer data type and shape std::vector reduce_sum_shape = {specs.input_size + specs.hidden_size, kDimMultiNum * specs.hidden_nz_size * kCubeSize}; - AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(dynamic_rnn_grad_cnode, 0)}, {reduce_sum_shape}, - reduce_sum.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({common::AnfAlgo::GetOutputInferDataType(dynamic_rnn_grad_cnode, 0)}, + {reduce_sum_shape}, reduce_sum.get()); // Set attr - AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(std::vector{0}), reduce_sum); - AnfAlgo::SetNodeAttr(kAttrKeepDims, MakeValue(false), reduce_sum); - AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), reduce_sum); + common::AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(std::vector{0}), reduce_sum); + common::AnfAlgo::SetNodeAttr(kAttrKeepDims, MakeValue(false), reduce_sum); + common::AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), reduce_sum); if (specs.shape_need_align) { std::vector in_shape = {specs.t_size, kDimMultiNum * specs.hidden_nz_size, specs.input_nz_size + specs.hidden_nz_size, kCubeSize, kCubeSize}; std::vector out_shape = {kDimMultiNum * specs.hidden_nz_size, specs.input_nz_size + specs.hidden_nz_size, kCubeSize, kCubeSize}; - AnfAlgo::SetNodeAttr(kAttrFixedInputDeviceShape, - MakeValue(std::vector>{Convert2Long(in_shape)}), reduce_sum); - AnfAlgo::SetNodeAttr(kAttrFixedOutputDeviceShape, - MakeValue(std::vector>{Convert2Long(out_shape)}), reduce_sum); + common::AnfAlgo::SetNodeAttr(kAttrFixedInputDeviceShape, + MakeValue(std::vector>{Convert2Long(in_shape)}), reduce_sum); + common::AnfAlgo::SetNodeAttr(kAttrFixedOutputDeviceShape, + MakeValue(std::vector>{Convert2Long(out_shape)}), reduce_sum); } auto ret_node = reduce_sum; @@ -751,9 +758,9 @@ AnfNodePtr DynamicRnnGradFissionV2::CreateDwReshape(const FuncGraphPtr &func_gra // Set infer data type and shape std::vector out_shape = {specs.input_size + specs.hidden_size, kDimMultiNum * specs.hidden_nz_size * kCubeSize}; - AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(dynamic_rnn_grad_cnode, 0)}, {out_shape}, - reshape.get()); - AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), reshape); + common::AnfAlgo::SetOutputInferTypeAndShape({common::AnfAlgo::GetOutputInferDataType(dynamic_rnn_grad_cnode, 0)}, + {out_shape}, reshape.get()); + common::AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), reshape); auto ret_node = reshape; if (specs.shape_need_align) { @@ -778,7 +785,7 @@ AnfNodePtr DynamicRnnGradFissionV2::CreateValueNode(const FuncGraphPtr &func_gra auto kernel_graph = func_graph->cast(); auto value_node = kernel_graph->NewValueNode(x_abstract, tensor); kernel_graph->AddValueNodeToGraph(value_node); - AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat32}, {shape}, value_node.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat32}, {shape}, value_node.get()); return value_node; } @@ -793,8 +800,8 @@ AnfNodePtr DynamicRnnGradFissionV2::CreateDbReduceSum(const FuncGraphPtr &func_g matmul}; auto reshape = NewCNode(reshape_inputs, func_graph); std::vector out_shape = {kDimMultiNum * specs.hidden_size}; - AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat16}, {out_shape}, reshape.get()); - AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), reshape); + common::AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat16}, {out_shape}, reshape.get()); + common::AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), reshape); return reshape; } else { std::vector reduce_sum_inputs = { @@ -802,16 +809,18 @@ AnfNodePtr DynamicRnnGradFissionV2::CreateDbReduceSum(const FuncGraphPtr &func_g auto reduce_sum = NewCNode(reduce_sum_inputs, func_graph); // Set infer data type and shape std::vector out_shape = {kDimMultiNum * specs.hidden_size}; - AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat16}, {out_shape}, reduce_sum.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat16}, {out_shape}, reduce_sum.get()); // Set attr - AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(std::vector{0}), reduce_sum); - AnfAlgo::SetNodeAttr(kAttrKeepDims, MakeValue(false), reduce_sum); - AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), reduce_sum); + common::AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(std::vector{0}), reduce_sum); + common::AnfAlgo::SetNodeAttr(kAttrKeepDims, MakeValue(false), reduce_sum); + common::AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), reduce_sum); if (specs.shape_need_align) { - AnfAlgo::SetNodeAttr(kAttrInputSize, MakeValue(SizeToLong(specs.input_size)), reduce_sum); - AnfAlgo::SetNodeAttr(kAttrHiddenSize, MakeValue(SizeToLong(specs.hidden_size)), reduce_sum); - AnfAlgo::SetNodeAttr(kAttrFixedInputFormat, MakeValue(std::vector{kOpFormat_DEFAULT}), reduce_sum); - AnfAlgo::SetNodeAttr(kAttrFixedOutputFormat, MakeValue(std::vector{kOpFormat_ND_RNN_BIAS}), reduce_sum); + common::AnfAlgo::SetNodeAttr(kAttrInputSize, MakeValue(SizeToLong(specs.input_size)), reduce_sum); + common::AnfAlgo::SetNodeAttr(kAttrHiddenSize, MakeValue(SizeToLong(specs.hidden_size)), reduce_sum); + common::AnfAlgo::SetNodeAttr(kAttrFixedInputFormat, MakeValue(std::vector{kOpFormat_DEFAULT}), + reduce_sum); + common::AnfAlgo::SetNodeAttr(kAttrFixedOutputFormat, MakeValue(std::vector{kOpFormat_ND_RNN_BIAS}), + reduce_sum); } return reduce_sum; } @@ -833,16 +842,16 @@ const AnfNodePtr DynamicRnnGradFissionV2::Process(const FuncGraphPtr &func_graph << (kDynamicRNNGradInputNum + 1) << " inputs"; return nullptr; } - if (AnfAlgo::IsDynamicShape(node)) { + if (common::AnfAlgo::IsDynamicShape(node)) { MS_LOG(INFO) << "DynamicRNNGrad is dynamic shape, can not do fission."; return nullptr; } - auto input0_shape = AnfAlgo::GetOutputInferShape(dynamic_rnn_grad_cnode->input(kIndex1), 0); + auto input0_shape = common::AnfAlgo::GetOutputInferShape(dynamic_rnn_grad_cnode->input(kIndex1), 0); RNNShapeSpecs specs; specs.t_size = input0_shape[0]; specs.batch_size = input0_shape[1]; specs.input_size = input0_shape[kDim2]; - specs.hidden_size = AnfAlgo::GetOutputInferShape(dynamic_rnn_grad_cnode->input(kIndex7), 0)[kDim2]; + specs.hidden_size = common::AnfAlgo::GetOutputInferShape(dynamic_rnn_grad_cnode->input(kIndex7), 0)[kDim2]; if (specs.input_size % kCubeSize != 0 || specs.hidden_size % kCubeSize != 0) { specs.shape_need_align = true; SetAttrInputAndHiddenSize(func_graph, dynamic_rnn_grad_cnode, SizeToLong(specs.input_size), diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/gather_v2_ds_fission.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/gather_v2_ds_fission.cc index 6482667ba6e..70e48dd43e2 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/gather_v2_ds_fission.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/gather_v2_ds_fission.cc @@ -18,8 +18,9 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/trace_base.h" namespace mindspore { @@ -32,15 +33,15 @@ constexpr size_t kGatherInputAxisIndex = 3; bool CheckInputs(const CNodePtr &origin_node) { MS_EXCEPTION_IF_NULL(origin_node); - if (AnfAlgo::GetInputTensorNum(origin_node) != kGatherV2DynInputTensorNum) { + if (common::AnfAlgo::GetInputTensorNum(origin_node) != kGatherV2DynInputTensorNum) { MS_LOG(DEBUG) << "GatherV2 in dynamic shape has wrong inputs num, not equal " << kGatherV2DynInputTensorNum << ". CNode= " << origin_node->DebugString(); return false; } - auto param_shape = AnfAlgo::GetPrevNodeOutputInferShape(origin_node, 0); - auto indice_shape = AnfAlgo::GetPrevNodeOutputInferShape(origin_node, 1); + auto param_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(origin_node, 0); + auto indice_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(origin_node, 1); // this optimizer only support embedding_table has dynamic shape - if (param_shape.empty() || indice_shape.empty() || AnfAlgo::IsDynamicShape(origin_node->input(kDim2))) { + if (param_shape.empty() || indice_shape.empty() || common::AnfAlgo::IsDynamicShape(origin_node->input(kDim2))) { return false; } if (param_shape[param_shape.size() - 1] != 1) { @@ -80,7 +81,7 @@ CNodePtr GatherV2DsFission::CreatePad(const FuncGraphPtr &graph, const CNodePtr << trace::DumpSourceLines(origin_node); } shape[shape.size() - 1] = SizeToLong(pad_dim_size); - auto type_id = AnfAlgo::GetPrevNodeOutputInferDataType(origin_node, 0); + auto type_id = common::AnfAlgo::GetPrevNodeOutputInferDataType(origin_node, 0); auto abstract = std::make_shared(TypeIdToType(type_id), shape); MS_EXCEPTION_IF_NULL(abstract); if (param_dyn_shape->max_shape().size() == param_dyn_shape->shape().size() && @@ -104,9 +105,9 @@ CNodePtr GatherV2DsFission::CreatePad(const FuncGraphPtr &graph, const CNodePtr auto last_padding_value = MakeValue(last_padding_vector); elements.push_back(last_padding_value); ValueTuplePtr paddings = std::make_shared(elements); - AnfAlgo::SetNodeAttr(kAttrPaddings, paddings, pad); - AnfAlgo::SetNodeAttr(kAttrInputIsDynamicShape, MakeValue(true), pad); - AnfAlgo::SetNodeAttr(kAttrOutputIsDynamicShape, MakeValue(true), pad); + common::AnfAlgo::SetNodeAttr(kAttrPaddings, paddings, pad); + common::AnfAlgo::SetNodeAttr(kAttrInputIsDynamicShape, MakeValue(true), pad); + common::AnfAlgo::SetNodeAttr(kAttrOutputIsDynamicShape, MakeValue(true), pad); return pad; } @@ -126,14 +127,15 @@ CNodePtr GatherV2DsFission::CreateGatherV2Ds(const FuncGraphPtr &graph, const CN MS_EXCEPTION_IF_NULL(gather_v2); gather_v2->set_scope(origin_node->scope()); - auto shape = AnfAlgo::GetOutputInferShape(origin_node, 0); + auto shape = common::AnfAlgo::GetOutputInferShape(origin_node, 0); shape[shape.size() - 1] = pad_dim_size; - AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(origin_node, 0)}, {shape}, gather_v2.get()); - AnfAlgo::SetNodeAttr(kAttrInputIsDynamicShape, MakeValue(true), gather_v2); - auto input_names = AnfAlgo::GetNodeAttr>(origin_node, kAttrInputNames); - AnfAlgo::SetNodeAttr(kAttrInputNames, MakeValue(input_names), gather_v2); - auto output_names = AnfAlgo::GetNodeAttr>(origin_node, kAttrOutputNames); - AnfAlgo::SetNodeAttr(kAttrOutputNames, MakeValue(output_names), gather_v2); + common::AnfAlgo::SetOutputInferTypeAndShape({common::AnfAlgo::GetOutputInferDataType(origin_node, 0)}, {shape}, + gather_v2.get()); + common::AnfAlgo::SetNodeAttr(kAttrInputIsDynamicShape, MakeValue(true), gather_v2); + auto input_names = common::AnfAlgo::GetNodeAttr>(origin_node, kAttrInputNames); + common::AnfAlgo::SetNodeAttr(kAttrInputNames, MakeValue(input_names), gather_v2); + auto output_names = common::AnfAlgo::GetNodeAttr>(origin_node, kAttrOutputNames); + common::AnfAlgo::SetNodeAttr(kAttrOutputNames, MakeValue(output_names), gather_v2); return gather_v2; } @@ -147,10 +149,10 @@ CNodePtr GatherV2DsFission::CreateSlice(const FuncGraphPtr &graph, const CNodePt MS_EXCEPTION_IF_NULL(slice); slice->set_scope(gather_v2->scope()); slice->set_abstract(gather_v2->abstract()); - auto gather_v2_shape = AnfAlgo::GetOutputInferShape(gather_v2, 0); + auto gather_v2_shape = common::AnfAlgo::GetOutputInferShape(gather_v2, 0); std::vector offsets(gather_v2_shape.size(), 0); - AnfAlgo::SetNodeAttr(kAttrBegin, MakeValue(Convert2Long(offsets)), slice); - AnfAlgo::SetNodeAttr(kAttrSize, MakeValue(Convert2Long(gather_v2_shape)), slice); + common::AnfAlgo::SetNodeAttr(kAttrBegin, MakeValue(Convert2Long(offsets)), slice); + common::AnfAlgo::SetNodeAttr(kAttrSize, MakeValue(Convert2Long(gather_v2_shape)), slice); return slice; } @@ -169,7 +171,7 @@ const AnfNodePtr GatherV2DsFission::Process(const FuncGraphPtr &graph, const Anf return nullptr; } size_t pad_dim_size; - auto input_dtype = AnfAlgo::GetPrevNodeOutputInferDataType(origin_node, 0); + auto input_dtype = common::AnfAlgo::GetPrevNodeOutputInferDataType(origin_node, 0); constexpr auto PADSIZE32 = 8; constexpr auto PADSIZE16 = 16; if (input_dtype == kNumberTypeFloat32) { diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/lars_v2_fission.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/lars_v2_fission.cc index 35442bdfc06..21ae78a1967 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/lars_v2_fission.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/lars_v2_fission.cc @@ -16,8 +16,9 @@ #include "plugin/device/ascend/optimizer/ir_fission/lars_v2_fission.h" #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/optimizer/helper.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/trace_base.h" namespace mindspore { @@ -44,7 +45,7 @@ void LarsV2Fission::CreateOutputsOfSquareSumAll(const FuncGraphPtr &graph, const auto types = {kNumberTypeFloat32, kNumberTypeFloat32}; std::vector shape; auto shapes = {shape, shape}; - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, square_sum_all.get()); + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, square_sum_all.get()); CreateMultipleOutputsOfAnfNode(graph, square_sum_all, kSquareSumOutputNum, square_sum_all_outputs); } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/layer_norm_grad_split.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/layer_norm_grad_split.cc index 8806951e60f..4b67dbfbc0f 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/layer_norm_grad_split.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/layer_norm_grad_split.cc @@ -19,9 +19,10 @@ #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "runtime/device/kernel_info.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/trace_base.h" namespace mindspore { @@ -46,14 +47,14 @@ void LayerNormGradSplit::CreateOutputsOfLayerNormXBackpropV2(const FuncGraphPtr auto layer_norm_x_backprop = NewCNode(layer_norm_x_backprop_inputs, graph); MS_EXCEPTION_IF_NULL(layer_norm_x_backprop); layer_norm_x_backprop->set_scope(layer_norm_grad->scope()); - auto types = {AnfAlgo::GetOutputInferDataType(layer_norm_grad, 0), kNumberTypeFloat32}; - auto shapes = {AnfAlgo::GetOutputDetailShape(layer_norm_grad, 0), - AnfAlgo::GetPrevNodeOutputDetailShape(layer_norm_grad, 1)}; + auto types = {common::AnfAlgo::GetOutputInferDataType(layer_norm_grad, 0), kNumberTypeFloat32}; + auto shapes = {common::AnfAlgo::GetOutputDetailShape(layer_norm_grad, 0), + common::AnfAlgo::GetPrevNodeOutputDetailShape(layer_norm_grad, 1)}; if (is_dynamic) { - AnfAlgo::SetNodeAttr(kAttrInputIsDynamicShape, MakeValue(true), layer_norm_x_backprop); - AnfAlgo::SetNodeAttr(kAttrOutputIsDynamicShape, MakeValue(true), layer_norm_x_backprop); + common::AnfAlgo::SetNodeAttr(kAttrInputIsDynamicShape, MakeValue(true), layer_norm_x_backprop); + common::AnfAlgo::SetNodeAttr(kAttrOutputIsDynamicShape, MakeValue(true), layer_norm_x_backprop); } - AnfAlgo::SetOutputTypeAndDetailShape(types, shapes, layer_norm_x_backprop.get()); + common::AnfAlgo::SetOutputTypeAndDetailShape(types, shapes, layer_norm_x_backprop.get()); CreateMultipleOutputsOfAnfNode(graph, layer_norm_x_backprop, kLayerNormXBackpropV2OutputNum, layer_norm_x_backprop_outputs); @@ -73,18 +74,19 @@ void LayerNormGradSplit::CreateOutputsOfLayerNormBetaGammaBackpropV2( layer_norm_beta_gamma_backprop->set_kernel_info(kernel_info); layer_norm_beta_gamma_backprop->set_scope(layer_norm_grad->scope()); if (is_dynamic) { - AnfAlgo::SetNodeAttr(kAttrInputIsDynamicShape, MakeValue(true), layer_norm_beta_gamma_backprop); + common::AnfAlgo::SetNodeAttr(kAttrInputIsDynamicShape, MakeValue(true), layer_norm_beta_gamma_backprop); } - auto types = {AnfAlgo::GetOutputInferDataType(layer_norm_grad, kLayerNormGradOutputGammaIndex), - AnfAlgo::GetOutputInferDataType(layer_norm_grad, kLayerNormGradOutputBetaIndex)}; - auto shapes = {AnfAlgo::GetOutputDetailShape(layer_norm_grad, kLayerNormGradOutputGammaIndex), - AnfAlgo::GetOutputDetailShape(layer_norm_grad, kLayerNormGradOutputBetaIndex)}; - AnfAlgo::SetOutputTypeAndDetailShape(types, shapes, layer_norm_beta_gamma_backprop.get()); + auto types = {common::AnfAlgo::GetOutputInferDataType(layer_norm_grad, kLayerNormGradOutputGammaIndex), + common::AnfAlgo::GetOutputInferDataType(layer_norm_grad, kLayerNormGradOutputBetaIndex)}; + auto shapes = {common::AnfAlgo::GetOutputDetailShape(layer_norm_grad, kLayerNormGradOutputGammaIndex), + common::AnfAlgo::GetOutputDetailShape(layer_norm_grad, kLayerNormGradOutputBetaIndex)}; + common::AnfAlgo::SetOutputTypeAndDetailShape(types, shapes, layer_norm_beta_gamma_backprop.get()); // get device shape of LayerNormGrad's 5th Input, and convert it to attr std::vector shape_gamma = - AnfAlgo::GetPrevNodeOutputInferShape(layer_norm_grad, kLayerNormGradInputGammaIndex); - AnfAlgo::SetNodeAttr(kAttrShapeGamma, MakeValue(opt::Convert2Long(shape_gamma)), layer_norm_beta_gamma_backprop); + common::AnfAlgo::GetPrevNodeOutputInferShape(layer_norm_grad, kLayerNormGradInputGammaIndex); + common::AnfAlgo::SetNodeAttr(kAttrShapeGamma, MakeValue(opt::Convert2Long(shape_gamma)), + layer_norm_beta_gamma_backprop); CreateMultipleOutputsOfAnfNode(graph, layer_norm_beta_gamma_backprop, kLayerNormBetaGammaBackpropOutputNum, layer_norm_beta_gamma_backprop_outputs); @@ -101,10 +103,10 @@ const AnfNodePtr LayerNormGradSplit::Process(const FuncGraphPtr &graph, const An MS_EXCEPTION_IF_NULL(graph); MS_EXCEPTION_IF_NULL(node); auto cnode = node->cast(); - if (AnfAlgo::GetInputTensorNum(cnode) != kLayerNormGradInputTensorNum) { + if (common::AnfAlgo::GetInputTensorNum(cnode) != kLayerNormGradInputTensorNum) { return nullptr; } - bool is_dynamic_shape = AnfAlgo::IsDynamicShape(cnode); + bool is_dynamic_shape = common::AnfAlgo::IsDynamicShape(cnode); // create layer_norm_x_backprop std::vector layer_norm_x_backprop_outputs; CreateOutputsOfLayerNormXBackpropV2(graph, cnode, &layer_norm_x_backprop_outputs, is_dynamic_shape); diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/lin_space_fission.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/lin_space_fission.cc index 02d1271f146..6b60ea46aa8 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/lin_space_fission.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/lin_space_fission.cc @@ -18,6 +18,7 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "frontend/optimizer/opt.h" #include "backend/common/optimizer/helper.h" @@ -114,7 +115,7 @@ const AnfNodePtr LinSpaceFission::Process(const FuncGraphPtr &graph, const AnfNo MS_EXCEPTION_IF_NULL(new_cnode); new_cnode->set_abstract(cnode->abstract()); new_cnode->set_scope(cnode->scope()); - AnfAlgo::CopyNodeAttrs(cnode, new_cnode); + common::AnfAlgo::CopyNodeAttrs(cnode, new_cnode); if (kernel_graph != nullptr) { kernel_graph->AddValueNodeToGraph(assist_const); MS_LOG(INFO) << "Split linspace op success."; diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/max_pool3d_grad_grad_fission.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/max_pool3d_grad_grad_fission.cc index 8b0eae87216..18acac9d950 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/max_pool3d_grad_grad_fission.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/max_pool3d_grad_grad_fission.cc @@ -18,6 +18,7 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "frontend/optimizer/opt.h" #include "backend/common/optimizer/helper.h" #include "utils/trace_base.h" @@ -33,8 +34,8 @@ tensor::TensorPtr CreateTensor(const AnfNodePtr &node) { MS_EXCEPTION_IF_NULL(node); auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - auto ksize = AnfAlgo::GetNodeAttr>(cnode, "kernel_size"); - auto data_format = AnfAlgo::GetNodeAttr(cnode, "format"); + auto ksize = common::AnfAlgo::GetNodeAttr>(cnode, "kernel_size"); + auto data_format = common::AnfAlgo::GetNodeAttr(cnode, "format"); if (data_format != kOpFormat_NCDHW) { MS_LOG(ERROR) << "MaxPool3DGradGrad only support NCDHW format, but got " << data_format; } @@ -120,7 +121,7 @@ const AnfNodePtr MaxPool3DGradGradFission::Process(const FuncGraphPtr &graph, co MS_EXCEPTION_IF_NULL(new_cnode); new_cnode->set_abstract(cnode->abstract()); new_cnode->set_scope(cnode->scope()); - AnfAlgo::CopyNodeAttrs(cnode, new_cnode); + common::AnfAlgo::CopyNodeAttrs(cnode, new_cnode); if (kernel_graph != nullptr) { kernel_graph->AddValueNodeToGraph(assist_const); MS_LOG(INFO) << "Split MaxPool3DGradGrad op success."; diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/pack_fission.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/pack_fission.cc index a3557e648c7..1fd4cee03bc 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/pack_fission.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/pack_fission.cc @@ -17,6 +17,7 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "utils/trace_base.h" namespace mindspore { @@ -33,14 +34,14 @@ AnfNodePtr PackFission::CreateNewPack(const FuncGraphPtr &func_graph, const CNod MS_EXCEPTION_IF_NULL(new_pack); new_pack->set_scope(origin_pack_cnode->scope()); new_pack->set_abstract(origin_pack_cnode->abstract()); - AnfAlgo::CopyNodeAttr(kAttrAxis, origin_pack_cnode, new_pack); - AnfAlgo::SetNodeAttr(kAttrN, MakeValue(SizeToLong(offset)), new_pack); - AnfAlgo::SetNodeAttr(kAttrNum, MakeValue(SizeToLong(offset)), new_pack); + common::AnfAlgo::CopyNodeAttr(kAttrAxis, origin_pack_cnode, new_pack); + common::AnfAlgo::SetNodeAttr(kAttrN, MakeValue(SizeToLong(offset)), new_pack); + common::AnfAlgo::SetNodeAttr(kAttrNum, MakeValue(SizeToLong(offset)), new_pack); std::vector dyn_input_sizes{SizeToLong(offset)}; - AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(dyn_input_sizes), new_pack); + common::AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(dyn_input_sizes), new_pack); // infer shape - auto output_shape = AnfAlgo ::GetOutputInferShape(origin_pack_cnode, 0); - auto axis = AnfAlgo::GetNodeAttr(new_pack, kAttrAxis); + auto output_shape = common::AnfAlgo::GetOutputInferShape(origin_pack_cnode, 0); + auto axis = common::AnfAlgo::GetNodeAttr(new_pack, kAttrAxis); if (axis < 0) { axis += SizeToLong(output_shape.size()); } @@ -53,8 +54,8 @@ AnfNodePtr PackFission::CreateNewPack(const FuncGraphPtr &func_graph, const CNod if (axis_l < new_shape.size()) { new_shape[axis_l] = offset; } - AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(origin_pack_cnode, 0)}, {new_shape}, - new_pack.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({common::AnfAlgo::GetOutputInferDataType(origin_pack_cnode, 0)}, + {new_shape}, new_pack.get()); return new_pack; } @@ -66,7 +67,7 @@ const BaseRef PackFission::DefinePattern() const { const AnfNodePtr PackFission::Process(const FuncGraphPtr &func_graph, const AnfNodePtr &node, const EquivPtr &) const { MS_EXCEPTION_IF_NULL(func_graph); MS_EXCEPTION_IF_NULL(node); - if (AnfAlgo::IsDynamicShape(node)) { + if (common::AnfAlgo::IsDynamicShape(node)) { return nullptr; } auto cnode = node->cast(); @@ -92,11 +93,11 @@ const AnfNodePtr PackFission::Process(const FuncGraphPtr &func_graph, const AnfN MS_EXCEPTION_IF_NULL(base_concat); base_concat->set_scope(cnode->scope()); base_concat->set_abstract(cnode->abstract()); - AnfAlgo::CopyNodeAttr(kAttrAxis, cnode, base_concat); - AnfAlgo::SetNodeAttr(kAttrN, MakeValue(SizeToLong(base_concat_inputs.size() - 1)), base_concat); - AnfAlgo::SetNodeAttr(kAttrInputNums, MakeValue(SizeToLong(base_concat_inputs.size() - 1)), base_concat); + common::AnfAlgo::CopyNodeAttr(kAttrAxis, cnode, base_concat); + common::AnfAlgo::SetNodeAttr(kAttrN, MakeValue(SizeToLong(base_concat_inputs.size() - 1)), base_concat); + common::AnfAlgo::SetNodeAttr(kAttrInputNums, MakeValue(SizeToLong(base_concat_inputs.size() - 1)), base_concat); std::vector dyn_input_sizes{SizeToLong(base_concat_inputs.size() - 1)}; - AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(dyn_input_sizes), base_concat); + common::AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(dyn_input_sizes), base_concat); return base_concat; } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/reduce_min_fission.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/reduce_min_fission.cc index 987eed08af5..7b7878aeb25 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/reduce_min_fission.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/reduce_min_fission.cc @@ -17,6 +17,7 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -94,7 +95,7 @@ CNodePtr ReduceMinFission::CreateReduceMin(const FuncGraphPtr &graph, const AnfN CNodePtr reduce_min = NewCNode(inputs, graph); MS_EXCEPTION_IF_NULL(reduce_min); reduce_min->set_scope(old_node->scope()); - AnfAlgo::CopyNodeAttr(kAttrKeepDims, old_node, reduce_min); + common::AnfAlgo::CopyNodeAttr(kAttrKeepDims, old_node, reduce_min); return reduce_min; } @@ -107,15 +108,15 @@ const AnfNodePtr ReduceMinFission::Process(const FuncGraphPtr &graph, const AnfN if (graph == nullptr || node == nullptr) { return nullptr; } - if (AnfAlgo::IsDynamicShape(node)) { + if (common::AnfAlgo::IsDynamicShape(node)) { return nullptr; } auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); CheckCNodeInputSize(cnode, 1); - auto shape = AnfAlgo::GetPrevNodeOutputInferShape(cnode, 0); - auto dtype = AnfAlgo::GetPrevNodeOutputInferDataType(cnode, 0); - auto prim = AnfAlgo::GetCNodePrimitive(cnode); + auto shape = common::AnfAlgo::GetPrevNodeOutputInferShape(cnode, 0); + auto dtype = common::AnfAlgo::GetPrevNodeOutputInferDataType(cnode, 0); + auto prim = common::AnfAlgo::GetCNodePrimitive(cnode); MS_EXCEPTION_IF_NULL(prim); if (!prim->HasAttr(kAttrAxis) || !prim->HasAttr(kAttrKeepDims)) { MS_LOG(INFO) << "ReduceMin has no axis or keep_dims, no need to optimize!"; @@ -126,8 +127,8 @@ const AnfNodePtr ReduceMinFission::Process(const FuncGraphPtr &graph, const AnfN if (!axis_value->isa()) { return nullptr; } - auto axis = AnfAlgo::GetNodeAttr>(cnode, kAttrAxis); - auto keep_dims = AnfAlgo::GetNodeAttr(cnode, kAttrKeepDims); + auto axis = common::AnfAlgo::GetNodeAttr>(cnode, kAttrAxis); + auto keep_dims = common::AnfAlgo::GetNodeAttr(cnode, kAttrKeepDims); if (!NeedOptimize(dtype, shape, axis)) { MS_LOG(INFO) << "No need to optimize for this ReduceMin. " << cnode->DebugString(); @@ -138,14 +139,14 @@ const AnfNodePtr ReduceMinFission::Process(const FuncGraphPtr &graph, const AnfN CNodePtr reduce_min1 = CreateReduceMin(graph, cnode->input(1), cnode); std::vector axis_first = CalFirstAxis(shape, axis); std::vector shape_first = GetInferShape(shape, axis_first, keep_dims); - AnfAlgo::SetOutputInferTypeAndShape({dtype}, {shape_first}, reduce_min1.get()); - AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(axis_first), reduce_min1); + common::AnfAlgo::SetOutputInferTypeAndShape({dtype}, {shape_first}, reduce_min1.get()); + common::AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(axis_first), reduce_min1); // Create reduce_min2 CNodePtr reduce_min2 = CreateReduceMin(graph, reduce_min1, cnode); reduce_min2->set_abstract(cnode->abstract()); std::vector axis_last = {-1}; - AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(axis_last), reduce_min2); + common::AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(axis_last), reduce_min2); return reduce_min2; } } // namespace opt diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/reduce_sum_fission.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/reduce_sum_fission.cc index d076b455483..4f0db11572e 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/reduce_sum_fission.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/reduce_sum_fission.cc @@ -19,6 +19,7 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -29,16 +30,16 @@ CNodePtr AddCastNode(const FuncGraphPtr &func_graph, const TypeId dst_type, cons std::vector shape; if (fir_flag) { new_cast_inputs.emplace_back(input_node->inputs()[kIndex1]); - shape = AnfAlgo::GetOutputInferShape(input_node->inputs()[kIndex1], 0); + shape = common::AnfAlgo::GetOutputInferShape(input_node->inputs()[kIndex1], 0); } else { new_cast_inputs.emplace_back(input_node); - shape = AnfAlgo::GetOutputInferShape(input_node, 0); + shape = common::AnfAlgo::GetOutputInferShape(input_node, 0); } CNodePtr new_cast = NewCNode(new_cast_inputs, func_graph); new_cast->set_scope(input_node->scope()); new_cast->set_abstract(input_node->abstract()); - AnfAlgo::SetNodeAttr(kAttrDstType, MakeValue(static_cast(dst_type)), new_cast); - AnfAlgo::SetOutputInferTypeAndShape({dst_type}, {shape}, new_cast.get()); + common::AnfAlgo::SetNodeAttr(kAttrDstType, MakeValue(static_cast(dst_type)), new_cast); + common::AnfAlgo::SetOutputInferTypeAndShape({dst_type}, {shape}, new_cast.get()); return new_cast; } } // namespace @@ -53,35 +54,35 @@ CNodePtr AddReduceSumNode(const FuncGraphPtr &func_graph, const CNodePtr &input_ const std::vector &axis, const std::vector &out_shape) { MS_EXCEPTION_IF_NULL(func_graph); MS_EXCEPTION_IF_NULL(input_node); - auto input_type = AnfAlgo::GetOutputInferDataType(input_node, 0); + auto input_type = common::AnfAlgo::GetOutputInferDataType(input_node, 0); std::vector inputs = {NewValueNode(std::make_shared(prim::kPrimReduceSum->name())), input_node}; CNodePtr reduce_sum = NewCNode(inputs, func_graph); MS_EXCEPTION_IF_NULL(reduce_sum); reduce_sum->set_scope(input_node->scope()); - AnfAlgo::SetNodeAttr(kAttrKeepDims, MakeValue(keep_dims), reduce_sum); - AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(axis), reduce_sum); - AnfAlgo::SetOutputInferTypeAndShape({input_type}, {out_shape}, reduce_sum.get()); + common::AnfAlgo::SetNodeAttr(kAttrKeepDims, MakeValue(keep_dims), reduce_sum); + common::AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(axis), reduce_sum); + common::AnfAlgo::SetOutputInferTypeAndShape({input_type}, {out_shape}, reduce_sum.get()); return reduce_sum; } const AnfNodePtr ReduceSumFission::Process(const FuncGraphPtr &graph, const AnfNodePtr &node, const EquivPtr &) const { - auto input_type = AnfAlgo::GetOutputInferDataType(node, 0); + auto input_type = common::AnfAlgo::GetOutputInferDataType(node, 0); if (input_type != kNumberTypeBool) { return nullptr; } auto cnode = node->cast(); - auto prim = AnfAlgo::GetCNodePrimitive(cnode); - auto keep_dims = AnfAlgo::GetNodeAttr(cnode, kAttrKeepDims); - auto out_shape = AnfAlgo::GetOutputInferShape(cnode, 0); + auto prim = common::AnfAlgo::GetCNodePrimitive(cnode); + auto keep_dims = common::AnfAlgo::GetNodeAttr(cnode, kAttrKeepDims); + auto out_shape = common::AnfAlgo::GetOutputInferShape(cnode, 0); std::vector inp_axis; auto axis_value = prim->GetAttr(kAttrAxis); MS_EXCEPTION_IF_NULL(axis_value); if (!axis_value->isa()) { - int64_t axis = AnfAlgo::GetNodeAttr(cnode, kAttrAxis); + int64_t axis = common::AnfAlgo::GetNodeAttr(cnode, kAttrAxis); inp_axis.emplace_back(axis); } else { - auto axis = AnfAlgo::GetNodeAttr>(cnode, kAttrAxis); + auto axis = common::AnfAlgo::GetNodeAttr>(cnode, kAttrAxis); inp_axis = axis; } auto cast_to_node = AddCastNode(graph, kNumberTypeFloat32, cnode, true); diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/seed_adapter.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/seed_adapter.cc index 65cdb7fe056..a76f36a826e 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/seed_adapter.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/seed_adapter.cc @@ -20,10 +20,11 @@ #include #include "backend/common/optimizer/helper.h" #include "kernel/kernel_build_info.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/trace_base.h" #include "backend/common/session/kernel_graph.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "runtime/device/kernel_info.h" #include "kernel/oplib/oplib.h" @@ -75,11 +76,11 @@ std::vector ConvertAttrToValueNode(const std::shared_ptrname(), cnode)) { + if (!common::AnfAlgo::HasNodeAttr(attr->name(), cnode)) { MS_LOG(EXCEPTION) << "Node(" << cnode->DebugString() << ") doesn't have attr(" << attr->name() << ")." << trace::DumpSourceLines(cnode); } - auto attr_value = AnfAlgo::GetNodeAttr(cnode, attr->name()); + auto attr_value = common::AnfAlgo::GetNodeAttr(cnode, attr->name()); auto value_node = CreateValueNode(attr_value); if (value_node == nullptr) { MS_LOG(EXCEPTION) << "Create value node error, node: " << cnode->DebugString() << ", seed value: " << attr_value @@ -113,7 +114,7 @@ const AnfNodePtr SeedAdapter::Process(const FuncGraphPtr &func_graph, const AnfN MS_EXCEPTION_IF_NULL(node); auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - auto cnode_type = AnfAlgo::GetCNodeName(node); + auto cnode_type = common::AnfAlgo::GetCNodeName(node); if (kNodeWithSeedOperators.find(cnode_type) == kNodeWithSeedOperators.end()) { return nullptr; } @@ -128,7 +129,7 @@ const AnfNodePtr SeedAdapter::Process(const FuncGraphPtr &func_graph, const AnfN kernel_graph->AddValueNodeToGraph(value_node); } // 2. set visited - AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node); + common::AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node); return node; } } // namespace mindspore::opt diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/single_batch_norm_fission.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/single_batch_norm_fission.cc index 641d2d0d0d0..7c14ec8185a 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/single_batch_norm_fission.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/single_batch_norm_fission.cc @@ -17,6 +17,7 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/optimizer/helper.h" #include "utils/trace_base.h" @@ -83,7 +84,7 @@ AnfNodePtr SingleBatchNormFission::CreateBNTrainingUpdateV3( } bn_training_update_v3->set_abstract(bn->abstract()); bn_training_update_v3->set_scope(bn->scope()); - AnfAlgo::CopyNodeAttr(kAttrEpsilon, bn_cnode, bn_training_update_v3); + common::AnfAlgo::CopyNodeAttr(kAttrEpsilon, bn_cnode, bn_training_update_v3); return bn_training_update_v3; } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/space_to_depth_split.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/space_to_depth_split.cc index 67f48e0fd12..d012122f23d 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/space_to_depth_split.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/space_to_depth_split.cc @@ -18,6 +18,7 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "frontend/optimizer/opt.h" #include "backend/common/optimizer/helper.h" @@ -33,8 +34,8 @@ tensor::TensorPtr CreateTensor(const AnfNodePtr &node) { auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); auto input_x = cnode->input(kSpaceToDepthInputNum); - int64_t block_size = AnfAlgo::GetNodeAttr(cnode, "block_size"); - std::vector x_shape = AnfAlgo::GetOutputInferShape(input_x, 0); + int64_t block_size = common::AnfAlgo::GetNodeAttr(cnode, "block_size"); + std::vector x_shape = common::AnfAlgo::GetOutputInferShape(input_x, 0); int64_t input_channel = SizeToLong(x_shape[kDim1]); int64_t assist_input_channel = SizeToLong(x_shape[kDim1]) * block_size * block_size; std::vector assist_input_shape = {assist_input_channel, input_channel, block_size, block_size}; @@ -107,7 +108,7 @@ const AnfNodePtr SpaceToDepthSplit::Process(const FuncGraphPtr &graph, const Anf return nullptr; } const auto &ori_inputs = cnode->inputs(); - TypeId x_dtype = AnfAlgo::GetOutputInferDataType(ori_inputs[kIndex1], 0); + TypeId x_dtype = common::AnfAlgo::GetOutputInferDataType(ori_inputs[kIndex1], 0); if (x_dtype != kNumberTypeFloat16) { MS_LOG(INFO) << "Node " << cnode->DebugString() << ": The data type of node's first input is: " << x_dtype << ", not fp16, cannot do fusion."; @@ -122,7 +123,7 @@ const AnfNodePtr SpaceToDepthSplit::Process(const FuncGraphPtr &graph, const Anf MS_EXCEPTION_IF_NULL(new_cnode); new_cnode->set_abstract(cnode->abstract()); new_cnode->set_scope(cnode->scope()); - AnfAlgo::CopyNodeAttrs(cnode, new_cnode); + common::AnfAlgo::CopyNodeAttrs(cnode, new_cnode); if (kernel_graph != nullptr) { kernel_graph->AddValueNodeToGraph(last_input_value); MS_LOG(INFO) << "Split SpaceToDepth op success."; diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/split_fission.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/split_fission.cc index 782a14ad1da..dde8e55e5e1 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/split_fission.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/split_fission.cc @@ -17,6 +17,7 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "utils/trace_base.h" namespace mindspore { @@ -24,13 +25,13 @@ namespace opt { namespace { void SetAttrForSplitVNode(const AnfNodePtr &splitv, const std::vector &size_splits, int64_t split_dim, int64_t num_split) { - AnfAlgo::SetNodeAttr(kAttrSizeSplits, MakeValue(size_splits), splitv); - AnfAlgo::SetNodeAttr(kAttrSplitDim, MakeValue(split_dim), splitv); - AnfAlgo::SetNodeAttr(kAttrNumSplit, MakeValue(num_split), splitv); + common::AnfAlgo::SetNodeAttr(kAttrSizeSplits, MakeValue(size_splits), splitv); + common::AnfAlgo::SetNodeAttr(kAttrSplitDim, MakeValue(split_dim), splitv); + common::AnfAlgo::SetNodeAttr(kAttrNumSplit, MakeValue(num_split), splitv); } size_t GetSmallSplitSize(const AnfNodePtr &split_node, int64_t split_dim, int64_t num_split) { - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(split_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(split_node, 0); if (split_dim < 0) { split_dim += SizeToLong(input_shape.size()); } @@ -68,12 +69,12 @@ void CreateOutputShapeAndTypeId(const CNodePtr &origin_cnode, int64_t split_dim, std::vector> *new_output_shapes) { MS_EXCEPTION_IF_NULL(new_type_ids); MS_EXCEPTION_IF_NULL(new_output_shapes); - auto output_shape = AnfAlgo::GetOutputInferShape(origin_cnode, 0); + auto output_shape = common::AnfAlgo::GetOutputInferShape(origin_cnode, 0); if (split_dim < 0) { split_dim += SizeToLong(output_shape.size()); } output_shape[LongToSize(split_dim)] = LongToSize(split_size); - TypeId type_id = AnfAlgo::GetOutputInferDataType(origin_cnode, 0); + TypeId type_id = common::AnfAlgo::GetOutputInferDataType(origin_cnode, 0); for (int64_t i = 0; i < num_split; ++i) { new_type_ids->emplace_back(type_id); new_output_shapes->emplace_back(output_shape); @@ -85,8 +86,8 @@ void SetAttrAndAbstractForBaseSplitv(const CNodePtr &origin_cnode, const CNodePt const std::vector &size_splits_base, int64_t split_dim, int64_t num_split) { SetAttrForSplitVNode(base_splitv, size_splits_base, split_dim, num_split); - auto output_shape = AnfAlgo::GetOutputInferShape(origin_cnode, 0); - TypeId type_id = AnfAlgo::GetOutputInferDataType(origin_cnode, 0); + auto output_shape = common::AnfAlgo::GetOutputInferShape(origin_cnode, 0); + TypeId type_id = common::AnfAlgo::GetOutputInferDataType(origin_cnode, 0); std::vector base_type_ids(num_split, type_id); std::vector> base_output_shapes_base; if (split_dim < 0) { @@ -100,9 +101,9 @@ void SetAttrAndAbstractForBaseSplitv(const CNodePtr &origin_cnode, const CNodePt for (size_t i = 0; i < num_split_l; ++i) { output_shape[split_dim_l] = LongToSize(size_splits_base[i]); base_output_shapes_base.emplace_back(output_shape); - AnfAlgo::SetOutputInferTypeAndShape({type_id}, {output_shape}, base_splitv_outputs[i].get()); + common::AnfAlgo::SetOutputInferTypeAndShape({type_id}, {output_shape}, base_splitv_outputs[i].get()); } - AnfAlgo::SetOutputInferTypeAndShape(base_type_ids, base_output_shapes_base, base_splitv.get()); + common::AnfAlgo::SetOutputInferTypeAndShape(base_type_ids, base_output_shapes_base, base_splitv.get()); } } // namespace @@ -148,7 +149,7 @@ AnfNodePtr SplitFission::DoFission(const FuncGraphPtr &func_graph, const CNodePt base_splitv_outputs.push_back(tuple_getitem); CNodePtr new_splitv = CreateSplitVNode(func_graph, tuple_getitem); SetAttrForSplitVNode(new_splitv, size_splits_new, split_dim, divisor); - AnfAlgo::SetOutputInferTypeAndShape(new_type_ids, new_output_shapes, new_splitv.get()); + common::AnfAlgo::SetOutputInferTypeAndShape(new_type_ids, new_output_shapes, new_splitv.get()); AddNewOutputs(func_graph, new_splitv, divisor, &make_tuple_inputs); cur_output_index += divisor; size_splits_base.emplace_back(base_split_size); @@ -167,7 +168,7 @@ AnfNodePtr SplitFission::DoFission(const FuncGraphPtr &func_graph, const CNodePt std::vector> last_new_output_shapes; CreateOutputShapeAndTypeId(cnode, split_dim, small_split_size, last_node_num_split, &last_new_type_ids, &last_new_output_shapes); - AnfAlgo::SetOutputInferTypeAndShape(last_new_type_ids, last_new_output_shapes, new_splitv.get()); + common::AnfAlgo::SetOutputInferTypeAndShape(last_new_type_ids, last_new_output_shapes, new_splitv.get()); AddNewOutputs(func_graph, new_splitv, last_node_num_split, &make_tuple_inputs); size_splits_base.emplace_back(last_node_num_split * small_split_size); } else { @@ -192,20 +193,21 @@ const BaseRef SplitFission::DefinePattern() const { const AnfNodePtr SplitFission::Process(const FuncGraphPtr &func_graph, const AnfNodePtr &node, const EquivPtr &) const { MS_EXCEPTION_IF_NULL(node); - if (AnfAlgo::IsDynamicShape(node)) { + if (common::AnfAlgo::IsDynamicShape(node)) { return nullptr; } auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); // Check output num - if (!AnfAlgo::HasNodeAttr(kAttrOutputNum, cnode)) { + if (!common::AnfAlgo::HasNodeAttr(kAttrOutputNum, cnode)) { return nullptr; } - auto num_split = AnfAlgo::GetNodeAttr(cnode, kAttrOutputNum); + auto num_split = common::AnfAlgo::GetNodeAttr(cnode, kAttrOutputNum); if (num_split <= outputs_divisor_) { return nullptr; } - return DoFission(func_graph, cnode, num_split, outputs_divisor_, AnfAlgo::GetNodeAttr(cnode, kAttrAxis)); + return DoFission(func_graph, cnode, num_split, outputs_divisor_, + common::AnfAlgo::GetNodeAttr(cnode, kAttrAxis)); } } // namespace opt } // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/splitv_fission.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/splitv_fission.cc index 4fa45dc08b4..30fafc7736a 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/splitv_fission.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/splitv_fission.cc @@ -16,6 +16,7 @@ #include "plugin/device/ascend/optimizer/ir_fission/splitv_fission.h" #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore::opt { const BaseRef SplitVFission::DefinePattern() const { @@ -27,19 +28,20 @@ const BaseRef SplitVFission::DefinePattern() const { const AnfNodePtr SplitVFission::Process(const FuncGraphPtr &func_graph, const AnfNodePtr &node, const EquivPtr &) const { MS_EXCEPTION_IF_NULL(node); - if (AnfAlgo::IsDynamicShape(node)) { + if (common::AnfAlgo::IsDynamicShape(node)) { return nullptr; } auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); // Check output num - if (!AnfAlgo::HasNodeAttr(kAttrNumSplit, cnode)) { + if (!common::AnfAlgo::HasNodeAttr(kAttrNumSplit, cnode)) { return nullptr; } - auto num_split = AnfAlgo::GetNodeAttr(cnode, kAttrNumSplit); + auto num_split = common::AnfAlgo::GetNodeAttr(cnode, kAttrNumSplit); if (num_split <= outputs_divisor_) { return nullptr; } - return DoFission(func_graph, cnode, num_split, outputs_divisor_, AnfAlgo::GetNodeAttr(cnode, kAttrSplitDim)); + return DoFission(func_graph, cnode, num_split, outputs_divisor_, + common::AnfAlgo::GetNodeAttr(cnode, kAttrSplitDim)); } } // namespace mindspore::opt diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/tensor_scatter_update_fission.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/tensor_scatter_update_fission.cc index 601f9d13eb9..21e4cc228f2 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/tensor_scatter_update_fission.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/tensor_scatter_update_fission.cc @@ -17,6 +17,7 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/optimizer/helper.h" namespace mindspore { @@ -31,7 +32,7 @@ CNodePtr TensorScatterUpdateFission::CreateTensorMove(const FuncGraphPtr &graph, MS_EXCEPTION_IF_NULL(tensor_move); tensor_move->set_scope(tensor_scatter_update->scope()); tensor_move->set_abstract(tensor_scatter_update->abstract()); - AnfAlgo::SetNodeAttr(kAttrUseLocking, MakeValue(false), tensor_move); + common::AnfAlgo::SetNodeAttr(kAttrUseLocking, MakeValue(false), tensor_move); return tensor_move; } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/topk_split.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/topk_split.cc index 58160b3f32b..4f22bb8cade 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/topk_split.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/topk_split.cc @@ -21,9 +21,10 @@ #include "utils/hash_set.h" #include "backend/common/optimizer/const_input_to_attr.h" #include "kernel/kernel_build_info.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/session/kernel_graph.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "runtime/device/kernel_info.h" #include "utils/ms_context.h" @@ -95,7 +96,7 @@ kernel::KernelBuildInfoPtr CreateKernelBuildInfo() { } bool CheckInputNamesSize(const CNodePtr &cnode) { - auto input_names_vec = AnfAlgo::GetNodeAttr>(cnode, kAttrInputNames); + auto input_names_vec = common::AnfAlgo::GetNodeAttr>(cnode, kAttrInputNames); if (input_names_vec.size() < kTopkIndexK + 1) { MS_LOG(INFO) << "The input k of topk has been converted to attr"; return false; @@ -104,7 +105,7 @@ bool CheckInputNamesSize(const CNodePtr &cnode) { } bool CheckOutputShape(const AnfNodePtr &node) { - auto shape = AnfAlgo::GetPrevNodeOutputInferShape(node, 0); + auto shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node, 0); if (shape.empty()) { MS_LOG(INFO) << "The output shape of topk to split must not be empty"; return false; @@ -119,7 +120,7 @@ bool CheckOutputShape(const AnfNodePtr &node) { } bool CheckInputType(const AnfNodePtr &node) { - auto dtype = AnfAlgo::GetPrevNodeOutputInferDataType(node, 0); + auto dtype = common::AnfAlgo::GetPrevNodeOutputInferDataType(node, 0); const std::set aicore_supported_types = {kNumberTypeFloat16, kNumberTypeFloat32, kNumberTypeFloat}; if (aicore_supported_types.find(dtype) == aicore_supported_types.end()) { MS_LOG(INFO) << "The input data type of topk to split must be float"; @@ -129,7 +130,7 @@ bool CheckInputType(const AnfNodePtr &node) { } bool CheckFusion(const CNodePtr &node) { - if (!AnfAlgo::HasNodeAttr(kAttrSorted, node) || !AnfAlgo::GetNodeAttr(node, kAttrSorted)) { + if (!common::AnfAlgo::HasNodeAttr(kAttrSorted, node) || !common::AnfAlgo::GetNodeAttr(node, kAttrSorted)) { return false; } if (!CheckInputNamesSize(node)) { @@ -155,7 +156,7 @@ const BaseRef TopKSplit::DefinePattern() const { const AnfNodePtr TopKSplit::Process(const FuncGraphPtr &func_graph, const AnfNodePtr &node, const EquivPtr &) const { MS_EXCEPTION_IF_NULL(func_graph); MS_EXCEPTION_IF_NULL(node); - if (AnfAlgo::IsDynamicShape(node)) { + if (common::AnfAlgo::IsDynamicShape(node)) { return nullptr; } auto kernel_graph = func_graph->cast(); @@ -171,7 +172,7 @@ const AnfNodePtr TopKSplit::Process(const FuncGraphPtr &func_graph, const AnfNod MS_EXCEPTION_IF_NULL(new_cnode); new_cnode->set_abstract(cnode->abstract()); new_cnode->set_scope(cnode->scope()); - AnfAlgo::CopyNodeAttrs(cnode, new_cnode); + common::AnfAlgo::CopyNodeAttrs(cnode, new_cnode); CheckCNodeInputSize(new_cnode, kTopkInputTensorNum); // Convert the tensor input to scalar and convert it to attr auto input_k = new_cnode->input(kTopkIndexK + 1); diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/transdata_split.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/transdata_split.cc index aa548a5e596..69daf693375 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/transdata_split.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/transdata_split.cc @@ -27,7 +27,7 @@ const std::set> invalid_formats_pair = { const AnfNodePtr TransDataSplit::Process(const FuncGraphPtr &func_graph, const AnfNodePtr &node, const EquivPtr &) const { MS_EXCEPTION_IF_NULL(func_graph); - if (node != nullptr && node->isa() && AnfAlgo::GetCNodeName(node) == kTransDataOpName) { + if (node != nullptr && node->isa() && common::AnfAlgo::GetCNodeName(node) == kTransDataOpName) { CheckCNodeInputSize(node->cast(), kTransOpInputTensorNum); if (IsFormatInvaild(node)) { TraceGuard guard(std::make_shared(node->debug_info())); @@ -71,7 +71,7 @@ CNodePtr TransDataSplit::DoSplit(const FuncGraphPtr &func_graph, const AnfNodePt // if output_format=default transdata need split transdata->transpose else transpose->transdata if (output_format == kOpFormat_DEFAULT || output_format == kOpFormat_NCHW) { // trans input_format to hwcn - new_transdata_node = NewTransOpNode(func_graph, AnfAlgo::GetInputNode(node->cast(), 0), node, + new_transdata_node = NewTransOpNode(func_graph, common::AnfAlgo::GetInputNode(node->cast(), 0), node, kernel_select_, false, prim::kPrimTransData->name()); RefreshKernelBuildInfo(input_format, kOpFormat_HWCN, new_transdata_node, padding_axis); // trans hwcn to default_format @@ -82,10 +82,10 @@ CNodePtr TransDataSplit::DoSplit(const FuncGraphPtr &func_graph, const AnfNodePt } else { // trans default to hwcn new_transpose_node = - NewTransOpNode(func_graph, AnfAlgo::GetInputNode(node->cast(), 0), node, kernel_select_, false, + NewTransOpNode(func_graph, common::AnfAlgo::GetInputNode(node->cast(), 0), node, kernel_select_, false, prim::kPrimTranspose->name(), std::vector{2, 3, 1, 0}); if (output_format == kOpFormat_FRACTAL_ZN_LSTM) { - AnfAlgo::SetNodeAttr(kAttrNopOp, MakeValue(true), new_transpose_node); + common::AnfAlgo::SetNodeAttr(kAttrNopOp, MakeValue(true), new_transpose_node); } RefreshKernelBuildInfo(input_format, kOpFormat_HWCN, new_transpose_node); diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/unsorted_segment_sum_fission.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/unsorted_segment_sum_fission.cc index 244f081e96d..89978371e96 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/unsorted_segment_sum_fission.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/unsorted_segment_sum_fission.cc @@ -17,21 +17,22 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace opt { namespace { bool CheckInputs(const CNodePtr &origin_node) { MS_EXCEPTION_IF_NULL(origin_node); - if (AnfAlgo::GetInputTensorNum(origin_node) != kUnsortedSegmentSumInputTensorNum) { + if (common::AnfAlgo::GetInputTensorNum(origin_node) != kUnsortedSegmentSumInputTensorNum) { MS_LOG(DEBUG) << "UnsortedSegmentSum has wrong inputs num, not equal " << kUnsortedSegmentSumInputTensorNum << ". CNode= " << origin_node->DebugString(); return false; } - auto x_shape = AnfAlgo::GetPrevNodeOutputInferShape(origin_node, 0); - auto y_shape = AnfAlgo::GetPrevNodeOutputInferShape(origin_node, 1); + auto x_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(origin_node, 0); + auto y_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(origin_node, 1); if (x_shape.empty() || y_shape.empty()) { return false; } @@ -53,11 +54,11 @@ CNodePtr UnsortSegmentSumFission::CreatePadding(const FuncGraphPtr &graph, const auto padding = NewCNode(padding_inputs, graph); MS_EXCEPTION_IF_NULL(padding); padding->set_scope(origin_node->scope()); - auto shape = AnfAlgo::GetPrevNodeOutputInferShape(origin_node, 0); + auto shape = common::AnfAlgo::GetPrevNodeOutputInferShape(origin_node, 0); shape[shape.size() - 1] = pad_dim_size; - AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetPrevNodeOutputInferDataType(origin_node, 0)}, {shape}, - padding.get()); - AnfAlgo::SetNodeAttr(kAttrPadDimSize, MakeValue(SizeToLong(pad_dim_size)), padding); + common::AnfAlgo::SetOutputInferTypeAndShape({common::AnfAlgo::GetPrevNodeOutputInferDataType(origin_node, 0)}, + {shape}, padding.get()); + common::AnfAlgo::SetNodeAttr(kAttrPadDimSize, MakeValue(SizeToLong(pad_dim_size)), padding); return padding; } @@ -72,11 +73,11 @@ CNodePtr UnsortSegmentSumFission::CreateUnsortedSegmentSum(const FuncGraphPtr &g auto unsorted_segment_sum = NewCNode(unsorted_segment_sum8_inputs, graph); MS_EXCEPTION_IF_NULL(unsorted_segment_sum); unsorted_segment_sum->set_scope(origin_node->scope()); - auto shape = AnfAlgo::GetOutputInferShape(origin_node, 0); + auto shape = common::AnfAlgo::GetOutputInferShape(origin_node, 0); shape[shape.size() - 1] = pad_dim_size; - AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(origin_node, 0)}, {shape}, - unsorted_segment_sum.get()); - AnfAlgo::SetNodeAttr(kAttrNumSegments, MakeValue(SizeToLong(shape[0])), unsorted_segment_sum); + common::AnfAlgo::SetOutputInferTypeAndShape({common::AnfAlgo::GetOutputInferDataType(origin_node, 0)}, {shape}, + unsorted_segment_sum.get()); + common::AnfAlgo::SetNodeAttr(kAttrNumSegments, MakeValue(SizeToLong(shape[0])), unsorted_segment_sum); return unsorted_segment_sum; } @@ -91,10 +92,10 @@ CNodePtr UnsortSegmentSumFission::CreateSlice(const FuncGraphPtr &graph, const C MS_EXCEPTION_IF_NULL(slice); slice->set_scope(unsort_segment_sum->scope()); slice->set_abstract(unsort_segment_sum->abstract()); - auto unsort_segment_sum_shape = AnfAlgo::GetOutputInferShape(unsort_segment_sum, 0); + auto unsort_segment_sum_shape = common::AnfAlgo::GetOutputInferShape(unsort_segment_sum, 0); std::vector offsets(unsort_segment_sum_shape.size(), 0); - AnfAlgo::SetNodeAttr(kAttrBegin, MakeValue(Convert2Long(offsets)), slice); - AnfAlgo::SetNodeAttr(kAttrSize, MakeValue(Convert2Long(unsort_segment_sum_shape)), slice); + common::AnfAlgo::SetNodeAttr(kAttrBegin, MakeValue(Convert2Long(offsets)), slice); + common::AnfAlgo::SetNodeAttr(kAttrSize, MakeValue(Convert2Long(unsort_segment_sum_shape)), slice); return slice; } @@ -114,7 +115,7 @@ const AnfNodePtr UnsortSegmentSumFission::Process(const FuncGraphPtr &graph, con return nullptr; } size_t pad_dim_size; - auto input_dtype = AnfAlgo::GetPrevNodeOutputInferDataType(origin_node, 0); + auto input_dtype = common::AnfAlgo::GetPrevNodeOutputInferDataType(origin_node, 0); constexpr auto PADSIZE32 = 8; constexpr auto PADSIZE16 = 16; if (input_dtype == kNumberTypeFloat32) { diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/adam_apply_one_fusion.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/adam_apply_one_fusion.cc index 1ffad3763fa..dcdeead5031 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/adam_apply_one_fusion.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/adam_apply_one_fusion.cc @@ -16,6 +16,7 @@ #include "plugin/device/ascend/optimizer/ir_fusion/adam_apply_one_fusion.h" #include "backend/common/optimizer/helper.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "utils/trace_base.h" namespace mindspore { namespace opt { @@ -209,7 +210,7 @@ AnfNodePtr AdamApplyOneFusion::CreateAdamApplyOneNode(const FuncGraphPtr &func_g MS_EXCEPTION_IF_NULL(func_graph); MS_EXCEPTION_IF_NULL(equiv); PrimitivePtr prim = nullptr; - if (AnfAlgo::CheckPrimitiveType(final_node, prim::kPrimDepend)) { + if (common::AnfAlgo::CheckPrimitiveType(final_node, prim::kPrimDepend)) { prim = std::make_shared(kAdamApplyOneAssignOpName); } else { prim = std::make_shared(kAdamApplyOneOpName); @@ -236,7 +237,7 @@ const AnfNodePtr AdamApplyOneFusion::Process(const FuncGraphPtr &func_graph, con const EquivPtr &equiv) const { MS_EXCEPTION_IF_NULL(func_graph); auto sub0 = node; - if (AnfAlgo::CheckPrimitiveType(node, prim::kPrimDepend)) { + if (common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimDepend)) { auto iter_sub0 = (*equiv).find(sub0_var_); if (iter_sub0 == (*equiv).end()) { MS_LOG(EXCEPTION) << "The equiv map is expected to contains the sub0 var after matched." diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/adam_apply_one_fusion.h b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/adam_apply_one_fusion.h index 3c968b63c00..270b5d96921 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/adam_apply_one_fusion.h +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/adam_apply_one_fusion.h @@ -20,7 +20,7 @@ #include #include #include "backend/common/optimizer/optimizer.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace opt { diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/adam_apply_one_with_decay_rule.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/adam_apply_one_with_decay_rule.cc index 9192d0a7e9c..123c285626b 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/adam_apply_one_with_decay_rule.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/adam_apply_one_with_decay_rule.cc @@ -15,6 +15,7 @@ */ #include "plugin/device/ascend/optimizer/ir_fusion/adam_apply_one_with_decay_rule.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" #include "backend/common/optimizer/helper.h" #include "utils/trace_base.h" @@ -36,7 +37,7 @@ std::vector AdamApplyOneWithDecayRule::GetFusionNodeInputs(const Equ auto mul4_x = utils::cast((*equiv)[mul4_x_]); auto add2_y = utils::cast((*equiv)[add2_y_]); PrimitivePtr prim = nullptr; - if (AnfAlgo::CheckPrimitiveType(final_node, prim::kPrimDepend)) { + if (common::AnfAlgo::CheckPrimitiveType(final_node, prim::kPrimDepend)) { prim = std::make_shared(kAdamApplyOneWithDecayAssignOpName); } else { prim = std::make_shared(kAdamApplyOneWithDecayOpName); @@ -275,7 +276,7 @@ const AnfNodePtr AdamApplyOneWithDecayRule::Process(const FuncGraphPtr &graph, c return nullptr; } auto sub0 = node; - if (AnfAlgo::CheckPrimitiveType(node, prim::kPrimDepend)) { + if (common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimDepend)) { auto iter_sub0 = (*equiv).find(sub0_var_); if (iter_sub0 == (*equiv).end()) { MS_LOG(EXCEPTION) << "The equiv map is expected to contains the sub0 var after matched." @@ -306,11 +307,11 @@ const AnfNodePtr AdamApplyOneWithDecayRule::Process(const FuncGraphPtr &graph, c MS_EXCEPTION_IF_NULL(add0); auto add1 = utils::cast(iter_add1->second); MS_EXCEPTION_IF_NULL(add1); - auto types = {AnfAlgo::GetOutputInferDataType(add1, 0), AnfAlgo::GetOutputInferDataType(add0, 0), - AnfAlgo::GetOutputInferDataType(sub0, 0)}; - auto shapes = {AnfAlgo::GetOutputInferShape(add1, 0), AnfAlgo::GetOutputInferShape(add0, 0), - AnfAlgo::GetOutputInferShape(sub0, 0)}; - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, fusion_node.get()); + auto types = {common::AnfAlgo::GetOutputInferDataType(add1, 0), common::AnfAlgo::GetOutputInferDataType(add0, 0), + common::AnfAlgo::GetOutputInferDataType(sub0, 0)}; + auto shapes = {common::AnfAlgo::GetOutputInferShape(add1, 0), common::AnfAlgo::GetOutputInferShape(add0, 0), + common::AnfAlgo::GetOutputInferShape(sub0, 0)}; + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, fusion_node.get()); std::vector fusion_node_outputs; CreateMultipleOutputsOfAnfNode(graph, fusion_node, kAdamApplyOneWithDecayOutputNum, &fusion_node_outputs); diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/adam_apply_one_with_decay_rule.h b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/adam_apply_one_with_decay_rule.h index b3709a16374..66c846bdc61 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/adam_apply_one_with_decay_rule.h +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/adam_apply_one_with_decay_rule.h @@ -20,7 +20,7 @@ #include #include #include "backend/common/optimizer/optimizer.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace opt { class AdamApplyOneWithDecayRule : public PatternProcessPass { diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/add_input_to_output.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/add_input_to_output.cc index 1135927733f..21cce56de34 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/add_input_to_output.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/add_input_to_output.cc @@ -18,6 +18,7 @@ #include #include "plugin/device/ascend/optimizer/ir_fusion/input_to_output_registry.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "kernel/oplib/oplib.h" #include "utils/trace_base.h" @@ -26,7 +27,7 @@ namespace opt { namespace { void GetInputOrOutputNames(const CNodePtr &cnode, const std::string &attr_name, std::vector *names_vec) { MS_EXCEPTION_IF_NULL(names_vec); - auto primitive = AnfAlgo::GetCNodePrimitive(cnode); + auto primitive = common::AnfAlgo::GetCNodePrimitive(cnode); MS_EXCEPTION_IF_NULL(primitive); ValuePtr names_value = primitive->GetAttr(attr_name); if (names_value == nullptr) { @@ -68,7 +69,7 @@ void AddOutputs(const CNodePtr &cnode, const std::vector &input_indices) } } if (!output_names_vec.empty()) { - AnfAlgo::SetNodeAttr(kAttrOutputNames, MakeValue(output_names_vec), cnode); + common::AnfAlgo::SetNodeAttr(kAttrOutputNames, MakeValue(output_names_vec), cnode); } auto abstract_tuple = std::make_shared(abstract_list); cnode->set_abstract(abstract_tuple); @@ -82,7 +83,7 @@ const AnfNodePtr AddInputToOutput::Process(const FuncGraphPtr &func_graph, const } auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - std::string op_name = AnfAlgo::GetCNodeName(cnode); + std::string op_name = common::AnfAlgo::GetCNodeName(cnode); InputToOutputRegister reg; if (!InputToOutputRegistry::Instance().GetRegisterByOpName(op_name, ®)) { return nullptr; @@ -93,10 +94,10 @@ const AnfNodePtr AddInputToOutput::Process(const FuncGraphPtr &func_graph, const return nullptr; } // No need add output if the output num matches the registered output num for tbe. - if (AnfAlgo::GetOutputTensorNum(cnode) >= IntToSize(output_num)) { + if (common::AnfAlgo::GetOutputTensorNum(cnode) >= IntToSize(output_num)) { return nullptr; } - bool is_origin_tuple_output = AnfAlgo::IsTupleOutput(cnode); + bool is_origin_tuple_output = common::AnfAlgo::IsTupleOutput(cnode); AddOutputs(cnode, reg.input_indices()); // No need to create tuple_getitem if the origin output is a tuple because there has already been some tuple_getitems // pointed to the outputs. diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/avgpool_3d_fusion.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/avgpool_3d_fusion.cc index b5d7c0e4970..69f9c07a8c5 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/avgpool_3d_fusion.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/avgpool_3d_fusion.cc @@ -20,6 +20,7 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -51,8 +52,8 @@ bool GetKernelSize(const AnfNodePtr &node, int64_t *kd, int64_t *kh, int64_t *kw MS_EXCEPTION_IF_NULL(kd); MS_EXCEPTION_IF_NULL(kh); MS_EXCEPTION_IF_NULL(kw); - if (AnfAlgo::HasNodeAttr("kernel_size", node->cast())) { - auto kernel_size = AnfAlgo::GetNodeAttr>(node, "kernel_size"); + if (common::AnfAlgo::HasNodeAttr("kernel_size", node->cast())) { + auto kernel_size = common::AnfAlgo::GetNodeAttr>(node, "kernel_size"); if (kernel_size.size() == 1) { *kd = kernel_size[kDim0]; *kh = kernel_size[kDim0]; @@ -79,8 +80,8 @@ bool GetStrideSize(const AnfNodePtr &node, int64_t *sd, int64_t *sh, int64_t *sw MS_EXCEPTION_IF_NULL(sd); MS_EXCEPTION_IF_NULL(sh); MS_EXCEPTION_IF_NULL(sw); - if (AnfAlgo::HasNodeAttr("strides", node->cast())) { - auto stride_size = AnfAlgo::GetNodeAttr>(node, "strides"); + if (common::AnfAlgo::HasNodeAttr("strides", node->cast())) { + auto stride_size = common::AnfAlgo::GetNodeAttr>(node, "strides"); if (stride_size.size() == 1) { *sd = stride_size[kDim0]; *sh = stride_size[kDim0]; @@ -105,18 +106,18 @@ bool GetStrideSize(const AnfNodePtr &node, int64_t *sd, int64_t *sh, int64_t *sw void GetAttrs(const AnfNodePtr &node, std::vector *pad_list, bool *count_include_pad, bool *ceil_mode, int64_t *divisor_override) { MS_EXCEPTION_IF_NULL(node); - if (!AnfAlgo::HasNodeAttr("pad_list", node->cast())) { + if (!common::AnfAlgo::HasNodeAttr("pad_list", node->cast())) { MS_LOG(EXCEPTION) << "AvgPool3D should has attr pad_list" << trace::DumpSourceLines(node); } - *pad_list = AnfAlgo::GetNodeAttr>(node, "pad_list"); - if (AnfAlgo::HasNodeAttr("count_include_pad", node->cast())) { - *count_include_pad = AnfAlgo::GetNodeAttr(node, "count_include_pad"); + *pad_list = common::AnfAlgo::GetNodeAttr>(node, "pad_list"); + if (common::AnfAlgo::HasNodeAttr("count_include_pad", node->cast())) { + *count_include_pad = common::AnfAlgo::GetNodeAttr(node, "count_include_pad"); } - if (AnfAlgo::HasNodeAttr("ceil_mode", node->cast())) { - *ceil_mode = AnfAlgo::GetNodeAttr(node, "ceil_mode"); + if (common::AnfAlgo::HasNodeAttr("ceil_mode", node->cast())) { + *ceil_mode = common::AnfAlgo::GetNodeAttr(node, "ceil_mode"); } - if (AnfAlgo::HasNodeAttr("divisor_override", node->cast())) { - *divisor_override = AnfAlgo::GetNodeAttr(node, "divisor_override"); + if (common::AnfAlgo::HasNodeAttr("divisor_override", node->cast())) { + *divisor_override = common::AnfAlgo::GetNodeAttr(node, "divisor_override"); } } @@ -205,7 +206,7 @@ AnfNodePtr ConstructMultiplier(const FuncGraphPtr &func_graph, int64_t fn, int64 MS_EXCEPTION_IF_NULL(kernel_graph); auto value_node = kernel_graph->NewValueNode(x_abstract, tensor); kernel_graph->AddValueNodeToGraph(value_node); - AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat16}, {infer_shape}, value_node.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat16}, {infer_shape}, value_node.get()); return value_node; } } // namespace @@ -233,7 +234,7 @@ AnfNodePtr ConstructFilterValueNode(const FuncGraphPtr &func_graph, float val, c MS_EXCEPTION_IF_NULL(kernel_graph); auto value_node = kernel_graph->NewValueNode(x_abstract, assist_tensor); kernel_graph->AddValueNodeToGraph(value_node); - AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat16}, {infer_shape}, value_node.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat16}, {infer_shape}, value_node.get()); return value_node; } @@ -253,8 +254,8 @@ const AnfNodePtr AvgPool3DFusion::Process(const FuncGraphPtr &func_graph, const << " inputs. Can not do fusion."; return nullptr; } - auto dims_in = AnfAlgo::GetPrevNodeOutputInferShape(avg_pool_3d_node, 0); - auto dims_out = AnfAlgo::GetOutputInferShape(avg_pool_3d_node, 0); + auto dims_in = common::AnfAlgo::GetPrevNodeOutputInferShape(avg_pool_3d_node, 0); + auto dims_out = common::AnfAlgo::GetOutputInferShape(avg_pool_3d_node, 0); if (dims_in.size() < k5DInferDims || dims_out.size() < k5DInferDims) { MS_LOG(EXCEPTION) << "AvgPool3D's in_out infer shape dims can not be less " << k5DInferDims << ", but got in_shape is " << dims_in.size() << "-D, out_shape is " << dims_out.size() @@ -307,7 +308,7 @@ const AnfNodePtr AvgPool3DFusion::Process(const FuncGraphPtr &func_graph, const MS_EXCEPTION_IF_NULL(new_3d); new_3d->set_scope(avg_pool_3d_node->scope()); new_3d->set_abstract(avg_pool_3d_node->abstract()); - AnfAlgo::CopyNodeAttrs(avg_pool_3d_node, new_3d); + common::AnfAlgo::CopyNodeAttrs(avg_pool_3d_node, new_3d); return new_3d; } } // namespace opt diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/avgpool_3d_fusion.h b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/avgpool_3d_fusion.h index 1249765c0bc..96842d860e2 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/avgpool_3d_fusion.h +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/avgpool_3d_fusion.h @@ -20,7 +20,7 @@ #include #include #include "backend/common/optimizer/optimizer.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace opt { diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/avgpool_3d_grad_fusion.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/avgpool_3d_grad_fusion.cc index 05593a7fd1f..bcede5fdccd 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/avgpool_3d_grad_fusion.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/avgpool_3d_grad_fusion.cc @@ -21,6 +21,7 @@ #include #include "plugin/device/ascend/optimizer/ir_fusion/avgpool_3d_fusion.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -41,39 +42,39 @@ void GetAttrs(const AnfNodePtr &node, std::vector *kernel_size, std::ve bool *count_include_pad, int64_t *divisor_override, std::string *format_str) { MS_EXCEPTION_IF_NULL(node); // attr kernel size - if (!AnfAlgo::HasNodeAttr("kernel_size", node->cast())) { + if (!common::AnfAlgo::HasNodeAttr("kernel_size", node->cast())) { MS_LOG(EXCEPTION) << "AvgPool3D should has attr kernel_size" << trace::DumpSourceLines(node); } - *kernel_size = AnfAlgo::GetNodeAttr>(node, "kernel_size"); + *kernel_size = common::AnfAlgo::GetNodeAttr>(node, "kernel_size"); // attr strides - if (!AnfAlgo::HasNodeAttr("strides", node->cast())) { + if (!common::AnfAlgo::HasNodeAttr("strides", node->cast())) { MS_LOG(EXCEPTION) << "AvgPool3D should has attr strides" << trace::DumpSourceLines(node); } - *strides = AnfAlgo::GetNodeAttr>(node, "strides"); + *strides = common::AnfAlgo::GetNodeAttr>(node, "strides"); // sttr pad_list - if (!AnfAlgo::HasNodeAttr("pad_list", node->cast())) { + if (!common::AnfAlgo::HasNodeAttr("pad_list", node->cast())) { MS_LOG(EXCEPTION) << "AvgPool3D should has attr pad_list" << trace::DumpSourceLines(node); } - *pad_list = AnfAlgo::GetNodeAttr>(node, "pad_list"); + *pad_list = common::AnfAlgo::GetNodeAttr>(node, "pad_list"); // attr origin input shape - if (!AnfAlgo::HasNodeAttr("origin_input_shape", node->cast())) { + if (!common::AnfAlgo::HasNodeAttr("origin_input_shape", node->cast())) { MS_LOG(EXCEPTION) << "AvgPool3D should has attr origin_input_shape" << trace::DumpSourceLines(node); } - *origin_input_shape = AnfAlgo::GetNodeAttr>(node, "origin_input_shape"); + *origin_input_shape = common::AnfAlgo::GetNodeAttr>(node, "origin_input_shape"); // attr count include pad - if (AnfAlgo::HasNodeAttr("count_include_pad", node->cast())) { - *count_include_pad = AnfAlgo::GetNodeAttr(node, "count_include_pad"); + if (common::AnfAlgo::HasNodeAttr("count_include_pad", node->cast())) { + *count_include_pad = common::AnfAlgo::GetNodeAttr(node, "count_include_pad"); } // attr ceil mode - if (AnfAlgo::HasNodeAttr("ceil_mode", node->cast())) { - *ceil_mode = AnfAlgo::GetNodeAttr(node, "ceil_mode"); + if (common::AnfAlgo::HasNodeAttr("ceil_mode", node->cast())) { + *ceil_mode = common::AnfAlgo::GetNodeAttr(node, "ceil_mode"); } // attr divisor override - if (AnfAlgo::HasNodeAttr("divisor_override", node->cast())) { - *divisor_override = AnfAlgo::GetNodeAttr(node, "divisor_override"); + if (common::AnfAlgo::HasNodeAttr("divisor_override", node->cast())) { + *divisor_override = common::AnfAlgo::GetNodeAttr(node, "divisor_override"); } - if (AnfAlgo::HasNodeAttr("format", node->cast())) { - *format_str = AnfAlgo::GetNodeAttr(node, "format"); + if (common::AnfAlgo::HasNodeAttr("format", node->cast())) { + *format_str = common::AnfAlgo::GetNodeAttr(node, "format"); } } @@ -178,7 +179,7 @@ AnfNodePtr ConstructMultiplier(const FuncGraphPtr &func_graph, const std::vector MS_EXCEPTION_IF_NULL(kernel_graph); auto value_node = kernel_graph->NewValueNode(x_abstract, tensor); kernel_graph->AddValueNodeToGraph(value_node); - AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat16}, {ori_shape}, value_node.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat16}, {ori_shape}, value_node.get()); return value_node; } } // namespace @@ -210,11 +211,11 @@ const AnfNodePtr AvgPool3DGradFusion::Process(const FuncGraphPtr &func_graph, co GetAttrs(avg_pool_3d_grad_node, &kernel_size, &strides, &pad_list, &origin_input_shape, &ceil_mode, &count_include_pad, &divisor_override, &format_str); const int64_t dim_one = SizeToLong(1); - AnfAlgo::SetNodeAttr( + common::AnfAlgo::SetNodeAttr( kKernelSize, MakeValue(std::vector{dim_one, dim_one, kernel_size[kDim0], kernel_size[kDim1], kernel_size[kDim2]}), avg_pool_3d_grad_node); - AnfAlgo::SetNodeAttr( + common::AnfAlgo::SetNodeAttr( kStrides, MakeValue(std::vector{dim_one, dim_one, strides[kDim0], strides[kDim1], strides[kDim2]}), avg_pool_3d_grad_node); if (IsVectorImpl(origin_input_shape, kernel_size, pad_list)) { @@ -234,7 +235,7 @@ const AnfNodePtr AvgPool3DGradFusion::Process(const FuncGraphPtr &func_graph, co MS_EXCEPTION_IF_NULL(filter_node); // after input to attr, the first input should be the 'grads', the index is 0; - auto dims_in = AnfAlgo::GetPrevNodeOutputInferShape(avg_pool_3d_grad_node, 0); + auto dims_in = common::AnfAlgo::GetPrevNodeOutputInferShape(avg_pool_3d_grad_node, 0); // assist node 2 if (divisor_override == 0 && (!IsZeroPads(pad_list) || ceil_mode)) { @@ -246,7 +247,7 @@ const AnfNodePtr AvgPool3DGradFusion::Process(const FuncGraphPtr &func_graph, co MS_EXCEPTION_IF_NULL(new_3d_grad); new_3d_grad->set_scope(avg_pool_3d_grad_node->scope()); new_3d_grad->set_abstract(avg_pool_3d_grad_node->abstract()); - AnfAlgo::CopyNodeAttrs(avg_pool_3d_grad_node, new_3d_grad); + common::AnfAlgo::CopyNodeAttrs(avg_pool_3d_grad_node, new_3d_grad); return new_3d_grad; } } // namespace opt diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/avgpool_3d_grad_fusion.h b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/avgpool_3d_grad_fusion.h index 8f2a36629cb..5a77dbb1f63 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/avgpool_3d_grad_fusion.h +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/avgpool_3d_grad_fusion.h @@ -20,7 +20,7 @@ #include #include #include "backend/common/optimizer/optimizer.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace opt { diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/batchnorm_to_bninfer.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/batchnorm_to_bninfer.cc index 63e12e7a8c9..7d30bc21682 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/batchnorm_to_bninfer.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/batchnorm_to_bninfer.cc @@ -17,8 +17,9 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "base/core_ops.h" #include "abstract/abstract_value.h" #include "backend/common/optimizer/helper.h" @@ -44,14 +45,14 @@ bool CheckIndex(const AnfNodePtr &index_node) { bool CheckBatchNorm(const FuncGraphPtr &graph, const CNodePtr &batchnorm) { MS_EXCEPTION_IF_NULL(graph); MS_EXCEPTION_IF_NULL(batchnorm); - if (AnfAlgo::GetInputTensorNum(batchnorm) < kBnInputTensorNum) { + if (common::AnfAlgo::GetInputTensorNum(batchnorm) < kBnInputTensorNum) { MS_LOG(DEBUG) << "BatchNorm's input number less than " << kBnInputTensorNum; return false; } - if (!AnfAlgo::HasNodeAttr(kAttrIsTraining, batchnorm)) { + if (!common::AnfAlgo::HasNodeAttr(kAttrIsTraining, batchnorm)) { return false; } - auto is_training = AnfAlgo::GetNodeAttr(batchnorm, kAttrIsTraining); + auto is_training = common::AnfAlgo::GetNodeAttr(batchnorm, kAttrIsTraining); if (is_training) { MS_LOG(DEBUG) << "Attr 'is_training' is true, no need do fusion"; return false; @@ -99,8 +100,8 @@ CNodePtr BatchNorm2BNInfer::CreateBNInfer(const FuncGraphPtr &graph, const CNode MS_EXCEPTION_IF_NULL(new_node); new_node->set_scope(batchnorm->scope()); new_node->set_abstract(node->abstract()); - AnfAlgo::CopyNodeAttr(kAttrIsTraining, batchnorm, new_node); - AnfAlgo::CopyNodeAttr(kAttrEpsilon, batchnorm, new_node); + common::AnfAlgo::CopyNodeAttr(kAttrIsTraining, batchnorm, new_node); + common::AnfAlgo::CopyNodeAttr(kAttrEpsilon, batchnorm, new_node); return new_node; } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/batchnormgrad_to_bninfergrad.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/batchnormgrad_to_bninfergrad.cc index ac58af54de6..b192ec0805a 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/batchnormgrad_to_bninfergrad.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/batchnormgrad_to_bninfergrad.cc @@ -17,8 +17,9 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "base/core_ops.h" #include "abstract/abstract_value.h" #include "backend/common/optimizer/helper.h" @@ -42,8 +43,8 @@ CNodePtr CreateBNInferGrad(const FuncGraphPtr &graph, const CNodePtr &batchnormg MS_EXCEPTION_IF_NULL(new_node); new_node->set_scope(batchnormgrad->scope()); new_node->set_abstract(node->abstract()); - AnfAlgo::CopyNodeAttr(kAttrIsTraining, batchnormgrad, new_node); - AnfAlgo::CopyNodeAttr(kAttrEpsilon, batchnormgrad, new_node); + common::AnfAlgo::CopyNodeAttr(kAttrIsTraining, batchnormgrad, new_node); + common::AnfAlgo::CopyNodeAttr(kAttrEpsilon, batchnormgrad, new_node); return new_node; } @@ -65,14 +66,14 @@ bool CheckIndex(const AnfNodePtr &index_node) { bool CheckBatchNormGrad(const FuncGraphPtr &graph, const CNodePtr &batchnormgrad) { MS_EXCEPTION_IF_NULL(graph); MS_EXCEPTION_IF_NULL(batchnormgrad); - if (AnfAlgo::GetInputTensorNum(batchnormgrad) < kBNGradInputTensorNum) { + if (common::AnfAlgo::GetInputTensorNum(batchnormgrad) < kBNGradInputTensorNum) { MS_LOG(DEBUG) << "BatchNormGrad's input number less than " << kBnInputTensorNum; return false; } - if (!AnfAlgo::HasNodeAttr(kAttrIsTraining, batchnormgrad)) { + if (!common::AnfAlgo::HasNodeAttr(kAttrIsTraining, batchnormgrad)) { return false; } - auto is_training = AnfAlgo::GetNodeAttr(batchnormgrad, kAttrIsTraining); + auto is_training = common::AnfAlgo::GetNodeAttr(batchnormgrad, kAttrIsTraining); if (is_training) { MS_LOG(DEBUG) << "Attr 'is_training' is true, no need do fusion"; return false; diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/bn_reduce_grad_conv2d_backprop_filter_fusion.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/bn_reduce_grad_conv2d_backprop_filter_fusion.cc index ec512ef2911..4c4f1439873 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/bn_reduce_grad_conv2d_backprop_filter_fusion.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/bn_reduce_grad_conv2d_backprop_filter_fusion.cc @@ -18,8 +18,9 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/trace_base.h" #include "base/core_ops.h" #include "abstract/abstract_value.h" @@ -35,9 +36,9 @@ constexpr auto kAttrFilterSizes = "filter_sizes"; constexpr auto kAttrPadList = "pad_list"; bool CheckSupported(const CNodePtr &conv_back_filter) { - auto y_shape = AnfAlgo::GetPrevNodeOutputInferShape(conv_back_filter, 0); - auto x_shape = AnfAlgo::GetPrevNodeOutputInferShape(conv_back_filter, 1); - auto out_shape = AnfAlgo::GetOutputInferShape(conv_back_filter, 0); + auto y_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(conv_back_filter, 0); + auto x_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(conv_back_filter, 1); + auto out_shape = common::AnfAlgo::GetOutputInferShape(conv_back_filter, 0); if (y_shape.size() != kNCHWShapeSize || x_shape.size() != kNCHWShapeSize || out_shape.size() != kNCHWShapeSize) { MS_LOG(EXCEPTION) << "The dim of Conv2dBackpropFilter's input and output should be 4, but got y_shape is " << y_shape.size() << "-D, x_shape is " << x_shape.size() << "-D, out_shape is " @@ -90,18 +91,19 @@ const AnfNodePtr BNReduceGradConv2dBackpropFilterFusion::Process(const FuncGraph } auto fused_dbn_dw = NewCNode(fused_dbn_dw_inputs, graph); MS_EXCEPTION_IF_NULL(fused_dbn_dw); - auto types = {AnfAlgo::GetOutputInferDataType(bnreduce_grad, 0), - AnfAlgo::GetOutputInferDataType(conv_back_filter, 0)}; - auto shapes = {AnfAlgo::GetOutputInferShape(bnreduce_grad, 0), AnfAlgo::GetOutputInferShape(conv_back_filter, 0)}; - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, fused_dbn_dw.get()); + auto types = {common::AnfAlgo::GetOutputInferDataType(bnreduce_grad, 0), + common::AnfAlgo::GetOutputInferDataType(conv_back_filter, 0)}; + auto shapes = {common::AnfAlgo::GetOutputInferShape(bnreduce_grad, 0), + common::AnfAlgo::GetOutputInferShape(conv_back_filter, 0)}; + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, fused_dbn_dw.get()); fused_dbn_dw->set_scope(bnreduce_grad->scope()); - AnfAlgo::CopyNodeAttr(kAttrFilterSizes, conv_back_filter, fused_dbn_dw); - AnfAlgo::CopyNodeAttr(kAttrStride, conv_back_filter, fused_dbn_dw); - AnfAlgo::CopyNodeAttr(kAttrPadList, conv_back_filter, fused_dbn_dw); - AnfAlgo::CopyNodeAttr(kAttrDilation, conv_back_filter, fused_dbn_dw); - AnfAlgo::CopyNodeAttr(kAttrGroups, conv_back_filter, fused_dbn_dw); - AnfAlgo::CopyNodeAttr(kAttrFormat, conv_back_filter, fused_dbn_dw); - AnfAlgo::CopyNodeAttr(kAttrEpsilon, bnreduce_grad, fused_dbn_dw); + common::AnfAlgo::CopyNodeAttr(kAttrFilterSizes, conv_back_filter, fused_dbn_dw); + common::AnfAlgo::CopyNodeAttr(kAttrStride, conv_back_filter, fused_dbn_dw); + common::AnfAlgo::CopyNodeAttr(kAttrPadList, conv_back_filter, fused_dbn_dw); + common::AnfAlgo::CopyNodeAttr(kAttrDilation, conv_back_filter, fused_dbn_dw); + common::AnfAlgo::CopyNodeAttr(kAttrGroups, conv_back_filter, fused_dbn_dw); + common::AnfAlgo::CopyNodeAttr(kAttrFormat, conv_back_filter, fused_dbn_dw); + common::AnfAlgo::CopyNodeAttr(kAttrEpsilon, bnreduce_grad, fused_dbn_dw); // replace bnreduce_grad's output std::vector fused_dbn_dw_outputs; diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/clip_by_norm_no_div_square_sum_fusion.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/clip_by_norm_no_div_square_sum_fusion.cc index 63fcbef3974..a6781bd1591 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/clip_by_norm_no_div_square_sum_fusion.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/clip_by_norm_no_div_square_sum_fusion.cc @@ -19,8 +19,9 @@ #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace opt { @@ -63,9 +64,9 @@ const AnfNodePtr ClipByNormNoDivSquareSumFusion::Process(const FuncGraphPtr &gra std::vector inputs = {NewValueNode(prim), input, constant_greater, constant_select, constant_maximum}; auto fusion_node = NewCNode(inputs, graph); MS_EXCEPTION_IF_NULL(fusion_node); - auto types = {AnfAlgo::GetOutputInferDataType(node, 0)}; - auto shapes = {AnfAlgo::GetOutputInferShape(node, 0)}; - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, fusion_node.get()); + auto types = {common::AnfAlgo::GetOutputInferDataType(node, 0)}; + auto shapes = {common::AnfAlgo::GetOutputInferShape(node, 0)}; + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, fusion_node.get()); fusion_node->set_scope(node->scope()); return fusion_node; } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/clip_by_value_fusion.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/clip_by_value_fusion.cc index 82d81eba24a..0c719962f19 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/clip_by_value_fusion.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/clip_by_value_fusion.cc @@ -20,8 +20,9 @@ #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/optimizer/helper.h" namespace mindspore { @@ -39,7 +40,7 @@ bool GetMinimumOp(const AnfNodePtr &input0, const AnfNodePtr &input1, CNodePtr * cnode = input1->cast(); *is_first_input = false; } else if (input0->isa() && input1->isa()) { - if (AnfAlgo::GetCNodeName(input0) == prim::kPrimMinimum->name()) { + if (common::AnfAlgo::GetCNodeName(input0) == prim::kPrimMinimum->name()) { cnode = input0->cast(); *is_first_input = true; } else { @@ -50,7 +51,7 @@ bool GetMinimumOp(const AnfNodePtr &input0, const AnfNodePtr &input1, CNodePtr * return false; } - if (AnfAlgo::GetCNodeName(cnode) != prim::kPrimMinimum->name()) { + if (common::AnfAlgo::GetCNodeName(cnode) != prim::kPrimMinimum->name()) { return false; } *minimum = cnode; @@ -79,7 +80,7 @@ const AnfNodePtr ClipByValueFusion::Process(const FuncGraphPtr &graph, const Anf return nullptr; } MS_EXCEPTION_IF_NULL(minimum); - if (AnfAlgo::GetInputTensorNum(minimum) != kMinimumInputTensorNum) { + if (common::AnfAlgo::GetInputTensorNum(minimum) != kMinimumInputTensorNum) { return nullptr; } @@ -89,9 +90,9 @@ const AnfNodePtr ClipByValueFusion::Process(const FuncGraphPtr &graph, const Anf is_first_input ? maximum_input1 : maximum_input0, minimum->input(kIndex2)}; auto clip_by_value = NewCNode(inputs, graph); MS_EXCEPTION_IF_NULL(clip_by_value); - auto types = {AnfAlgo::GetOutputInferDataType(node, 0)}; - auto shapes = {AnfAlgo::GetOutputInferShape(node, 0)}; - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, clip_by_value.get()); + auto types = {common::AnfAlgo::GetOutputInferDataType(node, 0)}; + auto shapes = {common::AnfAlgo::GetOutputInferShape(node, 0)}; + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, clip_by_value.get()); clip_by_value->set_scope(node->scope()); return clip_by_value; } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/confusion_mul_grad_fusion.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/confusion_mul_grad_fusion.cc index 8a9fee079ff..84273f232db 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/confusion_mul_grad_fusion.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/confusion_mul_grad_fusion.cc @@ -20,8 +20,9 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "abstract/abstract_value.h" #include "backend/common/optimizer/helper.h" #include "utils/trace_base.h" @@ -46,7 +47,7 @@ AnfNodePtr GetMul0(const FuncGraphPtr &graph, const AnfNodePtr &input2, const An constexpr int kMul0InputIndex2 = 2; return node_index.first != mul1 && node_index.second == kMul0InputIndex2; }); - if (it != outputs_set.end() && AnfAlgo::GetCNodeName(it->first) == prim::kPrimMul->name()) { + if (it != outputs_set.end() && common::AnfAlgo::GetCNodeName(it->first) == prim::kPrimMul->name()) { mul0 = it->first; } return mul0; @@ -62,14 +63,14 @@ bool QuitFusion(const FuncGraphPtr &graph, const AnfNodePtr &mul0_anf, const Anf constexpr size_t kInferShapeIndex = 2; constexpr size_t kShape2Dim1 = 1024; constexpr size_t kShape2Dim2 = 768; - if (addn == nullptr || AnfAlgo::GetCNodeName(addn) != prim::kPrimAddN->name()) { + if (addn == nullptr || common::AnfAlgo::GetCNodeName(addn) != prim::kPrimAddN->name()) { MS_LOG(INFO) << "Mul's second input is not Addn, quit fusion"; return true; } - if (AnfAlgo::IsDynamicShape(addn)) { + if (common::AnfAlgo::IsDynamicShape(addn)) { return true; } - std::vector shape = AnfAlgo::GetOutputInferShape(addn, 0); + std::vector shape = common::AnfAlgo::GetOutputInferShape(addn, 0); if (shape.size() != kInferShapeIndex || !(shape[1] == kShape2Dim1 || shape[1] == kShape2Dim2)) { MS_LOG(INFO) << "Addn's infer shape is not equal to [x,1024] or [x,768], quit fusion"; return true; @@ -108,11 +109,12 @@ CNodePtr ConfusionMulGradFusion::CreateFusionNode(const FuncGraphPtr &graph, con auto fusion_node = NewCNode(inputs, graph); MS_EXCEPTION_IF_NULL(fusion_node); fusion_node->set_scope(reduce_sum->scope()); - AnfAlgo::CopyNodeAttr(kAttrAxis, reduce_sum, fusion_node); - AnfAlgo::CopyNodeAttr(kAttrKeepDims, reduce_sum, fusion_node); - auto types = {AnfAlgo::GetOutputInferDataType(mul0, 0), AnfAlgo::GetOutputInferDataType(reduce_sum, 0)}; - auto shapes = {AnfAlgo::GetOutputInferShape(mul0, 0), AnfAlgo::GetOutputInferShape(reduce_sum, 0)}; - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, fusion_node.get()); + common::AnfAlgo::CopyNodeAttr(kAttrAxis, reduce_sum, fusion_node); + common::AnfAlgo::CopyNodeAttr(kAttrKeepDims, reduce_sum, fusion_node); + auto types = {common::AnfAlgo::GetOutputInferDataType(mul0, 0), + common::AnfAlgo::GetOutputInferDataType(reduce_sum, 0)}; + auto shapes = {common::AnfAlgo::GetOutputInferShape(mul0, 0), common::AnfAlgo::GetOutputInferShape(reduce_sum, 0)}; + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, fusion_node.get()); return fusion_node; } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/confusion_softmax_grad_rule.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/confusion_softmax_grad_rule.cc index 3edfd132eaf..379a8be60cb 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/confusion_softmax_grad_rule.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/confusion_softmax_grad_rule.cc @@ -19,8 +19,9 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/optimizer/helper.h" namespace mindspore { @@ -38,7 +39,7 @@ bool NeedFusion(const AnfNodePtr &sum_anf, const AnfNodePtr &input0, const AnfNo } // check axis should be last dim - auto prim = AnfAlgo::GetCNodePrimitive(reduce_sum); + auto prim = common::AnfAlgo::GetCNodePrimitive(reduce_sum); MS_EXCEPTION_IF_NULL(prim); if (!prim->HasAttr(kAttrAxis)) { MS_LOG(INFO) << "ReduceSum should have attr axis if do fusion."; @@ -58,7 +59,7 @@ bool NeedFusion(const AnfNodePtr &sum_anf, const AnfNodePtr &input0, const AnfNo } else { return false; } - auto sum_input_shape = AnfAlgo::GetPrevNodeOutputInferShape(reduce_sum, 0); + auto sum_input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(reduce_sum, 0); auto sum_input_size = SizeToLong(sum_input_shape.size()); if (sum_input_size == 0) { MS_LOG(INFO) << "ReduceSum's input should not be a scalar if do fusion."; @@ -71,7 +72,7 @@ bool NeedFusion(const AnfNodePtr &sum_anf, const AnfNodePtr &input0, const AnfNo } const size_t last_dim_limit = 30000; - auto input0_shape = AnfAlgo::GetOutputInferShape(input0, 0); + auto input0_shape = common::AnfAlgo::GetOutputInferShape(input0, 0); if (!input0_shape.empty() && input0_shape[input0_shape.size() - 1] > last_dim_limit) { MS_LOG(INFO) << "Input shape is too large to optimize, quit fusion, shape: " << input0_shape; return false; @@ -104,8 +105,8 @@ const AnfNodePtr ConfusionSoftmaxGradRule::Process(const FuncGraphPtr &graph, co MS_EXCEPTION_IF_NULL(fusion_node); fusion_node->set_abstract(node->abstract()); fusion_node->set_scope(node->scope()); - AnfAlgo::CopyNodeAttr(kAttrAxis, sum_anf, fusion_node); - AnfAlgo::CopyNodeAttr(kAttrKeepDims, sum_anf, fusion_node); + common::AnfAlgo::CopyNodeAttr(kAttrAxis, sum_anf, fusion_node); + common::AnfAlgo::CopyNodeAttr(kAttrKeepDims, sum_anf, fusion_node); return fusion_node; } } // namespace opt diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/conv2d_backprop_input_biasadd_fusion.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/conv2d_backprop_input_biasadd_fusion.cc index 2e2fea951d6..7c02a61a83e 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/conv2d_backprop_input_biasadd_fusion.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/conv2d_backprop_input_biasadd_fusion.cc @@ -17,8 +17,9 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/optimizer/helper.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/trace_base.h" #include "plugin/device/ascend/hal/device/lic_manager.h" @@ -55,7 +56,7 @@ const AnfNodePtr Conv2dBackpropInputBiasaddFusion::Process(const FuncGraphPtr &g new_node->set_scope(node->scope()); new_node->set_abstract(node->abstract()); - AnfAlgo::CopyNodeAttrs(conv2d_bp_input, new_node); + common::AnfAlgo::CopyNodeAttrs(conv2d_bp_input, new_node); return new_node; } } // namespace opt diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/derelu_fusion.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/derelu_fusion.cc index a0091574c6b..bb650ab33df 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/derelu_fusion.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/derelu_fusion.cc @@ -18,8 +18,9 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "abstract/abstract_value.h" #include "backend/common/optimizer/helper.h" @@ -50,15 +51,15 @@ CNodePtr DereluFusion::CreateReluV2(const FuncGraphPtr &graph, const CNodePtr &r // ReluV2's 2rd output is mask whose data type is uint8 TypeId mask_dtype = kNumberTypeUInt8; - if (AnfAlgo::IsDynamicShape(relu)) { + if (common::AnfAlgo::IsDynamicShape(relu)) { return nullptr; } - std::vector mask_shape = AnfAlgo::GetOutputInferShape(relu, 0); + std::vector mask_shape = common::AnfAlgo::GetOutputInferShape(relu, 0); if (mask_shape.size() != kMaskShapeSize) { MS_LOG(DEBUG) << "relu's infer shape size not equal 4"; return nullptr; } - auto input_dtype = AnfAlgo::GetPrevNodeOutputInferDataType(relu, 0); + auto input_dtype = common::AnfAlgo::GetPrevNodeOutputInferDataType(relu, 0); constexpr auto kMultiplierInt8 = 31; constexpr auto kDivisorInt8 = 32; constexpr auto kMultiplier = 15; @@ -74,9 +75,9 @@ CNodePtr DereluFusion::CreateReluV2(const FuncGraphPtr &graph, const CNodePtr &r mask_shape.push_back(kThirdShape); } - auto types = {AnfAlgo::GetOutputInferDataType(relu, 0), mask_dtype}; - auto shapes = {AnfAlgo::GetOutputInferShape(relu, 0), mask_shape}; - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, new_node.get()); + auto types = {common::AnfAlgo::GetOutputInferDataType(relu, 0), mask_dtype}; + auto shapes = {common::AnfAlgo::GetOutputInferShape(relu, 0), mask_shape}; + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, new_node.get()); return new_node; } @@ -123,10 +124,10 @@ const AnfNodePtr DereluFusion::Process(const FuncGraphPtr &graph, const AnfNodeP // Add attr mapping from original nodes to fusion nodes auto original_names = MakeValue>({relu->fullname_with_scope(), relu_grad->fullname_with_scope()}); - AnfAlgo::SetNodeAttr(kAttrDatadumpOriginalNames, original_names, relu_v2); - AnfAlgo::SetNodeAttr(kAttrDatadumpOriginalNames, original_names, relu_grad_v2); - AnfAlgo::SetNodeAttr(kAttrDatadumpIsMultiop, MakeValue(true), relu_v2); - AnfAlgo::SetNodeAttr(kAttrDatadumpIsMultiop, MakeValue(true), relu_grad_v2); + common::AnfAlgo::SetNodeAttr(kAttrDatadumpOriginalNames, original_names, relu_v2); + common::AnfAlgo::SetNodeAttr(kAttrDatadumpOriginalNames, original_names, relu_grad_v2); + common::AnfAlgo::SetNodeAttr(kAttrDatadumpIsMultiop, MakeValue(true), relu_v2); + common::AnfAlgo::SetNodeAttr(kAttrDatadumpIsMultiop, MakeValue(true), relu_grad_v2); auto manage = graph->manager(); MS_EXCEPTION_IF_NULL(manage); diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/fused_batch_norm_fusion.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/fused_batch_norm_fusion.cc index dad751894a6..92b93f35b49 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/fused_batch_norm_fusion.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/fused_batch_norm_fusion.cc @@ -18,7 +18,8 @@ #include #include "backend/common/optimizer/helper.h" #include "backend/common/session/anf_runtime_algorithm.h" -#include "utils/utils.h" +#include "include/common/utils/anfalgo.h" +#include "include/common/utils/utils.h" #include "utils/trace_base.h" namespace mindspore { @@ -159,13 +160,13 @@ AnfNodePtr FusedBatchNormFusion::CreateBNTrainingUpdate( GetBNTrainingUpdateAbstractList(equiv, bn, &abstract_list); auto abstract_tuple = std::make_shared(abstract_list); bn_training_update->set_abstract(abstract_tuple); - AnfAlgo::CopyNodeAttr(kAttrEpsilon, bn, bn_training_update); + common::AnfAlgo::CopyNodeAttr(kAttrEpsilon, bn, bn_training_update); ValuePtr factor = GetFactor(equiv); if (factor == nullptr) { return nullptr; } - AnfAlgo::SetNodeAttr(kAttrFactor, factor, bn_training_update); - AnfAlgo::SetNodeAttr(kAttrIsRef, MakeValue(true), bn_training_update); + common::AnfAlgo::SetNodeAttr(kAttrFactor, factor, bn_training_update); + common::AnfAlgo::SetNodeAttr(kAttrIsRef, MakeValue(true), bn_training_update); bn_training_update->set_scope(node->scope()); return bn_training_update; } @@ -181,7 +182,7 @@ void FusedBatchNormFusion::EliminateMonadNodes(const FuncGraphPtr &func_graph, c for (const auto &node_index : users) { const AnfNodePtr &output = node_index.first; MS_EXCEPTION_IF_NULL(output); - if (AnfAlgo::CheckPrimitiveType(output, prim::kPrimUpdateState)) { + if (common::AnfAlgo::CheckPrimitiveType(output, prim::kPrimUpdateState)) { (void)manager->Replace(output, GetAnfNodeByVar(equiv, monad0_var_)); break; } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/fused_batch_norm_fusion.h b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/fused_batch_norm_fusion.h index 7e82d7b8332..6eb0332819e 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/fused_batch_norm_fusion.h +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/fused_batch_norm_fusion.h @@ -20,7 +20,7 @@ #include #include #include "backend/common/optimizer/optimizer.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace opt { diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/input_to_output_registry.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/input_to_output_registry.cc index ce6f1020ef6..cffdc30b8be 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/input_to_output_registry.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/input_to_output_registry.cc @@ -15,49 +15,50 @@ */ #include "plugin/device/ascend/optimizer/ir_fusion/input_to_output_registry.h" #include -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { namespace { bool ApplyRMSPropPreCheck(const CNodePtr &node) { - return !(AnfAlgo::GetPrevNodeOutputInferDataType(node, 0) != kNumberTypeFloat32); + return !(common::AnfAlgo::GetPrevNodeOutputInferDataType(node, 0) != kNumberTypeFloat32); } bool FusedMulApplyMomentumPreCheck(const CNodePtr &node) { - TypeId data_type = AnfAlgo::GetPrevNodeOutputInferDataType(node, 0); + TypeId data_type = common::AnfAlgo::GetPrevNodeOutputInferDataType(node, 0); return !(data_type != kNumberTypeFloat32 && data_type != kNumberTypeFloat16); } bool SparseApplyRMSPropPreCheck(const CNodePtr &node) { - return !(AnfAlgo::GetPrevNodeOutputInferDataType(node, 0) != kNumberTypeFloat32); + return !(common::AnfAlgo::GetPrevNodeOutputInferDataType(node, 0) != kNumberTypeFloat32); } bool ApplyAdagradV2PreCheck(const CNodePtr &node) { - TypeId data_type = AnfAlgo::GetPrevNodeOutputInferDataType(node, 0); + TypeId data_type = common::AnfAlgo::GetPrevNodeOutputInferDataType(node, 0); return !(data_type != kNumberTypeFloat32 && data_type != kNumberTypeFloat16); } bool ApplyKerasMomentumPreCheck(const CNodePtr &node) { - TypeId data_type = AnfAlgo::GetPrevNodeOutputInferDataType(node, 0); + TypeId data_type = common::AnfAlgo::GetPrevNodeOutputInferDataType(node, 0); return !(data_type != kNumberTypeFloat32 && data_type != kNumberTypeFloat16); } bool SparseApplyFtrlPreCheck(const CNodePtr &node) { - return !(AnfAlgo::GetPrevNodeOutputInferDataType(node, 0) != kNumberTypeFloat32); + return !(common::AnfAlgo::GetPrevNodeOutputInferDataType(node, 0) != kNumberTypeFloat32); } bool SparseApplyFtrlV2PreCheck(const CNodePtr &node) { - return !(AnfAlgo::GetPrevNodeOutputInferDataType(node, 0) != kNumberTypeFloat32); + return !(common::AnfAlgo::GetPrevNodeOutputInferDataType(node, 0) != kNumberTypeFloat32); } bool SparseApplyAdagradV2PreCheck(const CNodePtr &node) { - return !(AnfAlgo::GetPrevNodeOutputInferDataType(node, 0) != kNumberTypeFloat32); + return !(common::AnfAlgo::GetPrevNodeOutputInferDataType(node, 0) != kNumberTypeFloat32); } bool SparseApplyAdadeltaPreCheck(const CNodePtr &node) { - return !(AnfAlgo::GetPrevNodeOutputInferDataType(node, 0) != kNumberTypeFloat32); + return !(common::AnfAlgo::GetPrevNodeOutputInferDataType(node, 0) != kNumberTypeFloat32); } } // namespace InputToOutputRegistry::InputToOutputRegistry() { diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/lamb_next_mv_rule.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/lamb_next_mv_rule.cc index c0dbe7af028..08381e2140d 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/lamb_next_mv_rule.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/lamb_next_mv_rule.cc @@ -17,7 +17,7 @@ #include "plugin/device/ascend/optimizer/ir_fusion/lamb_next_mv_rule.h" #include #include -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/optimizer/helper.h" #include "base/core_ops.h" diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/lamb_next_mv_with_decay_rule.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/lamb_next_mv_with_decay_rule.cc index 744109d0a91..2e299e03b19 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/lamb_next_mv_with_decay_rule.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/lamb_next_mv_with_decay_rule.cc @@ -16,6 +16,7 @@ #include "plugin/device/ascend/optimizer/ir_fusion/lamb_next_mv_with_decay_rule.h" #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "frontend/optimizer/opt.h" #include "utils/trace_base.h" namespace mindspore { diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/lamb_next_mv_with_decay_v1_rule.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/lamb_next_mv_with_decay_v1_rule.cc index 9aab4126f5c..4665bde6448 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/lamb_next_mv_with_decay_v1_rule.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/lamb_next_mv_with_decay_v1_rule.cc @@ -21,6 +21,7 @@ #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "frontend/optimizer/opt.h" #include "utils/trace_base.h" namespace mindspore { @@ -54,7 +55,8 @@ bool MatchAdd5Pattern(const AnfNodePtr &node, const AnfNodePtr &mul4, const AnfN return false; } auto add5 = node->cast(); - if (AnfAlgo::GetCNodeName(add5) != prim::kPrimAdd->name() || AnfAlgo::GetInputTensorNum(add5) != kAddInputTensorNum) { + if (common::AnfAlgo::GetCNodeName(add5) != prim::kPrimAdd->name() || + common::AnfAlgo::GetInputTensorNum(add5) != kAddInputTensorNum) { return false; } auto real_div4_anf = add5->input(kIndex1); @@ -62,8 +64,8 @@ bool MatchAdd5Pattern(const AnfNodePtr &node, const AnfNodePtr &mul4, const AnfN return false; } auto real_div4 = real_div4_anf->cast(); - if (AnfAlgo::GetCNodeName(real_div4) != kRealDivOpName || - AnfAlgo::GetInputTensorNum(real_div4) != kRealDivInputTensorNum) { + if (common::AnfAlgo::GetCNodeName(real_div4) != kRealDivOpName || + common::AnfAlgo::GetInputTensorNum(real_div4) != kRealDivInputTensorNum) { return false; } auto add4_anf = real_div4->input(kIndex2); @@ -71,7 +73,8 @@ bool MatchAdd5Pattern(const AnfNodePtr &node, const AnfNodePtr &mul4, const AnfN return false; } auto add4 = add4_anf->cast(); - if (AnfAlgo::GetCNodeName(add4) != prim::kPrimAdd->name() || AnfAlgo::GetInputTensorNum(add4) != kAddInputTensorNum) { + if (common::AnfAlgo::GetCNodeName(add4) != prim::kPrimAdd->name() || + common::AnfAlgo::GetInputTensorNum(add4) != kAddInputTensorNum) { return false; } auto sqrt1_anf = add4->input(kIndex1); @@ -79,7 +82,8 @@ bool MatchAdd5Pattern(const AnfNodePtr &node, const AnfNodePtr &mul4, const AnfN return false; } auto sqrt1 = sqrt1_anf->cast(); - if (AnfAlgo::GetCNodeName(sqrt1) != kSqrtOpName || AnfAlgo::GetInputTensorNum(sqrt1) != kSqrtInputTensorNum) { + if (common::AnfAlgo::GetCNodeName(sqrt1) != kSqrtOpName || + common::AnfAlgo::GetInputTensorNum(sqrt1) != kSqrtInputTensorNum) { return false; } MS_EXCEPTION_IF_NULL(add2_y); @@ -177,11 +181,11 @@ const AnfNodePtr LambNextMVWithDecayV1Rule::Process(const FuncGraphPtr &func_gra AnfNodePtr add1 = nullptr; AnfNodePtr add5 = iter->first; std::tie(add0, add1) = GetAdd0Add1Nodes(real_div0, real_div1); - auto types = {AnfAlgo::GetOutputInferDataType(node, 0), AnfAlgo::GetOutputInferDataType(add0, 0), - AnfAlgo::GetOutputInferDataType(add1, 0), AnfAlgo::GetOutputInferDataType(add5, 0)}; - auto shapes = {AnfAlgo::GetOutputInferShape(node, 0), AnfAlgo::GetOutputInferShape(add0, 0), - AnfAlgo::GetOutputInferShape(add1, 0), AnfAlgo::GetOutputInferShape(add5, 0)}; - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, fusion_node.get()); + auto types = {common::AnfAlgo::GetOutputInferDataType(node, 0), common::AnfAlgo::GetOutputInferDataType(add0, 0), + common::AnfAlgo::GetOutputInferDataType(add1, 0), common::AnfAlgo::GetOutputInferDataType(add5, 0)}; + auto shapes = {common::AnfAlgo::GetOutputInferShape(node, 0), common::AnfAlgo::GetOutputInferShape(add0, 0), + common::AnfAlgo::GetOutputInferShape(add1, 0), common::AnfAlgo::GetOutputInferShape(add5, 0)}; + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, fusion_node.get()); std::vector fusion_node_outputs; CreateMultipleOutputsOfAnfNode(func_graph, fusion_node, kLambNextMVWithDecayV1OutputNum, &fusion_node_outputs); diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/lamb_next_right_rule.h b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/lamb_next_right_rule.h index 4636e46bd29..e66fe95ab0f 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/lamb_next_right_rule.h +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/lamb_next_right_rule.h @@ -18,7 +18,7 @@ #include #include "backend/common/optimizer/optimizer.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace opt { diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/lamb_update_with_lr_rule_fusion.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/lamb_update_with_lr_rule_fusion.cc index 26960a527ef..22554b5e5da 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/lamb_update_with_lr_rule_fusion.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/lamb_update_with_lr_rule_fusion.cc @@ -19,8 +19,9 @@ #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace opt { @@ -68,9 +69,9 @@ const AnfNodePtr LambUpdateWithLRRuleFusion::Process(const FuncGraphPtr &graph, auto lamb_update_with_lr = NewCNode(inputs, graph); MS_EXCEPTION_IF_NULL(lamb_update_with_lr); - auto types = {AnfAlgo::GetOutputInferDataType(node, 0)}; - auto shapes = {AnfAlgo::GetOutputInferShape(node, 0)}; - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, lamb_update_with_lr.get()); + auto types = {common::AnfAlgo::GetOutputInferDataType(node, 0)}; + auto shapes = {common::AnfAlgo::GetOutputInferShape(node, 0)}; + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, lamb_update_with_lr.get()); lamb_update_with_lr->set_scope(node->scope()); return lamb_update_with_lr; } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/lamb_update_with_lr_v2.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/lamb_update_with_lr_v2.cc index 619747a8fb8..aaf5a840c32 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/lamb_update_with_lr_v2.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/lamb_update_with_lr_v2.cc @@ -17,7 +17,7 @@ #include "plugin/device/ascend/optimizer/ir_fusion/lamb_update_with_lr_v2.h" #include #include -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "base/core_ops.h" namespace mindspore { diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/layer_norm_beta_gamma_backprop_fusion.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/layer_norm_beta_gamma_backprop_fusion.cc index baca67356a2..d809cc754f5 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/layer_norm_beta_gamma_backprop_fusion.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/layer_norm_beta_gamma_backprop_fusion.cc @@ -17,6 +17,7 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "utils/trace_base.h" namespace mindspore { namespace opt { @@ -34,7 +35,7 @@ void GetOutputCastNodes(const FuncGraphPtr &func_graph, const AnfNodePtr &node, AnfNodePtr output = node_index.first; auto output_cnode = output->cast(); MS_EXCEPTION_IF_NULL(output_cnode); - if (AnfAlgo::GetCNodeName(output_cnode) != prim::kPrimTupleGetItem->name()) { + if (common::AnfAlgo::GetCNodeName(output_cnode) != prim::kPrimTupleGetItem->name()) { MS_LOG(EXCEPTION) << "The output of node " << node->DebugString() << " should be " << prim::kPrimTupleGetItem->name() << trace::DumpSourceLines(node); } @@ -46,7 +47,7 @@ void GetOutputCastNodes(const FuncGraphPtr &func_graph, const AnfNodePtr &node, MS_EXCEPTION_IF_NULL(transitive_output); auto transitive_output_cnode = transitive_output->cast(); MS_EXCEPTION_IF_NULL(transitive_output_cnode); - if (AnfAlgo::GetCNodeName(transitive_output_cnode) == prim::kPrimCast->name()) { + if (common::AnfAlgo::GetCNodeName(transitive_output_cnode) == prim::kPrimCast->name()) { cast_nodes->push_back(transitive_output_cnode); } } @@ -73,21 +74,21 @@ bool CheckKernelBuildInfo(const CNodePtr &cnode, const kernel::KernelBuildInfoPt bool CheckLayernormBetaGammaBackprop(const FuncGraphPtr &func_graph, const CNodePtr &cnode, std::vector *cast_nodes) { MS_EXCEPTION_IF_NULL(cnode); - if (!AnfAlgo::HasNodeAttr(kAttrShapeGamma, cnode)) { + if (!common::AnfAlgo::HasNodeAttr(kAttrShapeGamma, cnode)) { MS_LOG(INFO) << "The node " << cnode->DebugString() << " has no " << kAttrShapeGamma << " attr"; return false; } - if (AnfAlgo::GetInputTensorNum(cnode) != kLayerNormBetaGammaBackpropInputTensorNum) { + if (common::AnfAlgo::GetInputTensorNum(cnode) != kLayerNormBetaGammaBackpropInputTensorNum) { MS_LOG(INFO) << "The node " << cnode->DebugString() << " inputs num is not equal to " << kLayerNormBetaGammaBackpropInputTensorNum; return false; } - if (AnfAlgo::GetOutputTensorNum(cnode) != kLayerNormBetaGammaBackpropOutputNum) { + if (common::AnfAlgo::GetOutputTensorNum(cnode) != kLayerNormBetaGammaBackpropOutputNum) { MS_LOG(INFO) << "The node " << cnode->DebugString() << " outputs num is not equal to " << kLayerNormBetaGammaBackpropOutputNum; return false; } - size_t input_num = AnfAlgo::GetInputTensorNum(cnode); + size_t input_num = common::AnfAlgo::GetInputTensorNum(cnode); for (size_t i = 0; i < input_num; ++i) { if (AnfAlgo::GetInputDeviceDataType(cnode, i) != kNumberTypeFloat16) { MS_LOG(INFO) << "The data type of node " << cnode->DebugString() << " input " << i << " is not float16"; diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/matmul_biasadd_fusion.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/matmul_biasadd_fusion.cc index 65c8ffd3dbd..60e1e7827f6 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/matmul_biasadd_fusion.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/matmul_biasadd_fusion.cc @@ -17,7 +17,8 @@ #include #include "backend/common/optimizer/helper.h" #include "backend/common/session/anf_runtime_algorithm.h" -#include "utils/utils.h" +#include "include/common/utils/anfalgo.h" +#include "include/common/utils/utils.h" #include "utils/trace_base.h" namespace mindspore { @@ -43,7 +44,7 @@ AnfNodePtr MatmulBiasaddFusion::CreateMatmulWithBias(const FuncGraphPtr &graph, MS_EXCEPTION_IF_NULL(new_node); new_node->set_scope(node->scope()); new_node->set_abstract(node->abstract()); - AnfAlgo::CopyNodeAttrs(matmul, new_node); + common::AnfAlgo::CopyNodeAttrs(matmul, new_node); return new_node; } @@ -62,7 +63,7 @@ const AnfNodePtr MatmulBiasaddFusion::Process(const FuncGraphPtr &graph, const A } bool MatmulAddFusion::NeedFusion(const AnfNodePtr &add) const { - auto bias_shape = AnfAlgo::GetPrevNodeOutputInferShape(add, kIndex1); + auto bias_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(add, kIndex1); if (bias_shape.size() != 1) { return false; } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/momentum_lossscale_fusion.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/momentum_lossscale_fusion.cc index 5eb0e33c238..d3153518ba9 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/momentum_lossscale_fusion.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/momentum_lossscale_fusion.cc @@ -19,6 +19,7 @@ #include #include "backend/common/optimizer/helper.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -29,10 +30,10 @@ bool CheckValueNodeInputOfMul(const AnfNodePtr &node) { if (!node->isa()) { return false; } - if (AnfAlgo::IsDynamicShape(node)) { + if (common::AnfAlgo::IsDynamicShape(node)) { return false; } - std::vector mul_input_shape = AnfAlgo::GetOutputInferShape(node, 0); + std::vector mul_input_shape = common::AnfAlgo::GetOutputInferShape(node, 0); return mul_input_shape.empty() || (mul_input_shape.size() == 1 && mul_input_shape[0] == 1); } } // namespace @@ -87,11 +88,11 @@ const AnfNodePtr MomentumLossscaleFusion::Process(const FuncGraphPtr &func_graph mul_cnode->input(value_node_index)}; auto new_node = NewCNode(new_node_inputs, func_graph); MS_EXCEPTION_IF_NULL(new_node); - AnfAlgo::CopyNodeAttrs(node, new_node); - auto input_names_value = AnfAlgo::GetNodeAttr>(new_node, kAttrInputNames); + common::AnfAlgo::CopyNodeAttrs(node, new_node); + auto input_names_value = common::AnfAlgo::GetNodeAttr>(new_node, kAttrInputNames); input_names_value[kIndex3] = "x1"; input_names_value.emplace_back("x2"); - AnfAlgo::SetNodeAttr(kAttrInputNames, MakeValue(input_names_value), new_node); + common::AnfAlgo::SetNodeAttr(kAttrInputNames, MakeValue(input_names_value), new_node); new_node->set_abstract(node->abstract()); new_node->set_scope(node->scope()); return new_node; diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/mul_add_fusion.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/mul_add_fusion.cc index 0d9d90c6711..b3bb4705cb8 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/mul_add_fusion.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/mul_add_fusion.cc @@ -19,6 +19,7 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "frontend/optimizer/opt.h" #include "backend/common/optimizer/helper.h" @@ -35,7 +36,7 @@ bool GetMul(const FuncGraphPtr &graph, const CNodePtr &add, CNodePtr *mul, size_ if (input->isa()) { auto cnode = input->cast(); MS_EXCEPTION_IF_NULL(cnode); - if (AnfAlgo::GetCNodeName(cnode) == prim::kPrimMul->name()) { + if (common::AnfAlgo::GetCNodeName(cnode) == prim::kPrimMul->name()) { if (!opt::IsUsedByOthers(graph, cnode)) { auto full_name = cnode->fullname_with_scope(); // exclude lamb and adam, and only work in bert @@ -67,7 +68,7 @@ const AnfNodePtr MulAddFusion::Process(const FuncGraphPtr &graph, const AnfNodeP return nullptr; } auto add = node->cast(); - if (add == nullptr || AnfAlgo::GetInputTensorNum(add) != kAddInputTensorNum) { + if (add == nullptr || common::AnfAlgo::GetInputTensorNum(add) != kAddInputTensorNum) { return nullptr; } @@ -85,7 +86,7 @@ const AnfNodePtr MulAddFusion::Process(const FuncGraphPtr &graph, const AnfNodeP } auto another_input_node = add->input(add->size() - mul_index); if (another_input_node->isa() && - AnfAlgo::GetCNodeName(another_input_node) == prim::kPrimTupleGetItem->name()) { + common::AnfAlgo::GetCNodeName(another_input_node) == prim::kPrimTupleGetItem->name()) { MS_LOG(INFO) << "Add's another input node has multiple outputs, do not fuse"; return nullptr; } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/mul_addn_fusion.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/mul_addn_fusion.cc index 7bdc37df7c9..12091d7e6ce 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/mul_addn_fusion.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/mul_addn_fusion.cc @@ -17,6 +17,7 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "frontend/optimizer/opt.h" #include "backend/common/optimizer/helper.h" @@ -65,7 +66,7 @@ const AnfNodePtr MulAddNFusion::Process(const FuncGraphPtr &graph, const AnfNode return nullptr; } auto mul = mul_anf->cast(); - if (mul == nullptr || AnfAlgo::GetInputTensorNum(mul) != kMulInputTensorNum) { + if (mul == nullptr || common::AnfAlgo::GetInputTensorNum(mul) != kMulInputTensorNum) { return nullptr; } if (IsUsedByOthers(graph, mul)) { @@ -82,10 +83,10 @@ const AnfNodePtr MulAddNFusion::Process(const FuncGraphPtr &graph, const AnfNode break; } } - if (AnfAlgo::IsDynamicShape(mul->input(lossscale_input_index))) { + if (common::AnfAlgo::IsDynamicShape(mul->input(lossscale_input_index))) { return nullptr; } - auto constant_shape = AnfAlgo::GetOutputInferShape(mul->input(lossscale_input_index), 0); + auto constant_shape = common::AnfAlgo::GetOutputInferShape(mul->input(lossscale_input_index), 0); if (!(constant_shape.size() == 0 || (constant_shape.size() == 1 && constant_shape[0] == 1))) { MS_LOG(DEBUG) << "The const input of Mul node must be scalar or shape=(1,), but shape size is " << constant_shape.size() << " and shape[0] is " << constant_shape[0]; diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/parameter_and_transop_fusion.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/parameter_and_transop_fusion.cc index b81a5ece676..055d8aa6b58 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/parameter_and_transop_fusion.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/parameter_and_transop_fusion.cc @@ -17,7 +17,8 @@ #include "plugin/device/ascend/optimizer/ir_fusion/parameter_and_transop_fusion.h" #include #include "backend/common/session/anf_runtime_algorithm.h" -#include "utils/utils.h" +#include "include/common/utils/anfalgo.h" +#include "include/common/utils/utils.h" #include "base/core_ops.h" #include "runtime/device/kernel_info.h" #include "backend/common/optimizer/helper.h" @@ -33,7 +34,7 @@ const AnfNodePtr ParamTransRoad(const FuncGraphPtr &func_graph, const AnfNodePtr } if (node->isa()) { auto cnode = node->cast(); - auto op_name = AnfAlgo::GetCNodeName(cnode); + auto op_name = common::AnfAlgo::GetCNodeName(cnode); auto manager = func_graph->manager(); if (manager == nullptr) { return nullptr; @@ -47,7 +48,7 @@ const AnfNodePtr ParamTransRoad(const FuncGraphPtr &func_graph, const AnfNodePtr MS_EXCEPTION_IF_NULL(trans_road); trans_road->push_back(cnode); first_flag = false; - auto next_node = AnfAlgo::GetInputNode(cnode, 0); + auto next_node = common::AnfAlgo::GetInputNode(cnode, 0); MS_EXCEPTION_IF_NULL(next_node); if (next_node->isa() || next_node->isa()) { return next_node; @@ -95,21 +96,22 @@ bool ParameterTransOpFusion::Run(const FuncGraphPtr &func_graph) { continue; } auto cnode = node->cast(); - auto node_name = AnfAlgo::GetCNodeName(cnode); + auto node_name = common::AnfAlgo::GetCNodeName(cnode); if (node_name == prim::kPrimCast->name() || node_name == prim::kPrimTranspose->name() || node_name == prim::kPrimReshape->name() || node_name == kTransDataOpName) { MS_LOG(DEBUG) << "Skip trans op"; continue; } - size_t input_num = AnfAlgo::GetInputTensorNum(cnode); + size_t input_num = common::AnfAlgo::GetInputTensorNum(cnode); for (size_t input_index = 0; input_index < input_num; input_index++) { std::vector trans_road; bool first_flag = true; - auto final_node = ParamTransRoad(func_graph, AnfAlgo::GetInputNode(cnode, input_index), first_flag, &trans_road); + auto final_node = + ParamTransRoad(func_graph, common::AnfAlgo::GetInputNode(cnode, input_index), first_flag, &trans_road); if (final_node != nullptr && trans_road.size() == kTransRoadSize && - AnfAlgo::GetCNodeName(trans_road[kIndex0]) == kTransDataOpName && - AnfAlgo::GetCNodeName(trans_road[kIndex1]) == prim::kPrimCast->name() && - AnfAlgo::GetCNodeName(trans_road[kIndex2]) == kTransDataOpName) { + common::AnfAlgo::GetCNodeName(trans_road[kIndex0]) == kTransDataOpName && + common::AnfAlgo::GetCNodeName(trans_road[kIndex1]) == prim::kPrimCast->name() && + common::AnfAlgo::GetCNodeName(trans_road[kIndex2]) == kTransDataOpName) { auto cur_transop = trans_road[kIndex0]; auto format = AnfAlgo::GetOutputFormat(cur_transop, 0); auto dtype = AnfAlgo::GetOutputDeviceDataType(cur_transop, 0); diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/prelu_fusion.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/prelu_fusion.cc index 963c29e1f32..791351eb5af 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/prelu_fusion.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/prelu_fusion.cc @@ -19,8 +19,9 @@ #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/optimizer/helper.h" namespace mindspore { diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/refresh_parameter_format.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/refresh_parameter_format.cc index d32c70953e0..f4284b0816a 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/refresh_parameter_format.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/refresh_parameter_format.cc @@ -16,7 +16,8 @@ #include "plugin/device/ascend/optimizer/ir_fusion/refresh_parameter_format.h" #include "backend/common/session/anf_runtime_algorithm.h" -#include "utils/utils.h" +#include "include/common/utils/anfalgo.h" +#include "include/common/utils/utils.h" #include "runtime/device/kernel_info.h" #include "backend/common/optimizer/helper.h" @@ -24,9 +25,9 @@ namespace mindspore { namespace opt { void DoRefresh(const CNodePtr &cnode) { MS_EXCEPTION_IF_NULL(cnode); - size_t input_num = AnfAlgo::GetInputTensorNum(cnode); + size_t input_num = common::AnfAlgo::GetInputTensorNum(cnode); for (size_t input_index = 0; input_index < input_num; input_index++) { - auto input_kernel_node = AnfAlgo::VisitKernel(AnfAlgo::GetInputNode(cnode, input_index), 0).first; + auto input_kernel_node = common::AnfAlgo::VisitKernel(common::AnfAlgo::GetInputNode(cnode, input_index), 0).first; MS_EXCEPTION_IF_NULL(input_kernel_node); if (input_kernel_node->isa()) { std::shared_ptr builder = @@ -58,7 +59,7 @@ bool RefreshParameterFormat::Run(const FuncGraphPtr &func_graph) { if (cnode == nullptr) { continue; } - auto node_name = AnfAlgo::GetCNodeName(cnode); + auto node_name = common::AnfAlgo::GetCNodeName(cnode); if (node_name == kBNTrainingUpdateOpName) { DoRefresh(cnode); } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/remove_reshape_pair.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/remove_reshape_pair.cc index 4a3f5cf7655..3153536ba4e 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/remove_reshape_pair.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/remove_reshape_pair.cc @@ -17,6 +17,7 @@ #include "plugin/device/ascend/optimizer/ir_fusion/remove_reshape_pair.h" #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "kernel/common_utils.h" namespace mindspore { @@ -38,19 +39,19 @@ const AnfNodePtr RemoveReshapePair::Process(const FuncGraphPtr &func_graph, cons return nullptr; } auto in_reshape = - CheckAnfNodeIfCNodeAndInputSize(AnfAlgo::GetInputNode(out_reshape, 0), kBackendReshapeInputTensorNum); + CheckAnfNodeIfCNodeAndInputSize(common::AnfAlgo::GetInputNode(out_reshape, 0), kBackendReshapeInputTensorNum); MS_EXCEPTION_IF_NULL(in_reshape); if (IsUsedByOthers(func_graph, in_reshape)) { return nullptr; } - if (AnfAlgo::IsDynamicShape(out_reshape) || AnfAlgo::IsDynamicShape(in_reshape)) { + if (common::AnfAlgo::IsDynamicShape(out_reshape) || common::AnfAlgo::IsDynamicShape(in_reshape)) { return nullptr; } auto output_shape = AnfAlgo::GetOutputDeviceShape(out_reshape, 0); auto input_shape = AnfAlgo::GetInputDeviceShape(in_reshape, 0); if (kernel::IsSameShape(input_shape, output_shape)) { - auto input_node = AnfAlgo::GetInputNode(in_reshape, 0); + auto input_node = common::AnfAlgo::GetInputNode(in_reshape, 0); return input_node; } return nullptr; diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/reshape_transpose_fusion.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/reshape_transpose_fusion.cc index e5330a7507f..a0bc3aeb112 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/reshape_transpose_fusion.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/reshape_transpose_fusion.cc @@ -17,7 +17,8 @@ #include "plugin/device/ascend/optimizer/ir_fusion/reshape_transpose_fusion.h" #include #include "backend/common/session/anf_runtime_algorithm.h" -#include "utils/utils.h" +#include "include/common/utils/anfalgo.h" +#include "include/common/utils/utils.h" #include "backend/common/optimizer/helper.h" #include "base/core_ops.h" @@ -54,7 +55,7 @@ const AnfNodePtr ReshapeTransposeFusion::Process(const FuncGraphPtr &func_graph, MS_EXCEPTION_IF_NULL(transpose_cnode); auto reshape_cnode = CheckAnfNodeIfCNodeAndInputSize(transpose_cnode->input(1), kBackendReshapeInputTensorNum); MS_EXCEPTION_IF_NULL(reshape_cnode); - if (AnfAlgo::IsDynamicShape(transpose_cnode) || AnfAlgo::IsDynamicShape(reshape_cnode)) { + if (common::AnfAlgo::IsDynamicShape(transpose_cnode) || common::AnfAlgo::IsDynamicShape(reshape_cnode)) { return nullptr; } auto kernel_graph = func_graph->cast(); @@ -62,8 +63,8 @@ const AnfNodePtr ReshapeTransposeFusion::Process(const FuncGraphPtr &func_graph, (kernel_graph->IsInternalOutput(reshape_cnode, 0) || kernel_graph->IsInternalOutput(transpose_cnode, 0))) { return nullptr; } - std::vector reshape_input0_shape = AnfAlgo::GetPrevNodeOutputInferShape(reshape_cnode, 0); - std::vector transpose_output0_shape = AnfAlgo::GetOutputInferShape(transpose_cnode, 0); + std::vector reshape_input0_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(reshape_cnode, 0); + std::vector transpose_output0_shape = common::AnfAlgo::GetOutputInferShape(transpose_cnode, 0); if (!CheckShapeDimInfo(reshape_input0_shape) || !CheckShapeDimInfo(transpose_output0_shape)) { return nullptr; } @@ -74,11 +75,11 @@ const AnfNodePtr ReshapeTransposeFusion::Process(const FuncGraphPtr &func_graph, MS_EXCEPTION_IF_NULL(new_node); new_node->set_abstract(node->abstract()); - AnfAlgo::CopyNodeAttrs(reshape_cnode, new_node); - AnfAlgo::CopyNodeAttr(kAttrPerm, transpose_cnode, new_node); - AnfAlgo::SetNodeAttr(kAttrTransposeFirst, MakeValue(false), new_node); - auto reshape_output_shape = AnfAlgo::GetOutputInferShape(reshape_cnode, 0); - AnfAlgo::SetNodeAttr(kAttrShape, MakeValue(Convert2Long(reshape_output_shape)), new_node); + common::AnfAlgo::CopyNodeAttrs(reshape_cnode, new_node); + common::AnfAlgo::CopyNodeAttr(kAttrPerm, transpose_cnode, new_node); + common::AnfAlgo::SetNodeAttr(kAttrTransposeFirst, MakeValue(false), new_node); + auto reshape_output_shape = common::AnfAlgo::GetOutputInferShape(reshape_cnode, 0); + common::AnfAlgo::SetNodeAttr(kAttrShape, MakeValue(Convert2Long(reshape_output_shape)), new_node); return new_node; } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/softmax_dropout_do_mask_v3_fusion.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/softmax_dropout_do_mask_v3_fusion.cc index eebdca16c3e..8bddb3a58d7 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/softmax_dropout_do_mask_v3_fusion.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/softmax_dropout_do_mask_v3_fusion.cc @@ -16,8 +16,9 @@ #include "plugin/device/ascend/optimizer/ir_fusion/softmax_dropout_do_mask_v3_fusion.h" #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "base/core_ops.h" #include "backend/common/optimizer/helper.h" @@ -52,12 +53,13 @@ const AnfNodePtr SoftmaxDropoutDoMaskV3Fusion::Process(const FuncGraphPtr &graph dropout->input(kIndex2)}; auto softmax_dropout = NewCNode(softmax_dropout_inputs, graph); MS_EXCEPTION_IF_NULL(softmax_dropout); - auto types = {AnfAlgo::GetOutputInferDataType(softmax, 0), AnfAlgo::GetOutputInferDataType(dropout, 0)}; - auto shapes = {AnfAlgo::GetOutputInferShape(softmax, 0), AnfAlgo::GetOutputInferShape(dropout, 0)}; - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, softmax_dropout.get()); + auto types = {common::AnfAlgo::GetOutputInferDataType(softmax, 0), + common::AnfAlgo::GetOutputInferDataType(dropout, 0)}; + auto shapes = {common::AnfAlgo::GetOutputInferShape(softmax, 0), common::AnfAlgo::GetOutputInferShape(dropout, 0)}; + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, softmax_dropout.get()); softmax_dropout->set_scope(softmax->scope()); - AnfAlgo::CopyNodeAttr(kAttrAxis, softmax, softmax_dropout); - AnfAlgo::CopyNodeAttr(kAttrKeepProb, dropout, softmax_dropout); + common::AnfAlgo::CopyNodeAttr(kAttrAxis, softmax, softmax_dropout); + common::AnfAlgo::CopyNodeAttr(kAttrKeepProb, dropout, softmax_dropout); // replace softmax's output std::vector softmax_dropout_outputs; diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/softmax_grad_ext_fusion.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/softmax_grad_ext_fusion.cc index eb4235d7e6b..3ef318e5d08 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/softmax_grad_ext_fusion.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/softmax_grad_ext_fusion.cc @@ -16,8 +16,9 @@ #include "plugin/device/ascend/optimizer/ir_fusion/softmax_grad_ext_fusion.h" #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/optimizer/helper.h" namespace mindspore { @@ -69,8 +70,8 @@ const AnfNodePtr SoftmaxGradExtFusion::Process(const FuncGraphPtr &graph, const MS_EXCEPTION_IF_NULL(fusion_node); fusion_node->set_scope(node->scope()); fusion_node->set_abstract(node->abstract()); - AnfAlgo::CopyNodeAttr(kAttrKeepDims, "keepdims", sum, fusion_node); - AnfAlgo::CopyNodeAttr(kAttrAxis, sum, fusion_node); + common::AnfAlgo::CopyNodeAttr(kAttrKeepDims, "keepdims", sum, fusion_node); + common::AnfAlgo::CopyNodeAttr(kAttrAxis, sum, fusion_node); return fusion_node; } } // namespace opt diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/square_sum_fusion.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/square_sum_fusion.cc index ddb229520a9..04fc514d7c0 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/square_sum_fusion.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/square_sum_fusion.cc @@ -21,7 +21,8 @@ #include #include "backend/common/session/anf_runtime_algorithm.h" -#include "utils/utils.h" +#include "include/common/utils/anfalgo.h" +#include "include/common/utils/utils.h" #include "base/core_ops.h" #include "backend/common/optimizer/helper.h" #include "runtime/device/kernel_info.h" @@ -58,14 +59,14 @@ CNodePtr SquareSumFusion::GenerateSquareSumV1(const FuncGraphPtr &graph, const C auto kernel_info = std::make_shared(); MS_EXCEPTION_IF_NULL(kernel_info); square_sumv1->set_kernel_info(kernel_info); - auto types = {AnfAlgo::GetOutputInferDataType(sum, 0)}; - auto shapes = {AnfAlgo::GetOutputInferShape(sum, 0)}; - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, square_sumv1.get()); + auto types = {common::AnfAlgo::GetOutputInferDataType(sum, 0)}; + auto shapes = {common::AnfAlgo::GetOutputInferShape(sum, 0)}; + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, square_sumv1.get()); square_sumv1->set_scope(sum->scope()); - AnfAlgo::CopyNodeAttr(kAttrAxis, sum, square_sumv1); - AnfAlgo::CopyNodeAttr(kAttrKeepDims, sum, square_sumv1); + common::AnfAlgo::CopyNodeAttr(kAttrAxis, sum, square_sumv1); + common::AnfAlgo::CopyNodeAttr(kAttrKeepDims, sum, square_sumv1); auto names = MakeValue>({square->fullname_with_scope(), sum->fullname_with_scope()}); - AnfAlgo::SetNodeAttr(kAttrDatadumpOriginalNames, names, square_sumv1); + common::AnfAlgo::SetNodeAttr(kAttrDatadumpOriginalNames, names, square_sumv1); return square_sumv1; } @@ -80,14 +81,14 @@ CNodePtr SquareSumFusion::GenerateSquareSumV2(const FuncGraphPtr &graph, const C std::vector square_sumv2_inputs = {NewValueNode(prim), square->input(1)}; auto square_sumv2 = NewCNode(square_sumv2_inputs, graph); MS_EXCEPTION_IF_NULL(square_sumv2); - auto types = {AnfAlgo::GetOutputInferDataType(sum, 0), AnfAlgo::GetOutputInferDataType(square, 0)}; - auto shapes = {AnfAlgo::GetOutputInferShape(sum, 0), AnfAlgo::GetOutputInferShape(square, 0)}; - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, square_sumv2.get()); + auto types = {common::AnfAlgo::GetOutputInferDataType(sum, 0), common::AnfAlgo::GetOutputInferDataType(square, 0)}; + auto shapes = {common::AnfAlgo::GetOutputInferShape(sum, 0), common::AnfAlgo::GetOutputInferShape(square, 0)}; + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, square_sumv2.get()); square_sumv2->set_scope(sum->scope()); - AnfAlgo::CopyNodeAttr(kAttrAxis, sum, square_sumv2); - AnfAlgo::CopyNodeAttr(kAttrKeepDims, sum, square_sumv2); + common::AnfAlgo::CopyNodeAttr(kAttrAxis, sum, square_sumv2); + common::AnfAlgo::CopyNodeAttr(kAttrKeepDims, sum, square_sumv2); auto names = MakeValue>({square->fullname_with_scope(), sum->fullname_with_scope()}); - AnfAlgo::SetNodeAttr(kAttrDatadumpOriginalNames, names, square_sumv2); + common::AnfAlgo::SetNodeAttr(kAttrDatadumpOriginalNames, names, square_sumv2); return square_sumv2; } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/transpose_reshape_fusion.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/transpose_reshape_fusion.cc index d0e0b51991b..700e68edc41 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/transpose_reshape_fusion.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/transpose_reshape_fusion.cc @@ -17,7 +17,8 @@ #include "plugin/device/ascend/optimizer/ir_fusion/transpose_reshape_fusion.h" #include #include "backend/common/session/anf_runtime_algorithm.h" -#include "utils/utils.h" +#include "include/common/utils/anfalgo.h" +#include "include/common/utils/utils.h" #include "backend/common/optimizer/helper.h" #include "base/core_ops.h" @@ -53,7 +54,7 @@ const AnfNodePtr TransposeReshapeFusion::Process(const FuncGraphPtr &func_graph, MS_EXCEPTION_IF_NULL(reshape_cnode); auto transpose_cnode = CheckAnfNodeIfCNodeAndInputSize(reshape_cnode->input(1), kBackendReshapeInputTensorNum); MS_EXCEPTION_IF_NULL(transpose_cnode); - if (AnfAlgo::IsDynamicShape(transpose_cnode) || AnfAlgo::IsDynamicShape(reshape_cnode)) { + if (common::AnfAlgo::IsDynamicShape(transpose_cnode) || common::AnfAlgo::IsDynamicShape(reshape_cnode)) { return nullptr; } auto kernel_graph = func_graph->cast(); @@ -61,8 +62,8 @@ const AnfNodePtr TransposeReshapeFusion::Process(const FuncGraphPtr &func_graph, (kernel_graph->IsInternalOutput(reshape_cnode, 0) || kernel_graph->IsInternalOutput(transpose_cnode, 0))) { return nullptr; } - std::vector reshape_output0_shape = AnfAlgo::GetOutputInferShape(reshape_cnode, 0); - std::vector transpose_input0_shape = AnfAlgo::GetPrevNodeOutputInferShape(transpose_cnode, 0); + std::vector reshape_output0_shape = common::AnfAlgo::GetOutputInferShape(reshape_cnode, 0); + std::vector transpose_input0_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(transpose_cnode, 0); if (!CheckShapeDimInfo(reshape_output0_shape) || !CheckShapeDimInfo(transpose_input0_shape)) { return nullptr; } @@ -72,11 +73,11 @@ const AnfNodePtr TransposeReshapeFusion::Process(const FuncGraphPtr &func_graph, MS_EXCEPTION_IF_NULL(new_node); new_node->set_abstract(node->abstract()); - AnfAlgo::CopyNodeAttrs(reshape_cnode, new_node); - AnfAlgo::CopyNodeAttr(kAttrPerm, transpose_cnode, new_node); - AnfAlgo::SetNodeAttr(kAttrTransposeFirst, MakeValue(true), new_node); - auto reshape_output_shape = AnfAlgo::GetOutputInferShape(reshape_cnode, 0); - AnfAlgo::SetNodeAttr(kAttrShape, MakeValue(Convert2Long(reshape_output_shape)), new_node); + common::AnfAlgo::CopyNodeAttrs(reshape_cnode, new_node); + common::AnfAlgo::CopyNodeAttr(kAttrPerm, transpose_cnode, new_node); + common::AnfAlgo::SetNodeAttr(kAttrTransposeFirst, MakeValue(true), new_node); + auto reshape_output_shape = common::AnfAlgo::GetOutputInferShape(reshape_cnode, 0); + common::AnfAlgo::SetNodeAttr(kAttrShape, MakeValue(Convert2Long(reshape_output_shape)), new_node); return new_node; } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/transpose_transdata_fusion.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/transpose_transdata_fusion.cc index abe271d97db..60c7ca57410 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/transpose_transdata_fusion.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/transpose_transdata_fusion.cc @@ -17,7 +17,8 @@ #include "plugin/device/ascend/optimizer/ir_fusion/transpose_transdata_fusion.h" #include #include "backend/common/session/anf_runtime_algorithm.h" -#include "utils/utils.h" +#include "include/common/utils/anfalgo.h" +#include "include/common/utils/utils.h" #include "base/core_ops.h" namespace mindspore { @@ -60,8 +61,8 @@ const AnfNodePtr TransposeTransDataFusion::Process(const FuncGraphPtr &func_grap auto new_node = NewCNode(inputs, func_graph); MS_EXCEPTION_IF_NULL(new_node); new_node->set_abstract(node->abstract()); - AnfAlgo::CopyNodeAttrs(transdata_cnode, new_node); - AnfAlgo::SetNodeAttr(kAttrSrcFormat, MakeValue(transpose_input_formats[0]), new_node); + common::AnfAlgo::CopyNodeAttrs(transdata_cnode, new_node); + common::AnfAlgo::SetNodeAttr(kAttrSrcFormat, MakeValue(transpose_input_formats[0]), new_node); AnfAlgo::SetSelectKernelBuildInfo(new_transdata_builder->Build(), new_node.get()); MS_LOG(INFO) << "transpose transdata fusion node:" << node->fullname_with_scope() << " success"; return new_node; diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/transposed_update_fusion.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/transposed_update_fusion.cc index a9550df57df..932bde94600 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/transposed_update_fusion.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/transposed_update_fusion.cc @@ -18,6 +18,7 @@ #include #include "plugin/device/ascend/optimizer/ascend_helper.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "debug/anf_ir_dump.h" #include "utils/trace_base.h" @@ -27,7 +28,7 @@ namespace { constexpr size_t kInt32Len = 4; tensor::TensorPtr CreatePermTensor(const CNodePtr &transposed) { - auto perm_attr = AnfAlgo::GetNodeAttr>(transposed, kAttrPerm); + auto perm_attr = common::AnfAlgo::GetNodeAttr>(transposed, kAttrPerm); std::vector perm; (void)std::transform(perm_attr.begin(), perm_attr.end(), std::back_inserter(perm), LongToInt); std::vector perm_shape = {SizeToLong(perm.size())}; @@ -78,7 +79,8 @@ const AnfNodePtr TransposedUpdateFusion::Process(const FuncGraphPtr &func_graph, MS_EXCEPTION_IF_NULL(transposed); auto kernel_graph = func_graph->cast(); MS_EXCEPTION_IF_NULL(kernel_graph); - if (AnfAlgo::HasNodeAttr(kAttrNopOp, transposed) && AnfAlgo::GetNodeAttr(transposed, kAttrNopOp)) { + if (common::AnfAlgo::HasNodeAttr(kAttrNopOp, transposed) && + common::AnfAlgo::GetNodeAttr(transposed, kAttrNopOp)) { MS_LOG(INFO) << "Node [" << transposed->fullname_with_scope() << "] is a nop op, skip update."; return nullptr; } @@ -108,7 +110,7 @@ const AnfNodePtr TransposedUpdateFusion::Process(const FuncGraphPtr &func_graph, builder->SetOutputsFormat({output_format}); AnfAlgo::SetSelectKernelBuildInfo(builder->Build(), transpose.get()); kernel_graph->AddValueNodeToGraph(perm_vnode); - AnfAlgo::CopyNodeAttrs(transposed, transpose); + common::AnfAlgo::CopyNodeAttrs(transposed, transpose); return transpose; } } // namespace opt diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/all_to_all_unify_mindir.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/all_to_all_unify_mindir.cc index 65d4bfbd9df..46adb4d2cde 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/all_to_all_unify_mindir.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/all_to_all_unify_mindir.cc @@ -19,6 +19,7 @@ #include #include "utils/trace_base.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "plugin/device/ascend/hal/hccl_adapter/hccl_adapter.h" #include "backend/common/optimizer/helper.h" @@ -60,8 +61,8 @@ uint32_t GetRankSize(const std::string &group) { CNodePtr AllToAllUnifyMindIR::CreateSplitNode(const FuncGraphPtr &graph, const CNodePtr &all_to_all) const { MS_EXCEPTION_IF_NULL(graph); MS_EXCEPTION_IF_NULL(all_to_all); - int64_t split_count = AnfAlgo::GetNodeAttr(all_to_all, kAttrSplitCount); - int64_t split_dim = AnfAlgo::GetNodeAttr(all_to_all, kAttrSplitDim); + int64_t split_count = common::AnfAlgo::GetNodeAttr(all_to_all, kAttrSplitCount); + int64_t split_dim = common::AnfAlgo::GetNodeAttr(all_to_all, kAttrSplitDim); if (all_to_all->size() <= kAllToAllInputIdx) { MS_LOG(EXCEPTION) << "Inputs should not be empty for cnode " << all_to_all->DebugString() @@ -72,8 +73,8 @@ CNodePtr AllToAllUnifyMindIR::CreateSplitNode(const FuncGraphPtr &graph, const C all_to_all_input}; auto split_v = NewCNode(split_input, graph); MS_EXCEPTION_IF_NULL(split_v); - auto dtype = AnfAlgo::GetOutputInferDataType(all_to_all_input, 0); - auto shape = AnfAlgo::GetOutputInferShape(all_to_all_input, 0); + auto dtype = common::AnfAlgo::GetOutputInferDataType(all_to_all_input, 0); + auto shape = common::AnfAlgo::GetOutputInferShape(all_to_all_input, 0); split_dim = NormalizeDim(shape, split_dim); if (SizeToLong(shape.size()) <= split_dim) { MS_LOG(EXCEPTION) << "Invalid split dim " << split_dim << " is over the shape size " << shape.size() @@ -86,12 +87,12 @@ CNodePtr AllToAllUnifyMindIR::CreateSplitNode(const FuncGraphPtr &graph, const C shape[LongToSize(split_dim)] /= static_cast(split_count); std::vector dtypes(split_count, dtype); std::vector> shapes(split_count, shape); - AnfAlgo::SetOutputInferTypeAndShape(dtypes, shapes, split_v.get()); - AnfAlgo::SetNodeAttr(kAttrSplitDim, MakeValue(split_dim), split_v); - AnfAlgo::SetNodeAttr(kAttrNumSplit, MakeValue(split_count), split_v); - AnfAlgo::SetNodeAttr(kAttrSizeSplits, MakeValue(std::vector(split_count, shape[LongToSize(split_dim)])), - split_v); - AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), split_v); + common::AnfAlgo::SetOutputInferTypeAndShape(dtypes, shapes, split_v.get()); + common::AnfAlgo::SetNodeAttr(kAttrSplitDim, MakeValue(split_dim), split_v); + common::AnfAlgo::SetNodeAttr(kAttrNumSplit, MakeValue(split_count), split_v); + common::AnfAlgo::SetNodeAttr(kAttrSizeSplits, + MakeValue(std::vector(split_count, shape[LongToSize(split_dim)])), split_v); + common::AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), split_v); return split_v; } @@ -100,8 +101,8 @@ CNodePtr AllToAllUnifyMindIR::CreateAllToAllvNode(const FuncGraphPtr &graph, con MS_EXCEPTION_IF_NULL(graph); MS_EXCEPTION_IF_NULL(all_to_all); MS_EXCEPTION_IF_NULL(split); - int64_t split_count = AnfAlgo::GetNodeAttr(all_to_all, kAttrSplitCount); - std::string group = AnfAlgo::GetNodeAttr(all_to_all, kAttrGroup); + int64_t split_count = common::AnfAlgo::GetNodeAttr(all_to_all, kAttrSplitCount); + std::string group = common::AnfAlgo::GetNodeAttr(all_to_all, kAttrGroup); std::vector split_outputs; CreateMultipleOutputsOfAnfNode(graph, split, static_cast(split_count), &split_outputs); if (split_outputs.empty()) { @@ -112,20 +113,20 @@ CNodePtr AllToAllUnifyMindIR::CreateAllToAllvNode(const FuncGraphPtr &graph, con (void)all_to_all_v_input.insert(all_to_all_v_input.end(), split_outputs.begin(), split_outputs.end()); auto all_to_all_v = NewCNode(all_to_all_v_input, graph); MS_EXCEPTION_IF_NULL(all_to_all_v); - auto single_shape = AnfAlgo::GetOutputInferShape(split_outputs[0], 0); - auto single_type = AnfAlgo::GetOutputInferDataType(split_outputs[0], 0); + auto single_shape = common::AnfAlgo::GetOutputInferShape(split_outputs[0], 0); + auto single_type = common::AnfAlgo::GetOutputInferDataType(split_outputs[0], 0); std::vector dtypes(split_count, single_type); std::vector> shapes(split_count, single_shape); - AnfAlgo::SetOutputInferTypeAndShape(dtypes, shapes, all_to_all_v.get()); + common::AnfAlgo::SetOutputInferTypeAndShape(dtypes, shapes, all_to_all_v.get()); uint32_t rank_size = GetRankSize(group); std::vector rank_ids(rank_size, 0); for (uint32_t i = 0; i < rank_size; ++i) { rank_ids[i] = static_cast(i); } - AnfAlgo::SetNodeAttr(kAttrSendRankIds, MakeValue>(rank_ids), all_to_all_v); - AnfAlgo::SetNodeAttr(kAttrRecvRankIds, MakeValue>(rank_ids), all_to_all_v); - AnfAlgo::SetNodeAttr(kAttrGroup, MakeValue(group), all_to_all_v); + common::AnfAlgo::SetNodeAttr(kAttrSendRankIds, MakeValue>(rank_ids), all_to_all_v); + common::AnfAlgo::SetNodeAttr(kAttrRecvRankIds, MakeValue>(rank_ids), all_to_all_v); + common::AnfAlgo::SetNodeAttr(kAttrGroup, MakeValue(group), all_to_all_v); MS_LOG(INFO) << "Create AllToAllv success, split count " << split_count << ", rank size " << rank_size; return all_to_all_v; } @@ -135,8 +136,8 @@ CNodePtr AllToAllUnifyMindIR::CreateConcatNode(const FuncGraphPtr &graph, const MS_EXCEPTION_IF_NULL(graph); MS_EXCEPTION_IF_NULL(all_to_all); MS_EXCEPTION_IF_NULL(all_to_all_v); - int64_t split_count = AnfAlgo::GetNodeAttr(all_to_all, kAttrSplitCount); - int64_t concat_dim = AnfAlgo::GetNodeAttr(all_to_all, kAttrConcatDim); + int64_t split_count = common::AnfAlgo::GetNodeAttr(all_to_all, kAttrSplitCount); + int64_t concat_dim = common::AnfAlgo::GetNodeAttr(all_to_all, kAttrConcatDim); std::vector all_to_all_v_outputs; CreateMultipleOutputsOfAnfNode(graph, all_to_all_v, static_cast(split_count), &all_to_all_v_outputs); if (all_to_all_v_outputs.empty()) { @@ -147,19 +148,19 @@ CNodePtr AllToAllUnifyMindIR::CreateConcatNode(const FuncGraphPtr &graph, const (void)concat_input.insert(concat_input.end(), all_to_all_v_outputs.begin(), all_to_all_v_outputs.end()); auto concat = NewCNode(concat_input, graph); MS_EXCEPTION_IF_NULL(concat); - auto single_shape = AnfAlgo::GetOutputInferShape(all_to_all_v_outputs[0], 0); + auto single_shape = common::AnfAlgo::GetOutputInferShape(all_to_all_v_outputs[0], 0); concat_dim = NormalizeDim(single_shape, concat_dim); if (LongToSize(concat_dim) >= single_shape.size()) { MS_LOG(EXCEPTION) << "Invalid concat dim " << concat_dim << " is greater than shape size " << single_shape.size() << trace::DumpSourceLines(all_to_all); } single_shape[LongToSize(concat_dim)] *= static_cast(split_count); - AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(all_to_all_v_outputs[0], 0)}, {single_shape}, - concat.get()); - AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(concat_dim), concat); - AnfAlgo::SetNodeAttr(kAttrInputNums, MakeValue(split_count), concat); + common::AnfAlgo::SetOutputInferTypeAndShape({common::AnfAlgo::GetOutputInferDataType(all_to_all_v_outputs[0], 0)}, + {single_shape}, concat.get()); + common::AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(concat_dim), concat); + common::AnfAlgo::SetNodeAttr(kAttrInputNums, MakeValue(split_count), concat); std::vector dyn_input_size{split_count}; - AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(dyn_input_size), concat); + common::AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(dyn_input_size), concat); return concat; } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/avg_pool_grad_unify_mindir.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/avg_pool_grad_unify_mindir.cc index 06a0df9479b..a8d2386da7b 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/avg_pool_grad_unify_mindir.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/avg_pool_grad_unify_mindir.cc @@ -22,13 +22,14 @@ #include #include -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/check_convert_utils.h" #include "utils/convert_utils_base.h" #include "utils/trace_base.h" #include "backend/common/optimizer/helper.h" #include "runtime/device/kernel_info.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -40,7 +41,7 @@ constexpr size_t kFloat32Len = 4; // size of float32 std::vector GetInputXShape(const AnfNodePtr &node) { MS_EXCEPTION_IF_NULL(node); std::vector shapes; - auto shape_size_t = AnfAlgo::GetPrevNodeOutputInferShape(node, 0); + auto shape_size_t = common::AnfAlgo::GetPrevNodeOutputInferShape(node, 0); std::transform(shape_size_t.begin(), shape_size_t.end(), std::back_inserter(shapes), SizeToLong); return shapes; } @@ -198,10 +199,10 @@ const AnfNodePtr AvgPoolGradUnifyMindIR::Process(const FuncGraphPtr &graph, cons auto avgpool_grad = CheckAnfNodeIfCNodeAndInputSize(node, kAvgPoolGradInputNum); auto x_shape = GetInputXShape(avgpool_grad); - auto x_dtype = AnfAlgo::GetPrevNodeOutputInferDataType(avgpool_grad, 0); - auto k_size = AnfAlgo::GetNodeAttr>(avgpool_grad, kAttrKernelSize); - auto stride = AnfAlgo::GetNodeAttr>(avgpool_grad, kAttrStrides); - auto pad_mode = PadMode(AnfAlgo::GetNodeAttr(avgpool_grad, kAttrPadMode)); + auto x_dtype = common::AnfAlgo::GetPrevNodeOutputInferDataType(avgpool_grad, 0); + auto k_size = common::AnfAlgo::GetNodeAttr>(avgpool_grad, kAttrKernelSize); + auto stride = common::AnfAlgo::GetNodeAttr>(avgpool_grad, kAttrStrides); + auto pad_mode = PadMode(common::AnfAlgo::GetNodeAttr(avgpool_grad, kAttrPadMode)); auto x_shape_vnode = CreateShapeValueNode(graph, x_shape); auto mean_matrix_vnode = CreateMeanMatrixValueNode(graph, node, x_shape, k_size, stride, pad_mode, x_dtype); @@ -214,14 +215,14 @@ const AnfNodePtr AvgPoolGradUnifyMindIR::Process(const FuncGraphPtr &graph, cons MS_EXCEPTION_IF_NULL(avgpool_grad_vm); avgpool_grad_vm->set_scope(avgpool_grad->scope()); avgpool_grad_vm->set_abstract(avgpool_grad->abstract()); - AnfAlgo::CopyNodeAttr(kAttrKernelSize, avgpool_grad, avgpool_grad_vm); - AnfAlgo::CopyNodeAttr(kAttrStrides, avgpool_grad, avgpool_grad_vm); - AnfAlgo::CopyNodeAttr(kAttrPadMode, avgpool_grad, avgpool_grad_vm); - AnfAlgo::CopyNodeAttr(kAttrFormat, avgpool_grad, avgpool_grad_vm); + common::AnfAlgo::CopyNodeAttr(kAttrKernelSize, avgpool_grad, avgpool_grad_vm); + common::AnfAlgo::CopyNodeAttr(kAttrStrides, avgpool_grad, avgpool_grad_vm); + common::AnfAlgo::CopyNodeAttr(kAttrPadMode, avgpool_grad, avgpool_grad_vm); + common::AnfAlgo::CopyNodeAttr(kAttrFormat, avgpool_grad, avgpool_grad_vm); auto input_names = std::vector{"x_origin", "grad", "mean_matrix", "kernel_matrix"}; - AnfAlgo::SetNodeAttr(kAttrInputNames, MakeValue(input_names), avgpool_grad_vm); + common::AnfAlgo::SetNodeAttr(kAttrInputNames, MakeValue(input_names), avgpool_grad_vm); auto output_names = std::vector{"output"}; - AnfAlgo::SetNodeAttr(kAttrOutputNames, MakeValue(output_names), avgpool_grad_vm); + common::AnfAlgo::SetNodeAttr(kAttrOutputNames, MakeValue(output_names), avgpool_grad_vm); return avgpool_grad_vm; } } // namespace opt diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/bn_grad_unify_mindir.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/bn_grad_unify_mindir.cc index b740e459eb1..3dc69820842 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/bn_grad_unify_mindir.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/bn_grad_unify_mindir.cc @@ -17,6 +17,7 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -40,16 +41,19 @@ AnfNodePtr BatchNormGradUnifyMindIR::CreateNewBatchNormGrad(const FuncGraphPtr & auto new_bn_grad = NewCNode(bn_grad_inputs, graph); MS_EXCEPTION_IF_NULL(new_bn_grad); new_bn_grad->set_scope(bn_grad_node->scope()); - auto types = {AnfAlgo::GetOutputInferDataType(bn_grad_node, 0), AnfAlgo::GetOutputInferDataType(bn_grad_node, 1), - AnfAlgo::GetOutputInferDataType(bn_grad_node, 2), - AnfAlgo::GetPrevNodeOutputInferDataType(bn_grad_node, 3), - AnfAlgo::GetPrevNodeOutputInferDataType(bn_grad_node, 4)}; - auto shapes = {AnfAlgo::GetOutputInferShape(bn_grad_node, 0), AnfAlgo::GetOutputInferShape(bn_grad_node, 1), - AnfAlgo::GetOutputInferShape(bn_grad_node, 2), AnfAlgo::GetPrevNodeOutputInferShape(bn_grad_node, 3), - AnfAlgo::GetPrevNodeOutputInferShape(bn_grad_node, 4)}; - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, new_bn_grad.get()); - AnfAlgo::CopyNodeAttrs(bn_grad_node, new_bn_grad); - AnfAlgo::SetNodeAttr(kAttrUnifyIRPassed, MakeValue(true), new_bn_grad); + auto types = {common::AnfAlgo::GetOutputInferDataType(bn_grad_node, 0), + common::AnfAlgo::GetOutputInferDataType(bn_grad_node, 1), + common::AnfAlgo::GetOutputInferDataType(bn_grad_node, 2), + common::AnfAlgo::GetPrevNodeOutputInferDataType(bn_grad_node, 3), + common::AnfAlgo::GetPrevNodeOutputInferDataType(bn_grad_node, 4)}; + auto shapes = {common::AnfAlgo::GetOutputInferShape(bn_grad_node, 0), + common::AnfAlgo::GetOutputInferShape(bn_grad_node, 1), + common::AnfAlgo::GetOutputInferShape(bn_grad_node, 2), + common::AnfAlgo::GetPrevNodeOutputInferShape(bn_grad_node, 3), + common::AnfAlgo::GetPrevNodeOutputInferShape(bn_grad_node, 4)}; + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, new_bn_grad.get()); + common::AnfAlgo::CopyNodeAttrs(bn_grad_node, new_bn_grad); + common::AnfAlgo::SetNodeAttr(kAttrUnifyIRPassed, MakeValue(true), new_bn_grad); return new_bn_grad; } @@ -66,7 +70,7 @@ const AnfNodePtr BatchNormGradUnifyMindIR::Process(const FuncGraphPtr &func_grap auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - if (AnfAlgo::HasNodeAttr(kAttrUnifyIRPassed, cnode)) { + if (common::AnfAlgo::HasNodeAttr(kAttrUnifyIRPassed, cnode)) { return nullptr; } return CreateNewBatchNormGrad(func_graph, cnode); diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/conv2d_unify_mindir.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/conv2d_unify_mindir.cc index a870a90048f..e3a5770d876 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/conv2d_unify_mindir.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/conv2d_unify_mindir.cc @@ -21,13 +21,14 @@ #include #include -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/ms_context.h" #include "utils/check_convert_utils.h" #include "utils/trace_base.h" #include "backend/common/optimizer/helper.h" #include "runtime/device/kernel_info.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -44,7 +45,7 @@ constexpr auto kAttrInputSize = "input_size"; bool NeedUpdate(const CNodePtr &conv2d, std::vector in_shape, std::vector out_shape) { MS_EXCEPTION_IF_NULL(conv2d); - auto group = LongToSize(AnfAlgo::GetNodeAttr(conv2d, kAttrGroup)); + auto group = LongToSize(common::AnfAlgo::GetNodeAttr(conv2d, kAttrGroup)); if (group == 1) { return false; } @@ -115,73 +116,73 @@ CNodePtr CreateTranspose(const FuncGraphPtr &graph, const CNodePtr &conv2d, cons transpose->set_scope(conv2d->scope()); if (need_trans_output) { - auto types = {AnfAlgo::GetOutputInferDataType(input_node, 0)}; - auto out_shape = AnfAlgo::GetOutputInferShape(input_node, 0); + auto types = {common::AnfAlgo::GetOutputInferDataType(input_node, 0)}; + auto out_shape = common::AnfAlgo::GetOutputInferShape(input_node, 0); if (out_shape.size() != kConv2DAxisNum) { MS_LOG(EXCEPTION) << "Conv2D's output axis number should be " << kConv2DAxisNum << ", but got " << out_shape.size() << trace::DumpSourceLines(conv2d); } std::swap(out_shape[kDim0], out_shape[kDim1]); auto shapes = {out_shape}; - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, transpose.get()); + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, transpose.get()); } else { transpose->set_abstract(conv2d->abstract()); } auto input_names = std::vector{"x", "perm"}; auto output_names = std::vector{"output"}; - AnfAlgo::SetNodeAttr(kAttrInputNames, MakeValue(input_names), transpose); - AnfAlgo::SetNodeAttr(kAttrOutputNames, MakeValue(output_names), transpose); + common::AnfAlgo::SetNodeAttr(kAttrInputNames, MakeValue(input_names), transpose); + common::AnfAlgo::SetNodeAttr(kAttrOutputNames, MakeValue(output_names), transpose); if (IsPynative()) { - AnfAlgo::SetNodeAttr(kAttrPerm, MakeValue(perm), transpose); + common::AnfAlgo::SetNodeAttr(kAttrPerm, MakeValue(perm), transpose); } return transpose; } void SetCommonAttrs(const CNodePtr &conv2d, const CNodePtr &depth_conv) { - AnfAlgo::CopyNodeAttr(kAttrKernelSize, conv2d, depth_conv); - AnfAlgo::CopyNodeAttr(kAttrDilation, conv2d, depth_conv); - AnfAlgo::CopyNodeAttr(kAttrFormat, conv2d, depth_conv); - AnfAlgo::CopyNodeAttr(kAttrPadList, conv2d, depth_conv); - AnfAlgo::CopyNodeAttr(kAttrPadMode, conv2d, depth_conv); + common::AnfAlgo::CopyNodeAttr(kAttrKernelSize, conv2d, depth_conv); + common::AnfAlgo::CopyNodeAttr(kAttrDilation, conv2d, depth_conv); + common::AnfAlgo::CopyNodeAttr(kAttrFormat, conv2d, depth_conv); + common::AnfAlgo::CopyNodeAttr(kAttrPadList, conv2d, depth_conv); + common::AnfAlgo::CopyNodeAttr(kAttrPadMode, conv2d, depth_conv); constexpr auto kMode = 3; - AnfAlgo::SetNodeAttr(kAttrMode, MakeValue(kMode), depth_conv); - AnfAlgo::SetNodeAttr(kAttrChannelMultiplier, MakeValue(1), depth_conv); + common::AnfAlgo::SetNodeAttr(kAttrMode, MakeValue(kMode), depth_conv); + common::AnfAlgo::SetNodeAttr(kAttrChannelMultiplier, MakeValue(1), depth_conv); } void SetConv2DAttrs(const CNodePtr &conv2d, const CNodePtr &depth_conv) { SetCommonAttrs(conv2d, depth_conv); - AnfAlgo::CopyNodeAttr(kAttrInputNames, conv2d, depth_conv); - AnfAlgo::CopyNodeAttr(kAttrStride, conv2d, depth_conv); - if (AnfAlgo::HasNodeAttr(kAttrOffsetA, conv2d)) { - AnfAlgo::CopyNodeAttr(kAttrOffsetA, conv2d, depth_conv); + common::AnfAlgo::CopyNodeAttr(kAttrInputNames, conv2d, depth_conv); + common::AnfAlgo::CopyNodeAttr(kAttrStride, conv2d, depth_conv); + if (common::AnfAlgo::HasNodeAttr(kAttrOffsetA, conv2d)) { + common::AnfAlgo::CopyNodeAttr(kAttrOffsetA, conv2d, depth_conv); } else { - AnfAlgo::SetNodeAttr(kAttrOffsetA, MakeValue(0), depth_conv); + common::AnfAlgo::SetNodeAttr(kAttrOffsetA, MakeValue(0), depth_conv); } } void SetConv2DBackpropInputAttrs(const CNodePtr &conv2d_backin, const CNodePtr &depth_conv_backin) { SetCommonAttrs(conv2d_backin, depth_conv_backin); auto input_names = std::vector{"input_size", "filter", "dout"}; - AnfAlgo::SetNodeAttr(kAttrInputNames, MakeValue(input_names), depth_conv_backin); - auto stride = AnfAlgo::GetNodeAttr>(conv2d_backin, kAttrStride); + common::AnfAlgo::SetNodeAttr(kAttrInputNames, MakeValue(input_names), depth_conv_backin); + auto stride = common::AnfAlgo::GetNodeAttr>(conv2d_backin, kAttrStride); constexpr size_t kStrideSize = 2; if (stride.size() == kStrideSize) { (void)stride.insert(stride.begin(), kStrideSize, 1); } - AnfAlgo::SetNodeAttr(kAttrStride, MakeValue(stride), depth_conv_backin); + common::AnfAlgo::SetNodeAttr(kAttrStride, MakeValue(stride), depth_conv_backin); } void SetConv2DBackpropFilterAttrs(const CNodePtr &conv2d_backfil, const CNodePtr &depth_conv_backfil) { SetCommonAttrs(conv2d_backfil, depth_conv_backfil); auto input_names = std::vector{"input", "filter_size", "dout"}; - AnfAlgo::SetNodeAttr(kAttrInputNames, MakeValue(input_names), depth_conv_backfil); - auto stride = AnfAlgo::GetNodeAttr>(conv2d_backfil, kAttrStride); + common::AnfAlgo::SetNodeAttr(kAttrInputNames, MakeValue(input_names), depth_conv_backfil); + auto stride = common::AnfAlgo::GetNodeAttr>(conv2d_backfil, kAttrStride); constexpr size_t kStrideSize = 2; if (stride.size() == kStrideSize) { (void)stride.insert(stride.begin(), kStrideSize, 1); } - AnfAlgo::SetNodeAttr(kAttrStride, MakeValue(stride), depth_conv_backfil); + common::AnfAlgo::SetNodeAttr(kAttrStride, MakeValue(stride), depth_conv_backfil); } } // namespace @@ -212,8 +213,8 @@ const AnfNodePtr Conv2DUnifyMindIR::Process(const FuncGraphPtr &graph, const Anf auto conv2d = node->cast(); MS_EXCEPTION_IF_NULL(conv2d); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(conv2d, 0); - auto output_shape = AnfAlgo::GetOutputInferShape(conv2d, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(conv2d, 0); + auto output_shape = common::AnfAlgo::GetOutputInferShape(conv2d, 0); if (!NeedUpdate(conv2d, input_shape, output_shape)) { return nullptr; } @@ -243,7 +244,7 @@ CNodePtr Conv2DBackpropInputUnifyMindIR::CreateDepthwiseConv2DBackpropInput(cons NewValueNode(std::make_shared(kDepthwiseConv2dNativeBackpropInputOpName)), transpose, conv2d_backin->input(kIndex1)}; depth_conv_backin = NewCNode(depth_conv_backin_inputs, graph); - AnfAlgo::CopyNodeAttr(kAttrInputSizes, kAttrInputSize, conv2d_backin, depth_conv_backin); + common::AnfAlgo::CopyNodeAttr(kAttrInputSizes, kAttrInputSize, conv2d_backin, depth_conv_backin); } MS_EXCEPTION_IF_NULL(depth_conv_backin); depth_conv_backin->set_abstract(conv2d_backin->abstract()); @@ -264,8 +265,8 @@ const AnfNodePtr Conv2DBackpropInputUnifyMindIR::Process(const FuncGraphPtr &gra auto conv2d_backin = node->cast(); MS_EXCEPTION_IF_NULL(conv2d_backin); - auto input_shape = AnfAlgo::GetOutputInferShape(conv2d_backin, 0); - auto output_shape = AnfAlgo::GetPrevNodeOutputInferShape(conv2d_backin, 0); + auto input_shape = common::AnfAlgo::GetOutputInferShape(conv2d_backin, 0); + auto output_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(conv2d_backin, 0); if (!NeedUpdate(conv2d_backin, input_shape, output_shape)) { return nullptr; } @@ -309,15 +310,15 @@ CNodePtr Conv2DBackpropFilterUnifyMindIR::CreateDepthwiseConv2DBackpropFilter(co MS_EXCEPTION_IF_NULL(depth_conv_backfil); depth_conv_backfil->set_scope(conv2d_backfil->scope()); - auto types = {AnfAlgo::GetOutputInferDataType(conv2d_backfil, 0)}; - std::vector out_shape = AnfAlgo::GetOutputInferShape(conv2d_backfil, 0); + auto types = {common::AnfAlgo::GetOutputInferDataType(conv2d_backfil, 0)}; + std::vector out_shape = common::AnfAlgo::GetOutputInferShape(conv2d_backfil, 0); if (out_shape.size() != kConv2DAxisNum) { MS_LOG(EXCEPTION) << "Conv2DBackpropFilter's output axis number should be " << kConv2DAxisNum << ", but got " << out_shape.size() << trace::DumpSourceLines(conv2d_backfil); } std::swap(out_shape[0], out_shape[1]); auto shapes = {out_shape}; - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, depth_conv_backfil.get()); + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, depth_conv_backfil.get()); return depth_conv_backfil; } @@ -336,8 +337,8 @@ const AnfNodePtr Conv2DBackpropFilterUnifyMindIR::Process(const FuncGraphPtr &gr auto conv2d_backfil = node->cast(); MS_EXCEPTION_IF_NULL(conv2d_backfil); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(conv2d_backfil, 1); - auto output_shape = AnfAlgo::GetPrevNodeOutputInferShape(conv2d_backfil, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(conv2d_backfil, 1); + auto output_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(conv2d_backfil, 0); if (!NeedUpdate(conv2d_backfil, input_shape, output_shape)) { return nullptr; } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/dropout_unify_mindir.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/dropout_unify_mindir.cc index 50412bb13c7..f3fa9f2fb8b 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/dropout_unify_mindir.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/dropout_unify_mindir.cc @@ -22,8 +22,9 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "utils/log_adapter.h" -#include "utils/ms_device_shape_transfer.h" +#include "runtime/device/ms_device_shape_transfer.h" /* DropoutGenMask： @@ -48,7 +49,7 @@ constexpr size_t kInt64Len = 8; // size of int64 TypeId GetInputXDataType(const AnfNodePtr &node) { MS_EXCEPTION_IF_NULL(node); - auto dropout_input_type = AnfAlgo::GetPrevNodeOutputInferDataType(node, 0); + auto dropout_input_type = common::AnfAlgo::GetPrevNodeOutputInferDataType(node, 0); if (dropout_input_type != kNumberTypeFloat32 && dropout_input_type != kNumberTypeFloat && dropout_input_type != kNumberTypeFloat16) { dropout_input_type = kNumberTypeFloat16; @@ -63,15 +64,15 @@ ValueNodePtr CreateKeepPorbValueNode(const FuncGraphPtr &func_graph, const AnfNo auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); // Step1: get keep_prob - if (!AnfAlgo::HasNodeAttr(kAttrKeepProb, cnode)) { + if (!common::AnfAlgo::HasNodeAttr(kAttrKeepProb, cnode)) { MS_LOG(EXCEPTION) << "Dropout node does not have attr: keep_prob." << trace::DumpSourceLines(node); } - if (AnfAlgo::GetCNodeName(cnode) == kDropoutOpName) { - if (!AnfAlgo::HasNodeAttr(kAttrSeed0, cnode) || !AnfAlgo::HasNodeAttr(kAttrSeed1, cnode)) { + if (common::AnfAlgo::GetCNodeName(cnode) == kDropoutOpName) { + if (!common::AnfAlgo::HasNodeAttr(kAttrSeed0, cnode) || !common::AnfAlgo::HasNodeAttr(kAttrSeed1, cnode)) { MS_LOG(EXCEPTION) << "Dropout node does not have attr: seed0 or seed1." << trace::DumpSourceLines(node); } } - auto keep_prob = AnfAlgo::GetNodeAttr(node, kAttrKeepProb); + auto keep_prob = common::AnfAlgo::GetNodeAttr(node, kAttrKeepProb); MS_LOG(INFO) << "Keep_prob value: " << keep_prob; std::vector keep_prob_shape = {}; @@ -196,7 +197,7 @@ CNodePtr CreateDropoutGenMaskCNode(const FuncGraphPtr &func_graph, const CNodePt MS_EXCEPTION_IF_NULL(gen_mask_abstract); dropout_gen_mask->set_abstract(gen_mask_abstract); dropout_gen_mask->set_scope(dropout->scope()); - AnfAlgo::CopyNodeAttrs(dropout, dropout_gen_mask); + common::AnfAlgo::CopyNodeAttrs(dropout, dropout_gen_mask); if (dropout->HasPrimalAttr(kAttrMicro)) { dropout_gen_mask->AddPrimalAttr(kAttrMicro, dropout->GetPrimalAttr(kAttrMicro)); } @@ -217,7 +218,7 @@ CNodePtr CreateDropoutDoMaskCNode(const FuncGraphPtr &func_graph, const CNodePtr MS_EXCEPTION_IF_NULL(dropout_do_mask); dropout_do_mask->set_abstract(abstract); dropout_do_mask->set_scope(dropout->scope()); - AnfAlgo::CopyNodeAttr(kAttrKeepProb, dropout, dropout_do_mask); + common::AnfAlgo::CopyNodeAttr(kAttrKeepProb, dropout, dropout_do_mask); if (dropout->HasPrimalAttr(kAttrMicro)) { dropout_do_mask->AddPrimalAttr(kAttrMicro, dropout->GetPrimalAttr(kAttrMicro)); } @@ -279,9 +280,9 @@ const AnfNodePtr DropoutAndDropoutGradUnifyMindIR::Process(const FuncGraphPtr &f for (auto &node_index : iter->second) { auto used_node = node_index.first; MS_EXCEPTION_IF_NULL(used_node); - if (AnfAlgo::CheckPrimitiveType(used_node, prim::kPrimTupleGetItem)) { + if (common::AnfAlgo::CheckPrimitiveType(used_node, prim::kPrimTupleGetItem)) { // check if Dropout's first output, which is used by forward, is used - if (AnfAlgo::GetTupleGetItemOutIndex(used_node->cast()) == 0) { + if (common::AnfAlgo::GetTupleGetItemOutIndex(used_node->cast()) == 0) { // if Dropout's first output is used, create forward DropoutDoMask auto do_mask_abstract1 = std::make_shared(TypeIdToType(inputx_type_id), input_shape); diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/env_op_attr_update.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/env_op_attr_update.cc index bfd2f5b0f8c..fc1ab6e24ae 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/env_op_attr_update.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/env_op_attr_update.cc @@ -17,8 +17,9 @@ #include "plugin/device/ascend/optimizer/mindir/env_op_attr_update.h" #include #include -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -35,9 +36,9 @@ const AnfNodePtr EnvOpAttrUpdate::Process(const FuncGraphPtr &graph, const AnfNo if (!node->isa()) { return node; } - auto kernel_name = AnfAlgo::GetCNodeName(node); + auto kernel_name = common::AnfAlgo::GetCNodeName(node); if (kEnvOpNames.find(kernel_name) != kEnvOpNames.end()) { - AnfAlgo::SetNodeAttr(kAttrCustAicpu, MakeValue(kEnvOpSoNames), node); + common::AnfAlgo::SetNodeAttr(kAttrCustAicpu, MakeValue(kEnvOpSoNames), node); } return node; diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/fake_learned_scale_quant_grad_unify_mindir.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/fake_learned_scale_quant_grad_unify_mindir.cc index 24bc4cb9105..4684378153d 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/fake_learned_scale_quant_grad_unify_mindir.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/fake_learned_scale_quant_grad_unify_mindir.cc @@ -18,11 +18,12 @@ #include #include -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/ms_context.h" #include "backend/common/optimizer/helper.h" #include "runtime/device/kernel_info.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "utils/trace_base.h" namespace mindspore { @@ -46,13 +47,13 @@ void FakeLearnedScaleQuantPerLayerGradUnifyMindIR::CreateOutputsOfLSQPerLayerGra MS_EXCEPTION_IF_NULL(lsq_perlayer_grad_d); lsq_perlayer_grad_d->set_scope(lsq_perlayer_grad_node->scope()); - auto types = {AnfAlgo::GetOutputInferDataType(lsq_perlayer_grad_node, 0), - AnfAlgo::GetOutputInferDataType(lsq_perlayer_grad_node, 0)}; - auto shapes = {AnfAlgo::GetOutputInferShape(lsq_perlayer_grad_node, 0), - AnfAlgo::GetOutputInferShape(lsq_perlayer_grad_node, 0)}; - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, lsq_perlayer_grad_d.get()); + auto types = {common::AnfAlgo::GetOutputInferDataType(lsq_perlayer_grad_node, 0), + common::AnfAlgo::GetOutputInferDataType(lsq_perlayer_grad_node, 0)}; + auto shapes = {common::AnfAlgo::GetOutputInferShape(lsq_perlayer_grad_node, 0), + common::AnfAlgo::GetOutputInferShape(lsq_perlayer_grad_node, 0)}; + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, lsq_perlayer_grad_d.get()); - AnfAlgo::CopyNodeAttr(kAttrNeg_trunc, lsq_perlayer_grad_node, lsq_perlayer_grad_d); + common::AnfAlgo::CopyNodeAttr(kAttrNeg_trunc, lsq_perlayer_grad_node, lsq_perlayer_grad_d); CreateMultipleOutputsOfAnfNode(graph, lsq_perlayer_grad_d, kFakeLearnedScaleQuantGradDOutputNum, lsq_perlayer_grad_d_outputs); } @@ -82,9 +83,9 @@ void FakeLearnedScaleQuantPerLayerGradUnifyMindIR::CreateOutputsOfLSQPerLayerRed MS_EXCEPTION_IF_NULL(lsq_perlayer_reduce_grad); lsq_perlayer_reduce_grad->set_scope(lsq_perlayer_grad_node->scope()); - auto types = {AnfAlgo::GetOutputInferDataType(lsq_perlayer_grad_node, 1)}; - auto shapes = {AnfAlgo::GetOutputInferShape(lsq_perlayer_grad_node, 1)}; - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, lsq_perlayer_reduce_grad.get()); + auto types = {common::AnfAlgo::GetOutputInferDataType(lsq_perlayer_grad_node, 1)}; + auto shapes = {common::AnfAlgo::GetOutputInferShape(lsq_perlayer_grad_node, 1)}; + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, lsq_perlayer_reduce_grad.get()); (*lsq_perlayer_reduce_grad_outputs).push_back(lsq_perlayer_reduce_grad); } @@ -108,14 +109,14 @@ void FakeLearnedScaleQuantPerChannelGradUnifyMindIR::CreateOutputsOfLSQPerChanne MS_EXCEPTION_IF_NULL(lsq_perchannel_grad_d); lsq_perchannel_grad_d->set_scope(lsq_perchannel_grad_node->scope()); - auto types = {AnfAlgo::GetOutputInferDataType(lsq_perchannel_grad_node, 0), - AnfAlgo::GetOutputInferDataType(lsq_perchannel_grad_node, 0)}; - auto shapes = {AnfAlgo::GetOutputInferShape(lsq_perchannel_grad_node, 0), - AnfAlgo::GetOutputInferShape(lsq_perchannel_grad_node, 0)}; - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, lsq_perchannel_grad_d.get()); + auto types = {common::AnfAlgo::GetOutputInferDataType(lsq_perchannel_grad_node, 0), + common::AnfAlgo::GetOutputInferDataType(lsq_perchannel_grad_node, 0)}; + auto shapes = {common::AnfAlgo::GetOutputInferShape(lsq_perchannel_grad_node, 0), + common::AnfAlgo::GetOutputInferShape(lsq_perchannel_grad_node, 0)}; + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, lsq_perchannel_grad_d.get()); - AnfAlgo::CopyNodeAttr(kAttrNeg_trunc, lsq_perchannel_grad_node, lsq_perchannel_grad_d); - AnfAlgo::CopyNodeAttr(kAttrChannelAxis, lsq_perchannel_grad_node, lsq_perchannel_grad_d); + common::AnfAlgo::CopyNodeAttr(kAttrNeg_trunc, lsq_perchannel_grad_node, lsq_perchannel_grad_d); + common::AnfAlgo::CopyNodeAttr(kAttrChannelAxis, lsq_perchannel_grad_node, lsq_perchannel_grad_d); CreateMultipleOutputsOfAnfNode(graph, lsq_perchannel_grad_d, kFakeLearnedScaleQuantGradDOutputNum, lsq_perchannel_grad_d_outputs); } @@ -145,10 +146,10 @@ void FakeLearnedScaleQuantPerChannelGradUnifyMindIR::CreateOutputsOfLSQPerChanne MS_EXCEPTION_IF_NULL(lsq_perchannel_reduce_grad); lsq_perchannel_reduce_grad->set_scope(lsq_perchannel_grad_node->scope()); - auto types = {AnfAlgo::GetOutputInferDataType(lsq_perchannel_grad_node, 1)}; - auto shapes = {AnfAlgo::GetOutputInferShape(lsq_perchannel_grad_node, 1)}; - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, lsq_perchannel_reduce_grad.get()); - AnfAlgo::CopyNodeAttr(kAttrChannelAxis, lsq_perchannel_grad_node, lsq_perchannel_reduce_grad); + auto types = {common::AnfAlgo::GetOutputInferDataType(lsq_perchannel_grad_node, 1)}; + auto shapes = {common::AnfAlgo::GetOutputInferShape(lsq_perchannel_grad_node, 1)}; + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, lsq_perchannel_reduce_grad.get()); + common::AnfAlgo::CopyNodeAttr(kAttrChannelAxis, lsq_perchannel_grad_node, lsq_perchannel_reduce_grad); (*lsq_perchannel_reduce_grad_outputs).push_back(lsq_perchannel_reduce_grad); } @@ -164,7 +165,7 @@ const AnfNodePtr FakeLearnedScaleQuantPerLayerGradUnifyMindIR::Process(const Fun MS_EXCEPTION_IF_NULL(func_graph); auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - auto primitive = AnfAlgo::GetCNodePrimitive(cnode); + auto primitive = common::AnfAlgo::GetCNodePrimitive(cnode); MS_EXCEPTION_IF_NULL(primitive); std::vector lsq_perlayer_grad_d_outputs; @@ -203,7 +204,7 @@ const AnfNodePtr FakeLearnedScaleQuantPerChannelGradUnifyMindIR::Process(const F MS_EXCEPTION_IF_NULL(func_graph); auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - auto primitive = AnfAlgo::GetCNodePrimitive(cnode); + auto primitive = common::AnfAlgo::GetCNodePrimitive(cnode); MS_EXCEPTION_IF_NULL(primitive); std::vector lsq_perchannel_grad_d_outputs; diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/maxpool_to_maxpool_with_argmax.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/maxpool_to_maxpool_with_argmax.cc index b15a13605c0..27ccdada85e 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/maxpool_to_maxpool_with_argmax.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/maxpool_to_maxpool_with_argmax.cc @@ -19,12 +19,13 @@ #include #include -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/ms_context.h" #include "utils/trace_base.h" #include "backend/common/optimizer/helper.h" #include "runtime/device/kernel_info.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -61,10 +62,10 @@ CNodePtr MaxPool2MaxPoolWithArgmax::CreateMaxPoolWithArgmax(const FuncGraphPtr & // MaxPoolWithArgmax's second output is argmax, whose datatype is uint16 and with same shape as first output TypeId argmax_dtype = kNumberTypeUInt16; - auto types = {AnfAlgo::GetOutputInferDataType(maxpool, 0), argmax_dtype}; - auto out_shape = AnfAlgo::GetOutputInferShape(maxpool, 0); + auto types = {common::AnfAlgo::GetOutputInferDataType(maxpool, 0), argmax_dtype}; + auto out_shape = common::AnfAlgo::GetOutputInferShape(maxpool, 0); auto shapes = {out_shape, out_shape}; - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, maxpool_argmax.get()); + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, maxpool_argmax.get()); return maxpool_argmax; } @@ -92,8 +93,8 @@ CNodePtr MaxPool2MaxPoolWithArgmax::CreateMaxPoolGradWithArgmax( void MaxPool2MaxPoolWithArgmax::SetNodeAttrs(const CNodePtr &maxpool, const CNodePtr &maxpool_grad, const CNodePtr &maxpool_argmax, const CNodePtr &maxpool_grad_argmax) const { - auto strides = AnfAlgo::GetNodeAttr>(maxpool, kAttrStrides); - auto ksize = AnfAlgo::GetNodeAttr>(maxpool, kAttrKernelSize); + auto strides = common::AnfAlgo::GetNodeAttr>(maxpool, kAttrStrides); + auto ksize = common::AnfAlgo::GetNodeAttr>(maxpool, kAttrKernelSize); if (strides.size() != kMaxPoolAttrAxisNum) { MS_LOG(EXCEPTION) << "MaxPool's attr strides has wrong axis number, should be " << kMaxPoolAttrAxisNum << ", but got " << strides.size() << trace::DumpSourceLines(maxpool); @@ -111,12 +112,12 @@ void MaxPool2MaxPoolWithArgmax::SetNodeAttrs(const CNodePtr &maxpool, const CNod ksize[kIndex2] = ksize[kIndex3]; ksize[kIndex3] = 1; - AnfAlgo::CopyNodeAttrs(maxpool, maxpool_argmax); - AnfAlgo::CopyNodeAttrs(maxpool_grad, maxpool_grad_argmax); - AnfAlgo::SetNodeAttr(kAttrStrides, MakeValue(strides), maxpool_argmax); - AnfAlgo::SetNodeAttr(kAttrStrides, MakeValue(strides), maxpool_grad_argmax); - AnfAlgo::SetNodeAttr(kAttrKernelSize, MakeValue(ksize), maxpool_argmax); - AnfAlgo::SetNodeAttr(kAttrKernelSize, MakeValue(ksize), maxpool_grad_argmax); + common::AnfAlgo::CopyNodeAttrs(maxpool, maxpool_argmax); + common::AnfAlgo::CopyNodeAttrs(maxpool_grad, maxpool_grad_argmax); + common::AnfAlgo::SetNodeAttr(kAttrStrides, MakeValue(strides), maxpool_argmax); + common::AnfAlgo::SetNodeAttr(kAttrStrides, MakeValue(strides), maxpool_grad_argmax); + common::AnfAlgo::SetNodeAttr(kAttrKernelSize, MakeValue(ksize), maxpool_argmax); + common::AnfAlgo::SetNodeAttr(kAttrKernelSize, MakeValue(ksize), maxpool_grad_argmax); } const BaseRef MaxPool2MaxPoolWithArgmax::DefinePattern() const { diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/maxpool_with_argmax_unify_mindir.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/maxpool_with_argmax_unify_mindir.cc index 0f51f5ccf5e..816c0a65495 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/maxpool_with_argmax_unify_mindir.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/maxpool_with_argmax_unify_mindir.cc @@ -20,8 +20,9 @@ #include "backend/common/optimizer/helper.h" #include "runtime/device/kernel_info.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "base/core_ops.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/trace_base.h" namespace mindspore { @@ -63,8 +64,8 @@ const AnfNodePtr MaxPoolWithArgmaxUnifyMindIR::Process(const FuncGraphPtr &graph MS_EXCEPTION_IF_NULL(maxpool_with_argmax); TypeId argmax_dtype = kNumberTypeUInt16; - auto ksize = AnfAlgo::GetNodeAttr>(maxpool_with_argmax, kAttrKernelSize); - auto output_shape = AnfAlgo::GetOutputInferShape(maxpool_with_argmax, 0); + auto ksize = common::AnfAlgo::GetNodeAttr>(maxpool_with_argmax, kAttrKernelSize); + auto output_shape = common::AnfAlgo::GetOutputInferShape(maxpool_with_argmax, 0); auto argmax_shape = output_shape; if (argmax_shape.size() != kMaxPoolWithArgmaxShape || ksize.size() != kMaxPoolWithArgmaxShape) { MS_LOG(EXCEPTION) << "Argmax or kernel_size's shape dim should be equal to 4, but got argmax dim: " @@ -72,9 +73,9 @@ const AnfNodePtr MaxPoolWithArgmaxUnifyMindIR::Process(const FuncGraphPtr &graph } argmax_shape[kDim2] = LongToSize(ksize[kDim1] * ksize[kDim2]); argmax_shape[kDim3] = (output_shape[kDim2] * output_shape[kDim3] + kAlignBytes - 1) / kAlignBytes + 1; - auto types = {AnfAlgo::GetOutputInferDataType(maxpool_with_argmax, 0), argmax_dtype}; + auto types = {common::AnfAlgo::GetOutputInferDataType(maxpool_with_argmax, 0), argmax_dtype}; auto shapes = {output_shape, argmax_shape}; - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, maxpool_with_argmax.get()); + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, maxpool_with_argmax.get()); return maxpool_with_argmax; } @@ -99,15 +100,15 @@ const AnfNodePtr MaxPoolGradWithArgmaxUnifyMindIR::Process(const FuncGraphPtr &g MS_EXCEPTION_IF_NULL(tuple_getitem0_anf); TypeId argmax_dtype = kNumberTypeUInt16; - auto ksize = AnfAlgo::GetNodeAttr>(maxpool_grad_with_argmax, kAttrKernelSize); - auto argmax_shape = AnfAlgo::GetOutputInferShape(tuple_getitem0_anf, 0); + auto ksize = common::AnfAlgo::GetNodeAttr>(maxpool_grad_with_argmax, kAttrKernelSize); + auto argmax_shape = common::AnfAlgo::GetOutputInferShape(tuple_getitem0_anf, 0); if (argmax_shape.size() != kMaxPoolWithArgmaxShape || ksize.size() != kMaxPoolWithArgmaxShape) { MS_LOG(EXCEPTION) << "Argmax or kernel_size's shape dim should be equal to 4, but got argmax dim: " << argmax_shape.size() << ", kernel_size dim: " << ksize.size() << trace::DumpSourceLines(node); } argmax_shape[kDim3] = (argmax_shape[kDim2] * argmax_shape[kDim3] + kAlignBytes - 1) / kAlignBytes + 1; argmax_shape[kDim2] = LongToSize(ksize[kDim1] * ksize[kDim2]); - AnfAlgo::SetOutputInferTypeAndShape({argmax_dtype}, {argmax_shape}, tuple_getitem0_anf.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({argmax_dtype}, {argmax_shape}, tuple_getitem0_anf.get()); return maxpool_grad_with_argmax; } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/neighbor_exchange_v2_unify_mindir.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/neighbor_exchange_v2_unify_mindir.cc index 5f2089d1aa0..08fd138a0be 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/neighbor_exchange_v2_unify_mindir.