Generator — PyTorch 2.0 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')
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()
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 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())