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post quantization code review

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pull/5157/head
xutianchun 5 years ago
parent 2de216961f
commit b5f8556d63
6 changed files with 58 additions and 89 deletions
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30
mindspore/lite/tools/converter/converter.cc
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@ -15,9 +15,9 @@
*/ */
#include "tools/converter/converter.h" #include "tools/converter/converter.h"
#include <memory>
#include <vector> #include <vector>
#include <utility> #include <utility>
#include <memory>
#include "tools/converter/converter_flags.h" #include "tools/converter/converter_flags.h"
#include "src/common/common.h" #include "src/common/common.h"
#include "src/common/file_utils.h" #include "src/common/file_utils.h"
@ -141,31 +141,11 @@ MetaGraphT *Converter::Convert(const converter::Flags *flag) {
return meta_graph; return meta_graph;
} }
void Converter::CreateQuantizer(FuncGraphPtr funcGraph, const converter::Flags *flags) { void Converter::CreateQuantizer(FuncGraphPtr func_graph, const converter::Flags *flags) {
auto type = flags->quantType; auto type = flags->quantType;
switch (type) { if (type == mindspore::schema::QuantType_PostTraining) {
case mindspore::schema::QuantType_AwareTraining: { MS_LOG(INFO) << "create post training quantizer.";
// mQuantizer.reset(new AwareQuantizer(graphDefT, flags->inputInferenceTypeIn, flags->stdDev, flags->mean)); mQuantizer.reset(new quant::PostTrainingQuantizer(func_graph, flags->configFile, 8));
break;
}
// case mindspore::schema::QuantType_WeightQuant: {
// MS_LOG(INFO) << "create WeightQuantizer!";
// mQuantizer.reset(
// new quant::WeightQuantizer(funcGraph, flags->quantSize, flags->convWeightQuantChannelThreshold,
// flags->bitNum));
// break;
// }
case mindspore::schema::QuantType_PostTraining: {
MS_LOG(INFO) << "create PostTrainningQuantizer!";
mQuantizer.reset(new quant::PostTrainingQuantizer(funcGraph, flags->configFile, 8));
break;
}
case mindspore::schema::QuantType_QUANT_NONE:
MS_LOG(INFO) << "Not do quantization for model!";
break;
default:
MS_LOG(INFO) << "will support quntizer type " << flags->quantTypeIn.c_str() << " in the future!";
break;
} }
} }
int RunConverter(int argc, const char **argv) { int RunConverter(int argc, const char **argv) {

2
mindspore/lite/tools/converter/converter.h
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@ -34,7 +34,7 @@ class Converter {
Converter(); Converter();
virtual ~Converter(); virtual ~Converter();
virtual schema::MetaGraphT *Convert(const lite::converter::Flags *flags); virtual schema::MetaGraphT *Convert(const lite::converter::Flags *flags);
void CreateQuantizer(FuncGraphPtr funcGraph, const converter::Flags *flags); void CreateQuantizer(FuncGraphPtr func_graph, const converter::Flags *flags);
void FreeFuncGraph(const FuncGraphPtr &func_graph); void FreeFuncGraph(const FuncGraphPtr &func_graph);
protected: protected:

83
mindspore/lite/tools/converter/quantizer/post_training_quantizer.cc
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@ -80,7 +80,7 @@ struct DivergInfo {
this->interval = max_value / static_cast<float>(bin_num); this->interval = max_value / static_cast<float>(bin_num);
} }
STATUS UpdateHistogram(const std::vector<float> &data, const std::vector<int> &shape) { STATUS UpdateHistogram(const std::vector<float> &data) {
for (auto value : data) { for (auto value : data) {
if (value == 0) { if (value == 0) {
continue; continue;
@ -235,7 +235,7 @@ struct DivergInfo {
return std::make_pair(this->cnode, zero_point); return std::make_pair(this->cnode, zero_point);
} }
}; };
std::unordered_map<CNodePtr, float> Calibrator::GetResult( std::unordered_map<CNodePtr, float> Calibrator::GetScale(
