Change input data type to int64_t

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<int>, 2nd dim. equal to 1) "
+             "(2-D LoDTensor<int64_t>, 2nd dim. equal to 1) "
              "The indices for hypothesis strings.");
     AddInput("Refs",
-             "(2-D LoDTensor<int>, 2nd dim. equal to 1) "
+             "(2-D LoDTensor<int64_t>, 2nd dim. equal to 1) "
              "The indices for reference strings.");
     AddAttr<bool>("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");
   }

paddle/operators/edit_distance_op.cu

@@ -113,8 +113,8 @@ class EditDistanceGPUKernel : public framework::OpKernel<T> {
         dist_t.Resize({m + 1, n + 1});
         dist_t.mutable_data<T>(ctx.GetPlace());
         auto dist = dist_t.data<T>();
-        auto x1 = x1_t->data<int>() + hyp_lod[num];
-        auto x2 = x2_t->data<int>() + ref_lod[num];
+        auto x1 = x1_t->data<int64_t>() + hyp_lod[num];
+        auto x2 = x2_t->data<int64_t>() + ref_lod[num];
 
         FillFirstColumn<T><<<1 + m / PADDLE_CUDA_NUM_THREADS,
                              PADDLE_CUDA_NUM_THREADS, 0, stream>>>(dist, m, n);

paddle/operators/edit_distance_op.h

@@ -60,8 +60,8 @@ class EditDistanceKernel : public framework::OpKernel<T> {
         dist_t.Resize({m + 1, n + 1});
         dist_t.mutable_data<T>(ctx.GetPlace());
         auto dist = dist_t.data<T>();
-        auto x1 = x1_t->data<int>() + hyp_lod[num];
-        auto x2 = x2_t->data<int>() + ref_lod[num];
+        auto x1 = x1_t->data<int64_t>() + hyp_lod[num];
+        auto x2 = x2_t->data<int64_t>() + ref_lod[num];
         for (int64_t i = 0; i < m + 1; ++i) {
           dist[i * (n + 1)] = i;
         }