in muse/modeling_utils.py [0:0]
def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):
r"""
Instantiate a pretrained pytorch model from a pre-trained model configuration.
The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). To train
the model, you should first set it back in training mode with `model.train()`.
The warning *Weights from XXX not initialized from pretrained model* means that the weights of XXX do not come
pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning
task.
The warning *Weights from XXX not used in YYY* means that the layer XXX is not used by YYY, therefore those
weights are discarded.
Parameters:
pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
Can be either:
- A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids should have an organization name, like `google/ddpm-celebahq-256`.
- A path to a *directory* containing model weights saved using [`~ModelMixin.save_config`], e.g.,
`./my_model_directory/`.
cache_dir (`Union[str, os.PathLike]`, *optional*):
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.
torch_dtype (`str` or `torch.dtype`, *optional*):
Override the default `torch.dtype` and load the model under this dtype. If `"auto"` is passed the dtype
will be automatically derived from the model's weights.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download (`bool`, *optional*, defaults to `False`):
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
output_loading_info(`bool`, *optional*, defaults to `False`):
Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
local_files_only(`bool`, *optional*, defaults to `False`):
Whether or not to only look at local files (i.e., do not try to download the model).
use_auth_token (`str` or *bool*, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
when running `diffusers-cli login` (stored in `~/.huggingface`).
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
identifier allowed by git.
from_flax (`bool`, *optional*, defaults to `False`):
Load the model weights from a Flax checkpoint save file.
subfolder (`str`, *optional*, defaults to `""`):
In case the relevant files are located inside a subfolder of the model repo (either remote in
huggingface.co or downloaded locally), you can specify the folder name here.
mirror (`str`, *optional*):
Mirror source to accelerate downloads in China. If you are from China and have an accessibility
problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety.
Please refer to the mirror site for more information.
device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):
A map that specifies where each submodule should go. It doesn't need to be refined to each
parameter/buffer name, once a given module name is inside, every submodule of it will be sent to the
same device.
To have Accelerate compute the most optimized `device_map` automatically, set `device_map="auto"`. For
more information about each option see [designing a device
map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
Speed up model loading by not initializing the weights and only loading the pre-trained weights. This
also tries to not use more than 1x model size in CPU memory (including peak memory) while loading the
model. This is only supported when torch version >= 1.9.0. If you are using an older version of torch,
setting this argument to `True` will raise an error.
<Tip>
It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated
models](https://huggingface.co/docs/hub/models-gated#gated-models).
</Tip>
<Tip>
Activate the special ["offline-mode"](https://huggingface.co/diffusers/installation.html#offline-mode) to use
this method in a firewalled environment.
</Tip>
"""
cache_dir = kwargs.pop("cache_dir", MUSE_CACHE)
ignore_mismatched_sizes = kwargs.pop("ignore_mismatched_sizes", False)
force_download = kwargs.pop("force_download", False)
resume_download = kwargs.pop("resume_download", False)
proxies = kwargs.pop("proxies", None)
output_loading_info = kwargs.pop("output_loading_info", False)
local_files_only = kwargs.pop("local_files_only", False) # TODO
use_auth_token = kwargs.pop("use_auth_token", None)
revision = kwargs.pop("revision", None)
torch_dtype = kwargs.pop("torch_dtype", None)
subfolder = kwargs.pop("subfolder", None)
device_map = kwargs.pop("device_map", None)
low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT)
if low_cpu_mem_usage is False and device_map is not None:
raise ValueError(
f"You cannot set `low_cpu_mem_usage` to `False` while using device_map={device_map} for loading and"
" dispatching. Please make sure to set `low_cpu_mem_usage=True`."
)
user_agent = {
"diffusers": __version__,
"file_type": "model",
"framework": "pytorch",
}
# Load config if we don't provide a configuration
config_path = pretrained_model_name_or_path
# This variable will flag if we're loading a sharded checkpoint. In this case the archive file is just the
# Load model
model_file = None
if model_file is None:
model_file = _get_model_file(
pretrained_model_name_or_path,
weights_name=WEIGHTS_NAME,
cache_dir=cache_dir,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
revision=revision,
subfolder=subfolder,
user_agent=user_agent,
)
if low_cpu_mem_usage:
# Instantiate model with empty weights
with accelerate.init_empty_weights():
config, unused_kwargs = cls.load_config(
config_path,
cache_dir=cache_dir,
return_unused_kwargs=True,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
revision=revision,
subfolder=subfolder,
device_map=device_map,
**kwargs,
)
model = cls.from_config(config, **unused_kwargs)
# if device_map is None, load the state dict and move the params from meta device to the cpu
if device_map is None:
param_device = "cpu"
state_dict = load_state_dict(model_file)
# move the params from meta device to cpu
missing_keys = set(model.state_dict().keys()) - set(state_dict.keys())
if len(missing_keys) > 0:
raise ValueError(
f"Cannot load {cls} from {pretrained_model_name_or_path} because the following keys are"
f" missing: \n {', '.join(missing_keys)}. \n Please make sure to pass"
" `low_cpu_mem_usage=False` and `device_map=None` if you want to randomely initialize"
" those weights or else make sure your checkpoint file is correct."
)
for param_name, param in state_dict.items():
accepts_dtype = "dtype" in set(inspect.signature(set_module_tensor_to_device).parameters.keys())
if accepts_dtype:
set_module_tensor_to_device(model, param_name, param_device, value=param, dtype=torch_dtype)
else:
set_module_tensor_to_device(model, param_name, param_device, value=param)
else: # else let accelerate handle loading and dispatching.
# Load weights and dispatch according to the device_map
# by deafult the device_map is None and the weights are loaded on the CPU
accelerate.load_checkpoint_and_dispatch(model, model_file, device_map, dtype=torch_dtype)
loading_info = {
"missing_keys": [],
"unexpected_keys": [],
"mismatched_keys": [],
"error_msgs": [],
}
else:
config, unused_kwargs = cls.load_config(
config_path,
cache_dir=cache_dir,
return_unused_kwargs=True,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
revision=revision,
subfolder=subfolder,
device_map=device_map,
**kwargs,
)
model = cls.from_config(config, **unused_kwargs)
state_dict = load_state_dict(model_file)
model, missing_keys, unexpected_keys, mismatched_keys, error_msgs = cls._load_pretrained_model(
model,
state_dict,
model_file,
pretrained_model_name_or_path,
ignore_mismatched_sizes=ignore_mismatched_sizes,
)
loading_info = {
"missing_keys": missing_keys,
"unexpected_keys": unexpected_keys,
"mismatched_keys": mismatched_keys,
"error_msgs": error_msgs,
}
if torch_dtype is not None and not isinstance(torch_dtype, torch.dtype):
raise ValueError(
f"{torch_dtype} needs to be of type `torch.dtype`, e.g. `torch.float16`, but is {type(torch_dtype)}."
)
elif torch_dtype is not None:
model = model.to(torch_dtype)
model.register_to_config(_name_or_path=pretrained_model_name_or_path)
# Set model in evaluation mode to deactivate DropOut modules by default
model.eval()
if output_loading_info:
return model, loading_info
return model