chore: import upstream snapshot with attribution

python/paddle/framework/random.py

@@ -0,0 +1,305 @@
+#   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.
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import paddle
+from paddle.base import core
+from paddle.utils.decorator_utils import param_one_alias
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+__all__ = []
+
+
+def seed(seed: int) -> paddle.base.core.Generator:
+    """
+
+    Sets the seed for global default generator, which manages the random number generation.
+
+    Args:
+        seed(int): The random seed to set. It is recommend to set a large int number.
+
+    Returns:
+        Generator: The global default generator object.
+
+    Examples:
+        .. code-block:: pycon
+
+            >>> import paddle
+            >>> gen = paddle.seed(102)
+
+    """
+    # TODO(zhiqiu): 1. remove program.random_seed when all random-related op upgrade
+    # 2. support gpu generator by global device
+
+    seed = int(seed)
+
+    if paddle.is_compiled_with_cuda():
+        for i in range(core.get_cuda_device_count()):
+            core.default_cuda_generator(i).manual_seed(seed)
+    elif paddle.is_compiled_with_xpu():
+        for i in range(core.get_xpu_device_count()):
+            core.default_xpu_generator(i).manual_seed(seed)
+    place = paddle.framework._current_expected_place()
+    if isinstance(place, paddle.CustomPlace):
+        dev_cnt = sum(
+            [
+                place.get_device_type() == s.split(':')[0]
+                for s in core.get_available_custom_device()
+            ]
+        )
+        for i in range(dev_cnt):
+            core.default_custom_device_generator(
+                paddle.CustomPlace(place.get_device_type(), i)
+            ).manual_seed(seed)
+    return core.default_cpu_generator().manual_seed(seed)
+
+
+def get_rng_state(
+    device: str | None = None,
+) -> list[core.GeneratorState]:
+    """
+    Get all random states of random generators of specified device.
+
+    Args:
+        device(str): This parameter determines the specific running device.
+            It can be ``cpu``, ``gpu``, ``xpu``, Default is None.
+            If None, return the generators of current device (specified by ``set_device``).
+
+    Returns:
+        list[GeneratorState], object.
+
+    Examples:
+        .. code-block:: pycon
+
+            >>> import paddle
+            >>> sts = paddle.get_rng_state()
+    """
+    state_list = []
+    if device is None:
+        place = paddle.framework._current_expected_place_()
+    else:
+        place = paddle.device._convert_to_place(device)
+
+    if isinstance(place, paddle.CPUPlace):
+        state_list.append(core.default_cpu_generator().get_state())
+    elif isinstance(place, paddle.CUDAPlace):
+        for i in range(core.get_cuda_device_count()):
+            state_list.append(core.default_cuda_generator(i).get_state())
+    elif isinstance(place, paddle.XPUPlace):
+        for i in range(core.get_xpu_device_count()):
+            state_list.append(core.default_xpu_generator(i).get_state())
+    elif isinstance(place, paddle.CustomPlace):
+        dev_cnt = sum(
+            [
+                place.get_device_type() == s.split(':')[0]
+                for s in core.get_available_custom_device()
+            ]
+        )
+        for i in range(dev_cnt):
+            state_list.append(
+                core.default_custom_device_generator(
+                    core.CustomPlace(place.get_device_type(), i)
+                ).get_state()
+            )
+    else:
+        raise ValueError(
+            f"get_rng_state is not implemented for current device: {place}"
+        )
+
+    return state_list
+
+
+def get_cuda_rng_state() -> list[paddle.base.core.GeneratorState]:
+    """
+
+    Get random state of cuda generators.
+
+    Args:
+        None.
+
+    Returns:
+        GeneratorState:  object.
+
+    Examples:
+        .. code-block:: pycon
+
+            >>> import paddle
+            >>> sts = paddle.get_cuda_rng_state()
+
+    """
+    state_list = []
+    if paddle.is_compiled_with_cuda():
+        for i in range(core.get_cuda_device_count()):
+            state_list.append(core.default_cuda_generator(i).get_state())
+
+    return state_list
+
+
+@param_one_alias(["state_list", "new_state"])
+def set_rng_state(
+    state_list: Sequence[paddle.base.core.GeneratorState],
+    device: str | None = None,
+) -> None:
+    """
+
+    Sets generator state for all device generators.
+
+    Args:
+        state_list(list|tuple): The device states to set back to device generators. state_list is obtained from get_rng_state().
+            Alias: ``new_state``.
+        device(str): This parameter determines the specific running device.
+            It can be ``cpu``, ``gpu``, ``xpu``, Default is None.
+            If None, return the generators of current device (specified by ``set_device``).
