cjh9368
5 years ago
parent
cfd37ca90d
commit
18c6ac9988
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include "tools/converter/legacy_optimizer/graph/dtype_trans_pass.h"
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#include <string>
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#include "tools/common/converter_op_utils.h"
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#include "tools/common/node_util.h"
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#include "src/common/common.h"
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#include "src/common/utils.h"
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namespace mindspore {
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namespace lite {
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#define kMinInputNum 1
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#define kOutputNum 1
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STATUS DTypeTransPass::Run(schema::MetaGraphT *graph) {
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MS_ASSERT(graph != nullptr);
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auto status = DoModelInputDTypeTrans(graph);
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if (status != RET_OK) {
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MS_LOG(ERROR) << "DoModelInputDTypeTrans error: " << status;
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return status;
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}
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status = DoModelOutputDTypeTrans(graph);
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if (status != RET_OK) {
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MS_LOG(ERROR) << "DoModelOutputDTypeTrans error: " << status;
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return status;
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}
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status = DoNodeInoutDTypeTrans(graph);
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if (status != RET_OK) {
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MS_LOG(ERROR) << "DoNodeInoutDTypeTrans error: " << status;
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return status;
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}
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return RET_OK;
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}
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STATUS DTypeTransPass::DoModelInputDTypeTrans(schema::MetaGraphT *graph) {
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MS_ASSERT(graph != nullptr);
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// modify inputTensor first
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auto &graphInIdxes = graph->inputIndex;
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for (auto graphInIdx : graphInIdxes) {
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MS_ASSERT(graph->allTensors.size() > graphInIdx);
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auto &graphInTensor = graph->allTensors.at(graphInIdx);
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graphInTensor->dataType = TypeId::kNumberTypeUInt8;
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}
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if (this->inputDataDType == TypeId::kNumberTypeInt8) {
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return RET_OK;
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}
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if (this->inputDataDType != TypeId::kNumberTypeFloat && this->inputDataDType != TypeId::kNumberTypeUInt8) {
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MS_LOG(ERROR) << "Invalid inputDataType: " << this->inputDataDType;
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return RET_ERROR;
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}
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// insert fp2int8 node
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for (auto graphInIdx : graphInIdxes) {
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MS_ASSERT(graphInIdx < graph->allTensors.size());
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auto &tensor = graph->allTensors.at(graphInIdx);
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if (tensor->dims.size() != kNHWCDimNumber) {
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continue;
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}
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for (auto iter = graph->nodes.begin(); iter != graph->nodes.end(); iter++) {
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auto &node = *iter;
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auto nodeName = node->name;
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for (size_t inputIndexIdx = 0; inputIndexIdx < node->inputIndex.size(); inputIndexIdx++) {
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if (node->inputIndex.at(inputIndexIdx) == graphInIdx) {
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STATUS status = RET_OK;
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// insert dtype cast node between input tensor and input node
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if (inputDataDType == TypeId::kNumberTypeFloat) {
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iter = InsertDTypeTransNode(graph, iter, kBefore, inputIndexIdx, kFP32ToInt8, &status);
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} else {
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iter = InsertDTypeTransNode(graph, iter, kBefore, inputIndexIdx, kUInt8ToInt8, &status);
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}
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if (status != RET_OK) {
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MS_LOG(ERROR) << "InsertDTypeTransNode before " << nodeName.c_str() << " failed";
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return status;
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}
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}
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}
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}
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}
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return RET_OK;
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}
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STATUS DTypeTransPass::DoModelOutputDTypeTrans(schema::MetaGraphT *graph) {
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MS_ASSERT(graph != nullptr);
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if (inputDataDType == TypeId::kNumberTypeInt8) {
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return RET_OK;
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}
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MS_ASSERT(inputDataDType == TypeId::kNumberTypeFloat);
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auto &graphOutIdxes = graph->outputIndex;
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for (auto graphOutIdx : graphOutIdxes) {
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for (auto iter = graph->nodes.begin(); iter != graph->nodes.end(); iter++) {
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auto &node = *iter;
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auto nodeName = node->name;
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MS_ASSERT(node != nullptr);
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for (size_t outputIndexIdx = 0; outputIndexIdx < node->outputIndex.size(); outputIndexIdx++) {
