diff --git a/mindspore/ccsrc/backend/kernel_compiler/cpu/tile_cpu_kernel.cc b/mindspore/ccsrc/backend/kernel_compiler/cpu/tile_cpu_kernel.cc
index 1cf9d4835b..f3769ac05c 100644
--- a/mindspore/ccsrc/backend/kernel_compiler/cpu/tile_cpu_kernel.cc
+++ b/mindspore/ccsrc/backend/kernel_compiler/cpu/tile_cpu_kernel.cc
@@ -85,7 +85,7 @@ void TileCPUKernel::LaunchKernel(const std::vector<AddressPtr> &inputs, const st
   std::vector<size_t> cargo_x(d, 1);
   std::vector<size_t> cargo_y = x_shape_;
   for (int i = d - 2; i >= 0; --i) {
-    cargo_x[i] = x_shape_[i + 1] * cargo_x[i];
+    cargo_x[i] = x_shape_[i + 1] * cargo_x[i + 1];
     cargo_y[i] *= cargo_y[i + 1] * multiples_[i + 1];
   }
   size_t offset = 0;
diff --git a/mindspore/ops/_grad/grad_array_ops.py b/mindspore/ops/_grad/grad_array_ops.py
index 73afd6aff0..e48223ffea 100644
--- a/mindspore/ops/_grad/grad_array_ops.py
+++ b/mindspore/ops/_grad/grad_array_ops.py
@@ -380,10 +380,18 @@ def get_bprop_gather_v2(self):
 
 @bprop_getters.register(P.GatherD)
 def get_bprop_gather_d(self):
+    """Generate bprop for GatherD"""
+    gather_d = P.GatherD()
 
     def bprop(x, dim, index, out, dout):
         return P.GatherDGrad(dim)(index, dout)
 
+    def bprop_ascend(x, dim, index, out, dout):
+        return (gather_d(dout, dim, index), zeros_like(dim), zeros_like(index))
+
+    if context.get_context('device_target') == 'Ascend':
+        return bprop_ascend
+
     return bprop
 
 
diff --git a/mindspore/ops/operations/image_ops.py b/mindspore/ops/operations/image_ops.py
index 14545dd11e..e57be1d6ac 100644
--- a/mindspore/ops/operations/image_ops.py
+++ b/mindspore/ops/operations/image_ops.py
@@ -44,9 +44,9 @@ class CropAndResize(PrimitiveWithInfer):
           mapped to [0, image_height - 1] in image height coordinates. We do allow y1 > y2, in which case the sampled
           crop is an up-down flipped version of the original image. The width dimension is treated similarly.
           Normalized coordinates outside the [0, 1] range are allowed, in which case we use extrapolation_value to
-          extrapolate the input image values. Types allowd: float32.
+          extrapolate the input image values. Types allowed: float32.
         - **box_index** (Tensor) - A 1-D tensor of shape [num_boxes] with int32 values in [0, batch).
-          The value of box_ind[i] specifies the image that the i-th box refers to. Types allowd: int32.
+          The value of box_ind[i] specifies the image that the i-th box refers to. Types allowed: int32.
         - **crop_size** (Tuple[int]) - A tuple of two int32 elements: (crop_height, crop_width).
           Only constant value is allowed. All cropped image patches are resized to this size.
           The aspect ratio of the image content is not preserved. Both crop_height and crop_width need to be positive.