fix some typos

Author: wanghaox

paddle/operators/sequence_slice_op.cc

@@ -90,11 +90,11 @@ class SequenceSliceOpMaker : public framework::OpProtoAndCheckerMaker {
              "a vector<int> to describe the length of every input sequence for "
              "sub sequence item.");
     AddOutput("Out",
-              "(LoDTensor), The output of SequenceSliceOp.");
+              "(LoDTensor), the output of SequenceSliceOp.");
     AddComment(R"DOC(
 Sequence slice operator
 
-The operator crop a subsequence from given sequence with given start offset and subsequence length.
+The operator crops a subsequence from given sequence with given start offset and subsequence length.
 It only supports sequence (LoD Tensor with level number is 1).
 - Case:
     X = [[a1, a2;
@@ -109,7 +109,7 @@ It only supports sequence (LoD Tensor with level number is 1).
             b1, b2]
             [e1, e2]]
     LoD(Out) = {{0, 2, 3}}; Dims(Out) = (3, 2)
-NOTE: The length of the input, offset and length should be the same. The offset start from 0.
+NOTE: The first dimension size of input, the size of offset and Length, should be equal. The offset start from 0.
     )DOC");
   }
 };

paddle/operators/sequence_slice_op.h

@@ -83,7 +83,8 @@ class SequenceSliceOpKernel : public framework::OpKernel<T> {
       PADDLE_ENFORCE_LT(
           lod[0][i] + offset_data[i] + length_data[i],
           lod[0][i + 1],
-          "The target tensor's length overflow")}
+          "The target tensor's length overflow")
+    }
 
     out->mutable_data<T>(ctx.GetPlace());
     auto out_lod = SequenceSliceLoD(*in, offset_data, length_data);
@@ -140,27 +141,29 @@ class SequenceSliceGradOpKernel : public framework::OpKernel<T> {
     auto lod = in->lod();
     auto out_lod = out_grad->lod();
 
-    x_grad->mutable_data<T>(ctx.GetPlace());
-    math::SetConstant<Place, T> set_zero;
-    set_zero(ctx.device_context(), x_grad, static_cast<T>(0));
+    if (x_grad) {
+      x_grad->mutable_data<T>(ctx.GetPlace());
+      math::SetConstant<Place, T> set_zero;
+      set_zero(ctx.device_context(), x_grad, static_cast<T>(0));
 
-    auto out_grad_stride = framework::stride(out_grad->dims());
+      auto out_grad_stride = framework::stride(out_grad->dims());
 
-    for (size_t i = 0; i < out_lod[0].size() - 1; ++i) {
-      Tensor out_grad_t =
-          out_grad->Slice(static_cast<int>(out_lod[0][i]),
-                          static_cast<int>(out_lod[0][i + 1]));
-      auto out_grad_stride = framework::stride(out_grad_t.dims());
+      for (size_t i = 0; i < out_lod[0].size() - 1; ++i) {
+        Tensor out_grad_t =
+            out_grad->Slice(static_cast<int>(out_lod[0][i]),
+                            static_cast<int>(out_lod[0][i + 1]));
+        auto out_grad_stride = framework::stride(out_grad_t.dims());
 
-      auto x_grad_stride = framework::stride(x_grad->dims());
+        auto x_grad_stride = framework::stride(x_grad->dims());
 
-      Tensor x_grad_t = x_grad->Slice(
-          static_cast<int>(lod[0][i] + offset_data[i]),
-          static_cast<int>(lod[0][i] + offset_data[i] + length_data[i]));
+        Tensor x_grad_t = x_grad->Slice(
+            static_cast<int>(lod[0][i] + offset_data[i]),
+            static_cast<int>(lod[0][i] + offset_data[i] + length_data[i]));
 
-      StridedMemcpy<T>(ctx.device_context(), out_grad_t.data<T>(),
-                       out_grad_stride, out_grad_t.dims(), x_grad_stride,
-                       x_grad_t.data<T>());
+        StridedMemcpy<T>(ctx.device_context(), out_grad_t.data<T>(),
+                        out_grad_stride, out_grad_t.dims(), x_grad_stride,
+                        x_grad_t.data<T>());
+      }
     }
   }
 };