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85 lines
2.7 KiB
85 lines
2.7 KiB
/* Copyright (c) 2021 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 <bitset>
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#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
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namespace paddle {
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namespace framework {
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class Scope;
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namespace proto {
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class OpDesc;
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} // namespace proto
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} // namespace framework
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} // namespace paddle
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namespace paddle {
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namespace inference {
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namespace tensorrt {
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/*
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* TransposeOp
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*/
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class TransposeOpConverter : 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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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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int dims = input->getDimensions().nbDims;
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std::vector<int> axis =
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BOOST_GET_CONST(std::vector<int>, op_desc.GetAttr("axis"));
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if (!engine_->with_dynamic_shape()) {
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for (size_t i = 1; i < axis.size(); i++) {
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axis[i]--;
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}
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}
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nvinfer1::Permutation perm;
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for (int i = 0; i < dims; i++) {
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int j = engine_->with_dynamic_shape() ? i : i + 1;
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perm.order[i] = axis[j];
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}
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// Permutation is valid if it has nbDims unique values from range [0,
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// nbDims-1]
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auto is_valid_permutation = [&](int dims,
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const nvinfer1::Permutation& permutation) {
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std::bitset<nvinfer1::Dims::MAX_DIMS> found;
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for (int i = 0; i < dims; ++i) {
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const int x = permutation.order[i];
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if ((x < 0) || (x >= dims) || found[x])
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return false; // Out of bounds or duplicate
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found.set(x);
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}
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return true;
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};
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PADDLE_ENFORCE_EQ(is_valid_permutation(dims, perm), true,
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platform::errors::InvalidArgument(
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"Invalid permutation dimensions for trt transpose op "
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"converter: duplicate or out of bound."));
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auto* layer = TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *input);
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layer->setFirstTranspose(perm);
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auto output_name = op_desc.Output("Out")[0];
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RreplenishLayerAndOutput(layer, "transpose", {output_name}, test_mode);
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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(transpose, TransposeOpConverter);
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