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fix bugs, and fix coder graph

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pull/12443/head
yangjie159 4 years ago
parent 3c307979a4
commit eace3edd52
7 changed files with 148 additions and 139 deletions
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2
mindspore/lite/micro/coder/CMakeLists.txt
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@ -4,7 +4,7 @@ set(LITE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/../..)
set(3RD_DIR ${TOP_DIR}/third_party)
if(ENABLE_CONVERTER)
set(CODEGEN_PATH ${CMAKE_BINARY_DIR}/micro/coder/codegen)
elseif()
else()
set(CODEGEN_PATH ${CMAKE_BINARY_DIR}/coder/codegen)
endif()

137
mindspore/lite/micro/coder/graph.cc
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@ -21,8 +21,11 @@
#include <memory>
#include <algorithm>
#include <set>
#include "coder/log.h"
#include "schema/inner/model_generated.h"
#include "src/ops/primitive_c.h"
#include "securec/include/securec.h"
namespace mindspore::lite::micro {
CoderGraph::~CoderGraph() {
model_->Free();
@ -32,6 +35,140 @@ CoderGraph::~CoderGraph() {
}
}
int CoderGraph::ConvertTensors() {
if (model_ == nullptr) {
MS_LOG(ERROR) << "Graph model is nullptr";
return RET_ERROR;
}
std::vector<Tensor *> all_tensors;
auto clear_tensors = [&all_tensors]() {
std::for_each(all_tensors.begin(), all_tensors.end(), [](Tensor *&t) {
delete t;
t = nullptr;
});
all_tensors.clear();
};
auto check_dim = [](int dim) -> int {
MS_CHECK_TRUE(dim > 0, "invalid dim value!");
return RET_OK;
};
// deal with allTensors
uint32_t tensorCount = model_->all_tensors_.size();
for (uint32_t i = 0; i < tensorCount; ++i) {
schema::Tensor *origin_tensor = model_->all_tensors_.at(i);
MS_CHECK_PTR_WITH_EXE(origin_tensor, clear_tensors());
// tensor dims
std::vector<int> shape;
if (origin_tensor->nodeType() == schema::NodeType_ValueNode) {
MS_CHECK_PTR_WITH_EXE(origin_tensor->dims(), clear_tensors());
for (uint32_t j = 0; j < origin_tensor->dims()->size(); j++) {
MS_CHECK_PTR(origin_tensor->dims()->data());
int dim = static_cast<int>(origin_tensor->dims()->data()[j]);
MS_CHECK_RET_CODE_WITH_EXE(check_dim(dim), "parse shape failed!", clear_tensors());
shape.push_back(dim);
}
}
// tensor Datatype
int origin_data_type = static_cast<int>(origin_tensor->dataType());
Tensor *dstTensor = new (std::nothrow)
lite::Tensor(TypeId(origin_data_type), shape, origin_tensor->format(), TensorCategory(origin_tensor));
MS_CHECK_PTR(dstTensor);
if (origin_tensor->nodeType() == schema::NodeType_ValueNode && origin_tensor->data() != nullptr &&
origin_tensor->data()->size() > 0) {
if (shape.empty()) {
shape.push_back(1);
}
// copy data, this is weight && bias
MS_CHECK_TRUE_WITH_EXE(origin_tensor->data()->size() > 0, "invalid meta_tensor data size.", delete dstTensor);
auto data_size = static_cast<size_t>(origin_tensor->data()->size());
MS_CHECK_RET_CODE_WITH_EXE(dstTensor->MallocData(), "dst tensor malloc data failed!", delete dstTensor);
void *dst_data = dstTensor->data_c();
MS_CHECK_RET_CODE_WITH_EXE(memcpy_s(dst_data, data_size, origin_tensor->data()->data(), data_size),
"memcpy_s copy data failed!", delete dstTensor);
dstTensor->set_data(dst_data);
}
auto quant_params = origin_tensor->quantParams();
if (quant_params != nullptr) {
for (int j = 0; j < static_cast<int>(quant_params->size()); j++) {
QuantArg quant_arg{};
quant_arg.scale = quant_params->Get(j)->scale();
quant_arg.zeroPoint = quant_params->Get(j)->zeroPoint();
dstTensor->AddQuantParam(quant_arg);
}
}
all_tensors.emplace_back(dstTensor);
}
SetAllTensors(all_tensors);
return RET_OK;
}
int CoderGraph::InitGraphInOutTensors() {
if (model_ == nullptr) {
return RET_ERROR;
}
std::vector<size_t> graph_input_node_indexes = lite::GetGraphInputNodes(model_);
std::vector<uint32_t> input_indices;
for (auto in_node_index : graph_input_node_indexes) {
in_node_index = static_cast<uint32_t>(in_node_index);
auto *in_node = model_->all_nodes_.at(in_node_index);
if (in_node == nullptr) {
return RET_ERROR;
}
for (uint32_t i = 0; i < in_node->input_indices_.size(); i++) {
auto in_tensor_index = size_t(in_node->input_indices_.at(i));
bool is_graph_input = false;
for (uint32_t j = 0; j < model_->sub_graphs_.at(0)->input_indices_.size(); j++) {
if (in_tensor_index == size_t(model_->sub_graphs_.at(0)->input_indices_.at(j))) {
input_indices.push_back(static_cast<uint32_t>(in_tensor_index));
is_graph_input = true;
break;
}
}
if (!is_graph_input) {
continue;
}
if (in_tensor_index < all_tensors_.size()) {
lite::Tensor *in_tensor = all_tensors_.at(in_tensor_index);
AddInputMap(in_node->name_, in_tensor);
}
}
}
SetInputIndices(input_indices);
std::vector<uint32_t> output_indices;
auto graph_output_node_indexes = lite::GetGraphOutputNodes(model_);
for (auto out_node_index : graph_output_node_indexes) {
out_node_index = static_cast<uint32_t>(out_node_index);
auto *out_node = model_->all_nodes_.at(out_node_index);
for (uint32_t i = 0; i < out_node->output_indices_.size(); i++) {
auto out_tensor_index = size_t(out_node->output_indices_.at(i));
bool is_graph_output = false;
for (uint32_t j = 0; j < model_->sub_graphs_.at(0)->output_indices_.size(); j++) {
if (out_tensor_index == size_t(model_->sub_graphs_.at(0)->output_indices_.at(j))) {
output_indices.push_back(static_cast<uint32_t>(out_tensor_index));
is_graph_output = true;
break;
}
}
if (!is_graph_output) {
continue;
}
if (out_tensor_index < all_tensors_.size()) {
lite::Tensor *out_tensor = all_tensors_.at(out_tensor_index);
if (out_tensor == nullptr) {
MS_LOG(ERROR) << "can not find any output tensor in all_tensors";
return RET_ERROR;
}
AddOutputMap(out_node->name_, out_tensor);
}
}
}
SetOutputIndices(output_indices);
InitInputs();
InitOutputs();
return RET_OK;
}
std::vector<lite::Tensor *> CoderGraph::input_tensors() const { return input_tensors_; }
std::vector<lite::Tensor *> CoderGraph::output_tensors() const { return output_tensors_; }

