CUDA out of memory only during validation not training
I’m trying to fine-tune an AraBERT2GPT2 model using the EncoderDecoderModel class on a relatively small dataset. I train only for 1 epoch and evaluate after it. However, I get a CUDA out of memory error a few samples into the evaluation even after reducing the evaluation batch size to 1. What may be the cause of this and how can I solve it?RuntimeError                              Traceback (most recent call last)
<ipython-input-4-581ee471b86c> in <module>()
     18 trainer = Seq2SeqTrainer(model=model, args=training_args, compute_metrics=compute_metrics, train_dataset = train_dataset['train'], eval_dataset=train_dataset['test'])
     19 
---> 20 trainer.train()
     21 torch.cuda.empty_cache()

9 frames
/usr/local/lib/python3.7/dist-packages/transformers/trainer.py in train(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)
   1319             resume_from_checkpoint=resume_from_checkpoint,
   1320             trial=trial,
-> 1321             ignore_keys_for_eval=ignore_keys_for_eval,
   1322         )
   1323 

/usr/local/lib/python3.7/dist-packages/transformers/trainer.py in _inner_training_loop(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)
   1642 
   1643             self.control = self.callback_handler.on_epoch_end(args, self.state, self.control)
-> 1644             self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)
   1645 
   1646             if DebugOption.TPU_METRICS_DEBUG in self.args.debug:

/usr/local/lib/python3.7/dist-packages/transformers/trainer.py in _maybe_log_save_evaluate(self, tr_loss, model, trial, epoch, ignore_keys_for_eval)
   1794         metrics = None
   1795         if self.control.should_evaluate:
-> 1796             metrics = self.evaluate(ignore_keys=ignore_keys_for_eval)
   1797             self._report_to_hp_search(trial, epoch, metrics)
   1798 

/usr/local/lib/python3.7/dist-packages/transformers/trainer_seq2seq.py in evaluate(self, eval_dataset, ignore_keys, metric_key_prefix, max_length, num_beams)
     68         self._max_length = max_length if max_length is not None else self.args.generation_max_length
     69         self._num_beams = num_beams if num_beams is not None else self.args.generation_num_beams
---> 70         return super().evaluate(eval_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix)
     71 
     72     def predict(

/usr/local/lib/python3.7/dist-packages/transformers/trainer.py in evaluate(self, eval_dataset, ignore_keys, metric_key_prefix)
   2463             prediction_loss_only=True if self.compute_metrics is None else None,
   2464             ignore_keys=ignore_keys,
-> 2465             metric_key_prefix=metric_key_prefix,
   2466         )
   2467 

/usr/local/lib/python3.7/dist-packages/transformers/trainer.py in evaluation_loop(self, dataloader, description, prediction_loss_only, ignore_keys, metric_key_prefix)
   2660                 if self.preprocess_logits_for_metrics is not None:
   2661                     logits = self.preprocess_logits_for_metrics(logits, labels)
-> 2662                 preds_host = logits if preds_host is None else nested_concat(preds_host, logits, padding_index=-100)
   2663             self.control = self.callback_handler.on_prediction_step(args, self.state, self.control)
   2664 

/usr/local/lib/python3.7/dist-packages/transformers/trainer_pt_utils.py in nested_concat(tensors, new_tensors, padding_index)
    112     ), f"Expected `tensors` and `new_tensors` to have the same type but found {type(tensors)} and {type(new_tensors)}."
    113     if isinstance(tensors, (list, tuple)):
--> 114         return type(tensors)(nested_concat(t, n, padding_index=padding_index) for t, n in zip(tensors, new_tensors))
    115     elif isinstance(tensors, torch.Tensor):
    116         return torch_pad_and_concatenate(tensors, new_tensors, padding_index=padding_index)

/usr/local/lib/python3.7/dist-packages/transformers/trainer_pt_utils.py in <genexpr>(.0)
    112     ), f"Expected `tensors` and `new_tensors` to have the same type but found {type(tensors)} and {type(new_tensors)}."
    113     if isinstance(tensors, (list, tuple)):
--> 114         return type(tensors)(nested_concat(t, n, padding_index=padding_index) for t, n in zip(tensors, new_tensors))
    115     elif isinstance(tensors, torch.Tensor):
    116         return torch_pad_and_concatenate(tensors, new_tensors, padding_index=padding_index)

/usr/local/lib/python3.7/dist-packages/transformers/trainer_pt_utils.py in nested_concat(tensors, new_tensors, padding_index)
    114         return type(tensors)(nested_concat(t, n, padding_index=padding_index) for t, n in zip(tensors, new_tensors))
    115     elif isinstance(tensors, torch.Tensor):
--> 116         return torch_pad_and_concatenate(tensors, new_tensors, padding_index=padding_index)
    117     elif isinstance(tensors, np.ndarray):
    118         return numpy_pad_and_concatenate(tensors, new_tensors, padding_index=padding_index)

/usr/local/lib/python3.7/dist-packages/transformers/trainer_pt_utils.py in torch_pad_and_concatenate(tensor1, tensor2, padding_index)
     73 
     74     if len(tensor1.shape) == 1 or tensor1.shape[1] == tensor2.shape[1]:
---> 75         return torch.cat((tensor1, tensor2), dim=0)
     76 
     77     # Let's figure out the new shape

RuntimeError: CUDA out of memory. Tried to allocate 86.00 MiB (GPU 0; 15.90 GiB total capacity; 14.10 GiB already allocated; 79.75 MiB free; 14.78 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
2022-05-26T11:33:52Z
Here are a few things:Make sure your model only returns logits and not extra tensors (as everything is accumulated on the GPU)Useeval_accumulation_stepsto regularly offload the predictions on the GPU to the CPU (slower but will avoid this OOM error).
2022-05-30T18:05:54Z
Thank you! Setting predict_with_generate to True in the training args seemed to solve it for me
2023-05-09T06:05:02Z
What if I am using a HF pipeline and model?ex:model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=10)The model uses less than 10% of GPU memory during training and runs out of memory during validation.