def test_hf_storage():
key = "dummy"
storage = HuggingFaceStorage()
has_token = HfFolder().get_token()
if not has_token:
token = os.environ["HUGGING_FACE_TOKEN"]
HfFolder().save_token(token)
state_dict = storage.get(key)
model.load_state_dict(state_dict)
assert key in storage
assert key in storage.models
def _get_repo_url_from_name(
repo_name: str,
organization: Optional[str] = None,
private: bool = None,
use_auth_token: Optional[Union[bool, str]] = None,
) -> str:
if isinstance(use_auth_token, str):
token = use_auth_token
elif use_auth_token:
token = HfFolder.get_token()
if token is None:
raise ValueError(
"You must login to the Hugging Face hub on this computer by typing `transformers-cli login` and "
"entering your credentials to use `use_auth_token=True`. Alternatively, you can pass your own "
"token as the `use_auth_token` argument.")
else:
token = None
# Special provision for the test endpoint (CI)
return create_repo(
token,
repo_name,
organization=organization,
private=private,
repo_type=None,
exist_ok=True,
)
def has_file(
path_or_repo: Union[str, os.PathLike],
filename: str,
revision: Optional[str] = None,
mirror: Optional[str] = None,
proxies: Optional[Dict[str, str]] = None,
use_auth_token: Optional[Union[bool, str]] = None,
):
"""
Checks if a repo contains a given file wihtout downloading it. Works for remote repos and local folders.
<Tip warning={false}>
This function will raise an error if the repository `path_or_repo` is not valid or if `revision` does not exist for
this repo, but will return False for regular connection errors.
</Tip>
"""
if os.path.isdir(path_or_repo):
return os.path.isfile(os.path.join(path_or_repo, filename))
url = hf_bucket_url(path_or_repo,
filename=filename,
revision=revision,
mirror=mirror)
headers = {"user-agent": http_user_agent()}
if isinstance(use_auth_token, str):
headers["authorization"] = f"Bearer {use_auth_token}"
elif use_auth_token:
token = HfFolder.get_token()
if token is None:
raise EnvironmentError(
"You specified use_auth_token=True, but a huggingface token was not found."
)
headers["authorization"] = f"Bearer {token}"
r = requests.head(url,
headers=headers,
allow_redirects=False,
proxies=proxies,
timeout=10)
try:
_raise_for_status(r)
return True
except RepositoryNotFoundError as e:
logger.error(e)
raise EnvironmentError(
f"{path_or_repo} is not a local folder or a valid repository name on 'https://hf.co'."
)
except RevisionNotFoundError as e:
logger.error(e)
raise EnvironmentError(
f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for this "
"model name. Check the model page at 'https://huggingface.co/{path_or_repo}' for available revisions."
)
except requests.HTTPError:
# We return false for EntryNotFoundError (logical) as well as any connection error.
return False
def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
if token is None:
token = HfFolder.get_token()
if organization is None:
username = whoami(token)["name"]
return f"{username}/{model_id}"
else:
return f"{organization}/{model_id}"
def get_list_of_files(
path_or_repo: Union[str, os.PathLike],
revision: Optional[str] = None,
use_auth_token: Optional[Union[bool, str]] = None,
local_files_only: bool = False,
) -> List[str]:
"""
Gets the list of files inside `path_or_repo`.
Args:
path_or_repo (`str` or `os.PathLike`):
Can be either the id of a repo on huggingface.co or a path to a *directory*.
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.
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 `transformers-cli login` (stored in `~/.huggingface`).
local_files_only (`bool`, *optional*, defaults to `False`):
Whether or not to only rely on local files and not to attempt to download any files.
<Tip warning={true}>
This API is not optimized, so calling it a lot may result in connection errors.
</Tip>
Returns:
`List[str]`: The list of files available in `path_or_repo`.
"""
path_or_repo = str(path_or_repo)
# If path_or_repo is a folder, we just return what is inside (subdirectories included).
if os.path.isdir(path_or_repo):
list_of_files = []
for path, dir_names, file_names in os.walk(path_or_repo):
list_of_files.extend([os.path.join(path, f) for f in file_names])
return list_of_files
# Can't grab the files if we are on offline mode.
if is_offline_mode() or local_files_only:
return []
# Otherwise we grab the token and use the list_repo_files method.
if isinstance(use_auth_token, str):
token = use_auth_token
elif use_auth_token is True:
token = HfFolder.get_token()
else:
token = None
try:
return list_repo_files(path_or_repo, revision=revision, token=token)
except HTTPError as e:
raise ValueError(
f"{path_or_repo} is not a local path or a model identifier on the model Hub. Did you make a typo?"
