wehub-resource-sync
83 lines
3.1 KiB
C++
83 lines
3.1 KiB
C++
/* 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::inference::tensorrt {
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/*
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* DropoutOp. This Layer doesn't has weights.
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*/
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class DropoutOpConverter : 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,
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bool test_mode) override {
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VLOG(3) << "convert a dropout op to tensorrt dropout layer";
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framework::OpDesc op_desc(op, nullptr);
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// Declare inputs
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auto* input1 = engine_->GetITensor(op_desc.Input("X")[0]);
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float dropout_prob =
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PADDLE_GET_CONST(float, op_desc.GetAttr("dropout_prob"));
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std::string downgrade_in_infer = "";
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if (op_desc.HasAttr("dropout_implementation")) {
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downgrade_in_infer = PADDLE_GET_CONST(
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std::string, op_desc.GetAttr("dropout_implementation"));
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}
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if (!downgrade_in_infer.empty() &&
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downgrade_in_infer == "upscale_in_train") {
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auto* layer = TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *input1);
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auto output_name = op_desc.Output("Out")[0];
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ReplenishLayerAndOutput(layer, "dropout", {output_name}, test_mode);
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return;
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}
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CPUPlace cpu_place;
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std::unique_ptr<phi::DenseTensor> weight_tensor(new phi::DenseTensor());
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weight_tensor->Resize(common::make_ddim({1}));
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auto* weight_data = weight_tensor->mutable_data<float>(CPUPlace());
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weight_data[0] = 1 - dropout_prob;
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TensorRTEngine::Weight scale_weights{
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nvinfer1::DataType::kFLOAT,
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static_cast<void*>(weight_data),
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weight_tensor->memory_size() / sizeof(float)};
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TensorRTEngine::Weight shift_weights{
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nvinfer1::DataType::kFLOAT, nullptr, 0};
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TensorRTEngine::Weight power_weights{
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nvinfer1::DataType::kFLOAT, nullptr, 0};
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auto* layer = TRT_ENGINE_ADD_LAYER(engine_,
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Scale,
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*const_cast<nvinfer1::ITensor*>(input1),
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nvinfer1::ScaleMode::kUNIFORM,
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shift_weights.get(),
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scale_weights.get(),
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power_weights.get());
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engine_->SetWeights(op_desc.Output("Out").front() + "_dropout",
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std::move(weight_tensor));
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auto output_name = op_desc.Output("Out")[0];
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ReplenishLayerAndOutput(layer, "dropout", {output_name}, test_mode);
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}
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};
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} // namespace paddle::inference::tensorrt
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USE_OP_ITSELF(dropout);
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REGISTER_TRT_OP_CONVERTER(dropout, DropoutOpConverter);
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