HF compatibility issue
BenoitDalFerro opened this issue · 3 comments
BenoitDalFerro commented
Suggest to assign to
🐛 Bug
model = MaskedLanguageModelingTransformer(pretrained_model_name_or_path=
Failure to pass the Huggingface model name
modelname='flaubert/flaubert_base_cased'
model = LanguageModelingTransformer(pretrained_model_name_or_path=modelnameOr the path to the locally saved version of a successful object instanciation such as
model = FlaubertWithLMHeadModel.from_pretrained(modelname)
model.save_pretrained('./saved_models/FlauBERT_test')
model = LanguageModelingTransformer(pretrained_model_name_or_path=modelpathOSError: None is not a local folder and is not a valid model identifier listed on 'https://huggingface.co/models'
If this is a private repository, make sure to pass a token having permission to this repo withuse_auth_tokenor log in withhuggingface-cli loginand passuse_auth_token=True.
Note the logical divorce in between passing the model as a Huggingface model string name or local path and the next argument of passing a tokenizer as an object. Either/or we follow the first logic and the the model name or path suffices to instantiate calls to the relevant tokenizer as per the model name or local config.json OR one separately instantiates a model object which is passed as an argument. The second approach is much clearer to understand, transparent and less cumbersome to implement and finally less prone to compatibility breakage.
To Reproduce
modelname = 'flaubert/flaubert_base_cased'
modelpath = './saved_models/FlauBERT_test'
model = FlaubertWithLMHeadModel.from_pretrained(modelname).to(device)
model.save_pretrained(modelpath)
LM_tokenizer = FlaubertTokenizer.from_pretrained(pretrained_model_name_or_path=modelname, do_lowercase=False)
with init_empty_weights():
model = MaskedLanguageModelingTransformer(
pretrained_model=modelname, #modelpath
tokenizer=FlaubertTokenizer.from_pretrained(pretrained_model_name_or_path=modelname, do_lowercase=False),
load_weights=False,
low_cpu_mem_usage=True,
device_map="auto"
#deepspeed_sharding=True, # Linux only, defer initialization of the model to shard/load pre-train weights
)> --------------------------------------------------------------------------
> HTTPError Traceback (most recent call last)
> File ~\miniconda3\envs\MyEnv\lib\site-packages\huggingface_hub\utils\_errors.py:213, in hf_raise_for_status(response, endpoint_name)
> 212 try:
> --> 213 response.raise_for_status()
> 214 except HTTPError as e:
>
> File ~\miniconda3\envs\MyEnv\lib\site-packages\requests\models.py:1021, in Response.raise_for_status(self)
> 1020 if http_error_msg:
> -> 1021 raise HTTPError(http_error_msg, response=self)
>
> HTTPError: 401 Client Error: Unauthorized for url: https://huggingface.co/None/resolve/main/config.json
>
> The above exception was the direct cause of the following exception:
>
> RepositoryNotFoundError Traceback (most recent call last)
> File ~\miniconda3\envs\MyEnv\lib\site-packages\transformers\utils\hub.py:409, in cached_file(path_or_repo_id, filename, cache_dir, force_download, resume_download, proxies, use_auth_token, revision, local_files_only, subfolder, user_agent, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash)
> 407 try:
> 408 # Load from URL or cache if already cached
> --> 409 resolved_file = hf_hub_download(
> 410 path_or_repo_id,
> 411 filename,
> 412 subfolder=None if len(subfolder) == 0 else subfolder,
> 413 revision=revision,
> 414 cache_dir=cache_dir,
> 415 user_agent=user_agent,
> 416 force_download=force_download,
> 417 proxies=proxies,
> 418 resume_download=resume_download,
> 419 use_auth_token=use_auth_token,
> 420 local_files_only=local_files_only,
> 421 )
> 423 except RepositoryNotFoundError:
>
> File ~\miniconda3\envs\MyEnv\lib\site-packages\huggingface_hub\file_download.py:1053, in hf_hub_download(repo_id, filename, subfolder, repo_type, revision, library_name, library_version, cache_dir, user_agent, force_download, force_filename, proxies, etag_timeout, resume_download, use_auth_token, local_files_only, legacy_cache_layout)
> 1052 try:
> -> 1053 metadata = get_hf_file_metadata(
> 1054 url=url,
> 1055 use_auth_token=use_auth_token,
> 1056 proxies=proxies,
> 1057 timeout=etag_timeout,
> 1058 )
> 1059 except EntryNotFoundError as http_error:
> 1060 # Cache the non-existence of the file and raise
>
> File ~\miniconda3\envs\MyEnv\lib\site-packages\huggingface_hub\file_download.py:1359, in get_hf_file_metadata(url, use_auth_token, proxies, timeout)
> 1350 r = _request_wrapper(
> 1351 method="HEAD",
> 1352 url=url,
> (...)
