FM / torch

/usr/local/lib64/python3.6/site-packages/torch/

/usr/local/lib64/python3.6/site-packages/torch

Name	Size	Mode	Actions
ao/	-	0755	rm
autograd/	-	0755	rm
backends/	-	0755	rm
bin/	-	0755	rm
contrib/	-	0755	rm
cpu/	-	0755	rm
cuda/	-	0755	rm
distributed/	-	0755	rm
distributions/	-	0755	rm
fft/	-	0755	rm
for_onnx/	-	0755	rm
futures/	-	0755	rm
fx/	-	0755	rm
include/	-	0755	rm
jit/	-	0755	rm
lib/	-	0755	rm
linalg/	-	0755	rm
multiprocessing/	-	0755	rm
nn/	-	0755	rm
onnx/	-	0755	rm
optim/	-	0755	rm
package/	-	0755	rm
profiler/	-	0755	rm
quantization/	-	0755	rm
share/	-	0755	rm
sparse/	-	0755	rm
special/	-	0755	rm
testing/	-	0755	rm
utils/	-	0755	rm
_C/	-	0755	rm
__pycache__/	-	0755	rm
autocast_mode.py	9663	0644	edit dl rm
functional.py	71257	0644	edit dl rm
hub.py	23371	0644	edit dl rm
overrides.py	84146	0644	edit dl rm
py.typed	0	0644	edit dl rm
quasirandom.py	7489	0644	edit dl rm
random.py	4828	0644	edit dl rm
serialization.py	36544	0644	edit dl rm
storage.py	5762	0644	edit dl rm
torch_version.py	3468	0644	edit dl rm
types.py	1552	0644	edit dl rm
version.py	125	0644	edit dl rm
_appdirs.py	26245	0644	edit dl rm
_C.cpython-36m-x86_64-linux-gnu.so	29296	0755	edit dl rm
_classes.py	1717	0644	edit dl rm
_deploy.py	3100	0644	edit dl rm
_dl.cpython-36m-x86_64-linux-gnu.so	29832	0755	edit dl rm
_jit_internal.py	46910	0644	edit dl rm
_linalg_utils.py	2373	0644	edit dl rm
_lobpcg.py	44076	0644	edit dl rm
_lowrank.py	11031	0644	edit dl rm
_namedtensor_internals.py	5351	0644	edit dl rm
_ops.py	4457	0644	edit dl rm
_python_dispatcher.py	7011	0644	edit dl rm
_six.py	1858	0644	edit dl rm
_sources.py	3882	0644	edit dl rm
_storage_docs.py	1298	0644	edit dl rm
_tensor.py	50854	0644	edit dl rm
_tensor_docs.py	113474	0644	edit dl rm
_tensor_str.py	18091	0644	edit dl rm
_torch_docs.py	364678	0644	edit dl rm
_utils.py	21617	0644	edit dl rm
_utils_internal.py	1687	0644	edit dl rm
_VF.py	656	0644	edit dl rm
_vmap_internals.py	13219	0644	edit dl rm
__config__.py	551	0644	edit dl rm
__future__.py	813	0644	edit dl rm
__init__.py	31199	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/torch/random.py (4828B)

import contextlib
from typing import Generator
import warnings

from torch._C import default_generator
import torch

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)

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

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") -> 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 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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