[API Compatiblity] Support paddle.tanh

paddle/phi/ops/yaml/python_api_info.yaml

@@ -197,6 +197,11 @@
   args_mapper :
     func : ArgSumMapper
 
+- op : tanh
+  name : [paddle.tanh, paddle.Tensor.tanh, paddle.nn.functional.tanh]
+  args_alias:
+    use_default_mapping : True
+
 - op : exp
   name : [paddle.exp, paddle.Tensor.exp]
   args_alias:

python/paddle/_paddle_docs.py

@@ -2314,6 +2314,47 @@ def dot(
 """,
 )
 
+add_doc_and_signature(
+    "tanh",
+    r"""
+
+    Tanh Activation Operator.
+
+    .. math::
+        out = \frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}
+
+    .. note::
+        Alias Support:
+        1. The parameter name ``input`` can be used as an alias for ``x``.
+
+    Args:
+        x (Tensor): Input of Tanh operator, an N-D Tensor, with data type bfloat16, float32, float64,
+            float16, uint8, int8, int16, int32, int64. Alias: ``input``.
+        name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
+        out (Tensor|None, optional): The output tensor. Default: None.
+
+    Returns:
+        Output of Tanh operator, a Tensor with same data type and shape as input
+            (integer types are autocasted into float32).
+
+    Examples:
+        .. code-block:: python
+
+            >>> import paddle
+
+            >>> x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
+            >>> out = paddle.tanh(x)
+            >>> out
+            Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True,
+            [-0.37994900, -0.19737528,  0.09966799,  0.29131261])
+""",
+    """
+def tanh(
+    x: Tensor, *, out: Tensor | None = None, name: str | None = None,
+) -> Tensor
+""",
+)
+
 add_doc_and_signature(
     "exp",
     """
@@ -2331,7 +2372,7 @@ def dot(
         x (Tensor): Input of Exp operator, an N-D Tensor, with data type int32, int64, bfloat16, float16, float32, float64, complex64 or complex128.
             Alias: ``input``.
         name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
-        out (Tensor|None, optional): The output tensor.
+        out (Tensor|None, optional): The output tensor. Default: None.
 
     Returns:
         Tensor. Output of Exp operator, a Tensor with shape same as input.
@@ -2371,7 +2412,7 @@ def exp(
         x (Tensor): Input of Expm1 operator, an N-D Tensor, with data type int32, int64, bfloat16, float16, float32, float64, complex64 or complex128.
             Alias: ``input``.
         name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
-        out (Tensor|None, optional): The output tensor.
+        out (Tensor|None, optional): The output tensor. Default: None.
 
     Returns:
         Tensor. Output of Expm1 operator, a Tensor with shape same as input.
@@ -2494,10 +2535,16 @@ def diagonal(
           out.shape = [4]
           out.data = [1., -1., 3., 1.]
 
+    .. note::
+        Alias Support:
+        1. The parameter name ``input`` can be used as an alias for ``x``.
+
     Args:
         x (Tensor): Input of Round operator, an N-D Tensor, with data type bfloat16, int32, int64, float32, float64, float16, complex64 or complex128.
+            Alias: ``input``.
         decimals(int): Rounded decimal place (default: 0).
         name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
+        out (Tensor|None, optional): The output tensor. Default: None.
 
     Returns:
         Tensor. Output of Round operator, a Tensor with shape same as input.
@@ -2529,12 +2576,15 @@ def round(
 
         out = |x|
 
+    .. note::
+        Alias Support:
+        1. The parameter name ``input`` can be used as an alias for ``x``.
+
     Args:
         x (Tensor): The input Tensor with data type int32, int64, float16, float32, float64, complex64 and complex128.
+            Alias: ``input``.
         name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
-
-    Keyword args:
-        out (Tensor|None, optional): The output tensor.
+        out (Tensor|None, optional): The output tensor. Default: None.
 
     Returns:
         Tensor.A Tensor with the same data type and shape as :math:`x`.

python/paddle/tensor/math.py

@@ -40,6 +40,7 @@
     sign,
     sin,
     sum,
+    tanh,
 )
 from paddle.base.libpaddle import DataType
 from paddle.common_ops_import import VarDesc, dygraph_utils
@@ -4459,60 +4460,6 @@ def prod(
         return out
 
 
-def tanh(x: Tensor, name: str | None = None) -> Tensor:
-    r"""
-    Tanh Activation Operator.
-
-    .. math::
-        out = \frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}
-
-    Args:
-        x (Tensor): Input of Tanh operator, an N-D Tensor, with data type bfloat16, float32, float64,
-            float16, uint8, int8, int16, int32, int64.
-        name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
-
-    Returns:
-        Output of Tanh operator, a Tensor with same data type and shape as input
-            (integer types are autocasted into float32).
-
-    Examples:
-
-        .. code-block:: python
-
-            >>> import paddle
-
-            >>> x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
-            >>> out = paddle.tanh(x)
-            >>> out
-            Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True,
-            [-0.37994900, -0.19737528,  0.09966799,  0.29131261])
-    """
-    if in_dynamic_or_pir_mode():
-        return _C_ops.tanh(x)
-    else:
-        check_variable_and_dtype(
-            x,
-            'x',
-            [
-                'uint16',
-                'float16',
-                'float32',
-                'float64',
-                'uint8',
-                'int8',
-                'int16',
-                'int32',
-                'int64',
-            ],
-            'tanh',
-        )
-        check_type(x, 'x', (Variable), 'tanh')
-        helper = LayerHelper('tanh', **locals())
-        out = helper.create_variable_for_type_inference(x.dtype)
-        helper.append_op(type='tanh', inputs={'X': x}, outputs={'Out': out})
-        return out
-
-
 @inplace_apis_in_dygraph_only
 def tanh_(x: Tensor, name: str | None = None) -> Tensor:
     r"""

