C++ implementation of ChatGLM-6B and ChatGLM2-6B for real-time chatting on your MacBook.
Highlights:
- Pure C++ implementation based on ggml, working in the same way as llama.cpp.
- Accelerated memory-efficient CPU inference with int4/int8 quantization, optimized KV cache and parallel computing.
- Streaming generation with typewriter effect.
- Python binding, web demo, and more possibilities.
Support Matrix:
- Hardwares: x86/arm CPU, NVIDIA GPU, Apple Silicon GPU
- Platforms: Linux, MacOS, Windows
- Models: ChatGLM, ChatGLM2, CodeGeeX2
Preparation
Clone the ChatGLM.cpp repository into your local machine:
git clone --recursive https://github.com/li-plus/chatglm.cpp.git && cd chatglm.cpp
If you forgot the --recursive
flag when cloning the repository, run the following command in the chatglm.cpp
folder:
git submodule update --init --recursive
Quantize Model
Use convert.py
to transform ChatGLM-6B or ChatGLM2-6B into quantized GGML format. For example, to convert the fp16 original model to q4_0 (quantized int4) GGML model, run:
python3 convert.py -i THUDM/chatglm-6b -t q4_0 -o chatglm-ggml.bin
The original model (-i <model_name_or_path>
) can be a HuggingFace model name or a local path to your pre-downloaded model. Currently supported models are:
- ChatGLM-6B:
THUDM/chatglm-6b
,THUDM/chatglm-6b-int8
,THUDM/chatglm-6b-int4
- ChatGLM2-6B:
THUDM/chatglm2-6b
,THUDM/chatglm2-6b-int4
- CodeGeeX2:
THUDM/codegeex2-6b
,THUDM/codegeex2-6b-int4
You are free to try any of the below quantization types by specifying -t <type>
:
q4_0
: 4-bit integer quantization with fp16 scales.q4_1
: 4-bit integer quantization with fp16 scales and minimum values.q5_0
: 5-bit integer quantization with fp16 scales.q5_1
: 5-bit integer quantization with fp16 scales and minimum values.q8_0
: 8-bit integer quantization with fp16 scales.f16
: half precision floating point weights without quantization.f32
: single precision floating point weights without quantization.
For LoRA model, add -l <lora_model_name_or_path>
flag to merge your LoRA weights into the base model.
Build & Run
Compile the project using CMake:
cmake -B build
cmake --build build -j --config Release
Now you may chat with the quantized ChatGLM-6B model by running:
./build/bin/main -m chatglm-ggml.bin -p 你好
# 你好👋!我是人工智能助手 ChatGLM-6B,很高兴见到你,欢迎问我任何问题。
To run the model in interactive mode, add the -i
flag. For example:
./build/bin/main -m chatglm-ggml.bin -i
In interactive mode, your chat history will serve as the context for the next-round conversation.
Run ./build/bin/main -h
to explore more options!
Try Other Models
- ChatGLM2-6B
python3 convert.py -i THUDM/chatglm2-6b -t q4_0 -o chatglm2-ggml.bin
./build/bin/main -m chatglm2-ggml.bin -p 你好 --top_p 0.8 --temp 0.8
# 你好👋!我是人工智能助手 ChatGLM2-6B,很高兴见到你,欢迎问我任何问题。
- CodeGeeX2
$ python3 convert.py -i THUDM/codegeex2-6b -t q4_0 -o codegeex2-ggml.bin
$ ./build/bin/main -m codegeex2-ggml.bin --temp 0 --mode generate -p "\
# 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, 4, 3, 2, 1]))
BLAS library can be integrated to further accelerate matrix multiplication. However, in some cases, using BLAS may cause performance degradation. Whether to turn on BLAS should depend on the benchmarking result.
Accelerate Framework
Accelerate Framework is automatically enabled on macOS. To disable it, add the CMake flag -DGGML_NO_ACCELERATE=ON
.
OpenBLAS
OpenBLAS provides acceleration on CPU. Add the CMake flag -DGGML_OPENBLAS=ON
to enable it.
cmake -B build -DGGML_OPENBLAS=ON && cmake --build build -j
cuBLAS
cuBLAS uses NVIDIA GPU to accelerate BLAS. Add the CMake flag -DGGML_CUBLAS=ON
to enable it.
cmake -B build -DGGML_CUBLAS=ON && cmake --build build -j
Note that the current GGML CUDA implementation is really slow. The community is making efforts to optimize it.
Metal
MPS (Metal Performance Shaders) allows computation to run on powerful Apple Silicon GPU. Add the CMake flag -DGGML_METAL=ON
to enable it.
cmake -B build -DGGML_METAL=ON && cmake --build build -j
The Python binding provides high-level chat
and stream_chat
interface similar to the original Hugging Face ChatGLM(2)-6B.
Install from PyPI (recommended): will trigger compilation on your platform.
pip install -U chatglm-cpp
To enable cuBLAS acceleration on NVIDIA GPU:
CMAKE_ARGS="-DGGML_CUBLAS=ON" pip install -U chatglm-cpp
To enable Metal on Apple silicon devices:
CMAKE_ARGS="-DGGML_METAL=ON" pip install -U chatglm-cpp
You may also install from source. Add the corresponding CMAKE_ARGS
for acceleration.
# install from the latest source hosted on GitHub
pip install git+https://github.com/li-plus/chatglm.cpp.git@main
# or install from your local source after git cloning the repo
pip install .
