diff --git a/mindspore/nn/layer/conv.py b/mindspore/nn/layer/conv.py
index c57149aea8..6771a646c2 100644
--- a/mindspore/nn/layer/conv.py
+++ b/mindspore/nn/layer/conv.py
@@ -13,6 +13,7 @@
 # limitations under the License.
 # ============================================================================
 """conv"""
+
 import numpy as np
 from mindspore import log as logger
 from mindspore.ops import operations as P
@@ -20,7 +21,7 @@ from mindspore.ops.primitive import constexpr
 from mindspore.common.parameter import Parameter
 from mindspore.common.initializer import initializer
 from mindspore.common.tensor import Tensor
-from mindspore._checkparam import ParamValidator as validator, Rel
+from mindspore._checkparam import Rel
 from mindspore._checkparam import Validator
 from mindspore._checkparam import check_bool, twice, check_int_positive
 from mindspore._extends import cell_attr_register
@@ -807,8 +808,7 @@ class DepthwiseConv2d(Cell):
     filter and :math:`out_{j}` corresponds to the :math:`j`-th channel of the output. :math:`W_{ij}` is a slice
     of kernel and it has shape :math:`(\text{ks_h}, \text{ks_w})`, where :math:`\text{ks_h}` and
     :math:`\text{ks_w}` are the height and width of the convolution kernel. The full kernel has shape
-    :math:`(C_{out}, C_{in} // \text{group}, \text{ks_h}, \text{ks_w})`, where group is the group number
-    to split the input in the channel dimension.
+    :math:`(C_{out}, C_{in}, \text{ks_h}, \text{ks_w})` to split the input in the channel dimension.
 
     If the 'pad_mode' is set to be "valid", the output height and width will be
     :math:`\left \lfloor{1 + \frac{H_{in} + 2 \times \text{padding} - \text{ks_h} -
@@ -851,8 +851,6 @@ class DepthwiseConv2d(Cell):
                                       be :math:`k - 1` pixels skipped for each sampling location. Its value should
                                       be greater than or equal to 1 and bounded by the height and width of the
                                       input. Default: 1.
-        group (int): Split filter into groups, `in_ channels` and `out_channels` should be
-            divisible by the number of groups. Default: 1.
         has_bias (bool): Specifies whether the layer uses a bias vector. Default: False.
         weight_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the convolution kernel.
             It can be a Tensor, a string, an Initializer or a number. When a string is specified,
@@ -884,7 +882,6 @@ class DepthwiseConv2d(Cell):
                  pad_mode='same',
                  padding=0,
                  dilation=1,
-                 group=1,
                  has_bias=False,
                  weight_init='normal',
                  bias_init='zeros'):
@@ -894,13 +891,9 @@ class DepthwiseConv2d(Cell):
         self.dilation = twice(dilation)
         self.in_channels = check_int_positive(in_channels)
         self.out_channels = check_int_positive(out_channels)
-        validator.check_integer('group', group, in_channels, Rel.EQ)
-        validator.check_integer('group', group, out_channels, Rel.EQ)
-        validator.check_integer('group', group, 1, Rel.GE)
         self.pad_mode = pad_mode
         self.padding = padding
         self.dilation = dilation
-        self.group = group
         self.has_bias = has_bias
         self.weight_init = weight_init
         self.bias_init = bias_init
@@ -928,10 +921,10 @@ class DepthwiseConv2d(Cell):
 
     def extend_repr(self):
         s = 'input_channels={}, output_channels={}, kernel_size={}, stride={}, ' \
-            'pad_mode={}, padding={}, dilation={}, group={},' \
+            'pad_mode={}, padding={}, dilation={}' \
             'has_bias={}, weight_init={}, bias_init={}'.format(
                 self.in_channels, self.out_channels, self.kernel_size, self.stride,
-                self.pad_mode, self.padding, self.dilation, self.group,
+                self.pad_mode, self.padding, self.dilation,
                 self.has_bias, self.weight_init, self.bias_init)
 
         if self.has_bias: