codeshell

CodeShell-7B

In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on CodeShell models. For illustration purposes, we utilize the WisdomShell/CodeShell-7B as a reference CodeShell model.

Note: If you want to download the Hugging Face Transformers model, please refer to here.

BigDL-LLM optimizes the Transformers model in INT4 precision at runtime, and thus no explicit conversion is needed.

Requirements

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

Example: Predict Tokens using generate() API

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

1. Install

We suggest using conda to manage the Python environment. For more information about conda installation, please refer to here.

After installing conda, create a Python environment for BigDL-LLM:

conda create -n llm python=3.9 # recommend to use Python 3.9
conda activate llm

pip install --pre --upgrade bigdl-llm[all] # install the latest bigdl-llm nightly build with 'all' option

2. Run

After setting up the Python environment, you could run the example by following steps.

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 CodeShell model based on the capabilities of your machine.

2.1 Client

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

python ./generate.py --prompt 'def print_hello_world():'

More information about arguments can be found in Arguments Info section. The expected output can be found in Sample Output section.

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 --prompt 'def print_hello_world():'

More information about arguments can be found in Arguments Info section. The expected output can be found in Sample Output section.

2.3 Arguments Info

In the example, several arguments can be passed to satisfy your requirements:

• --repo-id-or-model-path: str, argument defining the huggingface repo id for the CodeShell model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be 'WisdomShell/CodeShell-7B'.
• --prompt: str, argument defining the prompt to be inferred (with integrated prompt format for code). It is default to be def print_hello_world():.
• --n-predict: int, argument defining the max number of tokens to predict. It is default to be 50.

2.4 Sample Output

WisdomShell/CodeShell-7B

Inference time: xxxx s
-------------------- Prompt --------------------
def print_hello_world():
-------------------- Output --------------------
def print_hello_world():
    print("Hello World")

print_hello_world()

# Function with parameters
def print_hello_name(name):
    print("Hello " + name)

print_hello_name("John")
print