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79 lines
2.9 KiB
79 lines
2.9 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/split_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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class SplitOpConverter : 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 a fluid split op to tensorrt split layer";
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framework::OpDesc op_desc(op, nullptr);
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// Declare inputs
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auto* input = engine_->GetITensor(op_desc.Input("X")[0]);
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auto input_dims = input->getDimensions();
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int input_num = op_desc.Input("X").size();
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size_t output_num = op_desc.Output("Out").size();
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// Get Attrs
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PADDLE_ENFORCE(input_num == 1);
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int axis = boost::get<int>(op_desc.GetAttr("axis"));
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// split on batch is not supported in TensorRT
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PADDLE_ENFORCE(axis != 0);
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axis += (axis < 0) ? input_dims.nbDims : -1;
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std::vector<int> output_lengths =
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boost::get<std::vector<int>>(op_desc.GetAttr("sections"));
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output_lengths.reserve(output_num);
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int num = boost::get<int>(op_desc.GetAttr("num"));
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if (num > 0) {
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int64_t in_axis_dim = input_dims.d[axis];
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PADDLE_ENFORCE_EQ(in_axis_dim % num, 0,
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"Tensor split does not result"
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" in an equal division");
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size_t out_axis_dim = in_axis_dim / num;
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for (size_t i = 0; i < output_num; ++i) {
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output_lengths.push_back(out_axis_dim);
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}
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}
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PADDLE_ENFORCE(output_lengths.size() == output_num);
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plugin::SplitPlugin* plugin = new plugin::SplitPlugin(axis, output_lengths);
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nvinfer1::IPluginLayer* layer =
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engine_->AddPlugin(&input, input_num, plugin);
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std::string layer_name = "split (Output: ";
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for (size_t i = 0; i < output_num; i++) {
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auto output_name = op_desc.Output("Out")[i];
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layer->getOutput(i)->setName(output_name.c_str());
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engine_->SetITensor(output_name, layer->getOutput(i));
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layer_name += output_name;
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if (test_mode) {
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engine_->DeclareOutput(output_name);
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}
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}
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layer->setName((layer_name + ")").c_str());
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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(split, SplitOpConverter);
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