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/neighbor_exchange_v2_unify_mindir.cc @@ -19,6 +19,7 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "plugin/device/ascend/hal/hccl_adapter/hccl_adapter.h" #include "backend/common/optimizer/helper.h" #include "utils/trace_base.h" @@ -124,11 +125,11 @@ CNodePtr CreateSplitNode(const FuncGraphPtr &graph, const std::vector dtypes(*num_split, input_dtype); - AnfAlgo::SetOutputInferTypeAndShape(dtypes, shapes, split_v.get()); - AnfAlgo::SetNodeAttr(kAttrSplitDim, MakeValue(split_dim), split_v); - AnfAlgo::SetNodeAttr(kAttrNumSplit, MakeValue(*num_split), split_v); - AnfAlgo::SetNodeAttr(kAttrSizeSplits, MakeValue>(size_splits), split_v); - AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), split_v); + common::AnfAlgo::SetOutputInferTypeAndShape(dtypes, shapes, split_v.get()); + common::AnfAlgo::SetNodeAttr(kAttrSplitDim, MakeValue(split_dim), split_v); + common::AnfAlgo::SetNodeAttr(kAttrNumSplit, MakeValue(*num_split), split_v); + common::AnfAlgo::SetNodeAttr(kAttrSizeSplits, MakeValue>(size_splits), split_v); + common::AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), split_v); return split_v; } @@ -279,12 +280,12 @@ CNodePtr CreateAllToAllvNode(const FuncGraphPtr &graph, const CNodePtr &neighbor MS_EXCEPTION_IF_NULL(graph); MS_EXCEPTION_IF_NULL(neighbor_exchange_v2_or_grad); std::vector send_rank_ids = - AnfAlgo::GetNodeAttr>(neighbor_exchange_v2_or_grad, kAttrSendRankIds); + common::AnfAlgo::GetNodeAttr>(neighbor_exchange_v2_or_grad, kAttrSendRankIds); std::vector recv_rank_ids = - AnfAlgo::GetNodeAttr>(neighbor_exchange_v2_or_grad, kAttrRecvRankIds); + common::AnfAlgo::GetNodeAttr>(neighbor_exchange_v2_or_grad, kAttrRecvRankIds); std::vector recv_lens = - AnfAlgo::GetNodeAttr>(neighbor_exchange_v2_or_grad, kAttrRecvLens); - std::string group = AnfAlgo::GetNodeAttr(neighbor_exchange_v2_or_grad, kAttrGroup); + common::AnfAlgo::GetNodeAttr>(neighbor_exchange_v2_or_grad, kAttrRecvLens); + std::string group = common::AnfAlgo::GetNodeAttr(neighbor_exchange_v2_or_grad, kAttrGroup); // get split nodes output, split_outputs: [top_bottom, left_right, top_corner, bottom_corner] std::vector> split_outputs; @@ -321,8 +322,8 @@ CNodePtr CreateAllToAllvNode(const FuncGraphPtr &graph, const CNodePtr &neighbor } // output shapes and dtypes - auto base_dtype = AnfAlgo::GetOutputInferDataType(base_node, 0); - auto base_shape = AnfAlgo::GetOutputInferShape(base_node, 0); + auto base_dtype = common::AnfAlgo::GetOutputInferDataType(base_node, 0); + auto base_shape = common::AnfAlgo::GetOutputInferShape(base_node, 0); if (SizeToLong(base_shape.size()) != kShapeSize) { MS_LOG(EXCEPTION) << "Invalid shape size " << base_shape.size() << ", only support NCHW input now!"; } @@ -343,15 +344,15 @@ CNodePtr CreateAllToAllvNode(const FuncGraphPtr &graph, const CNodePtr &neighbor // create alltoallv node auto all_to_all_v = pass.NewCNode(all_to_all_v_input, graph); MS_EXCEPTION_IF_NULL(all_to_all_v); - AnfAlgo::SetOutputInferTypeAndShape(dtypes, shapes, all_to_all_v.get()); + common::AnfAlgo::SetOutputInferTypeAndShape(dtypes, shapes, all_to_all_v.get()); - AnfAlgo::SetNodeAttr(kAttrSendRankIds, MakeValue>(real_send_rank_ids), all_to_all_v); - AnfAlgo::SetNodeAttr(kAttrRecvRankIds, MakeValue>(real_recv_rank_ids), all_to_all_v); - AnfAlgo::SetNodeAttr(kAttrRecvType, TypeIdToType(base_dtype), all_to_all_v); - AnfAlgo::SetNodeAttr(kAttrGroup, MakeValue(group), all_to_all_v); + common::AnfAlgo::SetNodeAttr(kAttrSendRankIds, MakeValue>(real_send_rank_ids), all_to_all_v); + common::AnfAlgo::SetNodeAttr(kAttrRecvRankIds, MakeValue>(real_recv_rank_ids), all_to_all_v); + common::AnfAlgo::SetNodeAttr(kAttrRecvType, TypeIdToType(base_dtype), all_to_all_v); + common::AnfAlgo::SetNodeAttr(kAttrGroup, MakeValue(group), all_to_all_v); // add depend for input & alltoallv in send_empty condition - AnfAlgo::SetNodeAttr(kAttrNeedDropInput, MakeValue(need_drop_input), all_to_all_v); + common::AnfAlgo::SetNodeAttr(kAttrNeedDropInput, MakeValue(need_drop_input), all_to_all_v); if (all_to_all_input_num == 0) { auto input = neighbor_exchange_v2_or_grad->input(kNeighborExchangeV2InputIdx); std::vector depend_input = {NewValueNode(std::make_shared(prim::kPrimDepend->name())), @@ -385,8 +386,9 @@ std::vector NeighborExchangeV2UnifyMindIR::CreateSplitNodes(const Func MS_EXCEPTION_IF_NULL(neighbor_exchange_v2); MS_EXCEPTION_IF_NULL(split_num); std::vector send_rank_ids = - AnfAlgo::GetNodeAttr>(neighbor_exchange_v2, kAttrSendRankIds); - std::vector send_lens = AnfAlgo::GetNodeAttr>(neighbor_exchange_v2, kAttrSendLens); + common::AnfAlgo::GetNodeAttr>(neighbor_exchange_v2, kAttrSendRankIds); + std::vector send_lens = + common::AnfAlgo::GetNodeAttr>(neighbor_exchange_v2, kAttrSendLens); if (neighbor_exchange_v2->size() <= kNeighborExchangeV2InputIdx) { MS_LOG(EXCEPTION) << "Invalid cnode " << neighbor_exchange_v2->DebugString() << " input size " @@ -397,8 +399,8 @@ std::vector NeighborExchangeV2UnifyMindIR::CreateSplitNodes(const Func auto neighbor_exchange_v2_input = neighbor_exchange_v2->input(kNeighborExchangeV2InputIdx); - auto dtype = AnfAlgo::GetOutputInferDataType(neighbor_exchange_v2_input, 0); - auto shape = AnfAlgo::GetOutputInferShape(neighbor_exchange_v2_input, 0); + auto dtype = common::AnfAlgo::GetOutputInferDataType(neighbor_exchange_v2_input, 0); + auto shape = common::AnfAlgo::GetOutputInferShape(neighbor_exchange_v2_input, 0); if (SizeToLong(shape.size()) != kShapeSize) { // only support NCHW now MS_LOG(EXCEPTION) << "Invalid shape size " << shape.size() << ", only support NCHW input now!" << trace::DumpSourceLines(neighbor_exchange_v2); @@ -479,11 +481,11 @@ CNodePtr NeighborExchangeV2UnifyMindIR::CreateConcatNode(const FuncGraphPtr &gra MS_EXCEPTION_IF_NULL(graph); auto concat = NewCNode(concat_input, graph); MS_EXCEPTION_IF_NULL(concat); - AnfAlgo::SetOutputInferTypeAndShape(output_dtype, output_shape, concat.get()); - AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(axis), concat); - AnfAlgo::SetNodeAttr(kAttrInputNums, MakeValue(input_nums), concat); + common::AnfAlgo::SetOutputInferTypeAndShape(output_dtype, output_shape, concat.get()); + common::AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(axis), concat); + common::AnfAlgo::SetNodeAttr(kAttrInputNums, MakeValue(input_nums), concat); std::vector dyn_input_size_empty{input_nums}; - AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(dyn_input_size_empty), concat); + common::AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(dyn_input_size_empty), concat); return concat; } @@ -500,7 +502,8 @@ CNodePtr NeighborExchangeV2UnifyMindIR::CreateLeftRightConcat(const FuncGraphPtr size_t middle_ids = is_left ? 6 : 2; size_t last_ids = is_left ? 5 : 3; - auto single_shape = AnfAlgo::GetOutputInferShape(all_to_all_v_outputs[AllToAllRealIds(middle_ids, recv_rank_ids)], 0); + auto single_shape = + common::AnfAlgo::GetOutputInferShape(all_to_all_v_outputs[AllToAllRealIds(middle_ids, recv_rank_ids)], 0); if (recv_rank_ids[first_ids] != kInvalidId) { ++input_num; @@ -519,7 +522,7 @@ CNodePtr NeighborExchangeV2UnifyMindIR::CreateLeftRightConcat(const FuncGraphPtr } std::vector concat_output_dtype = { - AnfAlgo::GetOutputInferDataType(all_to_all_v_outputs[AllToAllRealIds(middle_ids, recv_rank_ids)], 0)}; + common::AnfAlgo::GetOutputInferDataType(all_to_all_v_outputs[AllToAllRealIds(middle_ids, recv_rank_ids)], 0)}; auto concat = CreateConcatNode(graph, concat_input, {single_shape}, concat_output_dtype, kHDim, input_num); return concat; @@ -531,7 +534,7 @@ CNodePtr NeighborExchangeV2UnifyMindIR::CreateMiddleConcat( std::vector concat_input_all = {NewValueNode(std::make_shared(kConcatOpName))}; int64_t input_num_all = 0; auto neighbor_exchange_v2_input = neighbor_exchange_v2->input(kNeighborExchangeV2InputIdx); - auto single_shape = AnfAlgo::GetOutputInferShape(neighbor_exchange_v2_input, 0); + auto single_shape = common::AnfAlgo::GetOutputInferShape(neighbor_exchange_v2_input, 0); size_t first_idx = concat_dim == kWDim ? 6 : 0; size_t last_idx = concat_dim == kWDim ? 2 : 4; size_t first_len = concat_dim == kWDim ? static_cast(recv_lens[kDim2]) : static_cast(recv_lens[0]); @@ -567,7 +570,7 @@ CNodePtr NeighborExchangeV2UnifyMindIR::CreateMiddleConcat( single_shape[concat_dim] += last_len; } - std::vector concat_output_dtype = {AnfAlgo::GetOutputInferDataType(all_to_all_v_outputs[0], 0)}; + std::vector concat_output_dtype = {common::AnfAlgo::GetOutputInferDataType(all_to_all_v_outputs[0], 0)}; auto concat_all = CreateConcatNode(graph, concat_input_all, {single_shape}, concat_output_dtype, concat_dim, input_num_all); return concat_all; @@ -596,8 +599,9 @@ CNodePtr NeighborExchangeV2UnifyMindIR::CreateConcatNodes(const FuncGraphPtr &gr MS_EXCEPTION_IF_NULL(neighbor_exchange_v2); MS_EXCEPTION_IF_NULL(all_to_all_v); std::vector recv_rank_ids = - AnfAlgo::GetNodeAttr>(neighbor_exchange_v2, kAttrRecvRankIds); - std::vector recv_lens = AnfAlgo::GetNodeAttr>(neighbor_exchange_v2, kAttrRecvLens); + common::AnfAlgo::GetNodeAttr>(neighbor_exchange_v2, kAttrRecvRankIds); + std::vector recv_lens = + common::AnfAlgo::GetNodeAttr>(neighbor_exchange_v2, kAttrRecvLens); int64_t all_to_all_output_num = std::count_if(recv_rank_ids.begin(), recv_rank_ids.end(), [](int64_t ids) { return ids != kInvalidId; }); @@ -632,7 +636,7 @@ CNodePtr NeighborExchangeV2UnifyMindIR::CreateConcatNodes(const FuncGraphPtr &gr std::vector concat_input_all = {NewValueNode(std::make_shared(kConcatOpName))}; auto neighbor_exchange_v2_input = neighbor_exchange_v2->input(kNeighborExchangeV2InputIdx); - std::vector shape_all = AnfAlgo::GetOutputInferShape(neighbor_exchange_v2_input, 0); + std::vector shape_all = common::AnfAlgo::GetOutputInferShape(neighbor_exchange_v2_input, 0); shape_all[kDim2] = recv_rank_ids[kRankIdZero] != kInvalidId ? shape_all[kDim2] + static_cast(recv_lens[0]) : shape_all[kDim2]; shape_all[kDim2] = @@ -679,7 +683,7 @@ CNodePtr NeighborExchangeV2UnifyMindIR::CreateConcatNodes(const FuncGraphPtr &gr shape_all[kDim3] += recv_lens[kDim3]; } - std::vector concat_right_output_dtype = {AnfAlgo::GetOutputInferDataType(concat_input_all[1], 0)}; + std::vector concat_right_output_dtype = {common::AnfAlgo::GetOutputInferDataType(concat_input_all[1], 0)}; auto concat_all = CreateConcatNode(graph, concat_input_all, {shape_all}, concat_right_output_dtype, kWDim, input_nums_all); return concat_all; @@ -693,8 +697,9 @@ std::vector NeighborExchangeV2GradUnifyMindIR::CreateSplitNodesForGrad MS_EXCEPTION_IF_NULL(neighbor_exchange_v2_grad); MS_EXCEPTION_IF_NULL(split_num); std::vector send_rank_ids = - AnfAlgo::GetNodeAttr>(neighbor_exchange_v2_grad, kAttrSendRankIds); - std::vector send_lens = AnfAlgo::GetNodeAttr>(neighbor_exchange_v2_grad, kAttrSendLens); + common::AnfAlgo::GetNodeAttr>(neighbor_exchange_v2_grad, kAttrSendRankIds); + std::vector send_lens = + common::AnfAlgo::GetNodeAttr>(neighbor_exchange_v2_grad, kAttrSendLens); if (neighbor_exchange_v2_grad->size() <= kNeighborExchangeV2InputIdx) { MS_LOG(EXCEPTION) << "Invalid cnode " << neighbor_exchange_v2_grad->DebugString() << " input size " @@ -703,8 +708,8 @@ std::vector NeighborExchangeV2GradUnifyMindIR::CreateSplitNodesForGrad } auto neighbor_exchange_v2_grad_input = neighbor_exchange_v2_grad->input(kNeighborExchangeV2InputIdx); - auto dtype = AnfAlgo::GetOutputInferDataType(neighbor_exchange_v2_grad_input, 0); - auto shape = AnfAlgo::GetOutputInferShape(neighbor_exchange_v2_grad_input, 0); + auto dtype = common::AnfAlgo::GetOutputInferDataType(neighbor_exchange_v2_grad_input, 0); + auto shape = common::AnfAlgo::GetOutputInferShape(neighbor_exchange_v2_grad_input, 0); if (SizeToLong(shape.size()) != kShapeSize) { MS_LOG(EXCEPTION) << "Invalid shape size " << shape.size() << ", only support NCHW input now!" << trace::DumpSourceLines(neighbor_exchange_v2_grad); @@ -735,7 +740,7 @@ std::vector NeighborExchangeV2GradUnifyMindIR::CreateSplitNodesForGrad MS_LOG(EXCEPTION) << "The node " << split_nodes[0]->DebugString() << " should have at least one output, but got 0." << trace::DumpSourceLines(split_nodes[0]); } - size_split_h = AnfAlgo::GetNodeAttr>(split_nodes[0], kAttrSizeSplits); + size_split_h = common::AnfAlgo::GetNodeAttr>(split_nodes[0], kAttrSizeSplits); } else { // just middle split_outputs_top_bottom.push_back(neighbor_exchange_v2_grad_input); @@ -792,9 +797,9 @@ CNodePtr NeighborExchangeV2GradUnifyMindIR::CreatePadNode(const FuncGraphPtr &gr for (size_t i = 0; i < shape.size(); ++i) { (void)paddings.emplace_back(std::vector{begin[i], static_cast(shape[i]) - begin[i] - size[i]}); } - AnfAlgo::SetOutputInferTypeAndShape({dtype}, {shape}, pad.get()); - AnfAlgo::SetNodeAttr(kAttrPaddings, MakeValue(paddings), pad); - AnfAlgo::SetNodeAttr(kAttrInputNames, MakeValue(std::vector{"x"}), pad); + common::AnfAlgo::SetOutputInferTypeAndShape({dtype}, {shape}, pad.get()); + common::AnfAlgo::SetNodeAttr(kAttrPaddings, MakeValue(paddings), pad); + common::AnfAlgo::SetNodeAttr(kAttrInputNames, MakeValue(std::vector{"x"}), pad); return pad; } @@ -807,14 +812,15 @@ CNodePtr NeighborExchangeV2GradUnifyMindIR::CreateSplitGradNodes(const FuncGraph MS_EXCEPTION_IF_NULL(graph); MS_EXCEPTION_IF_NULL(neighbor_exchange_v2_grad); std::vector send_rank_ids = - AnfAlgo::GetNodeAttr>(neighbor_exchange_v2_grad, kAttrSendRankIds); + common::AnfAlgo::GetNodeAttr>(neighbor_exchange_v2_grad, kAttrSendRankIds); std::vector recv_rank_ids = - AnfAlgo::GetNodeAttr>(neighbor_exchange_v2_grad, kAttrRecvRankIds); - std::vector recv_lens = AnfAlgo::GetNodeAttr>(neighbor_exchange_v2_grad, kAttrRecvLens); + common::AnfAlgo::GetNodeAttr>(neighbor_exchange_v2_grad, kAttrRecvRankIds); + std::vector recv_lens = + common::AnfAlgo::GetNodeAttr>(neighbor_exchange_v2_grad, kAttrRecvLens); auto centerx = GetCenter(graph, neighbor_exchange_v2_grad, split_nodes, split_num, send_rank_ids); - auto centerx_dtype = AnfAlgo::GetOutputInferDataType(centerx, 0); - auto centerx_shape = AnfAlgo::GetOutputInferShape(centerx, 0); + auto centerx_dtype = common::AnfAlgo::GetOutputInferDataType(centerx, 0); + auto centerx_shape = common::AnfAlgo::GetOutputInferShape(centerx, 0); // empty int64_t all_to_all_output_num = std::count_if(recv_rank_ids.begin(), recv_rank_ids.end(), [](int64_t ids) { return ids != kInvalidId; }); @@ -885,9 +891,9 @@ CNodePtr NeighborExchangeV2GradUnifyMindIR::CreateSplitGradNodes(const FuncGraph } auto addn = NewCNode(addn_inputs, graph); MS_EXCEPTION_IF_NULL(addn); - AnfAlgo::SetOutputInferTypeAndShape({centerx_dtype}, {centerx_shape}, addn.get()); - AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue>({pad_num}), addn); - AnfAlgo::SetNodeAttr(kAttrN, MakeValue(pad_num), addn); + common::AnfAlgo::SetOutputInferTypeAndShape({centerx_dtype}, {centerx_shape}, addn.get()); + common::AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue>({pad_num}), addn); + common::AnfAlgo::SetNodeAttr(kAttrN, MakeValue(pad_num), addn); MS_LOG(DEBUG) << "Create splitvs grad nodes success."; return addn; } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/neighbor_exchange_v2_unify_mindir.h b/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/neighbor_exchange_v2_unify_mindir.h index dea994b6f0b..149fa3a8280 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/neighbor_exchange_v2_unify_mindir.h +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/neighbor_exchange_v2_unify_mindir.h @@ -20,6 +20,7 @@ #include #include "backend/common/optimizer/optimizer.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/optimizer_unify_output.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/optimizer_unify_output.cc index cc53afbe214..b7419a4adc4 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/optimizer_unify_output.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/optimizer_unify_output.cc @@ -21,6 +21,7 @@ #include "abstract/abstract_value.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -41,10 +42,10 @@ CNodePtr ProcessOutput(const FuncGraphPtr &graph, const AnfNodePtr &node, const auto abstract = cnode_ptr->abstract(); MS_EXCEPTION_IF_NULL(abstract); - if (AnfAlgo::HasNodeAttr("optim_output_passed", cnode_ptr) && abstract->isa()) { + if (common::AnfAlgo::HasNodeAttr("optim_output_passed", cnode_ptr) && abstract->isa()) { return nullptr; } - AnfAlgo::SetNodeAttr("optim_output_passed", MakeValue(true), cnode_ptr); + common::AnfAlgo::SetNodeAttr("optim_output_passed", MakeValue(true), cnode_ptr); std::vector abstract_list; for (size_t i = 0; i < output_size; i++) { diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/slice_grad_unify_mindir.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/slice_grad_unify_mindir.cc index 151227276ab..a21a05d1617 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/slice_grad_unify_mindir.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/slice_grad_unify_mindir.cc @@ -21,12 +21,13 @@ #include #include -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/ms_context.h" #include "utils/trace_base.h" #include "backend/common/optimizer/helper.h" #include "runtime/device/kernel_info.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -37,7 +38,7 @@ constexpr size_t kSliceGradCangjieInputTensorNum = 2; std::vector GetInputXShape(const AnfNodePtr &node) { MS_EXCEPTION_IF_NULL(node); std::vector shapes; - auto shape_size_t = AnfAlgo::GetPrevNodeOutputInferShape(node, 1); + auto shape_size_t = common::AnfAlgo::GetPrevNodeOutputInferShape(node, 1); std::transform(shape_size_t.begin(), shape_size_t.end(), std::back_inserter(shapes), SizeToLong); return shapes; } @@ -64,7 +65,7 @@ const AnfNodePtr SliceGradUnifyMindIR::Process(const FuncGraphPtr &graph, const auto slice_grad = node->cast(); MS_EXCEPTION_IF_NULL(slice_grad); - auto input_num = AnfAlgo::GetInputTensorNum(slice_grad); + auto input_num = common::AnfAlgo::GetInputTensorNum(slice_grad); if (input_num != kSliceGradInputTensorNum && input_num != kSliceGradCangjieInputTensorNum) { MS_LOG(EXCEPTION) << "The input tensor size[" << input_num << "] of node " + slice_grad->DebugString() + " is not equal to " << kSliceGradInputTensorNum @@ -86,8 +87,8 @@ const AnfNodePtr SliceGradUnifyMindIR::Process(const FuncGraphPtr &graph, const sizes = GetTupleValue(slice_grad->input(kIndex4)); } else { // if frontend is Cangjie and mode is pynative, input 2, 3 is already converted to attr - begins = AnfAlgo::GetNodeAttr>(slice_grad, kAttrBegin); - sizes = AnfAlgo::GetNodeAttr>(slice_grad, kAttrSize); + begins = common::AnfAlgo::GetNodeAttr>(slice_grad, kAttrBegin); + sizes = common::AnfAlgo::GetNodeAttr>(slice_grad, kAttrSize); } if (x_shape.size() != begins.size() || begins.size() != sizes.size()) { MS_LOG(EXCEPTION) @@ -99,8 +100,8 @@ const AnfNodePtr SliceGradUnifyMindIR::Process(const FuncGraphPtr &graph, const for (size_t i = 0; i < x_shape.size(); ++i) { paddings.emplace_back(std::vector{begins[i], x_shape[i] - begins[i] - sizes[i]}); } - AnfAlgo::SetNodeAttr(kAttrPaddings, MakeValue(paddings), pad); - AnfAlgo::SetNodeAttr(kAttrInputNames, MakeValue(std::vector{"x"}), pad); + common::AnfAlgo::SetNodeAttr(kAttrPaddings, MakeValue(paddings), pad); + common::AnfAlgo::SetNodeAttr(kAttrInputNames, MakeValue(std::vector{"x"}), pad); return pad; } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/space_batch_nd_attr_update.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/space_batch_nd_attr_update.cc index 7f173a9931f..129fa060181 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/space_batch_nd_attr_update.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/space_batch_nd_attr_update.cc @@ -20,8 +20,9 @@ #include "backend/common/optimizer/helper.h" #include "runtime/device/kernel_info.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "base/core_ops.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace opt { @@ -43,15 +44,15 @@ const AnfNodePtr SpaceToBatchNDAttrUpdate::Process(const FuncGraphPtr &graph, co MS_EXCEPTION_IF_NULL(graph); MS_EXCEPTION_IF_NULL(node); - auto block_shape = AnfAlgo::GetNodeAttr>(node, kAttrBlockShape); + auto block_shape = common::AnfAlgo::GetNodeAttr>(node, kAttrBlockShape); if (block_shape.size() == kBlockShapeDimNum) { block_shape.insert(block_shape.begin(), 1); - AnfAlgo::SetNodeAttr(kAttrBlockShape, MakeValue(block_shape), node); + common::AnfAlgo::SetNodeAttr(kAttrBlockShape, MakeValue(block_shape), node); } - auto paddings = AnfAlgo::GetNodeAttr>>(node, kAttrPaddings); + auto paddings = common::AnfAlgo::GetNodeAttr>>(node, kAttrPaddings); if (paddings.size() == kBlockShapeDimNum) { paddings.emplace(paddings.begin(), std::vector{0, 0}); - AnfAlgo::SetNodeAttr(kAttrPaddings, MakeValue(paddings), node); + common::AnfAlgo::SetNodeAttr(kAttrPaddings, MakeValue(paddings), node); } return node; } @@ -67,15 +68,15 @@ const AnfNodePtr BatchToSpaceNDAttrUpdate::Process(const FuncGraphPtr &graph, co MS_EXCEPTION_IF_NULL(graph); MS_EXCEPTION_IF_NULL(node); - auto block_shape = AnfAlgo::GetNodeAttr>(node, kAttrBlockShape); + auto block_shape = common::AnfAlgo::GetNodeAttr>(node, kAttrBlockShape); if (block_shape.size() == kBlockShapeDimNum) { block_shape.insert(block_shape.begin(), 1); - AnfAlgo::SetNodeAttr(kAttrBlockShape, MakeValue(block_shape), node); + common::AnfAlgo::SetNodeAttr(kAttrBlockShape, MakeValue(block_shape), node); } - auto crops = AnfAlgo::GetNodeAttr>>(node, kAttrCrops); + auto crops = common::AnfAlgo::GetNodeAttr>>(node, kAttrCrops); if (crops.size() == kBlockShapeDimNum) { crops.emplace(crops.begin(), std::vector{0, 0}); - AnfAlgo::SetNodeAttr(kAttrCrops, MakeValue(crops), node); + common::AnfAlgo::SetNodeAttr(kAttrCrops, MakeValue(crops), node); } return node; } diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/sparse_softmax_cross_entropy_with_logits_unify_mindir.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/sparse_softmax_cross_entropy_with_logits_unify_mindir.cc index dfb5efd84ed..bc4d7f962d6 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/sparse_softmax_cross_entropy_with_logits_unify_mindir.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/sparse_softmax_cross_entropy_with_logits_unify_mindir.cc @@ -19,8 +19,9 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/optimizer/helper.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "ir/primitive.h" #include "ir/tensor.h" #include "ir/dtype/type_id.h" @@ -57,7 +58,7 @@ CNodePtr CreateOneHot(const FuncGraphPtr &graph, const CNodePtr &sparse_softmax_ MS_EXCEPTION_IF_NULL(graph); MS_EXCEPTION_IF_NULL(sparse_softmax_node); - std::vector logits_shape = AnfAlgo::GetPrevNodeOutputInferShape(sparse_softmax_node, 0); + std::vector logits_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(sparse_softmax_node, 0); int64_t depth = 0; if (!logits_shape.empty()) { size_t index = logits_shape.size() - 1; @@ -101,11 +102,11 @@ CNodePtr CreateOneHot(const FuncGraphPtr &graph, const CNodePtr &sparse_softmax_ auto one_hot_node = pass.NewCNode(one_hot_inputs, graph); MS_EXCEPTION_IF_NULL(one_hot_node); one_hot_node->set_scope(sparse_softmax_node->scope()); - std::vector labels_shape = AnfAlgo ::GetPrevNodeOutputInferShape(sparse_softmax_node, 1); + std::vector labels_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(sparse_softmax_node, 1); labels_shape.emplace_back(depth); - AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat32}, {labels_shape}, one_hot_node.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat32}, {labels_shape}, one_hot_node.get()); if (is_convert_const_to_attr) { - AnfAlgo::SetNodeAttr(kAttrDepth, MakeValue(depth), one_hot_node); + common::AnfAlgo::SetNodeAttr(kAttrDepth, MakeValue(depth), one_hot_node); } return one_hot_node; } @@ -122,7 +123,7 @@ CNodePtr CreateSoftmaxCrossEntropyWithLogits(const FuncGraphPtr &graph, const CN MS_EXCEPTION_IF_NULL(softmax_node); softmax_node->set_scope(sparse_softmax_node->scope()); - std::vector labels_shape = AnfAlgo::GetOutputInferShape(one_hot_node, 0); + std::vector labels_shape = common::AnfAlgo::GetOutputInferShape(one_hot_node, 0); std::vector loss_shape; if (!labels_shape.empty()) { loss_shape.emplace_back(labels_shape[0]); @@ -130,16 +131,16 @@ CNodePtr CreateSoftmaxCrossEntropyWithLogits(const FuncGraphPtr &graph, const CN MS_LOG(EXCEPTION) << "One_hot output's shape is empty." << trace::DumpSourceLines(one_hot_node); } - auto shapes = {loss_shape, AnfAlgo::GetOutputInferShape(one_hot_node, 0)}; - auto data_types = AnfAlgo::GetOutputInferDataType(one_hot_node, 0); + auto shapes = {loss_shape, common::AnfAlgo::GetOutputInferShape(one_hot_node, 0)}; + auto data_types = common::AnfAlgo::GetOutputInferDataType(one_hot_node, 0); auto types = {data_types, data_types}; - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, softmax_node.get()); + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, softmax_node.get()); return softmax_node; } std::vector GetAxis(const AnfNodePtr &node) { MS_EXCEPTION_IF_NULL(node); - std::vector output_shape = AnfAlgo::GetOutputInferShape(node, 0); + std::vector output_shape = common::AnfAlgo::GetOutputInferShape(node, 0); if (output_shape.empty()) { MS_LOG(EXCEPTION) << node->fullname_with_scope() << "'s output shape is empty" << trace::DumpSourceLines(node); } @@ -192,7 +193,7 @@ CNodePtr CreateReduceMean(const FuncGraphPtr &graph, const CNodePtr &sparse_soft reduce_abstract->set_shape(std::make_shared()); reduce_node->set_abstract(reduce_abstract); if (is_pynative) { - AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(axis_value), reduce_node); + common::AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(axis_value), reduce_node); } return reduce_node; } @@ -220,10 +221,10 @@ CNodePtr CreateExpandDims(const FuncGraphPtr &graph, const CNodePtr &real_div_no MS_EXCEPTION_IF_NULL(expand_dims_node); expand_dims_node->set_scope(real_div_node->scope()); - std::vector y_shape = AnfAlgo::GetOutputInferShape(real_div_node, 0); + std::vector y_shape = common::AnfAlgo::GetOutputInferShape(real_div_node, 0); y_shape.emplace_back(1); - AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(real_div_node, 0)}, {y_shape}, - expand_dims_node.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({common::AnfAlgo::GetOutputInferDataType(real_div_node, 0)}, {y_shape}, + expand_dims_node.get()); return expand_dims_node; } @@ -244,11 +245,11 @@ CNodePtr CreateExpandDimsPynative(const FuncGraphPtr &graph, const CNodePtr &rea MS_EXCEPTION_IF_NULL(expand_dims_node); expand_dims_node->set_scope(real_div_node->scope()); - std::vector y_shape = AnfAlgo::GetOutputInferShape(real_div_node, 0); + std::vector y_shape = common::AnfAlgo::GetOutputInferShape(real_div_node, 0); y_shape.emplace_back(1); - AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(real_div_node, 0)}, {y_shape}, - expand_dims_node.get()); - AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(axis), expand_dims_node); + common::AnfAlgo::SetOutputInferTypeAndShape({common::AnfAlgo::GetOutputInferDataType(real_div_node, 0)}, {y_shape}, + expand_dims_node.get()); + common::AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(axis), expand_dims_node); return expand_dims_node; } @@ -260,7 +261,7 @@ CNodePtr CreateTile(const FuncGraphPtr &graph, const CNodePtr &sparse_softmax_no CheckCNodeInputSize(sparse_softmax_node, kSparseSoftmaxCrossEntropyWithLogitsInputTensorNum); CheckCNodeInputSize(mul_node, kMulInputTensorNum); - auto labels_shape = AnfAlgo::GetPrevNodeOutputInferShape(sparse_softmax_node, 1); + auto labels_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(sparse_softmax_node, 1); std::vector multiple_value; std::transform(labels_shape.begin(), labels_shape.end(), std::back_inserter(multiple_value), [](size_t label) { return static_cast(label); }); @@ -289,15 +290,15 @@ CNodePtr CreateTile(const FuncGraphPtr &graph, const CNodePtr &sparse_softmax_no auto tile_node = pass.NewCNode(tile_inputs, graph); MS_EXCEPTION_IF_NULL(tile_node); tile_node->set_scope(mul_node->scope()); - AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetPrevNodeOutputInferDataType(mul_node, 1)}, {labels_shape}, - tile_node.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({common::AnfAlgo::GetPrevNodeOutputInferDataType(mul_node, 1)}, + {labels_shape}, tile_node.get()); if (is_convert_const_to_attr) { - AnfAlgo::SetNodeAttr(kAttrMultiples, MakeValue(multiples), tile_node); + common::AnfAlgo::SetNodeAttr(kAttrMultiples, MakeValue(multiples), tile_node); } // feature map set std::vector feature_map_input_indexs; (void)feature_map_input_indexs.emplace_back(0); - AnfAlgo::SetNodeAttr(kIsFeatureMapInputList, MakeValue(feature_map_input_indexs), tile_node); + common::AnfAlgo::SetNodeAttr(kIsFeatureMapInputList, MakeValue(feature_map_input_indexs), tile_node); return tile_node; } @@ -307,7 +308,7 @@ CNodePtr CreateRealDiv(const FuncGraphPtr &graph, const CNodePtr &sparse_softmax MS_EXCEPTION_IF_NULL(sparse_softmax_node); MS_EXCEPTION_IF_NULL(tile_node); CheckCNodeInputSize(sparse_softmax_node, kSparseSoftmaxCrossEntropyWithLogitsInputTensorNum); - std::vector labels_shape = AnfAlgo::GetPrevNodeOutputInferShape(sparse_softmax_node, 1); + std::vector labels_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(sparse_softmax_node, 1); if (labels_shape.size() != 1) { MS_LOG(EXCEPTION) << "Label's shape should be 1-D, but got " << labels_shape.size() << trace::DumpSourceLines(sparse_softmax_node); @@ -355,7 +356,7 @@ CNodePtr CreateMul(const FuncGraphPtr &graph, const CNodePtr &sparse_softmax_nod MS_EXCEPTION_IF_NULL(graph); MS_EXCEPTION_IF_NULL(sparse_softmax_node); MS_EXCEPTION_IF_NULL(softmax_output_node); - auto softmax_output_shape = AnfAlgo::GetOutputInferShape(softmax_output_node, 0); + auto softmax_output_shape = common::AnfAlgo::GetOutputInferShape(softmax_output_node, 0); if (softmax_output_shape.size() != softmax_output_shape_size) { MS_LOG(EXCEPTION) << "SoftmaxCrossEntropyWithLogits the second output shape size should be " << softmax_output_shape_size << ", but got " << softmax_output_shape.size() @@ -408,13 +409,13 @@ CNodePtr CreateCast(const FuncGraphPtr &graph, const CNodePtr &cast, const AnfNo bool IsSparseSoftmaxCrossEntropyWithLogitsGrad(const CNodePtr &sparse, string pass_name) { MS_EXCEPTION_IF_NULL(sparse); - if (AnfAlgo::GetCNodeName(sparse) != kSparseSoftmaxCrossEntropyWithLogitsOpName) { + if (common::AnfAlgo::GetCNodeName(sparse) != kSparseSoftmaxCrossEntropyWithLogitsOpName) { MS_LOG(EXCEPTION) << "The pass of " << pass_name << "'s input node should be " - << kSparseSoftmaxCrossEntropyWithLogitsOpName << ", but got " << AnfAlgo::GetCNodeName(sparse) - << trace::DumpSourceLines(sparse); + << kSparseSoftmaxCrossEntropyWithLogitsOpName << ", but got " + << common::AnfAlgo::GetCNodeName(sparse) << trace::DumpSourceLines(sparse); } - if (AnfAlgo::HasNodeAttr(kAttrIsGrad, sparse)) { - return AnfAlgo::GetNodeAttr(sparse, kAttrIsGrad); + if (common::AnfAlgo::HasNodeAttr(kAttrIsGrad, sparse)) { + return common::AnfAlgo::GetNodeAttr(sparse, kAttrIsGrad); } else { MS_LOG(EXCEPTION) << "Node of " << sparse->fullname_with_scope() << " does not have the attr " << kAttrIsGrad << ", related pass: " << pass_name << trace::DumpSourceLines(sparse); @@ -568,8 +569,8 @@ const AnfNodePtr PynativeSparseSoftmaxCrossEntropyWithLogitsUnifyMindIR::Process CreateMultipleOutputsOfAnfNode(graph, softmax_node, kSoftmaxCrossEntropyWithLogitsOutputNum, &softmax_node_outputs); // Both of the forward loss function and the backward loss function from cangjie will match this pattern, // the true branch is for the backward loss function, and the false branch is for the other one. - if (AnfAlgo::HasNodeAttr(kAttrIsGrad, sparse_softmax_node) && - AnfAlgo::GetNodeAttr(sparse_softmax_node, kAttrIsGrad)) { + if (common::AnfAlgo::HasNodeAttr(kAttrIsGrad, sparse_softmax_node) && + common::AnfAlgo::GetNodeAttr(sparse_softmax_node, kAttrIsGrad)) { return softmax_node_outputs[1]; } else { auto reduce_node = CreateReduceMean(graph, sparse_softmax_node, softmax_node_outputs[0], *this, true); diff --git a/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/update_input_names_strided_slice_grad.cc b/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/update_input_names_strided_slice_grad.cc index 390bf40c641..1bd1f1db846 100644 --- a/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/update_input_names_strided_slice_grad.cc +++ b/mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/update_input_names_strided_slice_grad.cc @@ -18,7 +18,8 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" -#include "utils/utils.h" +#include "include/common/utils/anfalgo.h" +#include "include/common/utils/utils.h" #include "backend/common/optimizer/helper.h" namespace mindspore { @@ -37,14 +38,14 @@ const AnfNodePtr StridedSliceGradUpdateInputNames::Process(const FuncGraphPtr &g MS_EXCEPTION_IF_NULL(strided_slice_grad); const size_t shapex_index = 1; - if (AnfAlgo::IsDynamicShape(strided_slice_grad)) { - auto primitive = AnfAlgo::GetCNodePrimitive(strided_slice_grad); + if (common::AnfAlgo::IsDynamicShape(strided_slice_grad)) { + auto primitive = common::AnfAlgo::GetCNodePrimitive(strided_slice_grad); MS_EXCEPTION_IF_NULL(primitive); auto input_names_ptr = primitive->GetAttr(kAttrInputNames); MS_EXCEPTION_IF_NULL(input_names_ptr); auto input_names_vec = GetValue>(input_names_ptr); input_names_vec[shapex_index] = "shape"; - AnfAlgo::SetNodeAttr(kAttrInputNames, MakeValue(input_names_vec), strided_slice_grad); + common::AnfAlgo::SetNodeAttr(kAttrInputNames, MakeValue(input_names_vec), strided_slice_grad); } return nullptr; } diff --git a/mindspore/ccsrc/plugin/device/cpu/hal/device/cpu_kernel_runtime.cc b/mindspore/ccsrc/plugin/device/cpu/hal/device/cpu_kernel_runtime.cc index 38d08f497a6..bb787fe2398 100644 --- a/mindspore/ccsrc/plugin/device/cpu/hal/device/cpu_kernel_runtime.cc +++ b/mindspore/ccsrc/plugin/device/cpu/hal/device/cpu_kernel_runtime.cc @@ -28,6 +28,7 @@ #include "plugin/device/cpu/hal/device/cpu_memory_manager.h" #include "utils/ms_context.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/session/session_basic.h" #include "frontend/operator/ops.h" #include "profiler/device/cpu/cpu_profiling.h" @@ -105,7 +106,7 @@ void CPUKernelRuntime::AssignValueNodeAddress(session::KernelGraph *kernel_graph } TypeId output_type_id = AnfAlgo::GetOutputDeviceDataType(item_node, 0); if (output_type_id == kTypeUnknown) { - output_type_id = AnfAlgo::GetOutputInferDataType(item_node, 0); + output_type_id = common::AnfAlgo::GetOutputInferDataType(item_node, 0); } size_t type_size = GetTypeByte(TypeIdToType(output_type_id)); ShapeVector data_shape = tensor->shape(); @@ -134,11 +135,11 @@ void CPUKernelRuntime::AssignInputNodeAddress(const session::KernelGraph *kernel for (auto &item : kernel_graph->input_nodes()) { MS_EXCEPTION_IF_NULL(item); if (item->isa()) { - auto output_num = AnfAlgo::GetOutputTensorNum(item); + auto output_num = common::AnfAlgo::GetOutputTensorNum(item); for (size_t index = 0; index < output_num; index++) { TypeId output_type_id = AnfAlgo::GetOutputDeviceDataType(item, index); if (output_type_id == kTypeUnknown) { - output_type_id = AnfAlgo::GetOutputInferDataType(item, index); + output_type_id = common::AnfAlgo::GetOutputInferDataType(item, index); } std::vector fmt_shape = AnfAlgo::GetOutputDeviceShape(item, index); size_t type_size = GetTypeByte(TypeIdToType(output_type_id)); @@ -190,16 +191,16 @@ tensor::TensorPtr CPUKernelRuntime::CreateTensorForOutput(session::KernelGraph * MS_EXCEPTION_IF_NULL(kernel_graph); MS_EXCEPTION_IF_NULL(node); MS_EXCEPTION_IF_NULL(bound_addresses); - size_t output_size = AnfAlgo::GetOutputTensorNum(node); + size_t output_size = common::AnfAlgo::GetOutputTensorNum(node); if (index >= output_size) { MS_LOG(EXCEPTION) << "For node " << node->DebugString() << ", index " << index << " exceed output size " << output_size; } auto address = AnfAlgo::GetMutableOutputAddr(node, index); MS_EXCEPTION_IF_NULL(address); - TypeId infer_type_id = AnfAlgo::GetOutputInferDataType(node, index); + TypeId infer_type_id = common::AnfAlgo::GetOutputInferDataType(node, index); TypeId device_type_id = AnfAlgo::GetOutputDeviceDataType(node, index); - auto shape = AnfAlgo::GetOutputInferShape(node, index); + auto shape = common::AnfAlgo::GetOutputInferShape(node, index); ShapeVector temp_shape; tensor::TensorPtr tensor; bool is_internal_output = kernel_graph->IsInternalOutput(node, index); @@ -255,10 +256,10 @@ BaseRef CPUKernelRuntime::GetOrCreateTensorForOutput( if (input_node->isa()) { auto node = input_node->cast(); MS_EXCEPTION_IF_NULL(node); - if (AnfAlgo::GetCNodeName(input_node) == prim::kPrimMakeTuple->name()) { + if (common::AnfAlgo::GetCNodeName(input_node) == prim::kPrimMakeTuple->name()) { VectorRef ret; for (size_t i = 1; i < node->inputs().size(); i++) { - auto item_with_index = AnfAlgo::VisitKernelWithReturnType(node->input(i), 0); + auto item_with_index = common::AnfAlgo::VisitKernelWithReturnType(node->input(i), 0); auto out = GetOrCreateTensorForOutput(kernel_graph, item_with_index, tensor_to_node, input_param_tensor_map, bound_addresses); ret.push_back(out); @@ -303,7 +304,7 @@ void CPUKernelRuntime::CreateOutputTensors(session::KernelGraph *kernel_graph, std::set bound_addresses; auto output_nodes = kernel_graph->outputs(); for (const auto &item : output_nodes) { - auto item_with_index = AnfAlgo::VisitKernelWithReturnType(item, 0, false); + auto item_with_index = common::AnfAlgo::VisitKernelWithReturnType(item, 0, false); auto out = GetOrCreateTensorForOutput(kernel_graph, item_with_index, tensor_to_node, &input_param_tensor_map, &bound_addresses); outputs->push_back(std::move(out)); @@ -330,7 +331,7 @@ void CPUKernelRuntime::BindInputTensorAddressPtr(const session::KernelGraph &ker MS_EXCEPTION_IF_NULL(context_ptr); if (context_ptr->get_param(MS_CTX_EXECUTION_MODE) == kPynativeMode) { auto tensor_address = tensor->device_address(); - if (AnfAlgo::IsParameterWeight(item->cast()) && tensor_address != nullptr && + if (common::AnfAlgo::IsParameterWeight(item->cast()) && tensor_address != nullptr && tensor_address != address) { tensor->data_sync(); } @@ -355,10 +356,11 @@ void CPUKernelRuntime::BindInputTensorAddressPtr(const session::KernelGraph &ker auto tensor_shape = tensor->shape(); std::vector shape_tmp; (void)std::transform(tensor_shape.begin(), tensor_shape.end(), std::back_inserter(shape_tmp), IntToSize); - AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(item, 0)}, {shape_tmp}, item.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({common::AnfAlgo::GetOutputInferDataType(item, 0)}, {shape_tmp}, + item.get()); } address->ref_count_ = INIT_NODE_REF; - if (AnfAlgo::IsParameterWeight(input_param)) { + if (common::AnfAlgo::IsParameterWeight(input_param)) { tensor->set_device_address(address); } } @@ -434,19 +436,19 @@ bool CPUKernelRuntime::Run(const session::KernelGraph &kernel_graph, bool) { #ifdef ENABLE_PROFILE double start_time = GetTime(); #endif - if (AnfAlgo::IsDynamicShape(kernel)) { + if (common::AnfAlgo::IsDynamicShape(kernel)) { AnfAlgo::InferShape(kernel); } std::vector kernel_inputs; std::vector kernel_workspaces; std::vector kernel_outputs; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel); for (size_t i = 0; i < input_num; ++i) { auto device_address = AnfAlgo::GetPrevNodeMutableOutputAddr(kernel, i).get(); MS_EXCEPTION_IF_NULL(device_address); AddRuntimeAddress(device_address, &kernel_inputs); } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel); for (size_t i = 0; i < output_num; ++i) { auto device_address = AnfAlgo::GetMutableOutputAddr(kernel, i).get(); MS_EXCEPTION_IF_NULL(device_address); diff --git a/mindspore/ccsrc/plugin/device/cpu/hal/device/cpu_kernel_runtime.h b/mindspore/ccsrc/plugin/device/cpu/hal/device/cpu_kernel_runtime.h index 91fc696a252..8be6e5c3839 100644 --- a/mindspore/ccsrc/plugin/device/cpu/hal/device/cpu_kernel_runtime.h +++ b/mindspore/ccsrc/plugin/device/cpu/hal/device/cpu_kernel_runtime.h @@ -25,6 +25,7 @@ #include "backend/common/session/kernel_graph.h" #include "backend/common/session/session_basic.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "utils/any.h" #include "profiler/device/cpu/cpu_profiling.h" namespace mindspore { diff --git a/mindspore/ccsrc/plugin/device/cpu/hal/device/cpu_memory_manager.cc b/mindspore/ccsrc/plugin/device/cpu/hal/device/cpu_memory_manager.cc index 34a29715ada..826f5ead32a 100644 --- a/mindspore/ccsrc/plugin/device/cpu/hal/device/cpu_memory_manager.cc +++ b/mindspore/ccsrc/plugin/device/cpu/hal/device/cpu_memory_manager.cc @@ -16,8 +16,9 @@ #include "plugin/device/cpu/hal/device/cpu_memory_manager.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "utils/ms_context.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" namespace mindspore { namespace device { namespace cpu { @@ -172,7 +173,7 @@ void CPUMemoryManager::IncreaseAddressRefCount(const session::KernelGraph *graph auto kernels = graph->execution_order(); for (const auto &kernel : kernels) { MS_EXCEPTION_IF_NULL(kernel); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel); for (size_t i = 0; i < input_num; ++i) { auto address = AnfAlgo::GetPrevNodeMutableOutputAddr(kernel, i); MS_EXCEPTION_IF_NULL(address); @@ -193,7 +194,7 @@ void CPUMemoryManager::DecreaseAddressRefCount(const AnfNodePtr &kernel) { return; } MS_EXCEPTION_IF_NULL(kernel); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel); for (size_t i = 0; i < input_num; ++i) { auto address = AnfAlgo::GetPrevNodeMutableOutputAddr(kernel, i); MS_EXCEPTION_IF_NULL(address); diff --git a/mindspore/ccsrc/plugin/device/cpu/hal/device/cpu_simple_mem_plan.cc b/mindspore/ccsrc/plugin/device/cpu/hal/device/cpu_simple_mem_plan.cc index daa9fcbb238..4c5a47b3fdb 100644 --- a/mindspore/ccsrc/plugin/device/cpu/hal/device/cpu_simple_mem_plan.cc +++ b/mindspore/ccsrc/plugin/device/cpu/hal/device/cpu_simple_mem_plan.cc @@ -15,6 +15,7 @@ */ #include "plugin/device/cpu/hal/device/cpu_simple_mem_plan.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace device { @@ -25,9 +26,9 @@ size_t CPUSimpleMemPlan::MemPlan(const session::KernelGraph *graph) { auto kernels = graph->execution_order(); for (const auto &kernel : kernels) { MS_EXCEPTION_IF_NULL(kernel); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel); for (size_t i = 0; i < input_num; ++i) { - auto kernel_with_index = AnfAlgo::GetPrevNodeOutput(kernel, i); + auto kernel_with_index = common::AnfAlgo::GetPrevNodeOutput(kernel, i); MS_EXCEPTION_IF_NULL(kernel_with_index.first); if (kernel_with_index.first->isa()) { continue; @@ -39,7 +40,7 @@ size_t CPUSimpleMemPlan::MemPlan(const session::KernelGraph *graph) { } } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel); for (size_t i = 0; i < output_num; ++i) { auto address = AnfAlgo::GetOutputAddr(kernel, i); MS_EXCEPTION_IF_NULL(address); @@ -69,9 +70,9 @@ void CPUSimpleMemPlan::MemAssign(const session::KernelGraph *graph, uint8_t *bas auto kernels = graph->execution_order(); for (const auto &kernel : kernels) { MS_EXCEPTION_IF_NULL(kernel); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel); for (size_t i = 0; i < input_num; ++i) { - auto kernel_with_index = AnfAlgo::GetPrevNodeOutput(kernel, i); + auto kernel_with_index = common::AnfAlgo::GetPrevNodeOutput(kernel, i); MS_EXCEPTION_IF_NULL(kernel_with_index.first); if (kernel_with_index.first->isa()) { continue; @@ -84,7 +85,7 @@ void CPUSimpleMemPlan::MemAssign(const session::KernelGraph *graph, uint8_t *bas } } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel); for (size_t i = 0; i < output_num; ++i) { auto address = AnfAlgo::GetMutableOutputAddr(kernel, i); MS_EXCEPTION_IF_NULL(address); diff --git a/mindspore/ccsrc/plugin/device/cpu/hal/device/kernel_select_cpu.cc b/mindspore/ccsrc/plugin/device/cpu/hal/device/kernel_select_cpu.cc index af3deaf6032..d0140c9d800 100644 --- a/mindspore/ccsrc/plugin/device/cpu/hal/device/kernel_select_cpu.cc +++ b/mindspore/ccsrc/plugin/device/cpu/hal/device/kernel_select_cpu.cc @@ -37,7 +37,7 @@ namespace { constexpr auto kParamDynamic = "dynamic"; bool IsInputNotCNode(const CNodePtr &kernel_node, size_t input_index) { - auto input_node = AnfAlgo::VisitKernel(kernel_node->input(input_index + 1), 0).first; + auto input_node = common::AnfAlgo::VisitKernel(kernel_node->input(input_index + 1), 0).first; MS_EXCEPTION_IF_NULL(input_node); if (input_node->isa() || input_node->isa()) { return true; @@ -46,15 +46,15 @@ bool IsInputNotCNode(const CNodePtr &kernel_node, size_t input_index) { } void GetOutputDtypes(const CNodePtr &kernel_node, std::vector *output_types) { - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); for (size_t output_index = 0; output_index < output_num; ++output_index) { - TypeId dtype = AnfAlgo::GetOutputInferDataType(kernel_node, output_index); + TypeId dtype = common::AnfAlgo::GetOutputInferDataType(kernel_node, output_index); output_types->emplace_back(dtype); } } void GetOutputFormat(const CNodePtr &kernel_node, std::vector *output_formats) { - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); for (size_t output_index = 0; output_index < output_num; ++output_index) { output_formats->emplace_back(kOpFormat_DEFAULT); } @@ -62,12 +62,12 @@ void GetOutputFormat(const CNodePtr &kernel_node, std::vector *outp void GetInputDtypes(const CNodePtr &kernel_node, std::vector *input_types, std::vector *input_no_cnode_indexes) { - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); for (size_t input_index = 0; input_index < input_num; ++input_index) { TypeId dtype = kTypeUnknown; if (IsInputNotCNode(kernel_node, input_index)) { input_no_cnode_indexes->emplace_back(input_index); - dtype = AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, input_index); + dtype = common::AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, input_index); } else { dtype = AnfAlgo::GetPrevNodeOutputDeviceDataType(kernel_node, input_index); } @@ -76,7 +76,7 @@ void GetInputDtypes(const CNodePtr &kernel_node, std::vector *input_type } void GetInputFormat(const CNodePtr &kernel_node, std::vector *input_formats) { - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); for (size_t input_index = 0; input_index < input_num; ++input_index) { input_formats->emplace_back(kOpFormat_DEFAULT); } @@ -84,7 +84,7 @@ void GetInputFormat(const CNodePtr &kernel_node, std::vector *input void GetOutputFormatsAndDtypes(const CNodePtr &kernel_node, const KernelAttr &kernel_attr, std::vector *output_formats, std::vector *output_types) { - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); for (size_t output_index = 0; output_index < output_num; ++output_index) { output_formats->emplace_back(kernel_attr.GetOutputAttr(output_index).second); auto dtype = kernel_attr.GetOutputAttr(output_index).first; @@ -147,7 +147,7 @@ int GetInputDtypeFormatMatchedNum(const KernelAttr &kernel_attr, const std::vect void ExpandKernelAttr(const CNodePtr &kernel_node, KernelAttr *kernel_attr) { MS_EXCEPTION_IF_NULL(kernel_attr); size_t attr_num = kernel_attr->GetInputSize(); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (attr_num == 0) { MS_LOG(EXCEPTION) << "Input size is empty"; return; // To pass the CI Check_Cppcheck @@ -166,7 +166,7 @@ void ExpandKernelAttr(const CNodePtr &kernel_node, KernelAttr *kernel_attr) { kernel_attr->SetInputAttrList(attr_list); TypeId output_dtype = kernel_attr->GetOutputAttr(0).first; - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); for (size_t i = 1; i < output_num; ++i) { kernel_attr->AddOutputAttr(output_dtype); } @@ -186,10 +186,10 @@ void SetKernelBuildInfo(const std::vector &input_formats, const std void KernelNotSupportException(const AnfNodePtr &kernel_node, const std::vector &input_types, const std::vector &infer_output_types) { - std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node); + std::string kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); std::stringstream operator_info; operator_info << "Operator[" << kernel_name << "] "; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num > 0) { operator_info << " input("; for (size_t i = 0; i < input_num; ++i) { @@ -200,7 +200,7 @@ void KernelNotSupportException(const AnfNodePtr &kernel_node, const std::vector< } operator_info << ") "; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num > 0) { operator_info << "output("; for (size_t i = 0; i < output_num; ++i) { @@ -218,7 +218,7 @@ void KernelNotSupportException(const AnfNodePtr &kernel_node, const std::vector< } void UpdateDynamicKernelBuildInfoAndAttrs(const CNodePtr &kernel_node) { - const std::string &op_name = AnfAlgo::GetCNodeName(kernel_node); + const std::string &op_name = common::AnfAlgo::GetCNodeName(kernel_node); MS_LOG(INFO) << "Operator name: " << op_name; // Set kernel build info std::vector input_types; @@ -240,8 +240,8 @@ void UpdateDynamicKernelBuildInfoAndAttrs(const CNodePtr &kernel_node) { for (size_t j = 0; j < output_types.size(); j++) { attr.AddInputAttr(output_types[j]); } - std::vector kernel_attrs = - kernel::NativeCpuKernelModFactory::GetInstance().GetSupportedKernelAttrList(AnfAlgo::GetCNodeName(kernel_node)); + std::vector kernel_attrs = kernel::NativeCpuKernelModFactory::GetInstance().GetSupportedKernelAttrList( + common::AnfAlgo::GetCNodeName(kernel_node)); kernel_attrs.emplace_back(attr); kernel::NativeCpuKernelModFactory::GetInstance().UpdateKernelAttrs(op_name, kernel_attrs); return; @@ -249,13 +249,14 @@ void UpdateDynamicKernelBuildInfoAndAttrs(const CNodePtr &kernel_node) { void AddKernelAttr(const CNodePtr &kernel_node, const KernelAttr &kernel_attr) { MS_EXCEPTION_IF_NULL(kernel_node); - std::vector kernel_attrs = - kernel::NativeCpuKernelModFactory::GetInstance().GetSupportedKernelAttrList(AnfAlgo::GetCNodeName(kernel_node)); + std::vector kernel_attrs = kernel::NativeCpuKernelModFactory::GetInstance().GetSupportedKernelAttrList( + common::AnfAlgo::GetCNodeName(kernel_node)); if (kernel_attrs.size() == 1 && kernel_attrs[0].GetInputSize() == 0 && kernel_attrs[0].GetOutputSize() == 0) { kernel_attrs.clear(); } kernel_attrs.emplace_back(kernel_attr); - kernel::NativeCpuKernelModFactory::GetInstance().UpdateKernelAttrs(AnfAlgo::GetCNodeName(kernel_node), kernel_attrs); + kernel::NativeCpuKernelModFactory::GetInstance().UpdateKernelAttrs(common::AnfAlgo::GetCNodeName(kernel_node), + kernel_attrs); } void UpdateCustomKernelBuildInfoAndAttrs(const CNodePtr &kernel_node) { @@ -284,14 +285,14 @@ void UpdateCustomKernelBuildInfoAndAttrs(const CNodePtr &kernel_node) { KernelAttr attr; if (input_types.size() != input_formats.size()) { MS_LOG(EXCEPTION) << "input types size " << input_types.size() << " is not equal to input formats size " - << input_formats.size() << " in op: " << AnfAlgo::GetCNodeName(kernel_node); + << input_formats.size() << " in op: " << common::AnfAlgo::GetCNodeName(kernel_node); } for (size_t i = 0; i < input_types.size(); ++i) { attr.AddInputAttr(input_types[i], input_formats[i]); } if (output_types.size() != output_formats.size()) { MS_LOG(EXCEPTION) << "output types size " << output_types.size() << " is not equal to output formats size " - << output_formats.size() << " in op: " << AnfAlgo::GetCNodeName(kernel_node); + << output_formats.size() << " in op: " << common::AnfAlgo::GetCNodeName(kernel_node); } for (size_t i = 0; i < output_types.size(); ++i) { attr.AddOutputAttr(output_types[i], output_formats[i]); @@ -374,12 +375,12 @@ bool SelectKernel(const CNodePtr &kernel_node, KernelAttr *selected_kernel_attr, MS_EXCEPTION_IF_NULL(kernel_node); MS_EXCEPTION_IF_NULL(selected_kernel_attr); MS_EXCEPTION_IF_NULL(matched); - MS_LOG(DEBUG) << "Select kernel for op: " << AnfAlgo::GetCNodeName(kernel_node); + MS_LOG(DEBUG) << "Select kernel for op: " << common::AnfAlgo::GetCNodeName(kernel_node); for (auto kernel_attr : kernel_attrs) { if (kernel_attr.GetAllSame()) { ExpandKernelAttr(kernel_node, &kernel_attr); } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (kernel_attr.GetOutputSize() != output_num) { MS_LOG(DEBUG) << "Output num is not equal!"; continue; @@ -403,10 +404,10 @@ bool SelectKernel(const CNodePtr &kernel_node, KernelAttr *selected_kernel_attr, void SetKernelInfo(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - const std::string &op_name = AnfAlgo::GetCNodeName(kernel_node); + const std::string &op_name = common::AnfAlgo::GetCNodeName(kernel_node); if (IsPrimitiveCNode(kernel_node, prim::kPrimCustom)) { if (!kernel::NativeCpuKernelModFactory::GetInstance().SearchRegisteredOp(op_name)) { - auto tp = AnfAlgo::GetNodeAttr(kernel_node, kAttrFuncType); + auto tp = common::AnfAlgo::GetNodeAttr(kernel_node, kAttrFuncType); if (tp == kCustomTypePyfunc) { kernel::NativeCpuKernelRegistrar(op_name, KernelAttr(), []() { return std::make_shared(); }); @@ -441,8 +442,8 @@ void SetKernelInfo(const CNodePtr &kernel_node) { std::vector output_types; std::vector selected_output_types; MS_LOG(INFO) << "SetKernelInfo, CNode Name: " << op_name; - auto kernel_attrs = - kernel::NativeCpuKernelModFactory::GetInstance().GetSupportedKernelAttrList(AnfAlgo::GetCNodeName(kernel_node)); + auto kernel_attrs = kernel::NativeCpuKernelModFactory::GetInstance().GetSupportedKernelAttrList( + common::AnfAlgo::GetCNodeName(kernel_node)); if (kernel_attrs.empty() || (kernel_attrs[0].GetInputSize() == 0 && kernel_attrs[0].GetOutputSize() == 0)) { MS_LOG(DEBUG) << "Operator[" << op_name << "] will get ops attr info."; auto op_info_ptr = mindspore::kernel::OpLib::FindOp(op_name, kernel::OpImplyType::kCPU); diff --git a/mindspore/ccsrc/plugin/device/cpu/hal/device/kernel_select_cpu.h b/mindspore/ccsrc/plugin/device/cpu/hal/device/kernel_select_cpu.h index ecdc08d2929..7d518cf74fa 100644 --- a/mindspore/ccsrc/plugin/device/cpu/hal/device/kernel_select_cpu.h +++ b/mindspore/ccsrc/plugin/device/cpu/hal/device/kernel_select_cpu.h @@ -23,7 +23,7 @@ #include "ir/anf.h" #include "ir/dtype/type.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace device { diff --git a/mindspore/ccsrc/plugin/device/cpu/hal/hardware/cpu_device_context.cc b/mindspore/ccsrc/plugin/device/cpu/hal/hardware/cpu_device_context.cc index 45646e3e223..d204bdf2653 100644 --- a/mindspore/ccsrc/plugin/device/cpu/hal/hardware/cpu_device_context.cc +++ b/mindspore/ccsrc/plugin/device/cpu/hal/hardware/cpu_device_context.cc @@ -23,7 +23,7 @@ #include "kernel/kernel_build_info.h" #include "plugin/device/cpu/hal/device/kernel_select_cpu.h" #include "utils/trace_base.h" -#include "utils/context/graph_kernel_flags.h" +#include "include/common/utils/context/graph_kernel_flags.h" #include "backend/common/optimizer/optimizer.h" #include "backend/common/optimizer/pass_manager.h" #include "backend/common/optimizer/common_backend_optimization.h" @@ -33,6 +33,7 @@ #include "backend/common/pass/erase_visit_attr.h" #include "common/graph_kernel/adapter/graph_kernel_optimization.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "profiler/device/cpu/cpu_profiling.h" #if ((defined ENABLE_CPU) && (!defined _WIN32)) #include "plugin/device/cpu/hal/hardware/ms_collective_comm_lib.h" @@ -165,17 +166,17 @@ void SetControlOpInfo(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); std::vector inputs_format; std::vector inputs_type; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); for (size_t input_index = 0; input_index < input_num; ++input_index) { (void)inputs_format.emplace_back(kOpFormat_DEFAULT); - inputs_type.push_back(AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, input_index)); + inputs_type.push_back(common::AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, input_index)); } std::vector outputs_format; std::vector outputs_type; - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); for (size_t output_index = 0; output_index < output_num; ++output_index) { (void)outputs_format.emplace_back(kOpFormat_DEFAULT); - outputs_type.push_back(AnfAlgo::GetOutputInferDataType(kernel_node, output_index)); + outputs_type.push_back(common::AnfAlgo::GetOutputInferDataType(kernel_node, output_index)); } auto builder = std::make_shared(); @@ -191,7 +192,7 @@ void SetControlOpInfo(const CNodePtr &kernel_node) { void CPUDeviceContext::SetOperatorInfo(const std::vector &nodes) const { for (const auto &node : nodes) { MS_EXCEPTION_IF_NULL(node); - if (!AnfAlgo::IsControlOpExecInBackend(node)) { + if (!common::AnfAlgo::IsControlOpExecInBackend(node)) { SetKernelInfo(node); } else { SetControlOpInfo(node); @@ -205,7 +206,7 @@ void CPUDeviceContext::CreateKernel(const std::vector &nodes) const { std::vector akg_nodes; for (const auto &node : nodes) { MS_EXCEPTION_IF_NULL(node); - if (AnfAlgo::IsControlOpExecInBackend(node)) { + if (common::AnfAlgo::IsControlOpExecInBackend(node)) { continue; } if (session::AnfRuntimeAlgorithm::GetKernelType(node) == KernelType::AKG_KERNEL) { @@ -215,7 +216,7 @@ void CPUDeviceContext::CreateKernel(const std::vector &nodes) const { akg_nodes.push_back(node); continue; } - std::string kernel_name = AnfAlgo::GetCNodeName(node); + std::string kernel_name = common::AnfAlgo::GetCNodeName(node); std::shared_ptr cpu_kernel = kernel::NativeCpuKernelModFactory::GetInstance().Create(kernel_name, node); if (!cpu_kernel) { @@ -258,7 +259,7 @@ void CPUDeviceContext::PreprocessBeforeRunGraph(const KernelGraphPtr &graph) con MS_EXCEPTION_IF_NULL(graph); // Remove reorder after PS feature finish adapting push/pull in auto_monad. auto execution_order = graph->execution_order(); - AnfAlgo::ReorderPosteriorExecList(NOT_NULL(&execution_order)); + common::AnfAlgo::ReorderPosteriorExecList(NOT_NULL(&execution_order)); graph->set_execution_order(execution_order); } @@ -272,7 +273,8 @@ bool CPUDeviceContext::LaunchKernel(const CNodePtr &kernel, const std::vector(kernel_mod); MS_EXCEPTION_IF_NULL(cpu_kernel_mod); cpu_kernel_mod->InitKernel(kernel); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/adam_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/adam_cpu_kernel.cc index 608a5bf8505..208b01ff753 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/adam_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/adam_cpu_kernel.cc @@ -96,13 +96,13 @@ void AdamCpuKernelMod::LaunchAdamNnacl(const std::vector &in void AdamCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); CHECK_KERNEL_INPUTS_NUM(input_num, kAdamInputsNum, kernel_name_); - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); CHECK_KERNEL_OUTPUTS_NUM(output_num, kAdamOutputsNum, kernel_name_); - use_nesterov_ = AnfAlgo::GetNodeAttr(kernel_node, USE_NESTEROV); + use_nesterov_ = common::AnfAlgo::GetNodeAttr(kernel_node, USE_NESTEROV); } bool AdamCpuKernelMod::Launch(const std::vector &inputs, const std::vector &, diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/adam_delta_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/adam_delta_cpu_kernel.cc index 1dbb86c36eb..554bc9e570b 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/adam_delta_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/adam_delta_cpu_kernel.cc @@ -59,7 +59,7 @@ void AdamDeltaCpuKernelMod::LaunchAdamDelta(T *delta, T *m, T *v, float lr, floa void AdamDeltaCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); std::vector delta_shape = AnfAlgo::GetOutputDeviceShape(kernel_node, 0); std::vector m_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0); std::vector v_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 1); @@ -91,8 +91,8 @@ void AdamDeltaCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { if (elem_num_ < 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the 'delta' should be at least 1-D, but got empty shape!"; } - if (AnfAlgo::HasNodeAttr(USE_NESTEROV, kernel_node)) { - use_nesterov_ = AnfAlgo::GetNodeAttr(kernel_node, "use_nesterov"); + if (common::AnfAlgo::HasNodeAttr(USE_NESTEROV, kernel_node)) { + use_nesterov_ = common::AnfAlgo::GetNodeAttr(kernel_node, "use_nesterov"); } } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/adam_weight_decay_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/adam_weight_decay_cpu_kernel.cc index c90b539ca15..0d5cc2e83fc 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/adam_weight_decay_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/adam_weight_decay_cpu_kernel.cc @@ -90,7 +90,7 @@ void AdamWeightDecayCpuKernelMod::LaunchAdamWeightDecayNnacl(const std::vector #include "nlohmann/json.hpp" #include "kernel/common_utils.h" -#include "common/thread_pool.h" +#include "include/common/thread_pool.h" #include "utils/ms_utils.h" #include "mindspore/ccsrc/debug/common.h" diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/allgather_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/allgather_cpu_kernel.cc index ff3d995b445..0b075ca3890 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/allgather_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/allgather_cpu_kernel.cc @@ -29,10 +29,10 @@ constexpr auto kRanksGroup = "group"; void AllGatherCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); CHECK_KERNEL_INPUTS_NUM(input_num, kAllGatherInputsNum, kernel_name_); - ranks_group_ = AnfAlgo::GetNodeAttr>(kernel_node, kRanksGroup); + ranks_group_ = common::AnfAlgo::GetNodeAttr>(kernel_node, kRanksGroup); } bool AllGatherCpuKernelMod::Launch(const std::vector &inputs, diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/apply_adagrad_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/apply_adagrad_cpu_kernel.cc index 6ccd7694fe7..4e015a1d2aa 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/apply_adagrad_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/apply_adagrad_cpu_kernel.cc @@ -30,8 +30,8 @@ constexpr size_t kApplyAdagradOutputsNum = 2; void ApplyAdagradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - update_slots_ = AnfAlgo::GetNodeAttr(kernel_node, "update_slots"); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + update_slots_ = common::AnfAlgo::GetNodeAttr(kernel_node, "update_slots"); dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/apply_momentum_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/apply_momentum_cpu_kernel.cc index fa19db7679e..5bed4c60b3f 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/apply_momentum_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/apply_momentum_cpu_kernel.cc @@ -26,7 +26,7 @@ constexpr size_t kApplyMomentumInputsNum = 5; void ApplyMomentumCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); } bool ApplyMomentumCpuKernelMod::Launch(const std::vector &inputs, diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/argmax_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/argmax_cpu_kernel.cc index 12162fda876..e74df26ec8b 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/argmax_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/argmax_cpu_kernel.cc @@ -59,13 +59,13 @@ bool check_validation(const std::vector &shape, const size_t num_before_ template void ArgmaxCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 0); size_t shape_len = shape_.size(); if (shape_len == 0) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimension of 'input_x' should be at least 1, but got 0."; } - int64_t axis = AnfAlgo::GetNodeAttr(kernel_node, AXIS); + int64_t axis = common::AnfAlgo::GetNodeAttr(kernel_node, AXIS); axis += SizeToLong(shape_len); if (axis < 0) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the 'axis' should be in range [-1, " << (shape_len - 1) diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/argmax_with_value_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/argmax_with_value_cpu_kernel.cc index 7bea85d460e..253da57e25e 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/argmax_with_value_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/argmax_with_value_cpu_kernel.cc @@ -63,10 +63,10 @@ bool check_validation(const std::vector &shape, const size_t num_before_ template void ArgMaxWithValueCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 0); size_t shape_len = shape_.size(); - int64_t axis = AnfAlgo::GetNodeAttr(kernel_node, AXIS); + int64_t axis = common::AnfAlgo::GetNodeAttr(kernel_node, AXIS); axis += SizeToLong(shape_len); if (axis < 0) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the 'axis' should be in range [-1, " << (shape_len - 1) diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/argmin_with_value_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/argmin_with_value_cpu_kernel.cc index f69fbb87efd..19aad3dcd91 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/argmin_with_value_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/argmin_with_value_cpu_kernel.cc @@ -64,13 +64,13 @@ bool check_validation(const std::vector &shape, const size_t num_before_ template void ArgMinWithValueCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 0); size_t shape_len = shape_.size(); if (shape_len == 0) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimension of 'input_x' should be at least 1, but got 0."; } - int64_t axis = AnfAlgo::GetNodeAttr(kernel_node, AXIS); + int64_t axis = common::AnfAlgo::GetNodeAttr(kernel_node, AXIS); axis += static_cast(shape_len); if (axis < 0) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the 'axis' should be in range [-1, " << (shape_len - 1) diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/arithmetic_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/arithmetic_cpu_kernel.cc index 6696829f720..b61d3eb0144 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/arithmetic_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/arithmetic_cpu_kernel.cc @@ -425,10 +425,10 @@ void ArithmeticCpuKernelMod::InitComputeFunc() { template void ArithmeticCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - input_shape1_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - input_shape2_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - output_shape_ = AnfAlgo::GetOutputInferShape(kernel_node, 0); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + input_shape1_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + input_shape2_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + output_shape_ = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); if (output_shape_.size() == 0) { (void)output_shape_.insert(output_shape_.begin(), 1); } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/arithmetic_logic_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/arithmetic_logic_cpu_kernel.cc index 81048affa00..11780e6e0a3 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/arithmetic_logic_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/arithmetic_logic_cpu_kernel.cc @@ -155,10 +155,10 @@ void ArithmeticLogicCpuKernelMod::InitComputeFunc() { template void ArithmeticLogicCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - input_shape1_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - input_shape2_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - output_shape_ = AnfAlgo::GetOutputInferShape(kernel_node, 0); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + input_shape1_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + input_shape2_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + output_shape_ = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); if (output_shape_.empty()) { (void)output_shape_.insert(output_shape_.begin(), 1); } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/arithmetic_self_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/arithmetic_self_cpu_kernel.cc index 899fd268776..0de828ca38d 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/arithmetic_self_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/arithmetic_self_cpu_kernel.cc @@ -291,7 +291,7 @@ void Identity(const T *in, T *out, size_t size) { void ArithmeticSelfCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/assign_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/assign_cpu_kernel.cc index 94f37690cc2..d148c883e5b 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/assign_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/assign_cpu_kernel.cc @@ -20,7 +20,7 @@ #include #include "plugin/device/cpu/hal/device/cpu_device_address.h" -#include "common/thread_pool.h" +#include "include/common/thread_pool.h" namespace mindspore { namespace kernel { @@ -37,9 +37,9 @@ const std::map input_x_dtype_size_map = { void AssignCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - auto input_x_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto input_y_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + auto input_x_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_y_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); if (input_x_shape.size() != input_y_shape.size()) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the 'x' and 'y' should have the same dimension, but got the dimension of 'x': " diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/bias_add_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/bias_add_cpu_kernel.cc index d97ae793927..42ae20248b2 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/bias_add_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/bias_add_cpu_kernel.cc @@ -29,7 +29,7 @@ constexpr size_t kBiasAddOutputsNum = 1; void BiasAddCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); input_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 0); bias_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 1); data_shape_ = input_shape_.size(); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/bias_add_grad_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/bias_add_grad_cpu_kernel.cc index 02e54c82fd1..38cf193bde0 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/bias_add_grad_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/bias_add_grad_cpu_kernel.cc @@ -27,7 +27,7 @@ constexpr size_t kBiasAddGradOutputsNum = 1; void BiasAddGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); input_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 0); if (input_shape_.size() < 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', input tensor's dimension should be at least 2, but got " diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/binary_cross_entropy_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/binary_cross_entropy_cpu_kernel.cc index fd64c3b6de7..8662b2256a9 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/binary_cross_entropy_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/binary_cross_entropy_cpu_kernel.cc @@ -108,16 +108,16 @@ bool BinaryCrossEntropyCpuKernelMod::Launch(const std::vector &input void BinaryCrossEntropyCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); weight_defined_ = (input_num == kBceInputsNumWithWeight); dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); for (size_t i = 0; i < input_shape.size(); i++) { input_size_ *= input_shape[i]; } - const std::string reduction = AnfAlgo::GetNodeAttr(kernel_node, REDUCTION); + const std::string reduction = common::AnfAlgo::GetNodeAttr(kernel_node, REDUCTION); if (reduction == NONE) { reduction_ = kNone; } else if (reduction == MEAN) { diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/binary_cross_entropy_grad_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/binary_cross_entropy_grad_kernel.cc index c7f55f602e7..499814bfa48 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/binary_cross_entropy_grad_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/binary_cross_entropy_grad_kernel.cc @@ -87,16 +87,16 @@ bool BinaryCrossEntropyGradCpuKernelMod::Launch(const std::vector &i void BinaryCrossEntropyGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); weight_defined_ = (input_num == kBceGradInputsNumWithWeight); dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); for (size_t i = 0; i < input_shape.size(); i++) { input_size_ *= input_shape[i]; } - const std::string reduction = AnfAlgo::GetNodeAttr(kernel_node, REDUCTION); + const std::string reduction = common::AnfAlgo::GetNodeAttr(kernel_node, REDUCTION); if (reduction == NONE) { reduction_ = kNone; } else if (reduction == MEAN) { diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/boundingbox_decode_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/boundingbox_decode_cpu_kernel.cc index 15caeba0350..e0669095799 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/boundingbox_decode_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/boundingbox_decode_cpu_kernel.cc @@ -22,18 +22,18 @@ namespace kernel { template void BoundingBoxDecodeCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != INPUT_NUMS) { MS_LOG(ERROR) << "For '" << kernel_name_ << "', the number of inputs should be 2, but got " << input_num; } const size_t coordinate_size = 4; - if (AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("means")->isa() || - AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("means")->isa()) { - means_ = AnfAlgo::GetNodeAttr>(kernel_node, "means"); - } else if (AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("means")->isa()) { - float mean = AnfAlgo::GetNodeAttr(kernel_node, "means"); + if (common::AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("means")->isa() || + common::AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("means")->isa()) { + means_ = common::AnfAlgo::GetNodeAttr>(kernel_node, "means"); + } else if (common::AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("means")->isa()) { + float mean = common::AnfAlgo::GetNodeAttr(kernel_node, "means"); for (size_t i = 0; i < coordinate_size; i++) { (void)means_.emplace_back(mean); } @@ -42,11 +42,11 @@ void BoundingBoxDecodeCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { << "', the input 'means' should be a tuple or a list, and dtype should be float, but got is not."; } - if (AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("stds")->isa() || - AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("stds")->isa()) { - stds_ = AnfAlgo::GetNodeAttr>(kernel_node, "stds"); - } else if (AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("stds")->isa()) { - float std = AnfAlgo::GetNodeAttr(kernel_node, "stds"); + if (common::AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("stds")->isa() || + common::AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("stds")->isa()) { + stds_ = common::AnfAlgo::GetNodeAttr>(kernel_node, "stds"); + } else if (common::AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("stds")->isa()) { + float std = common::AnfAlgo::GetNodeAttr(kernel_node, "stds"); for (size_t i = 0; i < coordinate_size; i++) { (void)stds_.emplace_back(std); } @@ -62,10 +62,10 @@ void BoundingBoxDecodeCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { << means_.size() << ", and the length of 'stds': " << stds_.size(); } - std::vector max_shape_me = AnfAlgo::GetNodeAttr>(kernel_node, "max_shape"); + std::vector max_shape_me = common::AnfAlgo::GetNodeAttr>(kernel_node, "max_shape"); (void)std::transform(max_shape_me.begin(), max_shape_me.end(), std::back_inserter(max_shape_), [](const int64_t &value) { return LongToInt(value); }); - wh_ratio_clip_ = AnfAlgo::GetNodeAttr(kernel_node, "wh_ratio_clip"); + wh_ratio_clip_ = common::AnfAlgo::GetNodeAttr(kernel_node, "wh_ratio_clip"); if (max_shape_.size() < MIN_MAX_SHAPE_SIZE) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/boundingbox_encode_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/boundingbox_encode_cpu_kernel.cc index 08c4ccb2383..171b3eaa9a9 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/boundingbox_encode_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/boundingbox_encode_cpu_kernel.cc @@ -22,18 +22,18 @@ namespace kernel { template void BoundingBoxEncodeCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != INPUT_NUMS) { MS_LOG(ERROR) << "For '" << kernel_name_ << "', the number of inputs should be 2, but got " << input_num; } const size_t coordinate_size = 4; - if (AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("means")->isa() || - AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("means")->isa()) { - means_ = AnfAlgo::GetNodeAttr>(kernel_node, "means"); - } else if (AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("means")->isa()) { - float mean = AnfAlgo::GetNodeAttr(kernel_node, "means"); + if (common::AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("means")->isa() || + common::AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("means")->isa()) { + means_ = common::AnfAlgo::GetNodeAttr>(kernel_node, "means"); + } else if (common::AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("means")->isa()) { + float mean = common::AnfAlgo::GetNodeAttr(kernel_node, "means"); for (size_t i = 0; i < coordinate_size; i++) { means_.emplace_back(mean); } @@ -42,11 +42,11 @@ void BoundingBoxEncodeCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { << "', the input 'means' should be a tuple or a list, and dtype should be float, but got is not."; } - if (AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("stds")->isa() || - AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("stds")->isa()) { - stds_ = AnfAlgo::GetNodeAttr>(kernel_node, "stds"); - } else if (AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("stds")->isa()) { - float std = AnfAlgo::GetNodeAttr(kernel_node, "stds"); + if (common::AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("stds")->isa() || + common::AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("stds")->isa()) { + stds_ = common::AnfAlgo::GetNodeAttr>(kernel_node, "stds"); + } else if (common::AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("stds")->isa()) { + float std = common::AnfAlgo::GetNodeAttr(kernel_node, "stds"); for (size_t i = 0; i < coordinate_size; i++) { stds_.emplace_back(std); } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/broadcast_to_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/broadcast_to_cpu_kernel.cc index 36ce86369cb..30cebb08b2b 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/broadcast_to_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/broadcast_to_cpu_kernel.cc @@ -26,9 +26,9 @@ constexpr size_t kBroadcastToOutputsNum = 1; template void BroadcastToCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - input_shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - output_shape_ = AnfAlgo::GetOutputInferShape(kernel_node, 0); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + input_shape_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + output_shape_ = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); size_t input_shape_size = input_shape_.size(); size_t output_shape_size = output_shape_.size(); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/cast_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/cast_cpu_kernel.cc index eb5d1505658..c70d69e3c4d 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/cast_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/cast_cpu_kernel.cc @@ -42,7 +42,7 @@ void Cast(CastCpuKernelMod *content, const S *in, T *out, size_t size) { template void CastCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); source_dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); target_dtype_ = AnfAlgo::GetOutputDeviceDataType(kernel_node, 0); } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/check_valid_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/check_valid_cpu_kernel.cc index cbb02b70747..536e47177b7 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/check_valid_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/check_valid_cpu_kernel.cc @@ -32,7 +32,7 @@ constexpr size_t kIndex2 = 2; template void CheckValidCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); anchor_box_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 0); img_metas_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 1); output_shape_ = AnfAlgo::GetOutputDeviceShape(kernel_node, 0); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/cholesky_inverse_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/cholesky_inverse_cpu_kernel.cc index 021b59c8c69..c0f769e639d 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/cholesky_inverse_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/cholesky_inverse_cpu_kernel.cc @@ -24,10 +24,10 @@ constexpr size_t kDimNum = 2; template void CholeskyInverseCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { node_wpt_ = kernel_node; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); auto x_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); CHECK_KERNEL_INPUTS_NUM(input_num, kInputNum, kernel_name_); CHECK_KERNEL_OUTPUTS_NUM(output_num, kOutputNum, kernel_name_); if (x_shape.size() != kDimNum) { @@ -52,7 +52,7 @@ bool CholeskyInverseCpuKernelMod::Launch(const std::vector MatrixXd; Eigen::Map A(input_x0, n, n); MatrixXd result; - auto upper = AnfAlgo::GetNodeAttr(node_wpt_, "upper"); + auto upper = common::AnfAlgo::GetNodeAttr(node_wpt_, "upper"); if (upper) { result = (A.transpose() * A).inverse(); } else { diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/coalesce_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/coalesce_cpu_kernel.cc index fc31d40bdb0..190650acceb 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/coalesce_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/coalesce_cpu_kernel.cc @@ -26,9 +26,9 @@ constexpr char kKernelName[] = "Coalesce"; } // namespace void CoalesceCpuKernelMod::CheckParam(const CNodePtr &kernel_node) { - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); CHECK_KERNEL_INPUTS_NUM(input_num, kCoalesceInputsNum, kKernelName); - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); CHECK_KERNEL_OUTPUTS_NUM(output_num, kCoalesceOutputsNum, kKernelName); } @@ -46,7 +46,7 @@ bool CoalesceCpuKernelMod::Launch(const std::vector &inputs, if (!node_) { MS_LOG(EXCEPTION) << "node_wpt_ is expired."; } - size_t output_nm = AnfAlgo::GetOutputTensorNum(node_); + size_t output_nm = common::AnfAlgo::GetOutputTensorNum(node_); std::vector dtypes(output_nm); for (size_t i = 0; i < output_nm; i++) { dtypes[i] = AnfAlgo::GetOutputDeviceDataType(node_, i); @@ -56,7 +56,8 @@ bool CoalesceCpuKernelMod::Launch(const std::vector &inputs, (void)dims.emplace_back(jump + 1); std::vector dim; (void)dim.emplace_back(jump + 1); - AnfAlgo::SetOutputInferTypeAndShape(dtypes, {dims, dim, AnfAlgo::GetOutputInferShape(node_, 2)}, node_.get()); + common::AnfAlgo::SetOutputInferTypeAndShape(dtypes, {dims, dim, common::AnfAlgo::GetOutputInferShape(node_, 2)}, + node_.get()); return true; } @@ -65,7 +66,7 @@ void CoalesceCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { CheckParam(kernel_node); node_wpt_ = kernel_node; dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 1); - auto indices_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto indices_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); values_size_ = indices_shape[1]; shape_size_ = indices_shape[0]; } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/concat_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/concat_cpu_kernel.cc index 8d49c52158d..40c23ecf2fb 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/concat_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/concat_cpu_kernel.cc @@ -26,10 +26,10 @@ constexpr size_t kConcatOutputsNum = 1; template void ConcatCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); cnode_ptr_ = kernel_node; - axis_ = LongToInt(AnfAlgo::GetNodeAttr(kernel_node, AXIS)); - auto input_1_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + axis_ = LongToInt(common::AnfAlgo::GetNodeAttr(kernel_node, AXIS)); + auto input_1_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); if (axis_ < 0) { axis_ = axis_ + SizeToInt(input_1_shape.size()); } @@ -43,14 +43,14 @@ bool ConcatCpuKernelMod::Launch(const std::vector &inputs if (!node_) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', cnode_ptr_(kernel_node) is expired. Error no: " << node_; } - const size_t input_num = AnfAlgo::GetInputTensorNum(node_); + const size_t input_num = common::AnfAlgo::GetInputTensorNum(node_); CHECK_KERNEL_INPUTS_NUM(inputs.size(), input_num, kernel_name_); CHECK_KERNEL_OUTPUTS_NUM(outputs.size(), kConcatOutputsNum, kernel_name_); std::vector> input_flat_shape_list; input_flat_shape_list.reserve(input_num); for (size_t i = 0; i < input_num; i++) { - auto input_shape_i = AnfAlgo::GetPrevNodeOutputInferShape(node_, i); + auto input_shape_i = common::AnfAlgo::GetPrevNodeOutputInferShape(node_, i); auto flat_shape = CPUKernelUtils::FlatShapeByAxis(input_shape_i, axis_); (void)input_flat_shape_list.emplace_back(flat_shape); } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/concat_offset_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/concat_offset_cpu_kernel.cc index 2512b328f7b..f83175b3e95 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/concat_offset_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/concat_offset_cpu_kernel.cc @@ -28,10 +28,10 @@ template void ConcatOffsetCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { cnode_ptr_ = kernel_node; MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); - int64_t axis = AnfAlgo::GetNodeAttr(kernel_node, AXIS); - auto input_1_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + int64_t axis = common::AnfAlgo::GetNodeAttr(kernel_node, AXIS); + auto input_1_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); if (axis < 0) { axis_ = LongToSize(axis + input_1_shape.size()); } else { @@ -54,13 +54,13 @@ bool ConcatOffsetCpuKernelMod::Launch(const std::vector & MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', cnode_ptr_(kernel_node) is expired. Error no: " << node_; } auto output_addr = reinterpret_cast(outputs[0]->addr); - size_t input_num = AnfAlgo::GetInputTensorNum(node_); + size_t input_num = common::AnfAlgo::GetInputTensorNum(node_); std::vector offset{0}; - size_t all_shape = AnfAlgo::GetPrevNodeOutputInferShape(node_, 0)[axis_]; + size_t all_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node_, 0)[axis_]; // cal offset for (size_t i = 1; i < input_num; i++) { - auto input_shape_i = AnfAlgo::GetPrevNodeOutputInferShape(node_, i); + auto input_shape_i = common::AnfAlgo::GetPrevNodeOutputInferShape(node_, i); if (axis_ >= input_shape_i.size()) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the 'axis' should be less than the dimension of input, but got 'axis': " << axis_ @@ -69,7 +69,7 @@ bool ConcatOffsetCpuKernelMod::Launch(const std::vector & offset.emplace_back(all_shape); all_shape += input_shape_i[axis_]; } - auto output_shape = AnfAlgo::GetOutputInferShape(node_, 0); + auto output_shape = common::AnfAlgo::GetOutputInferShape(node_, 0); if (output_shape.size() != kConcatOffsetOutputShapeSize) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimension of output should be " << kConcatOffsetOutputShapeSize << ", but got:" << output_shape.size(); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/cpu_kernel.cc index 6e349178d97..d0d21bfaa29 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/cpu_kernel.cc @@ -43,7 +43,7 @@ void NativeCpuKernelMod::InitInputOutputSize(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); input_size_list_.clear(); output_size_list_.clear(); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); for (size_t input_index = 0; input_index < input_num; ++input_index) { TypeId type_id = AnfAlgo::GetInputDeviceDataType(kernel_node, input_index); size_t type_size = GetTypeByte(TypeIdToType(type_id)); @@ -53,7 +53,7 @@ void NativeCpuKernelMod::InitInputOutputSize(const CNodePtr &kernel_node) { tensor_size = std::max(tensor_size, type_size); (void)input_size_list_.emplace_back(tensor_size); } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); for (size_t output_index = 0; output_index < output_num; ++output_index) { TypeId type_id = AnfAlgo::GetOutputDeviceDataType(kernel_node, output_index); size_t type_size = GetTypeByte(TypeIdToType(type_id)); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/cpu_kernel.h b/mindspore/ccsrc/plugin/device/cpu/kernel/cpu_kernel.h index 40aff162017..6ff365a4a17 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/cpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/cpu_kernel.h @@ -27,11 +27,12 @@ #include "kernel/kernel.h" #include "plugin/device/cpu/kernel/cpu_kernel_mod.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "kernel/common_utils.h" #include "ir/anf.h" #include "runtime/graph_scheduler/graph_scheduler.h" #include "actor/actormgr.h" -#include "common/thread_pool.h" +#include "include/common/thread_pool.h" using mindspore::kernel::Address; using mindspore::kernel::AddressPtr; diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/crop_and_resize_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/crop_and_resize_cpu_kernel.cc index d92378742f5..ec5906a6547 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/crop_and_resize_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/crop_and_resize_cpu_kernel.cc @@ -22,19 +22,19 @@ namespace kernel { template void CropAndResizeCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != INPUT_NUM) { MS_LOG(ERROR) << "For '" << kernel_name_ << "', input number should be 4, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != OUTPUT_NUM) { MS_LOG(ERROR) << "For '" << kernel_name_ << "', output number should be 1, but got " << output_num; } // input image - auto input_image_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, IMAGE); + auto input_image_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, IMAGE); size_t input_image_shape_len = input_image_shape.size(); if (input_image_shape_len != IMAGE_DIM) { MS_LOG(ERROR) << "For '" << kernel_name_ << "', the dimension of 'image' should be " << IMAGE_DIM << "-D, but got " @@ -45,7 +45,7 @@ void CropAndResizeCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { input_width_ = SizeToInt(input_image_shape[IMAGE_WEIGHT]); // input boxes - auto input_boxes_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, BOXES); + auto input_boxes_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, BOXES); size_t input_boxes_shape_len = input_boxes_shape.size(); if (input_boxes_shape_len != BOX_RANK) { MS_LOG(ERROR) << "For '" << kernel_name_ << "', the dimension of 'boxes' should be " << BOX_RANK << ", but got " @@ -53,7 +53,7 @@ void CropAndResizeCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { } // input box_index - auto input_box_index_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, BOX_INDEX); + auto input_box_index_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, BOX_INDEX); size_t input_box_index_shape_len = input_box_index_shape.size(); if (input_box_index_shape_len != 1) { MS_LOG(ERROR) << "For '" << kernel_name_ << "', the dimension of 'box_index' should be 1, but got " @@ -61,7 +61,7 @@ void CropAndResizeCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { } // input crop_size - auto input_crop_size_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, CROP_SIZE); + auto input_crop_size_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, CROP_SIZE); size_t input_crop_size_shape_len = input_crop_size_shape.size(); if (input_crop_size_shape_len != 1) { MS_LOG(ERROR) << "For '" << kernel_name_ << "', the dimension of 'crop_size' should be 1, but got " @@ -76,7 +76,7 @@ void CropAndResizeCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { constexpr size_t HEIGHT = 1; constexpr size_t WEIGHT = 2; constexpr size_t CHANNEL = 3; - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); auto output_shape_len = output_shape.size(); output_size_ = 1; for (size_t i = 0; i < output_shape_len; i++) { @@ -89,7 +89,7 @@ void CropAndResizeCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { channel_ = SizeToInt(output_shape[CHANNEL]); // get op parameters - string method = AnfAlgo::GetNodeAttr(kernel_node, "method"); + string method = common::AnfAlgo::GetNodeAttr(kernel_node, "method"); if (method == "bilinear") { method_ = BILINEAR; } else if (method == "nearest") { @@ -97,7 +97,7 @@ void CropAndResizeCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { } else { // bilinear-v2 method_ = BILINEAR_V2; } - extrapolation_value_ = AnfAlgo::GetNodeAttr(kernel_node, "extrapolation_value"); + extrapolation_value_ = common::AnfAlgo::GetNodeAttr(kernel_node, "extrapolation_value"); } template diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/cross_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/cross_cpu_kernel.cc index 7b150619441..0e125bf97c2 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/cross_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/cross_cpu_kernel.cc @@ -33,7 +33,7 @@ void CrossCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { input2_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 1); output_shape_ = AnfAlgo::GetOutputDeviceShape(kernel_node, 0); input1_dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); - dim_ = AnfAlgo::GetNodeAttr(kernel_node, "dim"); + dim_ = common::AnfAlgo::GetNodeAttr(kernel_node, "dim"); int64_t default_dim = -65530; if (dim_ == default_dim) { size_t dim_size_value = 3; diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/ctcloss_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/ctcloss_cpu_kernel.cc index af160b754cc..59623ebf7e9 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/ctcloss_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/ctcloss_cpu_kernel.cc @@ -72,10 +72,10 @@ void MatrixFromVector(uint32_t row, uint32_t col, std::vector> *a void CTCLossCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - probs_shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - indices_dims_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - labels_dims_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + probs_shape_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + indices_dims_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + labels_dims_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); if (probs_shape_.size() != 3) { @@ -91,9 +91,9 @@ void CTCLossCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { << indices_dims_.size() << "-D."; } - preprocess_collapse_repeated_ = AnfAlgo::GetNodeAttr(kernel_node, PCR); - ctc_merge_repeated_ = AnfAlgo::GetNodeAttr(kernel_node, CTR); - ignore_longer_outputs_than_inputs_ = AnfAlgo::GetNodeAttr(kernel_node, ILOTI); + preprocess_collapse_repeated_ = common::AnfAlgo::GetNodeAttr(kernel_node, PCR); + ctc_merge_repeated_ = common::AnfAlgo::GetNodeAttr(kernel_node, CTR); + ignore_longer_outputs_than_inputs_ = common::AnfAlgo::GetNodeAttr(kernel_node, ILOTI); max_time_ = probs_shape_[0]; batch_size_ = probs_shape_[1]; num_class_ = probs_shape_[2]; diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/cummax_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/cummax_cpu_kernel.cc index d283d27fa80..71dd89ecd2e 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/cummax_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/cummax_cpu_kernel.cc @@ -24,7 +24,7 @@ void CummaxCPUKernelMod::InitKernel(const CNodePtr &kernel_node) { input_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 0); output1_shape_ = AnfAlgo::GetOutputDeviceShape(kernel_node, 0); output2_shape_ = AnfAlgo::GetOutputDeviceShape(kernel_node, 1); - dim_ = AnfAlgo::GetNodeAttr(kernel_node, "dim"); + dim_ = common::AnfAlgo::GetNodeAttr(kernel_node, "dim"); } template diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/cumsum_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/cumsum_cpu_kernel.cc index 4bdc0c2f4bc..dca9b9c0e12 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/cumsum_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/cumsum_cpu_kernel.cc @@ -29,13 +29,13 @@ constexpr size_t kCumSumOutputsNum = 1; void CumSumCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + shape_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); - axis_ = LongToInt(AnfAlgo::GetNodeAttr(kernel_node, AXIS)); - dst_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); - exclusive_ = AnfAlgo::GetNodeAttr(kernel_node, EXCLUSIVE); - reverse_ = AnfAlgo::GetNodeAttr(kernel_node, REVERSE); + axis_ = LongToInt(common::AnfAlgo::GetNodeAttr(kernel_node, AXIS)); + dst_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); + exclusive_ = common::AnfAlgo::GetNodeAttr(kernel_node, EXCLUSIVE); + reverse_ = common::AnfAlgo::GetNodeAttr(kernel_node, REVERSE); int input_dim_length = SizeToInt(shape_.size()); if (axis_ >= input_dim_length) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/custom/custom_aot_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/custom/custom_aot_cpu_kernel.cc index 3b35abe2b29..4ca45d25d62 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/custom/custom_aot_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/custom/custom_aot_cpu_kernel.cc @@ -39,8 +39,8 @@ CustomAOTCpuKernelMod::~CustomAOTCpuKernelMod() { } void CustomAOTCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - const auto &exec_info = AnfAlgo::GetNodeAttr(kernel_node, "func_name"); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + const auto &exec_info = common::AnfAlgo::GetNodeAttr(kernel_node, "func_name"); if (auto pos = exec_info.find(":"); pos != std::string::npos) { auto path = exec_info.substr(0, pos); auto real_path = FileUtils::GetRealPath(path.c_str()); @@ -55,7 +55,7 @@ void CustomAOTCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { << "' is illegal. Proper function path should follow the format of 'dir_path/file_name:func_name'"; } - num_input_ = AnfAlgo::GetInputTensorNum(kernel_node); + num_input_ = common::AnfAlgo::GetInputTensorNum(kernel_node); auto input_type_list = AnfAlgo::GetAllInputDeviceTypes(kernel_node); if (num_input_ != input_type_list.size()) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "' on CPU, number of input types '" << input_type_list.size() @@ -72,7 +72,7 @@ void CustomAOTCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { type_list_.emplace_back(TypeIdToString(input_type_list[i], true)); } - num_output_ = AnfAlgo::GetOutputTensorNum(kernel_node); + num_output_ = common::AnfAlgo::GetOutputTensorNum(kernel_node); auto output_type_list = AnfAlgo::GetAllOutputDeviceTypes(kernel_node); if (num_output_ != output_type_list.size()) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "' on CPU, number of outputs types '" << output_type_list.size() diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/custom/custom_julia_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/custom/custom_julia_cpu_kernel.cc index 2ca7522f62a..2914511ee5e 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/custom/custom_julia_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/custom/custom_julia_cpu_kernel.cc @@ -28,7 +28,7 @@ namespace mindspore { namespace kernel { void CustomJULIACpuKernelMod::InitKernel(const CNodePtr &kernel_node) { - const auto &exec_info = AnfAlgo::GetNodeAttr(kernel_node, "func_name"); + const auto &exec_info = common::AnfAlgo::GetNodeAttr(kernel_node, "func_name"); auto pos1 = exec_info.find(":"); auto pos2 = exec_info.rfind(":"); if (pos1 == std::string::npos || pos2 == std::string::npos || pos1 == pos2) { @@ -43,7 +43,7 @@ void CustomJULIACpuKernelMod::InitKernel(const CNodePtr &kernel_node) { module_name_ = exec_info.substr(pos1 + 1, pos2 - pos1 - 1); func_name_ = exec_info.substr(pos2 + 1); - num_input_ = AnfAlgo::GetInputTensorNum(kernel_node); + num_input_ = common::AnfAlgo::GetInputTensorNum(kernel_node); auto input_type_list = AnfAlgo::GetAllInputDeviceTypes(kernel_node); if (num_input_ != input_type_list.size()) { MS_LOG(EXCEPTION) << "Kernel[" << exec_info << "]'s input shapes'size is " << num_input_ @@ -60,7 +60,7 @@ void CustomJULIACpuKernelMod::InitKernel(const CNodePtr &kernel_node) { type_list_.emplace_back(TypeIdToString(input_type_list[i], true)); } - num_output_ = AnfAlgo::GetOutputTensorNum(kernel_node); + num_output_ = common::AnfAlgo::GetOutputTensorNum(kernel_node); auto output_type_list = AnfAlgo::GetAllOutputDeviceTypes(kernel_node); if (num_output_ != output_type_list.size()) { MS_LOG(EXCEPTION) << "Kernel[" << exec_info << "]'s output shapes'size is " << num_input_ diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/debug_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/debug_cpu_kernel.cc index f80ca62840b..d8577c19e83 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/debug_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/debug_cpu_kernel.cc @@ -27,7 +27,7 @@ constexpr size_t kDebugOutputsNum = 1; void DebugCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); } bool DebugCpuKernelMod::Launch(const std::vector &inputs, const std::vector &, diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/depthtospace_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/depthtospace_cpu_kernel.cc index 6c28a23cef9..edaae7139db 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/depthtospace_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/depthtospace_cpu_kernel.cc @@ -26,10 +26,10 @@ constexpr size_t kDepthToSpaceOutputsNum = 1; template void DepthToSpaceCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); input_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 0); output_shape_ = AnfAlgo::GetOutputDeviceShape(kernel_node, 0); - block_size_ = LongToSize(AnfAlgo::GetNodeAttr(kernel_node, "block_size")); + block_size_ = LongToSize(common::AnfAlgo::GetNodeAttr(kernel_node, "block_size")); } template diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/dropout_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/dropout_cpu_kernel.cc index 605179b7609..0ecc38ecb91 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/dropout_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/dropout_cpu_kernel.cc @@ -30,11 +30,11 @@ constexpr size_t kDropoutOutputsNum = 2; void DropoutCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - input_shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - output_shape_ = AnfAlgo::GetOutputInferShape(kernel_node, 0); - mask_shape_ = AnfAlgo::GetOutputInferShape(kernel_node, 1); - keep_prob_ = AnfAlgo::GetNodeAttr(kernel_node, "keep_prob"); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + input_shape_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + output_shape_ = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); + mask_shape_ = common::AnfAlgo::GetOutputInferShape(kernel_node, 1); + keep_prob_ = common::AnfAlgo::GetNodeAttr(kernel_node, "keep_prob"); if (keep_prob_ <= 0.0 || keep_prob_ > 1.0) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << ", the 'keep_prob' should be in (0.0, 1.0], but got " << keep_prob_; } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/dropout_grad_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/dropout_grad_kernel.cc index 3bd643cf700..d2be92584d3 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/dropout_grad_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/dropout_grad_kernel.cc @@ -30,7 +30,7 @@ constexpr size_t kDropoutGradOutputsNum = 1; void DropoutGradBwdCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); auto input_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0); auto input_mask_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 1); if (input_shape.size() != input_mask_shape.size()) { @@ -44,7 +44,7 @@ void DropoutGradBwdCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { num_count_ *= x; } dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); - keep_prob_ = AnfAlgo::GetNodeAttr(kernel_node, "keep_prob"); + keep_prob_ = common::AnfAlgo::GetNodeAttr(kernel_node, "keep_prob"); if (keep_prob_ <= 0.0 || keep_prob_ > 1.0) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << ", the 'keep_prob' should be in (0.0, 1.0], but got " << keep_prob_; } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/dynamic_assign_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/dynamic_assign_cpu_kernel.cc index f7dc7cac3fe..487878456e6 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/dynamic_assign_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/dynamic_assign_cpu_kernel.cc @@ -29,7 +29,7 @@ constexpr size_t kDynamicAssignOutputsNum = 1; void DynamicAssignCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); node_wpt_ = kernel_node; input_x_dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); input_x_dtype_size_ = GetTypeByte(TypeIdToType(input_x_dtype_)); @@ -63,8 +63,8 @@ void DynamicAssignCpuKernelMod::LaunchKernel(const std::vector &inpu if (!node) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', node_wpt_(kernel_node) is expired. Error no: " << node; } - auto input_x_shape = AnfAlgo::GetPrevNodeOutputInferShape(node, 0); - auto input_y_shape = AnfAlgo::GetPrevNodeOutputInferShape(node, 1); + auto input_x_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node, 0); + auto input_y_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node, 1); batch_size_ = 1; for (size_t i = 0; i < input_x_shape.size(); ++i) { batch_size_ *= input_x_shape[i]; @@ -98,7 +98,7 @@ void DynamicAssignCpuKernelMod::LaunchKernel(const std::vector &inpu MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', memcpy_s error. Error no: " << ret; } - auto node_with_idx = AnfAlgo::GetPrevNodeOutput(node, 0); + auto node_with_idx = common::AnfAlgo::GetPrevNodeOutput(node, 0); auto out_node = node_with_idx.first; if (out_node->isa()) { auto node_ptr = out_node->cast(); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/dynamic_shape_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/dynamic_shape_cpu_kernel.cc index fbc0bb8d90a..2594168f893 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/dynamic_shape_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/dynamic_shape_cpu_kernel.cc @@ -26,9 +26,9 @@ constexpr size_t kDynamicShapeOutputNum = 1; template void TensorShapeCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); cnode_ptr_ = kernel_node; - size_t input_count = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_count = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_count != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 1, but got " << input_count; } @@ -44,8 +44,8 @@ bool TensorShapeCpuKernelMod::Launch(const std::vector &i MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', cnode_ptr_(kernel_node) is expired. Error no: " << node_; } auto output_addr = reinterpret_cast(outputs[0]->addr); - std::vector input_shape = AnfAlgo::GetPrevNodeOutputInferShape(node_, 0); - auto output_shape = AnfAlgo::GetOutputInferShape(node_, 0); + std::vector input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node_, 0); + auto output_shape = common::AnfAlgo::GetOutputInferShape(node_, 0); if (output_shape.size() != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimension of output should be 1-D, but got: " << output_shape.size(); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/dynamic_stitch_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/dynamic_stitch_cpu_kernel.cc index 66aa4409319..26d4ccebe81 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/dynamic_stitch_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/dynamic_stitch_cpu_kernel.cc @@ -40,12 +40,12 @@ void DynamicStitchCpuKernelMod::LaunchKernel(const std::vector(inputs[i]->addr); - auto shape_size = GetShapeSize(AnfAlgo::GetPrevNodeOutputInferShape(node_, i)); + auto shape_size = GetShapeSize(common::AnfAlgo::GetPrevNodeOutputInferShape(node_, i)); for (size_t j = 0; j < shape_size; ++j) { max_index = std::max(indice[j], max_index); } @@ -54,12 +54,12 @@ void DynamicStitchCpuKernelMod::LaunchKernel(const std::vector dtypes{AnfAlgo::GetOutputDeviceDataType(node_, 0)}; std::vector result_shape{IntToSize(first_dim_size)}; - auto data0_shape = AnfAlgo::GetPrevNodeOutputInferShape(node_, input_tuple_num_); - auto indice_dims = AnfAlgo::GetPrevNodeOutputInferShape(node_, 0).size(); + auto data0_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node_, input_tuple_num_); + auto indice_dims = common::AnfAlgo::GetPrevNodeOutputInferShape(node_, 0).size(); for (size_t d = indice_dims; d < data0_shape.size(); ++d) { result_shape.emplace_back(data0_shape[d]); } - AnfAlgo::SetOutputInferTypeAndShape(dtypes, {result_shape}, node_.get()); + common::AnfAlgo::SetOutputInferTypeAndShape(dtypes, {result_shape}, node_.get()); size_t num_out_dims = 2; std::vector out_dims(num_out_dims, 0); @@ -76,7 +76,7 @@ void DynamicStitchCpuKernelMod::LaunchKernel(const std::vector(inputs[i]->addr); auto data = reinterpret_cast(inputs[i + input_tuple_num_]->addr); - auto shape_size = GetShapeSize(AnfAlgo::GetPrevNodeOutputInferShape(node_, i)); + auto shape_size = GetShapeSize(common::AnfAlgo::GetPrevNodeOutputInferShape(node_, i)); for (size_t j = 0; j < shape_size; ++j) { auto ret = memcpy_s(merged + indice[j] * slice_size, slice_bytes, data + j * slice_size, slice_bytes); if (ret != 0) { diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/cholesky_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/cholesky_cpu_kernel.cc index 499e1aafa43..98b62304e70 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/cholesky_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/cholesky_cpu_kernel.cc @@ -55,22 +55,22 @@ void CholeskyCpuKernelMod::InitMatrixInfo(const std::vector &shape, s template void CholeskyCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); CHECK_KERNEL_INPUTS_NUM(input_num, kInputsNum, kernel_name_); - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); CHECK_KERNEL_OUTPUTS_NUM(output_num, kOutputsNum, kernel_name_); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, kInputIndex); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, kInputIndex); InitMatrixInfo(input_shape, &input_row_, &input_col_); - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, kOutputIndex); + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, kOutputIndex); InitMatrixInfo(output_shape, &output_row_, &output_col_); // If clean attribute exits, we will remain rand triangular data by clean flag, otherwise clean it to zero. - if (AnfAlgo::HasNodeAttr(CLEAN, kernel_node)) { - clean_ = AnfAlgo::GetNodeAttr(kernel_node, CLEAN); + if (common::AnfAlgo::HasNodeAttr(CLEAN, kernel_node)) { + clean_ = common::AnfAlgo::GetNodeAttr(kernel_node, CLEAN); } - if (AnfAlgo::HasNodeAttr(LOWER, kernel_node)) { - lower_ = AnfAlgo::GetNodeAttr(kernel_node, LOWER); + if (common::AnfAlgo::HasNodeAttr(LOWER, kernel_node)) { + lower_ = common::AnfAlgo::GetNodeAttr(kernel_node, LOWER); } } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/cholesky_solve_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/cholesky_solve_cpu_kernel.cc index 540bb5eda57..30893ec344c 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/cholesky_solve_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/cholesky_solve_cpu_kernel.cc @@ -65,21 +65,21 @@ void CholeskySolveCpuKernelMod::InitLeftMatrixInfo(const std::vector template void CholeskySolveCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); CHECK_KERNEL_INPUTS_NUM(input_num, kInputsNum, kernel_name_); - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); CHECK_KERNEL_OUTPUTS_NUM(output_num, kOutputsNum, kernel_name_); - auto input_a_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, kInputAIndex); + auto input_a_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, kInputAIndex); InitRightMatrixInfo(input_a_shape, &input_a_row_, &input_a_col_); - auto input_b_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, kInputBIndex); + auto input_b_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, kInputBIndex); const bool is_right_equal_left = input_a_shape.size() == input_b_shape.size(); InitLeftMatrixInfo(input_b_shape, is_right_equal_left, &input_b_row_, &input_b_col_); - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, kOutputIndex); + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, kOutputIndex); InitLeftMatrixInfo(output_shape, is_right_equal_left, &output_row_, &output_col_); - if (AnfAlgo::HasNodeAttr(LOWER, kernel_node)) { - lower_ = AnfAlgo::GetNodeAttr(kernel_node, LOWER); + if (common::AnfAlgo::HasNodeAttr(LOWER, kernel_node)) { + lower_ = common::AnfAlgo::GetNodeAttr(kernel_node, LOWER); } if (input_a_row_ != input_a_col_ || input_a_row_ != input_b_row_) { MS_LOG_EXCEPTION << kernel_name_ << " llt solve input a row is not match to b row: " << input_a_row_ << " vs " diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/eig_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/eig_cpu_kernel.cc index 2b4e6cffb20..1d82f532d73 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/eig_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/eig_cpu_kernel.cc @@ -50,18 +50,18 @@ void EigCpuKernelMod::InitMatrixInfo(const std::vector &shape) { template void EigCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); // If compute_v_ is true, then: w, v = Eig(a) // If compute_v_ is false, then: w = Eig(a) - if (AnfAlgo::HasNodeAttr(COMPUTE_V, kernel_node)) { - compute_v_ = AnfAlgo::GetNodeAttr(kernel_node, COMPUTE_V); + if (common::AnfAlgo::HasNodeAttr(COMPUTE_V, kernel_node)) { + compute_v_ = common::AnfAlgo::GetNodeAttr(kernel_node, COMPUTE_V); } - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); CHECK_KERNEL_INPUTS_NUM(input_num, kInputsNum, kernel_name_); - size_t output_num = AnfAlgo ::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); auto expect_output_num = compute_v_ ? kOutputsNumV : kOutputsNumNV; CHECK_KERNEL_OUTPUTS_NUM(output_num, expect_output_num, kernel_name_); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); InitMatrixInfo(input_shape); } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/eigh_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/eigh_cpu_kernel.cc index db84bbd5baa..257b50b69b3 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/eigh_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/eigh_cpu_kernel.cc @@ -29,11 +29,11 @@ constexpr size_t kOutputsNum = 2; template void EighCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); - compute_eigen_vectors_ = AnfAlgo::GetNodeAttr(kernel_node, C_EIEH_VECTOR); - lower_ = AnfAlgo::GetNodeAttr(kernel_node, LOWER); - auto A_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + compute_eigen_vectors_ = common::AnfAlgo::GetNodeAttr(kernel_node, C_EIEH_VECTOR); + lower_ = common::AnfAlgo::GetNodeAttr(kernel_node, LOWER); + auto A_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); if (A_shape.size() != kShape2dDims) { MS_LOG(EXCEPTION) << "Wrong array shape. For '" << kernel_name_ << "', a should be 2D, but got [" << A_shape.size() << "] dimensions."; diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/lu_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/lu_cpu_kernel.cc index ac27fe59506..2e42d41e95b 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/lu_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/lu_cpu_kernel.cc @@ -67,19 +67,19 @@ void LUCpuKernelMod::InitPivotVecInfo(const std::vector &shape, size_ template void LUCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); CHECK_KERNEL_INPUTS_NUM(input_num, kLUInputsNum, kernel_name_); - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); CHECK_KERNEL_OUTPUTS_NUM(output_num, kLUOutputsNum, kernel_name_); - auto a_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, kLUaIndex); + auto a_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, kLUaIndex); InitMatrixInfo(a_shape, &a_row_, &a_col_); - auto lu_shape = AnfAlgo::GetOutputInferShape(kernel_node, kLuIndex); + auto lu_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, kLuIndex); InitMatrixInfo(lu_shape, &lu_row_, &lu_col_); - auto permutation_shape = AnfAlgo::GetOutputInferShape(kernel_node, kPermutationIndex); + auto permutation_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, kPermutationIndex); InitMatrixInfo(permutation_shape, &permutation_row_, &permutation_col_); - auto pivots_shape = AnfAlgo::GetOutputInferShape(kernel_node, kPivotsIndex); + auto pivots_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, kPivotsIndex); InitPivotVecInfo(pivots_shape, &pivots_row_, &pivots_col_); } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/lu_solve_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/lu_solve_cpu_kernel.cc index 64553ffd22c..6aa921ef2a5 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/lu_solve_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/lu_solve_cpu_kernel.cc @@ -37,14 +37,14 @@ constexpr size_t kColIndex = 1; template void LUSolverCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); CHECK_KERNEL_INPUTS_NUM(input_num, kLUInputsNum, kernel_name_); - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); CHECK_KERNEL_OUTPUTS_NUM(output_num, kLUOutputsNum, kernel_name_); - auto a_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, kLUaIndex); - auto b_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, kLUbIndex); + auto a_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, kLUaIndex); + auto b_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, kLUbIndex); if (a_shape.empty() || b_shape.empty()) { MS_LOG_EXCEPTION << kernel_name_ << " input a or b matrix shape invalid."; } @@ -60,7 +60,7 @@ void LUSolverCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { b_row_ = b_shape.at(b_shape.size() - kRowIndex); b_col_ = b_shape.at(b_shape.size() - kColIndex); } - auto output_lu_shape = AnfAlgo::GetOutputInferShape(kernel_node, kLuIndex); + auto output_lu_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, kLuIndex); if (output_lu_shape.empty()) { MS_LOG_EXCEPTION << kernel_name_ << " output lu shape invalid."; } @@ -70,7 +70,7 @@ void LUSolverCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { out_row_ = output_lu_shape.at(output_lu_shape.size() - kRowIndex); out_col_ = output_lu_shape.at(output_lu_shape.size() - kColIndex); } - trans_ = AnfAlgo ::GetNodeAttr(kernel_node, TRANS); + trans_ = common::AnfAlgo::GetNodeAttr(kernel_node, TRANS); } template diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/matmul_double_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/matmul_double_cpu_kernel.cc index ae96d0c06fb..865d44e0983 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/matmul_double_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/matmul_double_cpu_kernel.cc @@ -35,15 +35,15 @@ using DoubleMatrix = Eigen::Matrix a_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0); std::vector b_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 1); std::vector out_shape = AnfAlgo::GetOutputDeviceShape(kernel_node, 0); if (a_shape.size() != kAMatrixDimNum || b_shape.size() != kAMatrixDimNum || out_shape.size() != kAMatrixDimNum) { MS_LOG(EXCEPTION) << "The tensor rank of MatMul should be equal to 2."; } - trans_a_ = AnfAlgo::GetNodeAttr(kernel_node, TRANSPOSE_A); - trans_b_ = AnfAlgo::GetNodeAttr(kernel_node, TRANSPOSE_B); + trans_a_ = common::AnfAlgo::GetNodeAttr(kernel_node, TRANSPOSE_A); + trans_b_ = common::AnfAlgo::GetNodeAttr(kernel_node, TRANSPOSE_B); a_row_ = a_shape[kDim0]; a_col_ = a_shape[kDim1]; diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/matrix_inverse_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/matrix_inverse_cpu_kernel.cc index 2e9568cfdad..c38f2f7c6be 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/matrix_inverse_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/matrix_inverse_cpu_kernel.cc @@ -30,12 +30,12 @@ constexpr size_t kMatrixInverseOutIndex = 0; template void MatrixInverseCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); CHECK_KERNEL_INPUTS_NUM(input_num, kMatrixInverseInputsNum, kernel_name_); - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); CHECK_KERNEL_OUTPUTS_NUM(output_num, kMatrixInverseOutputsNum, kernel_name_); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, kMatrixInverseInIndex); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, kMatrixInverseInIndex); if (input_shape.size() < 2) { MS_LOG(EXCEPTION) << "The dim entered needs to be greater than 2, but " << input_shape.size() << " was taken"; } @@ -47,7 +47,7 @@ void MatrixInverseCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { for (size_t i = 0; i < last_index - 1; i++) { batch_size_ *= input_shape[i]; } - adjoint_ = AnfAlgo::GetNodeAttr(kernel_node, ADJOINT); + adjoint_ = common::AnfAlgo::GetNodeAttr(kernel_node, ADJOINT); } template diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/matrix_triangular_solve_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/matrix_triangular_solve_cpu_kernel.cc index 79ceced3de4..2d7dd35f6ef 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/matrix_triangular_solve_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/matrix_triangular_solve_cpu_kernel.cc @@ -39,8 +39,8 @@ constexpr size_t kRowIndex = 2; constexpr size_t kColIndex = 1; template void MatrixTriangularSolveCpuKernelMod::InitShape(const CNodePtr &kernel_node) { - auto a_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto b_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto a_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto b_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); // Since the shape check is done in frontend, we can suppose that the shape of a, b here is valid. size_t a_dims = a_shape.size(); size_t aRowIndex = a_dims - kRowIndex; @@ -60,19 +60,19 @@ void MatrixTriangularSolveCpuKernelMod::InitShape(const CNodePtr &kernel_node template void MatrixTriangularSolveCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); InitShape(kernel_node); - if (AnfAlgo::HasNodeAttr(ADJOINT, kernel_node)) { + if (common::AnfAlgo::HasNodeAttr(ADJOINT, kernel_node)) { // MatrixTriangularSolve attribute - trans_ = AnfAlgo::GetNodeAttr(kernel_node, ADJOINT); - if (AnfAlgo::HasNodeAttr(TRANS, kernel_node)) { + trans_ = common::AnfAlgo::GetNodeAttr(kernel_node, ADJOINT); + if (common::AnfAlgo::HasNodeAttr(TRANS, kernel_node)) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the attribute 'adjoint' and 'trans' could not exist at the same time."; } } else { - lower_ = AnfAlgo::GetNodeAttr(kernel_node, LOWER); - unit_diagonal_ = AnfAlgo::GetNodeAttr(kernel_node, UNIT_DIAGONAL); - const std::string trans = AnfAlgo::GetNodeAttr(kernel_node, TRANS); + lower_ = common::AnfAlgo::GetNodeAttr(kernel_node, LOWER); + unit_diagonal_ = common::AnfAlgo::GetNodeAttr(kernel_node, UNIT_DIAGONAL); + const std::string trans = common::AnfAlgo::GetNodeAttr(kernel_node, TRANS); if (trans == "N") { trans_ = false; } else if (trans == "T") { diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/qr_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/qr_cpu_kernel.cc index a4376f171cb..4b407031409 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/qr_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/eigen/qr_cpu_kernel.cc @@ -33,28 +33,28 @@ constexpr size_t kColIndex = 1; template void QRCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); CHECK_KERNEL_INPUTS_NUM(input_num, kQRInputsNum, kernel_name_); - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); CHECK_KERNEL_OUTPUTS_NUM(output_num, kQROutputsNum, kernel_name_); - const auto mode = AnfAlgo::GetNodeAttr(kernel_node, MODE); + const auto mode = common::AnfAlgo::GetNodeAttr(kernel_node, MODE); if (mode != "full" && mode != "r" && mode != "economic") { MS_LOG(EXCEPTION) << "mode should be in [full, r, economic], but got [" << mode << "]."; } - auto a_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto a_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); CHECK_KERNEL_INPUTS_NUM(a_shape.size(), kAMatrixDimNum, kernel_name_); a_row_ = a_shape[kDim0]; a_col_ = a_shape[kDim1]; - auto q_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto q_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); CHECK_KERNEL_INPUTS_NUM(q_shape.size(), kAMatrixDimNum, kernel_name_); q_row_ = q_shape[kDim0]; q_col_ = q_shape[kDim1]; - auto r_shape = AnfAlgo::GetOutputInferShape(kernel_node, 1); + auto r_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 1); CHECK_KERNEL_INPUTS_NUM(r_shape.size(), kAMatrixDimNum, kernel_name_); r_row_ = r_shape[kDim0]; r_col_ = r_shape[kDim1]; diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/eltwise_grad_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/eltwise_grad_cpu_kernel.cc index 6970d23d191..9602084458c 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/eltwise_grad_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/eltwise_grad_cpu_kernel.cc @@ -17,7 +17,7 @@ #include "plugin/device/cpu/kernel/eltwise_grad_cpu_kernel.h" #include #include -#include "common/thread_pool.h" +#include "include/common/thread_pool.h" #include "plugin/device/cpu/hal/device/cpu_device_address.h" #include "nnacl/fp32_grad/activation_grad.h" #include "nnacl/fp32_grad/arithmetic_grad.h" @@ -317,7 +317,7 @@ void EltWiseGradCpuKernelMod::InitComputeFunc() { template void EltWiseGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); InitComputeFunc(); } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/elu_grad_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/elu_grad_cpu_kernel.cc index a910363b02c..e55824d2150 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/elu_grad_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/elu_grad_cpu_kernel.cc @@ -29,7 +29,7 @@ constexpr size_t kEleGradOutputsNum = 1; void EluGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); auto dtype_1 = AnfAlgo::GetInputDeviceDataType(kernel_node, 1); if (dtype_ != dtype_1) { diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/embedding_look_up_comm_grad_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/embedding_look_up_comm_grad_cpu_kernel.cc index e171245e03a..c17beded8db 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/embedding_look_up_comm_grad_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/embedding_look_up_comm_grad_cpu_kernel.cc @@ -28,10 +28,10 @@ constexpr size_t kEmbeddingLookupCommGradOutputsNum = 1; void EmbeddingLookUpCommGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - split_num_ = AnfAlgo::GetNodeAttr(kernel_node, "split_num"); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + split_num_ = common::AnfAlgo::GetNodeAttr(kernel_node, "split_num"); MS_LOG(INFO) << "split_num: " << split_num_; - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); if (split_num_ == 0) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the 'split_num' should be greater than 0, but got 0."; } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/embedding_look_up_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/embedding_look_up_cpu_kernel.cc index 4e46790515b..01e3e30e02a 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/embedding_look_up_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/embedding_look_up_cpu_kernel.cc @@ -19,7 +19,7 @@ #include #include "plugin/device/cpu/hal/device/cpu_device_address.h" #include "ir/primitive.h" -#include "common/thread_pool.h" +#include "include/common/thread_pool.h" namespace mindspore { namespace kernel { @@ -55,9 +55,9 @@ void LookUpTableTask(const float *input_addr, const T *indices_addr, float *outp void EmbeddingLookUpCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); node_wpt_ = kernel_node; - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); if (input_shape.empty() || input_shape.size() > kEmbeddingLookupInputParamsMaxDim) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimension of input should be 1-" << kEmbeddingLookupInputParamsMaxDim << "D, but got " << input_shape.size() << "D."; @@ -68,13 +68,13 @@ void EmbeddingLookUpCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { outer_dim_size_ *= input_shape[i]; } indices_lens_ = 1; - std::vector indices_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + std::vector indices_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); for (const auto &shape : indices_shape) { indices_lens_ *= shape; } indices_data_type_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 1); - if (AnfAlgo::HasNodeAttr(kAttrOffset, kernel_node)) { - offset_ = AnfAlgo::GetNodeAttr(kernel_node, kAttrOffset); + if (common::AnfAlgo::HasNodeAttr(kAttrOffset, kernel_node)) { + offset_ = common::AnfAlgo::GetNodeAttr(kernel_node, kAttrOffset); } } @@ -86,7 +86,7 @@ void EmbeddingLookUpCpuKernelMod::LaunchKernel(const std::vector input_shape = AnfAlgo::GetPrevNodeOutputInferShape(node, 0); + std::vector input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node, 0); if (input_shape.empty()) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimension of input should be at least 1D, but got empty input."; @@ -98,7 +98,7 @@ void EmbeddingLookUpCpuKernelMod::LaunchKernel(const std::vector indices_shape = AnfAlgo::GetPrevNodeOutputInferShape(node, 1); + std::vector indices_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node, 1); for (const auto &shape : indices_shape) { indices_lens_ *= shape; } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/environ/environ_cpu_get.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/environ/environ_cpu_get.cc index e088c64f869..412b0e6e599 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/environ/environ_cpu_get.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/environ/environ_cpu_get.cc @@ -26,7 +26,7 @@ void EnvironGetCpuKernelMod::InitKernel(const CNodePtr &node) { MS_LOG(EXCEPTION) << "The input checks invalid, kernel: " << node->fullname_with_scope(); } - value_type_attr_ = TypeId(AnfAlgo::GetNodeAttr(node, kEnvValueTypeAttr)); + value_type_attr_ = TypeId(common::AnfAlgo::GetNodeAttr(node, kEnvValueTypeAttr)); MS_LOG(INFO) << "The EnvironGet kernel " << node->fullname_with_scope() << " value type: " << value_type_attr_; handle_size_ = sizeof(int64_t); key_size_ = sizeof(int64_t); @@ -77,7 +77,7 @@ bool EnvironGetCpuKernelMod::Launch(const std::vector &inputs, const value_type = env_value->value_type_; } else { auto node = cnode_ptr_.lock(); - const std::string &prim_name = (node == nullptr) ? "" : AnfAlgo::GetCNodeName(node); + const std::string &prim_name = (node == nullptr) ? "" : common::AnfAlgo::GetCNodeName(node); MS_LOG(INFO) << "Use the default input value for kernel: " << prim_name << ", env handle: " << host_handle << ", env key: " << host_key; } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/environ/environ_cpu_set.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/environ/environ_cpu_set.cc index d12cd36e28b..9d764e5ada1 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/environ/environ_cpu_set.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/environ/environ_cpu_set.cc @@ -34,7 +34,7 @@ void EnvironSetCpuKernelMod::InitKernel(const CNodePtr &node) { MS_LOG(EXCEPTION) << "The output handle checks invalid, kernel: " << node->fullname_with_scope(); } - value_type_attr_ = TypeId(AnfAlgo::GetNodeAttr(node, kEnvValueTypeAttr)); + value_type_attr_ = TypeId(common::AnfAlgo::GetNodeAttr(node, kEnvValueTypeAttr)); MS_LOG(INFO) << "The EnvironSet kernel " << node->fullname_with_scope() << " value type: " << value_type_attr_; handle_size_ = sizeof(int64_t); key_size_ = sizeof(int64_t); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/equal_count_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/equal_count_cpu_kernel.cc index 1abfc7d0d24..115538afc30 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/equal_count_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/equal_count_cpu_kernel.cc @@ -26,7 +26,7 @@ constexpr size_t kEqualCountOutputsNum = 1; void EqualCountCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); } bool EqualCountCpuKernelMod::Launch(const std::vector &inputs, diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/expm1_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/expm1_cpu_kernel.cc index 2fcbba5500b..a4fab926d55 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/expm1_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/expm1_cpu_kernel.cc @@ -27,7 +27,7 @@ constexpr size_t kExpm1OutputsNum = 1; void Expm1CpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); input_dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); if (input_dtype_ != kNumberTypeFloat16 && input_dtype_ != kNumberTypeFloat32) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/fl/fused_pull_weight_kernel.h b/mindspore/ccsrc/plugin/device/cpu/kernel/fl/fused_pull_weight_kernel.h index d56290c9346..5d0decbfd72 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/fl/fused_pull_weight_kernel.h +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/fl/fused_pull_weight_kernel.h @@ -126,16 +126,16 @@ class FusedPullWeightKernelMod : public NativeCpuKernelMod { void Init(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); for (size_t i = 0; i < input_num; i++) { - auto weight_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, i); + auto weight_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, i); size_t weight_size_ = std::accumulate(weight_shape.begin(), weight_shape.end(), sizeof(T), std::multiplies()); input_size_list_.push_back(weight_size_); } server_num_ = ps::PSContext::instance()->server_num(); - indices_ = AnfAlgo::GetNodeAttr>(kernel_node, kAttrIndex); - weight_full_names_ = AnfAlgo::GetNodeAttr>(kernel_node, kAttrPsKey); + indices_ = common::AnfAlgo::GetNodeAttr>(kernel_node, kAttrIndex); + weight_full_names_ = common::AnfAlgo::GetNodeAttr>(kernel_node, kAttrPsKey); MS_LOG(INFO) << "Weight full name as key " << weight_full_names_ << ", key index is " << indices_ << ", server number is " << server_num_; if (server_num_ == 0 || weight_full_names_.empty() || indices_.empty()) { @@ -144,7 +144,7 @@ class FusedPullWeightKernelMod : public NativeCpuKernelMod { "empty or indices_ is UINT32_MAX."; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); for (size_t i = 0; i < output_num; i++) { output_size_list_.push_back(sizeof(size_t)); } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/fl/fused_push_weight_kernel.h b/mindspore/ccsrc/plugin/device/cpu/kernel/fl/fused_push_weight_kernel.h index 936ea2cd7c9..89bd79fa972 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/fl/fused_push_weight_kernel.h +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/fl/fused_push_weight_kernel.h @@ -113,16 +113,16 @@ class FusedPushWeightKernelMod : public NativeCpuKernelMod { void Init(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); for (size_t i = 0; i < input_num; i++) { - auto weight_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, i); + auto weight_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, i); size_t weight_size_ = std::accumulate(weight_shape.begin(), weight_shape.end(), sizeof(T), std::multiplies()); input_size_list_.push_back(weight_size_); } server_num_ = ps::PSContext::instance()->server_num(); - indices_ = AnfAlgo::GetNodeAttr>(kernel_node, kAttrIndex); - weight_full_names_ = AnfAlgo::GetNodeAttr>(kernel_node, kAttrPsKey); + indices_ = common::AnfAlgo::GetNodeAttr>(kernel_node, kAttrIndex); + weight_full_names_ = common::AnfAlgo::GetNodeAttr>(kernel_node, kAttrPsKey); MS_LOG(INFO) << "Weight full name as key " << weight_full_names_ << ", key index is " << indices_ << ", server number is " << server_num_; if (server_num_ == 0 || weight_full_names_.empty() || indices_.empty()) { @@ -131,7 +131,7 @@ class FusedPushWeightKernelMod : public NativeCpuKernelMod { "empty or indices_ is UINT32_MAX."; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); for (size_t i = 0; i < output_num; i++) { output_size_list_.push_back(sizeof(size_t)); } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/fl/get_model_kernel.h b/mindspore/ccsrc/plugin/device/cpu/kernel/fl/get_model_kernel.h index 8f18b77f5b7..0147213ba89 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/fl/get_model_kernel.h +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/fl/get_model_kernel.h @@ -112,9 +112,10 @@ class GetModelKernelMod : public NativeCpuKernelMod { MS_LOG(INFO) << "Initializing GetModel kernel. fl_name: " << fl_name_ << ". Request will be sent to server " << target_server_rank_; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); for (size_t i = 0; i < input_num; i++) { - auto input_node = AnfAlgo::VisitKernelWithReturnType(AnfAlgo::GetInputNode(kernel_node, i), 0).first; + auto input_node = + common::AnfAlgo::VisitKernelWithReturnType(common::AnfAlgo::GetInputNode(kernel_node, i), 0).first; MS_EXCEPTION_IF_NULL(input_node); auto weight_node = input_node->cast(); MS_EXCEPTION_IF_NULL(weight_node); @@ -122,7 +123,7 @@ class GetModelKernelMod : public NativeCpuKernelMod { MS_LOG(INFO) << "Parameter name is " << weight_name; weight_name_to_input_idx_.insert(std::make_pair(weight_name, i)); - auto weight_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, i); + auto weight_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, i); size_t weight_size_ = std::accumulate(weight_shape.begin(), weight_shape.end(), sizeof(float), std::multiplies()); input_size_list_.push_back(weight_size_); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/fl/start_fl_job_kernel.h b/mindspore/ccsrc/plugin/device/cpu/kernel/fl/start_fl_job_kernel.h index 499053a3d75..787dcad0881 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/fl/start_fl_job_kernel.h +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/fl/start_fl_job_kernel.h @@ -80,7 +80,7 @@ class StartFLJobKernelMod : public NativeCpuKernelMod { target_server_rank_ = rank_id_ % server_num_; fl_name_ = fl::worker::FLWorker::GetInstance().fl_name(); fl_id_ = fl::worker::FLWorker::GetInstance().fl_id(); - data_size_ = LongToInt(AnfAlgo::GetNodeAttr(kernel_node, "data_size")); + data_size_ = LongToInt(common::AnfAlgo::GetNodeAttr(kernel_node, "data_size")); fl::worker::FLWorker::GetInstance().set_data_size(data_size_); MS_LOG(INFO) << "Initializing StartFLJob kernel. fl_name: " << fl_name_ << ", fl_id: " << fl_id_ << ", data_size: " << data_size_ << ". Request will be sent to server " << target_server_rank_; diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/fl/update_model_kernel.h b/mindspore/ccsrc/plugin/device/cpu/kernel/fl/update_model_kernel.h index 735af4d650d..9c046130490 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/fl/update_model_kernel.h +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/fl/update_model_kernel.h @@ -98,7 +98,7 @@ class UpdateModelKernelMod : public NativeCpuKernelMod { target_server_rank_ = rank_id_ % server_num_; fl_name_ = fl::worker::FLWorker::GetInstance().fl_name(); fl_id_ = fl::worker::FLWorker::GetInstance().fl_id(); - encrypt_mode = AnfAlgo::GetNodeAttr(kernel_node, "encrypt_mode"); + encrypt_mode = common::AnfAlgo::GetNodeAttr(kernel_node, "encrypt_mode"); if (encrypt_mode.compare("") != 0 && encrypt_mode.compare("STABLE_PW_ENCRYPT") != 0) { MS_LOG(EXCEPTION) << "Value Error: the parameter 'encrypt_mode' of updateModel kernel can only be '' or " "'STABLE_PW_ENCRYPT' until now, but got: " @@ -115,9 +115,10 @@ class UpdateModelKernelMod : public NativeCpuKernelMod { } } - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); for (size_t i = 0; i < input_num; i++) { - auto input_node = AnfAlgo::VisitKernelWithReturnType(AnfAlgo::GetInputNode(kernel_node, i), 0).first; + auto input_node = + common::AnfAlgo::VisitKernelWithReturnType(common::AnfAlgo::GetInputNode(kernel_node, i), 0).first; MS_EXCEPTION_IF_NULL(input_node); auto weight_node = input_node->cast(); MS_EXCEPTION_IF_NULL(weight_node); @@ -125,7 +126,7 @@ class UpdateModelKernelMod : public NativeCpuKernelMod { MS_LOG(INFO) << "Parameter name is " << weight_name; weight_full_names_.push_back(weight_name); - auto weight_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, i); + auto weight_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, i); size_t weight_size_ = std::accumulate(weight_shape.begin(), weight_shape.end(), sizeof(float), std::multiplies()); input_size_list_.push_back(weight_size_); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/fused_ada_factor_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/fused_ada_factor_cpu_kernel.cc index d3a8f5a40af..06c1dfb625a 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/fused_ada_factor_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/fused_ada_factor_cpu_kernel.cc @@ -44,7 +44,7 @@ void FusedAdaFactorCpuKernelMod::InitInputOutputSize(const CNodePtr &kernel_node void FusedAdaFactorCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); param_dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, PARAM); auto shape = AnfAlgo::GetInputDeviceShape(kernel_node, PARAM); elem_num_ = std::accumulate(shape.begin(), shape.end(), 1LL, std::multiplies()); @@ -60,14 +60,14 @@ void FusedAdaFactorCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { } } - if (AnfAlgo::HasNodeAttr(kEnableScaleParameter, kernel_node)) { - enable_scale_parameter_ = AnfAlgo::GetNodeAttr(kernel_node, kEnableScaleParameter); + if (common::AnfAlgo::HasNodeAttr(kEnableScaleParameter, kernel_node)) { + enable_scale_parameter_ = common::AnfAlgo::GetNodeAttr(kernel_node, kEnableScaleParameter); } - if (AnfAlgo::HasNodeAttr(kEnableFirstMoment, kernel_node)) { - enable_first_moment_ = AnfAlgo::GetNodeAttr(kernel_node, kEnableFirstMoment); + if (common::AnfAlgo::HasNodeAttr(kEnableFirstMoment, kernel_node)) { + enable_first_moment_ = common::AnfAlgo::GetNodeAttr(kernel_node, kEnableFirstMoment); } - if (AnfAlgo::HasNodeAttr(kEnableWeightDecay, kernel_node)) { - enable_weight_decay_ = AnfAlgo::GetNodeAttr(kernel_node, kEnableWeightDecay); + if (common::AnfAlgo::HasNodeAttr(kEnableWeightDecay, kernel_node)) { + enable_weight_decay_ = common::AnfAlgo::GetNodeAttr(kernel_node, kEnableWeightDecay); } } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/fused_cast_adam_weight_decay_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/fused_cast_adam_weight_decay_cpu_kernel.cc index c055c734b84..0432244252d 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/fused_cast_adam_weight_decay_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/fused_cast_adam_weight_decay_cpu_kernel.cc @@ -105,16 +105,16 @@ void FusedCastAdamWeightDecayCpuKernelMod::LaunchFusedCastAdamFp16(const std::ve void FusedCastAdamWeightDecayCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); std::vector var_shape = AnfAlgo::GetInputDeviceShape(kernel_node, VAR); var_dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, VAR); gradient_dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, GRAD); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != kFusedCastAdamWeightDecayInputNum) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be " << kFusedCastAdamWeightDecayInputNum << ", but got: " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != kFusedCastAdamWeightDecayOutputNum) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be " << kFusedCastAdamWeightDecayOutputNum << ", but got: " << output_num; diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/gather_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/gather_cpu_kernel.cc index 17eff03ef1d..edbe39fefe3 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/gather_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/gather_cpu_kernel.cc @@ -18,7 +18,7 @@ #include "plugin/device/cpu/hal/device/cpu_device_address.h" #include "nnacl/gather_parameter.h" #include "nnacl/base/gather_base.h" -#include "common/thread_pool.h" +#include "include/common/thread_pool.h" namespace mindspore { namespace kernel { @@ -30,7 +30,7 @@ constexpr size_t kGatherInputParamsMaxDim = 4; template void GatherV2CpuKernelMod::CheckParam(const CNodePtr &kernel_node) { - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num == kGatherInputsNum + 1) { is_dynamic_shape_ = true; MS_LOG(DEBUG) << " GatherV2CPUKernel running in Dynamic Mode."; @@ -45,16 +45,16 @@ template void GatherV2CpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); CheckParam(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - input_shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - indices_shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - output_shape_ = AnfAlgo::GetOutputInferShape(kernel_node, 0); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + input_shape_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + indices_shape_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + output_shape_ = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); if (input_shape_.size() > kGatherInputParamsMaxDim) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimension of 'input_params' should be " << kGatherInputParamsMaxDim << "D or lower, but got " << input_shape_.size() << "."; } if (!is_dynamic_shape_) { - axis_ = AnfAlgo::GetNodeAttr(kernel_node, AXIS); + axis_ = common::AnfAlgo::GetNodeAttr(kernel_node, AXIS); } } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/gather_d_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/gather_d_cpu_kernel.cc index 1d295ded9ec..f35f9c16b89 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/gather_d_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/gather_d_cpu_kernel.cc @@ -67,7 +67,7 @@ void CopyTask(size_t cur, std::vector *pos, T *input, const I *index, co template void GatherDCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); input_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 0); index_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 2); if (input_shape_.size() != index_shape_.size()) { diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/gather_d_grad_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/gather_d_grad_cpu_kernel.cc index e8b15747efb..9f5c4c1ff88 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/gather_d_grad_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/gather_d_grad_cpu_kernel.cc @@ -64,7 +64,7 @@ void GatherDGradCopyTask(size_t cur, std::vector *pos, T *input, I *inde template void GatherDGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); index_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 0); input_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 1); if (input_shape_ != index_shape_) { @@ -72,8 +72,8 @@ void GatherDGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { << "', shape size of 'x' should be equal to 'index', but got shape size of 'x': " << input_shape_.size() << ", and shape size of 'index': " << index_shape_.size(); } - axis_ = AnfAlgo::GetNodeAttr(kernel_node, DIM); - output_shape_ = AnfAlgo::GetOutputInferShape(kernel_node, 0); + axis_ = common::AnfAlgo::GetNodeAttr(kernel_node, DIM); + output_shape_ = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); } template diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/gathernd_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/gathernd_cpu_kernel.cc index 410982873f9..689317d32ad 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/gathernd_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/gathernd_cpu_kernel.cc @@ -29,10 +29,10 @@ constexpr size_t kGatherNdOutputsNum = 1; template void GatherNdCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - input_shapes_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - indices_shapes_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - output_shapes_ = AnfAlgo::GetOutputInferShape(kernel_node, 0); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + input_shapes_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + indices_shapes_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + output_shapes_ = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/grid_sampler_3d_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/grid_sampler_3d_cpu_kernel.cc index 2200c6bc6f9..f2a38387322 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/grid_sampler_3d_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/grid_sampler_3d_cpu_kernel.cc @@ -46,9 +46,9 @@ void GridSampler3DCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { stride_compute(x_stride_, x_shape_); stride_compute(grid_stride_, grid_shape_); stride_compute(output_stride_, output_shape_); - interpolation_mode = AnfAlgo::GetNodeAttr(kernel_node, "interpolation_mode"); - padding_mode = AnfAlgo::GetNodeAttr(kernel_node, "padding_mode"); - align_corners = AnfAlgo::GetNodeAttr(kernel_node, "align_corners"); + interpolation_mode = common::AnfAlgo::GetNodeAttr(kernel_node, "interpolation_mode"); + padding_mode = common::AnfAlgo::GetNodeAttr(kernel_node, "padding_mode"); + align_corners = common::AnfAlgo::GetNodeAttr(kernel_node, "align_corners"); } bool GridSampler3DCpuKernelMod::Launch(const std::vector &inputs, diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/grid_sampler_3d_grad_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/grid_sampler_3d_grad_cpu_kernel.cc index 55e2a5049ab..c7cb481deec 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/grid_sampler_3d_grad_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/grid_sampler_3d_grad_cpu_kernel.cc @@ -49,9 +49,9 @@ void GridSampler3DGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { stride_compute(grid_stride_, grid_shape_); stride_compute(dx_stride_, dx_shape_); stride_compute(dgrid_stride_, dgrid_shape_); - interpolation_mode = AnfAlgo::GetNodeAttr(kernel_node, "interpolation_mode"); - padding_mode = AnfAlgo::GetNodeAttr(kernel_node, "padding_mode"); - align_corners = AnfAlgo::GetNodeAttr(kernel_node, "align_corners"); + interpolation_mode = common::AnfAlgo::GetNodeAttr(kernel_node, "interpolation_mode"); + padding_mode = common::AnfAlgo::GetNodeAttr(kernel_node, "padding_mode"); + align_corners = common::AnfAlgo::GetNodeAttr(kernel_node, "align_corners"); } bool GridSampler3DGradCpuKernelMod::Launch(const std::vector &inputs, diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/hsigmoid_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/hsigmoid_cpu_kernel.cc index 0b7ec2f5d60..1040e17904d 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/hsigmoid_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/hsigmoid_cpu_kernel.cc @@ -28,8 +28,8 @@ constexpr size_t kHSigmoidOutputsNum = 1; template void HSigmoidCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - x_shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + x_shape_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); for (const uint64_t &d : x_shape_) { tensor_size_ *= d; } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/hsigmoid_grad_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/hsigmoid_grad_cpu_kernel.cc index c4e6ca0a9f4..1f039f6ecb3 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/hsigmoid_grad_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/hsigmoid_grad_cpu_kernel.cc @@ -28,8 +28,8 @@ constexpr size_t kHSigmoidGradOutputsNum = 1; template void HSigmoidGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - x_shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + x_shape_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); for (const uint64_t &d : x_shape_) { tensor_size_ *= d; } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/hsv_to_rgb_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/hsv_to_rgb_cpu_kernel.cc index a74e6f6a76e..9f6843d6416 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/hsv_to_rgb_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/hsv_to_rgb_cpu_kernel.cc @@ -24,8 +24,8 @@ template void HSVToRGBCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); const size_t kNumDims = 3; - const size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); - const size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + const size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); + const size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (input_num != kInputNum) { MS_LOG(EXCEPTION) << "Needs " << kInputNum << " input, but got " << input_num << "."; } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/hswish_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/hswish_cpu_kernel.cc index 4f9c69f7d19..574c64fbeed 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/hswish_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/hswish_cpu_kernel.cc @@ -28,8 +28,8 @@ constexpr size_t kHSwishOutputsNum = 1; template void HSwishCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - x_shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + x_shape_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); for (const uint64_t &d : x_shape_) { tensor_size_ *= d; } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/hswish_grad_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/hswish_grad_cpu_kernel.cc index 3b259071f3b..3730e393ad2 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/hswish_grad_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/hswish_grad_cpu_kernel.cc @@ -28,8 +28,8 @@ constexpr size_t kHSwishGradOutputsNum = 1; template void HSwishGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - x_shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + x_shape_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); for (const uint64_t &d : x_shape_) { tensor_size_ *= d; } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/iou_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/iou_cpu_kernel.cc index 8d65d3720c1..c6e8ea25e88 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/iou_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/iou_cpu_kernel.cc @@ -30,7 +30,7 @@ constexpr size_t kBoxCoordinateLen = 4; template void IOUCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); auto anchor_boxes_shape = AnfAlgo::GetInputDeviceShape(kernel_node, ANCHOR_BOXES); constexpr size_t BOX_SHAPE_SIZE = 2; constexpr size_t BOX_SIZE_INDEX = 0; @@ -48,7 +48,7 @@ void IOUCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { } gt_boxes_size_ = gt_boxes_shape[BOX_SIZE_INDEX]; iou_size_ = anchor_boxes_size_ * gt_boxes_size_; - std::string iou_mode = AnfAlgo::GetNodeAttr(kernel_node, "mode"); + std::string iou_mode = common::AnfAlgo::GetNodeAttr(kernel_node, "mode"); if (iou_mode != "iou" && iou_mode != "iof") { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the 'mode' should be 'iou' or 'iof', but got: " << iou_mode; } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/isfinite_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/isfinite_cpu_kernel.cc index d8817d24b89..c6078145fba 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/isfinite_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/isfinite_cpu_kernel.cc @@ -28,7 +28,7 @@ constexpr size_t kIsFiniteOutputsNum = 1; void IsFiniteCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); input_dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); if (dtype_map_.find(input_dtype_) == dtype_map_.end()) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/isinf_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/isinf_cpu_kernel.cc index 74a72b17fd2..6b695ae1ac9 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/isinf_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/isinf_cpu_kernel.cc @@ -23,12 +23,12 @@ namespace mindspore { namespace kernel { void IsInfCpuKernelMod::InitKernel(const CNodePtr &kernelNode) { MS_EXCEPTION_IF_NULL(kernelNode); - kernel_name_ = AnfAlgo::GetCNodeName(kernelNode); - size_t input_num = AnfAlgo::GetInputTensorNum(kernelNode); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernelNode); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernelNode); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 1, but got: " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernelNode); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernelNode); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got: " << output_num; } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/isnan_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/isnan_cpu_kernel.cc index e09b2cc7ea9..904dff09b14 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/isnan_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/isnan_cpu_kernel.cc @@ -28,7 +28,7 @@ constexpr size_t kIsNanOutputsNum = 1; void IsNanCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); input_dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); if (dtype_map_.find(input_dtype_) == dtype_map_.end()) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/l2_normalize_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/l2_normalize_cpu_kernel.cc index 54bf5aca8f5..a104692d50d 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/l2_normalize_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/l2_normalize_cpu_kernel.cc @@ -29,11 +29,11 @@ constexpr size_t kL2NormalizeOutputsNum = 1; template void L2NormalizeCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - epsilon_ = static_cast(AnfAlgo::GetNodeAttr(kernel_node, EPSILON)); - axis_ = LongToInt(AnfAlgo::GetNodeAttr(kernel_node, AXIS)); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + epsilon_ = static_cast(common::AnfAlgo::GetNodeAttr(kernel_node, EPSILON)); + axis_ = LongToInt(common::AnfAlgo::GetNodeAttr(kernel_node, AXIS)); input_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 0); - output_shape_ = AnfAlgo::GetOutputInferShape(kernel_node, 0); + output_shape_ = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); int dims = SizeToInt(input_shape_.size()); if (axis_ < -dims || axis_ >= dims) { diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/l2loss_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/l2loss_cpu_kernel.cc index c800a6e95bc..97babd56e11 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/l2loss_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/l2loss_cpu_kernel.cc @@ -27,8 +27,8 @@ constexpr size_t kL2LossOutputsNum = 1; template void L2LossCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - input_shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + input_shape_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); tensor_size_ = 1; for (const size_t &d : input_shape_) { tensor_size_ *= d; diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/l2normalize_grad_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/l2normalize_grad_cpu_kernel.cc index 3bd0bf7bd2e..3715683cba6 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/l2normalize_grad_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/l2normalize_grad_cpu_kernel.cc @@ -26,11 +26,11 @@ constexpr size_t kL2NormalizeGradOutputsNum = 1; template void L2NormalizeGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); for (size_t i = 0; i < kL2NormalizeGradInputsNum; i++) { - (void)input_shape_list_.emplace_back(AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, i)); + (void)input_shape_list_.emplace_back(common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, i)); } - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); CheckInputShape(output_shape); int output_dim_length = output_shape.size(); @@ -39,10 +39,10 @@ void L2NormalizeGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { dim_elem_num_list_[i] = output_shape[i + 1] * dim_elem_num_list_[i + 1]; } - int axis = LongToInt(AnfAlgo::GetNodeAttr(kernel_node, "axis")); + int axis = LongToInt(common::AnfAlgo::GetNodeAttr(kernel_node, "axis")); int input_dim_length = SizeToInt(input_shape_list_[0].size()); axis_ = axis < 0 ? (axis + input_dim_length) : axis; - epsilon_ = static_cast(AnfAlgo::GetNodeAttr(kernel_node, "epsilon")); + epsilon_ = static_cast(common::AnfAlgo::GetNodeAttr(kernel_node, "epsilon")); } template diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/layer_norm_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/layer_norm_cpu_kernel.cc index c41d7398684..623a0c162d9 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/layer_norm_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/layer_norm_cpu_kernel.cc @@ -17,7 +17,7 @@ #include "plugin/device/cpu/kernel/layer_norm_cpu_kernel.h" #include "kernel/common_utils.h" #include "plugin/device/cpu/hal/device/cpu_device_address.h" -#include "common/thread_pool.h" +#include "include/common/thread_pool.h" namespace mindspore { namespace kernel { @@ -28,11 +28,11 @@ constexpr size_t kLayerNormOutputsNum = 3; void LayerNormCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); - std::vector x_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto begin_norm_axis = AnfAlgo::GetNodeAttr(kernel_node, "begin_norm_axis"); - auto begin_params_axis = AnfAlgo::GetNodeAttr(kernel_node, "begin_params_axis"); + std::vector x_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto begin_norm_axis = common::AnfAlgo::GetNodeAttr(kernel_node, "begin_norm_axis"); + auto begin_params_axis = common::AnfAlgo::GetNodeAttr(kernel_node, "begin_params_axis"); if (begin_norm_axis < 0) { begin_norm_axis += SizeToLong(x_shape.size()); } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/layer_norm_grad_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/layer_norm_grad_cpu_kernel.cc index db8433f542f..95d33b702ec 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/layer_norm_grad_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/layer_norm_grad_cpu_kernel.cc @@ -17,7 +17,7 @@ #include "plugin/device/cpu/kernel/layer_norm_grad_cpu_kernel.h" #include "kernel/common_utils.h" #include "plugin/device/cpu/hal/device/cpu_device_address.h" -#include "common/thread_pool.h" +#include "include/common/thread_pool.h" namespace mindspore { namespace kernel { @@ -28,11 +28,11 @@ constexpr size_t kLayerNormGradOutputsNum = 3; void LayerNormGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); - std::vector x_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto begin_norm_axis = AnfAlgo::GetNodeAttr(kernel_node, "begin_norm_axis"); - auto begin_params_axis = AnfAlgo::GetNodeAttr(kernel_node, "begin_params_axis"); + std::vector x_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto begin_norm_axis = common::AnfAlgo::GetNodeAttr(kernel_node, "begin_norm_axis"); + auto begin_params_axis = common::AnfAlgo::GetNodeAttr(kernel_node, "begin_params_axis"); if (begin_norm_axis < 0) { begin_norm_axis += SizeToLong(x_shape.size()); } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/log_matrix_determinant_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/log_matrix_determinant_cpu_kernel.cc index 5f7bd35bd01..822c5b1b062 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/log_matrix_determinant_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/log_matrix_determinant_cpu_kernel.cc @@ -33,14 +33,14 @@ void LogMatrixDeterminantCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); node_wpt_ = kernel_node; dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); CHECK_KERNEL_INPUTS_NUM(input_num, kInputSize, kernel_name_); - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); CHECK_KERNEL_OUTPUTS_NUM(output_num, kOutputSize, kernel_name_); - auto shape_x = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_sign = AnfAlgo::GetOutputInferShape(kernel_node, 0); - auto shape_y = AnfAlgo::GetOutputInferShape(kernel_node, 1); + auto shape_x = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_sign = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto shape_y = common::AnfAlgo::GetOutputInferShape(kernel_node, 1); size_t shape_size_x = shape_x.size(); size_t shape_size_sign = shape_sign.size(); size_t shape_size_y = shape_y.size(); @@ -98,7 +98,7 @@ void LogMatrixDeterminantCpuKernelMod::LaunchLogMatrixDeterminant(const std::vec auto output_sign = reinterpret_cast(outputs[0]->addr); auto output_y = reinterpret_cast(outputs[1]->addr); - auto shape_x = AnfAlgo::GetPrevNodeOutputInferShape(node_, 0); + auto shape_x = common::AnfAlgo::GetPrevNodeOutputInferShape(node_, 0); size_t shape_size = shape_x.size(); size_t m = shape_x[shape_size - 1]; size_t size_mm = m * m; diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/lu_solve_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/lu_solve_cpu_kernel.cc index 93642b87f6e..51a797c462c 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/lu_solve_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/lu_solve_cpu_kernel.cc @@ -33,9 +33,9 @@ size_t get_element_num(const std::vector &shape) { template void LuSolveCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { node_wpt_ = kernel_node; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); CHECK_KERNEL_INPUTS_NUM(input_num, kInputNum, kernel_name_); CHECK_KERNEL_OUTPUTS_NUM(output_num, kOutputNum, kernel_name_); auto x_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/map_cache_idx_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/map_cache_idx_cpu_kernel.cc index 47514025bd1..6a9b11e7530 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/map_cache_idx_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/map_cache_idx_cpu_kernel.cc @@ -51,13 +51,13 @@ int Compress(HashmapEntry *entry_p, const size_t &length, T entry) { void UpdateShape(size_t miss_count, const CNodePtr &node) { std::vector out_shape; (void)out_shape.emplace_back(miss_count); - size_t output_num = AnfAlgo::GetOutputTensorNum(node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(node); std::vector dtypes(output_num); for (size_t i = 0; i < output_num; i++) { dtypes[i] = AnfAlgo::GetOutputDeviceDataType(node, i); } - AnfAlgo::SetOutputInferTypeAndShape(dtypes, {AnfAlgo::GetOutputInferShape(node, 0), out_shape, out_shape, out_shape}, - node.get()); + common::AnfAlgo::SetOutputInferTypeAndShape( + dtypes, {common::AnfAlgo::GetOutputInferShape(node, 0), out_shape, out_shape, out_shape}, node.get()); } void CheckMissCount(size_t miss_count, int count_size, float total_count, float hit_count) { @@ -72,9 +72,9 @@ void CheckMissCount(size_t miss_count, int count_size, float total_count, float void MapCacheIdxCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); node_wpt_ = kernel_node; - auto hashmap_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto hashmap_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); if (hashmap_shape.size() != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimension of 'HashMap' should be 2-D, but got " << hashmap_shape.size() << "-D."; @@ -107,7 +107,7 @@ template void MapCacheIdxCpuKernelMod::LaunchKernel(const std::vector &inputs, const std::vector &outputs) { auto node = node_wpt_.lock(); - auto emb_idx_shape = AnfAlgo::GetPrevNodeOutputInferShape(node, 1); + auto emb_idx_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node, 1); batch_size_ = 1; for (size_t i = 0; i < emb_idx_shape.size(); ++i) { batch_size_ *= emb_idx_shape[i]; diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/map_uniform_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/map_uniform_cpu_kernel.cc index 3fed7ff9959..395fac5a8be 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/map_uniform_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/map_uniform_cpu_kernel.cc @@ -29,7 +29,7 @@ constexpr size_t kMapUniformOutputsNum = 1; void MapUniformCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); node_wpt_ = kernel_node; dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); } @@ -57,7 +57,7 @@ void MapUniformCpuKernelMod::LaunchKernel(const std::vector &inputs, if (!node) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', node_wpt_(kernel_node) is expired. Error no: " << node; } - auto input_x_shape = AnfAlgo::GetPrevNodeOutputInferShape(node, 0); + auto input_x_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node, 0); batch_size_ = 1; for (size_t i = 0; i < input_x_shape.size(); ++i) { batch_size_ *= input_x_shape[i]; diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/masked_select_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/masked_select_cpu_kernel.cc index fd0284a01db..ec8eca200e6 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/masked_select_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/masked_select_cpu_kernel.cc @@ -26,7 +26,7 @@ constexpr size_t kMaskedSelectOutputsNum = 1; template void MaskedSelectCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); input_shape_a_ = AnfAlgo::GetInputDeviceShape(kernel_node, 0); input_shape_b_ = AnfAlgo::GetInputDeviceShape(kernel_node, 1); output_shape_ = CPUKernelUtils::GetBroadcastShape(input_shape_a_, input_shape_b_); @@ -69,12 +69,12 @@ bool MaskedSelectCpuKernelMod::Launch(const std::vector & } std::vector out_shape; (void)out_shape.emplace_back(j); - size_t output_num = AnfAlgo::GetOutputTensorNum(node_); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(node_); std::vector dtypes(output_num); for (size_t i = 0; i < output_num; i++) { dtypes[i] = AnfAlgo::GetOutputDeviceDataType(node_, i); } - AnfAlgo::SetOutputInferTypeAndShape(dtypes, {out_shape}, node_.get()); + common::AnfAlgo::SetOutputInferTypeAndShape(dtypes, {out_shape}, node_.get()); } return true; } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/masked_select_grad_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/masked_select_grad_cpu_kernel.cc index 5c1b3cf99be..d6a57a171d7 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/masked_select_grad_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/masked_select_grad_cpu_kernel.cc @@ -26,7 +26,7 @@ constexpr size_t kMaskedSelectGradOutputsNum = 1; template void MaskedSelectGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); input_shape_a_ = AnfAlgo::GetInputDeviceShape(kernel_node, INPUT); input_shape_b_ = AnfAlgo::GetInputDeviceShape(kernel_node, MASK); grad_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, GRAD); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/matrix_band_part_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/matrix_band_part_cpu_kernel.cc index 04ed371f2c8..2431ae6e299 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/matrix_band_part_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/matrix_band_part_cpu_kernel.cc @@ -22,7 +22,7 @@ namespace mindspore { namespace kernel { template void MatrixBandPartCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { - shapes_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + shapes_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); dim_size_ = shapes_.size(); if (shapes_.size() < kDim2) { MS_LOG(EXCEPTION) << "Wrong array shape, A should be a matrix max than 2."; diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/matrix_determinant_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/matrix_determinant_cpu_kernel.cc index ca6263dbc47..7872cfc71ee 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/matrix_determinant_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/matrix_determinant_cpu_kernel.cc @@ -33,10 +33,10 @@ void MatrixDeterminantCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); node_wpt_ = kernel_node; dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); CHECK_KERNEL_INPUTS_NUM(input_num, kInputSize, kernel_name_); - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); CHECK_KERNEL_OUTPUTS_NUM(output_num, kOutputSize, kernel_name_); } @@ -69,7 +69,7 @@ void MatrixDeterminantCpuKernelMod::LaunchMatrixDeterminant(const std::vector(outputs[0]->addr); MS_EXCEPTION_IF_NULL(output); // Check if it's a square array - auto dims = AnfAlgo::GetPrevNodeOutputInferShape(node_, 0); + auto dims = common::AnfAlgo::GetPrevNodeOutputInferShape(node_, 0); if (dims.size() < kNumber2) { MS_LOG(EXCEPTION) << "Input x must be at least rank 2."; } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/matrix_diag_part_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/matrix_diag_part_cpu_kernel.cc index 9eacb678760..fb4a1d9e6c5 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/matrix_diag_part_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/matrix_diag_part_cpu_kernel.cc @@ -22,7 +22,7 @@ namespace mindspore { namespace kernel { template void MatrixDiagPartCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { - shapes_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + shapes_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); out_shapes_ = shapes_; dim_size_ = shapes_.size(); if (shapes_.size() < kDim2) { @@ -34,7 +34,7 @@ void MatrixDiagPartCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { out_range_size_ *= shapes_[i]; } // Invalid alignment will throw an exception. - auto alignment = AnfAlgo::GetNodeAttr(kernel_node, ALIGNMENT); + auto alignment = common::AnfAlgo::GetNodeAttr(kernel_node, ALIGNMENT); alignment_ = GetAlignments(alignment); node_wpt_ = kernel_node; } @@ -72,7 +72,7 @@ bool MatrixDiagPartCpuKernelMod::Launch(const std::vector &inputs out_shapes_.erase(out_shapes_.begin() + shapes_.size() - kDim2); } auto dtype = AnfAlgo::GetOutputDeviceDataType(node_, 0); - AnfAlgo::SetOutputInferTypeAndShape({dtype}, {out_shapes_}, node_.