3
mindspore/lite/micro/coder/graph.h
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@ -35,6 +35,9 @@ class CoderGraph {
explicit CoderGraph(Model *model) : model_(model) {}
~CoderGraph();
int ConvertTensors();
int InitGraphInOutTensors();
void SetAllTensors(const std::vector<Tensor *> &all_tensors);
void InitInputs();

139
mindspore/lite/micro/coder/session.cc
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@ -231,75 +231,6 @@ int CoderSession::InitTensorsRef() {
return RET_OK;
}
int CoderSession::ConvertTensors() {
auto model = coder_graph_->model();
if (model == nullptr) {
MS_LOG(ERROR) << "Graph model is nullptr";
return RET_ERROR;
}
std::vector<Tensor *> all_tensors;
auto clear_tensors = [&all_tensors]() {
std::for_each(all_tensors.begin(), all_tensors.end(), [](Tensor *&t) {
delete t;
t = nullptr;
});
all_tensors.clear();
};
auto check_dim = [](int dim) -> int {
MS_CHECK_TRUE(dim > 0, "invalid dim value!");
return RET_OK;
};
// deal with allTensors
uint32_t tensorCount = model->all_tensors_.size();
for (uint32_t i = 0; i < tensorCount; ++i) {
schema::Tensor *origin_tensor = model->all_tensors_.at(i);
MS_CHECK_PTR_WITH_EXE(origin_tensor, clear_tensors());
// tensor dims
std::vector<int> shape;
if (origin_tensor->nodeType() == schema::NodeType_ValueNode) {
MS_CHECK_PTR_WITH_EXE(origin_tensor->dims(), clear_tensors());
for (uint32_t j = 0; j < origin_tensor->dims()->size(); j++) {
MS_CHECK_PTR(origin_tensor->dims()->data());
int dim = static_cast<int>(origin_tensor->dims()->data()[j]);
MS_CHECK_RET_CODE_WITH_EXE(check_dim(dim), "parse shape failed!", clear_tensors());
shape.push_back(dim);
}
}
// tensor Datatype
int origin_data_type = static_cast<int>(origin_tensor->dataType());
Tensor *dstTensor = new (std::nothrow)
lite::Tensor(TypeId(origin_data_type), shape, origin_tensor->format(), TensorCategory(origin_tensor));
MS_CHECK_PTR(dstTensor);
if (origin_tensor->nodeType() == schema::NodeType_ValueNode && origin_tensor->data() != nullptr &&
origin_tensor->data()->size() > 0) {
if (shape.empty()) {
shape.push_back(1);
}
// copy data, this is weight && bias
MS_CHECK_TRUE_WITH_EXE(origin_tensor->data()->size() > 0, "invalid meta_tensor data size.", delete dstTensor);
auto data_size = static_cast<size_t>(origin_tensor->data()->size());
MS_CHECK_RET_CODE_WITH_EXE(dstTensor->MallocData(), "dst tensor malloc data failed!", delete dstTensor);
void *dst_data = dstTensor->data_c();
MS_CHECK_RET_CODE_WITH_EXE(memcpy_s(dst_data, data_size, origin_tensor->data()->data(), data_size),
"memcpy_s copy data failed!", delete dstTensor);
dstTensor->set_data(dst_data);
}
auto quant_params = origin_tensor->quantParams();
if (quant_params != nullptr) {
for (int j = 0; j < static_cast<int>(quant_params->size()); j++) {
QuantArg quant_arg{};
quant_arg.scale = quant_params->Get(j)->scale();
quant_arg.zeroPoint = quant_params->Get(j)->zeroPoint();
dstTensor->AddQuantParam(quant_arg);
}
}
all_tensors.emplace_back(dstTensor);
}
coder_graph_->SetAllTensors(all_tensors);
return RET_OK;
}
int CoderSession::CreateOpCoders() {
const Model *model = coder_graph_->model();