Run the Python example to chat with the quantized model:
cd examples && python3 cli_chat.py -m ../chatglm-ggml.bin -i
Launch a web demo to chat in your browser:
cd examples && python3 web_demo.py -m ../chatglm-ggml.bin
For other models:
- ChatGLM2
python3 cli_chat.py -m ../chatglm2-ggml.bin -p 你好 --temp 0.8 --top_p 0.8 # CLI demo
python3 web_demo.py -m ../chatglm2-ggml.bin --temp 0.8 --top_p 0.8 # web demo
- CodeGeeX2
# CLI demo
python3 cli_chat.py -m ../codegeex2-ggml.bin --temp 0 --mode generate -p "\
# language: Python
# write a bubble sort function
"
# web demo
python3 web_demo.py -m ../codegeex2-ggml.bin --temp 0 --max_length 512 --mode generate --plain
We support various kinds of API servers to integrate with popular frontends. Extra dependencies can be installed by:
pip install 'chatglm-cpp[api]'
Remember to add the corresponding CMAKE_ARGS
to enable acceleration.
LangChain API
Start the api server for LangChain:
MODEL=./chatglm2-ggml.bin uvicorn chatglm_cpp.langchain_api:app --host 127.0.0.1 --port 8000
Test the api endpoint with curl
:
curl http://127.0.0.1:8000 -H 'Content-Type: application/json' -d '{"prompt": "你好"}'
Run with LangChain:
>>> from langchain.llms import ChatGLM
>>>
>>> llm = ChatGLM(endpoint_url="http://127.0.0.1:8000")
>>> llm.predict("你好")
'你好👋!我是人工智能助手 ChatGLM2-6B,很高兴见到你,欢迎问我任何问题。'
For more options, please refer to examples/langchain_client.py and LangChain ChatGLM Integration.
OpenAI API
Start an API server compatible with OpenAI chat completions protocol:
MODEL=./chatglm2-ggml.bin uvicorn chatglm_cpp.openai_api:app --host 127.0.0.1 --port 8000
Test your endpoint with curl
:
curl http://127.0.0.1:8000/v1/chat/completions -H 'Content-Type: application/json' \
-d '{"messages": [{"role": "user", "content": "你好"}]}'
Use the OpenAI client to make streaming request:
OPENAI_API_BASE=http://127.0.0.1:8000/v1 python3 examples/openai_client.py --stream --prompt 你好
With this API server as backend, ChatGLM.cpp models can be seamlessly integrated into any frontend that uses OpenAI-style API, including mckaywrigley/chatbot-ui, fuergaosi233/wechat-chatgpt, Yidadaa/ChatGPT-Next-Web, and more.
docker run -it --rm -v [model path]:/opt/ chulinx/chatglm /chatglm -m /opt/chatglm2-ggml.bin -p "你好啊"
你好👋!我是人工智能助手 ChatGLM2-6B,很高兴见到你,欢迎问我任何问题。
Environment:
- CPU backend performance is measured on a Linux server with Intel(R) Xeon(R) Platinum 8260 CPU @ 2.40GHz using 16 threads.
- CUDA backend is measured on a V100-SXM2-32GB GPU using 1 thread.
- MPS backend is measured on an Apple M2 Ultra device using 1 thread (currently only supports ChatGLM2).
ChatGLM-6B:
Q4_0 | Q4_1 | Q5_0 | Q5_1 | Q8_0 | F16 | F32 | |
---|---|---|---|---|---|---|---|
ms/token (CPU @ Platinum 8260) | 74 | 77 | 86 | 89 | 114 | 189 | 357 |
ms/token (CUDA @ V100 SXM2) | 10.0 | 9.8 | 10.7 | 10.6 | 14.6 | 19.8 | 34.2 |
file size | 3.3GB | 3.7GB | 4.0GB | 4.4GB | 6.2GB | 12GB | 23GB |
mem usage | 4.0GB | 4.4GB | 4.7GB | 5.1GB | 6.9GB | 13GB | 24GB |
ChatGLM2-6B:
Q4_0 | Q4_1 | Q5_0 | Q5_1 | Q8_0 | F16 | F32 | |
---|---|---|---|---|---|---|---|
ms/token (CPU @ Platinum 8260) | 64 | 71 | 79 | 83 | 106 | 189 | 372 |
ms/token (CUDA @ V100 SXM2) | 9.7 | 9.4 | 10.3 | 10.2 | 14.0 | 19.1 | 33.0 |
ms/token (MPS @ M2 Ultra) | 11.0 | 11.7 | N/A | N/A | N/A | 32.1 | N/A |
file size | 3.3GB | 3.7GB | 4.0GB | 4.4GB | 6.2GB | 12GB | 24GB |
mem usage | 3.4GB | 3.8GB | 4.1GB | 4.5GB | 6.2GB | 12GB | 23GB |
- To perform unit tests, add the CMake flag
-DCHATGLM_ENABLE_TESTING=ON
, recompile, and run./build/bin/chatglm_test
. For benchmark only, run./build/bin/chatglm_test --gtest_filter=ChatGLM.benchmark
. - To format the code, run
cmake --build build --target lint
. You should haveclang-format
,black
andisort
pre-installed. - To check performance issue, add the CMake flag
-DGGML_PERF=ON
. It will show timing for each graph operation when running the model.
- This project is greatly inspired by @ggerganov's llama.cpp and is based on his NN library ggml.
- Thank @THUDM for the amazing ChatGLM-6B and ChatGLM2-6B and for releasing the model sources and checkpoints.