> 1357 timeout=timeout,
> 1358 )
> -> 1359 hf_raise_for_status(r)
> 1361 # Return
>
> File ~\miniconda3\envs\MyEnv\lib\site-packages\huggingface_hub\utils\_errors.py:242, in hf_raise_for_status(response, endpoint_name)
> 234 message = (
> 235 f"{response.status_code} Client Error."
> 236 + "\n\n"
> (...)
> 240 + "\nIf the repo is private, make sure you are authenticated."
> 241 )
> --> 242 raise RepositoryNotFoundError(message, response) from e
> 244 elif response.status_code == 400:
>
> RepositoryNotFoundError: 401 Client Error. (Request ID: Rw-R4i-FiiczgT3V91VYq)
>
> Repository Not Found for url: https://huggingface.co/None/resolve/main/config.json.
> Please make sure you specified the correct `repo_id` and `repo_type`.
> If the repo is private, make sure you are authenticated.
>
> During handling of the above exception, another exception occurred:
>
> OSError Traceback (most recent call last)
> Input In [16], in <cell line: 1>()
> 1 with init_empty_weights():
> ----> 2 model = MaskedLanguageModelingTransformer(
> 3 pretrained_model=modelname,
> 4 tokenizer=FlaubertTokenizer.from_pretrained(pretrained_model_name_or_path=modelname, do_lowercase=False),
> 5 load_weights=False,
> 6 low_cpu_mem_usage=True,
> 7 device_map="auto"
> 8 #deepspeed_sharding=True, # Linux only, defer initialization of the model to shard/load pre-train weights
> 9 )
>
> File ~\miniconda3\envs\MyEnv\lib\site-packages\lightning_transformers\task\nlp\masked_language_modeling\model.py:34, in MaskedLanguageModelingTransformer.__init__(self, downstream_model_type, *args, **kwargs)
> 31 def __init__(
> 32 self, *args, downstream_model_type: Type[_BaseAutoModelClass] = transformers.AutoModelForMaskedLM, **kwargs
> 33 ) -> None:
> ---> 34 super().__init__(downstream_model_type, *args, **kwargs)
>
> File ~\miniconda3\envs\MyEnv\lib\site-packages\lightning_transformers\core\model.py:64, in TaskTransformer.__init__(self, downstream_model_type, pretrained_model_name_or_path, tokenizer, pipeline_kwargs, load_weights, deepspeed_sharding, **model_data_kwargs)
> 62 self.pretrained_model_name_or_path = pretrained_model_name_or_path
> 63 if not self.deepspeed_sharding:
> ---> 64 self.initialize_model(self.pretrained_model_name_or_path)
> 65 self._tokenizer = tokenizer # necessary for hf_pipeline
> 66 self._hf_pipeline = None
>
> File ~\miniconda3\envs\MyEnv\lib\site-packages\lightning_transformers\core\model.py:79, in TaskTransformer.initialize_model(self, pretrained_model_name_or_path)
> 75 self.model = self.downstream_model_type.from_pretrained(
> 76 pretrained_model_name_or_path, **self.model_data_kwargs
> 77 )
> 78 else:
> ---> 79 config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **self.model_data_kwargs)
> 80 self.model = self.downstream_model_type.from_config(config)
>
> File ~\miniconda3\envs\MyEnv\lib\site-packages\transformers\models\auto\configuration_auto.py:776, in AutoConfig.from_pretrained(cls, pretrained_model_name_or_path, **kwargs)
> 774 kwargs["name_or_path"] = pretrained_model_name_or_path
> 775 trust_remote_code = kwargs.pop("trust_remote_code", False)
> --> 776 config_dict, unused_kwargs = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
> 777 if "auto_map" in config_dict and "AutoConfig" in config_dict["auto_map"]:
> 778 if not trust_remote_code:
>
> File ~\miniconda3\envs\MyEnv\lib\site-packages\transformers\configuration_utils.py:559, in PretrainedConfig.get_config_dict(cls, pretrained_model_name_or_path, **kwargs)
> 557 original_kwargs = copy.deepcopy(kwargs)
> 558 # Get config dict associated with the base config file
> --> 559 config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs)
> 560 if "_commit_hash" in config_dict:
> 561 original_kwargs["_commit_hash"] = config_dict["_commit_hash"]
>
> File ~\miniconda3\envs\MyEnv\lib\site-packages\transformers\configuration_utils.py:614, in PretrainedConfig._get_config_dict(cls, pretrained_model_name_or_path, **kwargs)
> 610 configuration_file = kwargs.pop("_configuration_file", CONFIG_NAME)
> 612 try:
> 613 # Load from local folder or from cache or download from model Hub and cache
> --> 614 resolved_config_file = cached_file(
> 615 pretrained_model_name_or_path,
> 616 configuration_file,
> 617 cache_dir=cache_dir,
> 618 force_download=force_download,
> 619 proxies=proxies,
> 620 resume_download=resume_download,
> 621 local_files_only=local_files_only,
> 622 use_auth_token=use_auth_token,
> 623 user_agent=user_agent,
> 624 revision=revision,
> 625 subfolder=subfolder,
> 626 _commit_hash=commit_hash,
> 627 )
> 628 commit_hash = extract_commit_hash(resolved_config_file, commit_hash)
> 629 except EnvironmentError:
> 630 # Raise any environment error raise by `cached_file`. It will have a helpful error message adapted to
> 631 # the original exception.