if (model == nullptr) {
@ -375,77 +306,15 @@ int CoderSession::CreateOpCoders() {
return RET_OK;
}
int CoderSession::InitGraphInOutTensors() {
const Model *model = coder_graph_->model();
if (model == nullptr) {
return RET_ERROR;
}
std::vector<size_t> graph_input_node_indexes = lite::GetGraphInputNodes(model);
std::vector<uint32_t> input_indices;
for (auto in_node_index : graph_input_node_indexes) {
in_node_index = static_cast<uint32_t>(in_node_index);
auto *in_node = model->all_nodes_.at(in_node_index);
if (in_node == nullptr) {
return RET_ERROR;
}
for (uint32_t i = 0; i < in_node->input_indices_.size(); i++) {
auto in_tensor_index = size_t(in_node->input_indices_.at(i));
bool is_graph_input = false;
for (uint32_t j = 0; j < model->sub_graphs_.at(0)->input_indices_.size(); j++) {
if (in_tensor_index == size_t(model->sub_graphs_.at(0)->input_indices_.at(j))) {
input_indices.push_back(static_cast<uint32_t>(in_tensor_index));
is_graph_input = true;
break;
}
}
if (!is_graph_input) {
continue;
}
if (in_tensor_index < coder_graph_->all_tensors().size()) {
lite::Tensor *in_tensor = this->coder_graph_->all_tensors().at(in_tensor_index);
coder_graph_->AddInputMap(in_node->name_, in_tensor);
}
}
}
coder_graph_->SetInputIndices(input_indices);
std::vector<uint32_t> output_indices;
auto graph_output_node_indexes = lite::GetGraphOutputNodes(model);
for (auto out_node_index : graph_output_node_indexes) {
out_node_index = static_cast<uint32_t>(out_node_index);
auto *out_node = model->all_nodes_.at(out_node_index);
for (uint32_t i = 0; i < out_node->output_indices_.size(); i++) {
auto out_tensor_index = size_t(out_node->output_indices_.at(i));
bool is_graph_output = false;
for (uint32_t j = 0; j < model->sub_graphs_.at(0)->output_indices_.size(); j++) {
if (out_tensor_index == size_t(model->sub_graphs_.at(0)->output_indices_.at(j))) {
output_indices.push_back(static_cast<uint32_t>(out_tensor_index));
is_graph_output = true;
break;
}
}
if (!is_graph_output) {
continue;
}
if (out_tensor_index < coder_graph_->all_tensors().size()) {
lite::Tensor *out_tensor = this->coder_graph_->all_tensors().at(out_tensor_index);
if (out_tensor == nullptr) {
MS_LOG(ERROR) << "can not find any output tensor in all_tensors";
return RET_ERROR;
}
coder_graph_->AddOutputMap(out_node->name_, out_tensor);
}
}
}
coder_graph_->SetOutputIndices(output_indices);
coder_graph_->InitInputs();
coder_graph_->InitOutputs();
int CoderSession::InitCodeGraph() {
MS_CHECK_RET_CODE(coder_graph_->ConvertTensors(), "convert tensors failed");
MS_CHECK_RET_CODE(coder_graph_->InitGraphInOutTensors(), "init graph inputs and outputs failed");
return RET_OK;
}
int CoderSession::CompileGraph() {
MS_LOG(INFO) << "CompileGraph";
MS_CHECK_RET_CODE(ConvertTensors(), "ConvertTensors failed");
MS_CHECK_RET_CODE(InitGraphInOutTensors(), "InitGraphInOutTensors failed");
MS_CHECK_RET_CODE(InitCodeGraph(), "InitGraphInOutTensors failed");
MS_CHECK_RET_CODE(InferShape(), "do infershape failed!");
MS_CHECK_RET_CODE(CreateOpCoders(), "CreateOpCoders failed!");
MS_CHECK_RET_CODE(InitTensorsRef(), "InitTensorsRefcount failed!");

