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!5513 add aware quant converter 

Merge pull request !5513 from cjh9368/aware_quant
This commit is contained in:
mindspore-ci-bot 2020-08-31 14:36:55 +08:00 committed by Gitee
parent 577beb21ec 3818de15e8
commit 25d5423640
3 changed files with 137 additions and 83 deletions
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124
mindspore/lite/tools/benchmark/benchmark.cc
View File

@ -18,10 +18,8 @@
#define __STDC_FORMAT_MACROS
#include <cinttypes>
#undef __STDC_FORMAT_MACROS
#include <cmath>
#include <algorithm>
#include <utility>
#include <cfloat>
#include "src/common/common.h"
#include "include/ms_tensor.h"
#include "include/context.h"
@ -167,71 +165,6 @@ int Benchmark::ReadCalibData() {
return RET_OK;
}
// tensorData need to be converter first
float Benchmark::CompareData(const std::string &nodeName, std::vector<int> msShape, float *msTensorData) {
auto iter = this->calibData.find(nodeName);
if (iter != this->calibData.end()) {
std::vector<size_t> castedMSShape;
size_t shapeSize = 1;
for (int64_t dim : msShape) {
castedMSShape.push_back(size_t(dim));
shapeSize *= dim;
}
CheckTensor *calibTensor = iter->second;
if (calibTensor->shape != castedMSShape) {
std::ostringstream oss;
oss << "Shape of mslite output(";
for (auto dim : castedMSShape) {
oss << dim << ",";
}
oss << ") and shape source model output(";
for (auto dim : calibTensor->shape) {
oss << dim << ",";
}
oss << ") are different";
std::cerr << oss.str() << std::endl;
MS_LOG(ERROR) << oss.str().c_str();
return RET_ERROR;
}
size_t errorCount = 0;
float meanError = 0;
std::cout << "Data of node " << nodeName << " : ";
for (size_t j = 0; j < shapeSize; j++) {
if (j < 50) {
std::cout << msTensorData[j] << " ";
}
if (std::isnan(msTensorData[j]) || std::isinf(msTensorData[j])) {
std::cerr << "Output tensor has nan or inf data, compare fail" << std::endl;
MS_LOG(ERROR) << "Output tensor has nan or inf data, compare fail";
return RET_ERROR;
}
auto tolerance = absoluteTolerance + relativeTolerance * fabs(calibTensor->data.at(j));
auto absoluteError = std::fabs(msTensorData[j] - calibTensor->data.at(j));
if (absoluteError > tolerance) {
// just assume that atol = rtol
meanError += absoluteError / (fabs(calibTensor->data.at(j)) + FLT_MIN);
errorCount++;
}
}
std::cout << std::endl;
if (meanError > 0.0f) {
meanError /= errorCount;
}
if (meanError <= 0.0000001) {
std::cout << "Mean bias of node " << nodeName << " : 0%" << std::endl;
} else {
std::cout << "Mean bias of node " << nodeName << " : " << meanError * 100 << "%" << std::endl;
}
return meanError;
} else {
MS_LOG(INFO) << "%s is not in Source Model output", nodeName.c_str();
return RET_ERROR;
}
}
int Benchmark::CompareOutput() {
