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remove ge depend in cpu

This commit is contained in:
Wei Luning 2020-03-23 17:33:56 +08:00 committed by kingfo
parent 0f2ed0b134
commit 73ba399364
92 changed files with 1574 additions and 1237 deletions
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12
CMakeLists.txt
View File

@ -42,11 +42,13 @@ else()
include(${CMAKE_SOURCE_DIR}/cmake/dependency_graphengine.cmake)
endif()
include_directories(${CMAKE_CURRENT_SOURCE_DIR}/graphengine/inc)
include_directories(${CMAKE_CURRENT_SOURCE_DIR}/graphengine/inc/external)
include_directories(${CMAKE_CURRENT_SOURCE_DIR}/graphengine/inc/framework)
include_directories(${CMAKE_CURRENT_SOURCE_DIR}/graphengine/third_party/fwkacllib/inc)
include_directories(${CMAKE_CURRENT_SOURCE_DIR}/graphengine/third_party/fwkacllib/inc/toolchain)
if (ENABLE_GE OR ENABLE_D OR ENABLE_TESTCASES)
include_directories(${CMAKE_CURRENT_SOURCE_DIR}/graphengine/inc)
include_directories(${CMAKE_CURRENT_SOURCE_DIR}/graphengine/inc/external)
include_directories(${CMAKE_CURRENT_SOURCE_DIR}/graphengine/inc/framework)
include_directories(${CMAKE_CURRENT_SOURCE_DIR}/graphengine/third_party/fwkacllib/inc)
include_directories(${CMAKE_CURRENT_SOURCE_DIR}/graphengine/third_party/fwkacllib/inc/toolchain)
endif()
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fvisibility=hidden")
add_subdirectory(mindspore/ccsrc)

2
cmake/mind_expression.cmake
View File

@ -40,7 +40,7 @@ if (ENABLE_GE)
include_directories(${CMAKE_SOURCE_DIR}/third_party/ge/include)
include_directories(${CMAKE_SOURCE_DIR}/third_party/ge/include/external)
include_directories(${CMAKE_SOURCE_DIR}/third_party/ge/include/external/graph)
else()
elseif(ENABLE_D OR ENABLE_TESTCASES)
include_directories(${CMAKE_SOURCE_DIR}/graphengine/inc)
include_directories(${CMAKE_SOURCE_DIR}/graphengine/inc/ops)
include_directories(${CMAKE_SOURCE_DIR}/graphengine/inc/external)

26
mindspore/ccsrc/CMakeLists.txt
View File

@ -34,6 +34,8 @@ if(ENABLE_GPU)
"device/gpu/*.cu"
"kernel/gpu/*.cu"
"kernel/akg/gpu/*.cc"
"kernel/akg/akgkernelbuild.cc"
"kernel/akg/akg_kernel_attrs_process.cc"
)
file(GLOB_RECURSE GPU_KERNEL_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}
"kernel/gpu/*.cc"
@ -100,14 +102,14 @@ file(GLOB_RECURSE MINDSPORE_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}
"debug/*.cc"
"onnx/onnx_exporter.cc"
"operator/*.cc"
"transform/*.cc"
"session/kernel_graph.cc"
"utils/node_utils.cc"
"session/session_basic.cc"
"session/session_factory.cc"
"session/anf_runtime_algorithm.cc"
"vm/*.cc"
"pynative/*.cc"
"pynative/base.cc"
"pynative/pynative_execute.cc"
"pybind_api/*.cc"
"device/common/*.cc"
"kernel/kernel_query.cc"
@ -117,7 +119,6 @@ file(GLOB_RECURSE MINDSPORE_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}
"device/kernel_runtime.cc"
"device/kernel_runtime_manager.cc"
"device/convert_tensor_utils.cc"
"pre_activate/ascend/*.cc"
"pre_activate/common/*.cc"
"pre_activate/pass/*.cc"
"pre_activate/gpu/*.cc"
@ -168,6 +169,15 @@ if(ENABLE_DUMP_PROTO)
add_compile_definitions(ENABLE_DUMP_PROTO)
endif()
if(ENABLE_GE)
file(GLOB_RECURSE GE_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}
"transform/*.cc"
"pynative/pynative_execute_ge.cc"
"pipeline/pipeline_ge.cc"
)
list(APPEND MINDSPORE_SRC_LIST ${GE_SRC_LIST})
endif()
if(ENABLE_D)
include_directories("${CMAKE_BINARY_DIR}/kernel/aicpu")
file(GLOB_RECURSE PROTO_IN RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}
@ -188,6 +198,9 @@ if(ENABLE_D)
"device/kernel_adjust.cc"
"kernel/kernel_fusion.cc"
"kernel/tbe/*.cc"
"pre_activate/ascend/*.cc"
"transform/*.cc"
"pipeline/pipeline_ge.cc"
)
list(APPEND MINDSPORE_SRC_LIST ${D_SRC_LIST})
list(APPEND MINDSPORE_PROTO_AICPU_LIST ${PROTOSRCS})
@ -246,9 +259,11 @@ if (ENABLE_GE)
target_link_libraries(mindspore graph ge_client)
endif()
target_link_libraries(mindspore tsdclient)
else()
elseif(ENABLE_D)
add_compile_definitions(NO_GE_CLIENT)
target_link_libraries(mindspore graph)
else()
add_compile_definitions(NO_GE_CLIENT)
endif()
if(ENABLE_D)
@ -288,8 +303,6 @@ endif()
set(PYTHON_MODULE_SOURCE
pipeline/init.cc
kernel/oplib/oplib.cc
kernel/akg/akgkernelbuild.cc
kernel/akg/akg_kernel_attrs_process.cc
${MS_STEPS_SRC_LIST} ${MS_CCE_SRC_LIST} ${MS_AICPU_SRC_LIST} ${MS_TASKINFO_LIST} ${MS_RT_SRC_LIST}
${GPU_NCCL_LIST} ${MS_HCCL_SRC_LIST} ${MS_PREDICT_SRC_LIST} ${CPU_SRC_LIST} ${MEM_REUSE_SRC_LIST} ${GPU_KERNEL_SRC_LIST})
@ -350,6 +363,7 @@ if(ENABLE_GPU)
assign_source_group("Include" ${GROUP_INCLUDE})
file(GLOB COMPILER_SRCS
"pre_activate/gpu/*.cc"
${TVM_DIR}/src/api/*.cc
${TVM_DIR}/src/arithmetic/*.cc
${TVM_DIR}/src/autotvm/*.cc

2
mindspore/ccsrc/debug/e2e_dump.cc
View File

@ -49,7 +49,7 @@ bool Dump::IsKernelNeedDump(const std::string& kernel_name) {
return false;
}
bool Dump::ParseDumpConfig(const string& dump_config_file) {
bool Dump::ParseDumpConfig(const std::string& dump_config_file) {
std::ifstream jsonFile(dump_config_file);
if (!jsonFile.is_open()) {
MS_LOG(ERROR) << dump_config_file << " open failed.";

2
mindspore/ccsrc/device/ascend/kernel_build_ascend.cc
View File

@ -94,7 +94,7 @@ static bool KernelBuildParallelCompile(const mindspore::session::KernelGraph *ke
return ret;
}
static vector<int> CalCleanZerosSize(const CNodePtr &pre_node) {
static std::vector<int> CalCleanZerosSize(const CNodePtr &pre_node) {
MS_EXCEPTION_IF_NULL(pre_node);
std::vector<int> clean_size_list;
// clean output

1
mindspore/ccsrc/device/ascend/profiling/profiling_manager.cc
View File

@ -27,6 +27,7 @@
#include "utils/log_adapter.h"
#include "utils/context/ms_context.h"
#include "common/utils.h"
#include "utils/convert_utils.h"
using std::vector;
using Json = nlohmann::json;

4
mindspore/ccsrc/device/ascend/tasksink/task_generator.cc
View File

@ -121,8 +121,8 @@ bool TaskGenerator::LaunchKernel(const CNodePtr &anf_node_ptr, uint32_t stream_i
LaunchAddrCleanKernel(anf_node_ptr, &kernel_inputs);
}
std::vector<TaskInfoPtr> task_info_ptrs =
kernel_mod->GenTask(kernel_inputs, kernel_workspaces, kernel_outputs, stream_id);
std::vector<TaskInfoPtr> task_info_ptrs = dynamic_cast<kernel::AscendKernelMod *>(kernel_mod)
->GenTask(kernel_inputs, kernel_workspaces, kernel_outputs, stream_id);
task_info_list->insert(task_info_list->end(), task_info_ptrs.begin(), task_info_ptrs.end());
return true;
}

2
mindspore/ccsrc/device/ascend/tasksink/task_generator.h
View File

@ -24,7 +24,7 @@
#include <vector>
#include "device/kernel_runtime.h"
#include "ir/anf.h"
#include "kernel/kernel.h"
#include "kernel/ascend_kernel_mod.h"
#include "framework/ge_runtime/task_info.h"
namespace mindspore {

1
mindspore/ccsrc/device/gpu/gpu_kernel_build.cc
View File

@ -21,7 +21,6 @@
#include "kernel/gpu/gpu_kernel_factory.h"
#include "operator/ops.h"
#include "pybind11/stl.h"
#include "transform/convert.h"
#include "session/anf_runtime_algorithm.h"
namespace mindspore {
namespace device {

2
mindspore/ccsrc/device/gpu/kernel_info_setter.cc
View File

@ -91,7 +91,7 @@ std::string SupportedTypeList(const CNodePtr& kernel_node) {
return supported_type_lists;
}
bool SelectAkgKernel(const CNodePtr& kernel_node, const shared_ptr<KernelBuildInfo>& selected_kernel_info) {
bool SelectAkgKernel(const CNodePtr& kernel_node, const std::shared_ptr<KernelBuildInfo>& selected_kernel_info) {
MS_EXCEPTION_IF_NULL(kernel_node);
MS_EXCEPTION_IF_NULL(selected_kernel_info);
std::vector<std::shared_ptr<KernelBuildInfo>> kernel_info_list;

1
mindspore/ccsrc/device/kernel_adjust.cc
View File

@ -32,6 +32,7 @@
#include "device/ascend/profiling/profiling_manager.h"
#include "device/ascend/kernel_select_ascend.h"
#include "device/kernel_info.h"
#include "runtime/base.h"
constexpr auto kLoopCountParamName = "loop_count";
constexpr auto kIterLoopParamName = "iter_loop";

17
mindspore/ccsrc/ir/anf.cc
View File

@ -197,6 +197,23 @@ PrimitivePtr GetCNodePrimitive(const AnfNodePtr& node) {
return nullptr;
}
std::string GetCNodeFuncName(const CNodePtr cnode) {
if (cnode->inputs().empty()) {
return "";
}
AnfNodePtr valuenode = cnode->input(0);
if (valuenode->isa<ValueNode>()) {
auto value = GetValueNode(valuenode);
// check whether the valuenode is primitive
if (value->isa<Primitive>()) {
return value->cast<PrimitivePtr>()->name();
}
return value->ToString();
}
return "";
}
bool IsPrimitive(const AnfNodePtr& node, const PrimitivePtr& value) {
if (IsValueNode<Primitive>(node)) {
PrimitivePtr fn_value = GetValueNode<PrimitivePtr>(node);

2
mindspore/ccsrc/ir/anf.h
View File

@ -384,6 +384,8 @@ static S GetValue(const ValuePtr &value) {
return v;
}
std::string GetCNodeFuncName(CNodePtr cnode);
// used to check whether an AnfNode is a cnode with a kind of Primitive as first input
bool IsPrimitiveCNode(const AnfNodePtr &node, const PrimitivePtr &value);

1
mindspore/ccsrc/ir/meta_tensor.cc
View File

@ -25,7 +25,6 @@
#include "device/device_address.h"
#include "pybind_api/api_register.h"
#include "pybind_api/export_flags.h"
#include "pynative/pynative_execute.h"
#include "pipeline/static_analysis/abstract_value.h"
namespace mindspore {

4
mindspore/ccsrc/kernel/aicpu/aicpu_kernel_mod.h
View File

@ -18,11 +18,11 @@
#include <vector>
#include <memory>
#include <string>
#include "kernel/kernel.h"
#include "kernel/ascend_kernel_mod.h"
#include "kernel/aicpu/aicpu_util.h"
namespace mindspore {
namespace kernel {
class AicpuOpKernelMod : public KernelMod {
class AicpuOpKernelMod : public AscendKernelMod {
public:
AicpuOpKernelMod();
~AicpuOpKernelMod() override;

5
mindspore/ccsrc/kernel/akg/akgkernelbuild.cc
View File

@ -35,7 +35,6 @@
#include "utils/convert_utils.h"
#include "utils/any.h"
#include "utils/utils.h"
#include "transform/convert.h"
#include "session/anf_runtime_algorithm.h"
#include "kernel/akg/akg_kernel_attrs_process.h"
@ -240,8 +239,8 @@ bool AkgKernelBuild::CreateOutputDescJson(const AnfNodePtr &anf_node, nlohmann::
return true;
}
void GetJson(const AnfNodePtr &anf_node, const vector<int> &dyn_input_sizes, const shared_ptr<OpAttr> &op_attr,
nlohmann::json *const attr_json, const ValuePtr &attr_value) {
void GetJson(const AnfNodePtr &anf_node, const std::vector<int> &dyn_input_sizes,
const std::shared_ptr<OpAttr> &op_attr, nlohmann::json *const attr_json, const ValuePtr &attr_value) {
MS_EXCEPTION_IF_NULL(anf_node);
MS_EXCEPTION_IF_NULL(op_attr);
MS_EXCEPTION_IF_NULL(attr_json);

36
mindspore/ccsrc/kernel/ascend_kernel_mod.h Normal file
View File

@ -0,0 +1,36 @@
/**
* 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_KERNEL_ASCEND_KERNEL_MOD_H_
#define MINDSPORE_CCSRC_KERNEL_ASCEND_KERNEL_MOD_H_
#include <vector>
#include <memory>
#include "framework/ge_runtime/task_info.h"
#include "kernel/kernel.h"
using TaskInfoPtr = std::shared_ptr<ge::model_runner::TaskInfo>;
namespace mindspore {
namespace kernel {
class AscendKernelMod : public KernelMod {
public:
virtual std::vector<TaskInfoPtr> GenTask(const std::vector<AddressPtr> &, const std::vector<AddressPtr> &,
const std::vector<AddressPtr> &, uint32_t) = 0;
};
} // namespace kernel
} // namespace mindspore
#endif // MINDSPORE_CCSRC_KERNEL_ASCEND_KERNEL_MOD_H_

3
mindspore/ccsrc/kernel/common_utils.cc
View File

@ -19,7 +19,6 @@
#include <map>
#include <iostream>
#include <fstream>
#include "runtime/rt.h"
#include "nlohmann/json.hpp"
#include "session/anf_runtime_algorithm.h"
#include "common/utils.h"
@ -490,7 +489,7 @@ void SaveJsonInfo(const std::string &json_name, const std::string &info) {
if (!filewrite.is_open()) {
return;
}
filewrite << info << endl;
filewrite << info << std::endl;
filewrite.close();
if (nullptr == realpath(path.c_str(), real_path)) {
MS_LOG(DEBUG) << "dir " << path << " does not exit.";

4
mindspore/ccsrc/kernel/gpu/nn/lstm_gpu_kernel.h
View File

@ -226,12 +226,12 @@ class LstmGpuKernel : public GpuKernel {
size_t reserved_size_;
// input desc
unique_ptr<cudnnTensorDescriptor_t[]> x_desc_;
std::unique_ptr<cudnnTensorDescriptor_t[]> x_desc_;
cudnnTensorDescriptor_t hx_desc_;
cudnnTensorDescriptor_t cx_desc_;
cudnnFilterDescriptor_t w_desc_;
cudnnDropoutDescriptor_t dropout_desc_;
unique_ptr<cudnnTensorDescriptor_t[]> y_desc_;
std::unique_ptr<cudnnTensorDescriptor_t[]> y_desc_;
cudnnTensorDescriptor_t hy_desc_;
cudnnTensorDescriptor_t cy_desc_;
cudnnRNNDescriptor_t rnn_desc_;

6
mindspore/ccsrc/kernel/gpu/nn/lstm_grad_data_gpu_kernel.h
View File

@ -258,8 +258,8 @@ class LstmGradDataGpuKernel : public GpuKernel {
cudnnRNNDescriptor_t rnn_desc_;
// input desc
unique_ptr<cudnnTensorDescriptor_t[]> y_desc_;
unique_ptr<cudnnTensorDescriptor_t[]> dy_desc_;
std::unique_ptr<cudnnTensorDescriptor_t[]> y_desc_;
std::unique_ptr<cudnnTensorDescriptor_t[]> dy_desc_;
cudnnTensorDescriptor_t dhy_desc_;
cudnnTensorDescriptor_t dcy_desc_;
cudnnFilterDescriptor_t w_desc_;
@ -269,7 +269,7 @@ class LstmGradDataGpuKernel : public GpuKernel {
cudnnDropoutDescriptor_t dropout_desc_;
// output desc
unique_ptr<cudnnTensorDescriptor_t[]> dx_desc_;
std::unique_ptr<cudnnTensorDescriptor_t[]> dx_desc_;
cudnnTensorDescriptor_t dhx_desc_;
cudnnTensorDescriptor_t dcx_desc_;

4
mindspore/ccsrc/kernel/gpu/nn/lstm_grad_weight_gpu_kernel.h
View File

@ -214,9 +214,9 @@ class LstmGradWeightGpuKernel : public GpuKernel {
cudnnDropoutDescriptor_t dropout_desc_;
// input desc
unique_ptr<cudnnTensorDescriptor_t[]> x_desc_;
std::unique_ptr<cudnnTensorDescriptor_t[]> x_desc_;
cudnnTensorDescriptor_t hx_desc_;
unique_ptr<cudnnTensorDescriptor_t[]> y_desc_;
std::unique_ptr<cudnnTensorDescriptor_t[]> y_desc_;
// output desc
cudnnFilterDescriptor_t dw_desc_;

4
mindspore/ccsrc/kernel/hccl/hccl_kernel.h
View File

@ -23,14 +23,14 @@
#include <vector>
#include <algorithm>
#include <utility>
#include "kernel/kernel.h"
#include "kernel/ascend_kernel_mod.h"
#include "kernel/hccl/hcom_util.h"
#include "hccl/hcom.h"
#include "common/utils.h"
namespace mindspore {
namespace kernel {
class HcclKernel : public KernelMod {
class HcclKernel : public AscendKernelMod {
public:
HcclKernel();
~HcclKernel() override;

6
mindspore/ccsrc/kernel/kernel.h
View File

@ -25,7 +25,6 @@
#include "ir/meta_tensor.h"
#include "pipeline/static_analysis/dshape.h"
#include "utils/log_adapter.h"
#include "framework/ge_runtime/task_info.h"
namespace mindspore {
enum KernelType : int { UNKNOWN_KERNEL_TYPE = 0, AUTO_DIFF_KERNEL, AICPU_KERNEL, RT_KERNEL, HCCL_KERNEL, TBE_KERNEL };
@ -111,7 +110,6 @@ struct Address {
size_t size;
};
using AddressPtr = std::shared_ptr<Address>;
using TaskInfoPtr = std::shared_ptr<ge::model_runner::TaskInfo>;
class KernelMod {
public:
@ -120,10 +118,6 @@ class KernelMod {
virtual const std::vector<size_t> &GetWorkspaceSizeList() const = 0;
virtual bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) = 0;
virtual std::vector<TaskInfoPtr> GenTask(const std::vector<AddressPtr> &, const std::vector<AddressPtr> &,
const std::vector<AddressPtr> &, uint32_t) {
return {};
}
virtual std::vector<size_t> GenParameters() { return {}; }
virtual ~KernelMod() = default;

4
mindspore/ccsrc/kernel/mng/rt_kernel.h
View File

@ -22,12 +22,12 @@
#include <memory>
#include <map>
#include <string>
#include "kernel/kernel.h"
#include "kernel/ascend_kernel_mod.h"
#include "kernel/task_stream.h"
namespace mindspore {
namespace kernel {
class RtKernel : public KernelMod {
class RtKernel : public AscendKernelMod {
public:
RtKernel();
~RtKernel() override;

4
mindspore/ccsrc/kernel/oplib/oplib.cc
View File

@ -19,7 +19,7 @@
#include <unordered_map>
#include <memory>
#include "utils/log_adapter.h"
#include "kernel/oplib/opinfo.h"
#include "utils/overload.h"
#include "utils/context/ms_context.h"
namespace mindspore {
@ -50,7 +50,7 @@ constexpr auto kNeedCompile = "need_compile";
constexpr auto kShape = "shape";
std::vector<std::shared_ptr<OpInfo>> OpLib::op_info_;
string ImplTypeToStr(OpImplyType impl_type) {
std::string ImplTypeToStr(OpImplyType impl_type) {
switch (impl_type) {
case kTBE:
return kTbe;

