190 lines
8.5 KiB
Python
190 lines
8.5 KiB
Python
import ldm.modules.encoders.modules
|
|
import open_clip
|
|
import torch
|
|
import transformers.utils.hub
|
|
|
|
from modules import shared
|
|
|
|
|
|
class ReplaceHelper:
|
|
def __init__(self):
|
|
self.replaced = []
|
|
|
|
def replace(self, obj, field, func):
|
|
original = getattr(obj, field, None)
|
|
if original is None:
|
|
return None
|
|
|
|
self.replaced.append((obj, field, original))
|
|
setattr(obj, field, func)
|
|
|
|
return original
|
|
|
|
def restore(self):
|
|
for obj, field, original in self.replaced:
|
|
setattr(obj, field, original)
|
|
|
|
self.replaced.clear()
|
|
|
|
|
|
class DisableInitialization(ReplaceHelper):
|
|
"""
|
|
When an object of this class enters a `with` block, it starts:
|
|
- preventing torch's layer initialization functions from working
|
|
- changes CLIP and OpenCLIP to not download model weights
|
|
- changes CLIP to not make requests to check if there is a new version of a file you already have
|
|
|
|
When it leaves the block, it reverts everything to how it was before.
|
|
|
|
Use it like this:
|
|
```
|
|
with DisableInitialization():
|
|
do_things()
|
|
```
|
|
"""
|
|
|
|
def __init__(self, disable_clip=True):
|
|
super().__init__()
|
|
self.disable_clip = disable_clip
|
|
|
|
def replace(self, obj, field, func):
|
|
original = getattr(obj, field, None)
|
|
if original is None:
|
|
return None
|
|
|
|
self.replaced.append((obj, field, original))
|
|
setattr(obj, field, func)
|
|
|
|
return original
|
|
|
|
def __enter__(self):
|
|
def do_nothing(*args, **kwargs):
|
|
pass
|
|
|
|
def create_model_and_transforms_without_pretrained(*args, pretrained=None, **kwargs):
|
|
return self.create_model_and_transforms(*args, pretrained=None, **kwargs)
|
|
|
|
def CLIPTextModel_from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs):
|
|
res = self.CLIPTextModel_from_pretrained(None, *model_args, config=pretrained_model_name_or_path, state_dict={}, **kwargs)
|
|
res.name_or_path = pretrained_model_name_or_path
|
|
return res
|
|
|
|
def transformers_modeling_utils_load_pretrained_model(*args, **kwargs):
|
|
args = args[0:3] + ('/', ) + args[4:] # resolved_archive_file; must set it to something to prevent what seems to be a bug
|
|
return self.transformers_modeling_utils_load_pretrained_model(*args, **kwargs)
|
|
|
|
def transformers_utils_hub_get_file_from_cache(original, url, *args, **kwargs):
|
|
|
|
# this file is always 404, prevent making request
|
|
if url == 'https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/added_tokens.json' or url == 'openai/clip-vit-large-patch14' and args[0] == 'added_tokens.json':
|
|
return None
|
|
|
|
try:
|
|
res = original(url, *args, local_files_only=True, **kwargs)
|
|
if res is None:
|
|
res = original(url, *args, local_files_only=False, **kwargs)
|
|
return res
|
|
except Exception:
|
|
return original(url, *args, local_files_only=False, **kwargs)
|
|
|
|
def transformers_utils_hub_get_from_cache(url, *args, local_files_only=False, **kwargs):
|
|
return transformers_utils_hub_get_file_from_cache(self.transformers_utils_hub_get_from_cache, url, *args, **kwargs)
|
|
|
|
def transformers_tokenization_utils_base_cached_file(url, *args, local_files_only=False, **kwargs):
|
|
return transformers_utils_hub_get_file_from_cache(self.transformers_tokenization_utils_base_cached_file, url, *args, **kwargs)
|
|
|
|
def transformers_configuration_utils_cached_file(url, *args, local_files_only=False, **kwargs):
|
|
return transformers_utils_hub_get_file_from_cache(self.transformers_configuration_utils_cached_file, url, *args, **kwargs)
|
|
|
|
self.replace(torch.nn.init, 'kaiming_uniform_', do_nothing)
