chore: import upstream snapshot with attribution

python/paddle/nn/layer/distance.py

@@ -0,0 +1,117 @@
+#   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 annotations
+
+from typing import TYPE_CHECKING
+
+from paddle.utils.decorator_utils import param_one_alias, param_two_alias
+
+from .. import functional as F
+from .layers import Layer
+
+if TYPE_CHECKING:
+    import paddle
+
+__all__ = []
+
+
+class PairwiseDistance(Layer):
+    r"""
+
+    It computes the pairwise distance between two vectors. The
+    distance is calculated by p-order norm:
+
+    .. math::
+
+        \Vert x \Vert _p = \left( \sum_{i=1}^n \vert x_i \vert ^ p \right) ^ {1/p}.
+
+    Parameters:
+        p (float, optional): The order of norm. Default: :math:`2.0`.
+        epsilon (float, optional): Add small value to avoid division by zero.
+            Default: :math:`1e-6`.
+        keepdim (bool, optional): Whether to reserve the reduced dimension
+            in the output Tensor. The result tensor is one dimension less than
+            the result of ``|x-y|`` unless :attr:`keepdim` is True. Default: False.
+        name (str, optional): For details, please refer to :ref:`api_guide_Name`.
+            Generally, no setting is required. Default: None.
+
+    Shape:
+        - x: :math:`[N, D]` or :math:`[D]`, where :math:`N` is batch size, :math:`D`
+          is the dimension of the data. Available data type is float16, float32, float64.
+        - y: :math:`[N, D]` or :math:`[D]`, y have the same dtype as x.
+        - output: The same dtype as input tensor.
+            - If :attr:`keepdim` is True, the output shape is :math:`[N, 1]` or :math:`[1]`,
+              depending on whether the input has data shaped as :math:`[N, D]`.
+            - If :attr:`keepdim` is False, the output shape is :math:`[N]` or :math:`[]`,
+              depending on whether the input has data shaped as :math:`[N, D]`.
+
+    Examples:
+        .. code-block:: pycon
+
+            >>> import paddle
+            >>> x = paddle.to_tensor([[1.0, 3.0], [3.0, 5.0]], dtype=paddle.float64)
+            >>> y = paddle.to_tensor([[5.0, 6.0], [7.0, 8.0]], dtype=paddle.float64)
+            >>> dist = paddle.nn.PairwiseDistance()
+            >>> distance = dist(x, y)
+            >>> print(distance)
+            Tensor(shape=[2], dtype=float64, place=Place(cpu), stop_gradient=True,
+            [4.99999860, 4.99999860])
+    """
+
+    @param_one_alias(["epsilon", "eps"])
+    def __init__(
+        self,
+        p: float = 2.0,
+        epsilon: float = 1e-6,
+        keepdim: bool = False,
+        name: str | None = None,
+    ):
+        super().__init__()
+        self.p = p
+        self.epsilon = epsilon
+        self.keepdim = keepdim
+        self.name = name
+
+    @param_two_alias(["x", "x1"], ["y", "x2"])
+    def forward(self, x: paddle.Tensor, y: paddle.Tensor) -> paddle.Tensor:
+        return F.pairwise_distance(
+            x, y, self.p, self.epsilon, self.keepdim, self.name
+        )
+
+    def extra_repr(self) -> str:
+        main_str = 'p={p}'
+        if self.epsilon != 1e-6:
+            main_str += ', epsilon={epsilon}'
+        if self.keepdim is not False:
+            main_str += ', keepdim={keepdim}'
+        if self.name is not None:
+            main_str += ', name={name}'
+        return main_str.format(**self.__dict__)
+
+    @property
+    def eps(self) -> float:
+        return self.epsilon
+
+    @eps.setter
+    def eps(self, value: float) -> None:
+        self.epsilon = value
+
+    @property
+    def norm(self) -> float:
+        return self.p
+
+    @norm.setter
+    def norm(self, value: float) -> None:
+        self.p = value