Generator

class torch.Generator(device='cpu')

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')

clone_state() → torch.Generator

Clones the current state of the generator and returns a new generator pointing to this cloned state. This method is beneficial for preserving a particular state of a generator to restore at a later point.

Returns: A Generator pointing to the newly cloned state.
Return type: torch.Generator

Example

>>> g_cuda = torch.Generator(device='cuda')
>>> cloned_state = g_cuda.clone_state()

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()

graphsafe_get_state() → torch.Generator

Retrieves the current state of the generator in a manner that is safe for graph capture. This method is crucial for ensuring that the generator’s state can be captured in the CUDA graph.

Returns: A Generator point to the current state of the generator
Return type: torch.Generator

Example

>>> g_cuda = torch.Generator(device='cuda')
>>> current_state = g_cuda.graphsafe_get_state()

graphsafe_set_state(state) → None

Sets the state of the generator to the specified state in a manner that is safe for use in graph capture. This method is crucial for ensuring that the generator’s state can be captured in the CUDA graph.

Parameters: state (torch.Generator) – A Generator point to the new state for the generator, typically obtained from graphsafe_get_state.

Example

>>> g_cuda = torch.Generator(device='cuda')
>>> g_cuda_other = torch.Generator(device='cuda')
>>> current_state = g_cuda_other.graphsafe_get_state()
>>> g_cuda.graphsafe_set_state(current_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. Any 32-bit integer is a valid 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())