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!22665 fix pclint and error log 

Merge pull request !22665 from baihuawei/fixcodedex
This commit is contained in:
i-robot 2021-09-03 03:24:09 +00:00 committed by Gitee
parent c1ebbce297 af0d315279
commit 1da5f2faa0
16 changed files with 47 additions and 46 deletions
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16
mindspore/ccsrc/backend/kernel_compiler/aicpu/aicpu_kernel_build.cc
View File

@ -282,11 +282,11 @@ bool CreateNodeDefBytes(const std::shared_ptr<AnfNode> &anf_node,
uint64_t SetExtInfoShapeType(char *ext_info_buf, uint64_t ext_info_offset, UnknowShapeOpType type) {
// deal1: unknown shape type
auto *info = reinterpret_cast<ExtInfo *>(ext_info_buf + ext_info_offset);
info->infoType = FWK_ADPT_EXT_SHAPE_TYPE;
info->infoType = static_cast<int32_t>(FWK_ADPT_EXT_SHAPE_TYPE);
info->infoLen = sizeof(int32_t);
ext_info_offset += kExtInfoHeadSize;
auto *shape_type = reinterpret_cast<int32_t *>(ext_info_buf + ext_info_offset);
*shape_type = type;
*shape_type = static_cast<int32_t>(type);
ext_info_offset += info->infoLen;
return ext_info_offset;
}
@ -295,8 +295,8 @@ uint64_t SetExtInfoInputShapeType(char *ext_info_buf, uint64_t ext_info_offset,
const std::shared_ptr<AnfNode> &anf_node, size_t input_num) {
// deal2:input ShapeAndType
auto *info = reinterpret_cast<ExtInfo *>(ext_info_buf + ext_info_offset);
info->infoType = FWK_ADPT_EXT_INPUT_SHAPE;
info->infoLen = input_num * sizeof(ShapeAndType);
info->infoType = static_cast<int32_t>(FWK_ADPT_EXT_INPUT_SHAPE);
info->infoLen = SizeToInt(input_num * sizeof(ShapeAndType));
ext_info_offset += kExtInfoHeadSize;
auto *inputs = reinterpret_cast<ShapeAndType *>(ext_info_buf + ext_info_offset);
@ -335,8 +335,8 @@ uint64_t SetExtInfoOutputShapeType(char *ext_info_buf, uint64_t ext_info_offset,
const std::shared_ptr<AnfNode> &anf_node, size_t output_num) {
// deal3:output ShapeAndType
auto *info = reinterpret_cast<ExtInfo *>(ext_info_buf + ext_info_offset);
info->infoType = FWK_ADPT_EXT_OUTPUT_SHAPE;
info->infoLen = output_num * sizeof(ShapeAndType);
info->infoType = static_cast<int32_t>(FWK_ADPT_EXT_OUTPUT_SHAPE);
info->infoLen = SizeToInt(output_num * sizeof(ShapeAndType));
ext_info_offset += kExtInfoHeadSize;
auto *outputs = reinterpret_cast<ShapeAndType *>(ext_info_buf + ext_info_offset);
@ -418,9 +418,7 @@ KernelModPtr AicpuOpBuild(const std::shared_ptr<AnfNode> &anf_node) {
MS_LOG(EXCEPTION) << "Create nodeDefBytes failed!";
}
if (!CreateExtInfo(anf_node, kernel_mod_ptr)) {
MS_LOG(EXCEPTION) << "Create nodeDefBytes failed!";
}
CreateExtInfo(anf_node, kernel_mod_ptr);
if (!SetIOSize(anf_node, kernel_mod_ptr)) {
MS_LOG(EXCEPTION) << "Set input output size list failed.";

2
mindspore/ccsrc/backend/kernel_compiler/rts/assign.cc
View File

@ -50,7 +50,7 @@ bool AssignKernel::Launch(const std::vector<AddressPtr> &inputs, const std::vect
std::vector<TaskInfoPtr> AssignKernel::GenTask(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &,
const std::vector<AddressPtr> &, uint32_t stream_id) {
if (inputs.size() != 2) {
MS_LOG(EXCEPTION) << "inputs size is not two";
MS_LOG(EXCEPTION) << "inputs size is not two, got " << inputs.size();
}
stream_id_ = stream_id;

