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executor and subgraph optimize

This commit is contained in:
ling 2021-03-31 10:34:50 +08:00
parent d346a861bc
commit def805737c
8 changed files with 91 additions and 109 deletions
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21
mindspore/lite/src/common/tensor_util.cc
View File

@ -215,5 +215,26 @@ int GenerateInTensorC(const OpParameter *const parameter, const std::vector<lite
return ret;
}
int CheckTensorsInvalid(const std::vector<Tensor *> &tensors) {
for (auto &tensor : tensors) {
if (tensor == nullptr) {
MS_LOG(ERROR) << "check tensor is nullptr";
return RET_ERROR;
}
if (tensor->data_type() != kObjectTypeTensorType && tensor->data_c() == nullptr) {
MS_LOG(ERROR) << "check tensor data is nullptr " << tensors;
return RET_ERROR;
}
auto shape = tensor->shape();
bool valid = all_of(shape.begin(), shape.end(), [](int i) { return i >= 0; });
if (!valid) {
MS_LOG(ERROR) << "The shape of tensor contains negative dimension,"
<< "check the model and assign the input shape with method Resize().";
return RET_ERROR;
}
}
return RET_OK;
}
} // namespace lite
} // namespace mindspore

2
mindspore/lite/src/common/tensor_util.h
View File

@ -39,6 +39,8 @@ int GenerateInTensorC(const OpParameter *const parameter, const std::vector<lite
std::vector<lite::Tensor *> *outputs, std::vector<TensorC *> *in_tensor_c);
int GenerateOutTensorC(const OpParameter *const parameter, const std::vector<lite::Tensor *> &inputs,
std::vector<lite::Tensor *> *outputs, std::vector<TensorC *> *out_tensor_c);
int CheckTensorsInvalid(const std::vector<Tensor *> &tensors);
} // namespace lite
} // namespace mindspore

62
mindspore/lite/src/executor.cc
View File

@ -17,34 +17,14 @@
#include "src/executor.h"
#include <queue>
#include "include/errorcode.h"
#include "src/common/tensor_util.h"
namespace mindspore::lite {
int Executor::CheckInputs(const std::vector<Tensor *> &in_tensors) {
for (auto &inTensor : in_tensors) {
if (inTensor == nullptr) {
MS_LOG(ERROR) << "Graph input tensor is nullptr";
return RET_ERROR;
}
if (inTensor->data_type() != kObjectTypeTensorType && inTensor->data_c() == nullptr) {
MS_LOG(ERROR) << "Graph input tensor data is nullptr " << in_tensors;
return RET_ERROR;
}
auto shape = inTensor->shape();
bool valid = all_of(shape.begin(), shape.end(), [](int i) { return i >= 0; });
if (!valid) {
MS_LOG(ERROR) << "The shape of input tensor contains negative dimension,"
<< "check the model and assign the input shape with method Resize().";
return RET_ERROR;
}
}
return RET_OK;
}
int Executor::Run(const std::vector<Tensor *> &in_tensors, const std::vector<Tensor *> &out_tensors,
const std::vector<kernel::LiteKernel *> &kernels, mindspore::Allocator *allocator,
const KernelCallBack &before, const KernelCallBack &after) {
MS_ASSERT(nullptr != allocator);
auto ret = this->CheckInputs(in_tensors);
auto ret = CheckTensorsInvalid(in_tensors);
if (RET_OK != ret) {
MS_LOG(ERROR) << "CheckInputs failed";
return ret;
@ -88,42 +68,4 @@ int Executor::Run(const std::vector<Tensor *> &in_tensors, const std::vector<Ten
}
return RET_OK;
}
int CpuExecutor::Run(const std::vector<Tensor *> &in_tensors, const std::vector<Tensor *> &out_tensors,
const std::vector<kernel::LiteKernel *> &kernels, Allocator *allocator,
const KernelCallBack &before, const KernelCallBack &after) {
MS_ASSERT(nullptr != allocator);
// not check input for merge. too hard
if (kernels.front()->Type() != schema::PrimitiveType_Merge) {
auto ret = this->CheckInputs(in_tensors);
if (ret != RET_OK) {
MS_LOG(ERROR) << "CheckInputs failed";
return ret;
}
}
#ifdef SUPPORT_TRAIN
for (auto out_tensor : out_tensors) { // increase RefCount of output tensors, such that Run will not free them
out_tensor->set_ref_count(out_tensor->ref_count() + 1);
}
#endif
for (auto *kernel : kernels) {
MS_ASSERT(nullptr != kernel);
auto ret = kernel->PreProcess();
if (RET_OK != ret) {
MS_LOG(ERROR) << "PreProcess kernel failed, name: " << kernel->name();
return ret;
}
ret = kernel->Run(before, after);
if (RET_OK != ret) {
MS_LOG(ERROR) << "run kernel failed, name: " << kernel->name();
return ret;
}
ret = kernel->PostProcess();
if (RET_OK != ret) {
MS_LOG(ERROR) << "PostProcess kernel failed, name: " << kernel->name();
return ret;
}
}
return RET_OK;
}
} // namespace mindspore::lite

