{{ message }}
Repository navigation
Expand file tree
/
Copy pathdynamic_modules_utils.py
More file actions
544 lines (469 loc) · 23.7 KB
/
Copy pathdynamic_modules_utils.py
File metadata and controls
544 lines (469 loc) · 23.7 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
# coding=utf-8
# Copyright 2025 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Utilities to dynamically load objects from the Hub."""
import importlib
import inspect
import json
import os
import re
import shutil
import sys
import threading
from pathlib import Path
from types import ModuleType
from urllib import request
from huggingface_hub import hf_hub_download, model_info
from huggingface_hub.utils import RevisionNotFoundError, validate_hf_hub_args
from packaging import version
from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging
from .constants import DIFFUSERS_DISABLE_REMOTE_CODE
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
# See https://huggingface.co/datasets/diffusers/community-pipelines-mirror
COMMUNITY_PIPELINES_MIRROR_ID = "diffusers/community-pipelines-mirror"
TIME_OUT_REMOTE_CODE = int(os.getenv("DIFFUSERS_TIMEOUT_REMOTE_CODE", 15))
_HF_REMOTE_CODE_LOCK = threading.Lock()
def get_diffusers_versions():
url = "https://pypi.org/pypi/diffusers/json"
releases = json.loads(request.urlopen(url).read())["releases"].keys()
return sorted(releases, key=lambda x: version.Version(x))
def init_hf_modules():
"""
Creates the cache directory for modules with an init, and adds it to the Python path.
"""
# This function has already been executed if HF_MODULES_CACHE already is in the Python path.
if HF_MODULES_CACHE in sys.path:
return
sys.path.append(HF_MODULES_CACHE)
os.makedirs(HF_MODULES_CACHE, exist_ok=True)
init_path = Path(HF_MODULES_CACHE) / "__init__.py"
if not init_path.exists():
init_path.touch()
def create_dynamic_module(name: str | os.PathLike):
"""
Creates a dynamic module in the cache directory for modules.
"""
init_hf_modules()
dynamic_module_path = Path(HF_MODULES_CACHE) / name
# If the parent module does not exist yet, recursively create it.
if not dynamic_module_path.parent.exists():
create_dynamic_module(dynamic_module_path.parent)
os.makedirs(dynamic_module_path, exist_ok=True)
init_path = dynamic_module_path / "__init__.py"
if not init_path.exists():
init_path.touch()
def get_relative_imports(module_file):
"""
Get the list of modules that are relatively imported in a module file.
Args:
module_file (`str` or `os.PathLike`): The module file to inspect.
"""
with open(module_file, "r", encoding="utf-8") as f:
content = f.read()
# Imports of the form `import .xxx`
relative_imports = re.findall(r"^\s*import\s+\.(\S+)\s*$", content, flags=re.MULTILINE)
# Imports of the form `from .xxx import yyy`
relative_imports += re.findall(r"^\s*from\s+\.(\S+)\s+import", content, flags=re.MULTILINE)
# Unique-ify
return list(set(relative_imports))
def get_relative_import_files(module_file):
"""
Get the list of all files that are needed for a given module. Note that this function recurses through the relative
imports (if a imports b and b imports c, it will return module files for b and c).
Args:
module_file (`str` or `os.PathLike`): The module file to inspect.
"""
no_change = False
files_to_check = [module_file]
all_relative_imports = []
# Let's recurse through all relative imports
while not no_change:
new_imports = []
for f in files_to_check:
new_imports.extend(get_relative_imports(f))
module_path = Path(module_file).parent
new_import_files = [str(module_path / m) for m in new_imports]
new_import_files = [f for f in new_import_files if f not in all_relative_imports]
files_to_check = [f"{f}.py" for f in new_import_files]
no_change = len(new_import_files) == 0
all_relative_imports.extend(files_to_check)
return all_relative_imports
def check_imports(filename):
"""
Check if the current Python environment contains all the libraries that are imported in a file.
"""
with open(filename, "r", encoding="utf-8") as f:
content = f.read()
# Imports of the form `import xxx`
imports = re.findall(r"^\s*import\s+(\S+)\s*$", content, flags=re.MULTILINE)
# Imports of the form `from xxx import yyy`
imports += re.findall(r"^\s*from\s+(\S+)\s+import", content, flags=re.MULTILINE)
# Only keep the top-level module
imports = [imp.split(".")[0] for imp in imports if not imp.startswith(".")]
# Unique-ify and test we got them all
imports = list(set(imports))
missing_packages = []
for imp in imports:
try:
importlib.import_module(imp)
except ImportError:
missing_packages.append(imp)
if len(missing_packages) > 0:
logger.warning(
"This modeling file might require the following packages that were not found in your environment: "
f"{', '.join(missing_packages)}. Run `pip install {' '.join(missing_packages)}`"
)
return get_relative_imports(filename)
def resolve_trust_remote_code(trust_remote_code, model_name, has_remote_code):
trust_remote_code = trust_remote_code and not DIFFUSERS_DISABLE_REMOTE_CODE
if DIFFUSERS_DISABLE_REMOTE_CODE:
logger.warning(
"Downloading remote code is disabled globally via the DIFFUSERS_DISABLE_REMOTE_CODE environment variable. Ignoring `trust_remote_code`."
