add logaddexp

zhiboniu · zhiboniu · commit ddd729ad0dd8 · 2023-04-27T11:29:34.000Z
diff --git a/python/paddle/__init__.py b/python/paddle/__init__.py
@@ -258,6 +258,7 @@
 from .tensor.math import add  # noqa: F401
 from .tensor.math import subtract  # noqa: F401
 from .tensor.math import logsumexp  # noqa: F401
+from .tensor.math import logaddexp  # noqa: F401
 from .tensor.math import inverse  # noqa: F401
 from .tensor.math import log1p  # noqa: F401
 from .tensor.math import erf  # noqa: F401
diff --git a/python/paddle/fluid/tests/unittests/test_logaddexp.py b/python/paddle/fluid/tests/unittests/test_logaddexp.py
@@ -0,0 +1,73 @@
+#  Copyright (c) 2020 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.
+
+import unittest
+
+import numpy as np
+
+import paddle
+
+
+def ref_logaddexp_old(x, y):
+    y = np.broadcast_to(y, x.shape)
+    out = np.log(1 + np.exp(np.minimum(x, y) - np.maximum(x, y))) + np.maximum(
+        x, y
+    )
+    return out
+
+
+def ref_logaddexp(x, y):
+    return np.logaddexp(x, y)
+
+
+class TestLogsumexpAPI(unittest.TestCase):
+    def setUp(self):
+        self.place = (
+            paddle.CUDAPlace(0)
+            if paddle.fluid.core.is_compiled_with_cuda()
+            else paddle.CPUPlace()
+        )
+
+    def api_case(self):
+        self.x = np.random.uniform(-1, 1, self.xshape).astype(self.dtype)
+        self.y = np.random.uniform(-1, 1, self.yshape).astype(self.dtype)
+        out_ref = ref_logaddexp(self.x, self.y)
+
+        # paddle.disable_static(self.place)
+        x = paddle.to_tensor(self.x)
+        y = paddle.to_tensor(self.y)
+        out = paddle.logaddexp(x, y)
+        np.testing.assert_allclose(out.numpy(), out_ref, atol=1e-06)
+
+    def test_api(self):
+        self.xshape = [1, 2, 3, 4]
+        self.yshape = [1, 2, 3, 4]
+        self.dtype = np.float64
+        self.api_case()
+
+    def test_api_broadcast(self):
+        self.xshape = [1, 2, 3, 4]
+        self.yshape = [1, 2, 3, 1]
+        self.dtype = np.float32
+        self.api_case()
+
+    def test_api_bigdata(self):
+        self.xshape = [10, 200, 300]
+        self.yshape = [10, 200, 300]
+        self.dtype = np.float32
+        self.api_case()
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/python/paddle/tensor/__init__.py b/python/paddle/tensor/__init__.py
@@ -201,6 +201,7 @@
 from .math import subtract_  # noqa: F401
 from .math import atan2  # noqa: F401
 from .math import logsumexp  # noqa: F401
+from .math import logaddexp  # noqa: F401
 from .math import inverse  # noqa: F401
 from .math import log2  # noqa: F401
 from .math import log10  # noqa: F401
diff --git a/python/paddle/tensor/math.py b/python/paddle/tensor/math.py
@@ -627,6 +627,64 @@ def add_(x, y, name=None):
     return _C_ops.add_(x, y)
 
 
+def logaddexp(x, y, name=None):
+    """
+    Elementwise LogAddExp Operator.
+    Add of exponentiations of the inputs
+    The equation is:
+
+    ..  math::
+
+        Out=log(X.exp()+Y.exp())
+
+    $X$ the tensor of any dimension.
+    $Y$ the tensor whose dimensions must be less than or equal to the dimensions of $X$.
+
+    There are two cases for this operator:
+
+    1. The shape of $Y$ is the same with $X$.
+    2. The shape of $Y$ is a continuous subsequence of $X$.
+
+    For case 2:
+
+    1. Broadcast $Y$ to match the shape of $X$, where axis is the start dimension index for broadcasting $Y$ onto $X$.
+    2. If $axis$ is -1 (default), $axis$=rank($X$)-rank($Y$).
+    3. The trailing dimensions of size 1 for $Y$ will be ignored for the consideration of subsequence, such as shape($Y$) = (2, 1) => (2).
+
+        For example:
+
+        ..  code-block:: python
+
+            shape(X) = (2, 3, 4, 5), shape(Y) = (,)
+            shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
+            shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5), with axis=-1(default) or axis=2
+            shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
+            shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
+            shape(X) = (2, 3, 4, 5), shape(Y) = (2, 1), with axis=0
+
+    Args:
+        x (Tensor): Tensor or LoDTensor of any dimensions. Its dtype should be float32, float64, float16.
+        y (Tensor): Tensor or LoDTensor of any dimensions. Its dtype should be float32, float64, float16.
+        name (string, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
+
+    Returns:
+        N-D Tensor. A location into which the result is stored. It's dimension equals with x.
+
+    Examples:
+
+        ..  code-block:: python
+
+            import paddle
+
+            x = paddle.to_tensor([-1, -2, -3], 'float64')
+            y = paddle.to_tensor([-1], 'float64')
+            z = paddle.logaddexp(x, y)
+            print(z)  # [-0.30685282, -0.68673831, -0.87307199]
+    """
+
+    return paddle.log1p(paddle.exp(-paddle.abs(x - y))) + paddle.maximum(x, y)
+
+
 def subtract(x, y, name=None):
     """
     Substract two tensors element-wise. The equation is:
