126 lines
4.5 KiB
Python
126 lines
4.5 KiB
Python
import contextlib
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import warnings
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from torch._C import default_generator
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import torch
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def set_rng_state(new_state) -> None:
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r"""Sets the random number generator state.
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Args:
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new_state (torch.ByteTensor): The desired state
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"""
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default_generator.set_state(new_state)
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def get_rng_state() -> torch.Tensor:
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r"""Returns the random number generator state as a `torch.ByteTensor`."""
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return default_generator.get_state()
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def manual_seed(seed) -> torch._C.Generator:
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r"""Sets the seed for generating random numbers. Returns a
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`torch.Generator` object.
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Args:
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seed (int): The desired seed. Value must be within the inclusive range
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`[-0x8000_0000_0000_0000, 0xffff_ffff_ffff_ffff]`. Otherwise, a RuntimeError
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is raised. Negative inputs are remapped to positive values with the formula
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`0xffff_ffff_ffff_ffff + seed`.
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"""
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seed = int(seed)
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import torch.cuda
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if not torch.cuda._is_in_bad_fork():
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torch.cuda.manual_seed_all(seed)
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return default_generator.manual_seed(seed)
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def seed() -> int:
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r"""Sets the seed for generating random numbers to a non-deterministic
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random number. Returns a 64 bit number used to seed the RNG.
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"""
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seed = default_generator.seed()
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import torch.cuda
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if not torch.cuda._is_in_bad_fork():
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torch.cuda.manual_seed_all(seed)
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return seed
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def initial_seed() -> int:
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r"""Returns the initial seed for generating random numbers as a
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Python `long`.
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"""
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return default_generator.initial_seed()
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_fork_rng_warned_already = False
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@contextlib.contextmanager
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def fork_rng(devices=None, enabled=True, _caller="fork_rng", _devices_kw="devices"):
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"""
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Forks the RNG, so that when you return, the RNG is reset
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to the state that it was previously in.
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Args:
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devices (iterable of CUDA IDs): CUDA devices for which to fork
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the RNG. CPU RNG state is always forked. By default, :meth:`fork_rng` operates
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on all devices, but will emit a warning if your machine has a lot
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of devices, since this function will run very slowly in that case.
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If you explicitly specify devices, this warning will be suppressed
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enabled (bool): if ``False``, the RNG is not forked. This is a convenience
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argument for easily disabling the context manager without having
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to delete it and unindent your Python code under it.
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"""
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import torch.cuda
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global _fork_rng_warned_already
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# Internal arguments:
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# _caller: the function which called fork_rng, which the user used
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# _devices_kw: the devices keyword of _caller
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if not enabled:
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yield
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return
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if devices is None:
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num_devices = torch.cuda.device_count()
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if num_devices > 1 and not _fork_rng_warned_already:
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warnings.warn(
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("CUDA reports that you have {num_devices} available devices, and you "
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"have used {caller} without explicitly specifying which devices are being used. "
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"For safety, we initialize *every* CUDA device by default, which "
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"can be quite slow if you have a lot of GPUs. If you know that you are only "
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"making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES "
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"or the '{devices_kw}' keyword argument of {caller} with the set of devices "
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"you are actually using. For example, if you are using CPU only, "
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"set CUDA_VISIBLE_DEVICES= or devices=[]; if you are using "
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"GPU 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0]. To initialize "
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"all devices and suppress this warning, set the '{devices_kw}' keyword argument "
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"to `range(torch.cuda.device_count())`."
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).format(num_devices=num_devices, caller=_caller, devices_kw=_devices_kw))
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_fork_rng_warned_already = True
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devices = list(range(num_devices))
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else:
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# Protect against user passing us a generator; we need to traverse this
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# multiple times but a generator will be exhausted upon first traversal
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devices = list(devices)
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cpu_rng_state = torch.get_rng_state()
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gpu_rng_states = []
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for device in devices:
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gpu_rng_states.append(torch.cuda.get_rng_state(device))
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try:
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yield
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finally:
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torch.set_rng_state(cpu_rng_state)
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for device, gpu_rng_state in zip(devices, gpu_rng_states):
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torch.cuda.set_rng_state(gpu_rng_state, device)
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