wehub-resource-sync
95 lines
3.9 KiB
C++
95 lines
3.9 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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#include "paddle/fluid/inference/tensorrt/helper.h"
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namespace paddle::inference::tensorrt {
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/*
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* Stack converter from fluid to tensorRT.
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*/
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class RollOpConverter : 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 roll op to tensorrt Gather layer";
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framework::OpDesc op_desc(op, nullptr);
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auto* input = engine_->GetITensor(op_desc.Input("X")[0]);
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std::vector<int64_t> axis =
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PADDLE_GET_CONST(std::vector<int64_t>, op_desc.GetAttr("axis"));
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std::vector<int64_t> shifts =
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PADDLE_GET_CONST(std::vector<int64_t>, op_desc.GetAttr("shifts"));
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int axis_size = axis.size();
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nvinfer1::ITensor* input_shape_tensor = Shape(input);
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nvinfer1::ILayer* layer = nullptr;
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for (int i = 0; i < axis_size; i++) {
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auto axi = static_cast<int32_t>(axis[i]);
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auto shift = static_cast<int32_t>(shifts[i]);
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nvinfer1::ITensor* input_axis =
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GetEleTensorOfShape(input_shape_tensor, axi);
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nvinfer1::ITensor* input_shift = Add1DConstantLayer(shift);
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// 1.sub_value mod input_axis
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auto input1 = Sub(input_axis, input_shift);
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auto tmp_div_res = FloorDiv(input1, input_axis);
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auto tmp_prod_res = Prod(tmp_div_res, input_axis);
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auto start = Sub(input1, tmp_prod_res);
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// 2.avoid start less than 0,start mod input_axis
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start = Sum(start, input_axis);
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auto tmp_div_res1 = FloorDiv(start, input_axis);
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auto tmp_prod_res1 = Prod(tmp_div_res1, input_axis);
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start = Sub(start, tmp_prod_res1);
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auto zero_tensor = Add1DConstantLayer(0);
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auto step = Add1DConstantLayer(1);
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// 3.make index_tensor0
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auto quotient_tensor = FloorDiv(Sub(input_axis, start), step);
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auto* start1 = GetEleTensorOfShape(start, 0, true);
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auto fill_layer0 = TRT_ENGINE_ADD_LAYER(
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engine_, Fill, nvinfer1::Dims{}, nvinfer1::FillOperation::kLINSPACE);
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fill_layer0->setInput(0, *quotient_tensor);
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fill_layer0->setInput(1, *start1);
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fill_layer0->setInput(2, *step);
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auto* index_tensor0 = fill_layer0->getOutput(0);
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// 4.make index_tensor1
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quotient_tensor = FloorDiv(Sub(start, zero_tensor), step);
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auto* start2 = Add1DConstantLayer(0, "", true);
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auto fill_layer1 = TRT_ENGINE_ADD_LAYER(
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engine_, Fill, nvinfer1::Dims{}, nvinfer1::FillOperation::kLINSPACE);
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fill_layer1->setInput(0, *quotient_tensor);
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fill_layer1->setInput(1, *start2);
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fill_layer1->setInput(2, *step);
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auto* index_tensor1 = fill_layer1->getOutput(0);
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std::vector<nvinfer1::ITensor*> itensors;
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itensors.push_back(index_tensor0);
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itensors.push_back(index_tensor1);
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nvinfer1::ITensor* concat_input_tensor = Concat(itensors);
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if (layer == nullptr) {
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layer = TRT_ENGINE_ADD_LAYER(
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engine_, Gather, *input, *concat_input_tensor, axi);
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} else {
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layer = TRT_ENGINE_ADD_LAYER(
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engine_, Gather, *layer->getOutput(0), *concat_input_tensor, axi);
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}
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}
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auto output_name = op_desc.Output("Out")[0];
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ReplenishLayerAndOutput(layer, "roll", {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(roll, RollOpConverter);
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