chore: import upstream snapshot with attribution

python/paddle/distribution/transformed_distribution.py

@@ -0,0 +1,166 @@
+# 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
+
+import typing
+from typing import TYPE_CHECKING
+
+from paddle.distribution import distribution, independent, transform
+from paddle.utils.decorator_utils import param_one_alias
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+    from paddle import Tensor
+    from paddle.distribution.distribution import Distribution
+    from paddle.distribution.transform import Transform
+
+
+class TransformedDistribution(distribution.Distribution):
+    r"""
+    Applies a sequence of Transforms to a base distribution.
+
+    Args:
+        base (Distribution): The base distribution.
+        transforms (Sequence[Transform]): A sequence of ``Transform`` .
+
+    Examples:
+
+        .. code-block:: pycon
+
+            >>> import paddle
+            >>> paddle.seed(2023)
+            >>> from paddle.distribution import transformed_distribution
+
+            >>> d = transformed_distribution.TransformedDistribution(
+            ...     paddle.distribution.Normal(0.0, 1.0),
+            ...     [paddle.distribution.AffineTransform(paddle.to_tensor(1.0), paddle.to_tensor(2.0))],
+            ... )
+
+            >>> # doctest: +SKIP('random sample')
+            >>> print(d.sample([10]))
+            Tensor(shape=[10], dtype=float32, place=Place(cpu), stop_gradient=True,
+                [ 3.22699189,  1.12264419,  0.50283587,  1.83812487, -2.00740123,
+                -2.70338631,  1.26663208,  4.47909021, -0.11529565,  4.32719326])
+            >>> print(d.log_prob(paddle.to_tensor(0.5)))
+            Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
+                -1.64333570)
+            >>> # doctest: -SKIP
+    """
+
+    base: Distribution
+    transforms: Sequence[Transform]
+
+    def __init__(
+        self, base: Distribution, transforms: Sequence[Transform]
+    ) -> None:
+        if not isinstance(base, distribution.Distribution):
+            raise TypeError(
+                f"Expected type of 'base' is Distribution, but got {type(base)}."
+            )
+        if not isinstance(transforms, typing.Sequence):
+            raise TypeError(
+                f"Expected type of 'transforms' is Sequence[Transform] or Chain, but got {type(transforms)}."
+            )
+        if not all(isinstance(t, transform.Transform) for t in transforms):
+            raise TypeError("All element of transforms must be Transform type.")
+
+        chain = transform.ChainTransform(transforms)
+        base_shape = base.batch_shape + base.event_shape
+        self._base = base
+        self._transforms = transforms
+        if not transforms:
+            super().__init__(base.batch_shape, base.event_shape)
+            return
+        if len(base.batch_shape + base.event_shape) < chain._domain.event_rank:
+            raise ValueError(
+                f"'base' needs to have shape with size at least {chain._domain.event_rank}, bug got {len(base_shape)}."
+            )
+        if chain._domain.event_rank > len(base.event_shape):
+            base = independent.Independent(
+                base, chain._domain.event_rank - len(base.event_shape)
+            )
+
+        transformed_shape = chain.forward_shape(
+            base.batch_shape + base.event_shape
+        )
+        transformed_event_rank = chain._codomain.event_rank + max(
+            len(base.event_shape) - chain._domain.event_rank, 0
+        )
+        super().__init__(
+            transformed_shape[
+                : len(transformed_shape) - transformed_event_rank
+            ],
+            transformed_shape[
+                len(transformed_shape) - transformed_event_rank :
+            ],
+        )
+
+    @param_one_alias(["shape", "sample_shape"])
+    def sample(self, shape: Sequence[int] = []) -> Tensor:
+        """Sample from ``TransformedDistribution``.
+
+        Args:
+            shape (Sequence[int], optional): The sample shape. Defaults to [].
+
+        Returns:
+            [Tensor]: The sample result.
+        """
+        x = self._base.sample(shape)
+        for t in self._transforms:
+            x = t.forward(x)
+        return x
+
+    @param_one_alias(["shape", "sample_shape"])
+    def rsample(self, shape: Sequence[int] = []) -> Tensor:
+        """Reparameterized sample from ``TransformedDistribution``.
+
+        Args:
+            shape (Sequence[int], optional): The sample shape. Defaults to [].
+
+        Returns:
+            [Tensor]: The sample result.
+        """
+        x = self._base.rsample(shape)
+        for t in self._transforms:
+            x = t.forward(x)
+        return x
+
+    def log_prob(self, value: Tensor) -> Tensor:
+        """The log probability evaluated at value.
+
+        Args:
+            value (Tensor): The value to be evaluated.
+
+        Returns:
+            Tensor: The log probability.
+        """
+        log_prob = 0.0
+        y = value
+        event_rank = len(self.event_shape)
+        for t in reversed(self._transforms):
+            x = t.inverse(y)
+            event_rank += t._domain.event_rank - t._codomain.event_rank
+            log_prob = log_prob - _sum_rightmost(
+                t.forward_log_det_jacobian(x), event_rank - t._domain.event_rank
+            )
+            y = x
+        log_prob += _sum_rightmost(
+            self._base.log_prob(y), event_rank - len(self._base.event_shape)
+        )
+        return log_prob
+
+
+def _sum_rightmost(value: Tensor, n: int) -> Tensor:
+    return value.sum(list(range(-n, 0))) if n > 0 else value