ipex-llm/python/llm/example/GPU/HF-Transformers-AutoModels/Model/chatglm3

ChatGLM3

In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on ChatGLM3 models on Intel GPUs. For illustration purposes, we utilize the THUDM/chatglm3-6b as a reference ChatGLM3 model.

0. 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: Predict Tokens using generate() API

In the example generate.py, we show a basic use case for a ChatGLM3 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 environment:

conda create -n llm 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 --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 ChatGLM3 model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be 'THUDM/chatglm3-6b'.
• --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.

Sample Output

THUDM/chatglm3-6b

Inference time: xxxx s
-------------------- Prompt --------------------
<|user|>
AI是什么？
<|assistant|>
-------------------- Output --------------------
[gMASK]sop <|user|>
AI是什么？
<|assistant|> AI是人工智能(Artificial Intelligence)的缩写,指通过计算机程序或机器学习算法来模拟、延伸或扩展人类智能的技术。AI旨在

Inference time: xxxx s
-------------------- Prompt --------------------
<|user|>
What is AI?
<|assistant|>
-------------------- Output --------------------
[gMASK]sop <|user|>
What is AI?
<|assistant|>
AI stands for Artificial Intelligence. It refers to the development of computer systems or machines that can perform tasks that would normally require human intelligence, such as recognizing patterns

Example 2: Stream Chat using stream_chat() API

In the example streamchat.py, we show a basic use case for a ChatGLM3 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
# 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

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 ChatGLM3 model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be 'THUDM/chatglm3-6b'.
• --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.