|
|
self.replace(torch.nn.init, '_no_grad_normal_', do_nothing)
|
|
self.replace(torch.nn.init, '_no_grad_uniform_', do_nothing)
|
|
|
|
if self.disable_clip:
|
|
self.create_model_and_transforms = self.replace(open_clip, 'create_model_and_transforms', create_model_and_transforms_without_pretrained)
|
|
self.CLIPTextModel_from_pretrained = self.replace(ldm.modules.encoders.modules.CLIPTextModel, 'from_pretrained', CLIPTextModel_from_pretrained)
|
|
self.transformers_modeling_utils_load_pretrained_model = self.replace(transformers.modeling_utils.PreTrainedModel, '_load_pretrained_model', transformers_modeling_utils_load_pretrained_model)
|
|
self.transformers_tokenization_utils_base_cached_file = self.replace(transformers.tokenization_utils_base, 'cached_file', transformers_tokenization_utils_base_cached_file)
|
|
self.transformers_configuration_utils_cached_file = self.replace(transformers.configuration_utils, 'cached_file', transformers_configuration_utils_cached_file)
|
|
self.transformers_utils_hub_get_from_cache = self.replace(transformers.utils.hub, 'get_from_cache', transformers_utils_hub_get_from_cache)
|
|
|
|
def __exit__(self, exc_type, exc_val, exc_tb):
|
|
self.restore()
|
|
|
|
|
|
class InitializeOnMeta(ReplaceHelper):
|
|
"""
|
|
Context manager that causes all parameters for linear/conv2d/mha layers to be allocated on meta device,
|
|
which results in those parameters having no values and taking no memory. model.to() will be broken and
|
|
will need to be repaired by using LoadStateDictOnMeta below when loading params from state dict.
|
|
|
|
Usage:
|
|
```
|
|
with sd_disable_initialization.InitializeOnMeta():
|
|
sd_model = instantiate_from_config(sd_config.model)
|
|
```
|
|
"""
|
|
|
|
def __enter__(self):
|
|
if shared.cmd_opts.disable_model_loading_ram_optimization:
|
|
return
|
|
|
|
def set_device(x):
|
|
x["device"] = "meta"
|
|
return x
|
|
|
|
linear_init = self.replace(torch.nn.Linear, '__init__', lambda *args, **kwargs: linear_init(*args, **set_device(kwargs)))
|
|
conv2d_init = self.replace(torch.nn.Conv2d, '__init__', lambda *args, **kwargs: conv2d_init(*args, **set_device(kwargs)))
|
|
mha_init = self.replace(torch.nn.MultiheadAttention, '__init__', lambda *args, **kwargs: mha_init(*args, **set_device(kwargs)))
|
|
self.replace(torch.nn.Module, 'to', lambda *args, **kwargs: None)
|
|
|
|
def __exit__(self, exc_type, exc_val, exc_tb):
|
|
self.restore()
|
|
|
|
|
|
class LoadStateDictOnMeta(ReplaceHelper):
|
|
"""
|
|
Context manager that allows to read parameters from state_dict into a model that has some of its parameters in the meta device.
|
|
As those parameters are read from state_dict, they will be deleted from it, so by the end state_dict will be mostly empty, to save memory.
|
|
Meant to be used together with InitializeOnMeta above.
|
|
|
|
Usage:
|
|
```
|
|
with sd_disable_initialization.LoadStateDictOnMeta(state_dict):
|
|
model.load_state_dict(state_dict, strict=False)
|
|
```
|
|
"""
|
|
|
|
def __init__(self, state_dict, device):
|
|
super().__init__()
|
|
self.state_dict = state_dict
|
|
self.device = device
|
|
|
|
def __enter__(self):
|
|
if shared.cmd_opts.disable_model_loading_ram_optimization:
|
|
return
|
|
|
|
sd = self.state_dict
|
|
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]
|
|
|
|
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)
|
|
|
|
original(self, state_dict, prefix, *args, **kwargs)
|
|
|
|
for name, _ in params:
|
|
key = prefix + name
|
|
if key in sd:
|
|
del sd[key]
|
|
|
|
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))
|
|
|
|
def __exit__(self, exc_type, exc_val, exc_tb):
|
|
self.restore()
|