Hi! Currently I’m working on some utterance classification problem. Previously I was using Wav2Vec2 (facebook/wav2vec2-base) model and everything was fine. Now I tried to use this new model using the same code. The training process goes fine, but when it comes to evaluation, it runs out of CUDA memory:
 43%|████▎     | 55/129 [00:41<01:09,  1.07it/s]e[ATraceback (most recent call last):
  File "/a2e_workspace/train.py", line 331, in <module>
    trainer.train()
  File "/opt/conda/lib/python3.8/site-packages/transformers/trainer.py", line 1399, in train
    self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)
  File "/opt/conda/lib/python3.8/site-packages/transformers/trainer.py", line 1521, in _maybe_log_save_evaluate
    metrics = self.evaluate(ignore_keys=ignore_keys_for_eval)
  File "/opt/conda/lib/python3.8/site-packages/transformers/trainer.py", line 2158, in evaluate
    output = eval_loop(
  File "/opt/conda/lib/python3.8/site-packages/transformers/trainer.py", line 2341, in evaluation_loop
    preds_host = logits if preds_host is None else nested_concat(preds_host, logits, padding_index=-100)
  File "/opt/conda/lib/python3.8/site-packages/transformers/trainer_pt_utils.py", line 106, in nested_concat
    return type(tensors)(nested_concat(t, n, padding_index=padding_index) for t, n in zip(tensors, new_tensors))
  File "/opt/conda/lib/python3.8/site-packages/transformers/trainer_pt_utils.py", line 106, in <genexpr>
    return type(tensors)(nested_concat(t, n, padding_index=padding_index) for t, n in zip(tensors, new_tensors))
  File "/opt/conda/lib/python3.8/site-packages/transformers/trainer_pt_utils.py", line 106, in nested_concat
    return type(tensors)(nested_concat(t, n, padding_index=padding_index) for t, n in zip(tensors, new_tensors))
  File "/opt/conda/lib/python3.8/site-packages/transformers/trainer_pt_utils.py", line 106, in <genexpr>
    return type(tensors)(nested_concat(t, n, padding_index=padding_index) for t, n in zip(tensors, new_tensors))
  File "/opt/conda/lib/python3.8/site-packages/transformers/trainer_pt_utils.py", line 108, in nested_concat
    return torch_pad_and_concatenate(tensors, new_tensors, padding_index=padding_index)
  File "/opt/conda/lib/python3.8/site-packages/transformers/trainer_pt_utils.py", line 76, in torch_pad_and_concatenate
    result = tensor1.new_full(new_shape, padding_index)
RuntimeError: CUDA out of memory. Tried to allocate 480.00 MiB (GPU 0; 15.78 GiB total capacity; 13.21 GiB already allocated; 415.75 MiB free; 13.99 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

The GPU has 16 GB VRAM.
Batch size parameters:
  per_device_train_batch_size: 4
  per_device_eval_batch_size: 4
  gradient_accumulation_steps: 4

The code is pretty much default, it’s based on this guide
The strange thing is that the size of the model is approximately the same as Wav2Vec2, and also memory always runs out exactly on evalution. So maybe there is some memory leak bug in the code.
I’m gonna test this on 32 GB GPU and report the results.