Adam
6 years ago
committed by
Tao Luo
parent
50b1cab122
commit
b837689e97
@ -0,0 +1,101 @@
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// Copyright (c) 2019 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 "paddle/fluid/framework/ir/mkldnn/conv_activation_mkldnn_fuse_pass.h"
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#include <string>
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#include <vector>
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#include "paddle/fluid/platform/enforce.h"
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namespace paddle {
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namespace framework {
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namespace ir {
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void ConvActivationFusePass::ApplyImpl(ir::Graph* graph) const {
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PADDLE_ENFORCE_NOT_NULL(graph, "graph cannot be nullptr.");
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FusePassBase::Init("conv_activation_mkldnn_fuse", graph);
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GraphPatternDetector gpd;
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auto* conv_input = gpd.mutable_pattern()
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->NewNode("conv_activation_mkldnn_fuse/conv_input")
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->AsInput()
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->assert_is_op_input(conv_type(), "Input");
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patterns::ConvActivation conv_activation_pattern(
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gpd.mutable_pattern(), "conv_activation_mkldnn_fuse");
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conv_activation_pattern(conv_input, conv_type(), activation_type());
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int found_conv_activation_count = 0;
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auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
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Graph* g) {
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VLOG(4) << "handle " + conv_type() + "+" + activation_type() + " fuse";
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GET_IR_NODE_FROM_SUBGRAPH(conv_weight, conv_weight,
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conv_activation_pattern); // Filter
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GET_IR_NODE_FROM_SUBGRAPH(conv_out, conv_out,
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conv_activation_pattern); // tmp
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GET_IR_NODE_FROM_SUBGRAPH(conv, conv, conv_activation_pattern); // CONV op
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GET_IR_NODE_FROM_SUBGRAPH(activation_out, activation_out,
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conv_activation_pattern); // Out
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GET_IR_NODE_FROM_SUBGRAPH(activation, activation,
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conv_activation_pattern); // Activation op
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// Transform Conv node into ConvActivation node.
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OpDesc* desc = conv->Op();
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desc->SetOutput("Output",
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std::vector<std::string>({activation_out->Name()}));
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desc->SetAttr("fuse_activation", activation_type());
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// MKLDNN ops use alpha and beta as activation parameters but paddle ops are
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// not generalized
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if (activation_type() == "relu6") {
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desc->SetAttr("fuse_alpha",
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boost::get<float>(activation->Op()->GetAttr("threshold")));
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} else {
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desc->SetAttr("fuse_alpha",
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activation->Op()->HasAttr("alpha")
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? boost::get<float>(activation->Op()->GetAttr("alpha"))
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: 0.0f);
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}
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desc->SetAttr("fuse_beta",
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activation->Op()->HasAttr("beta")
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? boost::get<float>(activation->Op()->GetAttr("beta"))
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: 0.0f);
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GraphSafeRemoveNodes(graph, {activation, conv_out});
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PADDLE_ENFORCE_GT(subgraph.count(conv_input), 0UL,
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"subgraph has to contain conv_input node.");
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IR_NODE_LINK_TO(conv, activation_out);
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found_conv_activation_count++;
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};
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gpd(graph, handler);
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AddStatis(found_conv_activation_count);
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}
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} // namespace ir
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} // namespace framework
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} // namespace paddle
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REGISTER_PASS(conv_activation_mkldnn_fuse_pass,
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paddle::framework::ir::ConvActivationFusePass);
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REGISTER_PASS(conv_relu_mkldnn_fuse_pass,
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paddle::framework::ir::ConvActivationFusePass);
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REGISTER_PASS(conv_leaky_relu_mkldnn_fuse_pass,
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paddle::framework::ir::Conv2DLeakyReLUFusePass);
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REGISTER_PASS(conv_relu6_mkldnn_fuse_pass,
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paddle::framework::ir::Conv2DReLU6FusePass);
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@ -0,0 +1,55 @@
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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 <string>
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#include "paddle/fluid/framework/ir/fuse_pass_base.h"
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#include "paddle/fluid/framework/ir/graph.h"
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#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
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#include "paddle/fluid/framework/ir/pass.h"
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namespace paddle {
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namespace framework {
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namespace ir {
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/*
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* Fuse Conv and Activation base class.