13
mindspore/ccsrc/backend/kernel_compiler/rts/memcpy_async.cc
View File

@ -52,7 +52,7 @@ bool MemCpyAsyncKernel::Launch(const std::vector<AddressPtr> &inputs, const std:
return true;
}
if (outputs[0]->size < inputs[0]->size) {
MS_LOG(EXCEPTION) << "rtMemcpyAsync destMax < src size";
MS_LOG(EXCEPTION) << "rtMemcpyAsync destMax " << outputs[0]->size << " is less than src size " << inputs[0]->size;
}
// input x -> memcpy_async -> AllReduce
if (outputs[0]->size > inputs[0]->size) {
@ -78,7 +78,7 @@ void MemCpyAsyncKernel::GetInputOutputDataType(const AnfNodePtr &anf_node) {
MS_EXCEPTION_IF_NULL(anf_node);
size_t input_size = AnfAlgo::GetInputTensorNum(anf_node);
if (input_size != 1) {
MS_LOG(EXCEPTION) << "MemCpyAsync input size is not 1";
MS_LOG(EXCEPTION) << "MemCpyAsync input size is not 1, got " << input_size;
}
input_type_id_ = AnfAlgo::GetPrevNodeOutputDeviceDataType(anf_node, 0);
}
@ -87,7 +87,7 @@ void MemCpyAsyncKernel::GetInputOutputTotalCount(const AnfNodePtr &anf_node) {
MS_EXCEPTION_IF_NULL(anf_node);
size_t input_size = AnfAlgo::GetInputTensorNum(anf_node);
if (input_size != 1) {
MS_LOG(EXCEPTION) << "MemCpyAsync input size is not 1";
MS_LOG(EXCEPTION) << "MemCpyAsync input size is not 1, got " << input_size;
}
size_t type_size = abstract::TypeIdSize(input_type_id_);
std::vector<size_t> shape_i = AnfAlgo::GetInputDeviceShape(anf_node, 0);
@ -134,15 +134,16 @@ device::DynamicKernelPtr MemCpyAsyncKernel::GenDynamicKernel(const CNodePtr &cno
device::KernelRuntime::GenLaunchArgs(*this, cnode_ptr, &kernel_inputs, &kernel_workspaces, &kernel_outputs);
if (kernel_inputs.size() != 1) {
MS_LOG(EXCEPTION) << "MemCpyAsync op inputs is not one";
MS_LOG(EXCEPTION) << "MemCpyAsync op inputs is not one, got " << kernel_inputs.size();
}
if (kernel_outputs.size() != 1) {
MS_LOG(EXCEPTION) << "MemCpyAsync op output is not one";
MS_LOG(EXCEPTION) << "MemCpyAsync op output is not one, got " << kernel_outputs.size();
}
if (kernel_outputs[0]->size < kernel_inputs[0]->size) {
MS_LOG(EXCEPTION) << "Check rtMemcpyAsync destMax < src size";
MS_LOG(EXCEPTION) << "rtMemcpyAsync destMax " << kernel_outputs[0]->size << " is less than src size "
<< kernel_inputs[0]->size;
}
// input x -> memcpy_async -> AllReduce
if (kernel_outputs[0]->size > kernel_inputs[0]->size) {

2
mindspore/ccsrc/backend/kernel_compiler/rts/stream_active.cc
View File

@ -35,7 +35,7 @@ bool StreamActiveKernel::Init(const AnfNodePtr &anf_node) {
auto primitive = AnfAlgo::GetCNodePrimitive(anf_node);
MS_EXCEPTION_IF_NULL(primitive);
if (!AnfAlgo::HasNodeAttr(kAttrActiveStreamList, anf_node->cast<CNodePtr>())) {
MS_LOG(EXCEPTION) << "StreamActiveKernel has no attr kAttrActiveStreamList";
MS_LOG(EXCEPTION) << "StreamActiveKernel " << anf_node->DebugString() << "has no attr kAttrActiveStreamList";
}
active_streams_index_ = GetValue<std::vector<uint32_t>>(primitive->GetAttr(kAttrActiveStreamList));
return true;

