diff --git a/mmcv/ops/deform_conv.py b/mmcv/ops/deform_conv.py
index 73472dc9b1..6db4ddd2f6 100644
--- a/mmcv/ops/deform_conv.py
+++ b/mmcv/ops/deform_conv.py
@@ -54,7 +54,7 @@ def _npu_backward(ctx, grad_output):
         grad_input, grad_weight, grad_offset_all, grad_bias = \
             torch.npu_deformable_conv2dbk(
                 input_tensor, grad_output, offset_out, weight, offset_all,
-                kernel_size=[weight.shape[3], weight.shape[2]],
+                kernel_size=[weight.shape[2], weight.shape[3]],
                 stride=[1, 1, ctx.stride[0], ctx.stride[1]],
                 padding=[ctx.padding[0], ctx.padding[0], ctx.padding[1],
                          ctx.padding[1]],
diff --git a/mmcv/ops/masked_conv.py b/mmcv/ops/masked_conv.py
index ed0389c7f1..9ab724af8c 100644
--- a/mmcv/ops/masked_conv.py
+++ b/mmcv/ops/masked_conv.py
@@ -42,7 +42,7 @@ def forward(ctx,
         stride_h, stride_w = _pair(stride)
         if stride_h != 1 or stride_w != 1:
             raise ValueError(
-                'Stride could not only be 1 in masked_conv2d currently.')
+                'Stride could only be 1 in masked_conv2d currently.')
         out_channel, in_channel, kernel_h, kernel_w = weight.size()
 
         if features.device.type == 'npu':
diff --git a/mmcv/ops/modulated_deform_conv.py b/mmcv/ops/modulated_deform_conv.py
index 4c735e2a09..f66822771d 100644
--- a/mmcv/ops/modulated_deform_conv.py
+++ b/mmcv/ops/modulated_deform_conv.py
@@ -53,7 +53,7 @@ def _npu_forward(ctx, input_tensor, offset, mask, weight, bias):
         conv2d_bias = bias if len(bias) > 0 else None
         sort_index_fp, sort_index_bp = \
             ModulatedDeformConv2dFunction._calculate_sort_index(
-                kernel_w, kernel_h, ctx.deform_groups)
+                kernel_h, kernel_w, ctx.deform_groups)
         select_offset = offset.index_select(1, sort_index_fp)
         offset_all = torch.cat([select_offset, mask], dim=1)
         output, offset_out = torch.npu_deformable_conv2d(
@@ -61,7 +61,7 @@ def _npu_forward(ctx, input_tensor, offset, mask, weight, bias):
             weight,
             offset_all,
             conv2d_bias,
-            kernel_size=[kernel_w, kernel_h],
+            kernel_size=[kernel_h, kernel_w],
             stride=[1, 1, ctx.stride[0], ctx.stride[1]],
             padding=[
                 ctx.padding[0], ctx.padding[0], ctx.padding[1], ctx.padding[1]
@@ -83,7 +83,7 @@ def _npu_backward(ctx, grad_output):
         grad_input, grad_weight, grad_offset_all, grad_bias = \
             torch.npu_deformable_conv2dbk(
                 input_tensor, grad_output, offset_out, weight, offset_all,
-                kernel_size=[weight.shape[3], weight.shape[2]],
+                kernel_size=[weight.shape[2], weight.shape[3]],
                 stride=[1, 1, ctx.stride[0], ctx.stride[1]],
                 padding=[ctx.padding[0], ctx.padding[0], ctx.padding[1],
                          ctx.padding[1]],