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({dtype}, {out_shapes_}, node_.get()); } auto func = [m = m_, n = n_, max_diag_len, u, l, in_value, out_value, dest_inner_matrix_len, padding_value, alignment = alignment_](size_t spos, size_t epos) { diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/matrix_inverse_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/matrix_inverse_cpu_kernel.cc index a7d682b4637..41d449e0156 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/matrix_inverse_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/matrix_inverse_cpu_kernel.cc @@ -33,7 +33,7 @@ void MatrixInverseCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); node_wpt_ = kernel_node; dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); } bool MatrixInverseCpuKernelMod::Launch(const std::vector &inputs, @@ -68,7 +68,7 @@ void MatrixInverseCpuKernelMod::LaunchMatrixInverse(const std::vector(outputs[0]->addr); MS_EXCEPTION_IF_NULL(output_ptr); // Judge whether the input shape matches - auto shape = AnfAlgo::GetPrevNodeOutputInferShape(node_, 0); + auto shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node_, 0); if (shape.size() < kNumber2) { MS_LOG(EXCEPTION) << "Input x must be at least rank 2."; } @@ -92,7 +92,7 @@ void MatrixInverseCpuKernelMod::LaunchMatrixInverse(const std::vector(node_, "adjoint"); + adjoint_ = common::AnfAlgo::GetNodeAttr(node_, "adjoint"); auto one_size = sizeof(*input_ptr); if ((one_size * input_num) <= kParallelDataNums) { diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/matrix_set_diag_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/matrix_set_diag_cpu_kernel.cc index 33d490362e9..85b5573db5a 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/matrix_set_diag_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/matrix_set_diag_cpu_kernel.cc @@ -18,34 +18,34 @@ #include #include #include "plugin/device/cpu/hal/device/cpu_device_address.h" -#include "common/thread_pool.h" +#include "include/common/thread_pool.h" #include "kernel/common_utils.h" namespace mindspore { namespace kernel { void MatrixSetDiagCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); constexpr size_t required_input_nums = 3; constexpr size_t required_output_nums = 1; - if (AnfAlgo::GetInputNum(kernel_node) != required_input_nums || - AnfAlgo::GetOutputTensorNum(kernel_node) != required_output_nums) { + if (common::AnfAlgo::GetInputNum(kernel_node) != required_input_nums || + common::AnfAlgo::GetOutputTensorNum(kernel_node) != required_output_nums) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the input nums are required to [input, diagonal, " "k, alignment] for 3 and the output nums is require to 1."; } // invalid alignment will throw an exception. - auto alignment = AnfAlgo::GetNodeAttr(kernel_node, ALIGNMENT); + auto alignment = common::AnfAlgo::GetNodeAttr(kernel_node, ALIGNMENT); alignment_ = GetAlignments(alignment); constexpr size_t input_index = 0; constexpr size_t diag_index = 1; constexpr size_t diag_k_index = 2; constexpr size_t output_index = 0; - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, input_index); - auto diag_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, diag_index); - auto diag_k_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, diag_k_index); - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, output_index); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, input_index); + auto diag_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, diag_index); + auto diag_k_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, diag_k_index); + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, output_index); constexpr size_t temporary_2d_dim = 2; constexpr size_t temporary_1d_dim = 1; diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/maximum_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/maximum_cpu_kernel.cc index ce88b34d87b..46befcd469e 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/maximum_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/maximum_cpu_kernel.cc @@ -34,7 +34,7 @@ constexpr size_t kMaximumOutputsNum = 1; template void MaximumCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); input_x_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 0); input_y_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 1); output_shape_ = AnfAlgo::GetOutputDeviceShape(kernel_node, 0); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/maximum_grad_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/maximum_grad_cpu_kernel.cc index 5f4ec0f23e7..9bbf826c41d 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/maximum_grad_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/maximum_grad_cpu_kernel.cc @@ -34,12 +34,12 @@ void CheckShape(std::vector *shape) { void MaximumGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - x_shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - y_shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - dout_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - dx_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); - dy_shape = AnfAlgo::GetOutputInferShape(kernel_node, 1); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + x_shape_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + y_shape_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + dout_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + dx_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); + dy_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 1); dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); CheckShape(&x_shape_); CheckShape(&y_shape_); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/minimum_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/minimum_cpu_kernel.cc index 0dc6a341726..885da70dbbe 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/minimum_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/minimum_cpu_kernel.cc @@ -33,7 +33,7 @@ constexpr size_t kIdx6 = 6; template void MinimumCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); input_x_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 0); input_y_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 1); output_shape_ = AnfAlgo::GetOutputDeviceShape(kernel_node, 0); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/minimum_grad_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/minimum_grad_cpu_kernel.cc index a15a21452ff..aa67c7273aa 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/minimum_grad_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/minimum_grad_cpu_kernel.cc @@ -62,12 +62,12 @@ void CheckShape(std::vector *shape) { void MinimumGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - x_shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - y_shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - dout_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - dx_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); - dy_shape = AnfAlgo::GetOutputInferShape(kernel_node, 1); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + x_shape_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + y_shape_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + dout_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + dx_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); + dy_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 1); dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); CheckShape(&x_shape_); CheckShape(&y_shape_); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/mirror_pad_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/mirror_pad_cpu_kernel.cc index 86185b4971b..3a899c3c97c 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/mirror_pad_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/mirror_pad_cpu_kernel.cc @@ -40,8 +40,8 @@ constexpr size_t kPadMaxSupportDim = 4; void MirrorPadCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - std::string mode = AnfAlgo::GetNodeAttr(kernel_node, "mode"); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + std::string mode = common::AnfAlgo::GetNodeAttr(kernel_node, "mode"); dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); pad_dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 1); if (mode == "REFLECT") { @@ -53,7 +53,7 @@ void MirrorPadCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { << mode; } - std::vector input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + std::vector input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); shape_size_ = input_shape.size(); (void)input_shape.insert(input_shape.begin(), kPadMaxSupportDim - shape_size_, 1); shape_size_ = kPadMaxSupportDim; @@ -63,10 +63,10 @@ void MirrorPadCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { input_shape_.push_back(SizeToLong(input_shape[i])); } - std::vector padding_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + std::vector padding_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); num_paddings_ = SizeToLong(padding_shape[0]); - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); for (auto x : output_shape) { output_size_ *= x; output_shape_.push_back(SizeToLong(x)); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/mirror_pad_grad_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/mirror_pad_grad_cpu_kernel.cc index 0074bc83cca..d34659a6644 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/mirror_pad_grad_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/mirror_pad_grad_cpu_kernel.cc @@ -56,8 +56,8 @@ bool range_check(int64_t x, int64_t y, int64_t padded_width, int64_t padded_heig void MirrorPadGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - std::string mode = AnfAlgo::GetNodeAttr(kernel_node, "mode"); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + std::string mode = common::AnfAlgo::GetNodeAttr(kernel_node, "mode"); dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); pad_dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 1); if (mode == "REFLECT") { @@ -69,7 +69,7 @@ void MirrorPadGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { << mode; } - std::vector input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + std::vector input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); shape_size_ = input_shape.size(); (void)input_shape.insert(input_shape.begin(), kPadMaxSupportDim - shape_size_, 1); shape_size_ = kPadMaxSupportDim; @@ -79,10 +79,10 @@ void MirrorPadGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { input_shape_.push_back(SizeToLong(input_shape[i])); } - std::vector padding_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + std::vector padding_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); num_paddings_ = SizeToLong(padding_shape[0]); - std::vector output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + std::vector output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); if (output_shape.size() == 4) { } else if (output_shape.size() == 3) { diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/addn_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/addn_cpu_kernel.cc index 696aeb5a7b1..9289bb1f51b 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/addn_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/addn_cpu_kernel.cc @@ -19,7 +19,7 @@ #include "plugin/device/cpu/kernel/nnacl/fp32/add_fp32.h" #include "plugin/device/cpu/kernel/nnacl/errorcode.h" #include "utils/ms_utils.h" -#include "common/thread_pool.h" +#include "include/common/thread_pool.h" namespace mindspore { namespace kernel { @@ -45,8 +45,8 @@ void AddT(const T *in0, const T *in1, T *out, int start, int end) { template void AddNCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - input_num_ = AnfAlgo::GetInputTensorNum(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + input_num_ = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num_ < kAddNInputsMinNum) { MS_LOG(EXCEPTION) << "Input numbers should not less " << kAddNInputsMinNum << ", but got " << input_num_; } @@ -112,13 +112,13 @@ bool AddNCpuKernelMod::Launch(const std::vector &inputs, template void AddNCpuKernelMod::CheckParam(const CNodePtr &kernel_node) { - auto src0_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto src0_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); auto dst_shape = AnfAlgo::GetOutputDeviceShape(kernel_node, 0); if (src0_shape != dst_shape) { MS_LOG(EXCEPTION) << "AddN output shape must be equal to input shape."; } for (size_t index = 1; index < input_num_; ++index) { - auto src_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, index); + auto src_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, index); if (src0_shape != src_shape) { MS_LOG(EXCEPTION) << "AddN input shapes must be equal."; } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/assignadd_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/assignadd_cpu_kernel.cc index 6da458daecf..af5069e6843 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/assignadd_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/assignadd_cpu_kernel.cc @@ -27,7 +27,7 @@ constexpr size_t kAssignAddOutputsNum = 1; void AssignAddCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); std::vector src0_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0); std::vector src1_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 1); if (src1_shape.size() == 0 && src0_shape.size() == 0) { diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/batch_norm_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/batch_norm_cpu_kernel.cc index 67ece67ef48..0acb5b91752 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/batch_norm_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/batch_norm_cpu_kernel.cc @@ -37,9 +37,9 @@ void BatchNormCpuKernelMod::InitInputOutputSize(const CNodePtr &kernel_node) { void BatchNormCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - is_train = AnfAlgo::GetNodeAttr(kernel_node, "is_training"); - momentum = AnfAlgo::GetNodeAttr(kernel_node, "momentum"); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + is_train = common::AnfAlgo::GetNodeAttr(kernel_node, "is_training"); + momentum = common::AnfAlgo::GetNodeAttr(kernel_node, "momentum"); std::vector x_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0); if (x_shape.size() == kBatchNormInputShapeSize2) { (void)x_shape.insert(x_shape.end(), kBatchNormInputShapeSize - kBatchNormInputShapeSize2, 1); @@ -52,7 +52,7 @@ void BatchNormCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { nhw_size = x_shape[0] * hw_size; dnnl::memory::desc x_desc = GetDefaultMemDesc(x_shape); dnnl::memory::desc scale_bias_desc = GetDefaultMemDesc({2, channel}); - auto epsilon = AnfAlgo::GetNodeAttr(kernel_node, "epsilon"); + auto epsilon = common::AnfAlgo::GetNodeAttr(kernel_node, "epsilon"); auto prop_kind = dnnl::prop_kind::forward_inference; auto normalization_flags = dnnl::normalization_flags::use_scale_shift | dnnl::normalization_flags::use_global_stats; if (is_train) { diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/batch_norm_grad_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/batch_norm_grad_cpu_kernel.cc index a3dfd824a0f..55059f02655 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/batch_norm_grad_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/batch_norm_grad_cpu_kernel.cc @@ -41,7 +41,7 @@ void BatchNormGradCpuKernelMod::InitInputOutputSize(const CNodePtr &kernel_node) void BatchNormGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); std::vector x_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0); if (x_shape.size() == NC) { (void)x_shape.insert(x_shape.end(), (NCHW - NC), 1); @@ -54,8 +54,8 @@ void BatchNormGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { nhw_size = batch_size * hw_size; dnnl::memory::desc x_desc = GetDefaultMemDesc(x_shape); dnnl::memory::desc scale_bias_desc = GetDefaultMemDesc({SCALE_SHIFT_NUM, channel}); - auto epsilon = AnfAlgo::GetNodeAttr(kernel_node, "epsilon"); - auto is_train = AnfAlgo::GetNodeAttr(kernel_node, "is_training"); + auto epsilon = common::AnfAlgo::GetNodeAttr(kernel_node, "epsilon"); + auto is_train = common::AnfAlgo::GetNodeAttr(kernel_node, "is_training"); auto prop_kind = dnnl::prop_kind::forward_inference; auto normalization_flags = dnnl::normalization_flags::use_scale_shift | dnnl::normalization_flags::use_global_stats; if (is_train) { diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/conv2d_grad_filter_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/conv2d_grad_filter_cpu_kernel.cc index 1ecc7c590d3..91c959b938a 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/conv2d_grad_filter_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/conv2d_grad_filter_cpu_kernel.cc @@ -31,7 +31,7 @@ constexpr size_t kConv2dGradFilterOutputsNum = 1; void Conv2dGradFilterCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); std::vector src_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 1); std::vector weight_shape = AnfAlgo::GetOutputDeviceShape(kernel_node, 0); std::vector dst_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0); @@ -39,7 +39,7 @@ void Conv2dGradFilterCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_LOG(EXCEPTION) << ("Conv2d grad filter only support nchw input!"); } std::vector kernel_size({weight_shape[2], weight_shape[3]}); - size_t group = LongToSize(AnfAlgo::GetNodeAttr(kernel_node, GROUP)); + size_t group = LongToSize(common::AnfAlgo::GetNodeAttr(kernel_node, GROUP)); if (group > 1) { if (src_shape[1] % group != 0) { MS_LOG(EXCEPTION) << "Conv2d channels should be divided by group!"; @@ -52,8 +52,8 @@ void Conv2dGradFilterCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { dnnl::memory::desc dst_desc = GetDefaultMemDesc(dst_shape); std::vector stride_ori; std::vector dilation_ori; - auto stride_me = AnfAlgo::GetNodeAttr>(kernel_node, STRIDE); - auto dilation_me = AnfAlgo::GetNodeAttr>(kernel_node, DILATION); + auto stride_me = common::AnfAlgo::GetNodeAttr>(kernel_node, STRIDE); + auto dilation_me = common::AnfAlgo::GetNodeAttr>(kernel_node, DILATION); (void)std::transform(stride_me.begin(), stride_me.end(), std::back_inserter(stride_ori), [](const int64_t &value) { return LongToInt(value); }); (void)std::transform(dilation_me.begin(), dilation_me.end(), std::back_inserter(dilation_ori), @@ -72,7 +72,7 @@ void Conv2dGradFilterCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { std::vector dilation{dilation_ori[2], dilation_ori[3]}; dnnl::memory::dims strides{stride_ori[0], stride_ori[1]}; dnnl::memory::dims dilates{dilation_ori[2] - 1, dilation_ori[3] - 1}; - const std::string pad_mode = AnfAlgo::GetNodeAttr(kernel_node, PAD_MODE); + const std::string pad_mode = common::AnfAlgo::GetNodeAttr(kernel_node, PAD_MODE); std::vector int_padding_l; std::vector int_padding_r; GetPadding(kernel_node, pad_mode, src_shape, kernel_size, stride, &int_padding_l, &int_padding_r, dilation); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/conv2d_grad_input_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/conv2d_grad_input_cpu_kernel.cc index 84a7fabc7b1..4b49e18ddf1 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/conv2d_grad_input_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/conv2d_grad_input_cpu_kernel.cc @@ -33,7 +33,7 @@ const std::map kFormatIndexMap = {{"NCHW", 2}, {"HWCN", 0}, void Conv2dGradInputCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); std::vector src_shape = AnfAlgo::GetOutputDeviceShape(kernel_node, 0); std::vector weight_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 1); std::vector dst_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0); @@ -41,7 +41,7 @@ void Conv2dGradInputCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_LOG(EXCEPTION) << "Conv2d grad filter only support nchw input!"; } std::vector kernel_size({weight_shape[2], weight_shape[3]}); - size_t group = LongToSize(AnfAlgo::GetNodeAttr(kernel_node, GROUP)); + size_t group = LongToSize(common::AnfAlgo::GetNodeAttr(kernel_node, GROUP)); if (group > 1) { if (src_shape[1] % group != 0) { MS_LOG(EXCEPTION) << "Conv2d channels should be divided by group!"; @@ -55,9 +55,9 @@ void Conv2dGradInputCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { std::vector stride_ori; std::vector dilation_ori; - auto stride_me = AnfAlgo::GetNodeAttr>(kernel_node, STRIDE); - auto dilation_me = AnfAlgo::GetNodeAttr>(kernel_node, DILATION); - auto format_me = AnfAlgo::GetNodeAttr(kernel_node, FORMAT); + auto stride_me = common::AnfAlgo::GetNodeAttr>(kernel_node, STRIDE); + auto dilation_me = common::AnfAlgo::GetNodeAttr>(kernel_node, DILATION); + auto format_me = common::AnfAlgo::GetNodeAttr(kernel_node, FORMAT); auto iter = kFormatIndexMap.find(format_me); if (iter == kFormatIndexMap.end()) { MS_LOG(EXCEPTION) << "OriFormat is " << format_me << ", Please confirm that in {NCHW, HWCN, NHWC}."; @@ -87,7 +87,7 @@ void Conv2dGradInputCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { dnnl::memory::dims dilates{dilation_ori[2] - 1, dilation_ori[3] - 1}; std::vector int_padding_l; std::vector int_padding_r; - const std::string pad_mode = AnfAlgo::GetNodeAttr(kernel_node, PAD_MODE); + const std::string pad_mode = common::AnfAlgo::GetNodeAttr(kernel_node, PAD_MODE); GetPadding(kernel_node, pad_mode, src_shape, kernel_size, stride, &int_padding_l, &int_padding_r, dilation); if (int_padding_l.size() != 2 || int_padding_r.size() != 2) { MS_LOG(EXCEPTION) << "Conv2d grad get padding failed"; diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/conv_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/conv_cpu_kernel.cc index fdd947e8284..54f92fe2bb4 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/conv_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/conv_cpu_kernel.cc @@ -32,7 +32,7 @@ constexpr size_t kKernelStartAxis = 2; void ConvCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); std::vector src_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0); std::vector weight_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 1); std::vector dst_shape = AnfAlgo::GetOutputDeviceShape(kernel_node, 0); @@ -45,7 +45,7 @@ void ConvCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { for (size_t i = kKernelStartAxis; i < src_dim; ++i) { (void)kernel_size.emplace_back(weight_shape[i]); } - size_t group = LongToSize(AnfAlgo::GetNodeAttr(kernel_node, GROUP)); + size_t group = LongToSize(common::AnfAlgo::GetNodeAttr(kernel_node, GROUP)); if (group > 1) { if (src_shape[1] % group != 0) { MS_LOG(EXCEPTION) << "Conv channels should be divided by group!"; @@ -60,8 +60,8 @@ void ConvCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { std::vector dilation_ori; auto stride_attr = src_dim == kShapeSize4D ? STRIDE : STRIDES; auto dilation_attr = src_dim == kShapeSize4D ? DILATION : DILATIONS; - auto stride_me = AnfAlgo::GetNodeAttr>(kernel_node, stride_attr); - auto dilation_me = AnfAlgo::GetNodeAttr>(kernel_node, dilation_attr); + auto stride_me = common::AnfAlgo::GetNodeAttr>(kernel_node, stride_attr); + auto dilation_me = common::AnfAlgo::GetNodeAttr>(kernel_node, dilation_attr); (void)std::transform(stride_me.begin(), stride_me.end(), std::back_inserter(stride_ori), [](const int64_t &value) { return LongToInt(value); }); (void)std::transform(dilation_me.begin(), dilation_me.end(), std::back_inserter(dilation_ori), @@ -91,7 +91,7 @@ void ConvCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { } std::vector int_padding_l; std::vector int_padding_r; - const std::string pad_mode = AnfAlgo::GetNodeAttr(kernel_node, PAD_MODE); + const std::string pad_mode = common::AnfAlgo::GetNodeAttr(kernel_node, PAD_MODE); GetPadding(kernel_node, pad_mode, src_shape, kernel_size, stride, &int_padding_l, &int_padding_r, dilation); if (int_padding_l.size() + kKernelStartAxis != src_dim || int_padding_r.size() + kKernelStartAxis != src_dim) { MS_LOG(EXCEPTION) << "Get padding failed!"; diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/eltwise_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/eltwise_cpu_kernel.cc index 6c06353c289..f233d91bb40 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/eltwise_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/eltwise_cpu_kernel.cc @@ -57,7 +57,7 @@ dnnl::eltwise_forward::desc EltWiseCpuKernelMod::GetForwardEltwiseDesc(const dnn void EltWiseCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); std::vector src_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0); if (src_shape.empty()) { (void)src_shape.insert(src_shape.begin(), 1); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/log_softmax_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/log_softmax_cpu_kernel.cc index bccdae95ce7..65561f20eaa 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/log_softmax_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/log_softmax_cpu_kernel.cc @@ -28,9 +28,9 @@ constexpr size_t kLogSoftmaxOutputsNum = 1; void LogSoftmaxCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); std::vector src_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0); - int axis = AnfAlgo::GetNodeAttr(kernel_node, AXIS); + int axis = common::AnfAlgo::GetNodeAttr(kernel_node, AXIS); if (axis >= SizeToInt(src_shape.size())) { axis = SizeToInt(src_shape.size()) - 1; } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/log_softmax_grad_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/log_softmax_grad_cpu_kernel.cc index 70467ac0663..ce0af3080e3 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/log_softmax_grad_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/log_softmax_grad_cpu_kernel.cc @@ -28,9 +28,9 @@ constexpr size_t kLogSoftmaxGradOutputsNum = 1; void LogSoftmaxGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); std::vector src_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0); - int axis = AnfAlgo::GetNodeAttr(kernel_node, AXIS); + int axis = common::AnfAlgo::GetNodeAttr(kernel_node, AXIS); if (axis >= SizeToInt(src_shape.size())) { axis = SizeToInt(src_shape.size()) - 1; } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/lstm_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/lstm_cpu_kernel.cc index b8ca3881eef..f04c501adc7 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/lstm_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/lstm_cpu_kernel.cc @@ -37,22 +37,22 @@ using dt = dnnl::memory::data_type; void LstmCpuKernelMod::InitInputOutputSize(const CNodePtr &kernel_node) { NativeCpuKernelMod::InitInputOutputSize(kernel_node); output_size_list_[kOutputWorkSpaceIndex] = reserve_size_; - auto output_num = AnfAlgo::GetOutputTensorNum(kernel_node); - auto output_type = AnfAlgo::GetOutputInferDataType(kernel_node, 0); + auto output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); + auto output_type = common::AnfAlgo::GetOutputInferDataType(kernel_node, 0); auto output_types = std::vector(output_num, output_type); std::vector> output_shapes; for (size_t output_index = 0; output_index < output_num; ++output_index) { - auto shape = AnfAlgo::GetOutputInferShape(kernel_node, output_index); + auto shape = common::AnfAlgo::GetOutputInferShape(kernel_node, output_index); (void)output_shapes.emplace_back(shape); } size_t len = reserve_size_ / IntToSize(kGateNum); output_shapes[kOutputWorkSpaceIndex] = {len, 1}; - AnfAlgo::SetOutputInferTypeAndShape(output_types, output_shapes, kernel_node.get()); + common::AnfAlgo::SetOutputInferTypeAndShape(output_types, output_shapes, kernel_node.get()); } void LstmCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); CheckParam(kernel_node); auto eng = engine_; dnnl::rnn_direction direction = dnnl::rnn_direction::unidirectional; @@ -127,11 +127,11 @@ void LstmCpuKernelMod::CheckParam(const CNodePtr &kernel_node) { std::vector src_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0); std::vector src_h_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 1); std::vector src_c_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 2); - bidirectional_ = AnfAlgo::GetNodeAttr(kernel_node, "bidirectional"); - input_size_ = LongToInt(AnfAlgo::GetNodeAttr(kernel_node, "input_size")); - hidden_size_ = LongToInt(AnfAlgo::GetNodeAttr(kernel_node, "hidden_size")); - num_layers_ = LongToInt(AnfAlgo::GetNodeAttr(kernel_node, "num_layers")); - has_bias_ = AnfAlgo::GetNodeAttr(kernel_node, "has_bias"); + bidirectional_ = common::AnfAlgo::GetNodeAttr(kernel_node, "bidirectional"); + input_size_ = LongToInt(common::AnfAlgo::GetNodeAttr(kernel_node, "input_size")); + hidden_size_ = LongToInt(common::AnfAlgo::GetNodeAttr(kernel_node, "hidden_size")); + num_layers_ = LongToInt(common::AnfAlgo::GetNodeAttr(kernel_node, "num_layers")); + has_bias_ = common::AnfAlgo::GetNodeAttr(kernel_node, "has_bias"); batch_size_ = SizeToInt(src_shape[1]); seq_len_ = SizeToInt(src_shape[0]); num_directions_ = 1; diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/lstm_grad_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/lstm_grad_cpu_kernel.cc index d71c4c8350d..543d7517efe 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/lstm_grad_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/lstm_grad_cpu_kernel.cc @@ -53,7 +53,7 @@ void LSTMGradCpuKernelMod::InitInputOutputSize(const CNodePtr &kernel_node) { void LSTMGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); CheckParam(kernel_node); auto eng = engine_; dnnl::rnn_direction direction = dnnl::rnn_direction::unidirectional; @@ -148,11 +148,11 @@ void LSTMGradCpuKernelMod::CheckParam(const CNodePtr &kernel_node) { if (src_shape.size() != 3 || src_h_shape.size() != 3 || src_c_shape.size() != 3) { MS_LOG(EXCEPTION) << "Lstm only support 3-D input!"; } - bidirectional_ = AnfAlgo::GetNodeAttr(kernel_node, "bidirectional"); - input_size_ = AnfAlgo::GetNodeAttr(kernel_node, "input_size"); - hidden_size_ = AnfAlgo::GetNodeAttr(kernel_node, "hidden_size"); - num_layers_ = AnfAlgo::GetNodeAttr(kernel_node, "num_layers"); - has_bias_ = AnfAlgo::GetNodeAttr(kernel_node, "has_bias"); + bidirectional_ = common::AnfAlgo::GetNodeAttr(kernel_node, "bidirectional"); + input_size_ = common::AnfAlgo::GetNodeAttr(kernel_node, "input_size"); + hidden_size_ = common::AnfAlgo::GetNodeAttr(kernel_node, "hidden_size"); + num_layers_ = common::AnfAlgo::GetNodeAttr(kernel_node, "num_layers"); + has_bias_ = common::AnfAlgo::GetNodeAttr(kernel_node, "has_bias"); batch_size_ = SizeToInt(src_shape[1]); seq_len_ = SizeToInt(src_shape[0]); num_directions_ = 1; diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/matmul_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/matmul_cpu_kernel.cc index 90da564c233..489be0d5725 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/matmul_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/matmul_cpu_kernel.cc @@ -16,7 +16,7 @@ #include "plugin/device/cpu/kernel/mkldnn/matmul_cpu_kernel.h" #include -#include "common/thread_pool.h" +#include "include/common/thread_pool.h" #include "plugin/device/cpu/kernel/nnacl/op_base.h" #include "plugin/device/cpu/kernel/nnacl/matmul_parameter.h" #include "plugin/device/cpu/kernel/nnacl/fp32/matmul_fp32.h" @@ -35,15 +35,15 @@ using dims = dnnl::memory::dims; void MatMulCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); std::vector a_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0); std::vector b_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 1); std::vector o_shape = AnfAlgo::GetOutputDeviceShape(kernel_node, 0); if (a_shape.size() < kRankMin || b_shape.size() < kRankMin || o_shape.size() < kRankMin) { MS_LOG(EXCEPTION) << "The tensor rank of MatMul should be greater than or equal to " << kRankMin; } - bool trans_a = AnfAlgo::GetNodeAttr(kernel_node, TRANSPOSE_A); - bool trans_b = AnfAlgo::GetNodeAttr(kernel_node, TRANSPOSE_B); + bool trans_a = common::AnfAlgo::GetNodeAttr(kernel_node, TRANSPOSE_A); + bool trans_b = common::AnfAlgo::GetNodeAttr(kernel_node, TRANSPOSE_B); auto rank = a_shape.size(); int64_t batch = 1; for (size_t i = 0; i < rank - kIndexOffset; ++i) { diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/mkl_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/mkl_cpu_kernel.cc index 27ea020df36..5f947212ad1 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/mkl_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/mkl_cpu_kernel.cc @@ -58,7 +58,7 @@ void MKLCpuKernelMod::GetPadding(const CNodePtr &kernel_node, const std::string } } else { std::vector pad; - std::vector pad_me = AnfAlgo::GetNodeAttr>(kernel_node, PAD_LIST); + std::vector pad_me = common::AnfAlgo::GetNodeAttr>(kernel_node, PAD_LIST); (void)std::transform(pad_me.begin(), pad_me.end(), std::back_inserter(pad), [](const int64_t &value) { return static_cast(value); }); for (size_t i = 0; i < dim; i += 2) { diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/pooling_avg_grad_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/pooling_avg_grad_cpu_kernel.cc index 654b71ecf75..b705a8d8f61 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/pooling_avg_grad_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/pooling_avg_grad_cpu_kernel.cc @@ -33,15 +33,15 @@ constexpr size_t kkAvgPoolingGradPadSize = 2; void AvgPoolingGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); std::vector src_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0); std::vector dst_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 1); dnnl::memory::desc src_desc = GetDefaultMemDesc(src_shape); dnnl::memory::desc dst_desc = GetDefaultMemDesc(dst_shape); std::vector origin_kernel_sizes; std::vector strides; - std::vector kernel_sizes_me = AnfAlgo::GetNodeAttr>(kernel_node, KERNEL_SIZE); - std::vector strides_me = AnfAlgo::GetNodeAttr>(kernel_node, STRIDES); + std::vector kernel_sizes_me = common::AnfAlgo::GetNodeAttr>(kernel_node, KERNEL_SIZE); + std::vector strides_me = common::AnfAlgo::GetNodeAttr>(kernel_node, STRIDES); (void)std::transform(kernel_sizes_me.begin(), kernel_sizes_me.end(), std::back_inserter(origin_kernel_sizes), [](const int64_t &value) { return LongToInt(value); }); (void)std::transform(strides_me.begin(), strides_me.end(), std::back_inserter(strides), @@ -52,7 +52,7 @@ void AvgPoolingGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { std::vector stride{strides[2], strides[3]}; dnnl::memory::dims strides_dims{strides[2], strides[3]}; dnnl::memory::dims kernels_dims{origin_kernel_sizes[2], origin_kernel_sizes[3]}; - const std::string pad_mode = AnfAlgo::GetNodeAttr(kernel_node, PAD_MODE); + const std::string pad_mode = common::AnfAlgo::GetNodeAttr(kernel_node, PAD_MODE); std::vector int_padding_l; std::vector int_padding_r; std::vector kernel_size({IntToSize(origin_kernel_sizes[2]), IntToSize(origin_kernel_sizes[3])}); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/pooling_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/pooling_cpu_kernel.cc index 790a6d25435..259ac0f055a 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/pooling_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/pooling_cpu_kernel.cc @@ -33,15 +33,15 @@ void PoolingCpuKernelMod::InitInputOutputSize(const CNodePtr &kernel_node) { void PoolingCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); std::vector src_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0); std::vector dst_shape = AnfAlgo::GetOutputDeviceShape(kernel_node, 0); dnnl::memory::desc src_desc = GetDefaultMemDesc(src_shape); dnnl::memory::desc dst_desc = GetDefaultMemDesc(dst_shape); std::vector origin_kernel_sizes; std::vector strides; - std::vector kernel_sizes_me = AnfAlgo::GetNodeAttr>(kernel_node, KERNEL_SIZE); - std::vector strides_me = AnfAlgo::GetNodeAttr>(kernel_node, STRIDES); + std::vector kernel_sizes_me = common::AnfAlgo::GetNodeAttr>(kernel_node, KERNEL_SIZE); + std::vector strides_me = common::AnfAlgo::GetNodeAttr>(kernel_node, STRIDES); (void)std::transform(kernel_sizes_me.begin(), kernel_sizes_me.end(), std::back_inserter(origin_kernel_sizes), [](const int64_t &value) { return static_cast(value); }); (void)std::transform(strides_me.begin(), strides_me.end(), std::back_inserter(strides), @@ -65,7 +65,7 @@ void PoolingCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { std::vector int_padding_l; std::vector int_padding_r; - const std::string pad_mode = AnfAlgo::GetNodeAttr(kernel_node, PAD_MODE); + const std::string pad_mode = common::AnfAlgo::GetNodeAttr(kernel_node, PAD_MODE); GetPadding(kernel_node, pad_mode, src_shape, kernel_size, stride, &int_padding_l, &int_padding_r, dummy_dilation); if (int_padding_l.size() != dim - kPoolingOffsetDim || int_padding_r.size() != dim - kPoolingOffsetDim) { MS_LOG(EXCEPTION) << "Pooling get padding failed!"; diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/pooling_max_grad_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/pooling_max_grad_cpu_kernel.cc index 16d4e4424b1..f4c89bf233f 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/pooling_max_grad_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/pooling_max_grad_cpu_kernel.cc @@ -33,13 +33,13 @@ constexpr size_t kMaxPoolingGradInputShapeSize = 4; void MaxPoolingGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); src_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 0); dst_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 1); std::vector kernel_sizes; std::vector strides; - auto kernel_sizes_me = AnfAlgo::GetNodeAttr>(kernel_node, KERNEL_SIZE); - auto strides_me = AnfAlgo::GetNodeAttr>(kernel_node, STRIDES); + auto kernel_sizes_me = common::AnfAlgo::GetNodeAttr>(kernel_node, KERNEL_SIZE); + auto strides_me = common::AnfAlgo::GetNodeAttr>(kernel_node, STRIDES); (void)std::transform(kernel_sizes_me.begin(), kernel_sizes_me.end(), std::back_inserter(kernel_sizes), [](const int64_t &value) { return LongToInt(value); }); (void)std::transform(strides_me.begin(), strides_me.end(), std::back_inserter(strides), @@ -49,7 +49,7 @@ void MaxPoolingGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_LOG(EXCEPTION) << "Pooling grad invalid input size!"; } std::vector padding_r; - const std::string pad_mode = AnfAlgo::GetNodeAttr(kernel_node, PAD_MODE); + const std::string pad_mode = common::AnfAlgo::GetNodeAttr(kernel_node, PAD_MODE); kernel_size_ = {IntToSize(kernel_sizes[2]), IntToSize(kernel_sizes[3])}; stride_.push_back(strides[2]); stride_.push_back(strides[3]); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/softmax_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/softmax_cpu_kernel.cc index 0a449010ee2..592d4cc4ad1 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/softmax_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/softmax_cpu_kernel.cc @@ -28,10 +28,10 @@ constexpr size_t kSoftmaxOutputsNum = 1; void SoftmaxCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); std::vector src_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0); std::vector axis_list; - std::vector axis_list_me = AnfAlgo::GetNodeAttr>(kernel_node, AXIS); + std::vector axis_list_me = common::AnfAlgo::GetNodeAttr>(kernel_node, AXIS); (void)std::transform(axis_list_me.begin(), axis_list_me.end(), std::back_inserter(axis_list), [](const int64_t &value) { return static_cast(value); }); if (axis_list.size() != 1) { diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/softmax_cross_entropy_with_logits_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/softmax_cross_entropy_with_logits_cpu_kernel.cc index 5f36e2e20e7..6d4a131189c 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/softmax_cross_entropy_with_logits_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/softmax_cross_entropy_with_logits_cpu_kernel.cc @@ -40,7 +40,7 @@ void SoftmaxCrossEntropyWithLogitsCpuKernelMod::InitInputOutputSize(const CNodeP void SoftmaxCrossEntropyWithLogitsCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); std::vector shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0); dnnl::memory::dims mem_dims; (void)mem_dims.insert(mem_dims.end(), shape.begin(), shape.end()); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/sparse_softmax_cross_entropy_with_logits_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/sparse_softmax_cross_entropy_with_logits_cpu_kernel.cc index 5100b3b8b14..c88b0e109a4 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/sparse_softmax_cross_entropy_with_logits_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/sparse_softmax_cross_entropy_with_logits_cpu_kernel.cc @@ -41,7 +41,7 @@ void SparseSoftmaxCrossEntropyWithLogitsCpuKernelMod::InitInputOutputSize(const void SparseSoftmaxCrossEntropyWithLogitsCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); std::vector shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0); std::vector label_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 1); if (label_shape.size() > 1) { @@ -57,7 +57,7 @@ void SparseSoftmaxCrossEntropyWithLogitsCpuKernelMod::InitKernel(const CNodePtr if (batch_size_ == 0 || class_num_ == 0) { MS_LOG(EXCEPTION) << "Invalid batch size or class num input!"; } - is_grad_ = AnfAlgo::GetNodeAttr(kernel_node, IS_GRAD); + is_grad_ = common::AnfAlgo::GetNodeAttr(kernel_node, IS_GRAD); auto mem_desc = CreateDesc(mem_dims, dnnl::memory::data_type::f32, dnnl::memory::format_tag::nc); auto desc = CreateDesc(dnnl::prop_kind::forward_training, mem_desc, 1); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/multinomial_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/multinomial_cpu_kernel.cc index c4bf8d9c49c..85c81233db1 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/multinomial_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/multinomial_cpu_kernel.cc @@ -22,8 +22,8 @@ namespace mindspore { namespace kernel { void MultinomialCpuKernel::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - input_shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + input_shape_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); // The dimensions of input tensor must be 1 or 2, with data type of float32. if (input_shape_.size() == 1) { @@ -32,8 +32,8 @@ void MultinomialCpuKernel::InitKernel(const CNodePtr &kernel_node) { workspace_size_list_.push_back(input_shape_[1] * sizeof(float)); } - seed_ = static_cast(GetValue(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("seed"))); - seed2_ = static_cast(GetValue(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("seed2"))); + seed_ = static_cast(GetValue(common::AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("seed"))); + seed2_ = static_cast(GetValue(common::AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("seed2"))); int64_t RNG_seed = 0; if (seed2_ > 0) { RNG_seed = seed2_; diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/nms_with_mask_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/nms_with_mask_cpu_kernel.cc index 58ed3371bfb..a0949261e44 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/nms_with_mask_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/nms_with_mask_cpu_kernel.cc @@ -199,15 +199,15 @@ void NMSWithMaskCpuKernelMod::ReducePass(const int num, bool *sel_boxes, cons template void NMSWithMaskCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - iou_value_ = AnfAlgo::GetNodeAttr(kernel_node, "iou_threshold"); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + iou_value_ = common::AnfAlgo::GetNodeAttr(kernel_node, "iou_threshold"); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != INPUT_NUM) { MS_LOG(ERROR) << "For '" << kernel_name_ << "', the number of inputs should be 1, but got " << input_num << "input(s)."; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != OUTPUT_NUM) { MS_LOG(ERROR) << "For '" << kernel_name_ << "', the number of outputs should be 3, but got " << output_num << "output(s)."; @@ -217,7 +217,7 @@ void NMSWithMaskCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { template void NMSWithMaskCpuKernelMod::InitInputOutputSize(const CNodePtr &kernel_node) { NativeCpuKernelMod::InitInputOutputSize(kernel_node); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); num_input_ = SizeToInt(input_shape[0]); // Get N values in [N, 5] data. ceil_power_2 = static_cast(NmsRoundUpPower2(num_input_)); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/one_hot_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/one_hot_cpu_kernel.cc index 6496568538b..7d927956e4c 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/one_hot_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/one_hot_cpu_kernel.cc @@ -63,11 +63,11 @@ constexpr size_t kOneHotOutputsNum = 1; void OneHotCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); input_dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); output_dtype_ = AnfAlgo::GetOutputDeviceDataType(kernel_node, 0); - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); - int64_t axis = AnfAlgo::GetNodeAttr(kernel_node, AXIS); + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); + int64_t axis = common::AnfAlgo::GetNodeAttr(kernel_node, AXIS); if (axis != -1 && LongToSize(axis) >= output_shape.size()) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the 'axis' should be -1, or an int which is less than the dimension of output, but got " diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/ones_like_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/ones_like_cpu_kernel.cc index 8e1086c9a5f..8324ead5f82 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/ones_like_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/ones_like_cpu_kernel.cc @@ -27,7 +27,7 @@ constexpr size_t kOnesLikeOutputsNum = 1; template void OnesLikeCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); input_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 0); output_shape_ = AnfAlgo::GetOutputDeviceShape(kernel_node, 0); } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/pack_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/pack_cpu_kernel.cc index 521c4bf4caa..7d7efd8c163 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/pack_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/pack_cpu_kernel.cc @@ -27,9 +27,9 @@ constexpr size_t kPackOutputsNum = 1; template void PackFwdCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - input_num_ = AnfAlgo::GetInputTensorNum(kernel_node); - axis_ = AnfAlgo::GetNodeAttr(kernel_node, AXIS); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + input_num_ = common::AnfAlgo::GetInputTensorNum(kernel_node); + axis_ = common::AnfAlgo::GetNodeAttr(kernel_node, AXIS); if (axis_ < 0) { auto input_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0); axis_ += (SizeToInt(input_shape.size()) + 1); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/pad_and_shift_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/pad_and_shift_cpu_kernel.cc index de88de13f15..8af21dbc95b 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/pad_and_shift_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/pad_and_shift_cpu_kernel.cc @@ -25,17 +25,17 @@ constexpr size_t kPadAndShiftOutputsNum = 1; void PadAndShiftCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); node_wpt_ = kernel_node; input_x_dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); type_size_ = GetTypeByte(TypeIdToType(input_x_dtype_)); - auto indices_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto indices_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); batch_size_ = 1; for (size_t i = 0; i < indices_shape.size(); ++i) { batch_size_ *= indices_shape[i]; } MS_LOG(INFO) << "PadAndShift batch_size:" << batch_size_; - auto cum_sum_arr_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto cum_sum_arr_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); if (cum_sum_arr_shape.size() != 1) { MS_LOG(ERROR) << "For '" << kernel_name_ << "', the dimension of 'cum_sum_arr' should be 1, but got " << cum_sum_arr_shape.size() << "."; @@ -89,12 +89,12 @@ void PadAndShiftCpuKernelMod::LaunchKernel(const std::vector &inputs if (!node_) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', node_wpt_(kernel_node) is expired. Error no: " << node_; } - auto output_nums = AnfAlgo::GetOutputTensorNum(node_); + auto output_nums = common::AnfAlgo::GetOutputTensorNum(node_); std::vector dtypes(output_nums); for (size_t i = 0; i < output_nums; i++) { dtypes[i] = AnfAlgo::GetOutputDeviceDataType(node_, i); } - AnfAlgo::SetOutputInferTypeAndShape(dtypes, {out_shape}, node_.get()); + common::AnfAlgo::SetOutputInferTypeAndShape(dtypes, {out_shape}, node_.get()); } } // namespace kernel } // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/pad_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/pad_cpu_kernel.cc index e9ea64f5937..1429b952704 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/pad_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/pad_cpu_kernel.cc @@ -27,11 +27,11 @@ constexpr size_t kPadElemSize = 2; void PadCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); std::vector> paddings_ = - AnfAlgo::GetNodeAttr>>(kernel_node, "paddings"); + common::AnfAlgo::GetNodeAttr>>(kernel_node, "paddings"); dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); - input_shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + input_shape_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); std::vector output_shape = AnfAlgo::GetOutputDeviceShape(kernel_node, 0); input_rank_ = input_shape_.size(); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/print_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/print_cpu_kernel.cc index 21a82a9e712..4254db88a74 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/print_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/print_cpu_kernel.cc @@ -26,10 +26,10 @@ namespace kernel { template void PrintCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - size_t input_tensor_num = AnfAlgo::GetInputTensorNum(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_tensor_num = common::AnfAlgo::GetInputTensorNum(kernel_node); for (size_t i = 0; i < input_tensor_num; ++i) { - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, i); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, i); (void)input_shapes_.emplace_back(input_shape); size_t size = input_shape.size() ? 1 : 0; for (size_t j = 0; j < input_shape.size(); ++j) { diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/ps/embedding_look_up_proxy_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/ps/embedding_look_up_proxy_kernel.cc index d776bf0867d..22d2216918c 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/ps/embedding_look_up_proxy_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/ps/embedding_look_up_proxy_kernel.cc @@ -29,9 +29,9 @@ constexpr size_t kEmbeddingLookUpProxyOutputsNum = 1; void EmbeddingLookUpProxyKernel::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); EmbeddingLookUpCpuKernelMod::InitKernel(kernel_node); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto indices_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto indices_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); size_t axis = kShape2dDims - input_shape.size(); if (input_shape.empty() || input_shape.size() > kShape2dDims) { MS_LOG(EXCEPTION) << "Input shape should not empty or greater than " << kShape2dDims << "-D, but got " @@ -47,7 +47,7 @@ void EmbeddingLookUpProxyKernel::InitKernel(const CNodePtr &kernel_node) { } if (mindspore::ps::PSContext::instance()->is_worker()) { - key_ = AnfAlgo::GetNodeAttr(kernel_node, kAttrPsKey); + key_ = common::AnfAlgo::GetNodeAttr(kernel_node, kAttrPsKey); } std::vector values; (void)std::transform(input_shape.begin(), input_shape.end(), std::back_inserter(values), diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/ps/pull_kernel.h b/mindspore/ccsrc/plugin/device/cpu/kernel/ps/pull_kernel.h index ed9d702e25a..a9cb65e9e13 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/ps/pull_kernel.h +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/ps/pull_kernel.h @@ -45,26 +45,26 @@ class PullKernelMod : public NativeCpuKernelMod { } void Init(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 2) { MS_LOG(ERROR) << "Input number is " << input_num << ", but pull needs 2 inputs."; return; } - auto key_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto key_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); for (size_t i = 0; i < key_shape.size(); i++) { keys_size_ *= key_shape[i]; } - auto var_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto var_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); for (size_t i = 0; i < var_shape.size(); i++) { var_size_ *= var_shape[i]; } - auto param_node = AnfAlgo::GetInputNode(kernel_node, 1); + auto param_node = common::AnfAlgo::GetInputNode(kernel_node, 1); MS_EXCEPTION_IF_NULL(param_node); param_name_ = param_node->fullname_with_scope(); if (mindspore::ps::PSContext::instance()->is_worker()) { - key_ = AnfAlgo::GetNodeAttr(kernel_node, kAttrPsKey); + key_ = common::AnfAlgo::GetNodeAttr(kernel_node, kAttrPsKey); } InitSizeLists(); return; diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/ps/push_kernel.h b/mindspore/ccsrc/plugin/device/cpu/kernel/ps/push_kernel.h index a85757dba73..e8a60d8c599 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/ps/push_kernel.h +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/ps/push_kernel.h @@ -54,10 +54,10 @@ class PushKernelMod : public NativeCpuKernelMod { } void Init(const CNodePtr &kernel_node) { - key_ = AnfAlgo::GetNodeAttr(kernel_node, kAttrPsKey); + key_ = common::AnfAlgo::GetNodeAttr(kernel_node, kAttrPsKey); auto optim_input_shapes = - AnfAlgo::GetNodeAttr>>(kernel_node, "optim_input_shapes"); - auto only_shape_indices = AnfAlgo::GetNodeAttr>(kernel_node, "only_shape_indices"); + common::AnfAlgo::GetNodeAttr>>(kernel_node, "optim_input_shapes"); + auto only_shape_indices = common::AnfAlgo::GetNodeAttr>(kernel_node, "only_shape_indices"); MS_LOG(INFO) << "Key " << key_ << " optimizer input shapes are:" << optim_input_shapes; MS_LOG(INFO) << "Only init shape indices are " << only_shape_indices; for (size_t i = 0; i < optim_input_shapes.size(); i++) { diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/ps/sparse_apply_adam_ps_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/ps/sparse_apply_adam_ps_kernel.cc index 2310ef94379..c6c7d914193 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/ps/sparse_apply_adam_ps_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/ps/sparse_apply_adam_ps_kernel.cc @@ -68,8 +68,8 @@ void SparseApplyAdamPSKernelMod::InitKernel( if (grad_shape[0] != indices_size_) { MS_LOG(ERROR) << "The first dimension of grad shape must be equal to indices"; } - if (AnfAlgo::HasNodeAttr(USE_NESTEROV, cnode)) { - use_nesterov_ = AnfAlgo::GetNodeAttr(cnode, USE_NESTEROV); + if (common::AnfAlgo::HasNodeAttr(USE_NESTEROV, cnode)) { + use_nesterov_ = common::AnfAlgo::GetNodeAttr(cnode, USE_NESTEROV); } (void)workspace_size_list_.emplace_back(indices_size_ * var_outer_dim_size_ * sizeof(float) * worker_num_); (void)workspace_size_list_.emplace_back(indices_size_ * sizeof(int) * worker_num_); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/ps/sparse_apply_ftrl_ps_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/ps/sparse_apply_ftrl_ps_kernel.cc index c5ed3ccdcca..14ce720375d 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/ps/sparse_apply_ftrl_ps_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/ps/sparse_apply_ftrl_ps_kernel.cc @@ -63,23 +63,23 @@ void SparseApplyFtrlPSKernelMod::InitKernel( if (grad_shape[0] != indices_size_) { MS_LOG(EXCEPTION) << "The first dimension of grad shape must be equal to indices"; } - init_accum_ = AnfAlgo::GetNodeAttr(cnode, "init_accum"); + init_accum_ = common::AnfAlgo::GetNodeAttr(cnode, "init_accum"); if (init_accum_ < 0) { MS_LOG(EXCEPTION) << "init_accum should be a non-negative scalar"; } - lr_ = AnfAlgo::GetNodeAttr(cnode, "lr"); + lr_ = common::AnfAlgo::GetNodeAttr(cnode, "lr"); if (lr_ <= 0) { MS_LOG(EXCEPTION) << "lr should be a positive scalar"; } - l1_ = AnfAlgo::GetNodeAttr(cnode, "l1"); + l1_ = common::AnfAlgo::GetNodeAttr(cnode, "l1"); if (l1_ < 0) { MS_LOG(EXCEPTION) << "l1 should be a non-negative scalar"; } - l2_ = AnfAlgo::GetNodeAttr(cnode, "l2"); + l2_ = common::AnfAlgo::GetNodeAttr(cnode, "l2"); if (l2_ < 0) { MS_LOG(EXCEPTION) << "l2 should be a non-negative scalar"; } - lr_power_ = AnfAlgo::GetNodeAttr(cnode, "lr_power"); + lr_power_ = common::AnfAlgo::GetNodeAttr(cnode, "lr_power"); if (lr_power_ > 0) { MS_LOG(EXCEPTION) << "lr_power should be a non-positive scalar"; } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/ps/sparse_apply_lazy_adam_ps_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/ps/sparse_apply_lazy_adam_ps_kernel.cc index f8c9412c0d1..39949c1775e 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/ps/sparse_apply_lazy_adam_ps_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/ps/sparse_apply_lazy_adam_ps_kernel.cc @@ -70,8 +70,8 @@ void SparseApplyLazyAdamPSKernelMod::InitKernel( if (grad_shape[0] != indices_size_) { MS_LOG(ERROR) << "The first dimension of grad shape must be equal to indices"; } - if (AnfAlgo::HasNodeAttr(USE_NESTEROV, cnode)) { - use_nesterov_ = AnfAlgo::GetNodeAttr(cnode, USE_NESTEROV); + if (common::AnfAlgo::HasNodeAttr(USE_NESTEROV, cnode)) { + use_nesterov_ = common::AnfAlgo::GetNodeAttr(cnode, USE_NESTEROV); } (void)workspace_size_list_.emplace_back(indices_size_ * var_outer_dim_size_ * sizeof(float) * worker_num_); (void)workspace_size_list_.emplace_back(indices_size_ * sizeof(int) * worker_num_); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/pyfunc/py_func_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/pyfunc/py_func_cpu_kernel.cc index 5a1ee09338e..b253c5e012d 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/pyfunc/py_func_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/pyfunc/py_func_cpu_kernel.cc @@ -214,9 +214,9 @@ void PyObjectToRawMemorys(const py::object &object, const PyFuncArgumentInfo &ou } // namespace void PyFuncCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { - func_id_ = AnfAlgo::GetNodeAttr(kernel_node, "fn_id"); - fake_output_ = AnfAlgo::GetNodeAttr(kernel_node, "fake_output"); - single_scalar_output_ = AnfAlgo::GetNodeAttr(kernel_node, "single_scalar_output"); + func_id_ = common::AnfAlgo::GetNodeAttr(kernel_node, "fn_id"); + fake_output_ = common::AnfAlgo::GetNodeAttr(kernel_node, "fake_output"); + single_scalar_output_ = common::AnfAlgo::GetNodeAttr(kernel_node, "single_scalar_output"); BuildFuncInfo(kernel_node); } @@ -236,26 +236,26 @@ void PyFuncCpuKernelMod::BuildFuncInfo(const CNodePtr &kernel_node) { std::vector> in_shapes; std::vector> out_shapes; - if (AnfAlgo::HasNodeAttr("in_types", kernel_node)) { - const auto &in_type_ptrs = AnfAlgo::GetNodeAttr>(kernel_node, "in_types"); + if (common::AnfAlgo::HasNodeAttr("in_types", kernel_node)) { + const auto &in_type_ptrs = common::AnfAlgo::GetNodeAttr>(kernel_node, "in_types"); (void)std::for_each(in_type_ptrs.begin(), in_type_ptrs.end(), [&in_types](auto p) { (void)in_types.emplace_back(p->type_id()); }); } else { in_types = AnfAlgo::GetAllInputDeviceTypes(kernel_node); } - if (AnfAlgo::HasNodeAttr("out_types", kernel_node)) { - const auto &out_type_ptrs = AnfAlgo::GetNodeAttr>(kernel_node, "out_types"); + if (common::AnfAlgo::HasNodeAttr("out_types", kernel_node)) { + const auto &out_type_ptrs = common::AnfAlgo::GetNodeAttr>(kernel_node, "out_types"); (void)std::for_each(out_type_ptrs.begin(), out_type_ptrs.end(), [&out_types](auto p) { (void)out_types.emplace_back(p->type_id()); }); } else { out_types = AnfAlgo::GetAllOutputDeviceTypes(kernel_node); } - if (AnfAlgo::HasNodeAttr("in_shapes", kernel_node)) { - in_shapes = AnfAlgo::GetNodeAttr>>(kernel_node, "in_shapes"); + if (common::AnfAlgo::HasNodeAttr("in_shapes", kernel_node)) { + in_shapes = common::AnfAlgo::GetNodeAttr>>(kernel_node, "in_shapes"); } else { - for (size_t i = 0; i < AnfAlgo::GetInputTensorNum(kernel_node); i++) { + for (size_t i = 0; i < common::AnfAlgo::GetInputTensorNum(kernel_node); i++) { std::vector in_shape = AnfAlgo::GetInputDeviceShape(kernel_node, i); std::vector in_shape_tmp; (void)std::for_each(in_shape.begin(), in_shape.end(), @@ -264,10 +264,10 @@ void PyFuncCpuKernelMod::BuildFuncInfo(const CNodePtr &kernel_node) { } } - if (AnfAlgo::HasNodeAttr("out_shapes", kernel_node)) { - out_shapes = AnfAlgo::GetNodeAttr>>(kernel_node, "out_shapes"); + if (common::AnfAlgo::HasNodeAttr("out_shapes", kernel_node)) { + out_shapes = common::AnfAlgo::GetNodeAttr>>(kernel_node, "out_shapes"); } else { - for (size_t i = 0; i < AnfAlgo::GetOutputTensorNum(kernel_node); i++) { + for (size_t i = 0; i < common::AnfAlgo::GetOutputTensorNum(kernel_node); i++) { std::vector out_shape = AnfAlgo::GetOutputDeviceShape(kernel_node, i); std::vector out_shape_tmp; (void)std::for_each(out_shape.begin(), out_shape.end(), diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/random_choice_with_mask_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/random_choice_with_mask_cpu_kernel.cc index 2f6b9a1409b..cf2a4cbb8fa 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/random_choice_with_mask_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/random_choice_with_mask_cpu_kernel.cc @@ -83,34 +83,34 @@ void UpdateOutput(const std::vector &dims_, const int32_t &non_zero_num void RandomChoiceWithMaskCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != INPUT_NUM) { MS_LOG(ERROR) << "For '" << kernel_name_ << "', the number of inputs should be 1, but got " << input_num << "input(s)."; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != OUTPUT_NUM) { MS_LOG(ERROR) << "For '" << kernel_name_ << "', the number of outputs should be 2, but got " << output_num << "output(s)."; } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); input_shape_size = input_shape.size(); if (input_shape_size < 1 || input_shape_size > MAX_DIMENSION) { MS_LOG(ERROR) << "For '" << kernel_name_ << "', the dimension of 'input_x ' should be in range [1-D, 5-D], but got " << input_shape_size << "-D."; } - seed = static_cast(AnfAlgo::GetNodeAttr(kernel_node, "seed")); - seed2 = static_cast(AnfAlgo::GetNodeAttr(kernel_node, "seed2")); - count = static_cast(AnfAlgo::GetNodeAttr(kernel_node, "count")); + seed = static_cast(common::AnfAlgo::GetNodeAttr(kernel_node, "seed")); + seed2 = static_cast(common::AnfAlgo::GetNodeAttr(kernel_node, "seed2")); + count = static_cast(common::AnfAlgo::GetNodeAttr(kernel_node, "count")); MS_LOG(INFO) << "This op attr count is " << count; for (size_t i = 0; i < input_num; i++) { - auto input_i_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, i); + auto input_i_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, i); for (size_t j = 0; j < input_i_shape.size(); j++) { (void)dims.emplace_back(input_i_shape[j]); } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/random_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/random_cpu_kernel.cc index e968eca930a..6b3316e5d60 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/random_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/random_cpu_kernel.cc @@ -97,7 +97,7 @@ void LaunchUniformReal(unsigned int seed, const std::vector &, const void RandomCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); auto iter = kRandomOpTypeMap.find(kernel_name_); if (iter == kRandomOpTypeMap.end()) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ @@ -107,7 +107,7 @@ void RandomCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { random_op_type_ = iter->second; } - auto prim = AnfAlgo::GetCNodePrimitive(kernel_node); + auto prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(prim); seed_ = LongToInt(GetValue(prim->GetAttr("seed"))); seed2_ = LongToInt(GetValue(prim->GetAttr("seed2"))); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/range_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/range_cpu_kernel.cc index f18e7cecd25..99653d6bc95 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/range_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/range_cpu_kernel.cc @@ -26,7 +26,7 @@ constexpr size_t kRangeOutputsNum = 1; void RangeCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/rank_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/rank_cpu_kernel.cc index 8c031505fcb..6c770a1b6f1 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/rank_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/rank_cpu_kernel.cc @@ -18,7 +18,7 @@ #include #include #include -#include "common/thread_pool.h" +#include "include/common/thread_pool.h" namespace mindspore { namespace kernel { @@ -27,13 +27,13 @@ using rank::NaOption; template void RankCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); static const std::map kValidMethods = { {"max", Method::Max}, {"min", Method::Min}, {"average", Method::Average}, {"first", Method::First}, {"dense", Method::Dense}, }; - auto method = AnfAlgo::GetNodeAttr(kernel_node, METHOD); + auto method = common::AnfAlgo::GetNodeAttr(kernel_node, METHOD); if (kValidMethods.find(method) == kValidMethods.end()) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the method should be in " << Map2Str(kValidMethods) << ", but got " << method; @@ -45,16 +45,16 @@ void RankCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { {"top", NaOption::Top}, {"bottom", NaOption::Bottom}, }; - auto option = AnfAlgo::GetNodeAttr(kernel_node, NA_OPTION); + auto option = common::AnfAlgo::GetNodeAttr(kernel_node, NA_OPTION); if (kValidOptions.find(option) == kValidOptions.end()) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the option should be in " << Map2Str(kValidOptions) << ", but got " << option; } option_ = kValidOptions.at(option); - ascending_ = AnfAlgo::GetNodeAttr(kernel_node, ASCENDING); - pct_ = AnfAlgo::GetNodeAttr(kernel_node, PCT); - auto axis = AnfAlgo::GetNodeAttr(kernel_node, AXIS); + ascending_ = common::AnfAlgo::GetNodeAttr(kernel_node, ASCENDING); + pct_ = common::AnfAlgo::GetNodeAttr(kernel_node, PCT); + auto axis = common::AnfAlgo::GetNodeAttr(kernel_node, AXIS); axis_ = axis < 0 ? LongToSize(axis + SizeToLong(input_shape.size())) : LongToSize(axis); if (axis_ >= input_shape.size()) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/reduce_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/reduce_cpu_kernel.cc index 6a5637e3723..5d5617395d5 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/reduce_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/reduce_cpu_kernel.cc @@ -39,9 +39,9 @@ void UpdateAxis(const PrimitivePtr &prim, const CNodePtr &kernel_node, const std MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the 'axis' should be not null, but got empty value."; } if (axis_addr->isa() || axis_addr->isa()) { - *axis = AnfAlgo::GetNodeAttr>(kernel_node, AXIS); + *axis = common::AnfAlgo::GetNodeAttr>(kernel_node, AXIS); } else if (axis_addr->isa()) { - (void)axis->emplace_back(AnfAlgo::GetNodeAttr(kernel_node, AXIS)); + (void)axis->emplace_back(common::AnfAlgo::GetNodeAttr(kernel_node, AXIS)); } else { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the type of 'axis' should be tuple, list, or int, but got invalid type."; @@ -51,10 +51,10 @@ void UpdateAxis(const PrimitivePtr &prim, const CNodePtr &kernel_node, const std template void ReduceCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); axis_.clear(); input_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 0); - auto prim = AnfAlgo::GetCNodePrimitive(kernel_node); + auto prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(prim); UpdateAxis(prim, kernel_node, kernel_name_, &axis_); size_t dimension = input_shape_.size(); @@ -64,7 +64,7 @@ void ReduceCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { // Delete the duplicate axis. auto last = std::unique(axis_.begin(), axis_.end()); axis_.erase(last, axis_.end()); - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); if constexpr (std::is_same::value) { if (kernel_name_ == prim::kPrimReduceAll->name()) { diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/reduce_scatter_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/reduce_scatter_cpu_kernel.cc index b78c0ae16b7..3bf8af94ec0 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/reduce_scatter_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/reduce_scatter_cpu_kernel.cc @@ -31,8 +31,8 @@ ReduceScatterCpuKernelMod::ReduceScatterCpuKernelMod() : op_type_(kMPIOpTypeSum) void ReduceScatterCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - auto primitive = AnfAlgo::GetCNodePrimitive(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + auto primitive = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(primitive); auto op = primitive->GetAttr("op"); if (op != nullptr) { diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/reshape_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/reshape_cpu_kernel.cc index b7d97390a51..62aaec645bc 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/reshape_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/reshape_cpu_kernel.cc @@ -26,7 +26,7 @@ constexpr size_t kReshapeOutputsNum = 1; void ReshapeCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); } bool ReshapeCpuKernelMod::Launch(const std::vector &inputs, const std::vector &, diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/resize_bilinear_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/resize_bilinear_cpu_kernel.cc index 7e739163b5e..98c978d8166 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/resize_bilinear_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/resize_bilinear_cpu_kernel.cc @@ -29,10 +29,10 @@ constexpr size_t kResizeBilinearAttrSize = 2; void ResizeBilinearCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - size_ = AnfAlgo::GetNodeAttr>(kernel_node, SIZE); - align_corners_ = AnfAlgo::GetNodeAttr(kernel_node, "align_corners"); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + shape_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + size_ = common::AnfAlgo::GetNodeAttr>(kernel_node, SIZE); + align_corners_ = common::AnfAlgo::GetNodeAttr(kernel_node, "align_corners"); dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); if (shape_.size() != kResizeBilinearInputsShapeSize) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimension of 'x' should be " diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/resize_bilinear_grad_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/resize_bilinear_grad_cpu_kernel.cc index 332a843b32e..d9f3c20fbd5 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/resize_bilinear_grad_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/resize_bilinear_grad_cpu_kernel.cc @@ -29,11 +29,11 @@ constexpr size_t kResizeBilinearGradInputsXShapeSize = 4; void ResizeBilinearGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - size_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - align_corners_ = AnfAlgo::GetNodeAttr(kernel_node, "align_corners"); - dtype_ = AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, 0); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + shape_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + size_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + align_corners_ = common::AnfAlgo::GetNodeAttr(kernel_node, "align_corners"); + dtype_ = common::AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, 0); if (shape_.size() < kResizeBilinearGradInputsDoutShapeSize) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimension of input 'dout' should be " << kResizeBilinearGradInputsDoutShapeSize << ", but got " << shape_.size(); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/resize_nearest_neighbor_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/resize_nearest_neighbor_cpu_kernel.cc index 19fddba2dbb..da196108bda 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/resize_nearest_neighbor_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/resize_nearest_neighbor_cpu_kernel.cc @@ -29,10 +29,10 @@ constexpr size_t kResizeNearestNeighborAttrSize = 2; void ResizeNearestNeighborCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - std::vector input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - std::vector output_size = AnfAlgo::GetNodeAttr>(kernel_node, SIZE); - align_corners_ = AnfAlgo::GetNodeAttr(kernel_node, "align_corners"); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + std::vector input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + std::vector output_size = common::AnfAlgo::GetNodeAttr>(kernel_node, SIZE); + align_corners_ = common::AnfAlgo::GetNodeAttr(kernel_node, "align_corners"); dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); if (input_shape.size() != kResizeNearestNeighborInputsShapeSize) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimension of 'input_x' should be " diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/resize_nearest_neighbor_grad_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/resize_nearest_neighbor_grad_cpu_kernel.cc index 4a55ee577d3..4cebb9af8ee 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/resize_nearest_neighbor_grad_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/resize_nearest_neighbor_grad_cpu_kernel.cc @@ -29,11 +29,11 @@ constexpr size_t kResizeNearestNeighborGradOutputsShapeSize = 4; void ResizeNearestNeighborGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - std::vector input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - std::vector output_size = AnfAlgo::GetOutputInferShape(kernel_node, 0); - align_corners_ = AnfAlgo::GetNodeAttr(kernel_node, "align_corners"); - dtype_ = AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, 0); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + std::vector input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + std::vector output_size = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); + align_corners_ = common::AnfAlgo::GetNodeAttr(kernel_node, "align_corners"); + dtype_ = common::AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, 0); if (input_shape.size() != kResizeNearestNeighborGradInputsShapeSize) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimension of input should be " diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/rl/buffer_append_cpu_kernel.h b/mindspore/ccsrc/plugin/device/cpu/kernel/rl/buffer_append_cpu_kernel.h index 9ce1c5e39ee..f17f48eb776 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/rl/buffer_append_cpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/rl/buffer_append_cpu_kernel.h @@ -31,10 +31,10 @@ class BufferAppendCpuKernelMod : public NativeCpuKernelMod { ~BufferAppendCpuKernelMod() override = default; void Init(const CNodePtr &kernel_node) { - auto shapes = AnfAlgo::GetNodeAttr>(kernel_node, "buffer_elements"); - auto types = AnfAlgo::GetNodeAttr>(kernel_node, "buffer_dtype"); - capacity_ = AnfAlgo::GetNodeAttr(kernel_node, "capacity"); - exp_batch_ = AnfAlgo::GetNodeAttr(kernel_node, "exp_batch"); + auto shapes = common::AnfAlgo::GetNodeAttr>(kernel_node, "buffer_elements"); + auto types = common::AnfAlgo::GetNodeAttr>(kernel_node, "buffer_dtype"); + capacity_ = common::AnfAlgo::GetNodeAttr(kernel_node, "capacity"); + exp_batch_ = common::AnfAlgo::GetNodeAttr(kernel_node, "exp_batch"); element_nums_ = shapes.size(); for (size_t i = 0; i < element_nums_; i++) { exp_element_list.push_back(shapes[i] * UnitSizeInBytes(types[i]->type_id())); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/rl/buffer_get_cpu_kernel.h b/mindspore/ccsrc/plugin/device/cpu/kernel/rl/buffer_get_cpu_kernel.h index 850da512acb..2d0f9af5887 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/rl/buffer_get_cpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/rl/buffer_get_cpu_kernel.h @@ -31,9 +31,9 @@ class BufferGetCpuKernelMod : public NativeCpuKernelMod { ~BufferGetCpuKernelMod() override = default; void Init(const CNodePtr &kernel_node) { - auto shapes = AnfAlgo::GetNodeAttr>(kernel_node, "buffer_elements"); - auto types = AnfAlgo::GetNodeAttr>(kernel_node, "buffer_dtype"); - capacity_ = AnfAlgo::GetNodeAttr(kernel_node, "capacity"); + auto shapes = common::AnfAlgo::GetNodeAttr>(kernel_node, "buffer_elements"); + auto types = common::AnfAlgo::GetNodeAttr>(kernel_node, "buffer_dtype"); + capacity_ = common::AnfAlgo::GetNodeAttr(kernel_node, "capacity"); element_nums_ = shapes.size(); for (size_t i = 0; i < element_nums_; i++) { exp_element_list.push_back(shapes[i] * UnitSizeInBytes(types[i]->type_id())); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/rl/buffer_sample_cpu_kernel.h b/mindspore/ccsrc/plugin/device/cpu/kernel/rl/buffer_sample_cpu_kernel.h index e15b6b037aa..9569fd1d0c9 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/rl/buffer_sample_cpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/rl/buffer_sample_cpu_kernel.h @@ -33,12 +33,12 @@ class BufferCPUSampleKernelMod : public NativeCpuKernelMod { ~BufferCPUSampleKernelMod() override = default; void Init(const CNodePtr &kernel_node) { - auto shapes = AnfAlgo::GetNodeAttr>(kernel_node, "buffer_elements"); - auto types = AnfAlgo::GetNodeAttr>(kernel_node, "buffer_dtype"); - capacity_ = AnfAlgo::GetNodeAttr(kernel_node, "capacity"); - seed_ = AnfAlgo::GetNodeAttr(kernel_node, "seed"); - unique_ = AnfAlgo::GetNodeAttr(kernel_node, "unique"); - batch_size_ = LongToSize(AnfAlgo::GetNodeAttr(kernel_node, "batch_size")); + auto shapes = common::AnfAlgo::GetNodeAttr>(kernel_node, "buffer_elements"); + auto types = common::AnfAlgo::GetNodeAttr>(kernel_node, "buffer_dtype"); + capacity_ = common::AnfAlgo::GetNodeAttr(kernel_node, "capacity"); + seed_ = common::AnfAlgo::GetNodeAttr(kernel_node, "seed"); + unique_ = common::AnfAlgo::GetNodeAttr(kernel_node, "unique"); + batch_size_ = LongToSize(common::AnfAlgo::GetNodeAttr(kernel_node, "batch_size")); element_nums_ = shapes.size(); for (size_t i = 0; i < element_nums_; i++) { exp_element_list.push_back(shapes[i] * UnitSizeInBytes(types[i]->type_id())); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/rl/tensor_array_create_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/rl/tensor_array_create_kernel.cc index 2ccba5c7c99..369e8860768 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/rl/tensor_array_create_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/rl/tensor_array_create_kernel.cc @@ -28,14 +28,14 @@ TensorArrayCreateCpuKernelMod::TensorArrayCreateCpuKernelMod() : is_dynamic_(tru void TensorArrayCreateCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - auto shape = AnfAlgo::GetNodeAttr>(kernel_node, "element_shape"); + auto shape = common::AnfAlgo::GetNodeAttr>(kernel_node, "element_shape"); for (auto i : shape) { shapes_.push_back(LongToSize(i)); } - type_ = AnfAlgo::GetNodeAttr(kernel_node, "dtype"); - size_ = AnfAlgo::GetNodeAttr(kernel_node, "size"); - is_dynamic_ = AnfAlgo::GetNodeAttr(kernel_node, "dynamic_size"); - name_ = AnfAlgo::GetNodeAttr(kernel_node, "name"); + type_ = common::AnfAlgo::GetNodeAttr(kernel_node, "dtype"); + size_ = common::AnfAlgo::GetNodeAttr(kernel_node, "size"); + is_dynamic_ = common::AnfAlgo::GetNodeAttr(kernel_node, "dynamic_size"); + name_ = common::AnfAlgo::GetNodeAttr(kernel_node, "name"); output_size_list_.push_back(sizeof(int64_t)); } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/rl/tensor_array_read_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/rl/tensor_array_read_kernel.cc index da77db85e89..181124e02d5 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/rl/tensor_array_read_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/rl/tensor_array_read_kernel.cc @@ -27,8 +27,8 @@ TensorArrayReadCpuKernelMod::TensorArrayReadCpuKernelMod() : value_size_(0), typ void TensorArrayReadCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - shapes_ = AnfAlgo::GetNodeAttr>(kernel_node, "element_shape"); - type_ = AnfAlgo::GetNodeAttr(kernel_node, "dtype"); + shapes_ = common::AnfAlgo::GetNodeAttr>(kernel_node, "element_shape"); + type_ = common::AnfAlgo::GetNodeAttr(kernel_node, "dtype"); value_size_ = GetTypeByte(type_); for (auto i : shapes_) { value_size_ *= LongToSize(i); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/rl/tensor_array_stack_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/rl/tensor_array_stack_kernel.cc index 429e5a783ee..50b741eb016 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/rl/tensor_array_stack_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/rl/tensor_array_stack_kernel.cc @@ -32,15 +32,15 @@ TensorArrayStackCpuKernelMod::TensorArrayStackCpuKernelMod() void TensorArrayStackCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); kernel_node_ = kernel_node; - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - auto shape = AnfAlgo::GetNodeAttr>(kernel_node, "element_shape"); - auto max_element = AnfAlgo::GetNodeAttr(kernel_node, "max_element"); - is_dynamic_ = AnfAlgo::GetNodeAttr(kernel_node, "is_dynamic_shape"); - auto size = AnfAlgo::GetNodeAttr(kernel_node, "size"); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + auto shape = common::AnfAlgo::GetNodeAttr>(kernel_node, "element_shape"); + auto max_element = common::AnfAlgo::GetNodeAttr(kernel_node, "max_element"); + is_dynamic_ = common::AnfAlgo::GetNodeAttr(kernel_node, "is_dynamic_shape"); + auto size = common::AnfAlgo::GetNodeAttr(kernel_node, "size"); for (auto i : shape) { shapes_.push_back(LongToSize(i)); } - type_ = AnfAlgo::GetNodeAttr(kernel_node, "dtype"); + type_ = common::AnfAlgo::GetNodeAttr(kernel_node, "dtype"); ele_size_ = GetTypeByte(type_); for (auto i : shapes_) { ele_size_ *= i; @@ -61,7 +61,7 @@ void TensorArrayStackCpuKernelMod::PostExecute() { auto shape = shapes_; (void)shape.insert(shape.begin(), tensor_size); MS_LOG(DEBUG) << "After postexecute, the real shape of TensorArrayStack is " << shape; - AnfAlgo::SetOutputInferTypeAndShape({type_->type_id()}, {shape}, kernel_node_.lock().get()); + common::AnfAlgo::SetOutputInferTypeAndShape({type_->type_id()}, {shape}, kernel_node_.lock().get()); } void TensorArrayStackCpuKernelMod::ResetResource() noexcept { diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/rl/tensor_array_write_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/rl/tensor_array_write_kernel.cc index aa1627507ea..3c71260c415 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/rl/tensor_array_write_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/rl/tensor_array_write_kernel.cc @@ -29,7 +29,7 @@ TensorArrayWriteCpuKernelMod::TensorArrayWriteCpuKernelMod() : value_size_(0), t void TensorArrayWriteCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - type_ = AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, kSecondInputIndex); + type_ = common::AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, kSecondInputIndex); shapes_ = AnfAlgo::GetInputDeviceShape(kernel_node, kSecondInputIndex); value_size_ = GetTypeByte(TypeIdToType(type_)); for (auto i : shapes_) { diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/rmsprop_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/rmsprop_cpu_kernel.cc index 8a77d1410ab..91a8689812c 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/rmsprop_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/rmsprop_cpu_kernel.cc @@ -76,18 +76,18 @@ void RMSPropCpuKernelMod::LaunchRMSPropUseCenter(T *variable, T *mean_square, template void RMSPropCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - auto node_name = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + auto node_name = common::AnfAlgo::GetCNodeName(kernel_node); if (node_name == "ApplyCenteredRMSProp") { use_center_ = true; } if (node_name == "ApplyRMSProp") { - decay_ = AnfAlgo::GetNodeAttr(kernel_node, "rho"); - momentum_ = AnfAlgo::GetNodeAttr(kernel_node, "momentum"); - epsilon_ = AnfAlgo::GetNodeAttr(kernel_node, "epsilon"); + decay_ = common::AnfAlgo::GetNodeAttr(kernel_node, "rho"); + momentum_ = common::AnfAlgo::GetNodeAttr(kernel_node, "momentum"); + epsilon_ = common::AnfAlgo::GetNodeAttr(kernel_node, "epsilon"); } - auto input_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); for (auto &dim : input_shape) { size_ *= dim; diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/roi_align_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/roi_align_cpu_kernel.cc index 0993b4bd1e3..e2c16613c84 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/roi_align_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/roi_align_cpu_kernel.cc @@ -27,10 +27,10 @@ constexpr size_t kOutputSize = 1; template void ROIAlignCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); // Get the input shapes - auto x_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto rois_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto x_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto rois_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); auto x_shape_size = x_shape.size(); if (x_shape_size != X_DIMS) { @@ -44,11 +44,11 @@ void ROIAlignCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { roi_rows_ = SizeToInt(rois_shape[0]); roi_cols_ = SizeToInt(rois_shape[1]); - pooled_height_ = static_cast(AnfAlgo::GetNodeAttr(kernel_node, "pooled_height")); - pooled_width_ = static_cast(AnfAlgo::GetNodeAttr(kernel_node, "pooled_width")); - spatial_scale_ = static_cast(AnfAlgo::GetNodeAttr(kernel_node, "spatial_scale")); - sample_num_ = static_cast(AnfAlgo::GetNodeAttr(kernel_node, "sample_num")); - roi_end_mode_ = static_cast(AnfAlgo::GetNodeAttr(kernel_node, "roi_end_mode")); + pooled_height_ = static_cast(common::AnfAlgo::GetNodeAttr(kernel_node, "pooled_height")); + pooled_width_ = static_cast(common::AnfAlgo::GetNodeAttr(kernel_node, "pooled_width")); + spatial_scale_ = static_cast(common::AnfAlgo::GetNodeAttr(kernel_node, "spatial_scale")); + sample_num_ = static_cast(common::AnfAlgo::GetNodeAttr(kernel_node, "sample_num")); + roi_end_mode_ = static_cast(common::AnfAlgo::GetNodeAttr(kernel_node, "roi_end_mode")); MS_EXCEPTION_IF_ZERO("channel", channels_); MS_EXCEPTION_IF_ZERO("pooled_height", pooled_height_); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/roi_align_grad_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/roi_align_grad_cpu_kernel.cc index 9ae4b58f21f..a2ba1a674d8 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/roi_align_grad_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/roi_align_grad_cpu_kernel.cc @@ -59,21 +59,21 @@ void AtomicAdd(T *const address, const T val) { template void ROIAlignGradCpuKernelMod::CheckParam(const CNodePtr &kernel_node) { // Get the number of the input args - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != INPUT_NUM) { MS_LOG(ERROR) << "For '" << kernel_name_ << "', the number of inputs should be 2, but got " << input_num << " input(s)."; } // Get the number of the output args - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != OUTPUT_NUM) { MS_LOG(ERROR) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_num << " output(s)."; } // Get the input shapes - auto dy_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto dy_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); auto dy_shape_size = dy_shape.size(); if (dy_shape_size != DY_DIMS) { MS_LOG(ERROR) << "For '" << kernel_name_ << "', the dimension of output should be 4-D, but got " << dy_shape_size @@ -84,20 +84,20 @@ void ROIAlignGradCpuKernelMod::CheckParam(const CNodePtr &kernel_node) { template void ROIAlignGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); CheckParam(kernel_node); - auto rois_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto rois_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); roi_rows_ = SizeToInt(rois_shape[0]); roi_cols_ = SizeToInt(rois_shape[1]); - std::vector xdiff_shape_me = AnfAlgo::GetNodeAttr>(kernel_node, "xdiff_shape"); + std::vector xdiff_shape_me = common::AnfAlgo::GetNodeAttr>(kernel_node, "xdiff_shape"); (void)std::transform(xdiff_shape_me.begin(), xdiff_shape_me.end(), std::back_inserter(xdiff_shape_), [](const int64_t &value) { return static_cast(value); }); - pooled_height_ = static_cast(AnfAlgo::GetNodeAttr(kernel_node, "pooled_height")); - pooled_width_ = static_cast(AnfAlgo::GetNodeAttr(kernel_node, "pooled_width")); - spatial_scale_ = static_cast(AnfAlgo::GetNodeAttr(kernel_node, "spatial_scale")); - sample_num_ = static_cast(AnfAlgo::GetNodeAttr(kernel_node, "sample_num")); + pooled_height_ = static_cast(common::AnfAlgo::GetNodeAttr(kernel_node, "pooled_height")); + pooled_width_ = static_cast(common::AnfAlgo::GetNodeAttr(kernel_node, "pooled_width")); + spatial_scale_ = static_cast(common::AnfAlgo::GetNodeAttr(kernel_node, "spatial_scale")); + sample_num_ = static_cast(common::AnfAlgo::GetNodeAttr(kernel_node, "sample_num")); roi_end_mode_ = 1; batch_size_ = xdiff_shape_[BATCH]; diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/rolling_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/rolling_cpu_kernel.cc index c46060b487d..2c2545f01a1 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/rolling_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/rolling_cpu_kernel.cc @@ -18,7 +18,7 @@ #include #include #include -#include "common/thread_pool.h" +#include "include/common/thread_pool.h" namespace mindspore { namespace kernel { @@ -26,33 +26,33 @@ using rolling::Method; template void RollingCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); static const std::map kValidMethods = { {"max", Method::Max}, {"min", Method::Min}, {"mean", Method::Mean}, {"sum", Method::Sum}, {"std", Method::Std}, {"var", Method::Var}, }; - auto method = AnfAlgo::GetNodeAttr(kernel_node, METHOD); + auto method = common::AnfAlgo::GetNodeAttr(kernel_node, METHOD); if (kValidMethods.find(method) == kValidMethods.end()) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the 'method' should be in (max, min, sum, mean, std, var), but got " << method; } method_ = kValidMethods.at(method); - auto window = AnfAlgo::GetNodeAttr(kernel_node, WINDOW); + auto window = common::AnfAlgo::GetNodeAttr(kernel_node, WINDOW); if (window <= 0) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the 'window' should be greater than 0, but got " << window; } window_ = LongToInt(window); - min_periods_ = AnfAlgo::GetNodeAttr(kernel_node, MIN_PERIODS); + min_periods_ = common::AnfAlgo::GetNodeAttr(kernel_node, MIN_PERIODS); if (min_periods_ <= 0) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the 'min_periods' should be greater than 0, but got " << min_periods_; } - center_ = AnfAlgo::GetNodeAttr(kernel_node, CENTER); - auto axis = AnfAlgo::GetNodeAttr(kernel_node, AXIS); + center_ = common::AnfAlgo::GetNodeAttr(kernel_node, CENTER); + auto axis = common::AnfAlgo::GetNodeAttr(kernel_node, AXIS); size_t axis_t = axis < 0 ? LongToSize(axis + SizeToLong(input_shape.size())) : LongToSize(axis); - closed_ = AnfAlgo::GetNodeAttr(kernel_node, CLOSED); + closed_ = common::AnfAlgo::GetNodeAttr(kernel_node, CLOSED); if (axis_t >= input_shape.size()) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the 'axis' should be less than the dimension of input tensor " << input_shape.size() << "D, but got " << axis_t; diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/scatter_arithmetic_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/scatter_arithmetic_cpu_kernel.cc index 2f56005149c..7c963889163 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/scatter_arithmetic_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/scatter_arithmetic_cpu_kernel.cc @@ -45,8 +45,8 @@ void ScatterArithmeticCpuKernelMod::InitComputeFunc() { template void ScatterArithmeticCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); if (input_shape.size() < 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimension of 'input_x' should be greater than or equal to 1, but got " @@ -63,7 +63,7 @@ void ScatterArithmeticCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { inner_size_ *= input_shape[i]; } input_size_ = input_shape[0] * inner_size_; - auto indices_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto indices_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); indices_size_ = 1; for (size_t i = 0; i < indices_shape.size(); i++) { indices_size_ *= indices_shape[i]; diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/scatter_nd_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/scatter_nd_cpu_kernel.cc index 5c233d29b01..870307730a2 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/scatter_nd_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/scatter_nd_cpu_kernel.cc @@ -17,7 +17,7 @@ #include "plugin/device/cpu/kernel/scatter_nd_cpu_kernel.h" #include #include "plugin/device/cpu/hal/device/cpu_device_address.h" -#include "common/thread_pool.h" +#include "include/common/thread_pool.h" namespace mindspore { namespace kernel { @@ -58,11 +58,11 @@ void Compute(ScatterNdCpuKernelMod *content, const ComputeParams *pa template void ScatterNdCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); Check(kernel_node); - auto shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); - auto indices_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto updates_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto indices_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto updates_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); auto indices_unit_rank = indices_shape.back(); if (indices_unit_rank > shape.size()) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ @@ -137,12 +137,12 @@ bool ScatterNdCpuKernelMod::Launch(const std::vector & template void ScatterNdCpuKernelMod::Check(const CNodePtr &kernel_node) { - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != kScatterNdInputSize) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 2, but got " << input_num << " input(s)."; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != kScatterNdOutputSize) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_num << " output(s)."; diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/scatter_nd_update_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/scatter_nd_update_cpu_kernel.cc index 726d8c9d386..46c50e57013 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/scatter_nd_update_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/scatter_nd_update_cpu_kernel.cc @@ -17,7 +17,7 @@ #include "plugin/device/cpu/kernel/scatter_nd_update_cpu_kernel.h" #include #include "plugin/device/cpu/hal/device/cpu_device_address.h" -#include "common/thread_pool.h" +#include "include/common/thread_pool.h" namespace mindspore { namespace kernel { @@ -60,10 +60,10 @@ void Compute(const ComputeParams *params, const size_t start, const size_t en void ScatterUpdateCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - auto shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto indices_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto updates_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + auto shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto indices_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto updates_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); auto indices_unit_rank = indices_shape.back(); if (indices_unit_rank > shape.size()) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/searchsorted_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/searchsorted_cpu_kernel.cc index e2cbfe5f6f4..4248081b3f2 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/searchsorted_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/searchsorted_cpu_kernel.cc @@ -30,8 +30,8 @@ constexpr size_t kSearchSortedOutputsNum = 1; template void SearchSortedCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - right_ = AnfAlgo::GetNodeAttr(kernel_node, "right"); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + right_ = common::AnfAlgo::GetNodeAttr(kernel_node, "right"); sequence_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 0); values_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 1); search_len = sequence_shape_.back(); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/select_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/select_cpu_kernel.cc index 5b74d3d9ac4..ee972f3ac42 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/select_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/select_cpu_kernel.cc @@ -27,8 +27,8 @@ constexpr size_t kSelectOutputsNum = 1; template void SelectCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - auto shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + auto shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); for (size_t x : shape) { element_num_ *= x; } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/sgd_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/sgd_cpu_kernel.cc index a9d813d697c..f768ea81fbf 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/sgd_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/sgd_cpu_kernel.cc @@ -27,10 +27,10 @@ constexpr size_t kSGDOutputsNum = 1; template void SGDCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - dampening_ = AnfAlgo::GetNodeAttr(kernel_node, "dampening"); - weight_decay_ = AnfAlgo::GetNodeAttr(kernel_node, "weight_decay"); - nesterov_ = AnfAlgo::GetNodeAttr(kernel_node, "nesterov"); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + dampening_ = common::AnfAlgo::GetNodeAttr(kernel_node, "dampening"); + weight_decay_ = common::AnfAlgo::GetNodeAttr(kernel_node, "weight_decay"); + nesterov_ = common::AnfAlgo::GetNodeAttr(kernel_node, "nesterov"); } template diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/shift_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/shift_cpu_kernel.cc index c4ee4c2abe4..d12af087816 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/shift_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/shift_cpu_kernel.cc @@ -14,30 +14,30 @@ * limitations under the License. */ #include "plugin/device/cpu/kernel/shift_cpu_kernel.h" -#include "common/thread_pool.h" +#include "include/common/thread_pool.h" namespace mindspore { namespace kernel { template void ShiftCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - size_t input_count = AnfAlgo::GetInputTensorNum(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_count = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_count != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 2, but got " << input_count << " input(s)."; } - size_t output_count = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_count = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_count != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_count << " output(s)."; } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, kInputIndex); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, kInputIndex); - periods_ = AnfAlgo::GetNodeAttr(kernel_node, PERIODS); - auto axis = AnfAlgo::GetNodeAttr(kernel_node, AXIS); + periods_ = common::AnfAlgo::GetNodeAttr(kernel_node, PERIODS); + auto axis = common::AnfAlgo::GetNodeAttr(kernel_node, AXIS); size_t axis_t = axis < 0 ? LongToSize(axis + SizeToLong(input_shape.size())) : LongToSize(axis); if (axis_t >= input_shape.size()) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the 'axis' should be less than the dimension of input tensor " diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/sigmoid_cross_entropy_with_logits_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/sigmoid_cross_entropy_with_logits_cpu_kernel.cc index 117fda55b29..f8c2bf16e26 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/sigmoid_cross_entropy_with_logits_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/sigmoid_cross_entropy_with_logits_cpu_kernel.cc @@ -26,9 +26,9 @@ constexpr size_t kSigmoidCrossEntropyWithLogitsOutputsNum = 1; void SigmoidCrossEntropyWithLogitsCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); - std::vector x_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + std::vector x_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); for (const uint64_t &d : x_shape) { tensor_size_ *= d; } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/sigmoid_cross_entropy_with_logits_grad_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/sigmoid_cross_entropy_with_logits_grad_cpu_kernel.cc index cde1b6b8551..16644bd980d 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/sigmoid_cross_entropy_with_logits_grad_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/sigmoid_cross_entropy_with_logits_grad_cpu_kernel.cc @@ -26,9 +26,9 @@ constexpr size_t kSigmoidCrossEntropyWithLogitsGradOutputsNum = 1; void SigmoidCrossEntropyWithLogitsGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); - std::vector x_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + std::vector x_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); for (const uint64_t &d : x_shape) { tensor_size_ *= d; } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/slice_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/slice_cpu_kernel.cc index 83488e6e24f..fec5ec5739b 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/slice_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/slice_cpu_kernel.cc @@ -17,7 +17,7 @@ #include "plugin/device/cpu/kernel/slice_cpu_kernel.h" #include #include -#include "common/thread_pool.h" +#include "include/common/thread_pool.h" #include "plugin/device/cpu/hal/device/cpu_device_address.h" namespace mindspore { @@ -39,24 +39,24 @@ int NormalizeBeginPos(int begin_pos, int dim_len) { void SliceCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); cnode_ptr_ = kernel_node; static const std::unordered_map type_size_map = {{kNumberTypeBool, sizeof(bool)}, {kNumberTypeInt32, sizeof(int)}, {kNumberTypeFloat32, sizeof(float)}, {kNumberTypeFloat64, sizeof(double)}}; - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); if (input_shape.size() > DIMENSION_8D || input_shape.empty()) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimension of input tensor should be in range [1D, 8D], but got " << input_shape.size() << "D."; } - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); // begin and size are const input if (input_num == 1) { - auto size = AnfAlgo::GetNodeAttr>(kernel_node, SIZE); - auto begin = AnfAlgo::GetNodeAttr>(kernel_node, BEGIN); + auto size = common::AnfAlgo::GetNodeAttr>(kernel_node, SIZE); + auto begin = common::AnfAlgo::GetNodeAttr>(kernel_node, BEGIN); if (begin.size() != input_shape.size() || size.size() != input_shape.size()) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the lengths of 'begin' and 'size' should be equal to " @@ -133,9 +133,9 @@ bool SliceCpuKernelMod::Launch(const std::vector &inputs, co auto output_addr = outputs[0]->addr; if (inputs.size() == kSliceDynamicInputNum) { auto cnode = cnode_ptr_.lock(); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(cnode, 0); - auto begin_shape = AnfAlgo::GetPrevNodeOutputInferShape(cnode, 1); - auto size_shape = AnfAlgo::GetPrevNodeOutputInferShape(cnode, 2); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(cnode, 0); + auto begin_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(cnode, 1); + auto size_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(cnode, 2); if (begin_shape.size() != 1 || size_shape.size() != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimensions of 'begin' and 'size' should be 1, but got the dimension of 'begin': " diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/slice_grad_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/slice_grad_cpu_kernel.cc index b1bebd22cd2..3f6598e7f72 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/slice_grad_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/slice_grad_cpu_kernel.cc @@ -31,30 +31,30 @@ constexpr size_t kSliceGradMaxInputShapeSize = 4; void SliceGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); cnode_ptr_ = kernel_node; ClearVectors(); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); if (input_shape.size() > kSliceGradMaxInputShapeSize) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimension of input tensor should be 4D or lower, but got " << input_shape.size() << "D."; } - output_shape_ = AnfAlgo::GetOutputInferShape(kernel_node, 0); + output_shape_ = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num == kSliceGradDynamicInputsNum || input_num == kStridedSliceGradDynamicInputsNum) { return; } // in the case that begin, end, size, stride are const value. - std::vector begin_me = AnfAlgo::GetNodeAttr>(kernel_node, BEGIN); + std::vector begin_me = common::AnfAlgo::GetNodeAttr>(kernel_node, BEGIN); (void)std::transform(begin_me.begin(), begin_me.end(), std::back_inserter(begin_), [](const int64_t &value) { return LongToInt(value); }); - auto prim = AnfAlgo::GetCNodePrimitive(kernel_node); + auto prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(prim); auto strides = prim->GetAttr(STRIDES); if (strides != nullptr) { - std::vector strides_me = AnfAlgo::GetNodeAttr>(kernel_node, STRIDES); - std::vector end_me = AnfAlgo::GetNodeAttr>(kernel_node, END); + std::vector strides_me = common::AnfAlgo::GetNodeAttr>(kernel_node, STRIDES); + std::vector end_me = common::AnfAlgo::GetNodeAttr>(kernel_node, END); (void)std::transform(strides_me.begin(), strides_me.end(), std::back_inserter(strides_), [](const int64_t &value) { return LongToInt(value); }); (void)std::transform(end_me.begin(), end_me.end(), std::back_inserter(end_), @@ -67,7 +67,7 @@ void SliceGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { } FormatArgs(true); } else { - std::vector size_me = AnfAlgo::GetNodeAttr>(kernel_node, SIZE); + std::vector size_me = common::AnfAlgo::GetNodeAttr>(kernel_node, SIZE); (void)std::transform(size_me.begin(), size_me.end(), std::back_inserter(size_), [](const int64_t &value) { return LongToInt(value); }); if (size_.size() != output_shape_.size() || begin_.size() != output_shape_.size()) { @@ -119,16 +119,16 @@ void SliceGradCpuKernelMod::ExpandAllMemberDims() { void SliceGradCpuKernelMod::InitParams(const std::vector &inputs) { auto cnode = cnode_ptr_.lock(); ClearVectors(); - output_shape_ = AnfAlgo::GetOutputInferShape(cnode, 0); - std::string kernel_name = AnfAlgo::GetCNodeName(cnode); - auto begin_shape = AnfAlgo::GetPrevNodeOutputInferShape(cnode, 2); + output_shape_ = common::AnfAlgo::GetOutputInferShape(cnode, 0); + std::string kernel_name = common::AnfAlgo::GetCNodeName(cnode); + auto begin_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(cnode, 2); auto begin_ptr = reinterpret_cast(inputs[2]->addr); std::vector begin{begin_ptr, begin_ptr + begin_shape[0]}; (void)std::transform(begin.begin(), begin.end(), std::back_inserter(begin_), [](const int32_t &value) { return value; }); if (kernel_name == prim::kPrimStridedSliceGrad->name()) { - auto end_shape = AnfAlgo::GetPrevNodeOutputInferShape(cnode, 3); - auto stride_shape = AnfAlgo::GetPrevNodeOutputInferShape(cnode, 4); + auto end_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(cnode, 3); + auto stride_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(cnode, 4); if (begin_shape.size() != 1 || end_shape.size() != 1 || stride_shape.size() != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimensions of 'begin', 'end', 'strides' should be 1, " @@ -153,7 +153,7 @@ void SliceGradCpuKernelMod::InitParams(const std::vector &in } FormatArgs(true); } else { - auto size_shape = AnfAlgo::GetPrevNodeOutputInferShape(cnode, 3); + auto size_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(cnode, 3); if (begin_shape.size() != 1 || size_shape.size() != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimensions of 'begin', 'end' should be 1, but got the dimension of 'begin': " diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/smooth_l1_loss_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/smooth_l1_loss_cpu_kernel.cc index 6984bbad3f4..254f372c08a 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/smooth_l1_loss_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/smooth_l1_loss_cpu_kernel.cc @@ -27,12 +27,12 @@ constexpr size_t kSmoothL1LossOutputsNum = 1; template void SmoothL1LossCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - beta_ = AnfAlgo::GetNodeAttr(kernel_node, "beta"); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + beta_ = common::AnfAlgo::GetNodeAttr(kernel_node, "beta"); if (beta_ == 0.0) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << ", the 'beta' should not be 0."; } - std::vector x_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + std::vector x_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); for (const uint64_t &d : x_shape) { tensor_size_ *= d; } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/smooth_l1_loss_grad_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/smooth_l1_loss_grad_cpu_kernel.cc index ea974c4c3e2..80b88c2d39c 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/smooth_l1_loss_grad_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/smooth_l1_loss_grad_cpu_kernel.cc @@ -27,12 +27,12 @@ constexpr size_t kSmoothL1LossGradOutputsNum = 1; template void SmoothL1LossGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - beta_ = AnfAlgo::GetNodeAttr(kernel_node, "beta"); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + beta_ = common::AnfAlgo::GetNodeAttr(kernel_node, "beta"); if (beta_ == 0.0) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << ", the 'beta' should not be 0."; } - std::vector x_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + std::vector x_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); for (const uint64_t &d : x_shape) { tensor_size_ *= d; } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/sort_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/sort_cpu_kernel.cc index 738ab64e5b7..942361b034d 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/sort_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/sort_cpu_kernel.cc @@ -14,29 +14,29 @@ * limitations under the License. */ #include "plugin/device/cpu/kernel/sort_cpu_kernel.h" -#include "common/thread_pool.h" +#include "include/common/thread_pool.h" namespace mindspore { namespace kernel { template void SortCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - size_t input_count = AnfAlgo::GetInputTensorNum(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_count = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_count != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 1, but got " << input_count << " input(s)."; } - size_t output_count = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_count = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_count != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 2, but got " << output_count << " output(s)."; } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - descending_ = AnfAlgo::GetNodeAttr(kernel_node, "descending"); - auto axis = AnfAlgo::GetNodeAttr(kernel_node, AXIS); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + descending_ = common::AnfAlgo::GetNodeAttr(kernel_node, "descending"); + auto axis = common::AnfAlgo::GetNodeAttr(kernel_node, AXIS); size_t axis_t = axis < 0 ? LongToSize(axis + SizeToLong(input_shape.size())) : LongToSize(axis); if (axis_t >= input_shape.size()) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the 'axis' should be less than the dimension of input tensor " diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/spacetodepth_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/spacetodepth_cpu_kernel.cc index aa58345aea9..669f5bc8050 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/spacetodepth_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/spacetodepth_cpu_kernel.cc @@ -29,11 +29,11 @@ constexpr size_t kSpaceToDepthMinBlockSize = 2; template void SpaceToDepthCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); input_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 0); output_shape_ = AnfAlgo::GetOutputDeviceShape(kernel_node, 0); - block_size_ = LongToSize(AnfAlgo::GetNodeAttr(kernel_node, "block_size")); + block_size_ = LongToSize(common::AnfAlgo::GetNodeAttr(kernel_node, "block_size")); if (input_shape_.size() != kSpaceToDepthInputShapeSize) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimension of input tensor should be 4-D, but got " << input_shape_.size() << "-D"; diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/sparse_apply_adam_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/sparse_apply_adam_cpu_kernel.cc index 9834bd8d973..405f6ea345c 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/sparse_apply_adam_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/sparse_apply_adam_cpu_kernel.cc @@ -103,12 +103,12 @@ void SparseApplyAdamCpuKernelMod::InitInputOutputSize(const CNodePtr &kernel_nod void SparseApplyAdamCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - std::vector var_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - std::vector m_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - std::vector v_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - std::vector grad_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 9); - std::vector indices_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 10); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + std::vector var_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + std::vector m_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + std::vector v_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + std::vector grad_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 9); + std::vector indices_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 10); if (var_shape.empty()) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimension of 'var' should be at least 1-D, but got scalar or None."; @@ -149,8 +149,8 @@ void SparseApplyAdamCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { "the first dimension value of 'indices', but got the first dimension value of 'grad': " << grad_shape[0] << ", and the first dimension value of 'indices': " << indices_size_; } - if (AnfAlgo::HasNodeAttr(USE_NESTEROV, kernel_node)) { - use_nesterov_ = AnfAlgo::GetNodeAttr(kernel_node, "use_nesterov"); + if (common::AnfAlgo::HasNodeAttr(USE_NESTEROV, kernel_node)) { + use_nesterov_ = common::AnfAlgo::GetNodeAttr(kernel_node, "use_nesterov"); } indices_data_type_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 10); } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/sparse_apply_ftrl_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/sparse_apply_ftrl_cpu_kernel.cc index d1a41bba81d..f4260f49ff2 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/sparse_apply_ftrl_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/sparse_apply_ftrl_cpu_kernel.cc @@ -88,12 +88,12 @@ void SparseApplyFtrlCpuKernelMod::InitInputOutputSize(const CNodePtr &kernel_nod void SparseApplyFtrlCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - std::vector var_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - std::vector accum_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - std::vector linear_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - std::vector grad_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - std::vector indices_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + std::vector var_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + std::vector accum_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + std::vector linear_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + std::vector grad_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + std::vector indices_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); if (var_shape.empty()) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the 'var' should be at least 1-D, but got scalar or None."; } @@ -136,19 +136,19 @@ void SparseApplyFtrlCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { "the first dimension value of 'indices', but got the first dimension value of 'grad': " << grad_shape[0] << ", and the first dimension value of 'indices': " << indices_size_; } - lr_ = AnfAlgo::GetNodeAttr(kernel_node, "lr"); + lr_ = common::AnfAlgo::GetNodeAttr(kernel_node, "lr"); if (lr_ <= 0) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', 'lr' should be a positive scalar, but got " << lr_; } - l1_ = AnfAlgo::GetNodeAttr(kernel_node, "l1"); + l1_ = common::AnfAlgo::GetNodeAttr(kernel_node, "l1"); if (l1_ < 0) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', 'l1' should be a non-negative scalar, but got " << l1_; } - l2_ = AnfAlgo::GetNodeAttr(kernel_node, "l2"); + l2_ = common::AnfAlgo::GetNodeAttr(kernel_node, "l2"); if (l2_ < 0) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', 'l2' should be a non-negative scalar, but got " << l2_; } - lr_power_ = AnfAlgo::GetNodeAttr(kernel_node, "lr_power"); + lr_power_ = common::AnfAlgo::GetNodeAttr(kernel_node, "lr_power"); if (lr_power_ > 0) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', 'lr_power' should be a non-negative scalar, but got " << lr_power_; diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/sparse_apply_lazy_adam_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/sparse_apply_lazy_adam_cpu_kernel.cc index 92331f622cb..5594828dce3 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/sparse_apply_lazy_adam_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/sparse_apply_lazy_adam_cpu_kernel.cc @@ -83,12 +83,12 @@ void SparseApplyLazyAdamCpuKernelMod::InitInputOutputSize(const CNodePtr &kernel void SparseApplyLazyAdamCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - std::vector var_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - std::vector m_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - std::vector v_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - std::vector grad_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 9); - std::vector indices_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 10); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + std::vector var_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + std::vector m_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + std::vector v_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + std::vector grad_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 9); + std::vector indices_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 10); if (var_shape.empty()) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimension of 'var' should be at least 1-D, but got scalar or None."; @@ -130,8 +130,8 @@ void SparseApplyLazyAdamCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { "the first dimension value of 'indices', but got the first dimension value of 'grad': " << grad_shape[0] << ", and the first dimension value of 'indices': " << indices_size_; } - if (AnfAlgo::HasNodeAttr(USE_NESTEROV, kernel_node)) { - use_nesterov_ = AnfAlgo::GetNodeAttr(kernel_node, USE_NESTEROV); + if (common::AnfAlgo::HasNodeAttr(USE_NESTEROV, kernel_node)) { + use_nesterov_ = common::AnfAlgo::GetNodeAttr(kernel_node, USE_NESTEROV); } indices_data_type_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 10); } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/sparse_apply_proximal_adagrad_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/sparse_apply_proximal_adagrad_cpu_kernel.cc index 0cf5d22528e..fdf12f11a01 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/sparse_apply_proximal_adagrad_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/sparse_apply_proximal_adagrad_cpu_kernel.cc @@ -83,14 +83,14 @@ void SparseApplyProximalAdagradCpuKernelMod::InitInputOutputSize(const CNodePtr void SparseApplyProximalAdagradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - std::vector var_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - std::vector accum_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - std::vector lr_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - std::vector l1_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - std::vector l2_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - std::vector grad_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - std::vector indices_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + std::vector var_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + std::vector accum_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + std::vector lr_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + std::vector l1_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + std::vector l2_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + std::vector grad_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); + std::vector indices_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); if (var_shape.empty()) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimension of 'var' should be at least 1-D, but got scalar or None."; diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/sparse_optimizer_cpu_kernel.h b/mindspore/ccsrc/plugin/device/cpu/kernel/sparse_optimizer_cpu_kernel.h index 8490bb77859..6757928dea7 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/sparse_optimizer_cpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/sparse_optimizer_cpu_kernel.h @@ -24,7 +24,7 @@ #include #include "plugin/device/cpu/kernel/cpu_kernel.h" #include "plugin/device/cpu/kernel/cpu_kernel_factory.h" -#include "common/thread_pool.h" +#include "include/common/thread_pool.h" namespace mindspore { namespace kernel { template diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/sparse_tensor_dense_matmul_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/sparse_tensor_dense_matmul_cpu_kernel.cc index feb2d8d267b..860661e0418 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/sparse_tensor_dense_matmul_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/sparse_tensor_dense_matmul_cpu_kernel.cc @@ -31,26 +31,26 @@ constexpr size_t kIndices2rdDimNum = 2; template void SparseTensorDenseMatmulCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - adj_st_ = AnfAlgo::GetNodeAttr(kernel_node, ADJ_ST); - adj_dt_ = AnfAlgo::GetNodeAttr(kernel_node, ADJ_dT); - auto indices_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, INDICES); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + adj_st_ = common::AnfAlgo::GetNodeAttr(kernel_node, ADJ_ST); + adj_dt_ = common::AnfAlgo::GetNodeAttr(kernel_node, ADJ_dT); + auto indices_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, INDICES); if (indices_shape.size() != kIndicesSizeNum && indices_shape[1] != kIndices2rdDimNum) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', it requires 'indices' should be a 2-D Tensor and the second dimension length " "should be 2, but got 'indices' shape: " << Vector2Str(indices_shape); } - auto values_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, VALUES); + auto values_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, VALUES); if (values_shape.size() != 1 || values_shape[0] != indices_shape[0]) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', it requires 'values' should be a 1-D Tensor and the first dimension length " " should be equal to the first dimension length of 'indices', but got 'values' shape: " << Vector2Str(values_shape) << " and 'indices' shape: " << Vector2Str(indices_shape); } - output_shape_ = AnfAlgo::GetOutputInferShape(kernel_node, 0); + output_shape_ = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); values_size_ = values_shape[0]; - b_shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, DENSE); + b_shape_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, DENSE); if (b_shape_.size() != kSparseTensorDenseMatmulDenseShapeSize) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimension of 'dense' should be " << kSparseTensorDenseMatmulDenseShapeSize << "-D, but got " << b_shape_.size() << "-D"; diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/sparse_to_dense_cpu_kernal.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/sparse_to_dense_cpu_kernal.cc index a3e13c0c8fa..42318c55249 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/sparse_to_dense_cpu_kernal.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/sparse_to_dense_cpu_kernal.cc @@ -29,13 +29,13 @@ constexpr size_t kSparseToDenseOutputsNum = 1; template void SparseToDenseCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - auto indices_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + auto indices_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); if (indices_shape.size() != kIndicesShapeSize) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', it requires 'indices' should be a " << kIndicesShapeSize << "-D Tensor, but got " << indices_shape.size() << "-D"; } - auto values_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto values_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); if (values_shape.size() != 1 || values_shape[0] != indices_shape[0]) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', it requires 'values' should be a 1-D Tensor and the first dimension length " @@ -43,7 +43,7 @@ void SparseToDenseCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { << Vector2Str(values_shape) << " and 'indices' shape: " << Vector2Str(indices_shape); } values_size_ = values_shape[0]; - output_shape_ = AnfAlgo::GetOutputInferShape(kernel_node, 0); + output_shape_ = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); } template diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/split_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/split_cpu_kernel.cc index e440dcf4d07..4325c35d7d4 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/split_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/split_cpu_kernel.cc @@ -17,7 +17,7 @@ #include "plugin/device/cpu/kernel/split_cpu_kernel.h" #include #include "plugin/device/cpu/hal/device/cpu_device_address.h" -#include "common/thread_pool.h" +#include "include/common/thread_pool.h" namespace mindspore { namespace kernel { @@ -28,13 +28,13 @@ constexpr size_t kSplitInputsNum = 1; template void SplitCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - axis_ = AnfAlgo::GetNodeAttr(kernel_node, AXIS); - output_num_ = LongToSize(AnfAlgo::GetNodeAttr(kernel_node, "output_num")); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + axis_ = common::AnfAlgo::GetNodeAttr(kernel_node, AXIS); + output_num_ = LongToSize(common::AnfAlgo::GetNodeAttr(kernel_node, "output_num")); if (output_num_ == 0) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the 'output_num' should be positive int, but got 0."; } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); (void)std::transform(input_shape.begin(), input_shape.end(), std::back_inserter(input_shape_), [](const size_t &value) { return SizeToInt(value); }); if (input_shape_.size() < 1 || input_shape_.size() > SPLIT_STRIDES_SIZE) { diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/strided_slice_grad_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/strided_slice_grad_cpu_kernel.cc index e2d692594bc..6bef044f707 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/strided_slice_grad_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/strided_slice_grad_cpu_kernel.cc @@ -25,12 +25,12 @@ namespace mindspore { namespace kernel { void StridedSliceGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); param_ = (struct StridedSliceParameter *)malloc(sizeof(struct StridedSliceParameter)); if (param_ == nullptr) { MS_LOG(ERROR) << "For '" << kernel_name_ << "', malloc StridedSliceGradParameter failed."; } - output_shape_ = AnfAlgo::GetOutputInferShape(kernel_node, 0); + output_shape_ = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); switch (dtype_) { case kNumberTypeFloat32: @@ -44,14 +44,14 @@ void StridedSliceGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { [](const int64_t &value) { return static_cast(value); }); param_->num_axes_ = input_shape_me.size(); param_->in_shape_length_ = input_shape_me.size(); - std::vector begin_me = AnfAlgo::GetNodeAttr>(kernel_node, BEGIN); + std::vector begin_me = common::AnfAlgo::GetNodeAttr>(kernel_node, BEGIN); (void)std::transform(begin_me.begin(), begin_me.end(), std::back_inserter(begin_), [](const int64_t &value) { return static_cast(value); }); - auto prim = AnfAlgo::GetCNodePrimitive(kernel_node); + auto prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(prim); auto strides = prim->GetAttr(STRIDES); - std::vector strides_me = AnfAlgo::GetNodeAttr>(kernel_node, STRIDES); - std::vector end_me = AnfAlgo::GetNodeAttr>(kernel_node, END); + std::vector strides_me = common::AnfAlgo::GetNodeAttr>(kernel_node, STRIDES); + std::vector end_me = common::AnfAlgo::GetNodeAttr>(kernel_node, END); (void)std::transform(strides_me.begin(), strides_me.end(), std::back_inserter(strides_), [](const int64_t &value) { return static_cast(value); }); (void)std::transform(end_me.begin(), end_me.end(), std::back_inserter(end_), diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/stridedslice_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/stridedslice_cpu_kernel.cc index 03e0c8573ed..67e567add68 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/stridedslice_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/stridedslice_cpu_kernel.cc @@ -19,7 +19,7 @@ #include #include #include -#include "common/thread_pool.h" +#include "include/common/thread_pool.h" #include "plugin/device/cpu/hal/device/cpu_device_address.h" #include "kernel/common_utils.h" @@ -44,24 +44,24 @@ int NormalizePos(int pos, int dim_len, PosType pos_type) { void StridedSliceCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); cnode_ptr_ = kernel_node; - input_shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - output_shape_ = AnfAlgo::GetOutputInferShape(kernel_node, 0); + input_shape_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + output_shape_ = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); if (input_shape_.size() > DIMENSION_8D || input_shape_.empty()) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimension of 'input_x' should be in range [1D, 8D], but got " << input_shape_.size() << "D."; } dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { return; } // for begin, end, stride are const input - auto begin = AnfAlgo::GetNodeAttr>(kernel_node, kAttrBegin); - auto end = AnfAlgo::GetNodeAttr>(kernel_node, kAttrEnd); - auto stride = AnfAlgo::GetNodeAttr>(kernel_node, kAttrStrides); + auto begin = common::AnfAlgo::GetNodeAttr>(kernel_node, kAttrBegin); + auto end = common::AnfAlgo::GetNodeAttr>(kernel_node, kAttrEnd); + auto stride = common::AnfAlgo::GetNodeAttr>(kernel_node, kAttrStrides); InitSliceParam(kernel_node, &begin, &end, &stride); parallel_ = MatchParallelPattern(); @@ -210,12 +210,12 @@ bool StridedSliceCpuKernelMod::Launch(const std::vector &inp auto output_addr = reinterpret_cast(outputs[0]->addr); auto cnode = cnode_ptr_.lock(); - size_t input_num = AnfAlgo::GetInputTensorNum(cnode); + size_t input_num = common::AnfAlgo::GetInputTensorNum(cnode); if (input_num == kStridedSliceDynamicInputsNum) { // for begin, end, stride are not const input - auto begin_shape = AnfAlgo::GetPrevNodeOutputInferShape(cnode, 1); - auto end_shape = AnfAlgo::GetPrevNodeOutputInferShape(cnode, 2); - auto stride_shape = AnfAlgo::GetPrevNodeOutputInferShape(cnode, 3); + auto begin_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(cnode, 1); + auto end_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(cnode, 2); + auto stride_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(cnode, 3); if (begin_shape.size() != 1 || end_shape.size() != 1 || stride_shape.size() != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimension of 'begin', 'end', 'strides' should be equal " diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/sub_and_filter_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/sub_and_filter_cpu_kernel.cc index f1d09663df0..eff980c73a6 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/sub_and_filter_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/sub_and_filter_cpu_kernel.cc @@ -27,7 +27,7 @@ constexpr size_t kSubAndFilterOutputNum = 2; void SubAndFilterCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); node_wpt_ = kernel_node; input_x_dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); } @@ -53,7 +53,7 @@ void SubAndFilterCpuKernelMod::LaunchKernel(const std::vector &input const std::vector &outputs) { auto node = node_wpt_.lock(); MS_EXCEPTION_IF_NULL(node); - auto indices_shape = AnfAlgo::GetPrevNodeOutputInferShape(node, 0); + auto indices_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node, 0); batch_size_ = 1; for (size_t i = 0; i < indices_shape.size(); ++i) { @@ -78,12 +78,12 @@ void SubAndFilterCpuKernelMod::LaunchKernel(const std::vector &input MS_LOG(INFO) << "SubAndFilter output count is " << count; std::vector out_shape; (void)out_shape.emplace_back(count); - size_t output_num = AnfAlgo::GetOutputTensorNum(node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(node); std::vector dtypes(output_num); for (size_t i = 0; i < output_num; i++) { dtypes[i] = AnfAlgo::GetOutputDeviceDataType(node, i); } - AnfAlgo::SetOutputInferTypeAndShape(dtypes, {out_shape, out_shape}, node.get()); + common::AnfAlgo::SetOutputInferTypeAndShape(dtypes, {out_shape, out_shape}, node.get()); } } // namespace kernel } // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/tensor_copy_slices_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/tensor_copy_slices_cpu_kernel.cc index 083f297e8c7..d5445df5681 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/tensor_copy_slices_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/tensor_copy_slices_cpu_kernel.cc @@ -30,18 +30,18 @@ constexpr size_t kTensorCopySlicesOutputsNum = 1; void TensorCopySlicesCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto update_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto update_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); CastShapeSizeToLong(input_shape, &input_shape_); CastShapeSizeToLong(update_shape, &update_shape_); CastShapeSizeToLong(output_shape, &output_shape_); - auto begin = AnfAlgo::GetNodeAttr>(kernel_node, BEGIN); - auto end = AnfAlgo::GetNodeAttr>(kernel_node, END); - auto stride = AnfAlgo::GetNodeAttr>(kernel_node, STRIDES); + auto begin = common::AnfAlgo::GetNodeAttr>(kernel_node, BEGIN); + auto end = common::AnfAlgo::GetNodeAttr>(kernel_node, END); + auto stride = common::AnfAlgo::GetNodeAttr>(kernel_node, STRIDES); CheckSliceValid(begin, end, stride, input_shape_); data_type_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/tensoradd_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/tensoradd_cpu_kernel.cc index a01d7a54825..823831b850c 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/tensoradd_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/tensoradd_cpu_kernel.cc @@ -28,7 +28,7 @@ constexpr size_t kTensorAddOutputsSize = 1; template void TensorAddCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); // Init shape and strides input_shape_a_ = AnfAlgo::GetInputDeviceShape(kernel_node, 0); input_shape_b_ = AnfAlgo::GetInputDeviceShape(kernel_node, 1); diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/tile_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/tile_cpu_kernel.cc index fa65f407c88..d124b9af094 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/tile_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/tile_cpu_kernel.cc @@ -79,13 +79,13 @@ void TileCpuKernelMod::TileMultipleCompute() { } void TileCpuKernelMod::TileTensorParamrInit(const CNodePtr &kernel_node) { - x_shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - y_shape_ = AnfAlgo::GetOutputInferShape(kernel_node, 0); + x_shape_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + y_shape_ = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); multiples_.clear(); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num == kTileInputsNum) { - std::vector multiples_me = AnfAlgo::GetNodeAttr>(kernel_node, "multiples"); + std::vector multiples_me = common::AnfAlgo::GetNodeAttr>(kernel_node, "multiples"); (void)std::transform(multiples_me.begin(), multiples_me.end(), std::back_inserter(multiples_), [](const int64_t &value) { return static_cast(value); }); TileMultipleCompute(); @@ -94,7 +94,7 @@ void TileCpuKernelMod::TileTensorParamrInit(const CNodePtr &kernel_node) { void TileCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); TileTensorParamrInit(kernel_node); cnode_ptr_ = kernel_node; @@ -135,10 +135,10 @@ void TileCpuKernelMod::LaunchKernel(const std::vector &inputs, const auto y_addr = reinterpret_cast(outputs[0]->addr); auto cnode = cnode_ptr_.lock(); MS_EXCEPTION_IF_NULL(cnode); - size_t input_num = AnfAlgo::GetInputTensorNum(cnode); + size_t input_num = common::AnfAlgo::GetInputTensorNum(cnode); if (input_num == kTileDynamicInputsNum) { auto multiples_addr = reinterpret_cast(inputs[1]->addr); - auto multiple_shape = AnfAlgo::GetPrevNodeOutputInferShape(cnode, 1); + auto multiple_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(cnode, 1); size_t multiple_nums = 1; for (size_t i = 0; i < multiple_shape.size(); ++i) { multiple_nums *= multiple_shape[i]; diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/topk_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/topk_cpu_kernel.cc index 802871dabcb..92c501002f3 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/topk_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/topk_cpu_kernel.cc @@ -17,7 +17,7 @@ #include "plugin/device/cpu/kernel/topk_cpu_kernel.h" #include #include "plugin/device/cpu/hal/device/cpu_device_address.h" -#include "common/thread_pool.h" +#include "include/common/thread_pool.h" namespace mindspore { namespace kernel { @@ -91,8 +91,8 @@ void TopKCpuKernelMod::LaunchKernel(const std::vector &inputs, const void TopKCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - auto x_shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + auto x_shape_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); if (x_shape_.empty()) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimension of input should be greater than 0, but got empty input."; @@ -101,7 +101,7 @@ void TopKCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { outer_size_ *= x_shape_[i]; } inner_size_ = x_shape_[x_shape_.size() - 1]; - sorted_ = AnfAlgo::GetNodeAttr(kernel_node, "sorted"); + sorted_ = common::AnfAlgo::GetNodeAttr(kernel_node, "sorted"); dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/transpose_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/transpose_cpu_kernel.cc index f70edb1533c..ae3690da278 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/transpose_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/transpose_cpu_kernel.cc @@ -17,7 +17,7 @@ #include "plugin/device/cpu/kernel/transpose_cpu_kernel.h" #include #include "plugin/device/cpu/hal/device/cpu_device_address.h" -#include "common/thread_pool.h" +#include "include/common/thread_pool.h" #include "nnacl/fp32/transpose_fp32.h" #include "nnacl/int8/transpose_int8.h" @@ -33,10 +33,10 @@ constexpr size_t kMaxTransposeSerialSize = 50331648; void TransposeFwdCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); input_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 0); output_shape_ = AnfAlgo::GetOutputDeviceShape(kernel_node, 0); - auto tmp = AnfAlgo::GetNodeAttr>(kernel_node, "perm"); + auto tmp = common::AnfAlgo::GetNodeAttr>(kernel_node, "perm"); axes_ = {tmp.begin(), tmp.end()}; dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); if (axes_.size() > MAX_TRANSPOSE_DIM_SIZE) { diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/unary_op_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/unary_op_cpu_kernel.cc index 59c10452289..77d27d2f99a 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/unary_op_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/unary_op_cpu_kernel.cc @@ -72,7 +72,7 @@ void UnaryOpCpuKernelMod::GetUnaryOpFunc() { template void UnaryOpCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); GetUnaryOpFunc(); } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/unique_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/unique_cpu_kernel.cc index 915480d3ce9..172676c05ff 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/unique_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/unique_cpu_kernel.cc @@ -22,17 +22,17 @@ namespace kernel { constexpr size_t kBucketSortThreshold = 100000; void UniqueCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); node_wpt_ = kernel_node; - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); if (input_shape.size() != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimension of input should be 1D, but got " << input_shape.size() << "D"; } input_size_ = input_shape[0]; dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); - if (AnfAlgo::HasNodeAttr(SORTED, kernel_node)) { - sorted_ = AnfAlgo::GetNodeAttr(kernel_node, SORTED); + if (common::AnfAlgo::HasNodeAttr(SORTED, kernel_node)) { + sorted_ = common::AnfAlgo::GetNodeAttr(kernel_node, SORTED); } } @@ -64,12 +64,13 @@ bool UniqueCpuKernelMod::Launch(const std::vector &inputs, } std::vector out_shape; (void)out_shape.emplace_back(output_size_); - size_t output_num = AnfAlgo::GetOutputTensorNum(node_); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(node_); std::vector dtypes(output_num); for (size_t i = 0; i < output_num; i++) { dtypes[i] = AnfAlgo::GetOutputDeviceDataType(node_, i); } - AnfAlgo::SetOutputInferTypeAndShape(dtypes, {out_shape, AnfAlgo::GetOutputInferShape(node_, 1)}, node_.get()); + common::AnfAlgo::SetOutputInferTypeAndShape(dtypes, {out_shape, common::AnfAlgo::GetOutputInferShape(node_, 1)}, + node_.get()); } return true; } diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/unique_cpu_kernel.h b/mindspore/ccsrc/plugin/device/cpu/kernel/unique_cpu_kernel.h index bca7d8c9b2d..3d124e4a235 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/unique_cpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/unique_cpu_kernel.h @@ -24,7 +24,7 @@ #include #include "plugin/device/cpu/kernel/cpu_kernel.h" #include "plugin/device/cpu/kernel/cpu_kernel_factory.h" -#include "common/thread_pool.h" +#include "include/common/thread_pool.h" namespace mindspore { namespace kernel { diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/unpack_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/unpack_cpu_kernel.cc index a7ebfd485fd..34d7f7d3d05 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/unpack_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/unpack_cpu_kernel.cc @@ -28,13 +28,13 @@ constexpr size_t kUnpackWorkspaceMinNum = 1; template void UnpackCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - int64_t axis_tmp = AnfAlgo::GetNodeAttr(kernel_node, AXIS); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + int64_t axis_tmp = common::AnfAlgo::GetNodeAttr(kernel_node, AXIS); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); if (axis_tmp < 0) { axis_tmp += SizeToLong(input_shape.size()); } - output_num_ = LongToSize(AnfAlgo::GetNodeAttr(kernel_node, "num")); + output_num_ = LongToSize(common::AnfAlgo::GetNodeAttr(kernel_node, "num")); unstack_param_.num_ = SizeToInt(output_num_); unstack_param_.axis_ = LongToInt(axis_tmp); unstack_param_.pre_dims_ = 1; diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/unsorted_segment_sum_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/unsorted_segment_sum_cpu_kernel.cc index 307488ed766..24950a74c21 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/unsorted_segment_sum_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/unsorted_segment_sum_cpu_kernel.cc @@ -16,7 +16,7 @@ #include "plugin/device/cpu/kernel/unsorted_segment_sum_cpu_kernel.h" #include "plugin/device/cpu/hal/device/cpu_device_address.h" -#include "common/thread_pool.h" +#include "include/common/thread_pool.h" namespace mindspore { namespace kernel { @@ -27,12 +27,12 @@ constexpr size_t kUnsortedSegmentOutputsNum = 1; void UnsortedSegmentSumCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); segment_ids_dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 1); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto segment_ids_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto segment_ids_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); if (output_shape.empty()) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimension of output should be at least 1, but got shape: " << output_shape; diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/update_cache_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/update_cache_cpu_kernel.cc index 7d9f677a1f9..c5723bf0473 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/update_cache_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/update_cache_cpu_kernel.cc @@ -27,8 +27,8 @@ constexpr size_t kMinUpdateShapeSize = 2; void UpdateCacheCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); CHECK_KERNEL_INPUTS_NUM(input_num, kUpdateCacheInputsNum, kernel_name_); node_wpt_ = kernel_node; input_x_dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); @@ -64,8 +64,8 @@ void UpdateCacheCpuKernelMod::LaunchKernel(const std::vector &inputs const std::vector &outputs) { auto node = node_wpt_.lock(); MS_EXCEPTION_IF_NULL(node); - auto indices_shape = AnfAlgo::GetPrevNodeOutputInferShape(node, 1); - auto update_shape = AnfAlgo::GetPrevNodeOutputInferShape(node, 2); + auto indices_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node, 1); + auto update_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node, 2); if (update_shape.size() < kMinUpdateShapeSize) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimension of 'update' should be at least " << kMinUpdateShapeSize << "D, but got: " << update_shape.size() << "D"; diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/zeros_like_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/zeros_like_cpu_kernel.cc index 5a7a9cd78c5..c566c98e81e 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/zeros_like_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/zeros_like_cpu_kernel.cc @@ -27,7 +27,7 @@ constexpr size_t kZerosLikeOutputsNum = 1; template void ZerosLikeCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); input_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 0); output_shape_ = AnfAlgo::GetOutputDeviceShape(kernel_node, 0); } diff --git a/mindspore/ccsrc/plugin/device/cpu/optimizer/insert_cast_cpu.cc b/mindspore/ccsrc/plugin/device/cpu/optimizer/insert_cast_cpu.cc index 2da8ac99899..2cd31492327 100644 --- a/mindspore/ccsrc/plugin/device/cpu/optimizer/insert_cast_cpu.cc +++ b/mindspore/ccsrc/plugin/device/cpu/optimizer/insert_cast_cpu.cc @@ -25,8 +25,9 @@ #include "kernel/kernel_build_info.h" #include "plugin/device/cpu/kernel/cpu_kernel_factory.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/session/kernel_graph.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/ms_context.h" #include "kernel/common_utils.h" #include "base/core_ops.h" @@ -54,13 +55,13 @@ AnfNodePtr AddCastOpNodeToGraph(const FuncGraphPtr &func_graph, const AnfNodePtr cast->set_kernel_info(kernel_info); } if (origin_shape->IsDynamic()) { - AnfAlgo::SetNodeAttr(kAttrInputIsDynamicShape, MakeValue(true), cast); - AnfAlgo::SetNodeAttr(kAttrOutputIsDynamicShape, MakeValue(true), cast); + common::AnfAlgo::SetNodeAttr(kAttrInputIsDynamicShape, MakeValue(true), cast); + common::AnfAlgo::SetNodeAttr(kAttrOutputIsDynamicShape, MakeValue(true), cast); } - AnfAlgo::SetNodeAttr("dst_type", TypeIdToType(output_type), cast); + common::AnfAlgo::SetNodeAttr("dst_type", TypeIdToType(output_type), cast); AnfAlgo::SetSelectKernelBuildInfo(builder.Build(), cast.get()); - AnfAlgo::SetOutputTypeAndDetailShape({origin_type}, {origin_shape}, cast.get()); - AnfAlgo::SetNodeAttr(kIsBackendCast, MakeValue(true), cast); + common::AnfAlgo::SetOutputTypeAndDetailShape({origin_type}, {origin_shape}, cast.get()); + common::AnfAlgo::SetNodeAttr(kIsBackendCast, MakeValue(true), cast); std::shared_ptr cpu_kernel = kernel::NativeCpuKernelModFactory::GetInstance().Create(kCastOpName, cast); if (cpu_kernel == nullptr) { @@ -105,33 +106,34 @@ void SyncWeightNodeWithCast(const FuncGraphPtr &func_graph, const CNodePtr &cnod AddCastOpNodeToGraph(func_graph, first_depend_node, format, device_type, origin_type, origin_shape, origin_type); auto kernel_graph = func_graph->cast(); MS_EXCEPTION_IF_NULL(kernel_graph); - kernel_graph->AddRefCorrespondPairs(std::make_pair(post_cast, 0), AnfAlgo::VisitKernel(cur_input, 0)); + kernel_graph->AddRefCorrespondPairs(std::make_pair(post_cast, 0), common::AnfAlgo::VisitKernel(cur_input, 0)); make_tuple_inputs->push_back(post_cast); } void InsertCast(const FuncGraphPtr &func_graph, const CNodePtr &cnode) { MS_EXCEPTION_IF_NULL(cnode); - size_t in_num = AnfAlgo::GetInputTensorNum(cnode); + size_t in_num = common::AnfAlgo::GetInputTensorNum(cnode); std::vector make_tuple_inputs{NewValueNode(std::make_shared(prim::kPrimMakeTuple->name()))}; for (size_t input_index = 0; input_index < in_num; ++input_index) { - auto prev_node = AnfAlgo::GetPrevNodeOutput(cnode, input_index); + auto prev_node = common::AnfAlgo::GetPrevNodeOutput(cnode, input_index); auto origin_type = AnfAlgo::GetOutputDeviceDataType(prev_node.first, prev_node.second); if (origin_type == kTypeUnknown) { - origin_type = AnfAlgo::GetOutputInferDataType(prev_node.first, prev_node.second); + origin_type = common::AnfAlgo::GetOutputInferDataType(prev_node.first, prev_node.second); } - auto cur_input = AnfAlgo::GetInputNode(cnode, input_index); + auto cur_input = common::AnfAlgo::GetInputNode(cnode, input_index); MS_EXCEPTION_IF_NULL(cur_input); const std::string dev_fmt = AnfAlgo::GetInputFormat(cnode, input_index); - const abstract::BaseShapePtr origin_shape = AnfAlgo::GetOutputDetailShape(prev_node.first, prev_node.second); + const abstract::BaseShapePtr origin_shape = + common::AnfAlgo::GetOutputDetailShape(prev_node.first, prev_node.second); if (TypeId device_type = AnfAlgo::GetInputDeviceDataType(cnode, input_index); origin_type != device_type) { auto cast = AddCastOpNodeToGraph(func_graph, cur_input, dev_fmt, origin_type, device_type, origin_shape, device_type); MS_EXCEPTION_IF_NULL(cast); cast->set_scope(cnode->scope()); cnode->set_input(input_index + 1, cast); - auto real_input = AnfAlgo::VisitKernel(cur_input, 0).first; - if (AnfAlgo::IsUpdateParameterKernel(cnode) && real_input->isa() && - AnfAlgo::IsParameterWeight(real_input->cast())) { + auto real_input = common::AnfAlgo::VisitKernel(cur_input, 0).first; + if (common::AnfAlgo::IsUpdateParameterKernel(cnode) && real_input->isa() && + common::AnfAlgo::IsParameterWeight(real_input->cast())) { SyncWeightNodeWithCast(func_graph, cnode, cur_input, cast, dev_fmt, device_type, origin_type, origin_shape, &make_tuple_inputs); } @@ -158,9 +160,9 @@ void InsertCast(const FuncGraphPtr &func_graph, const CNodePtr &cnode) { void InsertCastForGraphOutput(const FuncGraphPtr &func_graph, const CNodePtr &cnode, const AnfNodePtr &func_output) { MS_EXCEPTION_IF_NULL(cnode); - size_t output_num = AnfAlgo::GetOutputTensorNum(cnode); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(cnode); for (size_t i = 0; i < output_num; i++) { - auto infer_type = AnfAlgo::GetOutputInferDataType(cnode, i); + auto infer_type = common::AnfAlgo::GetOutputInferDataType(cnode, i); auto device_type = AnfAlgo::GetOutputDeviceDataType(cnode, i); const std::string dev_fmt = AnfAlgo::GetOutputFormat(cnode, i); // The shape of LSTM's reserved output will be changed in InitKernel, and this output is only used @@ -176,9 +178,9 @@ void InsertCastForGraphOutput(const FuncGraphPtr &func_graph, const CNodePtr &cn continue; } auto used_node_index = static_cast(used_node_list->at(j).second - 1); - auto cur_input = AnfAlgo::GetInputNode(utils::cast(used_node), used_node_index); + auto cur_input = common::AnfAlgo::GetInputNode(utils::cast(used_node), used_node_index); const abstract::BaseShapePtr origin_shape = - AnfAlgo::GetPrevNodeOutputDetailShape(utils::cast(used_node), used_node_index); + common::AnfAlgo::GetPrevNodeOutputDetailShape(utils::cast(used_node), used_node_index); auto cast = AddCastOpNodeToGraph(func_graph, cur_input, dev_fmt, device_type, infer_type, origin_shape, infer_type); MS_EXCEPTION_IF_NULL(cast); diff --git a/mindspore/ccsrc/plugin/device/cpu/optimizer/insert_format_transform_op.cc b/mindspore/ccsrc/plugin/device/cpu/optimizer/insert_format_transform_op.cc index 3329cbb1b55..e9c1381c2ac 100644 --- a/mindspore/ccsrc/plugin/device/cpu/optimizer/insert_format_transform_op.cc +++ b/mindspore/ccsrc/plugin/device/cpu/optimizer/insert_format_transform_op.cc @@ -20,12 +20,11 @@ #include #include #include -#include #include "utils/hash_set.h" #include "kernel/kernel_build_info.h" #include "backend/common/session/anf_runtime_algorithm.h" -#include "backend/common/session/kernel_graph.h" -#include "utils/utils.h" +#include "include/common/utils/anfalgo.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace opt { @@ -63,10 +62,10 @@ CNodePtr InsertTransposeOp(const FuncGraphPtr &graph, const AnfNodePtr &node, co std::vector transpose_input = {NewValueNode(transpose_prim), node}; auto transpose_op = graph->NewCNode(transpose_input); // 3.Set the output info of transpose. - auto transpose_type = {AnfAlgo::GetPrevNodeOutputInferDataType(used_node, used_node_index)}; - auto transpose_shape = {AnfAlgo::GetPrevNodeOutputInferShape(used_node, used_node_index)}; - AnfAlgo::SetOutputInferTypeAndShape(transpose_type, transpose_shape, transpose_op.get()); - AnfAlgo::SetNodeAttr(kAttrPerm, MakeValue(transpose_perm), transpose_op); + auto transpose_type = {common::AnfAlgo::GetPrevNodeOutputInferDataType(used_node, used_node_index)}; + auto transpose_shape = {common::AnfAlgo::GetPrevNodeOutputInferShape(used_node, used_node_index)}; + common::AnfAlgo::SetOutputInferTypeAndShape(transpose_type, transpose_shape, transpose_op.get()); + common::AnfAlgo::SetNodeAttr(kAttrPerm, MakeValue(transpose_perm), transpose_op); // 4. Set the new edge of transpose op. FuncGraphManagerPtr manager = graph->manager(); MS_EXCEPTION_IF_NULL(manager); @@ -77,8 +76,8 @@ CNodePtr InsertTransposeOp(const FuncGraphPtr &graph, const AnfNodePtr &node, co void SetTransposeOpBuildInfo(const std::string &input_format, const std::string &output_format, const AnfNodePtr &node) { MS_EXCEPTION_IF_NULL(node); - auto input_type = AnfAlgo::GetPrevNodeOutputInferDataType(node, 0); - auto output_type = AnfAlgo::GetOutputInferDataType(node, 0); + auto input_type = common::AnfAlgo::GetPrevNodeOutputInferDataType(node, 0); + auto output_type = common::AnfAlgo::GetOutputInferDataType(node, 0); kernel::KernelBuildInfo::KernelBuildInfoBuilder builder; builder.SetInputsFormat({input_format}); builder.SetInputsDeviceType({input_type}); @@ -94,7 +93,7 @@ void ProcessForTupleItem(const FuncGraphPtr &graph, const AnfNodePtr &node, int for (size_t i = 0; i < used_node_list->size(); i++) { auto used_node = used_node_list->at(i).first; auto used_node_index = used_node_list->at(i).second - 1; - if (AnfAlgo::GetCNodeName(used_node) == prim::kPrimTupleGetItem->name()) { + if (common::AnfAlgo::GetCNodeName(used_node) == prim::kPrimTupleGetItem->name()) { MS_LOG(EXCEPTION) << "The used node of tuple item " << used_node->DebugString() << " can't be tuple item."; } @@ -120,12 +119,12 @@ void InsertTransformOpForInput(const FuncGraphPtr &graph, const AnfNodePtr &node if (inputs_format[i] == prev_input_format) { continue; } - auto in_shape = AnfAlgo::GetPrevNodeOutputInferShape(node, i); + auto in_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node, i); auto dim = in_shape.size(); if (dim < kMinDimNeedToTransform) { continue; } - auto input_node = AnfAlgo::GetInputNode(utils::cast(node), i); + auto input_node = common::AnfAlgo::GetInputNode(utils::cast(node), i); MS_EXCEPTION_IF_NULL(input_node); auto transpose_perm = TransposeAxis(dim, ChannelFirst2ChannelLast); auto transpose_op = InsertTransposeOp(graph, input_node, node, i, transpose_perm); @@ -140,7 +139,7 @@ void InsertTransformOpForOutput(const FuncGraphPtr &graph, const AnfNodePtr &nod if ((outputs_format[i] == kOpFormat_DEFAULT) || (outputs_format[i] == origin_format)) { continue; } - auto out_shape = AnfAlgo::GetOutputInferShape(node, i); + auto out_shape = common::AnfAlgo::GetOutputInferShape(node, i); auto dim = out_shape.size(); if (dim < kMinDimNeedToTransform) { continue; @@ -151,7 +150,7 @@ void InsertTransformOpForOutput(const FuncGraphPtr &graph, const AnfNodePtr &nod for (size_t j = 0; j < used_node_list->size(); ++j) { auto used_node = used_node_list->at(j).first; auto used_node_index = used_node_list->at(j).second - 1; - if (AnfAlgo::GetCNodeName(used_node) == prim::kPrimTupleGetItem->name()) { + if (common::AnfAlgo::GetCNodeName(used_node) == prim::kPrimTupleGetItem->name()) { MS_LOG(DEBUG) << "The used node of [" << node->fullname_with_scope() << "] is tuple item."; // The tuple item need get next used nodes again. ProcessForTupleItem(graph, used_node, used_node_index, transpose_perm, outputs_format[i]); @@ -185,7 +184,7 @@ bool InsertFormatTransformOpCPU::Run(const FuncGraphPtr &graph) { continue; } - auto iter = kChannelLastKernel.find(AnfAlgo::GetCNodeName(node)); + auto iter = kChannelLastKernel.find(common::AnfAlgo::GetCNodeName(node)); if (iter == kChannelLastKernel.end()) { continue; } diff --git a/mindspore/ccsrc/plugin/device/gpu/hal/device/CMakeLists.txt b/mindspore/ccsrc/plugin/device/gpu/hal/device/CMakeLists.txt index 2c198d6fc30..dc8de65a318 100644 --- a/mindspore/ccsrc/plugin/device/gpu/hal/device/CMakeLists.txt +++ b/mindspore/ccsrc/plugin/device/gpu/hal/device/CMakeLists.txt @@ -5,6 +5,7 @@ endif() if(ENABLE_GPU) list(APPEND DEVICE_SRC_LIST "distribution/collective_init.cc") + list(APPEND DEVICE_SRC_LIST "gpu_comm_manager.cc") else() list(APPEND DEVICE_SRC_LIST "distribution/collective_fake_init.cc") endif() diff --git a/mindspore/ccsrc/plugin/device/gpu/hal/device/cuda_driver.cc b/mindspore/ccsrc/plugin/device/gpu/hal/device/cuda_driver.cc index 08bd12745ab..17f1885a52a 100644 --- a/mindspore/ccsrc/plugin/device/gpu/hal/device/cuda_driver.cc +++ b/mindspore/ccsrc/plugin/device/gpu/hal/device/cuda_driver.cc @@ -16,7 +16,7 @@ #include "plugin/device/gpu/hal/device/cuda_driver.h" #include "utils/log_adapter.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" namespace mindspore { namespace device { diff --git a/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_comm_manager.cc b/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_comm_manager.cc new file mode 100644 index 00000000000..2eaa399acb3 --- /dev/null +++ b/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_comm_manager.cc @@ -0,0 +1,91 @@ +/** + * Copyright 2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "include/common/utils/comm_manager.h" +#include "include/common/utils/convert_utils.h" +#include "utils/ms_context.h" +#include "include/common/utils/parallel_context.h" +#include "plugin/device/gpu/hal/device/distribution/collective_init.h" + +using CollectiveInitializer = mindspore::device::gpu::CollectiveInitializer; +using CreateCommGroupFunc = mindspore::device::gpu::CreateCommGroupFunc; +using GetRankIDByGroupFunc = mindspore::device::gpu::GetRankIDByGroupFunc; +using GetGroupSizeFunc = mindspore::device::gpu::GetGroupSizeFunc; +using DestroyGroupFunc = mindspore::device::gpu::DestroyGroupFunc; + +namespace mindspore { +namespace { +constexpr char kNcclWorldGroup[] = "nccl_world_group"; + +class GpuCommManager : public CommManager { + public: + GpuCommManager() : CommManager("nccl") {} + ~GpuCommManager() override = default; + + bool CreateGroupSync(const std::string &group, const std::vector &rank_id_list) const override { + bool ret = CollectiveInitializer::instance().CreateCommunicationGroup(group, rank_id_list); + if (!ret) { + MS_LOG(ERROR) << "Failed to create group " << group << " for rank id list " << rank_id_list; + return ret; + } + + MS_LOG(INFO) << "Successfully create group " << group << " for rank id list " << rank_id_list; + return ret; + } + + bool GetRankID(const std::string &group, unsigned int *rank_id) const override { + *rank_id = CollectiveInitializer::instance().GetRankIDByGroup(group); + MS_LOG(INFO) << "This process rank id is " << *rank_id << " in group " << group; + return true; + } + + bool GetRankSize(const std::string &group, unsigned int *rank_size) const override { + *rank_size = CollectiveInitializer::instance().GetGroupSize(group); + MS_LOG(INFO) << "Group " << group << " size is " << *rank_size; + return true; + } + + bool DestroyGroup(const std::string &group) const override { + bool ret = CollectiveInitializer::instance().DestroyCommunicationGroup(group); + if (!ret) { + MS_LOG(ERROR) << "Failed to destroy group " << group; + return ret; + } + + MS_LOG(INFO) << "Successfully destroy group " << group; + return ret; + } + + uint32_t GetRank() override { + uint32_t rank_id = 0; + auto parallel_context = parallel::ParallelContext::GetInstance(); + MS_EXCEPTION_IF_NULL(parallel_context); + if (parallel_context->parallel_mode() != parallel::kStandalone) { + // Check NCCL inited. + if (!CollectiveInitializer::instance().collective_inited()) { + MS_LOG(DEBUG) << "NCLL not inited, return rank_id = 0"; + return rank_id; + } + if (!GetRankID(kNcclWorldGroup, &rank_id)) { + MS_LOG(EXCEPTION) << "Get rank id failed."; + } + } + return rank_id; + } +}; +COMM_MANAGER_REG(kGPUDevice, std::make_shared()); +} // namespace +} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_device_manager.cc b/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_device_manager.cc index f3231f8df3a..7e939dec200 100644 --- a/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_device_manager.cc +++ b/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_device_manager.cc @@ -17,7 +17,7 @@ #include "plugin/device/gpu/hal/device/gpu_device_manager.h" #include "plugin/device/gpu/hal/device/gpu_common.h" #include "utils/log_adapter.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" #include "plugin/device/gpu/hal/device/gpu_buffer_mgr.h" namespace mindspore { diff --git a/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_kernel_build.cc b/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_kernel_build.cc index 3c542e2cfe2..57f0dee1d60 100644 --- a/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_kernel_build.cc +++ b/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_kernel_build.cc @@ -21,6 +21,7 @@ #include "kernel/common_utils.h" #include "frontend/operator/ops.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/session/kernel_build_client.h" #include "plugin/device/gpu/hal/device/cuda_env_checker.h" @@ -34,7 +35,7 @@ void CreateGPUKernel(const std::vector &kernels) { std::vector akg_nodes; for (const auto &kernel : kernels) { MS_EXCEPTION_IF_NULL(kernel); - std::string kernel_name = session::AnfRuntimeAlgorithm::GetCNodeName(kernel); + std::string kernel_name = common::AnfAlgo::GetCNodeName(kernel); if (kernel_name == prim::kPrimTupleGetItem->name() || kernel_name == prim::kPrimMakeTuple->name() || kernel_name == prim::kPrimDepend->name() || kernel_name == prim::kPrimStateSetItem->name()) { continue; @@ -54,7 +55,7 @@ void CreateGPUKernel(const std::vector &kernels) { } } akg_nodes.push_back(kernel); - } else if (!AnfAlgo::IsControlOpExecInBackend(kernel)) { + } else if (!common::AnfAlgo::IsControlOpExecInBackend(kernel)) { auto gpu_kernel_ptr = kernel::NativeGpuKernelModFactory::GetInstance().Create(kernel_name, kernel); if (!gpu_kernel_ptr) { MS_LOG(EXCEPTION) << "Build gpu kernel op[" << kernel->fullname_with_scope() << "] failed"; diff --git a/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_kernel_runtime.cc b/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_kernel_runtime.cc index 5e7e8e7f41c..c7bf78673a0 100644 --- a/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_kernel_runtime.cc +++ b/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_kernel_runtime.cc @@ -24,16 +24,16 @@ #include "plugin/device/gpu/hal/device/gpu_device_manager.h" #include "plugin/device/gpu/hal/device/gpu_memory_allocator.h" #include "plugin/device/gpu/hal/device/distribution/collective_init.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" #include "utils/ms_context.h" -#include "utils/context/graph_kernel_flags.h" +#include "include/common/utils/context/graph_kernel_flags.h" #include "runtime/device/kernel_runtime_manager.h" #include "plugin/device/gpu/hal/device/gpu_common.h" #include "utils/ms_utils.h" #include "plugin/device/gpu/hal/device/gpu_memory_manager.h" #include "kernel/common_utils.h" #include "plugin/device/gpu/hal/device/gpu_memory_copy_manager.h" -#include "utils/ms_device_shape_transfer.h" +#include "runtime/device/ms_device_shape_transfer.h" #include "ir/dtype.h" #ifndef ENABLE_SECURITY #include "profiler/device/gpu/gpu_profiling.h" @@ -168,13 +168,13 @@ void LoadKernelData(Debugger *debugger, const CNodePtr &kernel, if (debugger->debugger_enabled() || dump_json_parser.InputNeedDump()) { // get inputs - auto input_size = AnfAlgo::GetInputTensorNum(kernel); + auto input_size = common::AnfAlgo::GetInputTensorNum(kernel); for (size_t j = 0; j < input_size; ++j) { auto input_kernel = kernel->input(j + 1); MS_EXCEPTION_IF_NULL(input_kernel); std::string input_kernel_name = GetKernelNodeName(input_kernel); auto addr = kernel_inputs[j]; - auto type = AnfAlgo::GetOutputInferDataType(input_kernel, PARAMETER_OUTPUT_INDEX); + auto type = common::AnfAlgo::GetOutputInferDataType(input_kernel, PARAMETER_OUTPUT_INDEX); // For example, this happens with the Depend op if (type == kMetaTypeNone) { continue; @@ -194,14 +194,14 @@ void LoadKernelData(Debugger *debugger, const CNodePtr &kernel, if (debugger->debugger_enabled() || dump_json_parser.OutputNeedDump()) { // get outputs - auto output_size = AnfAlgo::GetOutputTensorNum(kernel); - auto node_name = AnfAlgo::GetCNodeName(kernel); + auto output_size = common::AnfAlgo::GetOutputTensorNum(kernel); + auto node_name = common::AnfAlgo::GetCNodeName(kernel); std::vector real_outputs = CheckRealOutput(node_name, output_size); for (int j : real_outputs) { auto addr = kernel_outputs[j]; - auto type = AnfAlgo::GetOutputInferDataType(kernel, j); + auto type = common::AnfAlgo::GetOutputInferDataType(kernel, j); // For example, this happens with the Depend op if (type == kMetaTypeNone) { continue; @@ -320,10 +320,10 @@ void GPUKernelRuntime::AllocInplaceNodeMemory(const session::KernelGraph *graph) std::map> inplace_groups; auto kernel_cnodes = graph->execution_order(); for (auto &kernel : kernel_cnodes) { - if (!AnfAlgo::IsInplaceNode(kernel, "inplace_algo")) { + if (!common::AnfAlgo::IsInplaceNode(kernel, "inplace_algo")) { continue; } - auto primitive = AnfAlgo::GetCNodePrimitive(kernel); + auto primitive = common::AnfAlgo::GetCNodePrimitive(kernel); MS_EXCEPTION_IF_NULL(primitive); auto group_attr = primitive->GetAttr("inplace_group"); MS_EXCEPTION_IF_NULL(group_attr); @@ -338,7 +338,7 @@ void GPUKernelRuntime::AllocInplaceNodeMemory(const session::KernelGraph *graph) continue; } - auto primitive = AnfAlgo::GetCNodePrimitive(item[0]); + auto primitive = common::AnfAlgo::GetCNodePrimitive(item[0]); MS_EXCEPTION_IF_NULL(primitive); auto output_index = GetValue(primitive->GetAttr("inplace_output_index")); auto device_address = GetMutableOutputAddr(item[0], output_index, false); @@ -357,7 +357,7 @@ void GPUKernelRuntime::AllocInplaceNodeMemory(const session::KernelGraph *graph) } for (auto &node : item) { - auto prim = AnfAlgo::GetCNodePrimitive(node); + auto prim = common::AnfAlgo::GetCNodePrimitive(node); MS_EXCEPTION_IF_NULL(prim); auto index = GetValue(prim->GetAttr("inplace_output_index")); AnfAlgo::SetOutputAddr(device_address, index, node.get()); @@ -369,7 +369,7 @@ bool GPUKernelRuntime::IsDistributedTraining(const session::KernelGraph *graph) MS_EXCEPTION_IF_NULL(graph); const auto &kernels = graph->execution_order(); return std::any_of(kernels.begin(), kernels.end(), - [](const AnfNodePtr &kernel) { return AnfAlgo::IsCommunicationOp(kernel); }); + [](const AnfNodePtr &kernel) { return common::AnfAlgo::IsCommunicationOp(kernel); }); } void GPUKernelRuntime::FetchMemUnitSize(const session::KernelGraph *graph) { @@ -384,7 +384,7 @@ void GPUKernelRuntime::FetchMemUnitSize(const session::KernelGraph *graph) { MS_EXCEPTION_IF_NULL(kernel_mode); auto kernel = cnode->cast(); MS_EXCEPTION_IF_NULL(kernel); - if (AnfAlgo::IsCommunicationOp(kernel)) { + if (common::AnfAlgo::IsCommunicationOp(kernel)) { continue; } const auto &input_size_list = kernel_mode->GetInputSizeList(); @@ -400,7 +400,7 @@ void GPUKernelRuntime::FetchMemUnitSize(const session::KernelGraph *graph) { } // Free the input of kernel by reference count. - size_t input_num = AnfAlgo::GetInputTensorNum(kernel); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel); if (input_num != input_size_list.size()) { continue; } @@ -658,7 +658,7 @@ void GPUKernelRuntime::InitKernelWorkspaceAddress(const session::KernelGraph *gr void GPUKernelRuntime::SaveGraphOutputNode(const session::KernelGraph *graph) { MS_EXCEPTION_IF_NULL(graph); auto graph_id = graph->graph_id(); - const auto &output_nodes = AnfAlgo::GetAllOutput(graph->output(), {prim::kPrimTupleGetItem}); + const auto &output_nodes = common::AnfAlgo::GetAllOutput(graph->output(), {prim::kPrimTupleGetItem}); for (const auto &node : output_nodes) { graph_output_map_[graph_id].insert(node); } @@ -728,7 +728,7 @@ CNodePtr GetLastKernel(const session::KernelGraph *graph) { const auto &kernels = graph->execution_order(); CNodePtr last_kernel; for (const auto &kernel : kernels) { - if (AnfAlgo::IsInplaceNode(kernel, "skip")) { + if (common::AnfAlgo::IsInplaceNode(kernel, "skip")) { continue; } else { last_kernel = kernel; @@ -763,7 +763,7 @@ bool GPUKernelRuntime::LaunchKernelDynamic(const session::KernelGraph *graph, bo for (const auto &kernel : kernels) { auto kernel_mod = AnfAlgo::GetKernelMod(kernel); MS_EXCEPTION_IF_NULL(kernel_mod); - if (AnfAlgo::IsInplaceNode(kernel, "skip")) { + if (common::AnfAlgo::IsInplaceNode(kernel, "skip")) { continue; } @@ -1110,7 +1110,7 @@ bool GPUKernelRuntime::AllocKernelInputDynamicRes(const mindspore::AnfNodePtr &k MS_EXCEPTION_IF_NULL(kernel); MS_EXCEPTION_IF_NULL(kernel_inputs); MS_EXCEPTION_IF_NULL(mem_reuse_util_); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel); for (size_t i = 0; i < input_num; ++i) { DeviceAddressPtr device_address; if (mem_reuse_util_->is_all_nop_node()) { @@ -1122,12 +1122,12 @@ bool GPUKernelRuntime::AllocKernelInputDynamicRes(const mindspore::AnfNodePtr &k } // Get in-place output_address - if (AnfAlgo::IsInplaceNode(kernel, "aggregate")) { - auto primitive = AnfAlgo::GetCNodePrimitive(kernel); + if (common::AnfAlgo::IsInplaceNode(kernel, "aggregate")) { + auto primitive = common::AnfAlgo::GetCNodePrimitive(kernel); MS_EXCEPTION_IF_NULL(primitive); auto input_index = GetValue(primitive->GetAttr("aggregate_input_index")); if (i == input_index) { - auto skip_node = AnfAlgo::GetInputNode(utils::cast(kernel), input_index); + auto skip_node = common::AnfAlgo::GetInputNode(utils::cast(kernel), input_index); device_address = GetPrevNodeMutableOutputAddr(skip_node, 0, false); } } @@ -1150,7 +1150,7 @@ bool GPUKernelRuntime::AllocKernelOutputDynamicRes(const mindspore::kernel::Kern MS_EXCEPTION_IF_NULL(kernel); MS_EXCEPTION_IF_NULL(kernel_outputs); UpdateHostSwapOutQueue(mock); - if (AnfAlgo::IsCommunicationOp(kernel)) { + if (common::AnfAlgo::IsCommunicationOp(kernel)) { AllocCommunicationOpOutputDynamicRes(kernel); } auto output_sizes = kernel_mod.GetOutputSizeList(); @@ -1204,8 +1204,8 @@ void GPUKernelRuntime::AllocCommunicationOpDynamicRes(const session::KernelGraph auto &kernels = graph->execution_order(); for (auto &kernel : kernels) { MS_EXCEPTION_IF_NULL(kernel); - if (AnfAlgo::IsCommunicationOp(kernel) && AnfAlgo::GetCNodeName(kernel) != kHcomSendOpName && - AnfAlgo::GetCNodeName(kernel) != kReceiveOpName) { + if (common::AnfAlgo::IsCommunicationOp(kernel) && common::AnfAlgo::GetCNodeName(kernel) != kHcomSendOpName && + common::AnfAlgo::GetCNodeName(kernel) != kReceiveOpName) { AllocCommunicationOpInputDynamicRes(kernel); } } @@ -1285,14 +1285,14 @@ void GPUKernelRuntime::FreeKernelDynamicRes(const mindspore::AnfNodePtr &kernel) auto cnode = kernel->cast(); MS_EXCEPTION_IF_NULL(cnode); // Can not free the input addr of communication op when enable multi stream - if (AnfAlgo::IsCommunicationOp(kernel)) { + if (common::AnfAlgo::IsCommunicationOp(kernel)) { return; } // Free the input of kernel by reference count. - size_t input_num = AnfAlgo::GetInputTensorNum(kernel); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel); for (size_t i = 0; i < input_num; ++i) { - if (AnfAlgo::IsInplaceNode(kernel, "aggregate")) { - auto primitive = AnfAlgo::GetCNodePrimitive(kernel); + if (common::AnfAlgo::IsInplaceNode(kernel, "aggregate")) { + auto primitive = common::AnfAlgo::GetCNodePrimitive(kernel); MS_EXCEPTION_IF_NULL(primitive); auto index = GetValue(primitive->GetAttr("aggregate_input_index")); if (i == index) { @@ -1323,7 +1323,7 @@ void GPUKernelRuntime::FreeKernelDynamicRes(const mindspore::AnfNodePtr &kernel) } } // Free the output of kernel, if output has no reference. - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel); for (size_t i = 0; i < output_num; ++i) { auto kernel_ref_count_ptr = mem_reuse_util_->GetRef(cnode, i); if (kernel_ref_count_ptr == nullptr) { @@ -1356,13 +1356,13 @@ DeviceAddressPtr GPUKernelRuntime::GetPrevNodeMutableOutputAddr(const AnfNodePtr auto &addr_cache = skip_nop_node ? prev_node_mut_output_addr_cache_ : prev_node_mut_output_addr_skip_nop_node_cache_; std::unordered_map>::iterator addr_iter; if (auto iter = addr_cache.find(node); iter == addr_cache.end()) { - addr_iter = addr_cache.insert({node, {AnfAlgo::GetInputTensorNum(node), {nullptr, 0}}}).first; + addr_iter = addr_cache.insert({node, {common::AnfAlgo::GetInputTensorNum(node), {nullptr, 0}}}).first; } else { addr_iter = iter; } if (addr_iter->second[i].first == nullptr) { - addr_iter->second[i] = AnfAlgo::GetPrevNodeOutput(node, i, skip_nop_node); + addr_iter->second[i] = common::AnfAlgo::GetPrevNodeOutput(node, i, skip_nop_node); } session::KernelWithIndex prev_node_with_index = addr_iter->second[i]; @@ -1406,18 +1406,18 @@ DeviceAddressPtr GPUKernelRuntime::GetMutableOutputAddr(const AnfNodePtr &node, session::KernelWithIndex GPUKernelRuntime::GetPrevNodeOutput(const AnfNodePtr &node, size_t i) { if (!enable_relation_cache_) { - return AnfAlgo::GetPrevNodeOutput(node, i); + return common::AnfAlgo::GetPrevNodeOutput(node, i); } std::unordered_map>::iterator addr_iter; if (auto iter = prev_node_output_cache_.find(node); iter == prev_node_output_cache_.end()) { - addr_iter = prev_node_output_cache_.insert({node, {AnfAlgo::GetInputTensorNum(node), {nullptr, 0}}}).first; + addr_iter = prev_node_output_cache_.insert({node, {common::AnfAlgo::GetInputTensorNum(node), {nullptr, 0}}}).first; } else { addr_iter = iter; } if (addr_iter->second[i].first == nullptr) { - auto kernel_with_index = AnfAlgo::GetPrevNodeOutput(node, i); + auto kernel_with_index = common::AnfAlgo::GetPrevNodeOutput(node, i); addr_iter->second[i] = kernel_with_index; } diff --git a/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_kernel_runtime.h b/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_kernel_runtime.h index 1c85d18e1c2..9055c89f4c5 100644 --- a/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_kernel_runtime.h +++ b/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_kernel_runtime.h @@ -26,6 +26,7 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "runtime/device/kernel_runtime.h" #include "runtime/device/kernel_runtime_manager.h" #include "common/mem_reuse/mem_swap_manager.h" diff --git a/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_launch_mul.cc b/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_launch_mul.cc index a48da4008c2..c81a4d4dffc 100644 --- a/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_launch_mul.cc +++ b/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_launch_mul.cc @@ -19,7 +19,7 @@ #include "plugin/device/gpu/hal/device/gpu_memory_allocator.h" #include "plugin/device/gpu/hal/device/gpu_device_manager.h" #include "backend/common/session/single_kernel_graph.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" namespace mindspore::device::gpu { void GPULaunchMul::FreeDeviceMem(void *addr) { GPULaunchkernel::FreeDeviceMem(addr); } diff --git a/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_memory_copy_manager.cc b/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_memory_copy_manager.cc index 5042ddc120a..e6295227205 100644 --- a/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_memory_copy_manager.cc +++ b/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_memory_copy_manager.cc @@ -18,6 +18,7 @@ #include "plugin/device/gpu/hal/device/gpu_common.h" #include "plugin/device/gpu/hal/device/gpu_device_manager.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace device { diff --git a/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_memory_manager.cc b/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_memory_manager.cc index bb8e126ccb5..a7e125ad507 100644 --- a/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_memory_manager.cc +++ b/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_memory_manager.cc @@ -17,7 +17,7 @@ #include "plugin/device/gpu/hal/device/gpu_memory_manager.h" #include "plugin/device/gpu/hal/device/gpu_memory_allocator.h" #include "utils/ms_context.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" #include "ps/ps_cache/ps_cache_manager.h" #include "plugin/device/gpu/hal/device/gpu_device_manager.h" #include "plugin/device/gpu/hal/device/gpu_common.h" diff --git a/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_stream_assign.cc b/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_stream_assign.cc index f26e77533ca..55787dace8c 100644 --- a/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_stream_assign.cc +++ b/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_stream_assign.cc @@ -32,13 +32,13 @@ void AssignGpuStream(const std::shared_ptr &kernel_graph) std::vector allreduce_kernels; auto execution_kernels = kernel_graph->execution_order(); for (auto kernel_node : execution_kernels) { - std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node); + std::string kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); if (kernel_name == kAllReduceOpName) { allreduce_kernels.emplace_back(kernel_node); } else { CudaDeviceStream compute_stream = GPUDeviceManager::GetInstance().default_stream(); MS_EXCEPTION_IF_NULL(compute_stream); - AnfAlgo::SetNodeAttr(kAttrStreamId, MakeValue(reinterpret_cast(compute_stream)), kernel_node); + common::AnfAlgo::SetNodeAttr(kAttrStreamId, MakeValue(reinterpret_cast(compute_stream)), kernel_node); } } if (allreduce_kernels.size() > 1) { @@ -47,11 +47,12 @@ void AssignGpuStream(const std::shared_ptr &kernel_graph) if (FindAllReduceStreamSwitchPos(kernel_graph, &send_recv_pairs)) { CudaDeviceStream comm_stream = nullptr; GPUDeviceManager::GetInstance().CreateStream(&comm_stream); - std::transform( - allreduce_kernels.begin(), allreduce_kernels.end(), allreduce_kernels.begin(), [&](CNodePtr allreduce_kernel) { - AnfAlgo::SetNodeAttr(kAttrStreamId, MakeValue(reinterpret_cast(comm_stream)), allreduce_kernel); - return allreduce_kernel; - }); + std::transform(allreduce_kernels.begin(), allreduce_kernels.end(), allreduce_kernels.begin(), + [&](CNodePtr allreduce_kernel) { + common::AnfAlgo::SetNodeAttr(kAttrStreamId, MakeValue(reinterpret_cast(comm_stream)), + allreduce_kernel); + return allreduce_kernel; + }); InsertStreamSwitchNode(kernel_graph, send_recv_pairs); } else { return; @@ -68,14 +69,14 @@ bool FindAllReduceStreamSwitchPos(const std::shared_ptr &k iter = iter_begin = execution_kernels.begin(); std::vector::iterator iter_end = execution_kernels.end(); for (; iter != execution_kernels.end(); ++iter) { - std::string kernel_name = AnfAlgo::GetCNodeName(*iter); + std::string kernel_name = common::AnfAlgo::GetCNodeName(*iter); if (kernel_name == kAllReduceOpName) { // Find AllReduce node's last input node. std::vector::iterator mock_send_node_iter = FindSendNodePos(iter_begin, iter + 1, *iter, kAllReduceStreamSwitch); if (mock_send_node_iter == iter + 1) { MS_LOG(INFO) << "Can't find send node place before AllReduce node."; - } else if (AnfAlgo::GetCNodeName(*mock_send_node_iter) != kAllReduceOpName) { + } else if (common::AnfAlgo::GetCNodeName(*mock_send_node_iter) != kAllReduceOpName) { SendRecvPair pair1 = {kAllReduceStreamSwitch, *mock_send_node_iter, *iter, IntToSize(mock_send_node_iter - iter_begin + 1), IntToSize(iter - iter_begin)}; send_recv_pairs->push_back(pair1); @@ -90,7 +91,7 @@ bool FindAllReduceStreamSwitchPos(const std::shared_ptr &k // otherwise consider FindAllReduceStreamSwitchPos as failed. MS_LOG(INFO) << "Can't find recv node place after AllReduce node."; return false; - } else if (AnfAlgo::GetCNodeName(*mock_recv_node_iter) != kAllReduceOpName) { + } else if (common::AnfAlgo::GetCNodeName(*mock_recv_node_iter) != kAllReduceOpName) { SendRecvPair pair2 = {kAllReduceStreamSwitch, *iter, *mock_recv_node_iter, IntToSize(iter - iter_begin + 1), IntToSize(mock_recv_node_iter - iter_begin)}; send_recv_pairs->push_back(pair2); @@ -130,8 +131,8 @@ std::vector::iterator FindRecvNodePos(std::vector::iterator if (stream_switch_type == kAllReduceStreamSwitch) { MS_EXCEPTION_IF_NULL(node); for (auto input : node->inputs()) { - if (mock_send_node == AnfAlgo::VisitKernel(input, 0).first) { - if (AnfAlgo::GetCNodeName(node) != kAllReduceOpName) { + if (mock_send_node == common::AnfAlgo::VisitKernel(input, 0).first) { + if (common::AnfAlgo::GetCNodeName(node) != kAllReduceOpName) { return iter; } else if (ret == end) { ret = iter; @@ -187,13 +188,13 @@ bool GenSendRecvCNodesForAllReduce(const std::shared_ptr & std::weak_ptr send_node_ = *send_node; CHECK_CUDA_RET_WITH_EXCEPT(send_node_, cudaEventCreate(&event, cudaEventDisableTiming), "Creating cuda event failed."); - AnfAlgo::SetNodeAttr(kAttrRecordEvent, MakeValue(reinterpret_cast(event)), *send_node); - AnfAlgo::SetNodeAttr(kAttrWaitEvent, MakeValue(reinterpret_cast(event)), *recv_node); + common::AnfAlgo::SetNodeAttr(kAttrRecordEvent, MakeValue(reinterpret_cast(event)), *send_node); + common::AnfAlgo::SetNodeAttr(kAttrWaitEvent, MakeValue(reinterpret_cast(event)), *recv_node); - uintptr_t send_stream = AnfAlgo::GetNodeAttr(mock_send_node, kAttrStreamId); - AnfAlgo::SetNodeAttr(kAttrRecordEventStream, MakeValue(send_stream), *send_node); - uintptr_t recv_stream = AnfAlgo::GetNodeAttr(mock_recv_node, kAttrStreamId); - AnfAlgo::SetNodeAttr(kAttrWaitEventStream, MakeValue(recv_stream), *recv_node); + uintptr_t send_stream = common::AnfAlgo::GetNodeAttr(mock_send_node, kAttrStreamId); + common::AnfAlgo::SetNodeAttr(kAttrRecordEventStream, MakeValue(send_stream), *send_node); + uintptr_t recv_stream = common::AnfAlgo::GetNodeAttr(mock_recv_node, kAttrStreamId); + common::AnfAlgo::SetNodeAttr(kAttrWaitEventStream, MakeValue(recv_stream), *recv_node); return true; } @@ -220,11 +221,11 @@ void CacheSendRecvCNodesForAllReduce(const std::shared_ptr const CNodePtr &send_node, const CNodePtr &recv_node) { MS_EXCEPTION_IF_NULL(kernel_graph); std::pair send_recv_nodes(send_node, recv_node); - if (AnfAlgo::GetCNodeName(mock_send_node) == kAllReduceOpName) { + if (common::AnfAlgo::GetCNodeName(mock_send_node) == kAllReduceOpName) { kernel_graph->InsertToSendRecvPair(mock_send_node, send_recv_nodes); } - if (AnfAlgo::GetCNodeName(mock_recv_node) == kAllReduceOpName) { + if (common::AnfAlgo::GetCNodeName(mock_recv_node) == kAllReduceOpName) { kernel_graph->InsertFromSendRecvPair(mock_recv_node, send_recv_nodes); } } diff --git a/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_stream_assign.h b/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_stream_assign.h index 333576f4492..68c9ce90ff1 100644 --- a/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_stream_assign.h +++ b/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_stream_assign.h @@ -22,6 +22,7 @@ #include #include "backend/common/session/kernel_graph.