5
mindspore/ccsrc/backend/kernel_compiler/rts/tensor_copy_slices.cc
View File

@ -85,7 +85,7 @@ void TensorCopySlices::GetInputOutputInfo(const AnfNodePtr &anf_node) {
MS_EXCEPTION_IF_NULL(anf_node);
size_t input_size = AnfAlgo::GetInputTensorNum(anf_node);
if (input_size != 2) {
MS_LOG(EXCEPTION) << "TensorCopySlices input size is not 2";
MS_LOG(EXCEPTION) << "TensorCopySlices input size is not 2, got " << input_size;
}
input_type_id_ = AnfAlgo::GetPrevNodeOutputDeviceDataType(anf_node, 0);
update_type_id_ = AnfAlgo::GetPrevNodeOutputDeviceDataType(anf_node, 0);
@ -138,7 +138,8 @@ std::vector<TaskInfoPtr> TensorCopySlices::GenTask(const std::vector<AddressPtr>
MS_LOG(EXCEPTION) << "outputs size is not 1.";
}
if (outputs[0]->size != inputs[0]->size) {
MS_LOG(EXCEPTION) << "TensorCopySlices input size and output size not equal.";
MS_LOG(EXCEPTION) << "TensorCopySlices input size " << inputs[0]->size << " is not equal to output size "
<< outputs[0]->size;
}
stream_id_ = stream_id;

2
mindspore/ccsrc/backend/optimizer/cpu/insert_format_transform_op.cc
View File

@ -95,7 +95,7 @@ void ProcessForTupleItem(const FuncGraphPtr &graph, const AnfNodePtr &node, int
auto used_node = used_node_list->at(i).first;
auto used_node_index = used_node_list->at(i).second - 1;
if (AnfAlgo::GetCNodeName(used_node) == prim::kPrimTupleGetItem->name()) {
MS_LOG(EXCEPTION) << "The used node of tuple item can't be tuple item.";
MS_LOG(EXCEPTION) << "The used node of tuple item " << used_node->DebugString() << " can't be tuple item.";
}
// node->used_node, if output format of node equals input format of used_node,

9
mindspore/ccsrc/backend/session/ascend_session.cc
View File

@ -225,14 +225,13 @@ size_t LoadCtrlInputTensor(const std::shared_ptr<KernelGraph> &graph, std::vecto
*next_val = 0;
next_loop_tensor->set_sync_status(kNeedSyncHostToDevice);
// set loop_count to zero
MS_EXCEPTION_IF_NULL(inputs);
inputs->push_back(next_loop_tensor);
auto epoch_tensor = (*inputs_params)[kLoopSinkEpochIndex];
MS_EXCEPTION_IF_NULL(epoch_tensor);
auto *epoch_val = static_cast<int32_t *>(epoch_tensor->data_c());
MS_EXCEPTION_IF_NULL(epoch_val);
*epoch_val = graph->current_epoch();
*epoch_val = SizeToInt(graph->current_epoch());
epoch_tensor->set_sync_status(kNeedSyncHostToDevice);
inputs->push_back(epoch_tensor);
MS_LOG(DEBUG) << "Load epoch_val:" << *epoch_val;
@ -611,7 +610,7 @@ void AscendSession::PreExecuteGraph(const std::shared_ptr<KernelGraph> &kernel_g
}
void AscendSession::PostExecuteGraph(const std::shared_ptr<KernelGraph> &kernel_graph,
const std::vector<tensor::TensorPtr> &inputs, VectorRef *const) {
const std::vector<tensor::TensorPtr> &, VectorRef *const) {
// summary
Summary(kernel_graph.get());
// load tensor from device for debugger
@ -1517,7 +1516,7 @@ void AscendSession::SyncInitialTenosrToDevice() {
auto backend_parameter = graph_inputs[input_idx];
// sync data from host to device
MS_EXCEPTION_IF_NULL(front_tensor);
size_t tensor_size = front_tensor->data().nbytes();
size_t tensor_size = LongToSize(front_tensor->data().nbytes());
auto addr = AnfAlgo::GetOutputAddr(backend_parameter, 0);
MS_EXCEPTION_IF_NULL(addr);
if (!addr->SyncHostToDevice(trans::GetRuntimePaddingShape(backend_parameter, 0), tensor_size,
@ -1560,7 +1559,7 @@ void AscendSession::SelectKernel(NotNull<KernelGraphPtr> root_graph) {
size_t reduce_precision_count = 0;
std::set<KernelGraphPtr> memo;
(void)RecurseSelectKernelInfo(root_graph, NOT_NULL(&memo), &raise_precision_count, &reduce_precision_count);
RecurseSelectKernelInfo(root_graph, NOT_NULL(&memo), &raise_precision_count, &reduce_precision_count);
memo.clear();
auto ms_context = MsContext::GetInstance();

