/* Copyright (c) 2016 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/edit_distance_op.h" namespace paddle { namespace operators { class EditDistanceOp : public framework::OperatorWithKernel { public: using framework::OperatorWithKernel::OperatorWithKernel; void InferShape(framework::InferShapeContext *ctx) const override { PADDLE_ENFORCE(ctx->HasInput("Hyps"), "Input(Hyps) shouldn't be null."); PADDLE_ENFORCE(ctx->HasInput("Refs"), "Input(Refs) shouldn't be null."); PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) shouldn't be null."); PADDLE_ENFORCE(ctx->HasOutput("SequenceNum"), "Output(SequenceNum) shouldn't be null."); auto hyp_dims = ctx->GetInputDim("Hyps"); auto ref_dims = ctx->GetInputDim("Refs"); PADDLE_ENFORCE(hyp_dims.size() == 2 && hyp_dims[1] == 1, "Input(Hyps) must be a 2-D LoDTensor with the 2nd dimension " "equal to 1."); PADDLE_ENFORCE(ref_dims.size() == 2 && ref_dims[1] == 1, "Input(Refs) must be a 2-D LoDTensor with the 2nd dimension " "equal to 1."); ctx->SetOutputDim("Out", ctx->GetInputDim("Refs")); ctx->SetOutputDim("SequenceNum", {1}); } protected: framework::OpKernelType GetExpectedKernelType( const framework::ExecutionContext &ctx) const override { return framework::OpKernelType(framework::proto::VarType::FP32, ctx.device_context()); } }; class EditDistanceOpMaker : public framework::OpProtoAndCheckerMaker { public: void Make() override { AddInput("Hyps", "(2-D LoDTensor, 2nd dim. equal to 1) " "The indices for hypothesis strings."); AddInput("Refs", "(2-D LoDTensor, 2nd dim. equal to 1) " "The indices for reference strings."); AddOutput("SequenceNum", "The sequence count of current batch"); AddAttr("normalized", "(bool, default false) Indicated whether to normalize " "the edit distance by the length of reference string.") .SetDefault(false); AddOutput("Out", "(2-D Tensor with shape [`batch_size` x 1]) " "The output edit distances of EditDistance operator."); AddComment(R"DOC( EditDistance operator computes the edit distances between a batch of hypothesis strings and their references. Edit distance, also called Levenshtein distance, measures how dissimilar two strings are by counting the minimum number of operations to transform one string into anthor. Here the operations include insertion, deletion, and substitution. For example, given hypothesis string A = "kitten" and reference B = "sitting", the edit distance is 3 for A will be transformed into B at least after two substitutions and one insertion: "kitten" -> "sitten" -> "sittin" -> "sitting" Input(Hyps) is a LoDTensor consisting of all the hypothesis strings with the total number denoted by `batch_size`, and the separation is specified by the LoD information. And the `batch_size` reference strings are arranged in order in the same way in the LoDTensor Input(Refs). Output(Out) contains the `batch_size` results and each stands for the edit stance for a pair of strings respectively. If Attr(normalized) is true, the edit distance will be divided by the length of reference string. )DOC"); } }; } // namespace operators } // namespace paddle namespace ops = paddle::operators; REGISTER_OPERATOR(edit_distance, ops::EditDistanceOp, ops::EditDistanceOpMaker, paddle::framework::EmptyGradOpMaker); REGISTER_OP_CPU_KERNEL( edit_distance, ops::EditDistanceKernel);