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SOLAR
In this directory, you will find examples on how you could use IPEX-LLM optimize_model API to accelerate SOLAR models. For illustration purposes, we utilize the upstage/SOLAR-10.7B-Instruct-v1.0 as a reference SOLAR model.
Requirements
To run these examples with IPEX-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to here for more information.
Example: Predict Tokens using generate() API
In the example generate.py, we show a basic use case for a SOLAR model to predict the next N tokens using generate() API, with IPEX-LLM INT4 optimizations on Intel GPUs.
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 transformers==4.35.2 # required by SOLAR
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 use pip to install the Intel oneAPI Base Toolkit 2024.0
pip install dpcpp-cpp-rt==2024.0.2 mkl-dpcpp==2024.0.0 onednn==2024.0.0
# 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 transformers==4.35.2 # required by SOLAR
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.socan be installed byconda 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 examples
python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT
In the example, several arguments can be passed to satisfy your requirements:
--repo-id-or-model-path REPO_ID_OR_MODEL_PATH: argument defining the huggingface repo id for the SOLAR model (e.gupstage/SOLAR-10.7B-Instruct-v1.0) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be'upstage/SOLAR-10.7B-Instruct-v1.0'.--prompt PROMPT: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be'What is AI?'.--n-predict N_PREDICT: argument defining the max number of tokens to predict. It is default to be32.
4.1 Sample Output
upstage/SOLAR-10.7B-Instruct-v1.0
Inference time: XXXX s
-------------------- Output --------------------
### User:
What is AI?
### Assistant:
 AI, or Artificial Intelligence, refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. It involves the development of
Inference time: XXXX s
-------------------- Output --------------------
### User:
AI是什么?
### Assistant:
AI, 全称为人工智能(Artificial Intelligence),是计算机科学、心理学、语言学、逻