Generator¶
PyTorch provides methods to create random number generation (RNG) as part of your PyTorch program. torch.generator is the primary API to use for standard in the clear application where there is no application of privacy preserving techniques such as differential privacy. To create a cryptographically secure RNG, please use torchcsprng which can be found in the repo here.
-
class
torch.Generator(device='cpu') → Generator¶ Creates and returns a generator object which manages the state of the algorithm that 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
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')
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get_state() → Tensor¶ Returns the Generator state as a
torch.ByteTensor.- Returns
A
torch.ByteTensorwhich 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()
-
initial_seed() → int¶ Returns the initial seed for generating random numbers.
Example:
>>> g_cpu = torch.Generator() >>> g_cpu.initial_seed() 2147483647
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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.
Example:
>>> g_cpu = torch.Generator() >>> g_cpu.manual_seed(2147483647)
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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())
