Update trt5 for paddle-trt (#18645)
* update paddle-trt for:
1. fix bug: when batch > 2, core in split plugin.
2. add leaky_relu trt5.0 support (yolov3 from 65ms to 42ms.)
3. add new attr to dropout.
4. shuffle channel, swish, relu6 support
test=develop
* 1. fix ci
test=develop
DDDivano-patch-1
Zhaolong Xing
6 years ago
committed by
GitHub
parent
d8396281ef
commit
26ae6d49e4
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/* 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 {
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namespace inference {
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namespace tensorrt {
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/*
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* ConcatOp
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*/
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class ShuffleChannelOpConverter : 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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auto input_dims = input->getDimensions();
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PADDLE_ENFORCE(input_dims.nbDims == 3);
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int c = input_dims.d[0];
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int h = input_dims.d[1];
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int w = input_dims.d[2];
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int group = boost::get<int>(op_desc.GetAttr("group"));
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auto* layer = TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *input);
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nvinfer1::Dims4 reshape_dim(group, c / group, h, w);
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layer->setReshapeDimensions(reshape_dim);
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layer->setSecondTranspose({1, 0, 2, 3});
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auto* output = layer->getOutput(0);
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auto* reshape_layer = TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *output);
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nvinfer1::DimsCHW reshape_dim2(c, h, w);
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reshape_layer->setReshapeDimensions(reshape_dim2);
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auto output_name = op_desc.Output("Out")[0];
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RreplenishLayerAndOutput(reshape_layer, "concat", {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(shuffle_channel, ShuffleChannelOpConverter);
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@ -0,0 +1,53 @@
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/* 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/swish_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 SwishOpConverter : 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 fluid swish 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(input_num == 1);
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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(output_num == 1);
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// Get attrs
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float beta = boost::get<float>(op_desc.GetAttr("beta"));
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plugin::SwishPlugin* plugin = new plugin::SwishPlugin(beta);
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nvinfer1::IPluginLayer* layer =
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engine_->AddPlugin(&input, input_num, plugin);
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auto output_name = op_desc.Output("Out")[0];
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RreplenishLayerAndOutput(layer, "swish", {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(swish, SwishOpConverter);
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/* 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 <gtest/gtest.h>
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#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
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#include "paddle/fluid/inference/tensorrt/convert/ut_helper.h"
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namespace paddle {
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namespace inference {
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namespace tensorrt {
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TEST(leaky_relu_op, test_leaky_relu) {
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std::unordered_set<std::string> parameters;
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framework::Scope scope;
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TRTConvertValidation validator(10, parameters, scope, 1000);
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validator.DeclInputVar("sc_input", nvinfer1::DimsCHW(4, 2, 2));
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validator.DeclOutputVar("sc_out", nvinfer1::DimsCHW(4, 2, 2));
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// Prepare Op description
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framework::OpDesc desc;
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desc.SetType("shuffle_channel");
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desc.SetInput("X", {"sc_input"});
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desc.SetOutput("Out", {"sc_out"});
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int group = 2;
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desc.SetAttr("group", group);
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validator.SetOp(*desc.Proto());
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validator.Execute(1);
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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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// USE_OP(leaky_relu);
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USE_OP(shuffle_channel);
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@ -0,0 +1,47 @@
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/* 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 <gtest/gtest.h>
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#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
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#include "paddle/fluid/inference/tensorrt/convert/ut_helper.h"
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namespace paddle {
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namespace inference {
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namespace tensorrt {
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TEST(swish_op, test_swish) {
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std::unordered_set<std::string> parameters;
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framework::Scope scope;
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TRTConvertValidation validator(10, parameters, scope, 1000);
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validator.DeclInputVar("sw_input", nvinfer1::DimsCHW(3, 2, 2));
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validator.DeclOutputVar("sw_out", nvinfer1::DimsCHW(3, 2, 2));
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// Prepare Op description
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framework::OpDesc desc;
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desc.SetType("swish");
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desc.SetInput("X", {"sw_input"});
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desc.SetOutput("Out", {"sw_out"});
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desc.SetAttr("beta", 2.0f);
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validator.SetOp(*desc.Proto());
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validator.Execute(1);
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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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USE_OP(swish);
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@ -1,5 +1,5 @@
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nv_library(tensorrt_plugin
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SRCS trt_plugin.cc split_op_plugin.cu elementwise_op_plugin.cu
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prelu_op_plugin.cu trt_plugin_factory.cc
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avg_pool_op_plugin.cu
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avg_pool_op_plugin.cu swish_op_plugin.cu
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DEPS enforce tensorrt_engine prelu)
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// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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 <stdio.h>
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#include <cassert>
