PrecollatorForGeneAndCellClassification has no attribute save_pretrained
#528
by
zzbb2266
- opened
Hi there, thanks for the great pipeline and maintenance! Currently I am trying to replicate the cell_classification.ipynb from the example folder, but got an error from the PrecollatorForGeneAndCellClassification module just after finishing the first epoch:
Epoch Training Loss Validation Loss Accuracy Macro F1
0 0.133900 0.411489 0.883736 0.684715
0%| | 0/1 [28:44<?, ?it/s]
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[5], line 6
1 train_valid_id_split_dict = {"attr_key": "individual",
2 "train": train_ids,
3 "eval": eval_ids}
5 # Example 6 layer 30M Geneformer model: https://huggingface.co/ctheodoris/Geneformer/blob/main/gf-6L-30M-i2048/model.safetensors
----> 6 all_metrics = cc.validate(model_directory="/home/data1/geneformer/Geneformer/gf-6L-30M-i2048/",
7 prepared_input_data_file=f"{output_dir}/{output_prefix}_labeled_train.dataset",
8 id_class_dict_file=f"{output_dir}/{output_prefix}_id_class_dict.pkl",
9 output_directory=output_dir,
10 output_prefix=output_prefix,
11 split_id_dict=train_valid_id_split_dict)
12 # to optimize hyperparameters, set n_hyperopt_trials=100 (or alternative desired # of trials)
File /opt/miniconda/envs/gnenformer/lib/python3.13/site-packages/geneformer/classifier.py:791, in Classifier.validate(self, model_directory, prepared_input_data_file, id_class_dict_file, output_directory, output_prefix, split_id_dict, attr_to_split, attr_to_balance, gene_balance, max_trials, pval_threshold, save_eval_output, predict_eval, predict_trainer, n_hyperopt_trials, save_gene_split_datasets, debug_gene_split_datasets)
789 train_data = data.select(train_indices)
790 if n_hyperopt_trials == 0:
--> 791 trainer = self.train_classifier(
792 model_directory,
793 num_classes,
794 train_data,
795 eval_data,
796 ksplit_output_dir,
797 predict_trainer,
798 )
799 else:
800 trainer = self.hyperopt_classifier(
801 model_directory,
802 num_classes,
(...) 806 n_trials=n_hyperopt_trials,
807 )
File /opt/miniconda/envs/gnenformer/lib/python3.13/site-packages/geneformer/classifier.py:1269, in Classifier.train_classifier(self, model_directory, num_classes, train_data, eval_data, output_directory, predict)
1259 trainer = Trainer(
1260 model=model,
1261 args=training_args_init,
(...) 1265 compute_metrics=cu.compute_metrics,
1266 )
1268 # train the classifier
-> 1269 trainer.train()
1270 trainer.save_model(output_directory)
1271 if predict is True:
1272 # make eval predictions and save predictions and metrics
File /opt/miniconda/envs/gnenformer/lib/python3.13/site-packages/transformers/trainer.py:2245, in Trainer.train(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)
2243 hf_hub_utils.enable_progress_bars()
2244 else:
-> 2245 return inner_training_loop(
2246 args=args,
2247 resume_from_checkpoint=resume_from_checkpoint,
2248 trial=trial,
2249 ignore_keys_for_eval=ignore_keys_for_eval,
2250 )
File /opt/miniconda/envs/gnenformer/lib/python3.13/site-packages/transformers/trainer.py:2661, in Trainer._inner_training_loop(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)
