Hi 
I am writing to seek assistance regarding an error I encountered while running a custum BERT-based classifier.
I am currently working with a relatively small dataset, and I have set the batch sizes as follows:
train_batch_size: 4val_batch_size: 4
The validation set consists of fewer than 200 examples, while the training set has approximately 600 examples.
I would greatly appreciate your help in resolving this issue.
Here is the code snippet that I’m working with:
class BERTClassifier(BertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.bert = BertModel(config)
self.dropout = torch.nn.Dropout(0.3)
self.linear = torch.nn.Linear(768, self.num_labels)
self.post_init()
def forward(self, input_ids, attention_mask=None,
token_type_ids=None, labels=None,
features=None, output_attentions=False):
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
output_attentions=True
)
if features is not None:
dropout_output = torch.cat((dropout_output, features), dim=1)
logits = self.linear(dropout_output)
loss = None
if labels is not None:
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
return SequenceClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states,attentions=outputs.attentions)
model = BERTClassifier.from_pretrained('bert-base-uncased')
model.to(device)
I encountered the following error message:
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ /tmp/ipykernel_29/4032920361.py:1 in <module> │
│ │
│ [Errno 2] No such file or directory: '/tmp/ipykernel_29/4032920361.py' │
│ │
│ /opt/conda/lib/python3.10/site-packages/transformers/trainer.py:1645 in train │
│ │
│ 1642 │ │ inner_training_loop = find_executable_batch_size( │
│ 1643 │ │ │ self._inner_training_loop, self._train_batch_size, args.auto_find_batch_size │
│ 1644 │ │ ) │
│ ❱ 1645 │ │ return inner_training_loop( │
│ 1646 │ │ │ args=args, │
│ 1647 │ │ │ resume_from_checkpoint=resume_from_checkpoint, │
│ 1648 │ │ │ trial=trial, │
│ │
│ /opt/conda/lib/python3.10/site-packages/transformers/trainer.py:2035 in _inner_training_loop │
│ │
│ 2032 │ │ │ │ self.control.should_training_stop = True │
│ 2033 │ │ │ │
│ 2034 │ │ │ self.control = self.callback_handler.on_epoch_end(args, self.state, self.con │
│ ❱ 2035 │ │ │ self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_ │
│ 2036 │ │ │ │
│ 2037 │ │ │ if DebugOption.TPU_METRICS_DEBUG in self.args.debug: │
│ 2038 │ │ │ │ if is_torch_tpu_available(): │
│ │
│ /opt/conda/lib/python3.10/site-packages/transformers/trainer.py:2321 in _maybe_log_save_evaluate │
│ │
│ 2318 │ │ │ │ │ ) │
│ 2319 │ │ │ │ │ metrics.update(dataset_metrics) │
│ 2320 │ │ │ else: │
│ ❱ 2321 │ │ │ │ metrics = self.evaluate(ignore_keys=ignore_keys_for_eval) │
│ 2322 │ │ │ self._report_to_hp_search(trial, self.state.global_step, metrics) │
│ 2323 │ │ │ │
│ 2324 │ │ │ # Run delayed LR scheduler now that metrics are populated │
│ │
│ /opt/conda/lib/python3.10/site-packages/transformers/trainer.py:3053 in evaluate │
│ │
│ 3050 │ │ start_time = time.time() │
│ 3051 │ │ │
│ 3052 │ │ eval_loop = self.prediction_loop if self.args.use_legacy_prediction_loop else se │
│ ❱ 3053 │ │ output = eval_loop( │
│ 3054 │ │ │ eval_dataloader, │
│ 3055 │ │ │ description="Evaluation", │
│ 3056 │ │ │ # No point gathering the predictions if there are no metrics, otherwise we d │
│ │
│ /opt/conda/lib/python3.10/site-packages/transformers/trainer.py:3270 in evaluation_loop │
│ │
│ 3267 │ │ │ │ if self.preprocess_logits_for_metrics is not None: │
│ 3268 │ │ │ │ │ logits = self.preprocess_logits_for_metrics(logits, labels) │
│ 3269 │ │ │ │ logits = self._nested_gather(logits) │