33
mindspore/ccsrc/kernel/tbe/tbe_kernel_build.h
View File

@ -48,7 +48,7 @@ class TbeKernelBuild {
private:
TbeKernelBuild() = default;
~TbeKernelBuild() = default;
static bool GenFusionDataInputJson(const shared_ptr<mindspore::AnfNode> &data_input, nlohmann::json *data_str,
static bool GenFusionDataInputJson(const std::shared_ptr<mindspore::AnfNode> &data_input, nlohmann::json *data_str,
size_t *index);
static bool GenFusionComputeJson(const mindspore::AnfNodePtr &compute_node,
std::vector<std::vector<mindspore::AnfNodePtr>>::iterator *layer_iter,
@ -56,12 +56,13 @@ class TbeKernelBuild {
static bool GenFusionComputeInputeJson(const mindspore::CNodePtr &cnode,
std::vector<std::vector<mindspore::AnfNodePtr>>::iterator *layer_iter,
std::vector<nlohmann::json> *input_desc_list, size_t *index);
static void GenDescJson(const shared_ptr<mindspore::AnfNode> &anf_node, size_t out_idx, nlohmann::json *output_desc);
static void GenReusedOutputDesc(const shared_ptr<mindspore::AnfNode> &anf_node, size_t index, size_t output_index,
nlohmann::json *output_desc);
static void GenDescJson(const std::shared_ptr<mindspore::AnfNode> &anf_node, size_t out_idx,
nlohmann::json *output_desc);
static void GenReusedOutputDesc(const std::shared_ptr<mindspore::AnfNode> &anf_node, size_t index,
size_t output_index, nlohmann::json *output_desc);
static size_t GetIOSizeImpl(const nlohmann::json &desc);
static bool GetInputLayers(const vector<mindspore::AnfNodePtr> &input_nodes,
const vector<mindspore::AnfNodePtr> &compute_nodes,
static bool GetInputLayers(const std::vector<mindspore::AnfNodePtr> &input_nodes,
const std::vector<mindspore::AnfNodePtr> &compute_nodes,
std::vector<std::vector<mindspore::AnfNodePtr>> *input_layers);
static bool IsDynamicInput(const CNodePtr &cnode);
static size_t GetOptionalInput(const CNodePtr &cnode, bool is_dynamic_input);
@ -82,15 +83,17 @@ class TbeKernelJsonCreator {
bool GenTbeAttrJson(const std::shared_ptr<AnfNode> &anf_node, const std::shared_ptr<OpInfo> &op_info,
nlohmann::json *attrs_json);
void ParseAttrValue(const std::string &type, const ValuePtr &value, nlohmann::json *attr_obj);
bool GenInputDescJson(const shared_ptr<AnfNode> &anf_node, size_t real_input_index, bool value,
const shared_ptr<OpIOInfo> &input_ptr, const string &op_input_name, size_t input_i,
vector<nlohmann::json> *input_list);
bool GenOutputDescJson(const shared_ptr<AnfNode> &anf_node, const vector<std::shared_ptr<OpIOInfo>> &outputs_ptr,
nlohmann::json *outputs_json);
bool GenInputList(const shared_ptr<AnfNode> &anf_node, size_t input_tensor_num, const shared_ptr<OpIOInfo> &input_ptr,
size_t *real_input_index, string *op_input_name, vector<nlohmann::json> *input_list);
void GenOutputList(const shared_ptr<AnfNode> &anf_node, const size_t &output_obj_num,
const shared_ptr<OpIOInfo> &output_ptr, size_t *output_idx, vector<nlohmann::json> *output_list);
bool GenInputDescJson(const std::shared_ptr<AnfNode> &anf_node, size_t real_input_index, bool value,
const std::shared_ptr<OpIOInfo> &input_ptr, const string &op_input_name, size_t input_i,
std::vector<nlohmann::json> *input_list);
bool GenOutputDescJson(const std::shared_ptr<AnfNode> &anf_node,
const std::vector<std::shared_ptr<OpIOInfo>> &outputs_ptr, nlohmann::json *outputs_json);
bool GenInputList(const std::shared_ptr<AnfNode> &anf_node, size_t input_tensor_num,
const std::shared_ptr<OpIOInfo> &input_ptr, size_t *real_input_index, string *op_input_name,
std::vector<nlohmann::json> *input_list);
void GenOutputList(const std::shared_ptr<AnfNode> &anf_node, const size_t &output_obj_num,
const std::shared_ptr<OpIOInfo> &output_ptr, size_t *output_idx,
std::vector<nlohmann::json> *output_list);
kCreaterType creater_type_;
std::string json_name_;
std::string json_info_;

4
mindspore/ccsrc/kernel/tbe/tbe_kernel_mod.h
View File

@ -21,12 +21,12 @@
#include <string>
#include <vector>
#include <utility>
#include "kernel/kernel.h"
#include "kernel/ascend_kernel_mod.h"
#include "kernel/tbe/tbe_utils.h"
namespace mindspore {
namespace kernel {
class TbeKernelMod : public KernelMod {
class TbeKernelMod : public AscendKernelMod {
public:
explicit TbeKernelMod(KernelPackPtr kernel_pack) : kernel_pack_(std::move(kernel_pack)) {}
~TbeKernelMod() override = default;

5
mindspore/ccsrc/kernel/tbe/tbe_kernel_parallel_build.h
View File

@ -55,8 +55,9 @@ class ParallelBuildManager {
bool WaitOne(int *task_id, char **task_result) const;
bool IsAllTaskFinish() const;
std::pair<int32_t, KernelModPtr> TaskFinishProcess(int32_t task_id, bool set_kernel_mod = true);
KernelModPtr GenKernelMod(const string &json_name, const string &processor, const vector<size_t> &input_size_list,
const vector<size_t> &output_size_list, const KernelPackPtr &kernel_pack) const;
KernelModPtr GenKernelMod(const string &json_name, const string &processor,
const std::vector<size_t> &input_size_list, const std::vector<size_t> &output_size_list,
const KernelPackPtr &kernel_pack) const;
private:
PyObject *tbe_parallel_compiler_;

9
mindspore/ccsrc/kernel/tbe/tbe_kernel_select.cc
View File

@ -168,7 +168,7 @@ bool ParseDynamicFormatJson(const std::string &jsonStr, std::vector<std::shared_
return true;
}
std::string OpSelectFormat(const shared_ptr<AnfNode> &anf_node) {
std::string OpSelectFormat(const std::shared_ptr<AnfNode> &anf_node) {
nlohmann::json kernel_json;
std::string res_json_str;
TbeKernelJsonCreator creator(OP_SELECT_FORMAT);
@ -182,7 +182,7 @@ std::string OpSelectFormat(const shared_ptr<AnfNode> &anf_node) {
return res_json_str;
}
void SetTidyInputsInfo(const shared_ptr<AnfNode> &anf_node,
void SetTidyInputsInfo(const std::shared_ptr<AnfNode> &anf_node,
const std::shared_ptr<KernelBuildInfo::KernelBuildInfoBuilder> &builder,
const std::vector<std::shared_ptr<OpIOInfo>> &inputs) {
std::vector<TypeId> inputs_type;
@ -231,7 +231,7 @@ void SetTidyInputsInfo(const shared_ptr<AnfNode> &anf_node,
builder->SetInputsFormat(inputs_format);
}
void SetTidyOutputsInfo(const shared_ptr<AnfNode> &anf_node,
void SetTidyOutputsInfo(const std::shared_ptr<AnfNode> &anf_node,
const std::shared_ptr<KernelBuildInfo::KernelBuildInfoBuilder> &builder,
const std::vector<std::shared_ptr<OpIOInfo>> &outputs) {
std::vector<TypeId> outputs_type;
@ -268,7 +268,8 @@ void SetTidyOutputsInfo(const shared_ptr<AnfNode> &anf_node,
builder->SetOutputsFormat(outputs_format);
}
void GenTidyKernelBuildInfo(const shared_ptr<AnfNode> &anf_node, const std::vector<std::shared_ptr<OpIOInfo>> &inputs,
void GenTidyKernelBuildInfo(const std::shared_ptr<AnfNode> &anf_node,
const std::vector<std::shared_ptr<OpIOInfo>> &inputs,
const std::vector<std::shared_ptr<OpIOInfo>> &outputs) {
auto builder_tmp = std::make_shared<KernelBuildInfo::KernelBuildInfoBuilder>();
builder_tmp->SetKernelType(TBE_KERNEL);

1
mindspore/ccsrc/kernel/tbe/tbe_utils.cc
View File

@ -26,6 +26,7 @@
#include <iostream>
#include <fstream>
#include "runtime/kernel.h"
#include "kernel/oplib/oplib.h"
#include "utils/utils.h"
#include "session/anf_runtime_algorithm.h"

64
mindspore/ccsrc/pipeline/base.h Normal file
View File

@ -0,0 +1,64 @@
/**
* 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_PIPELINE_BASE_H_
#define MINDSPORE_CCSRC_PIPELINE_BASE_H_
#include <mutex>
#include <memory>
#include <string>
#include <sstream>
#include "ir/anf.h"
#include "pipeline/resource.h"
#include "utils/context/ms_context.h"
namespace mindspore {
namespace pipeline {
struct ExecutorInfo {
FuncGraphPtr func_graph;
ResourcePtr resource;
std::size_t arg_list_size;
};
using ExecutorInfoPtr = std::shared_ptr<ExecutorInfo>;
inline std::string GetPhasePrefix(const std::string& phase) {
auto pos = phase.find('.');
if (pos == std::string::npos) {
MS_LOG(EXCEPTION) << "phase has no . for prefix" << phase;
}
return phase.substr(0, pos);
}
inline std::string GetFilePathName(const std::string& file_name) {
std::ostringstream oss;
auto ms_context = MsContext::GetInstance();
if (ms_context == nullptr) {
MS_LOG(EXCEPTION) << "ms_context is nullptr";
}
auto save_graphs_path = ms_context->save_graphs_path();
if (save_graphs_path.empty()) {
save_graphs_path = ".";
}
oss << save_graphs_path << "/" << file_name;
return oss.str();
}
} // namespace pipeline
} // namespace mindspore
#endif // MINDSPORE_CCSRC_PIPELINE_BASE_H_

10
mindspore/ccsrc/pipeline/init.cc
View File

@ -73,7 +73,7 @@ PYBIND11_MODULE(_c_expression, m) {
"Get CNode Strategy Dictionary.")
.def("get_allreduce_fusion", &ExecutorPy::GetAllreduceFusion, py::arg("phase") = py::str("train"),
"Get Allreduce Fusion Dictionary.")
.def("build_data_graph", &ExecutorPy::BuildDFGraph, py::arg("build_params"), py::arg("phase") = py::str("train"),
.def("build_data_graph", &ExecutorPy::BuildGraph, py::arg("build_params"), py::arg("phase") = py::str("train"),
py::arg("broadcast_params") = py::dict(), "Build data graph.")
.def("has_compiled", &ExecutorPy::HasCompiled, py::arg("phase") = py::str(""), "get if cell compiled.")
.def("run_init_graph", &ExecutorPy::RunInitGraph, "Run init Graph.");
@ -86,19 +86,17 @@ PYBIND11_MODULE(_c_expression, m) {
(void)m.def("generate_key", &mindspore::pipeline::GenerateKey, "Generate the function graph key.");
(void)m.def("real_run_op", &mindspore::pynative::RunOp, "Run op pynatively.");
(void)m.def("initialize_distribute", &mindspore::pipeline::InitDistribute, "Initialize for Distribute.")
.def("init_ge", &mindspore::pipeline::InitGe, "Init GE");
(void)m.def("reset_op_id", &mindspore::pipeline::ResetOpId, "Reset Operator Id");
(void)m.def("init_hccl", &mindspore::pipeline::InitHccl, "Init Hccl");
(void)m.def("finalize_ge", &mindspore::pipeline::FinalizeGe, "Finalize Ge");
(void)m.def("finalize_hccl", &mindspore::pipeline::FinalizeHccl, "Finalize Hccl");
(void)m.def("set_ge_option", &mindspore::pipeline::SetGeOption, "API for set ge option.");
(void)m.def("verify_inputs_signature", &mindspore::pipeline::VerifyInputSignature, "Verify input signature.");
(void)m.def("init_exec_dataset", &mindspore::pipeline::InitExecDataset, py::arg("queue_name"), py::arg("size"),
py::arg("batch_size"), py::arg("types"), py::arg("shapes"), py::arg("input_indexs"),
py::arg("phase") = py::str("dataset"), "Init and exec dataset.");
(void)m.def("_set_dataset_mode_config", &mindspore::ConfigManager::SetDatasetModeConfig, "API for set dataset mode.");
(void)m.def("export_graph", &mindspore::pipeline::ExportDFGraph, "Export Graph.");
(void)m.def("init_ge", &mindspore::pipeline::InitGe, "Init GE");
(void)m.def("export_graph", &mindspore::pipeline::ExportGraph, "Export Graph.");
(void)py::class_<mindspore::MsContext, std::shared_ptr<mindspore::MsContext>>(m, "MSContext")
.def_static("get_instance", &mindspore::MsContext::GetInstance, "Get ms context instance.")

1
mindspore/ccsrc/pipeline/parse/python_adapter.cc
View File

@ -27,6 +27,7 @@ static std::shared_ptr<py::scoped_interpreter> scoped_ = nullptr;
// true: start process from python, false: start process from c++
static bool python_env_ = false;
static bool use_signature_in_resolve_ = true;
void ResetPythonScope() { scoped_ = nullptr; }
void set_use_signature_in_resolve(bool use_signature) noexcept { use_signature_in_resolve_ = use_signature; }
bool UseSignatureInResolve() { return use_signature_in_resolve_; }
void set_python_env_flag(bool python_env) noexcept { python_env_ = python_env; }

1
mindspore/ccsrc/pipeline/parse/python_adapter.h
View File

@ -55,6 +55,7 @@ void set_use_signature_in_resolve(bool use_signature) noexcept;
bool UseSignatureInResolve();
std::shared_ptr<py::scoped_interpreter> set_python_scoped();
void ResetPythonScope();
bool IsPythonEnv();
void SetPythonPath(const std::string& path);
void set_python_env_flag(bool python_env) noexcept;

