Generator¶

class torch.Generator(device='cpu') → Generator¶

Creates and returns a generator object that manages the state of the algorithm which produces pseudo random numbers. Used as a keyword argument in many In-place random sampling functions.

Parameters

device (torch.device, optional) – the desired device for the generator.

Returns

An torch.Generator object.

Return type

Generator

Example:

>>> g_cpu = torch.Generator()
>>> g_cuda = torch.Generator(device='cuda')

device¶

Generator.device -> device

Gets the current device of the generator.

Example:

>>> g_cpu = torch.Generator()
>>> g_cpu.device
device(type='cpu')

get_state() → Tensor¶

Returns the Generator state as a torch.ByteTensor.

Returns

A torch.ByteTensor which contains all the necessary bits to restore a Generator to a specific point in time.

Return type

Tensor

Example:

>>> g_cpu = torch.Generator()
>>> g_cpu.get_state()

initial_seed() → int¶

Returns the initial seed for generating random numbers.

Example:

>>> g_cpu = torch.Generator()
>>> g_cpu.initial_seed()
2147483647

manual_seed(seed) → Generator¶

Sets the seed for generating random numbers. Returns a torch.Generator object. It is recommended to set a large seed, i.e. a number that has a good balance of 0 and 1 bits. Avoid having many 0 bits in the seed.

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.

Returns

An torch.Generator object.

Return type

Generator

Example:

>>> g_cpu = torch.Generator()
>>> g_cpu.manual_seed(2147483647)

seed() → int¶

Gets a non-deterministic random number from std::random_device or the current time and uses it to seed a Generator.

Example:

>>> g_cpu = torch.Generator()
>>> g_cpu.seed()
1516516984916

set_state(new_state) → void¶

Sets the Generator state.

Parameters

new_state (torch.ByteTensor) – The desired state.

Example:

>>> g_cpu = torch.Generator()
>>> g_cpu_other = torch.Generator()
>>> g_cpu.set_state(g_cpu_other.get_state())