14
mindspore/lite/src/executor.h
View File

@ -33,20 +33,6 @@ class Executor {
virtual int Run(const std::vector<Tensor *> &in_tensors, const std::vector<Tensor *> &out_tensors,
const std::vector<kernel::LiteKernel *> &kernels, mindspore::Allocator *allocator = nullptr,
const KernelCallBack &before = nullptr, const KernelCallBack &after = nullptr);
protected:
static int CheckInputs(const std::vector<Tensor *> &in_tensors);
};
class CpuExecutor : public Executor {
public:
CpuExecutor() = default;
virtual ~CpuExecutor() = default;
int Run(const std::vector<Tensor *> &in_tensors, const std::vector<Tensor *> &out_tensors,
const std::vector<kernel::LiteKernel *> &kernels, mindspore::Allocator *allocator = nullptr,
const KernelCallBack &before = nullptr, const KernelCallBack &after = nullptr) override;
};
} // namespace mindspore::lite
#endif

3
mindspore/lite/src/mindrt_executor.cc
View File

@ -18,6 +18,7 @@
#include "src/mindrt_executor.h"
#include "src/lite_mindrt.h"
#include "include/errorcode.h"
#include "src/common/tensor_util.h"
namespace mindspore::lite {
@ -62,7 +63,7 @@ int MindrtExecutor::Run(const std::vector<Tensor *> &in_tensors, const std::vect
const KernelCallBack &before, const KernelCallBack &after) {
MS_ASSERT(nullptr != allocator);
if (kernels.front()->Type() != schema::PrimitiveType_Merge) {
auto ret = this->CheckInputs(in_tensors);
auto ret = CheckTensorsInvalid(in_tensors);
if (RET_OK != ret) {
MS_LOG(ERROR) << "CheckInputs failed";
return ret;

57
mindspore/lite/src/sub_graph_kernel.cc
View File

@ -22,6 +22,7 @@
#endif
#include "src/common/version_manager.h"
#include "src/runtime/infer_manager.h"
#include "src/common/tensor_util.h"
namespace mindspore::kernel {
using mindspore::lite::RET_ERROR;
@ -70,19 +71,6 @@ std::string SubGraphKernel::ToString() const {
return oss.str();
}
int SubGraphKernel::Run() {
if (this->executor_ == nullptr) {
MS_LOG(ERROR) << "executor is nullptr";
return RET_ERROR;
}
auto ret = executor_->Run(this->in_tensors_, this->out_tensors_, this->nodes_, this->context_->allocator.get());
if (ret != RET_OK) {
MS_LOG(ERROR) << "Run sub graph failed: " << ret;
return ret;
}
return RET_OK;
}
int SubGraphKernel::Run(const KernelCallBack &before, const KernelCallBack &after) {
if (this->executor_ == nullptr) {
MS_LOG(ERROR) << "executor is nullptr";
@ -165,15 +153,42 @@ int CpuSubGraph::Prepare() {
tensor->set_allocator(this->context_->allocator.get());
}
}
this->executor_ = new (std::nothrow) mindspore::lite::CpuExecutor;
if (this->executor_ == nullptr) {
MS_LOG(ERROR) << "new CpuExecutor failed";
return RET_ERROR;
return RET_OK;
}
int CpuSubGraph::Run(const KernelCallBack &before, const KernelCallBack &after) {
MS_ASSERT(nullptr != this->context_->allocator.get());
if (nodes_.front()->Type() != schema::PrimitiveType_Merge) {
auto ret = CheckTensorsInvalid(in_tensors_);
if (ret != RET_OK) {
MS_LOG(ERROR) << "CheckInputs failed";
return ret;
}
}
ret = this->executor_->Prepare(this->nodes_);
if (ret != RET_OK) {
MS_LOG(ERROR) << "Prepare CpuExecutor failed";
return ret;
#ifdef SUPPORT_TRAIN
for (auto out_tensor : out_tensors_) { // increase RefCount of output tensors, such that Run will not free them
out_tensor->set_ref_count(out_tensor->ref_count() + 1);
}
#endif
for (auto *kernel : nodes_) {
MS_ASSERT(nullptr != kernel);
auto ret = kernel->PreProcess();
if (RET_OK != ret) {
MS_LOG(ERROR) << "PreProcess kernel failed, name: " << kernel->name();
return ret;
}
ret = kernel->Run(before, after);
if (RET_OK != ret) {
MS_LOG(ERROR) << "run kernel failed, name: " << kernel->name();
return ret;
}
ret = kernel->PostProcess();
if (RET_OK != ret) {
MS_LOG(ERROR) << "PostProcess kernel failed, name: " << kernel->name();
return ret;
}
}
return RET_OK;
}

