LoRA

LoRA Finetuning with BigDL-LLM

This example ports Alpaca-LoRA to BigDL-LLM (using LoRA algorithm) on Intel GPU.

0. Requirements

To run this example with BigDL-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to here for more information.

1. Install

conda create -n llm python=3.9
conda activate llm
# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
pip install transformers==4.34.0 datasets
pip install fire peft==0.5.0
pip install oneccl_bind_pt==2.1.100 -f https://developer.intel.com/ipex-whl-stable-xpu # necessary to run distributed finetuning
pip install accelerate==0.23.0
pip install bitsandbytes scipy

2. Configures OneAPI environment variables

source /opt/intel/oneapi/setvars.sh

3. LoRA Finetune

Here, we provide example usages on different hardware. Please refer to the appropriate script based on your device:

Finetuning LLaMA2-7B on single Arc A770

bash lora_finetune_llama2_7b_arc_1_card.sh

Finetuning LLaMA2-7B on four Intel Data Center GPU Max 1100

bash lora_finetune_llama2_7b_pvc_1100_1_card.sh

Finetuning LLaMA2-7B on single tile of Intel Data Center GPU Max 1550

bash lora_finetune_llama2_7b_pvc_1550_1_tile.sh

Finetuning LLaMA2-7B on four Intel Data Center GPU Max 1550

bash lora_finetune_llama2_7b_pvc_1550_4_card.sh

4. (Optional) Resume Training

If you fail to complete the whole finetuning process, it is suggested to resume training from a previously saved checkpoint by specifying resume_from_checkpoint to the local checkpoint folder as following:

python ./alpaca_lora_finetuning.py \
    --base_model "meta-llama/Llama-2-7b-hf" \
    --data_path "yahma/alpaca-cleaned" \
    --output_dir "./bigdl-qlora-alpaca" \
    --resume_from_checkpoint "./bigdl-qlora-alpaca/checkpoint-1100"

5. Sample Output

{'loss': 1.9231, 'learning_rate': 2.9999945367033285e-05, 'epoch': 0.0}                                                                                                                            
{'loss': 1.8622, 'learning_rate': 2.9999781468531096e-05, 'epoch': 0.01}                                                                                                                           
{'loss': 1.9043, 'learning_rate': 2.9999508305687345e-05, 'epoch': 0.01}                                                                                                                           
{'loss': 1.8967, 'learning_rate': 2.999912588049185e-05, 'epoch': 0.01}                                                                                                                            
{'loss': 1.9658, 'learning_rate': 2.9998634195730358e-05, 'epoch': 0.01}                                                                                                                           
{'loss': 1.8386, 'learning_rate': 2.9998033254984483e-05, 'epoch': 0.02}                                                                                                                           
{'loss': 1.809, 'learning_rate': 2.999732306263172e-05, 'epoch': 0.02}                                                                                                                             
{'loss': 1.8552, 'learning_rate': 2.9996503623845395e-05, 'epoch': 0.02}                                                                                                                           
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6. Merge the adapter into the original model

python ./export_merged_model.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --adapter_path ./outputs/checkpoint-200 --output_path ./outputs/checkpoint-200-merged

Then you can use ./outputs/checkpoint-200-merged as a normal huggingface transformer model to do inference.

7. Troubleshooting

Please refer to here for solutions of common issues during finetuning.