wehub-resource-sync
64 lines
2.3 KiB
C++
64 lines
2.3 KiB
C++
/* Copyright (c) 2022 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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class SiluOpConverter : 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(4) << "convert silu 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_EQ(input_num,
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1,
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common::errors::InvalidArgument(
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"The input X's size must equal to 1 in TRT silu op."
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" But received X's size %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(
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output_num,
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1UL,
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common::errors::InvalidArgument(
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"The output Out's size must equal to 1 in TRT silu op. "
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"But received Out's size %u.",
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output_num));
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nvinfer1::ILayer* layer = nullptr;
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auto* sigmoid = TRT_ENGINE_ADD_LAYER(
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engine_, Activation, *input, nvinfer1::ActivationType::kSIGMOID);
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layer = TRT_ENGINE_ADD_LAYER(engine_,
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ElementWise,
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*input,
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*(sigmoid->getOutput(0)),
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nvinfer1::ElementWiseOperation::kPROD);
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auto output_name = op_desc.Output("Out")[0];
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ReplenishLayerAndOutput(layer, "silu", {output_name}, test_mode);
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}
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};
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} // namespace paddle::inference::tensorrt
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REGISTER_TRT_OP_CONVERTER(silu, SiluOpConverter);
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