ipex-llm/python/llm/dev/benchmark/ceval/README.md

C-Eval Benchmark Test Guide

This guide provides instructions for running the C-Eval benchmark test in both single-GPU and multi-GPU environments. C-Eval is a comprehensive multi-level, multi-discipline Chinese evaluation suite for foundational models. It consists of 13,948 multiple-choice questions spanning 52 diverse disciplines and four difficulty levels. For more details, see the C-Eval paper and GitHub repository.

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Single-GPU Environment

1. Download Dataset

Download and unzip the dataset for evaluation:

wget https://huggingface.co/datasets/ceval/ceval-exam/resolve/main/ceval-exam.zip
mkdir data
mv ceval-exam.zip data
cd data; unzip ceval-exam.zip

2. Run Evaluation

Use the following command to run the evaluation:

bash run.sh

Contents of run.sh:

export IPEX_LLM_LAST_LM_HEAD=0
python eval.py \
    --model_path "path to model" \
    --eval_type validation \
    --device xpu \
    --eval_data_path data \
    --qtype sym_int4

Note

• eval_type: There are two types of evaluations:
  • validation: Runs on the validation dataset and outputs evaluation scores.
  • test: Runs on the test dataset and outputs a submission.json file for submission on C-Eval to get evaluation scores.

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Multi-GPU Environment

1. Prepare Environment

1. Set Docker Image and Container Name:

   export DOCKER_IMAGE=intelanalytics/ipex-llm-serving-xpu:latest
   export CONTAINER_NAME=ceval-benchmark
   

2. Start Docker Container:

   docker run -td \
         --net=host \
         --group-add video \
         --device=/dev/dri \
         --name=$CONTAINER_NAME \
         -v /home/intel/LLM:/llm/models/ \
         -e no_proxy=localhost,127.0.0.1 \
         -e http_proxy=$HTTP_PROXY \
         -e https_proxy=$HTTPS_PROXY \
         --shm-size="16g" \
         $DOCKER_IMAGE
   

3. Enter the Container:

   docker exec -it $CONTAINER_NAME bash
   

2. Configure lm-evaluation-harness

1. Clone the Repository:

   git clone https://github.com/EleutherAI/lm-evaluation-harness
   cd lm-evaluation-harness
   

2. Update Multi-GPU Support File: Update lm_eval/models/vllm_causallms.py based on the following link: Update Multi-GPU Support File

3. Install Dependencies:

   pip install -e .
   

3. Configure Environment Variables

Set environment variables required for multi-GPU execution:

export CCL_WORKER_COUNT=2
export CCL_ATL_TRANSPORT=ofi
export CCL_ZE_IPC_EXCHANGE=sockets
export CCL_ATL_SHM=1
export CCL_SAME_STREAM=1
export CCL_BLOCKING_WAIT=0

export SYCL_CACHE_PERSISTENT=1
export FI_PROVIDER=shm
export USE_XETLA=OFF
export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=2
export TORCH_LLM_ALLREDUCE=0

Load Intel OneCCL environment variables:

source /opt/intel/1ccl-wks/setvars.sh

4. Run Evaluation

Use the following command to run the C-Eval benchmark:

lm_eval --model vllm \
  --model_args pretrained=/llm/models/CodeLlama-34b/,dtype=float16,max_model_len=2048,device=xpu,load_in_low_bit=fp8,tensor_parallel_size=4,distributed_executor_backend="ray",gpu_memory_utilization=0.90,trust_remote_code=True \
  --tasks ceval-valid \
  --batch_size 2 \
  --num_fewshot 0 \
  --output_path c-eval-result

5. Notes

• Model and Parameter Adjustments:

  • pretrained: Replace with the desired model path, e.g., /llm/models/CodeLlama-7b/.
  • load_in_low_bit: Set to fp8 or other precision options based on hardware and task requirements.
  • tensor_parallel_size: Adjust based on the number of GPUs and memory. Recommended to match the GPU count.
  • batch_size: Increase to accelerate testing, but ensure it does not cause OOM errors. Recommended values are 2 or 3.
  • num_fewshot: Specify the number of few-shot examples. Default is 0. Increasing this value can improve model contextual understanding but may significantly increase input length and runtime.

• Logging: To log both to the console and a file, use:

  lm_eval --model vllm ... | tee c-eval.log
  

• Container Debugging: Ensure the paths for the model and tasks are correctly set, e.g., check if /llm/models/ is properly mounted in the container.

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By following the above steps, you can successfully run the C-Eval benchmark in both single-GPU and multi-GPU environments.