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11/05 [19:25:05] 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 1183, in forward
logits = logits.float()
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 49.14 GiB. GPU 0 has a total capacity of 79.10 GiB of which 42.31 GiB is free. Including non-PyTorch memory, this process has 36.77 GiB memory in use. Of the allocated memory 29.84 GiB is allocated by PyTorch, and 1.02 GiB 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)
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