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
hello , I am also trying to replicate the cell_classification.ipynb. could you share your environment after correction?
Hey, I'm facing this same issue with the latest version of codebase. What version of transformers are you using to correct this? I'm not able to find a version that both bypasses this error and other new errors that come up on lower package versions since the latest codebase is updated for those transformer changes.
which version should be install? I also have same issue.
Update: This issue has already been solved by downgrading the version of package
transformersalong with another issueevaluation_strategy. FYI
Would you tell me which version is working?
In today vesion, transformers==4.46 is ok to solve this problem
transformers==4.46 works for me thanks!
The package has been updated to resolve this error for compatibility with the updated transformers. Thank you for pointing it out.
ctheodoris
changed discussion status to
closed