)
if has_remote_code and not trust_remote_code:
error_msg = f"The repository for {model_name} contains custom code. "
error_msg += (
"Downloading remote code is disabled globally via the DIFFUSERS_DISABLE_REMOTE_CODE environment variable."
if DIFFUSERS_DISABLE_REMOTE_CODE
else "Pass `trust_remote_code=True` to allow loading remote code modules."
)
raise ValueError(error_msg)
elif has_remote_code and trust_remote_code:
logger.warning(
f"`trust_remote_code` is enabled. Downloading code from {model_name}. Please ensure you trust the contents of this repository"
)
return trust_remote_code
def get_class_in_module(class_name, module_path, force_reload=False):
"""
Import a module on the cache directory for modules and extract a class from it.
"""
name = os.path.normpath(module_path)
if name.endswith(".py"):
name = name[:-3]
name = name.replace(os.path.sep, ".")
module_file: Path = Path(HF_MODULES_CACHE) / module_path
with _HF_REMOTE_CODE_LOCK:
if force_reload:
sys.modules.pop(name, None)
importlib.invalidate_caches()
cached_module: ModuleType | None = sys.modules.get(name)
module_spec = importlib.util.spec_from_file_location(name, location=module_file)
module: ModuleType
if cached_module is None:
module = importlib.util.module_from_spec(module_spec)
# insert it into sys.modules before any loading begins
sys.modules[name] = module
else:
module = cached_module
module_spec.loader.exec_module(module)
if class_name is None:
return find_pipeline_class(module)
return getattr(module, class_name)
def find_pipeline_class(loaded_module):
"""
Retrieve pipeline class that inherits from `DiffusionPipeline`. Note that there has to be exactly one class
inheriting from `DiffusionPipeline`.
"""
from ..pipelines import DiffusionPipeline
cls_members = dict(inspect.getmembers(loaded_module, inspect.isclass))
pipeline_class = None
for cls_name, cls in cls_members.items():
if (
cls_name != DiffusionPipeline.__name__
and issubclass(cls, DiffusionPipeline)
and cls.__module__.split(".")[0] != "diffusers"
):
if pipeline_class is not None:
raise ValueError(
f"Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:"
f" {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in"
f" {loaded_module}."
)
pipeline_class = cls
return pipeline_class
@validate_hf_hub_args
def get_cached_module_file(
pretrained_model_name_or_path: str | os.PathLike,
module_file: str,
subfolder: str | None = None,
cache_dir: str | os.PathLike | None = None,
force_download: bool = False,
proxies: dict[str, str] | None = None,
token: bool | str | None = None,
revision: str | None = None,
local_files_only: bool = False,
local_dir: str | None = None,
trust_remote_code: 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.
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 `transformers-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.
trust_remote_code (`bool`, *optional*, defaults to `False`):
Whether or not to allow for custom pipelines and components defined on the Hub in their own files. This
option should only be set to `True` for repositories you trust and in which you have read the code, as it
will execute code present on the Hub on your local machine.
> [!TIP] > You may pass a token in `token` if you are not logged in (`hf auth login`) and want to use private or
[gated > models](https://huggingface.co/docs/hub/models-gated#gated-models).
Returns:
`str`: The path to the module inside the cache.
"""
# 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 DIFFUSERS_DISABLE_REMOTE_CODE:
raise ValueError(
"Downloading remote code is disabled globally via the DIFFUSERS_DISABLE_REMOTE_CODE environment variable."
)
if subfolder is not None:
module_file_or_url = os.path.join(pretrained_model_name_or_path, subfolder, module_file)
else:
module_file_or_url = os.path.join(pretrained_model_name_or_path, module_file)
is_local_file = os.path.isfile(module_file_or_url)
is_community_pipeline = not is_local_file and pretrained_model_name_or_path.count("/") == 0
if is_local_file:
resolved_module_file = module_file_or_url
submodule = "local"
if not trust_remote_code:
raise ValueError(
f"The directory {pretrained_model_name_or_path} contains custom code in {module_file} which must be executed to correctly "
f"load the model. You can inspect the file content at {module_file_or_url}.\n"
f"Pass `trust_remote_code=True` to allow loading remote code modules."
)
elif is_community_pipeline:
available_versions = get_diffusers_versions()
# cut ".dev0"
latest_version = "v" + ".".join(__version__.split(".")[:3])
# retrieve github version that matches
if revision is None:
revision = latest_version if latest_version[1:] in available_versions else "main"
logger.info(f"Defaulting to latest_version: {revision}.")
elif revision in available_versions:
revision = f"v{revision}"
elif revision == "main":
revision = revision
else:
raise ValueError(
f"`custom_revision`: {revision} does not exist. Please make sure to choose one of"
f" {', '.join(available_versions + ['main'])}."
