【PaddlePaddle Hackathon 3 No.17】为 Paddle 新增 sgn

python/paddle/__init__.py

@@ -276,6 +276,7 @@
 from .tensor.math import outer  # noqa: F401
 from .tensor.math import heaviside  # noqa: F401
 from .tensor.math import frac  # noqa: F401
+from .tensor.math import sgn  # noqa: F401
 
 from .tensor.random import bernoulli  # noqa: F401
 from .tensor.random import poisson  # noqa: F401
@@ -649,4 +650,5 @@
     'put_along_axis',
     'heaviside',
     'tril_indices',
+    'sgn',
 ]

python/paddle/fluid/tests/unittests/test_sgn.py

@@ -0,0 +1,85 @@
+#   Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+import paddle
+
+
+def np_sgn(x: np.ndarray):
+    if x.dtype == 'complex128' or x.dtype == 'complex64':
+        x_abs = np.abs(x)
+        eps = np.finfo(x.dtype).eps
+        x_abs = np.maximum(x_abs, eps)
+        out = x / x_abs
+    else:
+        out = np.sign(x)
+    return out
+
+
+class TestSgnError(unittest.TestCase):
+
+    def test_errors(self):
+        # The input dtype of sgn must be float16, float32, float64,complex64,complex128.
+        input2 = paddle.to_tensor(
+            np.random.randint(-10, 10, size=[12, 20]).astype('int32'))
+        input3 = paddle.to_tensor(
+            np.random.randint(-10, 10, size=[12, 20]).astype('int64'))
+
+        self.assertRaises(TypeError, paddle.sgn, input2)
+        self.assertRaises(TypeError, paddle.sgn, input3)
+
+
+class TestSignAPI(unittest.TestCase):
+
+    def setUp(self) -> None:
+        self.support_dtypes = [
+            'float16', 'float32', 'float64', 'complex64', 'complex128'
+        ]
+        if paddle.device.get_device() == 'cpu':
+            self.support_dtypes = [
+                'float32', 'float64', 'complex64', 'complex128'
+            ]
+
+    def test_dtype(self):
+        for dtype in self.support_dtypes:
+            x = paddle.to_tensor(
+                np.random.randint(-10, 10, size=[12, 20, 2]).astype(dtype))
+
+            paddle.sgn(x)
+
+    def test_complex(self):
+        for dtype in ['complex64', 'complex128']:
+            np_x = np.array([[3 + 4j, 7 - 24j, 0, 1 + 2j], [6 + 8j, 3, 0, -2]],
+                            dtype=dtype)
+            x = paddle.to_tensor(np_x)
+            z = paddle.sgn(x)
+            np_z = z.numpy()
+            z_expected = np_sgn(np_x)
+            self.assertTrue(np.allclose(np_z, z_expected))
+
+    def test_float(self):
+        for dtype in self.support_dtypes:
+            np_x = np.random.randint(-10, 10, size=[12, 20, 2]).astype(dtype)
+            x = paddle.to_tensor(np_x)
+            z = paddle.sgn(x)
+            np_z = z.numpy()
+            z_expected = np_sgn(np_x)
+            self.assertTrue(np.allclose(np_z, z_expected))
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/tensor/__init__.py

@@ -233,6 +233,7 @@
 from .math import outer  # noqa: F401
 from .math import heaviside  # noqa: F401
 from .math import frac  # noqa: F401
+from .math import sgn  # noqa: F401
 
 from .random import multinomial  # noqa: F401
 from .random import standard_normal  # noqa: F401
@@ -505,6 +506,7 @@
     'exponential_',
     'heaviside',
     'bucketize',
+    'sgn',
 ]
 
 #this list used in math_op_patch.py for magic_method bind

python/paddle/tensor/math.py

@@ -4700,3 +4700,45 @@ def frac(x, name=None):
             helper.append_op(
                 type="trunc", inputs=inputs, attrs=attrs, outputs={"Out": y})
             return _elementwise_op(LayerHelper(op_type, **locals()))
+
+
+def sgn(x, name=None):
+    """
+    For complex tensor, this API returns a new tensor whose elements have the same angles as the corresponding
+    elements of input and absolute values of one.
+    For other float dtype tensor,
+    this API returns sign of every element in `x`: 1 for positive, -1 for negative and 0 for zero, same as paddle.sign.
+
+    Args:
+        x (Tensor): The input tensor, which data type should be float16, float32, float64, complex64, complex128.
+        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
+
+    Returns:
+        Tensor: A sign Tensor for real input, or normalized Tensor for complex input, shape and data type are same as input.
+
+    Examples:
+        .. code-block:: Python
+
+            import paddle
+
+            x = paddle.to_tensor([[3 + 4j, 7 - 24j, 0, 1 + 2j], [6 + 8j, 3, 0, -2]])
+            print(paddle.sgn(x))
+            #[[0.6+0.8j       0.28-0.96j      0.+0.j      0.4472136+0.8944272j]
+            # [0.6+0.8j       1.+0.j          0.+0.j      -1.+0.j]]
+
+    """
+    if x.dtype not in [paddle.float16, paddle.float32, paddle.float64, paddle.complex64, paddle.complex128]:
+        raise TypeError(
+            "The data type of input must be one of ['float16', 'float32', 'float64', 'complex64', 'complex128'], but got {}"
+                .format(x.dtype))
+    if paddle.is_complex(x):
+        expand_x = paddle.as_real(x)
+        x_abs = paddle.abs(x)
+        x_abs = paddle.unsqueeze(x_abs, axis=-1)
+        output = expand_x / x_abs
+        zeros = paddle.zeros_like(output)
+        output = paddle.where(paddle.isnan(output), zeros, output)
+
+        return paddle.as_complex(output)
+    else:
+        return paddle.sign(x)