) from e
def login_to_hub() -> None:
"""Login to huggingface hub"""
access_token = HfFolder.get_token()
if access_token is not None and HfApi()._is_valid_token(access_token):
logging.info("Huggingface Hub token found and valid")
HfApi().set_access_token(access_token)
else:
subprocess.call(["huggingface-cli", "login"])
HfApi().set_access_token(HfFolder().get_token())
# check if git lfs is installed
try:
subprocess.call(["git", "lfs", "version"])
except FileNotFoundError:
raise OSError(
"Looks like you do not have git-lfs installed, please install. \
You can install from https://git-lfs.github.com/. \
Then run `git lfs install` (you only have to do this once)."
)
def push_to_hf_hub(
model,
local_dir,
repo_namespace_or_url=None,
commit_message='Add model',
use_auth_token=True,
git_email=None,
git_user=None,
revision=None,
model_config=None,
):
if repo_namespace_or_url:
repo_owner, repo_name = repo_namespace_or_url.rstrip('/').split(
'/')[-2:]
else:
if isinstance(use_auth_token, str):
token = use_auth_token
else:
token = HfFolder.get_token()
if token is None:
raise ValueError(
"You must login to the Hugging Face hub on this computer by typing `transformers-cli login` and "
"entering your credentials to use `use_auth_token=True`. Alternatively, you can pass your own "
"token as the `use_auth_token` argument.")
repo_owner = HfApi().whoami(token)['name']
repo_name = Path(local_dir).name
repo_url = f'https://huggingface.co/{repo_owner}/{repo_name}'
repo = Repository(
local_dir,
clone_from=repo_url,
use_auth_token=use_auth_token,
git_user=git_user,
git_email=git_email,
revision=revision,
)
# Prepare a default model card that includes the necessary tags to enable inference.
readme_text = f'---\ntags:\n- image-classification\n- timm\nlibrary_tag: timm\n---\n# Model card for {repo_name}'
with repo.commit(commit_message):
# Save model weights and config.
save_for_hf(model, repo.local_dir, model_config=model_config)
# Save a model card if it doesn't exist.
readme_path = Path(repo.local_dir) / 'README.md'
if not readme_path.exists():
readme_path.write_text(readme_text)
return repo.git_remote_url()
def move_cache(cache_dir=None, new_cache_dir=None, token=None):
if new_cache_dir is None:
new_cache_dir = TRANSFORMERS_CACHE
if cache_dir is None:
# Migrate from old cache in .cache/huggingface/hub
old_cache = Path(TRANSFORMERS_CACHE).parent / "transformers"
if os.path.isdir(str(old_cache)):
cache_dir = str(old_cache)
else:
cache_dir = new_cache_dir
if token is None:
token = HfFolder.get_token()
cached_files = get_all_cached_files(cache_dir=cache_dir)
print(f"Moving {len(cached_files)} files to the new cache system")
hub_metadata = {}
for file_info in tqdm(cached_files):
url = file_info.pop("url")
if url not in hub_metadata:
try:
hub_metadata[url] = get_hub_metadata(url, token=token)
except requests.HTTPError:
continue
etag, commit_hash = hub_metadata[url]
if etag is None or commit_hash is None:
continue
if file_info["etag"] != etag:
# Cached file is not up to date, we just throw it as a new version will be downloaded anyway.
clean_files_for(os.path.join(cache_dir, file_info["file"]))
continue
url_info = extract_info_from_url(url)
if url_info is None:
# Not a file from huggingface.co
continue
repo = os.path.join(new_cache_dir, url_info["repo"])
move_to_new_cache(
file=os.path.join(cache_dir, file_info["file"]),
repo=repo,
filename=url_info["filename"],
revision=url_info["revision"],
etag=etag,
commit_hash=commit_hash,
)
def get_hub_metadata(url, token=None):
"""
Returns the commit hash and associated etag for a given url.
"""
if token is None:
token = HfFolder.get_token()
headers = {"user-agent": http_user_agent()}
headers["authorization"] = f"Bearer {token}"
r = huggingface_hub.file_download._request_with_retry(
method="HEAD", url=url, headers=headers, allow_redirects=False)
huggingface_hub.file_download._raise_for_status(r)
commit_hash = r.headers.get(HUGGINGFACE_HEADER_X_REPO_COMMIT)
etag = r.headers.get(HUGGINGFACE_HEADER_X_LINKED_ETAG) or r.headers.get(
"ETag")
if etag is not None:
etag = huggingface_hub.file_download._normalize_etag(etag)
return etag, commit_hash
def get_list_of_files(
path_or_repo: Union[str, os.PathLike],
revision: Optional[str] = None,
use_auth_token: Optional[Union[bool, str]] = None,
) -> List[str]:
"""
Gets the list of files inside :obj:`path_or_repo`.
