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Fix number of minimal calls to the Hub with peft integration (#25715)

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* Fix number of minimal calls to the Hub with peft integration

* Alternate design

* And this way?

* Revert

* Address comments
This commit is contained in:
Sylvain Gugger 2023-08-24 14:56:11 +02:00 committed by GitHub
parent 70b49f023c
commit 2febd50614
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GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 98 additions and 24 deletions
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35
src/transformers/modeling_utils.py
View File

@ -51,6 +51,7 @@ from .pytorch_utils import ( # noqa: F401
from .utils import (
ADAPTER_SAFE_WEIGHTS_NAME,
ADAPTER_WEIGHTS_NAME,
CONFIG_NAME,
DUMMY_INPUTS,
FLAX_WEIGHTS_NAME,
SAFE_WEIGHTS_INDEX_NAME,
@ -65,6 +66,7 @@ from .utils import (
cached_file,
copy_func,
download_url,
extract_commit_hash,
has_file,
is_accelerate_available,
is_auto_gptq_available,
@ -2368,13 +2370,39 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
" ignored."
)
if commit_hash is None:
if not isinstance(config, PretrainedConfig):
# We make a call to the config file first (which may be absent) to get the commit hash as soon as possible
resolved_config_file = cached_file(
pretrained_model_name_or_path,
CONFIG_NAME,
cache_dir=cache_dir,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
local_files_only=local_files_only,
token=token,
revision=revision,
subfolder=subfolder,
_raise_exceptions_for_missing_entries=False,
_raise_exceptions_for_connection_errors=False,
)
commit_hash = extract_commit_hash(resolved_config_file, commit_hash)
else:
commit_hash = getattr(config, "_commit_hash", None)
if is_peft_available() and _adapter_model_path is None:
maybe_adapter_model_path = find_adapter_config_file(
pretrained_model_name_or_path,
cache_dir=cache_dir,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
local_files_only=local_files_only,
token=token,
revision=revision,
subfolder=subfolder,
token=token,
commit_hash=commit_hash,
_commit_hash=commit_hash,
)
elif is_peft_available() and _adapter_model_path is not None:
maybe_adapter_model_path = _adapter_model_path
@ -2622,9 +2650,6 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
" `pip install --upgrade bitsandbytes`."
)
if commit_hash is None:
commit_hash = getattr(config, "_commit_hash", None)
# This variable will flag if we're loading a sharded checkpoint. In this case the archive file is just the
# index of the files.
is_sharded = False

37
src/transformers/models/auto/auto_factory.py
View File

@ -22,7 +22,16 @@ from collections import OrderedDict
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...utils import copy_func, find_adapter_config_file, is_peft_available, logging, requires_backends
from ...utils import (
CONFIG_NAME,
cached_file,
copy_func,
extract_commit_hash,
find_adapter_config_file,
is_peft_available,
logging,
requires_backends,
)
from .configuration_auto import AutoConfig, model_type_to_module_name, replace_list_option_in_docstrings
@ -443,7 +452,6 @@ class _BaseAutoModelClass:
kwargs["_from_auto"] = True
hub_kwargs_names = [
"cache_dir",
"code_revision",
"force_download",
"local_files_only",
"proxies",
@ -454,6 +462,8 @@ class _BaseAutoModelClass:
"token",
]
hub_kwargs = {name: kwargs.pop(name) for name in hub_kwargs_names if name in kwargs}
code_revision = kwargs.pop("code_revision", None)
commit_hash = kwargs.pop("_commit_hash", None)
token = hub_kwargs.pop("token", None)
use_auth_token = hub_kwargs.pop("use_auth_token", None)
@ -470,12 +480,23 @@ class _BaseAutoModelClass:
if token is not None:
hub_kwargs["token"] = token
if is_peft_available():
revision = kwargs.get("revision", None)
subfolder = kwargs.get("subfolder", None)
if commit_hash is None:
if not isinstance(config, PretrainedConfig):
# We make a call to the config file first (which may be absent) to get the commit hash as soon as possible
resolved_config_file = cached_file(
pretrained_model_name_or_path,
CONFIG_NAME,
_raise_exceptions_for_missing_entries=False,
_raise_exceptions_for_connection_errors=False,
**hub_kwargs,
)
commit_hash = extract_commit_hash(resolved_config_file, commit_hash)
else:
commit_hash = getattr(config, "_commit_hash", None)
if is_peft_available():
maybe_adapter_path = find_adapter_config_file(
pretrained_model_name_or_path, revision=revision, token=token, subfolder=subfolder
pretrained_model_name_or_path, _commit_hash=commit_hash, **hub_kwargs
)
if maybe_adapter_path is not None:
@ -499,6 +520,8 @@ class _BaseAutoModelClass:
pretrained_model_name_or_path,
return_unused_kwargs=True,
trust_remote_code=trust_remote_code,
code_revision=code_revision,
_commit_hash=commit_hash,
**hub_kwargs,
**kwargs,
)
@ -517,7 +540,7 @@ class _BaseAutoModelClass:
if has_remote_code and trust_remote_code:
class_ref = config.auto_map[cls.__name__]
model_class = get_class_from_dynamic_module(
class_ref, pretrained_model_name_or_path, **hub_kwargs, **kwargs
class_ref, pretrained_model_name_or_path, code_revision=code_revision, **hub_kwargs, **kwargs
)
_ = hub_kwargs.pop("code_revision", None)
if os.path.isdir(pretrained_model_name_or_path):

