提交 172e460d 编写于 作者: Q QI JUN 提交者: GitHub

Merge pull request #4797 from reyoung/feature/implenment_infer_var_type

Complete infer_var_type
......@@ -47,5 +47,7 @@ cc_library(executor SRCS executor.cc DEPS op_registry device_context scope frame
cc_library(tensor_array SRCS tensor_array.cc DEPS lod_tensor)
cc_test(tensor_array_test SRCS tensor_array_test.cc DEPS tensor_array place)
cc_test(var_type_inference_test SRCS var_type_inference_test.cc DEPS op_registry
proto_desc)
cc_library(selected_rows SRCS selected_rows.cc DEPS tensor)
cc_test(selected_rows_test SRCS selected_rows_test.cc DEPS selected_rows)
......@@ -18,6 +18,7 @@
#include "paddle/framework/op_info.h"
#include "paddle/framework/op_proto_maker.h"
#include "paddle/framework/operator.h"
#include "paddle/framework/var_type_inference.h"
namespace paddle {
namespace framework {
......@@ -26,7 +27,8 @@ namespace details {
enum OpInfoFillType {
kOperator = 0,
kOpProtoAndCheckerMaker = 1,
kGradOpDescMaker = 2
kGradOpDescMaker = 2,
kVarTypeInference = 3
};
template <typename T>
......@@ -38,7 +40,9 @@ struct OpInfoFillTypeID {
? kOpProtoAndCheckerMaker
: (std::is_base_of<GradOpDescMakerBase, T>::value
? kGradOpDescMaker
: static_cast<OpInfoFillType>(-1)));
: (std::is_base_of<VarTypeInference, T>::value
? kVarTypeInference
: static_cast<OpInfoFillType>(-1))));
}
};
......@@ -106,6 +110,17 @@ struct OpInfoFiller<T, kGradOpDescMaker> {
};
}
};
template <typename T>
struct OpInfoFiller<T, kVarTypeInference> {
void operator()(const char* op_type, OpInfo* info) const {
info->infer_var_type_ = [](const OpDescBind& fwd_op, BlockDescBind* block) {
T inference;
inference(fwd_op, block);
};
}
};
} // namespace details
} // namespace framework
......
......@@ -239,5 +239,19 @@ void OpDescBind::InferShape(const BlockDescBind &block) const {
it->second(&ctx);
}
void OpDescBind::InferVarType(BlockDescBind *block) const {
auto &info = OpInfoMap::Instance().Get(this->Type());
if (info.infer_var_type_) {
info.infer_var_type_(*this, block);
} else {
// all output type is LoDTensor by default
for (auto &out_pair : this->outputs_) {
for (auto &out_var_name : out_pair.second) {
block->Var(out_var_name)->SetType(VarDesc::LOD_TENSOR);
}
}
}
}
} // namespace framework
} // namespace paddle
......@@ -102,6 +102,8 @@ class OpDescBind {
void InferShape(const BlockDescBind &block) const;
void InferVarType(BlockDescBind *block) const;
void Flush();
private:
......
......@@ -19,7 +19,6 @@
#include <unordered_map>
#include "paddle/framework/attribute.h"
#include "paddle/framework/op_desc.h"
#include "paddle/framework/type_defs.h"
#include "paddle/platform/macros.h"
......@@ -31,6 +30,7 @@ struct OpInfo {
GradOpMakerFN grad_op_maker_;
OpProto* proto_{nullptr};
OpAttrChecker* checker_{nullptr};
InferVarTypeFN infer_var_type_;
bool HasOpProtoAndChecker() const {
return proto_ != nullptr && checker_ != nullptr;
......
......@@ -16,12 +16,18 @@
#include <functional>
#include <map>
#include <memory>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <vector>
#include "paddle/platform/variant.h"
namespace paddle {
namespace framework {
class OperatorBase;
class OpDescBind;
class BlockDescBind;
class BlockDesc;
using VariableNameMap = std::map<std::string, std::vector<std::string>>;
// The order should be as same as framework.proto
......@@ -40,5 +46,8 @@ using GradOpMakerFN = std::function<std::vector<std::unique_ptr<OpDescBind>>(
const OpDescBind&, const std::unordered_set<std::string>& /*no_grad_set*/,
std::unordered_map<std::string, std::string>* /*grad_to_var*/)>;
using InferVarTypeFN = std::function<void(const OpDescBind& /*op_desc*/,
BlockDescBind* /*block*/)>;
} // namespace framework
} // namespace paddle
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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. */
#pragma once
#include "paddle/framework/type_defs.h"
namespace paddle {
namespace framework {
class VarTypeInference {
public:
virtual ~VarTypeInference() {}
virtual void operator()(const OpDescBind& op_desc,
BlockDescBind* block) const = 0;
};
} // namespace framework
} // namespace paddle
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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/framework/var_type_inference.h"
#include "gtest/gtest.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
#include "paddle/framework/program_desc.h"
namespace paddle {
namespace framework {
class SumOpMaker : public OpProtoAndCheckerMaker {
public:
SumOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "").AsDuplicable();
AddOutput("Out", "");
AddComment("");
}
};
class SumOpVarTypeInference : public VarTypeInference {
public:
void operator()(const OpDescBind &op_desc,
BlockDescBind *block) const override {
auto &inputs = op_desc.Input("X");
auto default_var_type = VarDesc::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() == VarDesc::LOD_TENSOR;
});
if (any_input_is_lod_tensor) {
default_var_type = VarDesc::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(InferVarType, sum_op) {
auto &prog = ProgramDescBind::Instance(&GetProgramDesc());
auto *op = prog.Block(0)->AppendOp();
op->SetType("sum");
op->SetInput("X", {"test_a", "test_b", "test_c"});
op->SetOutput("Out", {"test_out"});
prog.Block(0)->NewVar("test_a")->SetType(VarDesc::SELECTED_ROWS);
prog.Block(0)->NewVar("test_b")->SetType(VarDesc::SELECTED_ROWS);
prog.Block(0)->NewVar("test_c")->SetType(VarDesc::SELECTED_ROWS);
prog.Block(0)->NewVar("test_out");
op->InferVarType(prog.Block(0));
ASSERT_EQ(VarDesc::SELECTED_ROWS, prog.Block(0)->Var("test_out")->GetType());
prog.Block(0)->Var("test_b")->SetType(VarDesc::LOD_TENSOR);
op->InferVarType(prog.Block(0));
ASSERT_EQ(VarDesc::LOD_TENSOR, prog.Block(0)->Var("test_out")->GetType());
}
TEST(InferVarType, sum_op_without_infer_var_type) {
auto &prog = ProgramDescBind::Instance(&GetProgramDesc());
auto *op = prog.Block(0)->AppendOp();
op->SetType("sum_without_infer_var_type");
op->SetInput("X", {"test2_a", "test2_b", "test2_c"});
op->SetOutput("Out", {"test2_out"});
prog.Block(0)->NewVar("test2_a")->SetType(VarDesc::SELECTED_ROWS);
prog.Block(0)->NewVar("test2_b")->SetType(VarDesc::SELECTED_ROWS);
prog.Block(0)->NewVar("test2_c")->SetType(VarDesc::SELECTED_ROWS);
prog.Block(0)->NewVar("test2_out");
op->InferVarType(prog.Block(0));
ASSERT_EQ(VarDesc_VarType_LOD_TENSOR,
prog.Block(0)->Var("test2_out")->GetType());
}
} // namespace framework
} // namespace paddle
\ No newline at end of file
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