torch.random

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

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.

• deivce (str) – device type str, default is cuda. As for custom device, see details in [Note: support the custom device with privateuse1]

Return type:

Generator

torch.random.get_rng_state()

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

Return type:

Tensor

torch.random.initial_seed()

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

Return type:

int

torch.random.manual_seed(seed)

Sets the seed for generating random numbers. 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()

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

Return type:

int

torch.random.set_rng_state(new_state)

Sets the random number generator state.

Parameters:

new_state (torch.ByteTensor) – The desired state