From 77cf21e53ec9f4cdc3881f03ca01b171459f8f18 Mon Sep 17 00:00:00 2001 From: wanghaoshuang Date: Thu, 18 Jan 2018 14:56:47 +0800 Subject: [PATCH] Change input data type to int64_t --- paddle/operators/edit_distance_op.cc | 26 +++++++++++++------------- paddle/operators/edit_distance_op.cu | 4 ++-- paddle/operators/edit_distance_op.h | 4 ++-- 3 files changed, 17 insertions(+), 17 deletions(-) diff --git a/paddle/operators/edit_distance_op.cc b/paddle/operators/edit_distance_op.cc index e383f07fa9b..62a1fcebe7b 100644 --- a/paddle/operators/edit_distance_op.cc +++ b/paddle/operators/edit_distance_op.cc @@ -49,10 +49,10 @@ class EditDistanceOpMaker : public framework::OpProtoAndCheckerMaker { EditDistanceOpMaker(OpProto *proto, OpAttrChecker *op_checker) : OpProtoAndCheckerMaker(proto, op_checker) { AddInput("Hyps", - "(2-D LoDTensor, 2nd dim. equal to 1) " + "(2-D LoDTensor, 2nd dim. equal to 1) " "The indices for hypothesis strings."); AddInput("Refs", - "(2-D LoDTensor, 2nd dim. equal to 1) " + "(2-D LoDTensor, 2nd dim. equal to 1) " "The indices for reference strings."); AddAttr("normalized", "(bool, default false) Indicated whether to normalize " @@ -66,22 +66,22 @@ class EditDistanceOpMaker : public framework::OpProtoAndCheckerMaker { 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 +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 +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 +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"); } diff --git a/paddle/operators/edit_distance_op.cu b/paddle/operators/edit_distance_op.cu index cf5ebc5c38f..c165bbae7fc 100644 --- a/paddle/operators/edit_distance_op.cu +++ b/paddle/operators/edit_distance_op.cu @@ -113,8 +113,8 @@ class EditDistanceGPUKernel : public framework::OpKernel { dist_t.Resize({m + 1, n + 1}); dist_t.mutable_data(ctx.GetPlace()); auto dist = dist_t.data(); - auto x1 = x1_t->data() + hyp_lod[num]; - auto x2 = x2_t->data() + ref_lod[num]; + auto x1 = x1_t->data() + hyp_lod[num]; + auto x2 = x2_t->data() + ref_lod[num]; FillFirstColumn<<<1 + m / PADDLE_CUDA_NUM_THREADS, PADDLE_CUDA_NUM_THREADS, 0, stream>>>(dist, m, n); diff --git a/paddle/operators/edit_distance_op.h b/paddle/operators/edit_distance_op.h index 537e70281a5..4c5a29813ce 100644 --- a/paddle/operators/edit_distance_op.h +++ b/paddle/operators/edit_distance_op.h @@ -60,8 +60,8 @@ class EditDistanceKernel : public framework::OpKernel { dist_t.Resize({m + 1, n + 1}); dist_t.mutable_data(ctx.GetPlace()); auto dist = dist_t.data(); - auto x1 = x1_t->data() + hyp_lod[num]; - auto x2 = x2_t->data() + ref_lod[num]; + auto x1 = x1_t->data() + hyp_lod[num]; + auto x2 = x2_t->data() + ref_lod[num]; for (int64_t i = 0; i < m + 1; ++i) { dist[i * (n + 1)] = i; } -- GitLab