wehub-resource-sync
86 lines
3.4 KiB
C++
86 lines
3.4 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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class CeluOpConverter : 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 celu 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 celu 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 celu op. "
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"But received Out's size %u.",
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output_num));
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// Get attrs
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float alpha = PADDLE_GET_CONST(float, op_desc.GetAttr("alpha"));
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nvinfer1::ILayer* layer = nullptr;
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int32_t rank = input->getDimensions().nbDims;
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nvinfer1::Dims constant_shape;
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constant_shape.nbDims = rank;
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std::fill(constant_shape.d, constant_shape.d + rank, 1);
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std::vector<float> weight_alpha_data{alpha};
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std::vector<float> weight_zero_data{0.f};
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std::vector<float> weight_one_data{1.f};
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auto* alpha_data =
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AddConstantLayer(weight_alpha_data.data(), constant_shape);
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auto* constant_zero_data =
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AddConstantLayer(weight_zero_data.data(), constant_shape);
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auto* constant_one_data =
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AddConstantLayer(weight_one_data.data(), constant_shape);
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auto* input_div_with_alpha = Div(input, alpha_data);
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auto* input_exp = TRT_ENGINE_ADD_LAYER(
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engine_, Unary, *input_div_with_alpha, nvinfer1::UnaryOperation::kEXP);
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auto* input_sub_with_one = Sub(input_exp->getOutput(0), constant_one_data);
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auto* input_prod_with_alpha = Prod(input_sub_with_one, alpha_data);
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auto* min_input = Min(input_prod_with_alpha, constant_zero_data);
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auto* relu = TRT_ENGINE_ADD_LAYER(
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engine_, Activation, *input, nvinfer1::ActivationType::kRELU);
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layer = TRT_ENGINE_ADD_LAYER(engine_,
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ElementWise,
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*relu->getOutput(0),
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*min_input,
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nvinfer1::ElementWiseOperation::kSUM);
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auto output_name = op_desc.Output("Out")[0];
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ReplenishLayerAndOutput(layer, "celu", {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(celu, CeluOpConverter);
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