std::unordered_map<std::string, std::unique_ptr<DivergInfo>> *diverg_info) { std::unordered_map<std::string, std::unique_ptr<DivergInfo>> *diverg_info) {
std::unordered_map<CNodePtr, float> result; std::unordered_map<CNodePtr, float> result;
for (auto iter = diverg_info->begin(); iter != diverg_info->end(); iter++) { for (auto iter = diverg_info->begin(); iter != diverg_info->end(); iter++) {
@ -246,9 +246,9 @@ std::unordered_map<CNodePtr, float> Calibrator::GetResult(
return result; return result;
} }
std::unordered_map<CNodePtr, int32_t> Calibrator::GetZeropoint( std::unordered_map<CNodePtr, int32_t> Calibrator::GetZeropoint(
std::unordered_map<std::string, std::unique_ptr<DivergInfo>> *mDivergInfo) { std::unordered_map<std::string, std::unique_ptr<DivergInfo>> *diverg_info) {
std::unordered_map<CNodePtr, int32_t> result; std::unordered_map<CNodePtr, int32_t> result;
for (auto iter = mDivergInfo->begin(); iter != mDivergInfo->end(); iter++) { for (auto iter = diverg_info->begin(); iter != diverg_info->end(); iter++) {
DivergInfo *info = iter->second.get(); DivergInfo *info = iter->second.get();
auto zeropoint = info->GetZeropoint(); auto zeropoint = info->GetZeropoint();
result.insert(zeropoint); result.insert(zeropoint);
@ -257,9 +257,9 @@ std::unordered_map<CNodePtr, int32_t> Calibrator::GetZeropoint(
} }
std::map<CNodePtr, MaxMin> Calibrator::GetMinMax( std::map<CNodePtr, MaxMin> Calibrator::GetMinMax(
std::unordered_map<std::string, std::unique_ptr<DivergInfo>> *mDivergInfo) { std::unordered_map<std::string, std::unique_ptr<DivergInfo>> *diverg_info) {
std::map<CNodePtr, MaxMin> result; std::map<CNodePtr, MaxMin> result;
for (auto iter = mDivergInfo->begin(); iter != mDivergInfo->end(); iter++) { for (auto iter = diverg_info->begin(); iter != diverg_info->end(); iter++) {
DivergInfo *info = iter->second.get(); DivergInfo *info = iter->second.get();
mindspore::lite::quant::MaxMin input_maxmin{}; mindspore::lite::quant::MaxMin input_maxmin{};
input_maxmin.min = info->min; input_maxmin.min = info->min;
@ -284,10 +284,10 @@ std::unordered_map<std::string, std::unique_ptr<DivergInfo>> *Calibrator::GetOut
return &this->output_diverg_info_; return &this->output_diverg_info_;
} }
STATUS Calibrator::RecordMaxValue(std::string opName, vector<float> data, STATUS Calibrator::RecordMaxValue(const std::string &op_name, const vector<float> &data,
std::unordered_map<std::string, std::unique_ptr<DivergInfo>> *mDivergInfo) { std::unordered_map<std::string, std::unique_ptr<DivergInfo>> *diverg_info) {
auto got = (*mDivergInfo).find(opName); auto got = (*diverg_info).find(op_name);
if (got != (*mDivergInfo).end()) { if (got != (*diverg_info).end()) {
((*got).second)->RecordMaxValue(data); ((*got).second)->RecordMaxValue(data);
} }
return RET_OK; return RET_OK;
@ -332,11 +332,11 @@ STATUS Calibrator::UpdateDivergInverval(std::unordered_map<std::string, std::uni
return RET_OK; return RET_OK;
} }
STATUS Calibrator::UpdateDataFrequency(std::string op_name, vector<float> data, vector<int> shape, STATUS Calibrator::UpdateDataFrequency(const std::string &op_name, const vector<float> &data,
std::unordered_map<std::string, std::unique_ptr<DivergInfo>> *diverg_info) { std::unordered_map<std::string, std::unique_ptr<DivergInfo>> *diverg_info) {
auto got = (*diverg_info).find(op_name); auto got = (*diverg_info).find(op_name);
if (got != (*diverg_info).end()) { if (got != (*diverg_info).end()) {
((*got).second)->UpdateHistogram(data, shape); ((*got).second)->UpdateHistogram(data);
} }
return RET_OK; return RET_OK;
} }
@ -347,10 +347,10 @@ STATUS Calibrator::AddQuantizedOp(CNodePtr node) {
return RET_ERROR; return RET_ERROR;
} }
string node_name = node->fullname_with_scope(); string node_name = node->fullname_with_scope();
