sail-sg/CLoT

How to run the program on MacbookPro with M2 Max CPU?

zhuchuji opened this issue · 2 comments

zhuchuji commented

I try to run the program on my macbook pro with M2 Max CPU, it throws AssertionError: Torch not compiled with CUDA enabled.

Detail log is shown as following:

/Users/chockiezhu/Library/Python/3.9/lib/python/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020
  warnings.warn(
Loading checkpoint shards:   0%|                         | 0/10 [00:01<?, ?it/s]
Traceback (most recent call last):
  File "/Users/chockiezhu/practice/CLoT/gradio_demo.py", line 369, in <module>
    main()
  File "/Users/chockiezhu/practice/CLoT/gradio_demo.py", line 365, in main
    _launch_demo(args)
  File "/Users/chockiezhu/practice/CLoT/gradio_demo.py", line 114, in _launch_demo
    model, tokenizer = _load_model_tokenizer(args.checkpoint_path)
  File "/Users/chockiezhu/practice/CLoT/gradio_demo.py", line 71, in _load_model_tokenizer
    model = AutoPeftModelForCausalLM.from_pretrained(
  File "/Users/chockiezhu/Library/Python/3.9/lib/python/site-packages/peft/auto.py", line 104, in from_pretrained
    base_model = target_class.from_pretrained(base_model_path, **kwargs)
  File "/Users/chockiezhu/Library/Python/3.9/lib/python/site-packages/transformers/models/auto/auto_factory.py", line 558, in from_pretrained
    return model_class.from_pretrained(
  File "/Users/chockiezhu/Library/Python/3.9/lib/python/site-packages/transformers/modeling_utils.py", line 3531, in from_pretrained
    ) = cls._load_pretrained_model(
  File "/Users/chockiezhu/Library/Python/3.9/lib/python/site-packages/transformers/modeling_utils.py", line 3958, in _load_pretrained_model
    new_error_msgs, offload_index, state_dict_index = _load_state_dict_into_meta_model(
  File "/Users/chockiezhu/Library/Python/3.9/lib/python/site-packages/transformers/modeling_utils.py", line 812, in _load_state_dict_into_meta_model
    set_module_tensor_to_device(model, param_name, param_device, **set_module_kwargs)
  File "/Users/chockiezhu/Library/Python/3.9/lib/python/site-packages/accelerate/utils/modeling.py", line 399, in set_module_tensor_to_device
    new_value = value.to(device)
  File "/Users/chockiezhu/Library/Python/3.9/lib/python/site-packages/torch/cuda/__init__.py", line 293, in _lazy_init
    raise AssertionError("Torch not compiled with CUDA enabled")
zhuchuji commented

By using MPS(Metal Performance Shaders) on my mac and modify AutoPeftModelForCausalLM.from_pretrained( checkpoint_path, device_map="cuda", trust_remote_code=True, fp16=True ).eval() to AutoPeftModelForCausalLM.from_pretrained( checkpoint_path, device_map=torch.device('mps'), trust_remote_code=True, fp16=True ).eval(), the error becoming the following:

Loading checkpoint shards: 100%|███████████████████████████████████████████████████████████████████████████████████████| 10/10 [01:32<00:00, 9.23s/it]
Traceback (most recent call last):
File "/Users/chockiezhu/practice/CLoT/inference.py", line 9, in
model = AutoPeftModelForCausalLM.from_pretrained(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/lib/python3.12/site-packages/peft/auto.py", line 126, in from_pretrained
base_model.resize_token_embeddings(len(tokenizer))
File "/opt/homebrew/lib/python3.12/site-packages/transformers/modeling_utils.py", line 1803, in resize_token_embeddings
model_embeds = self._resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/lib/python3.12/site-packages/transformers/modeling_utils.py", line 1818, in _resize_token_embeddings
new_embeddings = self._get_resized_embeddings(old_embeddings, new_num_tokens, pad_to_multiple_of)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/lib/python3.12/site-packages/transformers/modeling_utils.py", line 1928, in _get_resized_embeddings
new_embeddings = nn.Embedding(
^^^^^^^^^^^^^
File "/opt/homebrew/lib/python3.12/site-packages/torch/nn/modules/sparse.py", line 143, in init
self.weight = Parameter(torch.empty((num_embeddings, embedding_dim), **factory_kwargs),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: MPS backend out of memory (MPS allocated: 36.18 GB, other allocations: 384.00 KB, max allowed: 36.27 GB). Tried to allocate 2.32 GB on private pool. Use PYTORCH_MPS_HIGH_WATERMARK_RATIO=0.0 to disable upper limit for memory allocations (may cause system failure).

zhongshsh commented

Our testing was conducted on Linux. Due to a lack of Apple Mac devices, we're unable to address your query. Here is a suggestion.

"Out of memory" errors often occur due to insufficient GPU memory. You can try using some methods for transformers (like those outlined in https://huggingface.co/docs/accelerate/en/usage_guides/big_modeling) to resolve the issue.