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124 lines
4.6 KiB
124 lines
4.6 KiB
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
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namespace paddle {
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namespace framework {
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class Scope;
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namespace proto {
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class OpDesc;
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} // namespace proto
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} // namespace framework
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} // namespace paddle
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namespace paddle {
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namespace inference {
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namespace tensorrt {
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/*
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* ConcatOp
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*/
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class ScaleOpConverter : public OpConverter {
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public:
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void operator()(const framework::proto::OpDesc& op,
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const framework::Scope& scope, bool test_mode) override {
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VLOG(3) << "convert a fluid scale op to tensorrt mul layer without bias";
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framework::OpDesc op_desc(op, nullptr);
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// Declare inputs
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std::vector<nvinfer1::ITensor*> itensors;
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std::string input_name = op_desc.Input("X").front();
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std::string out_name = op_desc.Output("Out").front();
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auto input = engine_->GetITensor(input_name);
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bool bias_after_scale =
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BOOST_GET_CONST(bool, op_desc.GetAttr("bias_after_scale"));
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float bias = BOOST_GET_CONST(float, op_desc.GetAttr("bias"));
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float scale = BOOST_GET_CONST(float, op_desc.GetAttr("scale"));
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auto create_weights = [&](float data, std::string type) -> float* {
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std::unique_ptr<framework::Tensor> tmp_tensor(new framework::Tensor());
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tmp_tensor->Resize({1});
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auto* tmp_data = tmp_tensor->mutable_data<float>(platform::CPUPlace());
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tmp_data[0] = data;
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engine_->SetWeights(out_name + "_scale_op_" + type,
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std::move(tmp_tensor));
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return tmp_data;
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};
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float* bias_ptr = create_weights(bias, "bias");
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float* scale_ptr = create_weights(scale, "scale");
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TensorRTEngine::Weight scale_weights{nvinfer1::DataType::kFLOAT,
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static_cast<void*>(scale_ptr), 1};
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TensorRTEngine::Weight shift_weights{nvinfer1::DataType::kFLOAT,
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static_cast<void*>(bias_ptr), 1};
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TensorRTEngine::Weight power_weights{nvinfer1::DataType::kFLOAT, nullptr,
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0};
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nvinfer1::ILayer* layer = nullptr;
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auto input_dim = input->getDimensions();
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PADDLE_ENFORCE_GE(input_dim.nbDims, 3,
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platform::errors::Fatal(
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"Paddle-TRT scale mode only support dimension >= 3"));
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nvinfer1::IShuffleLayer* expand_layer = nullptr;
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nvinfer1::IShuffleLayer* squeeze_layer = nullptr;
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if (input_dim.nbDims == 3) {
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// TensorRT scale layer is not supporting input dims < 4 when using
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// explicit batch
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expand_layer = TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *input);
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nvinfer1::Dims4 target_shape(0, 0, 0, 1); // expand 1 dims
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expand_layer->setReshapeDimensions(target_shape);
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input = expand_layer->getOutput(0);
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}
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if (bias_after_scale) {
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layer = TRT_ENGINE_ADD_LAYER(
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engine_, Scale, *input, nvinfer1::ScaleMode::kUNIFORM,
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shift_weights.get(), scale_weights.get(), power_weights.get());
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} else {
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// add bias
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layer = TRT_ENGINE_ADD_LAYER(
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engine_, Scale, *(input), nvinfer1::ScaleMode::kUNIFORM,
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shift_weights.get(), power_weights.get(), power_weights.get());
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// mul scale
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layer = TRT_ENGINE_ADD_LAYER(
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engine_, Scale, *(layer->getOutput(0)), nvinfer1::ScaleMode::kUNIFORM,
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power_weights.get(), scale_weights.get(), power_weights.get());
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}
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PADDLE_ENFORCE_EQ(layer != nullptr, true,
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platform::errors::Fatal("Create scale layer failed."));
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if (input_dim.nbDims == 3) {
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// TensorRT scale layer is not supporting input dims < 4 when using
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// explicit batch
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squeeze_layer =
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TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *(layer->getOutput(0)));
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nvinfer1::Dims3 target_shape(0, 0, 0); // expand 1 dims
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squeeze_layer->setReshapeDimensions(target_shape);
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layer = static_cast<nvinfer1::ILayer*>(squeeze_layer);
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}
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RreplenishLayerAndOutput(layer, "scale", {out_name}, test_mode);
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}
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};
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} // namespace tensorrt
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} // namespace inference
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} // namespace paddle
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REGISTER_TRT_OP_CONVERTER(scale, ScaleOpConverter);
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