std::unique_ptr<DivergInfo> input_diverg = std::unique_ptr<DivergInfo> input_diverg = std::unique_ptr<DivergInfo>(
std::unique_ptr<DivergInfo>(new DivergInfo(node, 2048, bit_num_, quant_max_, quant_min_, config_param_.method_x)); new DivergInfo(node, kDefaultBinNumber, bit_num_, quant_max_, quant_min_, config_param_.method_x));
std::unique_ptr<DivergInfo> output_diverg = std::unique_ptr<DivergInfo> output_diverg = std::unique_ptr<DivergInfo>(
std::unique_ptr<DivergInfo>(new DivergInfo(node, 2048, bit_num_, quant_max_, quant_min_, config_param_.method_x)); new DivergInfo(node, kDefaultBinNumber, bit_num_, quant_max_, quant_min_, config_param_.method_x));
input_diverg_info_.insert(std::make_pair(string(node_name), std::move(input_diverg))); input_diverg_info_.insert(std::make_pair(string(node_name), std::move(input_diverg)));
output_diverg_info_.insert(std::make_pair(string(node_name), std::move(output_diverg))); output_diverg_info_.insert(std::make_pair(string(node_name), std::move(output_diverg)));
@ -359,29 +359,33 @@ STATUS Calibrator::AddQuantizedOp(CNodePtr node) {
void Calibrator::AddImage(const string file) { void Calibrator::AddImage(const string file) {
auto exist = [](const string file) { auto exist = [](const string file) {
struct stat buf; struct stat buf{};
return stat(file.c_str(), &buf) == 0; return stat(file.c_str(), &buf) == 0;
}; };
if (exist(file)) { if (exist(file)) {
MS_LOG(INFO) << "load image: " << file; MS_LOG(INFO) << "load image: " << file;
this->images_.push_back(file); this->images_.push_back(file);
} else { } else {
MS_LOG(WARNING) << "Invaild image file path: " << file; MS_LOG(WARNING) << "invalid image file path: " << file;
} }
} }
STATUS Calibrator::GenerateInputData(const int index, mindspore::tensor::MSTensor *tensor) const { STATUS Calibrator::GenerateInputData(int index, mindspore::tensor::MSTensor *tensor) const {
string path = images_[index]; string path = images_[index];
MS_LOG(INFO) << "read image: " << path; MS_LOG(INFO) << "read image: " << path;
size_t size; size_t size;
char *binBuf = ReadFile(path.c_str(), &size); char *bin_buf = ReadFile(path.c_str(), &size);
auto data = tensor->MutableData(); auto data = tensor->MutableData();
if (size != tensor->Size()) { if (size != tensor->Size()) {
MS_LOG(ERROR) << "the input data is not consistent with model input, file_size: " << size MS_LOG(ERROR) << "the input data is not consistent with model input, file_size: " << size
<< " input tensor size: " << tensor->Size(); << " input tensor size: " << tensor->Size();
return RET_ERROR; return RET_ERROR;
} }
memcpy(data, binBuf, size); auto ret = memcpy_s(data, tensor->Size(), bin_buf, size);
if (ret != EOK) {
MS_LOG(ERROR) << "memcpy_s error: " << ret;
return RET_ERROR;
}
return RET_OK; return RET_OK;
} }
@ -467,7 +471,7 @@ STATUS Calibrator::ReadConfig() {
} }
MS_LOG(DEBUG) << "image_path: " << config_param_.image_path << " " MS_LOG(DEBUG) << "image_path: " << config_param_.image_path << " "
<< "batch_count: " << config_param_.batch_count << " " << "batch_count: " << config_param_.batch_count << " "
<< "mothod_x: " << config_param_.method_x << " " << "method_x: " << config_param_.method_x << " "
<< "thread_num: " << config_param_.thread_num; << "thread_num: " << config_param_.thread_num;
delete[] resolved_path; delete[] resolved_path;
@ -475,8 +479,8 @@ STATUS Calibrator::ReadConfig() {
return RET_OK; return RET_OK;
} }
Calibrator::Calibrator(string path, size_t bitNum, int quantMax, int quantMin) Calibrator::Calibrator(string path, size_t bit_num, int quant_max, int quant_min)
: config_path_(path), bit_num_(bitNum), quant_max_(quantMax), quant_min_(quantMin) {} : config_path_(path), bit_num_(bit_num), quant_max_(quant_max), quant_min_(quant_min) {}
