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| Original file line number | Diff line number | Diff line change |
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| /* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve. | ||
| 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. */ | ||
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|
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| #include "paddle/fluid/operators/unzip_op.h" | ||
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| #include <memory> | ||
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| #include "paddle/phi/kernels/funcs/math_function.h" | ||
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| namespace paddle { | ||
| namespace operators { | ||
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| using Tensor = framework::Tensor; | ||
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| class unzipOp : public framework::OperatorWithKernel { | ||
| public: | ||
| using framework::OperatorWithKernel::OperatorWithKernel; | ||
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| void InferShape(framework::InferShapeContext* ctx) const override { | ||
| OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "lod"); | ||
| OP_INOUT_CHECK(ctx->HasOutput("Y"), "Output", "Y", "lod"); | ||
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|
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| auto x_dims = ctx->GetInputDim("X"); | ||
| PADDLE_ENFORCE_EQ( | ||
| x_dims.size(), | ||
| 2UL, | ||
| platform::errors::InvalidArgument( | ||
| "Input(X)'s rank should be 2, but got %d", x_dims.size())); | ||
|
|
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| auto lod_dims = ctx->GetInputDim("lod"); | ||
| PADDLE_ENFORCE_EQ( | ||
| lod_dims.size(), | ||
| 1UL, | ||
| platform::errors::InvalidArgument( | ||
| "Input(X)'s rank should be 1, but got %d", lod_dims.size())); | ||
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| ctx->SetOutputDim("Y", {lod_dims[0] - 1, x_dims[1]}); | ||
| } | ||
|
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| protected: | ||
| // Explicitly set that the data type of computation kernel of | ||
| // unzip | ||
| // is determined by its input "X". | ||
| framework::OpKernelType GetExpectedKernelType( | ||
| const framework::ExecutionContext& ctx) const override { | ||
| return framework::OpKernelType( | ||
| OperatorWithKernel::IndicateVarDataType(ctx, "X"), | ||
| ctx.device_context()); | ||
| } | ||
| }; | ||
|
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| class unzipGradientOp : public framework::OperatorWithKernel { | ||
| public: | ||
| using framework::OperatorWithKernel::OperatorWithKernel; | ||
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| void InferShape(framework::InferShapeContext* ctx) const override { | ||
| OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "unzipGradient"); | ||
| OP_INOUT_CHECK(ctx->HasInput("lod"), "Input", "unzip", "unzipGradient"); | ||
| OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Y")), | ||
| "Input", | ||
| framework::GradVarName("Y"), | ||
| "unzipGradient"); | ||
| OP_INOUT_CHECK(ctx->HasOutput(framework::GradVarName("X")), | ||
| "Output", | ||
| framework::GradVarName("X"), | ||
| "unzipGradient"); | ||
|
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| auto x_dims = ctx->GetInputDim("X"); | ||
| auto lod_dims = ctx->GetInputDim("lod"); | ||
| auto dy_dims = ctx->GetInputDim(framework::GradVarName("Y")); | ||
| PADDLE_ENFORCE_EQ( | ||
| x_dims.size(), | ||
| 2, | ||
| platform::errors::InvalidArgument( | ||
| "Expect Input(X)'s rank == 2, but got %d", x_dims.size())); | ||
| PADDLE_ENFORCE_EQ( | ||
| dy_dims.size(), | ||
| 2, | ||
| platform::errors::InvalidArgument( | ||
| "Expect Input(X)'s rank == 2, but got %d", dy_dims.size())); | ||
| PADDLE_ENFORCE_EQ( | ||
| lod_dims.size(), | ||
| 1, | ||
| platform::errors::InvalidArgument( | ||
| "Expect Input(X)'s rank == 1, but got %d", lod_dims.size())); | ||
|
|
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| PADDLE_ENFORCE_EQ( | ||
| x_dims[1], | ||
| dy_dims[1], | ||
| platform::errors::InvalidArgument( | ||
| "The 1st dimension of Input(X) and Input(Y@Grad) should " | ||
| "be equal, X is %d, Y@Grad is %d", | ||
| x_dims[1], | ||
| dy_dims[1])); | ||
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| ctx->SetOutputDim(framework::GradVarName("X"), x_dims); | ||
| ctx->ShareLoD("X", framework::GradVarName("X")); | ||
| } | ||
|
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| protected: | ||
| // Explicitly set that the data type of computation kernel of | ||
| // unzip | ||
| // is determined by its input "X". | ||
| framework::OpKernelType GetExpectedKernelType( | ||
| const framework::ExecutionContext& ctx) const override { | ||
| return framework::OpKernelType(OperatorWithKernel::IndicateVarDataType( | ||
| ctx, framework::GradVarName("Y")), | ||
| ctx.device_context()); | ||
| } | ||
| }; | ||
|
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| class unzipOpMaker : public framework::OpProtoAndCheckerMaker { | ||
| public: | ||
| void Make() override { | ||
| AddInput("X", | ||
| "(LodTensor, default LodTensor<float>), a 2-D tensor with shape " | ||
| "[M x N]," | ||
| " where N is the batch size and D is the emebdding dim. "); | ||
| AddInput("lod", | ||
| "(Tensor), a 1-D Tensor with shape [K]"); | ||
| AddOutput("Y", | ||
| "(LodTensor, default LodTensor<float>), a 2-D tensor with shape " | ||
| "[K-1 x N]."); | ||
| AddComment(R"DOC( | ||
| unzip Operator. | ||
| )DOC"); | ||
| } | ||
| }; | ||
|
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| template <typename T> | ||
| class unzipGradOpMaker : public framework::SingleGradOpMaker<T> { | ||
| public: | ||
| using framework::SingleGradOpMaker<T>::SingleGradOpMaker; | ||
|
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| protected: | ||
| void Apply(GradOpPtr<T> op) const override { | ||
| op->SetType("unzip_grad"); | ||
| op->SetInput("X", this->Input("X")); | ||
| op->SetInput("lod", this->Input("lod")); | ||
| op->SetInput(framework::GradVarName("Y"), this->OutputGrad("Y")); | ||
| op->SetOutput(framework::GradVarName("X"), this->InputGrad("X")); | ||
| op->SetAttrMap(this->Attrs()); | ||
| } | ||
| }; | ||
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| DECLARE_NO_NEED_BUFFER_VARS_INFERER(unzipNoNeedBufferVarInferer, "lod"); | ||
| DECLARE_NO_NEED_BUFFER_VARS_INFERER(unzipGradNoNeedBufferVarInferer, "X"); | ||
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| } // namespace operators | ||
| } // namespace paddle | ||
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| namespace ops = paddle::operators; | ||
| REGISTER_OPERATOR(unzip, | ||
| ops::unzipOp, | ||
| ops::unzipOpMaker, | ||
| ops::unzipGradOpMaker<paddle::framework::OpDesc>, | ||
| ops::unzipGradOpMaker<paddle::imperative::OpBase>, | ||
| ops::unzipNoNeedBufferVarInferer); | ||
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| REGISTER_OPERATOR(unzip_grad, | ||
| ops::unzipGradientOp, | ||
| ops::unzipGradNoNeedBufferVarInferer); | ||
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| REGISTER_OP_CPU_KERNEL(unzip, ops::unzipOpKernel<int64_t>, ops::unzipOpKernel<int64_t>); | ||
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| REGISTER_OP_CPU_KERNEL(unzip_grad, | ||
| ops::unzipGradOpKernel<int64_t>, | ||
| ops::unzipGradOpKernel<int64_t>); |
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