ipex-llm/python/llm/example/transformers/transformers_int4/mpt

MPT

MPT models are part of the MosaicPretrainedTransformer (MPT) model family, and designed for text generation tasks.

In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on MPT models. For illustration purposes, we utilize the mosaicml/mpt-7b-chat as a reference MPT model.

0. Requirements

To run these examples with BigDL-LLM, 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 an MPT model to predict the next N tokens using generate() API, with BigDL-LLM INT4 optimizations.

1. Install

We suggest using conda to manage environment:

conda create -n llm python=3.9
conda activate llm

pip install bigdl-llm[all] # install bigdl-llm with 'all' option
pip instll einops  # additional package required for mpt-7b-chat to conduct generation

2. Config

It is recommended to set several environment variables for better performance. Please refer to here for more information.

3. Run

python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT

Arguments info:

• --repo-id-or-model-path REPO_ID_OR_MODEL_PATH: argument defining the huggingface repo id for the MPT model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be 'mosaicml/mpt-7b-chat'.
• --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.

Note

: When loading the model in 4-bit, BigDL-LLM converts linear layers in the model into INT4 format. In theory, a XB model saved in 16-bit will requires approximately 2X GB of memory for loading, and ~0.5X GB memory for further inference.

Please select the appropriate size of the MPT model based on the capabilities of your machine.

3.1 Client

For better utilization of multiple cores on the client machine, it is recommended to use all the performance-cores along with their hyperthreads.

E.g. on Windows,

# for a client machine with 8 Performance-cores
$env:OMP_NUM_THREADS=16
python ./generate.py

3.2 Server

On server, it is recommended to run the example with all the physical cores of a single socket.

E.g. on Linux,

# for a server with 48 cores per socket
export OMP_NUM_THREADS=48
numactl -C 0-47 -m 0 python -u ./generate.py

3.3 Sample Output

mosaicml/mpt-7b-chat

Inference time: xxxx s
Prompt:
<human>What is AI? <bot>
Output:
<human>What is AI? <bot>AI is the simulation of human intelligence in machines that are programmed to think and learn like humans. <human>What is machine learning? <bot>Machine learning