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import torch
from contextlib import contextmanager
class Linear(torch.nn.Module):
def __init__(self, in_features: int, out_features: int, bias: bool = True,
device=None, dtype=None) -> None:
factory_kwargs = {'device': device, 'dtype': dtype}
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.weight = torch.nn.Parameter(torch.empty((out_features, in_features), **factory_kwargs))
if bias:
self.bias = torch.nn.Parameter(torch.empty(out_features, **factory_kwargs))
else:
self.register_parameter('bias', None)
def forward(self, input):
return torch.nn.functional.linear(input, self.weight, self.bias)
class Conv2d(torch.nn.Conv2d):
def reset_parameters(self):
return None
def conv_nd(dims, *args, **kwargs):
if dims == 2:
return Conv2d(*args, **kwargs)
else:
raise ValueError(f"unsupported dimensions: {dims}")
@contextmanager
def use_comfy_ops(device=None, dtype=None): # Kind of an ugly hack but I can't think of a better way
old_torch_nn_linear = torch.nn.Linear
force_device = device
force_dtype = dtype
def linear_with_dtype(in_features: int, out_features: int, bias: bool = True, device=None, dtype=None):
if force_device is not None:
device = force_device
if force_dtype is not None:
dtype = force_dtype
return Linear(in_features, out_features, bias=bias, device=device, dtype=dtype)
torch.nn.Linear = linear_with_dtype
try:
yield
finally:
torch.nn.Linear = old_torch_nn_linear
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