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*/
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class ConvActivationFusePass : public FusePassBase {
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public:
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virtual ~ConvActivationFusePass() {}
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virtual std::string conv_type() const { return "conv2d"; }
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virtual std::string activation_type() const { return "relu"; }
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protected:
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void ApplyImpl(ir::Graph* graph) const override;
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const std::string name_scope_{"conv_activation_mkldnn_fuse"};
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};
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/*
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* Fuse Conv and LeakyReLU class
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*/
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class Conv2DLeakyReLUFusePass : public ConvActivationFusePass {
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public:
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std::string activation_type() const { return "leaky_relu"; }
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};
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/*
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* Fuse Conv and BoundedReLU class
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*/
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class Conv2DReLU6FusePass : public ConvActivationFusePass {
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public:
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std::string activation_type() const { return "relu6"; }
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};
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} // namespace ir
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} // namespace framework
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} // namespace paddle
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@ -0,0 +1,145 @@
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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 "paddle/fluid/framework/ir/mkldnn/conv_activation_mkldnn_fuse_pass.h"
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#include <gtest/gtest.h>
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#include "paddle/fluid/framework/op_proto_maker.h"
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namespace paddle {
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namespace framework {
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namespace ir {
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void SetOp(ProgramDesc* prog, const std::string& type, const std::string& name,
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const std::vector<std::string>& inputs,
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const std::vector<std::string>& outputs, bool is_activation = false,
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bool use_mkldnn = false) {
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auto* op = prog->MutableBlock(0)->AppendOp();
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op->SetType(type);
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op->SetAttr("name", name);
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if (type == "conv2d") {
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op->SetAttr("use_mkldnn", use_mkldnn);
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op->SetInput("Input", {inputs[0]});
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op->SetInput("Filter", {inputs[1]});
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op->SetInput("Bias", {inputs[2]});
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} else if (is_activation) {
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op->SetAttr("use_mkldnn", use_mkldnn);
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op->SetInput("X", inputs);
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if (type == "leaky_relu") {
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op->SetAttr("alpha", 0.02f);
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} else if (type == "relu6") {
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op->SetAttr("threshold", 6.0f);
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}
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}
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op->SetOutput("Out", outputs);
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op->SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(),
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static_cast<int>(OpRole::kForward));
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}
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// a->OP0->b
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// b->OP1->c
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// (c, weights, bias)->conv->f
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// (f)->activation->g
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ProgramDesc BuildProgramDesc(std::string activation) {
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ProgramDesc prog;
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for (auto& v :
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std::vector<std::string>({"a", "b", "c", "weights", "bias", "f", "g",
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"h", "weights2", "bias2", "k", "l", "m"})) {
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auto* var = prog.MutableBlock(0)->Var(v);
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var->SetType(proto::VarType::SELECTED_ROWS);
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if (v == "weights" || v == "bias" || v == "weights2" || v == "bias2") {
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var->SetPersistable(true);
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}
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}
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SetOp(&prog, "OP0", "op0", std::vector<std::string>({"a"}),
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std::vector<std::string>({"b"}));
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SetOp(&prog, "OP1", "op1", std::vector<std::string>({"b"}),
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std::vector<std::string>({"c"}));
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// conv+activation, both with MKL-DNN
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SetOp(&prog, "conv2d", "conv1",
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std::vector<std::string>({"c", "weights", "bias"}),
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std::vector<std::string>({"f"}), false, true);
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SetOp(&prog, activation, "activation1", std::vector<std::string>({"f"}),
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std::vector<std::string>({"g"}), true, true);
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SetOp(&prog, "OP3", "op3", std::vector<std::string>({"g"}),
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std::vector<std::string>({"h"}));
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// conv+activation, only one with MKL-DNN
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SetOp(&prog, "conv2d", "conv2",
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std::vector<std::string>({"h", "weights2", "bias2"}),
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std::vector<std::string>({"k"}), false, true);
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SetOp(&prog, "activation", "activation2", std::vector<std::string>({"k"}),
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std::vector<std::string>({"l"}), true, false);
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SetOp(&prog, "OP4", "op4", std::vector<std::string>({"l"}),
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std::vector<std::string>({"m"}));
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return prog;
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}
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void MainTest(std::string activation) {
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auto prog = BuildProgramDesc(activation);
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std::unique_ptr<ir::Graph> graph(new ir::Graph(prog));
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auto pass =
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PassRegistry::Instance().Get("conv_" + activation + "_mkldnn_fuse_pass");
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int original_nodes_num = graph->Nodes().size();
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graph.reset(pass->Apply(graph.release()));
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int current_nodes_num = graph->Nodes().size();
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// Remove 3 Nodes: CONV, activation, conv_out
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// Add 1 Node: ConvActivation
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EXPECT_EQ(original_nodes_num - 2, current_nodes_num);
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// Assert conv_activation op in newly generated graph
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int conv_activation_count = 0;
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for (auto* node : graph->Nodes()) {
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if (node->IsOp() && node->Op()->Type() == "conv2d") {
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auto* op = node->Op();
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ASSERT_TRUE(op->HasAttr("use_mkldnn"));
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EXPECT_TRUE(boost::get<bool>(op->GetAttr("use_mkldnn")));
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auto op_name = boost::get<std::string>(op->GetAttr("name"));
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std::string fuse_activation =
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op->HasAttr("fuse_activation")
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? boost::get<std::string>(op->GetAttr("fuse_activation"))
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: "";
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if (fuse_activation == activation) {
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++conv_activation_count;
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}
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// check if only "conv1" convolution is fused
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if (op_name == "conv1") {
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ASSERT_TRUE(op->HasAttr("fuse_activation"));
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} else if (op_name == "conv2") {
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ASSERT_FALSE(op->HasAttr("fuse_activation"));
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}
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}
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}
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EXPECT_EQ(conv_activation_count, 1);
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}
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TEST(ConvActivationFusePass, conv_relu_fuse_pass) { MainTest("relu"); }
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TEST(ConvActivationFusePass, conv_leaky_relu_fuse_pass) {
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MainTest("leaky_relu");
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}
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TEST(ConvActivationFusePass, conv_relu6_fuse_pass) { MainTest("relu6"); }
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} // namespace ir
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} // namespace framework
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} // namespace paddle
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USE_PASS(conv_activation_mkldnn_fuse_pass);
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