2024
- [2024/06] PyTorch engine support DeepSeek-V2 and several VLMs, such as CogVLM2, Mini-InternVL, LlaVA-Next
- [2024/05] Balance vision model when deploying VLMs with multiple GPUs
- [2024/05] Support 4-bits weight-only quantization and inference on VMLs, such as InternVL v1.5, LLaVa, InternLMXComposer2
- [2024/04] Support Llama3 and more VLMs, such as InternVL v1.1, v1.2, MiniGemini, InternLMXComposer2.
- [2024/04] TurboMind adds online int8/int4 KV cache quantization and inference for all supported devices. Refer here for detailed guide
- [2024/04] TurboMind latest upgrade boosts GQA, rocketing the internlm2-20b model inference to 16+ RPS, about 1.8x faster than vLLM.
- [2024/04] Support Qwen1.5-MOE and dbrx.
- [2024/03] Support DeepSeek-VL offline inference pipeline and serving.
- [2024/03] Support VLM offline inference pipeline and serving.
- [2024/02] Support Qwen 1.5, Gemma, Mistral, Mixtral, Deepseek-MOE and so on.
- [2024/01] OpenAOE seamless integration with LMDeploy Serving Service.
- [2024/01] Support for multi-model, multi-machine, multi-card inference services. For usage instructions, please refer to here
- [2024/01] Support PyTorch inference engine, developed entirely in Python, helping to lower the barriers for developers and enable rapid experimentation with new features and technologies.
2023
- [2023/12] Turbomind supports multimodal input. Gradio Demo
- [2023/11] Turbomind supports loading hf model directly. Click here for details.
- [2023/11] TurboMind major upgrades, including: Paged Attention, faster attention kernels without sequence length limitation, 2x faster KV8 kernels, Split-K decoding (Flash Decoding), and W4A16 inference for sm_75
- [2023/09] TurboMind supports Qwen-14B
- [2023/09] TurboMind supports InternLM-20B
- [2023/09] TurboMind supports all features of Code Llama: code completion, infilling, chat / instruct, and python specialist. Click here for deployment guide
- [2023/09] TurboMind supports Baichuan2-7B
- [2023/08] TurboMind supports flash-attention2.
- [2023/08] TurboMind supports Qwen-7B, dynamic NTK-RoPE scaling and dynamic logN scaling
- [2023/08] TurboMind supports Windows (tp=1)
- [2023/08] TurboMind supports 4-bit inference, 2.4x faster than FP16, the fastest open-source implementation. Check this guide for detailed info
- [2023/08] LMDeploy has launched on the HuggingFace Hub, providing ready-to-use 4-bit models.
- [2023/08] LMDeploy supports 4-bit quantization using the AWQ algorithm.
- [2023/07] TurboMind supports Llama-2 70B with GQA.
- [2023/07] TurboMind supports Llama-2 7B/13B.
- [2023/07] TurboMind supports tensor-parallel inference of InternLM.
LMDeploy is a toolkit for compressing, deploying, and serving LLM, developed by the MMRazor and MMDeploy teams. It has the following core features:
-
Efficient Inference: LMDeploy delivers up to 1.8x higher request throughput than vLLM, by introducing key features like persistent batch(a.k.a. continuous batching), blocked KV cache, dynamic split&fuse, tensor parallelism, high-performance CUDA kernels and so on.
-
Effective Quantization: LMDeploy supports weight-only and k/v quantization, and the 4-bit inference performance is 2.4x higher than FP16. The quantization quality has been confirmed via OpenCompass evaluation.
-
Effortless Distribution Server: Leveraging the request distribution service, LMDeploy facilitates an easy and efficient deployment of multi-model services across multiple machines and cards.
-
Interactive Inference Mode: By caching the k/v of attention during multi-round dialogue processes, the engine remembers dialogue history, thus avoiding repetitive processing of historical sessions.
For detailed inference benchmarks in more devices and more settings, please refer to the following link:
- A100
- V100
- 4090
- 3090
- 2080
| LLMs | VLMs |
|
|
LMDeploy has developed two inference engines - TurboMind and PyTorch, each with a different focus. The former strives for ultimate optimization of inference performance, while the latter, developed purely in Python, aims to decrease the barriers for developers.
They differ in the types of supported models and the inference data type. Please refer to this table for each engine's capability and choose the proper one that best fits your actual needs.
Install lmdeploy with pip ( python 3.8+) or from source
pip install lmdeploySince v0.3.0, The default prebuilt package is compiled on CUDA 12. However, if CUDA 11+ is required, you can install lmdeploy by:
export LMDEPLOY_VERSION=0.5.0
export PYTHON_VERSION=38
pip install https://github.com/InternLM/lmdeploy/releases/download/v${LMDEPLOY_VERSION}/lmdeploy-${LMDEPLOY_VERSION}+cu118-cp${PYTHON_VERSION}-cp${PYTHON_VERSION}-manylinux2014_x86_64.whl --extra-index-url https://download.pytorch.org/whl/cu118import lmdeploy
pipe = lmdeploy.pipeline("internlm/internlm2-chat-7b")
response = pipe(["Hi, pls intro yourself", "Shanghai is"])
print(response)Note
By default, LMDeploy downloads model from HuggingFace. If you would like to use models from ModelScope, please install ModelScope by pip install modelscope and set the environment variable:
export LMDEPLOY_USE_MODELSCOPE=True
For more information about inference pipeline, please refer to here.
Please overview getting_started section for the basic usage of LMDeploy.
For detailed user guides and advanced guides, please refer to our tutorials:
- User Guide
- Advance Guide
-
Deploying LLMs offline on the NVIDIA Jetson platform by LMDeploy: LMDeploy-Jetson
-
Example project for deploying LLMs using LMDeploy and BentoML: BentoLMDeploy
We appreciate all contributions to LMDeploy. Please refer to CONTRIBUTING.md for the contributing guideline.
@misc{2023lmdeploy,
title={LMDeploy: A Toolkit for Compressing, Deploying, and Serving LLM},
author={LMDeploy Contributors},
howpublished = {\url{https://github.com/InternLM/lmdeploy}},
year={2023}
}This project is released under the Apache 2.0 license.
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