forked from huggingface/diffusers
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathconfiguration_utils.py
More file actions
642 lines (534 loc) 路 28.2 KB
/
Copy pathconfiguration_utils.py
File metadata and controls
642 lines (534 loc) 路 28.2 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
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# 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.
""" ConfigMixin base class and utilities."""
import dataclasses
import functools
import importlib
import inspect
import json
import os
import re
from collections import OrderedDict
from pathlib import PosixPath
from typing import Any, Dict, Tuple, Union
import numpy as np
from huggingface_hub import hf_hub_download
from huggingface_hub.utils import EntryNotFoundError, RepositoryNotFoundError, RevisionNotFoundError
from requests import HTTPError
from . import __version__
from .utils import (
DIFFUSERS_CACHE,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
DummyObject,
deprecate,
extract_commit_hash,
http_user_agent,
logging,
)
logger = logging.get_logger(__name__)
_re_configuration_file = re.compile(r"config\.(.*)\.json")
class FrozenDict(OrderedDict):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
for key, value in self.items():
setattr(self, key, value)
self.__frozen = True
def __delitem__(self, *args, **kwargs):
raise Exception(f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.")
def setdefault(self, *args, **kwargs):
raise Exception(f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance.")
def pop(self, *args, **kwargs):
raise Exception(f"You cannot use ``pop`` on a {self.__class__.__name__} instance.")
def update(self, *args, **kwargs):
raise Exception(f"You cannot use ``update`` on a {self.__class__.__name__} instance.")
def __setattr__(self, name, value):
if hasattr(self, "__frozen") and self.__frozen:
raise Exception(f"You cannot use ``__setattr__`` on a {self.__class__.__name__} instance.")
super().__setattr__(name, value)
def __setitem__(self, name, value):
if hasattr(self, "__frozen") and self.__frozen:
raise Exception(f"You cannot use ``__setattr__`` on a {self.__class__.__name__} instance.")
super().__setitem__(name, value)
class ConfigMixin:
r"""
Base class for all configuration classes. Stores all configuration parameters under `self.config` Also handles all
methods for loading/downloading/saving classes inheriting from [`ConfigMixin`] with
- [`~ConfigMixin.from_config`]
- [`~ConfigMixin.save_config`]
Class attributes:
- **config_name** (`str`) -- A filename under which the config should stored when calling
[`~ConfigMixin.save_config`] (should be overridden by parent class).
- **ignore_for_config** (`List[str]`) -- A list of attributes that should not be saved in the config (should be
overridden by subclass).
- **has_compatibles** (`bool`) -- Whether the class has compatible classes (should be overridden by subclass).
- **_deprecated_kwargs** (`List[str]`) -- Keyword arguments that are deprecated. Note that the init function
should only have a `kwargs` argument if at least one argument is deprecated (should be overridden by
subclass).
"""
config_name = None
ignore_for_config = []
has_compatibles = False
_deprecated_kwargs = []
def register_to_config(self, **kwargs):
if self.config_name is None:
raise NotImplementedError(f"Make sure that {self.__class__} has defined a class name `config_name`")
# Special case for `kwargs` used in deprecation warning added to schedulers
# TODO: remove this when we remove the deprecation warning, and the `kwargs` argument,
# or solve in a more general way.
kwargs.pop("kwargs", None)
for key, value in kwargs.items():
try:
setattr(self, key, value)
except AttributeError as err:
logger.error(f"Can't set {key} with value {value} for {self}")
raise err
if not hasattr(self, "_internal_dict"):
internal_dict = kwargs
else:
previous_dict = dict(self._internal_dict)
internal_dict = {**self._internal_dict, **kwargs}
logger.debug(f"Updating config from {previous_dict} to {internal_dict}")
self._internal_dict = FrozenDict(internal_dict)
def save_config(self, save_directory: Union[str, os.PathLike], push_to_hub: bool = False, **kwargs):
"""
Save a configuration object to the directory `save_directory`, so that it can be re-loaded using the
[`~ConfigMixin.from_config`] class method.
