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Merge pull request #13858 from Sand3r-/mgallus/conv-bias-pass
[MKLDNN] Fuse Conv + Bias using Pass
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‎paddle/fluid/framework/ir/CMakeLists.txt

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@@ -39,6 +39,7 @@ pass_library(seq_concat_fc_fuse_pass inference)
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pass_library(conv_bn_fuse_pass inference)
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if(WITH_MKLDNN)
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pass_library(mkldnn_placement_pass base)
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pass_library(conv_bias_mkldnn_fuse_pass inference)
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pass_library(conv_relu_mkldnn_fuse_pass inference)
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endif()
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‎paddle/fluid/framework/ir/conv_bias_mkldnn_fuse_pass.cc

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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_bias_mkldnn_fuse_pass.h"
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#include <functional>
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#include <string>
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#include <vector>
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#include "paddle/fluid/framework/lod_tensor.h"
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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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template <typename BinaryOperation>
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LoDTensor tensor_apply_eltwise(const LoDTensor& vec_a, const LoDTensor& vec_b,
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BinaryOperation f) {
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PADDLE_ENFORCE_EQ(vec_a.dims(), vec_b.dims());
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LoDTensor vec_y;
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vec_y.Resize(vec_a.dims());
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const float* a = vec_a.data<float>();
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const float* b = vec_b.data<float>();
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float* y = vec_y.mutable_data<float>(platform::CPUPlace());
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for (int i = 0; i < vec_a.numel(); i++) {
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y[i] = f(a[i], b[i]);
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}
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return vec_y;
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}
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std::unique_ptr<ir::Graph> ConvBiasFusePass::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(name_scope_, graph.get());
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auto* scope = param_scope();
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PADDLE_ENFORCE(scope);
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GraphPatternDetector gpd;
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auto* conv_input =
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gpd.mutable_pattern()
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->NewNode(patterns::PDNodeName(name_scope_, "conv_input"))
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->AsInput()
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->assert_is_op_input("conv2d", "Input");
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patterns::ConvBias conv_bias_pattern(gpd.mutable_pattern(), name_scope_);
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conv_bias_pattern(conv_input);
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int found_conv_bias_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 ConvBias fuse";
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GET_IR_NODE_FROM_SUBGRAPH(conv_weight, conv_weight,
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conv_bias_pattern); // Filter
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GET_IR_NODE_FROM_SUBGRAPH(conv_out, conv_out, conv_bias_pattern); // tmp
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GET_IR_NODE_FROM_SUBGRAPH(conv, conv, conv_bias_pattern); // CONV op
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// bias
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GET_IR_NODE_FROM_SUBGRAPH(eltwise_bias, eltwise_bias, conv_bias_pattern);
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// output
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GET_IR_NODE_FROM_SUBGRAPH(eltwise_out, eltwise_out, conv_bias_pattern);
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// elementwise_add op
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GET_IR_NODE_FROM_SUBGRAPH(eltwise, eltwise, conv_bias_pattern);
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PADDLE_ENFORCE(subgraph.count(conv_input));
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// check if fuse can be done and if MKL-DNN should be used
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FuseOptions fuse_option = FindFuseOption(*conv, *eltwise);
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if (fuse_option == DO_NOT_FUSE || fuse_option == FUSE_NATIVE) {
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VLOG(3) << "do not perform conv+bias fuse";
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return;
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}
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auto* eltwise_bias_tensor =
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scope->FindVar(eltwise_bias->Name())->GetMutable<LoDTensor>();
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auto input_names = conv->Op()->InputNames();
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bool has_bias = std::find(input_names.begin(), input_names.end(), "Bias") !=
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input_names.end();
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if (has_bias && conv->Op()->Input("Bias").size() > 0) {
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auto conv_bias_names = conv->Op()->Input("Bias");
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// add eltwise bias to existing conv bias
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PADDLE_ENFORCE_EQ(conv_bias_names.size(), 1);
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auto* conv_bias_var = scope->FindVar(conv_bias_names[0]);
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auto* conv_bias_tensor = conv_bias_var->GetMutable<LoDTensor>();
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PADDLE_ENFORCE_EQ(conv_bias_tensor->dims(), eltwise_bias_tensor->dims());
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*conv_bias_tensor = tensor_apply_eltwise(
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*conv_bias_tensor, *eltwise_bias_tensor, std::plus<float>());
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conv->Op()->SetOutput("Output",
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std::vector<std::string>({eltwise_out->Name()}));
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GraphSafeRemoveNodes(graph.get(), {eltwise, conv_out});
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IR_NODE_LINK_TO(conv, eltwise_out);
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} else {
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// take eltwise bias as conv bias
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OpDesc desc;
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desc.SetInput(
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"Input", std::vector<std::string>({subgraph.at(conv_input)->Name()}));
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desc.SetInput("Filter", std::vector<std::string>({conv_weight->Name()}));
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desc.SetInput("Bias", std::vector<std::string>({eltwise_bias->Name()}));
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desc.SetOutput("Output", std::vector<std::string>({eltwise_out->Name()}));
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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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auto conv_bias_node = g->CreateOpNode(&desc);
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IR_NODE_LINK_TO(subgraph.at(conv_input), conv_bias_node);
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IR_NODE_LINK_TO(conv_weight, conv_bias_node);
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IR_NODE_LINK_TO(eltwise_bias, conv_bias_node);
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IR_NODE_LINK_TO(conv_bias_node, eltwise_out);
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GraphSafeRemoveNodes(graph.get(), {conv, eltwise, conv_out});
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}
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found_conv_bias_count++;
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};
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gpd(graph.get(), handler);
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AddStatis(found_conv_bias_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_bias_mkldnn_fuse_pass,
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paddle::framework::ir::ConvBiasFusePass);