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace device { @@ -41,8 +42,10 @@ struct StreamSwitchNode { if (offset < n.offset) { return true; } else if (offset == n.offset) { - return (AnfAlgo::GetCNodeName(cnode) == kRecvOpName && AnfAlgo::GetCNodeName(n.cnode) == kSendOpName) ? false - : true; + return (common::AnfAlgo::GetCNodeName(cnode) == kRecvOpName && + common::AnfAlgo::GetCNodeName(n.cnode) == kSendOpName) + ? false + : true; } else { return false; } diff --git a/mindspore/ccsrc/plugin/device/gpu/hal/device/kernel_info_setter.cc b/mindspore/ccsrc/plugin/device/gpu/hal/device/kernel_info_setter.cc index 77e6aa1cb12..ddc22178db6 100644 --- a/mindspore/ccsrc/plugin/device/gpu/hal/device/kernel_info_setter.cc +++ b/mindspore/ccsrc/plugin/device/gpu/hal/device/kernel_info_setter.cc @@ -25,11 +25,12 @@ #include "kernel/oplib/opinfo.h" #include "kernel/oplib/oplib.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "plugin/device/gpu/kernel/custom/custom_aot_gpu_kernel.h" #include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" #include "utils/ms_context.h" #include "utils/ms_utils.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace device { @@ -86,19 +87,19 @@ std::string SupportedTypeList(const CNodePtr &kernel_node, KernelType kernel_typ // Custom op gets reg info from OpLib instead of NativeGpuKernelModFactory. if (!IsPrimitiveCNode(kernel_node, prim::kPrimCustom)) { supported_type_lists = - kernel::NativeGpuKernelModFactory::GetInstance().SupportedTypeList(AnfAlgo::GetCNodeName(kernel_node)); + kernel::NativeGpuKernelModFactory::GetInstance().SupportedTypeList(common::AnfAlgo::GetCNodeName(kernel_node)); if (!supported_type_lists.empty()) { return supported_type_lists; } } std::vector> kernel_info_list; - std::string op_name = AnfAlgo::GetCNodeName(kernel_node); + std::string op_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel::OpImplyType imply_type = GetImplyType(kernel_type); auto op_info_ptr = mindspore::kernel::OpLib::FindOp(op_name, imply_type); if (op_info_ptr == nullptr) { MS_LOG(EXCEPTION) << "Unsupported op [" << op_name << "] on GPU, Please confirm whether the device target setting is correct, or refer to the " - "official website https://mindspore.cn/ to query the operator support list."; + "official website to query the operator support list."; } (void)ParseMetadata(kernel_node, op_info_ptr, kernel::Processor::CUDA, &kernel_info_list); for (size_t i = 0; i < kernel_info_list.size(); i++) { @@ -122,13 +123,13 @@ bool SelectAkgKernel(const CNodePtr &kernel_node, const std::shared_ptr> kernel_info_list; - if (AnfAlgo::IsNodeInGraphKernel(kernel_node)) { + if (common::AnfAlgo::IsNodeInGraphKernel(kernel_node)) { // The op_info in OpLib is only used for basic ops, // we don't care it in GraphKernel. return true; } - std::string op_name = AnfAlgo::GetCNodeName(kernel_node); + std::string op_name = common::AnfAlgo::GetCNodeName(kernel_node); auto op_info_ptr = mindspore::kernel::OpLib::FindOp(op_name, kernel::OpImplyType::kAKG); if (op_info_ptr == nullptr) { @@ -158,9 +159,9 @@ bool SelectCustomKernel(const CNodePtr &kernel_node, const std::shared_ptr(kernel_node, kAttrFuncType); + auto func_type = common::AnfAlgo::GetNodeAttr(kernel_node, kAttrFuncType); if (func_type == kCustomTypeAOT) { *kernel_type = KernelType::GPU_KERNEL; if (!kernel::NativeGpuKernelModFactory::GetInstance().SearchRegistered(op_name, selected_kernel_info)) { @@ -200,11 +201,11 @@ bool SelectCustomKernel(const CNodePtr &kernel_node, const std::shared_ptrinput(input_index + 1); MS_EXCEPTION_IF_NULL(input_kernel_node); - auto input_with_index = AnfAlgo::VisitKernel(input_kernel_node, 0); + auto input_with_index = common::AnfAlgo::VisitKernel(input_kernel_node, 0); MS_EXCEPTION_IF_NULL(input_with_index.first); auto real_input_node = input_with_index.first; if (!real_input_node->isa()) { @@ -215,16 +216,16 @@ void SetTensorDeviceInfo(const kernel::KernelBuildInfo &selected_kernel_info, co auto param = real_input_node->cast(); MS_EXCEPTION_IF_NULL(param); - if (!AnfAlgo::IsParameterWeight(param)) { + if (!common::AnfAlgo::IsParameterWeight(param)) { std::vector output_format = {kOpFormat_DEFAULT}; builder->SetOutputsFormat(output_format); - std::vector output_type = {AnfAlgo::GetOutputInferDataType(real_input_node, 0)}; + std::vector output_type = {common::AnfAlgo::GetOutputInferDataType(real_input_node, 0)}; builder->SetOutputsDeviceType(output_type); AnfAlgo::SetSelectKernelBuildInfo(builder->Build(), real_input_node.get()); continue; } if ((AnfAlgo::GetOutputDeviceDataType(real_input_node, 0) == kTypeUnknown) || - (AnfAlgo::GetCNodeName(kernel_node) == "ApplyMomentum")) { + (common::AnfAlgo::GetCNodeName(kernel_node) == "ApplyMomentum")) { std::vector output_format = {selected_kernel_info.GetInputFormat(input_index)}; builder->SetOutputsFormat(output_format); auto reduce_flag = kernel::NativeGpuKernelModFactory::GetInstance().reduce_flag_; @@ -268,7 +269,7 @@ bool IsNeedProcessFormatInfo(const CNodePtr &kernel_node, const std::vectorsecond.first; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); TransformFormatPosition(&inputs_format_position, input_num); for (const auto &input_format_position : inputs_format_position) { - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, input_format_position); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, input_format_position); // Only support the transformer between NCHW and NHWC, so need the shape is 4 dimension. if (input_shape.size() != kFormatTransformDimension) { return false; @@ -289,10 +290,10 @@ bool IsNeedProcessFormatInfo(const CNodePtr &kernel_node, const std::vectorsecond.second; - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); TransformFormatPosition(&outputs_format_position, output_num); for (const auto &output_format_position : outputs_format_position) { - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, output_format_position); + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, output_format_position); // Only support the transformer between NCHW and NHWC, so need the shape is 4 dimension. if (output_shape.size() != kFormatTransformDimension) { return false; @@ -307,7 +308,7 @@ void UpdateKernelFormatInfo(const CNodePtr &kernel_node, const std::vectorfullname_with_scope() << ", format: " << cal_format; auto inputs_format_position = iter->second.first; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); TransformFormatPosition(&inputs_format_position, input_num); for (const auto &input_format_position : inputs_format_position) { if (input_format_position >= inputs_format->size()) { @@ -326,7 +327,7 @@ void UpdateKernelFormatInfo(const CNodePtr &kernel_node, const std::vectorsecond.second; - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); TransformFormatPosition(&outputs_format_position, output_num); for (const auto &output_format_position : outputs_format_position) { if (output_format_position >= outputs_format->size()) { @@ -335,10 +336,10 @@ void UpdateKernelFormatInfo(const CNodePtr &kernel_node, const std::vectorHasAttr("format")) { - *origin_data_format = AnfAlgo::GetNodeAttr(kernel_node, "format"); + *origin_data_format = common::AnfAlgo::GetNodeAttr(kernel_node, "format"); } } @@ -352,9 +353,9 @@ void SetGraphKernelInfo(const CNodePtr &kernel_node, const FuncGraphPtr &func_gr for (size_t i = 0; i < input_list.size(); ++i) { kernel::KernelBuildInfo::KernelBuildInfoBuilder builder; std::vector outputs_format = {kOpFormat_DEFAULT}; - std::vector outputs_device_type = {AnfAlgo::GetOutputInferDataType(input_list[i], 0)}; + std::vector outputs_device_type = {common::AnfAlgo::GetOutputInferDataType(input_list[i], 0)}; graph_input_format.push_back(kOpFormat_DEFAULT); - graph_input_type.push_back(AnfAlgo::GetOutputInferDataType(input_list[i], 0)); + graph_input_type.push_back(common::AnfAlgo::GetOutputInferDataType(input_list[i], 0)); builder.SetOutputsFormat(outputs_format); builder.SetOutputsDeviceType(outputs_device_type); AnfAlgo::SetSelectKernelBuildInfo(builder.Build(), input_list[i].get()); @@ -400,7 +401,7 @@ void SetGraphKernelInfo(const CNodePtr &kernel_node, const FuncGraphPtr &func_gr void PrintUnsupportedTypeException(const CNodePtr &kernel_node, const std::vector &inputs_type, const std::vector &outputs_type, KernelType kernel_type) { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); std::string build_type = "input["; std::for_each(std::begin(inputs_type), std::end(inputs_type), [&build_type](auto i) { build_type += TypeIdToString(i) + " "; }); @@ -427,12 +428,12 @@ void FormatTransformChecker::CheckSupportFormatTransform(const std::shared_ptrname()) { format_transform_ = false; return; } - auto value = AnfAlgo::GetCNodePrimitive(kernel); + auto value = common::AnfAlgo::GetCNodePrimitive(kernel); if (value != nullptr && value->GetAttr("format") != nullptr && GetValue(value->GetAttr("format")) == kOpFormat_NHWC) { format_transform_ = false; @@ -454,25 +455,25 @@ void FormatTransformChecker::CheckSupportFormatTransform(const std::shared_ptr inputs_format; std::vector inputs_type; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); for (size_t input_index = 0; input_index < input_num; ++input_index) { inputs_format.emplace_back(kOpFormat_DEFAULT); - inputs_type.push_back(AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, input_index)); + inputs_type.push_back(common::AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, input_index)); } std::vector outputs_format; std::vector outputs_type; - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); for (size_t output_index = 0; output_index < output_num; ++output_index) { outputs_format.emplace_back(kOpFormat_DEFAULT); - outputs_type.push_back(AnfAlgo::GetOutputInferDataType(kernel_node, output_index)); + outputs_type.push_back(common::AnfAlgo::GetOutputInferDataType(kernel_node, output_index)); } std::string origin_data_format = kOpFormat_DEFAULT; if (IsNeedProcessFormatInfo(kernel_node, inputs_type)) { @@ -489,20 +490,20 @@ void SetKernelInfo(const CNodePtr &kernel_node, KernelType kernel_type) { // Custom op select kernel from OpLib result = SelectCustomKernel(kernel_node, builder->Build(), &kernel_type); } else if (kernel_type == UNKNOWN_KERNEL_TYPE) { - result = kernel::NativeGpuKernelModFactory::GetInstance().SearchRegistered(AnfAlgo::GetCNodeName(kernel_node), - builder->Build()); + result = kernel::NativeGpuKernelModFactory::GetInstance().SearchRegistered( + common::AnfAlgo::GetCNodeName(kernel_node), builder->Build()); if (!result) { - result = - kernel::NativeGpuKernelModFactory::GetInstance().ReducePrecision(AnfAlgo::GetCNodeName(kernel_node), builder); + result = kernel::NativeGpuKernelModFactory::GetInstance().ReducePrecision( + common::AnfAlgo::GetCNodeName(kernel_node), builder); } - if (!result && (!AnfAlgo::IsControlOpExecInBackend(kernel_node))) { + if (!result && (!common::AnfAlgo::IsControlOpExecInBackend(kernel_node))) { result = SelectAkgKernel(kernel_node, builder->Build()); kernel_type = AKG_KERNEL; } } else if (kernel_type == AKG_KERNEL) { result = SelectAkgKernel(kernel_node, builder->Build()); } - if (!result && (!AnfAlgo::IsControlOpExecInBackend(kernel_node))) { + if (!result && (!common::AnfAlgo::IsControlOpExecInBackend(kernel_node))) { PrintUnsupportedTypeException(kernel_node, inputs_type, outputs_type, kernel_type); return; } diff --git a/mindspore/ccsrc/plugin/device/gpu/hal/device/kernel_info_setter.h b/mindspore/ccsrc/plugin/device/gpu/hal/device/kernel_info_setter.h index 3fbec884564..40fb5a0d67b 100644 --- a/mindspore/ccsrc/plugin/device/gpu/hal/device/kernel_info_setter.h +++ b/mindspore/ccsrc/plugin/device/gpu/hal/device/kernel_info_setter.h @@ -24,7 +24,7 @@ #include #include "ir/anf.h" #include "ir/dtype.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "kernel/kernel.h" #include "backend/common/session/kernel_graph.h" diff --git a/mindspore/ccsrc/plugin/device/gpu/hal/hardware/gpu_device_context.cc b/mindspore/ccsrc/plugin/device/gpu/hal/hardware/gpu_device_context.cc index 40d77f8d8b4..e711a70d0f4 100644 --- a/mindspore/ccsrc/plugin/device/gpu/hal/hardware/gpu_device_context.cc +++ b/mindspore/ccsrc/plugin/device/gpu/hal/hardware/gpu_device_context.cc @@ -30,8 +30,8 @@ #include "plugin/device/gpu/hal/device/gpu_common.h" #include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" #include "plugin/device/gpu/hal/hardware/optimizer.h" -#include "utils/ms_device_shape_transfer.h" -#include "utils/context/graph_kernel_flags.h" +#include "runtime/device/ms_device_shape_transfer.h" +#include "include/common/utils/context/graph_kernel_flags.h" #include "plugin/device/gpu/hal/device/gpu_bucket.h" #include "profiler/device/gpu/gpu_profiling.h" #include "profiler/device/gpu/gpu_profiling_utils.h" @@ -40,7 +40,7 @@ #ifdef ENABLE_DUMP_IR #include "debug/rdr/running_data_recorder.h" #endif -#include "utils/comm_manager.h" +#include "include/common/utils/comm_manager.h" #ifdef ENABLE_DEBUGGER #include "debug/debugger/debugger.h" #endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/akg/akg_gpu_kernel_build.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/akg/akg_gpu_kernel_build.cc index 93834d875a3..38cde05c05f 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/akg/akg_gpu_kernel_build.cc +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/akg/akg_gpu_kernel_build.cc @@ -23,6 +23,7 @@ #include "plugin/device/gpu/kernel/akg/akg_gpu_kernel_mod.h" #include "utils/ms_utils.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace kernel { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/argmax_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/argmax_gpu_kernel.h index cd09eb7013e..1ae0e3750ef 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/argmax_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/argmax_gpu_kernel.h @@ -52,9 +52,9 @@ class ArgmaxGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - auto shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + auto shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(shape, kernel_name_, "input") || CHECK_SHAPE_NULL(output_shape, kernel_name_, "output"); if (is_null_input_) { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/argmaxandminwithvalue_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/argmaxandminwithvalue_gpu_kernel.h index 72cbed431f1..57b0273ea40 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/argmaxandminwithvalue_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/argmaxandminwithvalue_gpu_kernel.h @@ -45,10 +45,10 @@ class ArgMaxAndMinWithValueGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node); + std::string kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); small_ = (kernel_name == "ArgMinWithValue") ? true : false; - std::vector shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 1); + std::vector shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 1); is_null_input_ = CHECK_SHAPE_NULL(shape, kernel_name, "input") || CHECK_SHAPE_NULL(output_shape, kernel_name, "output"); if (is_null_input_) { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/array_reduce_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/array_reduce_gpu_kernel.h index 309df27b842..a8672f153ec 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/array_reduce_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/array_reduce_gpu_kernel.h @@ -76,27 +76,27 @@ class ArrayReduceGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; InitResource(); auto type_id = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); auto type_name = TypeIdLabel(type_id); - auto node_name = AnfAlgo::GetCNodeName(kernel_node); + auto node_name = common::AnfAlgo::GetCNodeName(kernel_node); if ((node_name == kReduceAnyOpName || node_name == kReduceAllOpName) && type_id != kNumberTypeBool) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the input data type should be bool, but got " << type_name; } data_type_ = GetCudnnDataType(type_name); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 1, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_num; } int input_dim_length = SizeToInt(AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 0).size()); - auto prim = AnfAlgo::GetCNodePrimitive(kernel_node); + auto prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(prim); if (prim->GetAttr("axis")->isa() || prim->GetAttr("axis")->isa()) { std::vector attr_axis; @@ -197,7 +197,7 @@ class ArrayReduceGpuKernelMod : public NativeGpuKernelMod { private: void InferArrayReduceType(const CNodePtr &kernel_node) { - std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node); + std::string kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); auto iter = kReduceTypeMap.find(kernel_name); if (iter == kReduceTypeMap.end()) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "Only support these array reduce kernel types: " diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/batchtospace_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/batchtospace_gpu_kernel.h index f6a7b8b3a6e..4893613d98c 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/batchtospace_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/batchtospace_gpu_kernel.h @@ -47,7 +47,7 @@ class BatchToSpaceGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); (void)CheckParam(kernel_node); input_size_ = sizeof(T); for (size_t idx = 0; idx < input_shape_.size(); ++idx) { @@ -95,11 +95,11 @@ class BatchToSpaceGpuKernelMod : public NativeGpuKernelMod { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the 'block_size' cannot be less than 1, but got " << block_size_; } - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 1, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_num; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/broadcast_to_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/broadcast_to_gpu_kernel.h index b687014f980..ddead96ed1c 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/broadcast_to_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/broadcast_to_gpu_kernel.h @@ -46,9 +46,9 @@ class BroadcastToGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - auto input_shapes = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto output_shapes = AnfAlgo::GetOutputInferShape(kernel_node, 0); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + auto input_shapes = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto output_shapes = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shapes, kernel_name_, "input") || CHECK_SHAPE_NULL(output_shapes, kernel_name_, "output"); if (is_null_input_) { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/cast_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/cast_gpu_kernel.h index 664529e9ae9..2209f10b83b 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/cast_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/cast_gpu_kernel.h @@ -52,9 +52,9 @@ class CastGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - auto input_shapes = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto output_shapes = AnfAlgo::GetOutputInferShape(kernel_node, 0); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + auto input_shapes = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto output_shapes = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shapes, kernel_name_, "input") || CHECK_SHAPE_NULL(output_shapes, kernel_name_, "output"); if (is_null_input_) { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/concatv2_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/concatv2_gpu_kernel.h index 9b5dcc4c23c..aec7c6502de 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/concatv2_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/concatv2_gpu_kernel.h @@ -70,7 +70,7 @@ class ConcatV2FwdGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; if (!CheckParam(kernel_node)) { return false; @@ -89,7 +89,7 @@ class ConcatV2FwdGpuKernelMod : public NativeGpuKernelMod { auto input_format = AnfAlgo::GetInputFormat(kernel_node, 0); axis_ = AxisTransform(origin_data_format, input_format, axis_); - input_num_ = SizeToInt(AnfAlgo::GetInputTensorNum(kernel_node)); + input_num_ = SizeToInt(common::AnfAlgo::GetInputTensorNum(kernel_node)); inputs_host_ = std::make_unique(input_num_); len_axis_ = std::make_unique(input_num_); int current_dim = 0; @@ -145,7 +145,7 @@ class ConcatV2FwdGpuKernelMod : public NativeGpuKernelMod { private: bool CheckParam(const CNodePtr &kernel_node) { - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_num; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/crop_and_resize_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/crop_and_resize_gpu_kernel.h index 5a9c18ee4e5..5c1b7b01f65 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/crop_and_resize_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/crop_and_resize_gpu_kernel.h @@ -78,21 +78,21 @@ class CropAndResizeGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 4) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 4, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } // input image - auto input_image_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto input_boxes_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto input_box_index_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto input_crop_size_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto input_image_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_boxes_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto input_box_index_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto input_crop_size_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_image_shape, kernel_name, "x") || CHECK_SHAPE_NULL(input_boxes_shape, kernel_name, "boxes") || CHECK_SHAPE_NULL(input_box_index_shape, kernel_name, "boxes_index") || diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/depthtospace_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/depthtospace_gpu_kernel.h index 8b3458dd759..cd7fd54069b 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/depthtospace_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/depthtospace_gpu_kernel.h @@ -49,7 +49,7 @@ class DepthToSpaceFwdKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; block_size_ = static_cast(GetAttr(kernel_node, "block_size")); if (block_size_ < 2) { @@ -57,12 +57,12 @@ class DepthToSpaceFwdKernelMod : public NativeGpuKernelMod { << block_size_; } // check input num and output num - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 1, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/dynamic_range_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/dynamic_range_gpu_kernel.h index a4efb38bae0..e965fd973e6 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/dynamic_range_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/dynamic_range_gpu_kernel.h @@ -97,9 +97,9 @@ class DynamicRangeGpuKernelMod : public NativeGpuKernelMod { CHECK_CUDA_RET_WITH_EXCEPT(kernel_node_, cudaStreamSynchronize(reinterpret_cast(stream_ptr_)), "cudaStreamSynchronize failed"); - std::vector output_type = {AnfAlgo::GetOutputInferDataType(kernel_node_.lock(), 0)}; + std::vector output_type = {common::AnfAlgo::GetOutputInferDataType(kernel_node_.lock(), 0)}; std::vector> output_shape = {{static_cast(output_shape_)}}; - AnfAlgo::SetOutputInferTypeAndShape(output_type, output_shape, kernel_node_.lock().get()); + common::AnfAlgo::SetOutputInferTypeAndShape(output_type, output_shape, kernel_node_.lock().get()); } void ResetResource() noexcept override { @@ -112,8 +112,8 @@ class DynamicRangeGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - size_t input_count = AnfAlgo::GetInputTensorNum(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_count = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_count != 3) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 3, but got " << input_count; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/dynamic_shape_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/dynamic_shape_gpu_kernel.h index 76dc30407a3..741ab28c061 100755 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/dynamic_shape_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/dynamic_shape_gpu_kernel.h @@ -49,14 +49,14 @@ class TensorShapeGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; - size_t input_count = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_count = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_count != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 1, but got " << input_count; } - std::vector prev_node_output_shape_tmp = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + std::vector prev_node_output_shape_tmp = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(prev_node_output_shape_tmp, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/embedding_lookup_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/embedding_lookup_gpu_kernel.h index 52579606904..1f58b648758 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/embedding_lookup_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/embedding_lookup_gpu_kernel.h @@ -55,9 +55,9 @@ class EmbeddingLookupKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num == 3) { is_dynamic_shape_ = true; MS_LOG(INFO) << " EmbeddingLookup running in Dynamic Mode."; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/extract_image_patches_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/extract_image_patches_gpu_kernel.h index 52d091066d2..31a8911ec31 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/extract_image_patches_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/extract_image_patches_gpu_kernel.h @@ -82,18 +82,18 @@ class ExtractImagePatchesKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 1, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input") || CHECK_SHAPE_NULL(output_shape, kernel_name, "output"); if (is_null_input_) { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/gather_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/gather_gpu_kernel.h index 5beb63dcb3b..8106c942826 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/gather_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/gather_gpu_kernel.h @@ -45,15 +45,15 @@ class GatherFwdGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); InitResource(); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 2, but got " << input_num; } - input_shapes_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - index_shapes_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - output_shapes_ = AnfAlgo::GetOutputInferShape(kernel_node, 0); + input_shapes_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + index_shapes_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + output_shapes_ = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shapes_, kernel_name, "input") || CHECK_SHAPE_NULL(index_shapes_, kernel_name, "input_indices") || CHECK_SHAPE_NULL(output_shapes_, kernel_name, "output"); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/gather_grad_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/gather_grad_gpu_kernel.h index a106a12fb3b..a8b5e50684b 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/gather_grad_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/gather_grad_gpu_kernel.h @@ -45,15 +45,15 @@ class GatherGradGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); InitResource(); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 2, but got " << input_num; } - index_shapes_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - grad_shapes_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - output_shapes_ = AnfAlgo::GetOutputInferShape(kernel_node, 0); + index_shapes_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + grad_shapes_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + output_shapes_ = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(index_shapes_, kernel_name, "index") || CHECK_SHAPE_NULL(grad_shapes_, kernel_name, "grad") || CHECK_SHAPE_NULL(output_shapes_, kernel_name, "output"); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/gathernd_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/gathernd_gpu_kernel.h index 70ea77f3805..e080aaa81a3 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/gathernd_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/gathernd_gpu_kernel.h @@ -22,6 +22,7 @@ #include "plugin/device/gpu/kernel/gpu_kernel_factory.h" #include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/gathernd.cuh" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace kernel { @@ -67,17 +68,17 @@ class GatherNdFwdGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; InitResource(); memcpy_flag_ = false; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 2, but got " << input_num; } - input_shapes_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - indices_shapes_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - output_shapes_ = AnfAlgo::GetOutputInferShape(kernel_node, 0); + input_shapes_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + indices_shapes_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + output_shapes_ = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shapes_, kernel_name, "input_x") || CHECK_SHAPE_NULL(indices_shapes_, kernel_name, "indices") || CHECK_SHAPE_NULL(output_shapes_, kernel_name, "output"); @@ -124,13 +125,13 @@ class GatherNdFwdGpuKernelMod : public NativeGpuKernelMod { protected: void InitSizeLists() override { - size_t size = AnfAlgo::TensorSizeInByte(input_shapes_); + size_t size = common::AnfAlgo::TensorSizeInByte(input_shapes_); input_size_list_.push_back(size); - size = AnfAlgo::TensorSizeInByte(indices_shapes_); + size = common::AnfAlgo::TensorSizeInByte(indices_shapes_); input_size_list_.push_back(size); - size = AnfAlgo::TensorSizeInByte(output_shapes_); + size = common::AnfAlgo::TensorSizeInByte(output_shapes_); output_size_list_.push_back(size); } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/gatherv2_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/gatherv2_gpu_kernel.h index c4a63212d4f..8248049d7d5 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/gatherv2_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/gatherv2_gpu_kernel.h @@ -23,6 +23,7 @@ #include "plugin/device/gpu/kernel/gpu_kernel_factory.h" #include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/gatherv2.cuh" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace kernel { @@ -60,10 +61,10 @@ class GatherV2FwdGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; InitResource(); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num == 3) { is_dynamic_shape_ = true; MS_LOG(INFO) << " GatherGpuV2FwdKernel running in Dynamic Mode."; @@ -109,14 +110,14 @@ class GatherV2FwdGpuKernelMod : public NativeGpuKernelMod { protected: void InitSizeLists() override { - size_t size = AnfAlgo::TensorSizeInByte(input_shapes_); + size_t size = common::AnfAlgo::TensorSizeInByte(input_shapes_); input_size_list_.push_back(size); - size = AnfAlgo::TensorSizeInByte(indices_shapes_); + size = common::AnfAlgo::TensorSizeInByte(indices_shapes_); input_size_list_.push_back(size); if (is_dynamic_shape_) { input_size_list_.push_back(sizeof(int64_t)); } - size = AnfAlgo::TensorSizeInByte(output_shapes_); + size = common::AnfAlgo::TensorSizeInByte(output_shapes_); output_size_list_.push_back(size); } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/in_top_k_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/in_top_k_gpu_kernel.h index d75de64118e..0a3e8da6b9a 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/in_top_k_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/in_top_k_gpu_kernel.h @@ -91,14 +91,14 @@ class InTopKGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; - size_t input_count = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_count = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_count != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 2, but got " << input_count; } - size_t output_count = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_count = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_count != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of output should be 1, but got " << output_count; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/matrix_band_part_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/matrix_band_part_gpu_kernel.h index a8538d0fcda..72dc01be2e8 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/matrix_band_part_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/matrix_band_part_gpu_kernel.h @@ -42,9 +42,9 @@ class MatrixBandPartGpuKernelMod : public NativeGpuKernelMod { ~MatrixBandPartGpuKernelMod() = default; bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); - shapes_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + shapes_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); if (is_null_input_) { InitSizeLists(); return true; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/matrix_diag_part_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/matrix_diag_part_gpu_kernel.h index 8527e876bba..7dfb6e10f49 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/matrix_diag_part_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/matrix_diag_part_gpu_kernel.h @@ -46,9 +46,9 @@ class MatrixDiagPartGpuKernelMod : public NativeGpuKernelMod { ~MatrixDiagPartGpuKernelMod() = default; bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); - shapes_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + shapes_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); if (is_null_input_) { InitSizeLists(); return true; @@ -64,7 +64,7 @@ class MatrixDiagPartGpuKernelMod : public NativeGpuKernelMod { } matrix_size_ = out_range_size_ * m_ * n_; InitSizeLists(); - alignment_ = GetAlignments(AnfAlgo::GetNodeAttr(kernel_node, kAlignment)); + alignment_ = GetAlignments(common::AnfAlgo::GetNodeAttr(kernel_node, kAlignment)); kernel_node_ = kernel_node; return true; } @@ -78,7 +78,7 @@ class MatrixDiagPartGpuKernelMod : public NativeGpuKernelMod { output_shape.erase(output_shape.begin() + shapes_.size() - kDim2); } auto data_type = AnfAlgo::GetInputDeviceDataType(kernel_node_.lock(), 0); - AnfAlgo::SetOutputInferTypeAndShape({data_type}, {output_shape}, kernel_node_.lock().get()); + common::AnfAlgo::SetOutputInferTypeAndShape({data_type}, {output_shape}, kernel_node_.lock().get()); } void ResetResource() noexcept override { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/matrix_set_diag_gpu_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/matrix_set_diag_gpu_kernel.cc index 2b9e541e59c..a6098dfe9e9 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/matrix_set_diag_gpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/matrix_set_diag_gpu_kernel.cc @@ -18,7 +18,7 @@ #include #include #include "plugin/device/cpu/hal/device/cpu_device_address.h" -#include "common/thread_pool.h" +#include "include/common/thread_pool.h" #include "kernel/common_utils.h" namespace mindspore { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/matrix_set_diag_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/matrix_set_diag_gpu_kernel.h index b0bc14f5744..8c9805038b7 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/matrix_set_diag_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/matrix_set_diag_gpu_kernel.h @@ -37,27 +37,27 @@ class MatrixSetDiagGpuKernelMod : public NativeGpuKernelMod { bool Init(const CNodePtr &kernel_node) override { MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); constexpr size_t required_input_nums = 3; constexpr size_t required_output_nums = 1; - if (AnfAlgo::GetInputNum(kernel_node) != required_input_nums || - AnfAlgo::GetOutputTensorNum(kernel_node) != required_output_nums) { + if (common::AnfAlgo::GetInputNum(kernel_node) != required_input_nums || + common::AnfAlgo::GetOutputTensorNum(kernel_node) != required_output_nums) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the input nums are required to [input, diagonal, " "k, alignment] for 3 and the output nums is require to 1."; } // invalid alignment will throw an exception. - auto alignment = AnfAlgo::GetNodeAttr(kernel_node, kAlignment); + auto alignment = common::AnfAlgo::GetNodeAttr(kernel_node, kAlignment); alignment_ = GetAlignments(alignment); constexpr int input_index = 0; constexpr int diag_index = 1; constexpr int diag_k_index = 2; constexpr int output_index = 0; - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, input_index); - auto diag_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, diag_index); - auto diag_k_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, diag_k_index); - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, output_index); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, input_index); + auto diag_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, diag_index); + auto diag_k_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, diag_k_index); + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, output_index); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name_, "input_shape") || CHECK_SHAPE_NULL(diag_shape, kernel_name_, "diag_shape") || diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/meshgrid_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/meshgrid_gpu_kernel.h index 3271c11e2f9..8183d5ba349 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/meshgrid_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/meshgrid_gpu_kernel.h @@ -66,7 +66,7 @@ class MeshgridGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); std::string indexing = GetAttr(kernel_node, "indexing"); if (indexing == "xy") { swap_indexing_ = true; @@ -78,7 +78,7 @@ class MeshgridGpuKernelMod : public NativeGpuKernelMod { } input_size_ = 1; - input_count_ = AnfAlgo::GetInputTensorNum(kernel_node); + input_count_ = common::AnfAlgo::GetInputTensorNum(kernel_node); for (size_t i = 0; i < input_count_; i++) { auto input_shape = AnfAlgo::GetInputDeviceShape(kernel_node, i); if (input_shape.size() < 1) { @@ -91,7 +91,7 @@ class MeshgridGpuKernelMod : public NativeGpuKernelMod { } output_size_ = 1; - output_count_ = AnfAlgo::GetOutputTensorNum(kernel_node); + output_count_ = common::AnfAlgo::GetOutputTensorNum(kernel_node); // inferred shape swaps output shape for us if needed output_shape_ = AnfAlgo::GetOutputDeviceShape(kernel_node, 0); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/one_hot_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/one_hot_gpu_kernel.h index cf2893f063b..22248893b5a 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/one_hot_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/one_hot_gpu_kernel.h @@ -46,10 +46,10 @@ class OneHotFwdGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); int64_t axis = GetAttr(kernel_node, "axis"); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input") || CHECK_SHAPE_NULL(output_shape, kernel_name, "output"); if (is_null_input_) { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/oneslike_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/oneslike_gpu_kernel.h index 2e54f687f5e..42f3bafa403 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/oneslike_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/oneslike_gpu_kernel.h @@ -42,16 +42,16 @@ class OnesLikeGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 1, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/pack_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/pack_gpu_kernel.h index 1e9dc564d18..94e9cb6634b 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/pack_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/pack_gpu_kernel.h @@ -59,7 +59,7 @@ class PackFwdGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; (void)CheckParam(kernel_node); axis_ = static_cast(GetAttr(kernel_node, "axis")); @@ -71,7 +71,7 @@ class PackFwdGpuKernelMod : public NativeGpuKernelMod { auto input_format = AnfAlgo::GetInputFormat(kernel_node, 0); axis_ = AxisTransform(origin_data_format, input_format, axis_); - input_num_ = AnfAlgo::GetInputTensorNum(kernel_node); + input_num_ = common::AnfAlgo::GetInputTensorNum(kernel_node); inputs_host_ = std::make_unique(input_num_); for (size_t i = 0; i < input_num_; i++) { size_t input_size = 1; @@ -111,7 +111,7 @@ class PackFwdGpuKernelMod : public NativeGpuKernelMod { private: void CheckParam(const CNodePtr &kernel_node) { - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_num; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/range_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/range_gpu_kernel.h index 7acade9fcde..a2f3debf44a 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/range_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/range_gpu_kernel.h @@ -50,12 +50,12 @@ class RangeGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 1, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/resize_nearest_neighbor_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/resize_nearest_neighbor_gpu_kernel.h index 99cdf519e85..96dc6fb8957 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/resize_nearest_neighbor_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/resize_nearest_neighbor_gpu_kernel.h @@ -53,18 +53,18 @@ class ResizeNearestNeighborGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 1, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); shape_size_ = input_shape.size(); - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input") || CHECK_SHAPE_NULL(output_shape, kernel_name, "output"); if (is_null_input_) { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/resize_nearest_neighbor_grad_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/resize_nearest_neighbor_grad_gpu_kernel.h index b85f75b2347..872135c65b0 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/resize_nearest_neighbor_grad_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/resize_nearest_neighbor_grad_gpu_kernel.h @@ -53,18 +53,18 @@ class ResizeNearestNeighborGradGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 1, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); shape_size_ = input_shape.size(); - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input") || CHECK_SHAPE_NULL(output_shape, kernel_name, "output"); if (is_null_input_) { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/reverse_sequence_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/reverse_sequence_gpu_kernel.h index d073c531019..26f4ae37bd9 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/reverse_sequence_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/reverse_sequence_gpu_kernel.h @@ -63,19 +63,19 @@ class ReverseSequenceFwdGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); batch_dim_ = GetAttr(kernel_node, "batch_dim"); seq_dim_ = GetAttr(kernel_node, "seq_dim"); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 2, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } - input_shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto seq_len_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + input_shape_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto seq_len_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); is_null_input_ = CHECK_SHAPE_NULL(input_shape_, kernel_name, "x") || CHECK_SHAPE_NULL(seq_len_shape, kernel_name, "seq_lengths"); if (is_null_input_) { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/reverse_v2_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/reverse_v2_gpu_kernel.h index 251e856fc78..ffce2f1e30a 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/reverse_v2_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/reverse_v2_gpu_kernel.h @@ -65,13 +65,13 @@ class ReverseV2GpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - size_t input_count = AnfAlgo::GetInputTensorNum(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_count = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_count != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 1, but got " << input_count; } - size_t output_count = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_count = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_count != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 2, but got " << output_count; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/scatter_functor_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/scatter_functor_gpu_kernel.h index b960bee68fb..779f9471054 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/scatter_functor_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/scatter_functor_gpu_kernel.h @@ -56,7 +56,7 @@ class ScatterFunctorKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node); + std::string kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); auto iter = kScatterFunctorTypeMap.find(kernel_name); if (iter == kScatterFunctorTypeMap.end()) { MS_LOG(EXCEPTION) @@ -67,22 +67,22 @@ class ScatterFunctorKernelMod : public NativeGpuKernelMod { scatter_functor_type_ = iter->second; } kernel_node_ = kernel_node; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 3) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 3, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); input_size_ = 1; inner_size_ = 1; for (size_t i = 1; i < input_shape.size(); i++) { inner_size_ *= input_shape[i]; } input_size_ = input_shape[0] * inner_size_; - auto indices_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto indices_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); indices_size_ = 1; for (size_t i = 0; i < indices_shape.size(); i++) { indices_size_ *= indices_shape[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/scatter_nd_functor_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/scatter_nd_functor_gpu_kernel.h index 7d512f1e826..2dc43fd1f26 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/scatter_nd_functor_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/scatter_nd_functor_gpu_kernel.h @@ -62,7 +62,7 @@ class ScatterNdFunctorKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node); + std::string kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); auto iter = kScatterNdFunctorTypeMap.find(kernel_name); if (iter == kScatterNdFunctorTypeMap.end()) { MS_LOG(EXCEPTION) @@ -72,18 +72,18 @@ class ScatterNdFunctorKernelMod : public NativeGpuKernelMod { scatter_nd_functor_type_ = iter->second; } kernel_node_ = kernel_node; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 3) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 3, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto indices_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto updates_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto indices_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto updates_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); auto index_depth = indices_shape.back(); if (index_depth > input_shape.size()) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the last dimension value of indices should be greater than " diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/scatter_nd_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/scatter_nd_gpu_kernel.h index 79b1a9f7559..0b9331eeb4e 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/scatter_nd_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/scatter_nd_gpu_kernel.h @@ -86,21 +86,21 @@ class ScatterNdFwdGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; memcpy_flag_ = false; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 2, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } - input_shapes_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - indices_shapes_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - output_shapes_ = AnfAlgo::GetOutputInferShape(kernel_node, 0); + input_shapes_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + indices_shapes_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + output_shapes_ = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shapes_, kernel_name, "input") || CHECK_SHAPE_NULL(indices_shapes_, kernel_name, "indices") || CHECK_SHAPE_NULL(output_shapes_, kernel_name, "output"); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/select_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/select_gpu_kernel.h index 12bb75f4245..aafb6cc2c9a 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/select_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/select_gpu_kernel.h @@ -46,9 +46,9 @@ class SelectGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); (void)CheckParam(kernel_node); - auto shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(shape, kernel_name_, "input"); if (is_null_input_) { InitSizeLists(); @@ -74,11 +74,11 @@ class SelectGpuKernelMod : public NativeGpuKernelMod { private: void CheckParam(const CNodePtr &kernel_node) { - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 3) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 3, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_num; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/slice_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/slice_gpu_kernel.h index 8984340cab3..411dddf93ec 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/slice_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/slice_gpu_kernel.h @@ -103,7 +103,7 @@ class SliceFwdGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); (void)CheckParam(kernel_node); auto input_shape = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 0); @@ -176,7 +176,7 @@ class SliceFwdGpuKernelMod : public NativeGpuKernelMod { private: void CheckParam(const CNodePtr &kernel_node) { - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); constexpr size_t kDynamicSliceInputNum = 3; if (input_num != 1 && input_num != kDynamicSliceInputNum) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 1 or " << kDynamicSliceInputNum @@ -185,11 +185,11 @@ class SliceFwdGpuKernelMod : public NativeGpuKernelMod { if (input_num == kDynamicSliceInputNum) { is_dynamic_attr_ = true; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_num; } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); const size_t kInputNumUpperLimit = 7; if (input_shape.size() > kInputNumUpperLimit) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimension of input cannot be greater than 7, but got " diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/slice_grad_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/slice_grad_gpu_kernel.h index 1291ac7204c..3832aee61eb 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/slice_grad_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/slice_grad_gpu_kernel.h @@ -54,9 +54,9 @@ class SliceGradGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); (void)CheckParam(kernel_node); - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); auto data_format = AnfAlgo::GetInputFormat(kernel_node, 0); if (kernel_name == "StridedSliceGrad") { is_strided_slice_ = true; @@ -133,11 +133,11 @@ class SliceGradGpuKernelMod : public NativeGpuKernelMod { private: void CheckParam(const CNodePtr &kernel_node) { - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_num; } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); if (input_shape.size() > 4) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimension of input cannot be greater than 4, but got " << input_shape.size(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/sort_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/sort_gpu_kernel.h index 0cb93e68e98..66466e86141 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/sort_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/sort_gpu_kernel.h @@ -113,13 +113,13 @@ class SortGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - size_t input_count = AnfAlgo::GetInputTensorNum(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_count = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_count != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 1, but got " << input_count; } - size_t output_count = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_count = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_count != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 2, but got " << output_count; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/spacetobatch_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/spacetobatch_gpu_kernel.h index 81ffb8ee054..2bc1d1577f6 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/spacetobatch_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/spacetobatch_gpu_kernel.h @@ -49,7 +49,7 @@ class SpaceToBatchGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); (void)CheckParam(kernel_node); input_size_ = sizeof(T); for (size_t idx = 0; idx < input_shape_.size(); ++idx) { @@ -97,11 +97,11 @@ class SpaceToBatchGpuKernelMod : public NativeGpuKernelMod { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the 'block_size' cannot be less than 1, but got " << block_size_; } - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 1, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_num; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/spacetodepth_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/spacetodepth_gpu_kernel.h index 7edb4c39205..18dd0b04fd0 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/spacetodepth_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/spacetodepth_gpu_kernel.h @@ -50,7 +50,7 @@ class SpaceToDepthFwdKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; block_size_ = static_cast(GetAttr(kernel_node, "block_size")); if (block_size_ < 2) { @@ -58,12 +58,12 @@ class SpaceToDepthFwdKernelMod : public NativeGpuKernelMod { << block_size_; } // check input num and output num - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 1, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 2, but got " << output_num; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/split_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/split_gpu_kernel.h index c3016b7ad73..a05eb79e885 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/split_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/split_gpu_kernel.h @@ -52,7 +52,7 @@ class SplitFwdGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; auto input_shape = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name_, "input"); @@ -133,10 +133,10 @@ class SplitFwdGpuKernelMod : public NativeGpuKernelMod { private: void CheckParam(const CNodePtr &kernel_node) { - auto input_num = AnfAlgo::GetInputTensorNum(kernel_node); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); int dims = SizeToInt(input_shape.size()); - int output_num = SizeToInt(AnfAlgo::GetOutputTensorNum(kernel_node)); + int output_num = SizeToInt(common::AnfAlgo::GetOutputTensorNum(kernel_node)); if (output_num <= 0) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be greater than 0, but got " << output_num; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/squeeze_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/squeeze_gpu_kernel.h index 1a33b22617d..0fb1bbcb38c 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/squeeze_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/squeeze_gpu_kernel.h @@ -49,7 +49,7 @@ class SqueezeGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; auto axis = GetAttr>(kernel_node, "axis"); auto input_shape = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 0); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/strided_slice_gpu_common.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/strided_slice_gpu_common.h index f7088a87d46..221c346f13a 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/strided_slice_gpu_common.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/strided_slice_gpu_common.h @@ -20,6 +20,7 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "kernel/common_utils.h" namespace mindspore { @@ -31,9 +32,9 @@ class StridedSliceGpuCommon { ~StridedSliceGpuCommon() = default; void CollectInfo(const CNodePtr &kernel_node) { - begin_ = AnfAlgo::GetNodeAttr>(kernel_node, kAttrBegin); - end_ = AnfAlgo::GetNodeAttr>(kernel_node, kAttrEnd); - strides_ = AnfAlgo::GetNodeAttr>(kernel_node, kAttrStrides); + begin_ = common::AnfAlgo::GetNodeAttr>(kernel_node, kAttrBegin); + end_ = common::AnfAlgo::GetNodeAttr>(kernel_node, kAttrEnd); + strides_ = common::AnfAlgo::GetNodeAttr>(kernel_node, kAttrStrides); FillEmptyDims(kernel_node, &begin_, &end_, &strides_, &input_shape_); ParseStrideSliceMasks(kernel_node, &begin_, &end_, &strides_, input_shape_); FillOutputDim(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/strided_slice_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/strided_slice_gpu_kernel.h index 86a44e7c3db..dbee4b48e00 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/strided_slice_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/strided_slice_gpu_kernel.h @@ -47,8 +47,8 @@ class StridedSliceGpuKernelMod : public NativeGpuKernelMod, public StridedSliceG } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - input_shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + input_shape_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); null_output_ = CHECK_SHAPE_NULL(input_shape_, kernel_name, "input"); if (null_output_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/strided_slice_grad_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/strided_slice_grad_gpu_kernel.h index 0354b2cac75..b51126d132d 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/strided_slice_grad_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/strided_slice_grad_gpu_kernel.h @@ -47,7 +47,7 @@ class StridedSliceGradGpuKernelMod : public NativeGpuKernelMod, public StridedSl return true; } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); std::vector shapex = GetAttr>(kernel_node, "shapex"); for (auto x : shapex) { input_shape_.push_back(static_cast(x)); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/tensor_copy_slices_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/tensor_copy_slices_gpu_kernel.h index 1afea2fd7c6..5ef5c7e5421 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/tensor_copy_slices_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/tensor_copy_slices_gpu_kernel.h @@ -55,21 +55,21 @@ class TensorCopySlicesGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 2, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_num; } - input_shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto update_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + input_shape_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto update_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); is_null_input_ = CHECK_SHAPE_NULL(input_shape_, kernel_name_, "input") || CHECK_SHAPE_NULL(update_shape, kernel_name_, "update"); if (is_null_input_) { @@ -103,7 +103,7 @@ class TensorCopySlicesGpuKernelMod : public NativeGpuKernelMod { protected: void CheckAtrrAndShapeValid(const CNodePtr &kernel_node) { - auto update_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto update_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); size_t total_update_num = std::accumulate(update_shape.begin(), update_shape.end(), 1, std::multiplies()); if (begin_.size() != end_.size() || end_.size() != strides_.size()) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the size of 'begin', 'strides' and 'end' should be the same " diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/tensor_scatter_add_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/tensor_scatter_add_gpu_kernel.h index d4a3d268ea5..e83ec2e0c19 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/tensor_scatter_add_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/tensor_scatter_add_gpu_kernel.h @@ -91,22 +91,22 @@ class TensorScatterAddFwdGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; memcpy_flag_ = false; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 3) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 3, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_num; } - update_shapes_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - indices_shapes_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - input_shapes_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - output_shapes_ = AnfAlgo::GetOutputInferShape(kernel_node, 0); + update_shapes_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + indices_shapes_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + input_shapes_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + output_shapes_ = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_NULL_INPUT(update_shapes_) || CHECK_NULL_INPUT(indices_shapes_) || CHECK_NULL_INPUT(input_shapes_) || CHECK_NULL_INPUT(output_shapes_); is_null_input_ = CHECK_SHAPE_NULL(update_shapes_, kernel_name_, "updates") || diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/tensor_scatter_max_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/tensor_scatter_max_gpu_kernel.h index ba7fdb4b693..bbb5455f0c8 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/tensor_scatter_max_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/tensor_scatter_max_gpu_kernel.h @@ -86,22 +86,22 @@ class TensorScatterMaxGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; memcpy_flag_ = false; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 3) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 3, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } - update_shapes_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - indices_shapes_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - input_shapes_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - output_shapes_ = AnfAlgo::GetOutputInferShape(kernel_node, 0); + update_shapes_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + indices_shapes_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + input_shapes_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + output_shapes_ = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); std::vector shape_me = input_shapes_; (void)std::transform(shape_me.begin(), shape_me.end(), std::back_inserter(vec_work_shape_), diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/tensor_scatter_min_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/tensor_scatter_min_gpu_kernel.h index 60af63b88b0..d006e12db87 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/tensor_scatter_min_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/tensor_scatter_min_gpu_kernel.h @@ -85,22 +85,22 @@ class TensorScatterMinGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; memcpy_flag_ = false; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 3) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 3, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of output should be 1, but got " << output_num; } - update_shapes_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - indices_shapes_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - input_shapes_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - output_shapes_ = AnfAlgo::GetOutputInferShape(kernel_node, 0); + update_shapes_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + indices_shapes_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + input_shapes_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + output_shapes_ = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); std::vector shape_me = input_shapes_; (void)std::transform(shape_me.begin(), shape_me.end(), std::back_inserter(vec_work_shape_), diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/tensor_scatter_sub_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/tensor_scatter_sub_gpu_kernel.h index 6d1a44c9864..6f5962ba888 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/tensor_scatter_sub_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/tensor_scatter_sub_gpu_kernel.h @@ -86,22 +86,22 @@ class TensorScatterSubGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; memcpy_flag_ = false; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 3) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 3, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } - update_shapes_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - indices_shapes_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - input_shapes_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - output_shapes_ = AnfAlgo::GetOutputInferShape(kernel_node, 0); + update_shapes_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + indices_shapes_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + input_shapes_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + output_shapes_ = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); std::vector shape_me = input_shapes_; (void)std::transform(shape_me.begin(), shape_me.end(), std::back_inserter(vec_work_shape_), diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/tensor_scatter_update_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/tensor_scatter_update_gpu_kernel.h index 6f110d216e7..54f87139d48 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/tensor_scatter_update_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/tensor_scatter_update_gpu_kernel.h @@ -89,22 +89,22 @@ class TensorScatterUpdateFwdGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; memcpy_flag_ = false; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 3) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 3, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } - update_shapes_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - indices_shapes_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - input_shapes_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - output_shapes_ = AnfAlgo::GetOutputInferShape(kernel_node, 0); + update_shapes_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + indices_shapes_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + input_shapes_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + output_shapes_ = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(update_shapes_, kernel_name, "update") || CHECK_SHAPE_NULL(indices_shapes_, kernel_name, "indices") || CHECK_SHAPE_NULL(input_shapes_, kernel_name, "input_x") || diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/tile_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/tile_gpu_kernel.h index 57eec01d03b..8091aeb2887 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/tile_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/tile_gpu_kernel.h @@ -55,18 +55,18 @@ class TileGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 1, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } - input_shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - output_shape_ = AnfAlgo::GetOutputInferShape(kernel_node, 0); + input_shape_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + output_shape_ = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape_, kernel_name, "input") || CHECK_SHAPE_NULL(output_shape_, kernel_name, "output"); if (is_null_input_) { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/topk_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/topk_gpu_kernel.h index 9bef68dfbaf..0c1d09c8d57 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/topk_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/topk_gpu_kernel.h @@ -79,10 +79,10 @@ class TopKGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; - auto input_shapes = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto output_shapes = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto input_shapes = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto output_shapes = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shapes, kernel_name, "input") || CHECK_SHAPE_NULL(output_shapes, kernel_name, "output"); if (is_null_input_) { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/transpose_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/transpose_gpu_kernel.h index 9990dc0b04c..884761c60a5 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/transpose_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/transpose_gpu_kernel.h @@ -82,13 +82,13 @@ class TransposeFwdGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 1, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/unique_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/unique_gpu_kernel.h index a27a62d0585..551d811deae 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/unique_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/unique_gpu_kernel.h @@ -46,7 +46,7 @@ class UniqueGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; std::vector shape = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(shape, kernel_name, "input"); @@ -69,17 +69,17 @@ class UniqueGpuKernelMod : public NativeGpuKernelMod { "cudaStreamSynchronized failed"); std::vector type_ids; std::vector> shapes; - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node_.lock()); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node_.lock()); for (size_t i = 0; i < output_num; ++i) { - std::vector shape = AnfAlgo::GetOutputInferShape(kernel_node_.lock(), i); + std::vector shape = common::AnfAlgo::GetOutputInferShape(kernel_node_.lock(), i); if (i == 0) { shape[0] = post_output_size_; } - TypeId type_id = AnfAlgo::GetOutputInferDataType(kernel_node_.lock(), i); + TypeId type_id = common::AnfAlgo::GetOutputInferDataType(kernel_node_.lock(), i); type_ids.emplace_back(type_id); shapes.emplace_back(shape); } - AnfAlgo::SetOutputInferTypeAndShape(type_ids, shapes, kernel_node_.lock().get()); + common::AnfAlgo::SetOutputInferTypeAndShape(type_ids, shapes, kernel_node_.lock().get()); } void ResetResource() noexcept override { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/unpack_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/unpack_gpu_kernel.h index e8265dcbe7a..21884ba1657 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/unpack_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/unpack_gpu_kernel.h @@ -53,7 +53,7 @@ class UnpackFwdGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; (void)CheckParam(kernel_node); axis_ = static_cast(GetAttr(kernel_node, "axis")); @@ -104,7 +104,7 @@ class UnpackFwdGpuKernelMod : public NativeGpuKernelMod { private: void CheckParam(const CNodePtr &kernel_node) { - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 1, but got " << input_num; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/unsorted_segment_max_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/unsorted_segment_max_gpu_kernel.h index d94c242202c..5ae52c23572 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/unsorted_segment_max_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/unsorted_segment_max_gpu_kernel.h @@ -50,7 +50,7 @@ class UnsortedSegmentMaxGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; auto input_shapes = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 0); auto segment_ids_shapes = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 1); @@ -63,7 +63,7 @@ class UnsortedSegmentMaxGpuKernelMod : public NativeGpuKernelMod { return true; } - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num == 3) { MS_LOG(INFO) << "UnsortedSegmentMax Kernel Input count is 3 - dynamic mode"; } else { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/unsorted_segment_min_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/unsorted_segment_min_gpu_kernel.h index 915e098f585..f4e4a5980d6 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/unsorted_segment_min_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/unsorted_segment_min_gpu_kernel.h @@ -45,7 +45,7 @@ class UnsortedSegmentMinGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); auto input_shapes = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 0); auto segment_ids_shapes = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 1); auto output_shapes = AnfAlgo::GetOutputRealDeviceShapeIfExist(kernel_node, 0); @@ -57,7 +57,7 @@ class UnsortedSegmentMinGpuKernelMod : public NativeGpuKernelMod { return true; } - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num == 3) { MS_LOG(INFO) << "UnsortedSegmentMin Kernel Input count is 3 - dynamic mode"; } else { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/unsorted_segment_sum_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/unsorted_segment_sum_gpu_kernel.h index 5e8c9e83a52..87b5ac15252 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/unsorted_segment_sum_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/unsorted_segment_sum_gpu_kernel.h @@ -48,7 +48,7 @@ class UnsortedSegmentSumGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; auto input_shapes = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 0); auto ids_shapes = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 1); @@ -61,7 +61,7 @@ class UnsortedSegmentSumGpuKernelMod : public NativeGpuKernelMod { return true; } - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num == 3) { MS_LOG(INFO) << "UnsortedSegmentSum Kernel Input count is 3 - dynamic mode"; } else { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/zeroslike_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/zeroslike_gpu_kernel.h index 0770cc6d344..930d7e2ac49 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/zeroslike_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/arrays/zeroslike_gpu_kernel.h @@ -48,7 +48,7 @@ class ZerosLikeGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; std::vector input_shape = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 0); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/custom/custom_aot_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/custom/custom_aot_gpu_kernel.h index 9a7038a043d..8868c62dcdf 100755 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/custom/custom_aot_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/custom/custom_aot_gpu_kernel.h @@ -95,8 +95,8 @@ class CustomAOTGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - const auto &exec_info = AnfAlgo::GetNodeAttr(kernel_node, "func_name"); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + const auto &exec_info = common::AnfAlgo::GetNodeAttr(kernel_node, "func_name"); if (auto pos = exec_info.find(":"); pos != std::string::npos) { auto path = exec_info.substr(0, pos); auto real_path = FileUtils::GetRealPath(path.c_str()); @@ -111,7 +111,7 @@ class CustomAOTGpuKernelMod : public NativeGpuKernelMod { << "' is illegal. Proper function path should follow the format of 'dir_path/file_name:func_name'"; } - num_input_ = AnfAlgo::GetInputTensorNum(kernel_node); + num_input_ = common::AnfAlgo::GetInputTensorNum(kernel_node); auto input_type_list = AnfAlgo::GetAllInputDeviceTypes(kernel_node); if (num_input_ != input_type_list.size()) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "' on GPU, number of input types '" << input_type_list.size() @@ -128,7 +128,7 @@ class CustomAOTGpuKernelMod : public NativeGpuKernelMod { shape_list_.emplace_back(in_shape_tmp); } - num_output_ = AnfAlgo::GetOutputTensorNum(kernel_node); + num_output_ = common::AnfAlgo::GetOutputTensorNum(kernel_node); auto output_type_list = AnfAlgo::GetAllOutputDeviceTypes(kernel_node); if (num_output_ != output_type_list.size()) { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/data/dataset_init_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/data/dataset_init_kernel.cc index 115b8bdba00..44315ba709a 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/data/dataset_init_kernel.cc +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/data/dataset_init_kernel.cc @@ -19,7 +19,7 @@ #include "kernel/common_utils.h" #include "plugin/device/gpu/hal/device/gpu_buffer_mgr.h" #include "plugin/device/gpu/hal/device/gpu_memory_allocator.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" namespace mindspore { namespace kernel { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/data/dataset_iterator_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/data/dataset_iterator_kernel.cc index 86a55fce668..83f9ab68d60 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/data/dataset_iterator_kernel.cc +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/data/dataset_iterator_kernel.cc @@ -19,7 +19,7 @@ #include #include #include -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" #include "plugin/device/gpu/kernel/data/dataset_utils.h" #include "kernel/common_utils.h" @@ -44,7 +44,7 @@ DatasetIteratorKernelMod::~DatasetIteratorKernelMod() { GpuBufferMgr::GetInstanc bool DatasetIteratorKernelMod::Init(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); kernel_node_ = kernel_node; - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); queue_name_ = GetAttr(kernel_node, "shared_name"); std::vector> shapes; std::vector type_ptrs; @@ -164,7 +164,7 @@ void DatasetIteratorKernelMod::PostExecute() { std::transform(item.shapes_.begin(), item.shapes_.end(), std::back_inserter(shape), LongToSize); shapes.push_back(shape); } - AnfAlgo::SetOutputInferTypeAndShape(types_, shapes, kernel_node_.lock().get()); + common::AnfAlgo::SetOutputInferTypeAndShape(types_, shapes, kernel_node_.lock().get()); } } // namespace kernel } // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/data/dataset_profiling.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/data/dataset_profiling.cc index a06cfc6d29f..fca5639970e 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/data/dataset_profiling.cc +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/data/dataset_profiling.cc @@ -18,7 +18,7 @@ #include #include "utils/log_adapter.h" #include "utils/ms_utils.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/ms_context.h" namespace mindspore { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/data/dataset_utils.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/data/dataset_utils.cc index 0aa61439941..fb1b279f4ab 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/data/dataset_utils.cc +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/data/dataset_utils.cc @@ -17,6 +17,7 @@ #include "plugin/device/gpu/kernel/data/dataset_utils.h" #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace kernel { @@ -38,10 +39,10 @@ void GetShapeAndType(const CNodePtr &kernel_node, std::vector> MS_EXCEPTION_IF_NULL(shapes); MS_EXCEPTION_IF_NULL(types); std::vector> shapes_me; - if (AnfAlgo::IsDynamicShape(kernel_node)) { - shapes_me = AnfAlgo::GetNodeAttr>>(kernel_node, "max_shapes"); + if (common::AnfAlgo::IsDynamicShape(kernel_node)) { + shapes_me = common::AnfAlgo::GetNodeAttr>>(kernel_node, "max_shapes"); } else { - shapes_me = AnfAlgo::GetNodeAttr>>(kernel_node, "shapes"); + shapes_me = common::AnfAlgo::GetNodeAttr>>(kernel_node, "shapes"); } (void)std::transform(shapes_me.begin(), shapes_me.end(), std::back_inserter(*shapes), [](const std::vector &values) { @@ -55,7 +56,7 @@ void GetShapeAndType(const CNodePtr &kernel_node, std::vector> return shape; }); - *types = AnfAlgo::GetNodeAttr>(kernel_node, "types"); + *types = common::AnfAlgo::GetNodeAttr>(kernel_node, "types"); if (shapes->size() != types->size()) { MS_LOG(EXCEPTION) << "Invalid shapes: " << *shapes << ", types: " << *types; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/debug/print_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/debug/print_gpu_kernel.h index eed5fac02cf..2ecf343801e 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/debug/print_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/debug/print_gpu_kernel.h @@ -86,10 +86,10 @@ class PrintGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); MS_EXCEPTION_IF_NULL(kernel_node); kernel_node_ = kernel_node; - if (AnfAlgo::HasNodeAttr("string_pos", kernel_node)) { + if (common::AnfAlgo::HasNodeAttr("string_pos", kernel_node)) { string_value_ = GetAttr>(kernel_node, "string_value"); string_pos_ = GetAttr>(kernel_node, "string_pos"); auto value_type = GetAttr>(kernel_node, "value_type"); @@ -98,7 +98,7 @@ class PrintGpuKernelMod : public NativeGpuKernelMod { value_type_[value_type_pos[i]] = value_type[i]; } } - size_t input_tensor_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_tensor_num = common::AnfAlgo::GetInputTensorNum(kernel_node); input_flag_ = SetInputFlag(&string_pos_, input_tensor_num); for (size_t i = 0; i < input_tensor_num; i++) { auto input_shape = AnfAlgo::GetInputDeviceShape(kernel_node, i); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/environ/environ_gpu_get.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/environ/environ_gpu_get.cc index 7e1db75b85e..8ba1eaba4a4 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/environ/environ_gpu_get.cc +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/environ/environ_gpu_get.cc @@ -27,7 +27,7 @@ bool EnvironGetGpuKernelMod::Init(const CNodePtr &kernel_node) { return false; } - value_type_attr_ = TypeId(AnfAlgo::GetNodeAttr(kernel_node, kEnvValueTypeAttr)); + value_type_attr_ = TypeId(common::AnfAlgo::GetNodeAttr(kernel_node, kEnvValueTypeAttr)); MS_LOG(INFO) << "The EnvironGet kernel " << kernel_node->fullname_with_scope() << " value type: " << value_type_attr_; handle_size_ = sizeof(int64_t); key_size_ = sizeof(int64_t); @@ -93,7 +93,7 @@ bool EnvironGetGpuKernelMod::Launch(const std::vector &inputs, const value_type = env_value->value_type_; } else { auto kernel_node = kernel_node_.lock(); - const std::string &prim_name = (kernel_node == nullptr) ? "" : AnfAlgo::GetCNodeName(kernel_node); + const std::string &prim_name = (kernel_node == nullptr) ? "" : common::AnfAlgo::GetCNodeName(kernel_node); MS_LOG(INFO) << "Use the default input value for kernel: " << prim_name << ", env handle: " << host_handle << ", env key: " << host_key; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/environ/environ_gpu_set.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/environ/environ_gpu_set.cc index 5a5770bef6e..485e752e94c 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/environ/environ_gpu_set.cc +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/environ/environ_gpu_set.cc @@ -36,7 +36,7 @@ bool EnvironSetGpuKernelMod::Init(const CNodePtr &kernel_node) { return false; } - value_type_attr_ = TypeId(AnfAlgo::GetNodeAttr(kernel_node, kEnvValueTypeAttr)); + value_type_attr_ = TypeId(common::AnfAlgo::GetNodeAttr(kernel_node, kEnvValueTypeAttr)); MS_LOG(INFO) << "The EnvironSet kernel " << kernel_node->fullname_with_scope() << " value type: " << value_type_attr_; handle_size_ = sizeof(int64_t); key_size_ = sizeof(int64_t); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/gpu_kernel.h index cb175a906b0..8732d734268 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/gpu_kernel.h @@ -34,6 +34,7 @@ #include "plugin/device/gpu/hal/device/gpu_device_manager.h" #include "plugin/device/gpu/hal/device/gpu_common.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "runtime/device/executor/dynamic_kernel.h" using AnfAlgo = mindspore::session::AnfRuntimeAlgorithm; @@ -116,7 +117,7 @@ class NativeGpuKernelMod : public GpuKernelMod { if ((addr_list[index] == nullptr) || (addr_list[index]->addr == nullptr) || (addr_list[index]->size == 0)) { auto kernel_node = kernel_node_.lock(); - const std::string &prim_name = (kernel_node == nullptr ? "" : AnfAlgo::GetCNodeName(kernel_node)); + const std::string &prim_name = (kernel_node == nullptr ? "" : common::AnfAlgo::GetCNodeName(kernel_node)); MS_LOG(EXCEPTION) << "The device address is empty, address index: " << index << ", op name is: " << prim_name; } @@ -140,17 +141,17 @@ class NativeGpuKernelMod : public GpuKernelMod { template inline T GetAttr(const CNodePtr &kernel_node, const std::string &key) const { - const PrimitivePtr &prim = AnfAlgo::GetCNodePrimitive(kernel_node); + const PrimitivePtr &prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); const ValuePtr &attr = prim->GetAttr(key); if (attr == nullptr) { - const std::string &prim_name = AnfAlgo::GetCNodeName(kernel_node); + const std::string &prim_name = common::AnfAlgo::GetCNodeName(kernel_node); MS_LOG(EXCEPTION) << "The attr(" << key << ") of kernel(" << prim_name << ") not exist"; } return GetValue(attr); } template inline T GetAttrWithDefault(const CNodePtr &kernel_node, const std::string &key, const T &value) const { - const PrimitivePtr &prim = AnfAlgo::GetCNodePrimitive(kernel_node); + const PrimitivePtr &prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); const ValuePtr &attr = prim->GetAttr(key); if (attr == nullptr) { return value; @@ -375,7 +376,7 @@ class NativeGpuKernelMod : public GpuKernelMod { << "th input in the depend_tensor_map"; } auto input_tensor = depend_iter->second; - const auto &input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, input_index); + const auto &input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, input_index); if (!ShapeEqual(input_shape, input_tensor->shape())) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the " << input_index << "th input is different between the InferShape and the TensorShape"; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/math/addn_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/math/addn_gpu_kernel.h index 631ad498e49..9c480473ee4 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/math/addn_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/math/addn_gpu_kernel.h @@ -62,15 +62,15 @@ class AddNFwdGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); num_input_ = GetAttr(kernel_node, "n"); if (num_input_ != input_num) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be " << num_input_ << ", but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/math/assign_add_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/math/assign_add_gpu_kernel.h index 52388592dea..a1528d5246a 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/math/assign_add_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/math/assign_add_gpu_kernel.h @@ -45,16 +45,16 @@ class AssignAddFwdGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 2, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/math/broadcast_complex_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/math/broadcast_complex_gpu_kernel.h index febbe873e47..566817a3ca1 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/math/broadcast_complex_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/math/broadcast_complex_gpu_kernel.h @@ -27,6 +27,7 @@ #include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/broadcast_impl.cuh" #include "plugin/device/gpu/kernel/kernel_constants.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace kernel { @@ -53,12 +54,12 @@ class BroadcastComplexOpGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); GetOpType(kernel_node); auto shape1 = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 0); auto shape2 = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 1); auto shape3 = AnfAlgo::GetOutputRealDeviceShapeIfExist(kernel_node, 0); - need_broadcast_ = AnfAlgo::IsTensorBroadcast(shape1, shape2); + need_broadcast_ = common::AnfAlgo::IsTensorBroadcast(shape1, shape2); if (need_broadcast_ && shape1.size() > MAX_DIMS) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimension of input cannot be greater than " << MAX_DIMS << ", but got " << shape1.size(); @@ -115,7 +116,7 @@ class BroadcastComplexOpGpuKernelMod : public NativeGpuKernelMod { private: void GetOpType(const CNodePtr &kernel_node) { - std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node); + std::string kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); static std::map kBroadcastArithmetricTypeMap = { {"RealDiv", BROADCAST_TYPE_REALDIV}, {"Mul", BROADCAST_TYPE_MUL}, {"Sub", BROADCAST_TYPE_SUB}, {"Add", BROADCAST_TYPE_ADD}, {"Div", BROADCAST_TYPE_DIV}, {"Complex", BROADCAST_TYPE_COMPLEX}}; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/math/broadcast_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/math/broadcast_gpu_kernel.h index 5b965853545..d24d5beeffb 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/math/broadcast_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/math/broadcast_gpu_kernel.h @@ -26,6 +26,7 @@ #include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/broadcast_impl.cuh" #include "plugin/device/gpu/kernel/kernel_constants.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace kernel { @@ -66,7 +67,7 @@ class BroadcastOpGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); GetOpType(kernel_node); auto shape1 = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 0); auto shape2 = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 1); @@ -78,7 +79,7 @@ class BroadcastOpGpuKernelMod : public NativeGpuKernelMod { InitSizeLists(); return true; } - need_broadcast_ = AnfAlgo::IsTensorBroadcast(shape1, shape2); + need_broadcast_ = common::AnfAlgo::IsTensorBroadcast(shape1, shape2); if (need_broadcast_ && shape1.size() > MAX_DIMS) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimension of input cannot be greater than " << MAX_DIMS << ", but got " << shape1.size(); @@ -156,7 +157,7 @@ class BroadcastOpGpuKernelMod : public NativeGpuKernelMod { private: void GetOpType(const CNodePtr &kernel_node) { - std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node); + std::string kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); static const std::map kBroadcastCmpTypeMap = { {"Greater", BROADCAST_TYPE_GREATER}, diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/math/broadcast_grad_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/math/broadcast_grad_gpu_kernel.h index cdb0885abf1..526e592298b 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/math/broadcast_grad_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/math/broadcast_grad_gpu_kernel.h @@ -26,6 +26,7 @@ #include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/broadcast_grad_impl.cuh" #include "plugin/device/gpu/kernel/kernel_constants.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace kernel { @@ -65,12 +66,12 @@ class BroadcastOpGradGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; GetOpType(kernel_node); - auto shape1 = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape2 = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape3 = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape1 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape2 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape3 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); is_null_input_ = CHECK_SHAPE_NULL(shape1, kernel_name_, "input_1") || CHECK_SHAPE_NULL(shape2, kernel_name_, "input_2") || CHECK_SHAPE_NULL(shape3, kernel_name_, "input_3"); @@ -78,7 +79,7 @@ class BroadcastOpGradGpuKernelMod : public NativeGpuKernelMod { InitSizeLists(); return true; } - need_broadcast_ = AnfAlgo::IsTensorBroadcast(shape1, shape2); + need_broadcast_ = common::AnfAlgo::IsTensorBroadcast(shape1, shape2); if (need_broadcast_ && shape1.size() > kMaxShapeSize) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimension of input cannot be greater than " << kMaxShapeSize << ", but got " << shape1.size(); @@ -153,7 +154,7 @@ class BroadcastOpGradGpuKernelMod : public NativeGpuKernelMod { private: void GetOpType(const CNodePtr &kernel_node) { - std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node); + std::string kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); static std::map kBroadcastTypeMap = { {"MaximumGrad", BROADCAST_GRAD_TYPE_MAXIMUM}, diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/math/cast_all_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/math/cast_all_gpu_kernel.h index 2c335613362..dfa87ce9839 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/math/cast_all_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/math/cast_all_gpu_kernel.h @@ -63,12 +63,12 @@ class CastAllFwdGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; num_input_ = GetAttr(kernel_node, "n"); size_ = std::make_unique(num_input_); for (size_t i = 0; i < num_input_; i++) { - auto shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, i); + auto shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, i); is_null_input_ = CHECK_SHAPE_NULL(shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/math/cholesky_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/math/cholesky_gpu_kernel.h index 7da6c9b7241..aa1326a6659 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/math/cholesky_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/math/cholesky_gpu_kernel.h @@ -27,7 +27,7 @@ #include "plugin/device/gpu/kernel/gpu_kernel.h" #include "plugin/device/gpu/kernel/gpu_kernel_factory.h" #include "plugin/device/gpu/kernel/kernel_constants.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" namespace mindspore { namespace kernel { @@ -46,16 +46,16 @@ class CholeskyGpuKernelMod : public NativeGpuKernelMod { ~CholeskyGpuKernelMod() = default; bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; // if clean attribute exits, we will remain rand triangular data by clean flag, otherwise clean it to zero. - if (AnfAlgo::HasNodeAttr(kClean, kernel_node)) { + if (common::AnfAlgo::HasNodeAttr(kClean, kernel_node)) { clean_ = static_cast(GetAttr(kernel_node, kClean)); } - if (AnfAlgo::HasNodeAttr(kLower, kernel_node)) { + if (common::AnfAlgo::HasNodeAttr(kLower, kernel_node)) { lower_ = static_cast(GetAttr(kernel_node, kLower)); } - if (AnfAlgo::HasNodeAttr(kSplitDim, kernel_node)) { + if (common::AnfAlgo::HasNodeAttr(kSplitDim, kernel_node)) { split_dim_ = static_cast(GetAttr(kernel_node, kSplitDim)); } // Cholesky input is sys_positive_matrix and saved by col_major in gpu backend. @@ -68,7 +68,7 @@ class CholeskyGpuKernelMod : public NativeGpuKernelMod { // Get CuSolver Dense matrix handler handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCusolverDnHandle(); - auto in_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, kInputIndex); + auto in_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, kInputIndex); is_null_input_ = CHECK_SHAPE_NULL(in_shape, kernel_name_, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/math/cholesky_solve_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/math/cholesky_solve_gpu_kernel.h index d6af6258a42..8e9600d8c4b 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/math/cholesky_solve_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/math/cholesky_solve_gpu_kernel.h @@ -25,7 +25,7 @@ #include "plugin/device/gpu/kernel/gpu_kernel.h" #include "plugin/device/gpu/kernel/gpu_kernel_factory.h" #include "plugin/device/gpu/kernel/kernel_constants.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" namespace mindspore { namespace kernel { @@ -44,9 +44,9 @@ class CholeskySolveGpuKernelMod : public NativeGpuKernelMod { ~CholeskySolveGpuKernelMod() = default; bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; - if (AnfAlgo::HasNodeAttr(kLower, kernel_node)) { + if (common::AnfAlgo::HasNodeAttr(kLower, kernel_node)) { lower_ = static_cast(GetAttr(kernel_node, kLower)); } // Gpu input is col major default, so need to change row major. @@ -59,8 +59,8 @@ class CholeskySolveGpuKernelMod : public NativeGpuKernelMod { } handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCusolverDnHandle(); - auto in_a_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, kDim0); - auto in_b_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, kDim1); + auto in_a_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, kDim0); + auto in_b_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, kDim1); is_null_input_ = CHECK_SHAPE_NULL(in_a_shape, kernel_name_, "input_a") || CHECK_SHAPE_NULL(in_b_shape, kernel_name_, "input_b"); if (is_null_input_) { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/math/cholesky_trsm_solve_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/math/cholesky_trsm_solve_gpu_kernel.h index 603be4333ea..cbd6d914a85 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/math/cholesky_trsm_solve_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/math/cholesky_trsm_solve_gpu_kernel.h @@ -26,7 +26,7 @@ #include "plugin/device/gpu/kernel/gpu_kernel.h" #include "plugin/device/gpu/kernel/gpu_kernel_factory.h" #include "plugin/device/gpu/kernel/kernel_constants.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" namespace mindspore { namespace kernel { @@ -65,11 +65,11 @@ class CholeskyTrsmGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCusolverDnHandle(); blas_handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCublasHandle(); - auto in_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto in_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(in_shape, kernel_name_, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/math/cumprod_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/math/cumprod_gpu_kernel.h index 78dae0b7918..419ff65a926 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/math/cumprod_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/math/cumprod_gpu_kernel.h @@ -51,13 +51,13 @@ class CumProdGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 1, but got " << input_num; } input_size_0_ = sizeof(T); - shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + shape_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(shape_, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/math/cumsum_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/math/cumsum_gpu_kernel.h index cb2887af803..ae397ef471e 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/math/cumsum_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/math/cumsum_gpu_kernel.h @@ -51,13 +51,13 @@ class CumSumGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 1, but got " << input_num; } input_size_0_ = sizeof(T); - shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + shape_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(shape_, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/math/determinant_triangle_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/math/determinant_triangle_gpu_kernel.h index 824482d83e6..37bd8a5e264 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/math/determinant_triangle_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/math/determinant_triangle_gpu_kernel.h @@ -53,17 +53,17 @@ class DetTriangleGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 1, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_num; } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name_, "input") || CHECK_SHAPE_NULL(output_shape, kernel_name_, "output"); if (is_null_input_) { @@ -91,7 +91,7 @@ class DetTriangleGpuKernelMod : public NativeGpuKernelMod { if (input_shape[input_shape.size() - 2] != input_shape[input_shape.size() - 1]) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the shape of input should be square matrix"; } - auto prim = AnfAlgo::GetCNodePrimitive(kernel_node); + auto prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(prim); fill_mode_ = static_cast(GetValue(prim->GetAttr("fill_mode"))); InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/math/eigh_c_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/math/eigh_c_gpu_kernel.h index 9afa14b8df1..d9cec826a1c 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/math/eigh_c_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/math/eigh_c_gpu_kernel.h @@ -30,7 +30,7 @@ #include "plugin/device/gpu/kernel/gpu_kernel.h" #include "plugin/device/gpu/kernel/gpu_kernel_factory.h" #include "plugin/device/gpu/kernel/kernel_constants.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" #include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/complex.h" #include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/real_to_complex_impl.cuh" #include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/transpose_impl.cuh" @@ -57,10 +57,10 @@ class EighcGpuKernelMod : public NativeGpuKernelMod { ~EighcGpuKernelMod() = default; bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); blas_handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCublasHandle(); dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); - auto A_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto A_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); compute_eigen_vectors_ = static_cast(GetAttr(kernel_node, C_EIEH_VECTOR)); lower_ = static_cast(GetAttr(kernel_node, LOWER)); if (compute_eigen_vectors_) { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/math/eigh_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/math/eigh_gpu_kernel.h index cbb627cf771..3230e6ed483 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/math/eigh_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/math/eigh_gpu_kernel.h @@ -29,7 +29,7 @@ #include "plugin/device/gpu/kernel/gpu_kernel.h" #include "plugin/device/gpu/kernel/gpu_kernel_factory.h" #include "plugin/device/gpu/kernel/kernel_constants.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" #include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/transpose_impl.cuh" namespace mindspore { @@ -43,9 +43,9 @@ class EighGpuKernelMod : public NativeGpuKernelMod { ~EighGpuKernelMod() = default; bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0); - auto A_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto A_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); compute_eigen_vectors_ = static_cast(GetAttr(kernel_node, C_EIEH_VECTOR)); lower_ = static_cast(GetAttr(kernel_node, LOWER)); if (compute_eigen_vectors_) { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/math/einsum_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/math/einsum_gpu_kernel.h index 3c328d61929..7a14e348e66 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/math/einsum_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/math/einsum_gpu_kernel.h @@ -123,9 +123,9 @@ class EinsumGpuKernelMod : public NativeGpuKernelMod { bool Init(const CNodePtr &kernel_node) override { kernel_node_ = kernel_node; - auto node_name = AnfAlgo::GetCNodeName(kernel_node); + auto node_name = common::AnfAlgo::GetCNodeName(kernel_node); node_name_ = node_name; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num < 1) { MS_LOG(ERROR) << "For " << node_name << ", input number can not be less than 1, but got " << input_num; return false; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/math/einsum_grad_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/math/einsum_grad_gpu_kernel.h index 31b674642a5..627b3e5b7c4 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/math/einsum_grad_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/math/einsum_grad_gpu_kernel.h @@ -70,9 +70,9 @@ class EinsumGradGpuKernelMod : public NativeGpuKernelMod { bool Init(const CNodePtr &kernel_node) override { kernel_node_ = kernel_node; - auto node_name = AnfAlgo::GetCNodeName(kernel_node); + auto node_name = common::AnfAlgo::GetCNodeName(kernel_node); node_name_ = node_name; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num < INPUT_NUM_MIN) { MS_LOG(ERROR) << "For " << node_name_ << ", input number should be no less than 2, but got " << input_num; return false; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/math/equalcount_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/math/equalcount_gpu_kernel.h index 8acd40ebcbd..21332de129d 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/math/equalcount_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/math/equalcount_gpu_kernel.h @@ -45,19 +45,19 @@ class EqualCountGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 2, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } output_size_ = sizeof(T); input_size_ = sizeof(T); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/math/float_status_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/math/float_status_gpu_kernel.h index a789bfc9ccf..0253978ec33 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/math/float_status_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/math/float_status_gpu_kernel.h @@ -74,9 +74,9 @@ class FloatStatusGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); (void)CheckParam(kernel_node); - auto shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); @@ -110,12 +110,12 @@ class FloatStatusGpuKernelMod : public NativeGpuKernelMod { private: void CheckParam(const CNodePtr &kernel_node) { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 1, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/math/identity_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/math/identity_gpu_kernel.h index 91cca5c87f4..7a2171899f5 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/math/identity_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/math/identity_gpu_kernel.h @@ -48,12 +48,12 @@ class IdentityGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 1, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/math/index_add_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/math/index_add_gpu_kernel.h index 706d17c2fea..f8fa78a80fc 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/math/index_add_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/math/index_add_gpu_kernel.h @@ -58,15 +58,15 @@ class IndexAddGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 3) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 3, but got " << input_num; } - std::vector dst_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - std::vector index_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - std::vector src_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + std::vector dst_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + std::vector index_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + std::vector src_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); is_null_input_ = CHECK_SHAPE_NULL(dst_shape, kernel_name, "x") || CHECK_SHAPE_NULL(index_shape, kernel_name, "indices") || CHECK_SHAPE_NULL(src_shape, kernel_name, "y"); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/math/linspace.h b/mindspore/ccsrc/plugin/device/gpu/kernel/math/linspace.h index 738d602dedd..c02c7709d78 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/math/linspace.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/math/linspace.h @@ -47,12 +47,12 @@ class LinSpaceGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 3) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 3, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/math/logical_not_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/math/logical_not_gpu_kernel.h index 82709487c0b..6eb5b937378 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/math/logical_not_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/math/logical_not_gpu_kernel.h @@ -24,7 +24,7 @@ #include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/logical_not_impl.cuh" #include "plugin/device/gpu/kernel/gpu_kernel.h" #include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" namespace mindspore { namespace kernel { @@ -46,7 +46,7 @@ class LogicalNotGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; auto input_shape = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/math/lu_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/math/lu_gpu_kernel.h index 08bd7ec2fe1..1950db1cf5f 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/math/lu_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/math/lu_gpu_kernel.h @@ -25,7 +25,7 @@ #include "plugin/device/gpu/kernel/gpu_kernel.h" #include "plugin/device/gpu/kernel/gpu_kernel_factory.h" #include "plugin/device/gpu/kernel/kernel_constants.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" #include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/transpose_impl.cuh" namespace mindspore { @@ -153,11 +153,11 @@ class LUGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; // 1. get CuSolver Dense matrix handler handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCusolverDnHandle(); - auto in_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto in_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); // 2. check input shape not null is_null_input_ = CHECK_SHAPE_NULL(in_shape, kernel_name_, "input"); if (is_null_input_) { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/math/lu_solve_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/math/lu_solve_gpu_kernel.h index 4f11a03c568..78a3a837610 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/math/lu_solve_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/math/lu_solve_gpu_kernel.h @@ -25,8 +25,9 @@ #include "plugin/device/gpu/kernel/gpu_kernel.h" #include "plugin/device/gpu/kernel/gpu_kernel_factory.h" #include "plugin/device/gpu/kernel/kernel_constants.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" #include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/transpose_impl.cuh" + namespace mindspore { namespace kernel { template @@ -36,11 +37,11 @@ class LuSolveGpuKernelMod : public NativeGpuKernelMod { ~LuSolveGpuKernelMod() = default; bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCusolverDnHandle(); - auto input_a_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto input_b_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto input_a_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_b_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); is_null_input_ = (CHECK_SHAPE_NULL(input_a_shape, kernel_name_, " lu solve input a") && (CHECK_SHAPE_NULL(input_b_shape, kernel_name_, " lu solve input b"))); if (is_null_input_) { @@ -132,8 +133,8 @@ class LuSolveGpuKernelMod : public NativeGpuKernelMod { private: bool InitInputSize(const CNodePtr &kernel_node) { - auto input_a_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto input_b_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto input_a_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_b_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); constexpr size_t input_min_dim = 1; if (input_a_shape.size() <= input_min_dim || input_b_shape.size() <= input_min_dim) { MS_LOG_EXCEPTION << kernel_name_ << " LuSolveGpuKernelMod input shape size is " << input_a_shape.size() diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/math/matmul_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/math/matmul_gpu_kernel.h index c6114592578..b99d4cde540 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/math/matmul_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/math/matmul_gpu_kernel.h @@ -25,7 +25,7 @@ #include "plugin/device/gpu/kernel/gpu_kernel_factory.h" #include "plugin/device/gpu/kernel/kernel_constants.h" #include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/fill_impl.cuh" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" namespace mindspore { namespace kernel { @@ -95,13 +95,13 @@ class MatMulGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCublasHandle(); dtype_a_ = GetCudaDataType(TypeIdLabel(AnfAlgo::GetInputDeviceDataType(kernel_node, 0))); dtype_b_ = GetCudaDataType(TypeIdLabel(AnfAlgo::GetInputDeviceDataType(kernel_node, 1))); dtype_c_ = GetCudaDataType(TypeIdLabel(AnfAlgo::GetOutputDeviceDataType(kernel_node, 0))); - auto node_name = AnfAlgo::GetCNodeName(kernel_node); + auto node_name = common::AnfAlgo::GetCNodeName(kernel_node); if (dtype_a_ != dtype_b_ || dtype_a_ != dtype_c_) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', input and output types are not the same in " << node_name; } @@ -109,8 +109,8 @@ class MatMulGpuKernelMod : public NativeGpuKernelMod { MS_LOG(INFO) << "input and output type is float16, allow to use Tensor Core operations if possible"; algo_ = CUBLAS_GEMM_DEFAULT_TENSOR_OP; } - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); - auto input1_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto input1_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input1_shape, kernel_name_, "input") || CHECK_SHAPE_NULL(output_shape, kernel_name_, "output"); if (is_null_input_) { @@ -144,7 +144,7 @@ class MatMulGpuKernelMod : public NativeGpuKernelMod { transpose = GetAttr(kernel_node, "transpose_x2"); transpose_x2_ = transpose ? CUBLAS_OP_T : CUBLAS_OP_N; - const std::string &kernel_name = AnfAlgo::GetCNodeName(kernel_node); + const std::string &kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); if (kernel_name == kFusedMatMulBiasAddName) { is_fused_matmul_biasadd_ = true; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/math/matrix_inverse_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/math/matrix_inverse_gpu_kernel.h index 91a7e522989..36f7d5c4ed3 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/math/matrix_inverse_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/math/matrix_inverse_gpu_kernel.h @@ -95,10 +95,10 @@ class MatrixInverseGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCublasHandle(); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name_, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/math/matrix_triangular_solve_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/math/matrix_triangular_solve_gpu_kernel.h index db6b478ea3e..95c292e4d59 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/math/matrix_triangular_solve_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/math/matrix_triangular_solve_gpu_kernel.h @@ -146,7 +146,7 @@ class MatrixTriangularSolveGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; blas_handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCublasHandle(); @@ -159,23 +159,23 @@ class MatrixTriangularSolveGpuKernelMod : public NativeGpuKernelMod { lda_ = SizeToInt(m_); ldb_ = SizeToInt(m_); - if (AnfAlgo::HasNodeAttr("adjoint", kernel_node)) { + if (common::AnfAlgo::HasNodeAttr("adjoint", kernel_node)) { // MatrixTriangularSolve attribute - bool trans = AnfAlgo::GetNodeAttr(kernel_node, "adjoint"); + bool trans = common::AnfAlgo::GetNodeAttr(kernel_node, "adjoint"); // converting row major to col major is the same as reverting the trans flag trans_ = trans ? CUBLAS_OP_N : CUBLAS_OP_T; - if (AnfAlgo::HasNodeAttr("trans", kernel_node)) { + if (common::AnfAlgo::HasNodeAttr("trans", kernel_node)) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the attribute 'adjoint' and 'trans' could not exist at the same time."; } } else { - bool lower = AnfAlgo::GetNodeAttr(kernel_node, "lower"); + bool lower = common::AnfAlgo::GetNodeAttr(kernel_node, "lower"); // reverting the trans flag by default, so also flip the lower flag lower = !lower; uplo_ = lower ? CUBLAS_FILL_MODE_LOWER : CUBLAS_FILL_MODE_UPPER; - bool unit_diagonal = AnfAlgo::GetNodeAttr(kernel_node, "unit_diagonal"); + bool unit_diagonal = common::AnfAlgo::GetNodeAttr(kernel_node, "unit_diagonal"); unit_diagonal_ = unit_diagonal ? CUBLAS_DIAG_UNIT : CUBLAS_DIAG_NON_UNIT; - const std::string trans = AnfAlgo::GetNodeAttr(kernel_node, "trans"); + const std::string trans = common::AnfAlgo::GetNodeAttr(kernel_node, "trans"); // converting row major to col major is the same as reverting the trans flag if (trans == "N") { trans_ = CUBLAS_OP_T; @@ -223,8 +223,8 @@ class MatrixTriangularSolveGpuKernelMod : public NativeGpuKernelMod { } void InitShape(const CNodePtr &kernel_node) { - auto a_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto b_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto a_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto b_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); is_null_input_ = CHECK_SHAPE_NULL(a_shape, kernel_name_, "input_a") || CHECK_SHAPE_NULL(b_shape, kernel_name_, "input_b"); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/math/multinomial_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/math/multinomial_gpu_kernel.h index 297a7e20b1d..37b4d0cfd79 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/math/multinomial_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/math/multinomial_gpu_kernel.h @@ -79,17 +79,17 @@ class MultinomialGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 2, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_num; } - auto input_shape_0 = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto input_shape_0 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape_0, kernel_name_, "input") || CHECK_SHAPE_NULL(output_shape, kernel_name_, "output"); if (is_null_input_) { @@ -113,7 +113,7 @@ class MultinomialGpuKernelMod : public NativeGpuKernelMod { output_size_ *= output_shape[i]; } - auto prim = AnfAlgo::GetCNodePrimitive(kernel_node); + auto prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(prim); seed_ = static_cast(GetValue(prim->GetAttr("seed"))); seed2_ = static_cast(GetValue(prim->GetAttr("seed2"))); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/math/nms_with_mask_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/math/nms_with_mask_gpu_kernel.h index 521317e8963..f2d3bc2b539 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/math/nms_with_mask_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/math/nms_with_mask_gpu_kernel.h @@ -61,20 +61,20 @@ class NMSWithMaskFwdGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); iou_value_ = GetAttr(kernel_node, "iou_threshold"); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 1, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 3) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 3, but got " << output_num; } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/math/random_op_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/math/random_op_gpu_kernel.h index 810c1a28d86..6621f812b86 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/math/random_op_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/math/random_op_gpu_kernel.h @@ -123,7 +123,7 @@ class RandomOpGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node); + std::string kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); auto iter = kRandomOpTypeMap.find(kernel_name); if (iter == kRandomOpTypeMap.end()) { MS_LOG(EXCEPTION) << "For '" << kernel_name << ", only support these types: StandardNormal, CudnnUniformReal, " @@ -131,19 +131,19 @@ class RandomOpGpuKernelMod : public NativeGpuKernelMod { } else { random_op_type_ = iter->second; } - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if ((random_op_type_ == RANDOM_OP_NORMAL || random_op_type_ == RANDOM_OP_UNIFORM_REAL) && input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 1, but got " << input_num; } if (random_op_type_ == RANDOM_OP_UNIFORM_INT && input_num != 3) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 3, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } - auto input_shape_0 = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto input_shape_0 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape_0, kernel_name, "input") || CHECK_SHAPE_NULL(output_shape, kernel_name, "output"); if (is_null_input_) { @@ -168,7 +168,7 @@ class RandomOpGpuKernelMod : public NativeGpuKernelMod { workspace_size_ = 0; } - auto prim = AnfAlgo::GetCNodePrimitive(kernel_node); + auto prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(prim); seed_ = static_cast(GetValue(prim->GetAttr("seed"))); seed2_ = static_cast(GetValue(prim->GetAttr("seed2"))); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/math/square_sum_all_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/math/square_sum_all_gpu_kernel.h index 54e9c61330b..9f2520199f8 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/math/square_sum_all_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/math/square_sum_all_gpu_kernel.h @@ -48,7 +48,7 @@ class SquareSumAllFwdGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); auto input_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/math/squared_difference_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/math/squared_difference_kernel.h index 170a342e0ce..d6aaedf3fa7 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/math/squared_difference_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/math/squared_difference_kernel.h @@ -52,7 +52,7 @@ class SquaredDifferenceOpGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); auto input_shape1 = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 0); auto input_shape2 = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 1); auto output_shape = AnfAlgo::GetOutputRealDeviceShapeIfExist(kernel_node, 0); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/math/trsm_solve_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/math/trsm_solve_gpu_kernel.h index e262965f187..ac15da957d5 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/math/trsm_solve_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/math/trsm_solve_gpu_kernel.h @@ -115,11 +115,11 @@ class TrsmGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; blas_handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCublasHandle(); - auto A_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto b_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto A_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto b_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); is_null_input_ = CHECK_SHAPE_NULL(A_shape, kernel_name_, "input_A") || CHECK_SHAPE_NULL(b_shape, kernel_name_, "input_b"); if (is_null_input_) { @@ -160,10 +160,10 @@ class TrsmGpuKernelMod : public NativeGpuKernelMod { lda_ = SizeToInt(m_); ldb_ = SizeToInt(m_); - const std::string trans = AnfAlgo::GetNodeAttr(kernel_node, "trans"); + const std::string trans = common::AnfAlgo::GetNodeAttr(kernel_node, "trans"); SetOperation(trans); - bool lower = AnfAlgo::GetNodeAttr(kernel_node, "lower"); + bool lower = common::AnfAlgo::GetNodeAttr(kernel_node, "lower"); // reverting the trans flag by default, so also flip the lower flag lower = !lower; if (lower) { @@ -172,7 +172,7 @@ class TrsmGpuKernelMod : public NativeGpuKernelMod { uplo_ = CUBLAS_FILL_MODE_UPPER; } - bool unit_diagonal = AnfAlgo::GetNodeAttr(kernel_node, "unit_diagonal"); + bool unit_diagonal = common::AnfAlgo::GetNodeAttr(kernel_node, "unit_diagonal"); if (unit_diagonal) { unit_diagonal_ = CUBLAS_DIAG_UNIT; } else { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/math/unary_op_complex_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/math/unary_op_complex_gpu_kernel.h index 42436488575..3cdcd2e2c3c 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/math/unary_op_complex_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/math/unary_op_complex_gpu_kernel.h @@ -68,14 +68,14 @@ class UnaryOpComplexGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); GetOpType(kernel_node); - std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + std::string kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 1, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 3, but got " << output_num; } @@ -110,7 +110,7 @@ class UnaryOpComplexGpuKernelMod : public NativeGpuKernelMod { private: void GetOpType(const CNodePtr &kernel_node) { - std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node); + std::string kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); static std::map kComplexSupportedTypeMap = { {"Real", UNARY_OP_REAL}, {"Imag", UNARY_OP_IMAG}, {"Conj", UNARY_OP_CONJ}}; auto iter = kComplexSupportedTypeMap.find(kernel_name); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/math/unary_op_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/math/unary_op_gpu_kernel.h index d27110cf9b3..ac8abdcf8af 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/math/unary_op_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/math/unary_op_gpu_kernel.h @@ -114,7 +114,7 @@ class UnaryOpGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node); + std::string kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); auto iter = kUnaryOpTypeMap.find(kernel_name); if (iter == kUnaryOpTypeMap.end()) { MS_LOG(EXCEPTION) << "For '" << kernel_name << ", only support these types: Exp, Expm1, Log, Log1p, Erf, Erfc," @@ -122,11 +122,11 @@ class UnaryOpGpuKernelMod : public NativeGpuKernelMod { << "Floor, Rint, Round, Real, Imag, Sign, Conj currently, but got " << kernel_name; } unary_op_type_ = iter->second; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 1, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/math/unary_op_grad_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/math/unary_op_grad_gpu_kernel.h index a12768c26ed..c644117aacf 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/math/unary_op_grad_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/math/unary_op_grad_gpu_kernel.h @@ -116,7 +116,7 @@ class UnaryGradOpGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node); + std::string kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); auto iter = kUnaryGradOpTypeMap.find(kernel_name); if (iter == kUnaryGradOpTypeMap.end()) { MS_LOG(EXCEPTION) << "For '" << kernel_name << ", only support these types: SqrtGrad, RsqrtGrad, AsinGrad, " @@ -124,15 +124,15 @@ class UnaryGradOpGpuKernelMod : public NativeGpuKernelMod { << kernel_name; } unary_grad_op_type_ = iter->second; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 2, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); @@ -141,7 +141,7 @@ class UnaryGradOpGpuKernelMod : public NativeGpuKernelMod { for (size_t i = 0; i < input_shape.size(); i++) { input_size_ *= input_shape[i]; } - auto dx_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto dx_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); is_null_input_ = CHECK_SHAPE_NULL(dx_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/math/update_thor_gradient.h b/mindspore/ccsrc/plugin/device/gpu/kernel/math/update_thor_gradient.h index 3e17aeeb6c3..7186bab616b 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/math/update_thor_gradient.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/math/update_thor_gradient.h @@ -25,7 +25,7 @@ #include "plugin/device/gpu/kernel/kernel_constants.h" #include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/convert_gradient_impl.cuh" #include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/pad_impl.cuh" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" namespace mindspore { namespace kernel { @@ -187,9 +187,9 @@ class UpdateThorGradientGpuKernelMod : public NativeGpuKernelMod { private: void SetProperty(const CNodePtr &kernel_node) { - auto matrix_a_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto gradient_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto matrix_g_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto matrix_a_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto gradient_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto matrix_g_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); is_null_input_ = CHECK_SHAPE_NULL(matrix_a_shape, kernel_name_, "matrix_a") || CHECK_SHAPE_NULL(gradient_shape, kernel_name_, "gradient") || CHECK_SHAPE_NULL(matrix_g_shape, kernel_name_, "matrix_g"); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nccl/nccl_collective_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nccl/nccl_collective_gpu_kernel.h index 8f56968a028..e6f408ff23a 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nccl/nccl_collective_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nccl/nccl_collective_gpu_kernel.h @@ -75,14 +75,14 @@ class NcclCollectiveGpuKernel : public NcclGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); MS_EXCEPTION_IF_NULL(kernel_node); kernel_node_ = kernel_node; nccl_data_type_ = nccl_dtype(AnfAlgo::GetInputDeviceDataType(kernel_node, 0)); InferCommType(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); for (size_t i = 0; i < input_num; ++i) { auto shape = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, i); is_null_input_ = CHECK_SHAPE_NULL(shape, kernel_name_, "input"); @@ -115,8 +115,8 @@ class NcclCollectiveGpuKernel : public NcclGpuKernelMod { } group_name_ = GetAttr(kernel_node, kAttrGroup); - MS_LOG(INFO) << AnfAlgo::GetCNodeName(kernel_node) << " for group " << group_name_; - auto prim = AnfAlgo::GetCNodePrimitive(kernel_node); + MS_LOG(INFO) << common::AnfAlgo::GetCNodeName(kernel_node) << " for group " << group_name_; + auto prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(prim); auto comm_stream_attr = prim->GetAttr("stream_id"); if (comm_stream_attr) { @@ -186,7 +186,7 @@ class NcclCollectiveGpuKernel : public NcclGpuKernelMod { } void InferCommType(const CNodePtr &kernel_node) { - std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node); + std::string kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); auto iter = kNcclTypeMap.find(kernel_name); if (iter == kNcclTypeMap.end()) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << ", only support these types: AllReduce, AllGather, Broadcast, " @@ -195,7 +195,7 @@ class NcclCollectiveGpuKernel : public NcclGpuKernelMod { nccl_kernel_type_ = iter->second; } - auto prim = AnfAlgo::GetCNodePrimitive(kernel_node); + auto prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(prim); auto reduce_op = prim->GetAttr(kAttrOp); if (reduce_op) { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nccl/nccl_p2p_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nccl/nccl_p2p_gpu_kernel.h index 564bedbc3f9..5ff1810550e 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nccl/nccl_p2p_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nccl/nccl_p2p_gpu_kernel.h @@ -56,13 +56,13 @@ class NcclP2PGpuKernel : public NcclGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); MS_EXCEPTION_IF_NULL(kernel_node); kernel_node_ = kernel_node; InferCommType(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (input_num > 0) { input_nccl_data_type_ = nccl_dtype(AnfAlgo::GetInputDeviceDataType(kernel_node, 0)); } @@ -99,8 +99,8 @@ class NcclP2PGpuKernel : public NcclGpuKernelMod { } group_name_ = GetAttr(kernel_node, kAttrGroup); - MS_LOG(INFO) << AnfAlgo::GetCNodeName(kernel_node) << " for group " << group_name_; - auto prim = AnfAlgo::GetCNodePrimitive(kernel_node); + MS_LOG(INFO) << common::AnfAlgo::GetCNodeName(kernel_node) << " for group " << group_name_; + auto prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(prim); auto comm_stream_attr = prim->GetAttr("stream_id"); if (comm_stream_attr) { @@ -169,7 +169,7 @@ class NcclP2PGpuKernel : public NcclGpuKernelMod { } void InferCommType(const CNodePtr &kernel_node) { - std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node); + std::string kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); auto iter = kNcclTypeMap.find(kernel_name); if (iter == kNcclTypeMap.end()) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << ", only support these types: AllToAllv, NeighborExchange " @@ -178,7 +178,7 @@ class NcclP2PGpuKernel : public NcclGpuKernelMod { nccl_kernel_type_ = iter->second; } - auto prim = AnfAlgo::GetCNodePrimitive(kernel_node); + auto prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(prim); auto root_rank = prim->GetAttr(kAttrRootRank); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nccl/nccl_recv_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nccl/nccl_recv_gpu_kernel.h index 744ec9f28d0..dc12f073f31 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nccl/nccl_recv_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nccl/nccl_recv_gpu_kernel.h @@ -42,10 +42,10 @@ class NcclRecvGpuKernel : public NcclGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); MS_EXCEPTION_IF_NULL(kernel_node); kernel_node_ = kernel_node; - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } @@ -53,7 +53,7 @@ class NcclRecvGpuKernel : public NcclGpuKernelMod { group_name_ = GetAttr(kernel_node, kAttrGroup); nccl_data_type_ = nccl_dtype(AnfAlgo::GetOutputDeviceDataType(kernel_node, 0)); - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(output_shape, kernel_name, "output"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nccl/nccl_send_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nccl/nccl_send_gpu_kernel.h index 2e15a3fa734..7eaa3105278 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nccl/nccl_send_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nccl/nccl_send_gpu_kernel.h @@ -42,10 +42,10 @@ class NcclSendGpuKernel : public NcclGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); MS_EXCEPTION_IF_NULL(kernel_node); kernel_node_ = kernel_node; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 1, but got " << input_num; } @@ -55,7 +55,7 @@ class NcclSendGpuKernel : public NcclGpuKernelMod { nccl_data_type_ = nccl_dtype(AnfAlgo::GetInputDeviceDataType(kernel_node, 0)); MS_LOG(INFO) << "NcclSend dest rank is " << dest_rank_ << ", group name is " << group_name_; - auto input_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nccl/sync_batch_norm_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nccl/sync_batch_norm_gpu_kernel.h index a06f9cdd9fd..3bba8db2b32 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nccl/sync_batch_norm_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nccl/sync_batch_norm_gpu_kernel.h @@ -24,7 +24,7 @@ #include "plugin/device/gpu/kernel/nccl/nccl_gpu_kernel.h" #include "plugin/device/gpu/kernel/gpu_kernel_factory.h" #include "plugin/device/gpu/kernel/kernel_constants.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/sync_batch_norm_impl.cuh" namespace mindspore { @@ -77,22 +77,22 @@ class SyncBatchNormGpuKernel : public NcclGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - auto root_rank = AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr(kAttrRootRank); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + auto root_rank = common::AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr(kAttrRootRank); if (root_rank) { root_ = static_cast(GetValue(root_rank)); } nccl_data_type_ = nccl_dtype(AnfAlgo::GetInputDeviceDataType(kernel_node, 0)); group_name_ = GetAttr(kernel_node, kAttrGroup); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 5) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 5, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 5) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 5, but got " << output_num; } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); @@ -129,8 +129,8 @@ class SyncBatchNormGpuKernel : public NcclGpuKernelMod { } // MULTI DEVICE SPECIFICS group_name_ = GetAttr(kernel_node, kAttrGroup); - MS_LOG(INFO) << AnfAlgo::GetCNodeName(kernel_node) << " for group " << group_name_; - auto comm_stream_attr = AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("stream_id"); + MS_LOG(INFO) << common::AnfAlgo::GetCNodeName(kernel_node) << " for group " << group_name_; + auto comm_stream_attr = common::AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("stream_id"); if (comm_stream_attr) { comm_stream_ = reinterpret_cast(GetValue(comm_stream_attr)); MS_EXCEPTION_IF_NULL(comm_stream_); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nccl/sync_batch_norm_grad_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nccl/sync_batch_norm_grad_gpu_kernel.h index d4d34fc69e8..5c6b52f24eb 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nccl/sync_batch_norm_grad_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nccl/sync_batch_norm_grad_gpu_kernel.h @@ -24,7 +24,7 @@ #include "plugin/device/gpu/kernel/nccl/nccl_gpu_kernel.h" #include "plugin/device/gpu/kernel/gpu_kernel_factory.h" #include "plugin/device/gpu/kernel/kernel_constants.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/sync_batch_norm_grad_impl.cuh" namespace mindspore { @@ -64,22 +64,22 @@ class SyncBatchNormGradGpuKernel : public NcclGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - auto root_rank = AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr(kAttrRootRank); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + auto root_rank = common::AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr(kAttrRootRank); if (root_rank) { root_ = static_cast(GetValue(root_rank)); } nccl_data_type_ = nccl_dtype(AnfAlgo::GetInputDeviceDataType(kernel_node, 0)); group_name_ = GetAttr(kernel_node, kAttrGroup); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 5) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 5, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 3) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 3, but got " << output_num; } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); @@ -113,8 +113,8 @@ class SyncBatchNormGradGpuKernel : public NcclGpuKernelMod { epsilon_ = GetAttr(kernel_node, "epsilon"); // MULTIDEVICE SPECIFICS group_name_ = GetAttr(kernel_node, kAttrGroup); - MS_LOG(INFO) << AnfAlgo::GetCNodeName(kernel_node) << " for group " << group_name_; - auto comm_stream_attr = AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("stream_id"); + MS_LOG(INFO) << common::AnfAlgo::GetCNodeName(kernel_node) << " for group " << group_name_; + auto comm_stream_attr = common::AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("stream_id"); if (comm_stream_attr) { comm_stream_ = reinterpret_cast(GetValue(comm_stream_attr)); MS_EXCEPTION_IF_NULL(comm_stream_); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/activation_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/activation_gpu_kernel.h index 0267002ca20..d5ba369609f 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/activation_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/activation_gpu_kernel.h @@ -52,7 +52,7 @@ class ActivationFwdGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { kernel_node_ = kernel_node; - auto node_name = AnfAlgo::GetCNodeName(kernel_node); + auto node_name = common::AnfAlgo::GetCNodeName(kernel_node); auto iter = kernel_map.find(node_name); if (iter == kernel_map.end()) { MS_LOG(EXCEPTION) << "Only support these activations: ReLU6, Tanh, Elu, Sigmoid currently, but got " << node_name; @@ -61,7 +61,7 @@ class ActivationFwdGpuKernelMod : public NativeGpuKernelMod { InitResource(); cudnn_data_type_ = GetCudnnDataType(TypeIdLabel(AnfAlgo::GetInputDeviceDataType(kernel_node, 0))); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << node_name << "', the number of inputs should be 1, but got " << input_num; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/activation_grad_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/activation_grad_kernel.h index 85bbec1603b..4ca08ee0603 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/activation_grad_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/activation_grad_kernel.h @@ -61,7 +61,7 @@ class ActivationGradGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { kernel_node_ = kernel_node; - auto node_name = AnfAlgo::GetCNodeName(kernel_node); + auto node_name = common::AnfAlgo::GetCNodeName(kernel_node); auto iter = kernel_map.find(node_name); if (iter == kernel_map.end()) { MS_LOG(EXCEPTION) << "Only support these activations: ReLU6, Tanh, Elu, Sigmoid currently, but got " << node_name; @@ -70,11 +70,11 @@ class ActivationGradGpuKernelMod : public NativeGpuKernelMod { InitResource(); cudnn_data_type_ = GetCudnnDataType(TypeIdLabel(AnfAlgo::GetInputDeviceDataType(kernel_node, 0))); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 2) { MS_LOG(EXCEPTION) << "For '" << node_name << "', the number of inputs should be 2, but got " << input_num; } - auto input_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, node_name, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/adagrad_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/adagrad_gpu_kernel.h index 34b2471bbf2..878b413c281 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/adagrad_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/adagrad_gpu_kernel.h @@ -66,9 +66,9 @@ class AdagradGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); - update_slots = AnfAlgo::GetNodeAttr(kernel_node, "update_slots"); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); + update_slots = common::AnfAlgo::GetNodeAttr(kernel_node, "update_slots"); if (input_num != 4) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 4, but got " << input_num; } @@ -77,9 +77,9 @@ class AdagradGpuKernelMod : public NativeGpuKernelMod { learning_rate_size_ = sizeof(S); gradient_size_ = sizeof(G); - auto variable_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto accumulation_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto gradient_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto variable_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto accumulation_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto gradient_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); is_null_input_ = CHECK_SHAPE_NULL(variable_shape, kernel_name_, "var") || CHECK_SHAPE_NULL(accumulation_shape, kernel_name_, "accum") || CHECK_SHAPE_NULL(gradient_shape, kernel_name_, "grad"); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/adam_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/adam_gpu_kernel.h index e3c9a9cfdf3..fb5abca3bb9 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/adam_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/adam_gpu_kernel.h @@ -65,8 +65,8 @@ class AdamGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != INPUT_NUM) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be " << INPUT_NUM << ", but got " << input_num; @@ -83,10 +83,10 @@ class AdamGpuKernelMod : public NativeGpuKernelMod { epsilon_size_ = sizeof(T); gradient_size_ = sizeof(T); - auto variable_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto m_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto v_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto gradient_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 9); + auto variable_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto m_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto v_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto gradient_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 9); is_null_input_ = CHECK_SHAPE_NULL(variable_shape, kernel_name_, "var") || CHECK_SHAPE_NULL(m_shape, kernel_name_, "m") || CHECK_SHAPE_NULL(v_shape, kernel_name_, "v") || CHECK_SHAPE_NULL(gradient_shape, kernel_name_, "gradient"); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/adam_weight_decay_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/adam_weight_decay_gpu_kernel.h index c2260757903..692d2fe4ada 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/adam_weight_decay_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/adam_weight_decay_gpu_kernel.h @@ -63,8 +63,8 @@ class AdamWeightDecayGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != INPUT_NUM) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be " << INPUT_NUM << ", but got " << input_num; @@ -80,10 +80,10 @@ class AdamWeightDecayGpuKernelMod : public NativeGpuKernelMod { decay_size_ = sizeof(float); gradient_size_ = sizeof(T); - auto variable_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto m_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto v_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto gradient_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); + auto variable_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto m_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto v_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto gradient_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); is_null_input_ = CHECK_SHAPE_NULL(variable_shape, kernel_name_, "var") || CHECK_SHAPE_NULL(m_shape, kernel_name_, "m") || CHECK_SHAPE_NULL(v_shape, kernel_name_, "v") || CHECK_SHAPE_NULL(gradient_shape, kernel_name_, "gradient"); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/adaptive_avg_pool2d_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/adaptive_avg_pool2d_gpu_kernel.h index cab901423cb..6e2a64bc75a 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/adaptive_avg_pool2d_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/adaptive_avg_pool2d_gpu_kernel.h @@ -57,8 +57,8 @@ class AdaptiveAvgPool2DKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - auto shape_addr = AnfAlgo::GetNodeAttr>(kernel_node, "output_size"); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + auto shape_addr = common::AnfAlgo::GetNodeAttr>(kernel_node, "output_size"); if (shape_addr.size() == 1) { output_height = shape_addr[0]; output_width = shape_addr[0]; @@ -70,7 +70,7 @@ class AdaptiveAvgPool2DKernelMod : public NativeGpuKernelMod { << shape_addr.size(); } - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 1, but got " << input_num; } @@ -78,8 +78,8 @@ class AdaptiveAvgPool2DKernelMod : public NativeGpuKernelMod { input_size_ = sizeof(T); output_size_ = sizeof(T); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name_, "input") || CHECK_SHAPE_NULL(output_shape, kernel_name_, "output"); if (is_null_input_) { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/adaptive_avg_pool2d_grad_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/adaptive_avg_pool2d_grad_gpu_kernel.h index adc7b8f67cc..b0808047d26 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/adaptive_avg_pool2d_grad_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/adaptive_avg_pool2d_grad_gpu_kernel.h @@ -59,8 +59,8 @@ class AdaptiveAvgPool2DGradKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != kAdaptiveAvgPool2dGradInputNum) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be " << kAdaptiveAvgPool2dGradInputNum << ", but got " << input_num; @@ -69,8 +69,8 @@ class AdaptiveAvgPool2DGradKernelMod : public NativeGpuKernelMod { input_size_ = sizeof(T); output_size_ = sizeof(T); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); // dy - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); // dx + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); // dy + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); // dx is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name_, "input") || CHECK_SHAPE_NULL(output_shape, kernel_name_, "output"); if (is_null_input_) { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/apply_gradient_descent_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/apply_gradient_descent_gpu_kernel.h index a61fec56d51..4296e327dac 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/apply_gradient_descent_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/apply_gradient_descent_gpu_kernel.h @@ -46,17 +46,17 @@ class ApplyGradientDescentKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 3) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 3, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_num; } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name_, "var"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/batch_norm_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/batch_norm_gpu_kernel.h index 1cab486c851..9d82f6f296c 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/batch_norm_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/batch_norm_gpu_kernel.h @@ -22,7 +22,7 @@ #include "plugin/device/gpu/kernel/gpu_kernel.h" #include "plugin/device/gpu/kernel/gpu_kernel_factory.h" #include "plugin/device/gpu/kernel/kernel_constants.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace kernel { @@ -80,7 +80,7 @@ class BatchNormGpuKernelMod : public NativeGpuKernelMod { bool Init(const CNodePtr &kernel_node) override { kernel_node_ = kernel_node; MS_EXCEPTION_IF_NULL(kernel_node); - std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node); + std::string kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); if (kernel_name == kBatchNorm) { bn_ops_ = CUDNN_BATCHNORM_OPS_BN; } else if (kernel_name == kBatchNormWithActivation) { @@ -98,7 +98,7 @@ class BatchNormGpuKernelMod : public NativeGpuKernelMod { exp_avg_factor_ = GetAttr(kernel_node, "momentum"); cudnn_data_type_ = GetCudnnDataType(TypeIdLabel(AnfAlgo::GetInputDeviceDataType(kernel_node, 0))); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (bn_ops_ == CUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION) { if (input_num != CUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION_INPUT_NUM) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be " diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/batch_norm_grad_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/batch_norm_grad_gpu_kernel.h index a9d4dc3ff82..3eb3efe533e 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/batch_norm_grad_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/batch_norm_grad_gpu_kernel.h @@ -19,7 +19,7 @@ #include #include -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "plugin/device/gpu/kernel/gpu_kernel.h" #include "plugin/device/gpu/kernel/gpu_kernel_factory.h" @@ -112,7 +112,7 @@ class BatchNormGradGpuKernelMod : public NativeGpuKernelMod { bool Init(const CNodePtr &kernel_node) override { kernel_node_ = kernel_node; MS_EXCEPTION_IF_NULL(kernel_node); - std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node); + std::string kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); if (kernel_name == kBatchNormGradOpName) { bn_ops_ = CUDNN_BATCHNORM_OPS_BN; } else if (kernel_name == kBatchNormGradWithActivation) { @@ -129,7 +129,7 @@ class BatchNormGradGpuKernelMod : public NativeGpuKernelMod { epsilon_ = GetAttr(kernel_node, "epsilon"); cudnn_data_type_ = GetCudnnDataType(TypeIdLabel(AnfAlgo::GetInputDeviceDataType(kernel_node, 0))); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (bn_ops_ == CUDNN_BATCHNORM_OPS_BN) { if (input_num != CUDNN_BATCHNORM_OPS_BN_INPUT_NUM) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be " diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/bce_with_logits_loss_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/bce_with_logits_loss_kernel.h index 4c210ba30b2..947e382618f 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/bce_with_logits_loss_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/bce_with_logits_loss_kernel.h @@ -65,19 +65,19 @@ class BCEWithLogitsLossKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 4) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 4, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_num; } - input_shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - weight_shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - pos_weight_shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + input_shape_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + weight_shape_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + pos_weight_shape_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); is_null_input_ = CHECK_SHAPE_NULL(input_shape_, kernel_name_, "logits") || CHECK_SHAPE_NULL(weight_shape_, kernel_name_, "weight") || CHECK_SHAPE_NULL(pos_weight_shape_, kernel_name_, "pos_weight"); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/bias_add_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/bias_add_gpu_kernel.h index 4e1cc3e28fd..e3909c9c62a 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/bias_add_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/bias_add_gpu_kernel.h @@ -58,11 +58,11 @@ class BiasAddGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; InitResource(); cudnn_data_type_ = GetCudnnDataType(TypeIdLabel(AnfAlgo::GetInputDeviceDataType(kernel_node, 0))); - auto x_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto x_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); auto num_dims = x_shape.size(); is_null_input_ = CHECK_SHAPE_NULL(x_shape, kernel_name_, "input_x"); if (is_null_input_) { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/bias_add_grad_gpu_kenel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/bias_add_grad_gpu_kenel.h index ab56bc9c3d5..fddb8e59bff 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/bias_add_grad_gpu_kenel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/bias_add_grad_gpu_kenel.h @@ -84,9 +84,9 @@ class BiasAddGradGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; - auto dy_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto dy_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(dy_shape, kernel_name_, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/binary_cross_entropy_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/binary_cross_entropy_gpu_kernel.h index 8b12f8e0cc4..be4be676bf6 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/binary_cross_entropy_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/binary_cross_entropy_gpu_kernel.h @@ -58,14 +58,14 @@ class BinaryCrossEntropyGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name_, "logits"); if (is_null_input_) { InitSizeLists(); return true; } - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); weight_defined_ = (input_num == 3); for (size_t i = 0; i < input_shape.size(); i++) { input_size_ *= input_shape[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/binary_cross_entropy_grad_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/binary_cross_entropy_grad_kernel.h index d28c1b9370d..6d87bfc68b3 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/binary_cross_entropy_grad_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/binary_cross_entropy_grad_kernel.h @@ -58,14 +58,14 @@ class BinaryCrossEntropyGradGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name_, "input"); if (is_null_input_) { InitSizeLists(); return true; } - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); weight_defined_ = (input_num == 4); for (size_t i = 0; i < input_shape.size(); i++) { input_size_ *= input_shape[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/clip_grad_norm_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/clip_grad_norm_gpu_kernel.h index f73a5337ede..a2e45728b91 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/clip_grad_norm_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/clip_grad_norm_gpu_kernel.h @@ -99,7 +99,7 @@ class ClipGradNormGpuKernelMod : public NativeGpuKernelMod { bool Init(const CNodePtr &kernel_node) override { MS_EXCEPTION_IF_NULL(kernel_node); - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); MS_EXCEPTION_IF_CHECK_FAIL(kernel_name == "ClipGradNorm", "Kernel name is not ClipGradNorm"); kernel_node_ = kernel_node; // Init resource for cudnnreducetensor operation. @@ -108,8 +108,8 @@ class ClipGradNormGpuKernelMod : public NativeGpuKernelMod { return false; } // Check input and output shape - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); size_t input_dim = input_shape.size(); if (!CheckValidShape(input_shape, output_shape, input_dim)) { return true; @@ -178,13 +178,13 @@ class ClipGradNormGpuKernelMod : public NativeGpuKernelMod { bool CheckIONumber(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); constexpr size_t input_num_expected = 3; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != input_num_expected) { MS_LOG(ERROR) << "The input number of kernel node [" << kernel_node->DebugString() << "] should be " << input_num_expected << ", but got " << input_num; return false; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(ERROR) << "The output number of kernel node [" << kernel_node->DebugString() << "] should be " << 1 << ", but got " << output_num; @@ -214,7 +214,7 @@ class ClipGradNormGpuKernelMod : public NativeGpuKernelMod { void InitAxis(const CNodePtr &kernel_node, const std::vector &output_shape, int input_dim) { MS_EXCEPTION_IF_NULL(kernel_node); - auto prim = AnfAlgo::GetCNodePrimitive(kernel_node); + auto prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(prim); if (prim->GetAttr("axis")->isa() || prim->GetAttr("axis")->isa()) { std::vector attr_axis = GetAttr>(kernel_node, "axis"); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/combine_momentum_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/combine_momentum_gpu_kernel.h index 7652713b5f6..63f3a9b812c 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/combine_momentum_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/combine_momentum_gpu_kernel.h @@ -61,9 +61,9 @@ class CombineMomentumGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); num_ = GetAttr(kernel_node, "n"); - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); if (kernel_name == "CombineMomentum") { input_num_ = 6; } else { @@ -71,7 +71,7 @@ class CombineMomentumGpuKernelMod : public NativeGpuKernelMod { } for (size_t i = 0; i < num_; i++) { element_num_ = 1; - auto variable_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, i * input_num_ + input_num_ - 5); + auto variable_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, i * input_num_ + input_num_ - 5); is_null_input_ = CHECK_SHAPE_NULL(variable_shape, kernel_name_, "input[" + std::to_string(i * input_num_ + input_num_ - 5) + "]"); if (is_null_input_) { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/conv2d_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/conv2d_gpu_kernel.h index b107df67ece..3b7517d8502 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/conv2d_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/conv2d_gpu_kernel.h @@ -74,7 +74,7 @@ class Conv2dFwdGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; InitResource(); (void)CheckParam(kernel_node); @@ -274,11 +274,11 @@ class Conv2dFwdGpuKernelMod : public NativeGpuKernelMod { private: void CheckParam(const CNodePtr &kernel_node) { - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 2, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_num; } @@ -333,8 +333,8 @@ class Conv2dFwdGpuKernelMod : public NativeGpuKernelMod { } } void SetStrideAndDilation(const CNodePtr &kernel_node) { - std::vector stride_me = AnfAlgo::GetNodeAttr>(kernel_node, "stride"); - std::vector dilation_me = AnfAlgo::GetNodeAttr>(kernel_node, "dilation"); + std::vector stride_me = common::AnfAlgo::GetNodeAttr>(kernel_node, "stride"); + std::vector dilation_me = common::AnfAlgo::GetNodeAttr>(kernel_node, "dilation"); (void)std::transform(stride_me.begin(), stride_me.end(), std::back_inserter(stride_), [](const int64_t &value) { return static_cast(value); }); (void)std::transform(dilation_me.begin(), dilation_me.end(), std::back_inserter(dilation_), diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/conv2d_grad_filter_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/conv2d_grad_filter_gpu_kernel.h index ab5ad4245dd..831b7d33880 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/conv2d_grad_filter_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/conv2d_grad_filter_gpu_kernel.h @@ -117,7 +117,7 @@ class ConvGradFilterBkwGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; InitResource(); (void)CheckParam(kernel_node); @@ -282,11 +282,11 @@ class ConvGradFilterBkwGpuKernelMod : public NativeGpuKernelMod { private: void CheckParam(const CNodePtr &kernel_node) { - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 2, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_num; } @@ -315,7 +315,7 @@ class ConvGradFilterBkwGpuKernelMod : public NativeGpuKernelMod { } } void GetFilterShape(const CNodePtr &kernel_node, std::vector *filter_shape) { - auto prim = AnfAlgo::GetCNodePrimitive(kernel_node); + auto prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(prim); auto shp_tuple_x = prim->GetAttr("filter_sizes")->cast()->value(); (void)std::transform(std::begin(shp_tuple_x), std::end(shp_tuple_x), std::back_inserter(*filter_shape), @@ -346,8 +346,8 @@ class ConvGradFilterBkwGpuKernelMod : public NativeGpuKernelMod { "cudnnSetTensorNdDescriptor failed"); } void SetStrideAndDilation(const CNodePtr &kernel_node) { - std::vector stride_me = AnfAlgo::GetNodeAttr>(kernel_node, "stride"); - std::vector dilation_me = AnfAlgo::GetNodeAttr>(kernel_node, "dilation"); + std::vector stride_me = common::AnfAlgo::GetNodeAttr>(kernel_node, "stride"); + std::vector dilation_me = common::AnfAlgo::GetNodeAttr>(kernel_node, "dilation"); (void)std::transform(stride_me.begin(), stride_me.end(), std::back_inserter(stride_), [](const int64_t &value) { return static_cast(value); }); (void)std::transform(dilation_me.begin(), dilation_me.end(), std::back_inserter(dilation_), diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/conv2d_grad_input_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/conv2d_grad_input_gpu_kernel.h index 3b5660483b8..50bb70253a2 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/conv2d_grad_input_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/conv2d_grad_input_gpu_kernel.h @@ -128,7 +128,7 @@ class ConvGradInputBkwGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; InitResource(); (void)CheckParam(kernel_node); @@ -293,11 +293,11 @@ class ConvGradInputBkwGpuKernelMod : public NativeGpuKernelMod { private: void CheckParam(const CNodePtr &kernel_node) { - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 2, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_num; } @@ -326,7 +326,7 @@ class ConvGradInputBkwGpuKernelMod : public NativeGpuKernelMod { } } void GetInputShape(const CNodePtr &kernel_node, std::vector *input_shape) { - auto prim = AnfAlgo::GetCNodePrimitive(kernel_node); + auto prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(prim); auto shp_tuple_x = prim->GetAttr("input_sizes")->cast()->value(); (void)std::transform(std::begin(shp_tuple_x), std::end(shp_tuple_x), std::back_inserter(*input_shape), @@ -357,9 +357,9 @@ class ConvGradInputBkwGpuKernelMod : public NativeGpuKernelMod { "cudnnSetTensorNdDescriptor failed"); } void SetStrideAndDilation(const CNodePtr &kernel_node) { - std::vector stride_me = AnfAlgo::GetNodeAttr>(kernel_node, "stride"); - std::vector dilation_me = AnfAlgo::GetNodeAttr>(kernel_node, "dilation"); - std::string format_me = AnfAlgo::GetNodeAttr(kernel_node, "format"); + std::vector stride_me = common::AnfAlgo::GetNodeAttr>(kernel_node, "stride"); + std::vector dilation_me = common::AnfAlgo::GetNodeAttr>(kernel_node, "dilation"); + std::string format_me = common::AnfAlgo::GetNodeAttr(kernel_node, "format"); auto iter = kFormatIndexMap.find(format_me); if (iter == kFormatIndexMap.end()) { MS_LOG(EXCEPTION) << "OriFormat is " << format_me << ", Please confirm that in {NCHW, HWCN, NHWC}."; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/conv3d_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/conv3d_gpu_kernel.h index 9c38cf96184..c140c19ab10 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/conv3d_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/conv3d_gpu_kernel.h @@ -104,7 +104,7 @@ class Conv3dGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; InitResource(); (void)CheckParam(kernel_node); @@ -257,12 +257,12 @@ class Conv3dGpuKernelMod : public NativeGpuKernelMod { private: void CheckParam(const CNodePtr &kernel_node) { - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); const size_t kInputNum = 2; if (input_num != kInputNum) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 2, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_num; } @@ -306,8 +306,8 @@ class Conv3dGpuKernelMod : public NativeGpuKernelMod { } void SetStrideAndDilation(const CNodePtr &kernel_node) { - std::vector stride_me = AnfAlgo::GetNodeAttr>(kernel_node, "strides"); - std::vector dilation_me = AnfAlgo::GetNodeAttr>(kernel_node, "dilations"); + std::vector stride_me = common::AnfAlgo::GetNodeAttr>(kernel_node, "strides"); + std::vector dilation_me = common::AnfAlgo::GetNodeAttr>(kernel_node, "dilations"); (void)std::transform(stride_me.begin(), stride_me.end(), std::back_inserter(stride_), [](const int64_t &value) { return static_cast(value); }); (void)std::transform(dilation_me.begin(), dilation_me.end(), std::back_inserter(dilation_), diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/conv3d_grad_filter_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/conv3d_grad_filter_gpu_kernel.h index a47db13b235..e95aa44db5e 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/conv3d_grad_filter_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/conv3d_grad_filter_gpu_kernel.h @@ -117,7 +117,7 @@ class Conv3dGradFilterGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; InitResource(); (void)CheckParam(kernel_node); @@ -272,11 +272,11 @@ class Conv3dGradFilterGpuKernelMod : public NativeGpuKernelMod { private: void CheckParam(const CNodePtr &kernel_node) { - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 2, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_num; } @@ -298,7 +298,7 @@ class Conv3dGradFilterGpuKernelMod : public NativeGpuKernelMod { } void GetFilterShape(const CNodePtr &kernel_node, std::vector *filter_shape) { - auto prim = AnfAlgo::GetCNodePrimitive(kernel_node); + auto prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(prim); auto shp_tuple_x = prim->GetAttr("filter_size")->cast()->value(); (void)std::transform(std::begin(shp_tuple_x), std::end(shp_tuple_x), std::back_inserter(*filter_shape), @@ -330,8 +330,8 @@ class Conv3dGradFilterGpuKernelMod : public NativeGpuKernelMod { } void SetStrideAndDilation(const CNodePtr &kernel_node) { - std::vector stride_me = AnfAlgo::GetNodeAttr>(kernel_node, "strides"); - std::vector dilation_me = AnfAlgo::GetNodeAttr>(kernel_node, "dilations"); + std::vector stride_me = common::AnfAlgo::GetNodeAttr>(kernel_node, "strides"); + std::vector dilation_me = common::AnfAlgo::GetNodeAttr>(kernel_node, "dilations"); (void)std::transform(stride_me.begin(), stride_me.end(), std::back_inserter(stride_), [](const int64_t &value) { return static_cast(value); }); (void)std::transform(dilation_me.begin(), dilation_me.end(), std::back_inserter(dilation_), diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/conv3d_grad_input_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/conv3d_grad_input_gpu_kernel.h index 28e887bdf71..58538620721 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/conv3d_grad_input_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/conv3d_grad_input_gpu_kernel.h @@ -99,7 +99,7 @@ class Conv3dGradInputGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; InitResource(); (void)CheckParam(kernel_node); @@ -242,11 +242,11 @@ class Conv3dGradInputGpuKernelMod : public NativeGpuKernelMod { private: void CheckParam(const CNodePtr &kernel_node) { - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 2, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_num; } @@ -265,7 +265,7 @@ class Conv3dGradInputGpuKernelMod : public NativeGpuKernelMod { } void GetInputShape(const CNodePtr &kernel_node, std::vector *input_shape) { - auto prim = AnfAlgo::GetCNodePrimitive(kernel_node); + auto prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(prim); auto shp_tuple_x = prim->GetAttr("input_size")->cast()->value(); (void)std::transform(std::begin(shp_tuple_x), std::end(shp_tuple_x), std::back_inserter(*input_shape), @@ -298,8 +298,8 @@ class Conv3dGradInputGpuKernelMod : public NativeGpuKernelMod { } void SetStrideAndDilation(const CNodePtr &kernel_node) { - std::vector stride_me = AnfAlgo::GetNodeAttr>(kernel_node, "strides"); - std::vector dilation_me = AnfAlgo::GetNodeAttr>(kernel_node, "dilations"); + std::vector stride_me = common::AnfAlgo::GetNodeAttr>(kernel_node, "strides"); + std::vector dilation_me = common::AnfAlgo::GetNodeAttr>(kernel_node, "dilations"); (void)std::transform(stride_me.begin(), stride_me.end(), std::back_inserter(stride_), [](const int64_t &value) { return static_cast(value); }); (void)std::transform(dilation_me.begin(), dilation_me.end(), std::back_inserter(dilation_), diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/conv3d_transpose_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/conv3d_transpose_gpu_kernel.h index b47f90f2e8e..f2d383ff594 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/conv3d_transpose_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/conv3d_transpose_gpu_kernel.h @@ -135,7 +135,7 @@ class Conv3dTransposeFwdGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; InitResource(); (void)CheckParam(kernel_node); @@ -328,12 +328,12 @@ class Conv3dTransposeFwdGpuKernelMod : public NativeGpuKernelMod { private: void CheckParam(const CNodePtr &kernel_node) { - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); const size_t kInputNum = 2; if (input_num != kInputNum) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 2, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_num; } @@ -355,7 +355,8 @@ class Conv3dTransposeFwdGpuKernelMod : public NativeGpuKernelMod { } void GetInputShape(const CNodePtr &kernel_node, std::vector *input_shape) { - auto shp_tuple_x = AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("input_size")->cast()->value(); + auto shp_tuple_x = + common::AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("input_size")->cast()->value(); (void)std::transform(std::begin(shp_tuple_x), std::end(shp_tuple_x), std::back_inserter(*input_shape), [](const ValuePtr &e) -> size_t { return static_cast(e->cast()->value()); }); } @@ -386,8 +387,8 @@ class Conv3dTransposeFwdGpuKernelMod : public NativeGpuKernelMod { } void SetStrideAndDilation(const CNodePtr &kernel_node) { - std::vector stride_me = AnfAlgo::GetNodeAttr>(kernel_node, "strides"); - std::vector dilation_me = AnfAlgo::GetNodeAttr>(kernel_node, "dilations"); + std::vector stride_me = common::AnfAlgo::GetNodeAttr>(kernel_node, "strides"); + std::vector dilation_me = common::AnfAlgo::GetNodeAttr>(kernel_node, "dilations"); (void)std::transform(stride_me.begin(), stride_me.end(), std::back_inserter(stride_), [](const int64_t &value) { return static_cast(value); }); (void)std::transform(dilation_me.begin(), dilation_me.end(), std::back_inserter(dilation_), diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/ctcloss_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/ctcloss_gpu_kernel.h index 55f60491ec8..329afbaeba8 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/ctcloss_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/ctcloss_gpu_kernel.h @@ -99,13 +99,13 @@ class CtcLossGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; InitResource(); - auto probs_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, kPrevOutput0th); - auto indice_dims = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, kPrevOutput1st); - auto labels_dims = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, kPrevOutput2nd); - auto sequence_length_dims = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, kPrevOutput3rd); + auto probs_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, kPrevOutput0th); + auto indice_dims = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, kPrevOutput1st); + auto labels_dims = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, kPrevOutput2nd); + auto sequence_length_dims = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, kPrevOutput3rd); is_null_input_ = CHECK_SHAPE_NULL(probs_shape, kernel_name_, "x") || CHECK_SHAPE_NULL(indice_dims, kernel_name_, "labels_indices") || CHECK_SHAPE_NULL(labels_dims, kernel_name_, "labels_values") || diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/dropout3d_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/dropout3d_gpu_kernel.h index f922b08c458..60bf034fd0f 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/dropout3d_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/dropout3d_gpu_kernel.h @@ -66,15 +66,15 @@ class Dropout3DFwdGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); cudnn_handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle(); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 1, but got " << input_num; } - std::vector input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + std::vector input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name_, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/dropout_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/dropout_gpu_kernel.h index 717678c10cb..28706ec5932 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/dropout_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/dropout_gpu_kernel.h @@ -62,15 +62,15 @@ class DropoutFwdGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); InitResource(); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 1, but got " << input_num; } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name_, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/dropout_grad_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/dropout_grad_kernel.h index f29a282d694..83980445a64 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/dropout_grad_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/dropout_grad_kernel.h @@ -47,15 +47,15 @@ class DropoutGradBwdGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); InitResource(); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 2, but got " << input_num; } - auto input_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name_, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/flatten_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/flatten_gpu_kernel.h index e3c56ab9f5c..1c39f9df74f 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/flatten_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/flatten_gpu_kernel.h @@ -47,7 +47,7 @@ class FlattenFwdGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); auto shape = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(shape, kernel_name_, "input"); if (is_null_input_) { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/flatten_grad_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/flatten_grad_gpu_kernel.h index 3ba2547b727..3cfb61ee261 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/flatten_grad_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/flatten_grad_gpu_kernel.h @@ -48,8 +48,8 @@ class FlattenGardBkwGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 1, but got " << input_num; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/ftrl_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/ftrl_gpu_kernel.h index 381a2f6fe1e..64243e6ab61 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/ftrl_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/ftrl_gpu_kernel.h @@ -61,8 +61,8 @@ class FtrlGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != INPUT_NUM) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be " << INPUT_NUM << ", but got " << input_num; @@ -77,10 +77,10 @@ class FtrlGpuKernelMod : public NativeGpuKernelMod { l2_regularization_size_ = sizeof(T); learning_rate_power_size_ = sizeof(T); - auto variable_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto accumulation_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto linear_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto gradient_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto variable_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto accumulation_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto linear_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto gradient_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); is_null_input_ = CHECK_SHAPE_NULL(variable_shape, kernel_name_, "var") || CHECK_SHAPE_NULL(accumulation_shape, kernel_name_, "accum") || CHECK_SHAPE_NULL(linear_shape, kernel_name_, "linear") || diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/fused_adam_weight_decay.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/fused_adam_weight_decay.h index 8e95bcf250f..cfef3814cbb 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/fused_adam_weight_decay.