Put_along_axis #37921
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| /* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. | ||
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| 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 | ||
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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 | ||
| 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. */ | ||
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| #include "paddle/fluid/operators/put_along_axis_op.h" | ||
| #include <memory> | ||
| #include <string> | ||
| #include <vector> | ||
| #include "paddle/fluid/framework/ddim.h" | ||
| #include "paddle/fluid/framework/op_version_registry.h" | ||
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| namespace paddle { | ||
| namespace operators { | ||
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| class PutAlongAxisOp : public framework::OperatorWithKernel { | ||
| public: | ||
| using framework::OperatorWithKernel::OperatorWithKernel; | ||
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| void InferShape(framework::InferShapeContext* ctx) const override { | ||
| PADDLE_ENFORCE_EQ( | ||
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| ctx->HasInput("Input"), true, | ||
| platform::errors::InvalidArgument( | ||
| "Input(Input) of PutAlongAxisOpOp should not be null.")); | ||
| PADDLE_ENFORCE_EQ( | ||
| ctx->HasInput("Index"), true, | ||
| platform::errors::InvalidArgument( | ||
| "Input(Index) of PutAlongAxisOpOp should not be null.")); | ||
| PADDLE_ENFORCE_EQ( | ||
| ctx->HasInput("Value"), true, | ||
| platform::errors::InvalidArgument( | ||
| "Input(Value) of PutAlongAxisOpOp should not be null.")); | ||
| PADDLE_ENFORCE_EQ( | ||
| ctx->HasOutput("Result"), true, | ||
| platform::errors::InvalidArgument( | ||
| "Output(Result) of PutAlongAxisOpOp should not be null.")); | ||
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| auto index_dim = ctx->GetInputDim("Index"); | ||
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| ctx->SetOutputDim("Result", index_dim); | ||
| } | ||
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| protected: | ||
| framework::OpKernelType GetExpectedKernelType( | ||
| const framework::ExecutionContext& ctx) const override { | ||
| return framework::OpKernelType( | ||
| OperatorWithKernel::IndicateVarDataType(ctx, "Input"), | ||
| ctx.device_context()); | ||
| } | ||
| framework::OpKernelType GetKernelTypeForVar( | ||
| const std::string& var_name, const framework::Tensor& tensor, | ||
| const framework::OpKernelType& expected_kernel_type) const override { | ||
| return framework::OpKernelType(expected_kernel_type.data_type_, | ||
| tensor.place(), tensor.layout()); | ||
| } | ||
| }; | ||
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| class PutAlongAxisOpMaker : public framework::OpProtoAndCheckerMaker { | ||
| public: | ||
| void Make() override { | ||
| AddInput("Input", "The input tensor of PutAlongAxisOp"); | ||
| AddInput("Index", "The index tensor of PutAlongAxisOp"); | ||
| AddInput("Value", "The value tensor of PutAlongAxisOp"); | ||
| AddOutput("Result", "The result tensor of PutAlongAxisOp"); | ||
| AddAttr<int>("Axis", "The axis that we do PutAlongAxis operation"); | ||
| AddAttr<std::string>("Reduce", "The reduce operation for scatter") | ||
| .SetDefault("assign"); | ||
| AddComment(R"DOC( | ||
| PutAlongAxis Operator.) | ||
| )DOC"); | ||
| } | ||
| }; | ||
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| class PutAlongAxisGradOp : public framework::OperatorWithKernel { | ||
| public: | ||
| using framework::OperatorWithKernel::OperatorWithKernel; | ||
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| void InferShape(framework::InferShapeContext* ctx) const override { | ||
| ctx->SetOutputDim(framework::GradVarName("Input"), | ||
| ctx->GetInputDim("Input")); | ||
| } | ||
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| protected: | ||
| framework::OpKernelType GetExpectedKernelType( | ||
| const framework::ExecutionContext& ctx) const override { | ||
| return framework::OpKernelType(OperatorWithKernel::IndicateVarDataType( | ||
| ctx, framework::GradVarName("Result")), | ||
| ctx.device_context()); | ||
| } | ||
| framework::OpKernelType GetKernelTypeForVar( | ||
| const std::string& var_name, const framework::Tensor& tensor, | ||
| const framework::OpKernelType& expected_kernel_type) const override { | ||
| return framework::OpKernelType(expected_kernel_type.data_type_, | ||
| tensor.place(), tensor.layout()); | ||
| } | ||
| }; | ||
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| template <typename T> | ||
| class PutAlongAxisGradOpMaker : public framework::SingleGradOpMaker<T> { | ||
| public: | ||
| using framework::SingleGradOpMaker<T>::SingleGradOpMaker; | ||
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| protected: | ||
| void Apply(GradOpPtr<T> op) const override { | ||
| op->SetType("put_along_axis_grad"); | ||
| op->SetInput("Index", this->Input("Index")); | ||
| op->SetInput("Input", this->Input("Input")); | ||
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| op->SetInput(framework::GradVarName("Result"), this->OutputGrad("Result")); | ||
| op->SetOutput(framework::GradVarName("Input"), this->InputGrad("Input")); | ||
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| op->SetOutput(framework::GradVarName("Value"), this->InputGrad("Value")); | ||
| op->SetAttrMap(this->Attrs()); | ||
| } | ||
| }; | ||
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| DECLARE_INPLACE_OP_INFERER(PutAlongAxisInplaceInferer, {"Input", "Result"}); | ||
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| } // namespace operators | ||
| } // namespace paddle | ||
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| namespace ops = paddle::operators; | ||
| REGISTER_OPERATOR(put_along_axis, ops::PutAlongAxisOp, ops::PutAlongAxisOpMaker, | ||
| ops::PutAlongAxisGradOpMaker<paddle::framework::OpDesc>, | ||
| ops::PutAlongAxisGradOpMaker<paddle::imperative::OpBase>, | ||
| paddle::operators::PutAlongAxisInplaceInferer); | ||
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| REGISTER_OPERATOR(put_along_axis_grad, ops::PutAlongAxisGradOp); | ||
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| REGISTER_OP_CPU_KERNEL(put_along_axis, ops::PutAlongAxisOpKernel<float>, | ||
| ops::PutAlongAxisOpKernel<double>, | ||
| ops::PutAlongAxisOpKernel<int>, | ||
| ops::PutAlongAxisOpKernel<uint8_t>, | ||
| ops::PutAlongAxisOpKernel<int64_t>); | ||
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| REGISTER_OP_CPU_KERNEL(put_along_axis_grad, | ||
| ops::PutAlongAxisGradOpKernel<float>, | ||
| ops::PutAlongAxisGradOpKernel<double>, | ||
| ops::PutAlongAxisGradOpKernel<int>, | ||
| ops::PutAlongAxisGradOpKernel<uint8_t>, | ||
| ops::PutAlongAxisGradOpKernel<int64_t>); | ||
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I think it is better to split it into two classes; one for gather and the other for scatter. Reasons:
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I don't think split it is better since I think scatter and gather share a lot of logic. Making one kernel function can make the code more elegant. Don't want to make some redundant code.