| import torch
|
| import ldm_patched.modules.model_management
|
|
|
| def cast_bias_weight(s, input):
|
| bias = None
|
| non_blocking = ldm_patched.modules.model_management.device_supports_non_blocking(input.device)
|
| if s.bias is not None:
|
| bias = s.bias.to(device=input.device, dtype=input.dtype, non_blocking=non_blocking)
|
| weight = s.weight.to(device=input.device, dtype=input.dtype, non_blocking=non_blocking)
|
| return weight, bias
|
|
|
|
|
| class disable_weight_init:
|
| class Linear(torch.nn.Linear):
|
| ldm_patched_cast_weights = False
|
| def reset_parameters(self):
|
| return None
|
|
|
| def forward_ldm_patched_cast_weights(self, input):
|
| weight, bias = cast_bias_weight(self, input)
|
| return torch.nn.functional.linear(input, weight, bias)
|
|
|
| def forward(self, *args, **kwargs):
|
| if self.ldm_patched_cast_weights:
|
| return self.forward_ldm_patched_cast_weights(*args, **kwargs)
|
| else:
|
| return super().forward(*args, **kwargs)
|
|
|
| class Conv2d(torch.nn.Conv2d):
|
| ldm_patched_cast_weights = False
|
| def reset_parameters(self):
|
| return None
|
|
|
| def forward_ldm_patched_cast_weights(self, input):
|
| weight, bias = cast_bias_weight(self, input)
|
| return self._conv_forward(input, weight, bias)
|
|
|
| def forward(self, *args, **kwargs):
|
| if self.ldm_patched_cast_weights:
|
| return self.forward_ldm_patched_cast_weights(*args, **kwargs)
|
| else:
|
| return super().forward(*args, **kwargs)
|
|
|
| class Conv3d(torch.nn.Conv3d):
|
| ldm_patched_cast_weights = False
|
| def reset_parameters(self):
|
| return None
|
|
|
| def forward_ldm_patched_cast_weights(self, input):
|
| weight, bias = cast_bias_weight(self, input)
|
| return self._conv_forward(input, weight, bias)
|
|
|
| def forward(self, *args, **kwargs):
|
| if self.ldm_patched_cast_weights:
|
| return self.forward_ldm_patched_cast_weights(*args, **kwargs)
|
| else:
|
| return super().forward(*args, **kwargs)
|
|
|
| class GroupNorm(torch.nn.GroupNorm):
|
| ldm_patched_cast_weights = False
|
| def reset_parameters(self):
|
| return None
|
|
|
| def forward_ldm_patched_cast_weights(self, input):
|
| weight, bias = cast_bias_weight(self, input)
|
| return torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps)
|
|
|
| def forward(self, *args, **kwargs):
|
| if self.ldm_patched_cast_weights:
|
| return self.forward_ldm_patched_cast_weights(*args, **kwargs)
|
| else:
|
| return super().forward(*args, **kwargs)
|
|
|
|
|
| class LayerNorm(torch.nn.LayerNorm):
|
| ldm_patched_cast_weights = False
|
| def reset_parameters(self):
|
| return None
|
|
|
| def forward_ldm_patched_cast_weights(self, input):
|
| weight, bias = cast_bias_weight(self, input)
|
| return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps)
|
|
|
| def forward(self, *args, **kwargs):
|
| if self.ldm_patched_cast_weights:
|
| return self.forward_ldm_patched_cast_weights(*args, **kwargs)
|
| else:
|
| return super().forward(*args, **kwargs)
|
|
|
| @classmethod
|
| def conv_nd(s, dims, *args, **kwargs):
|
| if dims == 2:
|
| return s.Conv2d(*args, **kwargs)
|
| elif dims == 3:
|
| return s.Conv3d(*args, **kwargs)
|
| else:
|
| raise ValueError(f"unsupported dimensions: {dims}")
|
|
|
|
|
| class manual_cast(disable_weight_init):
|
| class Linear(disable_weight_init.Linear):
|
| ldm_patched_cast_weights = True
|
|
|
| class Conv2d(disable_weight_init.Conv2d):
|
| ldm_patched_cast_weights = True
|
|
|
| class Conv3d(disable_weight_init.Conv3d):
|
| ldm_patched_cast_weights = True
|
|
|
| class GroupNorm(disable_weight_init.GroupNorm):
|
| ldm_patched_cast_weights = True
|
|
|
| class LayerNorm(disable_weight_init.LayerNorm):
|
| ldm_patched_cast_weights = True
|
|
|
