Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into quantize_transpiler_update
Dang Qingqing
7 years ago
commit
8a850e21ad
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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/conv_relu_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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std::unique_ptr<ir::Graph> ConvReLUFusePass::ApplyImpl(
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std::unique_ptr<ir::Graph> graph) const {
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PADDLE_ENFORCE(graph.get());
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FusePassBase::Init("conv_relu_mkldnn_fuse", graph.get());
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std::unordered_set<Node*> nodes2delete;
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GraphPatternDetector gpd;
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auto* conv_input = gpd.mutable_pattern()
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->NewNode("conv_relu_mkldnn_fuse/conv_input")
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->AsInput()
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->assert_is_op_input("conv2d", "Input");
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patterns::ConvReLU conv_relu_pattern(gpd.mutable_pattern(),
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"conv_relu_mkldnn_fuse");
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conv_relu_pattern(conv_input);
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int found_conv_relu_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 ConvReLU fuse";
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GET_IR_NODE_FROM_SUBGRAPH(conv_weight, conv_weight,
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conv_relu_pattern); // Filter
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GET_IR_NODE_FROM_SUBGRAPH(conv_bias, conv_bias, conv_relu_pattern); // Bias
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GET_IR_NODE_FROM_SUBGRAPH(conv_out, conv_out, conv_relu_pattern); // tmp
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GET_IR_NODE_FROM_SUBGRAPH(conv, conv, conv_relu_pattern); // CONV op
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GET_IR_NODE_FROM_SUBGRAPH(relu_out, relu_out, conv_relu_pattern); // Out
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GET_IR_NODE_FROM_SUBGRAPH(relu, relu, conv_relu_pattern); // ReLU op
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// Create an ConvReLU Node.
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OpDesc desc;
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std::string conv_relu_i_in = subgraph.at(conv_input)->Name();
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std::string conv_relu_w_in = conv_weight->Name();
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std::string conv_relu_b_in = conv_bias->Name();
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std::string conv_relu_out = relu_out->Name();
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desc.SetInput("Input", std::vector<std::string>({conv_relu_i_in}));
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desc.SetInput("Filter", std::vector<std::string>({conv_relu_w_in}));
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desc.SetInput("Bias", std::vector<std::string>({conv_relu_b_in}));
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desc.SetOutput("Out", std::vector<std::string>({conv_relu_out}));
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desc.SetType("conv2d");
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for (auto& attr : conv->Op()->GetAttrMap()) {
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desc.SetAttr(attr.first, attr.second);
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}
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desc.SetAttr("fuse_relu", true);
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auto conv_relu_node = g->CreateOpNode(&desc); // OpDesc will be copied.
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GraphSafeRemoveNodes(graph.get(), {conv, relu, conv_out});
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PADDLE_ENFORCE(subgraph.count(conv_input));
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IR_NODE_LINK_TO(subgraph.at(conv_input), conv_relu_node);
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IR_NODE_LINK_TO(conv_weight, conv_relu_node);
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IR_NODE_LINK_TO(conv_bias, conv_relu_node);
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IR_NODE_LINK_TO(conv_relu_node, relu_out);
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found_conv_relu_count++;
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};
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gpd(graph.get(), handler);
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AddStatis(found_conv_relu_count);
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return graph;
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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_relu_mkldnn_fuse_pass,
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paddle::framework::ir::ConvReLUFusePass);
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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 "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 the CONV and ReLU to a ConvReLUOp.
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*/
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class ConvReLUFusePass : public FusePassBase {
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public:
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virtual ~ConvReLUFusePass() {}
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protected:
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std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
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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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// 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/conv_relu_mkldnn_fuse_pass.h"
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#include <gtest/gtest.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,
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const std::vector<std::string>& inputs,
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const std::vector<std::string>& outputs) {
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auto* op = prog->MutableBlock(0)->AppendOp();
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op->SetType(type);
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if (type == "conv2d") {
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op->SetAttr("use_mkldnn", true);
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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 (type == "relu") {
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op->SetInput("X", inputs);
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}
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op->SetOutput("Out", outputs);
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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)->relu->g
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ProgramDesc BuildProgramDesc() {
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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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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") {
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var->SetPersistable(true);
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}
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}
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SetOp(&prog, "OP0", std::vector<std::string>({"a"}),
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std::vector<std::string>({"b"}));
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SetOp(&prog, "OP1", std::vector<std::string>({"b"}),
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std::vector<std::string>({"c"}));
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SetOp(&prog, "conv2d", std::vector<std::string>({"c", "weights", "bias"}),
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std::vector<std::string>({"f"}));
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SetOp(&prog, "relu", std::vector<std::string>({"f"}),
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std::vector<std::string>({"g"}));
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return prog;
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}
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TEST(ConvReLUFusePass, basic) {
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auto prog = BuildProgramDesc();
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std::unique_ptr<ir::Graph> graph(new ir::Graph(prog));
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auto pass = PassRegistry::Instance().Get("conv_relu_mkldnn_fuse_pass");
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int original_nodes_num = graph->Nodes().size();
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graph = pass->Apply(std::move(graph));
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int current_nodes_num = graph->Nodes().size();
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// Remove 3 Nodes: CONV, RELU, conv_out
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// Add 1 Node: ConvReLU
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EXPECT_EQ(original_nodes_num - 2, current_nodes_num);
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// Assert conv_relu op in newly generated graph
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int conv_relu_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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if (node->Op()->HasAttr("use_mkldnn")) {
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bool use_mkldnn = boost::get<bool>(node->Op()->GetAttr("use_mkldnn"));
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if (use_mkldnn) {
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if (node->Op()->HasAttr("fuse_relu")) {
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bool fuse_relu = boost::get<bool>(node->Op()->GetAttr("fuse_relu"));
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if (fuse_relu) {
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++conv_relu_count;
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}
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}
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}
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}
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}
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}
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EXPECT_EQ(conv_relu_count, 1);
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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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USE_PASS(conv_relu_mkldnn_fuse_pass);
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