# Source code for torch.random

import contextlib
import warnings

from torch._C import default_generator
import torch

[docs]def set_rng_state(new_state) -> None:
r"""Sets the random number generator state.

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

torch.cuda.manual_seed_all(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

torch.cuda.manual_seed_all(seed)

return seed

[docs]def initial_seed() -> int:
r"""Returns the initial seed for generating random numbers as a
Python long.
"""
return default_generator.initial_seed()

[docs]@contextlib.contextmanager
def fork_rng(devices=None, enabled=True, _caller="fork_rng", _devices_kw="devices"):
"""
Forks the RNG, so that when you return, the RNG is reset
to the state that it was previously in.

Arguments:
devices (iterable of CUDA IDs): CUDA 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.
"""

import torch.cuda

# 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 = torch.cuda.device_count()
if num_devices > 1 and not _fork_rng_warned_already:
warnings.warn(
("CUDA reports that you have {num_devices} available devices, and you "
"have used {caller} without explicitly specifying which devices are being used. "
"For safety, we initialize *every* CUDA device by default, which "
"can be quite slow if you have a lot of GPUs.  If you know that you are only "
"making use of a few CUDA devices, set the environment variable CUDA_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 CUDA_VISIBLE_DEVICES= or devices=[]; if you are using "
"GPU 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize "
"all devices and suppress this warning, set the '{devices_kw}' keyword argument "
"to range(torch.cuda.device_count())."
).format(num_devices=num_devices, caller=_caller, devices_kw=_devices_kw))
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()
gpu_rng_states = []
for device in devices:
gpu_rng_states.append(torch.cuda.get_rng_state(device))

try:
yield
finally:
torch.set_rng_state(cpu_rng_state)
for device, gpu_rng_state in zip(devices, gpu_rng_states):
torch.cuda.set_rng_state(gpu_rng_state, device)