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GLM-4

In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on GLM-4 models. For illustration purposes, we utilize the THUDM/glm-4-9b-chat as a reference GLM-4 model.

0. Requirements

To run these examples with IPEX-LLM, we have some recommended requirements for your machine, please refer to here for more information.

1. Install

We suggest using conda to manage environment:

On Linux:

conda create -n llm python=3.11 # recommend to use Python 3.11
conda activate llm

# install the latest ipex-llm nightly build with 'all' option
pip install --pre --upgrade ipex-llm[all] --extra-index-url https://download.pytorch.org/whl/cpu

# install packages required for GLM-4
pip install "tiktoken>=0.7.0" transformers==4.42.4 "trl<0.12.0"

On Windows:

conda create -n llm python=3.11
conda activate llm

pip install --pre --upgrade ipex-llm[all]

pip install "tiktoken>=0.7.0" transformers==4.42.4 "trl<0.12.0"

2. Run

Example 1: Predict Tokens using generate() API

In the example generate.py, we show a basic use case for a GLM-4 model to predict the next N tokens using generate() API, with IPEX-LLM INT4 optimizations.

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 GLM-4 model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be 'THUDM/glm-4-9b-chat'.
  • --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, IPEX-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 GLM-4 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 IPEX-LLM env variables
source ipex-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/glm-4-9b-chat
Inference time: xxxx s
-------------------- Prompt --------------------
<|user|>
AI是什么？
<|assistant|>
-------------------- Output --------------------

AI是什么？

AI，即人工智能（Artificial Intelligence），是指由人创造出来的，能够模拟、延伸和扩展人的智能的计算机系统或机器。人工智能技术

Inference time: xxxx s
-------------------- Prompt --------------------
<|user|>
What is AI?
<|assistant|>
-------------------- Output --------------------

What is AI?

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term "art

Example 2: Stream Chat using stream_chat() API

In the example streamchat.py, we show a basic use case for a GLM-4 model to stream chat, with IPEX-LLM INT4 optimizations.

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 GLM-4 model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be 'THUDM/glm-4-9b-chat'.
  • --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, IPEX-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 GLM-4 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 IPEX-LLM env variables
source ipex-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