698
mindspore/ccsrc/pipeline/pipeline.cc
View File

@ -27,11 +27,6 @@
#include "pipeline/pass.h"
#include "pipeline/parse/data_converter.h"
#include "optimizer/ad/dfunctor.h"
#include "ir/meta_tensor.h"
#include "transform/convert.h"
#include "transform/df_graph_manager.h"
#include "transform/graph_builder.h"
#include "transform/graph_runner.h"
#include "debug/anf_ir_dump.h"
#include "debug/anf_ir_utils.h"
#include "utils/config_manager.h"
@ -44,6 +39,12 @@
#include "device/kernel_runtime_manager.h"
#include "debug/trace.h"
#if (ENABLE_GE || ENABLE_D)
#include "pipeline/pipeline_ge.h"
#include "transform/convert.h"
#include "transform/df_graph_manager.h"
#endif
namespace mindspore {
// namespace to support intermediate representation definition
namespace pipeline {
@ -54,12 +55,6 @@ using mindspore::abstract::AbstractTensor;
using mindspore::abstract::AbstractTensorPtr;
using mindspore::abstract::AbstractTuple;
using mindspore::abstract::AbstractTuplePtr;
using mindspore::transform::DfGraphConvertor;
using mindspore::transform::DfGraphManager;
using mindspore::transform::GeTensorPtr;
using mindspore::transform::MeTensorPtr;
using mindspore::transform::Status;
using mindspore::transform::TransformUtil;
const char IR_TYPE_ANF[] = "anf_ir";
const char IR_TYPE_ONNX[] = "onnx_ir";
@ -85,65 +80,8 @@ std::string GetBaseNameForIR(int stage_idx, const std::string& action_name) {
oss << save_graphs_path << "/" << stage_idx << "_" << action_name;
return oss.str();
}
std::string GetFilePathName(const std::string& file_name) {
std::ostringstream oss;
auto ms_context = MsContext::GetInstance();
if (ms_context == nullptr) {
MS_LOG(EXCEPTION) << "ms_context is nullptr";
}
auto save_graphs_path = ms_context->save_graphs_path();
if (save_graphs_path.empty()) {
save_graphs_path = ".";
}
oss << save_graphs_path << "/" << file_name;
return oss.str();
}
} // namespace
// We will not execute graph when output is constant or just input itself.
static bool IsGraphOutputValueNodeOrParameter(const AnfNodePtr& output, const py::tuple& args,
const std::shared_ptr<py::object>& ret_val) {
if (output->isa<ValueNode>()) {
MS_LOG(INFO) << "Graph's output is a constant. No need to execute.";
ValuePtr value = GetValueNode(output);
*ret_val = ValuePtrToPyData(value);
return true;
}
// Adapter will transform values in __init__() and construct() to parameters, this could cause
// inputs (a.k.a args in current function) size less than parameters'.
if (output->isa<Parameter>()) {
MS_LOG(INFO) << "Graph's output is a parameter. If all params are inputs, no need to execute.";
if (args.empty()) {
MS_LOG(EXCEPTION) << "Inputs size is 0, let graph to be executed.";
}
// Find the right parameter as ret_val.
auto func_graph = output->func_graph();
MS_EXCEPTION_IF_NULL(func_graph);
auto params = func_graph->parameters();
if (params.empty()) {
MS_EXCEPTION(UnknownError) << "Graph's parameters size is 0";
}
if (args.size() != params.size()) {
MS_LOG(EXCEPTION) << "Input size " << args.size() << " not equal to params size " << params.size()
<< ", let graph to be executed.";
}
auto it = std::find(params.begin(), params.end(), output);
if (it == params.end()) {
MS_EXCEPTION(UnknownError) << "When graph output is Parameter, it should be found in graph parameters";
}
size_t index = it - params.cbegin();
if (index >= args.size()) {
MS_EXCEPTION(UnknownError) << "Index " << index << " equal or larger than args size " << args.size() << ".";
}
*ret_val = args[index];
return true;
}
return false;
}
py::tuple GenerateKey(const std::string& name, const std::unordered_map<std::string, py::object>& defaults) {
MS_LOG(DEBUG) << "GenerateKey args size:" << defaults.size();
abstract::AbstractBasePtrList args_spec;
@ -207,11 +145,7 @@ py::bool_ VerifyInputSignature(const py::list input_signature, const py::tuple i
return true;
}
ExecutorPy::ExecutorPy() {
// because Ge only support one Session exist at the same time ,so we delete the old one
DfGraphManager::GetInstance().DeleteGraphRunner();
DfGraphManager::GetInstance().DeleteGeSession();
}
ExecutorPy::ExecutorPy() {}
ResourcePtr ExecutorPy::GetResource(const std::string& phase) {
MS_LOG(DEBUG) << "phase size:" << info_.size();
@ -221,14 +155,6 @@ ResourcePtr ExecutorPy::GetResource(const std::string& phase) {
return info_[phase]->resource;
}
std::string GetPhasePrefix(const std::string& phase) {
auto pos = phase.find('.');
if (pos == std::string::npos) {
MS_LOG(EXCEPTION) << "phase has no . for prefix" << phase;
}
return phase.substr(0, pos);
}
FuncGraphPtr ExecutorPy::GetFuncGraph(const std::string& phase) {
if (info_.count(phase) == 0) {
MS_LOG(EXCEPTION) << "no phase in executor:" << GetPhasePrefix(phase);
@ -323,11 +249,15 @@ void ExecutorPy::DelNetRes(const std::string& id) {
}
}
MS_LOG(INFO) << "Delete flag:" << flag;
#ifdef ENABLE_GE
if (flag && info_.size() == 0) {
DfGraphManager::GetInstance().DeleteGraphRunner();
DfGraphManager::GetInstance().EraseAnfGraph();
DfGraphManager::GetInstance().DeleteGeSession();
// because Ge only support one Session exist at the same time ,so we delete the old one
transform::DfGraphManager::GetInstance().DeleteGraphRunner();
transform::DfGraphManager::GetInstance().EraseAnfGraph();
transform::DfGraphManager::GetInstance().DeleteGeSession();
}
#endif
}
}
@ -405,7 +335,8 @@ bool ExecutorPy::CompileInner(const py::object& obj, const py::tuple& args, cons
use_vm = ChangeExportGeirUseVmFlag(use_vm, phase_s);
if (use_vm) {
std::string backend = MsContext::GetInstance()->backend_policy();
if (use_vm && backend != "ge") {
// Create backend and session
resource->results()[kBackend] = compile::CreateBackend();
p_actions = VmPipeline();
@ -497,30 +428,6 @@ bool ExecutorPy::Compile(const py::object& obj, const py::tuple& args, const py:
return ret_value;
}
void SetGeOption(const std::map<std::string, std::string>& options) {
ConfigManager::GetInstance().set_ge_initialize_options(options);
}
bool InitDistribute(const std::map<std::string, std::string>& options) {
ConfigManager::GetInstance().set_parallel_strategy(ParallelStrategy::DISTRIBUTION);
MS_LOG(INFO) << "ME run in DISTRIBUTION strategy mode";
SetGeOption(options);
#ifdef ENABLE_GE
auto ge_options = ConfigManager::GetInstance().ge_initialize_options();
{
// Release GIL before calling into (potentially long-running) C++ code
py::gil_scoped_release release;
if (ge::GEInitialize(ge_options) != ge::GRAPH_SUCCESS) {
MS_LOG(ERROR) << "Initialize GE failed!";
return false;
}
}
#endif
MS_LOG(DEBUG) << "Initialize Ge success";
return true;
}
#ifdef ENABLE_LOAD_ANF_IR
// get MindSpore Intermediate Representation File
std::string GetMsIrFile(void) {
@ -704,9 +611,25 @@ py::object ExecutorPy::Run(const py::tuple& args, const py::object& phase) {
}
auto phase_s = py::cast<std::string>(phase);
std::string backend = MsContext::GetInstance()->backend_policy();
#ifdef ENABLE_GE
if (backend == "ge") {
return ExecDFGraph(args, phase_s);
return ExecDFGraph(info_, args, phase_s);
}
#else
MS_LOG(WARNING) << "In ut test " << size << phase_s;
if (backend == "ge") {
std::shared_ptr<py::object> ret_val = std::make_shared<py::object>();
if (info_.count(phase_s) != 0 && info_[phase_s]->func_graph != nullptr) {
if (IsGraphOutputValueNodeOrParameter(info_[phase_s]->func_graph->output(), args, ret_val)) {
return *ret_val;
}
}
if (args.size() > 0) {
return args[0];
}
return args;
}
#endif
std::size_t full_arg_size = ArgListSize(phase_s);
if (size > full_arg_size) {
MS_LOG(WARNING) << "The arg num : size = " << size << ". full_arg_size = " << full_arg_size;
@ -719,435 +642,25 @@ py::object ExecutorPy::Run(const py::tuple& args, const py::object& phase) {
MS_LOG(EXCEPTION) << "Can't find run graph func for " << phase_s;
}
MS_LOG(DEBUG) << "eval run";
MS_LOG(DEBUG) << "eval run" << backend;
BaseRef value = (*run)(arg_list);
MS_LOG(DEBUG) << "run end";
return BaseRefToPyData(value);
}
py::object ExtractGeneralCnodeRet(const AbstractBasePtr& cnode_data, const py::tuple& data, size_t* count) {
MS_EXCEPTION_IF_NULL(cnode_data);
if (*count >= data.size()) {
MS_LOG(EXCEPTION) << "The number of elements in the outputs : " << data.size()
<< " less than the number of elements required. ";
}
if (cnode_data->isa<AbstractTensor>()) {
BaseShapePtr shape = cnode_data->BuildShape();
auto shape_act = shape->cast<abstract::ShapePtr>()->shape();
Tensor tensor_exp = py::cast<Tensor>(data[*count]);
if (shape_act != tensor_exp.shape()) {
MS_LOG(EXCEPTION) << "The shape of the tensor returned from GE is not the same as "
"the shape of the tensor derived from ME.";
}
return data[(*count)++];
}
if (!cnode_data->isa<AbstractTuple>()) {
MS_LOG(EXCEPTION) << "The output of operator in the final anf graph could "
<< "only be a tensor or a tuple of tensor, but got " << cnode_data->BuildValue()->ToString()
<< ".";
}
auto data_tp = cnode_data->cast<AbstractTuplePtr>();
auto elements = data_tp->elements();
size_t size = data_tp->size();
py::tuple tp = py::tuple(size);
for (size_t i = 0; i < size; i++) {
tp[i] = ExtractGeneralCnodeRet(elements[i], data, count);
}
return std::move(tp);
}
py::object StructureOutput(const AnfNodePtr& output_node, const py::tuple& data, size_t* count) {
MS_EXCEPTION_IF_NULL(output_node);
if (output_node->isa<ValueNode>()) {
return ValuePtrToPyData(GetValueNode(output_node));
}
if (*count >= data.size()) {
MS_LOG(EXCEPTION) << "The number of elements in the outputs : " << data.size()
<< " less than the number of elements required. ";
}
if (output_node->isa<Parameter>()) {
return data[(*count)++];
}
auto output_c = output_node->cast<CNodePtr>();
if (output_c == nullptr) {
MS_LOG(EXCEPTION) << "The final anf graph could only have constant, parameter, and operator, but got "
<< output_node->ToString();
}
if (output_c->IsApply(prim::kPrimMakeTuple)) {
auto input_list = output_c->inputs();
size_t size = input_list.size();
py::tuple tp = py::tuple(size - 1);
for (size_t i = 1; i < size; i++) {
tp[i - 1] = StructureOutput(input_list[i], data, count);
}
return std::move(tp);
}
if (output_c->IsApply(prim::kPrimDepend)) {
return StructureOutput(output_c->input(1), data, count);
}
return ExtractGeneralCnodeRet(output_c->abstract(), data, count);
}
std::shared_ptr<py::object> DoExecGraph(const FuncGraphPtr& graph, const std::vector<MeTensorPtr>& inputs,
const std::string& phase) {
std::vector<GeTensorPtr> ge_tensors = TransformUtil::ConvertInputTensors(inputs, kOpFormat_NCHW);
if (ge_tensors.size() != inputs.size()) {
MS_LOG(ERROR) << "args convert to ge tensor error";
return nullptr;
}
std::vector<GeTensorPtr> ge_outputs;
transform::RunOptions run_options;
run_options.name = phase;
auto graph_runner = DfGraphManager::GetInstance().GetGraphRunner();
if (graph_runner == nullptr) {
MS_LOG(ERROR) << "Can not found GraphRunner";
return nullptr;
}
{
// Release GIL before calling into (potentially long-running) C++ code
py::gil_scoped_release release;
MS_LOG(DEBUG) << "Run graph begin, inputs size is: " << inputs.size();
Status ret = graph_runner->RunGraph(run_options, ge_tensors, &ge_outputs);
MS_LOG(DEBUG) << "Run graph finish, outputs size is: " << ge_outputs.size();
if (ret != Status::SUCCESS) {
MS_LOG(ERROR) << "Exec graph failed";
return nullptr;
}
}
std::vector<MeTensorPtr> me_outputs = TransformUtil::ConvertGeTensors(ge_outputs);
if (me_outputs.size() != ge_outputs.size()) {
MS_LOG(ERROR) << "Convert output Ge tensor to Me tensor failed";
}
py::tuple outputs(me_outputs.size());
for (std::size_t i = 0; i < outputs.size(); i++) {
outputs[i] = *me_outputs[i];
}
std::shared_ptr<py::object> ret = nullptr;
#ifdef ENABLE_GE
AnfNodePtr output_node = graph->get_return()->input(1);
MS_EXCEPTION_IF_NULL(output_node);
size_t count = 0;
py::object oj = StructureOutput(output_node, outputs, &count);
ret = std::make_shared<py::object>(oj);
FuncGraphPtr ExecutorPy::BuildGraph(const py::dict& init_params, const std::string& phase,
const py::object& broadcast_params) {
#if (ENABLE_GE || ENABLE_D)
return BuildDFGraph(info_, init_params, phase, broadcast_params);
#else
if (outputs.size() == 1) {
ret = std::make_shared<py::object>(outputs[0]);
} else {
ret = std::make_shared<py::object>(outputs);
}
return nullptr;
#endif
return ret;
}
void DoExecNonInputGraph(const std::string& phase) {
std::vector<GeTensorPtr> ge_tensors;
std::vector<GeTensorPtr> ge_outputs;
transform::RunOptions run_options;
run_options.name = phase;
auto graph_runner = DfGraphManager::GetInstance().GetGraphRunner();
if (graph_runner == nullptr) {
MS_LOG(ERROR) << "Can not found GraphRunner";
return;
}
{
// Release GIL before calling into (potentially long-running) C++ code
py::gil_scoped_release release;
Status ret = graph_runner->RunGraph(run_options, ge_tensors, &ge_outputs);
if (ret != Status::SUCCESS) {
MS_LOG(ERROR) << "Exec graph:" << run_options.name << " failed";
return;
}
}
}
void ExecutorPy::ProcessGeArg(const py::tuple& args, const std::string& phase, std::vector<tensor::TensorPtr>* inputs) {
// check the arg and use the ExecutorPy args
std::size_t size = args.size();
if (size != ArgListSize(phase)) {
MS_LOG(EXCEPTION) << "The real arg num : size = " << size << ". graph_arg_size = " << ArgListSize(phase);
}
// process the first args of tensor
// only in Dataset Feed Mode, fp_bp graph need input tensors
if (ConfigManager::GetInstance().dataset_mode() == DS_FEED_MODE) {
for (std::size_t i = 0; i < size; i++) {
ValuePtr converted = nullptr;
bool succ = parse::ConvertData(args[i], &converted);
if (!succ) {
MS_LOG(EXCEPTION) << "args convert error";
}
if (converted->isa<tensor::Tensor>()) {
(*inputs).push_back(converted->cast<tensor::TensorPtr>());
} else {
MS_LOG(EXCEPTION) << "args, " << converted->ToString() << " is not tensor";
}
}
}
}
py::object ExecutorPy::ExecDFGraph(const py::tuple& args, const std::string& phase) {
std::string phase_prefix = GetPhasePrefix(phase);
if (phase_prefix == "save") {
DoExecNonInputGraph(phase);
ConfigManager::GetInstance().ResetConfig();
return py::none();
}
if (info_.count(phase) == 0) {
MS_LOG(EXCEPTION) << "has no phase:" << phase;
}
#if (!defined ENABLE_GE) || (defined ENABLE_INFER)
// Now don't use the graph because the exec ge function don't take effect
MS_EXCEPTION_IF_NULL(info_[phase]->func_graph);
if (ENABLE_TRAIN != info_[phase]->func_graph->flags()["training"]) {
MS_LOG(ERROR) << "Graph training mode mismatch mode of libraries";
ConfigManager::GetInstance().ResetConfig();
return py::none();
}
#endif
std::shared_ptr<py::object> ret_val = std::make_shared<py::object>();
if (IsGraphOutputValueNodeOrParameter(info_[phase]->func_graph->output(), args, ret_val)) {
ConfigManager::GetInstance().ResetConfig();
return *ret_val;
}
std::vector<tensor::TensorPtr> inputs;
ProcessGeArg(args, phase, &inputs);
std::shared_ptr<py::object> ret = DoExecGraph(GetFuncGraph(phase), inputs, phase);
ConfigManager::GetInstance().ResetConfig();
if (ret != nullptr) {
return *ret;
} else {
MS_LOG(EXCEPTION) << "exec graph failed";
}
}
void ExecutorPy::RunInitGraph(const py::dict& init_params, const std::string& phase) {
MS_LOG(DEBUG) << "ExecInitGraph start.";
TensorOrderMap inputs_with_name{};
ConvertObjectToTensors(init_params, &inputs_with_name);
std::vector<tensor::TensorPtr> inputs;
(void)std::transform(inputs_with_name.begin(), inputs_with_name.end(), std::back_inserter(inputs),
[](const std::pair<std::string, tensor::TensorPtr>& item) { return item.second; });
std::vector<GeTensorPtr> ge_tensors = TransformUtil::ConvertInputTensors(inputs, kOpFormat_NCHW);
if (ge_tensors.size() != inputs.size()) {
MS_LOG(ERROR) << "Args convert to ge tensor error.";
return;
}
MS_LOG(DEBUG) << "Run graph begin, inputs size is: " << inputs.size() << ".";
std::vector<GeTensorPtr> ge_outputs;
transform::RunOptions run_options;
run_options.name = phase;
if (DfGraphManager::GetInstance().GetGraphByName(phase) == nullptr) {
MS_LOG(WARNING) << "Can not find " << phase << " sub graph, don't need data init subgraph in INFER mode.";
return;
}
auto graph_runner = DfGraphManager::GetInstance().GetGraphRunner();
if (graph_runner == nullptr) {
MS_LOG(EXCEPTION) << "Can not found GraphRunner.";
}
{
// Release GIL before calling into (potentially long-running) C++ code
py::gil_scoped_release release;
Status ret = graph_runner->RunGraph(run_options, ge_tensors, &ge_outputs);
if (ret != Status::SUCCESS) {
MS_LOG(EXCEPTION) << "Exec " << phase << " graph failed.";
}
MS_LOG(INFO) << "Exec " << phase << " graph success.";
if ((ConfigManager::GetInstance().parallel_strategy() == ParallelStrategy::DISTRIBUTION) &&
(DfGraphManager::GetInstance().GetGraphByName(BROADCAST_GRAPH_NAME) != nullptr)) {
run_options.name = BROADCAST_GRAPH_NAME;
ret = graph_runner->RunGraph(run_options, ge_tensors, &ge_outputs);
if (ret != Status::SUCCESS) {
MS_LOG(EXCEPTION) << "Exec BROADCAST_GRAPH_NAME failed.";
}
MS_LOG(INFO) << "Exec broadcast graph success.";
}
}
}
Status CreateSessionAndGraphRunner(bool is_training = true) {
std::shared_ptr<ge::Session> sess = DfGraphManager::GetInstance().GetGeSession();
if (sess == nullptr) {
transform::SessionOptions options;
if (is_training) {
options["ge.trainFlag"] = "1";
options["ge.streamNum"] = "100";
options["ge.enabledLocalFmkop"] = "1";
options["ge.hcomParallel"] = "1";
} else {
options["ge.trainFlag"] = "0";
}
options["ge.enablePrintOpPass"] = "0";
sess = transform::GraphRunner::NewSession(options);
if (sess == nullptr) {
MS_LOG(ERROR) << "Init data graph failed, because of create Ge session failed";
return Status::FAILED;
} else {
DfGraphManager::GetInstance().SetGeSession(sess);
}
}
transform::GraphRunnerOptions options;
options.sess_ptr = sess;
auto graph_runner = std::make_shared<transform::GraphRunner>(options);
if (graph_runner == nullptr) {
MS_LOG(ERROR) << "Create new graph runner failed";
return Status::FAILED;
} else {
DfGraphManager::GetInstance().SetGraphRunner(graph_runner);
}
return Status::SUCCESS;
}
void ExecutorPy::ConvertObjectToTensors(const py::dict& dict, TensorOrderMap* const tensors) {
for (auto item : dict) {
if ((!py::isinstance<py::str>(item.first))) {
MS_LOG(WARNING) << "Type of key of py_dict is not string, ignore it.";
continue;
}
std::shared_ptr<Tensor> tensor;
std::string name = py::cast<std::string>(item.first);
if (py::isinstance<py::float_>(item.second.attr("default_input"))) {
// convert float to tensor with shape([1])
tensor = std::make_shared<Tensor>(kNumberTypeFloat32, std::vector<int>({1}));
*(static_cast<float*>(tensor->data_c(true))) = py::cast<float>(item.second.attr("default_input"));
} else if (py::isinstance<py::int_>(item.second.attr("default_input"))) {
// convert int to tensor with shape([1])
tensor = std::make_shared<Tensor>(kNumberTypeInt32, std::vector<int>({1}));
*(static_cast<float*>(tensor->data_c(true))) = py::cast<float>(item.second.attr("default_input"));
} else if (py::hasattr(item.second.attr("default_input"), PYTHON_TENSOR_FLAG)) {
// cast tensor
tensor = py::cast<std::shared_ptr<Tensor>>(item.second.attr("default_input"));
}
if (tensor == nullptr) {
MS_LOG(EXCEPTION) << "Get default value for " << name << " failed";
}
(void)tensors->emplace(name, tensor);
}
}
bool ExecutorPy::AddDFGraph(const py::dict& init_params, const std::string& phase, const py::object& broadcast_params) {
FuncGraphPtr anf_graph = info_[phase]->func_graph;
DfGraphConvertor convertor(anf_graph);
size_t pos = phase.find('.');
std::string net_id = ((pos == std::string::npos || pos == phase.size() - 1) ? phase : phase.substr(pos + 1));
std::string phase_prefix = phase.substr(0, pos);
if (phase_prefix == "export") {
MS_LOG(INFO) << "Set DfGraphConvertor training : false";
convertor.set_training(false);
}
TensorOrderMap init_tensors{};
ConvertObjectToTensors(init_params, &init_tensors);
(void)convertor.ConvertAllNode().InitParam(init_tensors).BuildGraph();
if (broadcast_params != py::none()) {
if (!py::isinstance<py::dict>(broadcast_params)) {
MS_LOG(ERROR) << "Invalid broadcast params, it must be py::dict type";
return false;
}
py::dict broadcast = broadcast_params.cast<py::dict>();
if (broadcast.empty()) {
(void)convertor.GenerateBroadcastGraph(init_tensors);
} else {
TensorOrderMap broadcast_tensors{};
ConvertObjectToTensors(broadcast, &broadcast_tensors);
(void)convertor.GenerateBroadcastGraph(broadcast_tensors);
}
MS_LOG(INFO) << "Generate broadcast graph with params and broadcast_empty is " << broadcast.empty();
}
(void)convertor.GenerateCheckpointGraph();
if (convertor.ErrCode() != 0) {
DfGraphManager::GetInstance().ClearGraph();
MS_LOG(ERROR) << "convert df graph failed, err:" << convertor.ErrCode();
return false;
}
if (MsContext::GetInstance()->save_graphs_flag()) {
convertor.DrawComputeGraph(GetFilePathName("ge_graph.dot")); // for debug
convertor.DrawInitGraph(GetFilePathName("init_graph.dot")); // for debug
convertor.DrawSaveCheckpointGraph(GetFilePathName("save_checkpoint_graph.dot")); // for debug
}
std::string init_graph = "init_subgraph." + net_id;
std::string checkpoint_name = "save." + net_id;
if (phase.find("train") != std::string::npos) {
(void)DfGraphManager::GetInstance().AddGraph(phase, convertor.GetComputeGraph(), {{"ge.exec.variable_acc", "1"}});
} else {
(void)DfGraphManager::GetInstance().AddGraph(phase, convertor.GetComputeGraph());
}
(void)DfGraphManager::GetInstance().AddGraph(init_graph, convertor.GetInitGraph());
(void)DfGraphManager::GetInstance().AddGraph(BROADCAST_GRAPH_NAME, convertor.GetBroadcastGraph());
Status ret = DfGraphManager::GetInstance().AddGraph(checkpoint_name, convertor.GetSaveCheckpointGraph());
if (ret == Status::SUCCESS) {
DfGraphManager::GetInstance().SetAnfGraph(checkpoint_name, anf_graph);
}
return true;
}
FuncGraphPtr ExecutorPy::BuildDFGraph(const py::dict& init_params, const std::string& phase,
const py::object& broadcast_params) {
if (info_.count(phase) == 0) {
MS_LOG(EXCEPTION) << "no phase in executor:" << GetPhasePrefix(phase);
}
FuncGraphPtr anf_graph = info_[phase]->func_graph;
if (MsContext::GetInstance()->save_graphs_flag()) {
draw::Draw(GetFilePathName("anf_graph.dot"), anf_graph); // for debug
DumpIR(GetFilePathName("anf_graph.ir"), anf_graph, true);
}
if (!AddDFGraph(init_params, phase, broadcast_params)) {
MS_LOG(ERROR) << "GenConvertor failed";
return nullptr;
}
#if ENABLE_TRAIN
(void)setenv("GE_TRAIN", "1", 1);
#else
(void)setenv("GE_TRAIN", "0", 1);
#if ENABLE_GE
RunGEInitGraph(init_params, phase);
#endif
if (CreateSessionAndGraphRunner(static_cast<bool>(ENABLE_TRAIN)) != Status::SUCCESS) {
MS_LOG(ERROR) << "Create GE Session or GraphRunner failed.";
return nullptr;
}
return anf_graph;
}
bool InitExecDataset(const std::string& queue_name, int64_t iter_num, int64_t batch_size,
@ -1156,47 +669,16 @@ bool InitExecDataset(const std::string& queue_name, int64_t iter_num, int64_t ba
std::string name = MsContext::GetInstance()->backend_policy();
if (name == kMsConvert || name == kMsVm) {
return InitExecDatasetVm(queue_name, iter_num, batch_size, types, shapes, input_indexes);
} else {
return InitExecDatasetGe(queue_name, iter_num, batch_size, types, shapes, input_indexes, phase);
}
}
bool InitExecDatasetGe(const std::string& queue_name, int64_t size, int64_t batch_size,
const std::vector<TypePtr>& types, const std::vector<std::vector<int64_t>>& shapes,
const std::vector<int64_t>& input_indexes, const std::string& phase) {
// Convert types to GE types and TF types
std::vector<int64_t> ge_types;
(void)std::transform(types.begin(), types.end(), std::back_inserter(ge_types), [](const TypePtr& i) -> int64_t {
return transform::TransformUtil::ConvertDataType(i->type_id());
});
ConfigManager::GetInstance().set_dataset_mode(DatasetMode::DS_GRAPH_MODE);
ConfigManager::GetInstance().set_iter_num(size);
ConfigManager::GetInstance().set_dataset_phase(phase);
DatasetGraphParam param(queue_name, size, batch_size, ge_types, shapes, input_indexes);
ConfigManager::GetInstance().set_dataset_param(param);
if (transform::BuildDatasetGraph(param, phase) != transform::SUCCESS) {
MS_LOG(ERROR) << "Build dateset graph failed.";
return false;
}
#if ENABLE_TRAIN
(void)setenv("GE_TRAIN", "1", 1);
#if ENABLE_GE
return InitExecDatasetGe(queue_name, iter_num, batch_size, types, shapes, input_indexes, phase);
#else
(void)setenv("GE_TRAIN", "0", 1);
#endif
if (CreateSessionAndGraphRunner(static_cast<bool>(ENABLE_TRAIN)) != Status::SUCCESS) {
MS_LOG(ERROR) << "Create GE Session or GraphRunner failed.";
return false;
std::string backend = MsContext::GetInstance()->backend_policy();
if (backend == "ge") {
return true;
}
MS_LOG(INFO) << "DoExecNonInputGraph:" << phase;
DoExecNonInputGraph(phase);
return true;
#endif
return false;
}
bool InitExecDatasetVm(const std::string& queue_name, int64_t size, int64_t batch_size,
@ -1259,25 +741,6 @@ bool InitExecDatasetVm(const std::string& queue_name, int64_t size, int64_t batc
return true;
}
void InitGe() {
// set python env flag
mindspore::parse::python_adapter::set_python_env_flag(true);
// open tsd before ge initialize
auto ms_context = MsContext::GetInstance();
MS_EXCEPTION_IF_NULL(ms_context);
if (!ms_context->OpenTsd()) {
MS_LOG(EXCEPTION) << "open tsd failed";
}
(void)ms_context->InitGe();
}
void FinalizeGe() {
auto context_ptr = MsContext::GetInstance();
MS_EXCEPTION_IF_NULL(context_ptr);
(void)context_ptr->FinalizeGe();
(void)context_ptr->CloseTsd();
}
void ResetOpId() { mindspore::id_generator::reset_id(); }
void InitHccl() {
@ -1309,24 +772,57 @@ void FinalizeHccl() {
device::KernelRuntimeManager::Instance().ClearRuntimeResource();
#endif
}
void ExportDFGraph(const std::string& file_name, const std::string&, const std::string& phase) {
MS_LOG(DEBUG) << "ExportGraph Begin";
transform::DfGraphWrapperPtr wrap_ptr = DfGraphManager::GetInstance().GetGraphByName(phase);
if (wrap_ptr == nullptr) {
MS_LOG(ERROR) << "Get graph form DfGraphManager failed!";
return;
}
transform::DfGraphPtr ge_graph = wrap_ptr->graph_ptr_;
if (nullptr == ge_graph) {
MS_LOG(ERROR) << "The export graph is null";
return;
}
(void)ge_graph->SaveToFile(file_name);
MS_LOG(DEBUG) << "ExportGraph End";
void ExportGraph(const std::string& file_name, const std::string&, const std::string& phase) {
#if (ENABLE_GE || ENABLE_D)
ExportDFGraph(file_name, phase);
#endif
MS_LOG(WARNING) << "In ut test no export_graph";
}
void ReleaseGeTsd() {
auto context_ptr = MsContext::GetInstance();
if (context_ptr != nullptr) {
(void)context_ptr->FinalizeGe(true);
(void)context_ptr->CloseTsd(true);
}
}
void InitGe() {
// set python env flag
mindspore::parse::python_adapter::set_python_env_flag(true);
// open tsd before ge initialize
auto ms_context = MsContext::GetInstance();
MS_EXCEPTION_IF_NULL(ms_context);
if (!ms_context->OpenTsd()) {
MS_LOG(EXCEPTION) << "open tsd failed";
}
(void)ms_context->InitGe();
}
void FinalizeGe() {
auto context_ptr = MsContext::GetInstance();
MS_EXCEPTION_IF_NULL(context_ptr);
(void)context_ptr->FinalizeGe();
(void)context_ptr->CloseTsd();
}
void ClearResAtexit() {
MS_LOG(DEBUG) << "Pipeline clear all resource";
device::KernelRuntimeManager::Instance().ClearRuntimeResource();
ad::g_k_prims.clear();
abstract::ClearPrimEvaluatorMap();
compile::ClearConvertCache();
pipeline::GetMethodMap().clear();
pipeline::ExecutorPy::ClearRes();
#ifdef ENABLE_GE
transform::DfGraphManager::GetInstance().ClearGraph();
transform::DfGraphConvertor::get_adpt_map().clear();
#endif
ReleaseGeTsd();
parse::python_adapter::ResetPythonScope();
}
} // namespace pipeline
} // namespace mindspore