‎paddle/fluid/framework/ir/conv_bias_mkldnn_fuse_pass.h

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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 the Conv and Elementwise_add to a ConvBiasOp.
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*/
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class ConvBiasFusePass : public FusePassBase {
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public:
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virtual ~ConvBiasFusePass() {}
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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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const std::string name_scope_{"conv_bias_mkldnn_fuse"};
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};
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} // namespace ir
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} // namespace framework
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} // namespace paddle

‎paddle/fluid/framework/ir/graph_pattern_detector.cc

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@@ -966,6 +966,39 @@ PDNode *patterns::ElewiseAddActInplaceGrad::operator()(
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return ele_add_grad;
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}
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PDNode *patterns::ConvBias::operator()(
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paddle::framework::ir::PDNode *conv_input) {
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// Create Operators
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conv_input->assert_is_op_input("conv2d", "Input");
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auto *conv_op = pattern->NewNode(conv_repr())->assert_is_op("conv2d");
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auto *eltiwse_op =
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pattern->NewNode(eltwise_repr())->assert_is_op("elementwise_add");
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// Create variables
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// Filter
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auto *conv_weight_var = pattern->NewNode(conv_weight_repr())
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->AsInput()
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->assert_is_persistable_var()
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->assert_is_op_input("conv2d", "Filter");
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// intermediate variable, will be removed in the IR after fuse.
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auto *conv_out_var = pattern->NewNode(conv_out_repr())
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->AsIntermediate()
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->assert_is_only_output_of_op("conv2d")
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->assert_is_op_input("elementwise_add");
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// Bias stored in elementwise_add
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auto *eltwise_bias_var = pattern->NewNode(eltwise_bias_repr())
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->AsInput()
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->assert_is_persistable_var()
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->assert_is_op_input("elementwise_add", "Y");
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// output
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auto *eltwise_out_var = pattern->NewNode(eltwise_out_repr())
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->AsOutput()
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->assert_is_op_output("elementwise_add");
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conv_op->LinksFrom({conv_input, conv_weight_var}).LinksTo({conv_out_var});
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eltiwse_op->LinksFrom({conv_out_var, eltwise_bias_var})
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.LinksTo({eltwise_out_var});
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return eltwise_out_var;
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}
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} // namespace ir
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} // namespace framework
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} // namespace paddle

‎paddle/fluid/framework/ir/graph_pattern_detector.h

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@@ -578,6 +578,27 @@ struct ElewiseAddActInplaceGrad : public PatternBase {
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PATTERN_DECL_NODE(d_ele_y);
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PATTERN_DECL_NODE(ele_y);
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};
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// Conv with Elementwise_add as bias
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// op: conv + elementwise_add
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// named nodes:
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// conv_input, conv_weight,
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// conv_out, conv,
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// eltwise_bias, eltwise_out,
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// elementwise_add
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struct ConvBias : public PatternBase {
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ConvBias(PDPattern* pattern, const std::string& name_scope)
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: PatternBase(pattern, name_scope, "conv_bias") {}
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PDNode* operator()(PDNode* conv_input);
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// declare operator node's name
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PATTERN_DECL_NODE(conv);
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PATTERN_DECL_NODE(eltwise);
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// declare variable node's name
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PATTERN_DECL_NODE(conv_weight);
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PATTERN_DECL_NODE(conv_out);
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PATTERN_DECL_NODE(eltwise_bias);
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PATTERN_DECL_NODE(eltwise_out);
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};
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} // namespace patterns
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// Link two ir::Nodes from each other.

‎paddle/fluid/inference/analysis/analyzer.h

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"conv_bn_fuse_pass", //
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"conv_eltwiseadd_bn_fuse_pass", //
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#ifdef PADDLE_WITH_MKLDNN
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"conv_bias_mkldnn_fuse_pass", //
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"conv_relu_mkldnn_fuse_pass", //
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#endif
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}};

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