>
> File ~\miniconda3\envs\MyEnv\lib\site-packages\transformers\utils\hub.py:424, in cached_file(path_or_repo_id, filename, cache_dir, force_download, resume_download, proxies, use_auth_token, revision, local_files_only, subfolder, user_agent, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash)
> 409 resolved_file = hf_hub_download(
> 410 path_or_repo_id,
> 411 filename,
> (...)
> 420 local_files_only=local_files_only,
> 421 )
> 423 except RepositoryNotFoundError:
> --> 424 raise EnvironmentError(
> 425 f"{path_or_repo_id} is not a local folder and is not a valid model identifier "
> 426 "listed on '[https://huggingface.co/models'\nIf](https://huggingface.co/models'/nIf) this is a private repository, make sure to "
> 427 "pass a token having permission to this repo with `use_auth_token` or log in with "
> 428 "`huggingface-cli login` and pass `use_auth_token=True`."
> 429 )
> 430 except RevisionNotFoundError:
> 431 raise EnvironmentError(
> 432 f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists "
> 433 "for this model name. Check the model page at "
> 434 f"'[https://huggingface.co/{](https://huggingface.co/%7Bpath_or_repo_id)[path_or_repo_id](https://huggingface.co/%7Bpath_or_repo_id)}' for available revisions."
> 435 )
>
> OSError: None is not a local folder and is not a valid model identifier listed on 'https://huggingface.co/models'
> If 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` and pass `use_auth_token=True`.
Code sample
# -*- coding: utf-8 -*-
import os
from accelerate import (init_empty_weights)
from transformers import (FlaubertTokenizer, FlaubertWithLMHeadModel, TrainingArguments, DataCollatorForLanguageModeling)
from datasets import (load_dataset)
import pytorch_lightning as pl
from lightning_transformers.task.nlp.masked_language_modeling import (MaskedLanguageModelingTransformer, MaskedLanguageModelingDataModule)
import torch
from tqdm.notebook import tqdm
torch.cuda.empty_cache()
dtype = torch.cuda.FloatTensor
torch.backends.cudnn.benchmark = True
n_gpus = torch.cuda.device_count()
if n_gpus <= 1:
local_rank = -1
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
modelname = 'flaubert/flaubert_base_cased'
modelpath = './saved_models/FlauBERT_test'
model = FlaubertWithLMHeadModel.from_pretrained(modelname).to(device)
model.save_pretrained(modelpath)
LM_tokenizer = FlaubertTokenizer.from_pretrained(pretrained_model_name_or_path=modelname, do_lowercase=False)
with init_empty_weights():
model = MaskedLanguageModelingTransformer(
pretrained_model=modelname, #modelpath
tokenizer=FlaubertTokenizer.from_pretrained(pretrained_model_name_or_path=modelname, do_lowercase=False),
load_weights=False,
low_cpu_mem_usage=True,
device_map="auto"
#deepspeed_sharding=True, # Linux only, defer initialization of the model to shard/load pre-train weights
)
further code not executed owing to above new issue
datamodel = MaskedLanguageModelingDataModule(
batch_size=2,
dataset_name="wikitext",
dataset_config_name="wikitext-2-raw-v1",
tokenizer=LM_tokenizer,
)
trainer = pl.Trainer(
accelerator="auto",
devices="auto",
#strategy="deepspeed_stage_3",
precision=16,
max_epochs=1,
#strategy='dp',
#auto_lr_find=True,
#detect_anomaly=True
#val_check_interval=0
#progress_bar_refresh_rate=50
)
trainer.fit(model, datamodel)Expected behavior
Environment
Environment
Lightning-Transformers Version: 0.2.4
PyTorch Version (e.g., 1.0): 1.2.1
OS (e.g., Linux): Windows 10
How you installed PyTorch (conda, pip, source): conda
py3.9_cuda11.6_cudnn8_0 pytorch
Python version: 3.9
CUDA/cuDNN version: CUDA 11.6, cuDNN 8.0
GPU models and configuration: NVIDIA Quadro RTX 3000
Any other relevant information: none
Additional context
Additional context
Comes on top of 0.2.3 and despite 0.2.4 release and issue whilst passing object
BenoitDalFerro commented
Borda commented
@ethanwharris I think it was the same one you faced a while back with Flash, can you quickly point to the root? 🐰
ethanwharris commented
Hi there, I'm afraid I don't remember seeing this issue before or what was needed to fix it if we did. Sorry I couldn't be of more help!