parent
a323b26372
commit
62e2aa115d
@ -1,3 +1,5 @@
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cc_library(graph SRCS graph.cc)
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cc_library(graph SRCS graph.cc node)
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cc_library(node SRCS node.cc)
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cc_library(pass SRCS pass.cc)
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cc_library(pass SRCS pass.cc graph node)
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cc_test(graph_test SRCS graph_test.cc DEPS graph proto_desc op_registry)
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@ -0,0 +1,112 @@
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/* Copyright (c) 2016 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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#include "paddle/fluid/framework/ir/graph.h"
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#include "gtest/gtest.h"
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/framework/operator.h"
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#include "paddle/fluid/framework/program_desc.h"
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namespace paddle {
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namespace framework {
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class NOP : public OperatorBase {
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public:
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NOP(const std::string &type, const VariableNameMap &inputs,
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const VariableNameMap &outputs, const AttributeMap &attrs)
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: OperatorBase(type, inputs, outputs, attrs) {}
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private:
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void RunImpl(const Scope &scope,
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const platform::Place &place) const override {}
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};
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class SumOpMaker : public OpProtoAndCheckerMaker {
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public:
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void Make() {
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AddInput("X", "").AsDuplicable();
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AddOutput("Out", "");
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AddComment("");
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}
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};
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class SumOpVarTypeInference : public VarTypeInference {
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public:
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void operator()(const OpDesc &op_desc, BlockDesc *block) const override {
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auto &inputs = op_desc.Input("X");
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auto default_var_type = proto::VarType::SELECTED_ROWS;
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bool any_input_is_lod_tensor = std::any_of(
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inputs.begin(), inputs.end(), [block](const std::string &name) {
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return block->Var(name)->GetType() == proto::VarType::LOD_TENSOR;
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});
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if (any_input_is_lod_tensor) {
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default_var_type = proto::VarType::LOD_TENSOR;
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}
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auto out_var_name = op_desc.Output("Out").front();
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block->Var(out_var_name)->SetType(default_var_type);
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}
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};
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} // namespace framework
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} // namespace paddle
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REGISTER_OPERATOR(sum, paddle::framework::NOP, paddle::framework::SumOpMaker,
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paddle::framework::SumOpVarTypeInference);
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REGISTER_OPERATOR(sum_without_infer_var_type, paddle::framework::NOP,
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paddle::framework::SumOpMaker);
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namespace paddle {
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namespace framework {
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TEST(GraphTest, Basic) {
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ProgramDesc prog;
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auto *op = prog.MutableBlock(0)->AppendOp();
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op->SetType("sum");
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op->SetInput("X", {"test_a", "test_b", "test_c"});
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op->SetOutput("Out", {"test_out"});
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prog.MutableBlock(0)->Var("test_a")->SetType(proto::VarType::SELECTED_ROWS);
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prog.MutableBlock(0)->Var("test_b")->SetType(proto::VarType::SELECTED_ROWS);
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prog.MutableBlock(0)->Var("test_c")->SetType(proto::VarType::SELECTED_ROWS);
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prog.MutableBlock(0)->Var("test_out");
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op->InferVarType(prog.MutableBlock(0));
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ASSERT_EQ(proto::VarType::SELECTED_ROWS,
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prog.MutableBlock(0)->Var("test_out")->GetType());
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prog.MutableBlock(0)->Var("test_b")->SetType(proto::VarType::LOD_TENSOR);
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op->InferVarType(prog.MutableBlock(0));
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ASSERT_EQ(proto::VarType::LOD_TENSOR,
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prog.MutableBlock(0)->Var("test_out")->GetType());
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std::unique_ptr<Graph> g(new Graph(prog));
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ASSERT_EQ(g->nodes[0]->Name(), "sum");
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ASSERT_EQ(g->nodes[0]->inputs[0]->Name(), "test_a");
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ASSERT_EQ(g->nodes[0]->inputs[1]->Name(), "test_b");
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ASSERT_EQ(g->nodes[0]->inputs[2]->Name(), "test_c");
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ASSERT_EQ(g->nodes[0]->outputs[0]->Name(), "test_out");
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ASSERT_EQ(g->nodes[1]->Name(), "test_a");
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ASSERT_EQ(g->nodes[1]->outputs[0]->Name(), "sum");
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ASSERT_EQ(g->nodes[2]->Name(), "test_b");
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ASSERT_EQ(g->nodes[2]->outputs[0]->Name(), "sum");
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ASSERT_EQ(g->nodes[3]->Name(), "test_c");
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ASSERT_EQ(g->nodes[3]->outputs[0]->Name(), "sum");
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ASSERT_EQ(g->nodes[4]->Name(), "test_out");
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ASSERT_EQ(g->nodes[4]->inputs[0]->Name(), "sum");
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ASSERT_EQ(g->nodes.size(), 5);
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}
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} // namespace framework
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} // namespace paddle
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