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118 lines
4.5 KiB
118 lines
4.5 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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#include "paddle/fluid/inference/tensorrt/plugin/prelu_op_plugin.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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* PRelu converter from fluid to tensorRT.
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*/
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class PReluOpConverter : 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 prelu op to tensorrt prelu layer";
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framework::OpDesc op_desc(op, nullptr);
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// Declare inputs
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size_t input_num = op_desc.Input("X").size();
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PADDLE_ENFORCE_EQ(input_num, 1UL,
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platform::errors::InvalidArgument(
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"Invalid input X's size of prelu TRT converter. "
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"Expected 1, received %d.",
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input_num));
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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_EQ(output_num, 1UL,
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platform::errors::InvalidArgument(
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"Invalid output Out's size of prelu TRT converter. "
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"Expected 1, received %d.",
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output_num));
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// Get attrs
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std::string mode = BOOST_GET_CONST(std::string, op_desc.GetAttr("mode"));
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//
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auto* alpha_var = scope.FindVar(op_desc.Input("Alpha")[0]);
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PADDLE_ENFORCE_NOT_NULL(
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alpha_var, platform::errors::NotFound(
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"Variable Alpha of prelu TRT converter is not found."));
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auto* alpha_tensor = alpha_var->GetMutable<framework::LoDTensor>();
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platform::CPUPlace cpu_place;
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std::unique_ptr<framework::LoDTensor> alpha_tensor_temp(
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new framework::LoDTensor());
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alpha_tensor_temp->Resize(alpha_tensor->dims());
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TensorCopySync(*alpha_tensor, cpu_place, alpha_tensor_temp.get());
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float* alpha_data = alpha_tensor_temp->mutable_data<float>(cpu_place);
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nvinfer1::ILayer* layer = nullptr;
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if (engine_->with_dynamic_shape()) {
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#if IS_TRT_VERSION_GE(6000)
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plugin::PReluPluginDynamic* plugin = new plugin::PReluPluginDynamic(
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alpha_data, alpha_tensor_temp->numel(), mode);
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layer = engine_->AddPluginV2(&input, input_num, plugin);
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#else
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PADDLE_THROW(platform::errors::Fatal(
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"You are running the TRT Dynamic Shape mode, need to confirm that "
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"your TRT version is no less than 6.0"));
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#endif
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} else {
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#if IS_TRT_VERSION_GE(7000)
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float* alpha_weight_data = engine_->GetWeightCPUData(
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op_desc.Input("Alpha")[0], alpha_tensor, false);
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TensorRTEngine::Weight alpha_weight{
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nvinfer1::DataType::kFLOAT, static_cast<void*>(alpha_weight_data),
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static_cast<size_t>(alpha_tensor->numel())};
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nvinfer1::Dims dims;
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dims.nbDims = 0;
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// jump batch dim
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for (int i = 1; i < alpha_tensor->dims().size(); i++) {
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dims.d[dims.nbDims++] = alpha_tensor->dims()[i];
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}
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for (; dims.nbDims < input->getDimensions().nbDims; dims.nbDims++) {
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dims.d[dims.nbDims] = 1;
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}
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auto alpha_layer =
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TRT_ENGINE_ADD_LAYER(engine_, Constant, dims, alpha_weight.get());
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auto alpha_layer_output = alpha_layer->getOutput(0);
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layer = TRT_ENGINE_ADD_LAYER(engine_, ParametricReLU, *input,
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*alpha_layer_output);
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#else
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plugin::PReluPlugin* plugin =
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new plugin::PReluPlugin(alpha_data, alpha_tensor_temp->numel(), mode);
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layer = engine_->AddPlugin(&input, input_num, plugin);
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#endif
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}
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// keep alpha tensor to avoid release it's memory
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engine_->SetWeights(op_desc.Input("Alpha")[0],
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std::move(alpha_tensor_temp));
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auto output_name = op_desc.Output("Out")[0];
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RreplenishLayerAndOutput(layer, "prelu", {output_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(prelu, PReluOpConverter);
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