2658 self.control.should_training_stop = True
2660 self.control = self.callback_handler.on_epoch_end(args, self.state, self.control)
-> 2661 self._maybe_log_save_evaluate(
2662 tr_loss, grad_norm, model, trial, epoch, ignore_keys_for_eval, start_time, learning_rate=learning_rate
2663 )
2665 if DebugOption.TPU_METRICS_DEBUG in self.args.debug:
2666 if is_torch_xla_available():
2667 # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
File /opt/miniconda/envs/gnenformer/lib/python3.13/site-packages/transformers/trainer.py:3103, in Trainer._maybe_log_save_evaluate(self, tr_loss, grad_norm, model, trial, epoch, ignore_keys_for_eval, start_time, learning_rate)
3100 self.control.should_save = is_new_best_metric
3102 if self.control.should_save:
-> 3103 self._save_checkpoint(model, trial)
3104 self.control = self.callback_handler.on_save(self.args, self.state, self.control)
File /opt/miniconda/envs/gnenformer/lib/python3.13/site-packages/transformers/trainer.py:3200, in Trainer._save_checkpoint(self, model, trial)
3198 run_dir = self._get_output_dir(trial=trial)
3199 output_dir = os.path.join(run_dir, checkpoint_folder)
-> 3200 self.save_model(output_dir, _internal_call=True)
3202 if self.args.save_strategy in [SaveStrategy.STEPS, SaveStrategy.EPOCH] and self.state.best_global_step:
3203 best_checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.state.best_global_step}"
File /opt/miniconda/envs/gnenformer/lib/python3.13/site-packages/transformers/trainer.py:3902, in Trainer.save_model(self, output_dir, _internal_call)
3899 self.model_wrapped.save_checkpoint(output_dir)
3901 elif self.args.should_save:
-> 3902 self._save(output_dir)
3904 # Push to the Hub when `save_model` is called by the user.
3905 if self.args.push_to_hub and not _internal_call:
File /opt/miniconda/envs/gnenformer/lib/python3.13/site-packages/transformers/trainer.py:4018, in Trainer._save(self, output_dir, state_dict)
4012 elif (
4013 self.data_collator is not None
4014 and hasattr(self.data_collator, "tokenizer")
4015 and self.data_collator.tokenizer is not None
4016 ):
4017 logger.info("Saving Trainer.data_collator.tokenizer by default as Trainer.processing_class is `None`")
-> 4018 self.data_collator.tokenizer.save_pretrained(output_dir)
4020 # Good practice: save your training arguments together with the trained model
4021 torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))
File /opt/miniconda/envs/gnenformer/lib/python3.13/site-packages/transformers/tokenization_utils_base.py:1108, in SpecialTokensMixin.__getattr__(self, key)
1105 return self.convert_tokens_to_ids(attr_as_tokens) if attr_as_tokens is not None else None
1107 if key not in self.__dict__:
-> 1108 raise AttributeError(f"{self.__class__.__name__} has no attribute {key}")
1109 else:
1110 return super().__getattr__(key)
AttributeError: PrecollatorForGeneAndCellClassification has no attribute save_pretrained
Should I add the parameter save_pretrained manually to trainer.py or classifier.py ? Seems that the issue may due to a different version of package transformers to me. my env is:
