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!7499 post training quantization support multi-input model 

Merge pull request !7499 from xutianchun/quant1020
This commit is contained in:
mindspore-ci-bot 2020-10-20 16:52:24 +08:00 committed by Gitee
parent 6e2010ae75 d401034645
commit 15f57029a5
2 changed files with 113 additions and 83 deletions
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184
mindspore/lite/tools/converter/quantizer/post_training_quantizer.cc
View File

@ -367,21 +367,25 @@ STATUS Calibrator::AddQuantizedOp(CNodePtr node) {
return RET_OK;
}
void Calibrator::AddImage(const string file) {
void Calibrator::AddImage(const string &file, size_t index) {
if (index >= images_.size()) {
MS_LOG(ERROR) << "images_ size: " << images_.size() << " but index: " << index;
return;
}
auto exist = [](const string file) {
struct stat buf {};
return stat(file.c_str(), &buf) == 0;
};
if (exist(file)) {
MS_LOG(INFO) << "load image: " << file;
this->images_.push_back(file);
this->images_[index].push_back(file);
} else {
MS_LOG(WARNING) << "invalid image file path: " << file;
}
}
STATUS Calibrator::GenerateInputData(int index, mindspore::tensor::MSTensor *tensor) const {
string path = images_[index];
STATUS Calibrator::GenerateInputData(int input_index, int image_index, mindspore::tensor::MSTensor *tensor) const {
string path = images_[input_index][image_index];
MS_LOG(INFO) << "read image: " << path;
size_t size;
char *bin_buf = ReadFile(path.c_str(), &size);
@ -405,30 +409,34 @@ STATUS Calibrator::GenerateInputData(int index, mindspore::tensor::MSTensor *ten
}
STATUS Calibrator::CollectImages() {
// check image file path
DIR *root = opendir(config_param_.image_path.c_str());
if (root == nullptr) {
MS_LOG(ERROR) << "invalid image path: " << config_param_.image_path;
return RET_PARAM_INVALID;
}
struct dirent *image_dir = readdir(root);
size_t count = 0;
while (image_dir != nullptr) {
if (image_dir->d_name[0] != '.') {
const std::string file_name = config_param_.image_path + "/" + image_dir->d_name;
if (config_param_.batch_count == 0) {
this->AddImage(file_name);
count++;
} else if (count < config_param_.batch_count) {
this->AddImage(file_name);
count++;
} else {
break;
}
this->images_.resize(config_param_.image_paths.size());
auto input_i = 0;
for (const auto &image_path : config_param_.image_paths) {
DIR *root = opendir(image_path.c_str());
if (root == nullptr) {
MS_LOG(ERROR) << "invalid image path: " << image_path;
return RET_PARAM_INVALID;
}
image_dir = readdir(root);
struct dirent *image_dir = readdir(root);
size_t count = 0;
while (image_dir != nullptr) {
if (image_dir->d_name[0] != '.') {
const std::string file_name = image_path + "/" + image_dir->d_name;
if (config_param_.batch_count == 0) {
this->AddImage(file_name, input_i);
count++;
} else if (count < config_param_.batch_count) {
this->AddImage(file_name, input_i);
count++;
} else {
break;
}
}
image_dir = readdir(root);
}
closedir(root);
input_i++;
}
closedir(root);
return RET_OK;
}
@ -471,7 +479,16 @@ STATUS Calibrator::ReadConfig() {
Trim(&key);
Trim(&value);
if (key == "image_path") {
config_param_.image_path = value;
auto &raw_image_paths = value;
auto ind = raw_image_paths.find(',');
while (ind != std::string::npos) {
auto image_path = raw_image_paths.substr(0, ind);
Trim(&image_path);
config_param_.image_paths.push_back(image_path);
raw_image_paths = raw_image_paths.substr(ind + 1);
Trim(&raw_image_paths);
}
config_param_.image_paths.push_back(raw_image_paths);
} else if (key == "batch_count") {
config_param_.batch_count = std::stoul(value);
} else if (key == "thread_num") {
@ -491,8 +508,11 @@ STATUS Calibrator::ReadConfig() {
MS_LOG(WARNING) << "unsupported parameter";
}
}
MS_LOG(DEBUG) << "image_path: " << config_param_.image_path << " "
<< "batch_count: " << config_param_.batch_count << " "
for (const auto &path : config_param_.image_paths) {
MS_LOG(DEBUG) << "calibration data_path: " << path;
}
MS_LOG(DEBUG) << "batch_count: " << config_param_.batch_count << " "
<< "method_x: " << config_param_.method_x << " "