35
mindspore/ccsrc/pipeline/pipeline.h
View File

@ -30,6 +30,7 @@
#include "pipeline/action.h"
#include "vm/segment_runner.h"
#include "vm/transform.h"
#include "pipeline/base.h"
namespace mindspore {
extern const char kMsConvert[];
@ -55,14 +56,6 @@ class Pipeline {
std::vector<ActionItem> actions_;
};
struct ExecutorInfo {
FuncGraphPtr func_graph;
ResourcePtr resource;
std::size_t arg_list_size;
};
using ExecutorInfoPtr = std::shared_ptr<ExecutorInfo>;
// A function pipeline.
class ExecutorPy : public std::enable_shared_from_this<ExecutorPy> {
public:
@ -80,11 +73,7 @@ class ExecutorPy : public std::enable_shared_from_this<ExecutorPy> {
bool CompileInner(const py::object& obj, const py::tuple& args, const py::object& phase, bool use_vm);
bool Compile(const py::object& obj, const py::tuple& args, const py::object& phase, bool use_vm);
// for graph mode
py::object ExecDFGraph(const py::tuple& args, const std::string& phase = "train");
void ProcessVmArg(const py::tuple& args, const std::string& phase, VectorRef* arg_list);
void ProcessGeArg(const py::tuple& args, const std::string& phase, std::vector<tensor::TensorPtr>* inputs);
// for pynative mode when use_vm is on
py::object Run(const py::tuple& args, const py::object& phase);
@ -95,9 +84,8 @@ class ExecutorPy : public std::enable_shared_from_this<ExecutorPy> {
compile::VmEvalFuncPtr GetVmEvalFunc(const std::string& phase);
bool HasCompiled(const std::string& phase) const;
bool AddDFGraph(const py::dict& init_params, const std::string& phase, const py::object& broadcast_params);
FuncGraphPtr BuildDFGraph(const py::dict& init_params, const std::string& phase,
const py::object& broadcast_params = {});
FuncGraphPtr BuildGraph(const py::dict& init_params, const std::string& phase,
const py::object& broadcast_params = {});
void RunInitGraph(const py::dict& init_params, const std::string& phase);
py::dict GetParameterLayout(const std::string& phase);
py::dict GetCNodeStrategy(const std::string& phase);
@ -122,32 +110,29 @@ using ExecutorPyPtr = std::shared_ptr<ExecutorPy>;
py::tuple GenerateKey(const std::string& name, const std::unordered_map<std::string, py::object>& defaults);
py::bool_ VerifyInputSignature(const py::list input_signature, const py::tuple inputs);
void SetGeOption(const std::map<std::string, std::string>& options);
bool InitDistribute(const std::map<std::string, std::string>& options);
void ResetOpId();
void InitGe();
void FinalizeGe();
void InitHccl();
void FinalizeHccl();
void InitGe();
void FinalizeGe();
void ClearResAtexit();
void ReleaseGeTsd();
void ExportGraph(const std::string& file_name, const std::string&, const std::string& phase);
// init and exec dataset sub graph
bool InitExecDataset(const std::string& queue_name, int64_t iter_num, int64_t batch_size,
const std::vector<TypePtr>& types, const std::vector<std::vector<int64_t>>& shapes,
const std::vector<int64_t>& input_indexes, const std::string& phase);
// init and exec dataset sub graph for GE backend
bool InitExecDatasetGe(const std::string& queue_name, int64_t size, int64_t batch_size,
const std::vector<TypePtr>& types, const std::vector<std::vector<int64_t>>& shapes,
const std::vector<int64_t>& input_indexes, const std::string& phase);
// Build and run dataset subgraph for ms backend
bool InitExecDatasetVm(const std::string& queue_name, int64_t size, int64_t batch_size,
const std::vector<TypePtr>& types, const std::vector<std::vector<int64_t>>& shapes,
const std::vector<int64_t>& input_indexes);
void ExportDFGraph(const std::string& file_name, const std::string&, const std::string& phase);
} // namespace pipeline
} // namespace mindspore

545
mindspore/ccsrc/pipeline/pipeline_ge.cc Normal file
View File

@ -0,0 +1,545 @@
/**
* 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.
*/
#include "pipeline/pipeline_ge.h"
#include <sstream>
#include <map>
#include <unordered_map>
#include <cstdlib>
#include <algorithm>
#include "debug/anf_ir_dump.h"
#include "ir/meta_tensor.h"
#include "transform/convert.h"
#include "transform/df_graph_manager.h"
#include "transform/graph_builder.h"
#include "transform/graph_runner.h"
#include "debug/draw.h"
#include "pipeline/static_analysis/abstract_value.h"
namespace mindspore {
namespace pipeline {
using Tensor = mindspore::tensor::Tensor;
using MetaTensor = mindspore::tensor::MetaTensor;
using TensorOrderMap = std::map<std::string, std::shared_ptr<Tensor>>;
using mindspore::abstract::AbstractTensor;
using mindspore::abstract::AbstractTuple;
using mindspore::abstract::AbstractTuplePtr;
using mindspore::transform::DfGraphConvertor;
using mindspore::transform::DfGraphManager;
using mindspore::transform::GeTensorPtr;
using mindspore::transform::MeTensorPtr;
using mindspore::transform::Status;
using mindspore::transform::TransformUtil;
void DoExecNonInputGraph(const std::string& phase) {
std::vector<GeTensorPtr> ge_tensors;
std::vector<GeTensorPtr> ge_outputs;
transform::RunOptions run_options;
run_options.name = phase;
auto graph_runner = DfGraphManager::GetInstance().GetGraphRunner();
if (graph_runner == nullptr) {
MS_LOG(ERROR) << "Can not found GraphRunner";
return;
}
{
// Release GIL before calling into (potentially long-running) C++ code
py::gil_scoped_release release;
Status ret = graph_runner->RunGraph(run_options, ge_tensors, &ge_outputs);
if (ret != Status::SUCCESS) {
MS_LOG(ERROR) << "Exec graph:" << run_options.name << " failed";
return;
}
}
}
void SetGeOption(const std::map<std::string, std::string>& options) {
ConfigManager::GetInstance().set_ge_initialize_options(options);
}
Status CreateSessionAndGraphRunner(bool is_training = true) {
std::shared_ptr<ge::Session> sess = DfGraphManager::GetInstance().GetGeSession();
if (sess == nullptr) {
transform::SessionOptions options;
if (is_training) {
options["ge.trainFlag"] = "1";
options["ge.streamNum"] = "100";
options["ge.enabledLocalFmkop"] = "1";
options["ge.hcomParallel"] = "1";
} else {
options["ge.trainFlag"] = "0";
}
options["ge.enablePrintOpPass"] = "0";
sess = transform::GraphRunner::NewSession(options);
if (sess == nullptr) {
MS_LOG(ERROR) << "Init data graph failed, because of create Ge session failed";
return Status::FAILED;
} else {
DfGraphManager::GetInstance().SetGeSession(sess);
}
}
transform::GraphRunnerOptions options;
options.sess_ptr = sess;
auto graph_runner = std::make_shared<transform::GraphRunner>(options);
if (graph_runner == nullptr) {
MS_LOG(ERROR) << "Create new graph runner failed";
return Status::FAILED;
} else {
DfGraphManager::GetInstance().SetGraphRunner(graph_runner);
}
return Status::SUCCESS;
}
bool InitExecDatasetGe(const std::string& queue_name, int64_t size, int64_t batch_size,
const std::vector<TypePtr>& types, const std::vector<std::vector<int64_t>>& shapes,
const std::vector<int64_t>& input_indexes, const std::string& phase) {
std::vector<int64_t> ge_types;
(void)std::transform(types.begin(), types.end(), std::back_inserter(ge_types), [](const TypePtr& i) -> int64_t {
return transform::TransformUtil::ConvertDataType(i->type_id());
});
ConfigManager::GetInstance().set_dataset_mode(DatasetMode::DS_GRAPH_MODE);
ConfigManager::GetInstance().set_iter_num(size);
ConfigManager::GetInstance().set_dataset_phase(phase);
DatasetGraphParam param(queue_name, size, batch_size, ge_types, shapes, input_indexes);
ConfigManager::GetInstance().set_dataset_param(param);
if (transform::BuildDatasetGraph(param, phase) != transform::SUCCESS) {
MS_LOG(ERROR) << "Build dateset graph failed.";
return false;
}
#if ENABLE_TRAIN
(void)setenv("GE_TRAIN", "1", 1);
#else
(void)setenv("GE_TRAIN", "0", 1);
#endif
if (CreateSessionAndGraphRunner(static_cast<bool>(ENABLE_TRAIN)) != Status::SUCCESS) {
MS_LOG(ERROR) << "Create GE Session or GraphRunner failed.";
return false;
}
MS_LOG(INFO) << "DoExecNonInputGraph:" << phase;
DoExecNonInputGraph(phase);
return true;
}
void ConvertObjectToTensors(const py::dict& dict, TensorOrderMap* const tensors) {
for (auto item : dict) {
if ((!py::isinstance<py::str>(item.first))) {
MS_LOG(WARNING) << "Type of key of py_dict is not string, ignore it.";
continue;
}
std::shared_ptr<Tensor> tensor;
std::string name = py::cast<std::string>(item.first);
if (py::isinstance<py::float_>(item.second.attr("default_input"))) {
// convert float to tensor with shape([1])
tensor = std::make_shared<Tensor>(kNumberTypeFloat32, std::vector<int>({1}));
*(static_cast<float*>(tensor->data_c(true))) = py::cast<float>(item.second.attr("default_input"));
} else if (py::isinstance<py::int_>(item.second.attr("default_input"))) {
// convert int to tensor with shape([1])
tensor = std::make_shared<Tensor>(kNumberTypeInt32, std::vector<int>({1}));
*(static_cast<float*>(tensor->data_c(true))) = py::cast<float>(item.second.attr("default_input"));
} else if (py::hasattr(item.second.attr("default_input"), PYTHON_TENSOR_FLAG)) {
// cast tensor
tensor = py::cast<std::shared_ptr<Tensor>>(item.second.attr("default_input"));
}
if (tensor == nullptr) {
MS_LOG(EXCEPTION) << "Get default value for " << name << " failed";
}
(void)tensors->emplace(name, tensor);
}
}
bool AddDFGraph(const std::map<std::string, ExecutorInfoPtr>& info, const py::dict& init_params,
const std::string& phase, const py::object& broadcast_params) {
FuncGraphPtr anf_graph = info.at(phase)->func_graph;
DfGraphConvertor convertor(anf_graph);
size_t pos = phase.find('.');
std::string net_id = ((pos == std::string::npos || pos == phase.size() - 1) ? phase : phase.substr(pos + 1));
std::string phase_prefix = phase.substr(0, pos);
if (phase_prefix == "export") {
MS_LOG(INFO) << "Set DfGraphConvertor training : false";
convertor.set_training(false);
}
TensorOrderMap init_tensors{};
ConvertObjectToTensors(init_params, &init_tensors);
(void)convertor.ConvertAllNode().InitParam(init_tensors).BuildGraph();
if (broadcast_params != py::none()) {
if (!py::isinstance<py::dict>(broadcast_params)) {
MS_LOG(ERROR) << "Invalid broadcast params, it must be py::dict type";
return false;
}
py::dict broadcast = broadcast_params.cast<py::dict>();
if (broadcast.empty()) {
(void)convertor.GenerateBroadcastGraph(init_tensors);
} else {
TensorOrderMap broadcast_tensors{};
ConvertObjectToTensors(broadcast, &broadcast_tensors);
(void)convertor.GenerateBroadcastGraph(broadcast_tensors);
}
MS_LOG(INFO) << "Generate broadcast graph with params and broadcast_empty is " << broadcast.empty();
}
(void)convertor.GenerateCheckpointGraph();
if (convertor.ErrCode() != 0) {
DfGraphManager::GetInstance().ClearGraph();
MS_LOG(ERROR) << "convert df graph failed, err:" << convertor.ErrCode();
return false;
}
if (MsContext::GetInstance()->save_graphs_flag()) {
convertor.DrawComputeGraph(GetFilePathName("ge_graph.dot")); // for debug
convertor.DrawInitGraph(GetFilePathName("init_graph.dot")); // for debug
convertor.DrawSaveCheckpointGraph(GetFilePathName("save_checkpoint_graph.dot")); // for debug
}
std::string init_graph = "init_subgraph." + net_id;
std::string checkpoint_name = "save." + net_id;
if (phase.find("train") != std::string::npos) {
(void)DfGraphManager::GetInstance().AddGraph(phase, convertor.GetComputeGraph(), {{"ge.exec.variable_acc", "1"}});
} else {
(void)DfGraphManager::GetInstance().AddGraph(phase, convertor.GetComputeGraph());
}
(void)DfGraphManager::GetInstance().AddGraph(init_graph, convertor.GetInitGraph());
(void)DfGraphManager::GetInstance().AddGraph(checkpoint_name, convertor.GetSaveCheckpointGraph());
(void)DfGraphManager::GetInstance().AddGraph(BROADCAST_GRAPH_NAME, convertor.GetBroadcastGraph());
DfGraphManager::GetInstance().SetAnfGraph(checkpoint_name, anf_graph);
return true;
}
FuncGraphPtr BuildDFGraph(const std::map<std::string, ExecutorInfoPtr>& info, const py::dict& init_params,
const std::string& phase, const py::object& broadcast_params) {
if (info.count(phase) == 0) {
MS_LOG(EXCEPTION) << "no phase in executor:" << GetPhasePrefix(phase);
}
FuncGraphPtr anf_graph = info.at(phase)->func_graph;
if (MsContext::GetInstance()->save_graphs_flag()) {
draw::Draw(GetFilePathName("anf_graph.dot"), anf_graph); // for debug
DumpIR(GetFilePathName("anf_graph.ir"), anf_graph, true);
}
if (!AddDFGraph(info, init_params, phase, broadcast_params)) {
MS_LOG(ERROR) << "GenConvertor failed";
return nullptr;
}
#if ENABLE_TRAIN
(void)setenv("GE_TRAIN", "1", 1);
#else
(void)setenv("GE_TRAIN", "0", 1);
#endif
if (CreateSessionAndGraphRunner(static_cast<bool>(ENABLE_TRAIN)) != Status::SUCCESS) {
MS_LOG(ERROR) << "Create GE Session or GraphRunner failed.";
return nullptr;
}
return anf_graph;
}
void RunGEInitGraph(const py::dict& init_params, const std::string& phase) {
MS_LOG(DEBUG) << "ExecInitGraph start.";
TensorOrderMap inputs_with_name{};
ConvertObjectToTensors(init_params, &inputs_with_name);
std::vector<tensor::TensorPtr> inputs;
(void)std::transform(inputs_with_name.begin(), inputs_with_name.end(), std::back_inserter(inputs),
[](const std::pair<std::string, tensor::TensorPtr>& item) { return item.second; });
std::vector<GeTensorPtr> ge_tensors = TransformUtil::ConvertInputTensors(inputs, kOpFormat_NCHW);
if (ge_tensors.size() != inputs.size()) {
MS_LOG(ERROR) << "Args convert to ge tensor error.";
return;
}
MS_LOG(DEBUG) << "Run graph begin, inputs size is: " << inputs.size() << ".";
std::vector<GeTensorPtr> ge_outputs;
transform::RunOptions run_options;
run_options.name = phase;
if (DfGraphManager::GetInstance().GetGraphByName(phase) == nullptr) {
MS_LOG(WARNING) << "Can not find " << phase << " sub graph, don't need data init subgraph in INFER mode.";
return;
}
auto graph_runner = DfGraphManager::GetInstance().GetGraphRunner();
if (graph_runner == nullptr) {
MS_LOG(EXCEPTION) << "Can not found GraphRunner.";
}
{
// Release GIL before calling into (potentially long-running) C++ code
py::gil_scoped_release release;
Status ret = graph_runner->RunGraph(run_options, ge_tensors, &ge_outputs);
if (ret != Status::SUCCESS) {
MS_LOG(EXCEPTION) << "Exec " << phase << " graph failed.";
}
MS_LOG(INFO) << "Exec " << phase << " graph success.";
if ((ConfigManager::GetInstance().parallel_strategy() == ParallelStrategy::DISTRIBUTION) &&
(DfGraphManager::GetInstance().GetGraphByName(BROADCAST_GRAPH_NAME) != nullptr)) {
run_options.name = BROADCAST_GRAPH_NAME;
ret = graph_runner->RunGraph(run_options, ge_tensors, &ge_outputs);
if (ret != Status::SUCCESS) {
MS_LOG(EXCEPTION) << "Exec BROADCAST_GRAPH_NAME failed.";
}
MS_LOG(INFO) << "Exec broadcast graph success.";
}
}
}
py::object ExtractGeneralCnodeRet(const AbstractBasePtr& cnode_data, const py::tuple& data, size_t* count) {
MS_EXCEPTION_IF_NULL(cnode_data);
if (*count >= data.size()) {
MS_LOG(EXCEPTION) << "The number of elements in the outputs : " << data.size()
<< " less than the number of elements required. ";
}
if (cnode_data->isa<AbstractTensor>()) {
BaseShapePtr shape = cnode_data->BuildShape();
auto shape_act = shape->cast<abstract::ShapePtr>()->shape();
Tensor tensor_exp = py::cast<Tensor>(data[*count]);
if (shape_act != tensor_exp.shape()) {
MS_LOG(EXCEPTION) << "The shape of the tensor returned from GE is not the same as "
"the shape of the tensor derived from ME.";
}
return data[(*count)++];
}
if (!cnode_data->isa<AbstractTuple>()) {
MS_LOG(EXCEPTION) << "The output of operator in the final anf graph could "
<< "only be a tensor or a tuple of tensor, but got " << cnode_data->BuildValue()->ToString()
<< ".";
}
auto data_tp = cnode_data->cast<AbstractTuplePtr>();
auto elements = data_tp->elements();
size_t size = data_tp->size();
py::tuple tp = py::tuple(size);
for (size_t i = 0; i < size; i++) {
tp[i] = ExtractGeneralCnodeRet(elements[i], data, count);
}
return std::move(tp);
}
py::object StructureOutput(const AnfNodePtr& output_node, const py::tuple& data, size_t* count) {
MS_EXCEPTION_IF_NULL(output_node);
if (output_node->isa<ValueNode>()) {
return ValuePtrToPyData(GetValueNode(output_node));
}
if (*count >= data.size()) {
MS_LOG(EXCEPTION) << "The number of elements in the outputs : " << data.size()
<< " less than the number of elements required. ";
}
if (output_node->isa<Parameter>()) {
return data[(*count)++];
}
auto output_c = output_node->cast<CNodePtr>();
if (output_c == nullptr) {
MS_LOG(EXCEPTION) << "The final anf graph could only have constant, parameter, and operator, but got "
<< output_node->ToString();
}
if (output_c->IsApply(prim::kPrimMakeTuple)) {
auto input_list = output_c->inputs();
size_t size = input_list.size();
py::tuple tp = py::tuple(size - 1);
for (size_t i = 1; i < size; i++) {
tp[i - 1] = StructureOutput(input_list[i], data, count);
}
return std::move(tp);
}
if (output_c->IsApply(prim::kPrimDepend)) {
return StructureOutput(output_c->input(1), data, count);
}
return ExtractGeneralCnodeRet(output_c->abstract(), data, count);
}
std::shared_ptr<py::object> DoExecGraph(const FuncGraphPtr& graph, const std::vector<MeTensorPtr>& inputs,
const std::string& phase) {
std::vector<GeTensorPtr> ge_tensors = TransformUtil::ConvertInputTensors(inputs, kOpFormat_NCHW);
if (ge_tensors.size() != inputs.size()) {
MS_LOG(ERROR) << "args convert to ge tensor error";
return nullptr;
}
std::vector<GeTensorPtr> ge_outputs;
transform::RunOptions run_options;
run_options.name = phase;
auto graph_runner = DfGraphManager::GetInstance().GetGraphRunner();
if (graph_runner == nullptr) {
MS_LOG(ERROR) << "Can not found GraphRunner";
return nullptr;
}
{
// Release GIL before calling into (potentially long-running) C++ code
py::gil_scoped_release release;
MS_LOG(DEBUG) << "Run graph begin, inputs size is: " << inputs.size();
Status ret = graph_runner->RunGraph(run_options, ge_tensors, &ge_outputs);
MS_LOG(DEBUG) << "Run graph finish, outputs size is: " << ge_outputs.size();
if (ret != Status::SUCCESS) {
MS_LOG(ERROR) << "Exec graph failed";
return nullptr;
}
}
std::vector<MeTensorPtr> me_outputs = TransformUtil::ConvertGeTensors(ge_outputs);
if (me_outputs.size() != ge_outputs.size()) {
MS_LOG(ERROR) << "Convert output Ge tensor to Me tensor failed";
}
py::tuple outputs(me_outputs.size());
for (std::size_t i = 0; i < outputs.size(); i++) {
outputs[i] = *me_outputs[i];
}
std::shared_ptr<py::object> ret = nullptr;
#ifdef ENABLE_GE
AnfNodePtr root = graph->get_return();
MS_EXCEPTION_IF_NULL(root);
AbstractBasePtr output = root->abstract();
size_t count = 0;
py::object oj = StructureOutput(output, outputs, &count);
ret = std::make_shared<py::object>(oj);
#else
if (outputs.size() == 1) {
ret = std::make_shared<py::object>(outputs[0]);
} else {
ret = std::make_shared<py::object>(outputs);
}
#endif
return ret;
}
void ProcessGeArg(const std::map<std::string, ExecutorInfoPtr>& info, const py::tuple& args, const std::string& phase,
std::vector<tensor::TensorPtr>* inputs) {
// check the arg and use the ExecutorPy args
std::size_t size = args.size();
if (info.count(phase) == 0) {
MS_LOG(EXCEPTION) << "no phase in executor:" << GetPhasePrefix(phase);
}
auto arg_size = info.at(phase)->arg_list_size;
if (size != arg_size) {
MS_LOG(EXCEPTION) << "The real arg num : size = " << size << ". graph_arg_size = " << arg_size;
}
// process the first args of tensor
// only in Dataset Feed Mode, fp_bp graph need input tensors
if (ConfigManager::GetInstance().dataset_mode() == DS_FEED_MODE) {
for (std::size_t i = 0; i < size; i++) {
ValuePtr converted = nullptr;
bool succ = parse::ConvertData(args[i], &converted);
if (!succ) {
MS_LOG(EXCEPTION) << "args convert error";
}
if (converted->isa<tensor::Tensor>()) {
(*inputs).push_back(converted->cast<tensor::TensorPtr>());
} else {
MS_LOG(EXCEPTION) << "args, " << converted->ToString() << " is not tensor";
}
}
}
}
py::object ExecDFGraph(const std::map<std::string, ExecutorInfoPtr>& info, const py::tuple& args,
const std::string& phase) {
std::string phase_prefix = GetPhasePrefix(phase);
if (phase_prefix == "save") {
DoExecNonInputGraph(phase);
ConfigManager::GetInstance().ResetConfig();
return py::none();
}
if (info.count(phase) == 0) {
MS_LOG(EXCEPTION) << "has no phase:" << phase;
}
FuncGraphPtr anf_graph = info.at(phase)->func_graph;
#if (!defined ENABLE_GE) || (defined ENABLE_INFER)
// Now don't use the graph because the exec ge function don't take effect
MS_EXCEPTION_IF_NULL(info.at(phase)->func_graph);
if (ENABLE_TRAIN != info.at(phase)->func_graph->flags()["training"]) {
MS_LOG(ERROR) << "Graph training mode mismatch mode of libraries";
ConfigManager::GetInstance().ResetConfig();
return py::none();
}
#endif
std::shared_ptr<py::object> ret_val = std::make_shared<py::object>();
// We will not execute graph when output is constant or just input itself.
if (IsGraphOutputValueNodeOrParameter(info.at(phase)->func_graph->output(), args, ret_val)) {
ConfigManager::GetInstance().ResetConfig();
return *ret_val;
}
std::vector<tensor::TensorPtr> inputs;
ProcessGeArg(info, args, phase, &inputs);
std::shared_ptr<py::object> ret = DoExecGraph(anf_graph, inputs, phase);
ConfigManager::GetInstance().ResetConfig();
if (ret != nullptr) {
return *ret;
} else {
MS_LOG(EXCEPTION) << "exec graph failed";
}
}
void ExportDFGraph(const std::string& file_name, const std::string& phase) {
MS_LOG(DEBUG) << "ExportGraph Begin";
transform::DfGraphWrapperPtr wrap_ptr = DfGraphManager::GetInstance().GetGraphByName(phase);
if (wrap_ptr == nullptr) {
MS_LOG(ERROR) << "Get graph form DfGraphManager failed!";
return;
}
transform::DfGraphPtr ge_graph = wrap_ptr->graph_ptr_;
if (nullptr == ge_graph) {
MS_LOG(ERROR) << "The export graph is null";
return;
}
(void)ge_graph->SaveToFile(file_name);
MS_LOG(DEBUG) << "ExportGraph End";
}
} // namespace pipeline
} // namespace mindspore

