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Source code for torch.random

import contextlib
import warnings

from torch._C import default_generator
import torch


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()
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) return default_generator.manual_seed(seed) 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) 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()
_fork_rng_warned_already = False @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 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 = 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)) _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() 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)

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