Source code for torch.random
import contextlib
from typing import Generator
import warnings
from torch._C import default_generator
import torch
[docs]def set_rng_state(new_state: torch.Tensor) -> None:
r"""Sets the random number generator state.
.. note: This function only works for CPU. For CUDA, please use
torch.manual_seed(seed), which works for both CPU and CUDA.
Args:
new_state (torch.ByteTensor): The desired state
"""
default_generator.set_state(new_state)
[docs]def get_rng_state() -> torch.Tensor:
r"""Returns the random number generator state as a `torch.ByteTensor`."""
return default_generator.get_state()
[docs]def manual_seed(seed) -> torch._C.Generator:
r"""Sets the seed for generating random numbers. Returns a
`torch.Generator` object.
Args:
seed (int): The desired seed. Value must be within the inclusive range
`[-0x8000_0000_0000_0000, 0xffff_ffff_ffff_ffff]`. Otherwise, a RuntimeError
is raised. Negative inputs are remapped to positive values with the formula
`0xffff_ffff_ffff_ffff + seed`.
"""
seed = int(seed)
import torch.cuda
if not torch.cuda._is_in_bad_fork():
torch.cuda.manual_seed_all(seed)
import torch.mps
if not torch.mps._is_in_bad_fork():
torch.mps.manual_seed(seed)
import torch.xpu
if not torch.xpu._is_in_bad_fork():
torch.xpu.manual_seed_all(seed)
_seed_custom_device(seed)
return default_generator.manual_seed(seed)
[docs]def seed() -> int:
r"""Sets the seed for generating random numbers to a non-deterministic
random number. Returns a 64 bit number used to seed the RNG.
"""
seed = default_generator.seed()
import torch.cuda
if not torch.cuda._is_in_bad_fork():
torch.cuda.manual_seed_all(seed)
import torch.mps
if not torch.mps._is_in_bad_fork():
torch.mps.manual_seed(seed)
import torch.xpu
if not torch.xpu._is_in_bad_fork():
torch.xpu.manual_seed_all(seed)
_seed_custom_device(seed)
return seed
def _seed_custom_device(seed) -> None:
r"""Sets the seed to generate random numbers for custom device.
Args:
seed (int): The desired seed.
See [Note: support the custom device with privateuse1]
"""
seed = int(seed)
custom_backend_name = torch._C._get_privateuse1_backend_name()
if hasattr(torch, custom_backend_name):
custom_device_mod = getattr(torch, custom_backend_name)
_bad_fork_name = "_is_in_bad_fork"
_seed_all_name = "manual_seed_all"
if hasattr(custom_device_mod, _bad_fork_name) and hasattr(custom_device_mod, _seed_all_name):
if not getattr(custom_device_mod, _bad_fork_name)():
getattr(custom_device_mod, _seed_all_name)(seed)
else:
message = f"Set seed for `{custom_backend_name}` device does not take effect, please add API's "
message += f"`{_bad_fork_name}` and `{_seed_all_name}` to `{custom_backend_name}` device module."
warnings.warn(message, UserWarning, stacklevel=3)
[docs]def initial_seed() -> int:
r"""Returns the initial seed for generating random numbers as a
Python `long`.
"""
return default_generator.initial_seed()
_fork_rng_warned_already = False
[docs]@contextlib.contextmanager
def fork_rng(devices=None, enabled=True, _caller="fork_rng", _devices_kw="devices", device_type="cuda") -> Generator:
"""
Forks the RNG, so that when you return, the RNG is reset
to the state that it was previously in.
Args:
devices (iterable of Device IDs): devices for which to fork
the RNG. CPU RNG state is always forked. By default, :meth:`fork_rng` operates
on all devices, but will emit a warning if your machine has a lot
of devices, since this function will run very slowly in that case.
If you explicitly specify devices, this warning will be suppressed
enabled (bool): if ``False``, the RNG is not forked. This is a convenience
argument for easily disabling the context manager without having
to delete it and unindent your Python code under it.
deivce_type (str): device type str, default is `cuda`. As for custom device,
see details in [Note: support the custom device with privateuse1]
"""
device_type = torch.device(device_type).type
device_mod = getattr(torch, device_type, None)
if device_mod is None:
raise RuntimeError(f"torch has no module of `{device_type}`, you should register " +
"a module by `torch._register_device_module`.")
global _fork_rng_warned_already
# Internal arguments:
# _caller: the function which called fork_rng, which the user used
# _devices_kw: the devices keyword of _caller
if not enabled:
yield
return
if devices is None:
num_devices = device_mod.device_count()
if num_devices > 1 and not _fork_rng_warned_already:
message = (f"{device_type.upper()} reports that you have {num_devices} available devices, and "
f"you have used {_caller} without explicitly specifying which devices are being used. "
f"For safety, we initialize *every* {device_type.upper()} device by default, which can "
f"be quite slow if you have a lot of {device_type.upper()}s. If you know that you are only"
f" making use of a few {device_type.upper()} devices, set the environment variable "
f"{device_type.upper()}_VISIBLE_DEVICES or the '{_devices_kw}' keyword argument of {_caller} "
"with the set of devices you are actually using. For example, if you are using CPU only, "
"set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, "
f"set {device_type.upper()}_VISIBLE_DEVICES=0 or devices=[0]. To initialize all devices "
f"and suppress this warning, set the '{_devices_kw}' keyword argument to "
f"`range(torch.{device_type}.device_count())`.")
warnings.warn(message)
_fork_rng_warned_already = True
devices = list(range(num_devices))
else:
# Protect against user passing us a generator; we need to traverse this
# multiple times but a generator will be exhausted upon first traversal
devices = list(devices)
cpu_rng_state = torch.get_rng_state()
device_rng_states = [device_mod.get_rng_state(device) for device in devices]
try:
yield
finally:
torch.set_rng_state(cpu_rng_state)
for device, device_rng_state in zip(devices, device_rng_states):
device_mod.set_rng_state(device_rng_state, device)