8
mindspore/lite/src/sub_graph_kernel.h
View File

@ -94,7 +94,7 @@ class SubGraphKernel : public LiteKernel {
// called before Run
int PreProcess() override { return mindspore::lite::RET_OK; }
int Run() override;
int Run() override { return Run(nullptr, nullptr); }
int Run(const KernelCallBack &before, const KernelCallBack &after) override;
// called after Run
@ -134,10 +134,8 @@ class CpuSubGraph : public SubGraphKernel {
int Prepare() override;
int Init() override { return SubGraphKernel::Init(); }
int PreProcess() override { return SubGraphKernel::PreProcess(); }
int Run() override { return SubGraphKernel::Run(); }
int Run(const KernelCallBack &before, const KernelCallBack &after) override {
return SubGraphKernel::Run(before, after);
};
int Run() override { return Run(nullptr, nullptr); }
int Run(const KernelCallBack &before, const KernelCallBack &after) override;
int PostProcess() override { return SubGraphKernel::PostProcess(); }
};

33
mindspore/lite/src/train/train_session.cc
View File

@ -35,6 +35,7 @@
#include "src/kernel_registry.h"
#include "src/runtime/kernel/arm/fp32_grad/convolution.h"
#include "src/runtime/kernel/arm/fp32/batchnorm_fp32.h"
#include "src/common/tensor_util.h"
namespace mindspore {
namespace lite {
@ -188,17 +189,33 @@ int TrainSession::RunGraph(const KernelCallBack &before, const KernelCallBack &a
return lite::RET_NULL_PTR;
}
auto run_kernel = (train_mode_) ? train_kernels_ : inference_kernels_;
lite::CpuExecutor executor;
auto ret = RET_OK;
if (before == nullptr && after == nullptr) {
ret = executor.Run(this->inputs_, this->outputs_, run_kernel, this->context_->allocator.get());
} else {
ret = executor.Run(this->inputs_, this->outputs_, run_kernel, this->context_->allocator.get(), before, after);
}
auto ret = CheckTensorsInvalid(inputs_);
if (ret != RET_OK) {
MS_LOG(ERROR) << "failed to run model";
MS_LOG(ERROR) << "CheckInputs failed";
return ret;
}
for (auto out_tensor : outputs_) { // increase RefCount of output tensors, such that Run will not free them
out_tensor->set_ref_count(out_tensor->ref_count() + 1);
}
for (auto *kernel : run_kernel) {
MS_ASSERT(nullptr != kernel);
ret = kernel->PreProcess();
if (RET_OK != ret) {
MS_LOG(ERROR) << "PreProcess kernel failed, name: " << kernel->name();
return ret;
}
ret = kernel->Run(before, after);
if (RET_OK != ret) {
MS_LOG(ERROR) << "run kernel failed, name: " << kernel->name();
return ret;
}
ret = kernel->PostProcess();
if (RET_OK != ret) {
MS_LOG(ERROR) << "PostProcess kernel failed, name: " << kernel->name();
return ret;
}
}
if (train_mode_ && virtual_batch_multiplier_) {
virtual_batch_idx_++;