)
try:
resolved_module_file = hf_hub_download(
repo_id=COMMUNITY_PIPELINES_MIRROR_ID,
repo_type="dataset",
filename=f"{revision}/{pretrained_model_name_or_path}.py",
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
local_files_only=local_files_only,
local_dir=local_dir,
)
submodule = "git"
module_file = pretrained_model_name_or_path + ".py"
except RevisionNotFoundError as e:
raise EnvironmentError(
f"Revision '{revision}' not found in the community pipelines mirror. Check available revisions on"
" https://huggingface.co/datasets/diffusers/community-pipelines-mirror/tree/main."
" If you don't find the revision you are looking for, please open an issue on https://github.com/huggingface/diffusers/issues."
) from e
except EnvironmentError:
logger.error(f"Could not locate the {module_file} inside {pretrained_model_name_or_path}.")
raise
else:
if not trust_remote_code:
raise ValueError(
f"The repository for {pretrained_model_name_or_path} contains custom code in {module_file} which must be executed to correctly "
f"load the model. You can inspect the repository content at https://hf.co/{pretrained_model_name_or_path}/blob/main/{module_file}.\n"
f"Pass `trust_remote_code=True` to allow loading remote code modules."
)
try:
# Load from URL or cache if already cached
resolved_module_file = hf_hub_download(
pretrained_model_name_or_path,
module_file,
subfolder=subfolder,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
local_files_only=local_files_only,
local_dir=local_dir,
revision=revision,
token=token,
)
submodule = os.path.join("local", "--".join(pretrained_model_name_or_path.split("/")))
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 = DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
create_dynamic_module(full_submodule)
submodule_path = Path(HF_MODULES_CACHE) / full_submodule
if submodule == "local" or submodule == "git":
# 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.copyfile(resolved_module_file, submodule_path / module_file)
for module_needed in modules_needed:
if len(module_needed.split(".")) == 2:
module_needed = "/".join(module_needed.split("."))
module_folder = module_needed.split("/")[0]
if not os.path.exists(submodule_path / module_folder):
os.makedirs(submodule_path / module_folder)
module_needed = f"{module_needed}.py"
if subfolder is not None:
source_path = os.path.join(pretrained_model_name_or_path, subfolder, module_needed)
else:
source_path = os.path.join(pretrained_model_name_or_path, module_needed)
shutil.copyfile(source_path, 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.
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():
if len(module_file.split("/")) == 2:
module_folder = module_file.split("/")[0]
if not os.path.exists(submodule_path / module_folder):
os.makedirs(submodule_path / module_folder)
shutil.copyfile(resolved_module_file, submodule_path / module_file)
# Make sure we also have every file with relative
for module_needed in modules_needed:
if len(module_needed.split(".")) == 2:
module_needed = "/".join(module_needed.split("."))
if not (submodule_path / module_needed).exists():
get_cached_module_file(
pretrained_model_name_or_path,
f"{module_needed}.py",
subfolder=subfolder,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
token=token,
revision=revision,
local_files_only=local_files_only,
local_dir=local_dir,
trust_remote_code=trust_remote_code,
)
return os.path.join(full_submodule, module_file)
@validate_hf_hub_args
def get_class_from_dynamic_module(
pretrained_model_name_or_path: str | os.PathLike,
module_file: str,
subfolder: str | None = None,
class_name: str | None = None,
cache_dir: str | os.PathLike | None = None,
force_download: bool = False,
proxies: dict[str, str] | None = None,
token: bool | str | None = None,
revision: str | None = None,
local_files_only: bool = False,
local_dir: str | None = None,
trust_remote_code: bool = False,
):
"""
Extracts a class from a module file, present in the local folder or repository of a model.
> [!WARNING] > Calling this function will execute the code in the module file found locally or downloaded from the
Hub. It should > therefore only be called on trusted repos.
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.
class_name (`str`):
The name of the class to import in the module.
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.
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 `transformers-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.
trust_remote_code (`bool`, *optional*, defaults to `False`):
Whether or not to allow for custom pipelines and components defined on the Hub in their own files. This
option should only be set to `True` for repositories you trust and in which you have read the code, as it
will execute code present on the Hub on your local machine.
> [!TIP] > You may pass a token in `token` if you are not logged in (`hf auth login`) and want to use private or
[gated > models](https://huggingface.co/docs/hub/models-gated#gated-models).
Returns:
`type`: The class, dynamically imported from the module.
Examples:
```python
# Download module `modeling.py` from huggingface.co and cache then extract the class `MyBertModel` from this
# module.
cls = get_class_from_dynamic_module("sgugger/my-bert-model", "modeling.py", "MyBertModel")
```"""
# And lastly we get the class inside our newly created module
final_module = get_cached_module_file(
pretrained_model_name_or_path,
module_file,
subfolder=subfolder,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
token=token,
revision=revision,
local_files_only=local_files_only,
local_dir=local_dir,
trust_remote_code=trust_remote_code,
)
return get_class_in_module(class_name, final_module)
You can’t perform that action at this time.