ipex-llm/python/llm/example/GPU/Lightweight-Serving/README.md

Running Lightweight Serving using IPEX-LLM on one Intel GPU

Requirements

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

Example

1. Install

1.1 Installation on Linux

We suggest using conda to manage environment:

conda create -n llm python=3.11
conda activate llm
# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
pip install fastapi uvicorn openai
pip install gradio # for gradio web UI
conda install -c conda-forge -y gperftools=2.10 # to enable tcmalloc

1.2 Installation on Windows

We suggest using conda to manage environment:

conda create -n llm python=3.11 libuv
conda activate llm

# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
pip install fastapi uvicorn openai
pip install gradio # for gradio web UI
conda install -c conda-forge -y gperftools=2.10 # to enable tcmalloc

2. Configures OneAPI environment variables for Linux

Note

Skip this step if you are running on Windows.

This is a required step on Linux for APT or offline installed oneAPI. Skip this step for PIP-installed oneAPI.

source /opt/intel/oneapi/setvars.sh

3. Runtime Configurations

For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.

3.1 Configurations for Linux

For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series

export USE_XETLA=OFF
export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
export SYCL_CACHE_PERSISTENT=1

For Intel Data Center GPU Max Series

export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so
export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
export SYCL_CACHE_PERSISTENT=1
export ENABLE_SDP_FUSION=1

Note: Please note that libtcmalloc.so can be installed by conda install -c conda-forge -y gperftools=2.10.

For Intel iGPU

export SYCL_CACHE_PERSISTENT=1
export BIGDL_LLM_XMX_DISABLED=1

3.2 Configurations for Windows

For Intel iGPU

set SYCL_CACHE_PERSISTENT=1
set BIGDL_LLM_XMX_DISABLED=1

For Intel Arc™ A-Series Graphics

set SYCL_CACHE_PERSISTENT=1

Note

For the first time that each model runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile.

4. Running example

python ./lightweight_serving.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --low-bit LOW_BIT --port PORT

Arguments info:

• --repo-id-or-model-path REPO_ID_OR_MODEL_PATH: argument defining the huggingface repo id for the model (e.g. meta-llama/Llama-2-7b-chat-hf and meta-llama/Llama-2-13b-chat-hf) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be 'meta-llama/Llama-2-7b-chat-hf'.
• --low-bit LOW_BIT: Sets the low bit optimizations (such as 'sym_int4', 'fp16', 'fp8' and 'fp6') for the model. It is default to be sym_int4.
• --port PORT: The serving access port. It is default to be 8000.

5. Sample Input and Output

We can use curl to test serving api. And need to set no_proxy to ensure that requests are not forwarded by a proxy. export no_proxy=localhost,127.0.0.1

/generate

curl -X POST -H "Content-Type: application/json" -d '{
  "inputs": "What is AI?",
  "parameters": {
    "max_new_tokens": 32,
    "min_new_tokens": 32,
    "repetition_penalty": 1.0,
    "temperature": 1.0,
    "do_sample": false,
    "top_k": 5,
    "tok_p": 1.0
  },
  "stream": false
}' http://localhost:8000/generate

/generate_stream

curl -X POST -H "Content-Type: application/json" -d '{
  "inputs": "What is AI?",
  "parameters": {
    "max_new_tokens": 32,
    "min_new_tokens": 32,
    "repetition_penalty": 1.0,
    "temperature": 1.0,
    "do_sample": false,
    "top_k": 5,
    "tok_p": 1.0
  },
  "stream": false
}' http://localhost:8000/generate_stream

/v1/chat/completions

curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Llama-2-7b-chat-hf",
    "messages": [{"role": "user", "content": "Hello! What is your name?"}]
  }'

/v1/completions

curl http://localhost:8000/v1/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Llama-2-7b-chat-hf",
    "prompt": "Once upon a time",
    "max_tokens": 32
  }'

6. Benchmark with wrk

Please refer to here for more details

7. Using the benchmark.py Script

Please refer to here for more details

8. Gradio Web UI

Please refer to here for more details