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/fused_adam_weight_decay.h @@ -32,12 +32,12 @@ class FusedAdamWeightDecayGpuKernelMod : public NativeGpuKernelMod { ~FusedAdamWeightDecayGpuKernelMod() override = default; bool Init(const CNodePtr &kernel_node) override { - auto node_name = AnfAlgo::GetCNodeName(kernel_node); + auto node_name = common::AnfAlgo::GetCNodeName(kernel_node); if (node_name == "FusedAdamWeightDecay") { weight_decay_ = true; } - auto shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); + auto shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); is_null_input_ = CHECK_SHAPE_NULL(shape, node_name, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/fused_add_relu_grad_v2_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/fused_add_relu_grad_v2_gpu_kernel.h index 32bcc041b97..a4c08ad724d 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/fused_add_relu_grad_v2_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/fused_add_relu_grad_v2_gpu_kernel.h @@ -48,7 +48,7 @@ class FusedAddReluGradV2GpuKernelMod : public NativeGpuKernelMod { bool Init(const CNodePtr &kernel_node) override { MS_EXCEPTION_IF_NULL(kernel_node); - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); auto shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(shape, kernel_name, "input"); if (is_null_input_) { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/fused_add_relu_v2_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/fused_add_relu_v2_gpu_kernel.h index c258eca61c2..87f94df9bde 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/fused_add_relu_v2_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/fused_add_relu_v2_gpu_kernel.h @@ -48,7 +48,7 @@ class FusedAddReluV2GpuKernelMod : public NativeGpuKernelMod { bool Init(const CNodePtr &kernel_node) override { MS_EXCEPTION_IF_NULL(kernel_node); - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); auto shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(shape, kernel_name, "input"); if (is_null_input_) { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/fused_scale_momentum_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/fused_scale_momentum_gpu_kernel.h index 9579ad694f0..2b802695fd6 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/fused_scale_momentum_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/fused_scale_momentum_gpu_kernel.h @@ -47,14 +47,14 @@ class FusedScaleMomentumGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != INPUT_NUM) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be " << INPUT_NUM << ", but got " << input_num; } - auto variable_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto variable_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); is_null_input_ = CHECK_SHAPE_NULL(variable_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/fused_weightdecay_momentum_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/fused_weightdecay_momentum_gpu_kernel.h index 5ce11019c63..47936b97109 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/fused_weightdecay_momentum_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/fused_weightdecay_momentum_gpu_kernel.h @@ -47,14 +47,14 @@ class FusedWeightDecayMomentumGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != INPUT_NUM) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be " << INPUT_NUM << ", but got " << input_num; } - auto variable_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto variable_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); is_null_input_ = CHECK_SHAPE_NULL(variable_shape, kernel_name, "variable"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/fused_weightdecay_scale_momentum_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/fused_weightdecay_scale_momentum_gpu_kernel.h index e86efa29e10..880def50181 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/fused_weightdecay_scale_momentum_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/fused_weightdecay_scale_momentum_gpu_kernel.h @@ -48,14 +48,14 @@ class FusedWeightDecayScaleMomentumGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != INPUT_NUM) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be " << INPUT_NUM << ", but got " << input_num; } - auto variable_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto variable_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); is_null_input_ = CHECK_SHAPE_NULL(variable_shape, kernel_name, "variable"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/gelu_grad_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/gelu_grad_kernel.h index 1080e6f9423..53016258400 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/gelu_grad_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/gelu_grad_kernel.h @@ -45,10 +45,10 @@ class GeLUGradGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); InitResource(); input_size_ = sizeof(T); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/gelu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/gelu_kernel.h index b37c8abc739..17977799c12 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/gelu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/gelu_kernel.h @@ -44,10 +44,10 @@ class GeluGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); InitResource(); input_size_ = sizeof(T); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/hsigmoid_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/hsigmoid_gpu_kernel.h index 39991192200..1d043ab2386 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/hsigmoid_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/hsigmoid_gpu_kernel.h @@ -43,17 +43,17 @@ class HSigmoidKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 1, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/hsigmoid_grad_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/hsigmoid_grad_gpu_kernel.h index e5e672d8141..4605548a351 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/hsigmoid_grad_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/hsigmoid_grad_gpu_kernel.h @@ -44,17 +44,17 @@ class HSigmoidGradKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 2, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/hswish_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/hswish_gpu_kernel.h index 01b248e015a..518b01d578a 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/hswish_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/hswish_gpu_kernel.h @@ -43,17 +43,17 @@ class HSwishKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 1, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/hswish_grad_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/hswish_grad_gpu_kernel.h index 2c6e4535a2d..858676ace4c 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/hswish_grad_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/hswish_grad_gpu_kernel.h @@ -44,17 +44,17 @@ class HSwishGradKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 2, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/im2col_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/im2col_gpu_kernel.h index 1730617aac6..3a87c3f3346 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/im2col_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/im2col_gpu_kernel.h @@ -93,12 +93,12 @@ class Im2ColFwdGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; InitResource(); (void)CheckParam(kernel_node); - auto in_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto in_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(in_shape, kernel_name_, "input") || CHECK_SHAPE_NULL(output_shape, kernel_name_, "output"); if (is_null_input_) { @@ -205,12 +205,12 @@ class Im2ColFwdGpuKernelMod : public NativeGpuKernelMod { private: void CheckParam(const CNodePtr &kernel_node) { cudnn_data_type_ = GetCudnnDataType(TypeIdLabel(AnfAlgo::GetInputDeviceDataType(kernel_node, 0))); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 1, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_num; } @@ -284,8 +284,8 @@ class Im2ColFwdGpuKernelMod : public NativeGpuKernelMod { } void SetStrideAndDilation(const CNodePtr &kernel_node) { - std::vector stride_me = AnfAlgo::GetNodeAttr>(kernel_node, "stride"); - std::vector dilation_me = AnfAlgo::GetNodeAttr>(kernel_node, "dilation"); + std::vector stride_me = common::AnfAlgo::GetNodeAttr>(kernel_node, "stride"); + std::vector dilation_me = common::AnfAlgo::GetNodeAttr>(kernel_node, "dilation"); (void)std::transform(stride_me.begin(), stride_me.end(), std::back_inserter(stride_), [](const int64_t &value) { return static_cast(value); }); (void)std::transform(dilation_me.begin(), dilation_me.end(), std::back_inserter(dilation_), diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/instance_norm_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/instance_norm_gpu_kernel.h index 759b319c067..2d49e6d8102 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/instance_norm_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/instance_norm_gpu_kernel.h @@ -22,7 +22,7 @@ #include "plugin/device/gpu/kernel/gpu_kernel.h" #include "plugin/device/gpu/kernel/gpu_kernel_factory.h" #include "plugin/device/gpu/kernel/kernel_constants.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/instance_norm_impl.cuh" namespace mindspore { @@ -95,7 +95,7 @@ class InstanceNormGpuKernelMod : public NativeGpuKernelMod { bool Init(const CNodePtr &kernel_node) override { kernel_node_ = kernel_node; MS_EXCEPTION_IF_NULL(kernel_node); - std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node); + std::string kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); bn_ops_ = CUDNN_BATCHNORM_OPS_BN; InitResource(); @@ -104,7 +104,7 @@ class InstanceNormGpuKernelMod : public NativeGpuKernelMod { exp_avg_factor_ = GetAttr(kernel_node, "momentum"); cudnn_data_type_ = GetCudnnDataType(TypeIdLabel(AnfAlgo::GetInputDeviceDataType(kernel_node, 0))); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 5) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 5, but got " << input_num; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/instance_norm_grad_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/instance_norm_grad_gpu_kernel.h index c9e9a9775ff..3b654eb20ca 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/instance_norm_grad_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/instance_norm_grad_gpu_kernel.h @@ -19,7 +19,7 @@ #include #include -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "plugin/device/gpu/kernel/gpu_kernel.h" #include "plugin/device/gpu/kernel/gpu_kernel_factory.h" @@ -99,7 +99,7 @@ class InstanceNormGradGpuKernelMod : public NativeGpuKernelMod { bool Init(const CNodePtr &kernel_node) override { kernel_node_ = kernel_node; MS_EXCEPTION_IF_NULL(kernel_node); - std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node); + std::string kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); bn_ops_ = CUDNN_BATCHNORM_OPS_BN; InitResource(); @@ -107,7 +107,7 @@ class InstanceNormGradGpuKernelMod : public NativeGpuKernelMod { epsilon_ = GetAttr(kernel_node, "epsilon"); cudnn_data_type_ = GetCudnnDataType(TypeIdLabel(AnfAlgo::GetInputDeviceDataType(kernel_node, 0))); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 5) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 5, but got " << input_num; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/kl_div_loss_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/kl_div_loss_gpu_kernel.h index 75f4fb23d3b..d2b3fdeb017 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/kl_div_loss_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/kl_div_loss_gpu_kernel.h @@ -47,8 +47,8 @@ class KLDivLossGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "logits"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/kl_div_loss_grad_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/kl_div_loss_grad_kernel.h index 03ce54b2374..22e36c19494 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/kl_div_loss_grad_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/kl_div_loss_grad_kernel.h @@ -47,8 +47,8 @@ class KLDivLossGradGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/l2_loss_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/l2_loss_gpu_kernel.h index fab679c2ea4..228dd0a29fa 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/l2_loss_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/l2_loss_gpu_kernel.h @@ -42,8 +42,8 @@ class L2LossGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/l2normalize_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/l2normalize_gpu_kernel.h index 9fe274988c0..acdb2657e8f 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/l2normalize_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/l2normalize_gpu_kernel.h @@ -83,19 +83,19 @@ class L2NormalizeGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; InitResource(); data_type_ = GetCudnnDataType(TypeIdLabel(AnfAlgo::GetInputDeviceDataType(kernel_node, 0))); (void)CheckIONumber(kernel_node); - int input_dim_length = SizeToInt(AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0).size()); + int input_dim_length = SizeToInt(common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0).size()); int axis = LongToInt(GetAttr(kernel_node, "axis")); axis_ = axis < 0 ? (axis + input_dim_length) : axis; epsilon_ = GetAttr(kernel_node, "epsilon"); - auto inputA_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto inputA_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(inputA_shape, kernel_name_, "input") || CHECK_SHAPE_NULL(output_shape, kernel_name_, "output"); if (is_null_input_) { @@ -176,11 +176,11 @@ class L2NormalizeGpuKernelMod : public NativeGpuKernelMod { private: void CheckIONumber(const CNodePtr &kernel_node) { - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 1, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_num; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/l2normalize_grad_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/l2normalize_grad_gpu_kernel.h index 9bc08a94ace..d12f83cccae 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/l2normalize_grad_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/l2normalize_grad_gpu_kernel.h @@ -127,21 +127,21 @@ class L2NormalizeGradGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; InitResource(); data_type_ = GetCudnnDataType(TypeIdLabel(AnfAlgo::GetInputDeviceDataType(kernel_node, 0))); (void)CheckIONumber(kernel_node); - int input_dim_length = SizeToInt(AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0).size()); + int input_dim_length = SizeToInt(common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0).size()); int axis = static_cast(GetAttr(kernel_node, "axis")); axis_ = axis < 0 ? (axis + input_dim_length) : axis; epsilon_ = GetAttr(kernel_node, "epsilon"); for (size_t i = 0; i < INPUT_SIZE; i++) { - input_shape_list_.emplace_back(AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, i)); + input_shape_list_.emplace_back(common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, i)); } - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(output_shape, kernel_name_, "output"); if (is_null_input_) { InitSizeLists(); @@ -185,12 +185,12 @@ class L2NormalizeGradGpuKernelMod : public NativeGpuKernelMod { protected: void CheckIONumber(const CNodePtr &kernel_node) { - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != INPUT_SIZE) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be " << INPUT_SIZE << ", but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_num; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/layer_norm_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/layer_norm_gpu_kernel.h index 5a398972e02..e6b3e4a0270 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/layer_norm_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/layer_norm_gpu_kernel.h @@ -48,11 +48,11 @@ class LayerNormGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); int begin_norm_axis = static_cast(GetAttr(kernel_node, "begin_norm_axis")); int begin_params_axis = static_cast(GetAttr(kernel_node, "begin_params_axis")); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input_x"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/layer_norm_grad_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/layer_norm_grad_gpu_kernel.h index 7d1790adccc..cfc891aacad 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/layer_norm_grad_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/layer_norm_grad_gpu_kernel.h @@ -50,11 +50,11 @@ class LayerNormGradGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); int begin_norm_axis = static_cast(GetAttr(kernel_node, "begin_norm_axis")); int begin_params_axis = static_cast(GetAttr(kernel_node, "begin_params_axis")); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/layer_norm_grad_grad_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/layer_norm_grad_grad_gpu_kernel.h index e64fd9b3058..cdbe9ae9525 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/layer_norm_grad_grad_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/layer_norm_grad_grad_gpu_kernel.h @@ -63,12 +63,12 @@ class LayerNormGradGradGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; int begin_norm_axis = static_cast(GetAttr(kernel_node, "begin_norm_axis")); int begin_params_axis = static_cast(GetAttr(kernel_node, "begin_params_axis")); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/local_response_norm_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/local_response_norm_gpu_kernel.h index eb9918ffaf2..d833ee59f7d 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/local_response_norm_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/local_response_norm_gpu_kernel.h @@ -24,7 +24,7 @@ #include "plugin/device/gpu/kernel/kernel_constants.h" #include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/local_response_norm_impl.cuh" #include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/transpose_impl_opt.cuh" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace kernel { @@ -93,7 +93,7 @@ class LocalResponseNormGpuKernelMod : public NativeGpuKernelMod { bool Init(const CNodePtr &kernel_node) override { kernel_node_ = kernel_node; MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); (void)CheckParam(kernel_node); depth_radius_ = static_cast(GetAttr(kernel_node, "depth_radius")); @@ -208,11 +208,11 @@ class LocalResponseNormGpuKernelMod : public NativeGpuKernelMod { private: void CheckParam(const CNodePtr &kernel_node) { - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 1, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_num; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/local_response_norm_grad_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/local_response_norm_grad_gpu_kernel.h index c0e0bd481b8..46156b52ab1 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/local_response_norm_grad_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/local_response_norm_grad_gpu_kernel.h @@ -24,7 +24,7 @@ #include "plugin/device/gpu/kernel/kernel_constants.h" #include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/local_response_norm_impl.cuh" #include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/transpose_impl_opt.cuh" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace kernel { @@ -113,7 +113,7 @@ class LocalResponseNormGradGpuKernelMod : public NativeGpuKernelMod { bool Init(const CNodePtr &kernel_node) override { kernel_node_ = kernel_node; MS_EXCEPTION_IF_NULL(kernel_node); - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); (void)CheckParam(kernel_node); depth_radius_ = static_cast(GetAttr(kernel_node, "depth_radius")); @@ -244,11 +244,11 @@ class LocalResponseNormGradGpuKernelMod : public NativeGpuKernelMod { private: void CheckParam(const CNodePtr &kernel_node) { - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 3) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 3, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_num; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/lstm_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/lstm_gpu_kernel.h index 778846b3dc0..8d856164c68 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/lstm_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/lstm_gpu_kernel.h @@ -89,11 +89,11 @@ class LstmGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; InitResource(); cudnn_data_type_ = GetCudnnDataType(TypeIdLabel(AnfAlgo::GetInputDeviceDataType(kernel_node, 0))); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); @@ -150,7 +150,7 @@ class LstmGpuKernelMod : public NativeGpuKernelMod { hidden_size_, hidden_size_, num_layers_, dropout_desc_, 0), "set rnn_desc failed"); #endif - auto weight_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto weight_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "weight"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/lstm_grad_data_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/lstm_grad_data_gpu_kernel.h index cf9a5714ede..4da7ffc7238 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/lstm_grad_data_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/lstm_grad_data_gpu_kernel.h @@ -109,11 +109,11 @@ class LstmGradDataGpuKernelMod : public NativeGpuKernelMod { dropout_ = GetAttr(kernel_node, "dropout"); } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; InitResource(); cudnn_data_type_ = GetCudnnDataType(TypeIdLabel(AnfAlgo::GetInputDeviceDataType(kernel_node, 0))); - auto input_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); @@ -169,7 +169,7 @@ class LstmGradDataGpuKernelMod : public NativeGpuKernelMod { "set rnn_desc failed"); #endif const size_t kPrevOutput4th = 4; - auto weight_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, kPrevOutput4th); + auto weight_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, kPrevOutput4th); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "weight"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/lstm_grad_weight_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/lstm_grad_weight_gpu_kernel.h index 9d53ba0dd2d..dd5bdd59a90 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/lstm_grad_weight_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/lstm_grad_weight_gpu_kernel.h @@ -86,11 +86,11 @@ class LstmGradWeightGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; InitResource(); cudnn_data_type_ = GetCudnnDataType(TypeIdLabel(AnfAlgo::GetInputDeviceDataType(kernel_node, 0))); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); @@ -139,7 +139,7 @@ class LstmGradWeightGpuKernelMod : public NativeGpuKernelMod { hidden_size_, hidden_size_, num_layers_, dropout_desc_, 0), "set rnn_desc failed"); #endif - auto weight_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto weight_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(weight_shape, kernel_name, "weight"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/maxpool_with_argmax_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/maxpool_with_argmax_gpu_kernel.h index 458b75890ce..0c6dd7271d9 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/maxpool_with_argmax_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/maxpool_with_argmax_gpu_kernel.h @@ -74,17 +74,17 @@ class MaxPoolWithArgmaxFwdGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 1, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 2, but got " << output_num; } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input") || CHECK_SHAPE_NULL(output_shape, kernel_name, "output"); if (is_null_input_) { @@ -111,7 +111,7 @@ class MaxPoolWithArgmaxFwdGpuKernelMod : public NativeGpuKernelMod { output_height_ = SizeToInt(output_shape[kOutputIndexForH]); output_width_ = SizeToInt(output_shape[kOutputIndexForW]); std::vector window; - auto prim = AnfAlgo::GetCNodePrimitive(kernel_node); + auto prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(prim); std::vector window_me = GetValue>(prim->GetAttr("kernel_size")); (void)std::transform(window_me.begin(), window_me.end(), std::back_inserter(window), diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/maxpool_with_argmax_grad_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/maxpool_with_argmax_grad_gpu_kernel.h index 065312c5d93..9367430cc38 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/maxpool_with_argmax_grad_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/maxpool_with_argmax_grad_gpu_kernel.h @@ -67,19 +67,19 @@ class MaxPoolWithArgmaxGradGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 3) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 3, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_num; } - auto x_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto dy_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto index_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto dx_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto x_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto dy_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto index_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto dx_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(x_shape, kernel_name, "x") || CHECK_SHAPE_NULL(dy_shape, kernel_name, "dy") || CHECK_SHAPE_NULL(index_shape, kernel_name, "index") || CHECK_SHAPE_NULL(dx_shape, kernel_name, "dx"); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/mirror_pad_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/mirror_pad_gpu_kernel.h index efb3a8f08b2..d21da31fd66 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/mirror_pad_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/mirror_pad_gpu_kernel.h @@ -71,16 +71,16 @@ class MirrorPadFwdGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != kInputNum) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 2, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_num; } - auto prim = AnfAlgo::GetCNodePrimitive(kernel_node); + auto prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(prim); string mode = GetValue(prim->GetAttr("mode")); if (mode == "REFLECT") { @@ -89,9 +89,9 @@ class MirrorPadFwdGpuKernelMod : public NativeGpuKernelMod { mode_ = 1; // symmetric mirroring } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto padding_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto padding_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input_x") || CHECK_SHAPE_NULL(padding_shape, kernel_name, "paddings") || CHECK_SHAPE_NULL(output_shape, kernel_name, "output"); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/mirror_pad_grad_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/mirror_pad_grad_gpu_kernel.h index c7d837fa2b4..91b0d2cf959 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/mirror_pad_grad_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/mirror_pad_grad_gpu_kernel.h @@ -73,9 +73,9 @@ class MirrorPadBackGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); (void)CheckParam(kernel_node); - auto prim = AnfAlgo::GetCNodePrimitive(kernel_node); + auto prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(prim); string mode = GetValue(prim->GetAttr("mode")); if (mode == "REFLECT") { @@ -84,9 +84,9 @@ class MirrorPadBackGpuKernelMod : public NativeGpuKernelMod { mode_ = 1; // symmetric mirroring } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto padding_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto padding_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name_, "input_x") || CHECK_SHAPE_NULL(padding_shape, kernel_name_, "paddings") || CHECK_SHAPE_NULL(output_shape, kernel_name_, "output"); @@ -173,11 +173,11 @@ class MirrorPadBackGpuKernelMod : public NativeGpuKernelMod { private: void CheckParam(const CNodePtr &kernel_node) { - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != kInputNum) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 2, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_num; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/momentum_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/momentum_gpu_kernel.h index 3673fb93302..d54f9db521b 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/momentum_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/momentum_gpu_kernel.h @@ -52,8 +52,8 @@ class MomentumGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != INPUT_NUM) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be " << INPUT_NUM << ", but got " << input_num; @@ -66,9 +66,9 @@ class MomentumGpuKernelMod : public NativeGpuKernelMod { gradient_size_ = sizeof(G); momentum_size_ = sizeof(S); - auto variable_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto accumulation_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto gradient_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto variable_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto accumulation_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto gradient_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); is_null_input_ = CHECK_SHAPE_NULL(variable_shape, kernel_name, "variable") || CHECK_SHAPE_NULL(accumulation_shape, kernel_name, "accumulation") || CHECK_SHAPE_NULL(gradient_shape, kernel_name, "gradient"); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/nll_loss_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/nll_loss_gpu_kernel.h index e1641a8f3d6..e2cb953bbc5 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/nll_loss_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/nll_loss_gpu_kernel.h @@ -55,8 +55,8 @@ class NLLLossGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - std::vector input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + std::vector input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "logits"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/nll_loss_grad_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/nll_loss_grad_gpu_kernel.h index 45193109ad8..78700af96b5 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/nll_loss_grad_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/nll_loss_grad_gpu_kernel.h @@ -52,8 +52,8 @@ class NLLLossGradGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - std::vector input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + std::vector input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "logits"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/pad_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/pad_gpu_kernel.h index f1aa84afe74..6e58583b77f 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/pad_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/pad_gpu_kernel.h @@ -72,10 +72,10 @@ class PadFwdGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); (void)CheckIONumber(kernel_node); - input_shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + input_shape_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); std::vector output_shape = AnfAlgo::GetOutputDeviceShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape_, kernel_name_, "input") || CHECK_SHAPE_NULL(output_shape, kernel_name_, "output"); @@ -85,7 +85,7 @@ class PadFwdGpuKernelMod : public NativeGpuKernelMod { } input_rank_ = input_shape_.size(); - auto prim = AnfAlgo::GetCNodePrimitive(kernel_node); + auto prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(prim); std::vector> paddings = GetValue>>(prim->GetAttr("paddings")); if (paddings.size() != input_rank_) { @@ -155,11 +155,11 @@ class PadFwdGpuKernelMod : public NativeGpuKernelMod { private: void CheckIONumber(const CNodePtr &kernel_node) { - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 1, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_num; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/pooling_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/pooling_gpu_kernel.h index ff1286d9161..76fac08d164 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/pooling_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/pooling_gpu_kernel.h @@ -74,7 +74,7 @@ class PoolingFwdGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; InitResource(); (void)CheckParam(kernel_node); @@ -158,14 +158,14 @@ class PoolingFwdGpuKernelMod : public NativeGpuKernelMod { private: void CheckParam(const CNodePtr &kernel_node) { - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 1, but got " << input_num; } } void SetPoolingMode(const CNodePtr &kernel_node) { - mode_ = AnfAlgo::GetCNodeName(kernel_node); + mode_ = common::AnfAlgo::GetCNodeName(kernel_node); if (mode_ == "AvgPool" || mode_ == "AvgPool3D") { pooling_mode_ = CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING; pad_value_ = 0.0; @@ -175,7 +175,7 @@ class PoolingFwdGpuKernelMod : public NativeGpuKernelMod { } } void SetPad(const CNodePtr &kernel_node) { - auto prim = AnfAlgo::GetCNodePrimitive(kernel_node); + auto prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(prim); pad_mode_ = GetValue(prim->GetAttr("pad_mode")); std::vector window; @@ -218,7 +218,7 @@ class PoolingFwdGpuKernelMod : public NativeGpuKernelMod { } void SetPad3D(const CNodePtr &kernel_node) { - auto prim = AnfAlgo::GetCNodePrimitive(kernel_node); + auto prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(prim); pad_mode_ = GetValue(prim->GetAttr("pad_mode")); std::vector window; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/pooling_grad_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/pooling_grad_gpu_kernel.h index 960db387035..98c51603197 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/pooling_grad_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/pooling_grad_gpu_kernel.h @@ -118,7 +118,7 @@ class PoolingGradGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; InitResource(); (void)CheckParam(kernel_node); @@ -209,7 +209,7 @@ class PoolingGradGpuKernelMod : public NativeGpuKernelMod { private: void CheckParam(const CNodePtr &kernel_node) { - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != INPUT_NUM) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be " << INPUT_NUM << ", but got " << input_num; @@ -340,7 +340,7 @@ class PoolingGradGpuKernelMod : public NativeGpuKernelMod { } void SetPoolingMode(const CNodePtr &kernel_node) { - mode_ = AnfAlgo::GetCNodeName(kernel_node); + mode_ = common::AnfAlgo::GetCNodeName(kernel_node); if (mode_ == "AvgPoolGrad") { pooling_mode_ = CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING; pad_value_ = 0.0; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/prelu_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/prelu_gpu_kernel.h index aa1d0743992..f39ee8b8a4a 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/prelu_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/prelu_gpu_kernel.h @@ -48,13 +48,13 @@ class PReLUGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); ResetResource(); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 2, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/prelu_grad_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/prelu_grad_gpu_kernel.h index cc3fbe80f5a..3d9c1de84a2 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/prelu_grad_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/prelu_grad_gpu_kernel.h @@ -51,13 +51,13 @@ class PReLUGradGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); ResetResource(); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 3) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 3, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 2, but got " << output_num; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/ps_roi_pooling_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/ps_roi_pooling_gpu_kernel.h index 4998d402856..945cf988a5d 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/ps_roi_pooling_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/ps_roi_pooling_gpu_kernel.h @@ -74,24 +74,24 @@ class PsROIPoolingFwdGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); // Get the number of input args - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != INPUT_NUM) { MS_LOG(ERROR) << "Input number is " << input_num << ", but PsROIPooling needs 2 input."; return false; } // Get the number of output args - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != OUTPUT_NUM) { MS_LOG(ERROR) << "Output number is " << output_num << ", but PsROIPooling needs 2 output."; return false; } // Get the input shapes - auto x_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto rois_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto x_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto rois_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); is_null_input_ = CHECK_SHAPE_NULL(x_shape, kernel_name, "input") || CHECK_SHAPE_NULL(rois_shape, kernel_name, "roi"); if (is_null_input_) { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/ps_roi_pooling_grad_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/ps_roi_pooling_grad_gpu_kernel.h index 9a481ae8515..6a435971b86 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/ps_roi_pooling_grad_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/ps_roi_pooling_grad_gpu_kernel.h @@ -75,25 +75,25 @@ class PsROIPoolingBackGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); // Get the number of input args - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != INPUT_NUM) { MS_LOG(ERROR) << "Input number is " << input_num << ", but PsROIPoolingBackGpuKernelMod needs 3 input."; return false; } // Get the number of output args - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != OUTPUT_NUM) { MS_LOG(ERROR) << "Output number is " << output_num << ", but PsROIPoolingBackGpuKernelMod needs 1 output."; return false; } // Get the input shapes - auto dx_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto rois_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto mapping_channel_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto dx_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto rois_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto mapping_channel_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); is_null_input_ = CHECK_SHAPE_NULL(dx_shape, kernel_name, "input") || CHECK_SHAPE_NULL(rois_shape, kernel_name, "rois") || diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/relu_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/relu_gpu_kernel.h index 51e700b3032..35d42a45dc0 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/relu_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/relu_gpu_kernel.h @@ -45,8 +45,8 @@ class ReLUFwdGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 1, but got " << input_num; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/relu_grad_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/relu_grad_gpu_kernel.h index 4022810d7dd..a7aef06f33d 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/relu_grad_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/relu_grad_gpu_kernel.h @@ -48,9 +48,9 @@ class ReluGradFwdGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); InitResource(); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 2, but got " << input_num; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/relu_grad_v2_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/relu_grad_v2_gpu_kernel.h index d9e0395bcab..fb3143ebf4b 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/relu_grad_v2_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/relu_grad_v2_gpu_kernel.h @@ -47,7 +47,7 @@ class ReluGradV2GpuKernelMod : public NativeGpuKernelMod { bool Init(const CNodePtr &kernel_node) override { MS_EXCEPTION_IF_NULL(kernel_node); - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); auto shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(shape, kernel_name, "input"); if (is_null_input_) { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/relu_v2_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/relu_v2_gpu_kernel.h index 5eae299261b..2c73084fc46 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/relu_v2_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/relu_v2_gpu_kernel.h @@ -47,7 +47,7 @@ class ReluV2GpuKernelMod : public NativeGpuKernelMod { bool Init(const CNodePtr &kernel_node) override { MS_EXCEPTION_IF_NULL(kernel_node); - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); auto shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(shape, kernel_name, "input"); if (is_null_input_) { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/resize_bilinear_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/resize_bilinear_gpu_kernel.h index eb3855ff0e5..951fcb5d6e8 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/resize_bilinear_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/resize_bilinear_gpu_kernel.h @@ -45,17 +45,17 @@ class ResizeBilinearGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 1, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } - std::vector input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - std::vector output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + std::vector input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + std::vector output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input") || CHECK_SHAPE_NULL(output_shape, kernel_name, "output"); if (is_null_input_) { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/resize_bilinear_grad_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/resize_bilinear_grad_gpu_kernel.h index dd83598bce8..5b2e1efbf6c 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/resize_bilinear_grad_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/resize_bilinear_grad_gpu_kernel.h @@ -63,19 +63,19 @@ class ResizeBilinearGradGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != kInputsNum) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 2, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } - std::vector dy_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - std::vector x_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - std::vector dx_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + std::vector dy_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + std::vector x_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + std::vector dx_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(dy_shape, kernel_name, "dy") || CHECK_SHAPE_NULL(x_shape, kernel_name, "x") || CHECK_SHAPE_NULL(dx_shape, kernel_name, "dx"); if (is_null_input_) { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/rmsprop_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/rmsprop_gpu_kernel.h index 28d8b6ca0ae..ac37258dc3e 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/rmsprop_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/rmsprop_gpu_kernel.h @@ -62,7 +62,7 @@ class RMSPropGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - auto node_name = AnfAlgo::GetCNodeName(kernel_node); + auto node_name = common::AnfAlgo::GetCNodeName(kernel_node); if (node_name == "ApplyCenteredRMSProp") { use_center_ = true; } @@ -72,7 +72,7 @@ class RMSPropGpuKernelMod : public NativeGpuKernelMod { momentum_ = GetAttr(kernel_node, "momentum"); epsilon_ = GetAttr(kernel_node, "epsilon"); } - auto input_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, node_name, "var"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/roi_align_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/roi_align_gpu_kernel.h index 52b08df28d3..b85c4de498e 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/roi_align_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/roi_align_gpu_kernel.h @@ -61,22 +61,22 @@ class ROIAlignFwdGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); // Get the number of input args - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 2, but got " << input_num; } // Get the number of output args - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } // Get the input shapes - auto x_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto rois_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto x_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto rois_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); is_null_input_ = CHECK_SHAPE_NULL(x_shape, kernel_name, "features") || CHECK_SHAPE_NULL(rois_shape, kernel_name, "rois"); if (is_null_input_) { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/roi_align_grad_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/roi_align_grad_gpu_kernel.h index f655555a152..5d172a18f02 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/roi_align_grad_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/roi_align_grad_gpu_kernel.h @@ -61,22 +61,22 @@ class ROIAlignGradFwdGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); // Get the number of input args - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 2, but got " << input_num; } // Get the number of output args - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } // Get the input shapes - auto dy_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto rois_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto dy_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto rois_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); is_null_input_ = CHECK_SHAPE_NULL(dy_shape, kernel_name, "dy") || CHECK_SHAPE_NULL(rois_shape, kernel_name, "rois"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/sgd_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/sgd_gpu_kernel.h index e78a7c82b20..18ec88374ee 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/sgd_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/sgd_gpu_kernel.h @@ -52,13 +52,13 @@ class SGDGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; dampening_ = GetAttr(kernel_node, "dampening"); weight_decay_ = GetAttr(kernel_node, "weight_decay"); nesterov_ = GetAttr(kernel_node, "nesterov"); - auto input_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "parameters"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/sigmoid_cross_entropy_with_logits_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/sigmoid_cross_entropy_with_logits_gpu_kernel.h index 70f6873cc58..9b5497553cc 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/sigmoid_cross_entropy_with_logits_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/sigmoid_cross_entropy_with_logits_gpu_kernel.h @@ -48,8 +48,8 @@ class SigmoidCrossEntropyWithLogitsGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != INPUT_NUM) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be " << INPUT_NUM << ", but got " << input_num; @@ -58,9 +58,9 @@ class SigmoidCrossEntropyWithLogitsGpuKernelMod : public NativeGpuKernelMod { labels_size_ = sizeof(S); outputs_size_ = sizeof(T); - auto logits_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto labels_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto logits_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto labels_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(logits_shape, kernel_name, "logits") || CHECK_SHAPE_NULL(labels_shape, kernel_name, "labels") || CHECK_SHAPE_NULL(output_shape, kernel_name, "output"); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/sigmoid_cross_entropy_with_logits_grad_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/sigmoid_cross_entropy_with_logits_grad_gpu_kernel.h index 31f51d8a64c..483d3d566e3 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/sigmoid_cross_entropy_with_logits_grad_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/sigmoid_cross_entropy_with_logits_grad_gpu_kernel.h @@ -47,8 +47,8 @@ class SigmoidCrossEntropyWithLogitsGradGpuKernelMod : public NativeGpuKernelMod } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 3) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 3, but got " << input_num; } @@ -56,9 +56,9 @@ class SigmoidCrossEntropyWithLogitsGradGpuKernelMod : public NativeGpuKernelMod labels_size_ = sizeof(S); outputs_size_ = sizeof(T); - auto logits_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto labels_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto logits_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto labels_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(logits_shape, kernel_name, "logits") || CHECK_SHAPE_NULL(labels_shape, kernel_name, "labels") || CHECK_SHAPE_NULL(output_shape, kernel_name, "output"); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/smooth_l1_loss_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/smooth_l1_loss_gpu_kernel.h index abf91d8fa05..e5d98c8f332 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/smooth_l1_loss_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/smooth_l1_loss_gpu_kernel.h @@ -43,8 +43,8 @@ class SmoothL1LossGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "logits"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/smooth_l1_loss_grad_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/smooth_l1_loss_grad_gpu_kernel.h index a119076e0ff..f5af302a370 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/smooth_l1_loss_grad_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/smooth_l1_loss_grad_gpu_kernel.h @@ -44,8 +44,8 @@ class SmoothL1LossGradGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "logits"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/softmax_cross_entropy_with_logits_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/softmax_cross_entropy_with_logits_gpu_kernel.h index 682036ae87c..1818eb0dc95 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/softmax_cross_entropy_with_logits_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/softmax_cross_entropy_with_logits_gpu_kernel.h @@ -74,14 +74,14 @@ class SoftmaxCrossEntropyWithLogitsGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; InitResource(); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 2, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 2, but got " << output_num; } @@ -126,8 +126,8 @@ class SoftmaxCrossEntropyWithLogitsGpuKernelMod : public NativeGpuKernelMod { private: void InferInputOutputSize(const CNodePtr &kernel_node) { - auto logits_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto labels_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto logits_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto labels_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); is_null_input_ = CHECK_SHAPE_NULL(logits_shape, kernel_name_, "logits") || CHECK_SHAPE_NULL(labels_shape, kernel_name_, "labels"); if (is_null_input_) { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/softmax_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/softmax_gpu_kernel.h index 08d49d889ff..e965d3a908a 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/softmax_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/softmax_gpu_kernel.h @@ -100,26 +100,26 @@ class SoftmaxGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; InitResource(); cudnn_data_type_ = GetCudnnDataType(TypeIdLabel(AnfAlgo::GetInputDeviceDataType(kernel_node, 0))); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 1, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_num; } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name_, "input"); if (is_null_input_) { InitSizeLists(); return true; } shape_size_ = input_shape.size(); - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); if (kernel_name == "LogSoftmax") { algo_ = CUDNN_SOFTMAX_LOG; auto axis = LongToInt(GetAttr(kernel_node, "axis")); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/softmax_grad_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/softmax_grad_gpu_kernel.h index 188059f7ab2..a540544dbbb 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/softmax_grad_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/softmax_grad_gpu_kernel.h @@ -101,19 +101,19 @@ class SoftmaxGradGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; InitResource(); cudnn_data_type_ = GetCudnnDataType(TypeIdLabel(AnfAlgo::GetInputDeviceDataType(kernel_node, 0))); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 2, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_num; } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name_, "input"); if (is_null_input_) { InitSizeLists(); @@ -124,7 +124,7 @@ class SoftmaxGradGpuKernelMod : public NativeGpuKernelMod { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimension of input should be equal to 2, but got " << shape_size_; } - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); if (kernel_name == "LogSoftmaxGrad") { algo_ = CUDNN_SOFTMAX_LOG; auto axis = static_cast(GetAttr(kernel_node, "axis")); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/softplus_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/softplus_gpu_kernel.h index a33f0f23326..4a535f847dd 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/softplus_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/softplus_gpu_kernel.h @@ -44,10 +44,10 @@ class SoftplusGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); InitResource(); input_size_ = sizeof(T); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/softplus_grad_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/softplus_grad_gpu_kernel.h index 887ba91629e..f2dce01819b 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/softplus_grad_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/softplus_grad_gpu_kernel.h @@ -45,10 +45,10 @@ class SoftplusGradGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); InitResource(); input_size_ = sizeof(T); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/sparse_apply_proximal_adagrad_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/sparse_apply_proximal_adagrad_kernel.h index 2916745ee4b..4e33ff27d01 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/sparse_apply_proximal_adagrad_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/sparse_apply_proximal_adagrad_kernel.h @@ -58,8 +58,8 @@ class SparseApplyProximalAdagradKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != INPUT_NUM) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be " << INPUT_NUM << ", but got " << input_num; @@ -73,11 +73,11 @@ class SparseApplyProximalAdagradKernelMod : public NativeGpuKernelMod { gradient_size_ = sizeof(T); indices_size_ = sizeof(int); - auto variable_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto accumulation_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto learning_rate_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto gradient_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto indices_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); + auto variable_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto accumulation_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto learning_rate_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto gradient_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); + auto indices_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); is_null_input_ = CHECK_SHAPE_NULL(variable_shape, kernel_name, "var") || CHECK_SHAPE_NULL(accumulation_shape, kernel_name, "accum") || CHECK_SHAPE_NULL(learning_rate_shape, kernel_name, "lr") || diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/sparse_ftrl_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/sparse_ftrl_gpu_kernel.h index 990127a0605..25de000b862 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/sparse_ftrl_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/sparse_ftrl_gpu_kernel.h @@ -61,9 +61,9 @@ class SparseFtrlGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != INPUT_NUM) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be " << INPUT_NUM << ", but got " << input_num; @@ -75,11 +75,11 @@ class SparseFtrlGpuKernelMod : public NativeGpuKernelMod { gradient_size_ = sizeof(T); indices_size_ = sizeof(S); - auto variable_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto accumulation_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto linear_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto gradient_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto indices_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + auto variable_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto accumulation_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto linear_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto gradient_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto indices_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); is_null_input_ = CHECK_SHAPE_NULL(variable_shape, kernel_name, "var") || CHECK_SHAPE_NULL(accumulation_shape, kernel_name, "accum") || CHECK_SHAPE_NULL(linear_shape, kernel_name, "linear") || diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/sparse_softmax_cross_entropy_with_logits_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/sparse_softmax_cross_entropy_with_logits_gpu_kernel.h index 1f467e88714..1e3176bd72b 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/sparse_softmax_cross_entropy_with_logits_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/sparse_softmax_cross_entropy_with_logits_gpu_kernel.h @@ -78,14 +78,14 @@ class SparseSoftmaxCrossEntropyWithLogitsGpuKernelMod : public NativeGpuKernelMo return true; } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; InitResource(); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 2, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_num; } @@ -130,8 +130,8 @@ class SparseSoftmaxCrossEntropyWithLogitsGpuKernelMod : public NativeGpuKernelMo private: void InferInputOutputSize(const CNodePtr &kernel_node) { - auto logits_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto labels_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto logits_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto labels_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); is_null_input_ = CHECK_SHAPE_NULL(logits_shape, kernel_name_, "logits") || CHECK_SHAPE_NULL(labels_shape, kernel_name_, "labels"); if (is_null_input_) { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/other/assign_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/other/assign_gpu_kernel.h index 686546664d3..d6a2db977c0 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/other/assign_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/other/assign_gpu_kernel.h @@ -50,11 +50,11 @@ class AssignGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); MS_EXCEPTION_IF_NULL(kernel_node); kernel_node_ = kernel_node; (void)CheckParam(kernel_node); - auto shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(shape, kernel_name_, "input"); if (is_null_input_) { InitSizeLists(); @@ -78,11 +78,11 @@ class AssignGpuKernelMod : public NativeGpuKernelMod { private: void CheckParam(const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 2, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_num; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/other/boundingbox_decode_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/other/boundingbox_decode_gpu_kernel.h index 390de32af9b..77988a0b88d 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/other/boundingbox_decode_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/other/boundingbox_decode_gpu_kernel.h @@ -63,9 +63,9 @@ class BoundingBoxDecodeGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); MS_EXCEPTION_IF_NULL(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 2, but got " << input_num; } @@ -73,9 +73,9 @@ class BoundingBoxDecodeGpuKernelMod : public NativeGpuKernelMod { deltas_size_ = sizeof(T); bboxes_size_ = sizeof(T); - auto logits_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto labels_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto logits_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto labels_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(logits_shape, kernel_name_, "anchor_box") || CHECK_SHAPE_NULL(labels_shape, kernel_name_, "deltas") || CHECK_SHAPE_NULL(output_shape, kernel_name_, "output"); @@ -98,7 +98,7 @@ class BoundingBoxDecodeGpuKernelMod : public NativeGpuKernelMod { InitSizeLists(); const size_t coordinate_size = 4; - auto prim = AnfAlgo::GetCNodePrimitive(kernel_node); + auto prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(prim); auto means = prim->GetAttr("means"); MS_EXCEPTION_IF_NULL(means); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/other/boundingbox_encode_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/other/boundingbox_encode_gpu_kernel.h index d89f80be69f..1d796d7986c 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/other/boundingbox_encode_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/other/boundingbox_encode_gpu_kernel.h @@ -61,9 +61,9 @@ class BoundingBoxEncodeGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); MS_EXCEPTION_IF_NULL(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 2, but got " << input_num; } @@ -71,9 +71,9 @@ class BoundingBoxEncodeGpuKernelMod : public NativeGpuKernelMod { groundtruth_size_ = sizeof(T); deltas_size_ = sizeof(T); - auto logits_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto labels_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto logits_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto labels_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(logits_shape, kernel_name_, "anchor_box") || CHECK_SHAPE_NULL(labels_shape, kernel_name_, "groundtruth_box") || CHECK_SHAPE_NULL(output_shape, kernel_name_, "output"); @@ -96,7 +96,7 @@ class BoundingBoxEncodeGpuKernelMod : public NativeGpuKernelMod { InitSizeLists(); const size_t coordinate_size = 4; - auto prim = AnfAlgo::GetCNodePrimitive(kernel_node); + auto prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(prim); auto means = prim->GetAttr("means"); MS_EXCEPTION_IF_NULL(means); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/other/check_valid_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/other/check_valid_gpu_kernel.h index 8dfa8e82cd9..9f28969e8a6 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/other/check_valid_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/other/check_valid_gpu_kernel.h @@ -54,9 +54,9 @@ class CheckValidGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); MS_EXCEPTION_IF_NULL(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 2, but got " << input_num; } @@ -64,9 +64,9 @@ class CheckValidGpuKernelMod : public NativeGpuKernelMod { img_metas_size_ = sizeof(T); valid_size_ = sizeof(S); - auto anchor_boxes_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto img_metas_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto valid_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto anchor_boxes_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto img_metas_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto valid_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(anchor_boxes_shape, kernel_name_, "bboxes") || CHECK_SHAPE_NULL(img_metas_shape, kernel_name_, "img_metas") || CHECK_SHAPE_NULL(valid_shape, kernel_name_, "output"); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/other/concat_offset_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/other/concat_offset_gpu_kernel.h index f77d23aac39..4e94797c5aa 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/other/concat_offset_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/other/concat_offset_gpu_kernel.h @@ -42,12 +42,12 @@ class ConcatOffsetGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; if (!CheckParam(kernel_node)) { return false; } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); auto rank = input_shape.size(); auto rank_int = SizeToInt(rank); auto axis = static_cast(GetAttr(kernel_node, "axis")); @@ -58,7 +58,7 @@ class ConcatOffsetGpuKernelMod : public NativeGpuKernelMod { if (axis < 0) { axis += rank_int; } - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num == 0) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of input should be greater than 0"; } @@ -71,10 +71,10 @@ class ConcatOffsetGpuKernelMod : public NativeGpuKernelMod { input_size_list_.push_back(input_size * sizeof(T)); } // cal offset - size_t shape_offset = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0)[axis]; + size_t shape_offset = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0)[axis]; std::vector offset(input_num, 0); for (size_t i = 1; i < input_num; i++) { - input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, i); + input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, i); if (input_shape.size() != rank) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << " the dimension of input should be equal, but got:" << " the dimension of the " << i << "'th input: " << input_shape.size() @@ -84,7 +84,7 @@ class ConcatOffsetGpuKernelMod : public NativeGpuKernelMod { shape_offset += input_shape[axis]; } constexpr size_t kConcatOffsetOutputShapeSize = 2; - auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto output_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); if (output_shape.size() != kConcatOffsetOutputShapeSize) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimension of output should be " << kConcatOffsetOutputShapeSize << ", but got:" << output_shape.size(); @@ -121,7 +121,7 @@ class ConcatOffsetGpuKernelMod : public NativeGpuKernelMod { private: bool CheckParam(const CNodePtr &kernel_node) { - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_num; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/other/dynamic_broadcast_grad_args_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/other/dynamic_broadcast_grad_args_gpu_kernel.h index 0eea5f82229..6e19765e109 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/other/dynamic_broadcast_grad_args_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/other/dynamic_broadcast_grad_args_gpu_kernel.h @@ -57,7 +57,7 @@ class DynamicBroadcastGradientArgsGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { kernel_node_ = kernel_node; - auto input_num = AnfAlgo::GetInputTensorNum(kernel_node); + auto input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != kInputNum) { MS_LOG(EXCEPTION) << "DynamicBroadcastGradiendArgs needs " << kInputNum << " inputs, but get " << input_num; } @@ -86,8 +86,8 @@ class DynamicBroadcastGradientArgsGpuKernelMod : public NativeGpuKernelMod { void PostExecute() override { std::vector r0_shape{r0_size_}; std::vector r1_shape{r1_size_}; - AnfAlgo::SetOutputInferTypeAndShape({TypeId::kNumberTypeInt64, TypeId::kNumberTypeInt64}, {r0_shape, r1_shape}, - kernel_node_.lock().get()); + common::AnfAlgo::SetOutputInferTypeAndShape({TypeId::kNumberTypeInt64, TypeId::kNumberTypeInt64}, + {r0_shape, r1_shape}, kernel_node_.lock().get()); MS_LOG(DEBUG) << "Run PostExecute for DynamicBroadcastGradientArgs, real r0 shape is " << r0_shape << ", r1 shape is " << r1_shape; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/other/dynamic_broadcastto_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/other/dynamic_broadcastto_gpu_kernel.h index 7a9a7ac6e69..9c6a253fe3a 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/other/dynamic_broadcastto_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/other/dynamic_broadcastto_gpu_kernel.h @@ -106,7 +106,7 @@ class DynamicBroadcastToGpuKernelMod : public NativeGpuKernelMod { std::vector output_shape; std::transform(real_output_shape_.begin(), real_output_shape_.end(), std::back_inserter(output_shape), [](const S &i) { return static_cast(i); }); - AnfAlgo::SetOutputInferTypeAndShape({data_type}, {output_shape}, kernel_node_.lock().get()); + common::AnfAlgo::SetOutputInferTypeAndShape({data_type}, {output_shape}, kernel_node_.lock().get()); MS_LOG(DEBUG) << "Run PostExecute for DynamicBroadcastTo, real output shape is " << output_shape; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/other/dynamic_reshape_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/other/dynamic_reshape_gpu_kernel.h index 806e58dde82..107fc9f4b48 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/other/dynamic_reshape_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/other/dynamic_reshape_gpu_kernel.h @@ -82,7 +82,7 @@ class DynamicReshapeKernelMod : public NativeGpuKernelMod { std::vector output_shape; std::transform(real_output_shape_.begin(), real_output_shape_.end(), std::back_inserter(output_shape), [](const S &value) { return static_cast(value); }); - AnfAlgo::SetOutputInferTypeAndShape({data_type}, {output_shape}, kernel_node_.lock().get()); + common::AnfAlgo::SetOutputInferTypeAndShape({data_type}, {output_shape}, kernel_node_.lock().get()); MS_LOG(DEBUG) << "Run PostExecute for DynamicReshape, real output shape is " << output_shape; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/other/dynamic_stitch_gpu_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/other/dynamic_stitch_gpu_kernel.cc index b846f51d774..a5b4698a9ab 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/other/dynamic_stitch_gpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/other/dynamic_stitch_gpu_kernel.cc @@ -31,7 +31,7 @@ DynamicStitchKernelMod::~DynamicStitchKernelMod() {} bool DynamicStitchKernelMod::Init(const CNodePtr &kernel_node) { kernel_node_ = kernel_node; // Inputs: (indexlist, datalist) - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); n_ = input_num / kDivNum2; auto output_shape = AnfAlgo::GetOutputRealDeviceShapeIfExist(kernel_node, 0); @@ -63,7 +63,7 @@ void DynamicStitchKernelMod::PostExecute() { auto output_shape = AnfAlgo::GetOutputRealDeviceShapeIfExist(kernel_node_.lock(), 0); output_shape[0] = max_index_ + 1; auto data_type = AnfAlgo::GetInputDeviceDataType(kernel_node_.lock(), n_); - AnfAlgo::SetOutputInferTypeAndShape({data_type}, {output_shape}, kernel_node_.lock().get()); + common::AnfAlgo::SetOutputInferTypeAndShape({data_type}, {output_shape}, kernel_node_.lock().get()); MS_LOG(DEBUG) << "Run PostExecute for dynamicstitch, real output shape is " << output_shape; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/other/gpu_convert_to_dynamic_shape_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/other/gpu_convert_to_dynamic_shape_gpu_kernel.h index eeca568b927..dd3715719d2 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/other/gpu_convert_to_dynamic_shape_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/other/gpu_convert_to_dynamic_shape_gpu_kernel.h @@ -52,21 +52,21 @@ class GpuConvertToDynamicShapeGpuKernelMod : public NativeGpuKernelMod { CHECK_CUDA_RET_WITH_EXCEPT(kernel_node_, cudaStreamSynchronize(reinterpret_cast(cuda_stream_ptr_)), "cudaStreamSynchronized failed"); - std::vector output_types = {AnfAlgo::GetOutputInferDataType(kernel_node_.lock(), 0)}; + std::vector output_types = {common::AnfAlgo::GetOutputInferDataType(kernel_node_.lock(), 0)}; std::vector> output_shapes = {input_shape_}; - AnfAlgo::SetOutputInferTypeAndShape(output_types, output_shapes, kernel_node_.lock().get()); + common::AnfAlgo::SetOutputInferTypeAndShape(output_types, output_shapes, kernel_node_.lock().get()); } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); MS_EXCEPTION_IF_NULL(kernel_node); kernel_node_ = kernel_node; - size_t input_count = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_count = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_count != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 1, but got " << input_count; } - input_shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + input_shape_ = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape_, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/other/iou_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/other/iou_gpu_kernel.h index 3c09ddee697..338e4bb711a 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/other/iou_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/other/iou_gpu_kernel.h @@ -58,7 +58,7 @@ class IOUGpuKernelMod : public NativeGpuKernelMod { bool Init(const CNodePtr &kernel_node) override { MS_EXCEPTION_IF_NULL(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 2, but got " << input_num; } @@ -66,9 +66,9 @@ class IOUGpuKernelMod : public NativeGpuKernelMod { anchor_boxes_size_ = sizeof(T); iou_size_ = sizeof(T); - auto gt_boxes_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto anchor_boxes_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto iou_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto gt_boxes_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto anchor_boxes_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto iou_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(gt_boxes_shape, kernel_name_, "anchor_boxes") || CHECK_SHAPE_NULL(anchor_boxes_shape, kernel_name_, "gt_boxes") || CHECK_SHAPE_NULL(iou_shape, kernel_name_, "output"); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/quant/batchnorm_fold2_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/quant/batchnorm_fold2_gpu_kernel.h index ffef444f504..ec84dba3d25 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/quant/batchnorm_fold2_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/quant/batchnorm_fold2_gpu_kernel.h @@ -62,16 +62,16 @@ class BatchNormFold2GpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); InitResource(); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != INPUT_NUM) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be " << INPUT_NUM << ", but got " << input_num; } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); @@ -86,7 +86,7 @@ class BatchNormFold2GpuKernelMod : public NativeGpuKernelMod { channel_ = input_shape[1]; height_ = input_shape[2]; width_ = input_shape[3]; - auto prim = AnfAlgo::GetCNodePrimitive(kernel_node); + auto prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(prim); freeze_bn_ = static_cast(GetValue(prim->GetAttr("freeze_bn"))); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/quant/batchnorm_fold2_grad_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/quant/batchnorm_fold2_grad_gpu_kernel.h index 92c8ef0f649..988b03e6545 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/quant/batchnorm_fold2_grad_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/quant/batchnorm_fold2_grad_gpu_kernel.h @@ -90,16 +90,16 @@ class BatchNormFold2GradGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; InitResource(); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != INPUT_NUM) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be " << INPUT_NUM << ", but got " << input_num; } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); @@ -114,7 +114,7 @@ class BatchNormFold2GradGpuKernelMod : public NativeGpuKernelMod { channel_ = input_shape[1]; height_ = input_shape[2]; width_ = input_shape[3]; - auto prim = AnfAlgo::GetCNodePrimitive(kernel_node); + auto prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(prim); freeze_bn_ = GetValue(prim->GetAttr("freeze_bn")); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/quant/batchnorm_fold_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/quant/batchnorm_fold_gpu_kernel.h index f24ff530b6d..6fd455ab823 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/quant/batchnorm_fold_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/quant/batchnorm_fold_gpu_kernel.h @@ -95,20 +95,20 @@ class BatchNormFoldGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; InitResource(); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 4) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 4, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 4) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 4, but got " << output_num; } - auto prim = AnfAlgo::GetCNodePrimitive(kernel_node); + auto prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(prim); T momentum = GetValue(prim->GetAttr("momentum")); exp_avg_factor_ = 1.0 - momentum; @@ -116,7 +116,7 @@ class BatchNormFoldGpuKernelMod : public NativeGpuKernelMod { is_training_ = GetValue(prim->GetAttr("is_training")); freeze_bn_ = static_cast(GetValue(prim->GetAttr("freeze_bn"))); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/quant/batchnorm_fold_grad_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/quant/batchnorm_fold_grad_gpu_kernel.h index 8ba620b24a7..7ca5b7584f1 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/quant/batchnorm_fold_grad_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/quant/batchnorm_fold_grad_gpu_kernel.h @@ -74,26 +74,26 @@ class BatchNormFoldGradGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != INPUT_NUM) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be " << INPUT_NUM << ", but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } - auto prim = AnfAlgo::GetCNodePrimitive(kernel_node); + auto prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(prim); epsilon_ = GetValue(prim->GetAttr("epsilon")); is_training_ = GetValue(prim->GetAttr("is_training")); freeze_bn_ = static_cast(GetValue(prim->GetAttr("freeze_bn"))); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/quant/correction_mul_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/quant/correction_mul_gpu_kernel.h index b17888a5d32..c659a76c656 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/quant/correction_mul_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/quant/correction_mul_gpu_kernel.h @@ -45,15 +45,15 @@ class CorrectionMulGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); InitResource(); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 3) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 3, but got " << input_num; } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/quant/correction_mul_grad_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/quant/correction_mul_grad_gpu_kernel.h index 996e26891c6..10d8e38acd6 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/quant/correction_mul_grad_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/quant/correction_mul_grad_gpu_kernel.h @@ -51,15 +51,15 @@ class CorrectionMulGradGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); InitResource(); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 4) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 4, but got " << input_num; } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/quant/fake_learned_scale_quant_perchannel_gpu_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/quant/fake_learned_scale_quant_perchannel_gpu_kernel.cc index e6e9fef93a2..3877906c3b4 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/quant/fake_learned_scale_quant_perchannel_gpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/quant/fake_learned_scale_quant_perchannel_gpu_kernel.cc @@ -33,24 +33,25 @@ FakeLearnedScaleQuantPerChannelGpuKernelMod::FakeLearnedScaleQuantPerChannelGpuK num_channels_(0) {} bool FakeLearnedScaleQuantPerChannelGpuKernelMod::Init(const CNodePtr &kernel_node) { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 3) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 3, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } - quant_delay_ = static_cast(GetValue(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("quant_delay"))); - training_ = GetValue(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("training")); - neg_trunc_ = GetValue(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("neg_trunc")); + quant_delay_ = + static_cast(GetValue(common::AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("quant_delay"))); + training_ = GetValue(common::AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("training")); + neg_trunc_ = GetValue(common::AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("neg_trunc")); // init size - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); num_channels_ = SizeToInt(input_shape[0]); for (size_t i = 0; i < input_shape.size(); ++i) { quant_num_ *= SizeToInt(input_shape[i]); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/quant/fake_learned_scale_quant_perchannel_grad_gpu_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/quant/fake_learned_scale_quant_perchannel_grad_gpu_kernel.cc index 704ee886b16..728a8f4525c 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/quant/fake_learned_scale_quant_perchannel_grad_gpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/quant/fake_learned_scale_quant_perchannel_grad_gpu_kernel.cc @@ -28,28 +28,29 @@ FakeLearnedScaleQuantPerChannelGradGpuKernelMod::FakeLearnedScaleQuantPerChannel num_channels_(0) {} bool FakeLearnedScaleQuantPerChannelGradGpuKernelMod::Init(const CNodePtr &kernel_node) { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 4) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 4, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 2, but got " << output_num; } - quant_delay_ = static_cast(GetValue(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("quant_delay"))); + quant_delay_ = + static_cast(GetValue(common::AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("quant_delay"))); if (quant_delay_ < 0) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the value of quant_delay_ cannot be less than 0, but got " << quant_delay_; } - neg_trunc_ = GetValue(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("neg_trunc")); + neg_trunc_ = GetValue(common::AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("neg_trunc")); // init size - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); num_channels_ = SizeToInt(input_shape[0]); for (size_t i = 0; i < input_shape.size(); ++i) { quant_num_ *= SizeToInt(input_shape[i]); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/quant/fake_learned_scale_quant_perlayer_gpu_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/quant/fake_learned_scale_quant_perlayer_gpu_kernel.cc index 74cca389fc4..cd52b0ae46d 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/quant/fake_learned_scale_quant_perlayer_gpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/quant/fake_learned_scale_quant_perlayer_gpu_kernel.cc @@ -27,24 +27,25 @@ FakeLearnedScaleQuantPerLayerGpuKernelMod::FakeLearnedScaleQuantPerLayerGpuKerne : input_size_(0), quant_num_(1), global_step_(0), quant_delay_(0), training_(false), neg_trunc_(false) {} bool FakeLearnedScaleQuantPerLayerGpuKernelMod::Init(const CNodePtr &kernel_node) { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 3) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 3, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } - quant_delay_ = static_cast(GetValue(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("quant_delay"))); - training_ = GetValue(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("training")); - neg_trunc_ = GetValue(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("neg_trunc")); + quant_delay_ = + static_cast(GetValue(common::AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("quant_delay"))); + training_ = GetValue(common::AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("training")); + neg_trunc_ = GetValue(common::AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("neg_trunc")); // init size - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); for (size_t i = 0; i < input_shape.size(); ++i) { quant_num_ *= SizeToInt(input_shape[i]); } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/quant/fake_learned_scale_quant_perlayer_grad_gpu_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/quant/fake_learned_scale_quant_perlayer_grad_gpu_kernel.cc index 30432bab050..587e9f24e96 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/quant/fake_learned_scale_quant_perlayer_grad_gpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/quant/fake_learned_scale_quant_perlayer_grad_gpu_kernel.cc @@ -22,28 +22,29 @@ FakeLearnedScaleQuantPerLayerGradGpuKernelMod::FakeLearnedScaleQuantPerLayerGrad : input_size_(0), workspace_size_(0), quant_num_(1), quant_delay_(0), global_step_(0), neg_trunc_(false) {} bool FakeLearnedScaleQuantPerLayerGradGpuKernelMod::Init(const CNodePtr &kernel_node) { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 4) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 4, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 2, but got " << output_num; } - quant_delay_ = static_cast(GetValue(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("quant_delay"))); + quant_delay_ = + static_cast(GetValue(common::AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("quant_delay"))); if (quant_delay_ < 0) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the value of quant_delay_ cannot be less than 0, but got " << quant_delay_; } - neg_trunc_ = GetValue(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("neg_trunc")); + neg_trunc_ = GetValue(common::AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("neg_trunc")); // init size - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); for (size_t i = 0; i < input_shape.size(); ++i) { quant_num_ *= SizeToInt(input_shape[i]); } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/quant/fake_quant_perchannel_gpu_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/quant/fake_quant_perchannel_gpu_kernel.cc index 3d907e915aa..ff3afb041db 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/quant/fake_quant_perchannel_gpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/quant/fake_quant_perchannel_gpu_kernel.cc @@ -37,20 +37,20 @@ FakeQuantPerChannelGpuKernelMod::FakeQuantPerChannelGpuKernelMod() global_step_(0) {} bool FakeQuantPerChannelGpuKernelMod::Init(const CNodePtr &kernel_node) { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 3) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 3, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } // get attribute - auto prim = AnfAlgo::GetCNodePrimitive(kernel_node); + auto prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(prim); num_bits_ = static_cast(GetValue(prim->GetAttr("num_bits"))); training_ = GetValue(prim->GetAttr("training")); @@ -76,7 +76,7 @@ bool FakeQuantPerChannelGpuKernelMod::Init(const CNodePtr &kernel_node) { } // shape info for gpu - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/quant/fake_quant_perchannel_grad_gpu_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/quant/fake_quant_perchannel_grad_gpu_kernel.cc index f2055b4e923..0876013615e 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/quant/fake_quant_perchannel_grad_gpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/quant/fake_quant_perchannel_grad_gpu_kernel.cc @@ -32,19 +32,19 @@ FakeQuantPerChannelGradGpuKernelMod::FakeQuantPerChannelGradGpuKernelMod() symmetric_(false) {} bool FakeQuantPerChannelGradGpuKernelMod::Init(const CNodePtr &kernel_node) { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 4) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 4, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } - auto prim = AnfAlgo::GetCNodePrimitive(kernel_node); + auto prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(prim); num_bits_ = static_cast(GetValue(prim->GetAttr("num_bits"))); if (num_bits_ <= 2 || num_bits_ >= 16) { @@ -68,7 +68,7 @@ bool FakeQuantPerChannelGradGpuKernelMod::Init(const CNodePtr &kernel_node) { quant_min_++; } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/quant/fake_quant_perlayer_gpu_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/quant/fake_quant_perlayer_gpu_kernel.cc index 514da145d36..56d40d8a2d2 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/quant/fake_quant_perlayer_gpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/quant/fake_quant_perlayer_gpu_kernel.cc @@ -37,19 +37,19 @@ FakeQuantPerLayerGpuKernelMod::FakeQuantPerLayerGpuKernelMod() symmetric_(false) {} bool FakeQuantPerLayerGpuKernelMod::Init(const CNodePtr &kernel_node) { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 3) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 3, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } - auto prim = AnfAlgo::GetCNodePrimitive(kernel_node); + auto prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(prim); num_bits_ = static_cast(GetValue(prim->GetAttr("num_bits"))); quant_delay_ = static_cast(GetValue(prim->GetAttr("quant_delay"))); @@ -74,7 +74,7 @@ bool FakeQuantPerLayerGpuKernelMod::Init(const CNodePtr &kernel_node) { } // init size - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/quant/fake_quant_perlayer_grad_gpu_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/quant/fake_quant_perlayer_grad_gpu_kernel.cc index 6259e78ad0a..267190ae469 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/quant/fake_quant_perlayer_grad_gpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/quant/fake_quant_perlayer_grad_gpu_kernel.cc @@ -33,19 +33,19 @@ FakeQuantPerLayerGradGpuKernelMod::FakeQuantPerLayerGradGpuKernelMod() symmetric_(false) {} bool FakeQuantPerLayerGradGpuKernelMod::Init(const CNodePtr &kernel_node) { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 4) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 4, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } - auto prim = AnfAlgo::GetCNodePrimitive(kernel_node); + auto prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(prim); num_bits_ = static_cast(GetValue(prim->GetAttr("num_bits"))); if (num_bits_ <= 2 || num_bits_ >= 16) { @@ -70,7 +70,7 @@ bool FakeQuantPerLayerGradGpuKernelMod::Init(const CNodePtr &kernel_node) { } // init size - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/quant/minmax_update_perchannel_gpu_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/quant/minmax_update_perchannel_gpu_kernel.cc index 0e387af2b73..f9a8b689dfc 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/quant/minmax_update_perchannel_gpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/quant/minmax_update_perchannel_gpu_kernel.cc @@ -27,24 +27,24 @@ MinMaxUpdatePerChannelGpuKernelMod::MinMaxUpdatePerChannelGpuKernelMod() : input_size_(0), quant_num_(1), ema_(false), is_null_input_(false), ema_decay_(0), num_channels_(0) {} bool MinMaxUpdatePerChannelGpuKernelMod::Init(const CNodePtr &kernel_node) { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 3) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 3, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 2, but got " << output_num; } - auto prim = AnfAlgo::GetCNodePrimitive(kernel_node); + auto prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(prim); ema_ = GetValue(prim->GetAttr("ema")); ema_decay_ = GetValue(prim->GetAttr("ema_decay")); // init size - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/quant/minmax_update_perlayer_gpu_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/quant/minmax_update_perlayer_gpu_kernel.cc index 08b6d8d6c66..1c09b89d7c1 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/quant/minmax_update_perlayer_gpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/quant/minmax_update_perlayer_gpu_kernel.cc @@ -27,24 +27,24 @@ MinMaxUpdatePerLayerGpuKernelMod::MinMaxUpdatePerLayerGpuKernelMod() : input_size_(0), quant_num_(1), ema_(false), is_null_input_(false), ema_decay_(0) {} bool MinMaxUpdatePerLayerGpuKernelMod::Init(const CNodePtr &kernel_node) { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 3) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 3, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 2, but got " << output_num; } - auto prim = AnfAlgo::GetCNodePrimitive(kernel_node); + auto prim = common::AnfAlgo::GetCNodePrimitive(kernel_node); MS_EXCEPTION_IF_NULL(prim); ema_ = GetValue(prim->GetAttr("ema")); ema_decay_ = GetValue(prim->GetAttr("ema_decay")); // init size - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/random/random_categorical_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/random/random_categorical_gpu_kernel.h index 22b7789319a..4aaaf66190b 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/random/random_categorical_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/random/random_categorical_gpu_kernel.h @@ -88,18 +88,18 @@ class RandomCategoricalGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); MS_EXCEPTION_IF_NULL(kernel_node); kernel_node_ = kernel_node; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 3) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 3, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 1, but got " << output_num; } - auto logits_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto logits_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(logits_shape, kernel_name, "logits"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/random/random_choice_with_mask_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/random/random_choice_with_mask_gpu_kernel.h index 7f290fbab07..469e64a5c26 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/random/random_choice_with_mask_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/random/random_choice_with_mask_gpu_kernel.h @@ -69,18 +69,18 @@ class RandomChoiceWithMaskGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); MS_EXCEPTION_IF_NULL(kernel_node); uint32_t time_interval = std::chrono::system_clock::now().time_since_epoch().count(); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be 1, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 2) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of outputs should be 2, but got " << output_num; } - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/random/randperm_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/random/randperm_gpu_kernel.h index f8f3b83e7a6..b5e67a96c36 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/random/randperm_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/random/randperm_gpu_kernel.h @@ -69,13 +69,13 @@ class RandpermGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); - size_t input_count = AnfAlgo::GetInputTensorNum(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_count = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_count != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 1, but got " << input_count; } - size_t output_count = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_count = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_count != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 1, but got " << output_count; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/random/uniform_candidate_sampler_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/random/uniform_candidate_sampler_gpu_kernel.h index 0b18458243f..f4a5ee9a898 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/random/uniform_candidate_sampler_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/random/uniform_candidate_sampler_gpu_kernel.h @@ -82,14 +82,14 @@ class UniformCandidateSamplerGpuKernelMod : public NativeGpuKernelMod { } bool Init(const CNodePtr &kernel_node) override { - kernel_name_ = AnfAlgo::GetCNodeName(kernel_node); + kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); MS_EXCEPTION_IF_NULL(kernel_node); kernel_node_ = kernel_node; - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != 1) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs should be 1, but got " << input_num; } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); if (output_num != 3) { MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of outputs should be 3, but got " << output_num; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/rl/buffer_sample_gpu_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/rl/buffer_sample_gpu_kernel.cc index d3ee193db2d..4890ab89184 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/rl/buffer_sample_gpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/rl/buffer_sample_gpu_kernel.cc @@ -38,7 +38,7 @@ BufferSampleKernelMod::~BufferSampleKernelMod() { void BufferSampleKernelMod::ReleaseResource() {} bool BufferSampleKernelMod::Init(const CNodePtr &kernel_node) { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; auto shapes = GetAttr>(kernel_node, "buffer_elements"); auto types = GetAttr>(kernel_node, "buffer_dtype"); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/rl/discounted_return_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/rl/discounted_return_gpu_kernel.h index 2c794882264..952d7e7e3e4 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/rl/discounted_return_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/rl/discounted_return_gpu_kernel.h @@ -21,6 +21,7 @@ #include #include "plugin/device/gpu/kernel/gpu_kernel.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "plugin/device/gpu/kernel/cuda_impl/rl/discounted_return_impl.cuh" namespace mindspore { @@ -35,10 +36,10 @@ class DiscountedReturnGpuKernelMod : public NativeGpuKernelMod { ~DiscountedReturnGpuKernelMod() = default; bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); MS_EXCEPTION_IF_NULL(kernel_node); - gamma_ = AnfAlgo::GetNodeAttr(kernel_node, kGammaAttrName); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + gamma_ = common::AnfAlgo::GetNodeAttr(kernel_node, kGammaAttrName); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); if (input_num != kInputNum) { MS_LOG(EXCEPTION) << "For '" << kernel_name << "', the number of inputs should be " << kInputNum << ", but got " << input_num; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/rl/env_gpu_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/rl/env_gpu_kernel.cc index 0e84262cf43..2eb6eb76f11 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/rl/env_gpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/rl/env_gpu_kernel.cc @@ -23,7 +23,7 @@ constexpr auto kHandleAttrName = "handle"; } // namespace bool EnvCreateKernelMod::Init(const CNodePtr &cnode) { - const auto &name = AnfAlgo::GetNodeAttr(cnode, kEnvTypeName); + const auto &name = common::AnfAlgo::GetNodeAttr(cnode, kEnvTypeName); std::tie(handle_, env_) = EnvironmentFactory::GetInstance().Create(name); MS_EXCEPTION_IF_NULL(env_); env_->Init(cnode, nullptr); @@ -43,7 +43,7 @@ bool EnvCreateKernelMod::Launch(const std::vector &inputs, const std } bool EnvResetKernelMod::Init(const CNodePtr &cnode) { - handle_ = AnfAlgo::GetNodeAttr(cnode, kHandleAttrName); + handle_ = common::AnfAlgo::GetNodeAttr(cnode, kHandleAttrName); env_ = EnvironmentFactory::GetInstance().GetByHandle(handle_); MS_EXCEPTION_IF_NULL(env_); InitSizeLists(); @@ -61,7 +61,7 @@ bool EnvResetKernelMod::Launch(const std::vector &inputs, const std: } bool EnvStepKernelMod::Init(const CNodePtr &cnode) { - handle_ = AnfAlgo::GetNodeAttr(cnode, kHandleAttrName); + handle_ = common::AnfAlgo::GetNodeAttr(cnode, kHandleAttrName); env_ = EnvironmentFactory::GetInstance().GetByHandle(handle_); MS_EXCEPTION_IF_NULL(env_); InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/rl/env_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/rl/env_gpu_kernel.h index 5146d879b3b..d29b708678f 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/rl/env_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/rl/env_gpu_kernel.h @@ -26,6 +26,7 @@ #include "plugin/device/gpu/kernel/gpu_kernel.h" #include "plugin/device/gpu/kernel/gpu_kernel_factory.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "plugin/device/gpu/kernel/rl/environment_factory.h" namespace mindspore { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/rl/environment_factory.h b/mindspore/ccsrc/plugin/device/gpu/kernel/rl/environment_factory.h index 37787da6349..288ca471ad6 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/rl/environment_factory.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/rl/environment_factory.h @@ -25,6 +25,7 @@ #include #include "plugin/device/gpu/kernel/gpu_kernel_factory.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace kernel { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/rl/gru_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/rl/gru_gpu_kernel.h index 0df0296983e..815df7c85d3 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/rl/gru_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/rl/gru_gpu_kernel.h @@ -95,11 +95,11 @@ class GruGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; InitResource(); cudnn_data_type_ = GetCudnnDataType(TypeIdLabel(AnfAlgo::GetInputDeviceDataType(kernel_node, 0))); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); @@ -156,7 +156,7 @@ class GruGpuKernelMod : public NativeGpuKernelMod { hidden_size_, hidden_size_, num_layers_, dropout_desc_, 0), "set rnn_desc failed"); #endif - auto weight_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto weight_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "weight"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/rl/gru_grad_data_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/rl/gru_grad_data_gpu_kernel.h index aafae033765..0f4dce42d22 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/rl/gru_grad_data_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/rl/gru_grad_data_gpu_kernel.h @@ -110,11 +110,11 @@ class GruGradDataGpuKernelMod : public NativeGpuKernelMod { dropout_ = GetAttr(kernel_node, "dropout"); } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; InitResource(); cudnn_data_type_ = GetCudnnDataType(TypeIdLabel(AnfAlgo::GetInputDeviceDataType(kernel_node, 0))); - auto input_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); @@ -169,7 +169,7 @@ class GruGradDataGpuKernelMod : public NativeGpuKernelMod { hidden_size_, hidden_size_, num_layers_, dropout_desc_, 0), "set rnn_desc failed"); #endif - auto weight_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto weight_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "weight"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/rl/gru_grad_weight_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/rl/gru_grad_weight_gpu_kernel.h index 506a7b7325d..37d18dc68fe 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/rl/gru_grad_weight_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/rl/gru_grad_weight_gpu_kernel.h @@ -90,11 +90,11 @@ class GruGradWeightGpuKernelMod : public NativeGpuKernelMod { return true; } bool Init(const CNodePtr &kernel_node) override { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); kernel_node_ = kernel_node; InitResource(); cudnn_data_type_ = GetCudnnDataType(TypeIdLabel(AnfAlgo::GetInputDeviceDataType(kernel_node, 0))); - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(input_shape, kernel_name, "input"); if (is_null_input_) { InitSizeLists(); @@ -142,7 +142,7 @@ class GruGradWeightGpuKernelMod : public NativeGpuKernelMod { hidden_size_, hidden_size_, num_layers_, dropout_desc_, 0), "set rnn_desc failed"); #endif - auto weight_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + auto weight_shape = common::AnfAlgo::GetOutputInferShape(kernel_node, 0); is_null_input_ = CHECK_SHAPE_NULL(weight_shape, kernel_name, "weight"); if (is_null_input_) { InitSizeLists(); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/rl/tag_environment.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/rl/tag_environment.cc index 71b02676f8c..aac77246da2 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/rl/tag_environment.cc +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/rl/tag_environment.cc @@ -50,18 +50,18 @@ TagEnvironment::~TagEnvironment() { bool TagEnvironment::InitGameSetting(const CNodePtr &cnode, GameSetting *setting_host) { MS_EXCEPTION_IF_NULL(setting_host); - setting_host->seed = AnfAlgo::GetNodeAttr(cnode, kSeedAttr); - setting_host->predator_num = AnfAlgo::GetNodeAttr(cnode, kPredatorNumAttr); - setting_host->max_timestep = AnfAlgo::GetNodeAttr(cnode, kMaxTimestepAttr); - setting_host->map_length = AnfAlgo::GetNodeAttr(cnode, kMapLengthAttr); - setting_host->map_width = AnfAlgo::GetNodeAttr(cnode, kMapWidthAttr); - setting_host->wall_hit_penalty = AnfAlgo::GetNodeAttr(cnode, kWallHitPenaltyAttr); - setting_host->catch_reward = AnfAlgo::GetNodeAttr(cnode, kCatchRewardAttr); - setting_host->caught_penalty = AnfAlgo::GetNodeAttr(cnode, kCaughtPenaltyAttr); - setting_host->step_cost = AnfAlgo::GetNodeAttr(cnode, kStepCostAttr); - setting_host->partially_observation = AnfAlgo::GetNodeAttr(cnode, kPartiallyObsAttr); + setting_host->seed = common::AnfAlgo::GetNodeAttr(cnode, kSeedAttr); + setting_host->predator_num = common::AnfAlgo::GetNodeAttr(cnode, kPredatorNumAttr); + setting_host->max_timestep = common::AnfAlgo::GetNodeAttr(cnode, kMaxTimestepAttr); + setting_host->map_length = common::AnfAlgo::GetNodeAttr(cnode, kMapLengthAttr); + setting_host->map_width = common::AnfAlgo::GetNodeAttr(cnode, kMapWidthAttr); + setting_host->wall_hit_penalty = common::AnfAlgo::GetNodeAttr(cnode, kWallHitPenaltyAttr); + setting_host->catch_reward = common::AnfAlgo::GetNodeAttr(cnode, kCatchRewardAttr); + setting_host->caught_penalty = common::AnfAlgo::GetNodeAttr(cnode, kCaughtPenaltyAttr); + setting_host->step_cost = common::AnfAlgo::GetNodeAttr(cnode, kStepCostAttr); + setting_host->partially_observation = common::AnfAlgo::GetNodeAttr(cnode, kPartiallyObsAttr); - env_num_ = AnfAlgo::GetNodeAttr(cnode, kEnvNumAttr); + env_num_ = common::AnfAlgo::GetNodeAttr(cnode, kEnvNumAttr); agent_num_ = setting_host->predator_num + 1; return true; } diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/rl/tensor_array_stack_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/rl/tensor_array_stack_kernel.cc index 954b9bd7d14..a1584b5f2d8 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/rl/tensor_array_stack_kernel.cc +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/rl/tensor_array_stack_kernel.cc @@ -67,7 +67,7 @@ void TensorArrayStackKernelMod::PostExecute() { auto shape = shapes_; shape.insert(shape.begin(), tensor_size); MS_LOG(DEBUG) << "After postexecute, the real shape of TensorArrayStack is " << shape; - AnfAlgo::SetOutputInferTypeAndShape({type_->type_id()}, {shape}, kernel_node_.lock().get()); + common::AnfAlgo::SetOutputInferTypeAndShape({type_->type_id()}, {shape}, kernel_node_.lock().get()); } void TensorArrayStackKernelMod::ResetResource() noexcept { diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_atom_energy_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_atom_energy_kernel.h index a22dd0e6a9d..333ec50779a 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_atom_energy_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_atom_energy_kernel.h @@ -35,13 +35,13 @@ class AngleAtomEnergyGpuKernelMod : public NativeGpuKernelMod { bool Init(const CNodePtr &kernel_node) override { kernel_node_ = kernel_node; angle_numbers = static_cast(GetAttr(kernel_node, "angle_numbers")); - auto shape_uint_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_scaler = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_atom_a = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_atom_b = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_atom_c = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_angle_k = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_angle_theta0 = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); + auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_atom_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_atom_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_atom_c = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + auto shape_angle_k = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); + auto shape_angle_theta0 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); for (size_t i = 0; i < shape_uint_crd.size(); i++) ele_uint_crd *= shape_uint_crd[i]; for (size_t i = 0; i < shape_scaler.size(); i++) ele_scaler *= shape_scaler[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_energy_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_energy_kernel.h index 21daaea65db..139a918f103 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_energy_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_energy_kernel.h @@ -35,13 +35,13 @@ class AngleEnergyGpuKernelMod : public NativeGpuKernelMod { bool Init(const CNodePtr &kernel_node) override { kernel_node_ = kernel_node; angle_numbers = static_cast(GetAttr(kernel_node, "angle_numbers")); - auto shape_uint_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_scaler = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_atom_a = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_atom_b = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_atom_c = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_angle_k = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_angle_theta0 = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); + auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_atom_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_atom_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_atom_c = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + auto shape_angle_k = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); + auto shape_angle_theta0 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); for (size_t i = 0; i < shape_uint_crd.size(); i++) ele_uint_crd *= shape_uint_crd[i]; for (size_t i = 0; i < shape_scaler.size(); i++) ele_scaler *= shape_scaler[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_force_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_force_kernel.h index b638c05b2f7..36be4cda81d 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_force_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_force_kernel.h @@ -35,13 +35,13 @@ class AngleForceGpuKernelMod : public NativeGpuKernelMod { bool Init(const CNodePtr &kernel_node) override { kernel_node_ = kernel_node; angle_numbers = static_cast(GetAttr(kernel_node, "angle_numbers")); - auto shape_uint_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_scaler = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_atom_a = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_atom_b = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_atom_c = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_angle_k = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_angle_theta0 = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); + auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_atom_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_atom_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_atom_c = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + auto shape_angle_k = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); + auto shape_angle_theta0 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); for (size_t i = 0; i < shape_uint_crd.size(); i++) ele_uint_crd *= shape_uint_crd[i]; for (size_t i = 0; i < shape_scaler.size(); i++) ele_scaler *= shape_scaler[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_force_with_atom_energy_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_force_with_atom_energy_kernel.h index 54e44c84521..bb88e008a45 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_force_with_atom_energy_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_force_with_atom_energy_kernel.h @@ -35,13 +35,13 @@ class AngleForceWithAtomEnergyGpuKernelMod : public NativeGpuKernelMod { bool Init(const CNodePtr &kernel_node) override { kernel_node_ = kernel_node; angle_numbers = static_cast(GetAttr(kernel_node, "angle_numbers")); - auto shape_uint_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_scaler = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_atom_a = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_atom_b = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_atom_c = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_angle_k = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_angle_theta0 = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); + auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_atom_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_atom_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_atom_c = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + auto shape_angle_k = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); + auto shape_angle_theta0 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); for (size_t i = 0; i < shape_uint_crd.size(); i++) ele_uint_crd *= shape_uint_crd[i]; for (size_t i = 0; i < shape_scaler.size(); i++) ele_scaler *= shape_scaler[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_atom_energy_cuda_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_atom_energy_cuda_gpu_kernel.h index 0aa39729ae5..539ccccd181 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_atom_energy_cuda_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_atom_energy_cuda_gpu_kernel.h @@ -40,12 +40,12 @@ class BondAtomEnergyCudaGpuKernelMod : public NativeGpuKernelMod { kernel_node_ = kernel_node; bond_numbers = static_cast(GetAttr(kernel_node, "bond_numbers")); atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - auto shape_uint_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_scaler = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_atom_a = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_atom_b = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_bond_k = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_bond_r0 = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); + auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_atom_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_atom_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_bond_k = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + auto shape_bond_r0 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); for (size_t i = 0; i < shape_uint_crd.size(); i++) ele_uint_crd *= shape_uint_crd[i]; for (size_t i = 0; i < shape_scaler.size(); i++) ele_scaler *= shape_scaler[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_energy_cuda_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_energy_cuda_gpu_kernel.h index 5be844e02c9..5ab7cc5ba0b 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_energy_cuda_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_energy_cuda_gpu_kernel.h @@ -41,12 +41,12 @@ class BondEnergyCudaGpuKernelMod : public NativeGpuKernelMod { kernel_node_ = kernel_node; bond_numbers = static_cast(GetAttr(kernel_node, "bond_numbers")); atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - auto shape_uint_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_scaler = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_atom_a = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_atom_b = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_bond_k = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_bond_r0 = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); + auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_atom_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_atom_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_bond_k = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + auto shape_bond_r0 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); for (size_t i = 0; i < shape_uint_crd.size(); i++) ele_uint_crd *= shape_uint_crd[i]; for (size_t i = 0; i < shape_scaler.size(); i++) ele_scaler *= shape_scaler[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_cuda_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_cuda_gpu_kernel.h index fcb51d8c4d2..5930908d277 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_cuda_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_cuda_gpu_kernel.h @@ -41,12 +41,12 @@ class BondForceCudaGpuKernelMod : public NativeGpuKernelMod { kernel_node_ = kernel_node; bond_numbers = static_cast(GetAttr(kernel_node, "bond_numbers")); atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - auto shape_uint_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_scaler = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_atom_a = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_atom_b = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_bond_k = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_bond_r0 = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); + auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_atom_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_atom_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_bond_k = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + auto shape_bond_r0 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); for (size_t i = 0; i < shape_uint_crd.size(); i++) ele_uint_crd *= shape_uint_crd[i]; for (size_t i = 0; i < shape_scaler.size(); i++) ele_scaler *= shape_scaler[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_with_atom_energy_and_virial_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_with_atom_energy_and_virial_kernel.h index ae7e0d0572e..181b8f3e4f9 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_with_atom_energy_and_virial_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_with_atom_energy_and_virial_kernel.h @@ -41,12 +41,12 @@ class BondForceWithAtomEnergyAndVirialGpuKernelMod : public NativeGpuKernelMod { kernel_node_ = kernel_node; bond_numbers = static_cast(GetAttr(kernel_node, "bond_numbers")); atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - auto shape_uint_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_scaler = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_atom_a = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_atom_b = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_bond_k = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_bond_r0 = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); + auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_atom_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_atom_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_bond_k = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + auto shape_bond_r0 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); for (size_t i = 0; i < shape_uint_crd.size(); i++) ele_uint_crd *= shape_uint_crd[i]; for (size_t i = 0; i < shape_scaler.size(); i++) ele_scaler *= shape_scaler[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_with_atom_energy_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_with_atom_energy_kernel.h index 4c735ca7512..24cfdb77390 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_with_atom_energy_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_with_atom_energy_kernel.h @@ -41,12 +41,12 @@ class BondForceWithAtomEnergyGpuKernelMod : public NativeGpuKernelMod { kernel_node_ = kernel_node; bond_numbers = static_cast(GetAttr(kernel_node, "bond_numbers")); atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - auto shape_uint_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_scaler = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_atom_a = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_atom_b = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_bond_k = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_bond_r0 = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); + auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_atom_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_atom_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_bond_k = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + auto shape_bond_r0 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); for (size_t i = 0; i < shape_uint_crd.size(); i++) ele_uint_crd *= shape_uint_crd[i]; for (size_t i = 0; i < shape_scaler.size(); i++) ele_scaler *= shape_scaler[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_with_atom_virial_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_with_atom_virial_kernel.h index 55c1977aeca..9dc988af70a 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_with_atom_virial_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_with_atom_virial_kernel.h @@ -41,12 +41,12 @@ class BondForceWithAtomVirialGpuKernelMod : public NativeGpuKernelMod { kernel_node_ = kernel_node; bond_numbers = static_cast(GetAttr(kernel_node, "bond_numbers")); atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - auto shape_uint_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_scaler = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_atom_a = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_atom_b = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_bond_k = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_bond_r0 = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); + auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_atom_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_atom_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_bond_k = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + auto shape_bond_r0 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); for (size_t i = 0; i < shape_uint_crd.size(); i++) ele_uint_crd *= shape_uint_crd[i]; for (size_t i = 0; i < shape_scaler.size(); i++) ele_scaler *= shape_scaler[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/atomcrdtocv_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/atomcrdtocv_kernel.h index 02b7550fca4..c65e63f5143 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/atomcrdtocv_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/atomcrdtocv_kernel.h @@ -42,9 +42,9 @@ class AtomCrdToCVGpuKernelMod : public NativeGpuKernelMod { end_serial = static_cast(GetAttr(kernel_node, "end_serial")); number = static_cast(GetAttr(kernel_node, "number")); atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - auto shape_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_old_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_box = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_old_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_box = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); for (size_t i = 0; i < shape_crd.size(); i++) ele_crd *= shape_crd[i]; for (size_t i = 0; i < shape_old_crd.size(); i++) ele_old_crd *= shape_old_crd[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/crd_to_uint_crd_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/crd_to_uint_crd_kernel.h index 9d0243d0977..324c149f90e 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/crd_to_uint_crd_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/crd_to_uint_crd_kernel.h @@ -38,8 +38,8 @@ class CrdToUintCrdGpuKernelMod : public NativeGpuKernelMod { kernel_node_ = kernel_node; atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - auto shape_crd_to_uint_crd_cof = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_crd_to_uint_crd_cof = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); for (size_t i = 0; i < shape_crd_to_uint_crd_cof.size(); i++) ele_crd_to_uint_crd_cof *= shape_crd_to_uint_crd_cof[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/crd_to_uint_crd_quarter_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/crd_to_uint_crd_quarter_kernel.h index 935965cc1c3..56e21944166 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/crd_to_uint_crd_quarter_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/crd_to_uint_crd_quarter_kernel.h @@ -38,8 +38,8 @@ class CrdToUintCrdQuarterGpuKernelMod : public NativeGpuKernelMod { kernel_node_ = kernel_node; atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - auto shape_crd_to_uint_crd_cof = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_crd_to_uint_crd_cof = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); for (size_t i = 0; i < shape_crd_to_uint_crd_cof.size(); i++) ele_crd_to_uint_crd_cof *= shape_crd_to_uint_crd_cof[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/get_center_of_mass_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/get_center_of_mass_kernel.h index 6c390793a89..63f045e2f39 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/get_center_of_mass_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/get_center_of_mass_kernel.h @@ -38,11 +38,11 @@ class GetCenterOfMassGpuKernelMod : public NativeGpuKernelMod { kernel_node_ = kernel_node; residue_numbers = static_cast(GetAttr(kernel_node, "residue_numbers")); - auto shape_start = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_end = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_atom_mass = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_residue_mass_inverse = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + auto shape_start = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_end = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_atom_mass = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_residue_mass_inverse = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); for (size_t i = 0; i < shape_start.size(); i++) ele_start *= shape_start[i]; for (size_t i = 0; i < shape_end.size(); i++) ele_end *= shape_end[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/getcenter_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/getcenter_kernel.h index f89ca37698f..f73470bf80f 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/getcenter_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/getcenter_kernel.h @@ -39,8 +39,8 @@ class GetCenterOfGeometryGpuKernelMod : public NativeGpuKernelMod { center_numbers = static_cast(GetAttr(kernel_node, "center_numbers")); center_numbers_inverse = static_cast(GetAttr(kernel_node, "center_numbers_inverse")); - auto shape_center_atoms = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_center_atoms = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); for (size_t i = 0; i < shape_center_atoms.size(); i++) ele_center_atoms *= shape_center_atoms[i]; for (size_t i = 0; i < shape_crd.size(); i++) ele_crd *= shape_crd[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/map_center_of_mass_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/map_center_of_mass_kernel.h index 97dc17d5e58..736e16f690e 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/map_center_of_mass_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/map_center_of_mass_kernel.h @@ -38,12 +38,12 @@ class MapCenterOfMassGpuKernelMod : public NativeGpuKernelMod { kernel_node_ = kernel_node; residue_numbers = static_cast(GetAttr(kernel_node, "residue_numbers")); - auto shape_start = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_end = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_center_of_mass = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_box_length = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_no_wrap_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); + auto shape_start = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_end = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_center_of_mass = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_box_length = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_no_wrap_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + auto shape_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); for (size_t i = 0; i < shape_start.size(); i++) ele_start *= shape_start[i]; for (size_t i = 0; i < shape_end.size(); i++) ele_end *= shape_end[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/mdtemperature_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/mdtemperature_kernel.h index f3f8339da2f..ad18e79d240 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/mdtemperature_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/mdtemperature_kernel.h @@ -38,10 +38,10 @@ class MDTemperatureGpuKernelMod : public NativeGpuKernelMod { kernel_node_ = kernel_node; residue_numbers = static_cast(GetAttr(kernel_node, "residue_numbers")); - auto shape_start = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_end = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_atom_vel = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_atom_mass = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_start = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_end = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_atom_vel = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_atom_mass = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); for (size_t i = 0; i < shape_start.size(); i++) ele_start *= shape_start[i]; for (size_t i = 0; i < shape_end.size(); i++) ele_end *= shape_end[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/total_c6_get_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/total_c6_get_kernel.h index a1bcd4a1dde..156547789d7 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/total_c6_get_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/total_c6_get_kernel.h @@ -38,8 +38,8 @@ class TotalC6GetGpuKernelMod : public NativeGpuKernelMod { kernel_node_ = kernel_node; atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - auto shape_atom_lj_type = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_lj_b = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_atom_lj_type = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_lj_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); for (size_t i = 0; i < shape_atom_lj_type.size(); i++) ele_atom_lj_type *= shape_atom_lj_type[i]; for (size_t i = 0; i < shape_lj_b.size(); i++) ele_lj_b *= shape_lj_b[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/crdmcmap/cal_no_wrap_crd_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/crdmcmap/cal_no_wrap_crd_kernel.h index 8a5cbda121e..21256cde82d 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/crdmcmap/cal_no_wrap_crd_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/crdmcmap/cal_no_wrap_crd_kernel.h @@ -40,9 +40,9 @@ class CalculateNoWrapCrdGpuKernelMod : public NativeGpuKernelMod { // get bond_numbers kernel_node_ = kernel_node; atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - auto shape_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_box = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_box_map_times = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_box = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_box_map_times = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); for (size_t i = 0; i < shape_crd.size(); i++) ele_crd *= shape_crd[i]; for (size_t i = 0; i < shape_box.size(); i++) ele_box *= shape_box[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/crdmcmap/refresh_boxmaptimes_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/crdmcmap/refresh_boxmaptimes_kernel.h index ba052a8f385..2abff75c3dd 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/crdmcmap/refresh_boxmaptimes_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/crdmcmap/refresh_boxmaptimes_kernel.h @@ -40,10 +40,10 @@ class RefreshBoxmaptimesGpuKernelMod : public NativeGpuKernelMod { // get bond_numbers kernel_node_ = kernel_node; atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - auto shape_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_old_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_box_length_inverse = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_box_map_times = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_old_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_box_length_inverse = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_box_map_times = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); for (size_t i = 0; i < shape_crd.size(); i++) ele_crd *= shape_crd[i]; for (size_t i = 0; i < shape_old_crd.size(); i++) ele_old_crd *= shape_old_crd[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_atom_energy_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_atom_energy_kernel.h index 94b6bd21e18..35cfcf59df9 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_atom_energy_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_atom_energy_kernel.h @@ -35,17 +35,17 @@ class DihedralAtomEnergyGpuKernelMod : public NativeGpuKernelMod { bool Init(const CNodePtr &kernel_node) override { kernel_node_ = kernel_node; dihedral_numbers = static_cast(GetAttr(kernel_node, "dihedral_numbers")); - auto shape_uint_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_scaler = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_atom_a = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_atom_b = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_atom_c = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_atom_d = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_ipn = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_pk = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); - auto shape_gamc = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); - auto shape_gams = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 9); - auto shape_pn = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 10); + auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_atom_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_atom_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_atom_c = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + auto shape_atom_d = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); + auto shape_ipn = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); + auto shape_pk = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); + auto shape_gamc = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); + auto shape_gams = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 9); + auto shape_pn = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 10); for (size_t i = 0; i < shape_uint_crd.size(); i++) ele_uint_crd *= shape_uint_crd[i]; for (size_t i = 0; i < shape_scaler.size(); i++) ele_scaler *= shape_scaler[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_energy_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_energy_kernel.h index a8a51293e04..dee16deb947 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_energy_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_energy_kernel.h @@ -35,17 +35,17 @@ class DihedralEnergyGpuKernelMod : public NativeGpuKernelMod { bool Init(const CNodePtr &kernel_node) override { kernel_node_ = kernel_node; dihedral_numbers = static_cast(GetAttr(kernel_node, "dihedral_numbers")); - auto shape_uint_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_scaler = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_atom_a = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_atom_b = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_atom_c = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_atom_d = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_ipn = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_pk = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); - auto shape_gamc = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); - auto shape_gams = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 9); - auto shape_pn = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 10); + auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_atom_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_atom_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_atom_c = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + auto shape_atom_d = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); + auto shape_ipn = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); + auto shape_pk = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); + auto shape_gamc = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); + auto shape_gams = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 9); + auto shape_pn = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 10); for (size_t i = 0; i < shape_uint_crd.size(); i++) ele_uint_crd *= shape_uint_crd[i]; for (size_t i = 0; i < shape_scaler.size(); i++) ele_scaler *= shape_scaler[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_force_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_force_kernel.h index d07471ee7d5..241e763d624 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_force_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_force_kernel.h @@ -35,17 +35,17 @@ class DihedralForceGpuKernelMod : public NativeGpuKernelMod { bool Init(const CNodePtr &kernel_node) override { kernel_node_ = kernel_node; dihedral_numbers = static_cast(GetAttr(kernel_node, "dihedral_numbers")); - auto shape_uint_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_scaler = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_atom_a = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_atom_b = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_atom_c = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_atom_d = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_ipn = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_pk = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); - auto shape_gamc = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); - auto shape_gams = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 9); - auto shape_pn = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 10); + auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_atom_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_atom_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_atom_c = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + auto shape_atom_d = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); + auto shape_ipn = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); + auto shape_pk = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); + auto shape_gamc = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); + auto shape_gams = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 9); + auto shape_pn = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 10); for (size_t i = 0; i < shape_uint_crd.size(); i++) ele_uint_crd *= shape_uint_crd[i]; for (size_t i = 0; i < shape_scaler.size(); i++) ele_scaler *= shape_scaler[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_force_with_atom_energy_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_force_with_atom_energy_kernel.h index 4d4086517dc..756dabb6daa 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_force_with_atom_energy_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_force_with_atom_energy_kernel.h @@ -35,17 +35,17 @@ class DihedralForceWithAtomEnergyGpuKernelMod : public NativeGpuKernelMod { bool Init(const CNodePtr &kernel_node) override { kernel_node_ = kernel_node; dihedral_numbers = static_cast(GetAttr(kernel_node, "dihedral_numbers")); - auto shape_uint_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_scaler = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_atom_a = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_atom_b = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_atom_c = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_atom_d = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_ipn = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_pk = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); - auto shape_gamc = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); - auto shape_gams = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 9); - auto shape_pn = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 10); + auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_atom_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_atom_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_atom_c = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + auto shape_atom_d = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); + auto shape_ipn = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); + auto shape_pk = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); + auto shape_gamc = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); + auto shape_gams = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 9); + auto shape_pn = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 10); for (size_t i = 0; i < shape_uint_crd.size(); i++) ele_uint_crd *= shape_uint_crd[i]; for (size_t i = 0; i < shape_scaler.size(); i++) ele_scaler *= shape_scaler[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_direct_cf_force_with_lj_virial_direct_cf_energy_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_direct_cf_force_with_lj_virial_direct_cf_energy_kernel.h index 4526cf2e1f6..8b3dc606446 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_direct_cf_force_with_lj_virial_direct_cf_energy_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_direct_cf_force_with_lj_virial_direct_cf_energy_kernel.h @@ -42,14 +42,14 @@ class LJForceWithVirialEnergyGpuKernelMod : public NativeGpuKernelMod { cutoff = static_cast(GetAttr(kernel_node, "cutoff")); pme_beta = static_cast(GetAttr(kernel_node, "pme_beta")); - auto shape_uint_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_LJtype = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_charge = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_scaler = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_nl_numbers = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_nl_serial = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_d_LJ_a = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_d_LJ_b = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); + auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_LJtype = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_charge = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_nl_numbers = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + auto shape_nl_serial = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); + auto shape_d_LJ_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); + auto shape_d_LJ_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); for (size_t i = 0; i < shape_uint_crd.size(); i++) ele_uint_crd *= shape_uint_crd[i]; for (size_t i = 0; i < shape_LJtype.size(); i++) ele_LJtype *= shape_LJtype[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_energy_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_energy_kernel.h index 34fc479bb57..20d542606cf 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_energy_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_energy_kernel.h @@ -36,14 +36,14 @@ class LJEnergyGpuKernelMod : public NativeGpuKernelMod { atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); cutoff_square = static_cast(GetAttr(kernel_node, "cutoff_square")); - auto shape_uint_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_LJtype = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_charge = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_scaler = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_nl_numbers = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_nl_serial = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_d_LJ_a = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_d_LJ_b = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); + auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_LJtype = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_charge = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_nl_numbers = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + auto shape_nl_serial = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); + auto shape_d_LJ_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); + auto shape_d_LJ_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); for (size_t i = 0; i < shape_uint_crd.size(); i++) ele_uint_crd *= shape_uint_crd[i]; for (size_t i = 0; i < shape_LJtype.size(); i++) ele_LJtype *= shape_LJtype[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_kernel.h index ffd02e02fa8..02cea76b769 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_kernel.h @@ -36,14 +36,14 @@ class LJForceGpuKernelMod : public NativeGpuKernelMod { atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); cutoff_square = static_cast(GetAttr(kernel_node, "cutoff_square")); - auto shape_uint_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_LJtype = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_charge = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_scaler = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_nl_numbers = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_nl_serial = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_d_LJ_a = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_d_LJ_b = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); + auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_LJtype = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_charge = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_nl_numbers = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + auto shape_nl_serial = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); + auto shape_d_LJ_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); + auto shape_d_LJ_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); for (size_t i = 0; i < shape_uint_crd.size(); i++) ele_uint_crd *= shape_uint_crd[i]; for (size_t i = 0; i < shape_LJtype.size(); i++) ele_LJtype *= shape_LJtype[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_with_pme_direct_force_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_with_pme_direct_force_kernel.h index f5eba16255f..b2dbae6f7c2 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_with_pme_direct_force_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_with_pme_direct_force_kernel.h @@ -37,14 +37,14 @@ class LJForceWithPMEDirectForceGpuKernelMod : public NativeGpuKernelMod { cutoff = static_cast(GetAttr(kernel_node, "cutoff")); pme_beta = static_cast(GetAttr(kernel_node, "pme_beta")); - auto shape_uint_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_LJtype = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_charge = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_scaler = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_nl_numbers = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_nl_serial = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_d_LJ_a = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_d_LJ_b = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); + auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_LJtype = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_charge = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_nl_numbers = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + auto shape_nl_serial = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); + auto shape_d_LJ_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); + auto shape_d_LJ_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); for (size_t i = 0; i < shape_uint_crd.size(); i++) ele_uint_crd *= shape_uint_crd[i]; for (size_t i = 0; i < shape_LJtype.size(); i++) ele_LJtype *= shape_LJtype[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_with_pme_direct_force_update_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_with_pme_direct_force_update_kernel.h index 388f5014082..82e69af1ff7 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_with_pme_direct_force_update_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_with_pme_direct_force_update_kernel.h @@ -42,14 +42,14 @@ class LJForceWithPMEDirectForceUpdateGpuKernelMod : public NativeGpuKernelMod { pme_beta = static_cast(GetAttr(kernel_node, "pme_beta")); need_update = static_cast(GetAttr(kernel_node, "need_update")); - auto shape_uint_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_LJtype = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_charge = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_scaler = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_nl_numbers = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_nl_serial = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_d_LJ_a = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_d_LJ_b = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); + auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_LJtype = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_charge = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_nl_numbers = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + auto shape_nl_serial = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); + auto shape_d_LJ_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); + auto shape_d_LJ_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); for (size_t i = 0; i < shape_uint_crd.size(); i++) ele_uint_crd *= shape_uint_crd[i]; for (size_t i = 0; i < shape_LJtype.size(); i++) ele_LJtype *= shape_LJtype[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_with_virial_energy_update_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_with_virial_energy_update_kernel.h index ac10ff932aa..feaa2231cd9 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_with_virial_energy_update_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_with_virial_energy_update_kernel.h @@ -43,14 +43,14 @@ class LJForceWithVirialEnergyUpdateGpuKernelMod : public NativeGpuKernelMod { pme_beta = static_cast(GetAttr(kernel_node, "pme_beta")); need_update = static_cast(GetAttr(kernel_node, "need_update")); - auto shape_uint_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_LJtype = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_charge = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_scaler = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_nl_numbers = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_nl_serial = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_d_LJ_a = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_d_LJ_b = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); + auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_LJtype = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_charge = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_nl_numbers = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + auto shape_nl_serial = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); + auto shape_d_LJ_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); + auto shape_d_LJ_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); for (size_t i = 0; i < shape_uint_crd.size(); i++) ele_uint_crd *= shape_uint_crd[i]; for (size_t i = 0; i < shape_LJtype.size(); i++) ele_LJtype *= shape_LJtype[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_cf_atom_energy_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_cf_atom_energy_kernel.h index 6c17cee4cdc..1493ef0db71 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_cf_atom_energy_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_cf_atom_energy_kernel.h @@ -39,13 +39,13 @@ class Dihedral14CFAtomEnergyGpuKernelMod : public NativeGpuKernelMod { dihedral_14_numbers = static_cast(GetAttr(kernel_node, "dihedral_14_numbers")); atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - auto shape_uint_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_LJtype = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_charge = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_boxlength_f = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_a_14 = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_b_14 = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_cf_scale_factor = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); + auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_LJtype = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_charge = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_boxlength_f = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_a_14 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + auto shape_b_14 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); + auto shape_cf_scale_factor = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); for (size_t i = 0; i < shape_uint_crd.size(); i++) ele_uint_crd *= shape_uint_crd[i]; for (size_t i = 0; i < shape_LJtype.size(); i++) ele_LJtype *= shape_LJtype[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_cf_energy_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_cf_energy_kernel.h index ef3bc821769..cfcdc876c59 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_cf_energy_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_cf_energy_kernel.h @@ -39,13 +39,13 @@ class Dihedral14CFEnergyGpuKernelMod : public NativeGpuKernelMod { dihedral_14_numbers = static_cast(GetAttr(kernel_node, "dihedral_14_numbers")); atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - auto shape_uint_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_LJtype = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_charge = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_boxlength_f = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_a_14 = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_b_14 = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_cf_scale_factor = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); + auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_LJtype = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_charge = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_boxlength_f = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_a_14 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + auto shape_b_14 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); + auto shape_cf_scale_factor = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); for (size_t i = 0; i < shape_uint_crd.size(); i++) ele_uint_crd *= shape_uint_crd[i]; for (size_t i = 0; i < shape_LJtype.size(); i++) ele_LJtype *= shape_LJtype[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_atom_energy_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_atom_energy_kernel.h index 51fd3e02153..f6ba9a6d53b 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_atom_energy_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_atom_energy_kernel.h @@ -39,15 +39,15 @@ class Dihedral14LJAtomEnergyGpuKernelMod : public NativeGpuKernelMod { dihedral_14_numbers = static_cast(GetAttr(kernel_node, "dihedral_14_numbers")); atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - auto shape_uint_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_LJtype = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_charge = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_boxlength_f = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_a_14 = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_b_14 = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_lj_scale_factor = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_LJ_type_A = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); - auto shape_LJ_type_B = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); + auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_LJtype = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_charge = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_boxlength_f = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_a_14 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + auto shape_b_14 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); + auto shape_lj_scale_factor = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); + auto shape_LJ_type_A = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); + auto shape_LJ_type_B = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); for (size_t i = 0; i < shape_uint_crd.size(); i++) ele_uint_crd *= shape_uint_crd[i]; for (size_t i = 0; i < shape_LJtype.size(); i++) ele_LJtype *= shape_LJtype[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_and_virial_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_and_virial_kernel.h index 17865345593..f084100cbb7 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_and_virial_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_and_virial_kernel.h @@ -40,16 +40,16 @@ class Dihedral14LJCFForceWithAtomEnergyAndVirialGpuKernelMod : public NativeGpuK dihedral_14_numbers = static_cast(GetAttr(kernel_node, "dihedral_14_numbers")); atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - auto shape_uint_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_LJtype = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_charge = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_boxlength_f = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_a_14 = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_b_14 = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_lj_scale_factor = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_cf_scale_factor = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); - auto shape_LJ_type_A = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); - auto shape_LJ_type_B = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 9); + auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_LJtype = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_charge = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_boxlength_f = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_a_14 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + auto shape_b_14 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); + auto shape_lj_scale_factor = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); + auto shape_cf_scale_factor = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); + auto shape_LJ_type_A = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); + auto shape_LJ_type_B = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 9); for (size_t i = 0; i < shape_uint_crd.size(); i++) ele_uint_crd *= shape_uint_crd[i]; for (size_t i = 0; i < shape_LJtype.size(); i++) ele_LJtype *= shape_LJtype[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_kernel.h index 6e33ebf2561..7e2423c2d3c 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_kernel.h @@ -39,16 +39,16 @@ class Dihedral14LJCFForceWithAtomEnergyGpuKernelMod : public NativeGpuKernelMod dihedral_14_numbers = static_cast(GetAttr(kernel_node, "dihedral_14_numbers")); atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - auto shape_uint_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_LJtype = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_charge = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_boxlength_f = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_a_14 = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_b_14 = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_lj_scale_factor = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_cf_scale_factor = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); - auto shape_LJ_type_A = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); - auto shape_LJ_type_B = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 9); + auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_LJtype = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_charge = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_boxlength_f = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_a_14 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + auto shape_b_14 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); + auto shape_lj_scale_factor = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); + auto shape_cf_scale_factor = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); + auto shape_LJ_type_A = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); + auto shape_LJ_type_B = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 9); for (size_t i = 0; i < shape_uint_crd.size(); i++) ele_uint_crd *= shape_uint_crd[i]; for (size_t i = 0; i < shape_LJtype.size(); i++) ele_LJtype *= shape_LJtype[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_energy_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_energy_kernel.h index edd9c83d3d3..99ecdfb8760 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_energy_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_energy_kernel.h @@ -39,15 +39,15 @@ class Dihedral14LJEnergyGpuKernelMod : public NativeGpuKernelMod { dihedral_14_numbers = static_cast(GetAttr(kernel_node, "dihedral_14_numbers")); atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - auto shape_uint_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_LJtype = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_charge = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_boxlength_f = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_a_14 = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_b_14 = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_lj_scale_factor = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_LJ_type_A = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); - auto shape_LJ_type_B = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); + auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_LJtype = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_charge = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_boxlength_f = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_a_14 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + auto shape_b_14 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); + auto shape_lj_scale_factor = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); + auto shape_LJ_type_A = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); + auto shape_LJ_type_B = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); for (size_t i = 0; i < shape_uint_crd.size(); i++) ele_uint_crd *= shape_uint_crd[i]; for (size_t i = 0; i < shape_LJtype.size(); i++) ele_LJtype *= shape_LJtype[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_force_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_force_gpu_kernel.h index f51ebc7cc54..8081aa143fc 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_force_gpu_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_force_gpu_kernel.h @@ -39,15 +39,15 @@ class Dihedral14LJForceGpuKernelMod : public NativeGpuKernelMod { dihedral_14_numbers = static_cast(GetAttr(kernel_node, "dihedral_14_numbers")); atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - auto shape_uint_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_LJtype = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_charge = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_boxlength_f = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_a_14 = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_b_14 = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_lj_scale_factor = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_LJ_type_A = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); - auto shape_LJ_type_B = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); + auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_LJtype = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_charge = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_boxlength_f = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_a_14 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + auto shape_b_14 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); + auto shape_lj_scale_factor = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); + auto shape_LJ_type_A = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); + auto shape_LJ_type_B = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); for (size_t i = 0; i < shape_uint_crd.size(); i++) ele_uint_crd *= shape_uint_crd[i]; for (size_t i = 0; i < shape_LJtype.size(); i++) ele_LJtype *= shape_LJtype[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_force_with_direct_cf_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_force_with_direct_cf_kernel.h index baa6a71b36d..10c0ab76c97 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_force_with_direct_cf_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_force_with_direct_cf_kernel.h @@ -39,16 +39,16 @@ class Dihedral14LJForceWithDirectCFGpuKernelMod : public NativeGpuKernelMod { dihedral_14_numbers = static_cast(GetAttr(kernel_node, "dihedral_14_numbers")); atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - auto shape_uint_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_LJtype = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_charge = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_boxlength_f = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_a_14 = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_b_14 = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_lj_scale_factor = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_cf_scale_factor = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); - auto shape_LJ_type_A = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); - auto shape_LJ_type_B = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 9); + auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_LJtype = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_charge = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_boxlength_f = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_a_14 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + auto shape_b_14 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); + auto shape_lj_scale_factor = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); + auto shape_cf_scale_factor = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); + auto shape_LJ_type_A = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); + auto shape_LJ_type_B = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 9); for (size_t i = 0; i < shape_uint_crd.size(); i++) ele_uint_crd *= shape_uint_crd[i]; for (size_t i = 0; i < shape_LJtype.size(); i++) ele_LJtype *= shape_LJtype[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/restrain/restrain_energy_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/restrain/restrain_energy_kernel.h index 441087ae770..8c98e3d785e 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/restrain/restrain_energy_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/restrain/restrain_energy_kernel.h @@ -42,10 +42,10 @@ class RestrainEnergyGpuKernelMod : public NativeGpuKernelMod { restrain_numbers = static_cast(GetAttr(kernel_node, "restrain_numbers")); atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); weight = static_cast(GetAttr(kernel_node, "weight")); - auto shape_restrain_list = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_crd_ref = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_scaler = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_restrain_list = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_crd_ref = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); for (size_t i = 0; i < shape_crd.size(); i++) ele_crd *= shape_crd[i]; for (size_t i = 0; i < shape_scaler.size(); i++) ele_scaler *= shape_scaler[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/restrain/restrain_force_atom_energy_virial_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/restrain/restrain_force_atom_energy_virial_kernel.h index e2a6da86d99..41ed646ac26 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/restrain/restrain_force_atom_energy_virial_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/restrain/restrain_force_atom_energy_virial_kernel.h @@ -42,10 +42,10 @@ class RestrainForceWithAtomenergyAndVirialGpuKernelMod : public NativeGpuKernelM restrain_numbers = static_cast(GetAttr(kernel_node, "restrain_numbers")); atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); weight = static_cast(GetAttr(kernel_node, "weight")); - auto shape_restrain_list = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_crd_ref = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_scaler = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_restrain_list = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_crd_ref = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); for (size_t i = 0; i < shape_crd.size(); i++) ele_crd *= shape_crd[i]; for (size_t i = 0; i < shape_scaler.size(); i++) ele_scaler *= shape_scaler[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/restrain/restrain_force_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/restrain/restrain_force_kernel.h index 8e180f58254..bb24727f7ca 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/restrain/restrain_force_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/restrain/restrain_force_kernel.h @@ -42,10 +42,10 @@ class RestrainForceGpuKernelMod : public NativeGpuKernelMod { restrain_numbers = static_cast(GetAttr(kernel_node, "restrain_numbers")); atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); factor = static_cast(GetAttr(kernel_node, "factor")); - auto shape_restrain_list = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_uint_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_uint_crd_ref = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_scaler = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_restrain_list = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_uint_crd_ref = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); for (size_t i = 0; i < shape_uint_crd.size(); i++) ele_uint_crd *= shape_uint_crd[i]; for (size_t i = 0; i < shape_scaler.size(); i++) ele_scaler *= shape_scaler[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_cycle_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_cycle_kernel.h index 078ae2c1a7d..401a80f7d78 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_cycle_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_cycle_kernel.h @@ -42,13 +42,13 @@ class ConstrainForceCycleGpuKernelMod : public NativeGpuKernelMod { atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); constrain_pair_numbers = static_cast(GetAttr(kernel_node, "constrain_pair_numbers")); - auto shape_uint_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_scaler = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_pair_dr = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_atom_i_serials = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_atom_j_serials = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_constant_rs = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_constrain_ks = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); + auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_pair_dr = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_atom_i_serials = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_atom_j_serials = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + auto shape_constant_rs = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); + auto shape_constrain_ks = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); for (size_t i = 0; i < shape_uint_crd.size(); i++) ele_uint_crd *= shape_uint_crd[i]; for (size_t i = 0; i < shape_scaler.size(); i++) ele_scaler *= shape_scaler[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_cycle_with_virial_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_cycle_with_virial_kernel.h index 9b3402134f4..6ed40fbf1f2 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_cycle_with_virial_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_cycle_with_virial_kernel.h @@ -42,13 +42,13 @@ class ConstrainForceCycleWithVirialGpuKernelMod : public NativeGpuKernelMod { atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); constrain_pair_numbers = static_cast(GetAttr(kernel_node, "constrain_pair_numbers")); - auto shape_uint_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_scaler = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_pair_dr = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_atom_i_serials = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_atom_j_serials = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_constant_rs = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_constrain_ks = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); + auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_pair_dr = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_atom_i_serials = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_atom_j_serials = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + auto shape_constant_rs = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); + auto shape_constrain_ks = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); for (size_t i = 0; i < shape_uint_crd.size(); i++) ele_uint_crd *= shape_uint_crd[i]; for (size_t i = 0; i < shape_scaler.size(); i++) ele_scaler *= shape_scaler[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_kernel.h index 672ba22f6c2..431b30591dd 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_kernel.h @@ -47,16 +47,16 @@ class ConstrainForceGpuKernelMod : public NativeGpuKernelMod { iteration_numbers = static_cast(GetAttr(kernel_node, "iteration_numbers")); half_exp_gamma_plus_half = static_cast(GetAttr(kernel_node, "half_exp_gamma_plus_half")); - auto shape_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_quarter_cof = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_mass_inverse = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_quarter_cof = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_mass_inverse = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_scaler = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_pair_dr = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_atom_i_serials = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_atom_j_serials = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_constant_rs = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); - auto shape_constrain_ks = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); + auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_pair_dr = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + auto shape_atom_i_serials = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); + auto shape_atom_j_serials = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); + auto shape_constant_rs = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); + auto shape_constrain_ks = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); for (size_t i = 0; i < shape_scaler.size(); i++) ele_scaler *= shape_scaler[i]; for (size_t i = 0; i < shape_pair_dr.size(); i++) ele_pair_dr *= shape_pair_dr[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_virial_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_virial_kernel.h index 983a233316d..010247a89e2 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_virial_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_virial_kernel.h @@ -46,16 +46,16 @@ class ConstrainForceVirialGpuKernelMod : public NativeGpuKernelMod { iteration_numbers = static_cast(GetAttr(kernel_node, "iteration_numbers")); half_exp_gamma_plus_half = static_cast(GetAttr(kernel_node, "half_exp_gamma_plus_half")); - auto shape_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_quarter_cof = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_mass_inverse = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_quarter_cof = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_mass_inverse = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_scaler = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_pair_dr = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_atom_i_serials = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_atom_j_serials = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_constant_rs = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); - auto shape_constrain_ks = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); + auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_pair_dr = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + auto shape_atom_i_serials = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); + auto shape_atom_j_serials = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); + auto shape_constant_rs = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); + auto shape_constrain_ks = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); for (size_t i = 0; i < shape_scaler.size(); i++) ele_scaler *= shape_scaler[i]; for (size_t i = 0; i < shape_pair_dr.size(); i++) ele_pair_dr *= shape_pair_dr[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_kernel.h index bdc2cf0f5bf..5b207fdb747 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_kernel.h @@ -47,16 +47,16 @@ class ConstrainGpuKernelMod : public NativeGpuKernelMod { iteration_numbers = static_cast(GetAttr(kernel_node, "iteration_numbers")); half_exp_gamma_plus_half = static_cast(GetAttr(kernel_node, "half_exp_gamma_plus_half")); - auto shape_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_quarter_cof = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_mass_inverse = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_quarter_cof = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_mass_inverse = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_scaler = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_pair_dr = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_atom_i_serials = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_atom_j_serials = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_constant_rs = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); - auto shape_constrain_ks = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); + auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_pair_dr = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + auto shape_atom_i_serials = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); + auto shape_atom_j_serials = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); + auto shape_constant_rs = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); + auto shape_constrain_ks = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); for (size_t i = 0; i < shape_scaler.size(); i++) ele_scaler *= shape_scaler[i]; for (size_t i = 0; i < shape_pair_dr.size(); i++) ele_pair_dr *= shape_pair_dr[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/last_crd_to_dr_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/last_crd_to_dr_kernel.h index 990a6b7e99e..cde54dbae7c 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/last_crd_to_dr_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/last_crd_to_dr_kernel.h @@ -41,13 +41,13 @@ class LastCrdToDrGpuKernelMod : public NativeGpuKernelMod { kernel_node_ = kernel_node; constrain_pair_numbers = static_cast(GetAttr(kernel_node, "constrain_pair_numbers")); - auto shape_atom_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_quater_cof = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_uint_dr_to_dr = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_atom_i_serials = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_atom_j_serials = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_constant_rs = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_constrain_ks = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); + auto shape_atom_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_quater_cof = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_uint_dr_to_dr = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_atom_i_serials = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_atom_j_serials = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); + auto shape_constant_rs = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); + auto shape_constrain_ks = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); for (size_t i = 0; i < shape_atom_crd.size(); i++) ele_atom_crd *= shape_atom_crd[i]; for (size_t i = 0; i < shape_quater_cof.size(); i++) ele_quater_cof *= shape_quater_cof[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/refresh_crd_vel_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/refresh_crd_vel_kernel.h index 3d12554afa9..d8413e5cd8f 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/refresh_crd_vel_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/refresh_crd_vel_kernel.h @@ -44,10 +44,10 @@ class RefreshCrdVelGpuKernelMod : public NativeGpuKernelMod { dt = static_cast(GetAttr(kernel_node, "dt")); exp_gamma = static_cast(GetAttr(kernel_node, "exp_gamma")); half_exp_gamma_plus_half = static_cast(GetAttr(kernel_node, "half_exp_gamma_plus_half")); - auto shape_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_vel = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_test_frc = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_mass_inverse = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_vel = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_test_frc = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_mass_inverse = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); for (size_t i = 0; i < shape_crd.size(); i++) ele_crd *= shape_crd[i]; for (size_t i = 0; i < shape_vel.size(); i++) ele_vel *= shape_vel[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/refresh_uint_crd_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/refresh_uint_crd_kernel.h index f1d7f0ec3c3..b4e42808fd1 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/refresh_uint_crd_kernel.h +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/refresh_uint_crd_kernel.h @@ -41,10 +41,10 @@ class RefreshUintCrdGpuKernelMod : public NativeGpuKernelMod { kernel_node_ = kernel_node; atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); half_exp_gamma_plus_half = static_cast(GetAttr(kernel_node, "half_exp_gamma_plus_half")); - auto shape_crd = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_quarter_cof = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_test_frc = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_mass_inverse = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); + auto shape_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + auto shape_quarter_cof = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + auto shape_test_frc = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + auto shape_mass_inverse = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); for (size_t i = 0; i < shape_crd.size(); i++) ele_crd *= shape_crd[i]; for (size_t i = 0; i < shape_quarter_cof.size(); i++) ele_quarter_cof *= shape_quarter_cof[i]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/trt/trt_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/trt/trt_kernel.cc index c24aed2021e..9862c6343ac 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/trt/trt_kernel.cc +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/trt/trt_kernel.cc @@ -25,8 +25,8 @@ namespace mindspore { namespace kernel { bool TrtKernelMod::Init(const CNodePtr &kernel_node) { - auto kernel_name = AnfAlgo::GetCNodeName(kernel_node); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel_node); for (size_t i = 0; i < input_num; i++) { auto input_shape = AnfAlgo::GetInputDeviceShape(kernel_node, i); auto type_id = AnfAlgo::GetInputDeviceDataType(kernel_node, i); @@ -35,7 +35,7 @@ bool TrtKernelMod::Init(const CNodePtr &kernel_node) { input_size_list_.push_back(size_in_byte); } - size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(kernel_node); for (size_t j = 0; j < output_num; j++) { auto output_shape = AnfAlgo::GetOutputDeviceShape(kernel_node, j); auto type_id = AnfAlgo::GetOutputDeviceDataType(kernel_node, j); diff --git a/mindspore/ccsrc/plugin/device/gpu/optimizer/adam_fusion.cc b/mindspore/ccsrc/plugin/device/gpu/optimizer/adam_fusion.cc index 4b0fd725592..9dfc8b86ccf 100644 --- a/mindspore/ccsrc/plugin/device/gpu/optimizer/adam_fusion.cc +++ b/mindspore/ccsrc/plugin/device/gpu/optimizer/adam_fusion.cc @@ -20,8 +20,9 @@ #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/optimizer/helper.h" namespace mindspore { @@ -35,14 +36,14 @@ kernel::KernelBuildInfoPtr GenerateKernelBuildInfo(CNodePtr node) { kernel::KernelBuildInfo::KernelBuildInfoBuilder builder; MS_EXCEPTION_IF_NULL(node); - size_t input_num = AnfAlgo::GetInputTensorNum(node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(node); for (size_t input_index = 0; input_index < input_num; ++input_index) { - inputs_type.push_back(AnfAlgo::GetPrevNodeOutputInferDataType(node, input_index)); + inputs_type.push_back(common::AnfAlgo::GetPrevNodeOutputInferDataType(node, input_index)); inputs_format.push_back(kOpFormat_DEFAULT); } - size_t output_num = AnfAlgo::GetOutputTensorNum(node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(node); for (size_t output_index = 0; output_index < output_num; ++output_index) { - outputs_type.push_back(AnfAlgo::GetOutputInferDataType(node, output_index)); + outputs_type.push_back(common::AnfAlgo::GetOutputInferDataType(node, output_index)); outputs_format.push_back(kOpFormat_DEFAULT); } builder.SetInputsDeviceType(inputs_type); @@ -163,9 +164,9 @@ const AnfNodePtr AdamFusion::Process(const FuncGraphPtr &graph, const AnfNodePtr m_input, v_input, gradient_input}; auto adam = graph->NewCNode(inputs); MS_EXCEPTION_IF_NULL(adam); - auto types = {AnfAlgo::GetOutputInferDataType(node, 0)}; - auto shapes = {AnfAlgo::GetOutputInferShape(node, 0)}; - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, adam.get()); + auto types = {common::AnfAlgo::GetOutputInferDataType(node, 0)}; + auto shapes = {common::AnfAlgo::GetOutputInferShape(node, 0)}; + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, adam.get()); adam->set_scope(node->scope()); auto build_info = GenerateKernelBuildInfo(adam); AnfAlgo::SetSelectKernelBuildInfo(build_info, adam.get()); diff --git a/mindspore/ccsrc/plugin/device/gpu/optimizer/adam_weight_decay_fusion.cc b/mindspore/ccsrc/plugin/device/gpu/optimizer/adam_weight_decay_fusion.cc index d295e2fa0c0..16b1c1c8f47 100644 --- a/mindspore/ccsrc/plugin/device/gpu/optimizer/adam_weight_decay_fusion.cc +++ b/mindspore/ccsrc/plugin/device/gpu/optimizer/adam_weight_decay_fusion.cc @@ -20,8 +20,9 @@ #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/optimizer/helper.h" namespace mindspore { @@ -35,14 +36,14 @@ kernel::KernelBuildInfoPtr GenerateKernelBuildInfo(CNodePtr node) { kernel::KernelBuildInfo::KernelBuildInfoBuilder builder; MS_EXCEPTION_IF_NULL(node); - size_t input_num = AnfAlgo::GetInputTensorNum(node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(node); for (size_t input_index = 0; input_index < input_num; ++input_index) { - inputs_type.push_back(AnfAlgo::GetPrevNodeOutputInferDataType(node, input_index)); + inputs_type.push_back(common::AnfAlgo::GetPrevNodeOutputInferDataType(node, input_index)); inputs_format.push_back(kOpFormat_DEFAULT); } - size_t output_num = AnfAlgo::GetOutputTensorNum(node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(node); for (size_t output_index = 0; output_index < output_num; ++output_index) { - outputs_type.push_back(AnfAlgo::GetOutputInferDataType(node, output_index)); + outputs_type.push_back(common::AnfAlgo::GetOutputInferDataType(node, output_index)); outputs_format.push_back(kOpFormat_DEFAULT); } builder.SetInputsDeviceType(inputs_type); @@ -168,9 +169,9 @@ const AnfNodePtr AdamWeightDecayFusion::Process(const FuncGraphPtr &graph, const m_input, v_input, gradient_input, weight_decay_input}; auto adam_weight_decay = graph->NewCNode(inputs); MS_EXCEPTION_IF_NULL(adam_weight_decay); - auto types = {AnfAlgo::GetOutputInferDataType(node, 0)}; - auto shapes = {AnfAlgo::GetOutputInferShape(node, 0)}; - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, adam_weight_decay.get()); + auto types = {common::AnfAlgo::GetOutputInferDataType(node, 0)}; + auto shapes = {common::AnfAlgo::GetOutputInferShape(node, 0)}; + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, adam_weight_decay.get()); adam_weight_decay->set_scope(node->scope()); auto build_info = GenerateKernelBuildInfo(adam_weight_decay); diff --git a/mindspore/ccsrc/plugin/device/gpu/optimizer/add_relu_grad_v2_fusion.cc b/mindspore/ccsrc/plugin/device/gpu/optimizer/add_relu_grad_v2_fusion.cc index 84cd9bf69ba..9bcddb0caa6 100644 --- a/mindspore/ccsrc/plugin/device/gpu/optimizer/add_relu_grad_v2_fusion.cc +++ b/mindspore/ccsrc/plugin/device/gpu/optimizer/add_relu_grad_v2_fusion.cc @@ -20,8 +20,9 @@ #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/optimizer/helper.h" namespace mindspore { @@ -34,14 +35,14 @@ kernel::KernelBuildInfoPtr GenerateKernelBuildInfo(CNodePtr node) { std::vector outputs_type; kernel::KernelBuildInfo::KernelBuildInfoBuilder builder; - size_t input_num = AnfAlgo::GetInputTensorNum(node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(node); for (size_t input_index = 0; input_index < input_num; ++input_index) { - inputs_type.push_back(AnfAlgo::GetPrevNodeOutputInferDataType(node, input_index)); + inputs_type.push_back(common::AnfAlgo::GetPrevNodeOutputInferDataType(node, input_index)); inputs_format.push_back(kOpFormat_DEFAULT); } - size_t output_num = AnfAlgo::GetOutputTensorNum(node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(node); for (size_t output_index = 0; output_index < output_num; ++output_index) { - outputs_type.push_back(AnfAlgo::GetOutputInferDataType(node, output_index)); + outputs_type.push_back(common::AnfAlgo::GetOutputInferDataType(node, output_index)); outputs_format.push_back(kOpFormat_DEFAULT); } builder.SetInputsDeviceType(inputs_type); @@ -69,15 +70,15 @@ const AnfNodePtr AddReluGradV2Fusion::Process(const FuncGraphPtr &graph, const A MS_EXCEPTION_IF_NULL(x2); MS_EXCEPTION_IF_NULL(mask); - auto tensor_add = AnfAlgo::GetInputNode(utils::cast(node), 0); + auto tensor_add = common::AnfAlgo::GetInputNode(utils::cast(node), 0); MS_EXCEPTION_IF_NULL(tensor_add); auto users = GetRealNodeUsedList(graph, tensor_add); if (users->size() > 1) { return nullptr; } - std::vector shape1 = AnfAlgo::GetPrevNodeOutputInferShape(tensor_add, 0); - std::vector shape2 = AnfAlgo::GetPrevNodeOutputInferShape(tensor_add, 1); + std::vector shape1 = common::AnfAlgo::GetPrevNodeOutputInferShape(tensor_add, 0); + std::vector shape2 = common::AnfAlgo::GetPrevNodeOutputInferShape(tensor_add, 1); if (shape1 != shape2) { return nullptr; } @@ -87,9 +88,9 @@ const AnfNodePtr AddReluGradV2Fusion::Process(const FuncGraphPtr &graph, const A std::vector inputs = {NewValueNode(prim), x1, x2, mask}; auto add_relugrad = graph->NewCNode(inputs); MS_EXCEPTION_IF_NULL(add_relugrad); - auto types = {AnfAlgo::GetOutputInferDataType(node, 0)}; - auto shapes = {AnfAlgo::GetOutputInferShape(node, 0)}; - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, add_relugrad.get()); + auto types = {common::AnfAlgo::GetOutputInferDataType(node, 0)}; + auto shapes = {common::AnfAlgo::GetOutputInferShape(node, 0)}; + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, add_relugrad.get()); add_relugrad->set_scope(node->scope()); auto build_info = GenerateKernelBuildInfo(add_relugrad); diff --git a/mindspore/ccsrc/plugin/device/gpu/optimizer/add_relu_v2_fusion.cc b/mindspore/ccsrc/plugin/device/gpu/optimizer/add_relu_v2_fusion.cc index 6fb9eef1216..b30411a8fdb 100644 --- a/mindspore/ccsrc/plugin/device/gpu/optimizer/add_relu_v2_fusion.cc +++ b/mindspore/ccsrc/plugin/device/gpu/optimizer/add_relu_v2_fusion.cc @@ -20,8 +20,9 @@ #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/optimizer/helper.h" namespace mindspore { @@ -34,14 +35,14 @@ kernel::KernelBuildInfoPtr GenerateKernelBuildInfo(CNodePtr node) { std::vector outputs_type; kernel::KernelBuildInfo::KernelBuildInfoBuilder builder; - size_t input_num = AnfAlgo::GetInputTensorNum(node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(node); for (size_t input_index = 0; input_index < input_num; ++input_index) { - inputs_type.push_back(AnfAlgo::GetPrevNodeOutputInferDataType(node, input_index)); + inputs_type.push_back(common::AnfAlgo::GetPrevNodeOutputInferDataType(node, input_index)); inputs_format.push_back(kOpFormat_DEFAULT); } - size_t output_num = AnfAlgo::GetOutputTensorNum(node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(node); for (size_t output_index = 0; output_index < output_num; ++output_index) { - outputs_type.push_back(AnfAlgo::GetOutputInferDataType(node, output_index)); + outputs_type.push_back(common::AnfAlgo::GetOutputInferDataType(node, output_index)); outputs_format.push_back(kOpFormat_DEFAULT); } builder.SetInputsDeviceType(inputs_type); @@ -67,15 +68,15 @@ const AnfNodePtr AddReluV2Fusion::Process(const FuncGraphPtr &graph, const AnfNo MS_EXCEPTION_IF_NULL(x1); MS_EXCEPTION_IF_NULL(x2); - auto tensor_add = AnfAlgo::GetInputNode(utils::cast(node), 0); + auto tensor_add = common::AnfAlgo::GetInputNode(utils::cast(node), 0); MS_EXCEPTION_IF_NULL(tensor_add); auto users = GetRealNodeUsedList(graph, tensor_add); if (users->size() > 1) { return nullptr; } - std::vector shape1 = AnfAlgo::GetPrevNodeOutputInferShape(tensor_add, 0); - std::vector shape2 = AnfAlgo::GetPrevNodeOutputInferShape(tensor_add, 1); + std::vector shape1 = common::AnfAlgo::GetPrevNodeOutputInferShape(tensor_add, 0); + std::vector shape2 = common::AnfAlgo::GetPrevNodeOutputInferShape(tensor_add, 1); if (shape1 != shape2) { return nullptr; } @@ -88,13 +89,13 @@ const AnfNodePtr AddReluV2Fusion::Process(const FuncGraphPtr &graph, const AnfNo std::vector types; std::vector> shapes; - size_t output_num = AnfAlgo::GetOutputTensorNum(node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(node); for (size_t i = 0; i < output_num; i++) { - types.push_back(AnfAlgo::GetOutputInferDataType(node, i)); - shapes.push_back(AnfAlgo::GetOutputInferShape(node, i)); + types.push_back(common::AnfAlgo::GetOutputInferDataType(node, i)); + shapes.push_back(common::AnfAlgo::GetOutputInferShape(node, i)); } - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, add_relu.get()); + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, add_relu.get()); add_relu->set_scope(node->scope()); auto build_info = GenerateKernelBuildInfo(add_relu); diff --git a/mindspore/ccsrc/plugin/device/gpu/optimizer/alltoall_fusion.cc b/mindspore/ccsrc/plugin/device/gpu/optimizer/alltoall_fusion.cc index fd346572047..8b2f9af922d 100644 --- a/mindspore/ccsrc/plugin/device/gpu/optimizer/alltoall_fusion.cc +++ b/mindspore/ccsrc/plugin/device/gpu/optimizer/alltoall_fusion.cc @@ -20,8 +20,9 @@ #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "plugin/device/gpu/hal/device/kernel_info_setter.h" #include "plugin/device/gpu/kernel/nccl/nccl_gpu_kernel.h" @@ -40,8 +41,8 @@ inline int64_t NormalizeDim(const std::vector &shape, int64_t dim) { CNodePtr CreateSplitNode(const FuncGraphPtr &graph, const CNodePtr &all_to_all) { MS_EXCEPTION_IF_NULL(graph); MS_EXCEPTION_IF_NULL(all_to_all); - int64_t split_count = AnfAlgo::GetNodeAttr(all_to_all, kAttrSplitCount); - int64_t split_dim = AnfAlgo::GetNodeAttr(all_to_all, kAttrSplitDim); + int64_t split_count = common::AnfAlgo::GetNodeAttr(all_to_all, kAttrSplitCount); + int64_t split_dim = common::AnfAlgo::GetNodeAttr(all_to_all, kAttrSplitDim); if (all_to_all->size() <= kAllToAllInputIdx) { MS_LOG(EXCEPTION) << "Invalid cnode " << all_to_all->DebugString() << " input size " << all_to_all->size(); } @@ -54,8 +55,8 @@ CNodePtr CreateSplitNode(const FuncGraphPtr &graph, const CNodePtr &all_to_all) MS_EXCEPTION_IF_NULL(split); // Judge validity of split_dim and shape - auto dtype = AnfAlgo::GetOutputInferDataType(all_to_all_input, 0); - auto shape = AnfAlgo::GetOutputInferShape(all_to_all_input, 0); + auto dtype = common::AnfAlgo::GetOutputInferDataType(all_to_all_input, 0); + auto shape = common::AnfAlgo::GetOutputInferShape(all_to_all_input, 0); split_dim = NormalizeDim(shape, split_dim); if (SizeToLong(shape.size()) <= split_dim) { MS_LOG(EXCEPTION) << "Invalid split dim " << split_dim << " is over the shape size " << shape.size(); @@ -69,9 +70,9 @@ CNodePtr CreateSplitNode(const FuncGraphPtr &graph, const CNodePtr &all_to_all) // Set Split CNode outputs type and shape, and CNode attributes. std::vector dtypes(split_count, dtype); std::vector> shapes(split_count, shape); - AnfAlgo::SetOutputInferTypeAndShape(dtypes, shapes, split.get()); - AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(split_dim), split); - AnfAlgo::SetNodeAttr(kAttrOutputNum, MakeValue(split_count), split); + common::AnfAlgo::SetOutputInferTypeAndShape(dtypes, shapes, split.get()); + common::AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(split_dim), split); + common::AnfAlgo::SetNodeAttr(kAttrOutputNum, MakeValue(split_count), split); return split; } @@ -79,8 +80,8 @@ CNodePtr CreateAllToAllvNode(const FuncGraphPtr &graph, const CNodePtr &all_to_a MS_EXCEPTION_IF_NULL(graph); MS_EXCEPTION_IF_NULL(all_to_all); MS_EXCEPTION_IF_NULL(split); - int64_t split_count = AnfAlgo::GetNodeAttr(all_to_all, kAttrSplitCount); - std::string group = AnfAlgo::GetNodeAttr(all_to_all, kAttrGroup); + int64_t split_count = common::AnfAlgo::GetNodeAttr(all_to_all, kAttrSplitCount); + std::string group = common::AnfAlgo::GetNodeAttr(all_to_all, kAttrGroup); std::vector split_outputs; CreateMultipleOutputsOfAnfNode(graph, split, split_count, &split_outputs); if (split_outputs.empty()) { @@ -94,11 +95,11 @@ CNodePtr CreateAllToAllvNode(const FuncGraphPtr &graph, const CNodePtr &all_to_a MS_EXCEPTION_IF_NULL(all_to_all_v); // Prepare dtypes, shapes and ranks vectors. - auto single_shape = AnfAlgo::GetOutputInferShape(split_outputs[0], 0); - auto single_type = AnfAlgo::GetOutputInferDataType(split_outputs[0], 0); + auto single_shape = common::AnfAlgo::GetOutputInferShape(split_outputs[0], 0); + auto single_type = common::AnfAlgo::GetOutputInferDataType(split_outputs[0], 0); std::vector dtypes(split_count, single_type); std::vector> shapes(split_count, single_shape); - AnfAlgo::SetOutputInferTypeAndShape(dtypes, shapes, all_to_all_v.get()); + common::AnfAlgo::SetOutputInferTypeAndShape(dtypes, shapes, all_to_all_v.get()); uint32_t rank_size = device::gpu::CollectiveInitializer::instance().GetGroupSize(group); std::vector rank_ids(rank_size, 0); for (uint32_t i = 0; i < rank_size; ++i) { @@ -106,9 +107,9 @@ CNodePtr CreateAllToAllvNode(const FuncGraphPtr &graph, const CNodePtr &all_to_a } // Set AllToAllv CNode outputs and attributes. - AnfAlgo::SetNodeAttr(kAttrSendRankIds, MakeValue>(rank_ids), all_to_all_v); - AnfAlgo::SetNodeAttr(kAttrRecvRankIds, MakeValue>(rank_ids), all_to_all_v); - AnfAlgo::SetNodeAttr(kAttrGroup, MakeValue(group), all_to_all_v); + common::AnfAlgo::SetNodeAttr(kAttrSendRankIds, MakeValue>(rank_ids), all_to_all_v); + common::AnfAlgo::SetNodeAttr(kAttrRecvRankIds, MakeValue>(rank_ids), all_to_all_v); + common::AnfAlgo::SetNodeAttr(kAttrGroup, MakeValue(group), all_to_all_v); MS_LOG(INFO) << "Create AllToAllv success, split count " << split_count << ", rank size " << rank_size; return all_to_all_v; } @@ -117,8 +118,8 @@ CNodePtr CreateConcatNode(const FuncGraphPtr &graph, const CNodePtr &all_to_all, MS_EXCEPTION_IF_NULL(graph); MS_EXCEPTION_IF_NULL(all_to_all); MS_EXCEPTION_IF_NULL(all_to_all_v); - int64_t split_count = AnfAlgo::GetNodeAttr(all_to_all, kAttrSplitCount); - int64_t concat_dim = AnfAlgo::GetNodeAttr(all_to_all, kAttrConcatDim); + int64_t split_count = common::AnfAlgo::GetNodeAttr(all_to_all, kAttrSplitCount); + int64_t concat_dim = common::AnfAlgo::GetNodeAttr(all_to_all, kAttrConcatDim); std::vector all_to_all_v_outputs; CreateMultipleOutputsOfAnfNode(graph, all_to_all_v, split_count, &all_to_all_v_outputs); if (all_to_all_v_outputs.empty()) { @@ -132,7 +133,7 @@ CNodePtr CreateConcatNode(const FuncGraphPtr &graph, const CNodePtr &all_to_all, MS_EXCEPTION_IF_NULL(concat); // Judge validity of concat_dim. - auto single_shape = AnfAlgo::GetOutputInferShape(all_to_all_v_outputs[0], 0); + auto single_shape = common::AnfAlgo::GetOutputInferShape(all_to_all_v_outputs[0], 0); concat_dim = NormalizeDim(single_shape, concat_dim); if (LongToSize(concat_dim) >= single_shape.size()) { MS_LOG(EXCEPTION) << "Invalid concat dim " << concat_dim << " is greater than shape size " << single_shape.size(); @@ -140,12 +141,12 @@ CNodePtr CreateConcatNode(const FuncGraphPtr &graph, const CNodePtr &all_to_all, // Set Concat CNode outputs and attributes. single_shape[LongToSize(concat_dim)] *= split_count; - AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(all_to_all_v_outputs[0], 0)}, {single_shape}, - concat.get()); - AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(concat_dim), concat); - AnfAlgo::SetNodeAttr(kAttrInputNums, MakeValue(split_count), concat); + common::AnfAlgo::SetOutputInferTypeAndShape({common::AnfAlgo::GetOutputInferDataType(all_to_all_v_outputs[0], 0)}, + {single_shape}, concat.get()); + common::AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(concat_dim), concat); + common::AnfAlgo::SetNodeAttr(kAttrInputNums, MakeValue(split_count), concat); std::vector dyn_input_size{split_count}; - AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(dyn_input_size), concat); + common::AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(dyn_input_size), concat); return concat; } } // namespace diff --git a/mindspore/ccsrc/plugin/device/gpu/optimizer/apply_momentum_scale_fusion.cc b/mindspore/ccsrc/plugin/device/gpu/optimizer/apply_momentum_scale_fusion.cc index 256dfe5e8d4..f8885d7678f 100644 --- a/mindspore/ccsrc/plugin/device/gpu/optimizer/apply_momentum_scale_fusion.cc +++ b/mindspore/ccsrc/plugin/device/gpu/optimizer/apply_momentum_scale_fusion.cc @@ -20,8 +20,9 @@ #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/optimizer/helper.h" namespace mindspore { @@ -87,9 +88,9 @@ const AnfNodePtr ApplyMomentumScaleFusion::Process(const FuncGraphPtr &graph, co learning_rate, gradient, momentum, monad_state}; auto replace_node = graph->NewCNode(inputs); MS_EXCEPTION_IF_NULL(replace_node); - auto types = {AnfAlgo::GetOutputInferDataType(node, 0)}; - auto shapes = {AnfAlgo::GetOutputInferShape(node, 0)}; - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, replace_node.get()); + auto types = {common::AnfAlgo::GetOutputInferDataType(node, 0)}; + auto shapes = {common::AnfAlgo::GetOutputInferShape(node, 0)}; + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, replace_node.get()); replace_node->set_scope(node->scope()); return replace_node; } diff --git a/mindspore/ccsrc/plugin/device/gpu/optimizer/apply_momentum_scale_fusion.h b/mindspore/ccsrc/plugin/device/gpu/optimizer/apply_momentum_scale_fusion.h index ba302d666be..6baf830fc38 100644 --- a/mindspore/ccsrc/plugin/device/gpu/optimizer/apply_momentum_scale_fusion.h +++ b/mindspore/ccsrc/plugin/device/gpu/optimizer/apply_momentum_scale_fusion.h @@ -19,6 +19,7 @@ #include #include "backend/common/optimizer/optimizer.