Args:
path_or_repo (:obj:`str` or :obj:`os.PathLike`):
Can be either the id of a repo on huggingface.co or a path to a `directory`.
revision (:obj:`str`, `optional`, defaults to :obj:`"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.
use_auth_token (:obj:`str` or `bool`, `optional`):
The token to use as HTTP bearer authorization for remote files. If :obj:`True`, will use the token
generated when running :obj:`transformers-cli login` (stored in :obj:`~/.huggingface`).
Returns:
:obj:`List[str]`: The list of files available in :obj:`path_or_repo`.
"""
path_or_repo = str(path_or_repo)
# If path_or_repo is a folder, we just return what is inside (subdirectories included).
if os.path.isdir(path_or_repo):
list_of_files = []
for path, dir_names, file_names in os.walk(path_or_repo):
list_of_files.extend([os.path.join(path, f) for f in file_names])
return list_of_files
# Can't grab the files if we are on offline mode.
if is_offline_mode():
return []
# Otherwise we grab the token and use the model_info method.
if isinstance(use_auth_token, str):
token = use_auth_token
elif use_auth_token is True:
token = HfFolder.get_token()
else:
token = None
model_info = HfApi(endpoint=HUGGINGFACE_CO_RESOLVE_ENDPOINT).model_info(
path_or_repo, revision=revision, token=token)
return [f.rfilename for f in model_info.siblings]
def _create_repo(
self,
repo_id: str,
private: Optional[bool] = None,
use_auth_token: Optional[Union[bool, str]] = None,
repo_url: Optional[str] = None,
organization: Optional[str] = None,
):
"""
Create the repo if needed, cleans up repo_id with deprecated kwards `repo_url` and `organization`, retrives the
token.
"""
if repo_url is not None:
warnings.warn(
"The `repo_url` argument is deprecated and will be removed in v5 of Transformers. Use `repo_id` "
"instead.")
repo_id = repo_url.replace(f"{HUGGINGFACE_CO_RESOLVE_ENDPOINT}/",
"")
if organization is not None:
warnings.warn(
"The `organization` argument is deprecated and will be removed in v5 of Transformers. Set your "
"organization directly in the `repo_id` passed instead (`repo_id={organization}/{model_id}`)."
)
if not repo_id.startswith(organization):
if "/" in repo_id:
repo_id = repo_id.split("/")[-1]
repo_id = f"{organization}/{repo_id}"
token = HfFolder.get_token(
) if use_auth_token is True else use_auth_token
url = create_repo(repo_id=repo_id,
token=token,
private=private,
exist_ok=True)
# If the namespace is not there, add it or `upload_file` will complain
if "/" not in repo_id and url != f"{HUGGINGFACE_CO_RESOLVE_ENDPOINT}/{repo_id}":
repo_id = get_full_repo_name(repo_id, token=token)
return repo_id, token
def get_from_cache(
url: str,
cache_dir=None,
force_download=False,
proxies=None,
etag_timeout=10,
resume_download=False,
user_agent: Union[Dict, str, None] = None,
use_auth_token: Union[bool, str, None] = None,
local_files_only=False,
) -> Optional[str]:
"""
Given a URL, look for the corresponding file in the local cache. If it's not there, download it. Then return the
path to the cached file.
Return:
Local path (string) of file or if networking is off, last version of file cached on disk.
Raises:
In case of non-recoverable file (non-existent or inaccessible url + no cache on disk).
"""
if cache_dir is None:
cache_dir = TRANSFORMERS_CACHE
if isinstance(cache_dir, Path):
cache_dir = str(cache_dir)
os.makedirs(cache_dir, exist_ok=True)
headers = {"user-agent": http_user_agent(user_agent)}
if isinstance(use_auth_token, str):
headers["authorization"] = f"Bearer {use_auth_token}"
elif use_auth_token:
token = HfFolder.get_token()
if token is None:
raise EnvironmentError(
"You specified use_auth_token=True, but a huggingface token was not found."
)
headers["authorization"] = f"Bearer {token}"
url_to_download = url
etag = None
if not local_files_only:
try:
r = requests.head(url,
headers=headers,
allow_redirects=False,
proxies=proxies,
timeout=etag_timeout)
_raise_for_status(r)
etag = r.headers.get("X-Linked-Etag") or r.headers.get("ETag")
# We favor a custom header indicating the etag of the linked resource, and
# we fallback to the regular etag header.
# If we don't have any of those, raise an error.
if etag is None:
raise OSError(
"Distant resource does not have an ETag, we won't be able to reliably ensure reproducibility."