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if (node->outputIndex.at(outputIndexIdx) == graphOutIdx) {
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// insert transNode
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STATUS status = RET_OK;
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if (inputDataDType == TypeId::kNumberTypeFloat) {
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iter = InsertDTypeTransNode(graph, iter, kAfter, outputIndexIdx, kInt8ToFP32, &status);
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} else {
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iter = InsertDTypeTransNode(graph, iter, kAfter, outputIndexIdx, kInt8ToUInt8, &status);
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}
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if (status != RET_OK) {
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MS_LOG(ERROR) << "InsertDTypeTransNode after " << nodeName.c_str() << " failed";
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return status;
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}
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break;
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}
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}
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}
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}
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return RET_OK;
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}
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STATUS DTypeTransPass::DoNodeInoutDTypeTrans(schema::MetaGraphT *graph) {
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MS_ASSERT(graph != nullptr);
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// insert transNode before and after existNode
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for (auto iter = graph->nodes.begin(); iter != graph->nodes.end(); iter++) {
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if (IsContain(GetUint8OpList(), GetCNodeTType(**iter)) && (*iter)->quantType == QuantType_AwareTrainning) {
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continue;
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}
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auto &node = *iter;
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if (GetCNodeTType(**iter) == PrimitiveType_QuantDTypeCast) {
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continue;
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}
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bool needInsertPost = true;
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if (GetCNodeTType(**iter) == PrimitiveType_Shape) {
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needInsertPost = false;
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}
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auto nodeName = node->name;
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if (node->inputIndex.size() < kMinInputNum) {
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MS_LOG(ERROR) << "Op " << nodeName.c_str() << " should have " << kMinInputNum << " input tensor at least";
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return RET_ERROR;
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}
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STATUS status;
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// insert pre
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for (size_t i = 0; i < (*iter)->inputIndex.size(); i++) {
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MS_ASSERT(graph->allTensors.size() > (*iter)->inputIndex.at(i));
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auto &preTensor = graph->allTensors.at((*iter)->inputIndex.at(i));
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auto &graphInIdxes = graph->inputIndex;
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if (preTensor->nodeType == NodeType_ValueNode && !IsContain(graphInIdxes, (*iter)->inputIndex.at(i))) {
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continue;
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}
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iter = InsertDTypeTransNode(graph, iter, kBefore, i, kInt8ToFP32, &status);
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if (status != RET_OK) {
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MS_LOG(ERROR) << "InsertInt8ToFloat32Node before " << nodeName.c_str() << " failed";
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return RET_ERROR;
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}
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}
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if (needInsertPost) {
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for (size_t i = 0; i < (*iter)->outputIndex.size(); i++) {
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iter = InsertDTypeTransNode(graph, iter, kAfter, i, kFP32ToInt8, &status);
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if (status != RET_OK) {
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MS_LOG(ERROR) << "InsertFloat32ToUint8Node after " << nodeName.c_str() << " failed";
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return RET_ERROR;
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}
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}
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}
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(*iter)->quantType = QuantType_QUANT_NONE;
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}
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return RET_OK;
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}
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NodeIter DTypeTransPass::InsertDTypeTransNode(schema::MetaGraphT *graph, NodeIter existNodeIter, InsertPlace place,
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size_t inoutIdx, DTypeTransNodeType nodeType, STATUS *errorCode) {
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MS_ASSERT((*existNodeIter) != nullptr);
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auto existNodeName = (*existNodeIter)->name;
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std::string tileName;
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if (place == kBefore) {
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tileName = existNodeName + "_pre";
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} else {
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tileName = existNodeName + "_post";
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}
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auto transNode = std::unique_ptr<CNodeT>(new (std::nothrow) CNodeT);
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if (transNode == nullptr) {
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MS_LOG(ERROR) << "new TransNode failed";
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*errorCode = RET_ERROR;
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return graph->nodes.end();
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}
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auto quantDTypeCastParam = new (std::nothrow) QuantDTypeCastT;
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if (quantDTypeCastParam == nullptr) {
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MS_LOG(ERROR) << "new quantDTypeCastParam failed";
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*errorCode = RET_ERROR;
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return graph->nodes.end();
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}
