[API Compatibility No.334-335] Add parameter alias support for i1、i1e -part

paddle/phi/ops/yaml/python_api_info.yaml

@@ -205,6 +205,16 @@
     x : [input]
     y : [values]
 
+- op : i1
+  name : [paddle.i1,paddle.Tensor.i1]
+  args_alias :
+    use_default_mapping : True
+
+- op : i1e
+  name : [paddle.i1e,paddle.Tensor.i1e]
+  args_alias :
+    use_default_mapping : True
+
 - op : index_put
   name : [paddle.index_put, paddle.Tensor.index_put]
   args_alias :

python/paddle/_paddle_docs.py

@@ -4124,3 +4124,72 @@ def conj(
 ) -> Tensor
 """,
 )
+
+add_doc_and_signature(
+    "i1",
+    """
+    The function is used to calculate modified bessel function of order 1.
+
+    Args:
+        x (Tensor): The input tensor, it's data type should be float32, float64,
+            uint8, int8, int16, int32, int64.
+        name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
+
+    Returns:
+        - out (Tensor), A Tensor. the value of the modified bessel function of order 1 at x
+            (integer types are autocasted into float32).
+
+    Examples:
+        .. code-block:: python
+
+            >>> import paddle
+
+            >>> x = paddle.to_tensor([0, 1, 2, 3, 4], dtype="float32")
+            >>> print(paddle.i1(x))
+            Tensor(shape=[5], dtype=float32, place=Place(cpu), stop_gradient=True,
+            [0.        , 0.56515908, 1.59063685, 3.95337057, 9.75946712])
+""",
+    """
+def i1(
+    x: Tensor,
+    name: str | None = None,
+    *,
+    out: Tensor | None = None
+) -> Tensor
+""",
+)
+
+add_doc_and_signature(
+    "i1e",
+    """
+    The function is used to calculate exponentially scaled modified Bessel function of order 1.
+
+    Args:
+
+        x (Tensor): The input tensor, it's data type should be float32, float64,
+            uint8, int8, int16, int32, int64.
+        name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
+
+    Returns:
+        - out (Tensor), A Tensor. the value of the exponentially scaled modified Bessel function of order 1 at x
+            (integer types are autocasted into float32).
+
+    Examples:
+        .. code-block:: python
+
+            >>> import paddle
+
+            >>> x = paddle.to_tensor([0, 1, 2, 3, 4], dtype="float32")
+            >>> print(paddle.i1e(x))
+            Tensor(shape=[5], dtype=float32, place=Place(cpu), stop_gradient=True,
+            [0.        , 0.20791042, 0.21526928, 0.19682673, 0.17875087])
+""",
+    """
+def i1e(
+    x: Tensor,
+    name: str | None = None,
+    *,
+    out: Tensor | None = None
+) -> Tensor
+""",
+)

python/paddle/special.py

@@ -13,10 +13,12 @@
 # limitations under the License.
 
 from .tensor.compat_softmax import softmax
-from .tensor.math import logsumexp
+from .tensor.math import i1, i1e, logsumexp
 from .tensor.ops import expm1
 
 __all__ = [
+    "i1",
+    "i1e",
     "logsumexp",
     "softmax",
     "expm1",

python/paddle/tensor/math.py

@@ -34,6 +34,8 @@
     fmax,
     fmin,
     heaviside,
+    i1,
+    i1e,
     isfinite,
     isinf,
     isnan,
@@ -6288,89 +6290,6 @@ def i0e(x: Tensor, name: str | None = None) -> Tensor:
     return out
 
