Generator#
- classtorch.Generator(device='cpu')#
Creates and returns a generator object that manages the state of the algorithm whichproduces pseudo random numbers. Used as a keyword argument in manyIn-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.devicedevice(type='cpu')
- get_state()→Tensor#
Returns the Generator state as a
torch.ByteTensor.- Returns
A
torch.ByteTensorwhich contains all the necessary bitsto 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 fromgraphsafe_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 atorch.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 RuntimeErroris 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)
- seed()→int#
Gets a non-deterministic random number from std::random_device or the currenttime 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())
