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121 lines
4.7 KiB
121 lines
4.7 KiB
/* Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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/masked_select_op.h"
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#include "paddle/fluid/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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class MaskedSelectOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override {
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OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "Input", "MaskedSelect");
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OP_INOUT_CHECK(ctx->HasInput("Mask"), "Input", "Mask", "MaskedSelect");
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OP_INOUT_CHECK(ctx->HasOutput("Y"), "Output", "Out", "MaskedSelect");
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framework::DDim output_dims(ctx->GetInputDim("X"));
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ctx->SetOutputDim("Y", output_dims);
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ctx->ShareLoD("X", /*->*/ "Y");
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}
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protected:
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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 MaskedSelectOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X", "The input tensor.");
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AddInput("Mask",
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"The mask of Input Tensor to be selected which is a bool Tensor.");
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AddOutput(
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"Y",
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"The returned tensor, the data type "
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"is same as input, will be on the same device with the input Tensor.");
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AddComment(R"DOC(
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Size Operator.
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Return a new 0-D tensor which indexes the indexed tensor according
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the mask which is a tensor withe data type bool.
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)DOC");
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}
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};
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class MaskedSelectOpGrad : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override {
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OP_INOUT_CHECK(ctx->HasOutput(framework::GradVarName("X")), "Input",
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"Input", "MaskedSelect");
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OP_INOUT_CHECK(ctx->HasInput("Mask"), "Input", "Mask", "MaskedSelect");
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ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
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ctx->ShareLoD("X", /*-->*/ framework::GradVarName("X"));
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}
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protected:
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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("Y")),
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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 MaskedSelectGradOpMaker : 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> op) const override {
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op->SetType("masked_select_grad");
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op->SetInput("X", this->Input("X"));
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op->SetInput("Mask", this->Input("Mask"));
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op->SetInput(framework::GradVarName("Y"), this->OutputGrad("Y"));
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op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
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}
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};
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DECLARE_NO_NEED_BUFFER_VARS_INFERER(MaskedSelectedGradNoNeedBufferVarsInferer,
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"X");
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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(masked_select, ops::MaskedSelectOp, ops::MaskedSelectOpMaker,
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ops::MaskedSelectGradOpMaker<paddle::framework::OpDesc>,
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ops::MaskedSelectGradOpMaker<paddle::imperative::OpBase>);
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REGISTER_OPERATOR(masked_select_grad, ops::MaskedSelectOpGrad,
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ops::MaskedSelectedGradNoNeedBufferVarsInferer);
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REGISTER_OP_CPU_KERNEL(
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masked_select,
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ops::MaskedSelectKernel<paddle::platform::CPUDeviceContext, float>,
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ops::MaskedSelectKernel<paddle::platform::CPUDeviceContext, double>,
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ops::MaskedSelectKernel<paddle::platform::CPUDeviceContext, int>,
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ops::MaskedSelectKernel<paddle::platform::CPUDeviceContext, int64_t>);
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REGISTER_OP_CPU_KERNEL(
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masked_select_grad,
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ops::MaskedSelectGradKernel<paddle::platform::CPUDeviceContext, float>,
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ops::MaskedSelectGradKernel<paddle::platform::CPUDeviceContext, double>,
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ops::MaskedSelectGradKernel<paddle::platform::CPUDeviceContext, int>,
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ops::MaskedSelectGradKernel<paddle::platform::CPUDeviceContext, int64_t>);
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