Args:
save_directory (`str` or `os.PathLike`):
Directory where the configuration JSON file will be saved (will be created if it does not exist).
"""
if os.path.isfile(save_directory):
raise AssertionError(f"Provided path ({save_directory}) should be a directory, not a file")
os.makedirs(save_directory, exist_ok=True)
# If we save using the predefined names, we can load using `from_config`
output_config_file = os.path.join(save_directory, self.config_name)
self.to_json_file(output_config_file)
logger.info(f"Configuration saved in {output_config_file}")
@classmethod
def from_config(cls, config: Union[FrozenDict, Dict[str, Any]] = None, return_unused_kwargs=False, **kwargs):
r"""
Instantiate a Python class from a config dictionary
Parameters:
config (`Dict[str, Any]`):
A config dictionary from which the Python class will be instantiated. Make sure to only load
configuration files of compatible classes.
return_unused_kwargs (`bool`, *optional*, defaults to `False`):
Whether kwargs that are not consumed by the Python class should be returned or not.
kwargs (remaining dictionary of keyword arguments, *optional*):
Can be used to update the configuration object (after it being loaded) and initiate the Python class.
`**kwargs` will be directly passed to the underlying scheduler/model's `__init__` method and eventually
overwrite same named arguments of `config`.
Examples:
```python
>>> from diffusers import DDPMScheduler, DDIMScheduler, PNDMScheduler
>>> # Download scheduler from huggingface.co and cache.
>>> scheduler = DDPMScheduler.from_pretrained("google/ddpm-cifar10-32")
>>> # Instantiate DDIM scheduler class with same config as DDPM
>>> scheduler = DDIMScheduler.from_config(scheduler.config)
>>> # Instantiate PNDM scheduler class with same config as DDPM
>>> scheduler = PNDMScheduler.from_config(scheduler.config)
```
"""
# <===== TO BE REMOVED WITH DEPRECATION
# TODO(Patrick) - make sure to remove the following lines when config=="model_path" is deprecated
if "pretrained_model_name_or_path" in kwargs:
config = kwargs.pop("pretrained_model_name_or_path")
if config is None:
raise ValueError("Please make sure to provide a config as the first positional argument.")
# ======>
if not isinstance(config, dict):
deprecation_message = "It is deprecated to pass a pretrained model name or path to `from_config`."
if "Scheduler" in cls.__name__:
deprecation_message += (
f"If you were trying to load a scheduler, please use {cls}.from_pretrained(...) instead."
" Otherwise, please make sure to pass a configuration dictionary instead. This functionality will"
" be removed in v1.0.0."
)
elif "Model" in cls.__name__:
deprecation_message += (
f"If you were trying to load a model, please use {cls}.load_config(...) followed by"
f" {cls}.from_config(...) instead. Otherwise, please make sure to pass a configuration dictionary"
" instead. This functionality will be removed in v1.0.0."
)
deprecate("config-passed-as-path", "1.0.0", deprecation_message, standard_warn=False)
config, kwargs = cls.load_config(pretrained_model_name_or_path=config, return_unused_kwargs=True, **kwargs)
init_dict, unused_kwargs, hidden_dict = cls.extract_init_dict(config, **kwargs)
# Allow dtype to be specified on initialization
if "dtype" in unused_kwargs:
init_dict["dtype"] = unused_kwargs.pop("dtype")
# add possible deprecated kwargs
for deprecated_kwarg in cls._deprecated_kwargs:
if deprecated_kwarg in unused_kwargs:
init_dict[deprecated_kwarg] = unused_kwargs.pop(deprecated_kwarg)
# Return model and optionally state and/or unused_kwargs
model = cls(**init_dict)
# make sure to also save config parameters that might be used for compatible classes
model.register_to_config(**hidden_dict)
# add hidden kwargs of compatible classes to unused_kwargs
unused_kwargs = {**unused_kwargs, **hidden_dict}
if return_unused_kwargs:
return (model, unused_kwargs)
else:
return model
@classmethod
def get_config_dict(cls, *args, **kwargs):
deprecation_message = (
f" The function get_config_dict is deprecated. Please use {cls}.load_config instead. This function will be"
" removed in version v1.0.0"
)
deprecate("get_config_dict", "1.0.0", deprecation_message, standard_warn=False)
return cls.load_config(*args, **kwargs)
@classmethod
def load_config(
cls,
pretrained_model_name_or_path: Union[str, os.PathLike],
return_unused_kwargs=False,
return_commit_hash=False,
**kwargs,
) -> Tuple[Dict[str, Any], Dict[str, Any]]:
r"""
Instantiate a Python class from a config dictionary
Parameters:
pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
Can be either:
- A string, the *model id* of 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 [`~ConfigMixin.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.
