# Chatglm3-32k In this directory, you will find examples on how you could apply IPEX-LLM INT4/FP8 optimizations on Chatglm3-32K models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [THUDM/chatglm3-6b-32k](https://huggingface.co/THUDM/chatglm3-6b-32k) as reference Chatglm3-32K models. ## 0. Requirements To run these examples with IPEX-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to [here](../../../README.md#requirements) for more information. ## Example: Predict Tokens using `generate()` API In the example [generate.py](./generate.py), we show a basic use case for a Chatglm3 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4/FP8 optimizations on Intel GPUs. ### 1. Install #### 1.1 Installation on Linux We suggest using conda to manage environment: ```bash conda create -n llm python=3.11 conda activate llm # below command will install intel_extension_for_pytorch==2.1.10+xpu as default pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ ``` #### 1.2 Installation on Windows We suggest using conda to manage environment: ```bash conda create -n llm python=3.11 libuv conda activate llm # below command will install intel_extension_for_pytorch==2.1.10+xpu as default pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ ``` ### 2. Configures OneAPI environment variables for Linux > [!NOTE] > Skip this step if you are running on Windows. This is a required step on Linux for APT or offline installed oneAPI. Skip this step for PIP-installed oneAPI. ```bash source /opt/intel/oneapi/setvars.sh ``` ### 3. Runtime Configurations For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device. #### 3.1 Configurations for Linux
For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series ```bash export USE_XETLA=OFF export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1 export SYCL_CACHE_PERSISTENT=1 ```
For Intel Data Center GPU Max Series ```bash export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1 export SYCL_CACHE_PERSISTENT=1 export ENABLE_SDP_FUSION=1 ``` > Note: Please note that `libtcmalloc.so` can be installed by `conda install -c conda-forge -y gperftools=2.10`.
For Intel iGPU ```bash export SYCL_CACHE_PERSISTENT=1 ```
#### 3.2 Configurations for Windows
For Intel iGPU and Intel Arc™ A-Series Graphics ```cmd set SYCL_CACHE_PERSISTENT=1 ```
> [!NOTE] > For the first time that each model runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile. ### 4. Running examples #### 4.1 Using simple prompt ``` python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT --low-bit LOW_BIT ``` Arguments info: - `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the Chatglm3 model (e.g. `THUDM/chatglm3-6b-32k`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'THUDM/chatglm3-6b-32k'`. - `--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`. - `--low-bit LOW_BIT`: argument defining which low bit optimization to use. Options are sym_int4 or fp8. It is default to be `sym_int4`. #### 4.2 Using long context input prompt You can set the `prompt` argument to be a `.txt` file path containing the long context prompt text. An example command using the 8k input size prompt we provide is given below: ``` python ./generate.py --repo-id-or-model-path togethercomputer/chatglm3-6b-32k --prompt 8k.txt ``` > Note: If you need to run longer input or use less memory, please set `IPEX_LLM_LOW_MEM=1`, which will enable memory optimization and may slightly affect the latency performance. #### Sample Output #### [THUDM/chatglm3-6b-32k](https://huggingface.co/THUDM/chatglm3-6b-32k) ```log 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 ability of computers and other machines to perform tasks that typically require human intelligence, such as recognizing patterns, making ```