)
# In case of a redirect,
# save an extra redirect on the request.get call,
# and ensure we download the exact atomic version even if it changed
# between the HEAD and the GET (unlikely, but hey).
if 300 <= r.status_code <= 399:
url_to_download = r.headers["Location"]
except (
requests.exceptions.SSLError,
requests.exceptions.ProxyError,
RepositoryNotFoundError,
EntryNotFoundError,
RevisionNotFoundError,
):
# Actually raise for those subclasses of ConnectionError
# Also raise the custom errors coming from a non existing repo/branch/file as they are caught later on.
raise
except (HTTPError, requests.exceptions.ConnectionError,
requests.exceptions.Timeout):
# Otherwise, our Internet connection is down.
# etag is None
pass
filename = url_to_filename(url, etag)
# get cache path to put the file
cache_path = os.path.join(cache_dir, filename)
# etag is None == we don't have a connection or we passed local_files_only.
# try to get the last downloaded one
if etag is None:
if os.path.exists(cache_path):
return cache_path
else:
matching_files = [
file for file in fnmatch.filter(os.listdir(cache_dir),
filename.split(".")[0] + ".*")
if not file.endswith(".json") and not file.endswith(".lock")
]
if len(matching_files) > 0:
return os.path.join(cache_dir, matching_files[-1])
else:
# If files cannot be found and local_files_only=True,
# the models might've been found if local_files_only=False
# Notify the user about that
if local_files_only:
raise FileNotFoundError(
"Cannot find the requested files in the cached path and outgoing traffic has been"
" disabled. To enable model look-ups and downloads online, set 'local_files_only'"
" to False.")
else:
raise ValueError(
"Connection error, and we cannot find the requested files in the cached path."
" Please try again or make sure your Internet connection is on."
)
# From now on, etag is not None.
if os.path.exists(cache_path) and not force_download:
return cache_path
# Prevent parallel downloads of the same file with a lock.
lock_path = cache_path + ".lock"
with FileLock(lock_path):
# If the download just completed while the lock was activated.
if os.path.exists(cache_path) and not force_download:
# Even if returning early like here, the lock will be released.
return cache_path
if resume_download:
incomplete_path = cache_path + ".incomplete"
@contextmanager
def _resumable_file_manager() -> "io.BufferedWriter":
with open(incomplete_path, "ab") as f:
yield f
temp_file_manager = _resumable_file_manager
if os.path.exists(incomplete_path):
resume_size = os.stat(incomplete_path).st_size
else:
resume_size = 0
else:
temp_file_manager = partial(tempfile.NamedTemporaryFile,
mode="wb",
dir=cache_dir,
delete=False)
resume_size = 0
# Download to temporary file, then copy to cache dir once finished.
# Otherwise you get corrupt cache entries if the download gets interrupted.
with temp_file_manager() as temp_file:
logger.info(
f"{url} not found in cache or force_download set to True, downloading to {temp_file.name}"
)
http_get(url_to_download,
temp_file,
proxies=proxies,
resume_size=resume_size,
headers=headers)
logger.info(f"storing {url} in cache at {cache_path}")
os.replace(temp_file.name, cache_path)
# NamedTemporaryFile creates a file with hardwired 0600 perms (ignoring umask), so fixing it.
umask = os.umask(0o666)
os.umask(umask)
os.chmod(cache_path, 0o666 & ~umask)
logger.info(f"creating metadata file for {cache_path}")
meta = {"url": url, "etag": etag}
meta_path = cache_path + ".json"
with open(meta_path, "w") as meta_file:
json.dump(meta, meta_file)
return cache_path
def push_to_hf(
repo_name: str,
serialization_dir: Optional[Union[str, PathLike]] = None,
archive_path: Optional[Union[str, PathLike]] = None,
organization: Optional[str] = None,
commit_message: str = "Update repository",
local_repo_path: Union[str, PathLike] = "hub",
use_auth_token: Union[bool, str] = True,
) -> str:
"""Pushes model and related files to the Hugging Face Hub ([hf.co](https://hf.co/))
# Parameters
repo_name: `str`
Name of the repository in the Hugging Face Hub.
serialization_dir : `Union[str, PathLike]`, optional (default = `None`)
Full path to a directory with the serialized model.
archive_path : `Union[str, PathLike]`, optional (default = `None`)
Full path to the zipped model (e.g. model/model.tar.gz). Use `serialization_dir` if possible.
organization : `Optional[str]`, optional (default = `None`)
Name of organization to which the model should be uploaded.
commit_message: `str` (default=`Update repository`)
Commit message to use for the push.
local_repo_path : `Union[str, Path]`, optional (default=`hub`)
Local directory where the repository will be saved.
use_auth_token (``str`` or ``bool``, `optional`, defaults ``True``):
huggingface_token can be extract from ``HfApi().login(username, password)`` and is used to authenticate
against the Hugging Face Hub (useful from Google Colab for instance). It's automatically retrieved
if you've done `huggingface-cli login` before.
