I am getting a None is not a local path or a model identifier on the model Hub error when I try to start training with the optimum.habana library. Wondered if I could get some help on it. Here is code and error:
Code:
from transformers import DistilBertConfig, DistilBertTokenizerFast, DistilBertForSequenceClassification
# from transformers import DistilBertForSequenceClassification, Trainer, TrainingArguments #for Habana version of transformers
from optimum.habana import GaudiTrainer, GaudiTrainingArguments #for Huggingface version
training_args = GaudiTrainingArguments(
use_habana=True,
use_lazy_mode=True,
output_dir='./results', # output directory
num_train_epochs=3, # total number of training epochs
per_device_train_batch_size=128, # batch size per device during training
per_device_eval_batch_size=128, # batch size for evaluation
warmup_steps=500, # number of warmup steps for learning rate scheduler
weight_decay=0.01, # strength of weight decay
logging_dir='./logs', # directory for storing logs
logging_steps=10,
report_to='all'
)
import os
if not os.path.isdir("./results/checkpoint-3500"):
model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased")
model.to(device)
trainer = GaudiTrainer(
model=model, # the instantiated Transformers model to be trained
args=training_args, # training arguments, defined above
train_dataset=train_dataset, # training dataset
eval_dataset=val_dataset # evaluation dataset
)
trainer.train()
else:
model = DistilBertForSequenceClassification.from_pretrained("./results/checkpoint-3500")
Error:
Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_projector.bias', 'vocab_transform.bias', 'vocab_projector.weight', 'vocab_layer_norm.weight', 'vocab_layer_norm.bias', 'vocab_transform.weight']
- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.weight', 'classifier.weight', 'classifier.bias', 'pre_classifier.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
---------------------------------------------------------------------------
RepositoryNotFoundError Traceback (most recent call last)
File /usr/local/lib/python3.8/dist-packages/transformers/utils/hub.py:836, in get_list_of_files(path_or_repo, revision, use_auth_token, local_files_only)
835 try:
--> 836 return list_repo_files(path_or_repo, revision=revision, token=token)
837 except HTTPError as e:
File /usr/local/lib/python3.8/dist-packages/huggingface_hub/hf_api.py:1334, in HfApi.list_repo_files(self, repo_id, revision, repo_type, token, timeout)
1312 """
1313 Get the list of files in a given repo.
1314
(...)
1332 `List[str]`: the list of files in a given repository.
1333 """
-> 1334 repo_info = self.repo_info(
1335 repo_id,
1336 revision=revision,
1337 repo_type=repo_type,
1338 token=token,
1339 timeout=timeout,
1340 )
1341 return [f.rfilename for f in repo_info.siblings]
File /usr/local/lib/python3.8/dist-packages/huggingface_hub/hf_api.py:1289, in HfApi.repo_info(self, repo_id, revision, repo_type, token, timeout)
1288 if repo_type is None or repo_type == "model":
-> 1289 return self.model_info(
1290 repo_id, revision=revision, token=token, timeout=timeout
1291 )
1292 elif repo_type == "dataset":
File /usr/local/lib/python3.8/dist-packages/huggingface_hub/hf_api.py:1136, in HfApi.model_info(self, repo_id, revision, token, timeout, securityStatus)
1133 r = requests.get(
1134 path, headers=headers, timeout=timeout, params=status_query_param
1135 )
-> 1136 _raise_for_status(r)
1137 d = r.json()
File /usr/local/lib/python3.8/dist-packages/huggingface_hub/utils/_errors.py:78, in _raise_for_status(request)
76 if request.status_code == 401:
77 # The repo was not found and the user is not Authenticated
---> 78 raise RepositoryNotFoundError(
79 f"401 Client Error: Repository Not Found for url: {request.url}. If the"
80 " repo is private, make sure you are authenticated. (Request ID:"
81 f" {request_id})"
82 )
84 _raise_with_request_id(request)
RepositoryNotFoundError: 401 Client Error: Repository Not Found for url: https://huggingface.co/api/models/None. If the repo is private, make sure you are authenticated. (Request ID: aHGbVXZi6yrQUYVhwq7a7)
The above exception was the direct cause of the following exception:
ValueError Traceback (most recent call last)
Input In [49], in <cell line: 2>()
3 model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased")
4 model.to(device)
----> 5 trainer = GaudiTrainer(
6 model=model, # the instantiated Transformers model to be trained
7 args=training_args, # training arguments, defined above
8 train_dataset=train_dataset, # training dataset
9 eval_dataset=val_dataset # evaluation dataset
10 )
11 trainer.train()
12 else:
File /usr/local/lib/python3.8/dist-packages/optimum/habana/trainer.py:148, in GaudiTrainer.__init__(self, model, gaudi_config, args, data_collator, train_dataset, eval_dataset, tokenizer, model_init, compute_metrics, callbacks, optimizers, preprocess_logits_for_metrics)
133 super().__init__(
134 model,
135 args,
(...)
144 preprocess_logits_for_metrics,
145 )
147 if gaudi_config is None:
--> 148 self.gaudi_config = GaudiConfig.from_pretrained(args.gaudi_config_name)
149 else:
150 self.gaudi_config = copy.deepcopy(gaudi_config)
File /usr/local/lib/python3.8/dist-packages/transformers/configuration_utils.py:534, in PretrainedConfig.from_pretrained(cls, pretrained_model_name_or_path, **kwargs)
457 @classmethod
458 def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
459 r"""
460 Instantiate a [`PretrainedConfig`] (or a derived class) from a pretrained model configuration.
461
(...)
532 assert unused_kwargs == {"foo": False}
533 ```"""
--> 534 config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
535 if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
536 logger.warning(
537 f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
538 f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
539 )
File /usr/local/lib/python3.8/dist-packages/optimum/configuration_utils.py:179, in BaseConfig.get_config_dict(cls, pretrained_model_name_or_path, **kwargs)
177 config_file = pretrained_model_name_or_path
178 else:
--> 179 configuration_file = cls.get_configuration_file(
180 pretrained_model_name_or_path,
181 revision=revision,
182 use_auth_token=use_auth_token,
183 local_files_only=local_files_only,
184 )
186 if os.path.isdir(pretrained_model_name_or_path):
187 config_file = os.path.join(pretrained_model_name_or_path, configuration_file)
File /usr/local/lib/python3.8/dist-packages/optimum/configuration_utils.py:115, in BaseConfig.get_configuration_file(cls, path_or_repo, revision, use_auth_token, local_files_only)
95 """
96 Get the configuration file to use for this version of transformers.
97
(...)
112 :obj:`str`: The configuration file to use.
113 """
114 # Inspect all files from the repo/folder.
--> 115 all_files = get_list_of_files(
116 path_or_repo, revision=revision, use_auth_token=use_auth_token, local_files_only=local_files_only
117 )
118 configuration_files_map = {}
119 _re_configuration_file = cls._re_configuration_file()
File /usr/local/lib/python3.8/dist-packages/transformers/utils/hub.py:838, in get_list_of_files(path_or_repo, revision, use_auth_token, local_files_only)
836 return list_repo_files(path_or_repo, revision=revision, token=token)
837 except HTTPError as e:
--> 838 raise ValueError(
839 f"{path_or_repo} is not a local path or a model identifier on the model Hub. Did you make a typo?"
840 ) from e
ValueError: None is not a local path or a model identifier on the model Hub. Did you make a typo?
Appreciate your help!