diff --git a/python/paddle/fluid/tests/unittests/test_conv2d_op.py b/python/paddle/fluid/tests/unittests/test_conv2d_op.py
index 85bf18c8c84eb72e15bdd6fcd84694490b964e2b..9992efee1b3053fec7515dd7ce063499af22ca16 100644
--- a/python/paddle/fluid/tests/unittests/test_conv2d_op.py
+++ b/python/paddle/fluid/tests/unittests/test_conv2d_op.py
@@ -17,6 +17,7 @@ from __future__ import print_function
 import unittest
 import numpy as np
 
+import paddle
 import paddle.fluid.core as core
 import paddle.fluid as fluid
 from op_test import OpTest
@@ -1328,6 +1329,16 @@ class TestConv2DAPI(unittest.TestCase):
             groups=1,
             data_format="NCHW")
 
+    def test_depthwise_conv2d(self):
+        x_var = paddle.uniform((2, 8, 8, 4), dtype='float32', min=-1., max=1.)
+        conv = paddle.nn.Conv2D(
+            in_channels=4,
+            out_channels=4,
+            kernel_size=(3, 3),
+            groups=4,
+            data_format='NHWC')
+        y_var = conv(x_var)
+
 
 class TestConv2DAPI_Error(unittest.TestCase):
     def test_api(self):
diff --git a/python/paddle/nn/functional/conv.py b/python/paddle/nn/functional/conv.py
index eaa4dc4d4f2cd959f20956a2c463655868a643eb..75dc62e530d0db81ee4126dc76918e2f08713d30 100644
--- a/python/paddle/nn/functional/conv.py
+++ b/python/paddle/nn/functional/conv.py
@@ -110,6 +110,12 @@ def _conv_nd(x,
              use_mkldnn=False,
              name=None):
 
+    # Due to the poor performance of NHWC, we transpose the input to NCHW.
+    origin_format = data_format
+    if origin_format == "NHWC" and op_type == "depthwise_conv2d":
+        x = nn.transpose(x, perm=[0, 3, 1, 2])
+        data_format = "NCHW"
+        channel_dim = 1
     if in_dygraph_mode():
         attrs = ('strides', stride, 'paddings', padding, 'dilations', dilation,
                  'groups', groups, 'use_cudnn', use_cudnn, 'use_mkldnn',
@@ -154,6 +160,9 @@ def _conv_nd(x,
         else:
             out = pre_bias
 
+    if origin_format == "NHWC" and op_type == "depthwise_conv2d":
+        out = nn.transpose(out, perm=[0, 2, 3, 1])
+
     return out