57
mindspore/ccsrc/pipeline/pipeline_ge.h Normal file
View File

@ -0,0 +1,57 @@
/**
* 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_PIPELINE_PIPELINE_GE_H_
#define MINDSPORE_CCSRC_PIPELINE_PIPELINE_GE_H_
#include <vector>
#include <utility>
#include <string>
#include <memory>
#include <unordered_map>
#include <map>
#include <mutex>
#include "pybind11/pybind11.h"
#include "pipeline/base.h"
#include "operator/ops.h"
namespace mindspore {
namespace pipeline {
namespace py = pybind11;
void SetGeOption(const std::map<std::string, std::string>& options);
void RunGEInitGraph(const py::dict& init_params, const std::string& phase);
py::object ExecDFGraph(const std::map<std::string, ExecutorInfoPtr>& info, const py::tuple& args,
const std::string& phase = "train");
FuncGraphPtr BuildDFGraph(const std::map<std::string, ExecutorInfoPtr>& info, const py::dict& init_params,
const std::string& phase, const py::object& broadcast_params = {});
// init and exec dataset sub graph for GE backend
bool InitExecDatasetGe(const std::string& queue_name, int64_t size, int64_t batch_size,
const std::vector<TypePtr>& types, const std::vector<std::vector<int64_t>>& shapes,
const std::vector<int64_t>& input_indexes, const std::string& phase);
void ExportDFGraph(const std::string& file_name, const std::string& phase);
} // namespace pipeline
} // namespace mindspore
#endif // MINDSPORE_CCSRC_PIPELINE_PIPELINE_GE_H_

29
mindspore/ccsrc/pipeline/resource.cc
View File

@ -25,19 +25,13 @@
#include "pipeline/parse/data_converter.h"
#include "operator/ops.h"
#include "utils/graph_utils.h"
#include "transform/convert.h"
#include "optimizer/ad/dfunctor.h"
#include "vm/segment_runner.h"
#include "utils/context/ms_context.h"
#include "transform/df_graph_manager.h"
#include "device/kernel_runtime_manager.h"
namespace mindspore {
// namespace to support opmap definition
namespace pipeline {
using MethodMap = std::unordered_map<int, std::unordered_map<std::string, Any>>;
MethodMap& GetMethodMap() {
static MethodMap method_map = {{kObjectTypeString,
{
@ -255,28 +249,5 @@ void Resource::Clean() {
trace::ClearTraceStack();
is_cleaned_ = true;
}
void ReleaseGeTsd() {
auto context_ptr = MsContext::GetInstance();
if (context_ptr != nullptr) {
(void)context_ptr->FinalizeGe(true);
(void)context_ptr->CloseTsd(true);
}
}
void ClearResAtexit() {
MS_LOG(DEBUG) << "pipeline clear all resource";
device::KernelRuntimeManager::Instance().ClearRuntimeResource();
transform::DfGraphManager::GetInstance().ClearGraph();
ad::g_k_prims.clear();
abstract::ClearPrimEvaluatorMap();
compile::ClearConvertCache();
transform::DfGraphConvertor::get_adpt_map().clear();
pipeline::GetMethodMap().clear();
pipeline::ExecutorPy::ClearRes();
ReleaseGeTsd();
}
} // namespace pipeline
} // namespace mindspore

7
mindspore/ccsrc/pipeline/resource.h
View File

@ -44,6 +44,10 @@ const char kOutput[] = "output";
class InferenceResource;
using MethodMap = std::unordered_map<int, std::unordered_map<std::string, Any>>;
MethodMap& GetMethodMap();
class ResourceBase {
public:
ResourceBase() { manager_ = MakeManager(); }
@ -110,9 +114,6 @@ class Resource : public ResourceBase {
using ResourcePtr = std::shared_ptr<pipeline::Resource>;
void ClearResAtexit();
void ReleaseGeTsd();
} // namespace pipeline
} // namespace mindspore

2
mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc
View File

@ -21,7 +21,7 @@
#include "pre_activate/ascend/ir_fission/bn_grad_split.h"
#include "pre_activate/ascend/ir_fusion/fused_batch_norm_fusion.h"
#include "pre_activate/ascend/ir_fission/layer_norm_grad_split.h"
#include "pre_activate/ascend/ir_fusion/allreduce_fusion.h"
#include "pre_activate/common/ir_fusion/allreduce_fusion.h"
#include "pre_activate/ascend/ir_fusion/square_sum_fusion.h"
#include "pre_activate/ascend/ir_fusion/clip_by_norm_no_div_square_sum_fusion.h"
#include "pre_activate/ascend/ir_fusion/lamb_update_with_lr_rule_fusion.h"

4
mindspore/ccsrc/pre_activate/ascend/buffer_fusion/buffer_fusion.cc
View File

@ -237,11 +237,11 @@ CNodePtr CreateFusionOp(const std::vector<AnfNodePtr> &inputs_list, const std::v
std::vector<std::string> input_names;
for (uint8_t i = 0; i < inputs_list.size(); i++) {
input_names.emplace_back("input" + to_string(i));
input_names.emplace_back("input" + std::to_string(i));
}
std::vector<std::string> output_names;
for (uint8_t i = 0; i < outputs_list.size(); i++) {
output_names.emplace_back("output" + to_string(i));
output_names.emplace_back("output" + std::to_string(i));
}
ValuePtr input_names_v = MakeValue(input_names);

2
mindspore/ccsrc/pre_activate/ascend/ir_fusion/allreduce_fusion.cc → mindspore/ccsrc/pre_activate/common/ir_fusion/allreduce_fusion.cc
View File

@ -13,7 +13,7 @@
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "pre_activate/ascend/ir_fusion/allreduce_fusion.h"
#include "pre_activate/common/ir_fusion/allreduce_fusion.h"
#include <vector>
#include <string>

6
mindspore/ccsrc/pre_activate/ascend/ir_fusion/allreduce_fusion.h → mindspore/ccsrc/pre_activate/common/ir_fusion/allreduce_fusion.h
View File

@ -13,8 +13,8 @@
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_IR_FUSION_ALLREDUCE_FUSION_H_
#define MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_IR_FUSION_ALLREDUCE_FUSION_H_
#ifndef MINDSPORE_CCSRC_PRE_ACTIVATE_COMMON_IR_FUSION_ALLREDUCE_FUSION_H_
#define MINDSPORE_CCSRC_PRE_ACTIVATE_COMMON_IR_FUSION_ALLREDUCE_FUSION_H_
#include <vector>
#include "pre_activate/common/pass.h"
@ -46,4 +46,4 @@ class AllReduceFusion : public Pass {
};
} // namespace opt
} // namespace mindspore
#endif // MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_IR_FUSION_ALLREDUCE_FUSION_H_
#endif // MINDSPORE_CCSRC_PRE_ACTIVATE_COMMON_IR_FUSION_ALLREDUCE_FUSION_H_

6
mindspore/ccsrc/predict/converter/kernel2ms.cc
View File

@ -16,7 +16,7 @@
#include "predict/converter/kernel2ms.h"
#include <algorithm>
#include "transform/convert.h"
#include "ir/anf.h"
#include "predict/converter/lite_model/op_attr_packer.h"
#include "mindspore/ccsrc/operator/ops.h"
@ -135,7 +135,7 @@ void Kernel2Ms::GetRealInpoutsPtr(const AnfNodePtr &node, std::vector<AnfNodePtr
if (node->isa<CNode>()) {
auto c_node = node->cast<CNodePtr>();
MS_EXCEPTION_IF_NULL(c_node);
std::string c_node_name = transform::GetCNodeFuncName(c_node);
std::string c_node_name = GetCNodeFuncName(c_node);
if (c_node_name == prim::kPrimTupleGetItem->name()) {
auto v_node = c_node->inputs()[kTupleGetItemIndex]->cast<ValueNodePtr>();
MS_EXCEPTION_IF_NULL(v_node);
@ -321,7 +321,7 @@ bool Kernel2Ms::SetGraphInputTensors(const KernelGraphPtr &kernel_graph_ptr, con
}
for (const auto &input_node : kernel_graph_ptr->inputs()) {
if (input_node->isa<Parameter>()) {
ParameterPtr pk_node = dynamic_pointer_cast<Parameter>(input_node);
ParameterPtr pk_node = std::dynamic_pointer_cast<Parameter>(input_node);
TensorPtr device_tensor;
if (convert_mode_ == kConvertCpuMode) {
device_tensor = predict::utils::GetParaCpuTensor(input_node);

67
mindspore/ccsrc/pynative/base.h Normal file
View File

@ -0,0 +1,67 @@
/**
* 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_PYNATIVE_BASE_H_
#define MINDSPORE_CCSRC_PYNATIVE_BASE_H_
#include <vector>
#include <utility>
#include <string>
#include <memory>
#include <unordered_map>
#include <unordered_set>
#include "pybind11/pybind11.h"
#include "ir/primitive.h"
#include "pipeline/static_analysis/abstract_value.h"
namespace mindspore {
namespace pynative {
namespace py = pybind11;
enum PynativeStatusCode {
PYNATIVE_SUCCESS = 0,
PYNATIVE_OP_NOT_IMPLEMENTED_ERR = 1,
PYNATIVE_OP_INPUTS_ERR = 2,
PYNATIVE_OP_PARAMS_ERR = 3,
PYNATIVE_OP_ATTRS_ERR = 4,
PYNATIVE_GRAPH_MANAGER_ERR = 5,
PYNATIVE_GRAPH_GE_BUILD_ERR = 6,
PYNATIVE_GRAPH_GE_RUN_ERR = 7,
PYNATIVE_UNKNOWN_STATE = 0XFF
};
enum RunOpArgsEnum { PY_PRIM = 0, PY_NAME, PY_INPUTS, PY_INPUT_MASK, PY_ARGS_NUM };
struct OpExecInfo {
PrimitivePyPtr py_primitive;
std::string op_name;
AbstractBasePtr abstract;
py::tuple op_inputs;
py::tuple inputs_mask;
py::dict op_attrs;
};
using OpExecInfoPtr = std::shared_ptr<OpExecInfo>;
OpExecInfoPtr GenerateOpExecInfo(const py::args& args);
const std::unordered_set<std::string> ignore_infer_prim = {"partial"};
} // namespace pynative
} // namespace mindspore
#endif // MINDSPORE_CCSRC_PYNATIVE_BASE_H_

