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138 lines
4.1 KiB
138 lines
4.1 KiB
// 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 <sstream>
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#include <string>
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#include <vector>
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namespace paddle {
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/*
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* This is a pass builder based on string. It is part of inference API.
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*/
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class PaddlePassBuilder {
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public:
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explicit PaddlePassBuilder(const std::vector<std::string> &passes)
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: passes_(passes) {}
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void AppendPass(const std::string &pass_type);
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void InsertPass(size_t idx, const std::string &pass_type);
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// Delete the `idx`-th pass.
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void DeletePass(size_t idx);
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// Delete all the passes that has type `pass_type`.
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void DeletePass(const std::string &pass_type);
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// Visualize the computation graph after each pass by generating a DOT
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// language file, one can draw them with the Graphviz toolkit.
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void TurnOnDebug();
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// Human-readible information.
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std::string DebugString();
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const std::vector<std::string> &AllPasses() const { return passes_; }
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protected:
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std::vector<std::string> passes_;
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};
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/*
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* Pass strategy to help control the IR passes.
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*/
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class PassStrategy : public PaddlePassBuilder {
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public:
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explicit PassStrategy(const std::vector<std::string> &passes)
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: PaddlePassBuilder(passes) {}
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// The MKLDNN control exists in both CPU and GPU mode, because there can be
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// still some CPU kernels running in CPU mode.
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virtual void EnableMKLDNN() = 0;
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virtual ~PassStrategy() = default;
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};
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/*
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* The CPU passes controller, it is used in AnalysisPredictor with CPU mode.
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*/
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class CpuPassStrategy : public PassStrategy {
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public:
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CpuPassStrategy() : PassStrategy({}) {
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// NOTE the large fusions should be located in the front, so that they will
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// not be damaged by smaller ones.
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passes_.assign({
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"infer_clean_graph_pass", //
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"attention_lstm_fuse_pass", //
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"seqconv_eltadd_relu_fuse_pass", //
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// "embedding_fc_lstm_fuse_pass", //
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"fc_lstm_fuse_pass", //
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"mul_lstm_fuse_pass", //
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"fc_gru_fuse_pass", //
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"mul_gru_fuse_pass", //
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"seq_concat_fc_fuse_pass", //
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"fc_fuse_pass", //
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"conv_bn_fuse_pass", //
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"conv_eltwiseadd_bn_fuse_pass", //
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"is_test_pass", //
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});
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}
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virtual ~CpuPassStrategy() = default;
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void EnableMKLDNN() override {
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// TODO(Superjomn) Consider the way to mix CPU with GPU.
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#ifdef PADDLE_WITH_MKLDNN
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passes_.insert(passes_.begin(), "mkldnn_placement_pass");
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for (auto &pass :
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std::vector<std::string>({"depthwise_conv_mkldnn_pass", //
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"conv_bias_mkldnn_fuse_pass", //
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"conv3d_bias_mkldnn_fuse_pass", //
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"conv_relu_mkldnn_fuse_pass", //
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"conv_elementwise_add_mkldnn_fuse_pass"})) {
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passes_.push_back(pass);
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}
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#endif
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}
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CpuPassStrategy(const CpuPassStrategy &other) : PassStrategy(other.passes_) {}
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};
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/*
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* The GPU passes strategy, it is used in
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*/
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class GpuPassStrategy : public PassStrategy {
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public:
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GpuPassStrategy() : PassStrategy({}) {
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passes_.assign({
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"infer_clean_graph_pass", //
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"conv_bn_fuse_pass", //
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"conv_elementwise_add_act_fuse_pass", //
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"conv_elementwise_add2_act_fuse_pass", //
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"conv_elementwise_add_fuse_pass", //
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});
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}
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GpuPassStrategy(const GpuPassStrategy &other)
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: PassStrategy(other.AllPasses()) {}
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void EnableMKLDNN() override;
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virtual ~GpuPassStrategy() = default;
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};
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} // namespace paddle
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