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MLC LLM

Machine Learning Compilation for Large Language Models (MLC LLM) is a high-performance universal deployment solution that allows native deployment of any large language models with native APIs with compiler acceleration. The mission of this project is to enable everyone to develop, optimize and deploy AI models natively on everyone's devices with ML compilation techniques.

Universal deployment. MLC LLM supports the following platforms and hardware:

<table style="width:100%"> <thead> <tr> <th style="width:15%"> </th> <th style="width:20%">AMD GPU</th> <th style="width:20%">NVIDIA GPU</th> <th style="width:20%">Apple GPU</th> <th style="width:24%">Intel GPU</th> </tr> </thead> <tbody> <tr> <td>Linux / Win</td> <td>✅ Vulkan, ROCm</td> <td>✅ Vulkan, CUDA</td> <td>N/A</td> <td>✅ Vulkan</td> </tr> <tr> <td>macOS</td> <td>✅ Metal (dGPU)</td> <td>N/A</td> <td>✅ Metal</td> <td>✅ Metal (iGPU)</td> </tr> <tr> <td>Web Browser</td> <td colspan=4>✅ WebGPU and WASM </td> </tr> <tr> <td>iOS / iPadOS</td> <td colspan=4>✅ Metal on Apple A-series GPU</td> </tr> <tr> <td>Android</td> <td colspan=2>✅ OpenCL on Adreno GPU</td> <td colspan=2>✅ OpenCL on Mali GPU</td> </tr> </tbody> </table>

Scalable. MLC LLM scales universally on NVIDIA and AMD GPUs, cloud and gaming GPUs. Below showcases our single batch decoding performance with prefilling = 1 and decoding = 256.

Performance of 4-bit CodeLlama-34B and Llama2-70B on two NVIDIA RTX 4090 and two AMD Radeon 7900 XTX:

Scaling of fp16 and 4-bit CodeLlama-34 and Llama2-70B on A100-80G-PCIe and A10G-24G-PCIe, up to 8 GPUs:

News

[10/18/2023] [Post] Scalable multi-GPU support for CUDA and ROCm are official.
[09/02/2023] Prebuilt ROCm 5.7 and CUDA 12.2 package is available.
[08/25/2023] CodeLlama support is up.
[08/14/2023] [Post] Mali GPU support is up on Orange Pi.
[08/09/2023] [Post] ROCm backend is mature to use.
[08/02/2023] Dockerfile is released for CUDA performance benchmarking.
[07/19/2023] Support for Llama2-7B/13B/70B is up.
[05/22/2023] [Post] RedPajama support is up.
[05/08/2023] [Post] MLC LLM is now available on Android.
[05/01/2023] [Post] MLC LLM is released with Metal, Vulkan and CUDA backends.
[04/14/2023] WebLLM is released prior to MLC LLM with WebGPU and WebAssembly backend.

Getting Started

Please visit our documentation for detailed instructions.

Model Support

MLC LLM supports a wide range of model architectures and variants. We have the following prebuilts which you can use off-the-shelf. Visit Prebuilt Models to see the full list, and Compile Models via MLC to see how to use models not on this list.

<table style="width:100%"> <thead> <tr> <th style="width:40%">Architecture</th> <th style="width:60%">Prebuilt Model Variants</th> </tr> </thead> <tbody> <tr> <td>Llama</td> <td>Llama-2, Code Llama, Vicuna, WizardLM, WizardMath, OpenOrca Platypus2, FlagAlpha Llama-2 Chinese, georgesung Llama-2 Uncensored</td> </tr> <tr> <td>GPT-NeoX</td> <td>RedPajama</td> </tr> <tr> <td>GPT-J</td> <td></td> </tr> <tr> <td>RWKV</td> <td>RWKV-raven</td> </tr> <tr> <td>MiniGPT</td> <td></td> </tr> <tr> <td>GPTBigCode</td> <td>WizardCoder</td> </tr> <tr> <td>ChatGLM</td> <td></td> </tr> <tr> <td>StableLM</td> <td></td> </tr> <tr> <td>Mistral</td> <td></td> </tr> <tr> <td>Phi</td> <td></td> </tr> </tbody> </table>

Universal Deployment APIs

MLC LLM provides multiple sets of APIs across platforms and environments. These include

Citation

Please consider citing our project if you find it useful:

@software{mlc-llm,
    author = {MLC team},
    title = {{MLC-LLM}},
    url = {https://github.com/mlc-ai/mlc-llm},
    year = {2023}
}

The underlying techniques of MLC LLM include:

<details> <summary>References (Click to expand)</summary>

@inproceedings{tensorir,
    author = {Feng, Siyuan and Hou, Bohan and Jin, Hongyi and Lin, Wuwei and Shao, Junru and Lai, Ruihang and Ye, Zihao and Zheng, Lianmin and Yu, Cody Hao and Yu, Yong and Chen, Tianqi},
    title = {TensorIR: An Abstraction for Automatic Tensorized Program Optimization},
    year = {2023},
    isbn = {9781450399166},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3575693.3576933},
    doi = {10.1145/3575693.3576933},
    booktitle = {Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2},
    pages = {804–817},
    numpages = {14},
    keywords = {Tensor Computation, Machine Learning Compiler, Deep Neural Network},
    location = {Vancouver, BC, Canada},
    series = {ASPLOS 2023}
}

@inproceedings{metaschedule,
    author = {Shao, Junru and Zhou, Xiyou and Feng, Siyuan and Hou, Bohan and Lai, Ruihang and Jin, Hongyi and Lin, Wuwei and Masuda, Masahiro and Yu, Cody Hao and Chen, Tianqi},
    booktitle = {Advances in Neural Information Processing Systems},
    editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh},
    pages = {35783--35796},
    publisher = {Curran Associates, Inc.},
    title = {Tensor Program Optimization with Probabilistic Programs},
    url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/e894eafae43e68b4c8dfdacf742bcbf3-Paper-Conference.pdf},
    volume = {35},
    year = {2022}
}

@inproceedings{tvm,
    author = {Tianqi Chen and Thierry Moreau and Ziheng Jiang and Lianmin Zheng and Eddie Yan and Haichen Shen and Meghan Cowan and Leyuan Wang and Yuwei Hu and Luis Ceze and Carlos Guestrin and Arvind Krishnamurthy},
    title = {{TVM}: An Automated {End-to-End} Optimizing Compiler for Deep Learning},
    booktitle = {13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18)},
    year = {2018},
    isbn = {978-1-939133-08-3},
    address = {Carlsbad, CA},
    pages = {578--594},
    url = {https://www.usenix.org/conference/osdi18/presentation/chen},
    publisher = {USENIX Association},
    month = oct,
}

</details>