285
mindspore/ccsrc/pynative/pynative_execute.cc
View File

@ -29,16 +29,18 @@
#include "pipeline/static_analysis/prim.h"
#include "session/session_factory.h"
#include "pynative/base.h"
#ifdef ENABLE_GE
#include "pynative/pynative_execute_ge.h"
#endif
const char SINGLE_OP_GRAPH[] = "single_op_graph";
// primitive unable to infer value for constant input in pynative mode
const std::unordered_set<std::string> ignore_infer_prim = {"partial"};
const std::unordered_set<std::string> vm_operators = {"partial", "depend"};
namespace mindspore {
namespace pynative {
using transform::GraphRunner;
using transform::GraphRunnerOptions;
using transform::OperatorPtr;
inline ValuePtr PyAttrValue(const py::object& obj) {
ValuePtr converted_ret = nullptr;
bool converted = parse::ConvertData(obj, &converted_ret);
@ -48,32 +50,12 @@ inline ValuePtr PyAttrValue(const py::object& obj) {
return converted_ret;
}
MeTensorPtr ConvertPyObjToTensor(const py::object& obj) {
MeTensorPtr me_tensor_ptr = nullptr;
if (py::isinstance<MeTensor>(obj)) {
me_tensor_ptr = py::cast<MeTensorPtr>(obj);
} else if (py::isinstance<py::tuple>(obj)) {
me_tensor_ptr = std::make_shared<MeTensor>(py::cast<py::tuple>(obj), nullptr);
} else if (py::isinstance<py::float_>(obj)) {
me_tensor_ptr = std::make_shared<MeTensor>(py::cast<py::float_>(obj), nullptr);
} else if (py::isinstance<py::int_>(obj)) {
me_tensor_ptr = std::make_shared<MeTensor>(py::cast<py::int_>(obj), nullptr);
} else if (py::isinstance<py::list>(obj)) {
me_tensor_ptr = std::make_shared<MeTensor>(py::cast<py::list>(obj), nullptr);
} else if (py::isinstance<py::array>(obj)) {
me_tensor_ptr = std::make_shared<MeTensor>(py::cast<py::array>(obj), nullptr);
} else {
MS_LOG(EXCEPTION) << "run op inputs type is invalid!";
}
return me_tensor_ptr;
}
void PynativeInfer(const PrimitivePyPtr& prim, const py::tuple& py_args, OpExecInfo* const op_exec_info) {
size_t size = py_args.size();
AbstractBasePtrList args_spec_list;
for (size_t i = 0; i < size; i++) {
ValuePtr input_value = PyAttrValue(py_args[i]);
if (py::isinstance<MeTensor>(py_args[i])) {
if (py::isinstance<tensor::Tensor>(py_args[i])) {
args_spec_list.emplace_back(abstract::FromValueInside(input_value, true));
} else {
args_spec_list.emplace_back(abstract::FromValueInside(input_value, false));
@ -140,241 +122,6 @@ std::string GetSingleOpGraphInfo(const OpExecInfoPtr& op_exec_info) {
return graph_info;
}
bool SetInputsForSingleOpGraph(const OpExecInfoPtr& op_exec_info, const std::vector<GeTensorPtr>& inputs,
const OperatorPtr& op, std::vector<GeOperator>* graph_input_nodes) {
MS_EXCEPTION_IF_NULL(op_exec_info);
MS_EXCEPTION_IF_NULL(graph_input_nodes);
auto op_inputs = op_exec_info->op_inputs;
std::string op_name = op_exec_info->op_name;
transform::OpAdapterPtr adapter = transform::DfGraphConvertor::FindAdapter(op_name, true);
if (adapter == nullptr) {
return false;
}
int op_input_idx = 1;
size_t size = inputs.size();
for (size_t i = 0; i < size; i++) {
if (inputs[i] == nullptr) {
continue;
}
auto const_op = std::make_shared<transform::Constant>();
MS_EXCEPTION_IF_NULL(const_op);
(void)const_op->set_attr_value(*inputs[i]);
MeTensorPtr me_tensor_ptr = ConvertPyObjToTensor(op_inputs[i]);
MS_EXCEPTION_IF_NULL(me_tensor_ptr);
auto const_op_desc =
transform::TransformUtil::GetGeTensorDesc(me_tensor_ptr->shape_c(), me_tensor_ptr->data_type(), kOpFormat_NCHW);
if (const_op_desc == nullptr) {
MS_LOG(ERROR) << "Create variable " << op_name << " ouptut descriptor failed!";
return false;
}
auto pointer_cast_const_op = std::static_pointer_cast<transform::Constant>(const_op);
MS_EXCEPTION_IF_NULL(pointer_cast_const_op);
(void)pointer_cast_const_op->update_output_desc_y(*const_op_desc);
auto& input_map = adapter->getInputMap();
if (input_map.find(op_input_idx) == input_map.end()) {
continue;
}
if (adapter->setInput(op, op_input_idx++, const_op)) {
MS_LOG(ERROR) << "fail to set params, index is " << op_input_idx;
return false;
}
graph_input_nodes->push_back(*const_op);
}
return true;
}
bool BuildSingleOpGraph(const OpExecInfoPtr& op_exec_info, const std::vector<GeTensorPtr>& inputs,
const std::unordered_map<std::string, ValuePtr>& attrs, const GeGraphPtr& graph) {
MS_EXCEPTION_IF_NULL(op_exec_info);
std::string op_name = op_exec_info->op_name;
auto op_inputs = op_exec_info->op_inputs;
transform::OpAdapterPtr adapter = transform::DfGraphConvertor::FindAdapter(op_name, true);
if (adapter == nullptr) {
MS_LOG(ERROR) << "Unable to find Adapter for " << ((std::string)py::str(op_name));
return false;
}
OperatorPtr op = adapter->generate(op_name);
MS_EXCEPTION_IF_NULL(op);
std::vector<GeOperator> graph_input_nodes;
// hold param nodes after setting input and output for the graph
// set input
if (!SetInputsForSingleOpGraph(op_exec_info, inputs, op, &graph_input_nodes)) {
return false;
}
// set attributes
for (auto attr : attrs) {
(void)adapter->setAttr(op, attr.first, attr.second);
}
// set default attributes
auto extra_attrs = adapter->GetExtraAttr();
for (auto attr : extra_attrs) {
(void)adapter->setAttr(op, attr.first, attr.second);
}
// set input attributes
auto& input_attr_map = adapter->getInputAttrMap();
for (auto& it : input_attr_map) {
if (op_inputs.size() < it.first) {
continue;
}
auto const_value = PyAttrValue(op_inputs[it.first - 1]);
if (const_value->isa<None>()) {
continue;
}
it.second.set_attr(op, const_value);
}
// construct output data nodes
std::vector<GeOperator> graph_outputs{*op};
// set input and output nodes for the graph
MS_EXCEPTION_IF_NULL(graph);
(void)graph->SetInputs(graph_input_nodes).SetOutputs(graph_outputs);
MS_LOG(INFO) << "BuildSingleOpGraph done";
return true;
}
void ToTensorPtr(const OpExecInfoPtr op_exec_info, std::vector<GeTensorPtr>* const inputs) {
MS_EXCEPTION_IF_NULL(inputs);
MS_EXCEPTION_IF_NULL(op_exec_info);
auto op_inputs = op_exec_info->op_inputs;
size_t size = op_inputs.size();
for (size_t i = 0; i < size; i++) {
if (py::isinstance<py::none>(op_inputs[i])) {
inputs->emplace_back(nullptr);
continue;
}
MeTensorPtr me_tensor_ptr = ConvertPyObjToTensor(op_inputs[i]);
auto ge_tensor_ptr = transform::TransformUtil::ConvertTensor(me_tensor_ptr, kOpFormat_NCHW);
if (ge_tensor_ptr == nullptr) {
MS_LOG(EXCEPTION) << "convert inputs to GE tensor failed in op " << op_exec_info->op_name << ".";
}
// set inputs for operator to build single node graph
inputs->push_back(ge_tensor_ptr);
}
}
PynativeStatusCode ConvertAttributes(const OpExecInfoPtr& op_exec_info, const std::vector<GeTensorPtr>& inputs) {
MS_EXCEPTION_IF_NULL(op_exec_info);
auto op_attrs = op_exec_info->op_attrs;
std::unordered_map<std::string, ValuePtr> attrs{};
for (auto& item : op_attrs) {
if (!py::isinstance<py::str>(item.first)) {
MS_LOG(ERROR) << "type error in py dict convert";
return PYNATIVE_OP_ATTRS_ERR;
}
std::string name = py::cast<std::string>(item.first);
auto attr_value = PyAttrValue(py::cast<py::object>(item.second));
(void)attrs.emplace(name, attr_value);
}
// build graph
GeGraphPtr graph = std::make_shared<GeGraph>(op_exec_info->op_name);
if (BuildSingleOpGraph(op_exec_info, inputs, attrs, graph) == false) {
MS_LOG(ERROR) << "Fail to BuildSingleOpGraph";
return PYNATIVE_GRAPH_GE_BUILD_ERR;
}
// add the single op graph into the graph manager, which will be iterated by session.
transform::Status ret =
transform::DfGraphManager::GetInstance().AddGraph(SINGLE_OP_GRAPH, std::shared_ptr<transform::DfGraph>(graph));
if (ret != transform::SUCCESS) {
MS_LOG(ERROR) << "Fail to AddGraph into graph manager";
return PYNATIVE_GRAPH_MANAGER_ERR;
}
return PYNATIVE_SUCCESS;
}
std::vector<MeTensorPtr> ConvertOutputTensors(const OpExecInfoPtr& op_exec_info,
const std::vector<GeTensorPtr>& ge_tensors) {
std::vector<MeTensorPtr> outputs;
AbstractBasePtr abs_base = op_exec_info->abstract;
std::vector<std::vector<int>> shapes;
if (abs_base != nullptr && abs_base->isa<abstract::AbstractTensor>()) {
auto arg_tensor = dyn_cast<abstract::AbstractTensor>(abs_base);
shapes.emplace_back(arg_tensor->shape()->shape());
outputs = transform::TransformUtil::ConvertGeTensors(ge_tensors, shapes);
return outputs;
}
if (abs_base != nullptr && abs_base->isa<abstract::AbstractTuple>()) {
auto arg_tuple = dyn_cast<abstract::AbstractTuple>(abs_base);
size_t len = arg_tuple->size();
for (size_t i = 0; i < len; i++) {
if (arg_tuple->elements()[i]->isa<abstract::AbstractTensor>()) {
auto arg_tensor = dyn_cast<abstract::AbstractTensor>(arg_tuple->elements()[i]);
shapes.emplace_back(arg_tensor->shape()->shape());
}
}
outputs = transform::TransformUtil::ConvertGeTensors(ge_tensors, shapes);
return outputs;
}
for (auto& it : ge_tensors) {
auto tensor = transform::TransformUtil::ConvertGeTensor(it);
if (tensor != nullptr) {
outputs.emplace_back(tensor);
}
}
return outputs;
}
py::object RunOpInGE(const OpExecInfoPtr& op_exec_info, PynativeStatusCode* status) {
MS_LOG(INFO) << "RunOpInGe start";
MS_EXCEPTION_IF_NULL(op_exec_info);
MS_EXCEPTION_IF_NULL(status);
// returns a null py::tuple on error
py::tuple err_ret(0);
auto op_name = op_exec_info->op_name;
transform::OpAdapterPtr adapter = transform::DfGraphConvertor::FindAdapter(op_name, true);
if (adapter == nullptr) {
MS_LOG(ERROR) << "Unable to find GE Adapter for " << ((std::string)py::str(op_name));
*status = PYNATIVE_OP_NOT_IMPLEMENTED_ERR;
return std::move(err_ret);
}
std::vector<GeTensorPtr> inputs{};
ToTensorPtr(op_exec_info, &inputs);
// convert me attr to ge AttrValue
PynativeStatusCode ret = ConvertAttributes(op_exec_info, inputs);
if (ret != PYNATIVE_SUCCESS) {
*status = ret;
return std::move(err_ret);
}
// run graph
transform::RunOptions run_options;
run_options.name = SINGLE_OP_GRAPH;
std::vector<GeTensorPtr> ge_inputs;
std::vector<GeTensorPtr> ge_outputs;
transform::GraphRunnerOptions graph_runner_options;
graph_runner_options.options["ge.trainFlag"] = "1";
auto graph_runner = std::make_shared<transform::GraphRunner>(graph_runner_options);
transform::Status run_ret;
{
// Release GIL before calling into (potentially long-running) C++ code
py::gil_scoped_release release;
run_ret = graph_runner->RunGraph(run_options, ge_inputs, &ge_outputs);
}
if (run_ret != transform::Status::SUCCESS) {
MS_LOG(ERROR) << "GraphRunner Fails to Run Graph";
*status = PYNATIVE_GRAPH_GE_RUN_ERR;
return std::move(err_ret);
}
std::vector<MeTensorPtr> graph_outputs = ConvertOutputTensors(op_exec_info, ge_outputs);
size_t output_size = graph_outputs.size();
py::tuple result(output_size);
for (size_t i = 0; i < output_size; i++) {
MS_EXCEPTION_IF_NULL(graph_outputs[i]);
result[i] = *graph_outputs[i];
}
*status = PYNATIVE_SUCCESS;
MS_LOG(INFO) << "RunOpInGe end";
return std::move(result);
}
py::object RunOpInVM(const OpExecInfoPtr& op_exec_info, PynativeStatusCode* status) {
MS_LOG(INFO) << "RunOpInVM start";
@ -423,12 +170,6 @@ py::object RunOpWithBackendPolicy(MsBackendPolicy backend_policy, const OpExecIn
MS_EXCEPTION_IF_NULL(status);
py::object result;
switch (backend_policy) {
case kMsBackendGeOnly: {
// use GE only
MS_LOG(INFO) << "RunOp use GE only backend";
result = RunOpInGE(op_exec_info, status);
break;
}
case kMsBackendVmOnly: {
// use vm only
MS_LOG(INFO) << "RunOp use VM only backend";
@ -436,22 +177,14 @@ py::object RunOpWithBackendPolicy(MsBackendPolicy backend_policy, const OpExecIn
break;
}
case kMsBackendGePrior: {
#ifdef ENABLE_GE
// use GE first, use vm when GE fails
MS_LOG(INFO) << "RunOp use GE first backend";
result = RunOpInGE(op_exec_info, status);
if (*status != PYNATIVE_SUCCESS) {
result = RunOpInVM(op_exec_info, status);
}
break;
}
case kMsBackendVmPrior: {
// GE_VM_SILENT
// (should not use this policy) use vm first, use GE when vm fails
MS_LOG(INFO) << "RunOp use VM first backend";
result = RunOpInVM(op_exec_info, status);
if (*status != PYNATIVE_SUCCESS) {
result = RunOpInGE(op_exec_info, status);
}
#endif
break;
}
case kMsBackendMsPrior: {

43
mindspore/ccsrc/pynative/pynative_execute.h
View File

@ -25,55 +25,14 @@
#include "pybind11/pybind11.h"
#include "transform/convert.h"
#include "transform/graph_runner.h"
#include "transform/types.h"
#include "pynative/base.h"
#include "utils/context/ms_context.h"
namespace mindspore {
namespace pynative {
using MeTensor = mindspore::tensor::Tensor;
using MeTensorPtr = mindspore::tensor::TensorPtr;
using GeTensor = ge::Tensor;
using GeTensorPtr = std::shared_ptr<GeTensor>;
using GeGraph = ge::Graph;
using GeGraphPtr = std::shared_ptr<GeGraph>;
using GeOperator = ge::Operator;
using GeOperatorPtr = std::shared_ptr<GeOperator>;
namespace py = pybind11;
enum PynativeStatusCode {
PYNATIVE_SUCCESS = 0,
PYNATIVE_OP_NOT_IMPLEMENTED_ERR = 1,
PYNATIVE_OP_INPUTS_ERR = 2,
PYNATIVE_OP_PARAMS_ERR = 3,
PYNATIVE_OP_ATTRS_ERR = 4,
PYNATIVE_GRAPH_MANAGER_ERR = 5,
PYNATIVE_GRAPH_GE_BUILD_ERR = 6,
PYNATIVE_GRAPH_GE_RUN_ERR = 7,
PYNATIVE_UNKNOWN_STATE = 0XFF
};
enum RunOpArgsEnum { PY_PRIM = 0, PY_NAME, PY_INPUTS, PY_INPUT_MASK, PY_ARGS_NUM };
struct OpExecInfo {
PrimitivePyPtr py_primitive;
std::string op_name;
AbstractBasePtr abstract;
py::tuple op_inputs;
py::tuple inputs_mask;
py::dict op_attrs;
};
using OpExecInfoPtr = std::shared_ptr<OpExecInfo>;
OpExecInfoPtr GenerateOpExecInfo(const py::args& args);
bool BuildSingleOpGraph(const OpExecInfoPtr& op_exec_info, const std::vector<GeTensorPtr>& inputs,
const std::unordered_map<std::string, ValuePtr>& attrs, const GeGraphPtr& graph);
py::object RunOpInGE(const OpExecInfoPtr& op_exec_info, PynativeStatusCode* status);
py::object RunOpInVM(const OpExecInfoPtr& op_exec_info, PynativeStatusCode* status);
py::tuple RunOp(const py::args& args);

311
mindspore/ccsrc/pynative/pynative_execute_ge.cc Normal file
View File

@ -0,0 +1,311 @@
/**
* 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.
*/
#include "pynative/pynative_execute_ge.h"
#include <typeinfo>
#include <map>
#include <set>
#include <unordered_set>
#include "utils/any.h"
#include "utils/utils.h"
#include "utils/context/ms_context.h"
#include "operator/ops.h"
#include "pipeline/parse/data_converter.h"
#include "pipeline/static_analysis/prim.h"
#include "session/session_factory.h"
const char SINGLE_OP_GRAPH[] = "single_op_graph";
namespace mindspore {
namespace pynative {
using MeTensor = mindspore::tensor::Tensor;
using MeTensorPtr = mindspore::tensor::TensorPtr;
using GeOperator = ge::Operator;
using GeOperatorPtr = std::shared_ptr<GeOperator>;
using transform::GraphRunner;
using transform::GraphRunnerOptions;
using transform::OperatorPtr;
static std::shared_ptr<session::SessionBasic> session = nullptr;
inline ValuePtr PyAttrValue(const py::object& obj) {
ValuePtr converted_ret = nullptr;
bool converted = parse::ConvertData(obj, &converted_ret);
if (!converted) {
MS_LOG(EXCEPTION) << "attribute convert error with type:" << std::string(py::str(obj));
}
return converted_ret;
}
MeTensorPtr ConvertPyObjToTensor(const py::object& obj) {
MeTensorPtr me_tensor_ptr = nullptr;
if (py::isinstance<MeTensor>(obj)) {
me_tensor_ptr = py::cast<MeTensorPtr>(obj);
} else if (py::isinstance<py::tuple>(obj)) {
me_tensor_ptr = std::make_shared<MeTensor>(py::cast<py::tuple>(obj), nullptr);
} else if (py::isinstance<py::float_>(obj)) {
me_tensor_ptr = std::make_shared<MeTensor>(py::cast<py::float_>(obj), nullptr);
} else if (py::isinstance<py::int_>(obj)) {
me_tensor_ptr = std::make_shared<MeTensor>(py::cast<py::int_>(obj), nullptr);
} else if (py::isinstance<py::list>(obj)) {
me_tensor_ptr = std::make_shared<MeTensor>(py::cast<py::list>(obj), nullptr);
} else if (py::isinstance<py::array>(obj)) {
me_tensor_ptr = std::make_shared<MeTensor>(py::cast<py::array>(obj), nullptr);
} else {
MS_LOG(EXCEPTION) << "run op inputs type is invalid!";
}
return me_tensor_ptr;
}
bool SetInputsForSingleOpGraph(const OpExecInfoPtr& op_exec_info, const std::vector<GeTensorPtr>& inputs,
const OperatorPtr& op, std::vector<GeOperator>* graph_input_nodes) {
MS_EXCEPTION_IF_NULL(op_exec_info);
MS_EXCEPTION_IF_NULL(graph_input_nodes);
auto op_inputs = op_exec_info->op_inputs;
std::string op_name = op_exec_info->op_name;
transform::OpAdapterPtr adapter = transform::DfGraphConvertor::FindAdapter(op_name, true);
if (adapter == nullptr) {
return false;
}
int op_input_idx = 1;
size_t size = inputs.size();
for (size_t i = 0; i < size; i++) {
if (inputs[i] == nullptr) {
continue;
}
auto const_op = std::make_shared<transform::Constant>();
MS_EXCEPTION_IF_NULL(const_op);
(void)const_op->set_attr_value(*inputs[i]);
MeTensorPtr me_tensor_ptr = ConvertPyObjToTensor(op_inputs[i]);
MS_EXCEPTION_IF_NULL(me_tensor_ptr);
auto const_op_desc =
transform::TransformUtil::GetGeTensorDesc(me_tensor_ptr->shape_c(), me_tensor_ptr->data_type(), kOpFormat_NCHW);
if (const_op_desc == nullptr) {
MS_LOG(ERROR) << "Create variable " << op_name << " ouptut descriptor failed!";
return false;
}
auto pointer_cast_const_op = std::static_pointer_cast<transform::Constant>(const_op);
MS_EXCEPTION_IF_NULL(pointer_cast_const_op);
(void)pointer_cast_const_op->update_output_desc_y(*const_op_desc);
auto& input_map = adapter->getInputMap();
if (input_map.find(op_input_idx) == input_map.end()) {
continue;
}
if (adapter->setInput(op, op_input_idx++, const_op)) {
MS_LOG(ERROR) << "fail to set params, index is " << op_input_idx;
return false;
}
graph_input_nodes->push_back(*const_op);
}
return true;
}
bool BuildSingleOpGraph(const OpExecInfoPtr& op_exec_info, const std::vector<GeTensorPtr>& inputs,
const std::unordered_map<std::string, ValuePtr>& attrs, const GeGraphPtr& graph) {
MS_EXCEPTION_IF_NULL(op_exec_info);
std::string op_name = op_exec_info->op_name;
auto op_inputs = op_exec_info->op_inputs;
transform::OpAdapterPtr adapter = transform::DfGraphConvertor::FindAdapter(op_name, true);
if (adapter == nullptr) {
MS_LOG(ERROR) << "Unable to find Adapter for " << ((std::string)py::str(op_name));
return false;
}
OperatorPtr op = adapter->generate(op_name);
MS_EXCEPTION_IF_NULL(op);
std::vector<GeOperator> graph_input_nodes;
// hold param nodes after setting input and output for the graph
// set input
if (!SetInputsForSingleOpGraph(op_exec_info, inputs, op, &graph_input_nodes)) {
return false;
}
// set attributes
for (auto attr : attrs) {
(void)adapter->setAttr(op, attr.first, attr.second);
}
// set default attributes
auto extra_attrs = adapter->GetExtraAttr();
for (auto attr : extra_attrs) {
(void)adapter->setAttr(op, attr.first, attr.second);
}
// set input attributes
auto& input_attr_map = adapter->getInputAttrMap();
for (auto& it : input_attr_map) {
if (op_inputs.size() < it.first) {
continue;
}
auto const_value = PyAttrValue(op_inputs[it.first - 1]);
if (const_value->isa<None>()) {
continue;
}
it.second.set_attr(op, const_value);
}
// construct output data nodes
std::vector<GeOperator> graph_outputs{*op};
// set input and output nodes for the graph
MS_EXCEPTION_IF_NULL(graph);
(void)graph->SetInputs(graph_input_nodes).SetOutputs(graph_outputs);
MS_LOG(INFO) << "BuildSingleOpGraph done";
return true;
}
void ToTensorPtr(const OpExecInfoPtr op_exec_info, std::vector<GeTensorPtr>* const inputs) {
MS_EXCEPTION_IF_NULL(inputs);
MS_EXCEPTION_IF_NULL(op_exec_info);
auto op_inputs = op_exec_info->op_inputs;
size_t size = op_inputs.size();
for (size_t i = 0; i < size; i++) {
if (py::isinstance<py::none>(op_inputs[i])) {
inputs->emplace_back(nullptr);
continue;
}
MeTensorPtr me_tensor_ptr = ConvertPyObjToTensor(op_inputs[i]);
auto ge_tensor_ptr = transform::TransformUtil::ConvertTensor(me_tensor_ptr, kOpFormat_NCHW);
if (ge_tensor_ptr == nullptr) {
MS_LOG(EXCEPTION) << "convert inputs to GE tensor failed in op " << op_exec_info->op_name << ".";
}
// set inputs for operator to build single node graph
inputs->push_back(ge_tensor_ptr);
}
}
PynativeStatusCode ConvertAttributes(const OpExecInfoPtr& op_exec_info, const std::vector<GeTensorPtr>& inputs) {
MS_EXCEPTION_IF_NULL(op_exec_info);
auto op_attrs = op_exec_info->op_attrs;
std::unordered_map<std::string, ValuePtr> attrs{};
for (auto& item : op_attrs) {
if (!py::isinstance<py::str>(item.first)) {
MS_LOG(ERROR) << "type error in py dict convert";
return PYNATIVE_OP_ATTRS_ERR;
}
std::string name = py::cast<std::string>(item.first);
auto attr_value = PyAttrValue(py::cast<py::object>(item.second));
(void)attrs.emplace(name, attr_value);
}
// build graph
GeGraphPtr graph = std::make_shared<GeGraph>(op_exec_info->op_name);
if (BuildSingleOpGraph(op_exec_info, inputs, attrs, graph) == false) {
MS_LOG(ERROR) << "Fail to BuildSingleOpGraph";
return PYNATIVE_GRAPH_GE_BUILD_ERR;
}
// add the single op graph into the graph manager, which will be iterated by session.
transform::Status ret =
transform::DfGraphManager::GetInstance().AddGraph(SINGLE_OP_GRAPH, std::shared_ptr<transform::DfGraph>(graph));
if (ret != transform::SUCCESS) {
MS_LOG(ERROR) << "Fail to AddGraph into graph manager";
return PYNATIVE_GRAPH_MANAGER_ERR;
}
return PYNATIVE_SUCCESS;
}
std::vector<MeTensorPtr> ConvertOutputTensors(const OpExecInfoPtr& op_exec_info,
const std::vector<GeTensorPtr>& ge_tensors) {
std::vector<MeTensorPtr> outputs;
AbstractBasePtr abs_base = op_exec_info->abstract;
std::vector<std::vector<int>> shapes;
if (abs_base != nullptr && abs_base->isa<abstract::AbstractTensor>()) {
auto arg_tensor = dyn_cast<abstract::AbstractTensor>(abs_base);
shapes.emplace_back(arg_tensor->shape()->shape());
outputs = transform::TransformUtil::ConvertGeTensors(ge_tensors, shapes);
return outputs;
}
if (abs_base != nullptr && abs_base->isa<abstract::AbstractTuple>()) {
auto arg_tuple = dyn_cast<abstract::AbstractTuple>(abs_base);
size_t len = arg_tuple->size();
for (size_t i = 0; i < len; i++) {
if (arg_tuple->elements()[i]->isa<abstract::AbstractTensor>()) {
auto arg_tensor = dyn_cast<abstract::AbstractTensor>(arg_tuple->elements()[i]);
shapes.emplace_back(arg_tensor->shape()->shape());
}
}
outputs = transform::TransformUtil::ConvertGeTensors(ge_tensors, shapes);
return outputs;
}
for (auto& it : ge_tensors) {
auto tensor = transform::TransformUtil::ConvertGeTensor(it);
if (tensor != nullptr) {
outputs.emplace_back(tensor);
}
}
return outputs;
}
py::object RunOpInGE(const OpExecInfoPtr& op_exec_info, PynativeStatusCode* status) {
MS_LOG(INFO) << "RunOpInGe start";
MS_EXCEPTION_IF_NULL(op_exec_info);
MS_EXCEPTION_IF_NULL(status);
// returns a null py::tuple on error
py::tuple err_ret(0);
auto op_name = op_exec_info->op_name;
transform::OpAdapterPtr adapter = transform::DfGraphConvertor::FindAdapter(op_name, true);
if (adapter == nullptr) {
MS_LOG(ERROR) << "Unable to find GE Adapter for " << ((std::string)py::str(op_name));
*status = PYNATIVE_OP_NOT_IMPLEMENTED_ERR;
return std::move(err_ret);
}
std::vector<GeTensorPtr> inputs{};
ToTensorPtr(op_exec_info, &inputs);
// convert me attr to ge AttrValue
PynativeStatusCode ret = ConvertAttributes(op_exec_info, inputs);
if (ret != PYNATIVE_SUCCESS) {
*status = ret;
return std::move(err_ret);
}
// run graph
transform::RunOptions run_options;
run_options.name = SINGLE_OP_GRAPH;
std::vector<GeTensorPtr> ge_inputs;
std::vector<GeTensorPtr> ge_outputs;
transform::GraphRunnerOptions graph_runner_options;
graph_runner_options.options["ge.trainFlag"] = "1";
auto graph_runner = std::make_shared<transform::GraphRunner>(graph_runner_options);
transform::Status run_ret;
{
// Release GIL before calling into (potentially long-running) C++ code
py::gil_scoped_release release;
run_ret = graph_runner->RunGraph(run_options, ge_inputs, &ge_outputs);
}
if (run_ret != transform::Status::SUCCESS) {
MS_LOG(ERROR) << "GraphRunner Fails to Run Graph";
*status = PYNATIVE_GRAPH_GE_RUN_ERR;
return std::move(err_ret);
}
std::vector<MeTensorPtr> graph_outputs = ConvertOutputTensors(op_exec_info, ge_outputs);
size_t output_size = graph_outputs.size();
py::tuple result(output_size);
for (size_t i = 0; i < output_size; i++) {
MS_EXCEPTION_IF_NULL(graph_outputs[i]);
result[i] = *graph_outputs[i];
}
*status = PYNATIVE_SUCCESS;
MS_LOG(INFO) << "RunOpInGe end";
return std::move(result);
}
} // namespace pynative
} // namespace mindspore

