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104 lines
4.3 KiB
104 lines
4.3 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 inference {
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namespace tensorrt {
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// LeakyRelu converter from fluid to tensorRT
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class LeakyReluOpConverter : 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(4) << "convert fluid leaky_relu op to tensorrt layer";
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framework::OpDesc op_desc(op, nullptr);
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// Declare inputs
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int input_num = op_desc.Input("X").size();
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PADDLE_ENFORCE(input_num == 1);
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auto* input = engine_->GetITensor(op_desc.Input("X")[0]);
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// Get output
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size_t output_num = op_desc.Output("Out").size();
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PADDLE_ENFORCE(output_num == 1);
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// Get attrs
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float alpha = boost::get<float>(op_desc.GetAttr("alpha"));
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nvinfer1::ILayer* output_layer = nullptr;
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#if IS_TRT_VERSION_GE(5100)
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nvinfer1::IActivationLayer* layer = TRT_ENGINE_ADD_LAYER(
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engine_, Activation, *input, nvinfer1::ActivationType::kLEAKY_RELU);
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layer->setAlpha(alpha);
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output_layer = layer;
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bool enable_int8 = boost::get<bool>(op_desc.HasAttr("enable_int8"));
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if (enable_int8) {
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CHECK(op_desc.HasAttr("X_scale"));
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float in_scale = boost::get<float>(op_desc.GetAttr("X_scale"));
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engine_->SetTensorDynamicRange(input, in_scale);
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}
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#else
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platform::CPUPlace place;
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std::unique_ptr<framework::LoDTensor> alpha_tensor(
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new framework::LoDTensor());
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alpha_tensor->Resize(framework::make_ddim({2}));
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float* alpha_data = alpha_tensor->mutable_data<float>(place);
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alpha_data[0] = alpha;
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alpha_data[1] = 1.f - alpha;
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// the leaky relu formula y = (x > 0) ? x : alpha * x is equal to
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// y = alpha * x + (x > 0) ? (1 - alpha) * x : 0
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TensorRTEngine::Weight scale{nvinfer1::DataType::kFLOAT, &alpha_data[0], 1};
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TensorRTEngine::Weight shift{nvinfer1::DataType::kFLOAT, nullptr, 0};
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TensorRTEngine::Weight power{nvinfer1::DataType::kFLOAT, nullptr, 0};
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// y_scale = alpha * x
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auto* scale_layer = TRT_ENGINE_ADD_LAYER(
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engine_, Scale, *input, nvinfer1::ScaleMode::kUNIFORM, shift.get(),
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scale.get(), power.get());
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PADDLE_ENFORCE(nullptr != scale_layer);
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// y_relu = (x > 0) : x : 0
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auto* relu_layer = TRT_ENGINE_ADD_LAYER(engine_, Activation, *input,
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nvinfer1::ActivationType::kRELU);
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PADDLE_ENFORCE(nullptr != relu_layer);
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//
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TensorRTEngine::Weight sub_scale{nvinfer1::DataType::kFLOAT, &alpha_data[1],
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1};
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auto* scale_relu_layer =
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TRT_ENGINE_ADD_LAYER(engine_, Scale, *(relu_layer->getOutput(0)),
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nvinfer1::ScaleMode::kUNIFORM, shift.get(),
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sub_scale.get(), power.get());
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PADDLE_ENFORCE(nullptr != scale_relu_layer);
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output_layer =
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TRT_ENGINE_ADD_LAYER(engine_, ElementWise, *(scale_layer->getOutput(0)),
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*(scale_relu_layer->getOutput(0)),
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nvinfer1::ElementWiseOperation::kSUM);
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PADDLE_ENFORCE(nullptr != output_layer);
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// keep alpha tensor to avoid release it's memory
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std::string alpha_name = op_desc.Output("Out")[0] + "_alpha";
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PADDLE_ENFORCE(engine_->weight_map.find(alpha_name) ==
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engine_->weight_map.end());
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engine_->SetWeights(alpha_name, std::move(alpha_tensor));
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#endif
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auto output_name = op_desc.Output("Out")[0];
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RreplenishLayerAndOutput(output_layer, "leaky_relu", {output_name},
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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(leaky_relu, LeakyReluOpConverter);
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