std::cout << "================ Comparing Output data ================" << std::endl;
@ -255,7 +188,24 @@ int Benchmark::CompareOutput() {
auto &tensor = tensors.front();
MS_ASSERT(tensor->GetDataType() == DataType_DT_FLOAT);
MS_ASSERT(tensor->GetData() != nullptr);
float bias = CompareData(nodeName, tensor->shape(), static_cast<float *>(tensor->MutableData()));
float bias = 0;
switch (msCalibDataType) {
case TypeId::kNumberTypeFloat: {
bias = CompareData<float>(nodeName, tensor->shape(), static_cast<float *>(tensor->MutableData()));
break;
}
case TypeId::kNumberTypeInt8: {
bias = CompareData<int8_t>(nodeName, tensor->shape(), static_cast<int8_t *>(tensor->MutableData()));
break;
}
case TypeId::kNumberTypeInt32: {
bias = CompareData<int32_t>(nodeName, tensor->shape(), static_cast<int32_t *>(tensor->MutableData()));
break;
}
default:
MS_LOG(ERROR) << "Datatype " << msCalibDataType << " is not supported.";
return RET_ERROR;
}
if (bias >= 0) {
totalBias += bias;
totalSize++;
@ -343,14 +293,26 @@ int Benchmark::MarkAccuracy() {
MS_LOG(INFO) << "MarkAccuracy";
std::cout << "MarkAccuracy" << std::endl;
for (size_t i = 0; i < msInputs.size(); i++) {
MS_ASSERT(msInputs.at(i) != nullptr);
MS_ASSERT(msInputs.at(i)->data_type() == TypeId::kNumberTypeFloat32);
auto inData = reinterpret_cast<float *>(msInputs.at(i)->MutableData());
std::cout << "InData" << i << ": ";
for (size_t j = 0; j < 20; j++) {
std::cout << inData[j] << " ";
}
std::cout << std::endl;
switch (msInputs.at(i)->data_type()) {
case TypeId::kNumberTypeFloat:
PrintInputData<float>(msInputs.at(i));
break;
case TypeId::kNumberTypeFloat32:
PrintInputData<float>(msInputs.at(i));
break;
case TypeId::kNumberTypeInt8:
PrintInputData<int8_t>(msInputs.at(i));
break;
case TypeId::kNumberTypeUInt8:
PrintInputData<uint8_t>(msInputs.at(i));
break;
case TypeId::kNumberTypeInt32:
PrintInputData<int>(msInputs.at(i));
break;
default:
MS_LOG(ERROR) << "Datatype " << msInputs.at(i)->data_type() << " is not supported.";
return RET_ERROR;
}
}
auto status = session->RunGraph();
if (status != RET_OK) {
@ -555,6 +517,16 @@ int Benchmark::Init() {
this->_flags->inDataType = this->_flags->inDataTypeIn == "img" ? kImage : kBinary;
if (!_flags->calibDataType.empty()) {
if (dataTypeMap.find(_flags->calibDataType) == dataTypeMap.end()) {
MS_LOG(ERROR) << "CalibDataType not supported: " << _flags->calibDataType.c_str();
return RET_ERROR;
}
msCalibDataType = dataTypeMap.at(_flags->calibDataType);
MS_LOG(INFO) << "CalibDataType = " << _flags->calibDataType.c_str();
std::cout << "CalibDataType = " << _flags->calibDataType.c_str() << std::endl;
}
if (_flags->modelPath.empty()) {
MS_LOG(ERROR) << "modelPath is required";
std::cerr << "modelPath is required" << std::endl;