+
+    Returns:
+        None.
+
+    Examples:
+        .. code-block:: pycon
+
+            >>> import paddle
+            >>> sts = paddle.get_rng_state()
+            >>> paddle.set_rng_state(sts)
+
+    """
+    if device is None:
+        place = paddle.framework._current_expected_place_()
+    else:
+        place = paddle.device._convert_to_place(device)
+
+    if isinstance(place, paddle.CUDAPlace):
+        if not len(state_list) == core.get_cuda_device_count():
+            raise ValueError(
+                "Length of gpu state list should be equal to the gpu device count"
+            )
+        for i in range(core.get_cuda_device_count()):
+            core.default_cuda_generator(i).set_state(state_list[i])
+    elif isinstance(place, paddle.XPUPlace):
+        if not len(state_list) == core.get_xpu_device_count():
+            raise ValueError(
+                "Length of xpu state list should be equal to the xpu device count"
+            )
+        for i in range(core.get_xpu_device_count()):
+            core.default_xpu_generator(i).set_state(state_list[i])
+    elif isinstance(place, paddle.CustomPlace):
+        dev_types = core.get_all_custom_device_type()
+        dev_type = dev_types[0]
+        dev_cnt = core.get_custom_device_count(dev_type)
+        if not len(state_list) == dev_cnt:
+            raise ValueError(
+                f"Length of custom device state list should be equal to the {dev_cnt} device count"
+            )
+        for i in range(dev_cnt):
+            core.default_custom_device_generator(
+                paddle.CustomPlace(place.get_device_type(), i)
+            ).set_state(state_list[i])
+    elif isinstance(place, core.CPUPlace):
+        if not len(state_list) == 1:
+            raise ValueError("Length of cpu state list should be equal to 1")
+        core.default_cpu_generator().set_state(state_list[0])
+    else:
+        raise ValueError(
+            f"set_rng_state is not implemented for current device: {place}"
+        )
+
+
+def set_cuda_rng_state(
+    state_list: Sequence[paddle.base.core.GeneratorState],
+) -> None:
+    """
+
+    Sets generator state for all cuda generators.
+
+    Args:
+        state_list(list|tuple): The cuda states to set back to cuda generators. state_list is obtained from get_cuda_rng_state().
+
+    Returns:
+        None.
+
+    Examples:
+        .. code-block:: pycon
+
+            >>> import paddle
+            >>> sts = paddle.get_cuda_rng_state()
+            >>> paddle.set_cuda_rng_state(sts)
+
+    """
+    if paddle.is_compiled_with_cuda():
+        if not len(state_list) == core.get_cuda_device_count():
+            raise ValueError(
+                "Length of cuda state list should be equal to the cuda device count"
+            )
+        for i in range(core.get_cuda_device_count()):
+            core.default_cuda_generator(i).set_state(state_list[i])
+
+
+def _manual_program_seed(seed: int) -> None:
+    """
+    Sets global seed for generating random numbers.
+
+    NOTE(zhiqiu): This is the original implementation of seed. Keeps it temporally
+    since CUDA generator is not developed, so we need it in the unittest.
+
+    Args:
+        seed(int): The random seed to set. It is recommend to set a large int number.
+
+    Returns:
+        None
+    """
+    paddle.static.default_main_program().random_seed = seed
+    paddle.static.default_startup_program().random_seed = seed
+    program = paddle.static.Program()
+    program.global_seed(seed)
+
+
+def set_random_seed_generator(name: str, seed: int) -> None:
+    core.set_random_seed_generator(name, seed)
+
+
+def get_random_seed_generator(name: str) -> paddle.base.core.Generator:
+    return core.get_random_seed_generator(name)
+
+
+class Generator:
+    def __new__(
+        cls, device: str | int | paddle.core.Place = None
+    ) -> core.Generator:
+        """
+        Generator is a random number generator.
+
+        Args:
+            device(str|int|paddle.core.Place): The device type to create the generator on.
+                It can be ``cpu``, ``gpu``, ``xpu``, or a paddle.core.Place instance.
+                default is None, which means using current device.
+
+        Examples:
+            .. code-block:: pycon
+
+                >>> import paddle
+                >>> g_cpu = paddle.Generator()
+        """
+        place = paddle.device.device_to_place(device)
+        if isinstance(place, core.CPUPlace):
+            return core.default_cpu_generator()
+        elif isinstance(place, core.CUDAPlace):
+            return core.default_cuda_generator(place.gpu_device_id())
+        elif isinstance(place, core.XPUPlace):
+            return core.default_xpu_generator(place.gpu_device_id())
+        elif isinstance(place, core.CustomPlace):
+            return core.default_custom_device_generator(place)