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

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==2.0.110+xpu as default
# you can install specific ipex/torch version for your need
pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu

2. Configures OneAPI environment variables

source /opt/intel/oneapi/setvars.sh

3. Run

For optimal performance on Arc, it is recommended to set several environment variables.

export USE_XETLA=OFF
export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1

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.