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transNode->primitive = std::make_unique<schema::PrimitiveT>();
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transNode->primitive->value.value = quantDTypeCastParam;
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transNode->primitive->value.type = PrimitiveType_QuantDTypeCast;
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transNode->quantType = QuantType_AwareTrainning;
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if (nodeType == kInt8ToFP32) {
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quantDTypeCastParam->srcT = TypeId::kNumberTypeInt8;
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quantDTypeCastParam->dstT = TypeId::kNumberTypeFloat32;
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transNode->name = "int8toft32_" + tileName + std::to_string(id++);
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} else if (nodeType == kFP32ToInt8) {
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quantDTypeCastParam->srcT = TypeId::kNumberTypeFloat32;
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quantDTypeCastParam->dstT = TypeId::kNumberTypeInt8;
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transNode->name = "ft32toint8_" + tileName + std::to_string(id++);
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} else if (nodeType == kUInt8ToInt8) {
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quantDTypeCastParam->srcT = TypeId::kNumberTypeUInt8;
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quantDTypeCastParam->dstT = TypeId::kNumberTypeInt8;
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transNode->name = "uint8toint8_" + tileName + std::to_string(id++);
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} else if (nodeType == kInt8ToUInt8) {
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quantDTypeCastParam->srcT = TypeId::kNumberTypeInt8;
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quantDTypeCastParam->dstT = TypeId::kNumberTypeUInt8;
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transNode->name = "int8touint8_" + tileName + std::to_string(id++);
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}
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transNode->primitive->value.value = quantDTypeCastParam;
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return InsertNode(graph, existNodeIter, place, inoutIdx, std::move(transNode), errorCode, castOpCopyer);
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}
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void DTypeTransPass::SetInputDataDType(TypeId dataType) { this->inputDataDType = dataType; }
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} // namespace lite
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} // namespace mindspore
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@ -0,0 +1,81 @@
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef MINDSPORE_PREDICT_DTYPE_TRANS_PASS_H
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#define MINDSPORE_PREDICT_DTYPE_TRANS_PASS_H
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#include <memory>
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#include <utility>
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#include "tools/converter/optimizer.h"
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#include "tools/common/graph_util.h"
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#include "tools/converter/converter_flags.h"
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#include "tools/common/tensor_util.h"
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namespace mindspore {
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namespace lite {
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enum DTypeTransNodeType { kInt8ToFP32, kFP32ToInt8, kUInt8ToInt8, kInt8ToUInt8 };
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class DTypeTransPass : public GraphPass {
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public:
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DTypeTransPass() : id(0) {}
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~DTypeTransPass() override = default;
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STATUS Run(schema::MetaGraphT *graph) override;
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void SetInputDataDType(TypeId dataType);
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private:
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STATUS DoModelInputDTypeTrans(schema::MetaGraphT *graph);
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STATUS DoModelOutputDTypeTrans(schema::MetaGraphT *graph);
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STATUS DoNodeInoutDTypeTrans(schema::MetaGraphT *graph);
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NodeIter InsertDTypeTransNode(schema::MetaGraphT *graph, NodeIter existNodeIter, InsertPlace place, size_t inoutIdx,
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DTypeTransNodeType nodeType, STATUS *errorCode);
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private:
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size_t id;
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TypeId inputDataDType = TypeId::kNumberTypeFloat;
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OpDefCopyer castOpCopyer = [](schema::CNodeT *inCNode) -> std::unique_ptr<schema::CNodeT> {
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std::unique_ptr<schema::CNodeT> newCNode(new (std::nothrow) schema::CNodeT);
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if (newCNode == nullptr) {
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MS_LOG(ERROR) << "new CNodeT failed";
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return nullptr;
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}
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newCNode->name = inCNode->name;
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newCNode->quantType = inCNode->quantType;
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newCNode->primitive = std::make_unique<schema::PrimitiveT>();
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newCNode->primitive->value.type = inCNode->primitive->value.type;
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auto oldQuantDTypeCastParam = inCNode->primitive->value.AsQuantDTypeCast();
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auto QuantDTypeCastParam = new (std::nothrow) QuantDTypeCastT;
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if (QuantDTypeCastParam == nullptr) {
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MS_LOG(ERROR) << "new QuantDTypeCast failed";
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return nullptr;
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}
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QuantDTypeCastParam->srcT = oldQuantDTypeCastParam->srcT;
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QuantDTypeCastParam->dstT = oldQuantDTypeCastParam->dstT;
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newCNode->primitive->value.value = QuantDTypeCastParam;
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return std::move(newCNode);
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};
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};
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} // namespace lite
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} // namespace mindspore
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#endif // MINDSPORE_PREDICT_DTYPE_TRANS_PASS_H
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