"""
if serialization_dir is not None:
working_dir = Path(serialization_dir)
if archive_path is not None:
raise ValueError(
"serialization_dir and archive_path are mutually exclusive, please just use one."
)
if not working_dir.exists() or not working_dir.is_dir():
raise ValueError(
f"Can't find path: {serialization_dir}, please point"
"to a directory with the serialized model.")
elif archive_path is not None:
working_dir = Path(archive_path)
if (not working_dir.exists() or not zipfile.is_zipfile(working_dir)
and not tarfile.is_tarfile(working_dir)):
raise ValueError(
f"Can't find path: {archive_path}, please point to a .tar.gz archive"
"or to a directory with the serialized model.")
else:
logging.info(
"Using the archive_path is discouraged. Using the serialization_dir"
"will also upload metrics and TensorBoard traces to the Hugging Face Hub."
)
else:
raise ValueError(
"please specify either serialization_dir or archive_path")
info_msg = f"Preparing repository '{use_auth_token}'"
if isinstance(use_auth_token, str):
huggingface_token = use_auth_token
elif use_auth_token:
huggingface_token = HfFolder.get_token()
# Create the repo (or clone its content if it's nonempty)
api = HfApi()
repo_url = api.create_repo(
name=repo_name,
token=huggingface_token,
organization=organization,
private=False,
exist_ok=True,
)
repo_local_path = Path(local_repo_path) / repo_name
repo = Repository(repo_local_path,
clone_from=repo_url,
use_auth_token=use_auth_token)
repo.git_pull(rebase=True)
# Model file should be tracked with Git LFS
repo.lfs_track(["*.th"])
info_msg = f"Preparing repository '{repo_name}'"
if organization is not None:
info_msg += f" ({organization})"
logging.info(info_msg)
# Extract information from either serializable directory or a
# .tar.gz file
if serialization_dir is not None:
for filename in working_dir.iterdir():
_copy_allowed_file(Path(filename), repo_local_path)
else:
with tempfile.TemporaryDirectory() as temp_dir:
extracted_dir = Path(
cached_path(working_dir, temp_dir, extract_archive=True))
for filename in extracted_dir.iterdir():
_copy_allowed_file(Path(filename), repo_local_path)
_create_model_card(repo_local_path)
logging.info(f"Pushing repo {repo_name} to the Hugging Face Hub")
repo.push_to_hub(commit_message=commit_message)
logging.info(f"View your model in {repo_url}")
return repo_url
def setUpClass(cls):
cls._token = TOKEN
set_access_token(TOKEN)
HfFolder.save_token(TOKEN)
def get_cached_module_file(
pretrained_model_name_or_path: Union[str, os.PathLike],
module_file: str,
cache_dir: Optional[Union[str, os.PathLike]] = None,
force_download: bool = False,
resume_download: bool = False,
proxies: Optional[Dict[str, str]] = None,
use_auth_token: Optional[Union[bool, str]] = None,
revision: Optional[str] = None,
local_files_only: bool = False,
):
"""
Prepares Downloads a module from a local folder or a distant repo and returns its path inside the cached
Transformers module.
Args:
pretrained_model_name_or_path (`str` or `os.PathLike`):
This can be either:
- a string, the *model id* of a pretrained model configuration hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced
under a user or organization name, like `dbmdz/bert-base-german-cased`.
- a path to a *directory* containing a configuration file saved using the
[`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`.
module_file (`str`):
The name of the module file containing the class to look for.
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.
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 `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
identifier allowed by git.
local_files_only (`bool`, *optional*, defaults to `False`):
If `True`, will only try to load the tokenizer configuration from local files.
<Tip>
Passing `use_auth_token=True` is required when you want to use a private model.
</Tip>
Returns:
`str`: The path to the module inside the cache.
