You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
Paddle/paddle/fluid/operators/bmm_op.cc

166 lines
6.1 KiB

/* Copyright (c) 2020 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/operators/bmm_op.h"
#include <vector>
namespace paddle {
namespace operators {
class BmmOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE_EQ(
ctx->HasInput("X"), true,
platform::errors::NotFound("Input(X) of BmmOp should not be null"));
PADDLE_ENFORCE_EQ(
ctx->HasInput("Y"), true,
platform::errors::NotFound("Input(Y) of BmmOp should not be null"));
PADDLE_ENFORCE_EQ(
ctx->HasOutput("Out"), true,
platform::errors::NotFound("Output(Out) of BmmOp should not be null."));
auto x_dims = ctx->GetInputDim("X");
auto y_dims = ctx->GetInputDim("Y");
PADDLE_ENFORCE_EQ(x_dims.size(), 3,
platform::errors::InvalidArgument(
"Input(X) of BmmOp must be 3-dimensional in BmmOp, "
"but received X's shape: [%s].",
x_dims));
PADDLE_ENFORCE_EQ(y_dims.size(), 3,
platform::errors::InvalidArgument(
"Input(Y) of BmmOp must be 3-dimensional in BmmOp, "
"but received Y's shape: [%s].",
y_dims));
PADDLE_ENFORCE_EQ(
x_dims[0], y_dims[0],
platform::errors::InvalidArgument(
"Input(X) and Input(Y) must have the same batch size in BmmOp, "
"but received X's batch size: [%s],"
"Y's batch size [%s]",
x_dims[0], y_dims[0]));
PADDLE_ENFORCE_EQ(
x_dims[2], y_dims[1],
platform::errors::InvalidArgument(
"Input(X)'s width must be equal with Input(Y)'s height in BmmOp,"
"but receive X's width: [%s],"
"Y's height: [%s].",
x_dims[2], y_dims[1]));
std::vector<int64_t> dim_out;
dim_out.push_back(x_dims[0]);
dim_out.push_back(x_dims[1]);
dim_out.push_back(y_dims[2]);
ctx->SetOutputDim("Out", framework::make_ddim(dim_out));
ctx->ShareLoD("X", /*->*/ "Out");
}
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
auto data_type = OperatorWithKernel::IndicateVarDataType(ctx, "X");
return framework::OpKernelType(data_type, ctx.device_context());
}
};
class BmmOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "(Tensor), The first input tensor of Bmm op.");
AddInput("Y", "(Tensor), The second input tensor of Bmm op.");
AddOutput("Out", "(Tensor), The output tensor of Bmm op.");
AddComment(R"DOC(
The Bmm operator is used to perform batched matrix multiplication
over the last two dimensions of the input tensors `X` and `Y`
which are both 3-dimentionsal.
Examples:
- X: [B, M, K], Y: [B, K, N] => Out: [B, M, N]
)DOC");
}
};
class BmmOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE_EQ(
ctx->HasInput("X"), true,
platform::errors::NotFound("Input(X) of BmmOp should not be null"));
PADDLE_ENFORCE_EQ(
ctx->HasInput("Y"), true,
platform::errors::NotFound("Input(Y) of BmmOp should not be null"));
PADDLE_ENFORCE_EQ(ctx->HasInput(framework::GradVarName("Out")), true,
platform::errors::NotFound(
"Output(Out@GRAD) of BmmOp should not be null."));
auto x_dims = ctx->GetInputDim("X");
auto y_dims = ctx->GetInputDim("Y");
auto x_grad_name = framework::GradVarName("X");
auto y_grad_name = framework::GradVarName("Y");
if (ctx->HasOutput(x_grad_name)) {
ctx->SetOutputDim(x_grad_name, x_dims);
}
if (ctx->HasOutput(y_grad_name)) {
ctx->SetOutputDim(y_grad_name, y_dims);
}
}
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(OperatorWithKernel::IndicateVarDataType(
ctx, framework::GradVarName("Out")),
ctx.device_context());
}
};
template <typename T>
class BmmOpGradMaker : public framework::SingleGradOpMaker<T> {
public:
using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
protected:
void Apply(GradOpPtr<T> retv) const override {
retv->SetType("bmm_grad");
retv->SetInput("X", this->Input("X"));
retv->SetInput("Y", this->Input("Y"));
retv->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
retv->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
retv->SetOutput(framework::GradVarName("Y"), this->InputGrad("Y"));
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(bmm, ops::BmmOp, ops::BmmOpMaker,
ops::BmmOpGradMaker<paddle::framework::OpDesc>,
ops::BmmOpGradMaker<paddle::imperative::OpBase>);
REGISTER_OPERATOR(bmm_grad, ops::BmmOpGrad);
REGISTER_OP_CPU_KERNEL(
bmm, ops::BmmKernel<paddle::platform::CPUDeviceContext, float>,
ops::BmmKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
bmm_grad, ops::BmmGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::BmmGradKernel<paddle::platform::CPUDeviceContext, double>);