test/legacy_test/test_activation_op.py

@@ -6214,6 +6214,7 @@ class TestActivationAPI_Compatibility(unittest.TestCase):
         ("paddle.exp", np.exp, {'min_val': -1.0, 'max_val': 1.0}),
         ("paddle.expm1", np.expm1, {'min_val': -1.0, 'max_val': 1.0}),
         ("paddle.round", np.round, {'min_val': -5.0, 'max_val': 5.0}),
+        ("paddle.tanh", np.tanh, {'min_val': -1.0, 'max_val': 1.0}),
     ]
 
     def setUp(self):

test/legacy_test/test_weight_decay.py

@@ -62,9 +62,9 @@ def bow_net(
     bow = paddle.static.nn.sequence_lod.sequence_pool(
         input=emb, pool_type='sum'
     )
-    bow_tanh = paddle.tanh(bow)
-    fc_1 = paddle.static.nn.fc(x=bow_tanh, size=hid_dim, activation="tanh")
-    fc_2 = paddle.static.nn.fc(x=fc_1, size=hid_dim2, activation="tanh")
+    bow_silu = paddle.nn.functional.silu(bow)
+    fc_1 = paddle.static.nn.fc(x=bow_silu, size=hid_dim, activation="silu")
+    fc_2 = paddle.static.nn.fc(x=fc_1, size=hid_dim2, activation="silu")
     prediction = paddle.static.nn.fc(
         x=[fc_2], size=class_dim, activation="softmax"
     )

test/standalone_executor/test_standalone_custom_event.py

@@ -40,12 +40,12 @@ def build_program():
         matmul_out = data @ weight
         bias = paddle.ones([1024, 2048], dtype='float32', name='bias')
         add_out = paddle.add(matmul_out, bias, name='add_out')
-        # add_out -> [sub] -> sub_out -> [tanh] -> tanh_out
+        # add_out -> [sub] -> sub_out -> [silu] -> silu_out
         sub_out = paddle.subtract(add_out, data, name='sub_out')
-        tanh_out = paddle.tanh(sub_out, name='tanh_out')
+        silu_out = paddle.nn.functional.silu(sub_out, name='silu_out')
         bias_1 = paddle.add(bias, sub_out, name='bias_1')
-        out_before = paddle.tanh(bias_1, name='out_before')
-        out_last = paddle.subtract(tanh_out, data, name='out_last')
+        out_before = paddle.nn.functional.silu(bias_1, name='out_before')
+        out_last = paddle.subtract(silu_out, data, name='out_last')
         out_last2 = out_last @ weight
 
         out = paddle.add(out_before, out_last2, name='out')
@@ -64,9 +64,9 @@ class TestManualEvent(unittest.TestCase):
       |     |           |              |
       |  elementwise_sub(s1)           |
       |     |           |              |
-      |  tanh(s1)     elementwise_add(s1)
+      |  silu(s1)     elementwise_add(s1)
       |     |                  |
-    elementwise_sub(s1)      tanh(s1)
+    elementwise_sub(s1)      silu(s1)
             |                  |
         matmul_v2(s1)          |
                  |             |    ---split prog----

test/standalone_executor/test_standalone_custom_stream.py

@@ -35,9 +35,9 @@ class TestCustomStream(unittest.TestCase):
       |     |           |              |
       |  elementwise_sub(cpu)          |
       |     |           |              |
-      |  tanh(cpu)     elementwise_add(s2)
+      |  silu(cpu)     elementwise_add(s2)
       |     |                  |
-    elementwise_sub(s1)      tanh(s2)
+    elementwise_sub(s1)      silu(s2)
                  |             |
                 elementwise_add(s2)
                         |

test/standalone_executor/test_standalone_executor.py

@@ -44,15 +44,15 @@ def build_program():
             bias = paddle.ones([4, 64], dtype='float32', name='bias')
             add_out = paddle.add(matmul_out, bias, name='add_out')
 
-        # add_out -> [memcpy_d2h] -> add_out' -> [sub] -> sub_out -> [tanh] -> tanh_out
+        # add_out -> [memcpy_d2h] -> add_out' -> [sub] -> sub_out -> [silu] -> silu_out
         with paddle.static.device_guard('cpu'):
             sub_out = paddle.subtract(add_out, data, name='sub_out')
-            tanh_out = paddle.tanh(sub_out, name='tanh_out')
+            silu_out = paddle.nn.functional.silu(sub_out, name='silu_out')
 
         with paddle.static.device_guard('gpu'):
             bias_1 = paddle.add(bias, sub_out, name='bias_1')
-            out_before = paddle.tanh(bias_1, name='out_before')
-            out_last = paddle.subtract(tanh_out, data, name='out_last')
+            out_before = paddle.nn.functional.silu(bias_1, name='out_before')
+            out_last = paddle.subtract(silu_out, data, name='out_last')
 
             out = paddle.add(out_before, out_last, name='out')
             mean = paddle.mean(out, name='mean_out')