"""
if is_offline_mode() and not local_files_only:
logger.info("Offline mode: forcing local_files_only=True")
local_files_only = True
# Download and cache module_file from the repo `pretrained_model_name_or_path` of grab it if it's a local file.
pretrained_model_name_or_path = str(pretrained_model_name_or_path)
if os.path.isdir(pretrained_model_name_or_path):
submodule = "local"
else:
submodule = pretrained_model_name_or_path.replace("/", os.path.sep)
try:
# Load from URL or cache if already cached
resolved_module_file = cached_file(
pretrained_model_name_or_path,
module_file,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
)
except EnvironmentError:
logger.error(
f"Could not locate the {module_file} inside {pretrained_model_name_or_path}."
)
raise
# Check we have all the requirements in our environment
modules_needed = check_imports(resolved_module_file)
# Now we move the module inside our cached dynamic modules.
full_submodule = TRANSFORMERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
create_dynamic_module(full_submodule)
submodule_path = Path(HF_MODULES_CACHE) / full_submodule
if submodule == "local":
# We always copy local files (we could hash the file to see if there was a change, and give them the name of
# that hash, to only copy when there is a modification but it seems overkill for now).
# The only reason we do the copy is to avoid putting too many folders in sys.path.
shutil.copy(resolved_module_file, submodule_path / module_file)
for module_needed in modules_needed:
module_needed = f"{module_needed}.py"
shutil.copy(
os.path.join(pretrained_model_name_or_path, module_needed),
submodule_path / module_needed)
else:
# Get the commit hash
# TODO: we will get this info in the etag soon, so retrieve it from there and not here.
if isinstance(use_auth_token, str):
token = use_auth_token
elif use_auth_token is True:
token = HfFolder.get_token()
else:
token = None
commit_hash = model_info(pretrained_model_name_or_path,
revision=revision,
token=token).sha
# The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
# benefit of versioning.
submodule_path = submodule_path / commit_hash
full_submodule = full_submodule + os.path.sep + commit_hash
create_dynamic_module(full_submodule)
if not (submodule_path / module_file).exists():
shutil.copy(resolved_module_file, submodule_path / module_file)
# Make sure we also have every file with relative
for module_needed in modules_needed:
if not (submodule_path / module_needed).exists():
get_cached_module_file(
pretrained_model_name_or_path,
f"{module_needed}.py",
cache_dir=cache_dir,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
use_auth_token=use_auth_token,
revision=revision,
local_files_only=local_files_only,
)
return os.path.join(full_submodule, module_file)
def push_to_huggingface_hub(self):
"""Creates a downstream repository on the Hub and pushes training artifacts to it."""
if self.args.hf_hub_org.lower() != "none":
organization = self.args.hf_hub_org
else:
organization = os.environ.get("HF_USERNAME")
huggingface_token = HfFolder.get_token()
print(f"[Runner] - Organisation to push fine-tuned model to: {organization}")
# Extract upstream repository metadata
if self.args.hub == "huggingface":
model_info = HfApi().model_info(self.args.upstream, token=huggingface_token)
downstream_model_id = model_info.sha
# Exclude "/" characters from downstream repo ID
upstream_model_id = model_info.modelId.replace("/", "__")
else:
upstream_model_id = self.args.upstream.replace("/", "__")
downstream_model_id = str(uuid.uuid4())[:8]
repo_name = f"{upstream_model_id}__{downstream_model_id}"
# Create downstream repo on the Hub
repo_url = HfApi().create_repo(
token=huggingface_token,
name=repo_name,
organization=organization,
exist_ok=True,
private=False,
)
print(f"[Runner] - Created Hub repo: {repo_url}")
# Download repo
HF_HUB_DIR = "hf_hub"
REPO_ROOT_DIR = os.path.join(self.args.expdir, HF_HUB_DIR, repo_name)
REPO_TASK_DIR = os.path.join(REPO_ROOT_DIR, self.args.downstream, self.args.expname)
print(f"[Runner] - Cloning Hub repo to {REPO_ROOT_DIR}")
model_repo = Repository(
local_dir=REPO_ROOT_DIR, clone_from=repo_url, use_auth_token=huggingface_token
)
# Pull latest changes if they exist
model_repo.git_pull()
# Copy checkpoints, tensorboard logs, and args / configs
# Note that this copies all files from the experiment directory,
# including those from multiple runs
shutil.copytree(self.args.expdir, REPO_TASK_DIR, dirs_exist_ok=True, ignore=shutil.ignore_patterns(HF_HUB_DIR))
# By default we use model.ckpt in the PreTrainedModel interface, so
# rename the best checkpoint to match this convention
checkpoints = list(Path(REPO_TASK_DIR).glob("*best*.ckpt"))
if len(checkpoints) == 0:
print("[Runner] - Did not find a best checkpoint! Using the final checkpoint instead ...")