46
mindspore/ccsrc/pynative/pynative_execute_ge.h Normal file
View File

@ -0,0 +1,46 @@
/**
* 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_PYNATIVE_PYNATIVE_EXECUTE_GE_H_
#define MINDSPORE_CCSRC_PYNATIVE_PYNATIVE_EXECUTE_GE_H_
#include <vector>
#include <utility>
#include <string>
#include <memory>
#include <unordered_map>
#include "pynative/base.h"
#include "transform/convert.h"
#include "transform/graph_runner.h"
#include "transform/types.h"
#include "utils/context/ms_context.h"
using GeTensor = ge::Tensor;
using GeTensorPtr = std::shared_ptr<GeTensor>;
using GeGraph = ge::Graph;
using GeGraphPtr = std::shared_ptr<GeGraph>;
namespace mindspore {
namespace pynative {
bool BuildSingleOpGraph(const OpExecInfoPtr& op_exec_info, const std::vector<GeTensorPtr>& inputs,
const std::unordered_map<std::string, ValuePtr>& attrs, const GeGraphPtr& graph);
py::object RunOpInGE(const OpExecInfoPtr& op_exec_info, PynativeStatusCode* status);
} // namespace pynative
} // namespace mindspore
#endif // MINDSPORE_CCSRC_PYNATIVE_PYNATIVE_EXECUTE_GE_H_

1
mindspore/ccsrc/session/ascend_session.cc
View File

@ -35,6 +35,7 @@
#include "pre_activate/common/helper.h"
#include "device/kernel_runtime_manager.h"
#include "kernel/tbe/tbe_python_funcs.h"
#include "utils/config_manager.h"
namespace mindspore {
namespace session {

2
mindspore/ccsrc/session/gpu_session.cc
View File

@ -19,7 +19,7 @@
#include "device/gpu/gpu_kernel_runtime.h"
#include "pre_activate/common/optimizer.h"
#include "pre_activate/common/pass_manager.h"
#include "pre_activate/ascend/ir_fusion/allreduce_fusion.h"
#include "pre_activate/common/ir_fusion/allreduce_fusion.h"
#include "device/kernel_runtime_manager.h"
#include "predict/predict.h"
#include "common/utils.h"

18
mindspore/ccsrc/transform/convert.cc
View File

@ -373,24 +373,6 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
}
// ---------------implement of DfGraphConvertor-------------
std::string GetCNodeFuncName(const CNodePtr cnode) {
if (cnode->inputs().empty()) {
return "";
}
AnfNodePtr valuenode = cnode->input(0);
if (valuenode->isa<ValueNode>()) {
auto value = GetValueNode(valuenode);
// check whether the valuenode is primitive
if (value->isa<Primitive>()) {
return value->cast<PrimitivePtr>()->name();
} else {
return value->ToString();
}
}
return "";
}
PrimType GetCNodeFuncType(const CNodePtr cnode) {
if (cnode->inputs().empty()) {
return kPrimTypeUnknown;

1
mindspore/ccsrc/transform/convert.h
View File

@ -253,7 +253,6 @@ class DfGraphConvertor {
bool distribute_ = false;
};
extern std::string GetCNodeFuncName(CNodePtr cnode);
} // namespace transform
} // namespace mindspore

13
mindspore/ccsrc/utils/callbacks.cc
View File

@ -20,16 +20,16 @@
#include <memory>
#include <vector>
#include "pybind11/pybind11.h"
#ifdef ENABLE_GE
#include "transform/df_graph_manager.h"
#include "transform/util.h"
#endif
#include "pipeline/parse/data_converter.h"
#include "pipeline/parse/python_adapter.h"
#include "utils/visible.h"
namespace mindspore {
namespace callbacks {
using mindspore::transform::Status;
using mindspore::transform::TransformUtil;
const char PYTHON_MOD_CALLBACK_MODULE[] = "mindspore.train.callback";
const char PYTHON_FUN_PROCESS_CHECKPOINT[] = "_checkpoint_cb_for_save_op";
@ -38,6 +38,10 @@ const char kSummary[] = "Summary";
const char kCheckPoint[] = "Save";
const int ONE_SHAPE = 1;
#ifdef ENABLE_GE
using mindspore::transform::Status;
using mindspore::transform::TransformUtil;
bool GetParameterShape(const FuncGraphPtr& graph, const std::string& param_name,
const std::shared_ptr<std::vector<int>>& shape) {
if (graph == nullptr) {
@ -181,6 +185,7 @@ uint32_t MS_EXPORT SummarySaveCallback(uint32_t graph_id, const std::map<std::st
MS_LOG(DEBUG) << "End the summary save callback function.";
return Status::SUCCESS;
}
#endif
// Cache the summary callback data from ME session
// Remove the GE module on new architecture
@ -208,10 +213,10 @@ uint32_t MS_EXPORT SummarySaveCallback(uint32_t graph_id, const std::map<std::st
auto bool_ret = py::cast<bool>(ret);
if (!bool_ret) {
MS_LOG(ERROR) << "Python checkpoint return false during callback";
return Status::FAILED;
return kCallbackFalied;
}
MS_LOG(DEBUG) << "End the summary save callback function.";
return Status::SUCCESS;
return kCallbackOk;
}
} // namespace callbacks
} // namespace mindspore

9
mindspore/ccsrc/utils/callbacks.h
View File

@ -20,8 +20,11 @@
#include <string>
#include <vector>
#include <memory>
#include "ir/meta_tensor.h"
#ifdef ENABLE_GE
#include "transform/types.h"
#include "transform/util.h"
#endif
namespace mindspore {
namespace callbacks {
@ -36,10 +39,16 @@ extern const char kSummary[];
extern const char kCheckPoint[];
extern const std::string kPythonCheckpointModuleName;
extern const std::string kPythonCheckpointFuncName;
const int kCallbackOk = 0;
const int kCallbackFalied = 1;
bool GetParameterShape(const FuncGraphPtr& anf_graph, const std::string& param_name,
const std::shared_ptr<std::vector<int>>& shape);
#ifdef ENABLE_GE
uint32_t CheckpointSaveCallback(uint32_t, const std::map<std::string, ge::Tensor>&);
uint32_t SummarySaveCallback(uint32_t, const std::map<std::string, ge::Tensor>&);
#endif
uint32_t SummarySaveCallback(uint32_t, const std::map<std::string, TensorPtr>&);
} // namespace callbacks

7
mindspore/ccsrc/utils/context/ms_context.cc
View File

@ -26,13 +26,15 @@
#include "tdt/tdt_host_interface.h"
#include "tdt/data_common.h"
#endif
#ifdef ENABLE_GE
#include "transform/df_graph_manager.h"
#endif
#include "ir/meta_tensor.h"
namespace mindspore {
#ifdef ENABLE_GE
using mindspore::transform::DfGraphManager;
using transform::GraphRunner;
using transform::GraphRunnerOptions;
#endif
std::atomic<bool> thread_1_must_end(false);
@ -81,6 +83,7 @@ MsContext::MsContext(const std::string& policy, const std::string& target) {
std::shared_ptr<MsContext> MsContext::GetInstance() {
if (inst_context_ == nullptr) {
MS_LOG(DEBUG) << "Create new mindspore context";
#ifdef ENABLE_GE
inst_context_.reset(new (std::nothrow) MsContext("ge", kAscendDevice));
#elif defined(ENABLE_D)

1
mindspore/ccsrc/utils/context/ms_context.h
View File

@ -23,7 +23,6 @@
#include <vector>
#include <string>
#include <utility>
#include "transform/graph_runner.h"
#include "utils/log_adapter.h"
namespace mindspore {

41
mindspore/ccsrc/utils/convert_utils.cc
View File

@ -373,4 +373,45 @@ AbstractBasePtr PyListDtype2AbstractTensor(const py::object &shape_obj, const py
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<py::object> &ret_val) {
if (output->isa<ValueNode>()) {
MS_LOG(INFO) << "Graph's output is a constant. No need to execute.";
ValuePtr value = GetValueNode(output);
*ret_val = ValuePtrToPyData(value);
return true;
}
// Adapter will transform values in __init__() and construct() to parameters, this could cause
// inputs (a.k.a args in current function) size less than parameters'.
if (output->isa<Parameter>()) {
MS_LOG(INFO) << "Graph's output is a parameter. If all params are inputs, no need to execute.";
if (args.empty()) {
MS_LOG(EXCEPTION) << "Inputs size is 0, let graph to be executed.";
}
// Find the right parameter as ret_val.
auto func_graph = output->func_graph();
MS_EXCEPTION_IF_NULL(func_graph);
auto params = func_graph->parameters();
if (params.empty()) {
MS_EXCEPTION(UnknownError) << "Graph's parameters size is 0";
}
if (args.size() != params.size()) {
MS_LOG(EXCEPTION) << "Input size " << args.size() << " not equal to params size " << params.size()
<< ", let graph to be executed.";
}
auto it = std::find(params.begin(), params.end(), output);
if (it == params.end()) {
MS_EXCEPTION(UnknownError) << "When graph output is Parameter, it should be found in graph parameters";
}
size_t index = it - params.cbegin();
if (index >= args.size()) {
MS_EXCEPTION(UnknownError) << "Index " << index << " equal or larger than args size " << args.size() << ".";
}
*ret_val = args[index];
return true;
}
return false;
}
} // namespace mindspore

4
mindspore/ccsrc/utils/convert_utils.h
View File

@ -18,6 +18,7 @@
#define MINDSPORE_CCSRC_UTILS_CONVERT_UTILS_H_
#include <limits>
#include <memory>
#include "pybind11/pybind11.h"
#include "utils/any.h"
@ -120,6 +121,9 @@ inline uint8_t *AddressOffset(void *address, size_t offset) {
AbstractBasePtr PyListDtype2AbstractTensor(const py::object &shape_obj, const py::object &type_obj);
bool IsGraphOutputValueNodeOrParameter(const AnfNodePtr &output, const py::tuple &args,
const std::shared_ptr<py::object> &ret_val);
} // namespace mindspore
#endif // MINDSPORE_CCSRC_UTILS_CONVERT_UTILS_H_

4
mindspore/ccsrc/vm/segment_runner.cc
View File

@ -178,14 +178,12 @@ LinConvertResult Convert(const AnfNodePtrList& lst) {
}
LinkFuncType MsVmConvert = Convert<VM>;
LinkFuncType GeVmConvert = Convert<GeVM>;
std::unordered_map<std::string, LinkFuncType> backends = {{kMsVm, MsVmConvert}, {kGeVm, GeVmConvert}};
std::unordered_map<std::string, LinkFuncType> backends = {{kMsVm, MsVmConvert}};
std::set<std::string> backend_list = {
kMsConvert,
kMsVm,
kGeVm,
};
} // namespace compile

16
mindspore/ccsrc/vm/transform.cc
View File

@ -24,7 +24,9 @@
#include <vector>
#include "pipeline/static_analysis/abstract_value.h"
#ifdef ENABLE_GE
#include "transform/convert.h"
#endif
#include "utils/graph_utils.h"
#include "utils/context/ms_context.h"
#include "debug/trace.h"
@ -55,7 +57,6 @@ CompileGraph::CompileGraph(const BackendPtr& backend, const std::vector<Primitiv
MS_LOG(INFO) << "Attribute 'is_gevm_convert' is true";
is_gevm_convert_ = true;
}
is_graph_cut = false;
}
bool CompileGraph::IsCut(const AnfNodePtr& node) {
@ -80,14 +81,15 @@ bool CompileGraph::IsCut(const AnfNodePtr& node) {
}
}
#ifdef ENABLE_GE
if (is_gevm_convert_) {
auto name = transform::GetCNodeFuncName(cnode);
auto name = GetCNodeFuncName(cnode);
auto adpt = transform::DfGraphConvertor::FindAdapter(name);
if (adpt == nullptr) {
is_graph_cut = true;
return true;
}
return true;
}
#endif
}
return false;
@ -605,12 +607,6 @@ FinalVMPtr CompileGraphs::CompileAndLink(const FuncGraphPtr& graph) {
(void)WrapPrimitives(graph);
Compile(graph);
#ifdef ENABLE_GE
if (!transform_->IsGraphCut()) {
return nullptr;
}
#endif
FinalVMPtr rt = Link(graph);
Reset();
MS_LOG(DEBUG) << "End";

2
mindspore/ccsrc/vm/transform.h
View File

@ -55,7 +55,6 @@ class CompileGraph {
InstSet Run(const FuncGraphPtr& func_graph);
InstSet GenMultiGraphsSinkInst(const FuncGraphPtr& graph);
bool IsGraphCut() const { return is_graph_cut; }
bool IsCut(const AnfNodePtr& node);
void Push(const AnfNodePtr& node);
void Tie(const AnfNodePtr& n1, const AnfNodePtr& n2) { slots_[n2] = slots_[n1]; }
@ -101,7 +100,6 @@ class CompileGraph {
BackendPtr backend_;
LinkFuncType lin_convert_;
bool is_gevm_convert_;
bool is_graph_cut;
int height_{0};
int max_height_{0};
std::vector<PrimitivePtr> cut_list_;

35
mindspore/ccsrc/vm/vmimpl.cc
View File

@ -26,8 +26,6 @@
#include <memory>
#include <set>
#include "transform/graph_runner.h"
#include "transform/convert.h"
#include "ir/meta_tensor.h"
#include "operator/ops.h"
#include "ir/manager.h"
@ -40,39 +38,6 @@ namespace compile {
using PrimitivePyPtr = std::shared_ptr<PrimitivePy>;
static const char SEGMENT_GRAPH_NAME[] = "runnable_segment";
VectorRef GeVM::RunGraph(const FuncGraphPtr& anf_graph, const VectorRef& args) {
// Convert graph
transform::DfGraphConvertor convertor(anf_graph);
(void)convertor.ConvertAllNode().BuildGraph();
if (convertor.ErrCode() == 0) {
(void)transform::DfGraphManager::GetInstance().AddGraph(SEGMENT_GRAPH_NAME, convertor.GetComputeGraph());
} else {
MS_LOG(EXCEPTION) << "convert df graph failed";
}
// Run graph
transform::GraphRunnerOptions options;
transform::GraphRunner graph_runner(options);
transform::RunOptions run_options;
run_options.name = SEGMENT_GRAPH_NAME;
std::vector<tensor::TensorPtr> inputs;
(void)std::transform(std::begin(args), std::end(args), std::back_inserter(inputs),
[](const BaseRef& arg) -> tensor::TensorPtr {
auto value_ref = utils::cast<PyObjectRef>(arg);
auto value = value_ref.object_;
return py::cast<tensor::TensorPtr>(value);
});
std::vector<tensor::TensorPtr> outputs;
(void)graph_runner.RunGraph(run_options, inputs, &outputs);
std::vector<BaseRef> ret;
(void)std::copy(outputs.begin(), outputs.end(), std::back_inserter(ret));
return VectorRef(ret);
}
// Indicate a call to a new frame.
struct CallWrap : public Base {
explicit CallWrap(const VMFramePtr& vm_frame) : frame(vm_frame) {}

6
mindspore/ccsrc/vm/vmimpl.h
View File

@ -64,12 +64,6 @@ class VMImpl {
virtual ~VMImpl() = default;
};
class GeVM : public VMImpl {
public:
VectorRef RunGraph(const FuncGraphPtr& fg, const VectorRef& args) override;
~GeVM() override = default;
};
// An execution frame.
// This holds the state for an application of a graph. The nodes list
// must contain free variables of graphs encountered before the

3
mindspore/common/api.py
View File

@ -22,7 +22,7 @@ from mindspore import context
from mindspore import log as logger
from mindspore.parallel._utils import _get_parallel_mode
from .._c_expression import generate_key, Executor_, Tensor, MetaTensor
from .._c_expression import verify_inputs_signature, init_exec_dataset, export_graph, _set_dataset_mode_config, init_ge
from .._c_expression import verify_inputs_signature, init_exec_dataset, _set_dataset_mode_config, init_ge
from .tensor import Tensor as MsTensor
# store ms_function class compiled pipeline cache
@ -501,6 +501,7 @@ class _Executor:
file_name (str): File name of model to export
file_format (str): MindSpore currently support 'GEIR' and 'ONNX' format for exported model
"""
from .._c_expression import export_graph
phase = 'export' + '.' + str(net.create_time)
export_graph(file_name, file_format, phase)

