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LightLLM is a Python-based LLM (Large Language Model) inference and serving framework, notable for its lightweight design, easy scalability, and high-speed performance. LightLLM harnesses the strengths of numerous well-regarded open-source implementations, including but not limited to FasterTransformer, TGI, vLLM, and FlashAttention.

Features

• Tri-process asynchronous collaboration: tokenization, model inference, and detokenization are performed asynchronously, leading to a considerable improvement in GPU utilization.
• Nopad (Unpad): offers support for nopad attention operations across multiple models to efficiently handle requests with large length disparities.
• Dynamic Batch: enables dynamic batch scheduling of requests
• FlashAttention: incorporates FlashAttention to improve speed and reduce GPU memory footprint during inference.
• Tensor Parallelism: utilizes tensor parallelism over multiple GPUs for faster inference.
• Token Attention: implements token-wise's KV cache memory management mechanism, allowing for zero memory waste during inference.
• High-performance Router: collaborates with Token Attention to meticulously manage the GPU memory of each token, thereby optimizing system throughput.
• Int8KV Cache: This feature will increase the capacity of tokens to almost twice as much. only llama support.

Supported Model List

• BLOOM
• LLaMA
• LLaMA V2
• StarCoder
• Qwen-7b
• ChatGLM2-6b
• Baichuan-7b
• Baichuan2-7b
• Baichuan2-13b
• Baichuan-13b
• InternLM-7b
• Yi-34b

When you start Qwen-7b, you need to set the parameter '--eos_id 151643 --trust_remote_code'.

ChatGLM2 needs to set the parameter '--trust_remote_code'.

Baichuan and Baichuan2 needs to set the parameter '--trust_remote_code'.

InternLM needs to set the parameter '--trust_remote_code'.

Get started

Requirements

The code has been tested with Pytorch>=1.3, CUDA 11.8, and Python 3.9. To install the necessary dependencies, please refer to the provided requirements.txt and follow the instructions as

pip install -r requirements.txt

Container

You can use the official Docker container to run the model more easily. To do this, follow these steps:

• Pull the container from the GitHub Container Registry:

  docker pull ghcr.io/modeltc/lightllm:main

• Run the container with GPU support and port mapping:

  docker run -it --gpus all -p 8080:8080                  \
          --shm-size 1g -v your_local_path:/data/         \
          ghcr.io/modeltc/lightllm:main /bin/bash

• Alternatively, you can build the container yourself:

  docker build -t <image_name> .
  docker run -it --gpus all -p 8080:8080                  \
          --shm-size 1g -v your_local_path:/data/         \
          <image_name> /bin/bash

• You can also use a helper script to launch both the container and the server:

  python tools/quick_launch_docker.py --help

• Note: If you use multiple GPUs, you may need to increase the shared memory size by adding --shm-size to the docker run command.

Installation

• Install from the source code by

python setup.py install

The code has been tested on a range of GPUs including A100, A800, 4090, and H800. If you are running the code on A100, A800, etc., we recommend using triton==2.1.0 or triton==2.0.0.dev20221202. If you are running the code on H800, etc., it is necessary to compile and install the source code of triton==2.1.0 from the GitHub repository. If the code doesn't work on other GPUs, try modifying the triton kernel used in model inference.

• Install Triton Package

use triton==2.1.0 (Better performance, but the code is under continuous development and may be unstable.)

pip install -U --index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/Triton-Nightly/pypi/simple/ triton-nightly

use triton==2.0.0.dev20221202 (This version has a memory leak bug. Refer to the issue #209 for the fix method. )

pip install triton==2.0.0.dev20221202

RUN LLaMA

With efficient Routers and TokenAttention, LightLLM can be deployed as a service and achieve the state-of-the-art throughput performance.

Launch the server:

python -m lightllm.server.api_server --model_dir /path/llama-7B     \
                                     --host 0.0.0.0                 \
                                     --port 8080                    \
                                     --tp 1                         \
                                     --max_total_token_num 120000

The parameter max_total_token_num is influenced by the GPU memory of the deployment environment. A larger value for this parameter allows for the processing of more concurrent requests, thereby increasing system concurrency. For more startup parameters, please refer to api_server.py or ApiServerArgs.md.

To initiate a query in the shell:

curl http://127.0.0.1:8080/generate     \
    -X POST                             \
    -d '{"inputs":"What is AI?","parameters":{"max_new_tokens":17, "frequency_penalty":1}}' \
    -H 'Content-Type: application/json'

To query from Python:

import time
import requests
import json

url = 'http://localhost:8080/generate'
headers = {'Content-Type': 'application/json'}
data = {
    'inputs': 'What is AI?',
    "parameters": {
        'do_sample': False,
        'ignore_eos': False,
        'max_new_tokens': 1024,
    }
}
response = requests.post(url, headers=headers, data=json.dumps(data))
if response.status_code == 200:
    print(response.json())
else:
    print('Error:', response.status_code, response.text)

Performance

Service Performance

We compared the service performance of LightLLM and vLLM==0.1.2 on LLaMA-7B using an A800 with 80G GPU memory.

To begin, prepare the data as follows:

wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json

Launch the service:

python -m lightllm.server.api_server --model_dir /path/llama-7b --tp 1 --max_total_token_num 121060 --tokenizer_mode auto

Evaluation:

cd test
python benchmark_serving.py --tokenizer /path/llama-7b --dataset /path/ShareGPT_V3_unfiltered_cleaned_split.json --num-prompts 2000 --request-rate 200

The performance comparison results are presented below:

vLLM	LightLLM
Total time: 361.79 s Throughput: 5.53 requests/s	Total time: 188.85 s Throughput: 10.59 requests/s

Static inference performance

For debugging, we offer static performance testing scripts for various models. For instance, you can evaluate the inference performance of the LLaMA model by

cd test/model
python test_llama.py

FAQ

• The LLaMA tokenizer fails to load.
  • consider resolving this by running the command pip install protobuf==3.20.0.
• error : PTX .version 7.4 does not support .target sm_89
  • launch with bash tools/resolve_ptx_version python -m lightllm.server.api_server ...

Community

For further information and discussion, join our discord server.

License

This repository is released under the Apache-2.0 license.

Acknowledgement

We learned a lot from the following projects when developing LightLLM.

• Faster Transformer
• Text Generation Inference
• vLLM
• Flash Attention 1&2
• OpenAI Triton