!5480 add infershape and trans op optimize
Merge pull request !5480 from zhengjun10/masterpull/5480/MERGE
mindspore-ci-bot
5 years ago
committed by
Gitee
commit
6d500c865d
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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/infershape_pass.h"
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#include <vector>
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#include "utils/log_adapter.h"
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#include "include/errorcode.h"
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#include "src/ir/tensor.h"
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#include "src/ops/primitive_c.h"
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using mindspore::lite::tensor::Tensor;
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using mindspore::lite::PrimitiveC;
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namespace mindspore {
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namespace lite {
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namespace {
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std::vector<tensor::Tensor *> ConvertTensorToLiteTensor(MetaGraphT *graph, const std::vector<uint32_t> &tensor_indexs,
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const schema::PrimitiveType node_type) {
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std::vector<tensor::Tensor *> lite_tensors;
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for (size_t i = 0; i < tensor_indexs.size(); i++) {
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auto &tensorT = graph->allTensors.at(tensor_indexs[i]);
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auto tensor_shape = tensorT->dims;
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auto lite_tensor =
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new(std::nothrow) tensor::Tensor(TypeId(tensorT->dataType), tensor_shape, tensorT->format, tensorT->nodeType);
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if (lite_tensor == nullptr) {
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MS_LOG(ERROR) << "lite tensor is nullptr";
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return std::vector<tensor::Tensor *>();
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}
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// reshape op must get tensor data to infershape
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if (node_type == schema::PrimitiveType_Reshape && i == 1 && tensorT->nodeType == NodeType_ValueNode) {
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auto lite_tensor_size = tensorT->data.size() * sizeof(uint8_t);
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// when tensorT as param input
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if (lite_tensor_size == 0) {
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delete lite_tensor;
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return std::vector<tensor::Tensor *>();
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}
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auto tensor_data = new(std::nothrow) char[lite_tensor_size / sizeof(char)];
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if (tensor_data == nullptr) {
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MS_LOG(ERROR) << "tensor_data is nullptr";
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delete lite_tensor;
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return std::vector<tensor::Tensor *>();
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}
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auto ret = memcpy_s(tensor_data, lite_tensor_size, tensorT->data.data(), lite_tensor_size);
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if (ret != EOK) {
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delete lite_tensor;
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delete[] tensor_data;
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MS_LOG(ERROR) << "memcpy error: " << ret;
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return std::vector<tensor::Tensor *>();
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}
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lite_tensor->SetData(tensor_data);
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lite_tensors.emplace_back(lite_tensor);
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continue;
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}
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lite_tensors.emplace_back(lite_tensor);
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}
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return lite_tensors;
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}
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} // namespace
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STATUS InferShapePass::Run(MetaGraphT *graph) {
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MS_ASSERT(graph != nullptr);
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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 input_tensors = ConvertTensorToLiteTensor(graph, node->inputIndex, node->primitive->value.type);
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if (input_tensors.empty() || input_tensors.size() != node->inputIndex.size()) {
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MS_LOG(ERROR) << "convert input lite tensor error";
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return RET_INFER_ERR;
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}
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auto output_tensors = ConvertTensorToLiteTensor(graph, node->outputIndex, node->primitive->value.type);
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if (output_tensors.empty() || output_tensors.size() != node->outputIndex.size()) {
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MS_LOG(ERROR) << "convert output lite tensor error";
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return RET_INFER_ERR;
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}
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std::unique_ptr<PrimitiveT> primitiveT(new (std::nothrow) PrimitiveT(*node->primitive));
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if (primitiveT == nullptr) {
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MS_LOG(ERROR) << "copy primitiveT error";
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return RET_ERROR;
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}
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auto primitiveC = std::shared_ptr<PrimitiveC>(PrimitiveC::UnPackFromSchemaPrimitiveT(primitiveT.release()));
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if (primitiveC == nullptr) {
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MS_LOG(ERROR) << "unpack primitiveT error";
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return RET_ERROR;
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}
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auto ret = primitiveC->InferShape(input_tensors, output_tensors);
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if (ret == RET_INFER_INVALID) {
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MS_LOG(INFO) << "InferShape shouldn't be done before runtime, name: " << node->name
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<< ", type: " << schema::EnumNamePrimitiveType(node->primitive->value.type)
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<< "flag set to false.";
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} else if (ret != RET_OK) {
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MS_LOG(WARNING) << "InferShape failed, name: " << node->name
