ipex-llm/docker/llm/finetune/qlora/cpu/docker/README.md

Fine-tune LLM with BigDL LLM Container

The following shows how to fine-tune LLM with Quantization (QLoRA built on BigDL-LLM 4bit optimizations) in a docker environment, which is accelerated by Intel CPU.

1. Prepare Docker Image

You can download directly from Dockerhub like:

# For standalone
docker pull intelanalytics/bigdl-llm-finetune-qlora-cpu-standalone:2.5.0-SNAPSHOT

# For k8s
docker pull intelanalytics/bigdl-llm-finetune-qlora-cpu-k8s:2.5.0-SNAPSHOT

Or build the image from source:

# For standalone
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/bigdl-llm-finetune-qlora-cpu-standalone:2.5.0-SNAPSHOT \
  -f ./Dockerfile .

# For k8s
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/bigdl-llm-finetune-qlora-cpu-k8s:2.5.0-SNAPSHOT \
  -f ./Dockerfile.k8s .

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 \
   --name=bigdl-llm-fintune-qlora-cpu \
   -e http_proxy=${HTTP_PROXY} \
   -e https_proxy=${HTTPS_PROXY} \
   -v $BASE_MODE_PATH:/bigdl/model \
   -v $DATA_PATH:/bigdl/data/alpaca-cleaned \
   intelanalytics/bigdl-llm-finetune-qlora-cpu-standalone:2.5.0-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 \
   --name=bigdl-llm-fintune-qlora-cpu \
   -e http_proxy=${HTTP_PROXY} \
   -e https_proxy=${HTTPS_PROXY} \
   intelanalytics/bigdl-llm-finetune-qlora-cpu-standalone:2.5.0-SNAPSHOT

However, we do recommend you to handle them manually, because the automatical 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 (Local Mode)

Enter the running container:

docker exec -it bigdl-llm-fintune-qlora-cpu bash

Then, start QLoRA fine-tuning: If the machine memory is not enough, you can try to set use_gradient_checkpointing=True.

bash start-qlora-finetuning-on-cpu.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})

4. Merge the adapter into the original model

Using the export_merged_model.py to merge.

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.

4. Start Multi-Porcess Fine-Tuning in One Docker

Multi-process parallelism enables higher performance for QLoRA fine-tuning, e.g. Xeon server series with multi-processor-socket architecture is suitable to run one instance on each QLoRA. This can be done by simply invoke >=2 OneCCL instances in BigDL QLoRA docker:

docker run -itd \
 --name=bigdl-llm-fintune-qlora-cpu \
 --cpuset-cpus="your_expected_range_of_cpu_numbers" \
 -e STANDALONE_DOCKER=TRUE \
 -e WORKER_COUNT_DOCKER=your_worker_count \
 -v your_downloaded_base_model_path:/bigdl/model \
 -v your_downloaded_data_path:/bigdl/data/alpaca_data_cleaned_archive.json \
 intelanalytics/bigdl-llm-finetune-qlora-cpu-standalone:2.5.0-SNAPSHOT

Note that STANDALONE_DOCKER is set to TRUE here.

Then following the same way as above to enter the docker container and start fine-tuning:

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

5. Start Distributed Fine-Tuning on Kubernetes

Besides multi-process mode, you can also run QLoRA on a kubernetes cluster. please refer here.