[API compatibility] torch.pow and torch.Tensor.pow

python/paddle/tensor/math.py

@@ -25,6 +25,7 @@
 from paddle.base.libpaddle import DataType
 from paddle.common_ops_import import VarDesc, dygraph_utils
 from paddle.pir import Value
+from paddle.utils.decorator_utils import ParamAliasDecorator
 from paddle.utils.inplace_utils import inplace_apis_in_dygraph_only
 
 from ..base.data_feeder import (
@@ -517,6 +518,7 @@ def scale_(
  return _C_ops.scale_(x, scale, float(bias), bias_after_scale)
 
 
+@ParamAliasDecorator({"x": ["input"], "y": ["exponent"]})
 def pow(x: Tensor, y: float | Tensor, name: str | None = None) -> Tensor:
  """
  Compute the power of Tensor elements. The equation is:
@@ -529,10 +531,15 @@ def pow(x: Tensor, y: float | Tensor, name: str | None = None) -> Tensor:
 
  .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor
 
+ .. note::
+ Alias Support: The parameter name ``input`` can be used as an alias for ``x``, The parameter name ``exponent`` can be used as an alias for ``y``.
+ For example, ``pow(input=2, exponent=1.1)`` is equivalent to ``pow(x=2, y=1.1)``.
 
  Args:
  x (Tensor): An N-D Tensor, the data type is bfloat16, float16, float32, float64, int32, int64, complex64 or complex128.
+ input: An alias for ``x`` , with identical behavior.
  y (float|int|Tensor): If it is an N-D Tensor, its data type should be the same as `x`.
+ exponent: An alias for ``y`` , with identical behavior.
  name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
 
  Returns:

test/legacy_test/test_pow.py

@@ -251,5 +251,58 @@ def test_power(self):
  self._test_power((0, 0))
 
 
+class TestPowerAPI_Alias(unittest.TestCase):
+ """
+ Test the alias of pow function.
+ ``pow(input=2, exponent=1.1)`` is equivalent to ``pow(x=2, y=1.1)``
+ """
+
+ def setUp(self):
+ self.places = get_devices()
+ self.test_cases = [
+ ([1.0, 2.0, 3.0], [1.1]), # 1D tensor
+ ([[1, 2], [3, 4]], 2), # 2D tensor with scalar exponent
+ (3.0, [2.0]), # Scalar input
+ ]
+
+ def test_powxy(self):
+ for alias_param_1 in ["x", "input"]:
+ for alias_param_2 in ["y", "exponent"]:
+ for place in self.places:
+ paddle.set_device(place)
+ paddle.disable_static(place)
+ for input_data, exp_data in self.test_cases:
+ input_tensor = paddle.to_tensor(input_data)
+ exp_tensor = paddle.to_tensor(exp_data)
+ output_alias = paddle.pow(
+ **{
+ alias_param_1: input_tensor,
+ alias_param_2: exp_tensor,
+ }
+ )
+ output_std = paddle.pow(x=input_tensor, y=exp_tensor)
+ self.assertTrue(
+ paddle.allclose(output_alias, output_std),
+ msg=f"Alias {alias_param_1}/{alias_param_2} failed on {place} with input {input_data}, exp {exp_data}",
+ )
+
+ def test_xpowy(self):
+ for alias_param_2 in ["y", "exponent"]:
+ for place in self.places:
+ paddle.set_device(place)
+ paddle.disable_static(place)
+ for input_data, exp_data in self.test_cases:
+ input_tensor = paddle.to_tensor(input_data)
+ exp_tensor = paddle.to_tensor(exp_data)
+ output_alias = input_tensor.pow(
+ **{alias_param_2: exp_tensor}
+ )
+ output_std = input_tensor.pow(y=exp_tensor)
+ self.assertTrue(
+ paddle.allclose(output_alias, output_std),
+ msg=f"Alias {alias_param_2} failed on {place} with input {input_data}, exp {exp_data}",
+ )
+
+
 if __name__ == '__main__':
  unittest.main()