/* Copyright (c) 2018 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/sequence_ops/sequence_scatter_op.h" #include #include "paddle/fluid/framework/eigen.h" #include "paddle/fluid/framework/op_registry.h" #include "paddle/fluid/operators/gather.h" #include "paddle/fluid/operators/scatter.h" namespace paddle { namespace operators { using Tensor = framework::Tensor; using LoDTensor = framework::LoDTensor; class SequenceScatterOpMaker : public framework::OpProtoAndCheckerMaker { public: void Make() override { AddInput("X", "(Tensor) The source input of sequence scatter op"); AddInput("Ids", "(LoDTensor) The index input of sequence scatter op where X" " will be updated, must be a LoDTensor"); AddInput("Updates", "(LoDTensor) The values to scatter to the input tensor " "X, must be a LoDTensor with the same LoD information as Ids"); AddOutput("Out", "(Tensor) The output tensor of sequence scatter op, which " "has the same dims as X"); AddComment(R"DOC( Sequence Scatter Operator. This operator scatters the Updates tensor to the input X. It uses the LoD information of Ids to select the rows to update, and use the values in Ids as the columns to update in each row of X. Following are cases to better explain how this works: Example 1: Given an all-ones Tensor input(X) X.data = [[1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0]] X.dims = [3, 6] a LoDTensor input(Ids) Ids.data = [[0], [1], [2], [5], [4], [3], [2], [1], [3], [2], [5], [4]] Ids.lod = [[0, 3, 8, 12]] and a Tensor input(Updates) Updates.data = [[0.3], [0.3], [0.4], [0.1], [0.2], [0.3], [0.4], [0.0], [0.2], [0.3], [0.1], [0.4]] Updates.lod = [[ 0, 3, 8, 12]] then we get an output Tensor Out.data = [[1.3, 1.3, 1.4, 1.0, 1.0, 1.0], [1.0, 1.0, 1.4, 1.3, 1.2, 1.1], [1.0, 1.0, 1.3, 1.2, 1.4, 1.1]] Out.dims = X.dims = [3, 6] )DOC"); } }; class SequenceScatterOp : public framework::OperatorWithKernel { public: using framework::OperatorWithKernel::OperatorWithKernel; void InferShape(framework::InferShapeContext* ctx) const override { // Enforce has inputs and outputs PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of SequenceScatterOp should not be null."); PADDLE_ENFORCE(ctx->HasInput("Ids"), "Input(Ids) of SequenceScatterOp should not be null."); PADDLE_ENFORCE(ctx->HasInput("Updates"), "Input(Updates) of SequenceScatterOp should not be null."); PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) of SequenceScatterOp should not be null."); // Set output dim the same as input auto ref_dims = ctx->GetInputDim("X"); ctx->SetOutputDim("Out", ref_dims); // Enforce the Updates and Ids are the same shape PADDLE_ENFORCE_EQ(ctx->GetInputDim("Updates")[0], ctx->GetInputDim("Ids")[0], "Updates and Ids should have same shape."); // Enforce LoD of ids and updates be the same if (ctx->IsRuntime()) { framework::Variable* ids_var = boost::get(ctx->GetInputVarPtrs("Ids")[0]); framework::Variable* updates_var = boost::get(ctx->GetInputVarPtrs("Updates")[0]); auto& ids_lod = ids_var->Get().lod(); auto& updates_lod = updates_var->Get().lod(); PADDLE_ENFORCE_EQ(ids_lod.size(), 1, "Currently only level 1 LoD could be" " processed by sequence scatter op."); PADDLE_ENFORCE_EQ(updates_lod.size(), 1, "Currently only level 1 LoD " "could be processed by sequence scatter op."); } } protected: framework::OpKernelType GetExpectedKernelType( const framework::ExecutionContext& ctx) const override { return framework::OpKernelType(ctx.Input("X")->type(), platform::CPUPlace()); } }; class SequenceScatterGradOp : public framework::OperatorWithKernel { public: using framework::OperatorWithKernel::OperatorWithKernel; void InferShape(framework::InferShapeContext* ctx) const override { ctx->SetOutputDim(framework::GradVarName("Updates"), ctx->GetInputDim("Updates")); ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim(framework::GradVarName("Out"))); } protected: framework::OpKernelType GetExpectedKernelType( const framework::ExecutionContext& ctx) const override { return framework::OpKernelType( ctx.Input(framework::GradVarName("Out"))->type(), platform::CPUPlace()); } }; class SequenceScatterGradDescMaker : public framework::SingleGradOpDescMaker { public: using framework::SingleGradOpDescMaker::SingleGradOpDescMaker; protected: std::unique_ptr Apply() const override { std::unique_ptr op(new framework::OpDesc()); op->SetType("sequence_scatter_grad"); op->SetInput("Ids", Input("Ids")); op->SetInput("Updates", Input("Updates")); op->SetInput(framework::GradVarName("Out"), OutputGrad("Out")); op->SetOutput(framework::GradVarName("X"), InputGrad("X")); op->SetOutput(framework::GradVarName("Updates"), InputGrad("Updates")); op->SetAttrMap(Attrs()); return op; } }; DECLARE_NO_NEED_BUFFER_VARS_INFERENCE( SequenceScatterGradNoNeedBufferVarsInference, "Updates"); } // namespace operators } // namespace paddle namespace ops = paddle::operators; REGISTER_OPERATOR(sequence_scatter, ops::SequenceScatterOp, ops::SequenceScatterOpMaker, ops::SequenceScatterGradDescMaker); REGISTER_OPERATOR(sequence_scatter_grad, ops::SequenceScatterGradOp, ops::SequenceScatterGradNoNeedBufferVarsInference); REGISTER_OP_CPU_KERNEL(sequence_scatter, ops::SequenceScatterOpKernel, ops::SequenceScatterOpKernel, ops::SequenceScatterOpKernel, ops::SequenceScatterOpKernel); REGISTER_OP_CPU_KERNEL(sequence_scatter_grad, ops::SequenceScatterGradientOpKernel, ops::SequenceScatterGradientOpKernel, ops::SequenceScatterGradientOpKernel, ops::SequenceScatterGradientOpKernel);