mindspore2022/mindspore/lite/tools/converter/quantizer/quantization_optimizer.cc

215 lines
7.7 KiB
C++

/**
* Copyright 2022 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "tools/converter/quantizer/quantization_optimizer.h"
#include <memory>
#include <string>
#include <unordered_map>
#include <deque>
#include <map>
#include <set>
#include "tools/anf_exporter/fetch_content.h"
#include "base/base.h"
#include "tools/converter/quantizer/quantize_util.h"
#include "tools/converter/quantizer/weight_quantizer.h"
#include "tools/converter/quantizer/full_quant_quantizer.h"
#include "tools/converter/quantizer/debug_info_manager.h"
#include "tools/converter/quantizer/parameter_tunner.h"
#include "tools/converter/quantizer/dynamic_quantizer.h"
namespace mindspore::lite::quant {
void GetFuncGraphs(const FuncGraphPtr &func_graph, std::set<FuncGraphPtr> *all_func_graphs) {
MS_ASSERT(func_graph != nullptr);
MS_ASSERT(all_func_graphs != nullptr);
all_func_graphs->insert(func_graph);
auto nodes = func_graph->GetOrderedCnodes();
std::deque<CNodePtr> to_process{};
to_process.insert(to_process.end(), nodes.begin(), nodes.end());
while (!to_process.empty()) {
auto &cur_cnode = to_process.front();
for (auto &input : cur_cnode->inputs()) {
if (!IsValueNode<FuncGraph>(input)) {
continue;
}
auto new_fg = GetValueNode<FuncGraphPtr>(input);
if (all_func_graphs->find(new_fg) != all_func_graphs->end()) {
continue;
}
all_func_graphs->insert(new_fg);
auto new_nodes = new_fg->GetOrderedCnodes();
to_process.insert(to_process.end(), new_nodes.begin(), new_nodes.end());
}
to_process.pop_front();
}
}
int DoFullQuant(const FuncGraphPtr &old_graph, const converter::Flags *config) {
auto quantizer = std::make_unique<FullQuantQuantizer>(*config);
if (quantizer == nullptr) {
MS_LOG(ERROR) << "New FullQuantQuantizer failed";
return RET_ERROR;
}
auto status = quantizer->DoQuantize(old_graph);
if (status != RET_OK) {
MS_LOG(ERROR) << "DoQuantization failed " << status;
return RET_ERROR;
}
return RET_OK;
}
int DoWeightQuant(const FuncGraphPtr &old_graph, const converter::Flags *config) {
double init_scale = config->mixedBitWeightQuantParam.init_scale;
if (config->commonQuantParam.bit_num == 0 && config->mixedBitWeightQuantParam.auto_tune) {
ParameterOptimizer optimizer;
auto status = optimizer.GridSearchForScale(old_graph, const_cast<converter::Flags *>(config), &init_scale);
if (status != RET_OK) {
MS_LOG(ERROR) << "Grid search with scale failed.";
return status;
}
auto quantizer = std::make_unique<WeightQuantizer>(*config);
if (quantizer == nullptr) {
MS_LOG(ERROR) << "New WeightQuantizer failed";
ReturnCode::GetSingleReturnCode()->UpdateReturnCode(RET_MEMORY_FAILED);
return RET_ERROR;
}
status = static_cast<WeightQuantizer *>(quantizer.get())->DoQuantize(old_graph, init_scale);
if (status != RET_OK) {
MS_LOG(ERROR) << "DoQuantization failed " << status;
ReturnCode::GetSingleReturnCode()->UpdateReturnCode(status);
return RET_ERROR;
}
} else {
auto quantizer = std::make_unique<WeightQuantizer>(*config);
if (quantizer == nullptr) {
MS_LOG(ERROR) << "New WeightQuantizer failed";
ReturnCode::GetSingleReturnCode()->UpdateReturnCode(RET_MEMORY_FAILED);
return RET_ERROR;
}
auto status = quantizer->DoQuantize(old_graph);
if (status != RET_OK) {
MS_LOG(ERROR) << "DoQuantization failed " << status;
ReturnCode::GetSingleReturnCode()->UpdateReturnCode(status);
return RET_ERROR;
}