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 `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.
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.
return_unused_kwargs (`bool`, *optional*, defaults to `False):
Whether unused keyword arguments of the config shall be returned.
return_commit_hash (`bool`, *optional*, defaults to `False):
Whether the commit_hash of the loaded configuration shall be returned.
<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/transformers/installation.html#offline-mode) to
use this method in a firewalled environment.
</Tip>
"""
cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
force_download = kwargs.pop("force_download", False)
resume_download = kwargs.pop("resume_download", False)
proxies = kwargs.pop("proxies", None)
use_auth_token = kwargs.pop("use_auth_token", None)
local_files_only = kwargs.pop("local_files_only", False)
revision = kwargs.pop("revision", None)
_ = kwargs.pop("mirror", None)
subfolder = kwargs.pop("subfolder", None)
user_agent = kwargs.pop("user_agent", {})
user_agent = {**user_agent, "file_type": "config"}
user_agent = http_user_agent(user_agent)
pretrained_model_name_or_path = str(pretrained_model_name_or_path)
if cls.config_name is None:
raise ValueError(
"`self.config_name` is not defined. Note that one should not load a config from "
"`ConfigMixin`. Please make sure to define `config_name` in a class inheriting from `ConfigMixin`"
)
if os.path.isfile(pretrained_model_name_or_path):
config_file = pretrained_model_name_or_path
elif os.path.isdir(pretrained_model_name_or_path):
if os.path.isfile(os.path.join(pretrained_model_name_or_path, cls.config_name)):
# Load from a PyTorch checkpoint
config_file = os.path.join(pretrained_model_name_or_path, cls.config_name)
elif subfolder is not None and os.path.isfile(
os.path.join(pretrained_model_name_or_path, subfolder, cls.config_name)
):
config_file = os.path.join(pretrained_model_name_or_path, subfolder, cls.config_name)
else:
raise EnvironmentError(
f"Error no file named {cls.config_name} found in directory {pretrained_model_name_or_path}."
)
else:
try:
# Load from URL or cache if already cached
config_file = hf_hub_download(
pretrained_model_name_or_path,
filename=cls.config_name,
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,
user_agent=user_agent,
subfolder=subfolder,
revision=revision,
)
except RepositoryNotFoundError:
raise EnvironmentError(
f"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier"
" listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a"
" token having permission to this repo with `use_auth_token` or log in with `huggingface-cli"
" login`."
)
except RevisionNotFoundError:
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"
f" 'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions."
)
except EntryNotFoundError:
raise EnvironmentError(
f"{pretrained_model_name_or_path} does not appear to have a file named {cls.config_name}."
)
except HTTPError as err:
raise EnvironmentError(
"There was a specific connection error when trying to load"
f" {pretrained_model_name_or_path}:\n{err}"
)
except ValueError:
raise EnvironmentError(
f"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"
f" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"
f" directory containing a {cls.config_name} file.\nCheckout your internet connection or see how to"
" run the library in offline mode at"
" 'https://huggingface.co/docs/diffusers/installation#offline-mode'."
