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283 lines
7.9 KiB
283 lines
7.9 KiB
/* Copyright (c) 2019 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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#pragma once
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#include <memory>
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#include <sstream>
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#include <string>
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#include <vector>
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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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struct Layers {
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public:
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const ProgramDesc& main_program() { return program_; }
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VarDesc* data(std::string name) { return lod_tensor(name); }
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VarDesc* conv2d(VarDesc* input, VarDesc* filter, VarDesc* bias,
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bool use_cudnn) {
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VarDesc* out = lod_tensor(unique_name());
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OpDesc* op = program_.MutableBlock(0)->AppendOp();
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op->SetType("conv2d");
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op->SetInput("Input", {input->Name()});
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op->SetInput("Filter", {filter->Name()});
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op->SetInput("Bias", {bias->Name()});
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op->SetOutput("Out", {out->Name()});
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op->SetAttr("use_cudnn", use_cudnn);
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op->SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(),
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static_cast<int>(OpRole::kForward));
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return out;
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}
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VarDesc* depthwise_conv2d(VarDesc* input, VarDesc* filter, VarDesc* bias,
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bool use_cudnn) {
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VarDesc* out = lod_tensor(unique_name());
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OpDesc* op = program_.MutableBlock(0)->AppendOp();
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op->SetType("depthwise_conv2d");
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op->SetInput("Input", {input->Name()});
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op->SetInput("Filter", {filter->Name()});
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op->SetInput("Bias", {bias->Name()});
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op->SetOutput("Out", {out->Name()});
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op->SetAttr("use_cudnn", use_cudnn);
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op->SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(),
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static_cast<int>(OpRole::kForward));
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return out;
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}
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VarDesc* pool2d(VarDesc* x, bool use_cudnn) {
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VarDesc* out = lod_tensor(unique_name());
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OpDesc* op = program_.MutableBlock(0)->AppendOp();
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op->SetType("pool2d");
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op->SetInput("X", {x->Name()});
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op->SetOutput("Out", {out->Name()});
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op->SetAttr("use_cudnn", use_cudnn);
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op->SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(),
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static_cast<int>(OpRole::kForward));
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return out;
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}
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VarDesc* relu(VarDesc* x, VarDesc* out = nullptr) {
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return unary_op("relu", x, out);
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}
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VarDesc* mul(VarDesc* x, VarDesc* y, VarDesc* out = nullptr) {
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return binary_op("mul", x, y, out);
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}
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VarDesc* elementwise_add(VarDesc* x, VarDesc* y, VarDesc* out = nullptr) {
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return binary_op("elementwise_add", x, y, out);
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}
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VarDesc* dropout(VarDesc* x, float dropout_prob,
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std::string dropout_implementation) {
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VarDesc* out = lod_tensor(unique_name());
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OpDesc* op = program_.MutableBlock(0)->AppendOp();
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op->SetType("dropout");
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op->SetInput("X", {x->Name()});
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op->SetOutput("Out", {out->Name()});
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op->SetAttr("is_test", true);
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op->SetAttr("dropout_prob", dropout_prob);
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op->SetAttr("dropout_implementation", dropout_implementation);
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op->SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(),
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static_cast<int>(OpRole::kForward));
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return out;
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}
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VarDesc* concat(std::vector<VarDesc*> inputs, int axis = -1) {
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VarDesc* out = lod_tensor(unique_name());
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OpDesc* op = program_.MutableBlock(0)->AppendOp();
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op->SetType("concat");
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std::vector<std::string> input_names(inputs.size());
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for (size_t i = 0; i < inputs.size(); ++i) {
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input_names[i] = inputs[i]->Name();
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}
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op->SetInput("X", input_names);
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op->SetOutput("Out", {out->Name()});
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op->SetAttr("axis", axis);
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op->SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(),
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static_cast<int>(OpRole::kForward));
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return out;
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}
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private:
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VarDesc* lod_tensor(std::string name) {
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auto* var = program_.MutableBlock(0)->Var(name);
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var->SetType(proto::VarType::LOD_TENSOR);
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return var;
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}
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VarDesc* unary_op(std::string type, VarDesc* x, VarDesc* out = nullptr) {
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if (!out) {
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out = lod_tensor(unique_name());
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}
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OpDesc* op = program_.MutableBlock(0)->AppendOp();
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op->SetType(type);
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op->SetInput("X", {x->Name()});
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op->SetOutput("Out", {out->Name()});
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op->SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(),
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static_cast<int>(OpRole::kForward));
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return out;
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}
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VarDesc* binary_op(std::string type, VarDesc* x, VarDesc* y,
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VarDesc* out = nullptr) {
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if (!out) {
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out = lod_tensor(unique_name());