CKPT_PATH = (
os.path.join(REPO_TASK_DIR, f"states-{self.config['runner']['total_steps']}.ckpt")
)
elif len(checkpoints) > 1:
print(f"[Runner] - More than one best checkpoint found! Using {checkpoints[0]} as default ...")
CKPT_PATH = checkpoints[0]
else:
print(f"[Runner] - Found best checkpoint {checkpoints[0]}!")
CKPT_PATH = checkpoints[0]
shutil.move(CKPT_PATH, os.path.join(REPO_TASK_DIR, "model.ckpt"))
model_repo.lfs_track("*.ckpt")
# Write model card
self._create_model_card(REPO_ROOT_DIR)
# Push everything to the Hub
print("[Runner] - Pushing model files to the Hub ...")
model_repo.push_to_hub()
print("[Runner] - Training run complete!")
def save_to_hub(self,
repo_name: str,
organization: Optional[str] = None,
private: Optional[bool] = None,
commit_message: str = "Add new SentenceTransformer model.",
local_model_path: Optional[str] = None,
exist_ok: bool = False,
replace_model_card: bool = False):
"""
Uploads all elements of this Sentence Transformer to a new HuggingFace Hub repository.
:param repo_name: Repository name for your model in the Hub.
:param organization: Organization in which you want to push your model or tokenizer (you must be a member of this organization).
:param private: Set to true, for hosting a prive model
:param commit_message: Message to commit while pushing.
:param local_model_path: Path of the model locally. If set, this file path will be uploaded. Otherwise, the current model will be uploaded
:param exist_ok: If true, saving to an existing repository is OK. If false, saving only to a new repository is possible
:param replace_model_card: If true, replace an existing model card in the hub with the automatically created model card
:return: The url of the commit of your model in the given repository.
"""
token = HfFolder.get_token()
if token is None:
raise ValueError(
"You must login to the Hugging Face hub on this computer by typing `transformers-cli login`."
)
if '/' in repo_name:
splits = repo_name.split('/', maxsplit=1)
if organization is None or organization == splits[0]:
organization = splits[0]
repo_name = splits[1]
else:
raise ValueError(
"You passed and invalid repository name: {}.".format(
repo_name))
endpoint = "https://huggingface.co"
repo_url = HfApi(endpoint=endpoint).create_repo(
token,
repo_name,
organization=organization,
private=private,
repo_type=None,
exist_ok=exist_ok,
)
full_model_name = repo_url[len(endpoint) + 1:].strip("/")
with tempfile.TemporaryDirectory() as tmp_dir:
# First create the repo (and clone its content if it's nonempty).
logging.info("Create repository and clone it if it exists")
repo = Repository(tmp_dir, clone_from=repo_url)
# If user provides local files, copy them.
if local_model_path:
copy_tree(local_model_path, tmp_dir)
else: # Else, save model directly into local repo.
create_model_card = replace_model_card or not os.path.exists(
os.path.join(tmp_dir, 'README.md'))
self.save(tmp_dir,
model_name=full_model_name,
create_model_card=create_model_card)
#Find files larger 5M and track with git-lfs
large_files = []
for root, dirs, files in os.walk(tmp_dir):
for filename in files:
file_path = os.path.join(root, filename)
rel_path = os.path.relpath(file_path, tmp_dir)
if os.path.getsize(file_path) > (5 * 1024 * 1024):
large_files.append(rel_path)
if len(large_files) > 0:
logging.info("Track files with git lfs: {}".format(
", ".join(large_files)))
repo.lfs_track(large_files)
logging.info("Push model to the hub. This might take a while")
push_return = repo.push_to_hub(commit_message=commit_message)
def on_rm_error(func, path, exc_info):
