This is a fork of vLLM to support xfastertransformer backend. This version is based on official vllm v0.4.2.

Notice

🎉🎉🎉Continuous batching and distributed is supported. 🎇🎇🎇

• BeamSearch is not support yet.(WIP)
• LORA is not support yet.(WIP)

Install

From PyPI

pip install vllm-xft

From Source

python3 setup.py bdist_wheel --verbose

Usage

Python offline

python examples/offline_inference_xfastertransformer.py

Serving(OpenAI Compatible Server)

python -m vllm.entrypoints.openai.api_server \
        --model /data/llama-2-7b-chat-cpu \
        --tokenizer /data/llama-2-7b-chat-hf \
        --dtype fp16 \
        --kv-cache-dtype fp16 \
        --served-model-name xft \
        --port 8000 \
        --trust-remote-code \

• --max-num-batched-tokens: max batched token, default value is max(MAX_SEQ_LEN_OF_MODEL, 2048).
• --max-num-seqs: max seqs batch, default is 256.

More Arguments please refer to vllm official docs

Query example

  curl http://localhost:8000/v1/completions \
  -H "Content-Type: application/json" \
  -d '{
  "model": "xft",
  "prompt": "San Francisco is a",
  "max_tokens": 512,
  "temperature": 0
  }'

Distributed(Multi-rank)

Use oneCCL's mpirun to run the workload. The master (rank 0) is the same as the single-rank above, and the slaves (rank > 0) should use the following command:

python -m vllm.entrypoints.slave --dtype fp16 --model ${MODEL_PATH} --kv-cache-dtype fp16

Please keep params of slaves align with master.

Serving(OpenAI Compatible Server)

Here is a example on 2Socket platform, 48 cores pre socket.

OMP_NUM_THREADS=48 mpirun \
        -n 1 numactl --all -C 0-47 -m 0 \
          python -m vllm.entrypoints.openai.api_server \
            --model ${MODEL_PATH} \
            --tokenizer ${TOKEN_PATH} \
            --dtype bf16 \
            --kv-cache-dtype fp16 \
            --served-model-name xft \
            --port 8000 \
            --trust-remote-code \
        : -n 1 numactl --all -C 48-95 -m 1 \
          python -m vllm.entrypoints.slave \
            --dtype bf16 \
            --model ${MODEL_PATH} \
            --kv-cache-dtype fp16

Easy, fast, and cheap LLM serving for everyone

| Documentation | Blog | Paper | Discord |

Latest News 🔥

• [2024/04] We hosted the third vLLM meetup with Roblox! Please find the meetup slides here.
• [2024/01] We hosted the second vLLM meetup in SF! Please find the meetup slides here.
• [2024/01] Added ROCm 6.0 support to vLLM.
• [2023/12] Added ROCm 5.7 support to vLLM.
• [2023/10] We hosted the first vLLM meetup in SF! Please find the meetup slides here.
• [2023/09] We created our Discord server! Join us to discuss vLLM and LLM serving! We will also post the latest announcements and updates there.
• [2023/09] We released our PagedAttention paper on arXiv!
• [2023/08] We would like to express our sincere gratitude to Andreessen Horowitz (a16z) for providing a generous grant to support the open-source development and research of vLLM.
• [2023/07] Added support for LLaMA-2! You can run and serve 7B/13B/70B LLaMA-2s on vLLM with a single command!
• [2023/06] Serving vLLM On any Cloud with SkyPilot. Check out a 1-click example to start the vLLM demo, and the blog post for the story behind vLLM development on the clouds.
• [2023/06] We officially released vLLM! FastChat-vLLM integration has powered LMSYS Vicuna and Chatbot Arena since mid-April. Check out our blog post.

---

About

vLLM is a fast and easy-to-use library for LLM inference and serving.

vLLM is fast with:

• State-of-the-art serving throughput
• Efficient management of attention key and value memory with PagedAttention
• Continuous batching of incoming requests
• Fast model execution with CUDA/HIP graph
• Quantization: GPTQ, AWQ, SqueezeLLM, FP8 KV Cache
• Optimized CUDA kernels

vLLM is flexible and easy to use with:

• Seamless integration with popular Hugging Face models
• High-throughput serving with various decoding algorithms, including parallel sampling, beam search, and more
• Tensor parallelism support for distributed inference
• Streaming outputs
• OpenAI-compatible API server
• Support NVIDIA GPUs and AMD GPUs
• (Experimental) Prefix caching support
• (Experimental) Multi-lora support

vLLM seamlessly supports many Hugging Face models, including the following architectures:

• Aquila & Aquila2 (BAAI/AquilaChat2-7B, BAAI/AquilaChat2-34B, BAAI/Aquila-7B, BAAI/AquilaChat-7B, etc.)
• Baichuan & Baichuan2 (baichuan-inc/Baichuan2-13B-Chat, baichuan-inc/Baichuan-7B, etc.)
• BLOOM (bigscience/bloom, bigscience/bloomz, etc.)
• ChatGLM (THUDM/chatglm2-6b, THUDM/chatglm3-6b, etc.)
• Command-R (CohereForAI/c4ai-command-r-v01, etc.)
• DBRX (databricks/dbrx-base, databricks/dbrx-instruct etc.)
• DeciLM (Deci/DeciLM-7B, Deci/DeciLM-7B-instruct, etc.)
• Falcon (tiiuae/falcon-7b, tiiuae/falcon-40b, tiiuae/falcon-rw-7b, etc.)
• Gemma (google/gemma-2b, google/gemma-7b, etc.)
• GPT-2 (gpt2, gpt2-xl, etc.)
• GPT BigCode (bigcode/starcoder, bigcode/gpt_bigcode-santacoder, etc.)
• GPT-J (EleutherAI/gpt-j-6b, nomic-ai/gpt4all-j, etc.)
• GPT-NeoX (EleutherAI/gpt-neox-20b, databricks/dolly-v2-12b, stabilityai/stablelm-tuned-alpha-7b, etc.)
• InternLM (internlm/internlm-7b, internlm/internlm-chat-7b, etc.)
• InternLM2 (internlm/internlm2-7b, internlm/internlm2-chat-7b, etc.)
• Jais (core42/jais-13b, core42/jais-13b-chat, core42/jais-30b-v3, core42/jais-30b-chat-v3, etc.)
• LLaMA, Llama 2, and Meta Llama 3 (meta-llama/Meta-Llama-3-8B-Instruct, meta-llama/Meta-Llama-3-70B-Instruct, meta-llama/Llama-2-70b-hf, lmsys/vicuna-13b-v1.3, young-geng/koala, openlm-research/open_llama_13b, etc.)
• MiniCPM (openbmb/MiniCPM-2B-sft-bf16, openbmb/MiniCPM-2B-dpo-bf16, etc.)
• Mistral (mistralai/Mistral-7B-v0.1, mistralai/Mistral-7B-Instruct-v0.1, etc.)
• Mixtral (mistralai/Mixtral-8x7B-v0.1, mistralai/Mixtral-8x7B-Instruct-v0.1, mistral-community/Mixtral-8x22B-v0.1, etc.)
• MPT (mosaicml/mpt-7b, mosaicml/mpt-30b, etc.)
• OLMo (allenai/OLMo-1B-hf, allenai/OLMo-7B-hf, etc.)
• OPT (facebook/opt-66b, facebook/opt-iml-max-30b, etc.)
• Orion (OrionStarAI/Orion-14B-Base, OrionStarAI/Orion-14B-Chat, etc.)
• Phi (microsoft/phi-1_5, microsoft/phi-2, etc.)
• Phi-3 (microsoft/Phi-3-mini-4k-instruct, microsoft/Phi-3-mini-128k-instruct, etc.)
• Qwen (Qwen/Qwen-7B, Qwen/Qwen-7B-Chat, etc.)
• Qwen2 (Qwen/Qwen1.5-7B, Qwen/Qwen1.5-7B-Chat, etc.)
• Qwen2MoE (Qwen/Qwen1.5-MoE-A2.7B, Qwen/Qwen1.5-MoE-A2.7B-Chat, etc.)
• StableLM(stabilityai/stablelm-3b-4e1t, stabilityai/stablelm-base-alpha-7b-v2, etc.)
• Starcoder2(bigcode/starcoder2-3b, bigcode/starcoder2-7b, bigcode/starcoder2-15b, etc.)
• Xverse (xverse/XVERSE-7B-Chat, xverse/XVERSE-13B-Chat, xverse/XVERSE-65B-Chat, etc.)
• Yi (01-ai/Yi-6B, 01-ai/Yi-34B, etc.)

Install vLLM with pip or from source:

pip install vllm

Getting Started

Visit our documentation to get started.

• Installation
• Quickstart
• Supported Models

Contributing

We welcome and value any contributions and collaborations. Please check out CONTRIBUTING.md for how to get involved.

Citation

If you use vLLM for your research, please cite our paper:

@inproceedings{kwon2023efficient,
  title={Efficient Memory Management for Large Language Model Serving with PagedAttention},
  author={Woosuk Kwon and Zhuohan Li and Siyuan Zhuang and Ying Sheng and Lianmin Zheng and Cody Hao Yu and Joseph E. Gonzalez and Hao Zhang and Ion Stoica},
  booktitle={Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles},
  year={2023}
}