提交 62e2aa11 编写于 作者: X Xin Pan

add a graph_test

上级 a323b263
cc_library(graph SRCS graph.cc) cc_library(graph SRCS graph.cc node)
cc_library(node SRCS node.cc) cc_library(node SRCS node.cc)
cc_library(pass SRCS pass.cc) cc_library(pass SRCS pass.cc graph node)
cc_test(graph_test SRCS graph_test.cc DEPS graph proto_desc op_registry)
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/framework/ir/graph.h"
#include "gtest/gtest.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/program_desc.h"
namespace paddle {
namespace framework {
class NOP : public OperatorBase {
public:
NOP(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs, const AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
private:
void RunImpl(const Scope &scope,
const platform::Place &place) const override {}
};
class SumOpMaker : public OpProtoAndCheckerMaker {
public:
void Make() {
AddInput("X", "").AsDuplicable();
AddOutput("Out", "");
AddComment("");
}
};
class SumOpVarTypeInference : public VarTypeInference {
public:
void operator()(const OpDesc &op_desc, BlockDesc *block) const override {
auto &inputs = op_desc.Input("X");
auto default_var_type = proto::VarType::SELECTED_ROWS;
bool any_input_is_lod_tensor = std::any_of(
inputs.begin(), inputs.end(), [block](const std::string &name) {
return block->Var(name)->GetType() == proto::VarType::LOD_TENSOR;
});
if (any_input_is_lod_tensor) {
default_var_type = proto::VarType::LOD_TENSOR;
}
auto out_var_name = op_desc.Output("Out").front();
block->Var(out_var_name)->SetType(default_var_type);
}
};
} // namespace framework
} // namespace paddle
REGISTER_OPERATOR(sum, paddle::framework::NOP, paddle::framework::SumOpMaker,
paddle::framework::SumOpVarTypeInference);
REGISTER_OPERATOR(sum_without_infer_var_type, paddle::framework::NOP,
paddle::framework::SumOpMaker);
namespace paddle {
namespace framework {
TEST(GraphTest, Basic) {
ProgramDesc prog;
auto *op = prog.MutableBlock(0)->AppendOp();
op->SetType("sum");
op->SetInput("X", {"test_a", "test_b", "test_c"});
op->SetOutput("Out", {"test_out"});
prog.MutableBlock(0)->Var("test_a")->SetType(proto::VarType::SELECTED_ROWS);
prog.MutableBlock(0)->Var("test_b")->SetType(proto::VarType::SELECTED_ROWS);
prog.MutableBlock(0)->Var("test_c")->SetType(proto::VarType::SELECTED_ROWS);
prog.MutableBlock(0)->Var("test_out");
op->InferVarType(prog.MutableBlock(0));
ASSERT_EQ(proto::VarType::SELECTED_ROWS,
prog.MutableBlock(0)->Var("test_out")->GetType());
prog.MutableBlock(0)->Var("test_b")->SetType(proto::VarType::LOD_TENSOR);
op->InferVarType(prog.MutableBlock(0));
ASSERT_EQ(proto::VarType::LOD_TENSOR,
prog.MutableBlock(0)->Var("test_out")->GetType());
std::unique_ptr<Graph> g(new Graph(prog));
ASSERT_EQ(g->nodes[0]->Name(), "sum");
ASSERT_EQ(g->nodes[0]->inputs[0]->Name(), "test_a");
ASSERT_EQ(g->nodes[0]->inputs[1]->Name(), "test_b");
ASSERT_EQ(g->nodes[0]->inputs[2]->Name(), "test_c");
ASSERT_EQ(g->nodes[0]->outputs[0]->Name(), "test_out");
ASSERT_EQ(g->nodes[1]->Name(), "test_a");
ASSERT_EQ(g->nodes[1]->outputs[0]->Name(), "sum");
ASSERT_EQ(g->nodes[2]->Name(), "test_b");
ASSERT_EQ(g->nodes[2]->outputs[0]->Name(), "sum");
ASSERT_EQ(g->nodes[3]->Name(), "test_c");
ASSERT_EQ(g->nodes[3]->outputs[0]->Name(), "sum");
ASSERT_EQ(g->nodes[4]->Name(), "test_out");
ASSERT_EQ(g->nodes[4]->inputs[0]->Name(), "sum");
ASSERT_EQ(g->nodes.size(), 5);
}
} // namespace framework
} // namespace paddle
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