Generator — PyTorch 2.7 documentation (original) (raw)
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 samplingfunctions.
Parameters
device (torch.device, optional) – the desired device for the generator.
Returns
An torch.Generator object.
Return type
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
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')
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
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
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)
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 formula0xffff_ffff_ffff_ffff + seed.
Returns
An torch.Generator object.
Return type
Example:
g_cpu = torch.Generator() g_cpu.manual_seed(2147483647)
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())