3
mindspore/lite/micro/coder/session.h
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@ -45,9 +45,8 @@ class CoderSession {
private:
int InitOpcodersInputsAndOutputs();
int InitTensorsRef();
int ConvertTensors();
int CreateOpCoders();
int InitGraphInOutTensors();
int InitCodeGraph();
int CompileGraph();
int InferShape();
void EndCode();

1
mindspore/lite/micro/test/CMakeLists.txt
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@ -8,6 +8,7 @@ set(3RD_DIR ${TOP_DIR}/third_party)
set(BUILD_LITE "on")
include(${MICRO_DIR}/../../../cmake/external_libs/gtest.cmake)
include(${MICRO_DIR}/cmake/file_list.cmake)
include(${MICRO_DIR}/cmake/wrapper.cmake)
include_directories(${TOP_DIR})
include_directories(${TOP_DIR}/mindspore/core/)

2
mindspore/lite/src/common/log_adapter.h
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@ -30,7 +30,7 @@
#define LOG_HEAR_FILE_REL_PATH "mindspore/lite/src/common/log_adapter.h"
// Get start index of file relative path in __FILE__
static constexpr int GetRealPathPos() noexcept {
static constexpr size_t GetRealPathPos() noexcept {
return sizeof(__FILE__) > sizeof(LOG_HEAR_FILE_REL_PATH) ? sizeof(__FILE__) - sizeof(LOG_HEAR_FILE_REL_PATH) : 0;
}

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