wehub-resource-sync
77 lines
2.8 KiB
C++
77 lines
2.8 KiB
C++
/* Copyright (c) 2023 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 <NvInferRuntimeCommon.h>
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#include <cstddef>
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#include <iostream>
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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 BitwiseNotConverter : 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 bitwise_not op to tensorrt layer";
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framework::OpDesc op_desc(op, nullptr);
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nvinfer1::ILayer* layer = nullptr;
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auto* input_tensor = engine_->GetITensor(op_desc.Input("X")[0]);
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nvinfer1::DataType data_type = input_tensor->getType();
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// for bool type: use UnaryOperation::kNOT, for int type: !x = -x - 1
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if (data_type == nvinfer1::DataType::kBOOL) {
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layer = TRT_ENGINE_ADD_LAYER(
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engine_, Unary, *input_tensor, nvinfer1::UnaryOperation::kNOT);
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} else {
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nvinfer1::Dims input_dims = input_tensor->getDimensions();
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// set up a elementwise -1 tensor, can not get the dims info for
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// dynamic_shape so just let it broadcast
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nvinfer1::Dims neg_one_tensor_dims;
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neg_one_tensor_dims.nbDims = input_dims.nbDims;
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for (int i = 0; i < input_dims.nbDims; ++i) {
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neg_one_tensor_dims.d[i] = 1;
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}
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nvinfer1::Weights weights{nvinfer1::DataType::kINT32, new int(-1), 1};
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auto neg_one_tensor =
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TRT_ENGINE_ADD_LAYER(engine_, Constant, neg_one_tensor_dims, weights)
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->getOutput(0);
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auto mul_neg_one =
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TRT_ENGINE_ADD_LAYER(engine_,
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ElementWise,
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*input_tensor,
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*neg_one_tensor,
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nvinfer1::ElementWiseOperation::kPROD);
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layer = TRT_ENGINE_ADD_LAYER(engine_,
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ElementWise,
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*(mul_neg_one->getOutput(0)),
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*neg_one_tensor,
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nvinfer1::ElementWiseOperation::kSUM);
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}
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auto output_name = op_desc.Output("Out")[0];
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ReplenishLayerAndOutput(layer, "bitwise_not", {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(bitwise_not, BitwiseNotConverter);
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