How to quantize NVILA with awq？

[Kibry-spin]

(vila) kirdo@kirdo-System-Product-Name:~/LLM/llm-awq$ python -m awq.entry --model_path /home/kirdo/LLM/NVILA-8B-Video/ --w_bit 4 --q_group_size 128 --run_awq --dump_awq awq_cache/$MODEL-w4-g128.pt
Quantization config: {'zero_point': True, 'q_group_size': 128}

• Building model /home/kirdo/LLM/NVILA-8B-Video/
  [2024-12-30 19:26:13,027] [INFO] [real_accelerator.py:110:get_accelerator] Setting ds_accelerator to cuda (auto detect)
  Loading checkpoint shards: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:03<00:00, 1.05it/s]
  You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with model.to('cuda').
  Repo card metadata block was not found. Setting CardData to empty.
  Token indices sequence length is longer than the specified maximum sequence length for this model (57053 > 16384). Running this sequence through the model will result in indexing errors
• Split into 59 blocks
  Traceback (most recent call last):
  File "/home/kirdo/miniconda3/envs/vila/lib/python3.10/runpy.py", line 196, in _run_module_as_main
  return _run_code(code, main_globals, None,
  File "/home/kirdo/miniconda3/envs/vila/lib/python3.10/runpy.py", line 86, in _run_code
  exec(code, run_globals)
  File "/home/kirdo/LLM/llm-awq/awq/entry.py", line 352, in
  main()
  File "/home/kirdo/LLM/llm-awq/awq/entry.py", line 293, in main
  model, enc = build_model_and_enc(args.model_path)
  File "/home/kirdo/LLM/llm-awq/awq/entry.py", line 199, in build_model_and_enc
  awq_results = run_awq(
  File "/home/kirdo/miniconda3/envs/vila/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
  return func(*args, **kwargs)
  File "/home/kirdo/LLM/llm-awq/awq/quantize/pre_quant.py", line 136, in run_awq
  model.llm(samples.to(next(model.parameters()).device))
  File "/home/kirdo/miniconda3/envs/vila/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
  return self._call_impl(*args, **kwargs)
  File "/home/kirdo/miniconda3/envs/vila/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
  return forward_call(*args, **kwargs)
  File "/home/kirdo/miniconda3/envs/vila/lib/python3.10/site-packages/transformers/models/qwen2/modeling_qwen2.py", line 1164, in forward
  outputs = self.model(
  File "/home/kirdo/miniconda3/envs/vila/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
  return self._call_impl(*args, **kwargs)
  File "/home/kirdo/miniconda3/envs/vila/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
  return forward_call(*args, **kwargs)
  File "/home/kirdo/miniconda3/envs/vila/lib/python3.10/site-packages/transformers/models/qwen2/modeling_qwen2.py", line 871, in forward
  position_embeddings = self.rotary_emb(hidden_states, position_ids)
  File "/home/kirdo/miniconda3/envs/vila/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
  return self._call_impl(*args, **kwargs)
  File "/home/kirdo/miniconda3/envs/vila/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
  return forward_call(*args, **kwargs)
  File "/home/kirdo/miniconda3/envs/vila/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
  return func(*args, **kwargs)
  File "/home/kirdo/miniconda3/envs/vila/lib/python3.10/site-packages/transformers/models/qwen2/modeling_qwen2.py", line 163, in forward
  freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
  RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cpu and cuda:0! (when checking argument for argument mat2 in method wrapper_CUDA_bmm)

I just follow the https://github.com/mit-han-lab/llm-awq.git to quantize NVILA-8B-Video，but when running awq search, i got an problem shown above.