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#include <vector>
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#include "glog/logging.h"
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#include "paddle/fluid/inference/tensorrt/plugin/swish_op_plugin.h"
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#include "paddle/fluid/inference/tensorrt/plugin/trt_plugin_factory.h"
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namespace paddle {
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namespace inference {
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namespace tensorrt {
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namespace plugin {
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SwishPlugin *CreateSwishPluginDeserialize(const void *buffer, size_t length) {
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return new SwishPlugin(buffer, length);
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}
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REGISTER_TRT_PLUGIN("swish_plugin", CreateSwishPluginDeserialize);
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int SwishPlugin::initialize() { return 0; }
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nvinfer1::Dims SwishPlugin::getOutputDimensions(int index,
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const nvinfer1::Dims *inputDims,
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int nbInputs) {
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assert(nbInputs == 1);
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assert(index < this->getNbOutputs());
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nvinfer1::Dims const &input_dims = inputDims[0];
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nvinfer1::Dims output_dims = input_dims;
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return output_dims;
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}
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__global__ void swish_kernel(int num, const float *input, float *output,
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float beta) {
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int index = blockIdx.x * blockDim.x + threadIdx.x;
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if (index < num) {
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#if __CUDA_ARCH__ >= 350
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output[index] =
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__ldg(input + index) / (1.0f + expf(-beta * __ldg(input + index)));
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#else
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output[index] = input[index] / (1.0f + expf(-beta * input[index]));
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#endif
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}
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}
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int SwishPlugin::enqueue(int batch_size, const void *const *inputs,
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void **outputs, void *workspace, cudaStream_t stream) {
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// input dims is CHW.
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const auto &input_dims = this->getInputDims(0);
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const float *input = reinterpret_cast<const float *>(inputs[0]);
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float *output = reinterpret_cast<float **>(outputs)[0];
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int num = batch_size;
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for (int i = 0; i < input_dims.nbDims; i++) {
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num *= input_dims.d[i];
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}
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int threads = 1024;
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int blocks = (num + threads - 1) / threads;
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swish_kernel<<<blocks, threads, 0, stream>>>(num, input, output, beta_);
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return cudaGetLastError() != cudaSuccess;
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}
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} // namespace plugin
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} // namespace tensorrt
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} // namespace inference
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} // namespace paddle
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@ -0,0 +1,72 @@
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// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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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#pragma once
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#include <algorithm>
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#include <string>
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#include <vector>
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#include "paddle/fluid/inference/tensorrt/engine.h"
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#include "paddle/fluid/inference/tensorrt/plugin/trt_plugin.h"
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namespace paddle {
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namespace inference {
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namespace tensorrt {
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namespace plugin {
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class SwishPlugin : public PluginTensorRT {
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private:
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float beta_;
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protected:
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size_t getSerializationSize() override {
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return getBaseSerializationSize() + SerializedSize(beta_);
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}
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// TRT will call this func when we need to serialize the configuration of
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// tensorrt.
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// It should not be called by users.
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void serialize(void *buffer) override {
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SerializeValue(&buffer, getPluginType());
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serializeBase(buffer);
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SerializeValue(&buffer, beta_);
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}
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public:
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explicit SwishPlugin(const float beta) : beta_(beta) {}
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// It was used for tensorrt deserialization.
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// It should not be called by users.
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SwishPlugin(void const *serialData, size_t serialLength) {
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deserializeBase(serialData, serialLength);
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DeserializeValue(&serialData, &serialLength, &beta_);
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}
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~SwishPlugin() {}
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int initialize() override;
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SwishPlugin *clone() const override { return new SwishPlugin(beta_); }
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const char *getPluginType() const override { return "swish_plugin"; }
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int getNbOutputs() const override { return 1; }
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nvinfer1::Dims getOutputDimensions(int index, const nvinfer1::Dims *inputs,
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int nbInputDims) override;
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int enqueue(int batchSize, const void *const *inputs, void **outputs,
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void *workspace, cudaStream_t stream) override;
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};
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} // namespace plugin
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} // namespace tensorrt
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} // namespace inference
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} // namespace paddle
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