7
src/transformers/models/auto/configuration_auto.py
View File

@ -1007,6 +1007,8 @@ class AutoConfig:
kwargs["_from_auto"] = True
kwargs["name_or_path"] = pretrained_model_name_or_path
trust_remote_code = kwargs.pop("trust_remote_code", None)
code_revision = kwargs.pop("code_revision", None)
config_dict, unused_kwargs = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
has_remote_code = "auto_map" in config_dict and "AutoConfig" in config_dict["auto_map"]
has_local_code = "model_type" in config_dict and config_dict["model_type"] in CONFIG_MAPPING
@ -1016,10 +1018,11 @@ class AutoConfig:
if has_remote_code and trust_remote_code:
class_ref = config_dict["auto_map"]["AutoConfig"]
config_class = get_class_from_dynamic_module(class_ref, pretrained_model_name_or_path, **kwargs)
config_class = get_class_from_dynamic_module(
class_ref, pretrained_model_name_or_path, code_revision=code_revision, **kwargs
)
if os.path.isdir(pretrained_model_name_or_path):
config_class.register_for_auto_class()
_ = kwargs.pop("code_revision", None)
return config_class.from_pretrained(pretrained_model_name_or_path, **kwargs)
elif "model_type" in config_dict:
config_class = CONFIG_MAPPING[config_dict["model_type"]]

43
src/transformers/utils/peft_utils.py
View File

@ -13,7 +13,7 @@
# limitations under the License.
import importlib
import os
from typing import Optional
from typing import Dict, Optional, Union
from packaging import version
@ -28,10 +28,15 @@ ADAPTER_SAFE_WEIGHTS_NAME = "adapter_model.safetensors"
def find_adapter_config_file(
model_id: str,
revision: str = None,
subfolder: str = None,
token: Optional[str] = None,
commit_hash: Optional[str] = None,
cache_dir: Optional[Union[str, os.PathLike]] = None,
force_download: bool = False,
resume_download: bool = False,
proxies: Optional[Dict[str, str]] = None,
token: Optional[Union[bool, str]] = None,
revision: Optional[str] = None,
local_files_only: bool = False,
subfolder: str = "",
_commit_hash: Optional[str] = None,
) -> Optional[str]:
r"""
Simply checks if the model stored on the Hub or locally is an adapter model or not, return the path the the adapter
@ -40,6 +45,20 @@ def find_adapter_config_file(
Args:
model_id (`str`):
The identifier of the model to look for, can be either a local path or an id to the repository on the Hub.
cache_dir (`str` or `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.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force to (re-)download the configuration files and override the cached versions if they
exist.
resume_download (`bool`, *optional*, defaults to `False`):
Whether or not to delete incompletely received file. Attempts 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.
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 `huggingface-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
@ -51,12 +70,11 @@ def find_adapter_config_file(
</Tip>
local_files_only (`bool`, *optional*, defaults to `False`):
If `True`, will only try to load the tokenizer configuration from local files.
subfolder (`str`, *optional*, defaults to `""`):
In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can
specify the folder name here.
token (`str`, `optional`):
Whether to use authentication token to load the remote folder. Userful to load private repositories that
are on HuggingFace Hub. You might need to call `huggingface-cli login` and paste your tokens to cache it.
"""
adapter_cached_filename = None
if model_id is None:
@ -69,10 +87,15 @@ def find_adapter_config_file(
adapter_cached_filename = cached_file(
model_id,
ADAPTER_CONFIG_NAME,
revision=revision,
cache_dir=cache_dir,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
token=token,
_commit_hash=commit_hash,
revision=revision,
local_files_only=local_files_only,
subfolder=subfolder,
_commit_hash=_commit_hash,
_raise_exceptions_for_missing_entries=False,
_raise_exceptions_for_connection_errors=False,
)