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[API Compatibility] paddle.sigmoid sink into C++ (#74802)
* feat(api sink): support paddle.sigmoid * feat(api sink): support paddle.sigmoid * feat(api sink): fix sigmoid doc
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‎paddle/phi/ops/yaml/ops.yaml‎

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- op : sigmoid
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args : (Tensor x)
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python_api:
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name : [paddle.sigmoid,paddle.Tensor.sigmoid,paddle.nn.functional.sigmoid]
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args_alias:
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use_default_mapping : True
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output : Tensor
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infer_meta :
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func : UnchangedInferMeta

‎python/paddle/_paddle_docs.py‎

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# shenwei
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add_doc_and_signature(
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"sigmoid",
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r"""
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Sigmoid Activation.
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.. math::
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out = \\frac{1}{1 + e^{-x}}
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Args:
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x (Tensor): Input of Sigmoid operator, an N-D Tensor, with data type bfloat16, float16, float32, float64,
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uint8, int8, int16, int32, int64, complex64 or complex128.
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name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
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Keyword Args:
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out (Tensor|optional): The output tensor.
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Returns:
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Tensor. Output of Sigmoid operator, a Tensor with shape same as input
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(integer types are autocasted into float32).
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Examples:
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.. code-block:: python
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>>> import paddle
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>>> import paddle.nn.functional as F
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>>> x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
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>>> out = F.sigmoid(x)
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>>> print(out)
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Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True,
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[0.40131235, 0.45016602, 0.52497917, 0.57444251])
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""",
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"""
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def sigmoid(
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x: paddle.Tensor,
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name: str | None = None,
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*,
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out: Tensor | None = None,
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) -> paddle.Tensor
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""",
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)
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# zhouxin
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# hehongyu

‎test/legacy_test/test_sigmoid.py‎

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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from op_test import get_places
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import paddle
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from paddle import base
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class TestSigmoidAPI_Compatibility(unittest.TestCase):
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def setUp(self):
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np.random.seed(123)
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paddle.enable_static()
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self.places = get_places()
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self.init_data()
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def init_data(self):
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self.shape = [10, 15]
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self.dtype = "float32"
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self.np_input = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
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def ref_forward(self, x):
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return 1 / (1 + np.exp(-x))
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def test_dygraph_Compatibility(self):
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paddle.disable_static()
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x = paddle.to_tensor(self.np_input)
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paddle_dygraph_out = []
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# Position args (args)
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out1 = paddle.sigmoid(x)
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paddle_dygraph_out.append(out1)
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# Key words args (kwargs) for paddle
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out2 = paddle.sigmoid(x=x)
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paddle_dygraph_out.append(out2)
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# Key words args for torch
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out3 = paddle.sigmoid(input=x)
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paddle_dygraph_out.append(out3)
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# Tensor method args
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out4 = x.sigmoid()
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paddle_dygraph_out.append(out4)
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# Test out
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out5 = paddle.empty([])
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paddle.sigmoid(x, out=out5)
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paddle_dygraph_out.append(out5)
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# Reference output
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ref_out = self.ref_forward(self.np_input)
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# Check
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for i in range(len(paddle_dygraph_out)):
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np.testing.assert_allclose(
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ref_out, paddle_dygraph_out[i].numpy(), rtol=1e-05
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)
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paddle.enable_static()
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def test_static_Compatibility(self):
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main = paddle.static.Program()
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startup = paddle.static.Program()
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with base.program_guard(main, startup):
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x = paddle.static.data(name="x", shape=self.shape, dtype=self.dtype)
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# Position args (args)
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out1 = paddle.sigmoid(x)
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# Key words args (kwargs) for paddle
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out2 = paddle.sigmoid(x=x)
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# Key words args for torch
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out3 = paddle.sigmoid(input=x)
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# Tensor method args
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out4 = x.sigmoid()
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exe = base.Executor(paddle.CPUPlace())
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fetches = exe.run(
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main,
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feed={"x": self.np_input},
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fetch_list=[out1, out2, out3, out4],
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)
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ref_out = self.ref_forward(self.np_input)
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for i in range(len(fetches)):
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np.testing.assert_allclose(fetches[i], ref_out, rtol=1e-05)
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class TestTensorSigmoidAPI_Compatibility(unittest.TestCase):
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def setUp(self):
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np.random.seed(123)
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paddle.enable_static()
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self.places = get_places()
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self.init_data()
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def init_data(self):
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self.shape = [10, 15]
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self.dtype = "float32"
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self.np_input = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
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def ref_forward(self, x):
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return 1 / (1 + np.exp(-x))
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def test_dygraph_Compatibility(self):
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paddle.disable_static()
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x = paddle.to_tensor(self.np_input)
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paddle_dygraph_out = []
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# Position args (args)
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out1 = paddle.Tensor.sigmoid(x)
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paddle_dygraph_out.append(out1)
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# Key words args (kwargs) for paddle
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out2 = paddle.Tensor.sigmoid(x=x)
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paddle_dygraph_out.append(out2)
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# Key words args for torch
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out3 = paddle.Tensor.sigmoid(input=x)
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paddle_dygraph_out.append(out3)
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# Tensor method args
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out4 = x.sigmoid()
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paddle_dygraph_out.append(out4)
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# Test out
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out5 = paddle.empty([])
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paddle.Tensor.sigmoid(x, out=out5)
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paddle_dygraph_out.append(out5)
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# Reference output
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ref_out = self.ref_forward(self.np_input)
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# Check
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for i in range(len(paddle_dygraph_out)):
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np.testing.assert_allclose(
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ref_out, paddle_dygraph_out[i].numpy(), rtol=1e-05
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)
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paddle.enable_static()
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def test_static_Compatibility(self):
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main = paddle.static.Program()
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startup = paddle.static.Program()
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with base.program_guard(main, startup):
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x = paddle.static.data(name="x", shape=self.shape, dtype=self.dtype)
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# Position args (args)
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out1 = paddle.Tensor.sigmoid(x)
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# Key words args (kwargs) for paddle
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out2 = paddle.Tensor.sigmoid(x=x)
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# Key words args for torch
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out3 = paddle.Tensor.sigmoid(input=x)
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# Tensor method args
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out4 = x.sigmoid()
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exe = base.Executor(paddle.CPUPlace())
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fetches = exe.run(
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main,
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feed={"x": self.np_input},
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fetch_list=[out1, out2, out3, out4],
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)
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ref_out = self.ref_forward(self.np_input)
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for i in range(len(fetches)):
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np.testing.assert_allclose(fetches[i], ref_out, rtol=1e-05)
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if __name__ == '__main__':
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unittest.main()

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