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jackaduma / Vicuna-LoRA-RLHF-PyTorch

A full pipeline to finetune Vicuna LLM with LoRA and RLHF on consumer hardware. Implementation of RLHF (Reinforcement Learning with Human Feedback) on top of the Vicuna architecture. Basically ChatGPT but with Vicuna
MIT License
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supervised_finetune.py failed with a wordaround #10

Open SeekPoint opened 1 year ago

SeekPoint commented 1 year ago

(gh_Vicuna-LoRA-RLHF-PyTorch) amd00@asus00:~/llm_dev/Vicuna-LoRA-RLHF-PyTorch$ python supervised_finetune.py --data_path './data/merge_sample.json' --output_path 'lora-Vicuna' --model_path './weights/vicuna-7b' --eval_steps 200 --save_steps 200 --test_size 1

===================================BUG REPORT=================================== Welcome to bitsandbytes. For bug reports, please run

python -m bitsandbytes

and submit this information together with your error trace to: https://github.com/TimDettmers/bitsandbytes/issues

bin /home/amd00/anaconda3/envs/gh_Vicuna-LoRA-RLHF-PyTorch/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cpu.so /home/amd00/anaconda3/envs/gh_Vicuna-LoRA-RLHF-PyTorch/lib/python3.10/site-packages/bitsandbytes/cextension.py:34: UserWarning: The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable. warn("The installed version of bitsandbytes was compiled without GPU support. " /home/amd00/anaconda3/envs/gh_Vicuna-LoRA-RLHF-PyTorch/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cpu.so: undefined symbol: cadam32bit_grad_fp32 CUDA SETUP: Loading binary /home/amd00/anaconda3/envs/gh_Vicuna-LoRA-RLHF-PyTorch/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cpu.so... ./weights/vicuna-7b Overriding torch_dtype=None with torch_dtype=torch.float16 due to requirements of bitsandbytes to enable model loading in mixed int8. Either pass torch_dtype=torch.float16 or don't pass this argument at all to remove this warning. ╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮ │ /home/amd00/llm_dev/Vicuna-LoRA-RLHF-PyTorch/supervised_finetune.py:72 in │ │ │ │ 69 │ device_map = {"": int(os.environ.get("LOCAL_RANK") or 0)} │ │ 70 │ GRADIENT_ACCUMULATION_STEPS = GRADIENT_ACCUMULATION_STEPS // world_size │ │ 71 print(args.model_path) │ │ ❱ 72 model = LlamaForCausalLM.from_pretrained( │ │ 73 │ args.model_path, │ │ 74 │ load_in_8bit=True, │ │ 75 │ device_map=device_map │ │ │ │ /home/amd00/.local/lib/python3.10/site-packages/transformers/modeling_utils.py:2740 in │ │ from_pretrained │ │ │ │ 2737 │ │ │ │ │ key: device_map[key] for key in device_map.keys() if key not in modu │ │ 2738 │ │ │ │ } │ │ 2739 │ │ │ │ if "cpu" in device_map_without_lm_head.values() or "disk" in devicemap │ │ ❱ 2740 │ │ │ │ │ raise ValueError( │ │ 2741 │ │ │ │ │ │ """ │ │ 2742 │ │ │ │ │ │ Some modules are dispatched on the CPU or the disk. Make sure yo │ │ 2743 │ │ │ │ │ │ the quantized model. If you want to dispatch the model on the CP │ ╰──────────────────────────────────────────────────────────────────────────────────────────────────╯ ValueError: Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit the quantized model. If you want to dispatch the model on the CPU or the disk while keeping these modules in 32-bit, you need to set load_in_8bit_fp32_cpu_offload=True and pass a custom device_map to from_pretrained. Check https://huggingface.co/docs/transformers/main/en/main_classes/quantization#offload-between-cpu-and-gpu for more details.

(gh_Vicuna-LoRA-RLHF-PyTorch) amd00@asus00:~/llm_dev/Vicuna-LoRA-RLHF-PyTorch$

SeekPoint commented 1 year ago

I got a workarond:

(gh_Vicuna-LoRA-RLHF-PyTorch) amd00@asus00:~/llm_dev/Vicuna-LoRA-RLHF-PyTorch$ git diff diff --git a/supervised_finetune.py b/supervised_finetune.py index 4cfbc76..a850789 100644 --- a/supervised_finetune.py +++ b/supervised_finetune.py @@ -71,7 +71,6 @@ if ddp: print(args.model_path) model = LlamaForCausalLM.from_pretrained( args.model_path,