Shortcuts

Source code for torch.cuda.random

import torch
from typing import cast, Iterable, List, Union
from . import _lazy_init, _lazy_call, device_count, current_device
from .. import Tensor

__all__ = ['get_rng_state', 'get_rng_state_all',
           'set_rng_state', 'set_rng_state_all',
           'manual_seed', 'manual_seed_all',
           'seed', 'seed_all', 'initial_seed']


def get_rng_state(device: Union[int, str, torch.device] = 'cuda') -> Tensor:
    r"""Returns the random number generator state of the specified GPU as a ByteTensor.

    Args:
        device (torch.device or int, optional): The device to return the RNG state of.
            Default: ``'cuda'`` (i.e., ``torch.device('cuda')``, the current CUDA device).

    .. warning::
        This function eagerly initializes CUDA.
    """
    _lazy_init()
    if isinstance(device, str):
        device = torch.device(device)
    elif isinstance(device, int):
        device = torch.device('cuda', device)
    idx = device.index
    if idx is None:
        idx = current_device()
    default_generator = torch.cuda.default_generators[idx]
    return default_generator.get_state()


def get_rng_state_all() -> List[Tensor]:
    r"""Returns a list of ByteTensor representing the random number states of all devices."""

    results = []
    for i in range(device_count()):
        results.append(get_rng_state(i))
    return results


[docs]def set_rng_state(new_state: Tensor, device: Union[int, str, torch.device] = 'cuda') -> None: r"""Sets the random number generator state of the specified GPU. Args: new_state (torch.ByteTensor): The desired state device (torch.device or int, optional): The device to set the RNG state. Default: ``'cuda'`` (i.e., ``torch.device('cuda')``, the current CUDA device). """ new_state_copy = new_state.clone(memory_format=torch.contiguous_format) if isinstance(device, str): device = torch.device(device) elif isinstance(device, int): device = torch.device('cuda', device) def cb(): idx = cast(torch.device, device).index if idx is None: idx = current_device() default_generator = torch.cuda.default_generators[idx] default_generator.set_state(new_state_copy) _lazy_call(cb)
[docs]def set_rng_state_all(new_states: Iterable[Tensor]) -> None: r"""Sets the random number generator state of all devices. Args: new_states (Iterable of torch.ByteTensor): The desired state for each device""" for i, state in enumerate(new_states): set_rng_state(state, i)
def manual_seed(seed: int) -> None: r"""Sets the seed for generating random numbers for the current GPU. It's safe to call this function if CUDA is not available; in that case, it is silently ignored. Args: seed (int): The desired seed. .. warning:: If you are working with a multi-GPU model, this function is insufficient to get determinism. To seed all GPUs, use :func:`manual_seed_all`. """ seed = int(seed) def cb(): idx = current_device() default_generator = torch.cuda.default_generators[idx] default_generator.manual_seed(seed) _lazy_call(cb, seed=True) def manual_seed_all(seed: int) -> None: r"""Sets the seed for generating random numbers on all GPUs. It's safe to call this function if CUDA is not available; in that case, it is silently ignored. Args: seed (int): The desired seed. """ seed = int(seed) def cb(): for i in range(device_count()): default_generator = torch.cuda.default_generators[i] default_generator.manual_seed(seed) _lazy_call(cb, seed_all=True)
[docs]def seed() -> None: r"""Sets the seed for generating random numbers to a random number for the current GPU. It's safe to call this function if CUDA is not available; in that case, it is silently ignored. .. warning:: If you are working with a multi-GPU model, this function will only initialize the seed on one GPU. To initialize all GPUs, use :func:`seed_all`. """ def cb(): idx = current_device() default_generator = torch.cuda.default_generators[idx] default_generator.seed() _lazy_call(cb)
[docs]def seed_all() -> None: r"""Sets the seed for generating random numbers to a random number on all GPUs. It's safe to call this function if CUDA is not available; in that case, it is silently ignored. """ def cb(): random_seed = 0 seeded = False for i in range(device_count()): default_generator = torch.cuda.default_generators[i] if not seeded: default_generator.seed() random_seed = default_generator.initial_seed() seeded = True else: default_generator.manual_seed(random_seed) _lazy_call(cb)
def initial_seed() -> int: r"""Returns the current random seed of the current GPU. .. warning:: This function eagerly initializes CUDA. """ _lazy_init() idx = current_device() default_generator = torch.cuda.default_generators[idx] return default_generator.initial_seed()

Docs

Access comprehensive developer documentation for PyTorch

View Docs

Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

Resources

Find development resources and get your questions answered

View Resources