Mixtral

In this directory, you will find examples on how you could use BigDL-LLM optimize_model API to accelerate Mixtral models. For illustration purposes, we utilize the DiscoResearch/mixtral-7b-8expert(https://huggingface.co/DiscoResearch/mixtral-7b-8expert) as a reference Mixtral model.

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.

Important: Please make sure you have installed transformers==4.36.0 to run the example.

Example: Predict Tokens using `generate()` API

In the example generate.py, we show a basic use case for a Mixtral 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

# Please make sure you are using a stable version of Transformers, 4.36.0 or newer.
pip install transformers==4.36.0

2. Download Model and Replace File

To run DiscoResearch/mixtral-7b-8expert model on Intel GPU, we have provided an updated version DiscoResearch-mixtral-7b-8expert/modeling_moe_mistral.py of modeling_moe_mistral.py.

2.1 Download Model

You could use the following code to download DiscoResearch/mixtral-7b-8expert.

from huggingface_hub import snapshot_download

# for DiscoResearch/mixtral-7b-8expert
model_path = snapshot_download(repo_id='DiscoResearch/mixtral-7b-8expert')
print(f'DiscoResearch/mixtral-7b-8expert checkpoint is downloaded to {model_path}')

2.2 Replace `modeling_moe_mistral.py`

For DiscoResearch/mixtral-7b-8expert, you should replace the modeling_moe_mistral.py with DiscoResearch-mixtral-7b-8expert/modeling_moe_mistral.py.

3. Configures OneAPI environment variables

source /opt/intel/oneapi/setvars.sh

4. 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 Mixtral model (e.g. DiscoResearch/mixtral-7b-8expert) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be 'DiscoResearch/mixtral-7b-8expert'. For model DiscoResearch/mixtral-7b-8expert, you should input the path to the model folder in which modeling_moe_mistral.py has been replaced.
--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

DiscoResearch/mixtral-7b-8expert

Inference time: xxxx s
-------------------- Output --------------------
[INST] What is AI? [/INST]

[INST] Artificial Intelligence (AI) is the ability of a computer program or a machine to think and learn. It is also a field of

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