ipex-llm/docker/llm/finetune/xpu

Finetune LLM with IPEX LLM Container

The following shows how to finetune LLM with IPEX-LLM optimizations in a docker environment, which is accelerated by Intel XPU.

With this docker image, we can use all ipex-llm finetune examples on Intel GPU, including:

• LoRA: examples of running LoRA finetuning
• QLoRA: examples of running QLoRA finetuning
• QA-LoRA: examples of running QA-LoRA finetuning
• ReLora: examples of running ReLora finetuning
• DPO: examples of running DPO finetuning
• HF-PEFT: run finetuning on Intel GPU using Hugging Face PEFT code without modification
• axolotl: LLM finetuning on Intel GPU using axolotl without writing code

1. Prepare Docker Image

You can download directly from Dockerhub like:

docker pull intelanalytics/ipex-llm-finetune-xpu:2.1.0-SNAPSHOT

Or build the image from source:

export HTTP_PROXY=your_http_proxy
export HTTPS_PROXY=your_https_proxy

docker build \
  --build-arg http_proxy=${HTTP_PROXY} \
  --build-arg https_proxy=${HTTPS_PROXY} \
  -t intelanalytics/ipex-llm-finetune-xpu:2.1.0-SNAPSHOT \
  -f ./Dockerfile .

2. Prepare Base Model, Data and Container

Here, we try to fine-tune a Llama2-7b with yahma/alpaca-cleaned dataset, and please download them and start a docker container with files mounted like below:

export BASE_MODE_PATH=your_downloaded_base_model_path
export DATA_PATH=your_downloaded_data_path
export HTTP_PROXY=your_http_proxy
export HTTPS_PROXY=your_https_proxy

docker run -itd \
   --net=host \
   --device=/dev/dri \
   --memory="32G" \
   --name=ipex-llm-finetune-xpu \
   -e http_proxy=${HTTP_PROXY} \
   -e https_proxy=${HTTPS_PROXY} \
   -v $BASE_MODE_PATH:/model \
   -v $DATA_PATH:/data/alpaca-cleaned \
   --shm-size="16g" \
   intelanalytics/ipex-llm-finetune-xpu:2.1.0-SNAPSHOT

The download and mount of base model and data to a docker container demonstrates a standard fine-tuning process. You can skip this step for a quick start, and in this way, the fine-tuning codes will automatically download the needed files:

export HTTP_PROXY=your_http_proxy
export HTTPS_PROXY=your_https_proxy

docker run -itd \
   --net=host \
   --device=/dev/dri \
   --memory="32G" \
   --name=ipex-llm-finetune-xpu \
   -e http_proxy=${HTTP_PROXY} \
   -e https_proxy=${HTTPS_PROXY} \
   --shm-size="16g" \
   intelanalytics/ipex-llm-finetune-xpu:2.1.0-SNAPSHOT

However, we do recommend you to handle them manually, because the download can be blocked by Internet access and Huggingface authentication etc. according to different environment, and the manual method allows you to fine-tune in a custom way (with different base model and dataset).

3. Start Fine-Tuning

3.1 QLoRA Llama2-7b example

Enter the running container:

docker exec -it ipex-llm-finetune-xpu bash

Then, start QLoRA fine-tuning:

bash start-qlora-finetuning-on-xpu.sh

After minutes, it is expected to get results like:

{'loss': 2.0251, 'learning_rate': 0.0002, 'epoch': 0.02}
{'loss': 1.2389, 'learning_rate': 0.00017777777777777779, 'epoch': 0.03}
{'loss': 1.032, 'learning_rate': 0.00015555555555555556, 'epoch': 0.05}
{'loss': 0.9141, 'learning_rate': 0.00013333333333333334, 'epoch': 0.06}
{'loss': 0.8505, 'learning_rate': 0.00011111111111111112, 'epoch': 0.08}
{'loss': 0.8713, 'learning_rate': 8.888888888888889e-05, 'epoch': 0.09}
{'loss': 0.8635, 'learning_rate': 6.666666666666667e-05, 'epoch': 0.11}
{'loss': 0.8853, 'learning_rate': 4.4444444444444447e-05, 'epoch': 0.12}
{'loss': 0.859, 'learning_rate': 2.2222222222222223e-05, 'epoch': 0.14}
{'loss': 0.8608, 'learning_rate': 0.0, 'epoch': 0.15}
{'train_runtime': xxxx, 'train_samples_per_second': xxxx, 'train_steps_per_second': xxxx, 'train_loss': 1.0400420665740966, 'epoch': 0.15}
100%|███████████████████████████████████████████████████████████████████████████████████| 200/200 [07:16<00:00,  2.18s/it]
TrainOutput(global_step=200, training_loss=1.0400420665740966, metrics={'train_runtime': xxxx, 'train_samples_per_second': xxxx, 'train_steps_per_second': xxxx, 'train_loss': 1.0400420665740966, 'epoch': 0.15})

3.2 QA-LoRA Llama2-7b example

Enter the running container:

docker exec -it ipex-llm-finetune-xpu bash

Enter QA-LoRA dir.

cd /LLM-Finetuning/QA-LoRA

Modify configuration in scripts, e.g., --base_model and --data_path in qalora_finetune_llama2_7b_arc_1_card.sh.

Then, start QA-LoRA fine-tuning:

bash qalora_finetune_llama2_7b_arc_1_card.sh

For more details, please refer to QA-LoRA example.

3.3 Axolotl LoRA

Enter the running container:

docker exec -it ipex-llm-finetune-xpu bash

Enter QA-LoRA dir.

cd /LLM-Finetuning/axolotl

Modify configuration in axolotl config, e.g., base_model and datasets.path in lora.yml.

Then, start QA-LoRA fine-tuning:

accelerate launch finetune.py lora.yml

For more details, please refer to axolotl example.