│ ❱ 3270 │ │ │ │ preds_host = logits if preds_host is None else nested_concat(preds_host, │
│ 3271 │ │ │ if labels is not None: │
│ 3272 │ │ │ │ labels = self._nested_gather(labels) │
│ 3273 │ │ │ │ labels_host = labels if labels_host is None else nested_concat(labels_ho │
│ │
│ /opt/conda/lib/python3.10/site-packages/transformers/trainer_pt_utils.py:114 in nested_concat │
│ │
│ 111 │ │ new_tensors │
│ 112 │ ), f"Expected `tensors` and `new_tensors` to have the same type but found {type(tens │
│ 113 │ if isinstance(tensors, (list, tuple)): │
│ ❱ 114 │ │ return type(tensors)(nested_concat(t, n, padding_index=padding_index) for t, n i │
│ 115 │ elif isinstance(tensors, torch.Tensor): │
│ 116 │ │ return torch_pad_and_concatenate(tensors, new_tensors, padding_index=padding_ind │
│ 117 │ elif isinstance(tensors, Mapping): │
│ │
│ /opt/conda/lib/python3.10/site-packages/transformers/trainer_pt_utils.py:114 in <genexpr> │
│ │
│ 111 │ │ new_tensors │
│ 112 │ ), f"Expected `tensors` and `new_tensors` to have the same type but found {type(tens │
│ 113 │ if isinstance(tensors, (list, tuple)): │
│ ❱ 114 │ │ return type(tensors)(nested_concat(t, n, padding_index=padding_index) for t, n i │
│ 115 │ elif isinstance(tensors, torch.Tensor): │
│ 116 │ │ return torch_pad_and_concatenate(tensors, new_tensors, padding_index=padding_ind │
│ 117 │ elif isinstance(tensors, Mapping): │
│ │
│ /opt/conda/lib/python3.10/site-packages/transformers/trainer_pt_utils.py:114 in nested_concat │
│ │
│ 111 │ │ new_tensors │
│ 112 │ ), f"Expected `tensors` and `new_tensors` to have the same type but found {type(tens │
│ 113 │ if isinstance(tensors, (list, tuple)): │
│ ❱ 114 │ │ return type(tensors)(nested_concat(t, n, padding_index=padding_index) for t, n i │
│ 115 │ elif isinstance(tensors, torch.Tensor): │
│ 116 │ │ return torch_pad_and_concatenate(tensors, new_tensors, padding_index=padding_ind │
│ 117 │ elif isinstance(tensors, Mapping): │
│ │
│ /opt/conda/lib/python3.10/site-packages/transformers/trainer_pt_utils.py:114 in <genexpr> │
│ │
│ 111 │ │ new_tensors │
│ 112 │ ), f"Expected `tensors` and `new_tensors` to have the same type but found {type(tens │
│ 113 │ if isinstance(tensors, (list, tuple)): │
│ ❱ 114 │ │ return type(tensors)(nested_concat(t, n, padding_index=padding_index) for t, n i │
│ 115 │ elif isinstance(tensors, torch.Tensor): │
│ 116 │ │ return torch_pad_and_concatenate(tensors, new_tensors, padding_index=padding_ind │
│ 117 │ elif isinstance(tensors, Mapping): │
│ │
│ /opt/conda/lib/python3.10/site-packages/transformers/trainer_pt_utils.py:116 in nested_concat │
│ │
│ 113 │ if isinstance(tensors, (list, tuple)): │
│ 114 │ │ return type(tensors)(nested_concat(t, n, padding_index=padding_index) for t, n i │
│ 115 │ elif isinstance(tensors, torch.Tensor): │
│ ❱ 116 │ │ return torch_pad_and_concatenate(tensors, new_tensors, padding_index=padding_ind │
│ 117 │ elif isinstance(tensors, Mapping): │
│ 118 │ │ return type(tensors)( │
│ 119 │ │ │ {k: nested_concat(t, new_tensors[k], padding_index=padding_index) for k, t i │
│ │
│ /opt/conda/lib/python3.10/site-packages/transformers/trainer_pt_utils.py:75 in │
│ torch_pad_and_concatenate │
│ │
│ 72 │ tensor2 = atleast_1d(tensor2) │
│ 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 │
│ 78 │ new_shape = (tensor1.shape[0] + tensor2.shape[0], max(tensor1.shape[1], tensor2.shap │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
OutOfMemoryError: CUDA out of memory. Tried to allocate 288.00 MiB (GPU 0; 15.90 GiB total capacity; 14.14 GiB
already allocated; 201.75 MiB free; 14.82 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
Thank you