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<< ", type: " << schema::EnumNamePrimitiveType(node->primitive->value.type);
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return RET_INFER_ERR;
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}
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// copy output shape to tensorT
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for (size_t i = 0; i < output_tensors.size(); i++) {
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auto output_dims = output_tensors[i]->shape();
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auto &output_tensor = graph->allTensors.at(node->outputIndex[i]);
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output_tensor->dims.swap(output_dims);
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output_tensor->format = output_tensors[i]->GetFormat();
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output_tensor->dataType = output_tensors[i]->data_type();
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}
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for (auto input_tensor : input_tensors) {
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delete input_tensor;
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}
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for (auto output_tensor : output_tensors) {
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delete output_tensor;
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}
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}
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return RET_OK;
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}
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} // namespace lite
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} // namespace mindspore
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@ -0,0 +1,40 @@
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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_INFERSHAPE_PASS_H
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#define MINDSPORE_PREDICT_INFERSHAPE_PASS_H
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#include <unordered_map>
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#include <memory>
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#include <string>
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#include <utility>
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#include "tools/common/graph_util.h"
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#include "tools/converter/optimizer.h"
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using mindspore::schema::TensorT;
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namespace mindspore {
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namespace lite {
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class InferShapePass : public GraphPass {
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public:
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InferShapePass() = default;
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~InferShapePass() = default;
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STATUS Run(MetaGraphT *graph) override;
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};
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} // namespace lite
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} // namespace mindspore
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#endif // MINDSPORE_PREDICT_INFERSHAPE_PASS_H
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@ -0,0 +1,49 @@
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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/trans_format_remove_pass.h"
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#include <vector>
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#include "utils/log_adapter.h"
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#include "include/errorcode.h"
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#include "tools/common/graph_util.h"
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#include "src/ir/tensor.h"
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#include "src/ops/primitive_c.h"
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using mindspore::lite::tensor::Tensor;
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using mindspore::lite::PrimitiveC;
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namespace mindspore {
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namespace lite {
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STATUS TransOpRemovePass::Run(MetaGraphT *graph) {
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MS_ASSERT(graph != nullptr);
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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 type = node->primitive->value.type;
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if (type == schema::PrimitiveType_Nchw2Nhwc || type == schema::PrimitiveType_Nhwc2Nchw) {
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auto &input_tensor = graph->allTensors.at(node->inputIndex.at(0));
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// less than 4 dims can delete
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if (!input_tensor->dims.empty() && input_tensor->dims.size() < 4) {
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auto status = IsolateOneWayNode(graph, node.get(), true);
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if (status != RET_OK) {
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MS_LOG(ERROR) << "IsolateOneWayNode failed, node: " << node->name.c_str() << ", error: " << status;
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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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return RET_OK;
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}
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} // namespace lite
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} // namespace mindspore
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@ -0,0 +1,40 @@
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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_TRANS_FORMAT_REMOVE_PASS_H
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#define MINDSPORE_PREDICT_TRANS_FORMAT_REMOVE_PASS_H
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#include <unordered_map>
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#include <memory>
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#include <string>
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#include <utility>
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#include "tools/common/graph_util.h"
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#include "tools/converter/optimizer.h"
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using mindspore::schema::TensorT;
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namespace mindspore {
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namespace lite {
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class TransOpRemovePass : public GraphPass {
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public:
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TransOpRemovePass() = default;
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~TransOpRemovePass() = default;
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STATUS Run(MetaGraphT *graph) override;
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};
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} // namespace lite
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} // namespace mindspore
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#endif // MINDSPORE_PREDICT_TRANS_FORMAT_REMOVE_PASS_H
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