# path contains the path of the file that couldn't be removed
# let's just assume that it's read-only and unlink it.
try:
os.chmod(path, stat.S_IWRITE)
os.unlink(path)
except:
pass
# Remove .git folder. On Windows, the .git folder might be read-only and cannot be deleted
# Hence, try to set write permissions on error
try:
for f in os.listdir(tmp_dir):
shutil.rmtree(os.path.join(tmp_dir, f),
onerror=on_rm_error)
except Exception as e:
logging.warning("Error when deleting temp folder: {}".format(
str(e)))
pass
return push_return
def push_to_hf_hub(model: Any, model_name: str, task: str, **kwargs) -> None:
"""Save model and its configuration on HF hub
>>> from doctr.models import login_to_hub, push_to_hf_hub
>>> from doctr.models.recognition import crnn_mobilenet_v3_small
>>> login_to_hub()
>>> model = crnn_mobilenet_v3_small(pretrained=True)
>>> push_to_hf_hub(model, 'my-model', 'recognition', arch='crnn_mobilenet_v3_small')
Args:
model: TF or PyTorch model to be saved
model_name: name of the model which is also the repository name
task: task name
**kwargs: keyword arguments for push_to_hf_hub
"""
run_config = kwargs.get("run_config", None)
arch = kwargs.get("arch", None)
if run_config is None and arch is None:
raise ValueError("run_config or arch must be specified")
if task not in [
"classification", "detection", "recognition", "obj_detection"
]:
raise ValueError(
"task must be one of classification, detection, recognition, obj_detection"
)
# default readme
readme = textwrap.dedent(f"""
---
language: en
---
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: {task}
https://github.com/mindee/doctr
### Example usage:
```python
>>> from doctr.io import DocumentFile
>>> from doctr.models import ocr_predictor, from_hub
>>> img = DocumentFile.from_images(['<image_path>'])
>>> # Load your model from the hub
>>> model = from_hub('mindee/my-model')
>>> # Pass it to the predictor
>>> # If your model is a recognition model:
>>> predictor = ocr_predictor(det_arch='db_mobilenet_v3_large',
>>> reco_arch=model,
>>> pretrained=True)
>>> # If your model is a detection model:
>>> predictor = ocr_predictor(det_arch=model,
>>> reco_arch='crnn_mobilenet_v3_small',
>>> pretrained=True)
>>> # Get your predictions
>>> res = predictor(img)
```
""")
# add run configuration to readme if available
if run_config is not None:
arch = run_config.arch
readme += textwrap.dedent(f"""### Run Configuration
\n{json.dumps(vars(run_config), indent=2, ensure_ascii=False)}"""
)
if arch not in AVAILABLE_ARCHS[task]: # type: ignore
raise ValueError(f"Architecture: {arch} for task: {task} not found.\
\nAvailable architectures: {AVAILABLE_ARCHS}")
commit_message = f"Add {model_name} model"
local_cache_dir = os.path.join(os.path.expanduser("~"), ".cache",
"huggingface", "hub", model_name)
repo_url = HfApi().create_repo(model_name,
token=HfFolder.get_token(),
exist_ok=False)
repo = Repository(local_dir=local_cache_dir,
clone_from=repo_url,
use_auth_token=True)
with repo.commit(commit_message):
_save_model_and_config_for_hf_hub(model,
repo.local_dir,
arch=arch,
task=task)
readme_path = Path(repo.local_dir) / "README.md"
readme_path.write_text(readme)
repo.git_push()
def main():
torch.multiprocessing.set_sharing_strategy('file_system')
torchaudio.set_audio_backend('sox_io')
hack_isinstance()
# get config and arguments
args, config, backup_files = get_downstream_args()
if args.cache_dir is not None:
torch.hub.set_dir(args.cache_dir)
# When torch.distributed.launch is used
if args.local_rank is not None:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(args.backend)
if args.mode == 'train' and args.past_exp:
ckpt = torch.load(args.init_ckpt, map_location='cpu')
now_use_ddp = is_initialized()
original_use_ddp = ckpt['Args'].local_rank is not None
assert now_use_ddp == original_use_ddp, f'{now_use_ddp} != {original_use_ddp}'
if now_use_ddp:
now_world = get_world_size()
original_world = ckpt['WorldSize']
assert now_world == original_world, f'{now_world} != {original_world}'
if args.hub == "huggingface":
args.from_hf_hub = True
# Setup auth
hf_user = os.environ.get("HF_USERNAME")
hf_password = os.environ.get("HF_PASSWORD")
huggingface_token = HfApi().login(username=hf_user, password=hf_password)
HfFolder.save_token(huggingface_token)
print(f"Logged into Hugging Face Hub with user: {hf_user}")
# Save command
if is_leader_process():
with open(os.path.join(args.expdir, f'args_{get_time_tag()}.yaml'), 'w') as file:
yaml.dump(vars(args), file)
with open(os.path.join(args.expdir, f'config_{get_time_tag()}.yaml'), 'w') as file:
yaml.dump(config, file)
for file in backup_files:
backup(file, args.expdir)
# Fix seed and make backends deterministic
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if torch.cuda.is_available(): torch.cuda.manual_seed_all(args.seed)
if args.disable_cudnn:
torch.backends.cudnn.enabled = False
else:
torch.backends.cudnn.enabled = True
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
runner = Runner(args, config)
eval(f'runner.{args.mode}')()
