refine LLM containers (#9109)
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Heyang Sun
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5 changed files with 17 additions and 118 deletions
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## Run BF16-Optimized Lora Finetuning on Kubernetes with OneCCL
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[Alpaca Lora](https://github.com/tloen/alpaca-lora/tree/main) uses [low-rank adaption](https://arxiv.org/pdf/2106.09685.pdf) to speed up the finetuning process of base model [Llama2-7b](https://huggingface.co/meta-llama/Llama-2-7b), and tries to reproduce the standard Alpaca, a general finetuned LLM. This is on top of Hugging Face transformers with Pytorch backend, which natively requires a number of expensive GPU resources and takes significant time.
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By constract, BigDL here provides a CPU optimization to accelerate the lora finetuning of Llama2-7b, in the power of mixed-precision and distributed training. Detailedly, [Intel OneCCL](https://www.intel.com/content/www/us/en/developer/tools/oneapi/oneccl.html), an available Hugging Face backend, is able to speed up the Pytorch computation with BF16 datatype on CPUs, as well as parallel processing on Kubernetes enabled by [Intel MPI](https://www.intel.com/content/www/us/en/developer/tools/oneapi/mpi-library.html).
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The architecture is illustrated in the following:
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As above, BigDL implements its MPI training build on [Kubeflow MPI operator](https://github.com/kubeflow/mpi-operator/tree/master), which encapsulates the deployment as MPIJob CRD, and assists users to handle the construction of a MPI worker cluster on Kubernetes, such as public key distribution, SSH connection, and log collection.
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Now, let's go to deploy a Lora finetuning to create a LLM from Llama2-7b.
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**Note: Please make sure you have already have an available Kubernetes infrastructure and NFS shared storage, and install [Helm CLI](https://helm.sh/docs/helm/helm_install/) for Kubernetes job submission.**
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### 1. Install Kubeflow MPI Operator
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Follow [here](https://github.com/kubeflow/mpi-operator/tree/master#installation) to install a Kubeflow MPI operator in your Kubernetes, which will listen and receive the following MPIJob request at backend.
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### 2. Download Image, Base Model and Finetuning Data
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Follow [here](https://github.com/intel-analytics/BigDL/tree/main/docker/llm/finetune/lora/docker#prepare-bigdl-image-for-lora-finetuning) to prepare BigDL Lora Finetuning image in your cluster.
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As finetuning is from a base model, first download [Llama2-7b model from the public download site of Hugging Face](https://huggingface.co/meta-llama/Llama-2-7b). Then, download [cleaned alpaca data](https://raw.githubusercontent.com/tloen/alpaca-lora/main/alpaca_data_cleaned_archive.json), which contains all kinds of general knowledge and has already been cleaned. Next, move the downloaded files to a shared directory on your NFS server.
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### 3. Deploy through Helm Chart
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You are allowed to edit and experiment with different parameters in `./kubernetes/values.yaml` to improve finetuning performance and accuracy. For example, you can adjust `trainerNum` and `cpuPerPod` according to node and CPU core numbers in your cluster to make full use of these resources, and different `microBatchSize` result in different training speed and loss (here note that `microBatchSize`×`trainerNum` should not more than 128, as it is the batch size).
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**Note: `dataSubPath` and `modelSubPath` need to have the same names as files under the NFS directory in step 2.**
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After preparing parameters in `./kubernetes/values.yaml`, submit the job as beflow:
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```bash
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cd ./kubernetes
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helm install bigdl-lora-finetuning .
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```
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### 4. Check Deployment
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```bash
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kubectl get all -n bigdl-lora-finetuning # you will see launcher and worker pods running
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```
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### 5. Check Finetuning Process
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After deploying successfully, you can find a launcher pod, and then go inside this pod and check the logs collected from all workers.
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```bash
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kubectl get all -n bigdl-lora-finetuning # you will see a launcher pod
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kubectl exec -it <launcher_pod_name> bash -n bigdl-ppml-finetuning # enter launcher pod
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cat launcher.log # display logs collected from other workers
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```
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From the log, you can see whether finetuning process has been invoked successfully in all MPI worker pods, and a progress bar with finetuning speed and estimated time will be showed after some data preprocessing steps (this may take quiet a while).
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For the fine-tuned model, it is written by the worker 0 (who holds rank 0), so you can find the model output inside the pod, which can be saved to host by command tools like `kubectl cp` or `scp`.
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## To run in TDX-CoCo and enable Remote Attestation API
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You can deploy this workload in TDX CoCo and enable Remote Attestation API Serving with setting `TEEMode` in `./kubernetes/values.yaml` to `tdx`. The main diffences are it's need to execute the pods as root and mount TDX device, and a flask service is responsible for generating launcher's quote and collecting workers' quotes.
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### (Optional) Enable TLS
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To enable TLS in Remote Attestation API Serving, you should provide a TLS certificate and setting `enableTLS` ( to `true` ), `base64ServerCrt` and `base64ServerKey` in `./kubernetes/values.yaml`.
