11/05 [19:26:59] INFO     | >> [*] Starting Training Loop        pretrain.py:227
Traceback (most recent call last):                                                    
  File "/hai/scratch/belkhale/openvla-mini/scripts/pretrain.py", line 241, in <module>
    pretrain()
  File "/hai/scratch/belkhale/miniforge3/envs/vla/lib/python3.10/site-packages/draccus/argparsing.py", line 203, in wrapper_inner
    response = fn(cfg, *args, **kwargs)
  File "/hai/scratch/belkhale/openvla-mini/scripts/pretrain.py", line 228, in pretrain
    train_strategy.run_training(train_dataset, collator, metrics, stage=cfg.stage, seed=cfg.seed)
  File "/hai/scratch/belkhale/openvla-mini/prismatic/training/strategies/base_strategy.py", line 190, in run_training
    output: CausalLMOutputWithPast = self.vlm(
  File "/hai/scratch/belkhale/miniforge3/envs/vla/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/hai/scratch/belkhale/miniforge3/envs/vla/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
    return forward_call(*args, **kwargs)
  File "/hai/scratch/belkhale/miniforge3/envs/vla/lib/python3.10/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py", line 849, in forward
    output = self._fsdp_wrapped_module(*args, **kwargs)
  File "/hai/scratch/belkhale/miniforge3/envs/vla/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/hai/scratch/belkhale/miniforge3/envs/vla/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
    return forward_call(*args, **kwargs)
  File "/hai/scratch/belkhale/openvla-mini/prismatic/models/vlms/prismatic.py", line 470, in forward
    return self.llm_backbone(
  File "/hai/scratch/belkhale/miniforge3/envs/vla/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/hai/scratch/belkhale/miniforge3/envs/vla/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
    return forward_call(*args, **kwargs)
  File "/hai/scratch/belkhale/openvla-mini/prismatic/models/backbones/llm/base_llm.py", line 221, in forward
    output: CausalLMOutputWithPast = self.llm(
  File "/hai/scratch/belkhale/miniforge3/envs/vla/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/hai/scratch/belkhale/miniforge3/envs/vla/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
    return forward_call(*args, **kwargs)
  File "/hai/scratch/belkhale/miniforge3/envs/vla/lib/python3.10/site-packages/transformers/models/qwen2/modeling_qwen2.py", line 1196, in forward
    loss = loss_fct(shift_logits, shift_labels)
  File "/hai/scratch/belkhale/miniforge3/envs/vla/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/hai/scratch/belkhale/miniforge3/envs/vla/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
    return forward_call(*args, **kwargs)
  File "/hai/scratch/belkhale/miniforge3/envs/vla/lib/python3.10/site-packages/torch/nn/modules/loss.py", line 1179, in forward
    return F.cross_entropy(input, target, weight=self.weight,
  File "/hai/scratch/belkhale/miniforge3/envs/vla/lib/python3.10/site-packages/torch/nn/functional.py", line 3059, in cross_entropy
    return torch._C._nn.cross_entropy_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index, label_smoothing)
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 24.57 GiB. GPU 0 has a total capacity of 79.10 GiB of which 20.18 GiB is free. Including non-PyTorch memory, this process has 58.91 GiB memory in use. Of the allocated memory 52.22 GiB is allocated by PyTorch, and 798.66 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)