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166 lines
6.1 KiB
166 lines
6.1 KiB
/* Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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*
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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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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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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/operators/bmm_op.h"
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#include <vector>
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namespace paddle {
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namespace operators {
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class BmmOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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protected:
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void InferShape(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE_EQ(
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ctx->HasInput("X"), true,
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platform::errors::NotFound("Input(X) of BmmOp should not be null"));
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PADDLE_ENFORCE_EQ(
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ctx->HasInput("Y"), true,
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platform::errors::NotFound("Input(Y) of BmmOp should not be null"));
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PADDLE_ENFORCE_EQ(
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ctx->HasOutput("Out"), true,
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platform::errors::NotFound("Output(Out) of BmmOp should not be null."));
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auto x_dims = ctx->GetInputDim("X");
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auto y_dims = ctx->GetInputDim("Y");
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PADDLE_ENFORCE_EQ(x_dims.size(), 3,
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platform::errors::InvalidArgument(
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"Input(X) of BmmOp must be 3-dimensional in BmmOp, "
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"but received X's shape: [%s].",
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x_dims));
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PADDLE_ENFORCE_EQ(y_dims.size(), 3,
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platform::errors::InvalidArgument(
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"Input(Y) of BmmOp must be 3-dimensional in BmmOp, "
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"but received Y's shape: [%s].",
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y_dims));
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PADDLE_ENFORCE_EQ(
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x_dims[0], y_dims[0],
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platform::errors::InvalidArgument(
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"Input(X) and Input(Y) must have the same batch size in BmmOp, "
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"but received X's batch size: [%s],"
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"Y's batch size [%s]",
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x_dims[0], y_dims[0]));
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PADDLE_ENFORCE_EQ(
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x_dims[2], y_dims[1],
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platform::errors::InvalidArgument(
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"Input(X)'s width must be equal with Input(Y)'s height in BmmOp,"
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"but receive X's width: [%s],"
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"Y's height: [%s].",
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x_dims[2], y_dims[1]));
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std::vector<int64_t> dim_out;
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dim_out.push_back(x_dims[0]);
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dim_out.push_back(x_dims[1]);
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dim_out.push_back(y_dims[2]);
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ctx->SetOutputDim("Out", framework::make_ddim(dim_out));
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ctx->ShareLoD("X", /*->*/ "Out");
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}
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const override {
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auto data_type = OperatorWithKernel::IndicateVarDataType(ctx, "X");
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return framework::OpKernelType(data_type, ctx.device_context());
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}
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};
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class BmmOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X", "(Tensor), The first input tensor of Bmm op.");
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AddInput("Y", "(Tensor), The second input tensor of Bmm op.");
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AddOutput("Out", "(Tensor), The output tensor of Bmm op.");
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AddComment(R"DOC(
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The Bmm operator is used to perform batched matrix multiplication
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over the last two dimensions of the input tensors `X` and `Y`
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which are both 3-dimentionsal.
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Examples:
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- X: [B, M, K], Y: [B, K, N] => Out: [B, M, N]
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)DOC");
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}
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};
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class BmmOpGrad : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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protected:
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void InferShape(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE_EQ(
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ctx->HasInput("X"), true,
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platform::errors::NotFound("Input(X) of BmmOp should not be null"));
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PADDLE_ENFORCE_EQ(
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ctx->HasInput("Y"), true,
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platform::errors::NotFound("Input(Y) of BmmOp should not be null"));
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PADDLE_ENFORCE_EQ(ctx->HasInput(framework::GradVarName("Out")), true,
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platform::errors::NotFound(
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"Output(Out@GRAD) of BmmOp should not be null."));
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auto x_dims = ctx->GetInputDim("X");
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auto y_dims = ctx->GetInputDim("Y");
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auto x_grad_name = framework::GradVarName("X");
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auto y_grad_name = framework::GradVarName("Y");
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if (ctx->HasOutput(x_grad_name)) {
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ctx->SetOutputDim(x_grad_name, x_dims);
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}
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if (ctx->HasOutput(y_grad_name)) {
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ctx->SetOutputDim(y_grad_name, y_dims);
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}
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}
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const override {
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return framework::OpKernelType(OperatorWithKernel::IndicateVarDataType(
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ctx, framework::GradVarName("Out")),
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ctx.device_context());
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}
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};
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template <typename T>
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class BmmOpGradMaker : public framework::SingleGradOpMaker<T> {
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public:
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using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
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protected:
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void Apply(GradOpPtr<T> retv) const override {
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retv->SetType("bmm_grad");
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retv->SetInput("X", this->Input("X"));
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retv->SetInput("Y", this->Input("Y"));
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retv->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
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retv->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
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retv->SetOutput(framework::GradVarName("Y"), this->InputGrad("Y"));
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OPERATOR(bmm, ops::BmmOp, ops::BmmOpMaker,
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ops::BmmOpGradMaker<paddle::framework::OpDesc>,
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ops::BmmOpGradMaker<paddle::imperative::OpBase>);
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REGISTER_OPERATOR(bmm_grad, ops::BmmOpGrad);
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REGISTER_OP_CPU_KERNEL(
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bmm, ops::BmmKernel<paddle::platform::CPUDeviceContext, float>,
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ops::BmmKernel<paddle::platform::CPUDeviceContext, double>);
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REGISTER_OP_CPU_KERNEL(
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bmm_grad, ops::BmmGradKernel<paddle::platform::CPUDeviceContext, float>,
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ops::BmmGradKernel<paddle::platform::CPUDeviceContext, double>);
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