<< "thread_num: " << config_param_.thread_num << " "
<< "bias_correction: " << config_param_.bias_correction;
@ -877,18 +897,23 @@ STATUS PostTrainingQuantizer::CheckFp32TensorVec(const std::string &node_name,
* 3. run session
**/
STATUS PostTrainingQuantizer::DoInference() {
// get input tensor
vector<mindspore::tensor::MSTensor *> inputs = fp32_session_->GetInputs();
if (inputs.size() != calibrator_->GetInputNum()) {
MS_LOG(ERROR) << "model's input tensor cnt: " << inputs.size() << " != " << calibrator_->GetInputNum();
return RET_ERROR;
}
for (size_t i = 0; i < calibrator_->GetBatchNum(); i++) {
// get input tensor
vector<mindspore::tensor::MSTensor *> inputs = fp32_session_->GetInputs();
if (inputs.size() > 1) {
MS_LOG(ERROR) << "model's input tensor size: " << inputs.size() << " >1";
return RET_ERROR;
}
STATUS status = calibrator_->GenerateInputData(i, inputs.front());
if (status != RET_OK) {
MS_LOG(ERROR) << "generate input data from images failed!";
return RET_ERROR;
// set multi-input data
for (size_t input_index = 0; input_index < inputs.size(); input_index++) {
STATUS status = calibrator_->GenerateInputData(input_index, i, inputs[input_index]);
if (status != RET_OK) {
MS_LOG(ERROR) << "generate input data from images failed!";
return RET_ERROR;
}
}
mindspore::session::KernelCallBack beforeCallBack =
[&](const std::vector<mindspore::tensor::MSTensor *> &beforeInputs,
const std::vector<mindspore::tensor::MSTensor *> &beforeOutputs,
@ -918,7 +943,7 @@ STATUS PostTrainingQuantizer::DoInference() {
this->calibrator_->RecordMaxValue(callParam.node_name, data, this->calibrator_->GetOutputDivergInfo());
return true;
};
status = fp32_session_->RunGraph(beforeCallBack, afterCallBack);
auto status = fp32_session_->RunGraph(beforeCallBack, afterCallBack);
if (status != RET_OK) {
MS_LOG(ERROR) << "run model failed!";
return RET_ERROR;
@ -928,21 +953,19 @@ STATUS PostTrainingQuantizer::DoInference() {
}
STATUS PostTrainingQuantizer::Int8Inference() {
// fp32 inference
// int8 inference
vector<mindspore::tensor::MSTensor *> inputs = int8_session_->GetInputs();
// get input tensor
if (inputs.size() != 1) {
MS_LOG(ERROR) << "model's input tensor size: " << inputs.size();
return RET_ERROR;
}
auto elem_count = inputs.front()->ElementsNum();
vector<float> dummy_data(elem_count);
std::fill(dummy_data.begin(), dummy_data.end(), 0.1);
auto ret = memcpy_s(inputs.front()->MutableData(), inputs.front()->Size(), dummy_data.data(),
sizeof(float) * dummy_data.size());
if (ret != EOK) {
MS_LOG(ERROR) << "memcpy_s error: " << ret;
return RET_ERROR;
for (auto input_tensor : inputs) {
// get input tensor
auto elem_count = input_tensor->ElementsNum();
vector<float> dummy_data(elem_count);
std::fill(dummy_data.begin(), dummy_data.end(), 0.1);
auto ret =
memcpy_s(input_tensor->MutableData(), input_tensor->Size(), dummy_data.data(), sizeof(float) * dummy_data.size());
if (ret != EOK) {
MS_LOG(ERROR) << "memcpy_s error: " << ret;
return RET_ERROR;
}
}
for (size_t i = 0; i < calibrator_->GetBatchNum(); i++) {
@ -1062,7 +1085,7 @@ STATUS PostTrainingQuantizer::Int8Inference() {
}
return true;
};
ret = int8_session_->RunGraph(beforeCallBack, afterCallBack);
auto ret = int8_session_->RunGraph(beforeCallBack, afterCallBack);
if (ret != RET_OK) {
MS_LOG(ERROR) << "run model failed!";
return RET_ERROR;
@ -1074,19 +1097,20 @@ STATUS PostTrainingQuantizer::Int8Inference() {
STATUS PostTrainingQuantizer::BiasCorrection(FuncGraphPtr func_graph) {
auto ret = RET_OK;
std::future<STATUS> int8_inference = std::async(std::launch::async, &PostTrainingQuantizer::Int8Inference, this);
// get input tensor
vector<mindspore::tensor::MSTensor *> inputs = fp32_session_->GetInputs();
if (inputs.size() != 1) {
MS_LOG(ERROR) << "model's input tensor size: " << inputs.size();
return RET_ERROR;
}
// fp32 inference
for (size_t i = 0; i < calibrator_->GetBatchNum(); i++) {
// get input tensor
vector<mindspore::tensor::MSTensor *> inputs = fp32_session_->GetInputs();
if (inputs.size() != 1) {
MS_LOG(ERROR) << "model's input tensor size: " << inputs.size();
return RET_ERROR;
}
