[API compatibility] update paddle group_norm api

python/paddle/nn/functional/norm.py

@@ -681,28 +681,34 @@ def local_response_norm(
     return res
 
 
+@param_two_alias(["x", "input"], ["epsilon", "eps"])
 def group_norm(
     x: Tensor,
     num_groups: int,
-    epsilon: float = 1e-05,
     weight: Tensor | None = None,
     bias: Tensor | None = None,
+    epsilon: float = 1e-05,
     data_format: DataLayout1D | DataLayout2D | DataLayout3D = 'NCHW',
     name: str | None = None,
 ) -> Tensor:
     """
     nn.GroupNorm is recommended.
     For more information, please refer to :ref:`api_paddle_nn_GroupNorm` .
 
+    .. note::
+        Alias Support: The parameter name ``input`` can be used as an alias for ``x``.
+        For example, ``group_norm(input=tensor_x, ...)`` is equivalent to ``group_norm(x=tensor_x, ...)``.
+
     Parameters:
         x(Tensor): Input Tensor with shape: attr:`(batch, num_features, *)`.
+            alias: ``input``.
         num_groups(int): The number of groups that divided from channels.
-        epsilon(float, optional): The small value added to the variance to prevent
-            division by zero. Default: 1e-05.
         weight(Tensor, optional): The weight Tensor of group_norm, with shape: attr:`[num_channels]`.
             Default: None.
         bias(Tensor, optional): The bias Tensor of group_norm, with shape: attr:`[num_channels]`.
             Default: None.
+        epsilon(float, optional): The small value added to the variance to prevent
+            division by zero. Default: 1e-05.
         data_format(str, optional): Specify the input data format. Support "NCL", "NCHW", "NCDHW", "NLC", "NHWC" or "NDHWC". Default: "NCHW".
         name(str|None, optional): Name for the GroupNorm, default is None. For more information, please refer to :ref:`api_guide_Name`..
 

python/paddle/nn/layer/norm.py

@@ -562,9 +562,9 @@ def forward(self, input: Tensor) -> Tensor:
         return group_norm(
             input,
             self._num_groups,
-            self._epsilon,
             self.weight,
             self.bias,
+            self._epsilon,
             self._data_format,
         )
 

test/legacy_test/test_group_norm_op_v2.py

@@ -618,5 +618,56 @@ def test_group_norm_cpu_with_optional_grad_nhwc(self):
             np.testing.assert_equal(dx.numpy(), dx_ref.numpy())
 
 
+class TestGroupNormParam(unittest.TestCase):
+    def setUp(self):
+        np.random.seed(42)
+        self.x_np = np.random.randn(2, 6, 4, 4).astype('float32')
+        self.weight_np = np.random.randn(6).astype('float32')
+        self.bias_np = np.random.randn(6).astype('float32')
+
+    def test_alias_input_for_x(self):
+        """test parameter alias input/x"""
+        x_tensor = paddle.to_tensor(self.x_np)
+        weight_tensor = paddle.to_tensor(self.weight_np)
+        bias_tensor = paddle.to_tensor(self.bias_np)
+
+        out_with_input = paddle.nn.functional.group_norm(
+            input=x_tensor,
+            num_groups=3,
+            weight=weight_tensor,
+            bias=bias_tensor,
+            eps=1e-5,
+        )
+        out_with_x = paddle.nn.functional.group_norm(
+            x=x_tensor,
+            num_groups=3,
+            weight=weight_tensor,
+            bias=bias_tensor,
+            eps=1e-5,
+        )
+
+        np.testing.assert_array_equal(
+            out_with_input.numpy(), out_with_x.numpy()
+        )
+
+    def test_param_order(self):
+        """test order of parameters"""
+        x_tensor = paddle.to_tensor(self.x_np)
+        weight_tensor = paddle.to_tensor(self.weight_np)
+        bias_tensor = paddle.to_tensor(self.bias_np)
+
+        try:
+            out = paddle.nn.functional.group_norm(
+                x_tensor,  # x
+                3,  # num_groups
+                weight_tensor,  # weight
+                bias_tensor,  # bias
+                1e-5,  # epsilon
+            )
+            self.assertTrue(True, "Function call succeeded without error")
+        except Exception as e:
+            self.fail(f"Function raised an unexpected exception: {e}")
+
+
 if __name__ == '__main__':
     unittest.main()