diff --git a/test/legacy_test/test_unfold_op.py b/test/legacy_test/test_unfold_op.py
index 111a153766df1e..7c8bdfbd904e22 100644
--- a/test/legacy_test/test_unfold_op.py
+++ b/test/legacy_test/test_unfold_op.py
@@ -161,6 +161,33 @@ def init_dtype(self):
         self.dtype = np.float16
 
 
+class TestUnfoldZeroSize(TestUnfoldOp):
+    """
+    This is for test on unfold Op with zero size input
+    """
+
+    def init_data(self):
+        self.batch_size = 3
+        self.input_channels = 0
+        self.input_height = 20
+        self.input_width = 20
+        self.kernel_sizes = [3, 3]
+        self.strides = [1, 1]
+        self.paddings = [1, 1, 1, 1]
+        self.dilations = [1, 1]
+        input_shape = [
+            self.batch_size,
+            self.input_channels,
+            self.input_height,
+            self.input_width,
+        ]
+        if self.dtype == np.uint16:
+            as_type = self.np_dtype
+        else:
+            as_type = self.dtype
+        self.x = np.random.rand(*input_shape).astype(as_type)
+
+
 @unittest.skipIf(
     not core.is_compiled_with_cuda()
     or not core.is_bfloat16_supported(core.CUDAPlace(0)),
diff --git a/test/xpu/test_unfold_op_xpu.py b/test/xpu/test_unfold_op_xpu.py
index d409167ecb0ea9..1d16b5d4bfa2bb 100644
--- a/test/xpu/test_unfold_op_xpu.py
+++ b/test/xpu/test_unfold_op_xpu.py
@@ -170,6 +170,29 @@ def test_dygraph(self):
         def test_info(self):
             str(paddle.nn.Unfold(**self.attrs))
 
+    class TestUnfoldZeroSize(TestUnfoldOp):
+        """
+        This is for test on unfold Op with zero size input
+        """
+
+        def init_data(self):
+            self.batch_size = 3
+            self.input_channels = 0
+            self.input_height = 20
+            self.input_width = 20
+
+            self.kernel_sizes = [2, 2]
+            self.strides = [1, 1]
+            self.paddings = [1, 1, 1, 1]
+            self.dilations = [1, 1]
+            input_shape = [
+                self.batch_size,
+                self.input_channels,
+                self.input_height,
+                self.input_width,
+            ]
+            self.x = np.random.rand(*input_shape).astype(self.dtype)
+
 
 support_types = get_xpu_op_support_types('unfold')
 for stype in support_types: