codegeex2

CodeGeeX2

In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on CodeGeeX2 models which is implemented based on the ChatGLM2 architecture trained on more code data on Intel GPUs. For illustration purposes, we utilize the THUDM/codegeex-6b as a reference CodeGeeX2 model.

0. Requirements

To run these examples with IPEX-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 CodeGeeX2 model to predict the next N tokens using generate() API, with IPEX-LLM INT4 optimizations on Intel GPUs.

1. Install

1.1 Installation on Linux

We suggest using conda to manage environment:

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:

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. Download Model and Replace File

If you select the codegeex2-6b model (THUDM/codegeex-6b), please note that their code (tokenization_chatglm.py) initialized tokenizer after the call of __init__ of its parent class, which may result in error during loading tokenizer. To address issue, we have provided an updated file (tokenization_chatglm.py)

def __init__(self, vocab_file, padding_side="left", clean_up_tokenization_spaces=False, **kwargs):
    self.tokenizer = SPTokenizer(vocab_file)
    super().__init__(padding_side=padding_side, clean_up_tokenization_spaces=clean_up_tokenization_spaces, **kwargs)

You could download the model from THUDM/codegeex-6b, and replace the file tokenization_chatglm.py with tokenization_chatglm.py.

3. 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.

source /opt/intel/oneapi/setvars.sh

4. 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

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

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

export SYCL_CACHE_PERSISTENT=1

3.2 Configurations for Windows

For Intel iGPU and Intel Arc™ A-Series Graphics

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.

5. Running examples

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 CodeGeeX2 model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be 'THUDM/codegeex-6b'.
• --prompt PROMPT: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be '# language: Python\n# write a bubble sort function\n'.
• --n-predict N_PREDICT: argument defining the max number of tokens to predict. It is default to be 128.

Sample Output

THUDM/codegeex-6b

Inference time: xxxx s
-------------------- Prompt --------------------
# language: Python
# write a bubble sort function

-------------------- Output --------------------
# language: Python
# write a bubble sort function


def bubble_sort(lst):
    for i in range(len(lst) - 1):
        for j in range(len(lst) - 1 - i):
            if lst[j] > lst[j + 1]:
                lst[j], lst[j + 1] = lst[j + 1], lst[j]
    return lst


print(bubble_sort([5, 2, 3, 4, 1]))