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { diff --git a/mindspore/ccsrc/plugin/device/gpu/optimizer/apply_momentum_weight_fusion.cc b/mindspore/ccsrc/plugin/device/gpu/optimizer/apply_momentum_weight_fusion.cc index 906896cc9f6..fb840d695f2 100644 --- a/mindspore/ccsrc/plugin/device/gpu/optimizer/apply_momentum_weight_fusion.cc +++ b/mindspore/ccsrc/plugin/device/gpu/optimizer/apply_momentum_weight_fusion.cc @@ -18,8 +18,9 @@ #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/optimizer/helper.h" namespace mindspore { @@ -59,9 +60,9 @@ const AnfNodePtr ApplyMomentumWeightDecayFusion::Process(const FuncGraphPtr &gra learning_rate, gradient, momentum, monad_state}; auto replace_node = graph->NewCNode(inputs); MS_EXCEPTION_IF_NULL(replace_node); - auto types = {AnfAlgo::GetOutputInferDataType(node, 0)}; - auto shapes = {AnfAlgo::GetOutputInferShape(node, 0)}; - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, replace_node.get()); + auto types = {common::AnfAlgo::GetOutputInferDataType(node, 0)}; + auto shapes = {common::AnfAlgo::GetOutputInferShape(node, 0)}; + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, replace_node.get()); replace_node->set_scope(node->scope()); return replace_node; } diff --git a/mindspore/ccsrc/plugin/device/gpu/optimizer/apply_momentum_weight_scale_fusion.cc b/mindspore/ccsrc/plugin/device/gpu/optimizer/apply_momentum_weight_scale_fusion.cc index bfa08c53759..fe820a4af5f 100644 --- a/mindspore/ccsrc/plugin/device/gpu/optimizer/apply_momentum_weight_scale_fusion.cc +++ b/mindspore/ccsrc/plugin/device/gpu/optimizer/apply_momentum_weight_scale_fusion.cc @@ -20,8 +20,9 @@ #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/optimizer/helper.h" namespace mindspore { @@ -124,9 +125,9 @@ const AnfNodePtr ApplyMomentumWeightDecayScaleFusion::Process(const FuncGraphPtr learning_rate, gradient, momentum, monad_state}; auto replace_node = graph->NewCNode(inputs); MS_EXCEPTION_IF_NULL(replace_node); - auto types = {AnfAlgo::GetOutputInferDataType(node, 0)}; - auto shapes = {AnfAlgo::GetOutputInferShape(node, 0)}; - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, replace_node.get()); + auto types = {common::AnfAlgo::GetOutputInferDataType(node, 0)}; + auto shapes = {common::AnfAlgo::GetOutputInferShape(node, 0)}; + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, replace_node.get()); replace_node->set_scope(node->scope()); return replace_node; } diff --git a/mindspore/ccsrc/plugin/device/gpu/optimizer/batch_norm_add_relu_fusion.cc b/mindspore/ccsrc/plugin/device/gpu/optimizer/batch_norm_add_relu_fusion.cc index 8c2a80ceb9b..af0b696525d 100644 --- a/mindspore/ccsrc/plugin/device/gpu/optimizer/batch_norm_add_relu_fusion.cc +++ b/mindspore/ccsrc/plugin/device/gpu/optimizer/batch_norm_add_relu_fusion.cc @@ -20,8 +20,9 @@ #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/optimizer/helper.h" #include "plugin/device/gpu/hal/device/kernel_info_setter.h" @@ -40,13 +41,13 @@ const AnfNodePtr BatchNormAddReluFusion::Process(const FuncGraphPtr &graph, cons MS_EXCEPTION_IF_NULL(graph); MS_EXCEPTION_IF_NULL(node); - auto tensor_add = AnfAlgo::GetInputNode(utils::cast(node), 0); + auto tensor_add = common::AnfAlgo::GetInputNode(utils::cast(node), 0); MS_EXCEPTION_IF_NULL(tensor_add); - auto tuple_get_item = AnfAlgo::GetInputNode(utils::cast(tensor_add), 0); + auto tuple_get_item = common::AnfAlgo::GetInputNode(utils::cast(tensor_add), 0); MS_EXCEPTION_IF_NULL(tuple_get_item); // Only fuse output[0] of BatchNorm with Add and ReLU - size_t output_index = AnfAlgo::GetTupleGetItemOutIndex(utils::cast(tuple_get_item)); + size_t output_index = common::AnfAlgo::GetTupleGetItemOutIndex(utils::cast(tuple_get_item)); if (output_index != 0) { return nullptr; } @@ -59,14 +60,14 @@ const AnfNodePtr BatchNormAddReluFusion::Process(const FuncGraphPtr &graph, cons return nullptr; } - auto batch_norm = AnfAlgo::GetInputNode(utils::cast(tuple_get_item), 0); + auto batch_norm = common::AnfAlgo::GetInputNode(utils::cast(tuple_get_item), 0); MS_EXCEPTION_IF_NULL(batch_norm); - auto is_train = AnfAlgo::GetCNodePrimitive(batch_norm)->GetAttr("is_training"); + auto is_train = common::AnfAlgo::GetCNodePrimitive(batch_norm)->GetAttr("is_training"); MS_EXCEPTION_IF_NULL(is_train); if (!GetValue(is_train)) { return nullptr; } - auto format_attr = AnfAlgo::GetCNodePrimitive(batch_norm)->GetAttr("format"); + auto format_attr = common::AnfAlgo::GetCNodePrimitive(batch_norm)->GetAttr("format"); MS_EXCEPTION_IF_NULL(format_attr); auto format = GetValue(format_attr); if (AnfAlgo::GetInputFormat(batch_norm, 0) != kOpFormat_NHWC && format != "NHWC") { @@ -77,12 +78,12 @@ const AnfNodePtr BatchNormAddReluFusion::Process(const FuncGraphPtr &graph, cons return nullptr; } - auto x = AnfAlgo::GetInputNode(utils::cast(batch_norm), 0); - auto scale = AnfAlgo::GetInputNode(utils::cast(batch_norm), 1); - auto bias = AnfAlgo::GetInputNode(utils::cast(batch_norm), 2); - auto mean = AnfAlgo::GetInputNode(utils::cast(batch_norm), 3); - auto var = AnfAlgo::GetInputNode(utils::cast(batch_norm), 4); - auto z = AnfAlgo::GetInputNode(utils::cast(tensor_add), 1); + auto x = common::AnfAlgo::GetInputNode(utils::cast(batch_norm), 0); + auto scale = common::AnfAlgo::GetInputNode(utils::cast(batch_norm), 1); + auto bias = common::AnfAlgo::GetInputNode(utils::cast(batch_norm), 2); + auto mean = common::AnfAlgo::GetInputNode(utils::cast(batch_norm), 3); + auto var = common::AnfAlgo::GetInputNode(utils::cast(batch_norm), 4); + auto z = common::AnfAlgo::GetInputNode(utils::cast(tensor_add), 1); MS_EXCEPTION_IF_NULL(x); MS_EXCEPTION_IF_NULL(scale); @@ -99,13 +100,13 @@ const AnfNodePtr BatchNormAddReluFusion::Process(const FuncGraphPtr &graph, cons std::vector outputs_type; std::vector> outputs_shape; - auto output_num = AnfAlgo::GetOutputTensorNum(batch_norm); + auto output_num = common::AnfAlgo::GetOutputTensorNum(batch_norm); for (size_t i = 0; i < output_num; i++) { - outputs_type.push_back(AnfAlgo::GetOutputInferDataType(batch_norm, i)); - outputs_shape.push_back(AnfAlgo::GetOutputInferShape(batch_norm, i)); + outputs_type.push_back(common::AnfAlgo::GetOutputInferDataType(batch_norm, i)); + outputs_shape.push_back(common::AnfAlgo::GetOutputInferShape(batch_norm, i)); } - AnfAlgo::SetOutputInferTypeAndShape(outputs_type, outputs_shape, fused_batch_norm_with_add_relu.get()); - AnfAlgo::CopyNodeAttrs(batch_norm, fused_batch_norm_with_add_relu); + common::AnfAlgo::SetOutputInferTypeAndShape(outputs_type, outputs_shape, fused_batch_norm_with_add_relu.get()); + common::AnfAlgo::CopyNodeAttrs(batch_norm, fused_batch_norm_with_add_relu); auto manager = graph->manager(); MS_EXCEPTION_IF_NULL(manager); diff --git a/mindspore/ccsrc/plugin/device/gpu/optimizer/batch_norm_add_relu_grad_fusion.cc b/mindspore/ccsrc/plugin/device/gpu/optimizer/batch_norm_add_relu_grad_fusion.cc index 6c6d4d6a70b..45a4365b8b5 100644 --- a/mindspore/ccsrc/plugin/device/gpu/optimizer/batch_norm_add_relu_grad_fusion.cc +++ b/mindspore/ccsrc/plugin/device/gpu/optimizer/batch_norm_add_relu_grad_fusion.cc @@ -21,8 +21,9 @@ #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/optimizer/helper.h" #include "plugin/device/gpu/hal/device/kernel_info_setter.h" @@ -69,15 +70,15 @@ void SetShapeAndType(const CNodePtr &bn_add_relu_grad, const AnfNodePtr &bn_grad // set output shape and dtype std::vector outputs_type; std::vector> outputs_shape; - auto output_num = AnfAlgo::GetOutputTensorNum(bn_grad); + auto output_num = common::AnfAlgo::GetOutputTensorNum(bn_grad); for (size_t i = 0; i < output_num; ++i) { - outputs_type.push_back(AnfAlgo::GetOutputInferDataType(bn_grad, i)); - outputs_shape.push_back(AnfAlgo::GetOutputInferShape(bn_grad, i)); + outputs_type.push_back(common::AnfAlgo::GetOutputInferDataType(bn_grad, i)); + outputs_shape.push_back(common::AnfAlgo::GetOutputInferShape(bn_grad, i)); } - outputs_type.push_back(AnfAlgo::GetOutputInferDataType(relu_grad, 0)); - outputs_shape.push_back(AnfAlgo::GetOutputInferShape(relu_grad, 0)); - AnfAlgo::SetOutputInferTypeAndShape(outputs_type, outputs_shape, bn_add_relu_grad.get()); + outputs_type.push_back(common::AnfAlgo::GetOutputInferDataType(relu_grad, 0)); + outputs_shape.push_back(common::AnfAlgo::GetOutputInferShape(relu_grad, 0)); + common::AnfAlgo::SetOutputInferTypeAndShape(outputs_type, outputs_shape, bn_add_relu_grad.get()); } void ReplaceOutput(const FuncGraphPtr &graph, const AnfNodePtr &bn_grad, const AnfNodePtr &relu_grad, @@ -115,7 +116,7 @@ void ReplaceOutput(const FuncGraphPtr &graph, const AnfNodePtr &bn_grad, const A bool PatternCheck(const FuncGraphPtr &graph, const AnfNodePtr &node) { MS_EXCEPTION_IF_NULL(graph); MS_EXCEPTION_IF_NULL(node); - auto format_attr = AnfAlgo::GetCNodePrimitive(node)->GetAttr("format"); + auto format_attr = common::AnfAlgo::GetCNodePrimitive(node)->GetAttr("format"); MS_EXCEPTION_IF_NULL(format_attr); auto format = GetValue(format_attr); if (AnfAlgo::GetInputFormat(node, 0) != kOpFormat_NHWC && format != "NHWC") { @@ -126,7 +127,7 @@ bool PatternCheck(const FuncGraphPtr &graph, const AnfNodePtr &node) { return false; } - auto relu_grad = AnfAlgo::GetInputNode(utils::cast(node), 0); + auto relu_grad = common::AnfAlgo::GetInputNode(utils::cast(node), 0); MS_EXCEPTION_IF_NULL(relu_grad); auto relu_users = GetRealNodeUsedList(graph, relu_grad); if (relu_users->size() != 2) { @@ -134,13 +135,14 @@ bool PatternCheck(const FuncGraphPtr &graph, const AnfNodePtr &node) { } // process pattern as Relu(TensorAdd(BN#0, BN#1)) - auto tuple_getitem = AnfAlgo::GetInputNode(utils::cast(node), 5); + auto tuple_getitem = common::AnfAlgo::GetInputNode(utils::cast(node), 5); MS_EXCEPTION_IF_NULL(tuple_getitem); - if (!utils::isa(tuple_getitem) || AnfAlgo::GetCNodeName(tuple_getitem) != prim::kPrimTupleGetItem->name()) { + if (!utils::isa(tuple_getitem) || + common::AnfAlgo::GetCNodeName(tuple_getitem) != prim::kPrimTupleGetItem->name()) { return false; } - auto forward_node = AnfAlgo::GetInputNode(utils::cast(tuple_getitem), 0); - if (AnfAlgo::GetCNodeName(forward_node) != kBatchNormWithAddAndActivation) { + auto forward_node = common::AnfAlgo::GetInputNode(utils::cast(tuple_getitem), 0); + if (common::AnfAlgo::GetCNodeName(forward_node) != kBatchNormWithAddAndActivation) { return false; } @@ -164,27 +166,27 @@ const AnfNodePtr BatchNormAddReluGradFusion::Process(const FuncGraphPtr &graph, return nullptr; } - auto relu_grad = AnfAlgo::GetInputNode(utils::cast(node), 0); + auto relu_grad = common::AnfAlgo::GetInputNode(utils::cast(node), 0); MS_EXCEPTION_IF_NULL(relu_grad); - auto dy = AnfAlgo::GetInputNode(utils::cast(relu_grad), 0); + auto dy = common::AnfAlgo::GetInputNode(utils::cast(relu_grad), 0); MS_EXCEPTION_IF_NULL(dy); - auto y = AnfAlgo::GetInputNode(utils::cast(relu_grad), 1); + auto y = common::AnfAlgo::GetInputNode(utils::cast(relu_grad), 1); MS_EXCEPTION_IF_NULL(y); - auto x = AnfAlgo::GetInputNode(utils::cast(node), 1); + auto x = common::AnfAlgo::GetInputNode(utils::cast(node), 1); MS_EXCEPTION_IF_NULL(x); - auto scale = AnfAlgo::GetInputNode(utils::cast(node), 2); + auto scale = common::AnfAlgo::GetInputNode(utils::cast(node), 2); MS_EXCEPTION_IF_NULL(scale); - auto save_mean = AnfAlgo::GetInputNode(utils::cast(node), 3); + auto save_mean = common::AnfAlgo::GetInputNode(utils::cast(node), 3); MS_EXCEPTION_IF_NULL(save_mean); - auto save_var = AnfAlgo::GetInputNode(utils::cast(node), 4); + auto save_var = common::AnfAlgo::GetInputNode(utils::cast(node), 4); MS_EXCEPTION_IF_NULL(save_var); - auto reserve = AnfAlgo::GetInputNode(utils::cast(node), 5); + auto reserve = common::AnfAlgo::GetInputNode(utils::cast(node), 5); MS_EXCEPTION_IF_NULL(reserve); - auto batch_norm = AnfAlgo::GetInputNode(utils::cast(save_mean), 0); + auto batch_norm = common::AnfAlgo::GetInputNode(utils::cast(save_mean), 0); MS_EXCEPTION_IF_NULL(batch_norm); - auto bias = AnfAlgo::GetInputNode(utils::cast(batch_norm), 2); + auto bias = common::AnfAlgo::GetInputNode(utils::cast(batch_norm), 2); MS_EXCEPTION_IF_NULL(bias); - auto is_train = AnfAlgo::GetCNodePrimitive(batch_norm)->GetAttr("is_training"); + auto is_train = common::AnfAlgo::GetCNodePrimitive(batch_norm)->GetAttr("is_training"); MS_EXCEPTION_IF_NULL(is_train); if (!GetValue(is_train)) { return nullptr; @@ -194,7 +196,7 @@ const AnfNodePtr BatchNormAddReluGradFusion::Process(const FuncGraphPtr &graph, std::vector inputs = {NewValueNode(prim), dy, x, scale, save_mean, save_var, reserve, bias, y}; auto fused_batch_norm_add_relu_grad = graph->NewCNode(inputs); MS_EXCEPTION_IF_NULL(fused_batch_norm_add_relu_grad); - AnfAlgo::CopyNodeAttrs(node, fused_batch_norm_add_relu_grad); + common::AnfAlgo::CopyNodeAttrs(node, fused_batch_norm_add_relu_grad); SetShapeAndType(fused_batch_norm_add_relu_grad, node, relu_grad); ReplaceOutput(graph, node, relu_grad, fused_batch_norm_add_relu_grad); device::gpu::SetKernelInfo(fused_batch_norm_add_relu_grad); diff --git a/mindspore/ccsrc/plugin/device/gpu/optimizer/batch_norm_relu_fusion.cc b/mindspore/ccsrc/plugin/device/gpu/optimizer/batch_norm_relu_fusion.cc index fd791c82e4d..bc4c5743589 100644 --- a/mindspore/ccsrc/plugin/device/gpu/optimizer/batch_norm_relu_fusion.cc +++ b/mindspore/ccsrc/plugin/device/gpu/optimizer/batch_norm_relu_fusion.cc @@ -20,8 +20,9 @@ #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/optimizer/helper.h" #include "plugin/device/gpu/hal/device/kernel_info_setter.h" @@ -39,11 +40,11 @@ const AnfNodePtr BatchNormReluFusion::Process(const FuncGraphPtr &graph, const A MS_EXCEPTION_IF_NULL(graph); MS_EXCEPTION_IF_NULL(node); - auto tuple_get_item = AnfAlgo::GetInputNode(utils::cast(node), 0); + auto tuple_get_item = common::AnfAlgo::GetInputNode(utils::cast(node), 0); MS_EXCEPTION_IF_NULL(tuple_get_item); // Only fuse output[0] of BatchNorm with ReLU - size_t output_index = AnfAlgo::GetTupleGetItemOutIndex(utils::cast(tuple_get_item)); + size_t output_index = common::AnfAlgo::GetTupleGetItemOutIndex(utils::cast(tuple_get_item)); if (output_index != 0) { return nullptr; } @@ -56,14 +57,14 @@ const AnfNodePtr BatchNormReluFusion::Process(const FuncGraphPtr &graph, const A return nullptr; } - auto batch_norm = AnfAlgo::GetInputNode(utils::cast(tuple_get_item), 0); + auto batch_norm = common::AnfAlgo::GetInputNode(utils::cast(tuple_get_item), 0); MS_EXCEPTION_IF_NULL(batch_norm); - auto is_train = AnfAlgo::GetCNodePrimitive(batch_norm)->GetAttr("is_training"); + auto is_train = common::AnfAlgo::GetCNodePrimitive(batch_norm)->GetAttr("is_training"); MS_EXCEPTION_IF_NULL(is_train); if (!GetValue(is_train)) { return nullptr; } - auto format_attr = AnfAlgo::GetCNodePrimitive(batch_norm)->GetAttr("format"); + auto format_attr = common::AnfAlgo::GetCNodePrimitive(batch_norm)->GetAttr("format"); MS_EXCEPTION_IF_NULL(format_attr); auto format = GetValue(format_attr); if (AnfAlgo::GetInputFormat(batch_norm, 0) != kOpFormat_NHWC && format != "NHWC") { @@ -74,11 +75,11 @@ const AnfNodePtr BatchNormReluFusion::Process(const FuncGraphPtr &graph, const A return nullptr; } - auto x = AnfAlgo::GetInputNode(utils::cast(batch_norm), 0); - auto scale = AnfAlgo::GetInputNode(utils::cast(batch_norm), 1); - auto bias = AnfAlgo::GetInputNode(utils::cast(batch_norm), 2); - auto mean = AnfAlgo::GetInputNode(utils::cast(batch_norm), 3); - auto var = AnfAlgo::GetInputNode(utils::cast(batch_norm), 4); + auto x = common::AnfAlgo::GetInputNode(utils::cast(batch_norm), 0); + auto scale = common::AnfAlgo::GetInputNode(utils::cast(batch_norm), 1); + auto bias = common::AnfAlgo::GetInputNode(utils::cast(batch_norm), 2); + auto mean = common::AnfAlgo::GetInputNode(utils::cast(batch_norm), 3); + auto var = common::AnfAlgo::GetInputNode(utils::cast(batch_norm), 4); MS_EXCEPTION_IF_NULL(x); MS_EXCEPTION_IF_NULL(scale); @@ -94,13 +95,13 @@ const AnfNodePtr BatchNormReluFusion::Process(const FuncGraphPtr &graph, const A std::vector outputs_type; std::vector> outputs_shape; - auto output_num = AnfAlgo::GetOutputTensorNum(batch_norm); + auto output_num = common::AnfAlgo::GetOutputTensorNum(batch_norm); for (size_t i = 0; i < output_num; i++) { - outputs_type.push_back(AnfAlgo::GetOutputInferDataType(batch_norm, i)); - outputs_shape.push_back(AnfAlgo::GetOutputInferShape(batch_norm, i)); + outputs_type.push_back(common::AnfAlgo::GetOutputInferDataType(batch_norm, i)); + outputs_shape.push_back(common::AnfAlgo::GetOutputInferShape(batch_norm, i)); } - AnfAlgo::SetOutputInferTypeAndShape(outputs_type, outputs_shape, fused_batch_norm_with_relu.get()); - AnfAlgo::CopyNodeAttrs(batch_norm, fused_batch_norm_with_relu); + common::AnfAlgo::SetOutputInferTypeAndShape(outputs_type, outputs_shape, fused_batch_norm_with_relu.get()); + common::AnfAlgo::CopyNodeAttrs(batch_norm, fused_batch_norm_with_relu); auto manager = graph->manager(); MS_EXCEPTION_IF_NULL(manager); diff --git a/mindspore/ccsrc/plugin/device/gpu/optimizer/batch_norm_relu_grad_fusion.cc b/mindspore/ccsrc/plugin/device/gpu/optimizer/batch_norm_relu_grad_fusion.cc index 2e7d9bbdd3a..095261e652a 100644 --- a/mindspore/ccsrc/plugin/device/gpu/optimizer/batch_norm_relu_grad_fusion.cc +++ b/mindspore/ccsrc/plugin/device/gpu/optimizer/batch_norm_relu_grad_fusion.cc @@ -20,8 +20,9 @@ #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/optimizer/helper.h" #include "plugin/device/gpu/hal/device/kernel_info_setter.h" #include "utils/ms_context.h" @@ -39,12 +40,12 @@ const AnfNodePtr BatchNormReluGradFusion::Process(const FuncGraphPtr &graph, con const EquivPtr &) const { MS_EXCEPTION_IF_NULL(graph); MS_EXCEPTION_IF_NULL(node); - auto is_train = AnfAlgo::GetCNodePrimitive(node)->GetAttr("is_training"); + auto is_train = common::AnfAlgo::GetCNodePrimitive(node)->GetAttr("is_training"); MS_EXCEPTION_IF_NULL(is_train); if (!GetValue(is_train)) { return nullptr; } - auto format_attr = AnfAlgo::GetCNodePrimitive(node)->GetAttr("format"); + auto format_attr = common::AnfAlgo::GetCNodePrimitive(node)->GetAttr("format"); MS_EXCEPTION_IF_NULL(format_attr); auto format = GetValue(format_attr); auto ms_context = MsContext::GetInstance(); @@ -60,7 +61,7 @@ const AnfNodePtr BatchNormReluGradFusion::Process(const FuncGraphPtr &graph, con return nullptr; } - auto relu_grad = AnfAlgo::GetInputNode(utils::cast(node), 0); + auto relu_grad = common::AnfAlgo::GetInputNode(utils::cast(node), 0); MS_EXCEPTION_IF_NULL(relu_grad); auto outlist = GetRealNodeUsedList(graph, relu_grad); @@ -69,23 +70,23 @@ const AnfNodePtr BatchNormReluGradFusion::Process(const FuncGraphPtr &graph, con return nullptr; } - auto dy = AnfAlgo::GetInputNode(utils::cast(relu_grad), 0); + auto dy = common::AnfAlgo::GetInputNode(utils::cast(relu_grad), 0); MS_EXCEPTION_IF_NULL(dy); - auto y = AnfAlgo::GetInputNode(utils::cast(relu_grad), 1); + auto y = common::AnfAlgo::GetInputNode(utils::cast(relu_grad), 1); MS_EXCEPTION_IF_NULL(y); - auto x = AnfAlgo::GetInputNode(utils::cast(node), 1); + auto x = common::AnfAlgo::GetInputNode(utils::cast(node), 1); MS_EXCEPTION_IF_NULL(x); - auto scale = AnfAlgo::GetInputNode(utils::cast(node), 2); + auto scale = common::AnfAlgo::GetInputNode(utils::cast(node), 2); MS_EXCEPTION_IF_NULL(scale); - auto save_mean = AnfAlgo::GetInputNode(utils::cast(node), 3); + auto save_mean = common::AnfAlgo::GetInputNode(utils::cast(node), 3); MS_EXCEPTION_IF_NULL(save_mean); - auto save_var = AnfAlgo::GetInputNode(utils::cast(node), 4); + auto save_var = common::AnfAlgo::GetInputNode(utils::cast(node), 4); MS_EXCEPTION_IF_NULL(save_var); - auto reserve = AnfAlgo::GetInputNode(utils::cast(node), 5); + auto reserve = common::AnfAlgo::GetInputNode(utils::cast(node), 5); MS_EXCEPTION_IF_NULL(reserve); - auto batch_norm = AnfAlgo::GetInputNode(utils::cast(save_mean), 0); + auto batch_norm = common::AnfAlgo::GetInputNode(utils::cast(save_mean), 0); MS_EXCEPTION_IF_NULL(batch_norm); - auto bias = AnfAlgo::GetInputNode(utils::cast(batch_norm), 2); + auto bias = common::AnfAlgo::GetInputNode(utils::cast(batch_norm), 2); MS_EXCEPTION_IF_NULL(bias); auto prim = std::make_shared(kBatchNormGradWithActivation); @@ -96,13 +97,13 @@ const AnfNodePtr BatchNormReluGradFusion::Process(const FuncGraphPtr &graph, con std::vector outputs_type; std::vector> outputs_shape; - auto output_num = AnfAlgo::GetOutputTensorNum(node); + auto output_num = common::AnfAlgo::GetOutputTensorNum(node); for (size_t i = 0; i < output_num; i++) { - outputs_type.push_back(AnfAlgo::GetOutputInferDataType(node, i)); - outputs_shape.push_back(AnfAlgo::GetOutputInferShape(node, i)); + outputs_type.push_back(common::AnfAlgo::GetOutputInferDataType(node, i)); + outputs_shape.push_back(common::AnfAlgo::GetOutputInferShape(node, i)); } - AnfAlgo::SetOutputInferTypeAndShape(outputs_type, outputs_shape, fused_batch_norm_grad_with_relu.get()); - AnfAlgo::CopyNodeAttrs(node, fused_batch_norm_grad_with_relu); + common::AnfAlgo::SetOutputInferTypeAndShape(outputs_type, outputs_shape, fused_batch_norm_grad_with_relu.get()); + common::AnfAlgo::CopyNodeAttrs(node, fused_batch_norm_grad_with_relu); device::gpu::SetKernelInfo(fused_batch_norm_grad_with_relu); return fused_batch_norm_grad_with_relu; } diff --git a/mindspore/ccsrc/plugin/device/gpu/optimizer/bce_with_logits_loss_fusion.cc b/mindspore/ccsrc/plugin/device/gpu/optimizer/bce_with_logits_loss_fusion.cc index e49af2baca0..d17a2130b82 100644 --- a/mindspore/ccsrc/plugin/device/gpu/optimizer/bce_with_logits_loss_fusion.cc +++ b/mindspore/ccsrc/plugin/device/gpu/optimizer/bce_with_logits_loss_fusion.cc @@ -19,8 +19,9 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/optimizer/helper.h" #include "plugin/device/gpu/hal/device/kernel_info_setter.h" @@ -37,12 +38,12 @@ AnfNodePtr AddReduceNode(const FuncGraphPtr &func_graph, const AnfNodePtr &node) CNodePtr new_cnode = func_graph->NewCNode(node_inputs); MS_EXCEPTION_IF_NULL(new_cnode); auto predict_input = cnode->inputs()[1]; - auto new_node_dtype = {AnfAlgo::GetOutputInferDataType(predict_input, 0)}; - auto new_node_shape = {AnfAlgo::GetOutputInferShape(predict_input, 0)}; - AnfAlgo::SetOutputInferTypeAndShape(new_node_dtype, new_node_shape, new_cnode.get()); + auto new_node_dtype = {common::AnfAlgo::GetOutputInferDataType(predict_input, 0)}; + auto new_node_shape = {common::AnfAlgo::GetOutputInferShape(predict_input, 0)}; + common::AnfAlgo::SetOutputInferTypeAndShape(new_node_dtype, new_node_shape, new_cnode.get()); // Add reduce node - string reduction = AnfAlgo::GetNodeAttr(node, kAttrReduction); + string reduction = common::AnfAlgo::GetNodeAttr(node, kAttrReduction); MS_LOG(INFO) << "Create reduce node for BCEWithLogitsLoss, reduction attr is: " << reduction; std::vector reduce_inputs; if (reduction == "sum") { @@ -55,11 +56,11 @@ AnfNodePtr AddReduceNode(const FuncGraphPtr &func_graph, const AnfNodePtr &node) } auto reduce_node = func_graph->NewCNode(reduce_inputs); MS_EXCEPTION_IF_NULL(reduce_node); - auto type = AnfAlgo::GetOutputInferDataType(node, 0); - auto shape = {AnfAlgo::GetOutputInferShape(node, 0)}; - AnfAlgo::SetOutputInferTypeAndShape({type}, shape, reduce_node.get()); - AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(std::vector{}), reduce_node); - AnfAlgo::SetNodeAttr("keep_dims", MakeValue(false), reduce_node); + auto type = common::AnfAlgo::GetOutputInferDataType(node, 0); + auto shape = {common::AnfAlgo::GetOutputInferShape(node, 0)}; + common::AnfAlgo::SetOutputInferTypeAndShape({type}, shape, reduce_node.get()); + common::AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(std::vector{}), reduce_node); + common::AnfAlgo::SetNodeAttr("keep_dims", MakeValue(false), reduce_node); reduce_node->set_scope(cnode->scope()); return reduce_node; } @@ -79,11 +80,11 @@ const AnfNodePtr BCEWithLogitsLossFusion::Process(const FuncGraphPtr &func_graph if (GetBoolAttr(cnode, kAttrVisited)) { return nullptr; } - AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node); + common::AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node); if (cnode->inputs().size() == 0) { return nullptr; } - if (!AnfAlgo::HasNodeAttr("reduction", cnode)) { + if (!common::AnfAlgo::HasNodeAttr("reduction", cnode)) { MS_LOG(INFO) << "Primitive BCEWithLogitsLoss doesn't not have reduction attr."; return nullptr; } diff --git a/mindspore/ccsrc/plugin/device/gpu/optimizer/combine_cast_fusion.cc b/mindspore/ccsrc/plugin/device/gpu/optimizer/combine_cast_fusion.cc index efa3d553ffe..454182321a9 100644 --- a/mindspore/ccsrc/plugin/device/gpu/optimizer/combine_cast_fusion.cc +++ b/mindspore/ccsrc/plugin/device/gpu/optimizer/combine_cast_fusion.cc @@ -15,8 +15,9 @@ */ #include "plugin/device/gpu/optimizer/combine_cast_fusion.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/optimizer/helper.h" namespace mindspore { @@ -24,7 +25,7 @@ namespace opt { namespace { bool IsParameter(const mindspore::AnfNodePtr &node) { return node->isa() || (IsPrimitiveCNode(node, prim::kPrimLoad) && - (AnfAlgo::GetInputNode(utils::cast(node), 0))->isa()); + (common::AnfAlgo::GetInputNode(utils::cast(node), 0))->isa()); } bool GetDealList(const std::vector &node_list, std::vector> *deal_list) { MS_EXCEPTION_IF_NULL(deal_list); @@ -40,10 +41,10 @@ bool GetDealList(const std::vector &node_list, std::vector(cast_node), 0); + auto input0 = common::AnfAlgo::GetInputNode(utils::cast(cast_node), 0); if (IsParameter(input0)) { - auto dst = AnfAlgo::GetOutputInferDataType(cast_node, 0); - auto src = AnfAlgo::GetPrevNodeOutputInferDataType(cast_node, 0); + auto dst = common::AnfAlgo::GetOutputInferDataType(cast_node, 0); + auto src = common::AnfAlgo::GetPrevNodeOutputInferDataType(cast_node, 0); if (dst == kNumberTypeFloat16 && src == kNumberTypeFloat32) { cast_32to16_list.push_back(cast_node); if (IsPrimitiveCNode(input0, prim::kPrimLoad)) { @@ -96,7 +97,7 @@ bool CastAllFusion::Run(const FuncGraphPtr &graph) { for (size_t idx = 0; idx < cast_list.size(); ++idx) { auto cnode = utils::cast(cast_list[idx]); MS_EXCEPTION_IF_NULL(cnode); - inputs.push_back(AnfAlgo::GetInputNode(cnode, 0)); + inputs.push_back(common::AnfAlgo::GetInputNode(cnode, 0)); } if (cast_list.size() > 0) { TraceGuard guard(std::make_shared(cast_list[0]->debug_info())); @@ -116,7 +117,7 @@ bool CastAllFusion::Run(const FuncGraphPtr &graph) { auto abstract_tuple = std::make_shared(abstract_list); MS_EXCEPTION_IF_NULL(abstract_tuple); cast_all->set_abstract(abstract_tuple); - AnfAlgo::SetNodeAttr("n", MakeValue(cast_list.size()), cast_all); + common::AnfAlgo::SetNodeAttr("n", MakeValue(cast_list.size()), cast_all); // 3 replace all the cast by CastAllv tuplegetitem[castall, idx] for (size_t idx = 0; idx < cast_list.size(); ++idx) { std::vector tuple_getitem_input; diff --git a/mindspore/ccsrc/plugin/device/gpu/optimizer/combine_momentum_fusion.cc b/mindspore/ccsrc/plugin/device/gpu/optimizer/combine_momentum_fusion.cc index 135c9632718..c8f0d689987 100644 --- a/mindspore/ccsrc/plugin/device/gpu/optimizer/combine_momentum_fusion.cc +++ b/mindspore/ccsrc/plugin/device/gpu/optimizer/combine_momentum_fusion.cc @@ -15,8 +15,9 @@ */ #include "plugin/device/gpu/optimizer/combine_momentum_fusion.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/optimizer/helper.h" namespace mindspore { @@ -27,9 +28,9 @@ bool GetDealList(const std::vector &node_list, std::vector momentum_decay; for (auto &momentum_node : node_list) { if (momentum_node != nullptr && momentum_node->isa()) { - if (AnfAlgo::GetCNodeName(momentum_node) == kFusedScaleApplyMomentum) { + if (common::AnfAlgo::GetCNodeName(momentum_node) == kFusedScaleApplyMomentum) { momentum.push_back(momentum_node); - } else if (AnfAlgo::GetCNodeName(momentum_node) == kFusedWeightScaleApplyMomentum) { + } else if (common::AnfAlgo::GetCNodeName(momentum_node) == kFusedWeightScaleApplyMomentum) { momentum_decay.push_back(momentum_node); } } @@ -61,7 +62,7 @@ bool CombineMomentumFusion::Run(const FuncGraphPtr &graph) { } // 2 create node momentum std::vector inputs = {}; - if (AnfAlgo::GetCNodeName(momentums[0]) == kFusedScaleApplyMomentum) { + if (common::AnfAlgo::GetCNodeName(momentums[0]) == kFusedScaleApplyMomentum) { auto prim = std::make_shared("CombineMomentum"); MS_EXCEPTION_IF_NULL(prim); inputs.push_back(NewValueNode(prim)); @@ -71,12 +72,12 @@ bool CombineMomentumFusion::Run(const FuncGraphPtr &graph) { inputs.push_back(NewValueNode(prim)); } // set inputs for momentum - size_t input_num = AnfAlgo::GetInputTensorNum(momentums[0]); + size_t input_num = common::AnfAlgo::GetInputTensorNum(momentums[0]); for (auto mom : momentums) { for (size_t i = 0; i < input_num; i++) { auto cnode = utils::cast(mom); MS_EXCEPTION_IF_NULL(cnode); - inputs.push_back(AnfAlgo::GetInputNode(cnode, i)); + inputs.push_back(common::AnfAlgo::GetInputNode(cnode, i)); } } TraceGuard guard(std::make_shared(momentums[0]->debug_info())); @@ -96,7 +97,7 @@ bool CombineMomentumFusion::Run(const FuncGraphPtr &graph) { auto abstract_tuple = std::make_shared(abstract_list); MS_EXCEPTION_IF_NULL(abstract_tuple); combine_mom->set_abstract(abstract_tuple); - AnfAlgo::SetNodeAttr("n", MakeValue(momentums.size()), combine_mom); + common::AnfAlgo::SetNodeAttr("n", MakeValue(momentums.size()), combine_mom); // 3 replace all the cast by momentum for (size_t idx = 0; idx < momentums.size(); ++idx) { if (!manager->Replace(momentums[idx], combine_mom)) { diff --git a/mindspore/ccsrc/plugin/device/gpu/optimizer/concat_outputs_for_all_gather.cc b/mindspore/ccsrc/plugin/device/gpu/optimizer/concat_outputs_for_all_gather.cc index d9f424ce094..f88a91123c9 100644 --- a/mindspore/ccsrc/plugin/device/gpu/optimizer/concat_outputs_for_all_gather.cc +++ b/mindspore/ccsrc/plugin/device/gpu/optimizer/concat_outputs_for_all_gather.cc @@ -19,6 +19,7 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore::opt { namespace { @@ -32,14 +33,14 @@ OutputInfo GetNodeOutputInfo(const AnfNodePtr &node) { std::vector output_device_dtype; auto type_ptr = node->Type(); auto shape_ptr = node->Shape(); - size_t output_num = AnfAlgo::GetOutputTensorNum(node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(node); auto kernel_info = dynamic_cast(node->kernel_info()); MS_EXCEPTION_IF_NULL(kernel_info); auto build_info = kernel_info->select_kernel_build_info(); MS_EXCEPTION_IF_NULL(build_info); for (size_t i = 0; i < output_num; i++) { - output_infer_dtype.emplace_back(AnfAlgo::GetOutputInferDataType(type_ptr, i)); - output_infer_shape.emplace_back(AnfAlgo::GetOutputInferShape(node, shape_ptr, i)); + output_infer_dtype.emplace_back(common::AnfAlgo::GetOutputInferDataType(type_ptr, i)); + output_infer_shape.emplace_back(common::AnfAlgo::GetOutputInferShape(node, shape_ptr, i)); output_format.emplace_back(build_info->GetOutputFormat(i)); output_device_dtype.emplace_back(build_info->GetOutputDeviceType(i)); } @@ -55,7 +56,7 @@ kernel::KernelBuildInfoPtr GenerateKernelBuildInfo(const AnfNodePtr &concat, con std::vector inputs_device_type; std::vector outputs_device_type; kernel::KernelBuildInfo::KernelBuildInfoBuilder builder; - size_t concat_input_num = AnfAlgo::GetInputTensorNum(concat); + size_t concat_input_num = common::AnfAlgo::GetInputTensorNum(concat); for (size_t i = 0; i < concat_input_num; ++i) { size_t input_index = allgather_input_idx + i * allgather_input_num; inputs_device_format.emplace_back(std::get<2>(allgather_output_info)[input_index]); @@ -90,7 +91,7 @@ AnfNodePtr InsertConcatForOutput(const FuncGraphPtr &func_graph, const AnfNodePt const std::vector &new_tuple_getitems, int64_t rank_size) { MS_EXCEPTION_IF_NULL(func_graph); std::vector make_tuple_inputs{NewValueNode(std::make_shared(prim::kPrimMakeTuple->name()))}; - size_t inputs_size = AnfAlgo::GetInputTensorNum(node); + size_t inputs_size = common::AnfAlgo::GetInputTensorNum(node); for (size_t i = 0; i < inputs_size; ++i) { std::vector concat_inputs{NewValueNode(std::make_shared(prim::kPrimConcat->name()))}; for (size_t j = 0, idx = i; j < LongToSize(rank_size); ++j, idx += inputs_size) { @@ -103,11 +104,11 @@ AnfNodePtr InsertConcatForOutput(const FuncGraphPtr &func_graph, const AnfNodePt const auto &shape = std::get<1>(output_info)[i]; std::vector> shapes = {shape}; shapes[0][0] *= rank_size; - AnfAlgo::SetOutputInferTypeAndShape(dtypes, shapes, concat.get()); - AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(static_cast(0)), concat); - AnfAlgo::SetNodeAttr(kAttrInputNums, MakeValue(rank_size), concat); + common::AnfAlgo::SetOutputInferTypeAndShape(dtypes, shapes, concat.get()); + common::AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(static_cast(0)), concat); + common::AnfAlgo::SetNodeAttr(kAttrInputNums, MakeValue(rank_size), concat); std::vector dyn_input_size{rank_size}; - AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(dyn_input_size), concat); + common::AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(dyn_input_size), concat); auto kernel_build_info = GenerateKernelBuildInfo(concat, output_info, inputs_size, i); AnfAlgo::SetSelectKernelBuildInfo(kernel_build_info, concat.get()); make_tuple_inputs.push_back(concat); @@ -129,21 +130,21 @@ const AnfNodePtr ConcatOutputsForAllGather::Process(const FuncGraphPtr &func_gra MS_EXCEPTION_IF_NULL(node); auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); - if (!AnfAlgo::HasNodeAttr(kAttrFusion, cnode) || !AnfAlgo::HasNodeAttr(kAttrRankSize, cnode)) { + if (!common::AnfAlgo::HasNodeAttr(kAttrFusion, cnode) || !common::AnfAlgo::HasNodeAttr(kAttrRankSize, cnode)) { return nullptr; } - auto fusion = AnfAlgo::GetNodeAttr(cnode, kAttrFusion); + auto fusion = common::AnfAlgo::GetNodeAttr(cnode, kAttrFusion); if (fusion <= 0) { return nullptr; } - if (AnfAlgo::HasNodeAttr("fused", cnode) || AnfAlgo::GetInputTensorNum(node) == 1) { + if (common::AnfAlgo::HasNodeAttr("fused", cnode) || common::AnfAlgo::GetInputTensorNum(node) == 1) { return nullptr; } - AnfAlgo::SetNodeAttr("fused", MakeValue(true), node); - auto rank_size = AnfAlgo::GetNodeAttr(node, kAttrRankSize); + common::AnfAlgo::SetNodeAttr("fused", MakeValue(true), node); + auto rank_size = common::AnfAlgo::GetNodeAttr(node, kAttrRankSize); std::vector new_outputs; OutputInfo output_info = GetNodeOutputInfo(node); - size_t output_num = AnfAlgo::GetOutputTensorNum(node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(node); for (size_t i = 0; i < output_num; ++i) { int64_t temp = SizeToLong(i); auto idx = NewValueNode(temp); @@ -153,8 +154,8 @@ const AnfNodePtr ConcatOutputsForAllGather::Process(const FuncGraphPtr &func_gra idx->set_abstract(abstract_scalar); auto tuple_getitem = func_graph->NewCNode({NewValueNode(prim::kPrimTupleGetItem), node, idx}); MS_EXCEPTION_IF_NULL(tuple_getitem); - AnfAlgo::SetOutputInferTypeAndShape({std::get<0>(output_info)[i]}, {std::get<1>(output_info)[i]}, - tuple_getitem.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({std::get<0>(output_info)[i]}, {std::get<1>(output_info)[i]}, + tuple_getitem.get()); new_outputs.emplace_back(std::move(tuple_getitem)); } return InsertConcatForOutput(func_graph, node, output_info, new_outputs, rank_size); diff --git a/mindspore/ccsrc/plugin/device/gpu/optimizer/cudnn_inplace_fusion.cc b/mindspore/ccsrc/plugin/device/gpu/optimizer/cudnn_inplace_fusion.cc index 8122235292d..59227074299 100644 --- a/mindspore/ccsrc/plugin/device/gpu/optimizer/cudnn_inplace_fusion.cc +++ b/mindspore/ccsrc/plugin/device/gpu/optimizer/cudnn_inplace_fusion.cc @@ -26,9 +26,10 @@ #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" -#include "utils/contract.h" +#include "include/common/utils/utils.h" +#include "include/common/utils/contract.h" #include "backend/common/optimizer/helper.h" #include "plugin/device/gpu/hal/device/kernel_info_setter.h" @@ -57,7 +58,7 @@ constexpr size_t inplace_node_size = 2; template void SetPrimAttr(AnfNodePtr inplace_node, const string &key, const T &value) { - auto primitive = AnfAlgo::GetCNodePrimitive(inplace_node); + auto primitive = common::AnfAlgo::GetCNodePrimitive(inplace_node); MS_EXCEPTION_IF_NULL(primitive); primitive->AddAttr(key, MakeValue(value)); } @@ -76,9 +77,9 @@ bool ExistRoute(const CNodePtr &src, const CNodePtr &dst) { to_do.push(dst); while (!to_do.empty()) { const auto ¤t_node = to_do.front(); - size_t input_num = AnfAlgo::GetInputTensorNum(current_node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(current_node); for (size_t input_index = 0; input_index < input_num; ++input_index) { - const AnfNodePtr &input_node = AnfAlgo::GetInputNode(current_node, input_index); + const AnfNodePtr &input_node = common::AnfAlgo::GetInputNode(current_node, input_index); const auto &cnode = input_node->cast(); if (cnode == nullptr) { continue; @@ -118,19 +119,19 @@ std::pair GetCoverIndex(const std::vector &inplace_n } auto first_node = inplace_node[0].node; auto second_node = inplace_node[1].node; - if (AnfAlgo::GetCNodeName(first_node) != kConv2DBackpropInputOpName || - AnfAlgo::GetCNodeName(second_node) != kConv2DBackpropInputOpName) { + if (common::AnfAlgo::GetCNodeName(first_node) != kConv2DBackpropInputOpName || + common::AnfAlgo::GetCNodeName(second_node) != kConv2DBackpropInputOpName) { return {0, false}; } - auto first_node_prim = AnfAlgo::GetCNodePrimitive(first_node); + auto first_node_prim = common::AnfAlgo::GetCNodePrimitive(first_node); MS_EXCEPTION_IF_NULL(first_node_prim); auto first_node_channel = first_node_prim.get()->GetAttr("out_channel"); MS_EXCEPTION_IF_NULL(first_node_channel); auto first_imm_ptr = first_node_channel->cast(); MS_EXCEPTION_IF_NULL(first_imm_ptr); size_t first_channel = first_imm_ptr->value(); - auto second_node_prim = AnfAlgo::GetCNodePrimitive(second_node); + auto second_node_prim = common::AnfAlgo::GetCNodePrimitive(second_node); MS_EXCEPTION_IF_NULL(second_node_prim); auto second_node_channel = second_node_prim.get()->GetAttr("out_channel"); MS_EXCEPTION_IF_NULL(second_node_channel); @@ -148,14 +149,14 @@ std::pair GetCoverIndex(const std::vector &inplace_n void CopyKernelInfo(AnfNodePtr src, AnfNodePtr dst) { auto build_info = AnfAlgo::GetSelectKernelBuildInfo(src); AnfAlgo::SetSelectKernelBuildInfo(build_info, dst.get()); - size_t output_num = AnfAlgo::GetOutputTensorNum(src); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(src); std::vector types; std::vector> shapes; for (size_t i = 0; i < output_num; i++) { - types.emplace_back(AnfAlgo::GetOutputInferDataType(src, i)); - shapes.emplace_back(AnfAlgo::GetOutputInferShape(src, i)); + types.emplace_back(common::AnfAlgo::GetOutputInferDataType(src, i)); + shapes.emplace_back(common::AnfAlgo::GetOutputInferShape(src, i)); } - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, dst.get()); + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, dst.get()); } void CheckInplaceNodeInputs(std::vector *inplace_node, size_t cover_index, const FuncGraphPtr &graph) { @@ -251,25 +252,25 @@ bool PatternMatch(const FuncGraphPtr &graph, const AnfNodePtr &node, AnfNodeInde if (!node->isa()) { return false; } - auto aggregate_iter = kAggregatesOpNames.find(AnfAlgo::GetCNodeName(node)); + auto aggregate_iter = kAggregatesOpNames.find(common::AnfAlgo::GetCNodeName(node)); if (aggregate_iter == kAggregatesOpNames.end()) { return false; } aggregate->node = node; aggregate->index = aggregate_iter->second; - *skip_node = AnfAlgo::GetInputNode(utils::cast(node), aggregate_iter->second); + *skip_node = common::AnfAlgo::GetInputNode(utils::cast(node), aggregate_iter->second); if (*skip_node == nullptr || !(*skip_node)->isa() || - kSkipOpNames.count(AnfAlgo::GetCNodeName(*skip_node)) == 0 || + kSkipOpNames.count(common::AnfAlgo::GetCNodeName(*skip_node)) == 0 || GetRealNodeUsedList(graph, *skip_node)->size() >= 2) { return false; } auto cnode = (*skip_node)->cast(); MS_EXCEPTION_IF_NULL(cnode); - size_t input_num = AnfAlgo::GetInputTensorNum(cnode); + size_t input_num = common::AnfAlgo::GetInputTensorNum(cnode); for (size_t i = 0; i < input_num; i++) { - auto inplace_node = AnfAlgo::GetInputNode(utils::cast(*skip_node), i); + auto inplace_node = common::AnfAlgo::GetInputNode(utils::cast(*skip_node), i); if (!inplace_node->isa()) { return false; } @@ -279,11 +280,11 @@ bool PatternMatch(const FuncGraphPtr &graph, const AnfNodePtr &node, AnfNodeInde } // skip TupleGetItem node - if (AnfAlgo::GetCNodeName(inplace_node) == prim::kPrimTupleGetItem->name()) { - inplace_node = AnfAlgo::GetInputNode(utils::cast(inplace_node), 0); + if (common::AnfAlgo::GetCNodeName(inplace_node) == prim::kPrimTupleGetItem->name()) { + inplace_node = common::AnfAlgo::GetInputNode(utils::cast(inplace_node), 0); } - auto inplace_iter = kInplaceOpNames.find(AnfAlgo::GetCNodeName(inplace_node)); + auto inplace_iter = kInplaceOpNames.find(common::AnfAlgo::GetCNodeName(inplace_node)); if (inplace_iter == kInplaceOpNames.end()) { return false; } diff --git a/mindspore/ccsrc/plugin/device/gpu/optimizer/insert_cast_gpu.cc b/mindspore/ccsrc/plugin/device/gpu/optimizer/insert_cast_gpu.cc index 06961cef304..ebab4e7851c 100644 --- a/mindspore/ccsrc/plugin/device/gpu/optimizer/insert_cast_gpu.cc +++ b/mindspore/ccsrc/plugin/device/gpu/optimizer/insert_cast_gpu.cc @@ -22,8 +22,9 @@ #include "utils/hash_set.h" #include "backend/common/optimizer/helper.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace opt { @@ -33,11 +34,11 @@ void InsertCast(const FuncGraphPtr &graph, const AnfNodePtr &node, size_t i, con MS_EXCEPTION_IF_NULL(node); auto prim = std::make_shared(prim::kPrimCast->name()); MS_EXCEPTION_IF_NULL(prim); - std::vector inputs = {NewValueNode(prim), AnfAlgo::GetInputNode(utils::cast(node), i)}; + std::vector inputs = {NewValueNode(prim), common::AnfAlgo::GetInputNode(utils::cast(node), i)}; auto cast = graph->NewCNode(inputs); MS_EXCEPTION_IF_NULL(cast); - auto cast_shape = {AnfAlgo::GetPrevNodeOutputInferShape(node, i)}; - AnfAlgo::SetOutputInferTypeAndShape({cast_type}, cast_shape, cast.get()); + auto cast_shape = {common::AnfAlgo::GetPrevNodeOutputInferShape(node, i)}; + common::AnfAlgo::SetOutputInferTypeAndShape({cast_type}, cast_shape, cast.get()); FuncGraphManagerPtr manager = graph->manager(); MS_EXCEPTION_IF_NULL(manager); manager->SetEdge(node, i + 1, cast); @@ -52,7 +53,7 @@ void ProcessTupleGetItem(const FuncGraphPtr &graph, const AnfNodePtr &node, size for (size_t i = 0; i < used_node_list->size(); i++) { auto used_node = used_node_list->at(i).first; auto used_node_index = used_node_list->at(i).second - 1; - if (AnfAlgo::GetCNodeName(used_node) == prim::kPrimTupleGetItem->name()) { + if (common::AnfAlgo::GetCNodeName(used_node) == prim::kPrimTupleGetItem->name()) { MS_LOG(EXCEPTION) << "TupleGetItem connect with TupleGetItem."; } InsertCast(graph, used_node, used_node_index, src_type, cast_type); @@ -70,24 +71,24 @@ bool InsertCastGPU::Run(const FuncGraphPtr &graph) { static const mindspore::HashSet kConv3DKernel = { prim::kPrimConv3DBackpropInput->name(), prim::kPrimConv3DBackpropFilter->name(), prim::kPrimConv3D->name(), prim::kPrimConv3DTranspose->name()}; - if (kConv3DKernel.find(AnfAlgo::GetCNodeName(node)) == kConv3DKernel.end()) { + if (kConv3DKernel.find(common::AnfAlgo::GetCNodeName(node)) == kConv3DKernel.end()) { continue; } - size_t input_num = AnfAlgo::GetInputTensorNum(node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(node); for (size_t i = 0; i < input_num; i++) { - auto inferType = AnfAlgo::GetPrevNodeOutputInferDataType(node, i); + auto inferType = common::AnfAlgo::GetPrevNodeOutputInferDataType(node, i); if (inferType == kNumberTypeFloat16) { InsertCast(graph, node, i, inferType, kNumberTypeFloat32); IsCasted = true; - MS_LOG(INFO) << "Improve precision for [" << AnfAlgo::GetCNodeName(utils::cast(node)) << "] input " - << i; + MS_LOG(INFO) << "Improve precision for [" << common::AnfAlgo::GetCNodeName(utils::cast(node)) + << "] input " << i; } } - size_t output_num = AnfAlgo::GetOutputTensorNum(node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(node); for (size_t i = 0; i < output_num; i++) { - auto inferType = AnfAlgo::GetOutputInferDataType(node, i); + auto inferType = common::AnfAlgo::GetOutputInferDataType(node, i); if (inferType != kNumberTypeFloat16) { continue; } @@ -96,7 +97,7 @@ bool InsertCastGPU::Run(const FuncGraphPtr &graph) { for (size_t j = 0; j < used_node_list->size(); j++) { auto used_node = used_node_list->at(j).first; auto used_node_index = used_node_list->at(j).second - 1; - if (AnfAlgo::GetCNodeName(used_node) == prim::kPrimTupleGetItem->name()) { + if (common::AnfAlgo::GetCNodeName(used_node) == prim::kPrimTupleGetItem->name()) { ProcessTupleGetItem(graph, used_node, used_node_index, kNumberTypeFloat32, inferType); } else { InsertCast(graph, used_node, used_node_index, kNumberTypeFloat32, inferType); @@ -108,10 +109,10 @@ bool InsertCastGPU::Run(const FuncGraphPtr &graph) { auto output_types = std::vector(output_num, kNumberTypeFloat32); std::vector> output_shapes; for (size_t output_index = 0; output_index < output_num; ++output_index) { - std::vector shape = AnfAlgo::GetOutputInferShape(node, output_index); + std::vector shape = common::AnfAlgo::GetOutputInferShape(node, output_index); (void)output_shapes.emplace_back(shape); } - AnfAlgo::SetOutputInferTypeAndShape(output_types, output_shapes, node.get()); + common::AnfAlgo::SetOutputInferTypeAndShape(output_types, output_shapes, node.get()); } } return true; diff --git a/mindspore/ccsrc/plugin/device/gpu/optimizer/insert_format_transform_op.cc b/mindspore/ccsrc/plugin/device/gpu/optimizer/insert_format_transform_op.cc index bd2fc2e8684..4d83aac540e 100644 --- a/mindspore/ccsrc/plugin/device/gpu/optimizer/insert_format_transform_op.cc +++ b/mindspore/ccsrc/plugin/device/gpu/optimizer/insert_format_transform_op.cc @@ -20,8 +20,9 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/optimizer/helper.h" #include "plugin/device/gpu/hal/device/kernel_info_setter.h" @@ -62,8 +63,8 @@ bool IsFakeTranspose(const std::vector &out_shape, const std::vectorNewCNode(transpose_input); MS_EXCEPTION_IF_NULL(transpose_op); // 3.Set the output info of transpose. - auto transpose_type = {AnfAlgo::GetPrevNodeOutputInferDataType(used_node, used_node_index)}; - auto transpose_shape = AnfAlgo::GetPrevNodeOutputInferShape(used_node, used_node_index); - AnfAlgo::SetOutputInferTypeAndShape(transpose_type, {transpose_shape}, transpose_op.get()); + auto transpose_type = {common::AnfAlgo::GetPrevNodeOutputInferDataType(used_node, used_node_index)}; + auto transpose_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(used_node, used_node_index); + common::AnfAlgo::SetOutputInferTypeAndShape(transpose_type, {transpose_shape}, transpose_op.get()); if (is_fake) { std::vector shape; std::transform(transpose_shape.begin(), transpose_shape.end(), std::back_inserter(shape), SizeToLong); - AnfAlgo::SetNodeAttr("shape", MakeValue(shape), transpose_op); + common::AnfAlgo::SetNodeAttr("shape", MakeValue(shape), transpose_op); } else { - AnfAlgo::SetNodeAttr(kAttrPerm, MakeValue(transpose_perm), transpose_op); + common::AnfAlgo::SetNodeAttr(kAttrPerm, MakeValue(transpose_perm), transpose_op); } // 4. Set the new edge of transpose op. FuncGraphManagerPtr manager = graph->manager(); @@ -125,7 +126,7 @@ const AnfNodePtr InsertFormatTransformOp::Process(const FuncGraphPtr &graph, con if (!AnfUtils::IsRealCNodeKernel(node)) { return nullptr; } - auto iter = device::gpu::kKernelFormatPositionMap.find(AnfAlgo::GetCNodeName(node)); + auto iter = device::gpu::kKernelFormatPositionMap.find(common::AnfAlgo::GetCNodeName(node)); if (iter == device::gpu::kKernelFormatPositionMap.end()) { return nullptr; } @@ -138,7 +139,7 @@ const AnfNodePtr InsertFormatTransformOp::Process(const FuncGraphPtr &graph, con auto inputs_format = AnfAlgo::GetAllInputFormats(node); for (size_t i = 0; i < inputs_format.size(); i++) { if ((inputs_format[i] != kOpFormat_DEFAULT) && (inputs_format[i] != origin_data_format)) { - auto input_node = AnfAlgo::GetInputNode(utils::cast(node), i); + auto input_node = common::AnfAlgo::GetInputNode(utils::cast(node), i); MS_EXCEPTION_IF_NULL(input_node); auto input_transpose_perm = TransposeAxis(origin_data_format, inputs_format[i]); auto input_transpose_op = InsertTransposeOp(graph, input_node, node, i, input_transpose_perm); @@ -157,7 +158,7 @@ const AnfNodePtr InsertFormatTransformOp::Process(const FuncGraphPtr &graph, con auto used_node = used_node_list->at(j).first; auto used_node_index = used_node_list->at(j).second - 1; auto output_transpose_perm = TransposeAxis(outputs_format[i], origin_data_format); - if (AnfAlgo::GetCNodeName(used_node) == prim::kPrimTupleGetItem->name()) { + if (common::AnfAlgo::GetCNodeName(used_node) == prim::kPrimTupleGetItem->name()) { MS_LOG(DEBUG) << "The used node of [" << node->fullname_with_scope() << "] is tuple item."; // The tuple item need get next used nodes again. ProcessForTupleItem(graph, used_node, used_node_index, output_transpose_perm, outputs_format[i]); @@ -179,7 +180,7 @@ void InsertFormatTransformOp::ProcessForTupleItem(const FuncGraphPtr &graph, con for (size_t i = 0; i < used_node_list->size(); i++) { auto used_node = used_node_list->at(i).first; auto used_node_index = used_node_list->at(i).second - 1; - if (AnfAlgo::GetCNodeName(used_node) == prim::kPrimTupleGetItem->name()) { + if (common::AnfAlgo::GetCNodeName(used_node) == prim::kPrimTupleGetItem->name()) { MS_LOG(EXCEPTION) << "The used node of tuple item can't be tuple item."; } auto transpose_op = InsertTransposeOp(graph, node, used_node, used_node_index, transpose_perm); diff --git a/mindspore/ccsrc/plugin/device/gpu/optimizer/matmul_biasadd_fusion.cc b/mindspore/ccsrc/plugin/device/gpu/optimizer/matmul_biasadd_fusion.cc index e8363c2470d..c0f48e44c1f 100644 --- a/mindspore/ccsrc/plugin/device/gpu/optimizer/matmul_biasadd_fusion.cc +++ b/mindspore/ccsrc/plugin/device/gpu/optimizer/matmul_biasadd_fusion.cc @@ -20,8 +20,9 @@ #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/optimizer/helper.h" namespace mindspore { @@ -34,14 +35,14 @@ kernel::KernelBuildInfoPtr GenerateKernelBuildInfo(CNodePtr node) { std::vector outputs_type; kernel::KernelBuildInfo::KernelBuildInfoBuilder builder; - size_t input_num = AnfAlgo::GetInputTensorNum(node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(node); for (size_t input_index = 0; input_index < input_num; ++input_index) { - inputs_type.push_back(AnfAlgo::GetPrevNodeOutputInferDataType(node, input_index)); + inputs_type.push_back(common::AnfAlgo::GetPrevNodeOutputInferDataType(node, input_index)); inputs_format.push_back(kOpFormat_DEFAULT); } - size_t output_num = AnfAlgo::GetOutputTensorNum(node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(node); for (size_t output_index = 0; output_index < output_num; ++output_index) { - outputs_type.push_back(AnfAlgo::GetOutputInferDataType(node, output_index)); + outputs_type.push_back(common::AnfAlgo::GetOutputInferDataType(node, output_index)); outputs_format.push_back(kOpFormat_DEFAULT); } builder.SetInputsDeviceType(inputs_type); @@ -74,7 +75,7 @@ const AnfNodePtr MatMulBiasAddFusion::Process(const FuncGraphPtr &graph, const A MS_EXCEPTION_IF_NULL(bias_input); // The `Matmul` node should have an unique user. - const AnfNodePtr &matmul = AnfAlgo::GetInputNode(utils::cast(node), 0); + const AnfNodePtr &matmul = common::AnfAlgo::GetInputNode(utils::cast(node), 0); MS_EXCEPTION_IF_NULL(matmul); auto outlist = GetRealNodeUsedList(graph, matmul); if (outlist->size() >= 2) { @@ -97,10 +98,10 @@ const AnfNodePtr MatMulBiasAddFusion::Process(const FuncGraphPtr &graph, const A MS_EXCEPTION_IF_NULL(fused_node); // Copy Abstract and KernelBuildInfo. - auto types = {AnfAlgo::GetOutputInferDataType(node, 0)}; - auto shapes = {AnfAlgo::GetOutputInferShape(node, 0)}; - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, fused_node.get()); - AnfAlgo::CopyNodeAttrs(matmul, fused_node); + auto types = {common::AnfAlgo::GetOutputInferDataType(node, 0)}; + auto shapes = {common::AnfAlgo::GetOutputInferShape(node, 0)}; + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, fused_node.get()); + common::AnfAlgo::CopyNodeAttrs(matmul, fused_node); fused_node->set_scope(node->scope()); auto build_info = GenerateKernelBuildInfo(fused_node); AnfAlgo::SetSelectKernelBuildInfo(build_info, fused_node.get()); diff --git a/mindspore/ccsrc/plugin/device/gpu/optimizer/neighbor_exchange_v2_fusion.cc b/mindspore/ccsrc/plugin/device/gpu/optimizer/neighbor_exchange_v2_fusion.cc index 4237e41d737..ac8e91473f9 100644 --- a/mindspore/ccsrc/plugin/device/gpu/optimizer/neighbor_exchange_v2_fusion.cc +++ b/mindspore/ccsrc/plugin/device/gpu/optimizer/neighbor_exchange_v2_fusion.cc @@ -21,8 +21,9 @@ #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "plugin/device/gpu/hal/device/kernel_info_setter.h" #include "backend/common/optimizer/helper.h" #include "plugin/device/gpu/kernel/nccl/nccl_gpu_kernel.h" @@ -139,9 +140,9 @@ CNodePtr CreateSliceNode(const FuncGraphPtr &graph, const std::vector> shapes = {slice_shape}; std::vector dtypes(1, slice_node_info.input_dtype); - AnfAlgo::SetOutputInferTypeAndShape(dtypes, shapes, slice.get()); - AnfAlgo::SetNodeAttr(kAttrBegin, MakeValue(Convert2Long(begins)), slice); - AnfAlgo::SetNodeAttr(kAttrSize, MakeValue(Convert2Long(slice_shape)), slice); + common::AnfAlgo::SetOutputInferTypeAndShape(dtypes, shapes, slice.get()); + common::AnfAlgo::SetNodeAttr(kAttrBegin, MakeValue(Convert2Long(begins)), slice); + common::AnfAlgo::SetNodeAttr(kAttrSize, MakeValue(Convert2Long(slice_shape)), slice); return slice; } @@ -177,7 +178,8 @@ CNodePtr CreateSplitNode(const FuncGraphPtr &graph, const AnfNodePtr &split_inpu } CNodePtr make_tuple = graph->NewCNode(make_tuple_inputs); make_tuple->set_abstract(std::make_shared(abstract_list)); - AnfAlgo::SetNodeAttr(kAttrSizeSplits, MakeValue>(splitv_node_info->size_splits), make_tuple); + common::AnfAlgo::SetNodeAttr(kAttrSizeSplits, MakeValue>(splitv_node_info->size_splits), + make_tuple); return make_tuple; } @@ -326,12 +328,12 @@ CNodePtr CreateAllToAllvNode(const FuncGraphPtr &graph, const CNodePtr &neighbor MS_EXCEPTION_IF_NULL(graph); MS_EXCEPTION_IF_NULL(neighbor_exchange_v2_or_grad); std::vector send_rank_ids = - AnfAlgo::GetNodeAttr>(neighbor_exchange_v2_or_grad, kAttrSendRankIds); + common::AnfAlgo::GetNodeAttr>(neighbor_exchange_v2_or_grad, kAttrSendRankIds); std::vector recv_rank_ids = - AnfAlgo::GetNodeAttr>(neighbor_exchange_v2_or_grad, kAttrRecvRankIds); + common::AnfAlgo::GetNodeAttr>(neighbor_exchange_v2_or_grad, kAttrRecvRankIds); std::vector recv_lens = - AnfAlgo::GetNodeAttr>(neighbor_exchange_v2_or_grad, kAttrRecvLens); - std::string group = AnfAlgo::GetNodeAttr(neighbor_exchange_v2_or_grad, kAttrGroup); + common::AnfAlgo::GetNodeAttr>(neighbor_exchange_v2_or_grad, kAttrRecvLens); + std::string group = common::AnfAlgo::GetNodeAttr(neighbor_exchange_v2_or_grad, kAttrGroup); // get split nodes output, split_outputs: [top_bottom, left_right, top_corner, bottom_corner] std::vector> split_outputs; @@ -360,8 +362,8 @@ CNodePtr CreateAllToAllvNode(const FuncGraphPtr &graph, const CNodePtr &neighbor } // output shapes and dtypes - auto base_dtype = AnfAlgo::GetOutputInferDataType(base_node, 0); - auto base_shape = AnfAlgo::GetOutputInferShape(base_node, 0); + auto base_dtype = common::AnfAlgo::GetOutputInferDataType(base_node, 0); + auto base_shape = common::AnfAlgo::GetOutputInferShape(base_node, 0); if (SizeToLong(base_shape.size()) != kShapeSize) { MS_LOG(EXCEPTION) << "Invalid shape size " << base_shape.size() << ", only support NCHW input now!"; } @@ -382,11 +384,11 @@ CNodePtr CreateAllToAllvNode(const FuncGraphPtr &graph, const CNodePtr &neighbor // create alltoallv node auto all_to_all_v = pass.NewCNode(all_to_all_v_input, graph); MS_EXCEPTION_IF_NULL(all_to_all_v); - AnfAlgo::SetOutputInferTypeAndShape(dtypes, shapes, all_to_all_v.get()); + common::AnfAlgo::SetOutputInferTypeAndShape(dtypes, shapes, all_to_all_v.get()); - AnfAlgo::SetNodeAttr(kAttrSendRankIds, MakeValue>(real_send_rank_ids), all_to_all_v); - AnfAlgo::SetNodeAttr(kAttrRecvRankIds, MakeValue>(real_recv_rank_ids), all_to_all_v); - AnfAlgo::SetNodeAttr(kAttrGroup, MakeValue(group), all_to_all_v); + common::AnfAlgo::SetNodeAttr(kAttrSendRankIds, MakeValue>(real_send_rank_ids), all_to_all_v); + common::AnfAlgo::SetNodeAttr(kAttrRecvRankIds, MakeValue>(real_recv_rank_ids), all_to_all_v); + common::AnfAlgo::SetNodeAttr(kAttrGroup, MakeValue(group), all_to_all_v); MS_LOG(INFO) << "Create AllToAllv success, send rank size " << send_rank_ids.size() << ", recv rank size " << recv_rank_ids.size(); return all_to_all_v; @@ -411,8 +413,9 @@ std::vector NeighborExchangeV2Fusion::CreateSplitNodes(const FuncGraph MS_EXCEPTION_IF_NULL(neighbor_exchange_v2); MS_EXCEPTION_IF_NULL(split_num); std::vector send_rank_ids = - AnfAlgo::GetNodeAttr>(neighbor_exchange_v2, kAttrSendRankIds); - std::vector send_lens = AnfAlgo::GetNodeAttr>(neighbor_exchange_v2, kAttrSendLens); + common::AnfAlgo::GetNodeAttr>(neighbor_exchange_v2, kAttrSendRankIds); + std::vector send_lens = + common::AnfAlgo::GetNodeAttr>(neighbor_exchange_v2, kAttrSendLens); if (neighbor_exchange_v2->size() <= kNeighborExchangeV2InputIdx) { MS_LOG(EXCEPTION) << "Invalid cnode " << neighbor_exchange_v2->DebugString() << " input size " @@ -422,8 +425,8 @@ std::vector NeighborExchangeV2Fusion::CreateSplitNodes(const FuncGraph std::vector split_nodes = {}; auto neighbor_exchange_v2_input = neighbor_exchange_v2->input(kNeighborExchangeV2InputIdx); - auto dtype = AnfAlgo::GetOutputInferDataType(neighbor_exchange_v2_input, 0); - auto shape = AnfAlgo::GetOutputInferShape(neighbor_exchange_v2_input, 0); + auto dtype = common::AnfAlgo::GetOutputInferDataType(neighbor_exchange_v2_input, 0); + auto shape = common::AnfAlgo::GetOutputInferShape(neighbor_exchange_v2_input, 0); // Splitv for top & bottom SplitvNodeInfo splitv_node_info; @@ -514,11 +517,11 @@ CNodePtr NeighborExchangeV2Fusion::CreateConcatNode(const FuncGraphPtr &graph, MS_EXCEPTION_IF_NULL(graph); auto concat = NewCNode(concat_input, graph); MS_EXCEPTION_IF_NULL(concat); - AnfAlgo::SetOutputInferTypeAndShape(output_dtype, output_shape, concat.get()); - AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(axis), concat); - AnfAlgo::SetNodeAttr(kAttrInputNums, MakeValue(input_nums), concat); + common::AnfAlgo::SetOutputInferTypeAndShape(output_dtype, output_shape, concat.get()); + common::AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(axis), concat); + common::AnfAlgo::SetNodeAttr(kAttrInputNums, MakeValue(input_nums), concat); std::vector dyn_input_size_empty{input_nums}; - AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(dyn_input_size_empty), concat); + common::AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(dyn_input_size_empty), concat); return concat; } @@ -534,7 +537,8 @@ CNodePtr NeighborExchangeV2Fusion::CreateLeftRightConcat(const FuncGraphPtr &gra size_t middle_ids = is_left ? 6 : 2; size_t last_ids = is_left ? 5 : 3; - auto single_shape = AnfAlgo::GetOutputInferShape(all_to_all_v_outputs[AllToAllRealIds(middle_ids, recv_rank_ids)], 0); + auto single_shape = + common::AnfAlgo::GetOutputInferShape(all_to_all_v_outputs[AllToAllRealIds(middle_ids, recv_rank_ids)], 0); if (recv_rank_ids[first_ids] != kInvalidId) { ++input_num; @@ -552,7 +556,7 @@ CNodePtr NeighborExchangeV2Fusion::CreateLeftRightConcat(const FuncGraphPtr &gra } std::vector concat_output_dtype = { - AnfAlgo::GetOutputInferDataType(all_to_all_v_outputs[AllToAllRealIds(middle_ids, recv_rank_ids)], 0)}; + common::AnfAlgo::GetOutputInferDataType(all_to_all_v_outputs[AllToAllRealIds(middle_ids, recv_rank_ids)], 0)}; auto concat = CreateConcatNode(graph, concat_input, {single_shape}, concat_output_dtype, kHDim, input_num); return concat; @@ -565,7 +569,7 @@ CNodePtr NeighborExchangeV2Fusion::CreateMiddleConcat(const FuncGraphPtr &graph, std::vector concat_input_all = {NewValueNode(std::make_shared(kConcatOpName))}; int64_t input_num_all = 0; auto neighbor_exchange_v2_input = neighbor_exchange_v2->input(kNeighborExchangeV2InputIdx); - auto single_shape = AnfAlgo::GetOutputInferShape(neighbor_exchange_v2_input, 0); + auto single_shape = common::AnfAlgo::GetOutputInferShape(neighbor_exchange_v2_input, 0); size_t first_idx = concat_dim == kWDim ? 6 : 0; size_t last_idx = concat_dim == kWDim ? 2 : 4; size_t first_len = concat_dim == kWDim ? static_cast(recv_lens[kDim2]) : static_cast(recv_lens[0]); @@ -599,7 +603,7 @@ CNodePtr NeighborExchangeV2Fusion::CreateMiddleConcat(const FuncGraphPtr &graph, single_shape[concat_dim] += last_len; } - std::vector concat_output_dtype = {AnfAlgo::GetOutputInferDataType(all_to_all_v_outputs[0], 0)}; + std::vector concat_output_dtype = {common::AnfAlgo::GetOutputInferDataType(all_to_all_v_outputs[0], 0)}; auto concat_all = CreateConcatNode(graph, concat_input_all, {single_shape}, concat_output_dtype, concat_dim, input_num_all); return concat_all; @@ -626,8 +630,9 @@ CNodePtr NeighborExchangeV2Fusion::CreateConcatNodes(const FuncGraphPtr &graph, MS_EXCEPTION_IF_NULL(neighbor_exchange_v2); MS_EXCEPTION_IF_NULL(all_to_all_v); std::vector recv_rank_ids = - AnfAlgo::GetNodeAttr>(neighbor_exchange_v2, kAttrRecvRankIds); - std::vector recv_lens = AnfAlgo::GetNodeAttr>(neighbor_exchange_v2, kAttrRecvLens); + common::AnfAlgo::GetNodeAttr>(neighbor_exchange_v2, kAttrRecvRankIds); + std::vector recv_lens = + common::AnfAlgo::GetNodeAttr>(neighbor_exchange_v2, kAttrRecvLens); int64_t all_to_all_output_num = std::count_if(recv_rank_ids.begin(), recv_rank_ids.end(), [](int64_t ids) { return ids != kInvalidId; }); @@ -662,7 +667,7 @@ CNodePtr NeighborExchangeV2Fusion::CreateConcatNodes(const FuncGraphPtr &graph, std::vector concat_input_all = {NewValueNode(std::make_shared(kConcatOpName))}; auto neighbor_exchange_v2_input = neighbor_exchange_v2->input(kNeighborExchangeV2InputIdx); - std::vector shape_all = AnfAlgo::GetOutputInferShape(neighbor_exchange_v2_input, 0); + std::vector shape_all = common::AnfAlgo::GetOutputInferShape(neighbor_exchange_v2_input, 0); shape_all[kDim2] = recv_rank_ids[kRankIdZero] != kInvalidId ? shape_all[kDim2] + static_cast(recv_lens[0]) : shape_all[kDim2]; shape_all[kDim2] = @@ -709,7 +714,7 @@ CNodePtr NeighborExchangeV2Fusion::CreateConcatNodes(const FuncGraphPtr &graph, shape_all[kDim3] += recv_lens[kDim3]; } - std::vector concat_right_output_dtype = {AnfAlgo::GetOutputInferDataType(concat_input_all[1], 0)}; + std::vector concat_right_output_dtype = {common::AnfAlgo::GetOutputInferDataType(concat_input_all[1], 0)}; auto concat_all = CreateConcatNode(graph, concat_input_all, {shape_all}, concat_right_output_dtype, kWDim, input_nums_all); return concat_all; @@ -725,8 +730,9 @@ std::vector NeighborExchangeV2GradFusion::CreateSplitNodesForGrad(cons MS_EXCEPTION_IF_NULL(neighbor_exchange_v2_grad); MS_EXCEPTION_IF_NULL(split_num); std::vector send_rank_ids = - AnfAlgo::GetNodeAttr>(neighbor_exchange_v2_grad, kAttrSendRankIds); - std::vector send_lens = AnfAlgo::GetNodeAttr>(neighbor_exchange_v2_grad, kAttrSendLens); + common::AnfAlgo::GetNodeAttr>(neighbor_exchange_v2_grad, kAttrSendRankIds); + std::vector send_lens = + common::AnfAlgo::GetNodeAttr>(neighbor_exchange_v2_grad, kAttrSendLens); if (neighbor_exchange_v2_grad->size() <= kNeighborExchangeV2InputIdx) { MS_LOG(EXCEPTION) << "Invalid cnode " << neighbor_exchange_v2_grad->DebugString() << " input size " @@ -735,8 +741,8 @@ std::vector NeighborExchangeV2GradFusion::CreateSplitNodesForGrad(cons } auto neighbor_exchange_v2_grad_input = neighbor_exchange_v2_grad->input(kNeighborExchangeV2InputIdx); - auto dtype = AnfAlgo::GetOutputInferDataType(neighbor_exchange_v2_grad_input, 0); - auto shape = AnfAlgo::GetOutputInferShape(neighbor_exchange_v2_grad_input, 0); + auto dtype = common::AnfAlgo::GetOutputInferDataType(neighbor_exchange_v2_grad_input, 0); + auto shape = common::AnfAlgo::GetOutputInferShape(neighbor_exchange_v2_grad_input, 0); if (SizeToLong(shape.size()) != kShapeSize) { MS_LOG(EXCEPTION) << "Invalid shape size " << shape.size() << ", only support NCHW input now!" << trace::DumpSourceLines(neighbor_exchange_v2_grad); @@ -771,7 +777,7 @@ std::vector NeighborExchangeV2GradFusion::CreateSplitNodesForGrad(cons MS_LOG(EXCEPTION) << "The node " << split_nodes[0]->DebugString() << " should have at least one output, but got 0." << trace::DumpSourceLines(split_nodes[0]); } - size_split_h = AnfAlgo::GetNodeAttr>(split_nodes[0], kAttrSizeSplits); + size_split_h = common::AnfAlgo::GetNodeAttr>(split_nodes[0], kAttrSizeSplits); } else { // just middle split_outputs_top_bottom.push_back(neighbor_exchange_v2_grad_input); @@ -832,9 +838,9 @@ CNodePtr NeighborExchangeV2GradFusion::CreatePadNode(const FuncGraphPtr &graph, for (size_t i = 0; i < shape.size(); ++i) { paddings.emplace_back(std::vector{begin[i], static_cast(shape[i]) - begin[i] - size[i]}); } - AnfAlgo::SetOutputInferTypeAndShape({dtype}, {shape}, pad.get()); - AnfAlgo::SetNodeAttr(kAttrPaddings, MakeValue(paddings), pad); - AnfAlgo::SetNodeAttr(kAttrInputNames, MakeValue(std::vector{"x"}), pad); + common::AnfAlgo::SetOutputInferTypeAndShape({dtype}, {shape}, pad.get()); + common::AnfAlgo::SetNodeAttr(kAttrPaddings, MakeValue(paddings), pad); + common::AnfAlgo::SetNodeAttr(kAttrInputNames, MakeValue(std::vector{"x"}), pad); return pad; } @@ -847,14 +853,15 @@ CNodePtr NeighborExchangeV2GradFusion::CreateSplitGradNodes(const FuncGraphPtr & MS_EXCEPTION_IF_NULL(graph); MS_EXCEPTION_IF_NULL(neighbor_exchange_v2_grad); std::vector send_rank_ids = - AnfAlgo::GetNodeAttr>(neighbor_exchange_v2_grad, kAttrSendRankIds); + common::AnfAlgo::GetNodeAttr>(neighbor_exchange_v2_grad, kAttrSendRankIds); std::vector recv_rank_ids = - AnfAlgo::GetNodeAttr>(neighbor_exchange_v2_grad, kAttrRecvRankIds); - std::vector recv_lens = AnfAlgo::GetNodeAttr>(neighbor_exchange_v2_grad, kAttrRecvLens); + common::AnfAlgo::GetNodeAttr>(neighbor_exchange_v2_grad, kAttrRecvRankIds); + std::vector recv_lens = + common::AnfAlgo::GetNodeAttr>(neighbor_exchange_v2_grad, kAttrRecvLens); auto centerx = GetCenter(graph, neighbor_exchange_v2_grad, split_nodes, split_num, send_rank_ids); - auto centerx_dtype = AnfAlgo::GetOutputInferDataType(centerx, 0); - auto centerx_shape = AnfAlgo::GetOutputInferShape(centerx, 0); + auto centerx_dtype = common::AnfAlgo::GetOutputInferDataType(centerx, 0); + auto centerx_shape = common::AnfAlgo::GetOutputInferShape(centerx, 0); // empty int64_t all_to_all_output_num = std::count_if(recv_rank_ids.begin(), recv_rank_ids.end(), [](int64_t ids) { return ids != kInvalidId; }); @@ -925,9 +932,9 @@ CNodePtr NeighborExchangeV2GradFusion::CreateSplitGradNodes(const FuncGraphPtr & } auto addn = NewCNode(addn_inputs, graph); MS_EXCEPTION_IF_NULL(addn); - AnfAlgo::SetOutputInferTypeAndShape({centerx_dtype}, {centerx_shape}, addn.get()); - AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue>({pad_num}), addn); - AnfAlgo::SetNodeAttr(kAttrN, MakeValue(pad_num), addn); + common::AnfAlgo::SetOutputInferTypeAndShape({centerx_dtype}, {centerx_shape}, addn.get()); + common::AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue>({pad_num}), addn); + common::AnfAlgo::SetNodeAttr(kAttrN, MakeValue(pad_num), addn); MS_LOG(DEBUG) << "Create splitvs grad nodes success."; return addn; } diff --git a/mindspore/ccsrc/plugin/device/gpu/optimizer/neighbor_exchange_v2_fusion.h b/mindspore/ccsrc/plugin/device/gpu/optimizer/neighbor_exchange_v2_fusion.h index 68536c0d223..2232d8d185a 100644 --- a/mindspore/ccsrc/plugin/device/gpu/optimizer/neighbor_exchange_v2_fusion.h +++ b/mindspore/ccsrc/plugin/device/gpu/optimizer/neighbor_exchange_v2_fusion.h @@ -20,6 +20,7 @@ #include #include "backend/common/optimizer/optimizer.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { diff --git a/mindspore/ccsrc/plugin/device/gpu/optimizer/post_batch_norm_add_relu_fusion.cc b/mindspore/ccsrc/plugin/device/gpu/optimizer/post_batch_norm_add_relu_fusion.cc index c02bb0fb659..e711b9286bc 100644 --- a/mindspore/ccsrc/plugin/device/gpu/optimizer/post_batch_norm_add_relu_fusion.cc +++ b/mindspore/ccsrc/plugin/device/gpu/optimizer/post_batch_norm_add_relu_fusion.cc @@ -20,8 +20,9 @@ #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/optimizer/helper.h" #include "plugin/device/gpu/hal/device/kernel_info_setter.h" @@ -40,18 +41,18 @@ const AnfNodePtr PostBatchNormAddReluFusion::Process(const FuncGraphPtr &graph, MS_EXCEPTION_IF_NULL(graph); MS_EXCEPTION_IF_NULL(node); - auto tensor_add = AnfAlgo::GetInputNode(utils::cast(node), 0); + auto tensor_add = common::AnfAlgo::GetInputNode(utils::cast(node), 0); MS_EXCEPTION_IF_NULL(tensor_add); - auto tuple_get_item = AnfAlgo::GetInputNode(utils::cast(tensor_add), 1); + auto tuple_get_item = common::AnfAlgo::GetInputNode(utils::cast(tensor_add), 1); MS_EXCEPTION_IF_NULL(tuple_get_item); - auto batch_norm = AnfAlgo::GetInputNode(utils::cast(tuple_get_item), 0); + auto batch_norm = common::AnfAlgo::GetInputNode(utils::cast(tuple_get_item), 0); MS_EXCEPTION_IF_NULL(batch_norm); - auto is_train = AnfAlgo::GetCNodePrimitive(batch_norm)->GetAttr("is_training"); + auto is_train = common::AnfAlgo::GetCNodePrimitive(batch_norm)->GetAttr("is_training"); MS_EXCEPTION_IF_NULL(is_train); if (!GetValue(is_train)) { return nullptr; } - auto format_attr = AnfAlgo::GetCNodePrimitive(batch_norm)->GetAttr("format"); + auto format_attr = common::AnfAlgo::GetCNodePrimitive(batch_norm)->GetAttr("format"); MS_EXCEPTION_IF_NULL(format_attr); auto format = GetValue(format_attr); if (AnfAlgo::GetInputFormat(batch_norm, 0) != kOpFormat_NHWC && format != "NHWC") { @@ -62,12 +63,12 @@ const AnfNodePtr PostBatchNormAddReluFusion::Process(const FuncGraphPtr &graph, return nullptr; } - auto x = AnfAlgo::GetInputNode(utils::cast(batch_norm), 0); - auto scale = AnfAlgo::GetInputNode(utils::cast(batch_norm), 1); - auto bias = AnfAlgo::GetInputNode(utils::cast(batch_norm), 2); - auto mean = AnfAlgo::GetInputNode(utils::cast(batch_norm), 3); - auto var = AnfAlgo::GetInputNode(utils::cast(batch_norm), 4); - auto z = AnfAlgo::GetInputNode(utils::cast(tensor_add), 0); + auto x = common::AnfAlgo::GetInputNode(utils::cast(batch_norm), 0); + auto scale = common::AnfAlgo::GetInputNode(utils::cast(batch_norm), 1); + auto bias = common::AnfAlgo::GetInputNode(utils::cast(batch_norm), 2); + auto mean = common::AnfAlgo::GetInputNode(utils::cast(batch_norm), 3); + auto var = common::AnfAlgo::GetInputNode(utils::cast(batch_norm), 4); + auto z = common::AnfAlgo::GetInputNode(utils::cast(tensor_add), 0); MS_EXCEPTION_IF_NULL(x); MS_EXCEPTION_IF_NULL(scale); @@ -84,13 +85,13 @@ const AnfNodePtr PostBatchNormAddReluFusion::Process(const FuncGraphPtr &graph, std::vector outputs_type; std::vector> outputs_shape; - auto output_num = AnfAlgo::GetOutputTensorNum(batch_norm); + auto output_num = common::AnfAlgo::GetOutputTensorNum(batch_norm); for (size_t i = 0; i < output_num; i++) { - outputs_type.push_back(AnfAlgo::GetOutputInferDataType(batch_norm, i)); - outputs_shape.push_back(AnfAlgo::GetOutputInferShape(batch_norm, i)); + outputs_type.push_back(common::AnfAlgo::GetOutputInferDataType(batch_norm, i)); + outputs_shape.push_back(common::AnfAlgo::GetOutputInferShape(batch_norm, i)); } - AnfAlgo::SetOutputInferTypeAndShape(outputs_type, outputs_shape, fused_batch_norm_with_add_relu.get()); - AnfAlgo::CopyNodeAttrs(batch_norm, fused_batch_norm_with_add_relu); + common::AnfAlgo::SetOutputInferTypeAndShape(outputs_type, outputs_shape, fused_batch_norm_with_add_relu.get()); + common::AnfAlgo::CopyNodeAttrs(batch_norm, fused_batch_norm_with_add_relu); auto manager = graph->manager(); MS_EXCEPTION_IF_NULL(manager); diff --git a/mindspore/ccsrc/plugin/device/gpu/optimizer/print_reduce_fusion.cc b/mindspore/ccsrc/plugin/device/gpu/optimizer/print_reduce_fusion.cc index c8e96d71dd4..b6d6c6626c0 100644 --- a/mindspore/ccsrc/plugin/device/gpu/optimizer/print_reduce_fusion.cc +++ b/mindspore/ccsrc/plugin/device/gpu/optimizer/print_reduce_fusion.cc @@ -21,8 +21,9 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/optimizer/helper.h" namespace mindspore { @@ -34,15 +35,15 @@ kernel::KernelBuildInfoPtr GenerateKernelBuildInfo(CNodePtr node) { std::vector outputs_type; kernel::KernelBuildInfo::KernelBuildInfoBuilder builder; - size_t input_num = AnfAlgo::GetInputTensorNum(node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(node); for (size_t input_index = 0; input_index < input_num; input_index++) { inputs_format.push_back(kOpFormat_DEFAULT); - inputs_type.push_back(AnfAlgo::GetPrevNodeOutputInferDataType(node, input_index)); + inputs_type.push_back(common::AnfAlgo::GetPrevNodeOutputInferDataType(node, input_index)); } - size_t output_num = AnfAlgo::GetOutputTensorNum(node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(node); for (size_t output_index = 0; output_index < output_num; output_index++) { outputs_format.push_back(kOpFormat_DEFAULT); - outputs_type.push_back(AnfAlgo::GetOutputInferDataType(node, output_index)); + outputs_type.push_back(common::AnfAlgo::GetOutputInferDataType(node, output_index)); } builder.SetInputsFormat(inputs_format); @@ -67,9 +68,9 @@ bool GetOptList(const std::vector &node_list, std::vector string_value; std::vector> value_type; if (IsPrimitiveCNode(node, prim::kPrimPrint)) { - size_t input_num = AnfAlgo::GetInputTensorNum(node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(node); for (size_t i = 0; i < input_num; i++) { - auto current_node = AnfAlgo::GetInputNode(utils::cast(node), i); + auto current_node = common::AnfAlgo::GetInputNode(utils::cast(node), i); // not tensor(tuple, scalar, string) if (current_node->cast() == nullptr) { continue; @@ -134,7 +135,7 @@ bool PrintReduceFusion::Run(const FuncGraphPtr &graph) { auto node = opt_list[idx]; CNodePtr cnode = utils::cast(node); MS_EXCEPTION_IF_NULL(cnode); - size_t input_num = AnfAlgo::GetInputTensorNum(cnode); + size_t input_num = common::AnfAlgo::GetInputTensorNum(cnode); auto prim = std::make_shared("Print"); std::vector inputs = {NewValueNode(prim)}; auto string_pos = string_pos_vec[idx]; @@ -150,12 +151,12 @@ bool PrintReduceFusion::Run(const FuncGraphPtr &graph) { if (input_flag[i] == -1) { continue; } - auto input_tensor = AnfAlgo::GetInputNode(cnode, i); + auto input_tensor = common::AnfAlgo::GetInputNode(cnode, i); MS_EXCEPTION_IF_NULL(input_tensor); inputs.push_back(input_tensor); } // add monad - auto monad_node = AnfAlgo::GetInputNode(cnode, input_flag.size()); + auto monad_node = common::AnfAlgo::GetInputNode(cnode, input_flag.size()); MS_EXCEPTION_IF_NULL(monad_node); inputs.push_back(monad_node); auto string_value = string_value_vec[idx]; @@ -171,19 +172,19 @@ bool PrintReduceFusion::Run(const FuncGraphPtr &graph) { auto print_fused = graph->NewCNode(inputs); MS_EXCEPTION_IF_NULL(print_fused); // hand over the attrs to new print - AnfAlgo::SetNodeAttr("string_pos", MakeValue>(string_pos), print_fused); - AnfAlgo::SetNodeAttr("string_value", MakeValue>(string_value), print_fused); - AnfAlgo::SetNodeAttr("value_type", MakeValue>(value_type), print_fused); - AnfAlgo::SetNodeAttr("value_type_pos", MakeValue>(value_type_pos), print_fused); + common::AnfAlgo::SetNodeAttr("string_pos", MakeValue>(string_pos), print_fused); + common::AnfAlgo::SetNodeAttr("string_value", MakeValue>(string_value), print_fused); + common::AnfAlgo::SetNodeAttr("value_type", MakeValue>(value_type), print_fused); + common::AnfAlgo::SetNodeAttr("value_type_pos", MakeValue>(value_type_pos), print_fused); // set output type and shape std::vector types; std::vector> shapes; - size_t output_num = AnfAlgo::GetOutputTensorNum(cnode); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(cnode); for (size_t i = 0; i < output_num; i++) { - types.push_back(AnfAlgo::GetOutputInferDataType(cnode, i)); - shapes.push_back(AnfAlgo::GetOutputInferShape(cnode, i)); + types.push_back(common::AnfAlgo::GetOutputInferDataType(cnode, i)); + shapes.push_back(common::AnfAlgo::GetOutputInferShape(cnode, i)); } - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, print_fused.get()); + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, print_fused.get()); // add build info auto build_info = GenerateKernelBuildInfo(print_fused); AnfAlgo::SetSelectKernelBuildInfo(build_info, print_fused.get()); diff --git a/mindspore/ccsrc/plugin/device/gpu/optimizer/reduce_precision_fusion.cc b/mindspore/ccsrc/plugin/device/gpu/optimizer/reduce_precision_fusion.cc index f2b18d90325..076d6b5911f 100644 --- a/mindspore/ccsrc/plugin/device/gpu/optimizer/reduce_precision_fusion.cc +++ b/mindspore/ccsrc/plugin/device/gpu/optimizer/reduce_precision_fusion.cc @@ -21,8 +21,9 @@ #include "backend/common/optimizer/helper.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace opt { @@ -33,12 +34,12 @@ void ReducePrecision(const FuncGraphPtr &graph, const AnfNodePtr &node, size_t i MS_EXCEPTION_IF_NULL(node); auto prim = std::make_shared(prim::kPrimCast->name()); MS_EXCEPTION_IF_NULL(prim); - std::vector inputs = {NewValueNode(prim), AnfAlgo::GetInputNode(utils::cast(node), i)}; + std::vector inputs = {NewValueNode(prim), common::AnfAlgo::GetInputNode(utils::cast(node), i)}; auto cast = graph->NewCNode(inputs); MS_EXCEPTION_IF_NULL(cast); prim->AddAttr(kAttrDstType, TypeIdToType(cast_type)); auto cast_shape = {AnfAlgo::GetInputDeviceShape(node, i)}; - AnfAlgo::SetOutputInferTypeAndShape({cast_type}, cast_shape, cast.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({cast_type}, cast_shape, cast.get()); FuncGraphManagerPtr manager = graph->manager(); MS_EXCEPTION_IF_NULL(manager); manager->SetEdge(node, i + 1, cast); @@ -60,7 +61,7 @@ void ProcessTupleGetItem(const FuncGraphPtr &graph, const AnfNodePtr &node, size for (size_t i = 0; i < used_node_list->size(); i++) { auto used_node = used_node_list->at(i).first; auto used_node_index = used_node_list->at(i).second - 1; - if (AnfAlgo::GetCNodeName(used_node) == prim::kPrimTupleGetItem->name()) { + if (common::AnfAlgo::GetCNodeName(used_node) == prim::kPrimTupleGetItem->name()) { MS_LOG(EXCEPTION) << "TupleGetItem connect with TupleGetItem."; } ReducePrecision(graph, used_node, used_node_index, src_type, cast_type); @@ -73,19 +74,19 @@ bool ReducePrecisionFusion::Run(const FuncGraphPtr &graph) { for (auto node : node_list) { MS_EXCEPTION_IF_NULL(node); if (node != nullptr && node->isa() && AnfUtils::IsRealKernel(node)) { - size_t input_num = AnfAlgo::GetInputTensorNum(node); - size_t output_num = AnfAlgo::GetOutputTensorNum(node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(node); for (size_t i = 0; i < input_num; i++) { - auto inferType = AnfAlgo::GetPrevNodeOutputInferDataType(node, i); + auto inferType = common::AnfAlgo::GetPrevNodeOutputInferDataType(node, i); auto deviceType = AnfAlgo::GetInputDeviceDataType(node, i); if (inferType == kNumberTypeInt64 && deviceType == kNumberTypeInt32) { ReducePrecision(graph, node, i, inferType, deviceType); - MS_LOG(WARNING) << "Reduce precision for [" << AnfAlgo::GetCNodeName(utils::cast(node)) + MS_LOG(WARNING) << "Reduce precision for [" << common::AnfAlgo::GetCNodeName(utils::cast(node)) << "] input " << i; } } for (size_t i = 0; i < output_num; i++) { - auto inferType = AnfAlgo::GetOutputInferDataType(node, i); + auto inferType = common::AnfAlgo::GetOutputInferDataType(node, i); auto deviceType = AnfAlgo::GetOutputDeviceDataType(node, i); if (inferType != kNumberTypeInt64 || deviceType != kNumberTypeInt32) { continue; @@ -95,7 +96,7 @@ bool ReducePrecisionFusion::Run(const FuncGraphPtr &graph) { for (size_t j = 0; j < used_node_list->size(); j++) { auto used_node = used_node_list->at(j).first; auto used_node_index = used_node_list->at(j).second - 1; - if (AnfAlgo::GetCNodeName(used_node) == prim::kPrimTupleGetItem->name()) { + if (common::AnfAlgo::GetCNodeName(used_node) == prim::kPrimTupleGetItem->name()) { ProcessTupleGetItem(graph, used_node, used_node_index, deviceType, inferType); } else { ReducePrecision(graph, used_node, used_node_index, deviceType, inferType); diff --git a/mindspore/ccsrc/plugin/device/gpu/optimizer/relu_v2_pass.cc b/mindspore/ccsrc/plugin/device/gpu/optimizer/relu_v2_pass.cc index 226918d1e2b..b58c1241710 100644 --- a/mindspore/ccsrc/plugin/device/gpu/optimizer/relu_v2_pass.cc +++ b/mindspore/ccsrc/plugin/device/gpu/optimizer/relu_v2_pass.cc @@ -20,8 +20,9 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "abstract/abstract_value.h" #include "backend/common/optimizer/helper.h" @@ -47,14 +48,14 @@ kernel::KernelBuildInfoPtr GenerateKernelBuildInfo(CNodePtr node) { std::vector outputs_type; kernel::KernelBuildInfo::KernelBuildInfoBuilder builder; - size_t input_num = AnfAlgo::GetInputTensorNum(node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(node); for (size_t input_index = 0; input_index < input_num; ++input_index) { - inputs_type.push_back(AnfAlgo::GetPrevNodeOutputInferDataType(node, input_index)); + inputs_type.push_back(common::AnfAlgo::GetPrevNodeOutputInferDataType(node, input_index)); inputs_format.push_back(kOpFormat_DEFAULT); } - size_t output_num = AnfAlgo::GetOutputTensorNum(node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(node); for (size_t output_index = 0; output_index < output_num; ++output_index) { - outputs_type.push_back(AnfAlgo::GetOutputInferDataType(node, output_index)); + outputs_type.push_back(common::AnfAlgo::GetOutputInferDataType(node, output_index)); outputs_format.push_back(kOpFormat_DEFAULT); } builder.SetInputsDeviceType(inputs_type); @@ -75,17 +76,17 @@ CNodePtr CreateReluV2(const FuncGraphPtr &graph, const CNodePtr &relu) { MS_EXCEPTION_IF_NULL(new_node); new_node->set_scope(relu->scope()); - if (AnfAlgo::IsDynamicShape(relu)) { + if (common::AnfAlgo::IsDynamicShape(relu)) { return nullptr; } - std::vector output_shape = AnfAlgo::GetOutputInferShape(relu, 0); + std::vector output_shape = common::AnfAlgo::GetOutputInferShape(relu, 0); auto element_num = std::accumulate(output_shape.begin(), output_shape.end(), static_cast(1), std::multiplies()); std::vector mask_shape = {(element_num + kBitPerUInt - 1) / kBitPerUInt}; - auto shapes = {AnfAlgo::GetOutputInferShape(relu, 0), mask_shape}; - auto types = {AnfAlgo::GetOutputInferDataType(relu, 0), kNumberTypeUInt32}; - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, new_node.get()); + auto shapes = {common::AnfAlgo::GetOutputInferShape(relu, 0), mask_shape}; + auto types = {common::AnfAlgo::GetOutputInferDataType(relu, 0), kNumberTypeUInt32}; + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, new_node.get()); auto build_info = GenerateKernelBuildInfo(new_node); AnfAlgo::SetSelectKernelBuildInfo(build_info, new_node.get()); @@ -106,13 +107,13 @@ CNodePtr CreateReluGradV2(const FuncGraphPtr &graph, const CNodePtr &relu_grad, std::vector types; std::vector> shapes; - size_t output_num = AnfAlgo::GetOutputTensorNum(relu_grad); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(relu_grad); for (size_t i = 0; i < output_num; i++) { - types.push_back(AnfAlgo::GetOutputInferDataType(relu_grad, i)); - shapes.push_back(AnfAlgo::GetOutputInferShape(relu_grad, i)); + types.push_back(common::AnfAlgo::GetOutputInferDataType(relu_grad, i)); + shapes.push_back(common::AnfAlgo::GetOutputInferShape(relu_grad, i)); } - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, new_node.get()); + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, new_node.get()); new_node->set_scope(relu_grad->scope()); auto build_info = GenerateKernelBuildInfo(new_node); diff --git a/mindspore/ccsrc/plugin/device/gpu/optimizer/remove_format_transform_pair.cc b/mindspore/ccsrc/plugin/device/gpu/optimizer/remove_format_transform_pair.cc index c5146128453..8313a3e1aeb 100644 --- a/mindspore/ccsrc/plugin/device/gpu/optimizer/remove_format_transform_pair.cc +++ b/mindspore/ccsrc/plugin/device/gpu/optimizer/remove_format_transform_pair.cc @@ -17,8 +17,9 @@ #include "plugin/device/gpu/optimizer/remove_format_transform_pair.h" #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/optimizer/helper.h" namespace mindspore { @@ -36,12 +37,13 @@ const AnfNodePtr RemoveFormatTransformPair::Process(const FuncGraphPtr &graph, c MS_EXCEPTION_IF_NULL(graph); MS_EXCEPTION_IF_NULL(node); MS_LOG(DEBUG) << "Process node:" << node->fullname_with_scope(); - auto input_node = AnfAlgo::GetInputNode(utils::cast(node), 0); + auto input_node = common::AnfAlgo::GetInputNode(utils::cast(node), 0); MS_EXCEPTION_IF_NULL(input_node); - if (AnfAlgo::GetCNodeName(node) != prim::kPrimTranspose->name() || - AnfAlgo::GetCNodeName(input_node) != prim::kPrimTranspose->name()) { + if (common::AnfAlgo::GetCNodeName(node) != prim::kPrimTranspose->name() || + common::AnfAlgo::GetCNodeName(input_node) != prim::kPrimTranspose->name()) { MS_LOG(EXCEPTION) << "The pattern is not transpose pair, " - << "node:" << AnfAlgo::GetCNodeName(node) << " node input:" << AnfAlgo::GetCNodeName(input_node); + << "node:" << common::AnfAlgo::GetCNodeName(node) + << " node input:" << common::AnfAlgo::GetCNodeName(input_node); } // If transpose operator used by more than one other operators, it cant not be deleted directly. if (IsUsedByOthers(graph, input_node)) { @@ -52,7 +54,7 @@ const AnfNodePtr RemoveFormatTransformPair::Process(const FuncGraphPtr &graph, c auto transpose1_input_shape = AnfAlgo::GetInputDeviceShape(input_node, 0); auto transpose2_output_shape = AnfAlgo::GetOutputDeviceShape(node, 0); if (transpose2_output_shape == transpose1_input_shape) { - auto transpose1_input_node = AnfAlgo::GetInputNode(utils::cast(input_node), 0); + auto transpose1_input_node = common::AnfAlgo::GetInputNode(utils::cast(input_node), 0); MS_EXCEPTION_IF_NULL(transpose1_input_node); return transpose1_input_node; } diff --git a/mindspore/ccsrc/plugin/device/gpu/optimizer/remove_redundant_format_transform.cc b/mindspore/ccsrc/plugin/device/gpu/optimizer/remove_redundant_format_transform.cc index 54d6104086c..46e786a5b97 100644 --- a/mindspore/ccsrc/plugin/device/gpu/optimizer/remove_redundant_format_transform.cc +++ b/mindspore/ccsrc/plugin/device/gpu/optimizer/remove_redundant_format_transform.cc @@ -18,8 +18,9 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/optimizer/helper.h" namespace mindspore { @@ -36,20 +37,20 @@ const AnfNodePtr RemoveRedundantFormatTransform::Process(const FuncGraphPtr &gra MS_EXCEPTION_IF_NULL(graph); MS_EXCEPTION_IF_NULL(node); MS_LOG(DEBUG) << "Process node:" << node->fullname_with_scope(); - auto input_node = AnfAlgo::GetInputNode(utils::cast(node), 0); + auto input_node = common::AnfAlgo::GetInputNode(utils::cast(node), 0); MS_EXCEPTION_IF_NULL(input_node); AnfNodePtr first_transpose = nullptr; auto used_node_list = GetRealNodeUsedList(graph, input_node); MS_EXCEPTION_IF_NULL(used_node_list); for (size_t j = 0; j < used_node_list->size(); j++) { auto used_node = used_node_list->at(j).first; - if (AnfAlgo::GetCNodeName(used_node) == prim::kPrimTranspose->name()) { + if (common::AnfAlgo::GetCNodeName(used_node) == prim::kPrimTranspose->name()) { first_transpose = used_node; break; } } - auto first_transpose_perm = AnfAlgo::GetNodeAttr>(first_transpose, "perm"); - auto node_perm = AnfAlgo::GetNodeAttr>(node, "perm"); + auto first_transpose_perm = common::AnfAlgo::GetNodeAttr>(first_transpose, "perm"); + auto node_perm = common::AnfAlgo::GetNodeAttr>(node, "perm"); if ((first_transpose != node) && (first_transpose_perm == node_perm)) { return first_transpose; } diff --git a/mindspore/ccsrc/plugin/device/gpu/optimizer/replace_addn_fusion.cc b/mindspore/ccsrc/plugin/device/gpu/optimizer/replace_addn_fusion.cc index 2c2c0da7953..5068f39dfe4 100644 --- a/mindspore/ccsrc/plugin/device/gpu/optimizer/replace_addn_fusion.cc +++ b/mindspore/ccsrc/plugin/device/gpu/optimizer/replace_addn_fusion.cc @@ -16,8 +16,9 @@ #include "plugin/device/gpu/optimizer/replace_addn_fusion.h" #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/optimizer/helper.h" namespace mindspore { @@ -30,11 +31,11 @@ const BaseRef ReplaceAddNFusion::DefinePattern() const { const AnfNodePtr ReplaceAddNFusion::Process(const FuncGraphPtr &graph, const AnfNodePtr &node, const EquivPtr &) const { MS_EXCEPTION_IF_NULL(graph); MS_EXCEPTION_IF_NULL(node); - auto A = AnfAlgo::GetInputNode(utils::cast(node), 0); - auto B = AnfAlgo::GetInputNode(utils::cast(node), 1); + auto A = common::AnfAlgo::GetInputNode(utils::cast(node), 0); + auto B = common::AnfAlgo::GetInputNode(utils::cast(node), 1); MS_EXCEPTION_IF_NULL(A); MS_EXCEPTION_IF_NULL(B); - int64_t num_input = AnfAlgo::GetNodeAttr(node, "n"); + int64_t num_input = common::AnfAlgo::GetNodeAttr(node, "n"); if (num_input == kAddNInputNum) { auto prim = std::make_shared(prim::kPrimAdd->name()); MS_EXCEPTION_IF_NULL(prim); @@ -43,9 +44,9 @@ const AnfNodePtr ReplaceAddNFusion::Process(const FuncGraphPtr &graph, const Anf MS_EXCEPTION_IF_NULL(add_new); std::vector outputs_type; std::vector> outputs_shape; - outputs_type.push_back(AnfAlgo::GetOutputInferDataType(A, 0)); - outputs_shape.push_back(AnfAlgo::GetOutputInferShape(A, 0)); - AnfAlgo::SetOutputInferTypeAndShape(outputs_type, outputs_shape, add_new.get()); + outputs_type.push_back(common::AnfAlgo::GetOutputInferDataType(A, 0)); + outputs_shape.push_back(common::AnfAlgo::GetOutputInferShape(A, 0)); + common::AnfAlgo::SetOutputInferTypeAndShape(outputs_type, outputs_shape, add_new.get()); auto manager = graph->manager(); MS_EXCEPTION_IF_NULL(manager); manager->Replace(utils::cast(node), utils::cast(add_new)); diff --git a/mindspore/ccsrc/plugin/device/gpu/optimizer/replace_momentum_cast_fusion.cc b/mindspore/ccsrc/plugin/device/gpu/optimizer/replace_momentum_cast_fusion.cc index 02b9b696810..3e6269ecfcb 100644 --- a/mindspore/ccsrc/plugin/device/gpu/optimizer/replace_momentum_cast_fusion.cc +++ b/mindspore/ccsrc/plugin/device/gpu/optimizer/replace_momentum_cast_fusion.cc @@ -16,8 +16,9 @@ #include "plugin/device/gpu/optimizer/replace_momentum_cast_fusion.h" #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/optimizer/helper.h" namespace mindspore { @@ -33,28 +34,28 @@ const AnfNodePtr ReplaceMomentumCastFusion::Process(const FuncGraphPtr &graph, c MS_EXCEPTION_IF_NULL(graph); MS_EXCEPTION_IF_NULL(node); - auto grad_cast = AnfAlgo::GetInputNode(utils::cast(node), kGradIndex); + auto grad_cast = common::AnfAlgo::GetInputNode(utils::cast(node), kGradIndex); MS_EXCEPTION_IF_NULL(grad_cast); - auto src = AnfAlgo::GetPrevNodeOutputInferDataType(grad_cast, 0); + auto src = common::AnfAlgo::GetPrevNodeOutputInferDataType(grad_cast, 0); // momentum only support fp32/fp16 by now, do nothing if not. if (src != kNumberTypeFloat16 || src != kNumberTypeFloat32) { return nullptr; } - auto grad = AnfAlgo::GetInputNode(utils::cast(grad_cast), 0); + auto grad = common::AnfAlgo::GetInputNode(utils::cast(grad_cast), 0); MS_EXCEPTION_IF_NULL(grad); auto manager = graph->manager(); MS_EXCEPTION_IF_NULL(manager); manager->Replace(utils::cast(grad_cast), utils::cast(grad)); std::vector outputs_type; std::vector> outputs_shape; - auto output_num = AnfAlgo::GetOutputTensorNum(node); + auto output_num = common::AnfAlgo::GetOutputTensorNum(node); for (size_t i = 0; i < output_num; i++) { - outputs_type.push_back(AnfAlgo::GetOutputInferDataType(node, i)); - outputs_shape.push_back(AnfAlgo::GetOutputInferShape(node, i)); + outputs_type.push_back(common::AnfAlgo::GetOutputInferDataType(node, i)); + outputs_shape.push_back(common::AnfAlgo::GetOutputInferShape(node, i)); } - outputs_type[kGradIndex] = AnfAlgo::GetPrevNodeOutputInferDataType(grad_cast, 0); + outputs_type[kGradIndex] = common::AnfAlgo::GetPrevNodeOutputInferDataType(grad_cast, 0); - AnfAlgo::SetOutputInferTypeAndShape(outputs_type, outputs_shape, node.get()); + common::AnfAlgo::SetOutputInferTypeAndShape(outputs_type, outputs_shape, node.get()); return node; } diff --git a/mindspore/ccsrc/plugin/device/gpu/optimizer/trt_pass/graph_converter.cc b/mindspore/ccsrc/plugin/device/gpu/optimizer/trt_pass/graph_converter.cc index 9e10d09524d..0fa35cac9c8 100644 --- a/mindspore/ccsrc/plugin/device/gpu/optimizer/trt_pass/graph_converter.cc +++ b/mindspore/ccsrc/plugin/device/gpu/optimizer/trt_pass/graph_converter.cc @@ -38,11 +38,11 @@ void CopyGraphOutputTypeAndShape(const std::vector &gr std::vector types; std::vector> shapes; for (const auto &item : graph_outputs) { - types.push_back(AnfAlgo::GetOutputInferDataType(item.first, item.second)); - shapes.push_back(AnfAlgo::GetOutputInferShape(item.first, item.second)); + types.push_back(common::AnfAlgo::GetOutputInferDataType(item.first, item.second)); + shapes.push_back(common::AnfAlgo::GetOutputInferShape(item.first, item.second)); } - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, trt_node.get()); + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, trt_node.get()); return; } @@ -84,9 +84,9 @@ CNodePtr BuildMakeTupleNode(const FuncGraphPtr root, const std::map types = {AnfAlgo::GetOutputInferDataType(trt_node, trt_index)}; - std::vector> shapes = {AnfAlgo::GetOutputInferShape(trt_node, trt_index)}; - AnfAlgo::SetOutputInferTypeAndShape(types, shapes, tuple_getitem_cnode.get()); + std::vector types = {common::AnfAlgo::GetOutputInferDataType(trt_node, trt_index)}; + std::vector> shapes = {common::AnfAlgo::GetOutputInferShape(trt_node, trt_index)}; + common::AnfAlgo::SetOutputInferTypeAndShape(types, shapes, tuple_getitem_cnode.get()); // Build make tuple inputs. make_tuple_inputs.push_back(tuple_getitem_cnode); @@ -96,7 +96,7 @@ CNodePtr BuildMakeTupleNode(const FuncGraphPtr root, const std::mapNewCNode(make_tuple_inputs); MS_EXCEPTION_IF_NULL(make_tuple_cnode); - AnfAlgo::SetOutputInferTypeAndShape(make_tuple_types, make_tuple_shapes, make_tuple_cnode.get()); + common::AnfAlgo::SetOutputInferTypeAndShape(make_tuple_types, make_tuple_shapes, make_tuple_cnode.get()); return make_tuple_cnode; } @@ -172,7 +172,7 @@ void GraphConverter::RemoveParameterWithoutUser(const FuncGraphPtr &graph) { const auto &input = inputs[i]; // Keep inputs of graph. - if (!input->isa() || !AnfAlgo::IsParameterWeight(input->cast())) { + if (!input->isa() || !common::AnfAlgo::IsParameterWeight(input->cast())) { graph_inputs.push_back(input); continue; } @@ -210,7 +210,7 @@ bool GraphConverter::ReplaceSubgraphWithTrtNode(const FuncGraphPtr &root, const auto manager = root->manager(); MS_EXCEPTION_IF_NULL(manager); if (outputs.size() == 1) { - if (AnfAlgo::CheckPrimitiveType(outputs[0], prim::kPrimMakeTuple)) { + if (common::AnfAlgo::CheckPrimitiveType(outputs[0], prim::kPrimMakeTuple)) { const CNodePtr &make_tuple_cnode = BuildMakeTupleNode(root, anf_trt_index_map, trt_node); manager->Replace(outputs[0], make_tuple_cnode); } else { diff --git a/mindspore/ccsrc/plugin/device/gpu/optimizer/trt_pass/graph_partitioner.cc b/mindspore/ccsrc/plugin/device/gpu/optimizer/trt_pass/graph_partitioner.cc index 5b1a68546ad..7f62e82de75 100644 --- a/mindspore/ccsrc/plugin/device/gpu/optimizer/trt_pass/graph_partitioner.cc +++ b/mindspore/ccsrc/plugin/device/gpu/optimizer/trt_pass/graph_partitioner.cc @@ -27,6 +27,7 @@ #include "utils/ms_context.h" #include "utils/trace_base.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "plugin/device/gpu/optimizer/trt_pass/trt_op_factory.h" #include "backend/graph_compiler/segment_runner.h" #include "debug/anf_ir_dump.h" @@ -39,11 +40,11 @@ bool WeightCheck(const AnfNodePtr &node) { {kConv2DOpName, {1}}, {kBatchNorm, {1, 2, 3, 4}}, {kConv2DBackpropInputOpName, {1}}}; MS_EXCEPTION_IF_NULL(node); - const std::string &op_name = AnfAlgo::GetCNodePrimitive(node)->name(); + const std::string &op_name = common::AnfAlgo::GetCNodePrimitive(node)->name(); auto iter = weight_list.find(op_name); if (iter != weight_list.end()) { std::vector real_inputs; - AnfAlgo::GetRealInputs(node, &real_inputs); + common::AnfAlgo::GetRealInputs(node, &real_inputs); for (auto index : iter->second) { if (index >= real_inputs.size()) { @@ -52,7 +53,7 @@ bool WeightCheck(const AnfNodePtr &node) { } if (real_inputs[index].first->isa() && - !AnfAlgo::IsParameterWeight(real_inputs[index].first->cast())) { + !common::AnfAlgo::IsParameterWeight(real_inputs[index].first->cast())) { return false; } } @@ -77,7 +78,7 @@ mindspore::HashMap CollectNodeInfo(const FuncGraphPtr &fun continue; } - const std::string &op_name = AnfAlgo::GetCNodePrimitive(node)->name(); + const std::string &op_name = common::AnfAlgo::GetCNodePrimitive(node)->name(); const auto &converter_factory = TrtOpFactory::GetInstance(); ConvertFunc convert_func = converter_factory.GetConvertFunc(op_name); if (!convert_func) { @@ -136,9 +137,9 @@ void GraphPartitioner::NewSubGraph(NodeInfo *node_info) { bool GraphPartitioner::ExistCycleAfterMerge(const AnfNodePtr &node, const std::string &target_graph_id) { MS_EXCEPTION_IF_NULL(node); - size_t input_num = AnfAlgo::GetInputTensorNum(node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(node); for (size_t input_index = 0; input_index < input_num; ++input_index) { - auto input_node = AnfAlgo::GetInputNode(node->cast(), input_index); + auto input_node = common::AnfAlgo::GetInputNode(node->cast(), input_index); if (input_node == nullptr || input_node->isa() || input_node->isa()) continue; if (node_info_[input_node].graph_id() == target_graph_id) { @@ -160,9 +161,9 @@ void GraphPartitioner::MergeParentBranchRecursively(const AnfNodePtr &node, cons MS_EXCEPTION_IF_NULL(node); node_info_[node].graph_id_ = new_graph_id; - size_t input_num = AnfAlgo::GetInputTensorNum(node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(node); for (size_t input_index = 0; input_index < input_num; ++input_index) { - auto input_node = AnfAlgo::GetInputNode(node->cast(), input_index); + auto input_node = common::AnfAlgo::GetInputNode(node->cast(), input_index); if (input_node == nullptr || input_node->isa() || input_node->isa()) continue; if (node_info_[input_node].graph_id() == old_graph_id) { @@ -182,16 +183,16 @@ bool GraphPartitioner::NodeGrouping(const FuncGraphPtr &func_graph) { NewSubGraph(¤t_node_info); // Merge the `TupleGetItem` node too parent sub graph. - if (AnfAlgo::CheckPrimitiveType(node, prim::kPrimTupleGetItem)) { - const AnfNodePtr &input = AnfAlgo::GetTupleGetItemRealInput(node); + if (common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimTupleGetItem)) { + const AnfNodePtr &input = common::AnfAlgo::GetTupleGetItemRealInput(node); const NodeInfo &parent_node_info = node_info_[input]; current_node_info.graph_id_ = parent_node_info.graph_id_; continue; } - size_t input_num = AnfAlgo::GetInputTensorNum(node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(node); for (size_t input_index = 0; input_index < input_num; ++input_index) { - auto input_node = AnfAlgo::GetInputNode(node->cast(), input_index); + auto input_node = common::AnfAlgo::GetInputNode(node->cast(), input_index); if (input_node == nullptr || input_node->isa() || input_node->isa()) continue; const NodeInfo &parent_node_info = node_info_[input_node]; @@ -200,7 +201,7 @@ bool GraphPartitioner::NodeGrouping(const FuncGraphPtr &func_graph) { // Annotation: Unsupported Support Support // Result: Native Native Trt if (current_node_info.type() != parent_node_info.type() || - (AnfAlgo::CheckPrimitiveType(input_node, prim::kPrimTupleGetItem) && + (common::AnfAlgo::CheckPrimitiveType(input_node, prim::kPrimTupleGetItem) && current_node_info.graph_id().at(0) != parent_node_info.graph_id().at(0))) { dependency_.AddDependency(current_node_info.graph_id(), parent_node_info.graph_id()); dependency_.InheritDependency(current_node_info.graph_id(), parent_node_info.graph_id()); @@ -240,7 +241,8 @@ std::map GraphPartitioner::CollectSegments() { std::map segments; for (const auto &item : node_info_) { const std::string &graph_id = item.second.graph_id(); - if (graph_id.find("T_") != graph_id.npos && AnfAlgo::GetCNodePrimitive(item.first)->name() != kReturnOpName) { + if (graph_id.find("T_") != graph_id.npos && + common::AnfAlgo::GetCNodePrimitive(item.first)->name() != kReturnOpName) { segments[graph_id].push_back(item.first); } } @@ -277,7 +279,7 @@ Subgraph GraphPartitioner::CreateNewGraph(const AnfNodePtrList &segment) { dst->set_name(src->name()); dst->debug_info()->set_name(src->name()); - if (AnfAlgo::IsParameterWeight(src)) { + if (common::AnfAlgo::IsParameterWeight(src)) { dst->set_default_param(src->default_param()); } } else { diff --git a/mindspore/ccsrc/plugin/device/gpu/optimizer/trt_pass/trt_converter_context.cc b/mindspore/ccsrc/plugin/device/gpu/optimizer/trt_pass/trt_converter_context.cc index bc469266ed8..7700d6ea746 100644 --- a/mindspore/ccsrc/plugin/device/gpu/optimizer/trt_pass/trt_converter_context.cc +++ b/mindspore/ccsrc/plugin/device/gpu/optimizer/trt_pass/trt_converter_context.cc @@ -21,8 +21,8 @@ #include "plugin/device/gpu/hal/device/trt_loader.h" #include "plugin/device/gpu/optimizer/trt_pass/trt_op_factory.h" #include "plugin/device/gpu/kernel/trt/trt_utils.h" -#include "utils/convert_utils.h" -#include "utils/utils.h" +#include "include/common/utils/convert_utils.h" +#include "include/common/utils/utils.h" #include "utils/singleton.h" #include "utils/ms_context.h" @@ -54,7 +54,7 @@ bool TrtConverterContext::Parser() { // Transform AnfNode To Trt layer. // Bypass control node including Depend, Load, UpdateState, TupleGetItem, MakeTuple. - std::string op_name = AnfAlgo::GetCNodePrimitive(node)->name(); + std::string op_name = common::AnfAlgo::GetCNodePrimitive(node)->name(); if (!AnfUtils::IsRealKernel(node) && op_name != "Return") { continue; } @@ -105,7 +105,7 @@ bool TrtConverterContext::InitInputTable() { } auto input = input_node->cast(); - if (AnfAlgo::IsParameterWeight(input)) { + if (common::AnfAlgo::IsParameterWeight(input)) { const auto ¶m_value = input->default_param(); MS_EXCEPTION_IF_NULL(param_value); auto tensor = std::dynamic_pointer_cast(param_value); @@ -153,8 +153,9 @@ bool TrtConverterContext::InitValueNodeTable() { } bool TrtConverterContext::StoreLayerOutput(const AnfNodePtr &node, const std::vector &nv_tensors) { - if (nv_tensors.size() != AnfAlgo::GetOutputTensorNum(node)) { - MS_LOG(INFO) << node->DebugString() << " output num not match. expect: " << AnfAlgo::GetOutputTensorNum(node) + if (nv_tensors.size() != common::AnfAlgo::GetOutputTensorNum(node)) { + MS_LOG(INFO) << node->DebugString() + << " output num not match. expect: " << common::AnfAlgo::GetOutputTensorNum(node) << ", while got: " << nv_tensors.size(); } @@ -179,10 +180,11 @@ bool TrtConverterContext::StoreLayerOutput(const AnfNodePtr &node, const std::ve LayerInput *TrtConverterContext::LoadInputOnDemand(const AnfNodePtr &node) { MS_EXCEPTION_IF_NULL(node); auto input = node->cast(); - std::variant type = TrtUtils::MsDtypeToTrtDtype(AnfAlgo::GetOutputInferDataType(node, 0)); + std::variant type = + TrtUtils::MsDtypeToTrtDtype(common::AnfAlgo::GetOutputInferDataType(node, 0)); TRT_VARIANT_CHECK(type, 1UL, nullptr); const auto &trt_dtype = std::get(type); - const nvinfer1::Dims &trt_dims = TrtUtils::MsDimsToTrtDims(AnfAlgo::GetOutputInferShape(node, 0), false); + const nvinfer1::Dims &trt_dims = TrtUtils::MsDimsToTrtDims(common::AnfAlgo::GetOutputInferShape(node, 0), false); nvinfer1::ITensor *tensor = network_->addInput(input->name().c_str(), trt_dtype, trt_dims); const std::vector &shape = TrtUtils::TrtDimsToMsDims(trt_dims); output_map_[node][0] = LayerInput(tensor, shape); @@ -191,7 +193,7 @@ LayerInput *TrtConverterContext::LoadInputOnDemand(const AnfNodePtr &node) { bool TrtConverterContext::LoadLayerInput(const AnfNodePtr &node, std::vector *inputs) { std::vector real_inputs; - AnfAlgo::GetRealInputs(node, &real_inputs); + common::AnfAlgo::GetRealInputs(node, &real_inputs); for (auto item : real_inputs) { auto node_iter = output_map_.find(item.first); if (node_iter == output_map_.end()) { @@ -228,7 +230,7 @@ std::vector TrtConverterContext::GetGraphInputs() const { } auto input = input_node->cast(); - if (!AnfAlgo::IsParameterWeight(input)) { + if (!common::AnfAlgo::IsParameterWeight(input)) { (void)graph_inputs.emplace(input->name(), input_node); } } @@ -251,7 +253,7 @@ std::vector TrtConverterContext::GetGraphInputs() const { std::tuple, std::vector> TrtConverterContext::GetGraphOutputs() const { std::vector anf_output_list; - AnfAlgo::GetRealInputs(func_graph_->get_return(), &anf_output_list); + common::AnfAlgo::GetRealInputs(func_graph_->get_return(), &anf_output_list); std::map anf_trt_index_map; std::vector trt_output_list(anf_output_list.size()); diff --git a/mindspore/ccsrc/plugin/device/gpu/optimizer/trt_pass/trt_converter_context.h b/mindspore/ccsrc/plugin/device/gpu/optimizer/trt_pass/trt_converter_context.h index a3a7322c85a..f15f14ee4e2 100644 --- a/mindspore/ccsrc/plugin/device/gpu/optimizer/trt_pass/trt_converter_context.h +++ b/mindspore/ccsrc/plugin/device/gpu/optimizer/trt_pass/trt_converter_context.h @@ -27,6 +27,7 @@ #include "base/base.h" #include "ir/anf.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "plugin/device/gpu/optimizer/trt_pass/layer_input.h" namespace mindspore { diff --git a/mindspore/ccsrc/plugin/device/gpu/optimizer/trt_pass/trt_op_converter.cc b/mindspore/ccsrc/plugin/device/gpu/optimizer/trt_pass/trt_op_converter.cc index 4f5ddab1f96..13b4fb6ec57 100644 --- a/mindspore/ccsrc/plugin/device/gpu/optimizer/trt_pass/trt_op_converter.cc +++ b/mindspore/ccsrc/plugin/device/gpu/optimizer/trt_pass/trt_op_converter.cc @@ -72,7 +72,7 @@ ConvertResult AddReshapeLayer(AnfNodePtr node, std::shared_ptrnetwork()->addShuffle(*inputs[0].tensor()); MS_EXCEPTION_IF_NULL(layer); - const auto &output_shape = AnfAlgo::GetOutputInferShape(node, 0); + const auto &output_shape = common::AnfAlgo::GetOutputInferShape(node, 0); const nvinfer1::Dims &dims = TrtUtils::MsDimsToTrtDims(output_shape, false); layer->setReshapeDimensions(dims); @@ -88,9 +88,9 @@ ConvertResult AddElementLayer(AnfNodePtr node, std::shared_ptr &x1_shape = AnfAlgo::GetPrevNodeOutputInferShape(node, 0); - const std::vector &x2_shape = AnfAlgo::GetPrevNodeOutputInferShape(node, 1); - const std::vector &y_shape = AnfAlgo::GetOutputInferShape(node, 0); + const std::vector &x1_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node, 0); + const std::vector &x2_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node, 1); + const std::vector &y_shape = common::AnfAlgo::GetOutputInferShape(node, 0); auto Broadcast = [&context, &y_shape](nvinfer1::ITensor *tensor, const std::vector &x_shape) { if (x_shape.size() == y_shape.size()) { @@ -134,21 +134,21 @@ ConvertResult AddPoolingLayer(AnfNodePtr node, std::shared_ptr(node, "format"); + const auto &format = common::AnfAlgo::GetNodeAttr(node, "format"); if (format != "NCHW") { MS_LOG(WARNING) << "The format: " << format << " not supported."; return {false, {}}; } - const auto &kernel_size = AnfAlgo::GetNodeAttr>(node, "kernel_size"); + const auto &kernel_size = common::AnfAlgo::GetNodeAttr>(node, "kernel_size"); auto *layer = context->network()->addPoolingNd( *(inputs[0].tensor()), pooling_type, nvinfer1::DimsHW{LongToInt(kernel_size[2]), LongToInt(kernel_size[3])}); MS_EXCEPTION_IF_NULL(layer); - const auto &strides = AnfAlgo::GetNodeAttr>(node, "strides"); + const auto &strides = common::AnfAlgo::GetNodeAttr>(node, "strides"); layer->setStride(nvinfer1::DimsHW{LongToInt(strides[2]), LongToInt(strides[3])}); - auto pad_mode = AnfAlgo::GetNodeAttr(node, "pad_mode"); + auto pad_mode = common::AnfAlgo::GetNodeAttr(node, "pad_mode"); std::transform(pad_mode.begin(), pad_mode.end(), pad_mode.begin(), toupper); if (pad_mode == "SAME") { layer->setPaddingMode(nvinfer1::PaddingMode::kSAME_UPPER); @@ -197,17 +197,17 @@ ConvertResult AddReduceLayer(AnfNodePtr node, std::shared_ptr &input_shape = AnfAlgo::GetPrevNodeOutputInferShape(node, 0); - const ValuePtr &value = AnfAlgo::GetCNodePrimitive(node)->GetAttr("axis"); + const std::vector &input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node, 0); + const ValuePtr &value = common::AnfAlgo::GetCNodePrimitive(node)->GetAttr("axis"); uint32_t reduce_axes = 0; if (value->isa() || value->isa()) { - const auto &axis = AnfAlgo::GetNodeAttr>(node, "axis"); + const auto &axis = common::AnfAlgo::GetNodeAttr>(node, "axis"); for (size_t i = 0; i < axis.size(); i++) { int offset = axis[i] >= 0 ? LongToInt(axis[i]) : LongToInt(axis[i] + input_shape.size()); reduce_axes |= 1UL << offset; } } else { - const auto &axis = AnfAlgo::GetNodeAttr(node, "axis"); + const auto &axis = common::AnfAlgo::GetNodeAttr(node, "axis"); int offset = axis >= 0 ? LongToInt(axis) : LongToInt(axis + input_shape.size()); reduce_axes = 1UL << offset; } @@ -219,7 +219,7 @@ ConvertResult AddReduceLayer(AnfNodePtr node, std::shared_ptr(node, "keep_dims"); + bool keep_dims = common::AnfAlgo::GetNodeAttr(node, "keep_dims"); // Tensor-RT do not support reduce all dimensions with keep_dims == false. // Reduce with keep_dims = true, add apply reshape latter. bool post_reshape = false; @@ -261,36 +261,36 @@ MS_TRT_CONVERTER_FUNC_REG(Conv2D) { return {false, {}}; } - const auto &data_format = AnfAlgo::GetNodeAttr(node, "format"); + const auto &data_format = common::AnfAlgo::GetNodeAttr(node, "format"); if (data_format != "NCHW") { MS_LOG(WARNING) << "The format: " << data_format << " not supported."; return {false, {}}; } - const auto &kernel_size = AnfAlgo::GetNodeAttr>(node, "kernel_size"); - const auto &out_channel = AnfAlgo::GetNodeAttr(node, "out_channel"); + const auto &kernel_size = common::AnfAlgo::GetNodeAttr>(node, "kernel_size"); + const auto &out_channel = common::AnfAlgo::GetNodeAttr(node, "out_channel"); nvinfer1::Weights bias{nvinfer1::DataType::kFLOAT, nullptr, 0}; auto *layer = context->network()->addConvolutionNd( *(inputs[0].tensor()), LongToInt(out_channel), nvinfer1::DimsHW{LongToInt(kernel_size[0]), LongToInt(kernel_size[1])}, *(inputs[1].weight()), bias); MS_EXCEPTION_IF_NULL(layer); - const auto &strides = AnfAlgo::GetNodeAttr>(node, "stride"); + const auto &strides = common::AnfAlgo::GetNodeAttr>(node, "stride"); layer->setStride(nvinfer1::DimsHW{LongToInt(strides[2]), LongToInt(strides[3])}); - auto pad_mode = AnfAlgo::GetNodeAttr(node, "pad_mode"); + auto pad_mode = common::AnfAlgo::GetNodeAttr(node, "pad_mode"); std::transform(pad_mode.begin(), pad_mode.end(), pad_mode.begin(), toupper); if (pad_mode == "SAME") { layer->setPaddingMode(nvinfer1::PaddingMode::kSAME_UPPER); } if (pad_mode == "PAD") { - const auto &pad_list = AnfAlgo::GetNodeAttr>(node, "pad_list"); + const auto &pad_list = common::AnfAlgo::GetNodeAttr>(node, "pad_list"); layer->setPrePadding(nvinfer1::DimsHW{LongToInt(pad_list[0]), LongToInt(pad_list[2])}); layer->setPostPadding(nvinfer1::DimsHW{LongToInt(pad_list[1]), LongToInt(pad_list[3])}); } - const auto &group = AnfAlgo::GetNodeAttr(node, "group"); + const auto &group = common::AnfAlgo::GetNodeAttr(node, "group"); layer->setNbGroups(SizeToInt(group)); return {true, {layer->getOutput(0)}}; @@ -355,7 +355,7 @@ MS_TRT_CONVERTER_FUNC_REG(ReLU6) { return {false, {}}; } - const std::vector &x_shape = AnfAlgo::GetPrevNodeOutputInferShape(node, 0); + const std::vector &x_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node, 0); nvinfer1::Dims dim; dim.nbDims = SizeToInt(x_shape.size()); std::fill(dim.d, dim.d + dim.nbDims, 1); @@ -390,7 +390,7 @@ MS_TRT_CONVERTER_FUNC_REG(GeLU) { return {false, {}}; } - const std::vector &x_shape = AnfAlgo::GetPrevNodeOutputInferShape(node, 0); + const std::vector &x_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node, 0); nvinfer1::Dims dim; dim.nbDims = SizeToInt(x_shape.size()); std::fill(dim.d, dim.d + dim.nbDims, 1); @@ -441,7 +441,7 @@ MS_TRT_CONVERTER_FUNC_REG(HSigmoid) { return {false, {}}; } - const std::vector &x_shape = AnfAlgo::GetPrevNodeOutputInferShape(node, 0); + const std::vector &x_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node, 0); nvinfer1::Dims dim; dim.nbDims = SizeToInt(x_shape.size()); std::fill(dim.d, dim.d + dim.nbDims, 1); @@ -482,7 +482,7 @@ MS_TRT_CONVERTER_FUNC_REG(HSwish) { return {false, {}}; } - const std::vector &x_shape = AnfAlgo::GetPrevNodeOutputInferShape(node, 0); + const std::vector &x_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node, 0); nvinfer1::Dims dim; dim.nbDims = SizeToInt(x_shape.size()); std::fill(dim.d, dim.d + dim.nbDims, 1); @@ -525,8 +525,8 @@ MS_TRT_CONVERTER_FUNC_REG(MatMul) { return {false, {}}; } - const auto &transpose_a = AnfAlgo::GetNodeAttr(node, "transpose_a"); - const auto &transpose_b = AnfAlgo::GetNodeAttr(node, "transpose_b"); + const auto &transpose_a = common::AnfAlgo::GetNodeAttr(node, "transpose_a"); + const auto &transpose_b = common::AnfAlgo::GetNodeAttr(node, "transpose_b"); if (inputs[0].IsTensor() && inputs[1].IsWeight() && transpose_a == false && transpose_b == true) { // Reshape x from (M, K) to (M, K, 1, 1) nvinfer1::Dims unsqueeze_dims = inputs[0].tensor()->getDimensions(); @@ -538,13 +538,13 @@ MS_TRT_CONVERTER_FUNC_REG(MatMul) { // Apply addFullyConnected: y = x * w^T + b nvinfer1::Weights bias{nvinfer1::DataType::kFLOAT, nullptr, 0}; - const auto &w_shape = AnfAlgo::GetPrevNodeOutputInferShape(node, 1); + const auto &w_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node, 1); auto *layer = context->network()->addFullyConnected(*x_reshape->getOutput(0), w_shape[0], *inputs[1].weight(), bias); MS_EXCEPTION_IF_NULL(layer); // Reshape x from (M, N, 1, 1) to (M, N) - const auto &y_shape = AnfAlgo::GetOutputInferShape(node, 0); + const auto &y_shape = common::AnfAlgo::GetOutputInferShape(node, 0); const nvinfer1::Dims &y_dims = TrtUtils::MsDimsToTrtDims(y_shape, false); auto *squeeze_y = context->network()->addShuffle(*layer->getOutput(0)); squeeze_y->setReshapeDimensions(y_dims); @@ -553,8 +553,8 @@ MS_TRT_CONVERTER_FUNC_REG(MatMul) { } else { auto op1 = transpose_a ? nvinfer1::MatrixOperation::kTRANSPOSE : nvinfer1::MatrixOperation::kNONE; auto op2 = transpose_b ? nvinfer1::MatrixOperation::kTRANSPOSE : nvinfer1::MatrixOperation::kNONE; - const std::vector &x1_shape = AnfAlgo::GetPrevNodeOutputInferShape(node, 0); - const std::vector &x2_shape = AnfAlgo::GetPrevNodeOutputInferShape(node, 1); + const std::vector &x1_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node, 0); + const std::vector &x2_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node, 1); nvinfer1::ITensor *x1 = ToTensor(&inputs[0], x1_shape, context); nvinfer1::ITensor *x2 = ToTensor(&inputs[1], x2_shape, context); auto *layer = context->network()->addMatrixMultiply(*x1, op1, *x2, op2); @@ -571,13 +571,13 @@ MS_TRT_CONVERTER_FUNC_REG(BatchMatMul) { return {false, {}}; } - const auto &transpose_a = AnfAlgo::GetNodeAttr(node, "transpose_a"); - const auto &transpose_b = AnfAlgo::GetNodeAttr(node, "transpose_b"); + const auto &transpose_a = common::AnfAlgo::GetNodeAttr(node, "transpose_a"); + const auto &transpose_b = common::AnfAlgo::GetNodeAttr(node, "transpose_b"); const auto &trt_transpose1 = transpose_a ? nvinfer1::MatrixOperation::kTRANSPOSE : nvinfer1::MatrixOperation::kNONE; const auto &trt_transpose2 = transpose_b ? nvinfer1::MatrixOperation::kTRANSPOSE : nvinfer1::MatrixOperation::kNONE; - std::vector shape1 = AnfAlgo::GetPrevNodeOutputInferShape(node, 0); - std::vector shape2 = AnfAlgo::GetPrevNodeOutputInferShape(node, 1); + std::vector shape1 = common::AnfAlgo::GetPrevNodeOutputInferShape(node, 0); + std::vector shape2 = common::AnfAlgo::GetPrevNodeOutputInferShape(node, 1); nvinfer1::ITensor *tensor1 = ToTensor(&inputs[0], shape1, context); nvinfer1::ITensor *tensor2 = ToTensor(&inputs[1], shape2, context); auto *layer = context->network()->addMatrixMultiply(*tensor1, trt_transpose1, *tensor2, trt_transpose2); @@ -594,9 +594,9 @@ MS_TRT_CONVERTER_FUNC_REG(BiasAdd) { return {false, {}}; } - const auto &x_shape = AnfAlgo::GetPrevNodeOutputInferShape(node, 0); - const auto &bias_shape = AnfAlgo::GetPrevNodeOutputInferShape(node, 1); - const auto &format = AnfAlgo::GetNodeAttr(node, "format"); + const auto &x_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node, 0); + const auto &bias_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node, 1); + const auto &format = common::AnfAlgo::GetNodeAttr(node, "format"); const string::size_type &pos = format.find("C"); if (pos == std::string::npos || pos >= x_shape.size()) { MS_LOG(WARNING) << "The format " << format << "' invalid"; @@ -632,13 +632,13 @@ MS_TRT_CONVERTER_FUNC_REG(BatchNorm) { auto primitive = GetCNodePrimitive(node); MS_EXCEPTION_IF_NULL(primitive); - auto is_training = AnfAlgo::GetNodeAttr(node, "is_training"); + auto is_training = common::AnfAlgo::GetNodeAttr(node, "is_training"); if (is_training != false) { MS_LOG(WARNING) << "Operation not support, is_training: " << is_training; return {false, {}}; } - const auto &format = AnfAlgo::GetNodeAttr(node, "format"); + const auto &format = common::AnfAlgo::GetNodeAttr(node, "format"); if (format != "NCHW") { MS_LOG(WARNING) << "The format " << format << "' invalid"; return {false, {}}; @@ -652,10 +652,10 @@ MS_TRT_CONVERTER_FUNC_REG(BatchNorm) { auto beta = static_cast(inputs[2].weight()->values); auto mean = static_cast(inputs[3].weight()->values); auto var = static_cast(inputs[4].weight()->values); - auto epsilon = AnfAlgo::GetNodeAttr(node, "epsilon"); + auto epsilon = common::AnfAlgo::GetNodeAttr(node, "epsilon"); - const TypeId &type = AnfAlgo::GetPrevNodeOutputInferDataType(node, 1); - const std::vector &shape = AnfAlgo::GetPrevNodeOutputInferShape(node, 1); + const TypeId &type = common::AnfAlgo::GetPrevNodeOutputInferDataType(node, 1); + const std::vector &shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node, 1); int64_t channel_num = SizeToLong(shape[0]); auto coeff = context->CreateTempWeight(type, shape); auto bias = context->CreateTempWeight(type, shape); @@ -696,9 +696,9 @@ MS_TRT_CONVERTER_FUNC_REG(Concat) { auto *layer = context->network()->addConcatenation(tensors.data(), tensors.size()); MS_EXCEPTION_IF_NULL(layer); - auto axis = static_cast(AnfAlgo::GetNodeAttr(node, "axis")); + auto axis = static_cast(common::AnfAlgo::GetNodeAttr(node, "axis")); if (axis < 0) { - auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(node, 0); + auto input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node, 0); axis += SizeToInt(input_shape.size()); } layer->setAxis(axis); @@ -714,31 +714,31 @@ MS_TRT_CONVERTER_FUNC_REG(Conv2DBackpropInput) { return {false, {}}; } - const auto &format = AnfAlgo::GetNodeAttr(node, "format"); + const auto &format = common::AnfAlgo::GetNodeAttr(node, "format"); if (format != "NCHW") { MS_LOG(WARNING) << "The format: " << format << " not supported."; return {false, {}}; } - const auto &kernel_size = AnfAlgo::GetNodeAttr>(node, "kernel_size"); - const auto &output_shape = AnfAlgo::GetOutputInferShape(node, 0); + const auto &kernel_size = common::AnfAlgo::GetNodeAttr>(node, "kernel_size"); + const auto &output_shape = common::AnfAlgo::GetOutputInferShape(node, 0); const nvinfer1::Weights &bias{nvinfer1::DataType::kFLOAT, nullptr, 0}; auto *layer = context->network()->addDeconvolutionNd( *(inputs[0].tensor()), SizeToInt(output_shape[1]), nvinfer1::DimsHW{LongToInt(kernel_size[0]), LongToInt(kernel_size[1])}, *(inputs[1].weight()), bias); MS_EXCEPTION_IF_NULL(layer); - const auto &strides = AnfAlgo::GetNodeAttr>(node, "stride"); + const auto &strides = common::AnfAlgo::GetNodeAttr>(node, "stride"); layer->setStride(nvinfer1::DimsHW{LongToInt(strides[2]), LongToInt(strides[3])}); - auto pad_mode = AnfAlgo::GetNodeAttr(node, "pad_mode"); + auto pad_mode = common::AnfAlgo::GetNodeAttr(node, "pad_mode"); std::transform(pad_mode.begin(), pad_mode.end(), pad_mode.begin(), toupper); if (pad_mode == "SAME") { layer->setPaddingMode(nvinfer1::PaddingMode::kSAME_UPPER); } if (pad_mode == "PAD") { - const auto &pad_list = AnfAlgo::GetNodeAttr>(node, "pad_list"); + const auto &pad_list = common::AnfAlgo::GetNodeAttr>(node, "pad_list"); layer->setPaddingMode(nvinfer1::PaddingMode::kEXPLICIT_ROUND_DOWN); layer->setPrePadding(nvinfer1::DimsHW{LongToInt(pad_list[0]), LongToInt(pad_list[2])}); layer->setPostPadding(nvinfer1::DimsHW{LongToInt(pad_list[1]), LongToInt(pad_list[3])}); @@ -755,8 +755,8 @@ MS_TRT_CONVERTER_FUNC_REG(Slice) { return {false, {}}; } - const auto &begin = AnfAlgo::GetNodeAttr>(node, "begin"); - const auto &size = AnfAlgo::GetNodeAttr>(node, "size"); + const auto &begin = common::AnfAlgo::GetNodeAttr>(node, "begin"); + const auto &size = common::AnfAlgo::GetNodeAttr>(node, "size"); nvinfer1::Dims trt_start = TrtUtils::MsDimsToTrtDims(begin, false); nvinfer1::Dims trt_size = TrtUtils::MsDimsToTrtDims(size, false); @@ -779,7 +779,7 @@ MS_TRT_CONVERTER_FUNC_REG(Transpose) { return {false, {}}; } - const auto &perm = AnfAlgo::GetNodeAttr>(node, "perm"); + const auto &perm = common::AnfAlgo::GetNodeAttr>(node, "perm"); nvinfer1::Permutation trt_perm; for (size_t i = 0; i < perm.size(); i++) { trt_perm.order[i] = LongToInt(perm[i]); @@ -800,11 +800,11 @@ MS_TRT_CONVERTER_FUNC_REG(Softmax) { return {false, {}}; } - const std::vector &input_shape = AnfAlgo::GetPrevNodeOutputInferShape(node, 0); - const ValuePtr &value = AnfAlgo::GetCNodePrimitive(node)->GetAttr("axis"); + const std::vector &input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node, 0); + const ValuePtr &value = common::AnfAlgo::GetCNodePrimitive(node)->GetAttr("axis"); uint32_t reduce_axes = 0; if (value->isa() || value->isa()) { - const auto &axis = AnfAlgo::GetNodeAttr>(node, "axis"); + const auto &axis = common::AnfAlgo::GetNodeAttr>(node, "axis"); if (axis.size() != 1) { MS_LOG(WARNING) << "Only one axis can be set. Axis size" << axis.size(); return {false, {}}; @@ -812,7 +812,7 @@ MS_TRT_CONVERTER_FUNC_REG(Softmax) { int offset = axis[0] >= 0 ? LongToInt(axis[0]) : LongToInt(axis[0] + input_shape.size()); reduce_axes = 1U << offset; } else { - const auto &axis = AnfAlgo::GetNodeAttr(node, "axis"); + const auto &axis = common::AnfAlgo::GetNodeAttr(node, "axis"); int offset = axis >= 0 ? LongToInt(axis) : LongToInt(axis + input_shape.size()); reduce_axes = 1UL << offset; } @@ -831,8 +831,8 @@ MS_TRT_CONVERTER_FUNC_REG(LogSoftmax) { return {false, {}}; } - const std::vector &input_shape = AnfAlgo::GetPrevNodeOutputInferShape(node, 0); - const auto &axis = AnfAlgo::GetNodeAttr(node, "axis"); + const std::vector &input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node, 0); + const auto &axis = common::AnfAlgo::GetNodeAttr(node, "axis"); int offset = axis >= 0 ? LongToInt(axis) : LongToInt(axis + input_shape.size()); uint32_t reduce_axes = 1UL << offset; @@ -854,12 +854,12 @@ MS_TRT_CONVERTER_FUNC_REG(Gather) { return {false, {}}; } - const std::vector &input_shape = AnfAlgo::GetPrevNodeOutputInferShape(node, 0); - auto axis = AnfAlgo::GetNodeAttr(node, "axis"); + const std::vector &input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node, 0); + auto axis = common::AnfAlgo::GetNodeAttr(node, "axis"); axis = axis >= 0 ? axis : axis + input_shape.size(); nvinfer1::ITensor *input = ToTensor(&inputs[0], input_shape, context); - const std::vector &indices_shape = AnfAlgo::GetPrevNodeOutputInferShape(node, 1); + const std::vector &indices_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node, 1); nvinfer1::ITensor *indices = ToTensor(&inputs[1], indices_shape, context); auto *layer = context->network()->addGather(*input, *indices, LongToInt(axis)); @@ -876,10 +876,10 @@ MS_TRT_CONVERTER_FUNC_REG(Cast) { return {false, {}}; } - const std::vector &input_shape = AnfAlgo::GetPrevNodeOutputInferShape(node, 0); + const std::vector &input_shape = common::AnfAlgo::GetPrevNodeOutputInferShape(node, 0); nvinfer1::ITensor *input = ToTensor(&inputs[0], input_shape, context); - const TypeId &dst_type = AnfAlgo::GetOutputInferDataType(node, 0); + const TypeId &dst_type = common::AnfAlgo::GetOutputInferDataType(node, 0); std::variant type = TrtUtils::MsDtypeToTrtDtype(dst_type); if (type.index() != 1) { return {false, {}}; @@ -906,8 +906,8 @@ MS_TRT_CONVERTER_FUNC_REG(LayerNorm) { } // Calculate reduce axes - const std::vector &input_shape = AnfAlgo::GetOutputInferShape(node, 0); - auto begin_norm_axis = AnfAlgo::GetNodeAttr(node, "begin_norm_axis"); + const std::vector &input_shape = common::AnfAlgo::GetOutputInferShape(node, 0); + auto begin_norm_axis = common::AnfAlgo::GetNodeAttr(node, "begin_norm_axis"); begin_norm_axis = begin_norm_axis >= 0 ? begin_norm_axis : begin_norm_axis + input_shape.size(); uint32_t reduce_axes = 0; for (size_t i = LongToSize(begin_norm_axis); i < input_shape.size(); i++) { @@ -915,14 +915,14 @@ MS_TRT_CONVERTER_FUNC_REG(LayerNorm) { } // Reshape gamma and beta for broadcast - auto begin_params_axis = AnfAlgo::GetNodeAttr(node, "begin_params_axis"); + auto begin_params_axis = common::AnfAlgo::GetNodeAttr(node, "begin_params_axis"); begin_params_axis = begin_params_axis >= 0 ? begin_params_axis : begin_params_axis + input_shape.size(); std::vector param_shape = input_shape; for (size_t j = 0; j < LongToSize(begin_params_axis); j++) { param_shape[j] = 1; } - auto epsilon = AnfAlgo::GetNodeAttr(node, "epsilon"); + auto epsilon = common::AnfAlgo::GetNodeAttr(node, "epsilon"); std::shared_ptr weight = context->CreateTempWeight(kNumberTypeFloat32, {1}); auto value = static_cast(weight->data_c()); value[0] = epsilon; diff --git a/mindspore/ccsrc/profiler/device/ascend/ascend_profiling.cc b/mindspore/ccsrc/profiler/device/ascend/ascend_profiling.cc index f47f8ed458e..b0d685490bc 100644 --- a/mindspore/ccsrc/profiler/device/ascend/ascend_profiling.cc +++ b/mindspore/ccsrc/profiler/device/ascend/ascend_profiling.cc @@ -19,7 +19,7 @@ #include "common/util/error_manager/error_manager.h" #include "pybind_api/api_register.h" #include "utils/log_adapter.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "profiler/device/ascend/memory_profiling.h" #include "plugin/device/ascend/hal/device/profiling/profiling_manager.h" #include "profiler/device/ascend/parallel_strategy_profiling.h" diff --git a/mindspore/ccsrc/profiler/device/ascend/parallel_strategy_profiling.cc b/mindspore/ccsrc/profiler/device/ascend/parallel_strategy_profiling.cc index c5c61a9a41c..fd6daefd658 100644 --- a/mindspore/ccsrc/profiler/device/ascend/parallel_strategy_profiling.cc +++ b/mindspore/ccsrc/profiler/device/ascend/parallel_strategy_profiling.cc @@ -19,13 +19,13 @@ #include "sys/stat.h" #include "debug/dump_proto.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" #include "frontend/parallel/device_manager.h" #include "profiler/device/ascend/options.h" #include "profiler/device/ascend/ascend_profiling.h" #include "proto/profiling_parallel.pb.h" #include "utils/ms_context.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "google/protobuf/util/json_util.h" @@ -58,8 +58,8 @@ bool IsProfilingParallelStrategyEnabled() { #endif std::string parallel_mode = parallel::ParallelContext::GetInstance()->parallel_mode(); - if ((parallel_mode == parallel::AUTO_PARALLEL) || (parallel_mode == parallel::SEMI_AUTO_PARALLEL) || - (parallel_mode == parallel::DATA_PARALLEL)) { + if ((parallel_mode == parallel::kAutoParallel) || (parallel_mode == parallel::kSemiAutoParallel) || + (parallel_mode == parallel::kDataParallel)) { return true; } diff --git a/mindspore/ccsrc/profiler/device/cpu/cpu_profiling.cc b/mindspore/ccsrc/profiler/device/cpu/cpu_profiling.cc index 96150bd901d..f8d8353388e 100644 --- a/mindspore/ccsrc/profiler/device/cpu/cpu_profiling.cc +++ b/mindspore/ccsrc/profiler/device/cpu/cpu_profiling.cc @@ -22,7 +22,7 @@ #include "profiler/device/cpu/cpu_data_saver.h" #include "pybind_api/api_register.h" #include "utils/log_adapter.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/ms_context.h" namespace mindspore { diff --git a/mindspore/ccsrc/profiler/device/gpu/gpu_profiling.cc b/mindspore/ccsrc/profiler/device/gpu/gpu_profiling.cc index 01551d23bc6..7b1a3393efc 100644 --- a/mindspore/ccsrc/profiler/device/gpu/gpu_profiling.cc +++ b/mindspore/ccsrc/profiler/device/gpu/gpu_profiling.cc @@ -24,7 +24,7 @@ #include "profiler/device/gpu/gpu_data_saver.h" #include "pybind_api/api_register.h" #include "utils/log_adapter.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/profile.h" #include "utils/ms_context.h" diff --git a/mindspore/ccsrc/profiler/device/gpu/gpu_profiling_utils.cc b/mindspore/ccsrc/profiler/device/gpu/gpu_profiling_utils.cc index 608fbba8613..77dcc5856c1 100644 --- a/mindspore/ccsrc/profiler/device/gpu/gpu_profiling_utils.cc +++ b/mindspore/ccsrc/profiler/device/gpu/gpu_profiling_utils.cc @@ -17,9 +17,10 @@ #include "profiler/device/gpu/gpu_profiling_utils.h" #include "kernel/kernel.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "utils/ms_utils.h" #include "utils/ms_context.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace profiler { @@ -71,7 +72,7 @@ void ProfilingUtils::OutputStepTraceOpNameStatus() { void ProfilingUtils::GetTraceHccl(const std::vector &cnode_exec_order) { for (const auto &node : cnode_exec_order) { - if (AnfAlgo::IsCommunicationOp(node)) { + if (common::AnfAlgo::IsCommunicationOp(node)) { MS_EXCEPTION_IF_NULL(node); if (std::find(profiling_trace.trace_custom_node.begin(), profiling_trace.trace_custom_node.end(), node->fullname_with_scope()) == profiling_trace.trace_custom_node.end()) { @@ -92,7 +93,7 @@ void ProfilingUtils::SetTraceFpStart(const std::vector &cnode_exec_ord auto first_node = cnode_exec_order.front(); MS_EXCEPTION_IF_NULL(first_node); - auto node_name = AnfAlgo::GetCNodeName(first_node); + auto node_name = common::AnfAlgo::GetCNodeName(first_node); if (node_name == kInitDatasetQueueOpName) { return; } @@ -120,7 +121,7 @@ void ProfilingUtils::SetTraceBpEnd(const std::vector &cnode_exec_order // Contain hccl kernel (try to find the last communication op) auto iter = cnode_exec_order.rbegin(); while (iter != cnode_exec_order.rend()) { - if (AnfAlgo::IsCommunicationOp(*iter)) { + if (common::AnfAlgo::IsCommunicationOp(*iter)) { break; } ++iter; @@ -129,9 +130,9 @@ void ProfilingUtils::SetTraceBpEnd(const std::vector &cnode_exec_order if (iter != cnode_exec_order.rend()) { // store communication op input nodes' name std::set ar_input_node_names; - size_t input_num = AnfAlgo::GetInputTensorNum(*iter); + size_t input_num = common::AnfAlgo::GetInputTensorNum(*iter); for (size_t i = 0; i < input_num; ++i) { - auto input_node_with_index = AnfAlgo::GetPrevNodeOutput(*iter, i); + auto input_node_with_index = common::AnfAlgo::GetPrevNodeOutput(*iter, i); auto input_node = input_node_with_index.first; ar_input_node_names.insert(input_node->fullname_with_scope()); } diff --git a/mindspore/ccsrc/profiler/device/profiling.cc b/mindspore/ccsrc/profiler/device/profiling.cc index 429b63bca52..79f24268f60 100644 --- a/mindspore/ccsrc/profiler/device/profiling.cc +++ b/mindspore/ccsrc/profiler/device/profiling.cc @@ -21,7 +21,7 @@ #include #include "pybind_api/api_register.h" #include "utils/log_adapter.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #if ENABLE_GPU #include "profiler/device/gpu/gpu_profiling.h" #endif diff --git a/mindspore/ccsrc/ps/core/communicator/http_communicator.cc b/mindspore/ccsrc/ps/core/communicator/http_communicator.cc index fe8f23a88ec..51d8d63d13c 100644 --- a/mindspore/ccsrc/ps/core/communicator/http_communicator.cc +++ b/mindspore/ccsrc/ps/core/communicator/http_communicator.cc @@ -16,7 +16,7 @@ #include "ps/core/communicator/http_communicator.h" #include -#include "common/thread_pool.h" +#include "include/common/thread_pool.h" namespace mindspore { namespace ps { diff --git a/mindspore/ccsrc/ps/core/file_configuration.h b/mindspore/ccsrc/ps/core/file_configuration.h index 2fb59d3a348..5a328d5b0ce 100644 --- a/mindspore/ccsrc/ps/core/file_configuration.h +++ b/mindspore/ccsrc/ps/core/file_configuration.h @@ -28,7 +28,7 @@ #include #include "utils/hash_map.h" -#include "utils/json_operation_utils.h" +#include "include/common/utils/json_operation_utils.h" #include "ps/constants.h" #include "utils/log_adapter.h" #include "ps/core/comm_util.h" diff --git a/mindspore/ccsrc/ps/parameter_server.cc b/mindspore/ccsrc/ps/parameter_server.cc index 012f006e84d..e9ff896d4c1 100644 --- a/mindspore/ccsrc/ps/parameter_server.cc +++ b/mindspore/ccsrc/ps/parameter_server.cc @@ -651,10 +651,10 @@ void ParameterServer::GetEmbeddingTableParamPtr() { MS_EXCEPTION_IF_NULL(cnode); std::string cnode_name = Util::GetPrimitiveName(cnode); if (cnode_name == kEmbeddingLookupOpName || cnode_name == kGatherV2OpName || cnode_name == kSparseGatherV2OpName) { - auto embedding_table = AnfAlgo::GetInputNode(cnode, 0); + auto embedding_table = common::AnfAlgo::GetInputNode(cnode, 0); if (IsPrimitiveCNode(embedding_table, prim::kPrimLoad)) { auto embedding_cnode = embedding_table->cast(); - embedding_table = AnfAlgo::GetInputNode(embedding_cnode, 0); + embedding_table = common::AnfAlgo::GetInputNode(embedding_cnode, 0); } MS_EXCEPTION_IF_NULL(embedding_table); if (embedding_table->isa()) { @@ -680,10 +680,10 @@ void ParameterServer::CacheEmbeddingTableParamPtr() { continue; } - auto embedding_table = AnfAlgo::GetInputNode(cnode, 0); + auto embedding_table = common::AnfAlgo::GetInputNode(cnode, 0); if (IsPrimitiveCNode(embedding_table, prim::kPrimLoad)) { auto embedding_cnode = embedding_table->cast(); - embedding_table = AnfAlgo::GetInputNode(embedding_cnode, 0); + embedding_table = common::AnfAlgo::GetInputNode(embedding_cnode, 0); } MS_EXCEPTION_IF_NULL(embedding_table); diff --git a/mindspore/ccsrc/ps/parameter_server.h b/mindspore/ccsrc/ps/parameter_server.h index 116eec7832c..7c817647e92 100644 --- a/mindspore/ccsrc/ps/parameter_server.h +++ b/mindspore/ccsrc/ps/parameter_server.h @@ -37,6 +37,7 @@ #include "ir/func_graph.h" #include "backend/common/session/session_basic.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/session/session_factory.h" #include "ps/optimizer_info.h" #include "ps/optimizer_info_builder.h" diff --git a/mindspore/ccsrc/ps/util.cc b/mindspore/ccsrc/ps/util.cc index 24d9759cf65..2c2a729ed9c 100644 --- a/mindspore/ccsrc/ps/util.cc +++ b/mindspore/ccsrc/ps/util.cc @@ -179,12 +179,12 @@ void Util::DoFusion(const FuncGraphPtr &func_graph, const std::string &cnode_nam single_nodes.push_back(node); auto weight_name_value_node = - AnfAlgo::GetInputNode(node->cast(), kNodeInputWeightNameOffset)->cast(); + common::AnfAlgo::GetInputNode(node->cast(), kNodeInputWeightNameOffset)->cast(); const std::string &weight_name = GetValue(weight_name_value_node->value()); weight_names.push_back(weight_name); auto weight_index_value_node = - AnfAlgo::GetInputNode(node->cast(), kNodeInputWeightIndexOffset)->cast(); + common::AnfAlgo::GetInputNode(node->cast(), kNodeInputWeightIndexOffset)->cast(); int64_t weight_index = GetValue(weight_index_value_node->value()); indices.push_back(weight_index); } @@ -196,14 +196,14 @@ void Util::DoFusion(const FuncGraphPtr &func_graph, const std::string &cnode_nam std::vector fused_node_inputs = {}; fused_node_inputs.push_back(NewValueNode(prim)); (void)std::for_each(single_nodes.begin(), single_nodes.end(), [&](const AnfNodePtr &node) { - fused_node_inputs.push_back(AnfAlgo::GetInputNode(node->cast(), 0)); + fused_node_inputs.push_back(common::AnfAlgo::GetInputNode(node->cast(), 0)); }); auto fused_cnode = func_graph->NewCNode(fused_node_inputs); MS_EXCEPTION_IF_NULL(fused_cnode); - AnfAlgo::SetNodeAttr(kAttrPsKey, MakeValue(weight_names), fused_cnode); - AnfAlgo::SetNodeAttr(kAttrIndex, MakeValue(indices), fused_cnode); - AnfAlgo::SetNodeAttr(kAttrPrimitiveTarget, MakeValue(kCPUDevice), fused_cnode); + common::AnfAlgo::SetNodeAttr(kAttrPsKey, MakeValue(weight_names), fused_cnode); + common::AnfAlgo::SetNodeAttr(kAttrIndex, MakeValue(indices), fused_cnode); + common::AnfAlgo::SetNodeAttr(kAttrPrimitiveTarget, MakeValue(kCPUDevice), fused_cnode); auto kernel_info = std::make_shared(); MS_EXCEPTION_IF_NULL(kernel_info); @@ -241,16 +241,16 @@ kernel::KernelBuildInfoPtr Util::GenerateKernelBuildInfo(const std::vector(node_list[idx]); MS_EXCEPTION_IF_NULL(cnode); - size_t input_num = AnfAlgo::GetInputTensorNum(cnode); + size_t input_num = common::AnfAlgo::GetInputTensorNum(cnode); for (size_t input_index = 0; input_index < input_num; ++input_index) { (void)inputs_device_format.emplace_back(kOpFormat_DEFAULT); - inputs_device_type.push_back(AnfAlgo::GetPrevNodeOutputInferDataType(cnode, input_index)); + inputs_device_type.push_back(common::AnfAlgo::GetPrevNodeOutputInferDataType(cnode, input_index)); } - size_t output_num = AnfAlgo::GetOutputTensorNum(cnode); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(cnode); for (size_t output_index = 0; output_index < output_num; ++output_index) { (void)outputs_device_format.emplace_back(kOpFormat_DEFAULT); - outputs_device_type.push_back(AnfAlgo::GetOutputInferDataType(cnode, output_index)); - outputs_shape.push_back(AnfAlgo::GetOutputInferShape(cnode, output_index)); + outputs_device_type.push_back(common::AnfAlgo::GetOutputInferDataType(cnode, output_index)); + outputs_shape.push_back(common::AnfAlgo::GetOutputInferShape(cnode, output_index)); } } builder.SetInputsFormat(inputs_device_format); diff --git a/mindspore/ccsrc/ps/util.h b/mindspore/ccsrc/ps/util.h index f68d55c206c..e3f479e5c32 100644 --- a/mindspore/ccsrc/ps/util.h +++ b/mindspore/ccsrc/ps/util.h @@ -25,6 +25,7 @@ #include "ps/constants.h" #include "frontend/optimizer/optimizer.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "kernel/common_utils.h" #include "plugin/device/cpu/kernel/sparse_optimizer_cpu_kernel.h" diff --git a/mindspore/ccsrc/pybind_api/gil_scoped_long_running.h b/mindspore/ccsrc/pybind_api/gil_scoped_long_running.h index a6874b3d34b..7b2d259ee76 100644 --- a/mindspore/ccsrc/pybind_api/gil_scoped_long_running.h +++ b/mindspore/ccsrc/pybind_api/gil_scoped_long_running.h @@ -21,7 +21,7 @@ #include "pybind11/pybind11.h" -#include "utils/scoped_long_running.h" +#include "include/common/utils/scoped_long_running.h" namespace py = pybind11; diff --git a/mindspore/ccsrc/pybind_api/ir/base_ref_py.h b/mindspore/ccsrc/pybind_api/ir/base_ref_py.h index b893db9cbb4..a27197c38db 100644 --- a/mindspore/ccsrc/pybind_api/ir/base_ref_py.h +++ b/mindspore/ccsrc/pybind_api/ir/base_ref_py.h @@ -39,8 +39,13 @@ class PyObjectRef : public BaseRef { uint32_t type() const override { return tid(); } std::string ToString() const override { return py::str(object_); } - bool operator==(const BaseRef &other) const override; - bool operator==(const PyObjectRef &other) const; + bool operator==(const PyObjectRef &other) const { return object_.is(other.object_); } + bool operator==(const BaseRef &other) const override { + if (!utils::isa(other)) { + return false; + } + return *this == utils::cast(other); + } py::object object_; }; diff --git a/mindspore/ccsrc/pybind_api/ir/cell_py.cc b/mindspore/ccsrc/pybind_api/ir/cell_py.cc index c7f5b115073..d901a9ca5be 100644 --- a/mindspore/ccsrc/pybind_api/ir/cell_py.cc +++ b/mindspore/ccsrc/pybind_api/ir/cell_py.cc @@ -19,7 +19,7 @@ #include "pybind_api/api_register.h" #include "abstract/abstract_value.h" -#include "pipeline/jit/parse/python_adapter.h" +#include "pipeline/jit/parse/data_converter.h" namespace mindspore { void CellPy::AddAttr(CellPtr cell, const std::string &name, const py::object &obj) { diff --git a/mindspore/ccsrc/pybind_api/ir/primitive_py.cc b/mindspore/ccsrc/pybind_api/ir/primitive_py.cc index 8ad9f9cf0b5..c9ea583db91 100644 --- a/mindspore/ccsrc/pybind_api/ir/primitive_py.cc +++ b/mindspore/ccsrc/pybind_api/ir/primitive_py.cc @@ -21,17 +21,16 @@ #include #include "ir/signature.h" #include "pipeline/jit/parse/data_converter.h" -#include "pipeline/jit/parse/python_adapter.h" +#include "include/common/utils/python_adapter.h" #include "pybind11/pytypes.h" #include "pybind_api/api_register.h" #include "pybind_api/export_flags.h" #include "pybind_api/ir/base_ref_py.h" #include "utils/convert_utils_base.h" -#include "utils/convert_utils_py.h" +#include "include/common/utils/convert_utils_py.h" #include "utils/ms_context.h" -#include "utils/primitive_utils.h" +#include "include/common/utils/primitive_utils.h" #include "utils/check_convert_utils.h" -#include "pipeline/jit/resource.h" #include "pipeline/pynative/pynative_execute.h" namespace mindspore { @@ -64,8 +63,8 @@ void ConvertCTensorToPyTensor(const py::tuple &input_args, py::tuple *convert_ar } for (size_t i = 0; i < input_args.size(); ++i) { if (py::isinstance(input_args[i])) { - (*convert_args)[i] = parse::python_adapter::CallPyFn(parse::PYTHON_MOD_PARSE_MODULE, - parse::PYTHON_MOD_CONVERT_TO_MS_TENSOR, input_args[i]); + (*convert_args)[i] = + python_adapter::CallPyFn(parse::PYTHON_MOD_PARSE_MODULE, parse::PYTHON_MOD_CONVERT_TO_MS_TENSOR, input_args[i]); } else if (py::isinstance(input_args[i])) { auto tuple_inp_arg = py::cast(input_args[i]); py::tuple convert_tuple_arg(tuple_inp_arg.size()); @@ -374,7 +373,7 @@ py::function PrimitivePy::GetComputeFunction() const { static const std::string vm_module = "mindspore.ops.vm_impl_registry"; static const std::string get_vm_impl_fn = "get_vm_impl_fn"; MS_LOG(DEBUG) << name() << ": get_vm_impl_fn"; - py::function get_fn = parse::python_adapter::GetPyFn(vm_module, get_vm_impl_fn); + py::function get_fn = python_adapter::GetPyFn(vm_module, get_vm_impl_fn); py::function vm_fn = get_fn(python_obj_); if (py::isinstance(vm_fn)) { MS_LOG(DEBUG) << "Cannot find " << python_obj_.attr("__class__").attr("__name__").cast(); diff --git a/mindspore/ccsrc/pybind_api/ir/primitive_py.h b/mindspore/ccsrc/pybind_api/ir/primitive_py.h index cfd00910896..b29bcec4022 100644 --- a/mindspore/ccsrc/pybind_api/ir/primitive_py.h +++ b/mindspore/ccsrc/pybind_api/ir/primitive_py.h @@ -25,7 +25,6 @@ #include "utils/hash_map.h" #include "abstract/abstract_value.h" -#include "frontend/parallel/ops_info/operator_info.h" #include "ir/primitive.h" #include "ir/signature.h" #include "pybind11/pybind11.h" diff --git a/mindspore/ccsrc/utils/security_py.cc b/mindspore/ccsrc/pybind_api/utils/security_py.cc similarity index 96% rename from mindspore/ccsrc/utils/security_py.cc rename to mindspore/ccsrc/pybind_api/utils/security_py.cc index 9be39768322..53b5c198fd5 100644 --- a/mindspore/ccsrc/utils/security_py.cc +++ b/mindspore/ccsrc/pybind_api/utils/security_py.cc @@ -14,7 +14,7 @@ * limitations under the License. */ -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "pybind_api/api_register.h" namespace mindspore { diff --git a/mindspore/ccsrc/runtime/device/CMakeLists.txt b/mindspore/ccsrc/runtime/device/CMakeLists.txt index 513200c04fa..21468b58b6c 100644 --- a/mindspore/ccsrc/runtime/device/CMakeLists.txt +++ b/mindspore/ccsrc/runtime/device/CMakeLists.txt @@ -2,6 +2,7 @@ file(GLOB_RECURSE DEVICE_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} "common/* "kernel_info.cc" "executor/dynamic_kernel.cc" "executor/executor_callback.cc" "kernel_runtime.cc" "memory_manager.cc" "kernel_runtime_manager.cc" "convert_tensor_utils.cc" "memory_scheduler.cc" "memory_offload_strategy.cc" "bucket.cc" "launch_kernel.cc" "launch_mul.cc" "tensor_array.cc" + "ms_device_shape_transfer.cc" "context_extends.cc" ) if("${ENABLE_HIDDEN}" STREQUAL "OFF") diff --git a/mindspore/ccsrc/runtime/device/bucket.cc b/mindspore/ccsrc/runtime/device/bucket.cc index 426cb34f74e..1df04198dfa 100644 --- a/mindspore/ccsrc/runtime/device/bucket.cc +++ b/mindspore/ccsrc/runtime/device/bucket.cc @@ -17,7 +17,7 @@ #include "runtime/device/bucket.h" #include "runtime/device/kernel_runtime_manager.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" #include "utils/profile.h" namespace mindspore::device { diff --git a/mindspore/ccsrc/utils/context/context_extends.cc b/mindspore/ccsrc/runtime/device/context_extends.cc similarity index 98% rename from mindspore/ccsrc/utils/context/context_extends.cc rename to mindspore/ccsrc/runtime/device/context_extends.cc index 58d952c5cb4..7e4bc63a4de 100644 --- a/mindspore/ccsrc/utils/context/context_extends.cc +++ b/mindspore/ccsrc/runtime/device/context_extends.cc @@ -14,14 +14,14 @@ * limitations under the License. */ -#include "utils/context/context_extends.h" +#include "runtime/device/context_extends.h" #include #include #include #include #include #include "pybind11/pybind11.h" -#include "utils/config_manager.h" +#include "include/common/utils/config_manager.h" #include "utils/ms_utils.h" #include "utils/convert_utils_base.h" #ifndef NO_DLIB @@ -31,7 +31,7 @@ #include "common/util/error_manager/error_manager.h" #endif #ifdef ENABLE_D -#include "transform/graph_ir/df_graph_manager.h" +#include "include/transform/graph_ir/df_graph_manager.h" #endif #include "profiler/device/profiling.h" diff --git a/mindspore/ccsrc/utils/context/context_extends.h b/mindspore/ccsrc/runtime/device/context_extends.h similarity index 97% rename from mindspore/ccsrc/utils/context/context_extends.h rename to mindspore/ccsrc/runtime/device/context_extends.h index 7c5641c6445..f2c188e85e1 100644 --- a/mindspore/ccsrc/utils/context/context_extends.h +++ b/mindspore/ccsrc/runtime/device/context_extends.h @@ -21,7 +21,7 @@ #include #include #include "utils/ms_context.h" -#include "utils/tensorprint_utils.h" +#include "include/common/utils/tensorprint_utils.h" namespace mindspore { namespace context { diff --git a/mindspore/ccsrc/runtime/device/executor/dynamic_kernel.cc b/mindspore/ccsrc/runtime/device/executor/dynamic_kernel.cc index 889e11d9da6..5cf1bad54a4 100644 --- a/mindspore/ccsrc/runtime/device/executor/dynamic_kernel.cc +++ b/mindspore/ccsrc/runtime/device/executor/dynamic_kernel.cc @@ -19,11 +19,13 @@ #include #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/optimizer/helper.h" -#include "utils/ms_device_shape_transfer.h" +#include "runtime/device/ms_device_shape_transfer.h" #include "pipeline/jit/static_analysis/static_analysis.h" #include "abstract/dshape.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" +#include "utils/ms_context.h" #include "abstract/param_validator.h" namespace mindspore { @@ -32,14 +34,14 @@ void DynamicKernel::Initialize() { MS_LOG(INFO) << "Init Start"; auto cnode = cnode_ptr_.lock(); MS_EXCEPTION_IF_NULL(cnode); - is_dynamic_shape_ = AnfAlgo::IsDynamicShape(cnode); + is_dynamic_shape_ = common::AnfAlgo::IsDynamicShape(cnode); if (!is_dynamic_shape_) { MS_LOG(DEBUG) << "cnode is not dynamic shape:" << cnode->fullname_with_scope(); return; } - is_input_dynamic_shape_ = AnfAlgo::GetBooleanAttr(cnode, kAttrInputIsDynamicShape); - is_output_dynamic_shape_ = AnfAlgo::GetBooleanAttr(cnode, kAttrOutputIsDynamicShape); + is_input_dynamic_shape_ = common::AnfAlgo::GetBooleanAttr(cnode, kAttrInputIsDynamicShape); + is_output_dynamic_shape_ = common::AnfAlgo::GetBooleanAttr(cnode, kAttrOutputIsDynamicShape); auto ret = abstract::GetDependsFormMap(cnode); if (ret.empty()) { @@ -67,21 +69,21 @@ void DynamicKernel::InferShape() { MS_EXCEPTION_IF_NULL(context); AbstractBasePtrList args_spec_list; auto primitive = GetValueNode(inputs[0]); - auto input_size = AnfAlgo::GetInputTensorNum(cnode); + auto input_size = common::AnfAlgo::GetInputTensorNum(cnode); for (size_t i = 0; i < input_size; i++) { - auto input_node_with_index = AnfAlgo::GetPrevNodeOutput(cnode, i); + auto input_node_with_index = common::AnfAlgo::GetPrevNodeOutput(cnode, i); auto real_input = input_node_with_index.first; MS_EXCEPTION_IF_NULL(real_input); auto cnode_input = cnode->input(i + 1); MS_EXCEPTION_IF_NULL(cnode_input); InferShapeForNopNode(&real_input); if (depend_list_.find(i) != depend_list_.end()) { - auto pre_node_with_index = AnfAlgo::GetPrevNodeOutput(cnode, i); + auto pre_node_with_index = common::AnfAlgo::GetPrevNodeOutput(cnode, i); bool skip_nop_node = !context->get_param(MS_CTX_ENABLE_MINDRT); auto output_addr = AnfAlgo::GetPrevNodeMutableOutputAddr(cnode, i, skip_nop_node); std::vector shapes = trans::GetRuntimePaddingShape(pre_node_with_index.first, pre_node_with_index.second); - auto host_type = AnfAlgo::GetOutputInferDataType(pre_node_with_index.first, pre_node_with_index.second); + auto host_type = common::AnfAlgo::GetOutputInferDataType(pre_node_with_index.first, pre_node_with_index.second); auto out_tensor = std::make_shared(host_type, shapes); MS_EXCEPTION_IF_NULL(out_tensor); // The second parameter must be false, otherwise the device address cannot be released and allocated, and the @@ -96,14 +98,14 @@ void DynamicKernel::InferShape() { if (real_abs->isa()) { real_input->abstract()->set_value(out_tensor); } else if (real_abs->isa()) { - auto tuple_get_item_index = AnfAlgo::GetTupleGetItemOutIndex(cnode_input->cast()); + auto tuple_get_item_index = common::AnfAlgo::GetTupleGetItemOutIndex(cnode_input->cast()); auto abstract_tuple = real_abs->cast(); MS_EXCEPTION_IF_NULL(abstract_tuple); auto tuple_elements = abstract_tuple->elements()[tuple_get_item_index]; tuple_elements->set_value(out_tensor); } } - (void)AnfAlgo::AddArgList(&args_spec_list, cnode_input, real_input, i); + (void)common::AnfAlgo::AddArgList(&args_spec_list, cnode_input, real_input, i); } auto eval_result = opt::CppInferShape(primitive, args_spec_list); cnode->set_abstract(eval_result); @@ -111,7 +113,7 @@ void DynamicKernel::InferShape() { void DynamicKernel::InferShapeForNopNode(AnfNodePtr *input_node) { MS_EXCEPTION_IF_NULL(*input_node); - if (!opt::IsNopNode(*input_node) || !AnfAlgo::IsDynamicShape(*input_node)) { + if (!common::AnfAlgo::IsNopNode(*input_node) || !common::AnfAlgo::IsDynamicShape(*input_node)) { MS_LOG(INFO) << "Input node is not a nop node, no need infer."; return; } @@ -120,10 +122,10 @@ void DynamicKernel::InferShapeForNopNode(AnfNodePtr *input_node) { nop_road.push(*input_node); while (true) { - auto input_node_with_idx = AnfAlgo::GetPrevNodeOutput(*input_node, 0); + auto input_node_with_idx = common::AnfAlgo::GetPrevNodeOutput(*input_node, 0); auto in_node = input_node_with_idx.first; MS_EXCEPTION_IF_NULL(in_node); - if (opt::IsNopNode(in_node)) { + if (common::AnfAlgo::IsNopNode(in_node)) { nop_road.push(in_node); *input_node = in_node; } else { diff --git a/mindspore/ccsrc/runtime/device/kernel_adjust.cc b/mindspore/ccsrc/runtime/device/kernel_adjust.cc index e36815ad0b1..a92abca3c9c 100644 --- a/mindspore/ccsrc/runtime/device/kernel_adjust.cc +++ b/mindspore/ccsrc/runtime/device/kernel_adjust.cc @@ -23,12 +23,13 @@ #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "utils/ms_context.h" -#include "utils/ms_device_shape_transfer.h" -#include "utils/config_manager.h" +#include "runtime/device/ms_device_shape_transfer.h" +#include "include/common/utils/config_manager.h" #include "utils/ms_utils.h" #include "kernel/kernel_build_info.