 
-def i1(x: Tensor, name: str | None = None) -> Tensor:
-    """
-    The function is used to calculate modified bessel function of order 1.
-
-    Args:
-        x (Tensor): The input tensor, it's data type should be float32, float64,
-            uint8, int8, int16, int32, int64.
-        name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
-
-    Returns:
-        - out (Tensor), A Tensor. the value of the modified bessel function of order 1 at x
-            (integer types are autocasted into float32).
-
-    Examples:
-        .. code-block:: python
-
-            >>> import paddle
-
-            >>> x = paddle.to_tensor([0, 1, 2, 3, 4], dtype="float32")
-            >>> print(paddle.i1(x))
-            Tensor(shape=[5], dtype=float32, place=Place(cpu), stop_gradient=True,
-            [0.        , 0.56515908, 1.59063685, 3.95337057, 9.75946712])
-    """
-    if in_dynamic_or_pir_mode():
-        return _C_ops.i1(x)
-    else:
-        check_variable_and_dtype(
-            x,
-            "x",
-            ["float32", "float64", "uint8", "int8", "int16", "int32", "int64"],
-            "i1",
-        )
-
-        helper = LayerHelper("i1", **locals())
-        out = helper.create_variable_for_type_inference(dtype=x.dtype)
-        helper.append_op(
-            type='i1', inputs={'x': x}, outputs={'out': out}, attrs={}
-        )
-    return out
-
-
-def i1e(x: Tensor, name: str | None = None) -> Tensor:
-    """
-    The function is used to calculate exponentially scaled modified Bessel function of order 1.
-
-    Args:
-
-        x (Tensor): The input tensor, it's data type should be float32, float64,
-            uint8, int8, int16, int32, int64.
-        name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
-
-    Returns:
-        - out (Tensor), A Tensor. the value of the exponentially scaled modified Bessel function of order 1 at x
-            (integer types are autocasted into float32).
-
-    Examples:
-        .. code-block:: python
-
-            >>> import paddle
-
-            >>> x = paddle.to_tensor([0, 1, 2, 3, 4], dtype="float32")
-            >>> print(paddle.i1e(x))
-            Tensor(shape=[5], dtype=float32, place=Place(cpu), stop_gradient=True,
-            [0.        , 0.20791042, 0.21526928, 0.19682673, 0.17875087])
-    """
-    if in_dynamic_or_pir_mode():
-        return _C_ops.i1e(x)
-    else:
-        check_variable_and_dtype(
-            x,
-            "x",
-            ["float32", "float64", "uint8", "int8", "int16", "int32", "int64"],
-            "i1e",
-        )
-
-        helper = LayerHelper("i1e", **locals())
-        out = helper.create_variable_for_type_inference(dtype=x.dtype)
-        helper.append_op(
-            type='i1e', inputs={'x': x}, outputs={'out': out}, attrs={}
-        )
-    return out
-
-
 def polygamma(x: Tensor, n: int, name: str | None = None) -> Tensor:
     r"""
     Calculates the polygamma of the given input tensor, element-wise.

test/legacy_test/test_i1_op.py

@@ -166,5 +166,75 @@ def test_check_grad(self):
         self.check_grad(['x'], 'out')
 