PostTrainingQuantizer::PostTrainingQuantizer(FuncGraphPtr graph, string path, int bit_num, TypeId target_type, PostTrainingQuantizer::PostTrainingQuantizer(FuncGraphPtr graph, string path, int bit_num, TypeId target_type,
bool per_channel) bool per_channel)
@ -669,11 +673,11 @@ STATUS PostTrainingQuantizer::DoBiasQuant(AnfNodePtr bias, std::shared_ptr<Primi
STATUS PostTrainingQuantizer::QuantNode() { STATUS PostTrainingQuantizer::QuantNode() {
auto input_min_max = this->calibrator_->GetMinMax(this->calibrator_->GetInputDivergInfo()); auto input_min_max = this->calibrator_->GetMinMax(this->calibrator_->GetInputDivergInfo());
auto input_scale = this->calibrator_->GetResult(this->calibrator_->GetInputDivergInfo()); auto input_scale = this->calibrator_->GetScale(this->calibrator_->GetInputDivergInfo());
auto input_zero_point = this->calibrator_->GetZeropoint(this->calibrator_->GetInputDivergInfo()); auto input_zero_point = this->calibrator_->GetZeropoint(this->calibrator_->GetInputDivergInfo());
auto output_min_max = this->calibrator_->GetMinMax(this->calibrator_->GetOutputDivergInfo()); auto output_min_max = this->calibrator_->GetMinMax(this->calibrator_->GetOutputDivergInfo());
auto output_scale = this->calibrator_->GetResult(this->calibrator_->GetOutputDivergInfo()); auto output_scale = this->calibrator_->GetScale(this->calibrator_->GetOutputDivergInfo());
auto output_zeropoint = this->calibrator_->GetZeropoint(this->calibrator_->GetOutputDivergInfo()); auto output_zeropoint = this->calibrator_->GetZeropoint(this->calibrator_->GetOutputDivergInfo());
auto cnodes = funcGraph->GetOrderedCnodes(); auto cnodes = funcGraph->GetOrderedCnodes();
@ -803,7 +807,7 @@ STATUS PostTrainingQuantizer::PreProcess() {
// from user input // from user input
QuantStrategy strategy(10); QuantStrategy strategy(10);
auto cnodes = funcGraph->GetOrderedCnodes(); auto cnodes = funcGraph->GetOrderedCnodes();
for (auto cnode : cnodes) { for (auto &cnode : cnodes) {
AnfNodePtr anf = std::dynamic_pointer_cast<AnfNode>(cnode); AnfNodePtr anf = std::dynamic_pointer_cast<AnfNode>(cnode);
if (strategy.CanOpPostQuantized(anf)) { if (strategy.CanOpPostQuantized(anf)) {
MS_LOG(INFO) << "node: " << cnode->fullname_with_scope() << " will be quantized"; MS_LOG(INFO) << "node: " << cnode->fullname_with_scope() << " will be quantized";
@ -813,16 +817,15 @@ STATUS PostTrainingQuantizer::PreProcess() {
return RET_OK; return RET_OK;
} }
STATUS PostTrainingQuantizer::CheckTensorVec(const std::string &nodeName, STATUS PostTrainingQuantizer::CheckTensorVec(const std::string &node_name,
const std::vector<mindspore::tensor::MSTensor *> &tensorVec) const { const std::vector<mindspore::tensor::MSTensor *> &tensor_vec) const {
if (tensorVec.size() < 1) { if (tensor_vec.size() < 1) {
MS_LOG(ERROR) << "node: " << nodeName << " input tensors is 0"; MS_LOG(ERROR) << "node: " << node_name << " input tensors is 0";
return RET_ERROR; return RET_ERROR;
} }
auto *tensor = tensorVec[0]; auto *tensor = tensor_vec[0];
if (tensor->data_type() != kNumberTypeFloat32) { if (tensor->data_type() != kNumberTypeFloat32) {
//&& tensor->RefCount() != MSCONST_WEIGHT_REFCOUNT MS_LOG(DEBUG) << "node: " << node_name << " will not quantize"
MS_LOG(DEBUG) << "node: " << nodeName << " will not quantize"
<< " tensor data_type: " << tensor->data_type(); << " tensor data_type: " << tensor->data_type();
return RET_ERROR; return RET_ERROR;
} }
@ -856,8 +859,8 @@ STATUS PostTrainingQuantizer::DoInference() {
} }
auto tensor = beforeInputs[0]; auto tensor = beforeInputs[0];
const float *tData = static_cast<const float *>(tensor->MutableData()); const float *tData = static_cast<const float *>(tensor->MutableData());
size_t shapeSize = tensor->ElementsNum(); size_t elem_count = tensor->ElementsNum();
