diff options
Diffstat (limited to 'modules/sd_disable_initialization.py')
| -rw-r--r-- | modules/sd_disable_initialization.py | 51 |
1 files changed, 44 insertions, 7 deletions
diff --git a/modules/sd_disable_initialization.py b/modules/sd_disable_initialization.py index 695c5736..719eeb93 100644 --- a/modules/sd_disable_initialization.py +++ b/modules/sd_disable_initialization.py @@ -168,22 +168,59 @@ class LoadStateDictOnMeta(ReplaceHelper): device = self.device
def load_from_state_dict(original, self, state_dict, prefix, *args, **kwargs):
- params = [(name, param) for name, param in self._parameters.items() if param is not None and param.is_meta]
+ used_param_keys = []
+
+ for name, param in self._parameters.items():
+ if param is None:
+ continue
+
+ key = prefix + name
+ sd_param = sd.pop(key, None)
+ if sd_param is not None:
+ state_dict[key] = sd_param
+ used_param_keys.append(key)
- for name, param in params:
if param.is_meta:
- self._parameters[name] = torch.nn.parameter.Parameter(torch.zeros_like(param, device=device), requires_grad=param.requires_grad)
+ dtype = sd_param.dtype if sd_param is not None else param.dtype
+ self._parameters[name] = torch.nn.parameter.Parameter(torch.zeros_like(param, device=device, dtype=dtype), requires_grad=param.requires_grad)
+
+ for name in self._buffers:
+ key = prefix + name
+
+ sd_param = sd.pop(key, None)
+ if sd_param is not None:
+ state_dict[key] = sd_param
+ used_param_keys.append(key)
original(self, state_dict, prefix, *args, **kwargs)
- for name, _ in params:
- key = prefix + name
- if key in sd:
- del sd[key]
+ for key in used_param_keys:
+ state_dict.pop(key, None)
+
+ def load_state_dict(original, self, state_dict, strict=True):
+ """torch makes a lot of copies of the dictionary with weights, so just deleting entries from state_dict does not help
+ because the same values are stored in multiple copies of the dict. The trick used here is to give torch a dict with
+ all weights on meta device, i.e. deleted, and then it doesn't matter how many copies torch makes.
+
+ In _load_from_state_dict, the correct weight will be obtained from a single dict with the right weights (sd).
+
+ The dangerous thing about this is if _load_from_state_dict is not called, (if some exotic module overloads
+ the function and does not call the original) the state dict will just fail to load because weights
+ would be on the meta device.
+ """
+
+ if state_dict == sd:
+ state_dict = {k: v.to(device="meta", dtype=v.dtype) for k, v in state_dict.items()}
+
+ original(self, state_dict, strict=strict)
+ module_load_state_dict = self.replace(torch.nn.Module, 'load_state_dict', lambda *args, **kwargs: load_state_dict(module_load_state_dict, *args, **kwargs))
+ module_load_from_state_dict = self.replace(torch.nn.Module, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(module_load_from_state_dict, *args, **kwargs))
linear_load_from_state_dict = self.replace(torch.nn.Linear, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(linear_load_from_state_dict, *args, **kwargs))
conv2d_load_from_state_dict = self.replace(torch.nn.Conv2d, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(conv2d_load_from_state_dict, *args, **kwargs))
mha_load_from_state_dict = self.replace(torch.nn.MultiheadAttention, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(mha_load_from_state_dict, *args, **kwargs))
+ layer_norm_load_from_state_dict = self.replace(torch.nn.LayerNorm, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(layer_norm_load_from_state_dict, *args, **kwargs))
+ group_norm_load_from_state_dict = self.replace(torch.nn.GroupNorm, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(group_norm_load_from_state_dict, *args, **kwargs))
def __exit__(self, exc_type, exc_val, exc_tb):
self.restore()
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