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "plugin/device/ascend/hal/device/profiling/profiling_manager.h" #include "runtime/base.h" #include "plugin/device/ascend/hal/device/ascend_stream_manager.h" @@ -54,7 +55,7 @@ void KernelAdjust::ReorderGetNext(const std::shared_ptr &k std::vector getnext_list; std::vector other_list; for (const auto &cnode : origin_cnode_list) { - if (AnfAlgo::GetCNodeName(cnode) == kGetNextOpName) { + if (common::AnfAlgo::GetCNodeName(cnode) == kGetNextOpName) { getnext_list.emplace_back(cnode); } else { other_list.emplace_back(cnode); @@ -86,7 +87,7 @@ CNodePtr KernelAdjust::CreateSendApplyKernel(const std::shared_ptr(); MS_EXCEPTION_IF_NULL(abstract_none); send_node_ptr->set_abstract(abstract_none); @@ -106,7 +107,7 @@ CNodePtr KernelAdjust::CreateRecvApplyKernel(const std::shared_ptr(); MS_EXCEPTION_IF_NULL(abstract_none); recv_node_ptr->set_abstract(abstract_none); @@ -117,7 +118,7 @@ bool KernelAdjust::ExistGetNext(const std::shared_ptr &ker MS_EXCEPTION_IF_NULL(kernel_graph_ptr); const std::vector &cnode_list = kernel_graph_ptr->execution_order(); for (const auto &cnode : cnode_list) { - if (AnfAlgo::GetCNodeName(cnode) == kGetNextOpName) { + if (common::AnfAlgo::GetCNodeName(cnode) == kGetNextOpName) { return true; } } @@ -147,8 +148,9 @@ void KernelAdjust::InsertIndepentParallel(const std::shared_ptr(true), independent_switch_app); - AnfAlgo::SetNodeAttr(kAttrStreamSwitchKind, MakeValue(kIndependentStreamSwitch), independent_switch_app); + common::AnfAlgo::SetNodeAttr(kStreamNeedActivedFirst, MakeValue(true), independent_switch_app); + common::AnfAlgo::SetNodeAttr(kAttrStreamSwitchKind, MakeValue(kIndependentStreamSwitch), + independent_switch_app); (*exec_order).push_back(independent_switch_app); MS_LOG(INFO) << "Independent op loop insert Stream Switch " << independent_switch_app->fullname_with_scope(); } @@ -167,10 +169,10 @@ void KernelAdjust::InsertFpBpLoopStreamSwitch(const std::shared_ptr(true), fpbp_switch_app); + common::AnfAlgo::SetNodeAttr(kStreamNeedActivedFirst, MakeValue(true), fpbp_switch_app); // update fpbp loop stream switch true_branch_stream attr - AnfAlgo::SetNodeAttr(kAttrTrueBranchStream, MakeValue(*fpbp_stream_id), fpbp_switch_app); - AnfAlgo::SetNodeAttr(kAttrStreamSwitchKind, MakeValue(kFpBpStreamSwitch), fpbp_switch_app); + common::AnfAlgo::SetNodeAttr(kAttrTrueBranchStream, MakeValue(*fpbp_stream_id), fpbp_switch_app); + common::AnfAlgo::SetNodeAttr(kAttrStreamSwitchKind, MakeValue(kFpBpStreamSwitch), fpbp_switch_app); (*exec_order).push_back(fpbp_switch_app); MS_LOG(INFO) << "FpBp loop insert Stream Switch " << fpbp_switch_app->fullname_with_scope(); } @@ -184,9 +186,9 @@ void KernelAdjust::CopyMemcpyList(const std::shared_ptr &k CNodePtr cur_cnode = nullptr; for (size_t idx = order_index + 1; idx < orders.size(); idx++) { cur_cnode = orders[idx]; - if (AnfAlgo::HasNodeAttr(kAttrLabelForInsertStreamActive, cur_cnode)) { + if (common::AnfAlgo::HasNodeAttr(kAttrLabelForInsertStreamActive, cur_cnode)) { auto pre_node = orders[idx - 1]; - auto pre_kernel_name = AnfAlgo::GetCNodeName(pre_node); + auto pre_kernel_name = common::AnfAlgo::GetCNodeName(pre_node); if (pre_kernel_name == kAtomicAddrCleanOpName) { (*other_list).pop_back(); (*memcpy_list).push_back(pre_node); @@ -216,8 +218,8 @@ void KernelAdjust::InsertGetNextLoopStreamActive(const std::shared_ptr>(getnext_active_streams), - getnext_active_app); + common::AnfAlgo::SetNodeAttr(kAttrActiveStreamList, MakeValue>(getnext_active_streams), + getnext_active_app); (*exec_order).push_back(getnext_active_app); MS_LOG(INFO) << "FpBp loop insert GetNext loop Stream Active " << getnext_active_app->fullname_with_scope(); } @@ -253,7 +255,7 @@ void KernelAdjust::InsertCurrentLoopAssignAdd(const std::shared_ptr(true), cur_assign_add); + common::AnfAlgo::SetNodeAttr(kAttrFpBpEnd, MakeValue(true), cur_assign_add); (*exec_order).push_back(cur_assign_add); MS_LOG(INFO) << "FpBp loop insert current loop AssignAdd " << cur_assign_add->fullname_with_scope(); } @@ -265,7 +267,8 @@ void KernelAdjust::InsertFpBpAndEosLoopStreamActive(const std::shared_ptr>(fpbp_active_streams), fpbp_active_app); + common::AnfAlgo::SetNodeAttr(kAttrActiveStreamList, MakeValue>(fpbp_active_streams), + fpbp_active_app); (*exec_order).push_back(fpbp_active_app); MS_LOG(INFO) << "FpBp loop insert FpBp loop and Eos loop Stream Active " << fpbp_active_app->fullname_with_scope(); } @@ -285,9 +288,9 @@ void KernelAdjust::InsertGetNextLoopStreamSwitch( MS_EXCEPTION_IF_NULL(getnext_switch_app); AnfAlgo::SetStreamId(*getnext_switch_stream_id, getnext_switch_app.get()); // update getnext loop stream switch true_branch_stream attr - AnfAlgo::SetNodeAttr(kStreamNeedActivedFirst, MakeValue(true), getnext_switch_app); - AnfAlgo::SetNodeAttr(kAttrTrueBranchStream, MakeValue(*getnext_stream_id), getnext_switch_app); - AnfAlgo::SetNodeAttr(kAttrStreamSwitchKind, MakeValue(kGetNextStreamSwitch), getnext_switch_app); + common::AnfAlgo::SetNodeAttr(kStreamNeedActivedFirst, MakeValue(true), getnext_switch_app); + common::AnfAlgo::SetNodeAttr(kAttrTrueBranchStream, MakeValue(*getnext_stream_id), getnext_switch_app); + common::AnfAlgo::SetNodeAttr(kAttrStreamSwitchKind, MakeValue(kGetNextStreamSwitch), getnext_switch_app); (*exec_order).push_back(getnext_switch_app); MS_LOG(INFO) << "GetNext loop insert Stream Switch " << getnext_switch_app->fullname_with_scope(); } @@ -300,7 +303,7 @@ void KernelAdjust::SetBeforeGetNextStreamID(std::vector *exec_order, c auto node = orders[*order_index]; (*exec_order).push_back(node); AnfAlgo::SetStreamId(getnext_stream_id, (*exec_order)[(*exec_order).size() - 1].get()); - if (AnfAlgo::GetCNodeName(node) == kGetNextOpName) { + if (common::AnfAlgo::GetCNodeName(node) == kGetNextOpName) { getnext_cnode = node; break; } @@ -349,10 +352,10 @@ void KernelAdjust::InsertEosStreamSwitch(const std::shared_ptr(true), eos_switch_app); + common::AnfAlgo::SetNodeAttr(kStreamNeedActivedFirst, MakeValue(true), eos_switch_app); // update eos loop stream switch true_branch_stream attr - AnfAlgo::SetNodeAttr(kAttrTrueBranchStream, MakeValue(*eos_stream_id), eos_switch_app); - AnfAlgo::SetNodeAttr(kAttrStreamSwitchKind, MakeValue(kEosStreamSwitch), eos_switch_app); + common::AnfAlgo::SetNodeAttr(kAttrTrueBranchStream, MakeValue(*eos_stream_id), eos_switch_app); + common::AnfAlgo::SetNodeAttr(kAttrStreamSwitchKind, MakeValue(kEosStreamSwitch), eos_switch_app); (*exec_order).push_back(eos_switch_app); MS_LOG(INFO) << "EoS loop insert Stream Switch " << eos_switch_app->fullname_with_scope(); } @@ -529,11 +532,11 @@ CNodePtr KernelAdjust::CreateStreamSwitchOp(const std::shared_ptr(RT_LESS_OR_EQUAL); ValuePtr cond = MakeValue(condition); - AnfAlgo::SetNodeAttr(kAttrSwitchCondition, cond, stream_switch_app); + common::AnfAlgo::SetNodeAttr(kAttrSwitchCondition, cond, stream_switch_app); // set attr:data_type int data_type = static_cast(RT_SWITCH_INT64); ValuePtr dt = MakeValue(data_type); - AnfAlgo::SetNodeAttr(kAttrDataType, dt, stream_switch_app); + common::AnfAlgo::SetNodeAttr(kAttrDataType, dt, stream_switch_app); // set distinction label and graph id return stream_switch_app; } @@ -563,9 +566,9 @@ CNodePtr KernelAdjust::CreatTupleGetItemNode(const std::shared_ptrNewCNode({NewValueNode(prim::kPrimTupleGetItem), node, idx}); MS_EXCEPTION_IF_NULL(tuple_getitem); tuple_getitem->set_scope(node->scope()); - std::vector origin_shape = AnfAlgo::GetOutputInferShape(node, output_idx); - TypeId origin_type = AnfAlgo::GetOutputInferDataType(node, output_idx); - AnfAlgo::SetOutputInferTypeAndShape({origin_type}, {origin_shape}, tuple_getitem.get()); + std::vector origin_shape = common::AnfAlgo::GetOutputInferShape(node, output_idx); + TypeId origin_type = common::AnfAlgo::GetOutputInferDataType(node, output_idx); + common::AnfAlgo::SetOutputInferTypeAndShape({origin_type}, {origin_shape}, tuple_getitem.get()); return tuple_getitem; } @@ -594,10 +597,10 @@ CNodePtr KernelAdjust::CreateEndOfSequenceOP(const std::shared_ptr input_names = {"x"}; ValuePtr input_names_v = MakeValue(input_names); - AnfAlgo::SetNodeAttr("input_names", input_names_v, end_of_sequence_node); + common::AnfAlgo::SetNodeAttr("input_names", input_names_v, end_of_sequence_node); std::vector output_names = {"y"}; ValuePtr output_names_v = MakeValue(output_names); - AnfAlgo::SetNodeAttr("output_names", output_names_v, end_of_sequence_node); + common::AnfAlgo::SetNodeAttr("output_names", output_names_v, end_of_sequence_node); end_of_sequence_node->set_abstract(tuple_get_item->abstract()); return end_of_sequence_node; } @@ -628,14 +631,15 @@ CNodePtr KernelAdjust::CreateStreamAssignAddnOP(const std::shared_ptr output_names = {"output"}; ValuePtr input_names_v = MakeValue(input_names); ValuePtr output_names_v = MakeValue(output_names); - AnfAlgo::SetNodeAttr("input_names", input_names_v, assign_add_one); - AnfAlgo::SetNodeAttr("output_names", output_names_v, assign_add_one); + common::AnfAlgo::SetNodeAttr("input_names", input_names_v, assign_add_one); + common::AnfAlgo::SetNodeAttr("output_names", output_names_v, assign_add_one); selected_kernel_builder.SetKernelType(KernelType::TBE_KERNEL); MS_EXCEPTION_IF_NULL(switch_loop_input.at(kCurLoopCountName)); assign_add_one->set_abstract(switch_loop_input.at(kCurLoopCountName)->abstract()); // add AssignAdd op to kernel ref node map session::AnfWithOutIndex final_pair = std::make_pair(assign_add_one, 0); - session::KernelWithIndex kernel_with_index = AnfAlgo::VisitKernel(AnfAlgo::GetInputNode(assign_add_one, 0), 0); + session::KernelWithIndex kernel_with_index = + common::AnfAlgo::VisitKernel(common::AnfAlgo::GetInputNode(assign_add_one, 0), 0); kernel_graph_ptr->AddRefCorrespondPairs(final_pair, kernel_with_index); return assign_add_one; } @@ -738,7 +742,7 @@ CNodePtr KernelAdjust::CreateNPUAllocStatus(const std::shared_ptrset_scope(kDefaultScope); std::vector npu_output_shape = {kNPUShape}; - AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat32}, {npu_output_shape}, npu_alloc_cnode.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat32}, {npu_output_shape}, npu_alloc_cnode.get()); kernel::KernelBuildInfo::KernelBuildInfoBuilder selected_kernel_builder; selected_kernel_builder.SetFusionType(kernel::FusionType::OPAQUE); @@ -772,12 +776,13 @@ CNodePtr KernelAdjust::CreateAssignAdd(const std::shared_ptr output_names = {"output"}; ValuePtr input_names_v = MakeValue(input_names); ValuePtr output_names_v = MakeValue(output_names); - AnfAlgo::SetNodeAttr("input_names", input_names_v, assign_add_cnode); - AnfAlgo::SetNodeAttr("output_names", output_names_v, assign_add_cnode); + common::AnfAlgo::SetNodeAttr("input_names", input_names_v, assign_add_cnode); + common::AnfAlgo::SetNodeAttr("output_names", output_names_v, assign_add_cnode); selected_kernel_builder.SetKernelType(KernelType::TBE_KERNEL); session::AnfWithOutIndex final_pair = std::make_pair(assign_add_cnode, 0); - session::KernelWithIndex kernel_with_index = AnfAlgo::VisitKernel(AnfAlgo::GetInputNode(assign_add_cnode, 0), 0); + session::KernelWithIndex kernel_with_index = + common::AnfAlgo::VisitKernel(common::AnfAlgo::GetInputNode(assign_add_cnode, 0), 0); kernel_graph_ptr->AddRefCorrespondPairs(final_pair, kernel_with_index); return assign_add_cnode; } @@ -821,12 +826,13 @@ CNodePtr KernelAdjust::CreateAssign(const std::shared_ptr std::vector output_names = {"output"}; ValuePtr input_names_v = MakeValue(input_names); ValuePtr output_names_v = MakeValue(output_names); - AnfAlgo::SetNodeAttr("input_names", input_names_v, assign_cnode); - AnfAlgo::SetNodeAttr("output_names", output_names_v, assign_cnode); + common::AnfAlgo::SetNodeAttr("input_names", input_names_v, assign_cnode); + common::AnfAlgo::SetNodeAttr("output_names", output_names_v, assign_cnode); selected_kernel_builder.SetKernelType(KernelType::TBE_KERNEL); session::AnfWithOutIndex final_pair = std::make_pair(assign_cnode, 0); - session::KernelWithIndex kernel_with_index = AnfAlgo::VisitKernel(AnfAlgo::GetInputNode(assign_cnode, 0), 0); + session::KernelWithIndex kernel_with_index = + common::AnfAlgo::VisitKernel(common::AnfAlgo::GetInputNode(assign_cnode, 0), 0); kernel_graph_ptr->AddRefCorrespondPairs(final_pair, kernel_with_index); return assign_cnode; } diff --git a/mindspore/ccsrc/runtime/device/kernel_runtime.cc b/mindspore/ccsrc/runtime/device/kernel_runtime.cc index acdedc704bb..7ddd95043c9 100644 --- a/mindspore/ccsrc/runtime/device/kernel_runtime.cc +++ b/mindspore/ccsrc/runtime/device/kernel_runtime.cc @@ -21,16 +21,17 @@ #include #include "backend/common/optimizer/helper.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/session/kernel_graph.h" -#include "utils/ms_device_shape_transfer.h" +#include "runtime/device/ms_device_shape_transfer.h" #include "debug/data_dump/dump_json_parser.h" #include "frontend/operator/ops.h" #include "ir/value.h" #include "utils/ms_context.h" #include "utils/ms_utils.h" #include "utils/shape_utils.h" -#include "utils/utils.h" -#include "frontend/parallel/context.h" +#include "include/common/utils/utils.h" +#include "include/common/utils/parallel_context.h" #include "debug/env_config_parser.h" #if ((defined ENABLE_CPU) && (!defined _WIN32)) #include "ps/ps_cache/ps_cache_manager.h" @@ -50,10 +51,10 @@ std::vector GetGraphInputs(const session::KernelGraph &graph) { auto kernels = graph.execution_order(); for (auto &kernel : kernels) { MS_EXCEPTION_IF_NULL(kernel); - auto input_num = AnfAlgo::GetInputTensorNum(kernel); + auto input_num = common::AnfAlgo::GetInputTensorNum(kernel); for (size_t i = 0; i < input_num; ++i) { auto input_node = kernel->input(i + 1); - auto input_real_node = AnfAlgo::VisitKernelWithReturnType(input_node, 0).first; + auto input_real_node = common::AnfAlgo::VisitKernelWithReturnType(input_node, 0).first; MS_EXCEPTION_IF_NULL(input_real_node); if (input_real_node->isa() && inputs_set.find(input_real_node) == inputs_set.end()) { (void)inputs_set.insert(input_real_node); @@ -119,9 +120,9 @@ void KernelRuntime::GetCommunicationInputInfo(const AnfNodePtr &node, size_t *to MS_EXCEPTION_IF_NULL(total_size); MS_EXCEPTION_IF_NULL(address_list); MS_EXCEPTION_IF_NULL(align_size_list); - size_t input_num = AnfAlgo::GetInputTensorNum(node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(node); for (size_t i = 0; i < input_num; ++i) { - auto input_node_with_index = AnfAlgo::GetPrevNodeOutput(node, i, true); + auto input_node_with_index = common::AnfAlgo::GetPrevNodeOutput(node, i, true); auto input_node = input_node_with_index.first; MS_EXCEPTION_IF_NULL(input_node); DeviceAddressPtr address = nullptr; @@ -139,7 +140,7 @@ void KernelRuntime::GetCommunicationInputInfo(const AnfNodePtr &node, size_t *to } void KernelRuntime::AssignCommunicationInputFromMemoryPool(const AnfNodePtr &node) const { - if (!AnfAlgo::IsCommunicationOp(node)) { + if (!common::AnfAlgo::IsCommunicationOp(node)) { return; } MS_EXCEPTION_IF_NULL(node); @@ -190,7 +191,7 @@ void KernelRuntime::GetCommunicationOutputInfo(const AnfNodePtr &node, size_t *t } void KernelRuntime::AssignCommunicationOutputFromMemoryPool(const AnfNodePtr &node) const { - if (!AnfAlgo::IsCommunicationOp(node)) { + if (!common::AnfAlgo::IsCommunicationOp(node)) { return; } MS_EXCEPTION_IF_NULL(node); @@ -215,7 +216,7 @@ void KernelRuntime::RunOpMallocPre(const session::KernelGraph &graph, const auto &nodes = graph.execution_order(); // Malloc for Node output for (const auto &node : nodes) { - auto output_num = AnfAlgo::GetOutputTensorNum(node); + auto output_num = common::AnfAlgo::GetOutputTensorNum(node); for (size_t i = 0; i < output_num; ++i) { MS_EXCEPTION_IF_NULL(node); auto runtime_info = node->user_data(); @@ -242,7 +243,7 @@ void KernelRuntime::RunOpMallocPre(const session::KernelGraph &graph, if (!item->isa()) { continue; } - auto output_size = AnfAlgo::GetOutputTensorNum(item); + auto output_size = common::AnfAlgo::GetOutputTensorNum(item); for (size_t index = 0; index < output_size; index++) { auto current_tensor = input_tensors[input_index]; MS_EXCEPTION_IF_NULL(current_tensor); @@ -271,10 +272,10 @@ void KernelRuntime::ResetNodeAddress(const session::KernelGraph &kernel_graph) { for (auto &kernel : kernels) { auto kernel_mod = AnfAlgo::GetKernelMod(kernel); MS_EXCEPTION_IF_NULL(kernel_mod); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel); for (size_t j = 0; j < input_num; ++j) { auto input_index = AnfAlgo::GetRealInputIndex(kernel, j); - KernelWithIndex kernel_with_index = AnfAlgo::GetPrevNodeOutput(kernel, input_index, true); + KernelWithIndex kernel_with_index = common::AnfAlgo::GetPrevNodeOutput(kernel, input_index, true); auto index = kernel_with_index.second; auto &input_node = kernel_with_index.first; if (NodeOutputDeviceAddressExist(input_node, index)) { @@ -340,7 +341,7 @@ void KernelRuntime::RunOpClearMemory(const session::KernelGraph &graph) const { for (const auto &cnode : graph.execution_order()) { MS_EXCEPTION_IF_NULL(cnode); // clear output memory resource - size_t output_num = AnfAlgo::GetOutputTensorNum(cnode); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(cnode); for (size_t index = 0; index < output_num; ++index) { AnfAlgo::SetOutputAddr(nullptr, index, cnode.get()); } @@ -396,7 +397,7 @@ void KernelRuntime::RunOpAssignInputMemory(const std::vector if (!item->isa()) { continue; } - auto output_size = AnfAlgo::GetOutputTensorNum(item); + auto output_size = common::AnfAlgo::GetOutputTensorNum(item); for (size_t index = 0; index < output_size; index++) { auto current_tensor = input_tensors[input_index]; MS_EXCEPTION_IF_NULL(current_tensor); @@ -414,7 +415,7 @@ void KernelRuntime::RunOpAssignInputMemory(const std::vector } TypeId output_type_id = AnfAlgo::GetOutputDeviceDataType(item, index); if (output_type_id == kTypeUnknown) { - output_type_id = AnfAlgo::GetOutputInferDataType(item, index); + output_type_id = common::AnfAlgo::GetOutputInferDataType(item, index); } auto tensor_size = AnfAlgo::GetOutputTensorMemSize(item, index); // Device address new create @@ -463,7 +464,7 @@ void KernelRuntime::RunOpAssignOutputMemory(const AnfNodePtr &kernel, } continue; } - if (AnfAlgo::GetCNodeName(kernel) == kApplyMomentumOpName) { + if (common::AnfAlgo::GetCNodeName(kernel) == kApplyMomentumOpName) { auto device_address = AnfAlgo::GetPrevNodeMutableOutputAddr(kernel, i); AnfAlgo::SetOutputAddr(device_address, i, kernel.get()); continue; @@ -516,7 +517,7 @@ void KernelRuntime::RunOpAssignOutputNodeMemory(const ValuePtr &pre_output_value } // share output address with pre output tensors for (size_t i = 0; i < output_nodes.size(); ++i) { - auto output_node_with_index = AnfAlgo::VisitKernel(output_nodes[i], 0); + auto output_node_with_index = common::AnfAlgo::VisitKernel(output_nodes[i], 0); auto output_node = output_node_with_index.first; MS_EXCEPTION_IF_NULL(output_node); if (!output_node->isa()) { @@ -535,7 +536,7 @@ void KernelRuntime::RunOpAssignOutputNodeMemory(const ValuePtr &pre_output_value MS_LOG(INFO) << "The address of pre output tensor [" << i << "] is a nullptr!"; continue; } - if (opt::IsNopNode(real_output_cnode)) { + if (common::AnfAlgo::IsNopNode(real_output_cnode)) { if (real_output_cnode->inputs().size() < kMinInputSize) { MS_LOG(EXCEPTION) << "The input size of output node: " << real_output_cnode->DebugString() << " should large than one!"; @@ -582,8 +583,8 @@ void KernelRuntime::AssignStaticMemoryInput(const session::KernelGraph &graph) { if (i < graph_valid_input.size() && !graph_valid_input[i]) { continue; } - if (AnfAlgo::CheckPrimitiveType(input_node, prim::kPrimMakeTuple)) { - auto outs = AnfAlgo::GetAllOutput(input_node); + if (common::AnfAlgo::CheckPrimitiveType(input_node, prim::kPrimMakeTuple)) { + auto outs = common::AnfAlgo::GetAllOutput(input_node); for (auto &out : outs) { MS_EXCEPTION_IF_NULL(out); add_need_alloc_nodes(out); @@ -598,7 +599,7 @@ void KernelRuntime::AssignStaticMemoryInput(const session::KernelGraph &graph) { GetShadowBackendNodeMap(graph, &shadow_backend_node_map); for (auto &item : need_alloc_nodes) { MS_EXCEPTION_IF_NULL(item); - auto output_size = AnfAlgo::GetOutputTensorNum(item); + auto output_size = common::AnfAlgo::GetOutputTensorNum(item); for (size_t index = 0; index < output_size; index++) { TypeId output_type_id = AnfAlgo::GetOutputDeviceDataType(item, index); // if graph output is a weight and doesn't link to any cnode, it's data type will be unknown @@ -665,18 +666,18 @@ void KernelRuntime::GetDeviceAddress(const AnfNodePtr &item, void KernelRuntime::AssignStaticMemoryOutput(const session::KernelGraph &graph) { MS_LOG(INFO) << "AssignStaticMemoryOutput start for graph " << graph.graph_id(); - auto nodes = AnfAlgo::GetAllOutput(graph.output(), {prim::kPrimTupleGetItem}); + auto nodes = common::AnfAlgo::GetAllOutput(graph.output(), {prim::kPrimTupleGetItem}); std::vector non_communication_op; // Assign Communicate Op Memory firstly. for (const auto &node : nodes) { // Assign output address to nop node that the attribute of "skip_nop_op_addr" is false; - auto is_skip = !opt::IsNopNode(node) || AnfAlgo::IsNeedSkipNopOpAddr(node); - auto kernel_with_index = AnfAlgo::VisitKernelWithReturnType(node, 0, is_skip); + auto is_skip = !common::AnfAlgo::IsNopNode(node) || common::AnfAlgo::IsNeedSkipNopOpAddr(node); + auto kernel_with_index = common::AnfAlgo::VisitKernelWithReturnType(node, 0, is_skip); MS_EXCEPTION_IF_NULL(kernel_with_index.first); if (!kernel_with_index.first->isa() || !AnfUtils::IsRealKernel(kernel_with_index.first)) { continue; } - if (AnfAlgo::IsCommunicationOp(kernel_with_index.first)) { + if (common::AnfAlgo::IsCommunicationOp(kernel_with_index.first)) { AssignCommunicationNodeMem(kStaticMem, kernel_with_index.first); } else { non_communication_op.emplace_back(kernel_with_index); @@ -695,7 +696,7 @@ void KernelRuntime::UpdateRefNodeOutputMem(const session::KernelGraph &graph) { auto &kernels = graph.execution_order(); for (auto &kernel : kernels) { MS_EXCEPTION_IF_NULL(kernel); - auto output_num = AnfAlgo::GetOutputTensorNum(kernel); + auto output_num = common::AnfAlgo::GetOutputTensorNum(kernel); if (output_num == 0) { MS_LOG(DEBUG) << "This kernel has no output size."; continue; @@ -791,8 +792,8 @@ bool KernelRuntime::KernelMemNotReuse(const AnfNodePtr &node) { DeviceAddressPtr KernelRuntime::PreAssignCNodeMemory(const AnfNodePtr &anf_node, size_t index) const { MS_EXCEPTION_IF_NULL(anf_node); - if (opt::IsNopNode(anf_node)) { - auto input_node_with_index = AnfAlgo::GetPrevNodeOutput(anf_node, index); + if (common::AnfAlgo::IsNopNode(anf_node)) { + auto input_node_with_index = common::AnfAlgo::GetPrevNodeOutput(anf_node, index); return PreAssignCNodeMemory(input_node_with_index.first, input_node_with_index.second); } @@ -811,9 +812,9 @@ void KernelRuntime::AssignCommunicationNodeInputMem(MemType type, const AnfNodeP MS_EXCEPTION_IF_NULL(mem_manager_); size_t total_size = 0; std::vector> addr_size; - size_t input_num = AnfAlgo::GetInputTensorNum(node); + size_t input_num = common::AnfAlgo::GetInputTensorNum(node); for (size_t i = 0; i < input_num; ++i) { - auto input_node_with_index = AnfAlgo::GetPrevNodeOutput(node, i, true); + auto input_node_with_index = common::AnfAlgo::GetPrevNodeOutput(node, i, true); auto input_node = input_node_with_index.first; MS_EXCEPTION_IF_NULL(input_node); if (AnfAlgo::OutputAddrExist(input_node, input_node_with_index.second)) { @@ -847,7 +848,7 @@ void KernelRuntime::AssignCommunicationNodeInputMem(MemType type, const AnfNodeP return; } auto first_input_node = cnode->input(1); - auto prenode_index = AnfAlgo::VisitKernelWithReturnType(first_input_node, 0, true); + auto prenode_index = common::AnfAlgo::VisitKernelWithReturnType(first_input_node, 0, true); uint8_t *input_ptr = mem_manager_->MallocOutputMem(prenode_index.first, prenode_index.second, type, total_size, addr_size[0].first, true); for (const auto &iter : addr_size) { @@ -941,7 +942,7 @@ void KernelRuntime::AssignValueNodeTensor(const ValueNodePtr &value_node, const auto node_size = AnfAlgo::GetOutputTensorMemSize(value_node, output_idx); TypeId output_type_id = AnfAlgo::GetOutputDeviceDataType(value_node, output_idx); if (output_type_id == kTypeUnknown) { - output_type_id = AnfAlgo::GetOutputInferDataType(value_node, output_idx); + output_type_id = common::AnfAlgo::GetOutputInferDataType(value_node, output_idx); } auto output_format = AnfAlgo::GetOutputFormat(value_node, output_idx); DeviceAddressPtr address = @@ -964,7 +965,8 @@ void KernelRuntime::AssignValueNodeTensor(const ValueNodePtr &value_node, const tensor->data_c(), tensor->device_info().host_format_)) { MS_EXCEPTION(NotExistsError) << "ValueNode SyncHostToDevice fail!" << value_node->DebugString() << "node format is" << AnfAlgo::GetOutputFormat(value_node, output_idx) - << "node dtype is " << AnfAlgo::GetOutputInferDataType(value_node, output_idx); + << "node dtype is " + << common::AnfAlgo::GetOutputInferDataType(value_node, output_idx); } } } @@ -1065,7 +1067,7 @@ void KernelRuntime::AssignDynamicMemory(const session::KernelGraph &graph) { std::vector compute_nodes; // communication nodes first for (auto &node : execution_nodes) { - if (AnfAlgo::IsCommunicationOp(node)) { + if (common::AnfAlgo::IsCommunicationOp(node)) { // skip if the memory is already allocated AssignCommunicationNodeMem(mem_type, node); } else { @@ -1103,15 +1105,15 @@ void KernelRuntime::GenLaunchArgs(const mindspore::kernel::KernelMod &kernel_mod MS_EXCEPTION_IF_NULL(kernel_launch_info); auto cnode = kernel->cast(); MS_EXCEPTION_IF_NULL(cnode); - if (AnfAlgo::GetCNodeName(cnode) == kAtomicAddrCleanOpName) { + if (common::AnfAlgo::GetCNodeName(cnode) == kAtomicAddrCleanOpName) { return GenAddrCleanLaunchArgs(cnode, &(kernel_launch_info->inputs_)); } auto ms_context = MsContext::GetInstance(); MS_EXCEPTION_IF_NULL(ms_context); auto skip_nop_node = (ms_context->get_param(MS_CTX_EXECUTION_MODE) != kPynativeMode); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel); for (size_t i = 0; i < input_num; ++i) { - if (AnfAlgo::IsNoneInput(kernel, i)) { + if (common::AnfAlgo::IsNoneInput(kernel, i)) { continue; } auto real_input = AnfAlgo::GetRealInputIndex(kernel, i); @@ -1168,7 +1170,7 @@ void KernelRuntime::GenKernelEvents(const session::KernelGraph &graph) { auto &kernel_post_run_events = kernel_events.second; for (size_t i = 0; i < kernels.size(); ++i) { auto &kernel = kernels[i]; - if (!AnfAlgo::IsCommunicationOp(kernel)) { + if (!common::AnfAlgo::IsCommunicationOp(kernel)) { continue; } auto pre_event = CreateDeviceEvent(); @@ -1188,12 +1190,13 @@ void KernelRuntime::GenKernelEvents(const session::KernelGraph &graph) { for (size_t j = i + 1; j < kernels.size(); ++j) { auto &child = kernels[j]; MS_EXCEPTION_IF_NULL(child); - if (AnfAlgo::IsCommunicationOp(child)) { + if (common::AnfAlgo::IsCommunicationOp(child)) { continue; } auto input_size = child->inputs().size() - 1; for (size_t k = 0; k < input_size; ++k) { - auto kernel_index = AnfAlgo::VisitKernelWithReturnType(AnfAlgo::GetInputNode(child, k), 0, true); + auto kernel_index = + common::AnfAlgo::VisitKernelWithReturnType(common::AnfAlgo::GetInputNode(child, k), 0, true); if (kernel_index.first == kernel) { found_nearest_child = true; break; @@ -1221,11 +1224,11 @@ void KernelRuntime::GenAddrCleanLaunchArgs(const CNodePtr &cnode, AddressPtrList MS_EXCEPTION_IF_NULL(cnode->inputs()[1]); auto pre_node = (cnode->inputs()[1])->cast(); // set clean output address - if (AnfAlgo::HasNodeAttr(kAttrAtomicOutputIndexs, pre_node)) { + if (common::AnfAlgo::HasNodeAttr(kAttrAtomicOutputIndexs, pre_node)) { #if defined(__APPLE__) - auto clean_output_indexes = AnfAlgo::GetNodeAttr>(pre_node, kAttrAtomicOutputIndexs); + auto clean_output_indexes = common::AnfAlgo::GetNodeAttr>(pre_node, kAttrAtomicOutputIndexs); #else - auto clean_output_indexes = AnfAlgo::GetNodeAttr>(pre_node, kAttrAtomicOutputIndexs); + auto clean_output_indexes = common::AnfAlgo::GetNodeAttr>(pre_node, kAttrAtomicOutputIndexs); #endif for (auto index : clean_output_indexes) { auto device_address = AnfAlgo::GetOutputAddr(pre_node, index); @@ -1243,11 +1246,13 @@ void KernelRuntime::GenAddrCleanLaunchArgs(const CNodePtr &cnode, AddressPtrList MS_LOG(DEBUG) << "AtomicAddClean clean output size:" << clean_output_indexes.size(); } // set clean workspace address - if (AnfAlgo::HasNodeAttr(kAttrAtomicWorkspaceIndexs, pre_node)) { + if (common::AnfAlgo::HasNodeAttr(kAttrAtomicWorkspaceIndexs, pre_node)) { #if defined(__APPLE__) - auto clean_workspaces_indexes = AnfAlgo::GetNodeAttr>(pre_node, kAttrAtomicWorkspaceIndexs); + auto clean_workspaces_indexes = + common::AnfAlgo::GetNodeAttr>(pre_node, kAttrAtomicWorkspaceIndexs); #else - auto clean_workspaces_indexes = AnfAlgo::GetNodeAttr>(pre_node, kAttrAtomicWorkspaceIndexs); + auto clean_workspaces_indexes = + common::AnfAlgo::GetNodeAttr>(pre_node, kAttrAtomicWorkspaceIndexs); #endif for (const auto &index : clean_workspaces_indexes) { auto device_address = AnfAlgo::GetWorkspaceAddr(pre_node, index); @@ -1326,16 +1331,16 @@ void KernelRuntime::AssignKernelAddress(const std::shared_ptr &mem MS_EXCEPTION_IF_NULL(kernel_launch_info); auto cnode = kernel->cast(); MS_EXCEPTION_IF_NULL(cnode); - if (AnfAlgo::GetCNodeName(cnode) == kAtomicAddrCleanOpName) { + if (common::AnfAlgo::GetCNodeName(cnode) == kAtomicAddrCleanOpName) { return GenAddrCleanLaunchArgs(cnode, &(kernel_launch_info->inputs_), mem_scheduler); } auto kernel_mod = AnfAlgo::GetKernelMod(kernel); MS_EXCEPTION_IF_NULL(kernel_mod); - size_t input_num = AnfAlgo::GetInputTensorNum(kernel); - const auto update_parameter = AnfAlgo::IsUpdateParameterKernel(cnode); + size_t input_num = common::AnfAlgo::GetInputTensorNum(kernel); + const auto update_parameter = common::AnfAlgo::IsUpdateParameterKernel(cnode); for (size_t j = 0; j < input_num; ++j) { auto real_input = AnfAlgo::GetRealInputIndex(kernel, j); - auto kernel_with_index = AnfAlgo::GetPrevNodeOutput(kernel, real_input, true); + auto kernel_with_index = common::AnfAlgo::GetPrevNodeOutput(kernel, real_input, true); auto index = kernel_with_index.second; auto &input_node = kernel_with_index.first; auto device_address = AnfAlgo::GetOutputAddr(input_node, index, true); @@ -1378,7 +1383,7 @@ void KernelRuntime::SyncNodeOutputTensors(const std::shared_ptr &m auto kernel_mod = AnfAlgo::GetKernelMod(kernel); MS_EXCEPTION_IF_NULL(kernel_mod); for (size_t input_idx = 0; input_idx < kernel_mod->GetInputSizeList().size(); ++input_idx) { - const auto input_node_index = AnfAlgo::GetPrevNodeOutput(kernel, input_idx, true); + const auto input_node_index = common::AnfAlgo::GetPrevNodeOutput(kernel, input_idx, true); if (input_node_index.first != nullptr && input_node_index.first->isa()) { SyncNodeOutputTensor(mem_scheduler, input_node_index, graph); } @@ -1459,7 +1464,7 @@ void KernelRuntime::InitGraphInputTensors(const std::shared_ptr &m } MemPriority priority = kMemPriorityLow; const auto ¶meter = input_node->cast(); - if (AnfAlgo::IsParameterWeight(parameter) || graph.IsUpdatedParameter(parameter)) { + if (common::AnfAlgo::IsParameterWeight(parameter) || graph.IsUpdatedParameter(parameter)) { priority = kMemPriorityHigh; } mem_scheduler->Init(device_address.get(), tensor->data_c(), tensor_size, priority); @@ -1469,7 +1474,7 @@ void KernelRuntime::InitGraphInputTensors(const std::shared_ptr &m void KernelRuntime::AssignCommunicationMem(const session::KernelGraph &graph) { for (const auto &kernel : graph.execution_order()) { - if (!AnfAlgo::IsCommunicationOp(kernel)) { + if (!common::AnfAlgo::IsCommunicationOp(kernel)) { continue; } AssignCommunicationInputFromMemoryPool(kernel); @@ -1485,7 +1490,7 @@ bool KernelRuntime::LaunchKernel(const session::KernelGraph &graph, const AnfNod KernelLaunchInfo kernel_launch_info; auto stream = kernel_mod->stream(); if (stream == nullptr) { - if (AnfAlgo::IsCommunicationOp(kernel)) { + if (common::AnfAlgo::IsCommunicationOp(kernel)) { stream = communication_stream_; } else { stream = stream_; @@ -1499,7 +1504,8 @@ bool KernelRuntime::LaunchKernel(const session::KernelGraph &graph, const AnfNod } AssignKernelAddress(mem_scheduler, kernel, &kernel_launch_info); auto cnode = kernel->cast(); - if (mock && AnfAlgo::HasNodeAttr(kAttrOffload, cnode) && AnfAlgo::GetNodeAttr(cnode, kAttrOffload)) { + if (mock && common::AnfAlgo::HasNodeAttr(kAttrOffload, cnode) && + common::AnfAlgo::GetNodeAttr(cnode, kAttrOffload)) { for (size_t i = 0; i < kernel_mod->GetOutputSizeList().size(); ++i) { auto device_address = AnfAlgo::GetOutputAddr(kernel, i, true); mem_scheduler->SetOffload(device_address); @@ -1580,8 +1586,8 @@ bool KernelRuntime::LaunchKernelMod(const session::KernelGraph &graph, bool mock // Skip transpose kernel with "nop_op" attr which is not hidden or removed in PyNative infer scenario. Transpose // kernel, which is not supposed to be executed, is generated in TransDataSplit to support specific Transdata. // And hard code here should be removed after new Transdata programme is implemented in the foreseeable future. - if (AnfAlgo::HasNodeAttr(kAttrNopOp, kernel)) { - for (size_t idx = 0; idx < AnfAlgo::GetOutputTensorNum(kernel); idx += 1) { + if (common::AnfAlgo::HasNodeAttr(kAttrNopOp, kernel)) { + for (size_t idx = 0; idx < common::AnfAlgo::GetOutputTensorNum(kernel); idx += 1) { auto real_input = AnfAlgo::GetRealInputIndex(kernel, idx); auto device_address = AnfAlgo::GetPrevNodeMutableOutputAddr(kernel, real_input); AnfAlgo::SetOutputAddr(device_address, idx, kernel.get()); @@ -1622,7 +1628,7 @@ void KernelRuntime::SyncParameter(const session::KernelGraph &graph, MS_EXCEPTION_IF_NULL(device_address); auto parameter = input_node->cast(); MS_EXCEPTION_IF_NULL(parameter); - if (!AnfAlgo::IsParameterWeight(parameter) && !graph.IsUpdatedParameter(parameter)) { + if (!common::AnfAlgo::IsParameterWeight(parameter) && !graph.IsUpdatedParameter(parameter)) { continue; } auto tensor = input_tensors[i]; @@ -1698,12 +1704,12 @@ void KernelRuntime::GetFirstPSEmbeddingCache(const session::KernelGraph &graph, size_t *const first_cache_size) { for (const auto &kernel : graph.execution_order()) { MS_EXCEPTION_IF_NULL(kernel); - auto kernel_name = AnfAlgo::GetCNodeName(kernel); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel); if (kernel_name != kGatherV2OpName && kernel_name != kSparseGatherV2OpName) { continue; } - auto input_param = AnfAlgo::GetPrevNodeOutput(kernel, 0, true); - auto input_index = AnfAlgo::GetPrevNodeOutput(kernel, 1, true); + auto input_param = common::AnfAlgo::GetPrevNodeOutput(kernel, 0, true); + auto input_index = common::AnfAlgo::GetPrevNodeOutput(kernel, 1, true); MS_EXCEPTION_IF_NULL(input_param.first); MS_EXCEPTION_IF_NULL(input_index.first); auto param_name = input_param.first->fullname_with_scope(); @@ -1711,18 +1717,19 @@ void KernelRuntime::GetFirstPSEmbeddingCache(const session::KernelGraph &graph, continue; } auto size = ps::ps_cache_instance.QueryHashTableSize(param_name); - while (input_index.first->isa() && (AnfAlgo::GetCNodeName(input_index.first) == kCastOpName)) { - input_index = AnfAlgo::GetPrevNodeOutput(input_index.first, 0, true); + while (input_index.first->isa() && (common::AnfAlgo::GetCNodeName(input_index.first) == kCastOpName)) { + input_index = common::AnfAlgo::GetPrevNodeOutput(input_index.first, 0, true); MS_EXCEPTION_IF_NULL(input_index.first); } - auto cnode = - AnfAlgo::IsGraphKernel(input_index.first) ? AnfAlgo::GetOutputOfGraphkernel(input_index) : input_index.first; + auto cnode = common::AnfAlgo::IsGraphKernel(input_index.first) + ? common::AnfAlgo::GetOutputOfGraphkernel(input_index) + : input_index.first; MS_EXCEPTION_IF_NULL(cnode); if (!cnode->isa()) { FinalizePsCache("The embeddingLookup whose input index should be a CNode but got " + cnode->fullname_with_scope()); } - auto input_index_node_name = AnfAlgo::GetCNodeName(cnode); + auto input_index_node_name = common::AnfAlgo::GetCNodeName(cnode); if (input_index_node_name != kGetNextOpName) { bool full_batch = parallel::ParallelContext::GetInstance()->full_batch(); if ((!full_batch && (input_index_node_name != kUniqueOpName)) || @@ -1744,23 +1751,23 @@ void KernelRuntime::GetFirstPSEmbeddingCache(const session::KernelGraph &graph, void KernelRuntime::CheckSparsePSEmbeddingCache(const CNodePtr &node) { MS_EXCEPTION_IF_NULL(node); - auto pre_node = AnfAlgo::GetPrevNodeOutput(node, 1, true); + auto pre_node = common::AnfAlgo::GetPrevNodeOutput(node, 1, true); MS_EXCEPTION_IF_NULL(pre_node.first); - while (pre_node.first->isa() && (AnfAlgo::GetCNodeName(pre_node.first) != kUniqueOpName)) { - pre_node = AnfAlgo::GetPrevNodeOutput(pre_node.first, 0, true); + while (pre_node.first->isa() && (common::AnfAlgo::GetCNodeName(pre_node.first) != kUniqueOpName)) { + pre_node = common::AnfAlgo::GetPrevNodeOutput(pre_node.first, 0, true); MS_EXCEPTION_IF_NULL(pre_node.first); } - if (!(pre_node.first->isa()) || (AnfAlgo::GetCNodeName(pre_node.first) != kUniqueOpName)) { + if (!(pre_node.first->isa()) || (common::AnfAlgo::GetCNodeName(pre_node.first) != kUniqueOpName)) { FinalizePsCache("The input_indices of kernel[SparseGatherV2] must be unique in parameter server cache mode"); } - pre_node = AnfAlgo::GetPrevNodeOutput(pre_node.first, 0, true); + pre_node = common::AnfAlgo::GetPrevNodeOutput(pre_node.first, 0, true); MS_EXCEPTION_IF_NULL(pre_node.first); - while (pre_node.first->isa() && (AnfAlgo::GetCNodeName(pre_node.first) == kCastOpName)) { - pre_node = AnfAlgo::GetPrevNodeOutput(pre_node.first, 0, true); + while (pre_node.first->isa() && (common::AnfAlgo::GetCNodeName(pre_node.first) == kCastOpName)) { + pre_node = common::AnfAlgo::GetPrevNodeOutput(pre_node.first, 0, true); MS_EXCEPTION_IF_NULL(pre_node.first); } - if (!(pre_node.first->isa()) || (AnfAlgo::GetCNodeName(pre_node.first) != kGetNextOpName)) { + if (!(pre_node.first->isa()) || (common::AnfAlgo::GetCNodeName(pre_node.first) != kGetNextOpName)) { FinalizePsCache( "The input indices of kernel[Unique] must be produced from dataset directly and the indices value can not be " "changed before delivering to kernel[Unique] in parameter server cache mode."); @@ -1774,12 +1781,12 @@ void KernelRuntime::CheckIfSupportPSEmbeddingCache(const session::KernelGraph &g MS_EXCEPTION_IF_NULL(first_cache_input_index); for (const auto &kernel : graph.execution_order()) { MS_EXCEPTION_IF_NULL(kernel); - auto kernel_name = AnfAlgo::GetCNodeName(kernel); + auto kernel_name = common::AnfAlgo::GetCNodeName(kernel); if (kernel_name != kGatherV2OpName && kernel_name != kSparseGatherV2OpName) { continue; } - auto input_param = AnfAlgo::GetPrevNodeOutput(kernel, 0, true); - auto input_index = AnfAlgo::GetPrevNodeOutput(kernel, 1, true); + auto input_param = common::AnfAlgo::GetPrevNodeOutput(kernel, 0, true); + auto input_index = common::AnfAlgo::GetPrevNodeOutput(kernel, 1, true); MS_EXCEPTION_IF_NULL(input_param.first); MS_EXCEPTION_IF_NULL(input_index.first); if (!input_param.first->isa()) { @@ -1789,12 +1796,13 @@ void KernelRuntime::CheckIfSupportPSEmbeddingCache(const session::KernelGraph &g if (ps::ps_cache_instance.IsHashTable(param_name) && (kernel_name == kSparseGatherV2OpName)) { CheckSparsePSEmbeddingCache(kernel); } - while (input_index.first->isa() && (AnfAlgo::GetCNodeName(input_index.first) == kCastOpName)) { - input_index = AnfAlgo::GetPrevNodeOutput(input_index.first, 0, true); + while (input_index.first->isa() && (common::AnfAlgo::GetCNodeName(input_index.first) == kCastOpName)) { + input_index = common::AnfAlgo::GetPrevNodeOutput(input_index.first, 0, true); MS_EXCEPTION_IF_NULL(input_index.first); } - auto cnode = - AnfAlgo::IsGraphKernel(input_index.first) ? AnfAlgo::GetOutputOfGraphkernel(input_index) : input_index.first; + auto cnode = common::AnfAlgo::IsGraphKernel(input_index.first) + ? common::AnfAlgo::GetOutputOfGraphkernel(input_index) + : input_index.first; MS_EXCEPTION_IF_NULL(cnode); if (cnode == first_cache_input_index) { if (!ps::ps_cache_instance.IsHashTable(param_name)) { @@ -1815,7 +1823,7 @@ void KernelRuntime::CheckIfSupportPSEmbeddingCache(const session::KernelGraph &g FinalizePsCache( "The embeddingLookup whose input index isn't from dataset doesn't support cache in parameter server training " "mode."); - } else if (cnode->isa() && (AnfAlgo::GetCNodeName(cnode) == kGetNextOpName)) { + } else if (cnode->isa() && (common::AnfAlgo::GetCNodeName(cnode) == kGetNextOpName)) { MS_LOG(ERROR) << "The EmbeddingLookup kernel(" << kernel->fullname_with_scope() << ") doesn't enable cache."; FinalizePsCache( "All EmbeddingLookup kernels whose input indices are from dataset must enable cache at " diff --git a/mindspore/ccsrc/runtime/device/kernel_runtime.h b/mindspore/ccsrc/runtime/device/kernel_runtime.h index 191646b5c3e..5caadcd05c3 100644 --- a/mindspore/ccsrc/runtime/device/kernel_runtime.h +++ b/mindspore/ccsrc/runtime/device/kernel_runtime.h @@ -24,12 +24,13 @@ #include #include "runtime/device/device_address.h" #include "ir/tensor.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" #ifdef ENABLE_DEBUGGER #include "debug/debugger/debugger.h" #endif #include "backend/common/session/kernel_graph.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "kernel/kernel.h" #include "utils/ms_context.h" #include "runtime/device/memory_manager.h" diff --git a/mindspore/ccsrc/runtime/device/launch_mul.cc b/mindspore/ccsrc/runtime/device/launch_mul.cc index 803ff627ce3..94cd1a1ab94 100644 --- a/mindspore/ccsrc/runtime/device/launch_mul.cc +++ b/mindspore/ccsrc/runtime/device/launch_mul.cc @@ -17,7 +17,9 @@ #include "runtime/device/launch_mul.h" #include "abstract/utils.h" #include "backend/common/session/single_kernel_graph.h" -#include "frontend/parallel/context.h" +#include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" +#include "include/common/utils/parallel_context.h" namespace mindspore::device { std::shared_ptr LaunchMul::ObtainMulKernelGraph() { diff --git a/mindspore/ccsrc/runtime/device/memory_manager.cc b/mindspore/ccsrc/runtime/device/memory_manager.cc index cc76782a399..cc1b4e8971e 100644 --- a/mindspore/ccsrc/runtime/device/memory_manager.cc +++ b/mindspore/ccsrc/runtime/device/memory_manager.cc @@ -17,6 +17,7 @@ #include "runtime/device/memory_manager.h" #include #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "debug/common.h" #ifdef ENABLE_DUMP_IR #include "debug/rdr/running_data_recorder.h" diff --git a/mindspore/ccsrc/utils/ms_device_shape_transfer.cc b/mindspore/ccsrc/runtime/device/ms_device_shape_transfer.cc similarity index 99% rename from mindspore/ccsrc/utils/ms_device_shape_transfer.cc rename to mindspore/ccsrc/runtime/device/ms_device_shape_transfer.cc index cbf6b694580..67ef56be7d9 100644 --- a/mindspore/ccsrc/utils/ms_device_shape_transfer.cc +++ b/mindspore/ccsrc/runtime/device/ms_device_shape_transfer.cc @@ -13,11 +13,12 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -#include "utils/ms_device_shape_transfer.h" +#include "runtime/device/ms_device_shape_transfer.h" #include #include #include #include + namespace mindspore { namespace trans { const int b1 = 1; @@ -232,7 +233,7 @@ ShapeVector GetRuntimePaddingShape(const AnfNodePtr &node, size_t index) { host_shape.push_back(1); } } else { - host_shape = AnfAlgo::GetOutputInferShape(node, index); + host_shape = common::AnfAlgo::GetOutputInferShape(node, index); } auto format = AnfAlgo::GetOutputFormat(node, index); if (IsNeedPadding(format, host_shape.size())) { @@ -369,7 +370,7 @@ ShapeVector DeviceShapeTransfer::GetDeviceShapeByFormat(const ShapeVector &shape } int64_t groups = 1; if (format == kOpFormat_FRAC_Z) { - groups = AnfAlgo::GetAttrGroups(node, index); + groups = common::AnfAlgo::GetAttrGroups(node, index); } ShapeVector input_hidden_size = {kAlign16, kAlign16}; if (format == kOpFormat_FRACTAL_ZN_RNN || format == kOpFormat_ND_RNN_BIAS) { @@ -395,11 +396,11 @@ std::optional DeviceShapeTransfer::GetFixedDeviceShape(const ShapeV } auto attr_name = is_output ? kAttrFixedOutputDeviceShape : kAttrFixedInputDeviceShape; auto cnode = node->cast(); - if (!AnfAlgo::HasNodeAttr(attr_name, cnode)) { + if (!common::AnfAlgo::HasNodeAttr(attr_name, cnode)) { return {}; } - auto shapes = AnfAlgo::GetNodeAttr>(cnode, attr_name); + auto shapes = common::AnfAlgo::GetNodeAttr>(cnode, attr_name); if (index >= shapes.size()) { MS_LOG(INFO) << "Index is out of range, got index: " << index << ", shape size: " << shapes.size(); return {}; @@ -779,14 +780,14 @@ ShapeVector DeviceShapeTransfer::GetAttrInputAndHiddenSize(const AnfNodePtr &nod input_hidden_size[1] = param->hidden_size(); } else { CNodePtr cnode = node->cast(); - if (cnode == nullptr || !AnfAlgo::HasNodeAttr(kAttrHiddenSize, cnode) || - !AnfAlgo::HasNodeAttr(kAttrInputSize, cnode)) { + if (cnode == nullptr || !common::AnfAlgo::HasNodeAttr(kAttrHiddenSize, cnode) || + !common::AnfAlgo::HasNodeAttr(kAttrInputSize, cnode)) { MS_LOG(EXCEPTION) << "Node with format FRACTAL_ZN_RNN or ND_RNN_BIAS should have hidden_size or input_size attr. Node info:" << node->DebugString(); } - input_hidden_size[0] = AnfAlgo::GetNodeAttr(cnode, kAttrInputSize); - input_hidden_size[1] = AnfAlgo::GetNodeAttr(cnode, kAttrHiddenSize); + input_hidden_size[0] = common::AnfAlgo::GetNodeAttr(cnode, kAttrInputSize); + input_hidden_size[1] = common::AnfAlgo::GetNodeAttr(cnode, kAttrHiddenSize); } return input_hidden_size; } @@ -816,7 +817,7 @@ bool FormatTransfer::TransDataByFormat(const FormatArgs &args, void *result, con bool is_forward) { int64_t groups = 1; if (args.device_format == kOpFormat_FRAC_Z && node != nullptr) { - groups = AnfAlgo::GetAttrGroups(node, index); + groups = common::AnfAlgo::GetAttrGroups(node, index); } if (is_forward) { return TransDataForwardCore(args, result, groups); diff --git a/mindspore/ccsrc/utils/ms_device_shape_transfer.h b/mindspore/ccsrc/runtime/device/ms_device_shape_transfer.h similarity index 98% rename from mindspore/ccsrc/utils/ms_device_shape_transfer.h rename to mindspore/ccsrc/runtime/device/ms_device_shape_transfer.h index 1b647fbc289..5784168e71f 100644 --- a/mindspore/ccsrc/utils/ms_device_shape_transfer.h +++ b/mindspore/ccsrc/runtime/device/ms_device_shape_transfer.h @@ -13,8 +13,8 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -#ifndef MINDSPORE_CCSRC_UTILS_MS_DEVICE_SHAPE_TRANSFER_H_ -#define MINDSPORE_CCSRC_UTILS_MS_DEVICE_SHAPE_TRANSFER_H_ +#ifndef MINDSPORE_CCSRC_RUNTIME_DEVICE_MS_DEVICE_SHAPE_TRANSFER_H_ +#define MINDSPORE_CCSRC_RUNTIME_DEVICE_MS_DEVICE_SHAPE_TRANSFER_H_ #include #include #include @@ -30,13 +30,14 @@ #include "ir/dtype/type.h" #include "utils/shape_utils.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "utils/ms_utils.h" #include "abstract/utils.h" #include "plugin/device/ascend/kernel/tbe/tbe_dynaminc_shape_util.h" #include "runtime/device/convert_tensor_utils.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" #include "utils/log_adapter.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace trans { @@ -474,4 +475,4 @@ std::vector TransShapeToDevice(const std::vector &shape, const std::string } // namespace trans } // namespace mindspore -#endif // MINDSPORE_CCSRC_UTILS_MS_DEVICE_SHAPE_TRANSFER_H_ +#endif // MINDSPORE_CCSRC_RUNTIME_DEVICE_MS_DEVICE_SHAPE_TRANSFER_H_ diff --git a/mindspore/ccsrc/runtime/device/tensor_array.h b/mindspore/ccsrc/runtime/device/tensor_array.h index 6281a5f770d..bacb9cee57e 100644 --- a/mindspore/ccsrc/runtime/device/tensor_array.h +++ b/mindspore/ccsrc/runtime/device/tensor_array.h @@ -22,6 +22,7 @@ #include #include "backend/common/session/kernel_graph.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "kernel/kernel.h" namespace mindspore { diff --git a/mindspore/ccsrc/runtime/device/tensor_array_manager.h b/mindspore/ccsrc/runtime/device/tensor_array_manager.h index fa1c0e0dab0..0660429887f 100644 --- a/mindspore/ccsrc/runtime/device/tensor_array_manager.h +++ b/mindspore/ccsrc/runtime/device/tensor_array_manager.h @@ -24,6 +24,7 @@ #include #include "backend/common/session/kernel_graph.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "runtime/device/tensor_array.h" namespace mindspore { diff --git a/mindspore/ccsrc/runtime/graph_scheduler/actor/actor_common.cc b/mindspore/ccsrc/runtime/graph_scheduler/actor/actor_common.cc index 33fc861e76e..d39130ded2d 100644 --- a/mindspore/ccsrc/runtime/graph_scheduler/actor/actor_common.cc +++ b/mindspore/ccsrc/runtime/graph_scheduler/actor/actor_common.cc @@ -17,6 +17,7 @@ #include "runtime/graph_scheduler/actor/actor_common.h" #include "runtime/graph_scheduler/device_tensor_store.h" #include "utils/ms_context.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace runtime { @@ -54,7 +55,7 @@ bool IsDeviceQueueDSActor(const AnfNodePtr &node, GraphExecutionStrategy strateg return false; } - if (node->isa() && (AnfAlgo::GetCNodeName(node) == kGetNextOpName)) { + if (node->isa() && (common::AnfAlgo::GetCNodeName(node) == kGetNextOpName)) { return true; } return false; @@ -64,7 +65,7 @@ bool IsHostQueueDSActor(const AnfNodePtr &node, const KernelGraphPtr &graph, const std::vector &host_parameters, GraphExecutionStrategy strategy) { MS_EXCEPTION_IF_NULL(node); - bool is_parameter_data = node->isa() && (!AnfAlgo::IsParameterWeight(node->cast())); + bool is_parameter_data = node->isa() && (!common::AnfAlgo::IsParameterWeight(node->cast())); if (!is_parameter_data) { return false; } @@ -92,12 +93,12 @@ bool IsHostQueueDSActor(const AnfNodePtr &node, const KernelGraphPtr &graph, return false; } -bool IsSwitchActor(const AnfNodePtr &node) { return AnfAlgo::CheckPrimitiveType(node, prim::kPrimSwitch); } +bool IsSwitchActor(const AnfNodePtr &node) { return common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimSwitch); } bool IsInternalParameter(const AnfNodePtr &node, const KernelGraphPtr &graph) { MS_EXCEPTION_IF_NULL(node); MS_EXCEPTION_IF_NULL(graph); - if (node->isa() && (!AnfAlgo::IsParameterWeight(node->cast()))) { + if (node->isa() && (!common::AnfAlgo::IsParameterWeight(node->cast()))) { // Judge whether node is internal parameter. const auto &front_node = graph->GetFrontNodeByInternalParameter(node); if (front_node.first != nullptr) { @@ -117,12 +118,12 @@ bool IsKernelActor(const AnfNodePtr &node, GraphExecutionStrategy strategy) { return true; } - return (AnfAlgo::GetCNodeName(node) != kGetNextOpName); + return (common::AnfAlgo::GetCNodeName(node) != kGetNextOpName); } bool IsSkippedKernelActor(const AnfNodePtr &node) { MS_EXCEPTION_IF_NULL(node); - if (IsKernelActor(node) && AnfAlgo::IsInplaceNode(node, "skip")) { + if (IsKernelActor(node) && common::AnfAlgo::IsInplaceNode(node, "skip")) { return true; } return false; @@ -130,8 +131,8 @@ bool IsSkippedKernelActor(const AnfNodePtr &node) { bool IsRpcActor(const AnfNodePtr &node) { MS_EXCEPTION_IF_NULL(node); - if (IsKernelActor(node) && - (AnfAlgo::GetCNodeName(node) == kRpcSendOpName || AnfAlgo::GetCNodeName(node) == kRpcRecvOpName)) { + if (IsKernelActor(node) && (common::AnfAlgo::GetCNodeName(node) == kRpcSendOpName || + common::AnfAlgo::GetCNodeName(node) == kRpcRecvOpName)) { return true; } return false; @@ -142,7 +143,7 @@ bool IsPersistentDeviceTensor(const AnfNodePtr &node) { if (node->isa()) { return true; } - if (node->isa() && AnfAlgo::IsParameterWeight(node->cast())) { + if (node->isa() && common::AnfAlgo::IsParameterWeight(node->cast())) { return true; } return false; @@ -329,7 +330,7 @@ std::set FetchModifiableRefOutputIndex(const CNodePtr &cnode, const Kern MS_EXCEPTION_IF_NULL(graph); std::set ref_output_indexes; - auto output_num = AnfAlgo::GetOutputTensorNum(cnode); + auto output_num = common::AnfAlgo::GetOutputTensorNum(cnode); for (size_t i = 0; i < output_num; ++i) { session::AnfWithOutIndex output_pair(cnode, i); // Only the ref node will modify the ref input corresponding to the output. diff --git a/mindspore/ccsrc/runtime/graph_scheduler/actor/actor_common.h b/mindspore/ccsrc/runtime/graph_scheduler/actor/actor_common.h index 9899c6f71f7..76ba1705276 100644 --- a/mindspore/ccsrc/runtime/graph_scheduler/actor/actor_common.h +++ b/mindspore/ccsrc/runtime/graph_scheduler/actor/actor_common.h @@ -27,10 +27,11 @@ #include "mindrt/include/actor/op_actor.h" #include "runtime/device/device_address.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/session/kernel_graph.h" #include "utils/log_adapter.h" #include "ir/tensor.h" -#include "utils/ms_device_shape_transfer.h" +#include "runtime/device/ms_device_shape_transfer.h" namespace mindspore { namespace runtime { diff --git a/mindspore/ccsrc/runtime/graph_scheduler/actor/actor_dump.cc b/mindspore/ccsrc/runtime/graph_scheduler/actor/actor_dump.cc index 44a57a17164..28baf524336 100644 --- a/mindspore/ccsrc/runtime/graph_scheduler/actor/actor_dump.cc +++ b/mindspore/ccsrc/runtime/graph_scheduler/actor/actor_dump.cc @@ -102,9 +102,9 @@ void DumpDSActor(const DataSourceActor *actor, std::ofstream &ofs) { const auto &data_kernel = device_queue_ds_actor->data_kernel(); MS_EXCEPTION_IF_NULL(data_kernel); ofs << "\t\tdata_kernel_name:" << data_kernel->fullname_with_scope() - << "\tinput_number:" << AnfAlgo::GetInputTensorNum(data_kernel) - << "\toutput_number:" << AnfAlgo::GetOutputTensorNum(data_kernel) << "\n"; - for (size_t i = 0; i < AnfAlgo::GetOutputTensorNum(data_kernel); ++i) { + << "\tinput_number:" << common::AnfAlgo::GetInputTensorNum(data_kernel) + << "\toutput_number:" << common::AnfAlgo::GetOutputTensorNum(data_kernel) << "\n"; + for (size_t i = 0; i < common::AnfAlgo::GetOutputTensorNum(data_kernel); ++i) { const auto &device_tensor = AnfAlgo::GetMutableOutputAddr(data_kernel, i, false); MS_EXCEPTION_IF_NULL(device_tensor); ofs << "\t\t\toutput_index:" << i << "\tptr:" << device_tensor->GetPtr() << "\tsize:" << device_tensor->GetSize() @@ -138,9 +138,10 @@ void DumpKernelActor(const KernelActor *actor, std::ofstream &ofs) { const auto &kernel = actor->kernel(); MS_EXCEPTION_IF_NULL(kernel); - ofs << "\t\tkernel_name:" << kernel->fullname_with_scope() << "\tinputs_num:" << AnfAlgo::GetInputTensorNum(kernel) - << "\toutputs_num:" << AnfAlgo::GetOutputTensorNum(kernel) << "\n"; - for (size_t i = 0; i < AnfAlgo::GetOutputTensorNum(kernel); ++i) { + ofs << "\t\tkernel_name:" << kernel->fullname_with_scope() + << "\tinputs_num:" << common::AnfAlgo::GetInputTensorNum(kernel) + << "\toutputs_num:" << common::AnfAlgo::GetOutputTensorNum(kernel) << "\n"; + for (size_t i = 0; i < common::AnfAlgo::GetOutputTensorNum(kernel); ++i) { const auto &device_tensor = AnfAlgo::GetMutableOutputAddr(kernel, i, false); MS_EXCEPTION_IF_NULL(device_tensor); ofs << "\t\t\toutput_index:" << i << "\tptr:" << device_tensor->GetPtr() << "\tsize:" << device_tensor->GetSize() diff --git a/mindspore/ccsrc/runtime/graph_scheduler/actor/data_prepare_actor.cc b/mindspore/ccsrc/runtime/graph_scheduler/actor/data_prepare_actor.cc index 1e38f21a9c9..4d7b30d27f8 100644 --- a/mindspore/ccsrc/runtime/graph_scheduler/actor/data_prepare_actor.cc +++ b/mindspore/ccsrc/runtime/graph_scheduler/actor/data_prepare_actor.cc @@ -24,7 +24,7 @@ #include "runtime/hardware/device_context_manager.h" #include "mindrt/include/async/async.h" #include "utils/log_adapter.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" namespace mindspore { namespace runtime { @@ -255,7 +255,8 @@ void DataPrepareActor::UpdateDynamicShape(const AnfNodePtr &input_node, const Te auto shape = input_tensor->shape(); std::vector shape_tmp; std::transform(shape.begin(), shape.end(), std::back_inserter(shape_tmp), IntToSize); - AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(input_node, 0)}, {shape_tmp}, input_node.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({common::AnfAlgo::GetOutputInferDataType(input_node, 0)}, {shape_tmp}, + input_node.get()); } void DataPrepareActor::PrepareData(const std::vector> &input_tensors, @@ -461,7 +462,7 @@ void DataPrepareActor::PrepareDataForStepMode(const std::vectorCreateDeviceAddress( diff --git a/mindspore/ccsrc/runtime/graph_scheduler/actor/data_source_actor.cc b/mindspore/ccsrc/runtime/graph_scheduler/actor/data_source_actor.cc index 56b920936e6..e59a2b3b529 100644 --- a/mindspore/ccsrc/runtime/graph_scheduler/actor/data_source_actor.cc +++ b/mindspore/ccsrc/runtime/graph_scheduler/actor/data_source_actor.cc @@ -145,7 +145,7 @@ void DeviceQueueDataSourceActor::OnMemoryAllocFinish(OpContext *co // Copy data from device queue by data kernel launching. try { auto ret = device_contexts_[0]->LaunchKernel(data_kernel_, launch_info_.inputs_, launch_info_.workspaces_, - launch_info_.outputs_, AnfAlgo::IsDynamicShape(data_kernel_)); + launch_info_.outputs_, common::AnfAlgo::IsDynamicShape(data_kernel_)); if (!ret) { std::string error_info = "Launch kernel failed: " + data_kernel_->fullname_with_scope(); SET_OPCONTEXT_FAIL_RET_WITH_ERROR((*context), error_info); diff --git a/mindspore/ccsrc/runtime/graph_scheduler/actor/kernel_actor.cc b/mindspore/ccsrc/runtime/graph_scheduler/actor/kernel_actor.cc index 40643553581..f39ffad92be 100644 --- a/mindspore/ccsrc/runtime/graph_scheduler/actor/kernel_actor.cc +++ b/mindspore/ccsrc/runtime/graph_scheduler/actor/kernel_actor.cc @@ -34,9 +34,9 @@ void KernelActor::Init() { running_dependent_msg_num_ = SizeToInt(input_datas_num_ + input_controls_num_); MS_EXCEPTION_IF_NULL(kernel_); - real_input_num_ = AnfAlgo::GetInputTensorNum(kernel_); + real_input_num_ = common::AnfAlgo::GetInputTensorNum(kernel_); kernel_info_ = dynamic_cast(kernel_->kernel_info()); - is_dynamic_shape_ = AnfAlgo::IsDynamicShape(kernel_); + is_dynamic_shape_ = common::AnfAlgo::IsDynamicShape(kernel_); // Init the device tensors and kernel launch info. copy_input_device_tensors_.resize(real_input_num_); @@ -128,7 +128,8 @@ void KernelActor::FetchWorkspaceDeviceTensor() { } else if (launch_info_.workspaces_.size() < workspace_sizes.size()) { for (size_t i = launch_info_.workspaces_.size(); i < workspace_sizes.size(); ++i) { auto device_address = device_contexts_[0]->CreateDeviceAddress(nullptr, workspace_sizes[i], "", kTypeUnknown); - MS_LOG(DEBUG) << "Create addr for node:" << AnfAlgo::GetNodeDebugString(kernel_) << " addr:" << device_address; + MS_LOG(DEBUG) << "Create addr for node:" << common::AnfAlgo::GetNodeDebugString(kernel_) + << " addr:" << device_address; AnfAlgo::SetWorkspaceAddr(device_address, i, kernel_.get()); // set to kernel_info MS_EXCEPTION_IF_NULL(device_address); (void)workspace_device_tensors_.emplace_back(device_address.get()); diff --git a/mindspore/ccsrc/runtime/graph_scheduler/actor/output_actor.cc b/mindspore/ccsrc/runtime/graph_scheduler/actor/output_actor.cc index 7afd37ad160..736d6743c50 100644 --- a/mindspore/ccsrc/runtime/graph_scheduler/actor/output_actor.cc +++ b/mindspore/ccsrc/runtime/graph_scheduler/actor/output_actor.cc @@ -68,7 +68,7 @@ void OutputActor::RunOpControl(AID *const, OpContext *const contex } // Update output tensor's shape for (size_t i = 0; i < outputs_.size(); ++i) { - auto shape = AnfAlgo::GetOutputInferShape(output_nodes_[i].first, output_nodes_[i].second); + auto shape = common::AnfAlgo::GetOutputInferShape(output_nodes_[i].first, output_nodes_[i].second); std::vector temp_shape; (void)std::copy(shape.begin(), shape.end(), std::back_inserter(temp_shape)); if (outputs_[i] == nullptr) { @@ -118,9 +118,9 @@ TensorPtr OutputActor::CreateOutputTensor(const AnfNodePtr &output_node, size_t // Create host tensor, the output tensor should use the infer type, it will be handed correctly by tensor data sync // when infer type is not equal to device type. - auto type_id = AnfAlgo::GetOutputInferDataType(output_node, output_index); + auto type_id = common::AnfAlgo::GetOutputInferDataType(output_node, output_index); std::vector temp_shape; - auto shape = AnfAlgo::GetOutputInferShape(output_node, output_index); + auto shape = common::AnfAlgo::GetOutputInferShape(output_node, output_index); (void)std::copy(shape.begin(), shape.end(), std::back_inserter(temp_shape)); auto tensor = std::make_shared(type_id, temp_shape); tensor->set_padding_type(AnfAlgo::GetOutputReshapeType(output_node, output_index)); diff --git a/mindspore/ccsrc/runtime/graph_scheduler/actor/output_actor.h b/mindspore/ccsrc/runtime/graph_scheduler/actor/output_actor.h index 5f551580d24..2927d3e9933 100644 --- a/mindspore/ccsrc/runtime/graph_scheduler/actor/output_actor.h +++ b/mindspore/ccsrc/runtime/graph_scheduler/actor/output_actor.h @@ -30,6 +30,7 @@ #include "runtime/graph_scheduler/actor/abstract_actor.h" #include "runtime/hardware/device_context.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "ir/tensor.h" namespace mindspore { diff --git a/mindspore/ccsrc/runtime/graph_scheduler/control_node_parser.cc b/mindspore/ccsrc/runtime/graph_scheduler/control_node_parser.cc index 8b0cfe42957..849f654b7e2 100644 --- a/mindspore/ccsrc/runtime/graph_scheduler/control_node_parser.cc +++ b/mindspore/ccsrc/runtime/graph_scheduler/control_node_parser.cc @@ -16,7 +16,7 @@ #include "runtime/graph_scheduler/control_node_parser.h" #include "runtime/graph_scheduler/actor/actor_common.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" #include "abstract/utils.h" #include "ir/tensor.h" #include "abstract/abstract_function.h" @@ -39,13 +39,13 @@ bool IsFrontValueNode(const KernelWithIndex &node_with_index) { KernelWithIndex FetchRealInputNode(const KernelWithIndex &node_with_index) { const auto &node = node_with_index.first; MS_EXCEPTION_IF_NULL(node); - if (!AnfAlgo::CheckPrimitiveType(node, prim::kPrimMakeTuple)) { + if (!common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimMakeTuple)) { return node_with_index; } const auto &abstract = node->abstract(); MS_EXCEPTION_IF_NULL(abstract); - size_t output_num = AnfAlgo::GetOutputNumByAbstract(abstract); + size_t output_num = common::AnfAlgo::GetOutputNumByAbstract(abstract); if (output_num <= node_with_index.second) { MS_LOG(EXCEPTION) << "Invalid index:" << node_with_index.second << "for tuple node:" << node->DebugString(); } @@ -57,7 +57,7 @@ KernelWithIndex FetchRealInputNode(const KernelWithIndex &node_with_index) { MS_EXCEPTION_IF_NULL(inputs[i]); const auto &sub_abstract = inputs[i]->abstract(); MS_EXCEPTION_IF_NULL(sub_abstract); - size_t tmp_index = AnfAlgo::GetOutputNumByAbstract(sub_abstract); + size_t tmp_index = common::AnfAlgo::GetOutputNumByAbstract(sub_abstract); // If it is not the output of node, need to subtract the number of inputs of it. if (real_index >= tmp_index) { real_index -= tmp_index; @@ -78,7 +78,7 @@ std::set FetchRealIndexByAbstract(const AbstractBasePtr &abstract, std:: size_t pre_abstract_num = 0; std::set output_indexs; if (indexes->empty()) { - size_t output_num = AnfAlgo::GetOutputNumByAbstract(abstract); + size_t output_num = common::AnfAlgo::GetOutputNumByAbstract(abstract); for (size_t i = 0; i < output_num; ++i) { (void)output_indexs.emplace(i); } @@ -121,7 +121,7 @@ std::set FetchRealIndexByAbstract(const AbstractBasePtr &abstract, std:: MS_LOG(EXCEPTION) << "Invalid index:" << index << " for abstract:" << abstract->ToString(); } for (size_t i = 0; i < index; ++i) { - pre_abstract_num += AnfAlgo::GetOutputNumByAbstract(sub_abstracts[i]); + pre_abstract_num += common::AnfAlgo::GetOutputNumByAbstract(sub_abstracts[i]); } dst_abstract = sub_abstracts[index]; } else { @@ -145,12 +145,12 @@ void FetchRealParameterByNode(const KernelWithIndex &node, std::set> &call_node_to_func_graphs) { auto node_with_index = node; if (!node.first->isa()) { - node_with_index = AnfAlgo::VisitKernelWithReturnType(node.first, node.second); + node_with_index = common::AnfAlgo::VisitKernelWithReturnType(node.first, node.second); } if (node_with_index.first->isa() || node_with_index.first->isa()) { // If node is a valuenode or parameter, the real parameter is itself. (void)real_parameters->emplace(node_with_index); - } else if (AnfAlgo::IsCallNode(node_with_index.first)) { + } else if (common::AnfAlgo::IsCallNode(node_with_index.first)) { // If node is a call node, the real parameters are the outputs of funcgraph the node called. if (invalid_call_nodes->find(node_with_index) != invalid_call_nodes->end()) { return; @@ -166,25 +166,25 @@ void FetchRealParameterByNode(const KernelWithIndex &node, std::setoutput(), node_with_index.second}, real_parameters, invalid_call_nodes, call_node_to_func_graphs); } - } else if (AnfAlgo::CheckPrimitiveType(node_with_index.first, prim::kPrimMakeTuple)) { + } else if (common::AnfAlgo::CheckPrimitiveType(node_with_index.first, prim::kPrimMakeTuple)) { // If node is a maketuple node, the real parameters are its total inputs. const auto &real_input = FetchRealInputNode(node_with_index); MS_LOG(DEBUG) << "Real input node:" << real_input.first->DebugString() << " index:" << real_input.second << " for tuple node:" << node_with_index.first->DebugString() << " index:" << node_with_index.second; FetchRealParameterByNode(real_input, real_parameters, invalid_call_nodes, call_node_to_func_graphs); - } else if (AnfAlgo::CheckPrimitiveType(node.first, prim::kPrimSwitch)) { + } else if (common::AnfAlgo::CheckPrimitiveType(node.first, prim::kPrimSwitch)) { // If node is a switch node, the real parameters are its both true and false branches. const auto cnode = node_with_index.first->cast(); const auto inputs = cnode->inputs(); for (size_t i = kSwitchTrueBranchPos; i < inputs.size(); ++i) { FetchRealParameterByNode({inputs[i], 0}, real_parameters, invalid_call_nodes, call_node_to_func_graphs); } - } else if (AnfAlgo::CheckPrimitiveType(node_with_index.first, prim::kPrimSwitchLayer)) { + } else if (common::AnfAlgo::CheckPrimitiveType(node_with_index.first, prim::kPrimSwitchLayer)) { // If node is a switchlyaer node, the real parameters are its total branches. const auto &switch_layer_cnode = node_with_index.first->cast(); const auto &switch_layer_inputs = switch_layer_cnode->inputs(); if (switch_layer_inputs.size() != kSwitchLayerInputNum || - (!AnfAlgo::CheckPrimitiveType(switch_layer_inputs[kSwitchLayerBranchPos], prim::kPrimMakeTuple))) { + (!common::AnfAlgo::CheckPrimitiveType(switch_layer_inputs[kSwitchLayerBranchPos], prim::kPrimMakeTuple))) { MS_LOG(EXCEPTION) << "Invalid switch layer node:" << switch_layer_cnode->DebugString(); } const auto &make_tuple_cnode = switch_layer_inputs[kSwitchLayerBranchPos]->cast(); @@ -220,11 +220,11 @@ void FetchWeightbyHostParameter(const AnfNodePtr &node, std::set *co // Recursive interface, get the real kernel that UpdateState node depends on. AnfNodePtr FetchSourceNodeByAutoMonad(const AnfNodePtr &node) { - if (AnfAlgo::CheckPrimitiveType(node, prim::kPrimUpdateState)) { + if (common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimUpdateState)) { const auto &cnode = node->cast(); const auto &inputs = cnode->inputs(); if (inputs.size() <= kUpdateStateRealInput) { - MS_LOG(EXCEPTION) << "Invalid updatestate node:" << AnfAlgo::GetNodeDebugString(node); + MS_LOG(EXCEPTION) << "Invalid updatestate node:" << common::AnfAlgo::GetNodeDebugString(node); } return FetchSourceNodeByAutoMonad(inputs[kUpdateStateRealInput]); @@ -297,17 +297,17 @@ std::vector FetchAllOutputWithIndex(const AnfNodePtr &node) { return result; } - size_t output_num = AnfAlgo::GetOutputTensorNum(node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(node); for (size_t i = 0; i < output_num; ++i) { - const auto node_with_index = AnfAlgo::VisitKernelWithReturnType(node, i); - if (AnfAlgo::CheckPrimitiveType(node_with_index.first, prim::kPrimMakeTuple)) { + const auto node_with_index = common::AnfAlgo::VisitKernelWithReturnType(node, i); + if (common::AnfAlgo::CheckPrimitiveType(node_with_index.first, prim::kPrimMakeTuple)) { const auto &cnode = node_with_index.first->cast(); const auto &inputs = cnode->inputs(); const auto &tmp_list = FetchAllOutputWithIndex(inputs[i + kMakeTupleInputStartPos]); (void)result.insert(result.end(), tmp_list.begin(), tmp_list.end()); - } else if (AnfAlgo::CheckPrimitiveType(node_with_index.first, prim::kPrimSwitch) || - AnfAlgo::CheckPrimitiveType(node_with_index.first, prim::kPrimSwitchLayer)) { - } else if (AnfAlgo::IsCallNode(node_with_index.first)) { + } else if (common::AnfAlgo::CheckPrimitiveType(node_with_index.first, prim::kPrimSwitch) || + common::AnfAlgo::CheckPrimitiveType(node_with_index.first, prim::kPrimSwitchLayer)) { + } else if (common::AnfAlgo::IsCallNode(node_with_index.first)) { (void)result.emplace_back(node_with_index.first, i); } else { (void)result.emplace_back(node_with_index); @@ -335,7 +335,7 @@ void CreateDeviceTensorForValueNode(const KernelWithIndex &front_node_with_index size_t tensor_size = AnfAlgo::GetOutputTensorMemSize(backend_node, 0); TypeId output_type_id = AnfAlgo::GetOutputDeviceDataType(backend_node, 0); if (output_type_id == kTypeUnknown) { - output_type_id = AnfAlgo::GetOutputInferDataType(backend_node, 0); + output_type_id = common::AnfAlgo::GetOutputInferDataType(backend_node, 0); } if (front_node->kernel_info() == nullptr) { @@ -354,7 +354,7 @@ void CreateDeviceTensorForValueNode(const KernelWithIndex &front_node_with_index device::DeviceAddressPtr address = device_context->CreateDeviceAddress(nullptr, tensor_size, output_format, output_type_id); MS_EXCEPTION_IF_NULL(address); - MS_LOG(DEBUG) << "Create address for node:" << AnfAlgo::GetNodeDebugString(front_node) << " addr:" << address + MS_LOG(DEBUG) << "Create address for node:" << common::AnfAlgo::GetNodeDebugString(front_node) << " addr:" << address << " size:" << tensor_size; AnfAlgo::SetOutputAddr(address, front_node_with_index.second, front_node.get()); UpdateRefCount(address.get(), true); @@ -367,7 +367,7 @@ void CreateDeviceTensorForFrontNode(const KernelWithIndex &front_node_with_index MS_EXCEPTION_IF_NULL(device_context); const auto &node = front_node_with_index.first; - TypeId type_id = AnfAlgo::GetOutputInferDataType(node, 0); + TypeId type_id = common::AnfAlgo::GetOutputInferDataType(node, 0); if (node->kernel_info() == nullptr) { auto kernel_info = std::make_shared(); std::shared_ptr builder = std::make_shared(); @@ -381,8 +381,9 @@ void CreateDeviceTensorForFrontNode(const KernelWithIndex &front_node_with_index // Create device tensor. device::DeviceAddressPtr address = device_context->CreateDeviceAddress(nullptr, size, kOpFormat_DEFAULT, type_id); MS_EXCEPTION_IF_NULL(address); - MS_LOG(INFO) << "Create address for node that has no corresponding backend node:" << AnfAlgo::GetNodeDebugString(node) - << " addr:" << address << " size:" << size << ", type id:" << type_id; + MS_LOG(INFO) << "Create address for node that has no corresponding backend node:" + << common::AnfAlgo::GetNodeDebugString(node) << " addr:" << address << " size:" << size + << ", type id:" << type_id; AnfAlgo::SetOutputAddr(address, front_node_with_index.second, node.get()); UpdateRefCount(address.get(), true); } @@ -408,14 +409,14 @@ void FetchAllExecutionFunction(const FuncGraphPtr &func_graph, std::setisa() || node->isa() || AnfAlgo::IsCallNode(node); + return node->isa() || node->isa() || common::AnfAlgo::IsCallNode(node); } // Fetch all inputs of node. std::vector FetchInputNodeByNode(const AnfNodePtr &node) { MS_EXCEPTION_IF_NULL(node); if (HasAbstractMonad(node)) { - const auto &real_node_with_index = AnfAlgo::VisitKernelWithReturnType(node, 0); + const auto &real_node_with_index = common::AnfAlgo::VisitKernelWithReturnType(node, 0); const auto &real_node = real_node_with_index.first; MS_EXCEPTION_IF_NULL(real_node); if (IsValidMonadNode(real_node)) { @@ -427,7 +428,7 @@ std::vector FetchInputNodeByNode(const AnfNodePtr &node) { // The node is divided into the following types: // 1. depend and load. const auto &node_with_index = - AnfAlgo::VisitKernelWithReturnType(node, 0, false, {prim::kPrimTupleGetItem, prim::kPrimMakeTuple}); + common::AnfAlgo::VisitKernelWithReturnType(node, 0, false, {prim::kPrimTupleGetItem, prim::kPrimMakeTuple}); auto real_node = node_with_index.first; size_t real_index = node_with_index.second; MS_EXCEPTION_IF_NULL(real_node); @@ -463,8 +464,8 @@ std::vector FetchInputNodeByNode(const AnfNodePtr &node) { MS_EXCEPTION_IF_NULL(src_node); const auto iter = sparse_attr_map.find(prim_value->name()); // Csr node from the make csr tensor node. - if (AnfAlgo::CheckPrimitiveType(src_node, prim::kPrimMakeCSRTensor) || - AnfAlgo::CheckPrimitiveType(src_node, prim::kPrimMakeCOOTensor)) { + if (common::AnfAlgo::CheckPrimitiveType(src_node, prim::kPrimMakeCSRTensor) || + common::AnfAlgo::CheckPrimitiveType(src_node, prim::kPrimMakeCOOTensor)) { const auto &make_tensor_cnode = src_node->cast(); const auto &make_tensor_inputs = make_tensor_cnode->inputs(); if (make_tensor_inputs.size() <= kMakeCSRTensorInputNum) { @@ -489,14 +490,14 @@ std::vector FetchInputNodeByNode(const AnfNodePtr &node) { const auto &abstract = real_node->abstract(); if (abstract == nullptr) { MS_LOG(WARNING) << "Empty abstract for node:" << real_node->DebugString(); - (void)results.emplace_back(AnfAlgo::VisitKernelWithReturnType(real_node, real_index)); + (void)results.emplace_back(common::AnfAlgo::VisitKernelWithReturnType(real_node, real_index)); return results; } // 5 Other. - if (AnfAlgo::CheckPrimitiveType(real_node, prim::kPrimTupleGetItem)) { + if (common::AnfAlgo::CheckPrimitiveType(real_node, prim::kPrimTupleGetItem)) { std::vector index_stack; - auto get_item_src_node = AnfAlgo::GetTupleIndexes(real_node, &index_stack); + auto get_item_src_node = common::AnfAlgo::GetTupleIndexes(real_node, &index_stack); MS_EXCEPTION_IF_NULL(get_item_src_node); if (index_stack.empty()) { const auto &sub_results = FetchInputNodeByNode(get_item_src_node); @@ -511,7 +512,7 @@ std::vector FetchInputNodeByNode(const AnfNodePtr &node) { return results; } - size_t output_num = AnfAlgo::GetOutputNumByAbstract(abstract); + size_t output_num = common::AnfAlgo::GetOutputNumByAbstract(abstract); for (size_t i = 0; i < output_num; ++i) { (void)results.emplace_back(real_node, i); } @@ -525,7 +526,7 @@ void AddFormalToRealParameter(const AnfNodePtr &formal_parameter, const AnfNodeP MS_EXCEPTION_IF_NULL(formal_parameter); auto abstract = formal_parameter->abstract(); MS_EXCEPTION_IF_NULL(abstract); - size_t output_num = AnfAlgo::GetOutputNumByAbstract(abstract); + size_t output_num = common::AnfAlgo::GetOutputNumByAbstract(abstract); for (size_t i = 0; i < output_num; ++i) { std::set real_parameters; @@ -554,7 +555,7 @@ bool IsFirstControlNode(const AnfNodePtr &node, std::set *checked_no const auto &inputs = cnode->inputs(); for (const auto &input : inputs) { MS_EXCEPTION_IF_NULL(input); - if ((AnfAlgo::IsCallNode(input) && unrecursion_call_nodes.find(input) == unrecursion_call_nodes.end()) || + if ((common::AnfAlgo::IsCallNode(input) && unrecursion_call_nodes.find(input) == unrecursion_call_nodes.end()) || (!IsFirstControlNode(input, checked_nodes, unrecursion_call_nodes))) { return false; } @@ -567,7 +568,7 @@ KernelWithIndex FetchRealNodeByGetItem(const KernelWithIndex &node_with_index) { MS_EXCEPTION_IF_NULL(node_with_index.first); std::vector index_stack{node_with_index.second}; - const auto &get_item_src_node = AnfAlgo::GetTupleIndexes(node_with_index.first, &index_stack); + const auto &get_item_src_node = common::AnfAlgo::GetTupleIndexes(node_with_index.first, &index_stack); const auto &get_item_src_abstract = get_item_src_node->abstract(); MS_EXCEPTION_IF_NULL(get_item_src_abstract); auto indexes = FetchRealIndexByAbstract(get_item_src_abstract, &index_stack); @@ -586,10 +587,10 @@ bool IsCsrNode(const AnfNodePtr &node) { if (!node->isa()) { return false; } - return AnfAlgo::CheckPrimitiveType(node, prim::kPrimCSRTensorGetIndptr) || - AnfAlgo::CheckPrimitiveType(node, prim::kPrimCSRTensorGetIndices) || - AnfAlgo::CheckPrimitiveType(node, prim::kPrimCSRTensorGetValues) || - AnfAlgo::CheckPrimitiveType(node, prim::kPrimCSRTensorGetDenseShape); + return common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimCSRTensorGetIndptr) || + common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimCSRTensorGetIndices) || + common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimCSRTensorGetValues) || + common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimCSRTensorGetDenseShape); } bool IsCooNode(const AnfNodePtr &node) { @@ -597,9 +598,9 @@ bool IsCooNode(const AnfNodePtr &node) { if (!node->isa()) { return false; } - return AnfAlgo::CheckPrimitiveType(node, prim::kPrimCOOTensorGetIndices) || - AnfAlgo::CheckPrimitiveType(node, prim::kPrimCOOTensorGetValues) || - AnfAlgo::CheckPrimitiveType(node, prim::kPrimCOOTensorGetDenseShape); + return common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimCOOTensorGetIndices) || + common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimCOOTensorGetValues) || + common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimCOOTensorGetDenseShape); } KernelWithIndex GetFrontNodeByKernelGraph(const AnfNodePtr &backend_node, const KernelGraph *const graph) { @@ -630,7 +631,7 @@ std::vector FetchInputNodeByCNode(const AnfNodePtr &node) { // The first input of normal cnode is the primitive of node, and the real input starts from the second input, // but in control flow, the call node has no primitive, and the 0th input is funcgraph or partial. size_t input_start_pos = kCNodeInputStartPos; - if (AnfAlgo::IsCallNode(node)) { + if (common::AnfAlgo::IsCallNode(node)) { input_start_pos = 0; } const auto &cnode = node->cast(); @@ -639,13 +640,13 @@ std::vector FetchInputNodeByCNode(const AnfNodePtr &node) { // The first branch of the input of the switch node is the true branch, and the second is the false branch. // But in switch actor, since the false value is 0, it corresponds to the first branch. Therefore, the input // of the switch node needs to exchange the positions of the two branches. So deal separately. - if (AnfAlgo::CheckPrimitiveType(node, prim::kPrimSwitch)) { + if (common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimSwitch)) { if (inputs.size() != kSwitchInputNum) { MS_LOG(EXCEPTION) << "Invalid switch node:" << node->DebugString(); } - (void)results.emplace_back(AnfAlgo::VisitKernelWithReturnType(inputs[kSwitchCondPos], 0)); - (void)results.emplace_back(AnfAlgo::VisitKernelWithReturnType(inputs[kSwitchFalseBranchPos], 0)); - (void)results.emplace_back(AnfAlgo::VisitKernelWithReturnType(inputs[kSwitchTrueBranchPos], 0)); + (void)results.emplace_back(common::AnfAlgo::VisitKernelWithReturnType(inputs[kSwitchCondPos], 0)); + (void)results.emplace_back(common::AnfAlgo::VisitKernelWithReturnType(inputs[kSwitchFalseBranchPos], 0)); + (void)results.emplace_back(common::AnfAlgo::VisitKernelWithReturnType(inputs[kSwitchTrueBranchPos], 0)); return results; } @@ -701,7 +702,7 @@ abstract::AbstractBasePtr FetchAbstractByIndex(const AbstractBasePtr &abstract, const auto &sub_abstracts = tuple_abstract->elements(); size_t real_index = index; for (const auto &sub_abstract : sub_abstracts) { - size_t tmp_index = AnfAlgo::GetOutputNumByAbstract(sub_abstract); + size_t tmp_index = common::AnfAlgo::GetOutputNumByAbstract(sub_abstract); if (real_index >= tmp_index) { real_index -= tmp_index; continue; @@ -849,7 +850,7 @@ bool ControlNodeParser::IsNeedStackControlNode(const AnfNodePtr &node) { bool ControlNodeParser::IsRecursionCallNode(const AnfNodePtr &node) { MS_EXCEPTION_IF_NULL(node); - if (!AnfAlgo::IsCallNode(node)) { + if (!common::AnfAlgo::IsCallNode(node)) { return false; } return find(unrecursion_call_nodes_.begin(), unrecursion_call_nodes_.end(), node) == unrecursion_call_nodes_.end(); @@ -928,7 +929,7 @@ void ControlNodeParser::ParseDeviceContextForFuncGraph(const std::vectorparameters()) { const auto &abstract = parameter->abstract(); MS_EXCEPTION_IF_NULL(abstract); - for (size_t i = 0; i < AnfAlgo::GetOutputNumByAbstract(abstract); ++i) { + for (size_t i = 0; i < common::AnfAlgo::GetOutputNumByAbstract(abstract); ++i) { (void)front_parameters.emplace_back(parameter, i); } } @@ -969,7 +970,7 @@ void ControlNodeParser::ParseDeviceContextForFuncGraph(const std::vectorparameters()) { const auto &abstract = parameter->abstract(); MS_EXCEPTION_IF_NULL(abstract); - output_num += AnfAlgo::GetOutputNumByAbstract(abstract); + output_num += common::AnfAlgo::GetOutputNumByAbstract(abstract); } func_graph_to_device_contexts_[sub_graph] = std::vector(output_num, default_context); } @@ -978,7 +979,7 @@ void ControlNodeParser::ParseDeviceContextForFuncGraph(const std::vector &control_nodes) { for (const auto &control_node : control_nodes) { - if (!AnfAlgo::CheckPrimitiveType(control_node, prim::kPrimPartial)) { + if (!common::AnfAlgo::CheckPrimitiveType(control_node, prim::kPrimPartial)) { continue; } @@ -1014,7 +1015,7 @@ void ControlNodeParser::ParseDeviceContextForPartialNode(const std::vectorabstract(); MS_EXCEPTION_IF_NULL(abstract); - input_num += AnfAlgo::GetOutputNumByAbstract(abstract); + input_num += common::AnfAlgo::GetOutputNumByAbstract(abstract); } if (input_num > iter->second.size()) { MS_LOG(EXCEPTION) << "Invalid input num:" << input_num << " for funcgraph:" << func_graph->ToString() @@ -1038,7 +1039,7 @@ void ControlNodeParser::ParseDeviceContextForPartialNode(const std::vector &control_nodes) { for (const auto &control_node : control_nodes) { MS_EXCEPTION_IF_NULL(control_node); - if (!AnfAlgo::IsCallNode(control_node)) { + if (!common::AnfAlgo::IsCallNode(control_node)) { continue; } @@ -1065,7 +1066,7 @@ void ControlNodeParser::ParseDeviceContextForCallNode(const std::vectorabstract(); MS_EXCEPTION_IF_NULL(abstract); - call_input_num += AnfAlgo::GetOutputNumByAbstract(abstract); + call_input_num += common::AnfAlgo::GetOutputNumByAbstract(abstract); } if (call_input_num > iter->second.size()) { @@ -1127,7 +1128,7 @@ void ControlNodeParser::ParseDeviceContextForReturnNode(const DeviceContext *def // If the output is parameter, used the default context type. MS_EXCEPTION_IF_NULL(default_context); (void)return_device_contexts.emplace_back(default_context); - } else if (AnfAlgo::IsCallNode(output_node.first)) { + } else if (common::AnfAlgo::IsCallNode(output_node.first)) { // If the output is call node, get the device context type by the output of funcgraph. const auto &func_graphs = call_node_to_func_graphs_[output_node.first]; std::vector call_device_contexts; @@ -1151,8 +1152,8 @@ void ControlNodeParser::ParseDeviceContextForReturnNode(const DeviceContext *def } MS_EXCEPTION_IF_NULL(call_device_contexts[output_node.second]); (void)return_device_contexts.emplace_back(call_device_contexts[output_node.second]); - } else if (AnfAlgo::CheckPrimitiveType(output_node.first, prim::kPrimPartial) || - AnfAlgo::CheckPrimitiveType(output_node.first, prim::kPrimSwitch)) { + } else if (common::AnfAlgo::CheckPrimitiveType(output_node.first, prim::kPrimPartial) || + common::AnfAlgo::CheckPrimitiveType(output_node.first, prim::kPrimSwitch)) { (void)return_device_contexts.emplace_back(default_context); } else if (output_node.first->isa()) { // If the output is a cnode, get the device context type by the kernel. @@ -1282,7 +1283,8 @@ void ControlNodeParser::FetchFrontValueNode(const std::vector &contr // Create device tensors for those value nodes which direct return by a return node. for (const auto &control_node : control_nodes) { - if ((!AnfAlgo::CheckPrimitiveType(control_node, prim::kPrimReturn)) && (!AnfAlgo::IsCallNode(control_node))) { + if ((!common::AnfAlgo::CheckPrimitiveType(control_node, prim::kPrimReturn)) && + (!common::AnfAlgo::IsCallNode(control_node))) { continue; } @@ -1319,7 +1321,7 @@ void ControlNodeParser::ParseFormalToRealParameter(const std::vector // 1. Input of partial node. // 2. Input of call node. for (const auto &node : control_nodes) { - if (AnfAlgo::IsCallNode(node)) { + if (common::AnfAlgo::IsCallNode(node)) { const auto &cnode = node->cast(); const auto &inputs = cnode->inputs(); const auto &func_graphs = FetchFuncGraphbyCallNode(node); @@ -1332,7 +1334,7 @@ void ControlNodeParser::ParseFormalToRealParameter(const std::vector &formal_to_real_parameters); } } - } else if (AnfAlgo::CheckPrimitiveType(node, prim::kPrimPartial)) { + } else if (common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimPartial)) { const auto &cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); const auto &inputs = cnode->inputs(); @@ -1423,9 +1425,9 @@ void ControlNodeParser::ParseControlNodeParameter(const std::vector for (const auto &control_node : control_nodes) { CNodePtr cnode = control_node->cast(); const auto &inputs = cnode->inputs(); - if (AnfAlgo::CheckPrimitiveType(control_node, prim::kPrimReturn)) { + if (common::AnfAlgo::CheckPrimitiveType(control_node, prim::kPrimReturn)) { break; - } else if (AnfAlgo::CheckPrimitiveType(control_node, prim::kPrimPartial)) { + } else if (common::AnfAlgo::CheckPrimitiveType(control_node, prim::kPrimPartial)) { for (size_t i = kPartialInputStartPos; i < inputs.size(); ++i) { if (inputs[i]->isa()) { (void)control_node_parameters_.emplace_back(inputs[i]); @@ -1437,16 +1439,16 @@ void ControlNodeParser::ParseControlNodeParameter(const std::vector (void)control_node_parameters_.emplace_back(inputs[i]); } } - } else if (AnfAlgo::CheckPrimitiveType(control_node, prim::kPrimSwitch)) { + } else if (common::AnfAlgo::CheckPrimitiveType(control_node, prim::kPrimSwitch)) { if (inputs.size() != kSwitchInputNum) { - MS_LOG(EXCEPTION) << "Invalid switch node:" << AnfAlgo::GetNodeDebugString(control_node); + MS_LOG(EXCEPTION) << "Invalid switch node:" << common::AnfAlgo::GetNodeDebugString(control_node); } if (inputs[kSwitchCondPos]->isa()) { (void)control_node_parameters_.emplace_back(inputs[kSwitchCondPos]); } - } else if (AnfAlgo::CheckPrimitiveType(control_node, prim::kPrimSwitchLayer)) { + } else if (common::AnfAlgo::CheckPrimitiveType(control_node, prim::kPrimSwitchLayer)) { if (inputs.size() != kSwitchLayerInputNum) { - MS_LOG(EXCEPTION) << "Invalid switch node:" << AnfAlgo::GetNodeDebugString(control_node); + MS_LOG(EXCEPTION) << "Invalid switch node:" << common::AnfAlgo::GetNodeDebugString(control_node); } if (inputs[kSwitchLayerCondPos]->isa()) { (void)control_node_parameters_.emplace_back(inputs[kSwitchLayerCondPos]); @@ -1460,7 +1462,7 @@ void ControlNodeParser::CreateBranchIDForCallNode(const std::vector for (const auto &control_node : control_nodes) { // Root funcgraph does not need to create a gather actor. - if (AnfAlgo::IsCallNode(control_node)) { + if (common::AnfAlgo::IsCallNode(control_node)) { call_node_to_branch_id_[control_node] = ++branch_id; } } @@ -1566,7 +1568,7 @@ std::vector GetFuncGraphs(const AnfNodePtr &anf_node) { void ControlNodeParser::ParseCallNodeToFuncGraph(const std::vector &control_nodes) { for (const auto &control_node : control_nodes) { MS_EXCEPTION_IF_NULL(control_node); - if (!AnfAlgo::IsCallNode(control_node)) { + if (!common::AnfAlgo::IsCallNode(control_node)) { continue; } std::vector func_graphs; @@ -1611,11 +1613,12 @@ void ControlNodeParser::FetchFrontToBackendKernel(const std::vectorGetFrontAnfByBackendAnf(kernel); if (front_node != nullptr) { - for (size_t j = 0; j < AnfAlgo::GetOutputTensorNum(kernel); ++j) { + for (size_t j = 0; j < common::AnfAlgo::GetOutputTensorNum(kernel); ++j) { front_to_backend_kernels_[{front_node, j}] = {{kernel, j}, device_context}; - MS_LOG(DEBUG) << "Add front to backend kernel, front:" << AnfAlgo::GetNodeDebugString(front_node) - << "index:" << j << " addr:" << front_node << " second:" << AnfAlgo::GetNodeDebugString(kernel) - << "index:" << j << " addr:" << kernel; + MS_LOG(DEBUG) << "Add front to backend kernel, front:" << common::AnfAlgo::GetNodeDebugString(front_node) + << "index:" << j << " addr:" << front_node + << " second:" << common::AnfAlgo::GetNodeDebugString(kernel) << "index:" << j + << " addr:" << kernel; } } } @@ -1634,14 +1637,14 @@ void ControlNodeParser::FetchAutoMonadNode(const std::vector &contro const auto &cnode = control_node->cast(); const auto &inputs = cnode->inputs(); if (inputs.empty()) { - MS_LOG(EXCEPTION) << "Invalid control node:" << AnfAlgo::GetNodeDebugString(control_node); + MS_LOG(EXCEPTION) << "Invalid control node:" << common::AnfAlgo::GetNodeDebugString(control_node); } if (inputs[0]->isa() && IsValueNode(inputs[0])) { for (size_t i = kCallInputStartPos; i < inputs.size(); ++i) { - if (AnfAlgo::CheckPrimitiveType(inputs[i], prim::kPrimUpdateState)) { + if (common::AnfAlgo::CheckPrimitiveType(inputs[i], prim::kPrimUpdateState)) { const auto &node = FetchSourceNodeByAutoMonad(inputs[i]); - const auto &iter = front_to_backend_kernels_.find(AnfAlgo::VisitKernelWithReturnType(node, 0)); + const auto &iter = front_to_backend_kernels_.find(common::AnfAlgo::VisitKernelWithReturnType(node, 0)); if (iter != front_to_backend_kernels_.end()) { kernel_to_call_nodes_[iter->second.first.first] = control_node; } @@ -1661,15 +1664,15 @@ AnfNodePtr ControlNodeParser::FetchRootGraphFrontNodeBySubFrontNode(const AnfNod void ControlNodeParser::ParseFirstControlNodeAndKernelGraphForFuncGraph(const std::vector &control_nodes) { for (const auto &control_node : control_nodes) { std::set checked_nodes; - if (((AnfAlgo::IsCallNode(control_node) && + if (((common::AnfAlgo::IsCallNode(control_node) && unrecursion_call_nodes_.find(control_node) == unrecursion_call_nodes_.end()) || - AnfAlgo::CheckPrimitiveType(control_node, prim::kPrimReturn)) && + common::AnfAlgo::CheckPrimitiveType(control_node, prim::kPrimReturn)) && IsFirstControlNode(control_node, &checked_nodes, unrecursion_call_nodes_)) { const auto &func_graph = control_node->func_graph(); MS_EXCEPTION_IF_NULL(func_graph); (void)func_graph_to_first_control_nodes_[func_graph].emplace(control_node); - if (!AnfAlgo::IsCallNode(control_node)) { + if (!common::AnfAlgo::IsCallNode(control_node)) { continue; } @@ -1750,7 +1753,7 @@ bool ControlNodeParser::IsCallNodeNeedStack(const AnfNodePtr &node) { for (const auto &input_with_index : input_with_indexs) { MS_EXCEPTION_IF_NULL(input_with_index.first); // If the call node has call or recursion graph input, a stack created for the call node is required. - if (!AnfAlgo::IsCallNode(input_with_index.first)) { + if (!common::AnfAlgo::IsCallNode(input_with_index.first)) { if (!input_with_index.first->isa()) { continue; } @@ -1767,7 +1770,7 @@ bool ControlNodeParser::IsCallNodeNeedStack(const AnfNodePtr &node) { void ControlNodeParser::ParseNeedStackControlNode(const std::vector &control_nodes) { for (const auto &control_node : control_nodes) { MS_EXCEPTION_IF_NULL(control_node); - if (AnfAlgo::IsCallNode(control_node) && IsCallNodeNeedStack(control_node)) { + if (common::AnfAlgo::IsCallNode(control_node) && IsCallNodeNeedStack(control_node)) { (void)need_stack_control_nodes_.emplace(control_node); MS_LOG(DEBUG) << "Add need stack control node:" << control_node->DebugString(); } @@ -1775,11 +1778,11 @@ void ControlNodeParser::ParseNeedStackControlNode(const std::vector for (const auto &control_node : control_nodes) { MS_EXCEPTION_IF_NULL(control_node); - if (AnfAlgo::CheckPrimitiveType(control_node, prim::kPrimReturn)) { + if (common::AnfAlgo::CheckPrimitiveType(control_node, prim::kPrimReturn)) { auto input_with_indexs = FetchInputNodeByCNode(control_node); size_t call_input_num = 0; for (auto input_with_index : input_with_indexs) { - if (AnfAlgo::IsCallNode(input_with_index.first)) { + if (common::AnfAlgo::IsCallNode(input_with_index.first)) { ++call_input_num; } } @@ -1792,12 +1795,12 @@ void ControlNodeParser::ParseNeedStackControlNode(const std::vector } if ((!IsInputInSameLevel(control_node)) || - (call_input_num != 0 && (AnfAlgo::CheckPrimitiveType(inputs[kReturnInputPos], prim::kPrimDepend)))) { + (call_input_num != 0 && (common::AnfAlgo::CheckPrimitiveType(inputs[kReturnInputPos], prim::kPrimDepend)))) { (void)need_stack_control_nodes_.emplace(control_node); } - } else if (AnfAlgo::CheckPrimitiveType(control_node, prim::kPrimPartial) || - AnfAlgo::CheckPrimitiveType(control_node, prim::kPrimSwitch) || - AnfAlgo::CheckPrimitiveType(control_node, prim::kPrimSwitchLayer)) { + } else if (common::AnfAlgo::CheckPrimitiveType(control_node, prim::kPrimPartial) || + common::AnfAlgo::CheckPrimitiveType(control_node, prim::kPrimSwitch) || + common::AnfAlgo::CheckPrimitiveType(control_node, prim::kPrimSwitchLayer)) { if (!IsInputInSameLevel(control_node)) { (void)need_stack_control_nodes_.emplace(control_node); MS_LOG(DEBUG) << "Add need stack control node:" << control_node->DebugString(); @@ -1825,7 +1828,7 @@ void CollectEffectiveInputByGraph(const KernelGraphPtr &graph, const FrontToBack outputs.find(front_node_with_index) != outputs.end() || front_node_with_index.first->isa()) { continue; } - if (AnfAlgo::IsCallNode(front_node_with_index.first)) { + if (common::AnfAlgo::IsCallNode(front_node_with_index.first)) { (*need_stack) = true; } (*inputs)[front_node_with_index] = device_context; @@ -1840,7 +1843,7 @@ void CollectEffectiveOutputByGraph(const KernelGraphPtr &graph, DeviceContext *c for (const auto &backend_to_front : graph->graph_output_map()) { if (HasAbstractMonad(backend_to_front.second.first) || HasAbstractMonad(backend_to_front.first.first) || - AnfAlgo::CheckPrimitiveType(backend_to_front.second.first, prim::kPrimPartial) || + common::AnfAlgo::CheckPrimitiveType(backend_to_front.second.first, prim::kPrimPartial) || backend_to_front.second.first->isa()) { continue; } @@ -1952,7 +1955,7 @@ void ControlNodeParser::ParseNodeLevel(const std::vector &control_no size_t level = 0; // 1. Parse levels of control nodes. for (const auto &control_node : control_nodes) { - if (AnfAlgo::CheckPrimitiveType(control_node, prim::kPrimReturn)) { + if (common::AnfAlgo::CheckPrimitiveType(control_node, prim::kPrimReturn)) { node_to_level_[control_node] = level; level = 0; const auto &func_graph = control_node->func_graph(); diff --git a/mindspore/ccsrc/runtime/graph_scheduler/control_node_scheduler.cc b/mindspore/ccsrc/runtime/graph_scheduler/control_node_scheduler.cc index 7030caa0f6f..c6d1339f60c 100644 --- a/mindspore/ccsrc/runtime/graph_scheduler/control_node_scheduler.cc +++ b/mindspore/ccsrc/runtime/graph_scheduler/control_node_scheduler.cc @@ -28,7 +28,7 @@ std::string GetActorName(const AnfNodePtr &node) { debug_name = debug_name.substr(0, index); } - if (AnfAlgo::IsCallNode(node)) { + if (common::AnfAlgo::IsCallNode(node)) { return "Call_" + node->UniqueName() + "_" + debug_name; } else { return node->UniqueName() + "_" + debug_name; @@ -49,7 +49,7 @@ void FetchRealDependNodeByAutoMonad(const AnfNodePtr &node, std::set // Find the real input node, include the monad node and make tuple node. const std::vector return_types = {prim::kPrimDepend, prim::kPrimUpdateState, prim::kPrimLoad, prim::kPrimMakeTuple}; - const auto &node_with_index = AnfAlgo::VisitKernelWithReturnType(node, 0, false, return_types); + const auto &node_with_index = common::AnfAlgo::VisitKernelWithReturnType(node, 0, false, return_types); auto real_node = node_with_index.first; MS_EXCEPTION_IF_NULL(real_node); if (!real_node->isa()) { @@ -61,7 +61,7 @@ void FetchRealDependNodeByAutoMonad(const AnfNodePtr &node, std::set const auto &real_inputs = real_cnode->inputs(); // Make tuple node needs to be expanded. - if (AnfAlgo::CheckPrimitiveType(real_node, prim::kPrimMakeTuple)) { + if (common::AnfAlgo::CheckPrimitiveType(real_node, prim::kPrimMakeTuple)) { for (size_t i = 1; i < real_inputs.size(); ++i) { MS_EXCEPTION_IF_NULL(real_inputs[i]); FetchRealDependNodeByAutoMonad(real_inputs[i], depend_nodes); @@ -71,15 +71,15 @@ void FetchRealDependNodeByAutoMonad(const AnfNodePtr &node, std::set const mindspore::HashSet recursion_prims = { prim::kPrimDepend, prim::kPrimUpdateState, prim::kPrimLoad, prim::kPrimMakeTuple}; - if (AnfAlgo::CheckPrimitiveType(real_node, prim::kPrimDepend) || - AnfAlgo::CheckPrimitiveType(real_node, prim::kPrimLoad)) { + if (common::AnfAlgo::CheckPrimitiveType(real_node, prim::kPrimDepend) || + common::AnfAlgo::CheckPrimitiveType(real_node, prim::kPrimLoad)) { FetchRealDependNodeByAutoMonad(real_inputs[kDependAttachNodeIndex], depend_nodes); // The real input may be this scene: depend/load --> load/depend, so need add the control arrow for real input // node in this scene. if (IsOneOfPrimitiveCNode(real_inputs[kRealInputIndexInDepend], recursion_prims)) { FetchRealDependNodeByAutoMonad(real_inputs[kRealInputIndexInDepend], depend_nodes); } - } else if (AnfAlgo::CheckPrimitiveType(real_node, prim::kPrimUpdateState)) { + } else if (common::AnfAlgo::CheckPrimitiveType(real_node, prim::kPrimUpdateState)) { for (size_t i = kUpdateStateRealInput; i < real_inputs.size(); ++i) { FetchRealDependNodeByAutoMonad(real_inputs[i], depend_nodes); } @@ -141,13 +141,14 @@ std::vector FetchAllMonadNodeByNode(const AnfNodePtr &node) { if (!node->isa()) { return {}; } - if (AnfAlgo::CheckPrimitiveType(node, prim::kPrimUpdateState) || - AnfAlgo::CheckPrimitiveType(node, prim::kPrimDepend) || AnfAlgo::CheckPrimitiveType(node, prim::kPrimLoad)) { + if (common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimUpdateState) || + common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimDepend) || + common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimLoad)) { return {node}; } std::vector results; - if (AnfAlgo::CheckPrimitiveType(node, prim::kPrimMakeTuple)) { + if (common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimMakeTuple)) { const auto &cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); for (const auto &input : cnode->inputs()) { @@ -183,8 +184,8 @@ std::vector ControlNodeScheduler::BuildSwitchActor(const GraphCo for (const auto &control_node : control_nodes) { // Switch node and switch layer node will be converted to switch actor. - if (AnfAlgo::CheckPrimitiveType(control_node, prim::kPrimSwitch) || - AnfAlgo::CheckPrimitiveType(control_node, prim::kPrimSwitchLayer)) { + if (common::AnfAlgo::CheckPrimitiveType(control_node, prim::kPrimSwitch) || + common::AnfAlgo::CheckPrimitiveType(control_node, prim::kPrimSwitchLayer)) { const auto &actor_name = GetActorName(control_node); const auto ¶meters = FetchInputNodeByCNode(control_node); const auto &switch_actor = @@ -236,7 +237,7 @@ std::vector ControlNodeScheduler::BuildGatherActor(const GraphCo for (const auto &control_node : control_nodes) { // Partial node and call node will be converted to gather actor. - if (IsValidPartialCNode(control_node) || AnfAlgo::IsCallNode(control_node)) { + if (IsValidPartialCNode(control_node) || common::AnfAlgo::IsCallNode(control_node)) { const auto &actor_name = GetActorName(control_node); const auto ¶meters = FetchInputNodeByCNode(control_node); const auto &gather_actor = @@ -245,7 +246,7 @@ std::vector ControlNodeScheduler::BuildGatherActor(const GraphCo InsertActor(gather_actor.get()); // The gather actor corresponding to a call node needs to set the branch id. - if (AnfAlgo::IsCallNode(control_node)) { + if (common::AnfAlgo::IsCallNode(control_node)) { gather_actor->output_branch_id_ = parser->FetchBranchIDByCallNode(control_node); } @@ -275,7 +276,7 @@ std::vector ControlNodeScheduler::BuildEntranceActor(const Gra std::vector entrance_actors; const auto &control_nodes = graph_compiler_info.control_nodes_; for (const auto &control_node : control_nodes) { - if (AnfAlgo::CheckPrimitiveType(control_node, prim::kPrimReturn)) { + if (common::AnfAlgo::CheckPrimitiveType(control_node, prim::kPrimReturn)) { const auto &func_graph = control_node->func_graph(); MS_EXCEPTION_IF_NULL(func_graph); const auto &actor_name = func_graph->ToString() + kEntranceActorNameSuffix; @@ -286,7 +287,7 @@ std::vector ControlNodeScheduler::BuildEntranceActor(const Gra for (const auto ¶meter : func_graph->parameters()) { const auto &abstract = parameter->abstract(); MS_EXCEPTION_IF_NULL(abstract); - size_t output_num = AnfAlgo::GetOutputNumByAbstract(abstract); + size_t output_num = common::AnfAlgo::GetOutputNumByAbstract(abstract); for (size_t i = 0; i < output_num; ++i) { (void)formal_parameters.emplace_back(parameter, i); } @@ -330,7 +331,7 @@ std::vector ControlNodeScheduler::BuildExitActor(const GraphCompil // The exit actor is used in 2 places: // 1.funcgraph output, that is the output of the return node. for (const auto &control_node : control_nodes) { - if (AnfAlgo::CheckPrimitiveType(control_node, prim::kPrimReturn)) { + if (common::AnfAlgo::CheckPrimitiveType(control_node, prim::kPrimReturn)) { const auto &func_graph = control_node->func_graph(); MS_EXCEPTION_IF_NULL(func_graph); const auto &actor_name = func_graph->ToString() + kExitActorNameSuffix; @@ -449,14 +450,14 @@ void ControlNodeScheduler::BuildStackActorForControlNode(const GraphCompilerInfo // Fetch the control actor of control node. std::string control_actor_name = ""; - if (AnfAlgo::CheckPrimitiveType(need_stack_control_node, prim::kPrimReturn)) { + if (common::AnfAlgo::CheckPrimitiveType(need_stack_control_node, prim::kPrimReturn)) { const auto &func_graph = need_stack_control_node->func_graph(); MS_EXCEPTION_IF_NULL(func_graph); control_actor_name = func_graph->ToString() + kExitActorNameSuffix; - } else if (AnfAlgo::CheckPrimitiveType(need_stack_control_node, prim::kPrimPartial) || - AnfAlgo::CheckPrimitiveType(need_stack_control_node, prim::kPrimSwitch) || - AnfAlgo::CheckPrimitiveType(need_stack_control_node, prim::kPrimSwitchLayer) || - AnfAlgo::IsCallNode(need_stack_control_node)) { + } else if (common::AnfAlgo::CheckPrimitiveType(need_stack_control_node, prim::kPrimPartial) || + common::AnfAlgo::CheckPrimitiveType(need_stack_control_node, prim::kPrimSwitch) || + common::AnfAlgo::CheckPrimitiveType(need_stack_control_node, prim::kPrimSwitchLayer) || + common::AnfAlgo::IsCallNode(need_stack_control_node)) { control_actor_name = GetActorName(need_stack_control_node); } else { MS_LOG(EXCEPTION) << "Invalid control node:" << need_stack_control_node->DebugString(); @@ -675,7 +676,8 @@ void ControlNodeScheduler::LinkArrowFromStackActor(StackActor *const stack_actor // Fetch the arrow type of input. if (to_actor->type_ == KernelTransformType::kExitActor && to_actor->node_ == nullptr && from_node->isa() && - (!AnfAlgo::IsCallNode(from_node)) && (!AnfAlgo::CheckPrimitiveType(from_node, prim::kPrimPartial)) && + (!common::AnfAlgo::IsCallNode(from_node)) && + (!common::AnfAlgo::CheckPrimitiveType(from_node, prim::kPrimPartial)) && to_actor->GetAID().Name().find( parser->FetchGroupNameByKernelGraph(parser->FetchKernelGraphByFrontNode(from_node))) != std::string::npos) { LinkArrowByKernel(from_node, to_actor, formal_parameter, {to_actor->node_, to_index}, graph_compiler_info); @@ -708,12 +710,12 @@ void ControlNodeScheduler::LinkArrowbyFormalParameter(ControlActor *const to_act } else if (from_node->isa()) { LinkArrowByParameter(from_node, to_actor, from_node_with_index, to_node_with_index, graph_compiler_info.control_node_parser_); - } else if (AnfAlgo::IsCallNode(from_node)) { + } else if (common::AnfAlgo::IsCallNode(from_node)) { // Link arrow by call node. LinkArrowByCallNode(from_node, to_actor, from_node_with_index, to_node_with_index, graph_compiler_info.control_node_parser_); - } else if (AnfAlgo::CheckPrimitiveType(from_node, prim::kPrimSwitch) || - AnfAlgo::CheckPrimitiveType(from_node, prim::kPrimSwitchLayer)) { + } else if (common::AnfAlgo::CheckPrimitiveType(from_node, prim::kPrimSwitch) || + common::AnfAlgo::CheckPrimitiveType(from_node, prim::kPrimSwitchLayer)) { // Link arrow from switch actor. const auto &actor_name = GetActorName(from_node); const auto &actor = FetchActor(actor_name); @@ -721,7 +723,7 @@ void ControlNodeScheduler::LinkArrowbyFormalParameter(ControlActor *const to_act const auto &switch_actor = dynamic_cast(actor); MS_EXCEPTION_IF_NULL(switch_actor); LinkPartialArrow(switch_actor, to_actor, from_node_with_index.second, to_node_with_index.second); - } else if (AnfAlgo::CheckPrimitiveType(from_node, prim::kPrimPartial)) { + } else if (common::AnfAlgo::CheckPrimitiveType(from_node, prim::kPrimPartial)) { // Link arrow from gather actor const auto &actor_name = GetActorName(from_node); const auto &actor = FetchActor(actor_name); @@ -1017,7 +1019,7 @@ void ControlNodeScheduler::LinkControlArrowForEntranceActor(ActorSet *const acto for (const auto &node : nodes) { // Fetch the source actor of control arrow. MS_EXCEPTION_IF_NULL(node); - if (AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { + if (common::AnfAlgo::CheckPrimitiveType(node, prim::kPrimReturn)) { actor_name = func_graph->ToString() + kExitActorNameSuffix; } else { actor_name = GetActorName(node); @@ -1155,7 +1157,7 @@ void ControlNodeScheduler::LinkControlArrowByAutoMonad(ControlActor *to_actor, c auto graph = parser->FetchKernelGraphByFrontNode(depend_node); std::vector from_actors; - if (AnfAlgo::IsCallNode(depend_node)) { + if (common::AnfAlgo::IsCallNode(depend_node)) { int branch_id = parser->FetchBranchIDByCallNode(depend_node); const auto &func_graphs = parser->FetchFuncGraphbyCallNode(depend_node); if (func_graphs.empty()) { @@ -1296,10 +1298,10 @@ void ControlNodeScheduler::LinkDataArrowByKernelGraph(const KernelGraphPtr &grap if ((!graph->is_executing_sink()) && (IsSkippedKernelActor(kernel) || !IsKernelActor(kernel))) { continue; } - for (size_t i = 0; i < AnfAlgo::GetInputNum(kernel); ++i) { - auto input_node = AnfAlgo::GetInputNode(kernel, i); + for (size_t i = 0; i < common::AnfAlgo::GetInputNum(kernel); ++i) { + auto input_node = common::AnfAlgo::GetInputNode(kernel, i); MS_EXCEPTION_IF_NULL(input_node); - auto input_with_index = AnfAlgo::VisitKernelWithReturnType(input_node, 0, false); + auto input_with_index = common::AnfAlgo::VisitKernelWithReturnType(input_node, 0, false); auto input = input_with_index.first; MS_EXCEPTION_IF_NULL(input); if (sink_input_node_linked.count(input) > 0 || HasAbstractMonad(input) || parser == nullptr || @@ -1315,7 +1317,7 @@ void ControlNodeScheduler::LinkDataArrowByKernelGraph(const KernelGraphPtr &grap from_node_with_index = tuple_node_with_index; } - if (AnfAlgo::CheckPrimitiveType(from_node_with_index.first, prim::kPrimTupleGetItem)) { + if (common::AnfAlgo::CheckPrimitiveType(from_node_with_index.first, prim::kPrimTupleGetItem)) { MS_LOG(WARNING) << "Input node:" << from_node_with_index.first->DebugString() << " for graph:" << graph->ToString() << " is a tuple get item"; from_node_with_index = FetchRealNodeByGetItem(from_node_with_index); @@ -1330,8 +1332,8 @@ void ControlNodeScheduler::LinkDataArrowByKernelGraph(const KernelGraphPtr &grap auto to_actor = FetchActor(type, "", kernel, graph); MS_EXCEPTION_IF_NULL(to_actor); size_t from_index = 0; - if (AnfAlgo::CheckPrimitiveType(from_node, prim::kPrimSwitch) || - AnfAlgo::CheckPrimitiveType(from_node, prim::kPrimSwitchLayer)) { + if (common::AnfAlgo::CheckPrimitiveType(from_node, prim::kPrimSwitch) || + common::AnfAlgo::CheckPrimitiveType(from_node, prim::kPrimSwitchLayer)) { const auto &actor_name = GetActorName(from_node); auto actor = FetchActor(actor_name); MS_EXCEPTION_IF_NULL(actor); diff --git a/mindspore/ccsrc/runtime/graph_scheduler/graph_compiler.cc b/mindspore/ccsrc/runtime/graph_scheduler/graph_compiler.cc index b73e37b9c39..83caef11f9b 100644 --- a/mindspore/ccsrc/runtime/graph_scheduler/graph_compiler.cc +++ b/mindspore/ccsrc/runtime/graph_scheduler/graph_compiler.cc @@ -22,9 +22,9 @@ #include "runtime/graph_scheduler/graph_scheduler.h" #include "runtime/op_builder/op_lazy_builder.h" #include "runtime/device/device_address.h" -#include "utils/ms_device_shape_transfer.h" -#include "utils/convert_utils.h" -#include "utils/context/graph_kernel_flags.h" +#include "runtime/device/ms_device_shape_transfer.h" +#include "include/common/utils/convert_utils.h" +#include "include/common/utils/context/graph_kernel_flags.h" #include "utils/ms_context.h" #include "ir/tensor.h" #include "backend/common/optimizer/helper.h" @@ -74,8 +74,8 @@ void CreateParameterDeviceAddress(const DeviceContext *device_context, const Ker continue; } - if (AnfAlgo::CheckPrimitiveType(item, prim::kPrimMakeTuple)) { - std::vector outs = AnfAlgo::GetAllOutput(item); + if (common::AnfAlgo::CheckPrimitiveType(item, prim::kPrimMakeTuple)) { + std::vector outs = common::AnfAlgo::GetAllOutput(item); for (const auto &out : outs) { MS_EXCEPTION_IF_NULL(out); if (!out->isa() || NodeDeviceAddressExist(device_context, out, 0)) { @@ -92,11 +92,11 @@ void CreateParameterDeviceAddress(const DeviceContext *device_context, const Ker // Create device address for anf node in nodes_list for (const auto &item : nodes_list) { - auto output_size = AnfAlgo::GetOutputTensorNum(item); + auto output_size = common::AnfAlgo::GetOutputTensorNum(item); for (size_t index = 0; index < output_size; index++) { TypeId output_type_id = AnfAlgo::GetOutputDeviceDataType(item, index); if (output_type_id == kTypeUnknown) { - output_type_id = AnfAlgo::GetOutputInferDataType(item, index); + output_type_id = common::AnfAlgo::GetOutputInferDataType(item, index); } size_t tensor_size = AnfAlgo::GetOutputTensorMemSize(item, index); @@ -104,7 +104,8 @@ void CreateParameterDeviceAddress(const DeviceContext *device_context, const Ker AnfAlgo::GetOutputFormat(item, index), output_type_id); device_address->set_from_persistent_mem(item->isa()); device_address->set_host_shape(trans::GetRuntimePaddingShape(item, index)); - MS_LOG(DEBUG) << "Create addr for node:" << AnfAlgo::GetNodeDebugString(item) << " addr:" << device_address; + MS_LOG(DEBUG) << "Create addr for node:" << common::AnfAlgo::GetNodeDebugString(item) + << " addr:" << device_address; AnfAlgo::SetOutputAddr(device_address, index, item.get()); } } @@ -139,13 +140,13 @@ void CreateDeviceAddressForTensorValue(const DeviceContext *device_context, cons size_t tensor_size = AnfAlgo::GetOutputTensorMemSize(value_node, output_idx); TypeId output_type_id = AnfAlgo::GetOutputDeviceDataType(value_node, output_idx); if (output_type_id == kTypeUnknown) { - output_type_id = AnfAlgo::GetOutputInferDataType(value_node, output_idx); + output_type_id = common::AnfAlgo::GetOutputInferDataType(value_node, output_idx); } std::string output_format = AnfAlgo::GetOutputFormat(value_node, output_idx); device::DeviceAddressPtr address = device_context->CreateDeviceAddress(nullptr, tensor_size, output_format, output_type_id); - MS_LOG(DEBUG) << "Create addr for node:" << AnfAlgo::GetNodeDebugString(value_node) << " addr:" << address; + MS_LOG(DEBUG) << "Create addr for node:" << common::AnfAlgo::GetNodeDebugString(value_node) << " addr:" << address; MS_EXCEPTION_IF_NULL(address); address->set_from_persistent_mem(true); AnfAlgo::SetOutputAddr(address, output_idx++, value_node.get()); @@ -171,7 +172,8 @@ void CreateValueNodeDeviceAddress(const DeviceContext *device_context, const Ker auto address = device_context->CreateDeviceAddress(nullptr, tensor_size, kOpFormat_DEFAULT, kNumberTypeUInt8); MS_EXCEPTION_IF_NULL(address); address->set_from_persistent_mem(true); - MS_LOG(DEBUG) << "Create addr for node:" << AnfAlgo::GetNodeDebugString(value_node) << " addr:" << address; + MS_LOG(DEBUG) << "Create addr for node:" << common::AnfAlgo::GetNodeDebugString(value_node) + << " addr:" << address; AnfAlgo::SetOutputAddr(address, 0, value_node.get()); } @@ -185,7 +187,7 @@ void CreateKernelOutputDeviceAddress(const DeviceContext *device_context, const const std::vector &kernels = graph->execution_order(); for (const auto &kernel : kernels) { MS_EXCEPTION_IF_NULL(kernel); - if (AnfAlgo::IsControlOpExecInBackend(kernel)) { + if (common::AnfAlgo::IsControlOpExecInBackend(kernel)) { continue; } @@ -202,7 +204,8 @@ void CreateKernelOutputDeviceAddress(const DeviceContext *device_context, const if (is_gradient_out) { device_address->set_from_persistent_mem(true); } - MS_LOG(DEBUG) << "Create addr for node:" << AnfAlgo::GetNodeDebugString(kernel) << " addr:" << device_address; + MS_LOG(DEBUG) << "Create addr for node:" << common::AnfAlgo::GetNodeDebugString(kernel) + << " addr:" << device_address; AnfAlgo::SetOutputAddr(device_address, i, kernel.get()); } } @@ -214,7 +217,7 @@ void CreateKernelWorkspaceDeviceAddress(const DeviceContext *device_context, con const std::vector &kernels = graph->execution_order(); for (const auto &kernel : kernels) { MS_EXCEPTION_IF_NULL(kernel); - if (AnfAlgo::IsControlOpExecInBackend(kernel)) { + if (common::AnfAlgo::IsControlOpExecInBackend(kernel)) { continue; } auto kernel_mod = AnfAlgo::GetKernelMod(kernel); @@ -225,7 +228,8 @@ void CreateKernelWorkspaceDeviceAddress(const DeviceContext *device_context, con break; } auto device_address = device_context->CreateDeviceAddress(nullptr, workspace_sizes[i], "", kTypeUnknown); - MS_LOG(DEBUG) << "Create addr for node:" << AnfAlgo::GetNodeDebugString(kernel) << " addr:" << device_address; + MS_LOG(DEBUG) << "Create addr for node:" << common::AnfAlgo::GetNodeDebugString(kernel) + << " addr:" << device_address; AnfAlgo::SetWorkspaceAddr(device_address, i, kernel.get()); } } @@ -237,10 +241,10 @@ void UpdateDeviceAddressForInplaceNode(const KernelGraphPtr &graph) { std::map> inplace_groups; const std::vector &kernels = graph->execution_order(); for (const auto &kernel : kernels) { - if (!AnfAlgo::IsInplaceNode(kernel, "inplace_algo")) { + if (!common::AnfAlgo::IsInplaceNode(kernel, "inplace_algo")) { continue; } - auto primitive = AnfAlgo::GetCNodePrimitive(kernel); + auto primitive = common::AnfAlgo::GetCNodePrimitive(kernel); MS_EXCEPTION_IF_NULL(primitive); auto inplace_group_attr = primitive->GetAttr("inplace_group"); MS_EXCEPTION_IF_NULL(inplace_group_attr); @@ -255,7 +259,7 @@ void UpdateDeviceAddressForInplaceNode(const KernelGraphPtr &graph) { continue; } // Get the device address of the first node in the inplace group. - auto node_primitive = AnfAlgo::GetCNodePrimitive(group_nodes[0]); + auto node_primitive = common::AnfAlgo::GetCNodePrimitive(group_nodes[0]); MS_EXCEPTION_IF_NULL(node_primitive); auto output_index = GetValue(node_primitive->GetAttr("inplace_output_index")); auto device_address = AnfAlgo::GetMutableOutputAddr(group_nodes[0], output_index, false); @@ -264,7 +268,7 @@ void UpdateDeviceAddressForInplaceNode(const KernelGraphPtr &graph) { // Update the device address of other nodes using device address of the first node in the inplace group. for (size_t i = 1; i < group_nodes.size(); ++i) { auto &group_node = group_nodes[i]; - auto prim = AnfAlgo::GetCNodePrimitive(group_node); + auto prim = common::AnfAlgo::GetCNodePrimitive(group_node); MS_EXCEPTION_IF_NULL(prim); auto index = GetValue(prim->GetAttr("inplace_output_index")); AnfAlgo::SetOutputAddr(device_address, index, group_node.get()); @@ -280,7 +284,7 @@ void UpdateDeviceAddressForRefNode(const KernelGraphPtr &graph) { auto &kernels = graph->execution_order(); for (auto &kernel : kernels) { MS_EXCEPTION_IF_NULL(kernel); - auto output_num = AnfAlgo::GetOutputTensorNum(kernel); + auto output_num = common::AnfAlgo::GetOutputTensorNum(kernel); if (output_num == 0) { MS_LOG(DEBUG) << "This kernel has no output size."; continue; @@ -542,7 +546,7 @@ GraphId GraphCompiler::CompileGraph(const session::OpRunInfo &op_run_info, bool auto &outputs_with_index = run_op_graph_output_nodes_[graph->graph_id()]; for (auto &node : output_nodes) { MS_EXCEPTION_IF_NULL(node); - (void)outputs_with_index.emplace_back(AnfAlgo::VisitKernelWithReturnType(node, 0, false)); + (void)outputs_with_index.emplace_back(common::AnfAlgo::VisitKernelWithReturnType(node, 0, false)); } UpdateRefCountForGraphOutput(outputs_with_index); diff --git a/mindspore/ccsrc/runtime/graph_scheduler/graph_scheduler.cc b/mindspore/ccsrc/runtime/graph_scheduler/graph_scheduler.cc index 90546bb4219..98a605ba133 100644 --- a/mindspore/ccsrc/runtime/graph_scheduler/graph_scheduler.cc +++ b/mindspore/ccsrc/runtime/graph_scheduler/graph_scheduler.cc @@ -23,15 +23,16 @@ #include "mindrt/src/actor/actormgr.h" #include "mindrt/include/async/async.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/optimizer/helper.h" #include "utils/anf_utils.h" -#include "utils/config_manager.h" +#include "include/common/utils/config_manager.h" #include "utils/log_adapter.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" #include "utils/ms_context.h" #include "utils/profile.h" #if !defined(_WIN32) && !defined(_WIN64) -#include "utils/signal_util.h" +#include "include/common/utils/signal_util.h" #endif #ifndef ENABLE_SECURITY #include "debug/data_dump/dump_json_parser.h" @@ -144,7 +145,7 @@ void ClearNodeInfo(const KernelGraphPtr &graph) { } auto front_input_node = FetchFrontNodeByBackendNode(input_node, graph); DeviceTensorStore::GetInstance().Remove(front_input_node.get()); - size_t output_num = AnfAlgo::GetOutputTensorNum(input_node); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(input_node); for (size_t index = 0; index < output_num; ++index) { if (AnfAlgo::OutputAddrExist(input_node, index)) { AnfAlgo::SetOutputAddr(nullptr, index, input_node.get()); @@ -163,7 +164,7 @@ void ClearNodeInfo(const KernelGraphPtr &graph) { // Clear cnode device tensor. for (const auto &cnode : graph->execution_order()) { - size_t output_num = AnfAlgo::GetOutputTensorNum(cnode); + size_t output_num = common::AnfAlgo::GetOutputTensorNum(cnode); for (size_t index = 0; index < output_num; ++index) { if (AnfAlgo::OutputAddrExist(cnode, index)) { AnfAlgo::SetOutputAddr(nullptr, index, cnode.get()); @@ -554,7 +555,7 @@ void GraphScheduler::CacheGraphOutputToActor(const GraphCompilerInfo &graph_comp for (const auto &graph : graph_compiler_info.graphs_) { MS_EXCEPTION_IF_NULL(graph); - auto outputs = AnfAlgo::GetAllOutputWithIndex(graph->output()); + auto outputs = common::AnfAlgo::GetAllOutputWithIndex(graph->output()); for (const auto &output_with_index : outputs) { auto output_kernel = output_with_index.first; MS_EXCEPTION_IF_NULL(output_kernel); @@ -903,15 +904,15 @@ DataPrepareActorPtr GraphScheduler::BuildDataPrepareActor(const GraphCompilerInf auto &execution_order = graph->execution_order(); for (auto &kernel : execution_order) { - if (!AnfAlgo::IsCommunicationOp(kernel)) { + if (!common::AnfAlgo::IsCommunicationOp(kernel)) { continue; } auto key = std::make_pair(kernel, graph_compiler_info.device_contexts_[index]); auto value = std::make_pair(false, false); - if (AnfAlgo::GetInputTensorNum(kernel) > 1) { + if (common::AnfAlgo::GetInputTensorNum(kernel) > 1) { value.first = true; } - if (AnfAlgo::GetOutputTensorNum(kernel) > 1) { + if (common::AnfAlgo::GetOutputTensorNum(kernel) > 1) { value.second = true; } if ((value.first == true) || (value.second == true)) { @@ -959,14 +960,14 @@ KernelActorPtr GraphScheduler::GenerateRpcActor(const CNodePtr &kernel, const De MS_EXCEPTION_IF_NULL(kernel); MS_EXCEPTION_IF_NULL(device_context); MS_EXCEPTION_IF_NULL(rpc_node_scheduler_); - if (AnfAlgo::GetCNodeName(kernel) == kRpcSendOpName) { + if (common::AnfAlgo::GetCNodeName(kernel) == kRpcSendOpName) { auto send_actor = std::make_shared(kernel->fullname_with_scope(), kernel, device_context, memory_manager_aid_, debug_aid_, recorder_aid_, strategy, ref_input_indexes, ref_output_indexes); MS_EXCEPTION_IF_NULL(send_actor); rpc_node_scheduler_->InsertSendActor(send_actor); return send_actor; - } else if (AnfAlgo::GetCNodeName(kernel) == kRpcRecvOpName) { + } else if (common::AnfAlgo::GetCNodeName(kernel) == kRpcRecvOpName) { auto recv_actor = std::make_shared(kernel->fullname_with_scope(), kernel, device_context, memory_manager_aid_, debug_aid_, recorder_aid_, strategy, ref_input_indexes, ref_output_indexes); @@ -1014,8 +1015,8 @@ void GraphScheduler::LinkDataArrowInSinkMode(const KernelGraphPtr &graph, const // Foreach the execution order to get the auto monad kernels. auto &execution_order = graph->execution_order(); (void)std::for_each(execution_order.begin(), execution_order.end(), [&](const CNodePtr &kernel) { - for (size_t i = 0; i < AnfAlgo::GetInputNum(kernel); ++i) { - auto input_node = AnfAlgo::GetInputNode(kernel, i); + for (size_t i = 0; i < common::AnfAlgo::GetInputNum(kernel); ++i) { + auto input_node = common::AnfAlgo::GetInputNode(kernel, i); if (HasAbstractMonad(input_node)) { (void)auto_monad_kernels.emplace_back(kernel); continue; @@ -1024,8 +1025,8 @@ void GraphScheduler::LinkDataArrowInSinkMode(const KernelGraphPtr &graph, const }); // Foreach auto monad kernels to get the auto monad device tensor stores. (void)std::for_each(auto_monad_kernels.begin(), auto_monad_kernels.end(), [&](const CNodePtr &kernel) { - for (size_t i = 0; i < AnfAlgo::GetInputTensorNum(kernel); ++i) { - KernelWithIndex from_kernel_with_output_idx = AnfAlgo::GetPrevNodeOutput(kernel, i, false); + for (size_t i = 0; i < common::AnfAlgo::GetInputTensorNum(kernel); ++i) { + KernelWithIndex from_kernel_with_output_idx = common::AnfAlgo::GetPrevNodeOutput(kernel, i, false); auto front_node = FetchFrontNodeByBackendNode(from_kernel_with_output_idx.first, graph); if (IsPersistentDeviceTensor(front_node)) { (void)to_actor->auto_monad_device_tensor_stores_.insert(front_node); @@ -1051,7 +1052,7 @@ void GraphScheduler::LinkDataArrowInNonSinkMode(const KernelGraphPtr &graph, // Foreach the execution order to link the actors. for (const auto &kernel : execution_order) { MS_EXCEPTION_IF_NULL(kernel); - if (AnfAlgo::IsCommunicationOp(kernel)) { + if (common::AnfAlgo::IsCommunicationOp(kernel)) { (void)communication_nodes->emplace_back(kernel); } if (IsSkippedKernelActor(kernel) || (!IsKernelActor(kernel, graph_compiler_info.strategy_))) { @@ -1060,8 +1061,8 @@ void GraphScheduler::LinkDataArrowInNonSinkMode(const KernelGraphPtr &graph, const auto &kernel_actor = FetchActor(kernel->fullname_with_scope()); MS_EXCEPTION_IF_NULL(kernel_actor); - for (size_t i = 0; i < AnfAlgo::GetInputNum(kernel); ++i) { - auto input_node = AnfAlgo::GetInputNode(kernel, i); + for (size_t i = 0; i < common::AnfAlgo::GetInputNum(kernel); ++i) { + auto input_node = common::AnfAlgo::GetInputNode(kernel, i); // Link the control arrows of kernel actor by the auto monad, the inputs include monad node. if (IsOneOfPrimitiveCNode(input_node, auto_monad_prims) || HasAbstractMonad(input_node)) { LinkControlArrowByAutoMonad(kernel_actor, input_node, graph, graph_compiler_info.control_node_parser_); @@ -1071,7 +1072,7 @@ void GraphScheduler::LinkDataArrowInNonSinkMode(const KernelGraphPtr &graph, continue; // No data arrow for monad input. } - KernelWithIndex from_kernel_with_output_idx = AnfAlgo::VisitKernelWithReturnType(input_node, 0, false); + KernelWithIndex from_kernel_with_output_idx = common::AnfAlgo::VisitKernelWithReturnType(input_node, 0, false); KernelWithIndex to_kernel_with_input_idx = std::make_pair(kernel, i); // The data arrow linking is taken over by the control flow. if (graph_compiler_info.control_node_parser_ != nullptr && @@ -1147,8 +1148,8 @@ void GraphScheduler::LinkDataArrowForInternalParameter(AbstractActor *const, Abs } else { // front node ---> actor. if (graph_output_to_actor_.count(front_output_with_index) == 0) { - MS_LOG(EXCEPTION) << "Can't find actor by front node:" << AnfAlgo::GetNodeDebugString(front_output_node) - << ", internal parameter:" << AnfAlgo::GetNodeDebugString(internal_parameter); + MS_LOG(EXCEPTION) << "Can't find actor by front node:" << common::AnfAlgo::GetNodeDebugString(front_output_node) + << ", internal parameter:" << common::AnfAlgo::GetNodeDebugString(internal_parameter); } auto actor_pair = graph_output_to_actor_[front_output_with_index]; MS_EXCEPTION_IF_NULL(actor_pair.first); @@ -1224,7 +1225,7 @@ void GraphScheduler::LinkDataArrowForKernelActor(AbstractActor *const from_actor // Update the from kernel info by the real node info. MS_EXCEPTION_IF_NULL(from_kernel); if (IsSkippedKernelActor(from_kernel)) { - real_from_kernel_with_output_idx = AnfAlgo::GetPrevNodeOutput(from_kernel, 0, false); + real_from_kernel_with_output_idx = common::AnfAlgo::GetPrevNodeOutput(from_kernel, 0, false); MS_EXCEPTION_IF_NULL(real_from_kernel_with_output_idx.first); LinkControlArrowBySkippedNode(to_actor, from_kernel); @@ -1308,7 +1309,8 @@ void GraphScheduler::LinkControlArrowByAutoMonad(AbstractActor *to_actor, const // Find the real input node, include the monad node and make tuple node. const std::vector return_types = {prim::kPrimDepend, prim::kPrimUpdateState, prim::kPrimLoad, prim::kPrimMakeTuple}; - const auto &input_kernel_with_output_idx = AnfAlgo::VisitKernelWithReturnType(from_node, 0, false, return_types); + const auto &input_kernel_with_output_idx = + common::AnfAlgo::VisitKernelWithReturnType(from_node, 0, false, return_types); MS_EXCEPTION_IF_NULL(input_kernel_with_output_idx.first); auto input_anfnode = input_kernel_with_output_idx.first; CNodePtr input_cnode = nullptr; @@ -1316,7 +1318,7 @@ void GraphScheduler::LinkControlArrowByAutoMonad(AbstractActor *to_actor, const input_cnode = input_anfnode->cast(); } // Make tuple node needs to be expanded. - if (AnfAlgo::CheckPrimitiveType(input_anfnode, prim::kPrimMakeTuple)) { + if (common::AnfAlgo::CheckPrimitiveType(input_anfnode, prim::kPrimMakeTuple)) { MS_EXCEPTION_IF_NULL(input_cnode); for (size_t i = 1; i < input_cnode->inputs().size(); ++i) { LinkControlArrowByAutoMonad(to_actor, input_cnode->input(i), graph, parser); @@ -1328,8 +1330,8 @@ void GraphScheduler::LinkControlArrowByAutoMonad(AbstractActor *to_actor, const prim::kPrimDepend, prim::kPrimUpdateState, prim::kPrimLoad, prim::kPrimMakeTuple}; // Get the real depend input by monad node which needs to link the control arrow. std::vector real_depend_inputs; - if (AnfAlgo::CheckPrimitiveType(input_anfnode, prim::kPrimDepend) || - AnfAlgo::CheckPrimitiveType(input_anfnode, prim::kPrimLoad)) { + if (common::AnfAlgo::CheckPrimitiveType(input_anfnode, prim::kPrimDepend) || + common::AnfAlgo::CheckPrimitiveType(input_anfnode, prim::kPrimLoad)) { MS_EXCEPTION_IF_NULL(input_cnode); real_depend_inputs.push_back(input_cnode->input(kDependAttachNodeIndex)); // The real input may be this scene: depend/load --> load/depend, so need add the control arrow for real input @@ -1337,7 +1339,7 @@ void GraphScheduler::LinkControlArrowByAutoMonad(AbstractActor *to_actor, const if (IsOneOfPrimitiveCNode(input_cnode->input(kRealInputIndexInDepend), recursion_prims)) { real_depend_inputs.push_back(input_cnode->input(kRealInputIndexInDepend)); } - } else if (AnfAlgo::CheckPrimitiveType(input_anfnode, prim::kPrimUpdateState)) { + } else if (common::AnfAlgo::CheckPrimitiveType(input_anfnode, prim::kPrimUpdateState)) { MS_EXCEPTION_IF_NULL(input_cnode); for (size_t i = kUpdateStateRealInput; i < input_cnode->inputs().size(); ++i) { real_depend_inputs.push_back(input_cnode->input(i)); @@ -1347,7 +1349,8 @@ void GraphScheduler::LinkControlArrowByAutoMonad(AbstractActor *to_actor, const } for (const auto &real_depend_input : real_depend_inputs) { - auto real_depend_input_with_idx = AnfAlgo::VisitKernelWithReturnType(real_depend_input, 0, false, return_types); + auto real_depend_input_with_idx = + common::AnfAlgo::VisitKernelWithReturnType(real_depend_input, 0, false, return_types); MS_EXCEPTION_IF_NULL(real_depend_input_with_idx.first); auto real_depend_kernel = real_depend_input_with_idx.first; // Update the real depend kernel in the subgraphs connecting scene. @@ -1355,7 +1358,7 @@ void GraphScheduler::LinkControlArrowByAutoMonad(AbstractActor *to_actor, const auto front_output_with_index = graph->GetFrontNodeByInternalParameter(real_depend_kernel); MS_EXCEPTION_IF_NULL(front_output_with_index.first); if (graph_output_to_actor_.count(front_output_with_index) == 0) { - if (AnfAlgo::IsCallNode(front_output_with_index.first)) { + if (common::AnfAlgo::IsCallNode(front_output_with_index.first)) { continue; } MS_LOG(EXCEPTION) << "Can't find graph output by front node:" << front_output_with_index.first->DebugString(); @@ -1406,9 +1409,9 @@ void GraphScheduler::LinkControlArrowBySkippedNode(AbstractActor *to_actor, cons MS_EXCEPTION_IF_NULL(skipped_node); // Link the control arrow from all the inputs of skipped node to the user of skipped node. - auto input_num = AnfAlgo::GetInputTensorNum(skipped_node); + auto input_num = common::AnfAlgo::GetInputTensorNum(skipped_node); for (size_t i = 0; i < input_num; ++i) { - auto kernel_with_index = AnfAlgo::GetPrevNodeOutput(skipped_node, i, false); + auto kernel_with_index = common::AnfAlgo::GetPrevNodeOutput(skipped_node, i, false); MS_EXCEPTION_IF_NULL(kernel_with_index.first); auto from_actor = FetchActor(kernel_with_index.first->fullname_with_scope()); MS_EXCEPTION_IF_NULL(from_actor); @@ -1477,7 +1480,7 @@ void GraphScheduler::LinkControlArrowBySendRecvNodes(const KernelGraphPtr &graph // In the scene of allreduce op and computing op parallel multi stream, the input memory of allreduce can be // reused only when the recv node runs finished, which is expressed by the reference count increased. - for (size_t i = 0; i < AnfAlgo::GetInputTensorNum(from_allreduce_node); ++i) { + for (size_t i = 0; i < common::AnfAlgo::GetInputTensorNum(from_allreduce_node); ++i) { auto device_tensor = AnfAlgo::GetPrevNodeMutableOutputAddr(from_allreduce_node, i, false); MS_EXCEPTION_IF_NULL(device_tensor); UpdateRefCount(device_tensor.get()); @@ -1542,7 +1545,7 @@ void GraphScheduler::LinkControlArrowForCustomActor(ActorSet *const actor_set, if (device_data_source_actor != nullptr) { auto kernel = device_data_source_actor->data_kernel(); MS_EXCEPTION_IF_NULL(kernel); - if (AnfAlgo::GetCNodeName(kernel) == kGetNextOpName) { + if (common::AnfAlgo::GetCNodeName(kernel) == kGetNextOpName) { kernel_to_actors.emplace(kernel, actor); } } @@ -1718,7 +1721,7 @@ void GraphScheduler::LinkOutputResultArrowForOutputActor(OutputActor *to_actor, for (size_t i = 0; i < graph_compiler_info.graphs_.size(); ++i) { const auto &graph = graph_compiler_info.graphs_[i]; MS_EXCEPTION_IF_NULL(graph); - auto outputs = AnfAlgo::GetAllOutputWithIndex(graph->output()); + auto outputs = common::AnfAlgo::GetAllOutputWithIndex(graph->output()); std::set> unique_output_positions; std::set unique_outputs; for (const auto &output : outputs) { @@ -1936,7 +1939,7 @@ void GraphScheduler::CheckActorValid(const ActorSet *actor_set) const { if (actor->type_ == KernelTransformType::kKernelActor) { auto kernel_actor = dynamic_cast(actor.get()); MS_EXCEPTION_IF_NULL(kernel_actor); - expect_toal_input_num = AnfAlgo::GetInputTensorNum(kernel_actor->kernel_); + expect_toal_input_num = common::AnfAlgo::GetInputTensorNum(kernel_actor->kernel_); } auto input_data_num = actor->input_datas_num_; auto device_tensor_store_num = actor->device_tensor_store_keys_.size(); diff --git a/mindspore/ccsrc/runtime/graph_scheduler/rpc_node_scheduler.cc b/mindspore/ccsrc/runtime/graph_scheduler/rpc_node_scheduler.cc index 7b36466f1ef..9ae106b5b37 100644 --- a/mindspore/ccsrc/runtime/graph_scheduler/rpc_node_scheduler.cc +++ b/mindspore/ccsrc/runtime/graph_scheduler/rpc_node_scheduler.cc @@ -15,6 +15,7 @@ */ #include "runtime/graph_scheduler/rpc_node_scheduler.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace runtime { @@ -38,10 +39,10 @@ void RpcNodeScheduler::Link(const ActorSetPtr &) { CNodePtr rpc_send_kernel = send_actor->kernel(); MS_EXCEPTION_IF_NULL(rpc_send_kernel); - auto send_dst_ranks = AnfAlgo::GetNodeAttr>(rpc_send_kernel, kAttrSendDstRanks); - auto send_dst_roles = AnfAlgo::GetNodeAttr>(rpc_send_kernel, kAttrSendDstRoles); - std::string send_src_node_name = AnfAlgo::GetNodeAttr(rpc_send_kernel, kAttrSendSrcNodeName); - std::string send_dst_node_name = AnfAlgo::GetNodeAttr(rpc_send_kernel, kAttrSendDstNodeName); + auto send_dst_ranks = common::AnfAlgo::GetNodeAttr>(rpc_send_kernel, kAttrSendDstRanks); + auto send_dst_roles = common::AnfAlgo::GetNodeAttr>(rpc_send_kernel, kAttrSendDstRoles); + std::string send_src_node_name = common::AnfAlgo::GetNodeAttr(rpc_send_kernel, kAttrSendSrcNodeName); + std::string send_dst_node_name = common::AnfAlgo::GetNodeAttr(rpc_send_kernel, kAttrSendDstNodeName); if (send_dst_ranks.empty() || send_dst_roles.empty()) { MS_LOG(EXCEPTION) << "The attributes of send node " << rpc_send_kernel->fullname_with_scope() @@ -55,10 +56,10 @@ void RpcNodeScheduler::Link(const ActorSetPtr &) { CNodePtr rpc_recv_kernel = recv_actor->kernel(); MS_EXCEPTION_IF_NULL(rpc_recv_kernel); - auto recv_src_ranks = AnfAlgo::GetNodeAttr>(rpc_recv_kernel, kAttrRecvSrcRanks); - auto recv_src_roles = AnfAlgo::GetNodeAttr>(rpc_recv_kernel, kAttrRecvSrcRoles); - std::string recv_src_node_name = AnfAlgo::GetNodeAttr(rpc_recv_kernel, kAttrRecvSrcNodeName); - std::string recv_dst_node_name = AnfAlgo::GetNodeAttr(rpc_recv_kernel, kAttrRecvDstNodeName); + auto recv_src_ranks = common::AnfAlgo::GetNodeAttr>(rpc_recv_kernel, kAttrRecvSrcRanks); + auto recv_src_roles = common::AnfAlgo::GetNodeAttr>(rpc_recv_kernel, kAttrRecvSrcRoles); + std::string recv_src_node_name = common::AnfAlgo::GetNodeAttr(rpc_recv_kernel, kAttrRecvSrcNodeName); + std::string recv_dst_node_name = common::AnfAlgo::GetNodeAttr(rpc_recv_kernel, kAttrRecvDstNodeName); if (recv_src_ranks.empty() || recv_src_roles.empty()) { MS_LOG(EXCEPTION) << "The attributes of recv node " << rpc_recv_kernel->fullname_with_scope() diff --git a/mindspore/ccsrc/runtime/hardware/device_context.h b/mindspore/ccsrc/runtime/hardware/device_context.h index b96e89af5e7..b258c73e508 100644 --- a/mindspore/ccsrc/runtime/hardware/device_context.h +++ b/mindspore/ccsrc/runtime/hardware/device_context.h @@ -27,6 +27,7 @@ #include "runtime/collective/collective_comm_lib_loader.h" #include "backend/common/session/kernel_graph.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "backend/common/optimizer/common_backend_optimization.h" namespace mindspore { diff --git a/mindspore/ccsrc/runtime/op_builder/op_lazy_builder.h b/mindspore/ccsrc/runtime/op_builder/op_lazy_builder.h index 236286601e9..7aaaa5c2b97 100644 --- a/mindspore/ccsrc/runtime/op_builder/op_lazy_builder.h +++ b/mindspore/ccsrc/runtime/op_builder/op_lazy_builder.h @@ -25,6 +25,7 @@ #include #include "backend/common/session/kernel_graph.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "runtime/hardware/device_context.h" #include "runtime/graph_scheduler/graph_scheduler.h" diff --git a/mindspore/ccsrc/transform/express_ir/mindir_exporter.cc b/mindspore/ccsrc/transform/express_ir/mindir_exporter.cc index cd74d57b5ab..81e74225b57 100644 --- a/mindspore/ccsrc/transform/express_ir/mindir_exporter.cc +++ b/mindspore/ccsrc/transform/express_ir/mindir_exporter.cc @@ -30,7 +30,7 @@ #include "utils/check_convert_utils.h" #include "debug/dump_proto.h" #include "utils/ms_utils.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "frontend/parallel/tensor_layout/tensor_layout.h" namespace mindspore { diff --git a/mindspore/ccsrc/transform/graph_ir/convert.cc b/mindspore/ccsrc/transform/graph_ir/convert.cc index b9a3db1d1a9..b6d13503c8c 100644 --- a/mindspore/ccsrc/transform/graph_ir/convert.cc +++ b/mindspore/ccsrc/transform/graph_ir/convert.cc @@ -14,23 +14,23 @@ * limitations under the License. */ -#include "transform/graph_ir/convert.h" +#include "include/transform/graph_ir/convert.h" #include #include #include -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "base/core_ops.h" #include "frontend/operator/ops.h" #include "utils/log_adapter.h" #include "ir/graph_utils.h" #include "utils/symbolic.h" -#include "utils/config_manager.h" -#include "utils/convert_utils.h" +#include "include/common/utils/config_manager.h" +#include "include/common/utils/convert_utils.h" #include "utils/ms_context.h" #include "utils/check_convert_utils.h" -#include "transform/graph_ir/op_adapter_map.h" +#include "include/transform/graph_ir/op_adapter_map.h" #include "ops/state_ops.h" #include "ops/array_ops.h" #include "ops/elewise_calculation_ops.h" @@ -38,6 +38,8 @@ #ifdef ENABLE_D #include "ops/save_ops.h" #endif +#include "transform/graph_ir/op_adapter.h" +#include "transform/graph_ir/op_adapter_desc.h" namespace mindspore { namespace transform { diff --git a/mindspore/ccsrc/transform/graph_ir/df_graph_manager.cc b/mindspore/ccsrc/transform/graph_ir/df_graph_manager.cc index d1f178bf485..0b0c87c3b57 100644 --- a/mindspore/ccsrc/transform/graph_ir/df_graph_manager.cc +++ b/mindspore/ccsrc/transform/graph_ir/df_graph_manager.cc @@ -14,12 +14,12 @@ * limitations under the License. */ -#include "transform/graph_ir/df_graph_manager.h" +#include "include/transform/graph_ir/df_graph_manager.h" #include #ifndef ENABLE_LITE_ACL -#include "pipeline/jit/parse/python_adapter.h" +#include "include/common/utils/python_adapter.h" #include "pipeline/jit/pipeline.h" #endif #ifndef NO_DLIB @@ -44,7 +44,7 @@ DfGraphManager::~DfGraphManager() { DeleteGeSession(); ClearGraph(); #ifndef ENABLE_LITE_ACL - parse::python_adapter::set_python_env_flag(false); + python_adapter::set_python_env_flag(false); #endif } diff --git a/mindspore/ccsrc/transform/graph_ir/graph_builder.cc b/mindspore/ccsrc/transform/graph_ir/graph_builder.cc index d807d0f2fc6..8ded1c5eeb8 100644 --- a/mindspore/ccsrc/transform/graph_ir/graph_builder.cc +++ b/mindspore/ccsrc/transform/graph_ir/graph_builder.cc @@ -14,7 +14,7 @@ * limitations under the License. */ -#include "transform/graph_ir/graph_builder.h" +#include "include/transform/graph_ir/graph_builder.h" #include diff --git a/mindspore/ccsrc/transform/graph_ir/graph_runner.cc b/mindspore/ccsrc/transform/graph_ir/graph_runner.cc index 18b5b837c9d..158dbc3975b 100644 --- a/mindspore/ccsrc/transform/graph_ir/graph_runner.cc +++ b/mindspore/ccsrc/transform/graph_ir/graph_runner.cc @@ -14,7 +14,7 @@ * Limitations under the License. */ -#include "transform/graph_ir/graph_runner.h" +#include "include/transform/graph_ir/graph_runner.h" #include #include #include @@ -23,12 +23,12 @@ #include "pybind11/pybind11.h" #endif #include "utils/log_adapter.h" -#include "utils/config_manager.h" +#include "include/common/utils/config_manager.h" #include "sys/time.h" -#include "utils/utils.h" -#include "utils/callbacks.h" +#include "include/common/utils/utils.h" +#include "include/common/utils/callbacks.h" #ifdef ENABLE_D -#include "utils/callbacks_ge.h" +#include "include/common/utils/callbacks_ge.h" #endif #include "utils/ms_context.h" diff --git a/mindspore/ccsrc/transform/graph_ir/op_adapter.h b/mindspore/ccsrc/transform/graph_ir/op_adapter.h index e6360b69260..c03f8c71542 100644 --- a/mindspore/ccsrc/transform/graph_ir/op_adapter.h +++ b/mindspore/ccsrc/transform/graph_ir/op_adapter.h @@ -23,7 +23,8 @@ #include "utils/hash_map.h" #include "transform/graph_ir/op_adapter_util.h" -#include "utils/utils.h" +#include "transform/graph_ir/op_adapter_base.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace transform { class OpAdapterImpl { diff --git a/mindspore/ccsrc/transform/graph_ir/op_adapter_base.h b/mindspore/ccsrc/transform/graph_ir/op_adapter_base.h index 1fdca5a2e33..4c4f7b5c21e 100644 --- a/mindspore/ccsrc/transform/graph_ir/op_adapter_base.h +++ b/mindspore/ccsrc/transform/graph_ir/op_adapter_base.h @@ -24,23 +24,13 @@ #include #include "utils/hash_map.h" -#include "transform/graph_ir/util.h" +#include "include/transform/graph_ir/util.h" #include "ir/anf.h" #include "ir/primitive.h" #include "ir/value.h" -#include "transform/graph_ir/types.h" -#ifdef ENABLE_D -#ifdef OPEN_SOURCE -#include "graph/types.h" -#endif -#endif - +#include "include/transform/graph_ir/types.h" #include "graph/operator_reg.h" -#ifdef OPEN_SOURCE -#include "ge/client/ge_api.h" -#else #include "external/ge/ge_api.h" -#endif #include "graph/tensor.h" namespace ge { @@ -64,21 +54,6 @@ namespace mindspore { namespace transform { using CusOperatorPtr = std::shared_ptr; using CustomOperator = ge::CustomOperator; - -struct OutHandler { - OperatorPtr op; - std::string out; - AnfNodePtr node; - OutHandler() : op(nullptr), out(""), node(nullptr) {} - OutHandler(const OperatorPtr &op, const std::string out, const AnfNodePtr &node = nullptr) - : op(op), out(out), node(node) {} -}; - -struct ControlEdge { - OperatorPtr src_op; - OperatorPtr dest_op; -}; - using AttrFunc = std::function; using OutputFunc = std::function; using InputOpFunc = std::function; diff --git a/mindspore/ccsrc/transform/graph_ir/op_adapter_map.cc b/mindspore/ccsrc/transform/graph_ir/op_adapter_map.cc index 9b9cf599200..eb75f792977 100644 --- a/mindspore/ccsrc/transform/graph_ir/op_adapter_map.cc +++ b/mindspore/ccsrc/transform/graph_ir/op_adapter_map.cc @@ -13,20 +13,23 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -#include "transform/graph_ir/op_adapter_map.h" +#include "include/transform/graph_ir/op_adapter_map.h" #include #include "graph/operator.h" +#include "transform/graph_ir/op_adapter_desc.h" namespace mindspore { namespace transform { +namespace { +mindspore::HashMap adpt_map_ = { + {kNameCustomOp, std::make_shared(std::make_shared>())}}; +} // namespace + template <> mindspore::HashMap> OpAdapter::cus_input_map_{}; template <> mindspore::HashMap> OpAdapter::cus_output_map_{}; -mindspore::HashMap OpAdapterMap::adpt_map_ = { - {kNameCustomOp, std::make_shared(std::make_shared>())}}; - mindspore::HashMap &OpAdapterMap::get() { return adpt_map_; } } // namespace transform } // namespace mindspore diff --git a/mindspore/ccsrc/transform/graph_ir/op_adapter_util.cc b/mindspore/ccsrc/transform/graph_ir/op_adapter_util.cc index 5f210767613..5165b6453ed 100644 --- a/mindspore/ccsrc/transform/graph_ir/op_adapter_util.cc +++ b/mindspore/ccsrc/transform/graph_ir/op_adapter_util.cc @@ -20,7 +20,7 @@ #include #include -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/check_convert_utils.h" #include "transform/graph_ir/op_adapter_base.h" #include "transform/graph_ir/io_format_map.h" diff --git a/mindspore/ccsrc/transform/graph_ir/op_declare/op_declare_macro.h b/mindspore/ccsrc/transform/graph_ir/op_declare/op_declare_macro.h index 8e205da8e57..f13bd2bd8c1 100644 --- a/mindspore/ccsrc/transform/graph_ir/op_declare/op_declare_macro.h +++ b/mindspore/ccsrc/transform/graph_ir/op_declare/op_declare_macro.h @@ -21,7 +21,8 @@ #include #include "utils/hash_map.h" #include "transform/graph_ir/op_adapter.h" -#include "transform/graph_ir/op_adapter_map.h" +#include "transform/graph_ir/op_adapter_desc.h" +#include "include/transform/graph_ir/op_adapter_map.h" #include "mindspore/core/base/core_ops.h" namespace mindspore::transform { diff --git a/mindspore/ccsrc/transform/graph_ir/util.cc b/mindspore/ccsrc/transform/graph_ir/util.cc index c84f4afd2d9..c208c87dd73 100644 --- a/mindspore/ccsrc/transform/graph_ir/util.cc +++ b/mindspore/ccsrc/transform/graph_ir/util.cc @@ -14,14 +14,14 @@ * limitations under the License. */ -#include "transform/graph_ir/util.h" +#include "include/transform/graph_ir/util.h" #include #include #include "securec/include/securec.h" -#include "utils/convert_utils.h" -#include "utils/utils.h" +#include "include/common/utils/convert_utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace transform { diff --git a/mindspore/ccsrc/utils/anfalgo.cc b/mindspore/ccsrc/utils/anfalgo.cc new file mode 100644 index 00000000000..54e4da2d200 --- /dev/null +++ b/mindspore/ccsrc/utils/anfalgo.cc @@ -0,0 +1,1624 @@ +/** + * Copyright 2019-2022 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "include/common/utils/anfalgo.h" +#include +#include +#include +#include +#include "ir/anf.h" +#include "ir/func_graph.h" +#include "base/core_ops.h" +#include "include/common/utils/utils.h" +#include "utils/shape_utils.h" +#include "utils/trace_base.h" +#include "utils/anf_utils.h" +#include "include/common/utils/parallel_context.h" +#include "utils/ms_context.h" + +namespace mindspore { +namespace common { +using abstract::AbstractTensor; +using abstract::AbstractTuple; + +namespace { +constexpr size_t kNopNodeInputSize = 2; +constexpr size_t kNopNodeRealInputIndex = 1; + +const std::set kNodeTupleOutSet = {prim::kMakeTuple, prim::kGetNext}; + +std::vector TransShapeToSizet(const abstract::ShapePtr &shape) { + MS_EXCEPTION_IF_NULL(shape); + std::vector shape_size_t; + if (AnfUtils::IsShapeDynamic(shape)) { + if (std::all_of(shape->max_shape().begin(), shape->max_shape().end(), [](int64_t s) { return s >= 0; })) { + std::transform(shape->max_shape().begin(), shape->max_shape().end(), std::back_inserter(shape_size_t), + LongToSize); + } else { + MS_LOG(EXCEPTION) << "Invalid Max Shape"; + } + } else { + std::transform(shape->shape().begin(), shape->shape().end(), std::back_inserter(shape_size_t), LongToSize); + } + return shape_size_t; +} + +enum class ShapeType { kMaxShape, kMinShape }; + +void GetRealOutputRecursively(const AnfNodePtr &node, size_t output_index, std::vector *inputs) { + MS_EXCEPTION_IF_NULL(node); + if (node->isa() || node->isa()) { + return inputs->push_back(std::make_pair(node, 0)); + } + + // Skip control node + if (AnfAlgo::CheckPrimitiveType(node, prim::kPrimDepend) || AnfAlgo::CheckPrimitiveType(node, prim::kPrimLoad) || + AnfAlgo::CheckPrimitiveType(node, prim::kPrimUpdateState)) { + return GetRealOutputRecursively(node->cast()->input(kRealInputIndexInDepend), 0, inputs); + } + + // Bypass TupleGetItem + if (AnfAlgo::CheckPrimitiveType(node, prim::kPrimTupleGetItem)) { + auto tuple_get_item = node->cast(); + MS_EXCEPTION_IF_NULL(tuple_get_item); + auto input = AnfAlgo::GetTupleGetItemRealInput(tuple_get_item); + auto index = AnfAlgo::GetTupleGetItemOutIndex(tuple_get_item); + + // Conceal MakeTuple + TupleGetItem pair. + if (AnfAlgo::CheckPrimitiveType(input, prim::kPrimMakeTuple)) { + auto make_tuple = input->cast(); + MS_EXCEPTION_IF_NULL(make_tuple); + auto real_input = AnfAlgo::GetInputNode(make_tuple, index); + return GetRealOutputRecursively(real_input, 0, inputs); + } + + // Skip TupleGetItem. + return GetRealOutputRecursively(input, index, inputs); + } + + // Flatten MakeTuple inputs. + if (AnfAlgo::CheckPrimitiveType(node, prim::kPrimMakeTuple)) { + auto make_tuple = node->cast(); + MS_EXCEPTION_IF_NULL(make_tuple); + size_t input_num = AnfAlgo::GetInputTensorNum(make_tuple); + for (size_t input_index = 0; input_index < input_num; ++input_index) { + auto input_node = AnfAlgo::GetInputNode(make_tuple, input_index); + GetRealOutputRecursively(input_node, 0, inputs); + } + return; + } + + return inputs->push_back(std::make_pair(node, output_index)); +} + +std::vector GetAllOutputWithIndexInner(const AnfNodePtr &node) { + std::vector ret; + std::vector ret_empty; + const PrimitiveSet expand_prims{ + prim::kPrimMakeTuple, + prim::kPrimMakeCSRTensor, + prim::kPrimMakeCOOTensor, + prim::kPrimMakeRowTensor, + }; + // The MakeTuple/MakeSparse node need expand and recurse. + if (IsOneOfPrimitiveCNode(node, expand_prims)) { + auto make_tuple = node->cast(); + MS_EXCEPTION_IF_NULL(make_tuple); + for (size_t i = 1; i < make_tuple->inputs().size(); i++) { + auto make_tuple_output = GetAllOutputWithIndexInner(make_tuple->input(i)); + (void)std::copy(make_tuple_output.begin(), make_tuple_output.end(), std::back_inserter(ret)); + } + return ret; + } + + // The depend node need get the real node. + if (AnfAlgo::CheckPrimitiveType(node, prim::kPrimDepend)) { + auto depend_node = node->cast(); + MS_EXCEPTION_IF_NULL(depend_node); + auto real_output = GetAllOutputWithIndexInner(depend_node->input(kRealInputIndexInDepend)); + (void)std::copy(real_output.begin(), real_output.end(), std::back_inserter(ret)); + return ret; + } + + // Value node need get all the elements. + if (node->isa()) { + auto value = node->cast()->value(); + MS_EXCEPTION_IF_NULL(value); + if (value->isa()) { + return ret; + } else if (value->isa()) { + auto value_tuple = value->cast(); + auto value_tuple_size = CountValueNum(value_tuple); + for (size_t i = 0; i < value_tuple_size; ++i) { + (void)ret.emplace_back(node, i); + } + } else { + (void)ret.emplace_back(node, 0); + } + return ret; + } + + size_t outputs_num = 1; + if (AnfUtils::IsRealCNodeKernel(node)) { + outputs_num = AnfAlgo::GetOutputTensorNum(node); + } + + // If the node is a call, the outputs num should get from the abstract. + if (AnfAlgo::IsCallNode(node) || AnfAlgo::CheckPrimitiveType(node, prim::kPrimTupleGetItem)) { + auto abstract = node->abstract(); + MS_EXCEPTION_IF_NULL(abstract); + outputs_num = AnfAlgo::GetOutputNumByAbstract(abstract); + } + + // The output may be the tuple of node, so need visit all the outputs of node. + for (size_t i = 0; i < outputs_num; ++i) { + // Maybe this scene: tupleGetItem + depend + makeTuple, can be done correctly in VisitKernelWithReturnType. + // The output may be updataState node for connecting dependencies between subgraphs. + auto output_with_index = + AnfAlgo::VisitKernelWithReturnType(node, i, false, {prim::kPrimMakeTuple, prim::kPrimUpdateState}); + MS_EXCEPTION_IF_NULL(output_with_index.first); + + // The MakeTuple/MakeSparse node need recurse. + if (IsOneOfPrimitiveCNode(output_with_index.first, expand_prims)) { + auto output_vector = GetAllOutputWithIndexInner(output_with_index.first); + (void)std::copy(output_vector.begin(), output_vector.end(), std::back_inserter(ret)); + continue; + } + + // The InitDataSetQueue node has no output. + if (AnfAlgo::CheckPrimitiveType(output_with_index.first, prim::kPrimInitDataSetQueue)) { + return ret_empty; + } + + MS_LOG(INFO) << "Output node: " << output_with_index.first->fullname_with_scope() + << " with output index: " << output_with_index.second; + ret.push_back(output_with_index); + } + return ret; +} + +bool IsNodeDynamicShape(const AnfNodePtr &node) { + MS_EXCEPTION_IF_NULL(node); + if (!node->isa()) { + MS_LOG(DEBUG) << "Node is not a cnode"; + return false; + } + auto cnode = node->cast(); + auto in_dynamic = AnfAlgo::IsNodeInputDynamicShape(cnode); + auto out_dynamic = AnfUtils::IsNodeOutputDynamicShape(cnode); + if (in_dynamic && !AnfAlgo::HasNodeAttr(kAttrInputIsDynamicShape, cnode)) { + AnfAlgo::SetNodeAttr(kAttrInputIsDynamicShape, MakeValue(true), cnode); + MS_LOG(DEBUG) << "Set Input Dynamic Shape Attr to Node:" << cnode->fullname_with_scope(); + } + if (out_dynamic && !AnfAlgo::HasNodeAttr(kAttrOutputIsDynamicShape, cnode)) { + AnfAlgo::SetNodeAttr(kAttrOutputIsDynamicShape, MakeValue(true), cnode); + MS_LOG(DEBUG) << "Set Output Dynamic Shape Attr to Node:" << cnode->fullname_with_scope(); + } + return in_dynamic || out_dynamic; +} +} // namespace + +AnfNodePtr AnfAlgo::GetTupleGetItemRealInput(const CNodePtr &tuple_get_item) { + MS_EXCEPTION_IF_NULL(tuple_get_item); + if (tuple_get_item->size() != kTupleGetItemInputSize) { + MS_LOG(EXCEPTION) << "The node tuple_get_item must have 2 inputs!"; + } + return tuple_get_item->input(kRealInputNodeIndexInTupleGetItem); +} + +size_t AnfAlgo::GetTupleGetItemOutIndex(const CNodePtr &tuple_get_item) { + MS_EXCEPTION_IF_NULL(tuple_get_item); + if (tuple_get_item->size() != kTupleGetItemInputSize) { + MS_LOG(EXCEPTION) << "The node tuple_get_item must have 2 inputs!"; + } + auto output_index_value_node = tuple_get_item->input(kInputNodeOutputIndexInTupleGetItem); + MS_EXCEPTION_IF_NULL(output_index_value_node); + auto value_node = output_index_value_node->cast(); + MS_EXCEPTION_IF_NULL(value_node); + return LongToSize(GetValue(value_node->value())); +} + +KernelWithIndex AnfAlgo::VisitKernel(const AnfNodePtr &anf_node, size_t index) { + // this function was moved to AnfUtils. + return AnfUtils::VisitKernel(anf_node, index); +} + +KernelWithIndex AnfAlgo::VisitKernelWithReturnType(const AnfNodePtr &anf_node, size_t index, bool skip_nop_node, + const std::vector &return_types, + abstract::AbstractBasePtr *abstract) { + MS_EXCEPTION_IF_NULL(anf_node); + if (std::any_of(return_types.begin(), return_types.end(), [&anf_node](const PrimitivePtr &prim_type) -> bool { + return CheckPrimitiveType(anf_node, prim_type); + })) { + return KernelWithIndex(anf_node, index); + } + if (!anf_node->isa()) { + return KernelWithIndex(anf_node, index); + } + auto cnode = anf_node->cast(); + MS_EXCEPTION_IF_NULL(cnode); + if (CheckPrimitiveType(cnode, prim::kPrimTupleGetItem)) { + abstract::AbstractBasePtr abs = nullptr; + auto item_with_index_tmp = VisitKernelWithReturnType( + GetTupleGetItemRealInput(cnode), GetTupleGetItemOutIndex(cnode), skip_nop_node, return_types, &abs); + if (CheckPrimitiveType(item_with_index_tmp.first, prim::kPrimMakeTuple)) { + MS_EXCEPTION_IF_NULL(item_with_index_tmp.first); + auto make_tuple = item_with_index_tmp.first->cast(); + MS_EXCEPTION_IF_NULL(make_tuple); + const std::vector &make_tuple_inputs = make_tuple->inputs(); + size_t make_tuple_input_index = item_with_index_tmp.second + 1; + if (make_tuple_input_index >= make_tuple_inputs.size()) { + MS_LOG(EXCEPTION) << "Index[" << make_tuple_input_index << "] out of range[" << make_tuple_inputs.size() + << "]."; + } + return VisitKernelWithReturnType(make_tuple_inputs[make_tuple_input_index], index, skip_nop_node, return_types); + } + if (IsCallNode(item_with_index_tmp.first) || item_with_index_tmp.first->isa()) { + size_t real_index = item_with_index_tmp.second; + if (abs == nullptr) { + abs = item_with_index_tmp.first->abstract(); + real_index = 0; + } + MS_EXCEPTION_IF_NULL(abs); + if (abs->isa()) { + auto tuple_abstract = abs->cast(); + MS_EXCEPTION_IF_NULL(tuple_abstract); + auto sub_abstracts = tuple_abstract->elements(); + if (sub_abstracts.size() <= GetTupleGetItemOutIndex(cnode)) { + MS_LOG(EXCEPTION) << "Invalid index:" << GetTupleGetItemOutIndex(cnode) + << " for abstract:" << abs->ToString(); + } + for (size_t i = 0; i < GetTupleGetItemOutIndex(cnode); ++i) { + MS_EXCEPTION_IF_NULL(sub_abstracts[i]); + real_index += AnfAlgo::GetOutputNumByAbstract(sub_abstracts[i]); + } + if (abstract != nullptr) { + (*abstract) = sub_abstracts[GetTupleGetItemOutIndex(cnode)]; + MS_EXCEPTION_IF_NULL((*abstract)); + } else { + // In recursion of getitem node, the index of the first input of its real node is returned. + // When the recursion ends, the outermost index needs to be accumulated. + real_index += index; + } + return {item_with_index_tmp.first, real_index}; + } + } + return item_with_index_tmp; + } + if (AnfAlgo::CheckPrimitiveType(cnode, prim::kPrimUpdateState)) { + return VisitKernelWithReturnType(cnode->input(kUpdateStateStateInput), index, skip_nop_node, return_types); + } + const PrimitiveSet follow_first_input_prims = {prim::kPrimDepend, prim::kPrimLoad}; + if (IsOneOfPrimitiveCNode(cnode, follow_first_input_prims)) { + return VisitKernelWithReturnType(cnode->input(kRealInputIndexInDepend), index, skip_nop_node, return_types); + } + if (IsNopNode(cnode) && skip_nop_node) { + if (cnode->size() != kNopNodeInputSize) { + MS_LOG(EXCEPTION) << "Invalid nop node " << cnode->DebugString() << trace::DumpSourceLines(cnode); + } + return VisitKernelWithReturnType(cnode->input(kNopNodeRealInputIndex), 0, skip_nop_node, return_types); + } + return KernelWithIndex(anf_node, index); +} + +std::vector AnfAlgo::GetAllOutput(const AnfNodePtr &node, const std::vector &return_types) { + std::vector ret; + auto return_prim_type = return_types; + // if visited make_tuple should return back + return_prim_type.push_back(prim::kPrimMakeTuple); + auto item_with_index = AnfAlgo::VisitKernelWithReturnType(node, 0, false, return_prim_type); + if (AnfAlgo::CheckPrimitiveType(item_with_index.first, prim::kPrimMakeTuple)) { + MS_EXCEPTION_IF_NULL(item_with_index.first); + auto make_tuple = item_with_index.first->cast(); + MS_EXCEPTION_IF_NULL(make_tuple); + for (size_t i = 1; i < make_tuple->inputs().size(); i++) { + auto input_i_vector = GetAllOutput(make_tuple->input(i), return_types); + (void)std::copy(input_i_vector.begin(), input_i_vector.end(), std::back_inserter(ret)); + } + return ret; + } + ret.push_back(item_with_index.first); + return ret; +} + +size_t AnfAlgo::GetOutputNumByAbstract(const AbstractBasePtr &node_abstract) { + MS_EXCEPTION_IF_NULL(node_abstract); + size_t result = 0; + if (node_abstract->isa()) { + auto csr_tensor_abstract = node_abstract->cast(); + MS_EXCEPTION_IF_NULL(csr_tensor_abstract); + result += GetOutputNumByAbstract(csr_tensor_abstract->indptr()); + result += GetOutputNumByAbstract(csr_tensor_abstract->indices()); + result += GetOutputNumByAbstract(csr_tensor_abstract->values()); + result += GetOutputNumByAbstract(csr_tensor_abstract->dense_shape()); + return result; + } + + if (node_abstract->isa()) { + auto coo_tensor_abstract = node_abstract->cast(); + MS_EXCEPTION_IF_NULL(coo_tensor_abstract); + result += GetOutputNumByAbstract(coo_tensor_abstract->dense_shape()); + result += GetOutputNumByAbstract(coo_tensor_abstract->indices()); + result += GetOutputNumByAbstract(coo_tensor_abstract->values()); + return result; + } + + if (!node_abstract->isa()) { + return 1; + } + + auto tuple_abstract = node_abstract->cast(); + MS_EXCEPTION_IF_NULL(tuple_abstract); + const auto &sub_abstracts = tuple_abstract->elements(); + for (const auto &sub_abstract : sub_abstracts) { + MS_EXCEPTION_IF_NULL(sub_abstract); + result += GetOutputNumByAbstract(sub_abstract); + } + return result; +} + +std::vector AnfAlgo::GetAllOutputWithIndex(const AnfNodePtr &node) { + auto ret = GetAllOutputWithIndexInner(node); + std::map value_node_index; + + // Unify the output of the front and back end to the ValueTuple + for (auto &output_with_index : ret) { + auto value_node = output_with_index.first; + if (!value_node->isa()) { + continue; + } + if (value_node_index.find(value_node) == value_node_index.end() || + value_node_index[value_node] < output_with_index.second) { + value_node_index[value_node] = output_with_index.second; + } else { + value_node_index[value_node]++; + MS_LOG(INFO) << "Set output value node new index, value node: " << value_node->fullname_with_scope() + << ", original index: " << output_with_index.second + << ", new index:" << value_node_index[value_node]; + output_with_index.second = value_node_index[value_node]; + } + } + return ret; +} + +bool AnfAlgo::CheckPrimitiveType(const AnfNodePtr &node, const PrimitivePtr &primitive_type) { + MS_EXCEPTION_IF_NULL(node); + if (!node->isa()) { + return false; + } + auto cnode = node->cast(); + MS_EXCEPTION_IF_NULL(cnode); + return IsPrimitive(cnode->input(kAnfPrimitiveIndex), primitive_type); +} + +FuncGraphPtr AnfAlgo::GetCNodeFuncGraphPtr(const AnfNodePtr &node) { + MS_EXCEPTION_IF_NULL(node); + auto cnode = node->cast(); + MS_EXCEPTION_IF_NULL(cnode); + auto attr_input = cnode->input(kAnfPrimitiveIndex); + MS_EXCEPTION_IF_NULL(attr_input); + auto value_node = attr_input->cast(); + MS_EXCEPTION_IF_NULL(value_node); + auto value = value_node->value(); + MS_EXCEPTION_IF_NULL(value); + return value->cast(); +} + +std::string AnfAlgo::GetCNodeName(const AnfNodePtr &node) { + // this function was moved to AnfUtils. + return AnfUtils::GetCNodeName(node); +} + +std::string AnfAlgo::GetNodeDebugString(const AnfNodePtr &node) { + MS_EXCEPTION_IF_NULL(node); + return node->DebugString(); +} + +void AnfAlgo::SetNodeAttr(const std::string &key, const ValuePtr &value, const AnfNodePtr &node) { + // this function was moved to AnfUtils. + return AnfUtils::SetNodeAttr(key, value, node); +} + +void AnfAlgo::CopyNodeAttr(const std::string &key, const AnfNodePtr &from, const AnfNodePtr &to) { + CopyNodeAttr(key, key, from, to); +} + +void AnfAlgo::CopyNodeAttr(const std::string &old_key, const std::string &new_key, const AnfNodePtr &from, + const AnfNodePtr &to) { + MS_EXCEPTION_IF_NULL(from); + MS_EXCEPTION_IF_NULL(to); + if (!from->isa() || !to->isa()) { + MS_LOG(EXCEPTION) << "Only cnode has attr, but this from_anf is " << from->DebugString() << " ,to_node is " + << to->DebugString() << trace::DumpSourceLines(from); + } + auto from_primitive = AnfAlgo::GetCNodePrimitive(from); + MS_EXCEPTION_IF_NULL(from_primitive); + auto to_primitive = AnfAlgo::GetCNodePrimitive(to); + MS_EXCEPTION_IF_NULL(to_primitive); + to_primitive->set_attr(new_key, from_primitive->GetAttr(old_key)); +} + +void AnfAlgo::CopyNodeAttrs(const AnfNodePtr &from, const AnfNodePtr &to) { + MS_EXCEPTION_IF_NULL(from); + MS_EXCEPTION_IF_NULL(to); + if (!from->isa() || !to->isa()) { + MS_LOG(EXCEPTION) << "Only cnode has attr, but this from_anf is " << from->DebugString() << ",to_node is " + << from->DebugString() << trace::DumpSourceLines(from); + } + auto from_primitive = AnfAlgo::GetCNodePrimitive(from); + MS_EXCEPTION_IF_NULL(from_primitive); + auto to_primitive = AnfAlgo::GetCNodePrimitive(to); + MS_EXCEPTION_IF_NULL(to_primitive); + auto from_cnode = from->cast(); + auto to_cnode = to->cast(); + if (from_cnode->HasPrimalAttr(kAttrMicro)) { + to_cnode->AddPrimalAttr(kAttrMicro, from_cnode->GetPrimalAttr(kAttrMicro)); + } + (void)to_primitive->SetAttrs(from_primitive->attrs()); +} + +void AnfAlgo::EraseNodeAttr(const std::string &key, const AnfNodePtr node) { + MS_EXCEPTION_IF_NULL(node); + if (!node->isa()) { + MS_LOG(EXCEPTION) << "Only cnode has attr, but this anf is " << node->DebugString() << trace::DumpSourceLines(node); + } + // single op cnode. + auto primitive = AnfAlgo::GetCNodePrimitive(node); + if (primitive != nullptr) { + primitive->EraseAttr(key); + return; + } + // graph kernel cnode. + auto fg = AnfAlgo::GetCNodeFuncGraphPtr(node); + MS_EXCEPTION_IF_NULL(fg); + fg->erase_flag(key); +} + +bool AnfAlgo::HasNodeAttr(const std::string &key, const CNodePtr &node) { + MS_EXCEPTION_IF_NULL(node); + if (!node->isa()) { + MS_LOG(WARNING) << "Only cnode has attr, but this anf is " << node->DebugString(); + return false; + } + // call node's input0 is not a primitive. + if (!IsValueNode(node->cast()->input(0))) { + return false; + } + // single op cnode. + auto primitive = AnfAlgo::GetCNodePrimitive(node); + if (primitive != nullptr) { + return primitive->HasAttr(key); + } + // graph kernel cnode. + auto fg = AnfAlgo::GetCNodeFuncGraphPtr(node); + MS_EXCEPTION_IF_NULL(fg); + return fg->has_attr(key); +} + +size_t AnfAlgo::GetInputNum(const CNodePtr &cnode) { + MS_EXCEPTION_IF_NULL(cnode); + size_t input_num = cnode->size(); + if (input_num == 0) { + MS_LOG(EXCEPTION) << "Cnode inputs size can't be zero." << trace::DumpSourceLines(cnode); + } + return input_num - 1; +} + +size_t AnfAlgo::GetInputTensorNum(const AnfNodePtr &node) { + // this function was moved to AnfUtils. + return AnfUtils::GetInputTensorNum(node); +} + +size_t AnfAlgo::GetOutputTensorNum(const AnfNodePtr &node) { + // this function was moved to AnfUtils. + return AnfUtils::GetOutputTensorNum(node); +} + +KernelWithIndex AnfAlgo::GetPrevNodeOutput(const AnfNodePtr &anf_node, size_t input_idx, bool skip_nop_node) { + MS_EXCEPTION_IF_NULL(anf_node); + if (!anf_node->isa()) { + MS_LOG(EXCEPTION) << anf_node->DebugString() << "anf_node is not CNode." << trace::DumpSourceLines(anf_node); + } + auto kernel_info = anf_node->kernel_info(); + if (kernel_info) { + auto runtime_cache = kernel_info->runtime_cache(); + if (runtime_cache.runtime_cache().is_valid()) { + auto output = runtime_cache.runtime_cache().get_prev_node_output(input_idx); + if (output.first != nullptr) { + return output; + } + } + } + KernelWithIndex res; + if (CheckPrimitiveType(anf_node, prim::kPrimTupleGetItem)) { + res = VisitKernelWithReturnType(anf_node, 0, skip_nop_node); + } else { + auto input_node = AnfAlgo::GetInputNode(anf_node->cast(), input_idx); + MS_EXCEPTION_IF_NULL(input_node); + res = VisitKernelWithReturnType(input_node, 0, skip_nop_node); + } + if (kernel_info) { + auto runtime_cache = kernel_info->runtime_cache(); + if (runtime_cache.runtime_cache().is_valid()) { + runtime_cache.runtime_cache().set_prev_node_output(input_idx, res); + } + } + return res; +} + +std::vector AnfAlgo::GetOutputInferShape(const AnfNodePtr &node, const abstract::BaseShapePtr &base_shape, + size_t output_idx) { + MS_EXCEPTION_IF_NULL(node); + MS_EXCEPTION_IF_NULL(base_shape); + if (base_shape->isa()) { + if (output_idx == 0) { + return TransShapeToSizet(base_shape->cast()); + } + MS_LOG(EXCEPTION) << "The node " << node->DebugString() << "is a single output node but got index [" << output_idx + << trace::DumpSourceLines(node); + } else if (base_shape->isa()) { + auto tuple_shape = base_shape->cast(); + MS_EXCEPTION_IF_NULL(tuple_shape); + if (output_idx >= tuple_shape->size()) { + MS_LOG(EXCEPTION) << "Output index " << output_idx << "is larger than output number " << tuple_shape->size() + << node->DebugString() << trace::DumpSourceLines(node); + } + auto b_shp = (*tuple_shape)[output_idx]; + if (b_shp->isa()) { + return TransShapeToSizet(b_shp->cast()); + } else if (b_shp->isa()) { + return std::vector(); + } else if (b_shp->isa()) { + // Usually there is no tuple in tuple for the shape of the kernel graph parameter, but there will be such a + // scenario when dump ir is in the compilation process, here return an empty shape so that dump ir can work + // normally. + MS_LOG(INFO) << "The output shape of node:" << node->DebugString() << " index:" << output_idx + << " is a TupleShape:" << base_shape->ToString(); + return std::vector(); + } else { + MS_LOG(EXCEPTION) << "The output type of ApplyKernel index:" << output_idx + << " should be a NoShape , ArrayShape or a TupleShape, but it is " << base_shape->ToString() + << "node :" << node->DebugString() << "." << trace::DumpSourceLines(node); + } + } else if (base_shape->isa()) { + return std::vector(); + } + MS_LOG(EXCEPTION) << "The output type of ApplyKernel should be a NoShape , ArrayShape or a TupleShape, but it is " + << base_shape->ToString() << " node : " << node->DebugString() << trace::DumpSourceLines(node); +} + +std::vector AnfAlgo::GetOutputInferShape(const AnfNodePtr &node, size_t output_idx) { + MS_EXCEPTION_IF_NULL(node); + return GetOutputInferShape(node, node->Shape(), output_idx); +} + +std::vector AnfAlgo::GetPrevNodeOutputInferShape(const AnfNodePtr &node, size_t input_idx) { + KernelWithIndex kernel_with_index = AnfAlgo::GetPrevNodeOutput(node, input_idx); + return AnfAlgo::GetOutputInferShape(kernel_with_index.first, kernel_with_index.second); +} + +TypeId AnfAlgo::GetOutputInferDataType(const TypePtr &type, size_t output_idx) { + auto type_ptr = type; + MS_EXCEPTION_IF_NULL(type_ptr); + if (type_ptr->isa()) { + auto tuple_ptr = type_ptr->cast(); + MS_EXCEPTION_IF_NULL(tuple_ptr); + if (output_idx >= tuple_ptr->size()) { + MS_LOG(EXCEPTION) << "Output index " << output_idx << " must be less than output number " << tuple_ptr->size(); + } + type_ptr = (*tuple_ptr)[output_idx]; + MS_EXCEPTION_IF_NULL(type_ptr); + } + + if (type_ptr->isa()) { + auto tensor_ptr = type_ptr->cast(); + MS_EXCEPTION_IF_NULL(tensor_ptr); + TypePtr elem = tensor_ptr->element(); + MS_EXCEPTION_IF_NULL(elem); + return elem->type_id(); + } + + if (type_ptr->isa()) { + auto tensor_ptr = type_ptr->cast(); + MS_EXCEPTION_IF_NULL(tensor_ptr); + TypePtr elem = tensor_ptr->element(); + MS_EXCEPTION_IF_NULL(elem); + return elem->type_id(); + } + + return type_ptr->type_id(); +} + +TypeId AnfAlgo::GetOutputInferDataType(const AnfNodePtr &node, size_t output_idx) { + MS_EXCEPTION_IF_NULL(node); + return GetOutputInferDataType(node->Type(), output_idx); +} + +TypeId AnfAlgo::GetPrevNodeOutputInferDataType(const AnfNodePtr &node, size_t input_idx) { + KernelWithIndex kernel_with_index = AnfAlgo::GetPrevNodeOutput(node, input_idx); + return AnfAlgo::GetOutputInferDataType(kernel_with_index.first, kernel_with_index.second); +} + +abstract::BaseShapePtr AnfAlgo::GetOutputDetailShape(const AnfNodePtr &node, size_t output_idx) { + MS_EXCEPTION_IF_NULL(node); + auto base_shape = node->Shape(); + MS_EXCEPTION_IF_NULL(base_shape); + if (base_shape->isa()) { + if (output_idx == 0) { + return base_shape; + } + MS_LOG(EXCEPTION) << "The node " << node->DebugString() << "is a single output node but got index [" << output_idx + << "]." << trace::DumpSourceLines(node); + } else if (base_shape->isa()) { + auto tuple_shape = base_shape->cast(); + MS_EXCEPTION_IF_NULL(tuple_shape); + if (output_idx >= tuple_shape->size()) { + MS_LOG(EXCEPTION) << "Output index " << output_idx << "is larger than output number " << tuple_shape->size() + << " node:" << node->DebugString() << "." << trace::DumpSourceLines(node); + } + auto b_shp = (*tuple_shape)[output_idx]; + if (b_shp->isa() || b_shp->isa()) { + return b_shp; + } else { + MS_LOG(EXCEPTION) << "The output type of ApplyKernel index:" << output_idx + << " should be a NoShape , ArrayShape or a TupleShape, but it is " << base_shape->ToString() + << "node :" << node->DebugString() << "." << trace::DumpSourceLines(node); + } + } else if (base_shape->isa()) { + return base_shape; + } + MS_LOG(EXCEPTION) << "The output type of ApplyKernel should be a NoShape , ArrayShape or a TupleShape, but it is " + << base_shape->ToString() << " node : " << node->DebugString() << trace::DumpSourceLines(node); +} + +abstract::BaseShapePtr AnfAlgo::GetPrevNodeOutputDetailShape(const AnfNodePtr &node, size_t input_idx) { + KernelWithIndex kernel_with_index = AnfAlgo::GetPrevNodeOutput(node, input_idx); + return AnfAlgo::GetOutputDetailShape(kernel_with_index.first, kernel_with_index.second); +} + +// set infer shapes and types of anf node +void AnfAlgo::SetOutputTypeAndDetailShape(const std::vector &types, + const std::vector &shapes, AnfNode *node) { + MS_EXCEPTION_IF_NULL(node); + auto node_ptr = node->cast(); + MS_EXCEPTION_IF_NULL(node_ptr); + std::string node_name = ""; + if (node_ptr->isa()) { + node_name = GetCNodeName(node_ptr); + } + if (types.size() != shapes.size()) { + MS_LOG(EXCEPTION) << "Types size " << types.size() << "should be same with shapes size " << shapes.size() << "." + << trace::DumpSourceLines(node); + } + + auto tuple_node = kNodeTupleOutSet.find(node_name); + if (shapes.empty() && tuple_node == kNodeTupleOutSet.end()) { + node->set_abstract(std::make_shared()); + } else if (shapes.size() == 1 && tuple_node == kNodeTupleOutSet.end()) { + // single output handle + auto abstract = std::make_shared(TypeIdToType(types[0]), shapes[0]); + node->set_abstract(abstract); + } else { + // multiple output handle + std::vector abstract_list; + for (size_t i = 0; i < types.size(); ++i) { + auto abstract = std::make_shared(TypeIdToType(types[i]), shapes[i]); + abstract_list.emplace_back(abstract); + } + auto abstract_tuple = std::make_shared(abstract_list); + node->set_abstract(abstract_tuple); + } +} + +// set infer shapes and types of anf node +void AnfAlgo::SetOutputInferTypeAndShape(const std::vector &types, + const std::vector> &shapes, AnfNode *node) { + MS_EXCEPTION_IF_NULL(node); + auto node_ptr = node->cast(); + std::string node_name = ""; + if (node_ptr->isa()) { + node_name = GetCNodeName(node_ptr); + } + MS_EXCEPTION_IF_NULL(node_ptr); + if (types.size() != shapes.size()) { + MS_LOG(EXCEPTION) << "Types size " << types.size() << "should be same with shapes size " << shapes.size() << "." + << trace::DumpSourceLines(node); + } + auto abstract_ptr = node_ptr->abstract(); + + auto tuple_node = kNodeTupleOutSet.find(node_name); + if (shapes.empty() && tuple_node == kNodeTupleOutSet.end()) { + node->set_abstract(std::make_shared()); + } else if (shapes.size() == 1 && tuple_node == kNodeTupleOutSet.end()) { + // single output handle + ShapeVector shape_int; + abstract::AbstractTensorPtr abstract = nullptr; + if (abstract_ptr != nullptr) { + auto max_shape0 = GetOutputMaxShape(node_ptr, 0); + auto min_shape0 = GetOutputMinShape(node_ptr, 0); + std::transform(shapes[0].begin(), shapes[0].end(), std::back_inserter(shape_int), SizeToLong); + abstract = std::make_shared(TypeIdToType(types[0]), + std::make_shared(shape_int, min_shape0, max_shape0)); + } else { + abstract = std::make_shared(TypeIdToType(types[0]), shape_int); + } + node->set_abstract(abstract); + } else { + // multiple output handle + std::vector abstract_list; + for (size_t i = 0; i < types.size(); ++i) { + ShapeVector shape_int; + abstract::AbstractTensorPtr abstract = nullptr; + if (abstract_ptr != nullptr) { + auto max_shape = GetOutputMaxShape(node_ptr, i); + auto min_shape = GetOutputMinShape(node_ptr, i); + std::transform(shapes[i].begin(), shapes[i].end(), std::back_inserter(shape_int), SizeToLong); + abstract = std::make_shared(TypeIdToType(types[i]), + std::make_shared(shape_int, min_shape, max_shape)); + } else { + abstract = + std::make_shared(TypeIdToType(types[i]), std::make_shared(shape_int)); + } + abstract_list.emplace_back(abstract); + } + auto abstract_tuple = std::make_shared(abstract_list); + node->set_abstract(abstract_tuple); + } +} +// copy an abstract of a node to another node +void AnfAlgo::CopyAbstract(const AnfNodePtr &from_node, AnfNode *to_node) { + MS_EXCEPTION_IF_NULL(from_node); + MS_EXCEPTION_IF_NULL(to_node); + to_node->set_abstract(from_node->abstract()); +} + +bool AnfAlgo::IsNodeInGraphKernel(const AnfNodePtr &node) { + // this function was moved to AnfUtils. + return AnfUtils::IsNodeInGraphKernel(node); +} + +AnfNodePtr AnfAlgo::GetOutputOfGraphkernel(const KernelWithIndex &kernel_with_index) { + auto func_graph = GetCNodeFuncGraph(kernel_with_index.first); + if (func_graph == nullptr) { + return kernel_with_index.first; + } + auto output = func_graph->output(); + if (CheckPrimitiveType(output, prim::kPrimMakeTuple)) { + return output->cast()->input(kernel_with_index.second + 1); + } + return output; +} + +bool AnfAlgo::IsParameterWeight(const ParameterPtr &node) { + MS_EXCEPTION_IF_NULL(node); + return node->has_default(); +} + +bool AnfAlgo::IsLabelIndexInNode(const AnfNodePtr &node, size_t label_index) { + MS_EXCEPTION_IF_NULL(node); + if (!node->isa()) { + return false; + } + auto cnode = node->cast(); + MS_EXCEPTION_IF_NULL(cnode); + if (AnfAlgo::GetCNodeName(cnode) == kLabelGotoOpName && + (AnfAlgo::GetNodeAttr(cnode, kAttrLabelIndex) == label_index)) { + return true; + } else if (AnfAlgo::GetCNodeName(cnode) == kLabelSwitchOpName) { + auto label_list = AnfAlgo::GetNodeAttr>(cnode, kAttrLabelSwitchList); + if (std::find(label_list.begin(), label_list.end(), label_index) != label_list.end()) { + return true; + } + } + return false; +} + +bool AnfAlgo::IsUpdateParameterKernel(const CNodePtr &node) { + MS_EXCEPTION_IF_NULL(node); + auto node_name = GetCNodeName(node); + if (HasNodeAttr(kAttrAsync, node) && GetNodeAttr(node, kAttrAsync)) { + return false; + } + if (kOptOperatorSet.find(node_name) == kOptOperatorSet.end() && node_name.find("Assign") == string::npos) { + return false; + } + return true; +} + +bool AnfAlgo::IsTupleOutput(const AnfNodePtr &anf) { + MS_EXCEPTION_IF_NULL(anf); + TypePtr type = anf->Type(); + if (type == nullptr) { + return false; + } + MS_EXCEPTION_IF_NULL(type); + return type->isa(); +} + +AnfNodePtr AnfAlgo::GetInputNode(const CNodePtr &node, size_t index) { + MS_EXCEPTION_IF_NULL(node); + auto get_input_index = index + 1; + if (get_input_index >= node->inputs().size()) { + MS_LOG(EXCEPTION) << "Input index size " << get_input_index << "but the node input size just" + << node->inputs().size() << "." << trace::DumpSourceLines(node); + } + // input 0 is primitive node + return node->input(get_input_index); +} + +void AnfAlgo::SetNodeInput(const CNodePtr &node, const AnfNodePtr &input_node, size_t index) { + MS_EXCEPTION_IF_NULL(node); + MS_EXCEPTION_IF_NULL(input_node); + node->set_input(index + 1, input_node); +} + +AnfNodePtr AnfAlgo::GetCNodePrimitiveNode(const CNodePtr &node) { + MS_EXCEPTION_IF_NULL(node); + return node->input(kAnfPrimitiveIndex); +} + +PrimitivePtr AnfAlgo::GetCNodePrimitive(const AnfNodePtr &node) { + MS_EXCEPTION_IF_NULL(node); + auto cnode = node->cast(); + MS_EXCEPTION_IF_NULL(cnode); + auto attr_input = GetCNodePrimitiveNode(cnode); + MS_EXCEPTION_IF_NULL(attr_input); + auto value_node = attr_input->cast(); + MS_EXCEPTION_IF_NULL(value_node); + auto value = value_node->value(); + MS_EXCEPTION_IF_NULL(value); + auto primitive = value->cast(); + return primitive; +} + +bool AnfAlgo::IsInplaceNode(const mindspore::AnfNodePtr &kernel, const string &type) { + MS_EXCEPTION_IF_NULL(kernel); + auto primitive = AnfAlgo::GetCNodePrimitive(kernel); + if (!primitive) { + return false; + } + + auto inplace_attr = primitive->GetAttr(type); + if (inplace_attr == nullptr) { + return false; + } + + return true; +} + +bool AnfAlgo::IsCommunicationOp(const AnfNodePtr &node) { + static const std::set kCommunicationOpNames = {kAllReduceOpName, kAllGatherOpName, kBroadcastOpName, + kReduceScatterOpName, kHcomSendOpName, kReceiveOpName, + kAllToAllVOpName}; + MS_EXCEPTION_IF_NULL(node); + if (!node->isa()) { + return false; + } + auto kernel_name = AnfAlgo::GetCNodeName(node); + return (kCommunicationOpNames.find(kernel_name) != kCommunicationOpNames.end()); +} + +bool AnfAlgo::IsFusedCommunicationOp(const AnfNodePtr &node) { + if (!IsCommunicationOp(node)) { + return false; + } + auto primitive = AnfAlgo::GetCNodePrimitive(node); + MS_EXCEPTION_IF_NULL(primitive); + ValuePtr attr_fusion = primitive->GetAttr(kAttrFusion); + ValuePtr attr_not_delay_fusion = primitive->GetAttr(kAttrNotDelayFusion); + if (attr_fusion == nullptr) { + return false; + } + + auto fusion = GetValue(attr_fusion); + if (fusion == 0) { + return false; + } + if (attr_not_delay_fusion && GetValue(attr_not_delay_fusion)) { + return false; + } + return true; +} + +bool AnfAlgo::IsGetNext(const NotNull &node) { + auto kernel_name = AnfAlgo::GetCNodeName(node); + return kernel_name == kGetNextOpName; +} + +bool AnfAlgo::IsGraphKernel(const AnfNodePtr &node) { + // this function was moved to AnfUtils. + return AnfUtils::IsGraphKernel(node); +} + +bool AnfAlgo::IsNeedSkipNopOpAddr(const AnfNodePtr &node) { + MS_EXCEPTION_IF_NULL(node); + if (!node->isa()) { + return false; + } + + auto primitive = AnfAlgo::GetCNodePrimitive(node); + if (primitive == nullptr) { + return false; + } + + auto skip_nop_op_addr_attr = primitive->GetAttr(kAttrSkipNopOpAddr); + if (skip_nop_op_addr_attr == nullptr) { + return false; + } + + return GetValue(skip_nop_op_addr_attr); +} + +FuncGraphPtr AnfAlgo::GetValueNodeFuncGraph(const AnfNodePtr &node) { + MS_EXCEPTION_IF_NULL(node); + auto value_node = node->cast(); + if (value_node == nullptr) { + return nullptr; + } + auto value = value_node->value(); + if (value == nullptr) { + return nullptr; + } + auto func_graph = value->cast(); + return func_graph; +} + +bool AnfAlgo::IsSwitchCall(const CNodePtr &call_node) { + MS_EXCEPTION_IF_NULL(call_node); + if (!CheckPrimitiveType(call_node, prim::kPrimCall)) { + MS_LOG(EXCEPTION) << "Call node should be a 'call', but is a " << call_node->DebugString() << "." + << trace::DumpSourceLines(call_node); + } + auto input1 = call_node->input(1); + MS_EXCEPTION_IF_NULL(input1); + if (input1->isa()) { + return false; + } else if (input1->isa() && AnfAlgo::CheckPrimitiveType(input1, prim::kPrimSwitch)) { + return true; + } + MS_LOG(EXCEPTION) << "Unexpected input1 of call