STATUS status = calibrator_->GenerateInputData(i, inputs.front());
if (status != RET_OK) {
MS_LOG(ERROR) << "generate input data from images failed!";
return RET_ERROR;
for (size_t input_index = 0; input_index < inputs.size(); input_index++) {
STATUS status = calibrator_->GenerateInputData(input_index, i, inputs[input_index]);
if (status != RET_OK) {
MS_LOG(ERROR) << "generate input data from images failed!";
return RET_ERROR;
}
}
mindspore::session::KernelCallBack beforeCallBack =
[this](const std::vector<mindspore::tensor::MSTensor *> &beforeInputs,
@ -1156,7 +1180,7 @@ STATUS PostTrainingQuantizer::BiasCorrection(FuncGraphPtr func_graph) {
return true;
};
status = fp32_session_->RunGraph(beforeCallBack, afterCallBack);
auto status = fp32_session_->RunGraph(beforeCallBack, afterCallBack);
if (status != RET_OK) {
MS_LOG(ERROR) << "run model failed!";
return RET_ERROR;
@ -1279,17 +1303,21 @@ STATUS PostTrainingQuantizer::BiasCorrection(FuncGraphPtr func_graph) {
}
STATUS PostTrainingQuantizer::CollectDataFrequency() {
// get input tensor
vector<mindspore::tensor::MSTensor *> inputs = fp32_session_->GetInputs();
if (inputs.size() != calibrator_->GetInputNum()) {
MS_LOG(ERROR) << "model's input tensor cnt: " << inputs.size() << " != " << calibrator_->GetInputNum();
return RET_ERROR;
}
for (size_t i = 0; i < calibrator_->GetBatchNum(); i++) {
// get input tensor
vector<mindspore::tensor::MSTensor *> inputs = fp32_session_->GetInputs();
if (inputs.size() > 1) {
MS_LOG(ERROR) << "model's input tensor size: " << inputs.size() << " > 1";
return RET_ERROR;
}
STATUS status = calibrator_->GenerateInputData(i, inputs.front());
if (status != RET_OK) {
MS_LOG(ERROR) << "generate input data from images failed!";
return RET_ERROR;
// set multi-input data
for (size_t input_index = 0; input_index < inputs.size(); input_index++) {
STATUS status = calibrator_->GenerateInputData(input_index, i, inputs[input_index]);
if (status != RET_OK) {
MS_LOG(ERROR) << "generate input data from images failed!";
return RET_ERROR;
}
}
mindspore::session::KernelCallBack beforeCallBack =
@ -1321,7 +1349,7 @@ STATUS PostTrainingQuantizer::CollectDataFrequency() {
this->calibrator_->UpdateDataFrequency(call_param.node_name, data, this->calibrator_->GetOutputDivergInfo());
return true;
};
status = fp32_session_->RunGraph(beforeCallBack, afterCallBack);
auto status = fp32_session_->RunGraph(beforeCallBack, afterCallBack);
if (status != RET_OK) {
MS_LOG(ERROR) << "run model failed!";
return RET_ERROR;

12
mindspore/lite/tools/converter/quantizer/post_training_quantizer.h
View File

@ -46,7 +46,7 @@ const char kMethodOutlier[] = "RemovalOutlier";
constexpr int kDefaultBinNumber = 2048;
struct ConfigParam {
std::string image_path;
std::vector<std::string> image_paths;
uint32_t batch_count{100};
std::string method_x{kMethodKL};
uint32_t thread_num{1};
@ -173,9 +173,9 @@ class Calibrator {
STATUS CollectImages();
STATUS GenerateInputData(int index, mindspore::tensor::MSTensor *tensor) const;
STATUS GenerateInputData(int input_index, int image_index, mindspore::tensor::MSTensor *tensor) const;
size_t GetBatchNum() const { return images_.size(); }
size_t GetBatchNum() const { return config_param_.batch_count; }
uint32_t GetThreadNum() const { return config_param_.thread_num; }
@ -183,6 +183,8 @@ class Calibrator {
bool GetBiasCorrection() const { return config_param_.bias_correction; }
size_t GetInputNum() const { return config_param_.image_paths.size(); }
STATUS AddQuantizedOp(CNodePtr node);
STATUS RecordMaxValue(const std::string &op_name, const std::vector<float> &data,
@ -209,7 +211,7 @@ class Calibrator {
std::unordered_map<std::string, std::unique_ptr<DivergInfo>> *GetOutputDivergInfo();
private:
std::vector<std::string> images_;
std::vector<std::vector<std::string>> images_; // multi_input, echo input has multi input data
std::string config_path_;
@ -223,7 +225,7 @@ class Calibrator {
int quant_max_;
int quant_min_;
void AddImage(std::string file);
void AddImage(const std::string &file, size_t index);
};
} // namespace quant
} // namespace lite