[Paddle-TRT]: add eltwise,pool2d, prelu, scale, concat, gelu dynamic shape support (#23396)
* add elementwise pool2d, prelu, shuffle channel test=develop * add scale and refine concat eltwise conveter test=develop * refine elementwise converter test=develop * refine ut test and enforce error. test=develop * modify const cast test=developrevert-23830-2.0-beta
Zhaolong Xing
5 years ago
committed by
GitHub
parent
c113302826
commit
3acb047a20
@ -0,0 +1,84 @@
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/* 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 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<bool>(op_desc.GetAttr("bias_after_scale"));
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float bias = boost::get<float>(op_desc.GetAttr("bias"));
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float scale = boost::get<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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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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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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