88
mindspore/lite/tools/benchmark/benchmark.h
View File

@ -23,9 +23,11 @@
#include <fstream>
#include <iostream>
#include <map>
#include <cmath>
#include <string>
#include <vector>
#include <memory>
#include <cfloat>
#include "include/model.h"
#include "tools/common/flag_parser.h"
#include "src/common/file_utils.h"
@ -64,6 +66,7 @@ class MS_API BenchmarkFlags : public virtual FlagParser {
AddFlag(&BenchmarkFlags::warmUpLoopCount, "warmUpLoopCount", "Run warm up loop", 3);
// MarkAccuracy
AddFlag(&BenchmarkFlags::calibDataPath, "calibDataPath", "Calibration data file path", "");
AddFlag(&BenchmarkFlags::calibDataType, "calibDataType", "Calibration data type. FLOAT | INT32 | INT8", "FLOAT");
AddFlag(&BenchmarkFlags::accuracyThreshold, "accuracyThreshold", "Threshold of accuracy", 0.5);
}
@ -88,6 +91,7 @@ class MS_API BenchmarkFlags : public virtual FlagParser {
int warmUpLoopCount;
// MarkAccuracy
std::string calibDataPath;
std::string calibDataType;
float accuracyThreshold;
// Resize
std::string resizeDimsIn = "";
@ -121,7 +125,85 @@ class MS_API Benchmark {
int CompareOutput();
float CompareData(const std::string &nodeName, std::vector<int> msShape, float *msTensorData);
template <typename T>
void PrintInputData(tensor::MSTensor *input) {
MS_ASSERT(input != nullptr);
static int i = 0;
auto inData = reinterpret_cast<T *>(input->MutableData());
std::cout << "InData" << i++ << ": ";
// int printSize = std::min(20, input->ElementsNum());
for (size_t j = 0; j < 20; j++) {
std::cout << static_cast<float >(inData[j]) << " ";
}
std::cout << std::endl;
}
// tensorData need to be converter first
template <typename T>
float CompareData(const std::string &nodeName, std::vector<int> msShape, T *msTensorData) {
auto iter = this->calibData.find(nodeName);
if (iter != this->calibData.end()) {
std::vector<size_t> castedMSShape;
size_t shapeSize = 1;
for (int64_t dim : msShape) {
castedMSShape.push_back(size_t(dim));
shapeSize *= dim;
}
CheckTensor *calibTensor = iter->second;
if (calibTensor->shape != castedMSShape) {
std::ostringstream oss;
oss << "Shape of mslite output(";
for (auto dim : castedMSShape) {
oss << dim << ",";
}
oss << ") and shape source model output(";
for (auto dim : calibTensor->shape) {
oss << dim << ",";
}
oss << ") are different";
std::cerr << oss.str() << std::endl;
MS_LOG(ERROR) << oss.str().c_str();
return RET_ERROR;
}
size_t errorCount = 0;
float meanError = 0;
std::cout << "Data of node " << nodeName << " : ";
for (size_t j = 0; j < shapeSize; j++) {
if (j < 50) {
std::cout << static_cast<float>(msTensorData[j]) << " ";
}
if (std::isnan(msTensorData[j]) || std::isinf(msTensorData[j])) {
std::cerr << "Output tensor has nan or inf data, compare fail" << std::endl;
MS_LOG(ERROR) << "Output tensor has nan or inf data, compare fail";
return RET_ERROR;
}
auto tolerance = absoluteTolerance + relativeTolerance * fabs(calibTensor->data.at(j));
auto absoluteError = std::fabs(msTensorData[j] - calibTensor->data.at(j));
if (absoluteError > tolerance) {
// just assume that atol = rtol
meanError += absoluteError / (fabs(calibTensor->data.at(j)) + FLT_MIN);
errorCount++;
}
}
std::cout << std::endl;
if (meanError > 0.0f) {
meanError /= errorCount;
}
if (meanError <= 0.0000001) {
std::cout << "Mean bias of node " << nodeName << " : 0%" << std::endl;
} else {
std::cout << "Mean bias of node " << nodeName << " : " << meanError * 100 << "%" << std::endl;
}
return meanError;
} else {
MS_LOG(INFO) << "%s is not in Source Model output", nodeName.c_str();
return RET_ERROR;
}
}
int MarkPerformance();
@ -133,6 +215,10 @@ class MS_API Benchmark {
std::vector<mindspore::tensor::MSTensor *> msInputs;
std::unordered_map<std::string, std::vector<mindspore::tensor::MSTensor *>> msOutputs;
std::unordered_map<std::string, CheckTensor *> calibData;
std::unordered_map<std::string, TypeId> dataTypeMap{
{"FLOAT", TypeId::kNumberTypeFloat}, {"INT8", TypeId::kNumberTypeInt8}, {"INT32", TypeId::kNumberTypeInt32}};
// TypeId msInputBinDataType = TypeId::kNumberTypeFloat;
TypeId msCalibDataType = TypeId::kNumberTypeFloat;
};
int MS_API RunBenchmark(int argc, const char **argv);

8
mindspore/lite/tools/converter/legacy_optimizer/graph/dtype_trans_pass.cc
View File

@ -104,7 +104,7 @@ STATUS DTypeTransPass::DoModelOutputDTypeTrans(schema::MetaGraphT *graph) {
if (outputDataDType == TypeId::kNumberTypeInt8) {
return RET_OK;
}
MS_ASSERT(inputDataDType == TypeId::kNumberTypeFloat);
MS_ASSERT(outputDataDType == TypeId::kNumberTypeFloat);
auto &graphOutIdxes = graph->outputIndex;
for (auto graphOutIdx : graphOutIdxes) {
for (auto iter = graph->nodes.begin(); iter != graph->nodes.end(); iter++) {
@ -115,11 +115,7 @@ STATUS DTypeTransPass::DoModelOutputDTypeTrans(schema::MetaGraphT *graph) {
if (node->outputIndex.at(outputIndexIdx) == graphOutIdx) {
// insert transNode
STATUS status = RET_OK;
if (inputDataDType == TypeId::kNumberTypeFloat) {
iter = InsertDTypeTransNode(graph, iter, kAfter, outputIndexIdx, kInt8ToFP32, &status);
} else {
iter = InsertDTypeTransNode(graph, iter, kAfter, outputIndexIdx, kInt8ToUInt8, &status);
}
iter = InsertDTypeTransNode(graph, iter, kAfter, outputIndexIdx, kInt8ToFP32, &status);
if (status != RET_OK) {
MS_LOG(ERROR) << "InsertDTypeTransNode after " << nodeName.c_str() << " failed";
return status;