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graph_send_ue_recv_op.cc
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graph_send_ue_recv_op.cc
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// Copyright (c) 2022 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/infershape_utils.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/phi/core/infermeta_utils.h"
#include "paddle/phi/infermeta/multiary.h"
namespace paddle {
namespace operators {
class GraphSendUERecvOP : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
OperatorWithKernel::IndicateVarDataType(ctx, "X"),
ctx.device_context());
}
};
class GraphSendUERecvGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
auto in_dims = ctx->GetInputDim("X");
ctx->SetOutputDim(framework::GradVarName("X"), in_dims);
auto y_dims = ctx->GetInputDim("Y");
ctx->SetOutputDim(framework::GradVarName("Y"), y_dims);
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(OperatorWithKernel::IndicateVarDataType(
ctx, framework::GradVarName("Out")),
ctx.device_context());
}
};
class GraphSendUERecvOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"The input tensor with data type float32, float64, int32, int64.");
AddInput("Y",
"The input edge weight tensor, data type should be same with X");
AddInput("Src_index", "The source index tensor.");
AddInput("Dst_index", "The destination index tensor.");
AddInput("Out_size",
"(Tensor<int>, optional). The 0th dimension of the output."
"It has a higher priority than Attr(out_size).")
.AsDispensable();
AddOutput("Out", "Output tensor of graph_send_ue_recv op.");
AddOutput("Dst_count",
"Count tensor of Dst_index, mainly for MEAN pool_type.")
.AsIntermediate();
AddAttr<std::string>("compute_type",
"(string, default 'ADD')"
"Define differenct computation types between X and E.")
.SetDefault("ADD")
.InEnum({"ADD", "MUL"});
AddAttr<std::string>("pool_type",
"(string, default 'SUM')"
"Define different pool types to receive the result "
"tensors of Dst_index.")
.SetDefault("SUM")
.InEnum({"SUM", "MEAN", "MIN", "MAX"});
AddAttr<std::vector<int64_t>>(
"out_size",
"(vector<int64_t>, default {0})"
"Define the first dimension of Output tensor."
"If set default {0}, then the shape of Out is the same with X.")
.SetDefault({0});
AddComment(R"DOC(
Graph Learning Send_UE_Recv combine operator.
$Out = Recv(Compute(Send(X, Src_index), Y, compute_type), Dst_index, pool_type)$
This operator is mainly used in Graph Learning domain, and the main purpose is to reduce
intermediate memory consumption in the process of message passing.
Take `X` as the input tensor, we first use `src_index` to gather corresponding data.
Then the gather data should compute with `Y` in different compute_types, like add, sub, mul, and div,
and get the computation result. Then, use `dst_index` to update the corresponding position of output
tensor in different pooling types, like sum, mean, max, or min.
)DOC");
}
};
template <typename T>
class GraphSendUERecvGradOpMaker : public framework::SingleGradOpMaker<T> {
public:
using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
protected:
void Apply(GradOpPtr<T> op) const override {
op->SetType("graph_send_ue_recv_grad");
op->SetInput("X", this->Input("X"));
op->SetInput("Y", this->Input("Y"));
op->SetInput("Src_index", this->Input("Src_index"));
op->SetInput("Dst_index", this->Input("Dst_index"));
if (PADDLE_GET_CONST(std::string, this->GetAttr("pool_type")) == "MEAN") {
op->SetInput("Dst_count", this->Output("Dst_count"));
}
if (PADDLE_GET_CONST(std::string, this->GetAttr("pool_type")) == "MIN" ||
PADDLE_GET_CONST(std::string, this->GetAttr("pool_type")) == "MAX") {
op->SetInput("Out", this->Output("Out"));
}
op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
op->SetOutput(framework::GradVarName("Y"), this->InputGrad("Y"));
op->SetAttrMap(this->Attrs());
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
DECLARE_INFER_SHAPE_FUNCTOR(graph_send_ue_recv,
GraphSendUERecvInferShapeFunctor,
PD_INFER_META(phi::GraphSendUERecvInferMeta));
REGISTER_OPERATOR(graph_send_ue_recv,
ops::GraphSendUERecvOP,
ops::GraphSendUERecvOpMaker,
ops::GraphSendUERecvGradOpMaker<paddle::framework::OpDesc>,
ops::GraphSendUERecvGradOpMaker<paddle::imperative::OpBase>,
GraphSendUERecvInferShapeFunctor);
REGISTER_OPERATOR(graph_send_ue_recv_grad, ops::GraphSendUERecvGradOp);