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}
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OpDesc* op = program_.MutableBlock(0)->AppendOp();
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op->SetType(type);
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op->SetInput("X", {x->Name()});
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op->SetInput("Y", {y->Name()});
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op->SetOutput("Out", {out->Name()});
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op->SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(),
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static_cast<int>(OpRole::kForward));
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return out;
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}
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std::string unique_name() { return "tmp_" + std::to_string(idx_++); }
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private:
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ProgramDesc program_;
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int idx_{0};
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};
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static std::string DebugString(OpDesc* op) {
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std::ostringstream os;
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os << "Op(" << op->Type() << "), inputs:{";
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bool is_first = true;
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for (auto& name : op->InputNames()) {
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if (!is_first) {
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os << ", ";
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}
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os << name << "[";
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bool is_first_var_name = true;
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for (auto& var_name : op->Input(name)) {
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if (!is_first_var_name) {
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os << ", ";
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}
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os << var_name;
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is_first_var_name = false;
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}
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os << "]";
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is_first = false;
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}
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os << "}, outputs:{";
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is_first = true;
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for (auto& name : op->OutputNames()) {
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if (!is_first) {
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os << ", ";
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}
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os << name << "[";
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bool is_first_var_name = true;
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for (auto& var_name : op->Output(name)) {
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if (!is_first_var_name) {
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os << ", ";
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}
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os << var_name;
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is_first_var_name = false;
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}
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os << "]";
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is_first = false;
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}
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os << "}";
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return os.str();
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}
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static std::string DebugString(Node* node) {
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std::ostringstream os;
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if (node->IsOp() && node->Op()) {
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OpDesc* op = node->Op();
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os << "Node(" << DebugString(op) << "), inputs:{";
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bool is_first = true;
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for (auto* in : node->inputs) {
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if (!is_first) {
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os << ", ";
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}
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os << in->Name();
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is_first = false;
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}
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os << "}, outputs:{";
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is_first = true;
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for (auto* out : node->outputs) {
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if (!is_first) {
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os << ", ";
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}
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os << out->Name();
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is_first = false;
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}
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os << "}.";
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} else if (node->IsVar() && node->Var()) {
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os << "Node(" << node->Name() << "), inputs:{";
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bool is_first = true;
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for (auto* in : node->inputs) {
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if (!is_first) {
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os << ", ";
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}
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if (in->IsOp() && in->Op()) {
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os << in->Op()->Type();
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}
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is_first = false;
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}
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os << "}, outputs:{";
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is_first = true;
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for (auto* out : node->outputs) {
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if (!is_first) {
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os << ", ";
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}
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if (out->IsOp() && out->Op()) {
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os << out->Op()->Type();
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}
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is_first = false;
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}
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os << "}";
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}
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return os.str();
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}
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static std::string DebugString(const std::unique_ptr<Graph>& graph) {
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std::ostringstream os;
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os << "Graph: {\n";
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for (auto* node : graph->Nodes()) {
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if (node->IsOp() && node->Op()) {
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os << " ";
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} else if (node->IsVar() && node->Var()) {
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os << " ";
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}
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os << DebugString(node) << "\n";
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}
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os << "}\n";
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return os.str();
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}
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static int GetNumOpNodes(const std::unique_ptr<Graph>& graph,
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std::string op_type) {
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int num_nodes = 0;
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for (auto* node : graph->Nodes()) {
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if (node->IsOp() && node->Op() && node->Op()->Type() == op_type) {
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num_nodes++;
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}
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}
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return num_nodes;
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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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