node,input1:" << input1->DebugString() << "." + << trace::DumpSourceLines(call_node); +} + +bool AnfAlgo::IsScalarInput(const CNodePtr &cnode, size_t index) { + auto shape = AnfAlgo::GetPrevNodeOutputInferShape(cnode, index); + if (shape.empty()) { + return true; + } + return shape.size() == kShape1dDims && shape[0] == 1; +} + +bool AnfAlgo::IsScalarOutput(const CNodePtr &cnode, size_t index) { + auto shape = AnfAlgo::GetPrevNodeOutputInferShape(cnode, index); + if (shape.empty()) { + return true; + } + return shape.size() == kShape1dDims && shape[0] == 1; +} + +namespace { +void FindDelayExecPosition(const std::vector &nodes, size_t current_index, std::set *invalid_position, + std::map> *insert_nodes) { + MS_EXCEPTION_IF_NULL(invalid_position); + MS_EXCEPTION_IF_NULL(insert_nodes); + if (current_index >= nodes.size()) { + return; + } + auto &node = nodes[current_index]; + for (size_t j = current_index + 1; j < nodes.size(); ++j) { + auto &child = nodes[j]; + auto input_size = child->inputs().size() - 1; + for (size_t k = 0; k < input_size; ++k) { + auto kernel_index = AnfAlgo::VisitKernelWithReturnType(AnfAlgo::GetInputNode(child, k), 0, true); + if (kernel_index.first != node) { + continue; + } + if (AnfAlgo::GetCNodeName(child) == kApplyMomentumOpName) { + return; + } + (void)invalid_position->insert(current_index); + auto iter = insert_nodes->find(j); + if (iter != insert_nodes->end()) { + iter->second.emplace_back(node); + } else { + (*insert_nodes)[j] = {node}; + } + return; + } + } +} + +std::vector DelayExecNode(const std::vector &nodes, const std::string &node_name, bool only_seed) { + std::map> insert_nodes; + std::set invalid_position; + for (size_t i = 0; i < nodes.size(); ++i) { + auto &node = nodes[i]; + if (AnfAlgo::GetCNodeName(node) != node_name) { + continue; + } + if (only_seed) { + bool is_seed = true; + auto input_size = node->inputs().size() - 1; + for (size_t k = 0; k < input_size; ++k) { + auto input = AnfAlgo::VisitKernelWithReturnType(AnfAlgo::GetInputNode(node, k), 0, true).first; + if (input != nullptr && input->isa()) { + is_seed = false; + break; + } + } + if (!is_seed) { + continue; + } + } + FindDelayExecPosition(nodes, i, &invalid_position, &insert_nodes); + } + std::vector result; + for (size_t i = 0; i < nodes.size(); ++i) { + auto iter = insert_nodes.find(i); + if (iter != insert_nodes.end()) { + (void)result.insert(result.end(), iter->second.rbegin(), iter->second.rend()); + } + if (invalid_position.find(i) != invalid_position.end()) { + continue; + } + result.emplace_back(nodes[i]); + } + return result; +} +} // namespace + +void AnfAlgo::ReorderExecList(NotNull *> node_list) { + std::vector result; + std::copy(node_list->begin(), node_list->end(), std::back_inserter(result)); + result = DelayExecNode(result, kTransDataOpName, true); + result = DelayExecNode(result, kCastOpName, true); + result = DelayExecNode(result, kAdamApplyOneWithDecayOpName, false); + result = DelayExecNode(result, kAdamApplyOneOpName, false); + if (parallel::ParallelContext::GetInstance()->pipeline_stage_split_num() > 1) { + result = DelayExecNode(result, kDropoutGenMaskOpName, true); + } + node_list->clear(); + std::copy(result.begin(), result.end(), std::back_inserter(*node_list)); +} + +void AnfAlgo::ReorderPosteriorExecList(NotNull *> node_list) { + std::vector ordinary_node_list; + std::vector posterior_node_list; + + for (const auto &node : *node_list) { + MS_EXCEPTION_IF_NULL(node); + if (kPosteriorOperatorSet.find(AnfAlgo::GetCNodeName(node)) != kPosteriorOperatorSet.end()) { + posterior_node_list.emplace_back(node); + } else { + ordinary_node_list.emplace_back(node); + } + } + node_list->clear(); + std::copy(ordinary_node_list.begin(), ordinary_node_list.end(), std::back_inserter(*node_list)); + std::copy(posterior_node_list.begin(), posterior_node_list.end(), std::back_inserter(*node_list)); +} + +TypeId AnfAlgo::GetCNodeOutputPrecision(const AnfNodePtr &node) { + MS_EXCEPTION_IF_NULL(node); + auto prim = AnfAlgo::GetCNodePrimitive(node); + if (prim == nullptr) { + return kTypeUnknown; + } + + TypeId except_type = kTypeUnknown; + if (prim->GetAttr(kAttrOutputPrecision) != nullptr) { + auto output_type_str = GetValue(prim->GetAttr(kAttrOutputPrecision)); + if (output_type_str == "float16") { + except_type = kNumberTypeFloat16; + } else if (output_type_str == "float32") { + except_type = kNumberTypeFloat32; + } else { + MS_LOG(EXCEPTION) << "The fix precision must be float16 or float32, but got " << output_type_str << "." + << trace::DumpSourceLines(node); + } + } + + return except_type; +} + +TypeId AnfAlgo::GetPrevNodeOutputPrecision(const AnfNodePtr &node, size_t input_idx) { + MS_EXCEPTION_IF_NULL(node); + if (!node->isa()) { + MS_LOG(EXCEPTION) << node->DebugString() << ", input node is not CNode." << trace::DumpSourceLines(node); + } + auto cnode = node->cast(); + MS_EXCEPTION_IF_NULL(cnode); + if (input_idx + 1 >= cnode->inputs().size()) { + MS_LOG(EXCEPTION) << "Input index " << input_idx << " is larger than input number " << GetInputTensorNum(cnode) + << "." << trace::DumpSourceLines(node); + } + auto input_node = cnode->input(input_idx + 1); + MS_EXCEPTION_IF_NULL(input_node); + auto kernel_with_index = VisitKernel(input_node, 0); + if (!kernel_with_index.first->isa()) { + return kTypeUnknown; + } + return GetCNodeOutputPrecision(kernel_with_index.first); +} + +bool AnfAlgo::IsCondControlKernel(const CNodePtr &node) { + MS_EXCEPTION_IF_NULL(node); + if (node->inputs().empty()) { + MS_LOG(EXCEPTION) << "Illegal null input of cnode." << trace::DumpSourceLines(node); + } + auto input = node->input(kAnfPrimitiveIndex); + return IsPrimitive(input, prim::kPrimLabelGoto) || IsPrimitive(input, prim::kPrimLabelSwitch); +} + +bool AnfAlgo::GetBooleanAttr(const AnfNodePtr &node, const std::string &attr) { + MS_EXCEPTION_IF_NULL(node); + if (!node->isa()) { + return false; + } + auto cnode = node->cast(); + MS_EXCEPTION_IF_NULL(cnode); + auto has_attr = AnfAlgo::HasNodeAttr(attr, cnode); + if (!has_attr) { + return false; + } + return AnfAlgo::GetNodeAttr(node, attr); +} + +std::optional AnfAlgo::GetDumpFlag(const AnfNodePtr &node) { + MS_EXCEPTION_IF_NULL(node); + auto cnode = node->cast(); + if (cnode == nullptr || !AnfAlgo::HasNodeAttr(kAttrDump, cnode)) { + return {}; + } + return std::optional{AnfAlgo::GetNodeAttr(node, kAttrDump)}; +} + +bool AnfAlgo::HasDynamicShapeFlag(const PrimitivePtr &prim) { + auto get_bool_attr = [](const PrimitivePtr &primitive, const std::string &attr_name) -> bool { + MS_EXCEPTION_IF_NULL(primitive); + if (!primitive->HasAttr(attr_name)) { + return false; + } + return GetValue(primitive->GetAttr(attr_name)); + }; + return get_bool_attr(prim, kAttrInputIsDynamicShape) || get_bool_attr(prim, kAttrOutputIsDynamicShape); +} + +bool AnfAlgo::IsDynamicShape(const AnfNodePtr &node) { + MS_EXCEPTION_IF_NULL(node); + if (!node->isa()) { + MS_LOG(DEBUG) << "Node is not a cnode."; + return false; + } + auto cnode = node->cast(); + if ((!HasNodeAttr(kAttrInputIsDynamicShape, cnode)) && (!HasNodeAttr(kAttrOutputIsDynamicShape, cnode))) { + auto ret = IsNodeDynamicShape(node); + MS_LOG(DEBUG) << "The Node:" << node->fullname_with_scope() << " is dynamic shape or not:" << ret; + return ret; + } + return GetBooleanAttr(node, kAttrInputIsDynamicShape) || GetBooleanAttr(node, kAttrOutputIsDynamicShape); +} + +void AnfAlgo::GetRealDynamicShape(const std::vector &shape, NotNull *> dynamic_shape) { + for (auto size : shape) { + if (size == SIZE_MAX) { + dynamic_shape->push_back(-1); + } else { + dynamic_shape->push_back(SizeToLong(size)); + } + } +} + +std::vector GetShapeFromSequenceShape(const abstract::SequenceShapePtr &sequeue_shape_ptr, size_t index, + ShapeType type) { + MS_EXCEPTION_IF_NULL(sequeue_shape_ptr); + auto shape_list = sequeue_shape_ptr->shape(); + if (index >= shape_list.size()) { + MS_LOG(EXCEPTION) << "Output Index:" << index << " >= " << shape_list.size(); + } + + auto shape = shape_list[index]; + MS_EXCEPTION_IF_NULL(shape); + if (shape->isa()) { + auto shape_ptr = shape->cast(); + if (type == ShapeType::kMaxShape) { + return shape_ptr->max_shape().empty() ? shape_ptr->shape() : shape_ptr->max_shape(); + } else { + return shape_ptr->min_shape().empty() ? shape_ptr->shape() : shape_ptr->min_shape(); + } + } else { + MS_LOG(EXCEPTION) << "Invalid Shape Type In Shape List"; + } +} + +std::vector AnfAlgo::GetInputMaxShape(const AnfNodePtr &anf_node, size_t index) { + auto input_node_with_index = AnfAlgo::GetPrevNodeOutput(anf_node, index); + return GetOutputMaxShape(input_node_with_index.first, input_node_with_index.second); +} + +std::vector AnfAlgo::GetInputMinShape(const AnfNodePtr &anf_node, size_t index) { + auto input_node_with_index = AnfAlgo::GetPrevNodeOutput(anf_node, index); + return GetOutputMinShape(input_node_with_index.first, input_node_with_index.second); +} + +std::vector AnfAlgo::GetOutputMaxShape(const AnfNodePtr &anf_node, size_t index) { + MS_EXCEPTION_IF_NULL(anf_node); + auto shape = anf_node->Shape(); + MS_EXCEPTION_IF_NULL(shape); + if (shape->isa()) { + auto shape_ptr = shape->cast(); + return shape_ptr->max_shape().empty() ? shape_ptr->shape() : shape_ptr->max_shape(); + } else if (shape->isa()) { + auto sequeue_shape_ptr = shape->cast(); + return GetShapeFromSequenceShape(sequeue_shape_ptr, index, ShapeType::kMaxShape); + } else if (shape->isa()) { + return {}; + } else { + MS_LOG(EXCEPTION) << "Invalid shape type." << trace::DumpSourceLines(anf_node); + } +} + +std::vector AnfAlgo::GetOutputMinShape(const AnfNodePtr &anf_node, size_t index) { + MS_EXCEPTION_IF_NULL(anf_node); + auto shape = anf_node->Shape(); + MS_EXCEPTION_IF_NULL(shape); + if (shape->isa()) { + auto shape_ptr = shape->cast(); + return shape_ptr->min_shape().empty() ? shape_ptr->shape() : shape_ptr->min_shape(); + } else if (shape->isa()) { + auto sequeue_shape_ptr = shape->cast(); + return GetShapeFromSequenceShape(sequeue_shape_ptr, index, ShapeType::kMinShape); + } else if (shape->isa()) { + return {}; + } else { + MS_LOG(EXCEPTION) << "Invalid shape type." << trace::DumpSourceLines(anf_node); + } +} + +bool AnfAlgo::IsNodeInputDynamicShape(const CNodePtr &anf_node_ptr) { + MS_EXCEPTION_IF_NULL(anf_node_ptr); + auto input_num = AnfAlgo::GetInputTensorNum(anf_node_ptr); + for (size_t i = 0; i < input_num; ++i) { + auto input_with_index = AnfAlgo::GetPrevNodeOutput(anf_node_ptr, i); + auto input = input_with_index.first; + auto index = input_with_index.second; + MS_EXCEPTION_IF_NULL(input); + auto base_shape = input->Shape(); + if (base_shape == nullptr) { + MS_LOG(INFO) << "Invalid shape ptr, node:" << input->fullname_with_scope(); + continue; + } + if (base_shape->isa()) { + if (AnfUtils::IsShapeDynamic(base_shape->cast())) { + return true; + } + } else if (base_shape->isa()) { + auto tuple_shape = base_shape->cast(); + MS_EXCEPTION_IF_NULL(tuple_shape); + + if (index >= tuple_shape->size()) { + MS_LOG(INFO) << "Node:" << anf_node_ptr->fullname_with_scope() << "Invalid index:" << index + << " and tuple_shape size:" << tuple_shape->size(); + continue; + } + auto b_shp = (*tuple_shape)[index]; + if (!b_shp->isa()) { + continue; + } + if (AnfUtils::IsShapeDynamic(b_shp->cast())) { + return true; + } + } + } + return false; +} + +void AnfAlgo::GetAllVisitedCNode(const CNodePtr &anf_node, std::vector *used_kernels, + std::set *visited) { + MS_EXCEPTION_IF_NULL(anf_node); + MS_EXCEPTION_IF_NULL(used_kernels); + MS_EXCEPTION_IF_NULL(visited); + if (visited->find(anf_node) != visited->end()) { + MS_LOG(INFO) << "Node:" << anf_node->fullname_with_scope() << " has already been visited"; + return; + } + visited->insert(anf_node); + auto input_size = anf_node->inputs().size() - 1; + for (size_t i = 0; i < input_size; ++i) { + auto input = AnfAlgo::GetInputNode(anf_node, i); + if (!input->isa()) { + continue; + } + if (!AnfUtils::IsRealKernel(input) || IsNopNode(input)) { + GetAllVisitedCNode(input->cast(), used_kernels, visited); + } else { + used_kernels->push_back(input); + } + } +} + +void AnfAlgo::GetAllFatherRealNode(const AnfNodePtr &anf_node, std::vector *result, + std::set *visited) { + MS_EXCEPTION_IF_NULL(anf_node); + MS_EXCEPTION_IF_NULL(result); + MS_EXCEPTION_IF_NULL(visited); + if (visited->find(anf_node) != visited->end()) { + MS_LOG(INFO) << "Node:" << anf_node->fullname_with_scope() << " has already been visited"; + return; + } + visited->insert(anf_node); + if (AnfUtils::IsRealKernel(anf_node)) { + result->emplace_back(anf_node); + return; + } + if (!anf_node->isa()) { + return; + } + auto cnode = anf_node->cast(); + MS_EXCEPTION_IF_NULL(cnode); + if (cnode->inputs().empty()) { + MS_LOG(EXCEPTION) << "Illegal null input of cnode(%s)" << anf_node->DebugString() << "." + << trace::DumpSourceLines(cnode); + } + auto input0 = cnode->input(0); + if (IsPrimitive(input0, prim::kPrimMakeTuple)) { + for (size_t i = 1; i < cnode->inputs().size(); ++i) { + GetAllFatherRealNode(cnode->input(i), result, visited); + } + } else if (IsPrimitive(input0, prim::kPrimTupleGetItem)) { + if (cnode->inputs().size() != kTupleGetItemInputSize) { + MS_LOG(EXCEPTION) << "The node tuple_get_item must have 2 inputs!"; + } + GetAllFatherRealNode(cnode->input(kRealInputNodeIndexInTupleGetItem), result, visited); + } else if (IsPrimitive(input0, prim::kPrimDepend)) { + if (cnode->inputs().size() != kDependInputSize) { + MS_LOG(EXCEPTION) << "Depend node must have 2 inputs!" << trace::DumpSourceLines(cnode); + } + GetAllFatherRealNode(cnode->input(kRealInputIndexInDepend), result, visited); + GetAllFatherRealNode(cnode->input(kDependAttachNodeIndex), result, visited); + } +} + +bool AnfAlgo::IsHostKernel(const CNodePtr &kernel_node) { + const std::set host_kernel = {prim::kPrimDynamicShape->name(), prim::kPrimReshape->name(), + prim::kPrimDynamicBroadcastGradientArgs->name(), + prim::kPrimTensorShape->name()}; + auto op_name = AnfAlgo::GetCNodeName(kernel_node); + if (host_kernel.find(op_name) == host_kernel.end()) { + return false; + } + return true; +} + +void AnfAlgo::AddArgList(AbstractBasePtrList *args_spec_list, const AnfNodePtr &cnode_input, + const AnfNodePtr &real_input, size_t index) { + if (AnfAlgo::CheckPrimitiveType(cnode_input, prim::kPrimTupleGetItem)) { + // cppcheck-suppress unreadVariable + auto lock = AnfUtils::GetAbstractLock(real_input.get()); + auto base_shape = real_input->Shape(); + if (!base_shape->isa()) { + MS_LOG(EXCEPTION) << "Node input is a tuple_get_item but real input node shape is not a TupleShape. trace: " + << trace::DumpSourceLines(real_input); + } + auto abs = real_input->abstract()->Clone()->cast(); + MS_EXCEPTION_IF_NULL(abs); + auto tuple_get_item_indexk = AnfAlgo::GetTupleGetItemOutIndex(cnode_input->cast()); + auto abs_i = abs->elements()[tuple_get_item_indexk]; + (void)args_spec_list->emplace_back(abs_i); + } else if (cnode_input->isa() && AnfAlgo::GetCNodeName(cnode_input) == prim::kPrimReshape->name()) { + // cppcheck-suppress unreadVariable + auto lock = AnfUtils::GetAbstractLock(cnode_input.get()); + (void)args_spec_list->emplace_back(cnode_input->abstract()->Clone()); + } else { + // cppcheck-suppress unreadVariable + auto lock = AnfUtils::GetAbstractLock(real_input.get()); + (void)args_spec_list->emplace_back(real_input->abstract()->Clone()); + } +} + +AnfNodeIndexSet AnfAlgo::GetUpdateStateUsers(const FuncGraphManagerPtr &manager, const AnfNodePtr &node) { + AnfNodeIndexSet update_states; + for (auto &user : manager->node_users()[node]) { + if (AnfAlgo::CheckPrimitiveType(user.first, prim::kPrimUpdateState)) { + update_states.insert(user); + } + } + return update_states; +} + +void AnfAlgo::GetRealInputs(const AnfNodePtr &node, std::vector *inputs) { + size_t input_num = AnfAlgo::GetInputTensorNum(node); + for (size_t input_index = 0; input_index < input_num; ++input_index) { + auto input_node = AnfAlgo::GetInputNode(node->cast(), input_index); + GetRealOutputRecursively(input_node, 0, inputs); + } +} + +bool AnfAlgo::IsTensorBroadcast(const std::vector &lhs, const std::vector &rhs) { + if (lhs.size() != rhs.size()) { + return true; + } + for (size_t i = 0; i < lhs.size(); i++) { + if (lhs[i] != rhs[i]) { + return true; + } + } + return false; +} + +bool AnfAlgo::IsControlOpExecInBackend(const AnfNodePtr &node) { + if (!node->isa()) { + return false; + } + // Operators in set control_ops_exec_in_backend will be compiled into kernel graph, rather than be cut into single op + // and executed in VM. + static std::set control_ops_exec_in_backend = {kBpropCutOpName}; + return control_ops_exec_in_backend.find(AnfAlgo::GetCNodeName(node)) != control_ops_exec_in_backend.end(); +} + +bool AnfAlgo::IsNodeInputContainMonad(const AnfNodePtr &node) { + auto input_size = GetInputTensorNum(node); + for (size_t i = 0; i < input_size; ++i) { + auto input_with_index = GetPrevNodeOutput(node, i); + if (HasAbstractMonad(input_with_index.first)) { + return true; + } + } + return false; +} + +bool AnfAlgo::IsNonTaskOp(const CNodePtr &node) { + auto op_name = GetCNodeName(node); + return (op_name == kSplitOpName || op_name == kSplitVOpName) && AnfAlgo::HasNodeAttr(kAttrNonTask, node); +} + +bool AnfAlgo::IsNoneInput(const AnfNodePtr &node, size_t index) { + auto op_name = GetCNodeName(node); + constexpr auto none_placeholder_index = 3; + if (op_name == kDynamicRNNOpName && index == none_placeholder_index) { + return true; + } + if (op_name == kDynamicGRUV2OpName) { + auto none_index = AnfAlgo::GetNodeAttr>(node, kAttrPlaceHolderIndex); + auto item = std::find(none_index.begin(), none_index.end(), index); + if (item != none_index.end()) { + return true; + } + } + return false; +} + +bool AnfAlgo::IsCallNode(const AnfNodePtr &node) { + MS_EXCEPTION_IF_NULL(node); + if (!node->isa()) { + return false; + } + auto input0 = node->cast()->input(0); + if (IsValueNode(input0)) { + return false; + } + return true; +} + +int64_t AnfAlgo::GetAttrGroups(const AnfNodePtr &node, size_t index) { + if (node == nullptr) { + return 1; + } + if (node->isa()) { + auto cnode = node->cast(); + if (AnfAlgo::HasNodeAttr(kAttrFracZGroup, cnode)) { + auto node_name = AnfAlgo::GetCNodeName(cnode); + if (node_name == kAllReduceOpName || node_name == kBroadcastOpName) { + // if index not exists in fracz_group_idx, return default value 1 + auto fz_group_idx = AnfAlgo::GetNodeAttr>(cnode, kAttrFracZGroupIdx); + int64_t out_index = SizeToLong(index); + auto fz_iter = std::find(std::begin(fz_group_idx), std::end(fz_group_idx), out_index); + if (fz_iter == std::end(fz_group_idx)) { + return 1; + } + } + return AnfAlgo::GetNodeAttr(cnode, kAttrFracZGroup); + } + } else if (node->isa()) { + auto param = node->cast(); + MS_EXCEPTION_IF_NULL(param); + return param->fracz_group(); + } + return 1; +} + +AnfNodePtr AnfAlgo::GetTupleIndexes(const AnfNodePtr &node, std::vector *const index_stack) { + MS_EXCEPTION_IF_NULL(node); + MS_EXCEPTION_IF_NULL(index_stack); + + if (IsPrimitiveCNode(node, prim::kPrimTupleGetItem)) { + auto tuple_getitem = node->cast(); + MS_EXCEPTION_IF_NULL(tuple_getitem); + // Get cur index + auto output_index_value_node = tuple_getitem->input(kInputNodeOutputIndexInTupleGetItem); + MS_EXCEPTION_IF_NULL(output_index_value_node); + auto value_node = output_index_value_node->cast(); + MS_EXCEPTION_IF_NULL(value_node); + auto output_idx = LongToSize(GetValue(value_node->value())); + index_stack->push_back(output_idx); + auto real_input = tuple_getitem->input(kRealInputNodeIndexInTupleGetItem); + return GetTupleIndexes(real_input, index_stack); + } + if (IsPrimitiveCNode(node, prim::kPrimMakeTuple)) { + // If make_tuple in make_tuple, visit may start with inner tuple_getitem. + if (index_stack->empty()) { + MS_LOG(WARNING) << "Visit make tuple: " << node->DebugString() + << ", but index are empty, visit should not start with inner tuple_getitem."; + return nullptr; + } + auto make_tuple = node->cast(); + MS_EXCEPTION_IF_NULL(make_tuple); + auto output_idx = index_stack->back(); + index_stack->pop_back(); + return GetTupleIndexes(make_tuple->input(1 + output_idx), index_stack); + } + if (IsPrimitiveCNode(node, prim::kPrimDepend)) { + return GetTupleIndexes(node->cast()->input(kRealInputIndexInDepend), index_stack); + } + if (IsPrimitiveCNode(node, prim::kPrimLoad)) { + return GetTupleIndexes(node->cast()->input(1), index_stack); + } + MS_LOG(DEBUG) << "Get real node:" << node->DebugString(); + return node; +} + +bool AnfAlgo::IsNopNode(const AnfNodePtr &node) { + auto context_ptr = MsContext::GetInstance(); + MS_EXCEPTION_IF_NULL(context_ptr); + auto target = GetCNodeTarget(node); + if (target == kCPUDevice) { + return false; + } + if (context_ptr->get_param(MS_CTX_DEVICE_TARGET) != kAscendDevice && + context_ptr->get_param(MS_CTX_DEVICE_TARGET) != kGPUDevice) { + return false; + } + + static mindspore::HashSet nop_nodes = {prim::kPrimReshape->name(), kExpandDimsOpName, + prim::kPrimSqueeze->name(), prim::kPrimFlatten->name(), + kFlattenGradOpName, prim::kPrimReformat->name()}; + if (node == nullptr || !node->isa()) { + return false; + } + CNodePtr cnode = node->cast(); + MS_EXCEPTION_IF_NULL(cnode); + if (cnode->inputs().empty()) { + return false; + } + auto input0 = cnode->input(0); + MS_EXCEPTION_IF_NULL(input0); + if (!input0->isa()) { + return false; + } + bool is_nop_node = false; + if (AnfAlgo::HasNodeAttr(kAttrNopOp, cnode)) { + is_nop_node = AnfAlgo::GetNodeAttr(cnode, kAttrNopOp); + } + if (nop_nodes.find(AnfAlgo::GetCNodeName(cnode)) == nop_nodes.end() && !is_nop_node) { + return false; + } + + if (cnode->size() != kNopNodeInputSize) { + return false; + } + return true; +} +} // namespace common +} // namespace mindspore diff --git a/mindspore/ccsrc/utils/callbacks.cc b/mindspore/ccsrc/utils/callbacks.cc index 6bc326dfaeb..a6b759252db 100644 --- a/mindspore/ccsrc/utils/callbacks.cc +++ b/mindspore/ccsrc/utils/callbacks.cc @@ -14,13 +14,12 @@ * limitations under the License. */ -#include "utils/callbacks.h" +#include "include/common/utils/callbacks.h" #include #include #include #include "pybind11/pybind11.h" -#include "pipeline/jit/parse/python_adapter.h" -#include "utils/visible.h" +#include "include/common/utils/python_adapter.h" namespace mindspore { namespace callbacks { @@ -53,7 +52,7 @@ uint32_t SummarySaveCallback(uint32_t graph_id, const std::map(ret); if (!bool_ret) { MS_LOG(ERROR) << "Python checkpoint return false during callback"; diff --git a/mindspore/ccsrc/utils/callbacks_ge.cc b/mindspore/ccsrc/utils/callbacks_ge.cc index 6bd3d135a00..51f571d49cb 100644 --- a/mindspore/ccsrc/utils/callbacks_ge.cc +++ b/mindspore/ccsrc/utils/callbacks_ge.cc @@ -15,14 +15,13 @@ */ #ifdef ENABLE_D -#include "utils/callbacks_ge.h" +#include "include/common/utils/callbacks_ge.h" #include "pybind11/pybind11.h" #include "ir/param_info.h" -#include "transform/graph_ir/df_graph_manager.h" -#include "transform/graph_ir/util.h" +#include "include/transform/graph_ir/df_graph_manager.h" +#include "include/transform/graph_ir/util.h" #include "pipeline/jit/parse/data_converter.h" -#include "pipeline/jit/parse/python_adapter.h" -#include "utils/visible.h" +#include "include/common/utils/python_adapter.h" #include "utils/shape_utils.h" namespace mindspore { @@ -107,8 +106,7 @@ uint32_t CheckpointSaveCallback(uint32_t graph_id, const std::map(ret); uint32_t status = Status::SUCCESS; @@ -176,7 +174,7 @@ uint32_t SummarySaveCallback(uint32_t graph_id, const std::map(ret); if (!bool_ret) { MS_LOG(ERROR) << "Python checkpoint return false during callback"; diff --git a/mindspore/ccsrc/utils/comm_manager.cc b/mindspore/ccsrc/utils/comm_manager.cc index a017977c52a..9bfb540aa0e 100644 --- a/mindspore/ccsrc/utils/comm_manager.cc +++ b/mindspore/ccsrc/utils/comm_manager.cc @@ -1,5 +1,5 @@ /** - * Copyright 2019 Huawei Technologies Co., Ltd + * Copyright 2019-2022 Huawei Technologies Co., Ltd * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -14,213 +14,78 @@ * limitations under the License. */ -#include "utils/comm_manager.h" -#include "utils/convert_utils.h" +#include "include/common/utils/comm_manager.h" +#include "include/common/utils/convert_utils.h" #include "utils/ms_context.h" -#include "frontend/parallel/context.h" -#include "frontend/parallel/group_manager.h" - -#ifndef NO_DLIB -#include "plugin/device/ascend/hal/hccl_adapter/hccl_adapter.h" -#include "hccl/hcom.h" -#include "plugin/device/ascend/hal/device/distribute/ascend_collective.h" -#endif - -#if defined(ENABLE_GPU) -#include "plugin/device/gpu/hal/device/distribution/collective_init.h" -using CollectiveInitializer = mindspore::device::gpu::CollectiveInitializer; -using CreateCommGroupFunc = mindspore::device::gpu::CreateCommGroupFunc; -using GetRankIDByGroupFunc = mindspore::device::gpu::GetRankIDByGroupFunc; -using GetGroupSizeFunc = mindspore::device::gpu::GetGroupSizeFunc; -using DestroyGroupFunc = mindspore::device::gpu::DestroyGroupFunc; -#endif +#include "include/common/utils/parallel_context.h" namespace mindspore { -#ifndef NO_DLIB -CommManager &CommManager::GetInstance() noexcept { - static CommManager instance("hccl"); - return instance; +namespace { +constexpr auto kDefaultCommManagerName = "default_comm_manager_name"; +constexpr unsigned int kNoCommDlibRankSize = 2048; + +std::map> &GetInstanceMap() { + static std::map> kCommInstanceMap = {}; + return kCommInstanceMap; } -#define HCCL_RUN_CHECK(op_name, group, op) \ - do { \ - auto hccl_result = (op); \ - if (hccl_result != 0) { \ - MS_LOG(ERROR) << op_name << " failed: #" << group << "#"; \ - return false; \ - } \ - } while (0) +class DefaultCommManager : public CommManager { + public: + DefaultCommManager() : CommManager("hccl") {} + ~DefaultCommManager() override = default; -#define HCCL_GROUP_CHECK_EMPTY(group) \ - do { \ - if (group.length() == 0) { \ - MS_LOG(ERROR) << "The length of group name should not be 0"; \ - return false; \ - } \ - } while (0) + bool CreateGroupSync(const string &, const std::vector &) const override { return true; } + bool GetRankID(const string &group, unsigned int *rank_id) const override { return true; } + bool GetRankSize(const string &group, unsigned int *rank_size) const override { + *rank_size = kNoCommDlibRankSize; + return true; + } -#define HCCL_GROUP_CHECK_IS_WORLD(group) \ - do { \ - if (group == "hccl_world_group") { \ - MS_LOG(ERROR) << "The group name should not be hccl_world_group"; \ - return false; \ - } \ - } while (0) + bool DestroyGroup(const string &group) const override { return true; } + + uint32_t GetRank() override { return 0; } +}; +COMM_MANAGER_REG(kDefaultCommManagerName, std::make_shared()); +} // namespace + +bool CommManager::Register(const std::string &name, const std::shared_ptr &instance) { + if (GetInstanceMap().find(name) != GetInstanceMap().end()) { + return false; + } + + GetInstanceMap().emplace(name, instance); + return true; +} + +CommManager &CommManager::GetInstance() noexcept { + if (GetInstanceMap().empty()) { + MS_LOG(EXCEPTION) << "No CommManager instance found."; + } + + auto default_iter = GetInstanceMap().find(kDefaultCommManagerName); + if (default_iter == GetInstanceMap().end()) { + MS_LOG(EXCEPTION) << "Default CommManager instance not found."; + } -bool CommManager::CreateGroupSync(const string &group, const vector &rank_id_list) const { - auto rank_size = rank_id_list.size(); - HCCL_GROUP_CHECK_EMPTY(group); - HCCL_GROUP_CHECK_IS_WORLD(group); auto context_ptr = MsContext::GetInstance(); MS_EXCEPTION_IF_NULL(context_ptr); - bool is_task_sink = context_ptr->get_param(MS_CTX_ENABLE_TASK_SINK); - auto mode = context_ptr->get_param(MS_CTX_EXECUTION_MODE); - if (!is_task_sink && mode == kGraphMode) { - HcclCollectiveGroup::instance().CreateCommGroup(group, rank_id_list); - } else { - HCCL_RUN_CHECK(string("create communicate group"), group, - hccl::HcclAdapter::GetInstance().HcclCreateGroup(group, UlongToUint(rank_size), - vector(rank_id_list).data())); - } - return true; -} - -bool CommManager::GetRankID(const string &group, unsigned int *rank_id) const { - auto context = MsContext::GetInstance(); - MS_EXCEPTION_IF_NULL(context); - if (context->get_param(MS_CTX_EXECUTION_MODE) == kGraphMode) { - HCCL_GROUP_CHECK_EMPTY(group); - if (!context->get_param(MS_CTX_ENABLE_TASK_SINK)) { - *rank_id = static_cast(HcclCollectiveGroup::instance().GetRankId(group)); - } else { - HCCL_RUN_CHECK(string("get rank_id"), group, hccl::HcclAdapter::GetInstance().HcclGetRankId(group, rank_id)); - } - } else { - HCCL_RUN_CHECK(string("get rank_id"), group, hccl::HcclAdapter::GetInstance().HcclGetRankId(rank_id)); - } - return true; -} - -bool CommManager::GetRankSize(const string &group, unsigned int *rank_size) const { - auto context = MsContext::GetInstance(); - MS_EXCEPTION_IF_NULL(context); - if (context->get_param(MS_CTX_EXECUTION_MODE) == kGraphMode) { - HCCL_GROUP_CHECK_EMPTY(group); - if (!context->get_param(MS_CTX_ENABLE_TASK_SINK)) { - *rank_size = static_cast(HcclCollectiveGroup::instance().GetRankSize(group)); - } else { - HCCL_RUN_CHECK(string("get rank size"), group, - hccl::HcclAdapter::GetInstance().HcclGetRankSize(group, rank_size)); - } - } else { - HCCL_RUN_CHECK(string("get rank size"), group, hccl::HcclAdapter::GetInstance().HcclGetRankSize(rank_size)); - } - return true; -} - -bool CommManager::DestroyGroup(const string &group) const { - HCCL_GROUP_CHECK_EMPTY(group); - HCCL_GROUP_CHECK_IS_WORLD(group); - HCCL_RUN_CHECK(string("destroy communicate group"), group, hccl::HcclAdapter::GetInstance().HcclDestroyGroup(group)); - return true; -} -#elif defined(ENABLE_GPU) -CommManager &CommManager::GetInstance() noexcept { - static CommManager instance("nccl"); - return instance; -} - -bool CommManager::CreateGroupSync(const string &group, const vector &rank_id_list) const { - bool ret = CollectiveInitializer::instance().CreateCommunicationGroup(group, rank_id_list); - if (!ret) { - MS_LOG(ERROR) << "Failed to create group " << group << " for rank id list " << rank_id_list; - return ret; + std::string device_name = context_ptr->get_param(MS_CTX_DEVICE_TARGET); + if (auto iter = GetInstanceMap().find(device_name); iter != GetInstanceMap().end()) { + return *(iter->second); } - MS_LOG(INFO) << "Successfully create group " << group << " for rank id list " << rank_id_list; - return ret; -} - -bool CommManager::GetRankID(const string &group, unsigned int *rank_id) const { - *rank_id = CollectiveInitializer::instance().GetRankIDByGroup(group); - MS_LOG(INFO) << "This process rank id is " << *rank_id << " in group " << group; - return true; -} - -bool CommManager::GetRankSize(const string &group, unsigned int *rank_size) const { - *rank_size = CollectiveInitializer::instance().GetGroupSize(group); - MS_LOG(INFO) << "Group " << group << " size is " << *rank_size; - return true; -} - -bool CommManager::DestroyGroup(const string &group) const { - bool ret = CollectiveInitializer::instance().DestroyCommunicationGroup(group); - if (!ret) { - MS_LOG(ERROR) << "Failed to destroy group " << group; - return ret; + if (static bool first_warning = true; first_warning) { + MS_LOG(WARNING) << "CommManager instance for " << device_name << " not found, return default instance."; + first_warning = false; } - - MS_LOG(INFO) << "Successfully destroy group " << group; - return ret; -} -#else -CommManager &CommManager::GetInstance() noexcept { - static CommManager instance("hccl"); - return instance; + return *(default_iter->second); } -bool CommManager::CreateGroupSync(const string &, const vector &) const { return true; } - -bool CommManager::GetRankID(const string &group, unsigned int *rank_id) const { return true; } - -bool CommManager::GetRankSize(const string &group, unsigned int *rank_size) const { - *rank_size = NO_COMM_DLIB_RANK_SIZE; - return true; -} - -bool CommManager::DestroyGroup(const string &group) const { return true; } -#endif - -uint32_t GetRank() { - uint32_t rank_id = 0; - auto ms_context = MsContext::GetInstance(); - MS_EXCEPTION_IF_NULL(ms_context); - std::string world_group; - std::string backend = ms_context->get_param(MS_CTX_DEVICE_TARGET); - if (backend == kAscendDevice) { - world_group = parallel::HCCL_WORLD_GROUP; - } else if (backend == kGPUDevice) { - world_group = parallel::NCCL_WORLD_GROUP; - } else { - // Other backends like CPU not support parallel, return rank_id with default 0. - return rank_id; - } - auto parallel_context = parallel::ParallelContext::GetInstance(); - MS_EXCEPTION_IF_NULL(parallel_context); - if (parallel_context->parallel_mode() != parallel::STAND_ALONE) { -#ifndef NO_DLIB - // Check HCCL inited. - if (!hccl::HcclAdapter::GetInstance().Inited()) { - MS_LOG(DEBUG) << "HCCL not inited, return rank_id = 0"; - return rank_id; - } -#elif defined(ENABLE_GPU) - // Check NCCL inited. - if (!CollectiveInitializer::instance().collective_inited()) { - MS_LOG(DEBUG) << "NCLL not inited, return rank_id = 0"; - return rank_id; - } -#endif - if (!CommManager::GetInstance().GetRankID(world_group, &rank_id)) { - MS_LOG(EXCEPTION) << "Get rank id failed."; - } - } - return rank_id; -} +uint32_t GetRank() { return CommManager::GetInstance().GetRank(); } bool IsStandAlone() { auto parallel_context = parallel::ParallelContext::GetInstance(); MS_EXCEPTION_IF_NULL(parallel_context); - return parallel_context->parallel_mode() == parallel::STAND_ALONE; + return parallel_context->parallel_mode() == parallel::kStandalone; } } // namespace mindspore diff --git a/mindspore/ccsrc/utils/comm_manager.h b/mindspore/ccsrc/utils/comm_manager.h deleted file mode 100644 index dc45589399e..00000000000 --- a/mindspore/ccsrc/utils/comm_manager.h +++ /dev/null @@ -1,51 +0,0 @@ -/** - * Copyright 2019 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_UTILS_COMM_MANAGER_H -#define MINDSPORE_CCSRC_UTILS_COMM_MANAGER_H - -#include -#include -#include -#include "utils/log_adapter.h" - -using std::string; -using std::vector; - -namespace mindspore { -constexpr unsigned int NO_COMM_DLIB_RANK_SIZE = 2048; - -class CommManager { - public: - static CommManager &GetInstance() noexcept; - bool CreateGroupSync(const string &group, const vector &rank_id_list) const; - bool DestroyGroup(const string &group) const; - bool GetRankID(const string &group, unsigned int *rank_id) const; - bool GetRankSize(const string &group, unsigned int *rank_size) const; - ~CommManager() = default; - - CommManager(const CommManager &) = delete; - - private: - explicit CommManager(string backend) : backend_(std::move(backend)) {} - string backend_; -}; - -uint32_t GetRank(); - -bool IsStandAlone(); -} // namespace mindspore -#endif // MINDSPORE_CCSRC_UTILS_COMMUNICATION_MANAGER_H diff --git a/mindspore/ccsrc/utils/config_manager.cc b/mindspore/ccsrc/utils/config_manager.cc index 58bdd70108b..f616dadef5f 100644 --- a/mindspore/ccsrc/utils/config_manager.cc +++ b/mindspore/ccsrc/utils/config_manager.cc @@ -14,7 +14,7 @@ * limitations under the License. */ -#include "utils/config_manager.h" +#include "include/common/utils/config_manager.h" #include diff --git a/mindspore/ccsrc/utils/context/graph_kernel_flags.cc b/mindspore/ccsrc/utils/context/graph_kernel_flags.cc index e721202ccfc..e356488d136 100644 --- a/mindspore/ccsrc/utils/context/graph_kernel_flags.cc +++ b/mindspore/ccsrc/utils/context/graph_kernel_flags.cc @@ -14,7 +14,7 @@ * limitations under the License. */ -#include "utils/context/graph_kernel_flags.h" +#include "include/common/utils/context/graph_kernel_flags.h" #include #include @@ -149,6 +149,16 @@ class FlagRegister { }; } // namespace +const GraphKernelFlags &GraphKernelFlags::GetInstance() { + static std::unique_ptr flags(nullptr); + auto contexts = GetGraphKernelContext(); + if (flags == nullptr || contexts.first != flags->flags_cache_ || contexts.second != flags->enable_graph_kernel_) { + flags.reset(new GraphKernelFlags(contexts.first, contexts.second)); + flags->Refresh(); + } + return *flags; +} + std::pair GraphKernelFlags::GetGraphKernelContext() { // This environment variable is deprecated. auto flags = common::GetEnv("MS_GRAPH_KERNEL_FLAGS"); diff --git a/mindspore/ccsrc/utils/convert_utils.cc b/mindspore/ccsrc/utils/convert_utils.cc index dcb6f8bee99..5ed8bb5c161 100644 --- a/mindspore/ccsrc/utils/convert_utils.cc +++ b/mindspore/ccsrc/utils/convert_utils.cc @@ -14,7 +14,7 @@ * limitations under the License. */ -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" #include #include diff --git a/mindspore/ccsrc/utils/convert_utils_py.cc b/mindspore/ccsrc/utils/convert_utils_py.cc index 73ad39c07c5..868c3554c16 100644 --- a/mindspore/ccsrc/utils/convert_utils_py.cc +++ b/mindspore/ccsrc/utils/convert_utils_py.cc @@ -14,7 +14,7 @@ * limitations under the License. */ -#include "utils/convert_utils_py.h" +#include "include/common/utils/convert_utils_py.h" #include #include @@ -26,7 +26,6 @@ #include "abstract/abstract_value.h" #include "abstract/utils.h" -#include "pipeline/jit/parse/parse.h" #include "pipeline/jit/parse/parse_base.h" #include "pipeline/jit/parse/resolve.h" #include "ir/value.h" @@ -36,8 +35,7 @@ #include "pybind_api/ir/base_ref_py.h" #include "ir/dtype/tensor_type.h" #include "utils/ms_context.h" -#include "utils/convert_utils.h" -#include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/convert_utils.h" namespace mindspore { py::object BuiltinsToPyData(const Any &value); @@ -185,8 +183,8 @@ static ValueNameToConverterVector value_name_to_converter = { [](const ValuePtr &value) -> py::object { auto value_list = value->cast()->value(); py::dict res_dict; - for (const auto &value : value_list) { - res_dict[py::str(value.first)] = ValueToPyData(value.second); + for (const auto &v : value_list) { + res_dict[py::str(v.first)] = ValueToPyData(v.second); } return res_dict; }}, @@ -197,8 +195,7 @@ static ValueNameToConverterVector value_name_to_converter = { auto start = ValueToPyData(slice->start()); auto end = ValueToPyData(slice->stop()); auto step = ValueToPyData(slice->step()); - return parse::python_adapter::CallPyFn(parse::PYTHON_MOD_PARSE_MODULE, parse::PYTHON_PARSE_CLASS_SLICE, start, end, - step); + return python_adapter::CallPyFn(parse::PYTHON_MOD_PARSE_MODULE, parse::PYTHON_PARSE_CLASS_SLICE, start, end, step); }}, // KeywordArg {KeywordArg::kTypeId, @@ -441,149 +438,6 @@ py::object VectorRefToPyData(const VectorRef &value_list, const AbstractBasePtr return ret; } -void SetValueRange(const AbstractBasePtr &tensor, const py::object &output) { - if (output.is_none()) { - return; - } - py::object obj_min = - output.contains(py::str(ATTR_MIN_VALUE)) ? (py::object)output[ATTR_MIN_VALUE] : (py::object)py::none(); - py::object obj_max = - output.contains(py::str(ATTR_MAX_VALUE)) ? (py::object)output[ATTR_MAX_VALUE] : (py::object)py::none(); - - if (!obj_min.is_none() && !obj_max.is_none()) { - bool converted = true; - ValuePtr min_value = nullptr; - ValuePtr max_value = nullptr; - converted = parse::ConvertData(obj_min, &min_value); - if (!converted) { - MS_LOG(EXCEPTION) << "Convert shape min value data failed"; - } - converted = parse::ConvertData(obj_max, &max_value); - if (!converted) { - MS_LOG(EXCEPTION) << "Convert shape max value data failed"; - } - auto abs_tensor = dyn_cast(tensor); - abs_tensor->set_value_range(min_value, max_value); - } -} - -AbstractBasePtr MakePyInferRes2AbstractTensor(const py::object &shape_obj, const py::object &type_obj, - const py::object &output) { - auto ret_vec = shape_obj.cast(); - auto ret_dtype = type_obj.cast(); - ShapeVector min_shape_vec; - ShapeVector max_shape_vec; - - if (!output.is_none()) { - py::object min_shape = - output.contains(py::str(ATTR_MIN_SHAPE)) ? (py::object)output[ATTR_MIN_SHAPE] : (py::object)py::none(); - py::object max_shape = - output.contains(py::str(ATTR_MAX_SHAPE)) ? (py::object)output[ATTR_MAX_SHAPE] : (py::object)py::none(); - if (!min_shape.is_none()) { - min_shape_vec = min_shape.cast(); - } - if (!max_shape.is_none()) { - max_shape_vec = max_shape.cast(); - } - } - - auto ret_shape = std::make_shared(ret_vec, min_shape_vec, max_shape_vec); - AbstractBasePtr tensor = MakeAbstractTensor(ret_shape, ret_dtype); - - SetValueRange(tensor, output); - return tensor; -} - -static bool IsMonadType(const py::object &type_obj) { - if (py::isinstance(type_obj)) { - auto type = type_obj.cast(); - return type->isa(); - } - return false; -} - -static AbstractBasePtr ToMonadAbstract(const py::object &type_obj) { - if (py::isinstance(type_obj)) { - auto type = type_obj.cast(); - if (!type->isa()) { - MS_LOG(EXCEPTION) << "Not a monad type object: " << py::str(type_obj); - } - return abstract::MakeMonadAbstract(type->cast()); - } - MS_LOG(EXCEPTION) << "Not a type object: " << py::str(type_obj); -} - -static py::object GetPyAbsItemOfTupleOut(const py::object &output, const size_t index) { - auto out_dict = output.cast(); - auto type_obj = out_dict[ATTR_DTYPE]; - auto shape_obj = out_dict[ATTR_SHAPE]; - auto out_item = py::dict(); - auto shape_tuple = shape_obj.cast(); - auto typeid_tuple = type_obj.cast(); - out_item[ATTR_DTYPE] = typeid_tuple[index]; - out_item[ATTR_SHAPE] = shape_tuple[index]; - if (output.contains(py::str(ATTR_MIN_SHAPE))) { - out_item[ATTR_MIN_SHAPE] = output[ATTR_MIN_SHAPE].cast()[index]; - } - if (output.contains(py::str(ATTR_MAX_SHAPE))) { - out_item[ATTR_MAX_SHAPE] = output[ATTR_MAX_SHAPE].cast()[index]; - } - out_item[ATTR_VALUE] = py::none(); - return out_item; -} - -AbstractBasePtr MakePyInferRes2Abstract(const py::object &output) { - auto out_dict = output.cast(); - auto type_obj = out_dict[ATTR_DTYPE]; - auto shape_obj = out_dict[ATTR_SHAPE]; - if ((py::isinstance(shape_obj) || py::isinstance(shape_obj)) && py::isinstance(type_obj)) { - auto ret_vec = shape_obj.cast(); - auto ret_dtype = type_obj.cast(); - MS_EXCEPTION_IF_NULL(ret_dtype); - // if the size of shape list is empty, return an scalar abstract - if (ret_vec.empty() && (!ret_dtype->isa())) { - abstract::AbstractScalarPtr abs_scalar = std::make_shared(kAnyValue, ret_dtype); - return abs_scalar; - } - return MakePyInferRes2AbstractTensor(shape_obj, type_obj, output); - } else if (py::isinstance(shape_obj) && py::isinstance(type_obj)) { - auto typeid_tuple = type_obj.cast(); - AbstractBasePtrList ptr_list; - for (size_t it = 0; it < typeid_tuple.size(); ++it) { - auto output_it = GetPyAbsItemOfTupleOut(output, it); - auto tensor_it = MakePyInferRes2Abstract(output_it); - ptr_list.push_back(tensor_it); - } - auto tuple = std::make_shared(ptr_list); - return tuple; - } else if (py::isinstance(shape_obj) && py::isinstance(type_obj)) { - auto typeid_list = type_obj.cast(); - AbstractBasePtrList ptr_list; - for (size_t it = 0; it < typeid_list.size(); ++it) { - auto output_it = GetPyAbsItemOfTupleOut(output, it); - auto tensor_it = MakePyInferRes2Abstract(output_it); - ptr_list.push_back(tensor_it); - } - auto list = std::make_shared(ptr_list); - return list; - } else if (shape_obj.is_none() && type_obj.is_none()) { - // AbstractNone indicates there is no output for this CNode node. - auto abstract_none = std::make_shared(); - return abstract_none; - } else if (IsMonadType(type_obj)) { - // Return monad abstract if it is monad type. - return ToMonadAbstract(type_obj); - } else { - // When sparse enabled, the undetermined might be raised and eliminated in opt passes - auto context = MsContext::GetInstance(); - MS_EXCEPTION_IF_NULL(context); - bool enable_sparse = context->get_param(MS_CTX_ENABLE_SPARSE); - if (enable_sparse) { - return std::make_shared(); - } - MS_LOG(EXCEPTION) << "Python evaluator return invalid shape or type. " << (std::string)py::str(type_obj); - } -} bool IsGraphOutputValueNodeOrParameter(const AnfNodePtr &output, const py::tuple &args, const std::shared_ptr &ret_val) { if (output->isa()) { diff --git a/mindspore/ccsrc/utils/json_operation_utils.cc b/mindspore/ccsrc/utils/json_operation_utils.cc index 72720acc85e..78c7cba224f 100644 --- a/mindspore/ccsrc/utils/json_operation_utils.cc +++ b/mindspore/ccsrc/utils/json_operation_utils.cc @@ -14,7 +14,7 @@ * limitations under the License. */ -#include "utils/json_operation_utils.h" +#include "include/common/utils/json_operation_utils.h" #include #include #include diff --git a/mindspore/ccsrc/utils/mpi/mpi_config.cc b/mindspore/ccsrc/utils/mpi/mpi_config.cc index e8d81cf8438..cb043c7f139 100644 --- a/mindspore/ccsrc/utils/mpi/mpi_config.cc +++ b/mindspore/ccsrc/utils/mpi/mpi_config.cc @@ -14,12 +14,12 @@ * limitations under the License. */ -#include "utils/mpi/mpi_config.h" +#include "include/common/utils/mpi/mpi_config.h" +#include "utils/log_adapter.h" namespace mindspore { -std::shared_ptr MpiConfig::instance_ = nullptr; - std::shared_ptr MpiConfig::GetInstance() { + static std::shared_ptr instance_ = nullptr; if (instance_ == nullptr) { MS_LOG(DEBUG) << "Create new mpi config instance."; instance_.reset(new (std::nothrow) MpiConfig()); diff --git a/mindspore/ccsrc/frontend/parallel/context.cc b/mindspore/ccsrc/utils/parallel_context.cc similarity index 82% rename from mindspore/ccsrc/frontend/parallel/context.cc rename to mindspore/ccsrc/utils/parallel_context.cc index 0a00eb8e63e..5441d85a06d 100644 --- a/mindspore/ccsrc/frontend/parallel/context.cc +++ b/mindspore/ccsrc/utils/parallel_context.cc @@ -14,36 +14,29 @@ * limitations under the License. */ -#include "frontend/parallel/context.h" +#include "include/common/utils/parallel_context.h" #include #include #include #include #include -#include -#include "frontend/parallel/device_manager.h" +namespace mindspore::parallel { +namespace { +std::map> param_shapes; -namespace mindspore { -namespace parallel { -std::map param_shapes; +std::vector kParallelModeList = {kStandalone, kDataParallel, kHybridParallel, kSemiAutoParallel, + kAutoParallel}; +std::vector kStrategySearchModeList = {kDynamicProgramming, kRecursiveProgramming, kShardingPropagation}; -std::vector PARALLEL_MODE_LIST = {STAND_ALONE, DATA_PARALLEL, HYBRID_PARALLEL, SEMI_AUTO_PARALLEL, - AUTO_PARALLEL}; -std::vector STRATEGY_SEARCH_MODE_LIST = {DYNAMIC_PROGRAMMING, RECURSIVE_PROGRAMMING, SHARDING_PROPAGATION}; +std::vector kCommuniParallelModeList = {kAllGroupParallel, kSameServerGroupParallel, kNoGroupParallel}; -std::vector COMMUNI_PARALLEL_MODE_LIST = {ALL_GROUP_PARALLEL, SAME_SERVER_GROUP_PARALLEL, - NO_GROUP_PARALLEL}; - -std::vector FUSION_MODE_LIST = {FUSION_AUTO, FUSION_SIZE, FUSION_INDEX}; - -std::shared_ptr ParallelContext::inst_context_ = nullptr; +std::vector kFusionModeList = {kFusionAuto, kFusionSize, kFusionIndex}; +} // namespace std::shared_ptr ParallelContext::GetInstance() { - if (inst_context_ == nullptr) { - inst_context_.reset(new (std::nothrow) ParallelContext()); - } + static std::shared_ptr inst_context_ = std::shared_ptr(new ParallelContext()); return inst_context_; } @@ -59,7 +52,7 @@ void ParallelContext::Reset() { global_rank_ = 0; device_num_is_set_ = false; global_rank_is_set_ = false; - parallel_mode_ = STAND_ALONE; + parallel_mode_ = kStandalone; parameter_broadcast_ = false; parameter_broadcast_is_set_ = false; enable_all_reduce_fusion_ = true; @@ -70,10 +63,10 @@ void ParallelContext::Reset() { enable_parallel_optimizer_ = false; all_reduce_fusion_split_indices_.clear(); all_reduce_fusion_split_sizes_.clear(); - strategy_search_mode_ = DYNAMIC_PROGRAMMING; + strategy_search_mode_ = kDynamicProgramming; pipeline_stage_split_num_ = 1; grad_accumulation_step_ = 1; - communi_parallel_mode_ = ALL_GROUP_PARALLEL; + communi_parallel_mode_ = kAllGroupParallel; optimizer_weight_shard_size_ = -1; optimizer_weight_shard_aggregated_save_ = false; enable_all2all_ = false; @@ -81,11 +74,11 @@ void ParallelContext::Reset() { parallel_optimizer_threshold_ = -1; sharding_propagation_ = false; dataset_strategy_.clear(); - fusion_threshold_mb_ = FUSUION_THRESHOLD; - allgather_fusion_threshold_mb_ = FUSUION_THRESHOLD; - reducescatter_fusion_threshold_mb_ = FUSUION_THRESHOLD; + fusion_threshold_mb_ = kFusionThreshold; + allgather_fusion_threshold_mb_ = kFusionThreshold; + reducescatter_fusion_threshold_mb_ = kFusionThreshold; fusion_threshold_is_set_ = true; - fusion_mode_ = FUSION_AUTO; + fusion_mode_ = kFusionAuto; } void ParallelContext::set_device_num(int64_t device_num) { @@ -110,8 +103,8 @@ void ParallelContext::set_reducescatter_fusion_threshold_mb(int64_t fusion_thres } bool ParallelContext::set_fusion_mode(const std::string &fusion_mode) { - auto iter = std::find(FUSION_MODE_LIST.begin(), FUSION_MODE_LIST.end(), fusion_mode); - if (iter == FUSION_MODE_LIST.end()) { + auto iter = std::find(kFusionModeList.begin(), kFusionModeList.end(), fusion_mode); + if (iter == kFusionModeList.end()) { MS_LOG(INFO) << "Invalid fusion mode:" << fusion_mode; return false; } @@ -143,8 +136,8 @@ void ParallelContext::set_loss_repeated_mean(bool loss_repeated_mean) { loss_rep void ParallelContext::set_pipeline_stage_split_num(const int64_t stage_num) { pipeline_stage_split_num_ = stage_num; } bool ParallelContext::set_parallel_mode(const std::string ¶llel_mode) { - auto iter = std::find(PARALLEL_MODE_LIST.begin(), PARALLEL_MODE_LIST.end(), parallel_mode); - if (iter == PARALLEL_MODE_LIST.end()) { + auto iter = std::find(kParallelModeList.begin(), kParallelModeList.end(), parallel_mode); + if (iter == kParallelModeList.end()) { MS_LOG(INFO) << "Invalid parallel mode:" << parallel_mode; return false; } @@ -153,8 +146,8 @@ bool ParallelContext::set_parallel_mode(const std::string ¶llel_mode) { } bool ParallelContext::set_strategy_search_mode(const std::string &strategy_search_mode) { - auto iter = std::find(STRATEGY_SEARCH_MODE_LIST.begin(), STRATEGY_SEARCH_MODE_LIST.end(), strategy_search_mode); - if (iter == STRATEGY_SEARCH_MODE_LIST.end()) { + auto iter = std::find(kStrategySearchModeList.begin(), kStrategySearchModeList.end(), strategy_search_mode); + if (iter == kStrategySearchModeList.end()) { MS_LOG(INFO) << "Invalid strategy search mode mode: " << strategy_search_mode; return false; } @@ -226,8 +219,8 @@ std::vector ParallelContext::GetAllReduceFusionSplitSizes(const std::s } bool ParallelContext::set_communi_parallel_mode(const std::string &communi_parallel_mode) { - auto iter = std::find(COMMUNI_PARALLEL_MODE_LIST.begin(), COMMUNI_PARALLEL_MODE_LIST.end(), communi_parallel_mode); - if (iter == COMMUNI_PARALLEL_MODE_LIST.end()) { + auto iter = std::find(kCommuniParallelModeList.begin(), kCommuniParallelModeList.end(), communi_parallel_mode); + if (iter == kCommuniParallelModeList.end()) { MS_LOG(INFO) << "Invalid communication parallel mode:" << communi_parallel_mode; return false; } @@ -239,22 +232,22 @@ bool ParallelContext::set_communi_parallel_mode(const std::string &communi_paral // Clear param_shapes before training in auto-parallel or semi-auto-parallel mode void ParallelContext::ParallelParameterContextInitShape(const FuncGraphPtr &func_graph) { MS_EXCEPTION_IF_NULL(func_graph); - if (!func_graph->has_flag(AUTO_PARALLEL)) { + if (!func_graph->has_flag(kAutoParallel)) { return; } - if (func_graph->has_flag(IS_FIRST_ITERATION)) { + if (func_graph->has_flag(kIsFirstIteration)) { param_shapes.clear(); init_param_shape_ = true; MS_LOG(INFO) << "Init the parameter shape dict in increment predict with two graph"; return; } - if (!func_graph->has_flag(TRAINING)) { + if (!func_graph->has_flag(kTraining)) { init_param_shape_ = false; MS_LOG(INFO) << "In parallel evaluation or prediction, may be need to restore the parameter shape"; return; } - if ((ParallelContext::GetInstance()->grad_accumulation_step() > 1) && !func_graph->has_flag(ACCUMULATION)) { + if ((ParallelContext::GetInstance()->grad_accumulation_step() > 1) && !func_graph->has_flag(kAccumulation)) { init_param_shape_ = false; MS_LOG(INFO) << "In parallel grad accumulation second graph, need to restore the parameter shape"; } else { @@ -270,7 +263,7 @@ void ParallelContext::ParallelParameterContextRestoreShape(const FuncGraphPtr &f MS_EXCEPTION_IF_NULL(func_graph); MS_EXCEPTION_IF_NULL(param_node); MS_EXCEPTION_IF_NULL(ptr); - if (!func_graph->has_flag(AUTO_PARALLEL)) { + if (!func_graph->has_flag(kAutoParallel)) { return; } @@ -282,7 +275,7 @@ void ParallelContext::ParallelParameterContextRestoreShape(const FuncGraphPtr &f MS_LOG(WARNING) << "Can not found the shape for parameter " << param_node->name(); return; } - Shape shape = iter->second; + auto shape = iter->second; std::shared_ptr base_shape = std::make_shared(shape); ptr->set_shape(base_shape); MS_LOG(INFO) << "The parameter name is " << param_node->name() << ", the shape is " << shape; @@ -295,7 +288,7 @@ void ParallelContext::ParallelParameterContextCkptShape(const FuncGraphPtr &func MS_EXCEPTION_IF_NULL(func_graph); MS_EXCEPTION_IF_NULL(param_node); MS_EXCEPTION_IF_NULL(ptr); - if (!func_graph->has_flag(AUTO_PARALLEL)) { + if (!func_graph->has_flag(kAutoParallel)) { return; } @@ -315,5 +308,4 @@ void ParallelContext::ParallelParameterContextCkptShape(const FuncGraphPtr &func void ParallelContext::set_enable_all2all(const bool enable) { enable_all2all_ = enable; } void ParallelContext::set_sharding_propagation(const bool stra_pto) { sharding_propagation_ = stra_pto; } -} // namespace parallel -} // namespace mindspore +} // namespace mindspore::parallel diff --git a/mindspore/ccsrc/utils/primitive_utils.cc b/mindspore/ccsrc/utils/primitive_utils.cc index 95656fe9d30..cc2f7062db0 100644 --- a/mindspore/ccsrc/utils/primitive_utils.cc +++ b/mindspore/ccsrc/utils/primitive_utils.cc @@ -14,14 +14,14 @@ * limitations under the License. */ -#include "utils/primitive_utils.h" +#include "include/common/utils/primitive_utils.h" #include -#include "pipeline/jit/parse/python_adapter.h" +#include "include/common/utils/python_adapter.h" #include "utils/log_adapter.h" #include "utils/ms_utils.h" -#include "utils/convert_utils_py.h" +#include "include/common/utils/convert_utils_py.h" #include "pybind_api/ir/base_ref_py.h" namespace mindspore { @@ -29,9 +29,9 @@ py::function GetBpropFunctionByObj(const py::object &obj) { static const std::string get_bprop_fn = "get_bprop_fn"; static const std::string ad_module = "mindspore.ops._grad"; static const std::string ad_experimental_module = "mindspore.ops._grad_experimental"; - py::function fn = parse::python_adapter::GetPyFn(ad_module, get_bprop_fn)(obj); + py::function fn = python_adapter::GetPyFn(ad_module, get_bprop_fn)(obj); if (!fn || py::isinstance(fn)) { - fn = parse::python_adapter::GetPyFn(ad_experimental_module, get_bprop_fn)(obj); + fn = python_adapter::GetPyFn(ad_experimental_module, get_bprop_fn)(obj); } return fn; } @@ -67,7 +67,7 @@ py::tuple ConvertDatatoPyTuple(const VectorRef &args) { py::function GetComputeFunctionWithoutPyObj(const std::string &name) { static const std::string vm_module = "mindspore.ops.vm_impl_registry"; static const std::string get_vm_impl_fn = "get_vm_impl_fn"; - py::function get_fn = parse::python_adapter::GetPyFn(vm_module, get_vm_impl_fn); + py::function get_fn = python_adapter::GetPyFn(vm_module, get_vm_impl_fn); if (py::isinstance(get_fn)) { MS_LOG(DEBUG) << "Failed to get the function 'get_vm_impl_fn'"; return py::none(); diff --git a/mindspore/ccsrc/utils/primitive_utils.h b/mindspore/ccsrc/utils/primitive_utils.h deleted file mode 100644 index b10bc48cf7b..00000000000 --- a/mindspore/ccsrc/utils/primitive_utils.h +++ /dev/null @@ -1,43 +0,0 @@ -/** - * Copyright 2020 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_UTILS_PRIMITIVE_UTILS_H_ -#define MINDSPORE_CCSRC_UTILS_PRIMITIVE_UTILS_H_ - -#include -#include "pybind11/pybind11.h" -#include "base/base_ref.h" -#include "utils/convert_utils.h" - -namespace py = pybind11; - -namespace mindspore { -py::function GetBpropFunctionByObj(const py::object &obj); - -py::function GetBpropFunction(const std::string &name); - -py::function GetComputeFunction(const std::string &name); - -BaseRef RunComputeFunction(const PrimitivePtr &prim, const VectorRef &args); - -py::function GetComputeFunctionWithoutPyObj(const std::string &name); - -BaseRef RunComputeFunctionWithoutPyObj(const PrimitivePtr &prim, const VectorRef &args); - -py::tuple ConvertDatatoPyTuple(const VectorRef &args); -} // namespace mindspore - -#endif // MINDSPORE_CCSRC_UTILS_PRIMITIVE_UTILS_H_ diff --git a/mindspore/ccsrc/pipeline/jit/parse/python_adapter.cc b/mindspore/ccsrc/utils/python_adapter.cc similarity index 97% rename from mindspore/ccsrc/pipeline/jit/parse/python_adapter.cc rename to mindspore/ccsrc/utils/python_adapter.cc index b4b4b1b1afc..d10b5f8e374 100644 --- a/mindspore/ccsrc/pipeline/jit/parse/python_adapter.cc +++ b/mindspore/ccsrc/utils/python_adapter.cc @@ -14,12 +14,11 @@ * limitations under the License. */ -#include "pipeline/jit/parse/python_adapter.h" +#include "include/common/utils/python_adapter.h" #include #include namespace mindspore { -namespace parse { namespace python_adapter { // python scoped env, should only have one scoped_ instance static std::shared_ptr scoped_ = nullptr; @@ -91,5 +90,4 @@ py::object GetPyFn(const std::string &module, const std::string &name) { } } // namespace python_adapter -} // namespace parse } // namespace mindspore diff --git a/mindspore/ccsrc/utils/runtime_error_codes.cc b/mindspore/ccsrc/utils/runtime_error_codes.cc index ae1d6e12fe3..285770c8bfd 100644 --- a/mindspore/ccsrc/utils/runtime_error_codes.cc +++ b/mindspore/ccsrc/utils/runtime_error_codes.cc @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -#include "utils/runtime_error_codes.h" +#include "include/common/utils/runtime_error_codes.h" #include #include #include "graphengine/inc/external/runtime/rt_error_codes.h" diff --git a/mindspore/ccsrc/utils/scoped_long_running.cc b/mindspore/ccsrc/utils/scoped_long_running.cc index 9f251b6a648..8cbc3a48e1f 100644 --- a/mindspore/ccsrc/utils/scoped_long_running.cc +++ b/mindspore/ccsrc/utils/scoped_long_running.cc @@ -1,5 +1,5 @@ /** - * Copyright 2020 Huawei Technologies Co., Ltd + * Copyright 2022 Huawei Technologies Co., Ltd * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -13,8 +13,24 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -#include "utils/scoped_long_running.h" +#include "include/common/utils/scoped_long_running.h" namespace mindspore { -ScopedLongRunningHookPtr ScopedLongRunning::hook_; +ScopedLongRunning::ScopedLongRunning() { + if (hook_ != nullptr) { + hook_->Enter(); + } +} + +ScopedLongRunning::~ScopedLongRunning() { + if (hook_ != nullptr) { + hook_->Leave(); + } +} + +void ScopedLongRunning::SetHook(ScopedLongRunningHookPtr hook) { + if (hook_ == nullptr) { + hook_ = std::move(hook); + } +} } // namespace mindspore diff --git a/mindspore/ccsrc/utils/signal_util.cc b/mindspore/ccsrc/utils/signal_util.cc index b4c5562ed14..e4d2fc83332 100644 --- a/mindspore/ccsrc/utils/signal_util.cc +++ b/mindspore/ccsrc/utils/signal_util.cc @@ -14,10 +14,9 @@ * limitations under the License. */ -#include "utils/signal_util.h" +#include "include/common/utils/signal_util.h" #include #include "utils/log_adapter.h" -#include "backend/common/session/kernel_build_client.h" namespace mindspore { SignalGuard::SignalGuard(IntHandlerFunc IntHandler) { RegisterHandlers(IntHandler); } diff --git a/mindspore/ccsrc/utils/summary/event_writer.cc b/mindspore/ccsrc/utils/summary/event_writer.cc index 4f2557f0bb0..f7f04a85fd1 100644 --- a/mindspore/ccsrc/utils/summary/event_writer.cc +++ b/mindspore/ccsrc/utils/summary/event_writer.cc @@ -14,17 +14,27 @@ * limitations under the License. */ -#include "utils/summary/event_writer.h" +#include "include/common/utils/summary/event_writer.h" #include +#include "utils/system/base.h" +#include "utils/system/file_system.h" +#include "utils/system/crc32c.h" +#include "utils/system/env.h" #include "utils/log_adapter.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" namespace mindspore { namespace summary { +namespace py = pybind11; +using string = std::string; +using Env = system::Env; +using WriteFile = system::WriteFile; +using WriteFilePtr = std::shared_ptr; +using FileSystem = system::FileSystem; // implement the EventWriter EventWriter::EventWriter(const std::string &file_full_name) : filename_(file_full_name), events_write_count_(0) { - fs_ = system::Env::GetFileSystem(); + fs_ = Env::GetFileSystem(); if (fs_ == nullptr) { MS_LOG(EXCEPTION) << "Get the file system failed."; } diff --git a/mindspore/ccsrc/utils/tensorprint_utils.cc b/mindspore/ccsrc/utils/tensorprint_utils.cc index 42fcd70017a..07ff21052a5 100644 --- a/mindspore/ccsrc/utils/tensorprint_utils.cc +++ b/mindspore/ccsrc/utils/tensorprint_utils.cc @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -#include "utils/tensorprint_utils.h" +#include "include/common/utils/tensorprint_utils.h" #include #include #include @@ -21,7 +21,7 @@ #include #include "ir/tensor.h" #include "pybind11/pybind11.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/ms_utils.h" #include "utils/shape_utils.h" diff --git a/mindspore/core/ir/graph_utils.cc b/mindspore/core/ir/graph_utils.cc index e4a8c98ba28..50990f166a6 100644 --- a/mindspore/core/ir/graph_utils.cc +++ b/mindspore/core/ir/graph_utils.cc @@ -23,7 +23,7 @@ #include "ir/func_graph.h" #include "utils/log_adapter.h" #include "utils/ms_context.h" -#include "mindspore/ccsrc/utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { // Dump the circle from the strike node `next`. diff --git a/mindspore/core/utils/anf_utils.cc b/mindspore/core/utils/anf_utils.cc index 7c6a4f66a43..d188c9f92db 100644 --- a/mindspore/core/utils/anf_utils.cc +++ b/mindspore/core/utils/anf_utils.cc @@ -20,7 +20,7 @@ #include #include "base/core_ops.h" #include "utils/trace_base.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace { diff --git a/mindspore/core/utils/file_utils.cc b/mindspore/core/utils/file_utils.cc index 2e3f4eda7b3..788221eced9 100644 --- a/mindspore/core/utils/file_utils.cc +++ b/mindspore/core/utils/file_utils.cc @@ -23,7 +23,7 @@ #include #include "utils/system/file_system.h" #include "utils/system/env.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #if defined(_WIN32) || defined(_WIN64) #include diff --git a/mindspore/lite/tools/converter/adapter/acl/common/utils.cc b/mindspore/lite/tools/converter/adapter/acl/common/utils.cc index bc67a1be86b..b9cde4dcb7c 100644 --- a/mindspore/lite/tools/converter/adapter/acl/common/utils.cc +++ b/mindspore/lite/tools/converter/adapter/acl/common/utils.cc @@ -21,7 +21,7 @@ #include "base/core_ops.h" #include "abstract/dshape.h" #include "abstract/abstract_value.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "src/common/log_util.h" #include "ir/func_graph.h" #include "nnacl/op_base.h" diff --git a/mindspore/lite/tools/converter/parser/lstm_adjust_pass.h b/mindspore/lite/tools/converter/parser/lstm_adjust_pass.h index cfec1caf837..97f029221ca 100644 --- a/mindspore/lite/tools/converter/parser/lstm_adjust_pass.h +++ b/mindspore/lite/tools/converter/parser/lstm_adjust_pass.h @@ -22,7 +22,7 @@ #include #include #include "backend/common/optimizer/optimizer.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "tools/converter/converter_flags.h" #include "tools/optimizer/common/format_utils.h" diff --git a/mindspore/lite/tools/converter/parser/tf_bidirection_gru_cf_fusion.cc b/mindspore/lite/tools/converter/parser/tf_bidirection_gru_cf_fusion.cc index 1f103d06849..b3c0bbf8597 100644 --- a/mindspore/lite/tools/converter/parser/tf_bidirection_gru_cf_fusion.cc +++ b/mindspore/lite/tools/converter/parser/tf_bidirection_gru_cf_fusion.cc @@ -18,7 +18,7 @@ #include #include #include "src/common/utils.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "tools/optimizer/common/gllo_utils.h" #include "securec/include/securec.h" #include "tools/converter/ops/ops_def.h" diff --git a/mindspore/lite/tools/converter/parser/tf_bidirection_gru_cf_fusion.h b/mindspore/lite/tools/converter/parser/tf_bidirection_gru_cf_fusion.h index e785608d084..32cd1949c33 100644 --- a/mindspore/lite/tools/converter/parser/tf_bidirection_gru_cf_fusion.h +++ b/mindspore/lite/tools/converter/parser/tf_bidirection_gru_cf_fusion.h @@ -22,7 +22,7 @@ #include "tools/optimizer/fusion/tf_bidirection_gru_fusion.h" #include "schema/inner/model_generated.h" #include "backend/common/optimizer/optimizer.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "include/errorcode.h" namespace mindspore { diff --git a/mindspore/lite/tools/dataset/cropper/dependencies.txt b/mindspore/lite/tools/dataset/cropper/dependencies.txt index eecc2169bab..211aa9694a9 100644 --- a/mindspore/lite/tools/dataset/cropper/dependencies.txt +++ b/mindspore/lite/tools/dataset/cropper/dependencies.txt @@ -1 +1 @@ -{"ESSENTIAL": ["mindspore/core/ir/signature.h", "mindspore/ccsrc/minddata/dataset/engine/datasetops/batch_op.cc", "mindspore/ccsrc/minddata/dataset/engine/tree_adapter.cc", "mindspore/lite/build/_deps/jpeg_turbo-src/./jmorecfg.h", "mindspore/ccsrc/minddata/dataset/engine/datasetops/source/io_block.cc", "mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/random_node.cc", "mindspore/core/utils/log_adapter.cc", "mindspore/ccsrc/minddata/dataset/engine/dataset_iterator.cc", "mindspore/ccsrc/minddata/dataset/api/python/pybind_conversion.cc", "mindspore/ccsrc/minddata/dataset/engine/datasetops/rename_op.cc", "mindspore/ccsrc/minddata/dataset/util/log_adapter.h", "mindspore/ccsrc/minddata/dataset/engine/datasetops/source/sampler/sampler.cc", "mindspore/ccsrc/minddata/dataset/engine/datasetops/source/nonmappable_leaf_op.cc", "mindspore/ccsrc/utils/system/env.h", "mindspore/ccsrc/minddata/dataset/engine/datasetops/source/random_data_op.cc", "mindspore/core/abstract/utils.cc", "mindspore/lite/build/_deps/nlohmann_json-src/nlohmann/detail/iterators/primitive_iterator.hpp", "mindspore/core/utils/trace_base.cc", "mindspore/ccsrc/minddata/dataset/util/intrp_service.cc", "mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/repeat_node.cc", "mindspore/lite/build/_deps/nlohmann_json-src/nlohmann/detail/meta/detected.hpp", "mindspore/lite/build/_deps/nlohmann_json-src/nlohmann/detail/value_t.hpp", "mindspore/ccsrc/minddata/dataset/engine/datasetops/source/mnist_op.cc", "mindspore/ccsrc/minddata/dataset/kernels/c_func_op.cc", "build/mindspore/_deps/pybind11-src/include/pybind11/detail/descr.h", "mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/filter_node.cc", "mindspore/lite/build/_deps/nlohmann_json-src/nlohmann/detail/iterators/iterator_traits.hpp", "mindspore/lite/build/_deps/nlohmann_json-src/nlohmann/detail/meta/is_sax.hpp", "mindspore/core/base/base_ref.cc", "mindspore/ccsrc/utils/utils.h", "mindspore/lite/build/_deps/nlohmann_json-src/nlohmann/detail/json_pointer.hpp", "mindspore/ccsrc/minddata/dataset/engine/datasetops/zip_op.cc", "mindspore/ccsrc/minddata/dataset/engine/perf/connector_throughput.cc", "mindspore/ccsrc/minddata/dataset/util/allocator.h", "mindspore/ccsrc/minddata/dataset/util/bit.h", "build/mindspore/_deps/pybind11-src/include/pybind11/buffer_info.h", "mindspore/ccsrc/minddata/dataset/engine/db_connector.h", "mindspore/ccsrc/minddata/dataset/engine/datasetops/source/csv_op.cc", "mindspore/ccsrc/minddata/dataset/engine/consumers/pull_based_tree_consumer.cc", "mindspore/core/ir/func_graph_cloner.cc", "mindspore/ccsrc/minddata/dataset/api/iterator.cc", "mindspore/ccsrc/minddata/dataset/engine/datasetops/cache_op.cc", "mindspore/lite/build/_deps/nlohmann_json-src/nlohmann/detail/exceptions.hpp", "mindspore/core/ir/param_info.h", "mindspore/ccsrc/minddata/dataset/engine/perf/dataset_iterator_tracing.cc", "mindspore/ccsrc/minddata/dataset/core/device_tensor.cc", "mindspore/ccsrc/minddata/dataset/core/tensor_helpers.cc", "mindspore/core/utils/misc.cc", "mindspore/ccsrc/minddata/dataset/core/global_context.cc", "include/api/context.h", "mindspore/ccsrc/minddata/dataset/engine/ir/cache/pre_built_dataset_cache.cc", "mindspore/ccsrc/minddata/dataset/kernels/image/lite_cv/image_process.cc", "mindspore/ccsrc/minddata/dataset/engine/datasetops/repeat_op.cc", "mindspore/lite/build/_deps/nlohmann_json-src/nlohmann/json.hpp", "build/mindspore/_deps/pybind11-src/include/pybind11/detail/common.h", "mindspore/core/ir/manager.cc", "mindspore/lite/build/_deps/nlohmann_json-src/nlohmann/detail/iterators/internal_iterator.hpp", "mindspore/ccsrc/minddata/dataset/util/path.cc", "mindspore/lite/build/_deps/nlohmann_json-src/nlohmann/detail/macro_scope.hpp", "mindspore/core/utils/any.cc", "mindspore/core/ir/kernel_info_dev.h", "mindspore/lite/build/_deps/nlohmann_json-src/nlohmann/detail/meta/cpp_future.hpp", "mindspore/ccsrc/minddata/dataset/util/json_helper.cc", "mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/dataset_node.cc", "mindspore/core/utils/profile.cc", "mindspore/lite/build/_deps/jpeg_turbo-src/_build/jconfig.h", "third_party/securec/include/./securectype.h", "mindspore/lite/build/_deps/nlohmann_json-src/nlohmann/detail/meta/void_t.hpp", "mindspore/ccsrc/minddata/dataset/engine/datasetops/device_queue_op.cc", "mindspore/ccsrc/minddata/dataset/engine/datasetops/pipeline_op.cc", "mindspore/ccsrc/minddata/dataset/engine/opt/pre/epoch_ctrl_pass.cc", "mindspore/ccsrc/minddata/dataset/api/vision.cc", "mindspore/ccsrc/minddata/dataset/engine/perf/cpu_sampling.cc", "mindspore/ccsrc/minddata/dataset/engine/consumers/tree_consumer.cc", "mindspore/ccsrc/minddata/dataset/core/de_tensor.cc", "mindspore/core/ir/dtype/type.cc", "mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/concat_node.cc", "mindspore/ccsrc/minddata/dataset/engine/datasetops/source/album_op.cc", "mindspore/core/utils/visible.h", "build/mindspore/_deps/pybind11-src/include/pybind11/detail/init.h", "mindspore/ccsrc/minddata/dataset/util/list.h", "mindspore/ccsrc/minddata/dataset/engine/perf/profiling.cc", "mindspore/ccsrc/minddata/dataset/engine/perf/cyclic_array.h", "mindspore/core/utils/flags.cc", "mindspore/ccsrc/minddata/dataset/kernels/data/data_utils.cc", "mindspore/ccsrc/minddata/dataset/util/auto_index.h", "mindspore/ccsrc/minddata/dataset/engine/cache/cache_common.h", "mindspore/lite/build/_deps/nlohmann_json-src/nlohmann/detail/macro_unscope.hpp", "mindspore/core/utils/parallel_node_check.cc", "mindspore/ccsrc/minddata/dataset/kernels/ir/data/transforms_ir.cc", "mindspore/ccsrc/minddata/dataset/engine/connector.h", "mindspore/ccsrc/minddata/dataset/kernels/tensor_op.cc", "mindspore/ccsrc/minddata/dataset/engine/opt/pass.cc", "mindspore/ccsrc/minddata/dataset/kernels/py_func_op.cc", "mindspore/ccsrc/utils/system/file_system.cc", "mindspore/ccsrc/minddata/dataset/engine/perf/device_queue_tracing.cc", "mindspore/lite/build/_deps/jpeg_turbo-src/./jpeglib.h", "mindspore/ccsrc/minddata/dataset/core/device_resource.cc", "mindspore/ccsrc/minddata/dataset/util/task.cc", "mindspore/core/ir/device_sync.h", "mindspore/ccsrc/minddata/dataset/engine/cache/cache_request.cc", "mindspore/lite/build/_deps/nlohmann_json-src/nlohmann/detail/input/input_adapters.hpp", "mindspore/ccsrc/minddata/dataset/api/data_helper.cc", "mindspore/ccsrc/minddata/dataset/include/dataset/text.h", "mindspore/ccsrc/minddata/dataset/kernels/image/lite_cv/lite_mat.cc", "mindspore/core/ir/device_event.h", "third_party/securec/include/./securec.h", "mindspore/core/utils/symbolic.cc", "mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/project_node.cc", "mindspore/ccsrc/minddata/dataset/engine/datasetops/epoch_ctrl_op.cc", "mindspore/ccsrc/minddata/dataset/engine/opt/pre/deep_copy_pass.cc", "mindspore/ccsrc/minddata/dataset/include/dataset/execute.h", "mindspore/ccsrc/minddata/dataset/engine/cache/cache_fbb.cc", "mindspore/core/ir/dtype/container.cc", "build/mindspore/_deps/pybind11-src/include/pybind11/detail/internals.h", "build/mindspore/_deps/pybind11-src/include/pybind11/options.h", "mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/samplers/prebuilt_sampler_ir.cc", "mindspore/ccsrc/minddata/dataset/core/tensor_shape.cc", "mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/map_node.cc", "mindspore/core/abstract/dshape.cc", "mindspore/ccsrc/minddata/dataset/util/services.cc", "mindspore/ccsrc/minddata/dataset/kernels/ir/validators.cc", "mindspore/lite/build/_deps/nlohmann_json-src/nlohmann/detail/input/position_t.hpp", "mindspore/lite/build/_deps/nlohmann_json-src/nlohmann/detail/output/output_adapters.hpp", "mindspore/core/ir/value.cc", "mindspore/ccsrc/minddata/dataset/include/dataset/constants.h", "mindspore/ccsrc/minddata/dataset/text/vocab.cc", "mindspore/ccsrc/minddata/dataset/engine/tree_adapter_lite.cc", "build/mindspore/_deps/pybind11-src/include/pybind11/cast.h", "build/mindspore/_deps/pybind11-src/include/pybind11/stl_bind.h", "mindspore/core/abstract/analysis_context.cc", "mindspore/core/utils/trace_info.cc", "akg/third_party/fwkacllib/inc/toolchain/slog.h", "mindspore/lite/build/_deps/nlohmann_json-src/nlohmann/detail/input/binary_reader.hpp", "mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/transfer_node.cc", "mindspore/ccsrc/minddata/dataset/util/semaphore.cc", "mindspore/ccsrc/minddata/dataset/core/config_manager.cc", "mindspore/ccsrc/minddata/dataset/engine/datasetops/filter_op.cc", "mindspore/core/utils/overload.h", "mindspore/ccsrc/minddata/dataset/engine/datasetops/source/sampler/sequential_sampler.cc", "mindspore/core/base/base.cc", "mindspore/ccsrc/minddata/dataset/include/dataset/datasets.h", "mindspore/core/ir/graph_utils.cc", "mindspore/ccsrc/minddata/dataset/util/btree_impl.tpp", "mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/rename_node.cc", "mindspore/ccsrc/minddata/dataset/engine/data_schema.cc", "mindspore/lite/build/_deps/nlohmann_json-src/nlohmann/detail/iterators/iter_impl.hpp", "mindspore/core/abstract/abstract_value.cc", "mindspore/ccsrc/minddata/dataset/util/treap.h", "mindspore/ccsrc/minddata/dataset/kernels/ir/tensor_operation.h", "mindspore/core/base/user_data.h", "mindspore/core/utils/counter.h", "mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/skip_node.cc", "mindspore/core/ir/primitive.cc", "mindspore/ccsrc/utils/system/crc32c.cc", "mindspore/core/abstract/param_validator.cc", 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"mindspore/core/ir/named.cc", "mindspore/lite/build/_deps/nlohmann_json-src/nlohmann/detail/input/parser.hpp", "build/mindspore/_deps/icu4c-src/icu4c/source/common/unicode/utf.h", "build/mindspore/_deps/icu4c-src/icu4c/source/common/unicode/char16ptr.h", "mindspore/core/utils/info.cc", "mindspore/core/ir/dtype/monad_type.cc", "mindspore/core/ir/scalar.h", "mindspore/core/ir/func_graph.cc", "mindspore/core/ir/meta_tensor.cc", "build/mindspore/_deps/icu4c-src/icu4c/source/common/unicode/stringoptions.h", "build/mindspore/_deps/pybind11-src/include/pybind11/detail/typeid.h", "build/mindspore/_deps/pybind11-src/include/pybind11/attr.h", "build/mindspore/_deps/icu4c-src/icu4c/source/i18n/unicode/regex.h", "include/api/data_type.h", "mindspore/core/abstract/abstract_function.cc", "include/api/dual_abi_helper.h", "mindspore/core/utils/ms_context.cc", "mindspore/ccsrc/cxx_api/types.cc", "mindspore/ccsrc/minddata/dataset/text/kernels/sliding_window_op.cc", 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"mindspore/core/utils/shape_utils.h", "build/mindspore/_deps/icu4c-src/icu4c/source/common/unicode/unifunct.h", "mindspore/ccsrc/minddata/dataset/text/kernels/jieba_tokenizer_op.cc", "mindspore/ccsrc/minddata/dataset/kernels/data/type_cast_op.cc", "build/mindspore/_deps/icu4c-src/icu4c/source/common/unicode/platform.h", "mindspore/lite/build/_deps/nlohmann_json-src/nlohmann/detail/conversions/from_json.hpp", "third_party/securec/include/securectype.h", "mindspore/ccsrc/minddata/dataset/util/status.cc", "mindspore/ccsrc/minddata/dataset/text/kernels/to_number_op.cc", "mindspore/core/ir/dtype/number.cc", "mindspore/core/utils/signal.h", "mindspore/core/utils/ms_exception.h", "mindspore/core/ir/tensor.cc", "mindspore/lite/build/_deps/nlohmann_json-src/nlohmann/detail/conversions/to_chars.hpp", "mindspore/core/ir/scope.cc", "mindspore/lite/build/_deps/nlohmann_json-src/nlohmann/detail/input/json_sax.hpp", "mindspore/core/utils/label.cc", "mindspore/core/base/float16.h", "mindspore/core/ir/dtype.cc", "mindspore/lite/build/_deps/nlohmann_json-src/nlohmann/detail/output/serializer.hpp", "build/mindspore/_deps/icu4c-src/icu4c/source/common/unicode/unimatch.h", "mindspore/lite/build/_deps/nlohmann_json-src/nlohmann/detail/iterators/iteration_proxy.hpp", "mindspore/lite/src/common/log.cc", "build/mindspore/_deps/pybind11-src/include/pybind11/pytypes.h", "build/mindspore/_deps/icu4c-src/icu4c/source/common/unicode/uscript.h", "mindspore/core/ir/dtype/empty.cc", "mindspore/core/utils/ordered_set.h", "build/mindspore/_deps/icu4c-src/icu4c/source/common/unicode/unifilt.h", "mindspore/core/ir/dtype/ref.cc"]} \ No newline at end of file diff --git a/mindspore/lite/tools/optimizer/fisson/eliminate_concat_split.cc b/mindspore/lite/tools/optimizer/fisson/eliminate_concat_split.cc index ca815b4cc05..631cc2826d6 100644 --- a/mindspore/lite/tools/optimizer/fisson/eliminate_concat_split.cc +++ b/mindspore/lite/tools/optimizer/fisson/eliminate_concat_split.cc @@ -20,7 +20,7 @@ #include #include "tools/optimizer/fisson/eliminate_concat_split.h" #include "schema/inner/model_generated.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "tools/optimizer/common/gllo_utils.h" #include "ops/split_with_overlap.h" #include "ops/concat.h" diff --git a/mindspore/lite/tools/optimizer/fisson/fisson_util.h b/mindspore/lite/tools/optimizer/fisson/fisson_util.h index dc8ed6f3624..73c1aa95981 100644 --- a/mindspore/lite/tools/optimizer/fisson/fisson_util.h +++ b/mindspore/lite/tools/optimizer/fisson/fisson_util.h @@ -22,7 +22,7 @@ #include #include #include "schema/inner/model_generated.h" -#include "mindspore/ccsrc/utils/utils.h" +#include "mindspore/ccsrc/include/common/utils/utils.h" #include "tools/optimizer/common/gllo_utils.h" #include "tools/converter/converter_flags.h" #include "mindspore/lite/include/context.h" diff --git a/mindspore/lite/tools/optimizer/fisson/multi_conv_split_pass.cc b/mindspore/lite/tools/optimizer/fisson/multi_conv_split_pass.cc index a5ba4ad65e3..3fc15d893ba 100644 --- a/mindspore/lite/tools/optimizer/fisson/multi_conv_split_pass.cc +++ b/mindspore/lite/tools/optimizer/fisson/multi_conv_split_pass.cc @@ -17,7 +17,7 @@ #include "tools/optimizer/fisson/multi_conv_split_pass.h" #include #include -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "base/base.h" #include "ops/fusion/conv2d_fusion.h" #include "tools/optimizer/common/gllo_utils.h" diff --git a/mindspore/lite/tools/optimizer/fusion/batchnorm_to_scale_fusion.cc b/mindspore/lite/tools/optimizer/fusion/batchnorm_to_scale_fusion.cc index a317281deb9..a55df429763 100644 --- a/mindspore/lite/tools/optimizer/fusion/batchnorm_to_scale_fusion.cc +++ b/mindspore/lite/tools/optimizer/fusion/batchnorm_to_scale_fusion.cc @@ -18,7 +18,7 @@ #include #include "ops/batch_norm.h" #include "ops/fused_batch_norm.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "ops/fusion/scale_fusion.h" #include "tools/optimizer/common/gllo_utils.h" #include "tools/common/tensor_util.h" diff --git a/mindspore/lite/tools/optimizer/fusion/conv_bn_fusion.cc b/mindspore/lite/tools/optimizer/fusion/conv_bn_fusion.cc index 34d495b4426..e09539a9146 100644 --- a/mindspore/lite/tools/optimizer/fusion/conv_bn_fusion.cc +++ b/mindspore/lite/tools/optimizer/fusion/conv_bn_fusion.cc @@ -16,7 +16,7 @@ #include "tools/optimizer/fusion/conv_bn_fusion.h" #include -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "tools/optimizer/common/gllo_utils.h" #include "tools/optimizer/fusion/batchnorm_to_scale_fusion.h" #include "nnacl/op_base.h" diff --git a/mindspore/lite/tools/optimizer/fusion/gelu_fusion.cc b/mindspore/lite/tools/optimizer/fusion/gelu_fusion.cc index af7f2dd54bb..e3598fb79d6 100644 --- a/mindspore/lite/tools/optimizer/fusion/gelu_fusion.cc +++ b/mindspore/lite/tools/optimizer/fusion/gelu_fusion.cc @@ -18,7 +18,7 @@ #include #include #include "ops/fusion/activation.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "tools/optimizer/common/gllo_utils.h" #include "nnacl/op_base.h" diff --git a/mindspore/lite/tools/optimizer/fusion/gelu_fusion.h b/mindspore/lite/tools/optimizer/fusion/gelu_fusion.h index 196928b57bb..7fc1a53e54c 100644 --- a/mindspore/lite/tools/optimizer/fusion/gelu_fusion.h +++ b/mindspore/lite/tools/optimizer/fusion/gelu_fusion.h @@ -21,7 +21,7 @@ #include #include #include "backend/common/optimizer/optimizer.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "tools/optimizer/common/gllo_utils.h" namespace mindspore { diff --git a/mindspore/lite/tools/optimizer/fusion/glu_fusion.cc b/mindspore/lite/tools/optimizer/fusion/glu_fusion.cc index 0592d72a09e..47616a2627c 100644 --- a/mindspore/lite/tools/optimizer/fusion/glu_fusion.cc +++ b/mindspore/lite/tools/optimizer/fusion/glu_fusion.cc @@ -17,7 +17,7 @@ #include #include #include "ops/glu.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "ops/op_utils.h" #include "nnacl/op_base.h" diff --git a/mindspore/lite/tools/optimizer/fusion/glu_fusion.h b/mindspore/lite/tools/optimizer/fusion/glu_fusion.h index a596d69f619..1c15b6022c4 100644 --- a/mindspore/lite/tools/optimizer/fusion/glu_fusion.h +++ b/mindspore/lite/tools/optimizer/fusion/glu_fusion.h @@ -20,7 +20,7 @@ #include #include #include "backend/common/optimizer/optimizer.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "tools/optimizer/common/gllo_utils.h" namespace mindspore { diff --git a/mindspore/lite/tools/optimizer/fusion/matmul_activation_fusion.cc b/mindspore/lite/tools/optimizer/fusion/matmul_activation_fusion.cc index 57d70f0f50c..1e3dde0a78b 100644 --- a/mindspore/lite/tools/optimizer/fusion/matmul_activation_fusion.cc +++ b/mindspore/lite/tools/optimizer/fusion/matmul_activation_fusion.cc @@ -18,7 +18,7 @@ #include #include "ops/fusion/activation.h" #include "ops/fusion/mat_mul_fusion.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "tools/optimizer/common/gllo_utils.h" #include "nnacl/op_base.h" diff --git a/mindspore/lite/tools/optimizer/fusion/multi_head_attention_fusion.h b/mindspore/lite/tools/optimizer/fusion/multi_head_attention_fusion.h index 1719d4c59b5..4d53531a2bf 100644 --- a/mindspore/lite/tools/optimizer/fusion/multi_head_attention_fusion.h +++ b/mindspore/lite/tools/optimizer/fusion/multi_head_attention_fusion.h @@ -21,7 +21,7 @@ #include #include #include "tools/optimizer/common/multiple_pattern_process_pass.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "include/errorcode.h" #include "ops/attention.h" diff --git a/mindspore/lite/tools/optimizer/fusion/norm_fusion.cc b/mindspore/lite/tools/optimizer/fusion/norm_fusion.cc index 3bfc10f2a68..c63acff4bab 100644 --- a/mindspore/lite/tools/optimizer/fusion/norm_fusion.cc +++ b/mindspore/lite/tools/optimizer/fusion/norm_fusion.cc @@ -19,7 +19,7 @@ #include "ops/fusion/layer_norm_fusion.h" #include "ops/fusion/reduce_fusion.h" #include "mindspore/core/ops/instance_norm.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "tools/optimizer/common/gllo_utils.h" #include "securec/include/securec.h" #include "nnacl/op_base.h" diff --git a/mindspore/lite/tools/optimizer/fusion/norm_fusion.h b/mindspore/lite/tools/optimizer/fusion/norm_fusion.h index a03512584aa..c3a0608612f 100644 --- a/mindspore/lite/tools/optimizer/fusion/norm_fusion.h +++ b/mindspore/lite/tools/optimizer/fusion/norm_fusion.h @@ -23,7 +23,7 @@ #include #include "schema/inner/model_generated.h" #include "backend/common/optimizer/optimizer.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "tools/optimizer/common/gllo_utils.h" namespace mindspore { diff --git a/mindspore/lite/tools/optimizer/fusion/sigmoid_mul_fusion.cc b/mindspore/lite/tools/optimizer/fusion/sigmoid_mul_fusion.cc index 1a3ec822a69..b2f90a629c5 100644 --- a/mindspore/lite/tools/optimizer/fusion/sigmoid_mul_fusion.cc +++ b/mindspore/lite/tools/optimizer/fusion/sigmoid_mul_fusion.cc @@ -17,7 +17,7 @@ #include #include "ops/fusion/activation.h" #include "ops/op_utils.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "tools/optimizer/common/gllo_utils.h" #include "nnacl/op_base.h" diff --git a/mindspore/lite/tools/optimizer/fusion/tf_bidirection_gru_fusion.cc b/mindspore/lite/tools/optimizer/fusion/tf_bidirection_gru_fusion.cc index 929584d1204..74ef4785323 100644 --- a/mindspore/lite/tools/optimizer/fusion/tf_bidirection_gru_fusion.cc +++ b/mindspore/lite/tools/optimizer/fusion/tf_bidirection_gru_fusion.cc @@ -24,7 +24,7 @@ #include "ops/transpose.h" #include "src/common/utils.h" #include "tools/common/tensor_util.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "securec/include/securec.h" #include "nnacl/op_base.h" diff --git a/mindspore/lite/tools/optimizer/fusion/tf_bidirection_gru_fusion.h b/mindspore/lite/tools/optimizer/fusion/tf_bidirection_gru_fusion.h index 42dd8dc28fd..1ee237d6c76 100644 --- a/mindspore/lite/tools/optimizer/fusion/tf_bidirection_gru_fusion.h +++ b/mindspore/lite/tools/optimizer/fusion/tf_bidirection_gru_fusion.h @@ -22,7 +22,7 @@ #include "tools/optimizer/common/gllo_utils.h" #include "schema/inner/model_generated.h" #include "backend/common/optimizer/optimizer.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "include/errorcode.h" namespace mindspore { diff --git a/mindspore/lite/tools/optimizer/fusion/tf_lstm_cell_fusion.cc b/mindspore/lite/tools/optimizer/fusion/tf_lstm_cell_fusion.cc index 5c9b6227b6c..1d23e6204a2 100644 --- a/mindspore/lite/tools/optimizer/fusion/tf_lstm_cell_fusion.cc +++ b/mindspore/lite/tools/optimizer/fusion/tf_lstm_cell_fusion.cc @@ -18,7 +18,7 @@ #include "ops/lstm.h" #include "src/common/utils.h" #include "tools/common/tensor_util.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "tools/optimizer/common/gllo_utils.h" #include "securec/include/securec.h" #include "tools/optimizer/fusion/tflite_lstm_cell_fusion.h" diff --git a/mindspore/lite/tools/optimizer/fusion/tf_lstm_cell_fusion.h b/mindspore/lite/tools/optimizer/fusion/tf_lstm_cell_fusion.h index 95d9ed99438..7cd5d4e2545 100644 --- a/mindspore/lite/tools/optimizer/fusion/tf_lstm_cell_fusion.h +++ b/mindspore/lite/tools/optimizer/fusion/tf_lstm_cell_fusion.h @@ -21,7 +21,7 @@ #include "schema/inner/model_generated.h" #include "tools/optimizer/fusion/tflite_lstm_cell_fusion.h" #include "backend/common/optimizer/optimizer.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "include/errorcode.h" namespace mindspore { diff --git a/mindspore/lite/tools/optimizer/fusion/tflite_lstm_cell_fusion.cc b/mindspore/lite/tools/optimizer/fusion/tflite_lstm_cell_fusion.cc index 2955ffbc381..1351d38615c 100644 --- a/mindspore/lite/tools/optimizer/fusion/tflite_lstm_cell_fusion.cc +++ b/mindspore/lite/tools/optimizer/fusion/tflite_lstm_cell_fusion.cc @@ -22,7 +22,7 @@ #include "ops/tuple_get_item.h" #include "src/common/utils.h" #include "tools/common/tensor_util.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "tools/optimizer/common/gllo_utils.h" #include "securec/include/securec.h" #include "nnacl/op_base.h" diff --git a/mindspore/lite/tools/optimizer/fusion/tflite_lstm_cell_fusion.h b/mindspore/lite/tools/optimizer/fusion/tflite_lstm_cell_fusion.h index a69c6823a4d..1c23a108953 100644 --- a/mindspore/lite/tools/optimizer/fusion/tflite_lstm_cell_fusion.h +++ b/mindspore/lite/tools/optimizer/fusion/tflite_lstm_cell_fusion.h @@ -20,7 +20,7 @@ #include #include #include "backend/common/optimizer/optimizer.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "include/errorcode.h" namespace mindspore { diff --git a/mindspore/lite/tools/optimizer/fusion/tflite_rel_pos_multi_head_attention_fusion.h b/mindspore/lite/tools/optimizer/fusion/tflite_rel_pos_multi_head_attention_fusion.h index 792fa1602ab..c454086ccb9 100644 --- a/mindspore/lite/tools/optimizer/fusion/tflite_rel_pos_multi_head_attention_fusion.h +++ b/mindspore/lite/tools/optimizer/fusion/tflite_rel_pos_multi_head_attention_fusion.h @@ -21,7 +21,7 @@ #include #include #include "backend/common/optimizer/optimizer.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "include/errorcode.h" #include "tools/optimizer/fusion/multi_head_attention_fusion.h" diff --git a/mindspore/lite/tools/optimizer/graph/decrease_transpose_algo.h b/mindspore/lite/tools/optimizer/graph/decrease_transpose_algo.h index 175512fe095..c3b7b5dc806 100644 --- a/mindspore/lite/tools/optimizer/graph/decrease_transpose_algo.h +++ b/mindspore/lite/tools/optimizer/graph/decrease_transpose_algo.h @@ -23,7 +23,7 @@ #include #include #include "backend/common/optimizer/optimizer.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "tools/converter/converter_flags.h" #include "tools/optimizer/common/format_utils.h" #include "tools/optimizer/format/delete_redundant_transpose.h" diff --git a/mindspore/lite/tools/optimizer/graph/reduce_same_act_pass.h b/mindspore/lite/tools/optimizer/graph/reduce_same_act_pass.h index 7d4b5d32559..28acc1b89ad 100644 --- a/mindspore/lite/tools/optimizer/graph/reduce_same_act_pass.h +++ b/mindspore/lite/tools/optimizer/graph/reduce_same_act_pass.h @@ -22,7 +22,7 @@ #include #include #include "backend/common/optimizer/optimizer.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "tools/converter/converter_flags.h" #include "tools/optimizer/common/format_utils.h" #include "tools/optimizer/graph/transpose_strategy.h" diff --git a/mindspore/lite/tools/optimizer/graph/split_one_pass.h b/mindspore/lite/tools/optimizer/graph/split_one_pass.h index 18845e32768..0b51175f464 100644 --- a/mindspore/lite/tools/optimizer/graph/split_one_pass.h +++ b/mindspore/lite/tools/optimizer/graph/split_one_pass.h @@ -22,7 +22,7 @@ #include #include #include "backend/common/optimizer/optimizer.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "tools/converter/converter_flags.h" #include "tools/optimizer/common/format_utils.h" #include "tools/optimizer/graph/transpose_strategy.h" diff --git a/mindspore/lite/tools/optimizer/parallel/conv2d_info.cc b/mindspore/lite/tools/optimizer/parallel/conv2d_info.cc index f54ce7d40c8..5800f960fbe 100644 --- a/mindspore/lite/tools/optimizer/parallel/conv2d_info.cc +++ b/mindspore/lite/tools/optimizer/parallel/conv2d_info.cc @@ -22,7 +22,7 @@ #include "ops/fusion/conv2d_fusion.h" #include "ops/split_with_overlap.h" #include "tools/optimizer/common/gllo_utils.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "tools/converter/converter_flags.h" #include "include/errorcode.h" #include "tools/optimizer/parallel/operator_info_register.h" diff --git a/mindspore/lite/tools/optimizer/parallel/depthwise_conv2d_info.cc b/mindspore/lite/tools/optimizer/parallel/depthwise_conv2d_info.cc index 5ddbaf8aa93..1bd99cd6337 100644 --- a/mindspore/lite/tools/optimizer/parallel/depthwise_conv2d_info.cc +++ b/mindspore/lite/tools/optimizer/parallel/depthwise_conv2d_info.cc @@ -23,7 +23,7 @@ #include "nnacl/op_base.h" #include "ops/fusion/conv2d_fusion.h" #include "tools/optimizer/common/gllo_utils.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "tools/converter/converter_flags.h" #include "include/errorcode.h" #include "tools/optimizer/parallel/operator_info_register.h" diff --git a/mindspore/lite/tools/optimizer/parallel/operator_info.cc b/mindspore/lite/tools/optimizer/parallel/operator_info.cc index 8243b5bd9f8..537f575bc28 100644 --- a/mindspore/lite/tools/optimizer/parallel/operator_info.cc +++ b/mindspore/lite/tools/optimizer/parallel/operator_info.cc @@ -20,7 +20,7 @@ #include "ops/concat.h" #include "ops/addn.h" #include "ops/tuple_get_item.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "base/core_ops.h" #include "include/errorcode.h" #include "nnacl/op_base.h" diff --git a/mindspore/lite/tools/optimizer/parallel/spliter.h b/mindspore/lite/tools/optimizer/parallel/spliter.h index d2e68ec0bd2..26244eab456 100644 --- a/mindspore/lite/tools/optimizer/parallel/spliter.h +++ b/mindspore/lite/tools/optimizer/parallel/spliter.h @@ -21,7 +21,7 @@ #include #include #include "schema/inner/model_generated.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "tools/optimizer/common/gllo_utils.h" #include "tools/converter/converter_flags.h" #include "include/context.h" diff --git a/tests/ut/cpp/CMakeLists.txt b/tests/ut/cpp/CMakeLists.txt index b738c81c4ca..ff3c3fc8035 100644 --- a/tests/ut/cpp/CMakeLists.txt +++ b/tests/ut/cpp/CMakeLists.txt @@ -240,7 +240,8 @@ add_dependencies(_ut_mindspore_obj graph) add_dependencies(_ut_ut_obj engine-cache-server graph) set(ut_objects $ $ $ $ $ - $) + $ $ + $) if(ENABLE_MINDDATA) set(ut_objects ${ut_objects} ${dataengine_submodules} $) endif() diff --git a/tests/ut/cpp/abstract/abstract_test.cc b/tests/ut/cpp/abstract/abstract_test.cc index 5cfd19c6b77..6c62fdfb3da 100644 --- a/tests/ut/cpp/abstract/abstract_test.cc +++ b/tests/ut/cpp/abstract/abstract_test.cc @@ -44,7 +44,7 @@ TEST_F(TestAbstract, TestParseDataClass) { } PyEval_InitThreads(); - py::object fn = parse::python_adapter::GetPyFn("gtest_input.pipeline.parse.parser_test", "TestFoo"); + py::object fn = python_adapter::GetPyFn("gtest_input.pipeline.parse.parser_test", "TestFoo"); ClassPtr cls_ptr = parse::ParseDataClass(fn); ASSERT_TRUE(nullptr != cls_ptr); diff --git a/tests/ut/cpp/common/py_func_graph_fetcher.cc b/tests/ut/cpp/common/py_func_graph_fetcher.cc index 78d5b682f7d..483bb07dd73 100644 --- a/tests/ut/cpp/common/py_func_graph_fetcher.cc +++ b/tests/ut/cpp/common/py_func_graph_fetcher.cc @@ -16,7 +16,7 @@ #include "common/py_func_graph_fetcher.h" namespace UT { -namespace python_adapter = mindspore::parse::python_adapter; +namespace python_adapter = mindspore::python_adapter; void InitPythonPath() { python_adapter::set_python_env_flag(false); python_adapter::SetPythonPath("../../../../"); diff --git a/tests/ut/cpp/common/py_func_graph_fetcher.h b/tests/ut/cpp/common/py_func_graph_fetcher.h index 6696a04ca65..26a0609bd39 100644 --- a/tests/ut/cpp/common/py_func_graph_fetcher.h +++ b/tests/ut/cpp/common/py_func_graph_fetcher.h @@ -44,11 +44,11 @@ class PyFuncGraphFetcher { template mindspore::FuncGraphPtr CallAndParseRet(std::string func_name, T... args) { try { - py::function fn = mindspore::parse::python_adapter::CallPyFn(model_path_.c_str(), func_name.c_str(), args...); + py::function fn = mindspore::python_adapter::CallPyFn(model_path_.c_str(), func_name.c_str(), args...); mindspore::FuncGraphPtr func_graph = mindspore::parse::ParsePythonCode(fn); if (doResolve_) { std::shared_ptr manager = mindspore::Manage(func_graph, false); - mindspore::parse::python_adapter::set_use_signature_in_resolve(false); + mindspore::python_adapter::set_use_signature_in_resolve(false); mindspore::parse::ResolveAll(manager); } return func_graph; @@ -68,11 +68,11 @@ class PyFuncGraphFetcher { if ("" != model_path) { path = model_path; } - py::function fn = mindspore::parse::python_adapter::GetPyFn(path.c_str(), func_name.c_str()); + py::function fn = mindspore::python_adapter::GetPyFn(path.c_str(), func_name.c_str()); mindspore::FuncGraphPtr func_graph = mindspore::parse::ParsePythonCode(fn); if (doResolve_) { std::shared_ptr manager = mindspore::Manage(func_graph, false); - mindspore::parse::python_adapter::set_use_signature_in_resolve(false); + mindspore::python_adapter::set_use_signature_in_resolve(false); mindspore::parse::ResolveAll(manager); } return func_graph; diff --git a/tests/ut/cpp/common/trans_test.cc b/tests/ut/cpp/common/trans_test.cc index ba2dda5e854..0da49facbe9 100644 --- a/tests/ut/cpp/common/trans_test.cc +++ b/tests/ut/cpp/common/trans_test.cc @@ -16,8 +16,8 @@ #include #include "common/common_test.h" -#include "utils/ms_device_shape_transfer.h" -#include "utils/utils.h" +#include "runtime/device/ms_device_shape_transfer.h" +#include "include/common/utils/utils.h" using namespace std; namespace mindspore { diff --git a/tests/ut/cpp/device/hccl_adapter_test.cc b/tests/ut/cpp/device/hccl_adapter_test.cc index c6e5d191565..dd54cb638f1 100644 --- a/tests/ut/cpp/device/hccl_adapter_test.cc +++ b/tests/ut/cpp/device/hccl_adapter_test.cc @@ -17,6 +17,7 @@ #include "common/common_test.h" #include "plugin/device/ascend/hal/hccl_adapter/all_to_all_v_calc_param.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "mindspore/core/ir/dtype/type_id.h" namespace mindspore::hccl { @@ -32,15 +33,15 @@ class TestHcclAdapter : public UT::Common { all_to_all_v_input.insert(all_to_all_v_input.end(), inputs.begin(), inputs.end()); auto all_to_all_v = graph->NewCNode(all_to_all_v_input); MS_EXCEPTION_IF_NULL(all_to_all_v); - AnfAlgo::SetNodeAttr(kAttrSendRankIds, MakeValue>(send_rank_ids), all_to_all_v); - AnfAlgo::SetNodeAttr(kAttrRecvRankIds, MakeValue>(recv_rank_ids), all_to_all_v); - AnfAlgo::SetNodeAttr(kAttrGroup, MakeValue("default_group"), all_to_all_v); + common::AnfAlgo::SetNodeAttr(kAttrSendRankIds, MakeValue>(send_rank_ids), all_to_all_v); + common::AnfAlgo::SetNodeAttr(kAttrRecvRankIds, MakeValue>(recv_rank_ids), all_to_all_v); + common::AnfAlgo::SetNodeAttr(kAttrGroup, MakeValue("default_group"), all_to_all_v); return all_to_all_v; } void SetOutputs(const CNodePtr &cnode, const std::vector> &shape, const std::vector &data_type) { - AnfAlgo::SetOutputInferTypeAndShape(data_type, shape, cnode.get()); + common::AnfAlgo::SetOutputInferTypeAndShape(data_type, shape, cnode.get()); kernel::KernelBuildInfo::KernelBuildInfoBuilder builder; builder.SetFusionType(kernel::FusionType::OPAQUE); builder.SetProcessor(kernel::Processor::AICORE); @@ -62,8 +63,8 @@ class TestHcclAdapter : public UT::Common { std::vector res; for (size_t i = 0; i < shape.size(); ++i) { auto node = graph->NewCNode(std::vector{NewValueNode(std::make_shared("AnyNameOp"))}); - AnfAlgo::SetOutputInferTypeAndShape(std::vector{data_type[i]}, std::vector>{shape[i]}, - node.get()); + common::AnfAlgo::SetOutputInferTypeAndShape(std::vector{data_type[i]}, + std::vector>{shape[i]}, node.get()); kernel::KernelBuildInfo::KernelBuildInfoBuilder builder; builder.SetFusionType(kernel::FusionType::OPAQUE); builder.SetProcessor(kernel::Processor::AICORE); diff --git a/tests/ut/cpp/operator/composite_test.cc b/tests/ut/cpp/operator/composite_test.cc index 29e941e89d5..8ddbc738200 100644 --- a/tests/ut/cpp/operator/composite_test.cc +++ b/tests/ut/cpp/operator/composite_test.cc @@ -145,7 +145,7 @@ TEST_F(TestComposite, test_TupleSlice_arg_one_number) { } TEST_F(TestComposite, test_TupleSlice_arg_slice) { - std::shared_ptr env = parse::python_adapter::set_python_scoped(); + std::shared_ptr env = python_adapter::set_python_scoped(); MetaFuncGraphPtr tupleSlicePtr = std::make_shared("tuple_slice"); FuncGraphPtr tupleSliceGraphPtr = UTCompositeUtils::MakeFuncGraph(tupleSlicePtr, 2); @@ -291,7 +291,7 @@ TEST_F(TestComposite, test_ListSlice_arg_one_number) { /// Description: Test List slice /// Expectation: No Expectation TEST_F(TestComposite, test_ListSlice_arg_slice) { - std::shared_ptr env = parse::python_adapter::set_python_scoped(); + std::shared_ptr env = python_adapter::set_python_scoped(); MetaFuncGraphPtr list_slice = std::make_shared("list_slice"); FuncGraphPtr list_slice_graph = UTCompositeUtils::MakeFuncGraph(list_slice, 2); diff --git a/tests/ut/cpp/operator/grad_implementations_test.cc b/tests/ut/cpp/operator/grad_implementations_test.cc index 39e999366d0..74fd7327acf 100644 --- a/tests/ut/cpp/operator/grad_implementations_test.cc +++ b/tests/ut/cpp/operator/grad_implementations_test.cc @@ -23,6 +23,7 @@ #include "frontend/optimizer/ad/dfunctor.h" #include "debug/draw.h" #include "common/py_func_graph_fetcher.h" +#include "include/common/utils/convert_utils.h" namespace mindspore { namespace prim { diff --git a/tests/ut/cpp/operator/ops_test.cc b/tests/ut/cpp/operator/ops_test.cc index 2a1d99f3805..c85aa2c64d2 100644 --- a/tests/ut/cpp/operator/ops_test.cc +++ b/tests/ut/cpp/operator/ops_test.cc @@ -20,7 +20,8 @@ #include "common/common_test.h" #include "ir/value.h" #include "pybind_api/ir/primitive_py.h" -#include "pipeline/jit/parse/python_adapter.h" +#include "pipeline/jit/parse/parse_base.h" +#include "include/common/utils/python_adapter.h" #include "frontend/operator/ops.h" #include "base/core_ops.h" @@ -439,7 +440,7 @@ TEST_F(TestOps, GetConv2DPrimPyTest) { MS_LOG(EXCEPTION) << "" << conv2d_ptr->name() << "'s compute function is not implemented"; } - py::object conv2d_pyobj = parse::python_adapter::GetPyFn("gtest_input.pynative", "conv2d_prim"); + py::object conv2d_pyobj = python_adapter::GetPyFn("gtest_input.pynative", "conv2d_prim"); py::dict opAttrs = py::getattr(conv2d_pyobj, "attrs"); mindspore::HashMap attrs{}; for (auto item : opAttrs) { diff --git a/tests/ut/cpp/optimizer/lib_test.cc b/tests/ut/cpp/optimizer/lib_test.cc index d4cf5286841..7a2089eec3e 100644 --- a/tests/ut/cpp/optimizer/lib_test.cc +++ b/tests/ut/cpp/optimizer/lib_test.cc @@ -29,6 +29,7 @@ #include "pipeline/jit/resource.h" #include "debug/draw.h" #include "pipeline/jit/parse/data_converter.h" +#include "include/common/utils/convert_utils.h" namespace mindspore { namespace opt { diff --git a/tests/ut/cpp/optimizer/opt_test.cc b/tests/ut/cpp/optimizer/opt_test.cc index c6414ac3c50..7800c70c99f 100644 --- a/tests/ut/cpp/optimizer/opt_test.cc +++ b/tests/ut/cpp/optimizer/opt_test.cc @@ -30,6 +30,7 @@ #include "debug/draw.h" #include "frontend/operator/ops.h" #include "frontend/optimizer/cse.h" +#include "include/common/utils/convert_utils.h" namespace mindspore { namespace opt { diff --git a/tests/ut/cpp/parallel/step_parallel_test.cc b/tests/ut/cpp/parallel/step_parallel_test.cc index 35a58f2b378..293e73b615e 100644 --- a/tests/ut/cpp/parallel/step_parallel_test.cc +++ b/tests/ut/cpp/parallel/step_parallel_test.cc @@ -20,7 +20,7 @@ #include "debug/draw.h" #include "frontend/operator/ops.h" #include "pipeline/jit/static_analysis/static_analysis.h" -#include "utils/convert_utils_py.h" +#include "include/common/utils/convert_utils_py.h" namespace mindspore { namespace parallel { @@ -295,7 +295,7 @@ TEST_F(TestStepParallel, CreateOpInstance) { std::vector arglist; (void)std::transform(attrs.begin(), attrs.end(), std::back_inserter(arglist), [](Attr attr) { return ValueToPyData(attr.second); }); - py::object allreduce_pyobj = parse::python_adapter::CallPyFn( + py::object allreduce_pyobj = python_adapter::CallPyFn( "mindspore.parallel._utils", "_get_python_op", "AllReduce", "mindspore.ops.operations", "test", arglist); py::dict opAttr = py::getattr(allreduce_pyobj, "attrs"); mindspore::HashMap attributes{}; diff --git a/tests/ut/cpp/pipeline/static_analysis/evaluator_test.cc b/tests/ut/cpp/pipeline/static_analysis/evaluator_test.cc index b9032543dee..6e3ae62c698 100644 --- a/tests/ut/cpp/pipeline/static_analysis/evaluator_test.cc +++ b/tests/ut/cpp/pipeline/static_analysis/evaluator_test.cc @@ -26,8 +26,6 @@ namespace mindspore { namespace abstract { -namespace python_adapter = mindspore::parse::python_adapter; - class TestEvaluatorCacheMap : public UT::Common { public: void SetUp() {} diff --git a/tests/ut/cpp/pipeline/static_analysis/prim_test.cc b/tests/ut/cpp/pipeline/static_analysis/prim_test.cc index 7d7fbcb5f44..29fea0900cc 100644 --- a/tests/ut/cpp/pipeline/static_analysis/prim_test.cc +++ b/tests/ut/cpp/pipeline/static_analysis/prim_test.cc @@ -32,7 +32,6 @@ namespace mindspore { namespace abstract { namespace py = pybind11; -namespace python_adapter = mindspore::parse::python_adapter; class UTPrimUtils { public: using AbstractTensorPtr = std::shared_ptr; diff --git a/tests/ut/cpp/pipeline/static_analysis/static_analysis_test.cc b/tests/ut/cpp/pipeline/static_analysis/static_analysis_test.cc index d0f2218453e..b95c518712f 100644 --- a/tests/ut/cpp/pipeline/static_analysis/static_analysis_test.cc +++ b/tests/ut/cpp/pipeline/static_analysis/static_analysis_test.cc @@ -32,7 +32,6 @@ namespace mindspore { namespace abstract { -namespace python_adapter = mindspore::parse::python_adapter; namespace { AbstractBasePtr InferImplScalarAddStub(const AnalysisEnginePtr &engine, const PrimitivePtr &, diff --git a/tests/ut/cpp/pre_activate/ascend/dynamic_shape/dynamic_shape_pass_test.cc b/tests/ut/cpp/pre_activate/ascend/dynamic_shape/dynamic_shape_pass_test.cc index 8f5a9ba8159..06c8e55c1a0 100644 --- a/tests/ut/cpp/pre_activate/ascend/dynamic_shape/dynamic_shape_pass_test.cc +++ b/tests/ut/cpp/pre_activate/ascend/dynamic_shape/dynamic_shape_pass_test.cc @@ -21,7 +21,7 @@ #include "common/backend_common_test.h" #include "debug/anf_ir_dump.h" #include "debug/dump_proto.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/ms_utils.h" namespace mindspore { diff --git a/tests/ut/cpp/pre_activate/ascend/enhancer/getnext_tensor_move_elimination_test.cc b/tests/ut/cpp/pre_activate/ascend/enhancer/getnext_tensor_move_elimination_test.cc index da5c760fbad..c741bb4d512 100644 --- a/tests/ut/cpp/pre_activate/ascend/enhancer/getnext_tensor_move_elimination_test.cc +++ b/tests/ut/cpp/pre_activate/ascend/enhancer/getnext_tensor_move_elimination_test.cc @@ -19,7 +19,7 @@ #include "frontend/operator/ops.h" #include "ir/tensor.h" #include "debug/anf_ir_dump.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "kernel/kernel_build_info.h" #include "backend/common/optimizer/optimizer.h" #include "plugin/device/ascend/optimizer/enhancer/getnext_tensor_move_elimination.h" diff --git a/tests/ut/cpp/pre_activate/ascend/enhancer/insert_tensor_move_for_getnext_test.cc b/tests/ut/cpp/pre_activate/ascend/enhancer/insert_tensor_move_for_getnext_test.cc index 8fbefaf41a2..21caa431780 100644 --- a/tests/ut/cpp/pre_activate/ascend/enhancer/insert_tensor_move_for_getnext_test.cc +++ b/tests/ut/cpp/pre_activate/ascend/enhancer/insert_tensor_move_for_getnext_test.cc @@ -21,7 +21,7 @@ #include "frontend/operator/ops.h" #include "ir/manager.h" #include "debug/anf_ir_dump.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "kernel/kernel_build_info.h" #include "backend/common/optimizer/optimizer.h" #include "plugin/device/ascend/optimizer/enhancer/insert_tensor_move_for_getnext.h" diff --git a/tests/ut/cpp/pre_activate/ascend/enhancer/insert_tensor_move_for_hccl_op_test.cc b/tests/ut/cpp/pre_activate/ascend/enhancer/insert_tensor_move_for_hccl_op_test.cc index e38f69bdd09..da508296d9f 100644 --- a/tests/ut/cpp/pre_activate/ascend/enhancer/insert_tensor_move_for_hccl_op_test.cc +++ b/tests/ut/cpp/pre_activate/ascend/enhancer/insert_tensor_move_for_hccl_op_test.cc @@ -19,10 +19,12 @@ #include "frontend/operator/ops.h" #include "ir/tensor.h" #include "debug/anf_ir_dump.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "kernel/kernel_build_info.h" #include "backend/common/optimizer/optimizer.h" #include "ir/param_info.h" +#include "include/common/utils/anfalgo.h" + #define private public #define protected public #include "plugin/device/ascend/optimizer/enhancer/insert_tensor_move_for_hccl_op.h" @@ -48,7 +50,7 @@ class MockInsertTensorMoveForHcclKernelQuery : public KernelQuery { if (!node->isa()) { return false; } - auto node_name = AnfAlgo::GetCNodeName(node->cast()); + auto node_name = common::AnfAlgo::GetCNodeName(node->cast()); return node_name == "ApplyMomentum" || node_name == "AssignAdd"; } }; diff --git a/tests/ut/cpp/pre_activate/ascend/format_type/check_consistency_test.cc b/tests/ut/cpp/pre_activate/ascend/format_type/check_consistency_test.cc index 8ceafca11e8..cd425cb27cb 100644 --- a/tests/ut/cpp/pre_activate/ascend/format_type/check_consistency_test.cc +++ b/tests/ut/cpp/pre_activate/ascend/format_type/check_consistency_test.cc @@ -24,7 +24,7 @@ #include "ir/tensor.h" #include "ir/manager.h" #include "debug/anf_ir_dump.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "kernel/kernel_build_info.h" #include "backend/common/optimizer/optimizer.h" #include "plugin/device/ascend/optimizer/format_type/check_consistency.h" diff --git a/tests/ut/cpp/pre_activate/ascend/format_type/insert_cast_test.cc b/tests/ut/cpp/pre_activate/ascend/format_type/insert_cast_test.cc index f2608506c2b..b2b5459100e 100644 --- a/tests/ut/cpp/pre_activate/ascend/format_type/insert_cast_test.cc +++ b/tests/ut/cpp/pre_activate/ascend/format_type/insert_cast_test.cc @@ -25,7 +25,7 @@ #include "runtime/device/kernel_info.h" #include "plugin/device/ascend/optimizer/format_type/insert_cast.h" #include "kernel/kernel_build_info.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/ms_context.h" namespace mindspore { diff --git a/tests/ut/cpp/pre_activate/ascend/format_type/merge_cast_to_op_test.cc b/tests/ut/cpp/pre_activate/ascend/format_type/merge_cast_to_op_test.cc index 4dfde823741..a24e548480a 100644 --- a/tests/ut/cpp/pre_activate/ascend/format_type/merge_cast_to_op_test.cc +++ b/tests/ut/cpp/pre_activate/ascend/format_type/merge_cast_to_op_test.cc @@ -19,9 +19,10 @@ #include "frontend/operator/ops.h" #include "ir/tensor.h" #include "debug/anf_ir_dump.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "kernel/kernel_build_info.h" #include "backend/common/optimizer/optimizer.h" +#include "include/common/utils/anfalgo.h" #define private public #define protected public @@ -45,7 +46,7 @@ class MockMergeCastToOpKernelQuery : public KernelQuery { ~MockMergeCastToOpKernelQuery() override = default; void Query(const CNodePtr &kernel_node, std::vector> *kernel_info_list) override { - std::string op_name = AnfAlgo::GetCNodeName(kernel_node); + std::string op_name = common::AnfAlgo::GetCNodeName(kernel_node); if (op_name == kFour2FiveOpName) { kernel::KernelBuildInfo::KernelBuildInfoBuilder builder; builder.SetInputsFormat({kOpFormat_NCHW}); @@ -153,7 +154,7 @@ TEST_F(TestHWMergeCastToOp, test_merge_cast_to_prior_op) { EXPECT_NE(make_tuple->input(1), nullptr); EXPECT_TRUE(make_tuple->input(1)->isa()); auto cast = make_tuple->input(1)->cast(); - AnfAlgo::SetNodeAttr("dst_type", MakeValue("float32"), cast); + common::AnfAlgo::SetNodeAttr("dst_type", MakeValue("float32"), cast); // set kernel for cast kernel::KernelBuildInfo::KernelBuildInfoBuilder builder1; diff --git a/tests/ut/cpp/pre_activate/ascend/ir_fission/bn_grad_split_test.cc b/tests/ut/cpp/pre_activate/ascend/ir_fission/bn_grad_split_test.cc index fb6474b4464..4a950f2496a 100644 --- a/tests/ut/cpp/pre_activate/ascend/ir_fission/bn_grad_split_test.cc +++ b/tests/ut/cpp/pre_activate/ascend/ir_fission/bn_grad_split_test.cc @@ -19,7 +19,7 @@ #include "frontend/operator/ops.h" #include "ir/tensor.h" #include "debug/anf_ir_dump.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "kernel/kernel_build_info.h" #include "backend/common/optimizer/optimizer.h" diff --git a/tests/ut/cpp/pre_activate/ascend/ir_fission/bn_split_test.cc b/tests/ut/cpp/pre_activate/ascend/ir_fission/bn_split_test.cc index 1bbeb49a400..4c6f53dfbc7 100644 --- a/tests/ut/cpp/pre_activate/ascend/ir_fission/bn_split_test.cc +++ b/tests/ut/cpp/pre_activate/ascend/ir_fission/bn_split_test.cc @@ -22,7 +22,7 @@ #include "ir/tensor.h" #include "ir/manager.h" #include "debug/anf_ir_dump.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "kernel/kernel_build_info.h" #include "backend/common/optimizer/optimizer.h" diff --git a/tests/ut/cpp/pre_activate/ascend/ir_fission/layer_norm_grad_split_test.cc b/tests/ut/cpp/pre_activate/ascend/ir_fission/layer_norm_grad_split_test.cc index f83d611dca1..624e6c200b7 100644 --- a/tests/ut/cpp/pre_activate/ascend/ir_fission/layer_norm_grad_split_test.cc +++ b/tests/ut/cpp/pre_activate/ascend/ir_fission/layer_norm_grad_split_test.cc @@ -19,7 +19,7 @@ #include "frontend/operator/ops.h" #include "ir/tensor.h" #include "debug/anf_ir_dump.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "kernel/kernel_build_info.h" #include "backend/common/optimizer/optimizer.h" diff --git a/tests/ut/cpp/pre_activate/ascend/ir_fission/topk_split_test.cc b/tests/ut/cpp/pre_activate/ascend/ir_fission/topk_split_test.cc index d4019a4e82e..42e457dc06a 100644 --- a/tests/ut/cpp/pre_activate/ascend/ir_fission/topk_split_test.cc +++ b/tests/ut/cpp/pre_activate/ascend/ir_fission/topk_split_test.cc @@ -21,6 +21,8 @@ #include "backend/common/pass/convert_const_input_to_attr.h" #include "debug/anf_ir_dump.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" + #define private public #define protected public #include "plugin/device/ascend/optimizer/ir_fission/topk_split.h" @@ -108,12 +110,12 @@ TEST_F(TestHWTopKSplit, test_topk_no_split) { CNodePtr topk_cnode = GetTopkCNodeFromKernelGraph(kernel_graph); EXPECT_EQ(topk_cnode->inputs().size(), 3); - auto input_names_vec = AnfAlgo::GetNodeAttr>(topk_cnode, kAttrInputNames); + auto input_names_vec = common::AnfAlgo::GetNodeAttr>(topk_cnode, kAttrInputNames); EXPECT_EQ(input_names_vec.size(), 2); mindspore::HashSet attr_index{1}; ConstInputToAttr(topk_cnode, attr_index); EXPECT_EQ(topk_cnode->inputs().size(), 2); - input_names_vec = AnfAlgo::GetNodeAttr>(topk_cnode, kAttrInputNames); + input_names_vec = common::AnfAlgo::GetNodeAttr>(topk_cnode, kAttrInputNames); EXPECT_EQ(input_names_vec.size(), 2); auto optimizer = std::make_shared(); diff --git a/tests/ut/cpp/pre_activate/ascend/ir_fission/transdata_split_test.cc b/tests/ut/cpp/pre_activate/ascend/ir_fission/transdata_split_test.cc index 9501da0027a..768d1d04b22 100644 --- a/tests/ut/cpp/pre_activate/ascend/ir_fission/transdata_split_test.cc +++ b/tests/ut/cpp/pre_activate/ascend/ir_fission/transdata_split_test.cc @@ -21,6 +21,7 @@ #include "kernel/oplib/oplib.h" #include "debug/anf_ir_dump.h" #include "utils/ms_context.h" +#include "include/common/utils/anfalgo.h" #define private public #define protected public @@ -45,7 +46,7 @@ class MockInsertTransOpKernelSelectTrans4Dto5D : public KernelSelect { MockInsertTransOpKernelSelectTrans4Dto5D() = default; ~MockInsertTransOpKernelSelectTrans4Dto5D() override = default; void SelectKernel(const CNodePtr &cnode) override { - if (AnfAlgo::GetCNodeName(cnode) == "Four2Five") { + if (common::AnfAlgo::GetCNodeName(cnode) == "Four2Five") { KernelBuildInfoBuilder builder; builder.SetInputsFormat({"NCHW"}); builder.SetInputsDeviceType({kFloat16->type_id()}); @@ -73,7 +74,7 @@ class MockTransdataSplitKernelSelect : public KernelSelect { MockTransdataSplitKernelSelect() = default; ~MockTransdataSplitKernelSelect() override = default; void SelectKernel(const CNodePtr &cnode) override { - if (AnfAlgo::GetCNodeName(cnode) == kTransDataOpName) { + if (common::AnfAlgo::GetCNodeName(cnode) == kTransDataOpName) { KernelBuildInfoBuilder builder; builder.SetInputsFormat({"NCHW"}); builder.SetInputsDeviceType({kFloat16->type_id()}); diff --git a/tests/ut/cpp/pre_activate/ascend/ir_fusion/layer_norm_beta_gamma_backprop_fusion_test.cc b/tests/ut/cpp/pre_activate/ascend/ir_fusion/layer_norm_beta_gamma_backprop_fusion_test.cc index 8f215c116d5..510f1dc56e7 100644 --- a/tests/ut/cpp/pre_activate/ascend/ir_fusion/layer_norm_beta_gamma_backprop_fusion_test.cc +++ b/tests/ut/cpp/pre_activate/ascend/ir_fusion/layer_norm_beta_gamma_backprop_fusion_test.cc @@ -18,6 +18,7 @@ #include "runtime/device/kernel_info.h" #include "debug/anf_ir_dump.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #define private public #define protected public @@ -42,7 +43,7 @@ class MockLayerNormBetaGammaBackpropFusionKernelQuery : public KernelQuery { MockLayerNormBetaGammaBackpropFusionKernelQuery() = default; ~MockLayerNormBetaGammaBackpropFusionKernelQuery() override = default; void Query(const CNodePtr &cnode, std::vector> *kernel_info_list) override { - if (AnfAlgo::GetCNodeName(cnode) == kLayerNormBetaGammaBackpropOpName) { + if (common::AnfAlgo::GetCNodeName(cnode) == kLayerNormBetaGammaBackpropOpName) { kernel::KernelBuildInfo::KernelBuildInfoBuilder builder; builder.SetInputsFormat({kOpFormat_DEFAULT, kOpFormat_DEFAULT, kOpFormat_DEFAULT, kOpFormat_DEFAULT}); builder.SetOutputsFormat({kOpFormat_DEFAULT, kOpFormat_DEFAULT}); @@ -99,7 +100,7 @@ TEST_F(TestHWLayerNormBetaGammaBackpropFusion, layernorm_beta_gamma_backprop_fus builder2.SetOutputsDeviceType({kNumberTypeFloat16, kNumberTypeFloat16}); layer->set_kernel_info(std::make_shared()); AnfAlgo::SetSelectKernelBuildInfo(builder2.Build(), layer.get()); - AnfAlgo::SetNodeAttr("shape_gamma", MakeValue(""), layer); + common::AnfAlgo::SetNodeAttr("shape_gamma", MakeValue(""), layer); auto optimizer = std::make_shared(); auto pm = std::make_shared(); @@ -158,7 +159,7 @@ TEST_F(TestHWLayerNormBetaGammaBackpropFusion, layernorm_beta_gamma_backprop_fus builder2.SetOutputsDeviceType({kNumberTypeFloat16, kNumberTypeFloat16}); layer->set_kernel_info(std::make_shared()); AnfAlgo::SetSelectKernelBuildInfo(builder2.Build(), layer.get()); - AnfAlgo::SetNodeAttr("shape_gamma", MakeValue(""), layer); + common::AnfAlgo::SetNodeAttr("shape_gamma", MakeValue(""), layer); auto optimizer = std::make_shared(); auto pm = std::make_shared(); @@ -217,7 +218,7 @@ TEST_F(TestHWLayerNormBetaGammaBackpropFusion, layernorm_beta_gamma_backprop_fus builder2.SetOutputsDeviceType({kNumberTypeFloat16, kNumberTypeFloat16}); layer->set_kernel_info(std::make_shared()); AnfAlgo::SetSelectKernelBuildInfo(builder2.Build(), layer.get()); - AnfAlgo::EraseNodeAttr("shape_gamma", layer); + common::AnfAlgo::EraseNodeAttr("shape_gamma", layer); auto optimizer = std::make_shared(); auto pm = std::make_shared(); @@ -266,7 +267,7 @@ TEST_F(TestHWLayerNormBetaGammaBackpropFusion, layernorm_beta_gamma_backprop_fus builder2.SetOutputsDeviceType({kNumberTypeFloat16, kNumberTypeFloat16}); layer->set_kernel_info(std::make_shared()); AnfAlgo::SetSelectKernelBuildInfo(builder2.Build(), layer.get()); - AnfAlgo::SetNodeAttr("shape_gamma", MakeValue(""), layer); + common::AnfAlgo::SetNodeAttr("shape_gamma", MakeValue(""), layer); auto optimizer = std::make_shared(); auto pm = std::make_shared(); @@ -325,7 +326,7 @@ TEST_F(TestHWLayerNormBetaGammaBackpropFusion, layernorm_beta_gamma_backprop_fus builder2.SetOutputsDeviceType({kNumberTypeFloat16, kNumberTypeFloat16}); layer->set_kernel_info(std::make_shared()); AnfAlgo::SetSelectKernelBuildInfo(builder2.Build(), layer.get()); - AnfAlgo::SetNodeAttr("shape_gamma", MakeValue(""), layer); + common::AnfAlgo::SetNodeAttr("shape_gamma", MakeValue(""), layer); auto optimizer = std::make_shared(); auto pm = std::make_shared(); diff --git a/tests/ut/cpp/pre_activate/ascend/ir_fusion/reshape_transpose_fusion_test.cc b/tests/ut/cpp/pre_activate/ascend/ir_fusion/reshape_transpose_fusion_test.cc index 82530489ce6..75c04e03ba2 100644 --- a/tests/ut/cpp/pre_activate/ascend/ir_fusion/reshape_transpose_fusion_test.cc +++ b/tests/ut/cpp/pre_activate/ascend/ir_fusion/reshape_transpose_fusion_test.cc @@ -17,7 +17,7 @@ #include "common/backend_common_test.h" #include "common/py_func_graph_fetcher.h" #include "debug/anf_ir_dump.h" -#include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "plugin/device/ascend/optimizer/ir_fusion/reshape_transpose_fusion.h" namespace mindspore { @@ -48,7 +48,7 @@ TEST_F(TestHWReshapeTransposeFusion, test_reshape_transpose_fusion) { auto ret = kg->get_return(); EXPECT_NE(ret->input(1), nullptr); auto transpose = ret->input(1)->cast(); - AnfAlgo::SetNodeAttr(kAttrPerm, MakeValue("perm"), transpose); + common::AnfAlgo::SetNodeAttr(kAttrPerm, MakeValue("perm"), transpose); auto optimizer = std::make_shared(); auto pm = std::make_shared(); diff --git a/tests/ut/cpp/pre_activate/ascend/ir_fusion/transpose_reshape_fusion_test.cc b/tests/ut/cpp/pre_activate/ascend/ir_fusion/transpose_reshape_fusion_test.cc index 58448a07a6a..1ad2f419e9c 100644 --- a/tests/ut/cpp/pre_activate/ascend/ir_fusion/transpose_reshape_fusion_test.cc +++ b/tests/ut/cpp/pre_activate/ascend/ir_fusion/transpose_reshape_fusion_test.cc @@ -17,7 +17,7 @@ #include "common/backend_common_test.h" #include "common/py_func_graph_fetcher.h" #include "debug/anf_ir_dump.h" -#include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "plugin/device/ascend/optimizer/ir_fusion/transpose_reshape_fusion.h" namespace mindspore { @@ -50,7 +50,7 @@ TEST_F(TestHWTransposeReshapeFusion, test_transpose_reshape_fusion) { auto reshape = ret->input(1)->cast(); EXPECT_NE(reshape->input(1), nullptr); auto transpose = reshape->input(1)->cast(); - AnfAlgo::SetNodeAttr(kAttrPerm, MakeValue("perm"), transpose); + common::AnfAlgo::SetNodeAttr(kAttrPerm, MakeValue("perm"), transpose); auto optimizer = std::make_shared(); auto pm = std::make_shared(); diff --git a/tests/ut/cpp/pre_activate/ascend/ir_fusion/transpose_transdata_fusion_test.cc b/tests/ut/cpp/pre_activate/ascend/ir_fusion/transpose_transdata_fusion_test.cc index df40ea7413d..bc0a6559e7a 100644 --- a/tests/ut/cpp/pre_activate/ascend/ir_fusion/transpose_transdata_fusion_test.cc +++ b/tests/ut/cpp/pre_activate/ascend/ir_fusion/transpose_transdata_fusion_test.cc @@ -20,6 +20,8 @@ #include "backend/common/session/anf_runtime_algorithm.h" #include "kernel/oplib/oplib.h" #include "utils/ms_context.h" +#include "include/common/utils/anfalgo.h" + #define private public #define protected public #include "plugin/device/ascend/optimizer/format_type/insert_trans_op.h" @@ -52,7 +54,7 @@ class MockInsertTransOpKernelSelectTrans4Dto5D : public KernelSelect { MockInsertTransOpKernelSelectTrans4Dto5D() = default; ~MockInsertTransOpKernelSelectTrans4Dto5D() override = default; void SelectKernel(const CNodePtr &cnode) override { - if (AnfAlgo::GetCNodeName(cnode) == "TransData") { + if (common::AnfAlgo::GetCNodeName(cnode) == "TransData") { KernelBuildInfoBuilder builder; builder.SetInputsFormat({"NCHW"}); builder.SetInputsDeviceType({kFloat16->type_id()}); diff --git a/tests/ut/cpp/pre_activate/ascend/mindir/all_to_all_unify_mindir_test.cc b/tests/ut/cpp/pre_activate/ascend/mindir/all_to_all_unify_mindir_test.cc index 373eedc4a4b..f31b4dd266a 100644 --- a/tests/ut/cpp/pre_activate/ascend/mindir/all_to_all_unify_mindir_test.cc +++ b/tests/ut/cpp/pre_activate/ascend/mindir/all_to_all_unify_mindir_test.cc @@ -21,7 +21,7 @@ #include "backend/common/optimizer/optimizer.h" #include "backend/common/optimizer/pass_manager.h" #include "backend/common/pass/convert_const_input_to_attr.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/ms_utils.h" namespace mindspore { diff --git a/tests/ut/cpp/pre_activate/common/pattern_engine_test.cc b/tests/ut/cpp/pre_activate/common/pattern_engine_test.cc index 132c7adc7a7..fea9d9724a2 100644 --- a/tests/ut/cpp/pre_activate/common/pattern_engine_test.cc +++ b/tests/ut/cpp/pre_activate/common/pattern_engine_test.cc @@ -27,7 +27,7 @@ #include "base/base_ref.h" #include "base/core_ops.h" #include "ir/anf.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { using PatternListType = std::initializer_list; diff --git a/tests/ut/cpp/pre_activate/common/pattern_process_pass_test.cc b/tests/ut/cpp/pre_activate/common/pattern_process_pass_test.cc index 8ec8d8d2474..287486eabc0 100644 --- a/tests/ut/cpp/pre_activate/common/pattern_process_pass_test.cc +++ b/tests/ut/cpp/pre_activate/common/pattern_process_pass_test.cc @@ -29,7 +29,7 @@ #include "base/core_ops.h" #include "ir/anf.h" #include "ir/value.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/session/anf_runtime_algorithm.h" namespace mindspore { diff --git a/tests/ut/cpp/pre_activate/common/restore_abs_input_in_backed_infer_test.cc b/tests/ut/cpp/pre_activate/common/restore_abs_input_in_backed_infer_test.cc index c245ef7edd8..6cab1707b0b 100644 --- a/tests/ut/cpp/pre_activate/common/restore_abs_input_in_backed_infer_test.cc +++ b/tests/ut/cpp/pre_activate/common/restore_abs_input_in_backed_infer_test.cc @@ -17,7 +17,7 @@ #include #include #include "ir/primitive.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "abstract/abstract_value.h" #include "abstract/primitive_infer_map.h" #include "backend/common/optimizer/const_input_to_attr.h" diff --git a/tests/ut/cpp/pre_activate/mem_reuse/kernel_ref_test.cc b/tests/ut/cpp/pre_activate/mem_reuse/kernel_ref_test.cc index ef022b61366..dcfc1b6576d 100644 --- a/tests/ut/cpp/pre_activate/mem_reuse/kernel_ref_test.cc +++ b/tests/ut/cpp/pre_activate/mem_reuse/kernel_ref_test.cc @@ -20,7 +20,7 @@ #include "common/mem_reuse/kernel_refcount.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "common/common_test.h" #include "common/py_func_graph_fetcher.h" diff --git a/tests/ut/cpp/pre_activate/mem_reuse/mem_reuse_test.cc b/tests/ut/cpp/pre_activate/mem_reuse/mem_reuse_test.cc index 4ac6c923925..b47f04e1213 100644 --- a/tests/ut/cpp/pre_activate/mem_reuse/mem_reuse_test.cc +++ b/tests/ut/cpp/pre_activate/mem_reuse/mem_reuse_test.cc @@ -25,6 +25,7 @@ #include "frontend/operator/ops.h" #include "utils/log_adapter.h" #include "backend/common/session/anf_runtime_algorithm.h" +#include "include/common/utils/anfalgo.h" #include "utils/ms_utils.h" #include "pipeline/jit/resource.h" #include "common/mem_reuse/mem_reuse.h" @@ -197,11 +198,11 @@ static KernelGraphPtr CreateGraphWithExecOrder() { kernel_graph->SetExecOrderByDefault(); auto execution_order = kernel_graph->execution_order(); EXPECT_EQ(execution_order.size(), 2); - EXPECT_EQ(AnfAlgo::GetCNodeName(execution_order[0]), prim::kPrimAdd->name()); - EXPECT_EQ(AnfAlgo::GetCNodeName(execution_order[1]), prim::kPrimMul->name()); + EXPECT_EQ(common::AnfAlgo::GetCNodeName(execution_order[0]), prim::kPrimAdd->name()); + EXPECT_EQ(common::AnfAlgo::GetCNodeName(execution_order[1]), prim::kPrimMul->name()); auto new_outputs = kernel_graph->outputs(); EXPECT_EQ(new_outputs.size(), 1); - EXPECT_EQ(AnfAlgo::GetCNodeName(new_outputs[0]), prim::kPrimMul->name()); + EXPECT_EQ(common::AnfAlgo::GetCNodeName(new_outputs[0]), prim::kPrimMul->name()); return kernel_graph; } diff --git a/tests/ut/cpp/pre_activate/pass/allreduce_fusion_test.cc b/tests/ut/cpp/pre_activate/pass/allreduce_fusion_test.cc index 0c64c766f3d..925442a8f6a 100644 --- a/tests/ut/cpp/pre_activate/pass/allreduce_fusion_test.cc +++ b/tests/ut/cpp/pre_activate/pass/allreduce_fusion_test.cc @@ -25,7 +25,8 @@ #include "runtime/device/kernel_info.h" #include "backend/common/optimizer/pass_manager.h" #include "kernel/kernel_build_info.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" +#include "include/common/utils/anfalgo.h" #include "utils/ms_context.h" namespace mindspore { @@ -62,7 +63,7 @@ TEST_F(TestHWAllReduceFusion, test_fusion_all) { if (node == nullptr) { continue; } - if ((node->isa() && AnfAlgo::GetCNodeName(node) == kAllReduceOpName) || node->isa()) { + if ((node->isa() && common::AnfAlgo::GetCNodeName(node) == kAllReduceOpName) || node->isa()) { node->set_kernel_info(std::make_shared()); AnfAlgo::SetSelectKernelBuildInfo(builder.Build(), node.get()); } @@ -103,7 +104,7 @@ TEST_F(TestHWAllReduceFusion, test_fusion_group) { if (node == nullptr) { continue; } - if ((node->isa() && AnfAlgo::GetCNodeName(node) == kAllReduceOpName) || node->isa()) { + if ((node->isa() && common::AnfAlgo::GetCNodeName(node) == kAllReduceOpName) || node->isa()) { node->set_kernel_info(std::make_shared()); AnfAlgo::SetSelectKernelBuildInfo(builder.Build(), node.get()); } @@ -145,16 +146,16 @@ TEST_F(TestHWAllReduceFusion, test_fusion_op) { if (node == nullptr) { continue; } - if ((node->isa() && AnfAlgo::GetCNodeName(node) == kAllReduceOpName) || node->isa()) { + if ((node->isa() && common::AnfAlgo::GetCNodeName(node) == kAllReduceOpName) || node->isa()) { node->set_kernel_info(std::make_shared()); AnfAlgo::SetSelectKernelBuildInfo(builder.Build(), node.get()); } - if (node->isa() && AnfAlgo::GetCNodeName(node) == kAllReduceOpName) { + if (node->isa() && common::AnfAlgo::GetCNodeName(node) == kAllReduceOpName) { if (count == 0) { - AnfAlgo::SetNodeAttr("op", MakeValue("max"), node); + common::AnfAlgo::SetNodeAttr("op", MakeValue("max"), node); count = 1; } else { - AnfAlgo::SetNodeAttr("op", MakeValue("sum"), node); + common::AnfAlgo::SetNodeAttr("op", MakeValue("sum"), node); count = 0; } } @@ -185,7 +186,7 @@ TEST_F(TestHWAllReduceFusion, test_fusion_sorted) { auto make_tuple = ret->input(1); auto make_tuple1 = make_tuple->cast()->input(1)->cast(); for (size_t i = 1; i < make_tuple1->inputs().size(); ++i) { - AnfAlgo::SetNodeAttr(kAttrIndex, MakeValue(SizeToLong(i)), make_tuple1->input(i)); + common::AnfAlgo::SetNodeAttr(kAttrIndex, MakeValue(SizeToLong(i)), make_tuple1->input(i)); } // set kernel build info kernel::KernelBuildInfo::KernelBuildInfoBuilder builder; @@ -201,7 +202,7 @@ TEST_F(TestHWAllReduceFusion, test_fusion_sorted) { if (node == nullptr) { continue; } - if ((node->isa() && AnfAlgo::GetCNodeName(node) == kAllReduceOpName) || node->isa()) { + if ((node->isa() && common::AnfAlgo::GetCNodeName(node) == kAllReduceOpName) || node->isa()) { node->set_kernel_info(std::make_shared()); AnfAlgo::SetSelectKernelBuildInfo(builder.Build(), node.get()); } diff --git a/tests/ut/cpp/pre_activate/pass/common_subexpression_elimination_test.cc b/tests/ut/cpp/pre_activate/pass/common_subexpression_elimination_test.cc index 7dc2b4ffe7e..fad991fdac9 100644 --- a/tests/ut/cpp/pre_activate/pass/common_subexpression_elimination_test.cc +++ b/tests/ut/cpp/pre_activate/pass/common_subexpression_elimination_test.cc @@ -25,7 +25,7 @@ #include "runtime/device/kernel_info.h" #include "backend/common/pass/common_subexpression_elimination.h" #include "kernel/kernel_build_info.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/ms_context.h" namespace mindspore { diff --git a/tests/ut/cpp/pre_activate/pass/convert_const_input_to_attr_test.cc b/tests/ut/cpp/pre_activate/pass/convert_const_input_to_attr_test.cc index b7c0da91143..29e05b96b8b 100644 --- a/tests/ut/cpp/pre_activate/pass/convert_const_input_to_attr_test.cc +++ b/tests/ut/cpp/pre_activate/pass/convert_const_input_to_attr_test.cc @@ -21,7 +21,8 @@ #include "backend/common/optimizer/optimizer.h" #include "backend/common/optimizer/pass_manager.h" #include "backend/common/pass/convert_const_input_to_attr.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" +#include "include/common/utils/anfalgo.h" #include "utils/ms_utils.h" namespace mindspore { @@ -88,7 +89,7 @@ TEST_F(TestHWConstInputToAttr, test_onehot) { EXPECT_NE(ret->input(1), nullptr); EXPECT_NE(ret->input(1)->cast(), nullptr); auto cnode = ret->input(1)->cast(); - EXPECT_FALSE(AnfAlgo::HasNodeAttr("depth", cnode)); + EXPECT_FALSE(common::AnfAlgo::HasNodeAttr("depth", cnode)); EXPECT_FALSE(CheckEqualGraph(g, g_after)); std::vector shp_x{16}; @@ -106,7 +107,7 @@ TEST_F(TestHWConstInputToAttr, test_onehot) { EXPECT_NE(make_tuple->input(1), nullptr); EXPECT_NE(make_tuple->input(1)->cast(), nullptr); cnode = make_tuple->input(1)->cast(); - EXPECT_TRUE(AnfAlgo::HasNodeAttr("depth", cnode)); + EXPECT_TRUE(common::AnfAlgo::HasNodeAttr("depth", cnode)); EXPECT_TRUE(CheckEqualGraph(func_graph, g_after)); } } // namespace opt diff --git a/tests/ut/cpp/pre_activate/pass/convert_const_input_to_tensor_input_test.cc b/tests/ut/cpp/pre_activate/pass/convert_const_input_to_tensor_input_test.cc index 5b215cefe09..c2c113dadb9 100644 --- a/tests/ut/cpp/pre_activate/pass/convert_const_input_to_tensor_input_test.cc +++ b/tests/ut/cpp/pre_activate/pass/convert_const_input_to_tensor_input_test.cc @@ -22,7 +22,8 @@ #include "backend/common/optimizer/optimizer.h" #include "backend/common/optimizer/pass_manager.h" #include "backend/common/pass/convert_const_input_to_tensor_input.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace opt { @@ -59,7 +60,7 @@ TEST_F(TestHWConstInputToTensorInput, test_onehot_fg) { EXPECT_NE(ret->input(1), nullptr); EXPECT_NE(ret->input(1)->cast(), nullptr); auto cnode = ret->input(1)->cast(); - EXPECT_FALSE(AnfAlgo::HasNodeAttr("depth", cnode)); + EXPECT_FALSE(common::AnfAlgo::HasNodeAttr("depth", cnode)); EXPECT_TRUE(IsValueNode(cnode->input(2))); } @@ -80,7 +81,7 @@ TEST_F(TestHWConstInputToTensorInput, test_onehot_kg) { EXPECT_NE(ret->input(1), nullptr); EXPECT_NE(ret->input(1)->cast(), nullptr); auto cnode = ret->input(1)->cast()->input(1)->cast(); - EXPECT_TRUE(AnfAlgo::HasNodeAttr("depth", cnode)); + EXPECT_TRUE(common::AnfAlgo::HasNodeAttr("depth", cnode)); EXPECT_TRUE(CheckEqualGraph(func_graph, g_after)); } @@ -98,7 +99,7 @@ TEST_F(TestHWConstInputToTensorInput, test_value_tuple_tensor_input) { EXPECT_NE(ret->input(1), nullptr); EXPECT_NE(ret->input(1)->cast(), nullptr); auto cnode = ret->input(1)->cast()->input(1)->cast(); - EXPECT_EQ(AnfAlgo::GetCNodeName(cnode), prim::kPrimDropoutGenMask->name()); + EXPECT_EQ(common::AnfAlgo::GetCNodeName(cnode), prim::kPrimDropoutGenMask->name()); auto input1 = cnode->input(1); ASSERT_TRUE(input1 != nullptr); EXPECT_TRUE(IsValueNode(input1)); diff --git a/tests/ut/cpp/pre_activate/pass/convert_tuple_input_to_dynamic_input_test.cc b/tests/ut/cpp/pre_activate/pass/convert_tuple_input_to_dynamic_input_test.cc index a51e3118923..9de674fbfee 100644 --- a/tests/ut/cpp/pre_activate/pass/convert_tuple_input_to_dynamic_input_test.cc +++ b/tests/ut/cpp/pre_activate/pass/convert_tuple_input_to_dynamic_input_test.cc @@ -22,7 +22,7 @@ #include "backend/common/optimizer/optimizer.h" #include "backend/common/optimizer/pass_manager.h" #include "backend/common/pass/convert_tuple_input_to_dynamic_input.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace opt { diff --git a/tests/ut/cpp/pre_activate/pass/convert_tuple_output_to_maketuple_test.cc b/tests/ut/cpp/pre_activate/pass/convert_tuple_output_to_maketuple_test.cc index 2017bd240d0..ef6cad9f0a4 100644 --- a/tests/ut/cpp/pre_activate/pass/convert_tuple_output_to_maketuple_test.cc +++ b/tests/ut/cpp/pre_activate/pass/convert_tuple_output_to_maketuple_test.cc @@ -22,7 +22,7 @@ #include "backend/common/optimizer/optimizer.h" #include "backend/common/optimizer/pass_manager.h" #include "backend/common/pass/convert_tuple_output_to_maketuple.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace mindspore { namespace opt { diff --git a/tests/ut/cpp/pre_activate/pass/eliminate_redundant_op_test.cc b/tests/ut/cpp/pre_activate/pass/eliminate_redundant_op_test.cc index 8c50500e310..f049b70f987 100644 --- a/tests/ut/cpp/pre_activate/pass/eliminate_redundant_op_test.cc +++ b/tests/ut/cpp/pre_activate/pass/eliminate_redundant_op_test.cc @@ -26,7 +26,7 @@ #include "backend/common/pass/eliminate_redundant_op.h" #include "backend/common/optimizer/optimizer.h" #include "backend/common/optimizer/pass_manager.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "utils/ms_context.h" #include "backend/common/session/anf_runtime_algorithm.h" #include "runtime/device/kernel_info.h" diff --git a/tests/ut/cpp/pre_activate/pass/getitem_tuple_test.cc b/tests/ut/cpp/pre_activate/pass/getitem_tuple_test.cc index 740f04da417..d96c93156c5 100644 --- a/tests/ut/cpp/pre_activate/pass/getitem_tuple_test.cc +++ b/tests/ut/cpp/pre_activate/pass/getitem_tuple_test.cc @@ -20,7 +20,7 @@ #include "frontend/operator/ops.h" #include "ir/manager.h" #include "debug/anf_ir_dump.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" #include "backend/common/optimizer/optimizer.h" #include "backend/common/pass/getitem_tuple.h" diff --git a/tests/ut/cpp/pynative/pynative_execute_test.cc b/tests/ut/cpp/pynative/pynative_execute_test.cc index 25fafa8041a..69754060b7f 100644 --- a/tests/ut/cpp/pynative/pynative_execute_test.cc +++ b/tests/ut/cpp/pynative/pynative_execute_test.cc @@ -16,12 +16,12 @@ #include #include #include "common/common_test.h" -#include "pipeline/jit/parse/python_adapter.h" +#include "include/common/utils/python_adapter.h" #include "pipeline/jit/parse/data_converter.h" #include "frontend/operator/ops.h" #include "pipeline/pynative/pynative_execute.h" #include "utils/ms_context.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" namespace py = pybind11; using pybind11::literals::operator"" _a; diff --git a/tests/ut/cpp/session/anf_runtime_algorithm_test.cc b/tests/ut/cpp/session/anf_runtime_algorithm_test.cc index 369ea80a6fb..ae53302bf3e 100644 --- a/tests/ut/cpp/session/anf_runtime_algorithm_test.cc +++ b/tests/ut/cpp/session/anf_runtime_algorithm_test.cc @@ -21,7 +21,8 @@ #include "backend/common/session/anf_runtime_algorithm.h" #include "mindspore/ccsrc/runtime/device/kernel_info.h" #include "mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_device_address.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace session { @@ -40,23 +41,23 @@ TEST_F(AnfRuntimeAlgorithmTest, VisitKernel) { auto kernel_graph = std::make_shared(); KernelWithIndex kernel_with_index; // test nullptr as input - EXPECT_THROW(AnfAlgo::VisitKernel(nullptr, 0), std::runtime_error); + EXPECT_THROW(common::AnfAlgo::VisitKernel(nullptr, 0), std::runtime_error); // test value node as input ValueNodePtr value_node = NewValueNode(prim::kPrimAdd); - kernel_with_index = AnfAlgo::VisitKernel(value_node, 0); + kernel_with_index = common::AnfAlgo::VisitKernel(value_node, 0); EXPECT_NE(kernel_with_index.first->cast(), nullptr); EXPECT_EQ((kernel_with_index.first->cast()).get(), value_node.get()); EXPECT_EQ(kernel_with_index.second, 0); // test parameter node as input ParameterPtr parameter_node = kernel_graph->add_parameter(); - kernel_with_index = AnfAlgo::VisitKernel(parameter_node, 0); + kernel_with_index = common::AnfAlgo::VisitKernel(parameter_node, 0); EXPECT_NE(kernel_with_index.first->cast(), nullptr); EXPECT_EQ((kernel_with_index.first->cast()).get(), parameter_node.get()); EXPECT_EQ(kernel_with_index.second, 0); // test cnode as input std::vector inputs{value_node}; auto add = kernel_graph->NewCNode(inputs); - kernel_with_index = AnfAlgo::VisitKernel(add, 0); + kernel_with_index = common::AnfAlgo::VisitKernel(add, 0); EXPECT_NE(kernel_with_index.first->cast(), nullptr); EXPECT_EQ((kernel_with_index.first->cast()).get(), add.get()); EXPECT_EQ(kernel_with_index.second, 0); @@ -70,11 +71,11 @@ TEST_F(AnfRuntimeAlgorithmTest, VisitKernel) { auto x_abstract = std::make_shared(kFloat32, shp); AbstractBasePtrList args_spec_list{x_abstract, x_abstract}; make_tuple->set_abstract(std::make_shared(args_spec_list)); - kernel_with_index = AnfAlgo::VisitKernel(make_tuple, 0); + kernel_with_index = common::AnfAlgo::VisitKernel(make_tuple, 0); EXPECT_NE(kernel_with_index.first->cast(), nullptr); EXPECT_EQ((kernel_with_index.first->cast()).get(), add.get()); EXPECT_EQ(kernel_with_index.second, 0); - kernel_with_index = AnfAlgo::VisitKernel(make_tuple, 1); + kernel_with_index = common::AnfAlgo::VisitKernel(make_tuple, 1); EXPECT_NE(kernel_with_index.first->cast(), nullptr); EXPECT_EQ((kernel_with_index.first->cast()).get(), add_second.get()); EXPECT_EQ(kernel_with_index.second, 0); @@ -82,14 +83,14 @@ TEST_F(AnfRuntimeAlgorithmTest, VisitKernel) { std::vector tuple_get_item_inputs{NewValueNode(prim::kPrimTupleGetItem), make_tuple, NewValueNode(static_cast(1))}; auto tuple_get_item = kernel_graph->NewCNode(tuple_get_item_inputs); - kernel_with_index = AnfAlgo::VisitKernel(tuple_get_item, 0); + kernel_with_index = common::AnfAlgo::VisitKernel(tuple_get_item, 0); EXPECT_NE(kernel_with_index.first->cast(), nullptr); EXPECT_EQ((kernel_with_index.first->cast()).get(), add_second.get()); EXPECT_EQ(kernel_with_index.second, 0); // test depend node as input std::vector depend_inputs{NewValueNode(prim::kPrimDepend), add, add_second}; auto depend = kernel_graph->NewCNode(depend_inputs); - kernel_with_index = AnfAlgo::VisitKernel(depend, 0); + kernel_with_index = common::AnfAlgo::VisitKernel(depend, 0); EXPECT_NE(kernel_with_index.first->cast(), nullptr); EXPECT_EQ((kernel_with_index.first->cast()).get(), add.get()); EXPECT_EQ(kernel_with_index.second, 0); @@ -101,9 +102,9 @@ TEST_F(AnfRuntimeAlgorithmTest, GetCNodePrimitive) { PrimitivePtr add_primitive = prim::kPrimAdd; std::vector inputs{NewValueNode(add_primitive)}; auto add = kernel_graph->NewCNode(inputs); - EXPECT_NE(AnfAlgo::GetCNodePrimitive(add), nullptr); - EXPECT_EQ(AnfAlgo::GetCNodePrimitive(add).get(), add_primitive.get()); - EXPECT_THROW(AnfAlgo::GetCNodePrimitive(nullptr), std::runtime_error); + EXPECT_NE(common::AnfAlgo::GetCNodePrimitive(add), nullptr); + EXPECT_EQ(common::AnfAlgo::GetCNodePrimitive(add).get(), add_primitive.get()); + EXPECT_THROW(common::AnfAlgo::GetCNodePrimitive(nullptr), std::runtime_error); } TEST_F(AnfRuntimeAlgorithmTest, GetCNodeName) { @@ -111,11 +112,11 @@ TEST_F(AnfRuntimeAlgorithmTest, GetCNodeName) { // test cnode node std::vector inputs{NewValueNode(prim::kPrimAdd)}; auto add = kernel_graph->NewCNode(inputs); - EXPECT_EQ(AnfAlgo::GetCNodeName(add), prim::kPrimAdd->name()); - EXPECT_THROW(AnfAlgo::GetCNodeName(nullptr), std::runtime_error); + EXPECT_EQ(common::AnfAlgo::GetCNodeName(add), prim::kPrimAdd->name()); + EXPECT_THROW(common::AnfAlgo::GetCNodeName(nullptr), std::runtime_error); // test parameter auto parameter_node = kernel_graph->add_parameter(); - EXPECT_THROW(AnfAlgo::GetCNodeName(parameter_node), std::runtime_error); + EXPECT_THROW(common::AnfAlgo::GetCNodeName(parameter_node), std::runtime_error); } TEST_F(AnfRuntimeAlgorithmTest, GetNodeDebugString) { @@ -123,8 +124,8 @@ TEST_F(AnfRuntimeAlgorithmTest, GetNodeDebugString) { // test cnode node std::vector inputs{NewValueNode(prim::kPrimAdd)}; auto add = kernel_graph->NewCNode(inputs); - EXPECT_EQ(AnfAlgo::GetNodeDebugString(add), add->DebugString()); - EXPECT_THROW(AnfAlgo::GetNodeDebugString(nullptr), std::runtime_error); + EXPECT_EQ(common::AnfAlgo::GetNodeDebugString(add), add->DebugString()); + EXPECT_THROW(common::AnfAlgo::GetNodeDebugString(nullptr), std::runtime_error); } TEST_F(AnfRuntimeAlgorithmTest, SetNodeAttr) { @@ -132,13 +133,13 @@ TEST_F(AnfRuntimeAlgorithmTest, SetNodeAttr) { // test cnode node std::vector inputs{NewValueNode(prim::kPrimAdd)}; auto add = kernel_graph->NewCNode(inputs); - AnfAlgo::SetNodeAttr("test_set_attr", MakeValue("test_value"), add); - auto primitive = AnfAlgo::GetCNodePrimitive(add); + common::AnfAlgo::SetNodeAttr("test_set_attr", MakeValue("test_value"), add); + auto primitive = common::AnfAlgo::GetCNodePrimitive(add); MS_EXCEPTION_IF_NULL(primitive); EXPECT_EQ(GetValue(primitive->GetAttr("test_set_attr")), "test_value"); // test parameter node auto parameter = kernel_graph->add_parameter(); - EXPECT_THROW(AnfAlgo::SetNodeAttr("test_set_attr", MakeValue("test_value"), parameter), std::runtime_error); + EXPECT_THROW(common::AnfAlgo::SetNodeAttr("test_set_attr", MakeValue("test_value"), parameter), std::runtime_error); } TEST_F(AnfRuntimeAlgorithmTest, CopyNodeAttr) { @@ -146,23 +147,23 @@ TEST_F(AnfRuntimeAlgorithmTest, CopyNodeAttr) { // test cnode node std::vector add_inputs{NewValueNode(prim::kPrimAdd)}; auto add = kernel_graph->NewCNode(add_inputs); - AnfAlgo::SetNodeAttr("test_set_attr", MakeValue("test_value"), add); + common::AnfAlgo::SetNodeAttr("test_set_attr", MakeValue("test_value"), add); std::vector mul_inputs{NewValueNode(prim::kPrimMul)}; auto mul = kernel_graph->NewCNode(mul_inputs); - AnfAlgo::SetNodeAttr("test_set_attr", MakeValue("test_value_v2"), mul); - AnfAlgo::CopyNodeAttr("test_set_attr", mul, add); - auto primitive = AnfAlgo::GetCNodePrimitive(add); + common::AnfAlgo::SetNodeAttr("test_set_attr", MakeValue("test_value_v2"), mul); + common::AnfAlgo::CopyNodeAttr("test_set_attr", mul, add); + auto primitive = common::AnfAlgo::GetCNodePrimitive(add); MS_EXCEPTION_IF_NULL(primitive); EXPECT_EQ(GetValue(primitive->GetAttr("test_set_attr")), "test_value_v2"); // test parameter node auto parameter = kernel_graph->add_parameter(); - EXPECT_THROW(AnfAlgo::CopyNodeAttr("test_set_attr", parameter, add), std::runtime_error); - EXPECT_THROW(AnfAlgo::CopyNodeAttr("test_set_attr", mul, parameter), std::runtime_error); - EXPECT_THROW(AnfAlgo::CopyNodeAttr("test_set_attr", parameter, parameter), std::runtime_error); - EXPECT_THROW(AnfAlgo::CopyNodeAttr("test_set_attr", nullptr, add), std::runtime_error); - EXPECT_THROW(AnfAlgo::CopyNodeAttr("test_set_attr", mul, nullptr), std::runtime_error); - EXPECT_THROW(AnfAlgo::CopyNodeAttr("test_set_attr", nullptr, nullptr), std::runtime_error); + EXPECT_THROW(common::AnfAlgo::CopyNodeAttr("test_set_attr", parameter, add), std::runtime_error); + EXPECT_THROW(common::AnfAlgo::CopyNodeAttr("test_set_attr", mul, parameter), std::runtime_error); + EXPECT_THROW(common::AnfAlgo::CopyNodeAttr("test_set_attr", parameter, parameter), std::runtime_error); + EXPECT_THROW(common::AnfAlgo::CopyNodeAttr("test_set_attr", nullptr, add), std::runtime_error); + EXPECT_THROW(common::AnfAlgo::CopyNodeAttr("test_set_attr", mul, nullptr), std::runtime_error); + EXPECT_THROW(common::AnfAlgo::CopyNodeAttr("test_set_attr", nullptr, nullptr), std::runtime_error); } TEST_F(AnfRuntimeAlgorithmTest, CopyNodeAttrs) { @@ -170,23 +171,23 @@ TEST_F(AnfRuntimeAlgorithmTest, CopyNodeAttrs) { // test cnode node std::vector add_inputs{NewValueNode(prim::kPrimAdd)}; auto add = kernel_graph->NewCNode(add_inputs); - AnfAlgo::SetNodeAttr("test_set_attr", MakeValue("test_value"), add); + common::AnfAlgo::SetNodeAttr("test_set_attr", MakeValue("test_value"), add); std::vector mul_inputs{NewValueNode(prim::kPrimMul)}; auto mul = kernel_graph->NewCNode(mul_inputs); - AnfAlgo::SetNodeAttr("test_set_attr", MakeValue("test_value_v2"), mul); - AnfAlgo::CopyNodeAttrs(mul, add); - auto primitive = AnfAlgo::GetCNodePrimitive(add); + common::AnfAlgo::SetNodeAttr("test_set_attr", MakeValue("test_value_v2"), mul); + common::AnfAlgo::CopyNodeAttrs(mul, add); + auto primitive = common::AnfAlgo::GetCNodePrimitive(add); MS_EXCEPTION_IF_NULL(primitive); EXPECT_EQ(GetValue(primitive->GetAttr("test_set_attr")), "test_value_v2"); // test parameter node auto parameter = kernel_graph->add_parameter(); - EXPECT_THROW(AnfAlgo::CopyNodeAttrs(parameter, add), std::runtime_error); - EXPECT_THROW(AnfAlgo::CopyNodeAttrs(mul, parameter), std::runtime_error); - EXPECT_THROW(AnfAlgo::CopyNodeAttrs(parameter, parameter), std::runtime_error); - EXPECT_THROW(AnfAlgo::CopyNodeAttrs(nullptr, add), std::runtime_error); - EXPECT_THROW(AnfAlgo::CopyNodeAttrs(mul, nullptr), std::runtime_error); - EXPECT_THROW(AnfAlgo::CopyNodeAttrs(nullptr, nullptr), std::runtime_error); + EXPECT_THROW(common::AnfAlgo::CopyNodeAttrs(parameter, add), std::runtime_error); + EXPECT_THROW(common::AnfAlgo::CopyNodeAttrs(mul, parameter), std::runtime_error); + EXPECT_THROW(common::AnfAlgo::CopyNodeAttrs(parameter, parameter), std::runtime_error); + EXPECT_THROW(common::AnfAlgo::CopyNodeAttrs(nullptr, add), std::runtime_error); + EXPECT_THROW(common::AnfAlgo::CopyNodeAttrs(mul, nullptr), std::runtime_error); + EXPECT_THROW(common::AnfAlgo::CopyNodeAttrs(nullptr, nullptr), std::runtime_error); } TEST_F(AnfRuntimeAlgorithmTest, EraseNodeAttr) { @@ -194,14 +195,14 @@ TEST_F(AnfRuntimeAlgorithmTest, EraseNodeAttr) { // test cnode node std::vector add_inputs{NewValueNode(prim::kPrimAdd)}; auto add = kernel_graph->NewCNode(add_inputs); - AnfAlgo::SetNodeAttr("test_set_attr", MakeValue("test_value"), add); - AnfAlgo::SetNodeAttr("test_set_attr_v2", MakeValue("test_value_v2"), add); - AnfAlgo::EraseNodeAttr("test_set_attr_v2", add); - EXPECT_THROW(AnfAlgo::GetNodeAttr(add, "test_set_attr_v2"), std::runtime_error); - EXPECT_THROW(AnfAlgo::EraseNodeAttr("test_set_attr_v2", nullptr), std::runtime_error); + common::AnfAlgo::SetNodeAttr("test_set_attr", MakeValue("test_value"), add); + common::AnfAlgo::SetNodeAttr("test_set_attr_v2", MakeValue("test_value_v2"), add); + common::AnfAlgo::EraseNodeAttr("test_set_attr_v2", add); + EXPECT_THROW(common::AnfAlgo::GetNodeAttr(add, "test_set_attr_v2"), std::runtime_error); + EXPECT_THROW(common::AnfAlgo::EraseNodeAttr("test_set_attr_v2", nullptr), std::runtime_error); // test parameter node auto parameter = kernel_graph->add_parameter(); - EXPECT_THROW(AnfAlgo::EraseNodeAttr("test_set_attr_v2", parameter), std::runtime_error); + EXPECT_THROW(common::AnfAlgo::EraseNodeAttr("test_set_attr_v2", parameter), std::runtime_error); } TEST_F(AnfRuntimeAlgorithmTest, GetInputTensorNum) { @@ -211,10 +212,10 @@ TEST_F(AnfRuntimeAlgorithmTest, GetInputTensorNum) { auto parameter_two = kernel_graph->NewParameter(); std::vector add_inputs{NewValueNode(prim::kPrimAdd), parameter_one, parameter_two}; auto add = kernel_graph->NewCNode(add_inputs); - EXPECT_EQ(AnfAlgo::GetInputTensorNum(add), 2); - EXPECT_THROW(AnfAlgo::GetInputTensorNum(nullptr), std::runtime_error); + EXPECT_EQ(common::AnfAlgo::GetInputTensorNum(add), 2); + EXPECT_THROW(common::AnfAlgo::GetInputTensorNum(nullptr), std::runtime_error); // test parameter node - EXPECT_THROW(AnfAlgo::GetInputTensorNum(parameter_one), std::runtime_error); + EXPECT_THROW(common::AnfAlgo::GetInputTensorNum(parameter_one), std::runtime_error); } TEST_F(AnfRuntimeAlgorithmTest, GetOutputTensorNum) { @@ -228,17 +229,17 @@ TEST_F(AnfRuntimeAlgorithmTest, GetOutputTensorNum) { auto x_abstract = std::make_shared(kFloat32, shp); AbstractBasePtrList args_spec_list{x_abstract, x_abstract, x_abstract, x_abstract, x_abstract}; bn->set_abstract(std::make_shared(args_spec_list)); - EXPECT_EQ(AnfAlgo::GetOutputTensorNum(bn), 5); - EXPECT_THROW(AnfAlgo::GetOutputTensorNum(nullptr), std::runtime_error); + EXPECT_EQ(common::AnfAlgo::GetOutputTensorNum(bn), 5); + EXPECT_THROW(common::AnfAlgo::GetOutputTensorNum(nullptr), std::runtime_error); // test add as input inputs.clear(); inputs.push_back(NewValueNode(prim::kPrimAdd)); auto add = kernel_graph->NewCNode(inputs); MS_EXCEPTION_IF_NULL(add); add->set_abstract(std::make_shared()); - EXPECT_EQ(AnfAlgo::GetOutputTensorNum(add), 0); + EXPECT_EQ(common::AnfAlgo::GetOutputTensorNum(add), 0); add->set_abstract(x_abstract); - EXPECT_EQ(AnfAlgo::GetOutputTensorNum(add), 1); + EXPECT_EQ(common::AnfAlgo::GetOutputTensorNum(add), 1); } TEST_F(AnfRuntimeAlgorithmTest, GetOutputFormat) { @@ -247,7 +248,7 @@ TEST_F(AnfRuntimeAlgorithmTest, GetOutputFormat) { kernel_graph->NewParameter()}; auto add = kernel_graph->NewCNode(inputs); std::vector shape = {1, 2, 3, 4}; - AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat32, kNumberTypeFloat32}, {shape, shape}, add.get()); + common::AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat32, kNumberTypeFloat32}, {shape, shape}, add.get()); MS_EXCEPTION_IF_NULL(add); add->set_kernel_info(std::make_shared()); auto d_kernel_info = dynamic_cast(add->kernel_info()); @@ -314,28 +315,28 @@ TEST_F(AnfRuntimeAlgorithmTest, GetOutputInferShape) { auto value_node = NewValueNode(prim::kPrimAdd); MS_EXCEPTION_IF_NULL(value_node); value_node->set_abstract(x_abstract); - EXPECT_EQ(AnfAlgo::GetOutputInferShape(value_node, 0)[1], 32); - EXPECT_THROW(AnfAlgo::GetOutputInferShape(nullptr, 0), std::runtime_error); + EXPECT_EQ(common::AnfAlgo::GetOutputInferShape(value_node, 0)[1], 32); + EXPECT_THROW(common::AnfAlgo::GetOutputInferShape(nullptr, 0), std::runtime_error); // test parameter node as input auto parameter_node = kernel_graph->add_parameter(); MS_EXCEPTION_IF_NULL(parameter_node); parameter_node->set_abstract(x_abstract); - EXPECT_EQ(AnfAlgo::GetOutputInferShape(parameter_node, 0)[2], 224); + EXPECT_EQ(common::AnfAlgo::GetOutputInferShape(parameter_node, 0)[2], 224); // test cnode as input std::vector inputs; inputs.push_back(NewValueNode(prim::kPrimAdd)); auto add = kernel_graph->NewCNode(inputs); MS_EXCEPTION_IF_NULL(add); add->set_abstract(std::make_shared()); - EXPECT_TRUE(AnfAlgo::GetOutputInferShape(add, 0).empty()); + EXPECT_TRUE(common::AnfAlgo::GetOutputInferShape(add, 0).empty()); add->set_abstract(x_abstract); - EXPECT_EQ(AnfAlgo::GetOutputInferShape(add, 0)[3], 224); - EXPECT_THROW(AnfAlgo::GetOutputInferShape(add, 1), std::runtime_error); + EXPECT_EQ(common::AnfAlgo::GetOutputInferShape(add, 0)[3], 224); + EXPECT_THROW(common::AnfAlgo::GetOutputInferShape(add, 1), std::runtime_error); add->set_abstract(tuple_abstract); - EXPECT_EQ(AnfAlgo::GetOutputInferShape(add, 0)[0], 2); - EXPECT_TRUE(AnfAlgo::GetOutputInferShape(add, 1).empty()); - EXPECT_EQ(AnfAlgo::GetOutputInferShape(add, 2)[1], 32); - EXPECT_THROW(AnfAlgo::GetOutputInferShape(add, 3), std::runtime_error); + EXPECT_EQ(common::AnfAlgo::GetOutputInferShape(add, 0)[0], 2); + EXPECT_TRUE(common::AnfAlgo::GetOutputInferShape(add, 1).empty()); + EXPECT_EQ(common::AnfAlgo::GetOutputInferShape(add, 2)[1], 32); + EXPECT_THROW(common::AnfAlgo::GetOutputInferShape(add, 3), std::runtime_error); } TEST_F(AnfRuntimeAlgorithmTest, GetPrevNodeOutputInferShape) { @@ -346,12 +347,12 @@ TEST_F(AnfRuntimeAlgorithmTest, GetPrevNodeOutputInferShape) { auto parameter_node = kernel_graph->NewParameter(); MS_EXCEPTION_IF_NULL(parameter_node); parameter_node->set_abstract(x_abstract); - EXPECT_THROW(AnfAlgo::GetPrevNodeOutputInferShape(parameter_node, 0), std::runtime_error); + EXPECT_THROW(common::AnfAlgo::GetPrevNodeOutputInferShape(parameter_node, 0), std::runtime_error); // test cnode as input std::vector inputs{NewValueNode(prim::kPrimAdd), parameter_node}; auto add = kernel_graph->NewCNode(inputs); - EXPECT_EQ(AnfAlgo::GetPrevNodeOutputInferShape(add, 0)[1], 32); - EXPECT_THROW(AnfAlgo::GetPrevNodeOutputInferShape(add, 1), std::runtime_error); + EXPECT_EQ(common::AnfAlgo::GetPrevNodeOutputInferShape(add, 0)[1], 32); + EXPECT_THROW(common::AnfAlgo::GetPrevNodeOutputInferShape(add, 1), std::runtime_error); } TEST_F(AnfRuntimeAlgorithmTest, GetOutputDeviceShape) { @@ -410,7 +411,7 @@ TEST_F(AnfRuntimeAlgorithmTest, GetInputDeviceShape) { EXPECT_EQ(AnfAlgo::GetInputDeviceShape(add, 1)[1], 32); std::vector expect_shape{2, 224, 224, 32}; EXPECT_EQ(AnfAlgo::GetInputDeviceShape(add, 2), expect_shape); - EXPECT_THROW(AnfAlgo::GetPrevNodeOutputInferShape(nullptr, 0), std::runtime_error); + EXPECT_THROW(common::AnfAlgo::GetPrevNodeOutputInferShape(nullptr, 0), std::runtime_error); } TEST_F(AnfRuntimeAlgorithmTest, GetOutputInferDataTypeTest) { @@ -423,9 +424,9 @@ TEST_F(AnfRuntimeAlgorithmTest, GetOutputInferDataTypeTest) { auto x_abstract = std::make_shared(kFloat32, shp); AbstractBasePtrList args_spec_list{x_abstract, x_abstract, x_abstract, x_abstract, x_abstract}; bn->set_abstract(std::make_shared(args_spec_list)); - EXPECT_EQ(AnfAlgo::GetOutputInferDataType(bn, 0), kFloat32->type_id()); - EXPECT_EQ(AnfAlgo::GetOutputInferDataType(bn, 4), kFloat32->type_id()); - EXPECT_THROW(AnfAlgo::GetOutputInferDataType(bn, 5), std::runtime_error); + EXPECT_EQ(common::AnfAlgo::GetOutputInferDataType(bn, 0), kFloat32->type_id()); + EXPECT_EQ(common::AnfAlgo::GetOutputInferDataType(bn, 4), kFloat32->type_id()); + EXPECT_THROW(common::AnfAlgo::GetOutputInferDataType(bn, 5), std::runtime_error); } TEST_F(AnfRuntimeAlgorithmTest, GetPrevNodeOutputInferDataType) { @@ -439,12 +440,12 @@ TEST_F(AnfRuntimeAlgorithmTest, GetPrevNodeOutputInferDataType) { pre_add->set_abstract(x_abstract); std::vector inputs{NewValueNode(prim::kPrimAdd), pre_add}; auto add = kernel_graph->NewCNode(inputs); - EXPECT_EQ(AnfAlgo::GetPrevNodeOutputInferDataType(add, 0), kFloat32->type_id()); - EXPECT_THROW(AnfAlgo::GetPrevNodeOutputInferDataType(add, 1), std::runtime_error); - EXPECT_THROW(AnfAlgo::GetPrevNodeOutputInferDataType(nullptr, 0), std::runtime_error); + EXPECT_EQ(common::AnfAlgo::GetPrevNodeOutputInferDataType(add, 0), kFloat32->type_id()); + EXPECT_THROW(common::AnfAlgo::GetPrevNodeOutputInferDataType(add, 1), std::runtime_error); + EXPECT_THROW(common::AnfAlgo::GetPrevNodeOutputInferDataType(nullptr, 0), std::runtime_error); // test parameter as input auto parameter_node = kernel_graph->add_parameter(); - EXPECT_THROW(AnfAlgo::GetPrevNodeOutputInferDataType(parameter_node, 0), std::runtime_error); + EXPECT_THROW(common::AnfAlgo::GetPrevNodeOutputInferDataType(parameter_node, 0), std::runtime_error); } TEST_F(AnfRuntimeAlgorithmTest, GetOutputDeviceDataTypeTest) { @@ -586,26 +587,26 @@ TEST_F(AnfRuntimeAlgorithmTest, SetOutputInferTypeAndShape) { // set none abstract std::vector none_types = {}; std::vector> none_shapes = {}; - EXPECT_THROW(AnfAlgo::SetOutputInferTypeAndShape(none_types, none_shapes, nullptr), std::runtime_error); - AnfAlgo::SetOutputInferTypeAndShape(none_types, none_shapes, add.get()); + EXPECT_THROW(common::AnfAlgo::SetOutputInferTypeAndShape(none_types, none_shapes, nullptr), std::runtime_error); + common::AnfAlgo::SetOutputInferTypeAndShape(none_types, none_shapes, add.get()); EXPECT_EQ((*add->abstract()), abstract::AbstractNone()); // set single input std::vector single_types = {kFloat32->type_id()}; std::vector> single_shapes = {{2, 32, 224, 224}}; - EXPECT_THROW(AnfAlgo::SetOutputInferTypeAndShape(none_types, single_shapes, add.get()), std::runtime_error); - AnfAlgo::SetOutputInferTypeAndShape(single_types, single_shapes, add.get()); - EXPECT_EQ(AnfAlgo::GetOutputInferDataType(add, 0), kFloat32->type_id()); - EXPECT_EQ(AnfAlgo::GetOutputInferShape(add, 0).size(), 4); + EXPECT_THROW(common::AnfAlgo::SetOutputInferTypeAndShape(none_types, single_shapes, add.get()), std::runtime_error); + common::AnfAlgo::SetOutputInferTypeAndShape(single_types, single_shapes, add.get()); + EXPECT_EQ(common::AnfAlgo::GetOutputInferDataType(add, 0), kFloat32->type_id()); + EXPECT_EQ(common::AnfAlgo::GetOutputInferShape(add, 0).size(), 4); // set multiple input std::vector mutiple_types = {kFloat16->type_id(), kFloat32->type_id(), kFloat64->type_id()}; std::vector> mutiple_shapes = {{2, 32, 224, 224}, {2, 32, 224, 224}, {2, 32, 224, 224}}; - AnfAlgo::SetOutputInferTypeAndShape(mutiple_types, mutiple_shapes, add.get()); - EXPECT_EQ(AnfAlgo::GetOutputInferDataType(add, 0), kFloat16->type_id()); - EXPECT_EQ(AnfAlgo::GetOutputInferDataType(add, 1), kFloat32->type_id()); - EXPECT_EQ(AnfAlgo::GetOutputInferDataType(add, 2), kFloat64->type_id()); - EXPECT_EQ(AnfAlgo::GetOutputInferShape(add, 0).size(), 4); - EXPECT_EQ(AnfAlgo::GetOutputInferShape(add, 1).size(), 4); - EXPECT_EQ(AnfAlgo::GetOutputInferShape(add, 2).size(), 4); + common::AnfAlgo::SetOutputInferTypeAndShape(mutiple_types, mutiple_shapes, add.get()); + EXPECT_EQ(common::AnfAlgo::GetOutputInferDataType(add, 0), kFloat16->type_id()); + EXPECT_EQ(common::AnfAlgo::GetOutputInferDataType(add, 1), kFloat32->type_id()); + EXPECT_EQ(common::AnfAlgo::GetOutputInferDataType(add, 2), kFloat64->type_id()); + EXPECT_EQ(common::AnfAlgo::GetOutputInferShape(add, 0).size(), 4); + EXPECT_EQ(common::AnfAlgo::GetOutputInferShape(add, 1).size(), 4); + EXPECT_EQ(common::AnfAlgo::GetOutputInferShape(add, 2).size(), 4); } TEST_F(AnfRuntimeAlgorithmTest, CopyAbstract) { @@ -616,21 +617,21 @@ TEST_F(AnfRuntimeAlgorithmTest, CopyAbstract) { // set single input std::vector single_types = {kFloat32->type_id()}; std::vector> single_shapes = {{2, 32, 224, 224}}; - AnfAlgo::SetOutputInferTypeAndShape(single_types, single_shapes, first_add.get()); + common::AnfAlgo::SetOutputInferTypeAndShape(single_types, single_shapes, first_add.get()); // set multiple input std::vector second_inputs; second_inputs.push_back(NewValueNode(prim::kPrimAdd)); auto second_add = kernel_graph->NewCNode(second_inputs); std::vector mutiple_types = {kFloat16->type_id(), kFloat32->type_id(), kFloat64->type_id()}; std::vector> mutiple_shapes = {{2, 32, 224, 224}, {2, 32, 224, 224}, {2, 32, 224, 224}}; - AnfAlgo::SetOutputInferTypeAndShape(mutiple_types, mutiple_shapes, second_add.get()); - AnfAlgo::CopyAbstract(second_add, first_add.get()); - EXPECT_EQ(AnfAlgo::GetOutputInferDataType(first_add, 0), kFloat16->type_id()); - EXPECT_EQ(AnfAlgo::GetOutputInferDataType(first_add, 1), kFloat32->type_id()); - EXPECT_EQ(AnfAlgo::GetOutputInferDataType(first_add, 2), kFloat64->type_id()); - EXPECT_EQ(AnfAlgo::GetOutputInferShape(first_add, 0).size(), 4); - EXPECT_EQ(AnfAlgo::GetOutputInferShape(first_add, 1).size(), 4); - EXPECT_EQ(AnfAlgo::GetOutputInferShape(first_add, 2).size(), 4); + common::AnfAlgo::SetOutputInferTypeAndShape(mutiple_types, mutiple_shapes, second_add.get()); + common::AnfAlgo::CopyAbstract(second_add, first_add.get()); + EXPECT_EQ(common::AnfAlgo::GetOutputInferDataType(first_add, 0), kFloat16->type_id()); + EXPECT_EQ(common::AnfAlgo::GetOutputInferDataType(first_add, 1), kFloat32->type_id()); + EXPECT_EQ(common::AnfAlgo::GetOutputInferDataType(first_add, 2), kFloat64->type_id()); + EXPECT_EQ(common::AnfAlgo::GetOutputInferShape(first_add, 0).size(), 4); + EXPECT_EQ(common::AnfAlgo::GetOutputInferShape(first_add, 1).size(), 4); + EXPECT_EQ(common::AnfAlgo::GetOutputInferShape(first_add, 2).size(), 4); } TEST_F(AnfRuntimeAlgorithmTest, GetKernelType) { @@ -765,8 +766,8 @@ TEST_F(AnfRuntimeAlgorithmTest, IsParameterWeight) { MS_EXCEPTION_IF_NULL(parameter_node); auto param_value_new = std::make_shared(int64_t(0), kInt32); parameter_node->set_default_param(param_value_new); - EXPECT_TRUE(AnfAlgo::IsParameterWeight(parameter_node)); - EXPECT_THROW(AnfAlgo::IsParameterWeight(nullptr), std::runtime_error); + EXPECT_TRUE(common::AnfAlgo::IsParameterWeight(parameter_node)); + EXPECT_THROW(common::AnfAlgo::IsParameterWeight(nullptr), std::runtime_error); } TEST_F(AnfRuntimeAlgorithmTest, GetStreamId) { diff --git a/tests/ut/cpp/session/kernel_graph_test.cc b/tests/ut/cpp/session/kernel_graph_test.cc index db8529290fd..759e3850cac 100644 --- a/tests/ut/cpp/session/kernel_graph_test.cc +++ b/tests/ut/cpp/session/kernel_graph_test.cc @@ -20,7 +20,8 @@ #include "backend/common/session/kernel_graph.h" #include "backend/common/session/anf_runtime_algorithm.h" #include "mindspore/ccsrc/runtime/device/kernel_info.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace session { @@ -50,8 +51,8 @@ TEST_F(KernelGraphTest, NewValueNode) { mutable_kernel_info->set_select_kernel_build_info(builder->Build()); auto new_value = kernel_graph->NewValueNode(add_value); EXPECT_NE(new_value, nullptr); - EXPECT_EQ(AnfAlgo::GetOutputInferShape(new_value, 0)[0], 1); - EXPECT_EQ(AnfAlgo::GetOutputInferDataType(new_value, 0), kFloat32->type_id()); + EXPECT_EQ(common::AnfAlgo::GetOutputInferShape(new_value, 0)[0], 1); + EXPECT_EQ(common::AnfAlgo::GetOutputInferDataType(new_value, 0), kFloat32->type_id()); EXPECT_EQ(AnfAlgo::GetOutputFormat(new_value, 0), kOpFormat_DEFAULT); EXPECT_EQ(AnfAlgo::GetOutputDeviceDataType(new_value, 0), kTypeUnknown); } @@ -74,8 +75,8 @@ TEST_F(KernelGraphTest, NewParameter) { auto new_non_weight_parameter = kernel_graph->NewParameter(non_weight_parameter); EXPECT_NE(new_non_weight_parameter, nullptr); new_non_weight_parameter->set_name("non_weight_parameter"); - EXPECT_EQ(AnfAlgo::GetOutputInferShape(new_non_weight_parameter, 0)[1], 32); - EXPECT_EQ(AnfAlgo::GetOutputInferDataType(new_non_weight_parameter, 0), kFloat32->type_id()); + EXPECT_EQ(common::AnfAlgo::GetOutputInferShape(new_non_weight_parameter, 0)[1], 32); + EXPECT_EQ(common::AnfAlgo::GetOutputInferDataType(new_non_weight_parameter, 0), kFloat32->type_id()); EXPECT_EQ(AnfAlgo::GetOutputFormat(new_non_weight_parameter, 0), kOpFormat_DEFAULT); EXPECT_EQ(AnfAlgo::GetOutputDeviceDataType(new_non_weight_parameter, 0), kFloat32->type_id()); EXPECT_EQ(new_non_weight_parameter->name(), "non_weight_parameter"); @@ -88,8 +89,8 @@ TEST_F(KernelGraphTest, NewParameter) { auto new_weight_parameter_node = kernel_graph->NewParameter(weight_parameter_node); EXPECT_NE(new_weight_parameter_node, nullptr); EXPECT_TRUE(new_weight_parameter_node->has_default()); - EXPECT_EQ(AnfAlgo::GetOutputInferShape(new_weight_parameter_node, 0)[2], 224); - EXPECT_EQ(AnfAlgo::GetOutputInferDataType(new_weight_parameter_node, 0), kFloat32->type_id()); + EXPECT_EQ(common::AnfAlgo::GetOutputInferShape(new_weight_parameter_node, 0)[2], 224); + EXPECT_EQ(common::AnfAlgo::GetOutputInferDataType(new_weight_parameter_node, 0), kFloat32->type_id()); EXPECT_EQ(AnfAlgo::GetOutputFormat(new_weight_parameter_node, 0), kOpFormat_DEFAULT); EXPECT_EQ(AnfAlgo::GetOutputDeviceDataType(new_weight_parameter_node, 0), kTypeUnknown); } @@ -100,9 +101,9 @@ TEST_F(KernelGraphTest, NewCNode) { std::vector inputs = {add_value}; auto new_cnode = kernel_graph->NewCNode(inputs); EXPECT_NE(new_cnode, nullptr); - EXPECT_EQ(AnfAlgo::GetCNodeName(new_cnode), prim::kPrimAdd->name()); - EXPECT_TRUE(AnfAlgo::GetOutputInferShape(new_cnode, 0).empty()); - EXPECT_EQ(AnfAlgo::GetOutputInferDataType(new_cnode, 0), kMetaTypeNone); + EXPECT_EQ(common::AnfAlgo::GetCNodeName(new_cnode), prim::kPrimAdd->name()); + EXPECT_TRUE(common::AnfAlgo::GetOutputInferShape(new_cnode, 0).empty()); + EXPECT_EQ(common::AnfAlgo::GetOutputInferDataType(new_cnode, 0), kMetaTypeNone); } TEST_F(KernelGraphTest, MutableInputs) { @@ -169,18 +170,18 @@ TEST_F(KernelGraphTest, SetExecOrderByDefault) { // test outputs() function auto outputs = kernel_graph->outputs(); EXPECT_EQ(outputs.size(), 1); - EXPECT_EQ(AnfAlgo::GetCNodeName(outputs[0]), prim::kPrimMul->name()); + EXPECT_EQ(common::AnfAlgo::GetCNodeName(outputs[0]), prim::kPrimMul->name()); // test SetExecOrderByDefault() function kernel_graph->SetExecOrderByDefault(); auto execution_order = kernel_graph->execution_order(); EXPECT_EQ(execution_order.size(), 2); - EXPECT_EQ(AnfAlgo::GetCNodeName(execution_order[0]), prim::kPrimAdd->name()); - EXPECT_EQ(AnfAlgo::GetCNodeName(execution_order[1]), prim::kPrimMul->name()); + EXPECT_EQ(common::AnfAlgo::GetCNodeName(execution_order[0]), prim::kPrimAdd->name()); + EXPECT_EQ(common::AnfAlgo::GetCNodeName(execution_order[1]), prim::kPrimMul->name()); // test set_execution_order() function kernel_graph->set_execution_order({add}); execution_order = kernel_graph->execution_order(); EXPECT_EQ(execution_order.size(), 1); - EXPECT_EQ(AnfAlgo::GetCNodeName(execution_order[0]), prim::kPrimAdd->name()); + EXPECT_EQ(common::AnfAlgo::GetCNodeName(execution_order[0]), prim::kPrimAdd->name()); } TEST_F(KernelGraphTest, SetGraphId) { diff --git a/tests/ut/cpp/session/session_basic_test.cc b/tests/ut/cpp/session/session_basic_test.cc index bcb024c85f0..a0b229c8c10 100644 --- a/tests/ut/cpp/session/session_basic_test.cc +++ b/tests/ut/cpp/session/session_basic_test.cc @@ -19,7 +19,8 @@ #include "backend/common/session/ascend_session.h" #include "backend/common/session/kernel_graph.h" #include "backend/common/session/anf_runtime_algorithm.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" +#include "include/common/utils/anfalgo.h" namespace mindspore { namespace session { @@ -87,11 +88,11 @@ TEST_F(SessionBasicTest, ConstructKernelGraph) { kernel_graph->SetExecOrderByDefault(); auto execution_order = kernel_graph->execution_order(); EXPECT_EQ(execution_order.size(), 2); - EXPECT_EQ(AnfAlgo::GetCNodeName(execution_order[0]), prim::kPrimAdd->name()); - EXPECT_EQ(AnfAlgo::GetCNodeName(execution_order[1]), prim::kPrimMul->name()); + EXPECT_EQ(common::AnfAlgo::GetCNodeName(execution_order[0]), prim::kPrimAdd->name()); + EXPECT_EQ(common::AnfAlgo::GetCNodeName(execution_order[1]), prim::kPrimMul->name()); auto new_outputs = kernel_graph->outputs(); EXPECT_EQ(new_outputs.size(), 1); - EXPECT_EQ(AnfAlgo::GetCNodeName(new_outputs[0]), prim::kPrimMul->name()); + EXPECT_EQ(common::AnfAlgo::GetCNodeName(new_outputs[0]), prim::kPrimMul->name()); }; } // namespace session diff --git a/tests/ut/cpp/tbe/tbe_json_creator_test.cc b/tests/ut/cpp/tbe/tbe_json_creator_test.cc index 13040a1ee6c..d5768f7a836 100644 --- a/tests/ut/cpp/tbe/tbe_json_creator_test.cc +++ b/tests/ut/cpp/tbe/tbe_json_creator_test.cc @@ -25,6 +25,7 @@ #include "plugin/device/ascend/optimizer/buffer_fusion/ub_pattern_fusion.h" #include "plugin/device/ascend/kernel/tbe/tbe_json/single_tbe_json_creator.h" #include "plugin/device/ascend/kernel/tbe/tbe_json/fusion_tbe_json_creator.h" +#include "include/common/utils/anfalgo.h" namespace mindspore::kernel { @@ -233,7 +234,7 @@ TEST_F(TestHWTBEJsonCreator, test_tbe_single_layer_norm) { EXPECT_TRUE(tbe_json_creator_select->GenJson(layer_norm, &kernel_json)); EXPECT_EQ(tbe_json_creator_select->GetJsonHash(), 6545088373747371515U); EXPECT_TRUE(tbe_json_creator_check->GenJson(layer_norm, &kernel_json)); - EXPECT_EQ(tbe_json_creator_check->GetJsonHash(), 10583210293426000299U); + EXPECT_EQ(tbe_json_creator_check->GetJsonHash(), 10583210293426000299U); EXPECT_TRUE(tbe_json_creator_build->GenJson(layer_norm, &kernel_json)); EXPECT_EQ(tbe_json_creator_build->GetJsonHash(), 10583210293426000299U); } @@ -288,12 +289,13 @@ TEST_F(TestHWTBEJsonCreator, test_tbe_fusion_common) { std::vector input_nodes; std::vector compute_nodes = {relu1, relu2}; - std::string full_name = "FusionOp_" + AnfAlgo::GetCNodeName(relu1) + "_" + AnfAlgo::GetCNodeName(relu2); + std::string full_name = + "FusionOp_" + common::AnfAlgo::GetCNodeName(relu1) + "_" + common::AnfAlgo::GetCNodeName(relu2); for (auto &node : compute_nodes) { auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); for (size_t idx = 1; idx < cnode->inputs().size(); ++idx) { - auto real_input = AnfAlgo::VisitKernel(cnode->input(idx), 0); + auto real_input = common::AnfAlgo::VisitKernel(cnode->input(idx), 0); if (std::find(compute_nodes.begin(), compute_nodes.end(), real_input.first) == compute_nodes.end()) { if (auto in = cnode->input(idx); std::find(input_nodes.begin(), input_nodes.end(), in) == input_nodes.end()) { input_nodes.push_back(in); @@ -347,12 +349,13 @@ TEST_F(TestHWTBEJsonCreator, test_fusion_add_conv2d) { std::vector input_nodes; std::vector compute_nodes = {add, conv2d}; - std::string full_name = "FusionOp_" + AnfAlgo::GetCNodeName(add) + "_" + AnfAlgo::GetCNodeName(conv2d); + std::string full_name = + "FusionOp_" + common::AnfAlgo::GetCNodeName(add) + "_" + common::AnfAlgo::GetCNodeName(conv2d); for (auto &node : compute_nodes) { auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); for (size_t idx = 1; idx < cnode->inputs().size(); ++idx) { - auto real_input = AnfAlgo::VisitKernel(cnode->input(idx), 0); + auto real_input = common::AnfAlgo::VisitKernel(cnode->input(idx), 0); if (std::find(compute_nodes.begin(), compute_nodes.end(), real_input.first) == compute_nodes.end()) { if (auto in = cnode->input(idx); std::find(input_nodes.begin(), input_nodes.end(), in) == input_nodes.end()) { input_nodes.push_back(in); diff --git a/tests/ut/cpp/transform/convert_test.cc b/tests/ut/cpp/transform/convert_test.cc index 61a7f88695a..730247d0a93 100644 --- a/tests/ut/cpp/transform/convert_test.cc +++ b/tests/ut/cpp/transform/convert_test.cc @@ -28,10 +28,10 @@ #include "common/common_test.h" #define private public -#include "transform/graph_ir/types.h" -#include "transform/graph_ir/convert.h" +#include "include/transform/graph_ir/types.h" +#include "include/transform/graph_ir/convert.h" #include "securec/include/securec.h" -#include "utils/utils.h" +#include "include/common/utils/utils.h" using std::cout; using std::endl; using std::string; diff --git a/tests/ut/cpp/transform/graph_builder_test.cc b/tests/ut/cpp/transform/graph_builder_test.cc index e4d72b33cb2..02d196683f0 100644 --- a/tests/ut/cpp/transform/graph_builder_test.cc +++ b/tests/ut/cpp/transform/graph_builder_test.cc @@ -25,8 +25,8 @@ #endif #define private public -#include "transform/graph_ir/graph_builder.h" -#include "transform/graph_ir/df_graph_manager.h" +#include "include/transform/graph_ir/graph_builder.h" +#include "include/transform/graph_ir/df_graph_manager.h" using UT::Common; diff --git a/tests/ut/cpp/transform/graph_manager_test.cc b/tests/ut/cpp/transform/graph_manager_test.cc index 9e55e1725bb..314eed8449d 100644 --- a/tests/ut/cpp/transform/graph_manager_test.cc +++ b/tests/ut/cpp/transform/graph_manager_test.cc @@ -25,7 +25,7 @@ #endif #define private public -#include "transform/graph_ir/df_graph_manager.h" +#include "include/transform/graph_ir/df_graph_manager.h" using UT::Common; diff --git a/tests/ut/cpp/transform/graph_runner_test.cc b/tests/ut/cpp/transform/graph_runner_test.cc index d5cf5348360..6559b14e9b4 100644 --- a/tests/ut/cpp/transform/graph_runner_test.cc +++ b/tests/ut/cpp/transform/graph_runner_test.cc @@ -23,9 +23,9 @@ #include "common/py_func_graph_fetcher.h" #include "pipeline/jit/static_analysis/static_analysis.h" #include "frontend/operator/ops.h" -#include "transform/graph_ir/df_graph_manager.h" -#include "transform/graph_ir/convert.h" -#include "utils/utils.h" +#include "include/transform/graph_ir/df_graph_manager.h" +#include "include/transform/graph_ir/convert.h" +#include "include/common/utils/utils.h" #ifdef OPEN_SOURCE #include "ge/client/ge_api.h" @@ -34,7 +34,7 @@ #endif #define private public -#include "transform/graph_ir/graph_runner.h" +#include "include/transform/graph_ir/graph_runner.h" using mindspore::tensor::TensorPy; diff --git a/tests/ut/cpp/transform/transform_base_test.cc b/tests/ut/cpp/transform/transform_base_test.cc index 0c73fe842b7..8235c10fdf8 100644 --- a/tests/ut/cpp/transform/transform_base_test.cc +++ b/tests/ut/cpp/transform/transform_base_test.cc @@ -24,7 +24,7 @@ using mindspore::tensor::TensorPy; namespace mindspore { namespace transform { using mindspore::parse::ParsePythonCode; -namespace python_adapter = mindspore::parse::python_adapter; +namespace python_adapter = mindspore::python_adapter; using mindspore::parse::ResolveAll; std::vector getAnfGraph(string package, string function) { py::function fn_ = python_adapter::GetPyFn(package, function); diff --git a/tests/ut/cpp/transform/transform_base_test.h b/tests/ut/cpp/transform/transform_base_test.h index 84fa05d30f9..0a0a9a03455 100644 --- a/tests/ut/cpp/transform/transform_base_test.h +++ b/tests/ut/cpp/transform/transform_base_test.h @@ -20,7 +20,7 @@ #include #include #include -#include "transform/graph_ir/util.h" +#include "include/transform/graph_ir/util.h" #include "ir/tensor.h" #include "common/common_test.h" diff --git a/tests/ut/cpp/utils/callback_test.cc b/tests/ut/cpp/utils/callback_test.cc index c34c1c0dcd6..88813e4150b 100644 --- a/tests/ut/cpp/utils/callback_test.cc +++ b/tests/ut/cpp/utils/callback_test.cc @@ -16,19 +16,17 @@ #include #include #include "pybind11/pybind11.h" -#include "utils/callbacks.h" +#include "include/common/utils/callbacks.h" #include "common/common_test.h" #include "pipeline/jit/pipeline.h" -#include "pipeline/jit/parse/python_adapter.h" -#include "transform/graph_ir/df_graph_manager.h" +#include "include/common/utils/python_adapter.h" +#include "include/transform/graph_ir/df_graph_manager.h" #include "debug/draw.h" #ifdef ENABLE_D -#include "utils/callbacks_ge.h" +#include "include/common/utils/callbacks_ge.h" #endif namespace mindspore { -namespace python_adapter = mindspore::parse::python_adapter; - class TestCallback : public UT::Common { public: TestCallback() {} diff --git a/tests/ut/cpp/utils/comm_manager_test.cc b/tests/ut/cpp/utils/comm_manager_test.cc index a8f5eb27d44..c434a39276a 100644 --- a/tests/ut/cpp/utils/comm_manager_test.cc +++ b/tests/ut/cpp/utils/comm_manager_test.cc @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -#include "utils/comm_manager.h" +#include "include/common/utils/comm_manager.h" #include "common/common_test.h" namespace mindspore { @@ -24,14 +24,14 @@ class TestCommManager : public UT::Common { TEST_F(TestCommManager, TestCreate) { std::vector devices{0, 1, 2}; - ASSERT_TRUE(CommManager::GetInstance().CreateGroupSync(string("1-2-3"), devices)); - ASSERT_TRUE(CommManager::GetInstance().CreateGroupSync(string("hccl_world_group"), devices)); + ASSERT_TRUE(CommManager::GetInstance().CreateGroupSync(std::string("1-2-3"), devices)); + ASSERT_TRUE(CommManager::GetInstance().CreateGroupSync(std::string("hccl_world_group"), devices)); } TEST_F(TestCommManager, TestGetSize) { unsigned int rank_size = 0; - ASSERT_TRUE(CommManager::GetInstance().GetRankSize(string("1-2-3"), &rank_size)); - ASSERT_TRUE(CommManager::GetInstance().GetRankSize(string("hccl_world_group"), &rank_size)); + ASSERT_TRUE(CommManager::GetInstance().GetRankSize(std::string("1-2-3"), &rank_size)); + ASSERT_TRUE(CommManager::GetInstance().GetRankSize(std::string("hccl_world_group"), &rank_size)); } } // namespace mindspore diff --git a/tests/ut/cpp/utils/config_manager_test.cc b/tests/ut/cpp/utils/config_manager_test.cc index 6928ec0baaf..c47beca4952 100644 --- a/tests/ut/cpp/utils/config_manager_test.cc +++ b/tests/ut/cpp/utils/config_manager_test.cc @@ -14,7 +14,7 @@ * limitations under the License. */ #include "common/common_test.h" -#include "utils/config_manager.h" +#include "include/common/utils/config_manager.h" namespace mindspore { class TestConfigManager : public UT::Common { diff --git a/tests/ut/cpp/utils/graph_utils_test.cc b/tests/ut/cpp/utils/graph_utils_test.cc index 60bc3f75ec7..c5906266cc9 100644 --- a/tests/ut/cpp/utils/graph_utils_test.cc +++ b/tests/ut/cpp/utils/graph_utils_test.cc @@ -23,7 +23,7 @@ #include "ir/anf.h" #include "ir/graph_utils.h" -#include "utils/convert_utils.h" +#include "include/common/utils/convert_utils.h" #include "pipeline/jit/parse/parse_base.h" #include "pipeline/jit/parse/parse.h" diff --git a/tests/ut/cpp/vm/segment_runner_test.cc b/tests/ut/cpp/vm/segment_runner_test.cc index 418ab3f88fa..96768be0acc 100644 --- a/tests/ut/cpp/vm/segment_runner_test.cc +++ b/tests/ut/cpp/vm/segment_runner_test.cc @@ -28,8 +28,8 @@ #include "backend/graph_compiler/segment_runner.h" #include "backend/graph_compiler/transform.h" #include "ir/tensor.h" -#include "utils/convert_utils.h" -#include "utils/convert_utils_py.h" +#include "include/common/utils/convert_utils.h" +#include "include/common/utils/convert_utils_py.h" #include "utils/log_adapter.h" #include "base/core_ops.h"