)
except EnvironmentError:
raise EnvironmentError(
f"Can't load config for '{pretrained_model_name_or_path}'. If you were trying to load it from "
"'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory "
f"containing a {cls.config_name} file"
)
try:
# Load config dict
config_dict = cls._dict_from_json_file(config_file)
commit_hash = extract_commit_hash(config_file)
except (json.JSONDecodeError, UnicodeDecodeError):
raise EnvironmentError(f"It looks like the config file at '{config_file}' is not a valid JSON file.")
if not (return_unused_kwargs or return_commit_hash):
return config_dict
outputs = (config_dict,)
if return_unused_kwargs:
outputs += (kwargs,)
if return_commit_hash:
outputs += (commit_hash,)
return outputs
@staticmethod
def _get_init_keys(cls):
return set(dict(inspect.signature(cls.__init__).parameters).keys())
@classmethod
def extract_init_dict(cls, config_dict, **kwargs):
# 0. Copy origin config dict
original_dict = dict(config_dict.items())
# 1. Retrieve expected config attributes from __init__ signature
expected_keys = cls._get_init_keys(cls)
expected_keys.remove("self")
# remove general kwargs if present in dict
if "kwargs" in expected_keys:
expected_keys.remove("kwargs")
# remove flax internal keys
if hasattr(cls, "_flax_internal_args"):
for arg in cls._flax_internal_args:
expected_keys.remove(arg)
# 2. Remove attributes that cannot be expected from expected config attributes
# remove keys to be ignored
if len(cls.ignore_for_config) > 0:
expected_keys = expected_keys - set(cls.ignore_for_config)
# load diffusers library to import compatible and original scheduler
diffusers_library = importlib.import_module(__name__.split(".")[0])
if cls.has_compatibles:
compatible_classes = [c for c in cls._get_compatibles() if not isinstance(c, DummyObject)]
else:
compatible_classes = []
expected_keys_comp_cls = set()
for c in compatible_classes:
expected_keys_c = cls._get_init_keys(c)
expected_keys_comp_cls = expected_keys_comp_cls.union(expected_keys_c)
expected_keys_comp_cls = expected_keys_comp_cls - cls._get_init_keys(cls)
config_dict = {k: v for k, v in config_dict.items() if k not in expected_keys_comp_cls}
# remove attributes from orig class that cannot be expected
orig_cls_name = config_dict.pop("_class_name", cls.__name__)
if orig_cls_name != cls.__name__ and hasattr(diffusers_library, orig_cls_name):
orig_cls = getattr(diffusers_library, orig_cls_name)
unexpected_keys_from_orig = cls._get_init_keys(orig_cls) - expected_keys
config_dict = {k: v for k, v in config_dict.items() if k not in unexpected_keys_from_orig}
# remove private attributes
config_dict = {k: v for k, v in config_dict.items() if not k.startswith("_")}
# 3. Create keyword arguments that will be passed to __init__ from expected keyword arguments
init_dict = {}
for key in expected_keys:
# if config param is passed to kwarg and is present in config dict
# it should overwrite existing config dict key
if key in kwargs and key in config_dict:
config_dict[key] = kwargs.pop(key)
if key in kwargs:
# overwrite key
init_dict[key] = kwargs.pop(key)
elif key in config_dict:
# use value from config dict
init_dict[key] = config_dict.pop(key)
# 4. Give nice warning if unexpected values have been passed
if len(config_dict) > 0:
logger.warning(
f"The config attributes {config_dict} were passed to {cls.__name__}, "
"but are not expected and will be ignored. Please verify your "
f"{cls.config_name} configuration file."
)
# 5. Give nice info if config attributes are initiliazed to default because they have not been passed
passed_keys = set(init_dict.keys())
if len(expected_keys - passed_keys) > 0:
logger.info(
f"{expected_keys - passed_keys} was not found in config. Values will be initialized to default values."
)
# 6. Define unused keyword arguments
unused_kwargs = {**config_dict, **kwargs}
# 7. Define "hidden" config parameters that were saved for compatible classes
hidden_config_dict = {k: v for k, v in original_dict.items() if k not in init_dict}
return init_dict, unused_kwargs, hidden_config_dict
@classmethod
def _dict_from_json_file(cls, json_file: Union[str, os.PathLike]):
with open(json_file, "r", encoding="utf-8") as reader:
text = reader.read()
return json.loads(text)
def __repr__(self):
return f"{self.__class__.__name__} {self.to_json_string()}"
@property
def config(self) -> Dict[str, Any]:
"""
Returns the config of the class as a frozen dictionary
Returns:
`Dict[str, Any]`: Config of the class.