12
mindspore/common/parameter.py
View File

@ -155,6 +155,18 @@ class Parameter:
def data(self):
return self.default_input
def __add__(self, other):
return self.default_input + other
def __sub__(self, other):
return self.default_input - other
def __mul__(self, other):
return self.default_input * other
def __truediv__(self, other):
return self.default_input / other
def set_parameter_data(self, data):
if isinstance(data, (Tensor, list, int, float,
np.float16, np.float32, np.int32, np.int16, np.ndarray)) and not isinstance(data, bool):

10
mindspore/common/tensor.py
View File

@ -89,6 +89,16 @@ class Tensor(Tensor_):
out = self.__mul__(other)
return out
def __truediv__(self, other):
if isinstance(other, (int, float)):
other_tensor = Tensor(other, self.dtype())
elif isinstance(other, Tensor):
other_tensor = other
else:
raise TypeError("unsupported type for div operation")
out = tensor_operator_registry.get('__div__')(self, other_tensor)
return out
def __sub__(self, other):
if not isinstance(other, Tensor):
raise TypeError("input_data must be a tensor")

2
mindspore/ops/functional.py
View File

@ -125,5 +125,5 @@ shape_mul = Primitive("shape_mul")
stop_gradient = Primitive("stop_gradient")
tensor_operator_registry.register('__add__', tensor_add)
tensor_operator_registry.register('__mul__', tensor_mul)
tensor_operator_registry.register('__div__', tensor_div)

3
mindspore/train/model.py
View File

@ -161,6 +161,9 @@ class Model:
def _update_metrics(self, outputs):
"""Update metrics local values."""
if not isinstance(outputs, tuple):
raise ValueError("The `outputs` is not tuple.")
if self._eval_indexes is not None and len(outputs) < 3:
raise ValueError("The length of `outputs` must be greater than or equal to 3, \
but got {}".format(len(outputs)))

2
tests/ut/cpp/device/ascend_kernel_select_test.cc
View File

@ -231,7 +231,7 @@ void test_select(const CNodePtr &kernel_node, std::vector<std::shared_ptr<kernel
AnfAlgo::SetSelectKernelBuildInfo(selected_kernel_info_ptr, kernel_node.get());
}
void SetParentAbstract(std::vector<AnfNodePtr> parent_list, std::vector<vector<size_t>> shapes,
void SetParentAbstract(std::vector<AnfNodePtr> parent_list, std::vector<std::vector<size_t>> shapes,
std::vector<TypeId> types) {
for (const auto &node : parent_list) {
AnfAlgo::SetOutputInferTypeAndShape(types, shapes, node.get());

2
tests/ut/cpp/device/ascend_profiling_test.cc
View File

@ -16,10 +16,10 @@
#include <iostream>
#include <memory>
#include "./prof_reporter.h"
#include "common/common_test.h"
#include "device/ascend/profiling/profiling_manager.h"
#include "./common.h"
#include "./prof_reporter.h"
#define private public
#include "device/ascend/profiling/plugin_impl.h"
#undef private

2
tests/ut/cpp/pre_activate/ascend/ir_fusion/allreduce_fusion_test.cc → tests/ut/cpp/pre_activate/common/ir_fusion/allreduce_fusion_test.cc
View File

@ -20,7 +20,7 @@
#include "ir/manager.h"
#include "debug/anf_ir_dump.h"
#include "session/anf_runtime_algorithm.h"
#include "pre_activate/ascend/ir_fusion/allreduce_fusion.h"
#include "pre_activate/common/ir_fusion/allreduce_fusion.h"
#include "pre_activate/common/optimizer.h"
#include "device/kernel_info.h"
#include "pre_activate/common/pass_manager.h"

2
tests/ut/cpp/pre_activate/pass/convert_const_input_to_tensor_input_test.cc
View File

@ -105,7 +105,7 @@ TEST_F(TestHWConstInputToTensorInput, test_value_tuple_tensor_input) {
auto tensor = input1->cast<ValueNodePtr>()->value()->cast<tensor::TensorPtr>();
ASSERT_TRUE(tensor != nullptr);
auto data = tensor->data_c(false);
EXPECT_EQ(vector<int>((int *)data, (int *)data + 4), vector<int>({2, 4, 2, 2}));
EXPECT_EQ(std::vector<int>((int *)data, (int *)data + 4), std::vector<int>({2, 4, 2, 2}));
}
} // namespace opt
} // namespace mindspore

18
tests/ut/python/ir/test_tensor.py
View File

@ -24,6 +24,8 @@ import pytest
import mindspore as ms
import mindspore.common.api as me
import mindspore.nn as nn
from mindspore.common.parameter import Parameter
from mindspore.common.initializer import initializer
from ..ut_filter import non_graph_engine
@ -199,6 +201,21 @@ def test_sub():
z = x - y
assert isinstance(z, ms.Tensor)
@non_graph_engine
def test_div():
x = ms.Tensor(np.array([[2,6,10],[12, 4, 8]]).astype(np.float32))
y = ms.Tensor(np.array([[2,2,5],[6, 1, 2]]).astype(np.float32))
z = x / y
z2 = x / 2
assert isinstance(z, ms.Tensor)
assert isinstance(z2, ms.Tensor)
@non_graph_engine
def test_parameter():
x = Parameter(initializer(1, [1], ms.float32), name="beta1_power")
z = x / 2
print(z)
class Net(nn.Cell):
"""Net definition"""
@ -378,3 +395,4 @@ def test_tensor_dtype_fp32_to_bool():
input = np.random.randn(2, 3, 4, 5).astype(np.float32)
input = ms.Tensor(input)
input_me = ms.Tensor(input, dtype=ms.bool_)

14
tests/ut/python/ops/test_array_ops.py
View File

@ -97,20 +97,6 @@ def test_select():
assert np.all(output.asnumpy() == expect)
def test_scalar_cast_grad():
""" test_scalar_cast_grad """
input_x = 255.5
input_t = get_py_obj_dtype(ms.int8)
def fx_cast(x):
output = F.scalar_cast(x, input_t)
return output
gfn = C.grad(fx_cast)(input_x)
expect_dx = 1
assert gfn == expect_dx
class CustomOP(PrimitiveWithInfer):
__mindspore_signature__ = (sig_dtype.T, sig_dtype.T, sig_dtype.T1,
sig_dtype.T1, sig_dtype.T2, sig_dtype.T2,

5
tests/ut/python/parallel/__init__.py
View File

@ -13,11 +13,14 @@
# limitations under the License.
import mindspore.context as context
from mindspore.parallel._utils import _reset_op_id
def setup_module(module):
context.set_context(mode=context.GRAPH_MODE)
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False)
_reset_op_id()
def teardown_module():
context.reset_auto_parallel_context()
_reset_op_id()

11
tests/ut/python/parallel/test_alltoall.py
View File

@ -97,13 +97,10 @@ def test_all_to_all():
strategys = all_to_all_common(strategy1)
print(strategys)
expect_dict = {'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_loss_fn-SoftmaxCrossEntropyWithLogits'
'/SoftmaxCrossEntropyWithLogits-op43': [[8, 1], [8, 1]],
'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_loss_fn-SoftmaxCrossEntropyWithLogits'
'/OneHot-op44': [[8, 1], [], []],
'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_backbone-AllToAllNet/Transpose-op1':
[[8, 1]],
'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_backbone-AllToAllNet/MatMul-op0':
[[1, 1], [1, 8]]}
'/SoftmaxCrossEntropyWithLogits-op3': [[8, 1], [8, 1]],
'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_loss_fn-SoftmaxCrossEntropyWithLogits/OneHot-op4': [[8, 1], [], []],
'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_backbone-AllToAllNet/Transpose-op1': [[8, 1]],
'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_backbone-AllToAllNet/MatMul-op0': [[1, 1], [1, 8]]}
assert (strategys == expect_dict)
context.set_context(save_graphs=False)

16
tests/ut/python/parallel/test_auto_parallel_arithmetic.py
View File

@ -65,8 +65,8 @@ def test_auto_parallel_arithmetic():
b = Tensor(np.ones([64, 128]), dtype=ms.float32)
_executor.compile(net, x, y, b, phase='train')
strategies = _executor._get_strategy(net)
expected_strategies = {'Default/network-Net/FloorDiv-op2': [[2, 4], [2, 4]],
'Default/network-Net/MatMul-op3': [[2, 1], [1, 4]]}
expected_strategies = {'Default/network-Net/FloorDiv-op0': [[2, 4], [2, 4]],
'Default/network-Net/MatMul-op1': [[2, 1], [1, 4]]}
assert strategies == expected_strategies
def test_auto_parallel_arithmetic_broadcast_both():
@ -91,8 +91,8 @@ def test_auto_parallel_arithmetic_broadcast_both():
b = Tensor(np.ones([1, 64]), dtype=ms.float32)
_executor.compile(net, x, y, b, phase='train')
strategies = _executor._get_strategy(net)
expected_strategies = {'Default/network-Net/FloorDiv-op2': [[8, 1], [1, 1]],
'Default/network-Net/MatMul-op3': [[8, 1], [1, 1]]}
expected_strategies = {'Default/network-Net/FloorDiv-op0': [[8, 1], [1, 1]],
'Default/network-Net/MatMul-op1': [[8, 1], [1, 1]]}
assert strategies == expected_strategies
@ -118,8 +118,8 @@ def test_auto_parallel_arithmetic_broadcast_right():
b = Tensor(np.ones([32]), dtype=ms.float32)
_executor.compile(net, x, y, b, phase='train')
strategies = _executor._get_strategy(net)
expected_strategies = {'Default/network-Net/FloorDiv-op2': [[4, 2], [2]],
'Default/network-Net/MatMul-op3': [[4, 1], [1, 2]]}
expected_strategies = {'Default/network-Net/FloorDiv-op0': [[4, 2], [2]],
'Default/network-Net/MatMul-op1': [[4, 1], [1, 2]]}
assert strategies == expected_strategies
@ -145,6 +145,6 @@ def test_auto_parallel_arithmetic_broadcast_left():
b = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
_executor.compile(net, x, y, b, phase="train")
strategies = _executor._get_strategy(net)
expected_strategies = {'Default/network-Net/FloorDiv-op2': [[4, 2], [1, 4, 2]],
'Default/network-Net/MatMul-op3': [[4, 1], [1, 2]]}
expected_strategies = {'Default/network-Net/FloorDiv-op0': [[4, 2], [1, 4, 2]],
'Default/network-Net/MatMul-op1': [[4, 1], [1, 2]]}
assert strategies == expected_strategies

10
tests/ut/python/parallel/test_auto_parallel_assign_sub_with_ref_key.py
View File

@ -12,6 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import re
import numpy as np
from mindspore import context
import mindspore.nn as nn
@ -55,6 +56,9 @@ def test_auto_parallel_assign_sub_with_ref_key():
_executor.compile(net, x, phase="train")
strategies = _executor._get_strategy(net)
expected_strategies = {'Default/network-PReLU/PReLU-op2': [[1, 1, 1, 8], [1]],
'Default/network-PReLU/ReLU-op3': [[1]]}
assert strategies == expected_strategies
for (k, v) in strategies.items():
if re.search('PReLU-op', k) is not None:
assert v == [[1, 1, 1, 8], [1]]
elif re.search('ReLU-op', k) is not None:
assert v == [[1]]

10
tests/ut/python/parallel/test_auto_parallel_cast.py
View File

@ -75,9 +75,9 @@ def test_double_star_graph():
_executor.compile(net, x, y, z, w, phase='train')
strategies = _executor._get_strategy(net)
expected_strategies = {'Default/network-Net/MatMul-op0': [[1, 8], [8, 1]],
'Default/network-Net/Cast-op7': [[8, 1]],
'Default/network-Net/MatMul-op8': [[8, 1], [1, 1]],
'Default/network-Net/Cast-op9': [[1, 8]],
'Default/network-Net/MatMul-op10': [[1, 1], [1, 8]]}
expected_strategies = {'Default/network-Net/Cast-op1': [[8, 1]],
'Default/network-Net/Cast-op3': [[1, 8]],
'Default/network-Net/MatMul-op2': [[8, 1], [1, 1]],
'Default/network-Net/MatMul-op4': [[1, 1], [1, 8]],
'Default/network-Net/MatMul-op0': [[1, 8], [8, 1]]}
assert strategies == expected_strategies

8
tests/ut/python/parallel/test_auto_parallel_matmul_prelu.py
View File

@ -12,6 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import re
import numpy as np
from mindspore import context
import mindspore.nn as nn
@ -66,7 +67,10 @@ def test_matmul_prelu():
_executor.compile(net, x, y, b, phase='train')
strategies = _executor._get_strategy(net)
assert strategies['Default/network-Net/PReLU-op2'] == [[16, 1, 1, 1], [1]]
assert strategies['Default/network-Net/Mul-op3'] == [[16, 1, 1, 1], [16, 1, 1, 1]]
for (k, v) in strategies.items():
if re.search('PReLU-op', k) is not None:
assert v == [[16, 1, 1, 1], [1]]
elif re.search('Mul-op', k) is not None:
assert v == [[16, 1, 1, 1], [16, 1, 1, 1]]

8
tests/ut/python/parallel/test_auto_parallel_parameter_cast.py
View File

@ -80,9 +80,9 @@ def test_common_parameter():
_executor.compile(net, x, y, z, w, phase='train')
strategies = _executor._get_strategy(net)
expected_strategies = {'Default/network-Net/MatMul-op6': [[8, 1], [1, 1]],
'Default/network-Net/MatMul-op8': [[8, 1], [1, 1]],
'Default/network-Net/Cast-op7': [[1, 1]],
expected_strategies = {'Default/network-Net/MatMul-op1': [[8, 1], [1, 1]],
'Default/network-Net/MatMul-op3': [[8, 1], [1, 1]],
'Default/network-Net/Cast-op2': [[1, 1]],
'Default/network-Net/MatMul-op0': [[8, 1], [1, 1]],
'Default/network-Net/Cast-op9': [[1, 1]]}
'Default/network-Net/Cast-op4': [[1, 1]]}
assert strategies == expected_strategies

8
tests/ut/python/parallel/test_auto_parallel_transpose.py
View File

@ -71,8 +71,8 @@ def test_two_matmul_transpose():
_executor.compile(net, x, y, b, phase='train')
strategies = _executor._get_strategy(net)
expected_strategies = {'Default/network-Net/Transpose-op4': [[1, 16]],
'Default/network-Net/Transpose-op5': [[16, 1]],
'Default/network-Net/MatMul-op6': [[16, 1], [1, 1]],
'Default/network-Net/MatMul-op7': [[16, 1], [1, 1]]}
expected_strategies = {'Default/network-Net/Transpose-op0': [[1, 16]],
'Default/network-Net/Transpose-op1': [[16, 1]],
'Default/network-Net/MatMul-op2': [[16, 1], [1, 1]],
'Default/network-Net/MatMul-op3': [[16, 1], [1, 1]]}
assert strategies == expected_strategies

5
tests/ut/python/parallel/test_auto_parallel_two_matmul.py
View File

@ -135,7 +135,6 @@ def test_two_matmul():
_executor.compile(net, x, y, b, phase='train')
strategies = _executor._get_strategy(net)
expected_strategies = {'Default/network-Net/MatMul-op2': [[16, 1], [1, 1]],
'Default/network-Net/MatMul-op3': [[16, 1], [1, 1]]}
expected_strategies = {'Default/network-Net/MatMul-op0': [[16, 1], [1, 1]],
'Default/network-Net/MatMul-op1': [[16, 1], [1, 1]]}
assert strategies == expected_strategies

2
tests/ut/python/parallel/test_dataset_interface.py
View File

@ -84,7 +84,7 @@ def loss_scale_manager_common(strategy1):
opt = Momentum(net.trainable_params(), learning_rate, momentum)
scale_manager = DynamicLossScaleManager(32, 2, 2000)
model = Model(net, loss, opt, loss_scale_manager=scale_manager)
# if no GE exists, outputs = self._train_network(*next_element) outputs is None, TypeError is caught.
# if no GE exists, outputs = self._train_network(*next_element) outputs inputs tensor.
try:
model.train(epoch_size, dataset, dataset_sink_mode=False)
except TypeError:

20
tests/ut/python/parallel/test_one_dev.py
View File

@ -12,6 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from mindspore.train import Model, ParallelMode
from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
from mindspore.nn.optim.momentum import Momentum
@ -89,16 +90,13 @@ def all_to_all_common():
def test_one_dev():
_reset_op_id()
strategys = all_to_all_common()
expect_dict = {'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_loss_fn-SoftmaxCrossEntropyWithLogits'
'/SoftmaxCrossEntropyWithLogits-op9': [[1, 1], [1, 1]],
'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_loss_fn-SoftmaxCrossEntropyWithLogits'
'/OneHot-op10': [[1, 1], [], []],
'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_backbone-AllToAllNet/Transpose-op11':
[[1, 1]],
'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_backbone-AllToAllNet/MatMul-op12':
[[1, 1], [1, 1]]}
assert (strategys == expect_dict)
strategies = all_to_all_common()
for (k, v) in strategies.items():
if re.search('SoftmaxCrossEntropyWithLogits-op', k) is not None:
assert v == [[1, 1], [1, 1]]
elif re.search('Transpose-op', k) is not None:
assert v == [[1, 1]]
elif re.search('MatMul-op', k) is not None:
assert v == [[1, 1], [1, 1]]

3
tests/ut/python/pipeline/parse/test_create_obj.py
View File

@ -24,6 +24,7 @@
import logging
import numpy as np
import mindspore.nn as nn
from mindspore import context
from mindspore.ops import operations as P
from mindspore.common.api import ms_function
from mindspore.common.tensor import Tensor
@ -50,6 +51,7 @@ class Net(nn.Cell):
def test_create_cell_object_on_construct():
""" test_create_cell_object_on_construct """
log.debug("begin test_create_object_on_construct")
context.set_context(mode=context.GRAPH_MODE)
np1 = np.random.randn(2, 3, 4, 5).astype(np.float32)
input_me = Tensor(np1)
@ -118,6 +120,7 @@ class NetC(nn.Cell):
def test_create_cell_object_on_construct_use_many_parameter():
""" test_create_cell_object_on_construct_use_many_parameter """
log.debug("begin test_create_object_on_construct")
context.set_context(mode=context.GRAPH_MODE)
np1 = np.random.randn(2, 3, 4, 5).astype(np.float32)
input_me = Tensor(np1)

3
tests/ut/python/pipeline/parse/test_dtype.py
View File

@ -28,5 +28,4 @@ def try_type():
def test_dtype_convert():
with pytest.raises(RuntimeError):
try_type()
try_type()

16
tests/ut/python/pynative_mode/ops/test_grad.py
View File

@ -19,8 +19,10 @@ from mindspore.common.api import ms_function
from mindspore import Tensor
from mindspore.ops import composite as C
from mindspore.ops.composite import grad_all_with_sens
from mindspore.common.dtype import get_py_obj_dtype
import mindspore.nn as nn
import mindspore.ops.operations as P
from mindspore.ops import functional as F
from ...ut_filter import non_graph_engine
@ -78,6 +80,20 @@ def test_cast_grad():
assert np.all(gout[0].asnumpy() == expect)
def test_scalar_cast_grad():
""" test_scalar_cast_grad """
input_x = 255.5
input_t = get_py_obj_dtype(ms.int8)
def fx_cast(x):
output = F.scalar_cast(x, input_t)
return output
gfn = C.grad(fx_cast)(input_x)
expect_dx = 1
assert gfn == expect_dx
@non_graph_engine
def test_reshape_grad():
""" test_reshape_grad """

21
tests/ut/python/train/summary/test_summary_ops_params_valid_check.py
View File

@ -163,12 +163,7 @@ def test_scalar_summary_use_invalid_tag_None():
def test_scalar_summary_use_invalid_tag_Bool():
log.debug("begin test_scalar_summary_use_invalid_tag_Bool")
net = SummaryDemoTag(True, True, True)
try:
run_case(net)
except:
assert True
else:
assert False
run_case(net)
log.debug("finished test_scalar_summary_use_invalid_tag_Bool")
@ -176,12 +171,7 @@ def test_scalar_summary_use_invalid_tag_Bool():
def test_scalar_summary_use_invalid_tag_null():
log.debug("begin test_scalar_summary_use_invalid_tag_null")
net = SummaryDemoTag("", "", "")
try:
run_case(net)
except:
assert True
else:
assert False
run_case(net)
log.debug("finished test_scalar_summary_use_invalid_tag_null")
@ -189,12 +179,7 @@ def test_scalar_summary_use_invalid_tag_null():
def test_scalar_summary_use_invalid_tag_Int():
log.debug("begin test_scalar_summary_use_invalid_tag_Int")
net = SummaryDemoTag(1, 2, 3)
try:
run_case(net)
except:
assert True
else:
assert False
run_case(net)
log.debug("finished test_scalar_summary_use_invalid_tag_Int")

3
tests/ut/python/utils/test_serialize.py
View File

@ -30,7 +30,7 @@ from mindspore.nn import WithLossCell, TrainOneStepCell
from mindspore.train.callback import _CheckpointManager
from mindspore.train.serialization import save_checkpoint, load_checkpoint,load_param_into_net, \
_exec_save_checkpoint, export, _save_graph
from ..ut_filter import run_on_onnxruntime
from ..ut_filter import run_on_onnxruntime, non_graph_engine
from mindspore import context
@ -306,6 +306,7 @@ class MYNET(nn.Cell):
return out
@non_graph_engine
def test_export():
net = MYNET()
input_data = Tensor(np.random.randint(0, 255, [1, 3, 224, 224]).astype(np.float32))