5
mindspore/ccsrc/backend/session/cpu_session.cc
View File

@ -163,7 +163,8 @@ void CPUSession::LoadInputData(const std::shared_ptr<KernelGraph> &kernel_graph,
MS_EXCEPTION_IF_NULL(kernel_graph);
auto &input_nodes = kernel_graph->inputs();
if (input_nodes.size() != inputs_const.size()) {
MS_LOG(EXCEPTION) << "Input size not equal to input node size!";
MS_LOG(EXCEPTION) << "Input size " << inputs_const.size() << " is not equal to input node size "
<< input_nodes.size();
}
for (size_t input_idx = 0; input_idx < input_nodes.size(); ++input_idx) {
auto &input_node = input_nodes[input_idx];
@ -201,7 +202,7 @@ void CPUSession::PreExecuteGraph(const std::shared_ptr<KernelGraph> &kernel_grap
}
void CPUSession::PostExecuteGraph(const std::shared_ptr<KernelGraph> &kernel_graph,
const std::vector<tensor::TensorPtr> &inputs, VectorRef *const outputs) {
const std::vector<tensor::TensorPtr> &, VectorRef *const) {
Summary(kernel_graph.get());
}

2
mindspore/ccsrc/pipeline/jit/pipeline.cc
View File

@ -1262,7 +1262,7 @@ void InitHccl() {
if (!task_sink && mode == kGraphMode) {
MS_LOG(INFO) << "mpi collective init.";
if (!HcclCollectiveGroup::instance().InitCollective()) {
MS_LOG(EXCEPTION) << "HcclCollectiveGroup init failed.";
MS_LOG(EXCEPTION) << "Mpi init failed, please check if mpirun is used correctly.";
}
device_id = IntToUint(HcclCollectiveGroup::instance().GetDeviceId());
ms_context->set_param<uint32_t>(MS_CTX_DEVICE_ID, device_id);

3
mindspore/ccsrc/runtime/device/ascend/distribute/ascend_collective.cc
View File

@ -48,7 +48,8 @@ bool HcclCollectiveGroup::InitCollective() {
MS_LOG(EXCEPTION)
<< "Loading libascend_collective.so failed. Many reasons could cause this:\n1.libascend_collective.so is not "
"installed.\n2.hccl is not "
"installed or found.\n3.mpi is not installed or found";
"installed or found.\n3.mpi is not installed or found, please check if lib files of OpenMPI is added to "
"LD_LIBRATY_PATH.";
}
init_mpi_ = DlsymFuncObj(InitMPI, collective_handle_);
finalize_mpi_ = DlsymFuncObj(FinalizeMPI, collective_handle_);

2
mindspore/ccsrc/runtime/device/ascend/kernel_select_ascend.cc
View File

@ -214,7 +214,7 @@ std::shared_ptr<kernel::KernelBuildInfo> ChooseMatchedKernelInfo(
UpdateCurMatchCounts(*kernel_info_ptr, kernel_node, &cur_kernel_info_match_counts);
// Currently the selection policy is the match format count first, and then is datatype counts.
if (PriorityChooseItem(cur_kernel_info_match_counts, &most_match_counts)) {
selected_index = SizeToInt(info_index);
selected_index = info_index;
}
}
return kernel_info_list[selected_index];

4
mindspore/ccsrc/runtime/device/cpu/cpu_device_address.cc
View File

@ -96,8 +96,8 @@ bool CPUDeviceAddress::SyncDeviceToHost(const ShapeVector &, size_t size, TypeId
return true;
}
bool CPUDeviceAddress::SyncHostToDevice(const ShapeVector & /* shape */, size_t size, TypeId type, const void *host_ptr,
const std::string &format) const {
bool CPUDeviceAddress::SyncHostToDevice(const ShapeVector &, size_t size, TypeId type, const void *host_ptr,
const std::string &) const {
// The input or output may be empty.
if ((size == 0) || (size_ == 0)) {
MS_LOG(INFO) << "No need sync, host size: " << size << ", device size: " << size_;