"""
return self._internal_dict
def to_json_string(self) -> str:
"""
Serializes this instance to a JSON string.
Returns:
`str`: String containing all the attributes that make up this configuration instance in JSON format.
"""
config_dict = self._internal_dict if hasattr(self, "_internal_dict") else {}
config_dict["_class_name"] = self.__class__.__name__
config_dict["_diffusers_version"] = __version__
def to_json_saveable(value):
if isinstance(value, np.ndarray):
value = value.tolist()
elif isinstance(value, PosixPath):
value = str(value)
return value
config_dict = {k: to_json_saveable(v) for k, v in config_dict.items()}
return json.dumps(config_dict, indent=2, sort_keys=True) + "\n"
def to_json_file(self, json_file_path: Union[str, os.PathLike]):
"""
Save this instance to a JSON file.
Args:
json_file_path (`str` or `os.PathLike`):
Path to the JSON file in which this configuration instance's parameters will be saved.
"""
with open(json_file_path, "w", encoding="utf-8") as writer:
writer.write(self.to_json_string())
def register_to_config(init):
r"""
Decorator to apply on the init of classes inheriting from [`ConfigMixin`] so that all the arguments are
automatically sent to `self.register_for_config`. To ignore a specific argument accepted by the init but that
shouldn't be registered in the config, use the `ignore_for_config` class variable
Warning: Once decorated, all private arguments (beginning with an underscore) are trashed and not sent to the init!
"""
@functools.wraps(init)
def inner_init(self, *args, **kwargs):
# Ignore private kwargs in the init.
init_kwargs = {k: v for k, v in kwargs.items() if not k.startswith("_")}
config_init_kwargs = {k: v for k, v in kwargs.items() if k.startswith("_")}
if not isinstance(self, ConfigMixin):
raise RuntimeError(
f"`@register_for_config` was applied to {self.__class__.__name__} init method, but this class does "
"not inherit from `ConfigMixin`."
)
ignore = getattr(self, "ignore_for_config", [])
# Get positional arguments aligned with kwargs
new_kwargs = {}
signature = inspect.signature(init)
parameters = {
name: p.default for i, (name, p) in enumerate(signature.parameters.items()) if i > 0 and name not in ignore
}
for arg, name in zip(args, parameters.keys()):
new_kwargs[name] = arg
# Then add all kwargs
new_kwargs.update(
{
k: init_kwargs.get(k, default)
for k, default in parameters.items()
if k not in ignore and k not in new_kwargs
}
)
new_kwargs = {**config_init_kwargs, **new_kwargs}
getattr(self, "register_to_config")(**new_kwargs)
init(self, *args, **init_kwargs)
return inner_init
def flax_register_to_config(cls):
original_init = cls.__init__
@functools.wraps(original_init)
def init(self, *args, **kwargs):
if not isinstance(self, ConfigMixin):
raise RuntimeError(
f"`@register_for_config` was applied to {self.__class__.__name__} init method, but this class does "
"not inherit from `ConfigMixin`."
)
# Ignore private kwargs in the init. Retrieve all passed attributes
init_kwargs = dict(kwargs.items())
# Retrieve default values
fields = dataclasses.fields(self)
default_kwargs = {}
for field in fields:
# ignore flax specific attributes
if field.name in self._flax_internal_args:
continue
if type(field.default) == dataclasses._MISSING_TYPE:
default_kwargs[field.name] = None
else:
default_kwargs[field.name] = getattr(self, field.name)
# Make sure init_kwargs override default kwargs
new_kwargs = {**default_kwargs, **init_kwargs}
# dtype should be part of `init_kwargs`, but not `new_kwargs`
if "dtype" in new_kwargs:
new_kwargs.pop("dtype")
# Get positional arguments aligned with kwargs
for i, arg in enumerate(args):
name = fields[i].name
new_kwargs[name] = arg
getattr(self, "register_to_config")(**new_kwargs)
original_init(self, *args, **kwargs)
cls.__init__ = init
return cls