torch.random

Created On: Aug 07, 2019 | Last Updated On: Jun 18, 2025

torch.random.fork_rng(devices=None, enabled=True, _caller='fork_rng', _devices_kw='devices', device_type='cuda') [source]

Forks the RNG, so that when you return, the RNG is reset to the state that it was previously in.

Parameters

devices (iterable of Device IDs) – devices for which to fork the RNG. CPU RNG state is always forked. By default, 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.
device_type (str) – device type str, default is cuda. As for supported device, see details in accelerator

Return type

Generator

torch.random.get_rng_state() [source]

Returns the random number generator state as a torch.ByteTensor.

Note

The returned state is for the default generator on CPU only.

torch.random.initial_seed() [source]

Returns the initial seed for generating random numbers as a Python long.

Note

The returned seed is for the default generator on CPU only.

torch.random.manual_seed(seed) [source]

Sets the seed for generating random numbers on all devices. Returns a torch.Generator object.

Parameters: 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.
Return type: Generator

torch.random.seed() [source]

Sets the seed for generating random numbers to a non-deterministic random number on all devices. Returns a 64 bit number used to seed the RNG.

torch.random.set_rng_state(new_state) [source]

Sets the random number generator state.

Note

This function only works for CPU. For CUDA, please use torch.manual_seed(), which works for both CPU and CUDA.