llama2

Llama2

In this directory, you will find examples on how you could use BigDL-LLM optimize_model API to accelerate Llama2 models. For illustration purposes, we utilize the meta-llama/Llama-2-7b-chat-hf, meta-llama/Llama-2-13b-chat-hf and meta-llama/Llama-2-70b-chat-hf as reference Llama2 models.

Requirements

To run these examples with BigDL-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to here for more information.

Example 1 - Basic Version: Predict Tokens using generate() API

In the example generate.py, we show a basic use case for a Llama2 model to predict the next N tokens using generate() API, with BigDL-LLM INT4 optimizations on Intel GPUs.

1. Install

1.1 Installation on Linux

We suggest using conda to manage the Python environment. For more information about conda installation, please refer to here.

After installing conda, create a Python environment for BigDL-LLM:

conda create -n llm python=3.9 # recommend to use Python 3.9
conda activate llm

# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu

1.2 Installation on Windows

We suggest using conda to manage environment:

conda create -n llm python=3.9 libuv
conda activate llm
# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu

2. Configures OneAPI environment variables

2.1 Configurations for Linux

source /opt/intel/oneapi/setvars.sh

2.2 Configurations for Windows

call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat"

Note: Please make sure you are using CMD (Anaconda Prompt if using conda) to run the command as PowerShell is not supported.

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

```bash export USE_XETLA=OFF export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=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 ENABLE_SDP_FUSION=1

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

3.2 Configurations for Windows

For Intel iGPU

set SYCL_CACHE_PERSISTENT=1
set BIGDL_LLM_XMX_DISABLED=1

For Intel Arc™ A300-Series or Pro A60

set SYCL_CACHE_PERSISTENT=1

For other Intel dGPU Series

There is no need to set further environment variables.

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 --prompt 'What is AI?'

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 Llama2 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'.
• --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 be 32.

Sample Output

meta-llama/Llama-2-7b-chat-hf

Inference time: xxxx s
-------------------- Output --------------------
### HUMAN:
What is AI?

### RESPONSE:

AI is a field of computer science that focuses on creating intelligent machines that can perform tasks that typically require human intelligence, such as understanding natural language,

meta-llama/Llama-2-13b-chat-hf

Inference time: xxxx s
-------------------- Output --------------------
### HUMAN:
What is AI?

### RESPONSE:

AI, or artificial intelligence, refers to the ability of machines to perform tasks that would typically require human intelligence, such as learning, problem-solving,

Example 2 - Low Memory Version: Predict Tokens using generate() API

If you're not able to load the full 4-bit model (e.g. meta-llama/Llama-2-70b-chat-hf) in one GPU as shown in Example 1, you may try this example instead.

In low_memory_generate.py, we show a way to load very large models with very low GPU memory footprint. However this could be much slower than the standard way. The implementation is adapted from here.

1. Environment setup

Please refer to Example 1 for more information.

2. Run

python ./low_memory_generate.py --split-weight --splitted-weights-path ${SPLITTED_WEIGHTS_PATH}

In the example, besides arguments in Example 1, several other arguments can be passed to satisfy your requirements:

• --splitted-weights-path: argument defining folder saving per-layer weights.
• --split-weight: argument defining whether to split weights by layer. If this argument is enabled, per-layer weights will be generated and saved to --splitted-weights-path. This argument only needs to be enabled once for the same model.
• --max-cache-num: argument defining the maximum number of weights saved in the cache. You can adjust this argument based on your GPU memory. It is default to be 200. For meta-llama/Llama-2-70b-chat-hf, GPU peak memory is around 3G when it is set to 0 and 15G when it is set to 200.