vector<float> data(tData, tData + shapeSize); vector<float> data(tData, tData + elem_count);
this->calibrator_->RecordMaxValue(callParam.name_callback_param, data, this->calibrator_->GetInputDivergInfo()); this->calibrator_->RecordMaxValue(callParam.name_callback_param, data, this->calibrator_->GetInputDivergInfo());
return true; return true;
}; };
@ -871,8 +874,8 @@ STATUS PostTrainingQuantizer::DoInference() {
} }
auto tensor = afterOutputs[0]; auto tensor = afterOutputs[0];
const float *tensor_data = static_cast<const float *>(tensor->MutableData()); const float *tensor_data = static_cast<const float *>(tensor->MutableData());
size_t shape_size = tensor->ElementsNum(); size_t elem_count = tensor->ElementsNum();
vector<float> data(tensor_data, tensor_data + shape_size); vector<float> data(tensor_data, tensor_data + elem_count);
this->calibrator_->RecordMaxValue(callParam.name_callback_param, data, this->calibrator_->GetOutputDivergInfo()); this->calibrator_->RecordMaxValue(callParam.name_callback_param, data, this->calibrator_->GetOutputDivergInfo());
return true; return true;
}; };
@ -910,7 +913,7 @@ STATUS PostTrainingQuantizer::CollectDataFrequency() {
const float *tensor_data = static_cast<const float *>(tensor->MutableData()); const float *tensor_data = static_cast<const float *>(tensor->MutableData());
size_t shape_size = tensor->ElementsNum(); size_t shape_size = tensor->ElementsNum();
vector<float> data(tensor_data, tensor_data + shape_size); vector<float> data(tensor_data, tensor_data + shape_size);
this->calibrator_->UpdateDataFrequency(callParam.name_callback_param, data, tensor->shape(), this->calibrator_->UpdateDataFrequency(callParam.name_callback_param, data,
this->calibrator_->GetInputDivergInfo()); this->calibrator_->GetInputDivergInfo());
return true; return true;
}; };
@ -926,7 +929,7 @@ STATUS PostTrainingQuantizer::CollectDataFrequency() {
const float *tenosr_data = static_cast<const float *>(tensor->MutableData()); const float *tenosr_data = static_cast<const float *>(tensor->MutableData());
size_t shape_size = tensor->ElementsNum(); size_t shape_size = tensor->ElementsNum();
vector<float> data(tenosr_data, tenosr_data + shape_size); vector<float> data(tenosr_data, tenosr_data + shape_size);
this->calibrator_->UpdateDataFrequency(call_param.name_callback_param, data, tensor->shape(), this->calibrator_->UpdateDataFrequency(call_param.name_callback_param, data,
this->calibrator_->GetOutputDivergInfo()); this->calibrator_->GetOutputDivergInfo());
return true; return true;
}; };

18
mindspore/lite/tools/converter/quantizer/post_training_quantizer.h
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@ -39,14 +39,9 @@ struct MaxMin {
float max; float max;
}; };
enum ImageFormat {
RGB = 0,
GRAY = 1,
BGR = 2,
};
const char kMethodMaxMin[] = "MAX_MIN"; const char kMethodMaxMin[] = "MAX_MIN";
const char kMethodKL[] = "KL"; const char kMethodKL[] = "KL";
constexpr int kDefaultBinNumber = 2048;
struct ConfigParam { struct ConfigParam {
// ImageFormat imageFormat; // ImageFormat imageFormat;
@ -78,7 +73,8 @@ class PostTrainingQuantizer : public Quantizer {
STATUS PreProcess(); STATUS PreProcess();
STATUS CheckTensorVec(const std::string &nodeName, const std::vector<mindspore::tensor::MSTensor *> &tensorVec) const; STATUS CheckTensorVec(const std::string &node_name,
const std::vector<mindspore::tensor::MSTensor *> &tensor_vec) const;
STATUS DoInference(); STATUS DoInference();
@ -105,7 +101,7 @@ struct DivergInfo;
class Calibrator { class Calibrator {
public: public:
explicit Calibrator(std::string path, size_t quant_size, int quant_max, int quant_msin); explicit Calibrator(std::string path, size_t bit_num, int quant_max, int quant_min);
~Calibrator() = default; ~Calibrator() = default;
