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PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice (『飞桨』核心框架,深度学习&机器学习高性能单机、分布式训练和跨平台部署)

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# 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
if TYPE_CHECKING:
from collections.abc import Sequence
__all__ = []
def seed(seed: int) -> paddle.base.core.Generator:
"""
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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():
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for i in range(core.get_cuda_device_count()):
core.default_cuda_generator(i).manual_seed(seed)
elif paddle.is_compiled_with_xpu():
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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[paddle.base.core.GeneratorState]:
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"""
Get all random states of random generators of specified device.
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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``).
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Returns:
list[GeneratorState], object.
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Examples:
.. code-block:: pycon
>>> import paddle
>>> sts = paddle.get_rng_state()
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"""
state_list = []
if device is None:
place = paddle.framework._current_expected_place_()
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else:
place = paddle.device._convert_to_place(device)
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if isinstance(place, paddle.CPUPlace):
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state_list.append(core.default_cpu_generator().get_state())
elif isinstance(place, paddle.CUDAPlace):
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for i in range(core.get_cuda_device_count()):
state_list.append(core.default_cuda_generator(i).get_state())
elif isinstance(place, paddle.XPUPlace):
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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()
)
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else:
raise ValueError(
f"get_rng_state is not implemented for current device: {place}"
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)
return state_list
def get_cuda_rng_state() -> list[paddle.base.core.GeneratorState]:
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"""
Get random state of cuda generators.
Args:
None.
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Returns:
GeneratorState: object.
Examples:
.. code-block:: pycon
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>>> import paddle
>>> sts = paddle.get_cuda_rng_state()
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"""
state_list = []
if paddle.is_compiled_with_cuda():
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for i in range(core.get_cuda_device_count()):
state_list.append(core.default_cuda_generator(i).get_state())
return state_list
def set_rng_state(
state_list: Sequence[paddle.base.core.GeneratorState],
device: str | None = None,
) -> None:
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"""
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().
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
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>>> import paddle
>>> sts = paddle.get_rng_state()
>>> paddle.set_rng_state(sts)
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"""
if device is None:
place = paddle.framework._current_expected_place_()
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else:
place = paddle.device._convert_to_place(device)
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if isinstance(place, paddle.CUDAPlace):
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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"
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)
for i in range(core.get_cuda_device_count()):
core.default_cuda_generator(i).set_state(state_list[i])
elif isinstance(place, paddle.XPUPlace):
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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"
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)
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])
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elif isinstance(place, core.CPUPlace):
if not len(state_list) == 1:
raise ValueError("Length of cpu state list should be equal to 1")
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core.default_cpu_generator().set_state(state_list[0])
else:
raise ValueError(
f"set_rng_state is not implemented for current device: {place}"
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)
def set_cuda_rng_state(
state_list: Sequence[paddle.base.core.GeneratorState],
) -> None:
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"""
Sets generator state for all cuda generators.
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Args:
state_list(list|tuple): The cuda states to set back to cuda generators. state_list is obtained from get_cuda_rng_state().
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Returns:
None.
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Examples:
.. code-block:: pycon
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>>> import paddle
>>> sts = paddle.get_cuda_rng_state()
>>> paddle.set_cuda_rng_state(sts)
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"""
if paddle.is_compiled_with_cuda():
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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"
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)
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)