 
+class TestI1API_Compatibility(unittest.TestCase):
+    DTYPE = "float64"
+    DATA = [0, 1, 2, 3, 4, 5]
+
+    def setUp(self):
+        self.x = np.array(self.DATA).astype(self.DTYPE)
+        self.place = get_places()
+
+    def test_dygraph_Compatibility(self):
+        def run(place):
+            paddle.disable_static(place)
+            x = paddle.to_tensor(self.x)
+            paddle_dygraph_out = []
+            # Position args (args)
+            out1 = paddle.i1(x)
+            paddle_dygraph_out.append(out1)
+            # Key words args (kwargs) for paddle
+            out2 = paddle.i1(x=x)
+            paddle_dygraph_out.append(out2)
+            # Key words args for torch
+            out3 = paddle.i1(input=x)
+            paddle_dygraph_out.append(out3)
+            # Tensor method kwargs
+            out4 = x.i1()
+            paddle_dygraph_out.append(out4)
+            # Test out
+            out5 = paddle.empty([])
+            paddle.i1(x, out=out5)
+            paddle_dygraph_out.append(out5)
+            # scipy reference  out
+            ref_out = reference_i1(self.x)
+            # Check
+            for out in paddle_dygraph_out:
+                np.testing.assert_allclose(out.numpy(), ref_out, rtol=1e-5)
+            paddle.enable_static()
+
+        for place in self.place:
+            run(place)
+
+    def test_static_Compatibility(self):
+        def run(place):
+            paddle.enable_static()
+            with paddle.static.program_guard(paddle.static.Program()):
+                x = paddle.static.data(
+                    name="x", shape=self.x.shape, dtype=self.DTYPE
+                )
+                # Position args (args)
+                out1 = paddle.i1(x)
+                # Key words args (kwargs) for paddle
+                out2 = paddle.i1(x=x)
+                # Key words args for torch
+                out3 = paddle.i1(input=x)
+                # Tensor method args
+                out4 = x.i1()
+
+                exe = paddle.static.Executor(place)
+                fetches = exe.run(
+                    paddle.static.default_main_program(),
+                    feed={"x": self.x},
+                    fetch_list=[out1, out2, out3, out4],
+                )
+                ref_out = reference_i1(self.x)
+                for out in fetches:
+                    np.testing.assert_allclose(out, ref_out, rtol=1e-5)
+            paddle.disable_static()
+
+        for place in self.place:
+            run(place)
+
+
 if __name__ == "__main__":
     unittest.main()

test/legacy_test/test_i1e_op.py

@@ -166,5 +166,75 @@ def test_check_grad(self):
         self.check_grad(['x'], 'out')
 
 
+class TestI1EAPI_Compatibility(unittest.TestCase):
+    DTYPE = "float64"
+    DATA = [0, 1, 2, 3, 4, 5]
+
+    def setUp(self):
+        self.x = np.array(self.DATA).astype(self.DTYPE)
+        self.place = get_places()
+
+    def test_dygraph_Compatibility(self):
+        def run(place):
+            paddle.disable_static(place)
+            x = paddle.to_tensor(self.x)
+            paddle_dygraph_out = []
+            # Position args (args)
+            out1 = paddle.i1e(x)
+            paddle_dygraph_out.append(out1)
+            # Key words args (kwargs) for paddle
+            out2 = paddle.i1e(x=x)
+            paddle_dygraph_out.append(out2)
+            # Key words args for torch
+            out3 = paddle.i1e(input=x)
+            paddle_dygraph_out.append(out3)
+            # Tensor method kwargs
+            out4 = x.i1e()
+            paddle_dygraph_out.append(out4)
+            # Test out
+            out5 = paddle.empty([])
+            paddle.i1e(x, out=out5)
+            paddle_dygraph_out.append(out5)
+            # scipy reference  out
+            ref_out = reference_i1e(self.x)
+            # Check
+            for out in paddle_dygraph_out:
+                np.testing.assert_allclose(out.numpy(), ref_out, rtol=1e-5)
+            paddle.enable_static()
+
+        for place in self.place:
+            run(place)
+
+    def test_static_Compatibility(self):
+        def run(place):
+            paddle.enable_static()
+            with paddle.static.program_guard(paddle.static.Program()):
+                x = paddle.static.data(
+                    name="x", shape=self.x.shape, dtype=self.DTYPE
+                )
+                # Position args (args)
+                out1 = paddle.i1e(x)
+                # Key words args (kwargs) for paddle
+                out2 = paddle.i1e(x=x)
+                # Key words args for torch
+                out3 = paddle.i1e(input=x)
+                # Tensor method args
+                out4 = x.i1e()
+
+                exe = paddle.static.Executor(place)
+                fetches = exe.run(
+                    paddle.static.default_main_program(),
+                    feed={"x": self.x},
+                    fetch_list=[out1, out2, out3, out4],
+                )
+                ref_out = reference_i1e(self.x)
+                for out in fetches:
+                    np.testing.assert_allclose(out, ref_out, rtol=1e-5)
+            paddle.disable_static()
+
+        for place in self.place:
+            run(place)
+
+
 if __name__ == "__main__":
     unittest.main()