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```bash
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# Generate a self-signed TLS certificate (DEBUG USE ONLY)
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export COUNTRY_NAME=your_country_name
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export CITY_NAME=your_city_name
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export ORGANIZATION_NAME=your_organization_name
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export COMMON_NAME=your_common_name
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export EMAIL_ADDRESS=your_email_address
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openssl req -x509 -newkey rsa:4096 -nodes -out server.crt -keyout server.key -days 365 -subj "/C=$COUNTRY_NAME/ST=$CITY_NAME/L=$CITY_NAME/O=$ORGANIZATION_NAME/OU=$ORGANIZATION_NAME/CN=$COMMON_NAME/emailAddress=$EMAIL_ADDRESS/"
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# Calculate Base64 format string in values.yaml
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cat server.crt | base64 -w 0 # Set in base64ServerCrt
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cat server.key | base64 -w 0 # Set in base64ServerKey
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```
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To use RA Rest API, you need to get the IP of job-launcher:
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``` bash
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kubectl get all -n bigdl-lora-finetuning
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```
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You will find a line like:
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```bash
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service/bigdl-lora-finetuning-launcher-attestation-api-service ClusterIP 10.109.87.248 <none> 9870/TCP 17m
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```
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Here are IP and port of the Remote Attestation API service.
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The RA Rest API are listed below:
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### 1. Generate launcher's quote
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```bash
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curl -X POST -H "Content-Type: application/json" -d '{"user_report_data": "<your_user_report_data>"}' http://<your_ra_api_service_ip>:<your_ra_api_service_port>/gen_quote
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```
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Example responce:
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```json
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{"quote":"BAACAIEAAAAAAAA..."}
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```
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### 2. Collect all cluster components' quotes (launcher and workers)
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```bash
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curl -X POST -H "Content-Type: application/json" -d '{"user_report_data": "<your_user_report_data>"}' http://<your_ra_api_service_ip>:<your_ra_api_service_port>/attest
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```
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Example responce:
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```json
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{"quote_list":{"bigdl-lora-finetuning-job-worker-0":"BAACAIEAAAAAAA...","bigdl-lora-finetuning-job-worker-1":"BAACAIEAAAAAAA...","launcher":"BAACAIEAAAAAA..."}}
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```
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@ -3,7 +3,7 @@
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You can download directly from Dockerhub like:
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```bash
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docker pull intelanalytics/bigdl-llm-finetune-cpu:2.4.0-SNAPSHOT
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docker pull intelanalytics/bigdl-llm-finetune-lora-cpu:2.4.0-SNAPSHOT
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```
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Or build the image from source:
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@ -15,6 +15,6 @@ export HTTPS_PROXY=your_https_proxy
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docker build \
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--build-arg http_proxy=${HTTP_PROXY} \
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--build-arg https_proxy=${HTTPS_PROXY} \
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-t intelanalytics/bigdl-llm-finetune-cpu:2.4.0-SNAPSHOT \
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-t intelanalytics/bigdl-llm-finetune-lora-cpu:2.4.0-SNAPSHOT \
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-f ./Dockerfile .
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```
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@ -1,4 +1,4 @@
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imageName: intelanalytics/bigdl-llm-finetune-cpu:2.4.0-SNAPSHOT
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imageName: intelanalytics/bigdl-llm-finetune-lora-cpu:2.4.0-SNAPSHOT
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trainerNum: 8
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microBatchSize: 8
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nfsServerIp: your_nfs_server_ip
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@ -28,14 +28,18 @@ docker build \
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Here, we try to fine-tune a [Llama2-7b](https://huggingface.co/meta-llama/Llama-2-7b) with [English Quotes](https://huggingface.co/datasets/Abirate/english_quotes) dataset, and please download them and start a docker container with files mounted like below:
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```bash
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export BASE_MODE_PATH=<your_downloaded_base_model_path>
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export DATA_PATH=<your_downloaded_data_path>
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export BASE_MODE_PATH=your_downloaded_base_model_path
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export DATA_PATH=your_downloaded_data_path
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export HTTP_PROXY=your_http_proxy
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export HTTPS_PROXY=your_https_proxy
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docker run -itd \
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--net=host \
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--device=/dev/dri \
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--memory="32G" \
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--name=bigdl-llm-fintune-qlora-xpu \
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-e http_proxy=${HTTP_PROXY} \
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-e https_proxy=${HTTPS_PROXY} \
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-v $BASE_MODE_PATH:/model \
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-v $DATA_PATH:/data/english_quotes \
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--shm-size="16g" \
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@ -45,11 +49,16 @@ docker run -itd \
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The download and mount of base model and data to a docker container demonstrates a standard fine-tuning process. You can skip this step for a quick start, and in this way, the fine-tuning codes will automatically download the needed files:
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```bash
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export HTTP_PROXY=your_http_proxy
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export HTTPS_PROXY=your_https_proxy
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docker run -itd \
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--net=host \
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--device=/dev/dri \
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--memory="32G" \
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--name=bigdl-llm-fintune-qlora-xpu \
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-e http_proxy=${HTTP_PROXY} \
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-e https_proxy=${HTTPS_PROXY} \
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--shm-size="16g" \
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intelanalytics/bigdl-llm-fintune-qlora-xpu:2.4.0-SNAPSHOT
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```
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@ -8,7 +8,9 @@ ENV TZ=Asia/Shanghai
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# Disable pip's cache behavior
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ARG PIP_NO_CACHE_DIR=false
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RUN apt-get update && \
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RUN curl -fsSL https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS-2023.PUB | gpg --dearmor | tee /usr/share/keyrings/intel-oneapi-archive-keyring.gpg && \
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echo "deb [signed-by=/usr/share/keyrings/intel-oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main " > /etc/apt/sources.list.d/oneAPI.list && \
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apt-get update && \
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apt-get install -y curl wget git gnupg gpg-agent && \
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wget -qO - https://repositories.intel.com/graphics/intel-graphics.key | gpg --dearmor --output /usr/share/keyrings/intel-graphics.gpg && \
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echo 'deb [arch=amd64,i386 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/graphics/ubuntu jammy arc' | tee /etc/apt/sources.list.d/intel.gpu.jammy.list && \
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