@ -123,18 +119,18 @@ class Calibrator {
STATUS AddQuantizedOp(CNodePtr node); STATUS AddQuantizedOp(CNodePtr node);
STATUS RecordMaxValue(std::string opName, std::vector<float> data, STATUS RecordMaxValue(const std::string &op_name, const std::vector<float> &data,
std::unordered_map<std::string, std::unique_ptr<DivergInfo>> *diverg_info); std::unordered_map<std::string, std::unique_ptr<DivergInfo>> *diverg_info);
STATUS UpdateDivergInverval(std::unordered_map<std::string, std::unique_ptr<DivergInfo>> *diverg_info); STATUS UpdateDivergInverval(std::unordered_map<std::string, std::unique_ptr<DivergInfo>> *diverg_info);
STATUS UpdateDataFrequency(std::string op_name, std::vector<float> data, std::vector<int> shape, STATUS UpdateDataFrequency(const std::string& op_name, const std::vector<float>& data,
std::unordered_map<std::string, std::unique_ptr<DivergInfo>> *diverg_info); std::unordered_map<std::string, std::unique_ptr<DivergInfo>> *diverg_info);
void Dump(); void Dump();
STATUS ComputeThreshold(); STATUS ComputeThreshold();
std::unordered_map<CNodePtr, float> GetResult( std::unordered_map<CNodePtr, float> GetScale(
std::unordered_map<std::string, std::unique_ptr<DivergInfo>> *diverg_info); std::unordered_map<std::string, std::unique_ptr<DivergInfo>> *diverg_info);
std::unordered_map<CNodePtr, int32_t> GetZeropoint( std::unordered_map<CNodePtr, int32_t> GetZeropoint(

12
mindspore/lite/tools/converter/quantizer/quantize_util.cc
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@ -349,16 +349,12 @@ STATUS QuantFilter(ParamValueLitePtr weight, std::shared_ptr<PrimitiveC> primiti
quant_datas[index] = quant_data; quant_datas[index] = quant_data;
} }
} }
auto ret = memcpy_s(const_cast<float *>(raw_datas), weight->tensor_size(), quant_datas.data(), auto ret = memcpy_s(raw_datas, weight->tensor_size(), quant_datas.data(),
elem_count * sizeof(int8_t)); elem_count * sizeof(int8_t));
if (ret != EOK) { if (ret != EOK) {
MS_LOG(ERROR) << "memcpy error: " << ret; MS_LOG(ERROR) << "memcpy error: " << ret;
return RET_ERROR; return RET_ERROR;
} }
if (quantType == QuantType_WeightQuant) {
PostBitPack(const_cast<float *>(raw_datas), elem_count, bitNum);
}
weight->set_tensor_size(elem_count * sizeof(int8_t)); weight->set_tensor_size(elem_count * sizeof(int8_t));
} else { } else {
// channel at first // channel at first
@ -407,9 +403,6 @@ STATUS QuantFilter(ParamValueLitePtr weight, std::shared_ptr<PrimitiveC> primiti
MS_LOG(ERROR) << "memcpy error: " << ret; MS_LOG(ERROR) << "memcpy error: " << ret;
return RET_ERROR; return RET_ERROR;
} }
if (quantType == QuantType_WeightQuant) {
PostBitPack(const_cast<float *>(raw_datas), elem_count, bitNum);
}
weight->set_tensor_size(elem_count * sizeof(int8_t)); weight->set_tensor_size(elem_count * sizeof(int8_t));
} }
@ -441,9 +434,6 @@ STATUS QuantFilter(ParamValueLitePtr weight, std::shared_ptr<PrimitiveC> primiti
MS_LOG(ERROR) << "memcpy error: " << ret; MS_LOG(ERROR) << "memcpy error: " << ret;
return RET_ERROR; return RET_ERROR;
} }
if (quantType == QuantType_WeightQuant) {
PostBitPack(raw_datas, elem_count, bitNum);
}
weight->set_tensor_size(elem_count * sizeof(int8_t)); weight->set_tensor_size(elem_count * sizeof(int8_t));
} }
if (quant_params.empty()) { if (quant_params.empty()) {

2
mindspore/lite/tools/converter/quantizer/quantizer.h
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@ -51,7 +51,7 @@ class Quantizer {
virtual STATUS DetermineNodeQuantType(); virtual STATUS DetermineNodeQuantType();
virtual STATUS DoQuantize(FuncGraphPtr funcGraph) = 0; virtual STATUS DoQuantize(FuncGraphPtr func_graph) = 0;
mindspore::lite::converter::Flags flags; mindspore::lite::converter::Flags flags;
protected: protected:

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