Vehicle-cpp/hkpc/software/torch/utils/_device.py

import torch
from torch.overrides import TorchFunctionMode
from torch.utils._contextlib import context_decorator
import functools

@functools.lru_cache(1)
def _device_constructors():
    return {
        # standard ones
        torch.empty,
        torch.empty_strided,
        torch.empty_quantized,
        torch.ones,
        torch.arange,
        torch.bartlett_window,
        torch.blackman_window,
        torch.eye,
        torch.fft.fftfreq,
        torch.fft.rfftfreq,
        torch.full,
        torch.fill,
        torch.hamming_window,
        torch.hann_window,
        torch.kaiser_window,
        torch.linspace,
        torch.logspace,
        torch.nested.nested_tensor,
        # This function doesn't actually take a device argument
        # torch.normal,
        torch.ones,
        torch.rand,
        torch.randn,
        torch.randint,
        torch.randperm,
        torch.range,
        torch.sparse_coo_tensor,
        torch.sparse_compressed_tensor,
        torch.sparse_csr_tensor,
        torch.sparse_csc_tensor,
        torch.sparse_bsr_tensor,
        torch.sparse_bsc_tensor,
        torch.tril_indices,
        torch.triu_indices,
        torch.vander,
        torch.zeros,
        torch.asarray,
        # weird ones
        torch.tensor,
        torch.as_tensor,
        torch.scalar_tensor,
    }

# NB: This is directly called from C++ in torch/csrc/Device.cpp
class DeviceContext(TorchFunctionMode):
    def __init__(self, device):
        self.device = torch.device(device)

    def __torch_function__(self, func, types, args=(), kwargs=None):
        kwargs = kwargs or {}
        if func in _device_constructors() and kwargs.get('device') is None:
            kwargs['device'] = self.device
        return func(*args, **kwargs)

# NB: This is directly called from C++ in torch/csrc/Device.cpp
def device_decorator(device, func):
    return context_decorator(lambda: device, func)

def set_device(device):
    """
    Decorator which sets the default device inside of the wrapped
    function.  If you would like to use this as a context manager,
    use device as a context manager directly, e.g.,
    ``with torch.device(device)``.
    """
    return lambda func: device_decorator(torch.device(device), func)