}
return RET_OK;
}
int DoDynamicQuant(const FuncGraphPtr &old_graph, const converter::Flags *config) {
auto quantizer = std::make_unique<DynamicQuantizer>(*config);
if (quantizer == nullptr) {
MS_LOG(ERROR) << "New DynamicQuantizer failed";
ReturnCode::GetSingleReturnCode()->UpdateReturnCode(RET_MEMORY_FAILED);
return RET_ERROR;
}
auto status = quantizer->DoQuantize(old_graph);
if (status != RET_OK) {
MS_LOG(ERROR) << "DoQuantization failed " << status;
ReturnCode::GetSingleReturnCode()->UpdateReturnCode(status);
return RET_ERROR;
}
return RET_OK;
}
int DoQuantDebug(const FuncGraphPtr &old_graph, const converter::Flags *config, const SessionModel &origin) {
auto quant = CreateSessionByFuncGraph(old_graph, *config, config->commonQuantParam.thread_num);
std::map<std::string, OpParameter *> op_parameters;
FetchOpParameterFromFuncGraph(old_graph, &op_parameters);
DebugInfoManager manager;
CHECK_NULL_RETURN(origin.model);
CHECK_NULL_RETURN(origin.session);
CHECK_NULL_RETURN(quant.model);
CHECK_NULL_RETURN(quant.session);
auto status = manager.CompareOriginWithQuant(
origin, quant, op_parameters, config->commonQuantParam.debug_info_save_path, config->dataPreProcessParam);
auto free_buffer = [&] {
delete origin.session;
delete origin.model;
delete quant.session;
delete quant.model;
for (auto parameter : op_parameters) {
if (parameter.second != nullptr) {
free(parameter.second);
parameter.second = nullptr;
}
}
op_parameters.clear();
};
if (status != RET_OK) {
MS_LOG(ERROR) << "Compare origin with quant failed.";
free_buffer();
return status;
}
free_buffer();
return RET_OK;
}
int DoSingleGraphQuantize(const FuncGraphPtr &old_graph, const converter::Flags *config) {
if (config->commonQuantParam.quant_type == schema::QuantType_QUANT_NONE) {
return RET_OK;
}
int status;
SessionModel origin;
if (config->commonQuantParam.is_debug) { // Bak fp32 model for debug
converter::Flags new_flag = *config;
new_flag.commonQuantParam.quant_type = schema::QuantType_QUANT_NONE;
origin = CreateSessionByFuncGraph(old_graph, new_flag, config->commonQuantParam.thread_num);
}
if (config->commonQuantParam.quant_type == schema::QuantType_QUANT_ALL) { // Full Quantization
status = DoFullQuant(old_graph, config);
if (status != RET_OK) {
MS_LOG(ERROR) << "Do full quant failed.";
return status;
}
} else if (config->commonQuantParam.quant_type == schema::QuantType_QUANT_WEIGHT) { // Weight Quantization
status = DoWeightQuant(old_graph, config);
if (status != RET_OK) {
MS_LOG(ERROR) << "Do weight quant failed.";
return status;
}
} else if (config->commonQuantParam.quant_type == schema::QuantType_QUANT_DYNAMIC) { // Dynamic Quantization
status = DoDynamicQuant(old_graph, config);
if (status != RET_OK) {
MS_LOG(ERROR) << "Do dynamic quant failed.";
return status;
}
}
if (config->commonQuantParam.is_debug) {
status = DoQuantDebug(old_graph, config, origin);
if (status != RET_OK) {
MS_LOG(ERROR) << "Do quant debug failed.";
return status;
}
}
return RET_OK;
}
int QuantizationOptimizer::Run(const mindspore::FuncGraphPtr &func_graph) {
std::set<FuncGraphPtr> all_func_graphs{};
GetFuncGraphs(func_graph, &all_func_graphs);
// Support for multi-subgraph models
for (auto &item : all_func_graphs) {
auto status = DoSingleGraphQuantize(item, flags_);
if (status != RET_OK) {
MS_LOG(ERROR) << "Do Quantize failed.";
return status;
}
}
return RET_OK;
}
} // namespace mindspore::lite::quant