Package Version
------------------------- -----------
absl-py 2.2.2
accelerate 1.6.0
accumulation_tree 0.6.4
aiohappyeyeballs 2.6.1
aiohttp 3.11.18
aiosignal 1.3.2
alembic 1.15.2
anndata 0.11.4
annotated-types 0.7.0
array_api_compat 1.11.2
asttokens 3.0.0
attrs 25.3.0
certifi 2025.4.26
charset-normalizer 3.4.1
click 8.1.8
colorlog 6.9.0
comm 0.2.2
contourpy 1.3.2
cycler 0.12.1
datasets 3.5.1
debugpy 1.8.14
decorator 5.2.1
dill 0.3.8
docker-pycreds 0.4.0
exceptiongroup 1.2.2
executing 2.2.0
filelock 3.18.0
fonttools 4.57.0
frozenlist 1.6.0
fsspec 2025.3.0
geneformer 0.1.0
gitdb 4.0.12
GitPython 3.1.44
greenlet 3.2.1
grpcio 1.71.0
h5py 3.13.0
huggingface-hub 0.30.2
idna 3.10
importlib_metadata 8.6.1
ipykernel 6.29.5
ipython 9.2.0
ipython_pygments_lexers 1.1.1
jedi 0.19.2
Jinja2 3.1.6
joblib 1.4.2
jsonschema 4.23.0
jsonschema-specifications 2025.4.1
jupyter_client 8.6.3
jupyter_core 5.7.2
kiwisolver 1.4.8
legacy-api-wrap 1.4.1
llvmlite 0.44.0
loompy 3.0.8
Mako 1.3.10
Markdown 3.8
MarkupSafe 3.0.2
matplotlib 3.10.1
matplotlib-inline 0.1.7
mpmath 1.3.0
msgpack 1.1.0
multidict 6.4.3
multiprocess 0.70.16
natsort 8.4.0
nest_asyncio 1.6.0
networkx 3.4.2
numba 0.61.2
numpy 2.2.5
numpy-groupies 0.11.2
nvidia-cublas-cu12 12.6.4.1
nvidia-cuda-cupti-cu12 12.6.80
nvidia-cuda-nvrtc-cu12 12.6.77
nvidia-cuda-runtime-cu12 12.6.77
nvidia-cudnn-cu12 9.5.1.17
nvidia-cufft-cu12 11.3.0.4
nvidia-cufile-cu12 1.11.1.6
nvidia-curand-cu12 10.3.7.77
nvidia-cusolver-cu12 11.7.1.2
nvidia-cusparse-cu12 12.5.4.2
nvidia-cusparselt-cu12 0.6.3
nvidia-nccl-cu12 2.26.2
nvidia-nvjitlink-cu12 12.6.85
nvidia-nvtx-cu12 12.6.77
optuna 4.3.0
optuna-integration 4.3.0
packaging 25.0
pandas 2.2.3
parso 0.8.4
patsy 1.0.1
peft 0.15.2
pexpect 4.9.0
pickleshare 0.7.5
pillow 11.2.1
pip 25.1
platformdirs 4.3.7
prompt_toolkit 3.0.51
propcache 0.3.1
protobuf 6.30.2
psutil 7.0.0
ptyprocess 0.7.0
pure_eval 0.2.3
pyarrow 20.0.0
pydantic 2.11.4
pydantic_core 2.33.2
Pygments 2.19.1
pynndescent 0.5.13
pyparsing 3.2.3
python-dateutil 2.9.0.post0
pytz 2025.2
pyudorandom 1.0.0
PyYAML 6.0.2
pyzmq 26.4.0
ray 2.45.0
referencing 0.36.2
regex 2024.11.6
requests 2.32.3
rpds-py 0.24.0
safetensors 0.5.3
scanpy 1.11.1
scikit-learn 1.5.2
scikit-misc 0.5.1
scipy 1.15.2
seaborn 0.13.2
sentry-sdk 2.27.0
session-info2 0.1.2
setproctitle 1.3.6
setuptools 80.0.1
six 1.17.0
smmap 5.0.2
SQLAlchemy 2.0.40
stack_data 0.6.3
statsmodels 0.14.4
sympy 1.14.0
tdigest 0.5.2.2
tensorboard 2.19.0
tensorboard-data-server 0.7.2
threadpoolctl 3.6.0
tokenizers 0.21.1
torch 2.7.0
tornado 6.4.2
tqdm 4.67.1
traitlets 5.14.3
transformers 4.51.3
triton 3.3.0
typing_extensions 4.13.2
typing-inspection 0.4.0
tzdata 2025.2
umap-learn 0.5.7
urllib3 2.4.0
wandb 0.19.10
wcwidth 0.2.13
Werkzeug 3.1.3
xxhash 3.5.0
yarl 1.20.0
zipp 3.21.0
Colud I know how could I solve this issue, or jsut downgrade the package transformers? Thank you!
Update: This issue has already been solved by downgrading the version of package transformers along with another issue evaluation_strategy. FYI