chatglm2

ChatGLM2

In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on ChatGLM2 models. For illustration purposes, we utilize the THUDM/chatglm2-6b as a reference ChatGLM2 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 1: Predict Tokens using generate() API

In the example generate.py, we show a basic use case for a ChatGLM2 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

2. 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 ChatGLM2 model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be 'THUDM/chatglm2-6b'.
• --prompt PROMPT: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be 'AI是什么？'.
• --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 ChatGLM2 model based on the capabilities of your machine.

2.1 Client

On client Windows machine, it is recommended to run directly with full utilization of all cores:

python ./generate.py 

2.2 Server

For optimal performance on server, it is recommended to set several environment variables (refer to here for more information), and run the example with all the physical cores of a single socket.

E.g. on Linux,

# set BigDL-LLM env variables
source bigdl-llm-init

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

2.3 Sample Output

THUDM/chatglm2-6b

Inference time: xxxx s
-------------------- Prompt --------------------
问：AI是什么？

答：
-------------------- Output --------------------
问：AI是什么？

答： AI指的是人工智能,是一种能够通过学习和推理来执行任务的计算机程序。它可以模仿人类的思维方式,做出类似人类的决策,并且具有自主学习、自我

Inference time: xxxx s
-------------------- Prompt --------------------
问：What is AI?

答：
-------------------- Output --------------------
问：What is AI?

答： Artificial Intelligence (AI) refers to the ability of a computer or machine to perform tasks that typically require human-like intelligence, such as understanding language, recognizing patterns

Example 2: Stream Chat using stream_chat() API

In the example streamchat.py, we show a basic use case for a ChatGLM2 model to stream chat, 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

2. Run

Stream Chat using stream_chat() API:

python ./streamchat.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --question QUESTION

Chat using chat() API:

python ./streamchat.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --question QUESTION --disable-stream

Arguments info:

• --repo-id-or-model-path REPO_ID_OR_MODEL_PATH: argument defining the huggingface repo id for the ChatGLM2 model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be 'THUDM/chatglm2-6b'.
• --question QUESTION: argument defining the question to ask. It is default to be "晚上睡不着应该怎么办".
• --disable-stream: argument defining whether to stream chat. If include --disable-stream when running the script, the stream chat is disabled and chat() API is used.

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 ChatGLM2 model based on the capabilities of your machine.

2.1 Client

On client Windows machine, it is recommended to run directly with full utilization of all cores:

$env:PYTHONUNBUFFERED=1  # ensure stdout and stderr streams are sent straight to terminal without being first buffered
python ./streamchat.py

2.2 Server

For optimal performance on server, it is recommended to set several environment variables (refer to here for more information), and run the example with all the physical cores of a single socket.

E.g. on Linux,

# set BigDL-LLM env variables
source bigdl-llm-init

# e.g. for a server with 48 cores per socket
export OMP_NUM_THREADS=48
export PYTHONUNBUFFERED=1  # ensure stdout and stderr streams are sent straight to terminal without being first buffered
numactl -C 0-47 -m 0 python ./streamchat.py