12
mindspore/ccsrc/runtime/device/cpu/cpu_kernel_runtime.cc
View File

@ -189,7 +189,8 @@ tensor::TensorPtr CPUKernelRuntime::CreatTensorForOutput(
MS_EXCEPTION_IF_NULL(tensor_to_node);
size_t output_size = AnfAlgo::GetOutputTensorNum(node);
if (index >= output_size) {
MS_LOG(EXCEPTION) << "Invalid input index " << index;
MS_LOG(EXCEPTION) << "For node " << node->DebugString() << ", index " << index << " exceed output size "
<< output_size;
}
auto address = AnfAlgo::GetMutableOutputAddr(node, index);
MS_EXCEPTION_IF_NULL(address);
@ -204,6 +205,9 @@ tensor::TensorPtr CPUKernelRuntime::CreatTensorForOutput(
tensor = kernel_graph->GetInternalOutputTensor(node, index);
if (tensor == nullptr) {
size_t type_size = GetTypeByte(TypeIdToType(device_type_id));
if (type_size == 0) {
MS_LOG(EXCEPTION) << "Invalid type_size " << type_size;
}
size_t tensor_size = std::accumulate(temp_shape.begin(), temp_shape.end(), type_size, std::multiplies<size_t>());
if (tensor_size < address->size_) {
temp_shape.clear();
@ -276,7 +280,7 @@ void CPUKernelRuntime::CreateOutputTensors(session::KernelGraph *kernel_graph,
MS_EXCEPTION_IF_NULL(tensor_to_node);
auto &input_nodes = kernel_graph->inputs();
if (input_nodes.size() != inputs.size()) {
MS_LOG(EXCEPTION) << "Input size not equal to input node size!";
MS_LOG(EXCEPTION) << "Input size " << inputs.size() << " is not equal to input node size " << input_nodes.size();
}
size_t input_idx = 0;
@ -300,7 +304,7 @@ void CPUKernelRuntime::BindInputTensorAddressPtr(const session::KernelGraph &ker
const std::vector<tensor::TensorPtr> &inputs) {
auto &input_nodes = kernel_graph.inputs();
if (input_nodes.size() != inputs.size()) {
MS_LOG(EXCEPTION) << "Input size not equal to input node size!";
MS_LOG(EXCEPTION) << "Input size" << inputs.size() << " is not equal to input node size " << input_nodes.size();
}
for (size_t input_idx = 0; input_idx < input_nodes.size(); ++input_idx) {
auto &item = input_nodes[input_idx];
@ -344,9 +348,11 @@ void CPUKernelRuntime::BindInputTensorAddressPtr(const session::KernelGraph &ker
AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(item, 0)}, {shape_tmp}, item.get());
}
address->ref_count_ = INIT_NODE_REF;
if (AnfAlgo::IsParameterWeight(input_param)) {
tensor->set_device_address(address);
}
}
}
void CPUKernelRuntime::BindOutputTensorAddressPtr(const VectorRef *outputs) {
MS_EXCEPTION_IF_NULL(outputs);

10
mindspore/ccsrc/runtime/device/cpu/cpu_memory_manager.cc
View File

@ -72,15 +72,9 @@ void CPUMemoryManager::ResetDynamicMemory() {
dynamic_mem_.clear();
}
CPUMemoryManager::~CPUMemoryManager() {
try {
MemFree();
} catch (std::exception &e) {
MS_LOG(EXCEPTION) << "MemFree exception in ~CPUMemoryManager(), " << e.what();
}
}
CPUMemoryManager::~CPUMemoryManager() { MemFree(); }
void CPUMemoryManager::MemFree() {
void CPUMemoryManager::MemFree() noexcept {
if (mem_ptr_ != nullptr) {
mem_ptr_ = nullptr;
mem_size_ = 0;

2
mindspore/ccsrc/runtime/device/cpu/cpu_memory_manager.h
View File

@ -58,7 +58,7 @@ class CPUMemoryManager : public MemoryManager {
private:
uint8_t *MemMalloc(size_t size);
void MemFree();
void MemFree() noexcept;
CPUSimpleMemPlan mem_plan_;
size_t mem_size_{0};

2
mindspore/parallel/_utils.py
View File

@ -50,7 +50,7 @@ def _check_task_sink_envs():
return True if task_sink environment variables have been exported, False otherwise.
"""
import os
task_sink = os.getenv("SINGLE_OP_MODE")
task_sink = os.getenv("GRAPH_OP_RUN")
if task_sink:
try:
if int(task_sink) == 1: