supervised_finetune.py failed with a wordaround

[SeekPoint]

(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===================================
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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 device_map_ │
│ ❱ 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]

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,

• load_in_8bit=True,
  device_map=device_map,
  )
  tokenizer = LlamaTokenizer.from_pretrained(
  @@ -159,7 +158,6 @@ trainer = transformers.Trainer(
  num_train_epochs=EPOCHS,
  max_steps=MAX_STEPS,
  learning_rate=LEARNING_RATE,

•    fp16=True,
     logging_steps=20,
     evaluation_strategy="steps" if VAL_SET_SIZE > 0 else "no",
     save_strategy="steps",
  

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