| 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): |
| 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 |
|
|
