Merge branch 'v0.5.4-dtk24.04.1'

Dockerfile.tpu

-ARG NIGHTLY_DATE="20240601"
+ARG NIGHTLY_DATE="20240726"
 ARG BASE_IMAGE="us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:nightly_3.10_tpuvm_$NIGHTLY_DATE"
 
 FROM $BASE_IMAGE
-
 WORKDIR /workspace
-COPY . /workspace/vllm
 
-ENV VLLM_TARGET_DEVICE="tpu"
 # Install aiohttp separately to avoid build errors.
 RUN pip install aiohttp
+# Install NumPy 1 instead of NumPy 2.
+RUN pip install "numpy<2"
 # Install the TPU and Pallas dependencies.
 RUN pip install torch_xla[tpu] -f https://storage.googleapis.com/libtpu-releases/index.html
 RUN pip install torch_xla[pallas] -f https://storage.googleapis.com/jax-releases/jax_nightly_releases.html -f https://storage.googleapis.com/jax-releases/jaxlib_nightly_releases.html
 
+# Fix FastAPI dependence
+RUN pip install "starlette<0.38.0"
+
 # Build vLLM.
+COPY . /workspace/vllm
+ENV VLLM_TARGET_DEVICE="tpu"
 RUN cd /workspace/vllm && python setup.py develop
 
 CMD ["/bin/bash"]

Dockerfile.xpu

+FROM intel/oneapi-basekit:2024.1.0-devel-ubuntu20.04
+
+RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | tee /usr/share/keyrings/intel-oneapi-archive-keyring.gpg > /dev/null && \
+    echo "deb [signed-by=/usr/share/keyrings/intel-oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main " | tee /etc/apt/sources.list.d/oneAPI.list && \
+    chmod 644 /usr/share/keyrings/intel-oneapi-archive-keyring.gpg && \
+    rm /etc/apt/sources.list.d/intel-graphics.list && \
+    wget -O- https://repositories.intel.com/graphics/intel-graphics.key | gpg --dearmor | tee /usr/share/keyrings/intel-graphics.gpg > /dev/null && \
+    echo "deb [arch=amd64,i386 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/graphics/ubuntu jammy arc" | tee /etc/apt/sources.list.d/intel.gpu.jammy.list && \
+    chmod 644 /usr/share/keyrings/intel-graphics.gpg
+
+RUN apt-get update  -y \
+&& apt-get install -y curl libicu70 lsb-release git wget vim numactl python3 python3-pip
+
+COPY ./ /workspace/vllm
+
+WORKDIR /workspace/vllm
+
+RUN pip install -v -r requirements-xpu.txt
+
+RUN VLLM_TARGET_DEVICE=xpu python3 setup.py install
+
+CMD ["/bin/bash"]

MANIFEST.in

 include LICENSE
+include requirements-adag.txt
 include requirements-common.txt
 include requirements-cuda.txt
 include requirements-rocm.txt

README.md

@@ -4,7 +4,7 @@ vLLM是一个快速且易于使用的LLM推理和服务库，使用PageAttention
 
 ## 暂不支持的官方功能
 - **量化推理**：目前支持fp16的推理和gptq,awq-int4推理，mralin的权重量化、kv-cache fp8推理方案暂不支持
-- **模块支持**：目前不支持Sliding window attention、 moe kernel和lora模块
+- **模块支持**：目前不支持Sliding window attention、 moe kernel模块
 
 
 ## 支持模型结构列表
@@ -82,7 +82,7 @@ VLLM_INSTALL_PUNICA_KERNELS=1 python3 setup.py install
 + 若使用 pip install 下载安装过慢，可添加源：-i https://pypi.tuna.tsinghua.edu.cn/simple/
 
 ## 验证
-- python -c "import vllm; print(vllm.\_\_version__)"，版本号与官方版本同步，查询该软件的版本号，例如0.5.0；
+- python -c "import vllm; print(vllm.\_\_version__)"，版本号与官方版本同步，查询该软件的版本号，例如0.5.4；
 
 ## Known Issue
 - 无

README_ORIGIN.md

@@ -16,33 +16,14 @@ Easy, fast, and cheap LLM serving for everyone
 
 ---
 
-**Ray Summit CPF is Open (June 4th to June 20th)!**
-
-There will be a track for vLLM at the Ray Summit (09/30-10/02, SF) this year!
-If you have cool projects related to vLLM or LLM inference, we would love to see your proposals.
-This will be a great chance for everyone in the community to get together and learn.
-Please submit your proposal [here](https://raysummit.anyscale.com/flow/anyscale/raysummit2024/landing/page/eventsite)
-
-**The Fourth vLLM Bay Area Meetup (June 11th 5:30pm-8pm PT)**
-
-We are thrilled to announce our fourth vLLM Meetup!
-The vLLM team will share recent updates and roadmap.
-We will also have vLLM collaborators from BentoML and Cloudflare coming up to the stage to discuss their experience in deploying LLMs with vLLM.
-Please register [here](https://lu.ma/agivllm) and join us!
-
----
-
 *Latest News* 🔥
+- [2024/07] We hosted [the fifth vLLM meetup](https://lu.ma/lp0gyjqr) with AWS! Please find the meetup slides [here](https://docs.google.com/presentation/d/1RgUD8aCfcHocghoP3zmXzck9vX3RCI9yfUAB2Bbcl4Y/edit?usp=sharing).
+- [2024/07] In partnership with Meta, vLLM officially supports Llama 3.1 with FP8 quantization and pipeline parallelism! Please check out our blog post [here](https://blog.vllm.ai/2024/07/23/llama31.html).
+- [2024/06] We hosted [the fourth vLLM meetup](https://lu.ma/agivllm) with Cloudflare and BentoML! Please find the meetup slides [here](https://docs.google.com/presentation/d/1iJ8o7V2bQEi0BFEljLTwc5G1S10_Rhv3beed5oB0NJ4/edit?usp=sharing).
 - [2024/04] We hosted [the third vLLM meetup](https://robloxandvllmmeetup2024.splashthat.com/) with Roblox! Please find the meetup slides [here](https://docs.google.com/presentation/d/1A--47JAK4BJ39t954HyTkvtfwn0fkqtsL8NGFuslReM/edit?usp=sharing).
-- [2024/01] We hosted [the second vLLM meetup](https://lu.ma/ygxbpzhl) in SF! Please find the meetup slides [here](https://docs.google.com/presentation/d/12mI2sKABnUw5RBWXDYY-HtHth4iMSNcEoQ10jDQbxgA/edit?usp=sharing).
-- [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](https://lu.ma/first-vllm-meetup) in SF! Please find the meetup slides [here](https://docs.google.com/presentation/d/1QL-XPFXiFpDBh86DbEegFXBXFXjix4v032GhShbKf3s/edit?usp=sharing).
-- [2023/09] We created our [Discord server](https://discord.gg/jz7wjKhh6g)! 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](https://arxiv.org/abs/2309.06180) on arXiv!
+- [2024/01] We hosted [the second vLLM meetup](https://lu.ma/ygxbpzhl) with IBM! Please find the meetup slides [here](https://docs.google.com/presentation/d/12mI2sKABnUw5RBWXDYY-HtHth4iMSNcEoQ10jDQbxgA/edit?usp=sharing).
+- [2023/10] We hosted [the first vLLM meetup](https://lu.ma/first-vllm-meetup) with a16z! Please find the meetup slides [here](https://docs.google.com/presentation/d/1QL-XPFXiFpDBh86DbEegFXBXFXjix4v032GhShbKf3s/edit?usp=sharing).
 - [2023/08] We would like to express our sincere gratitude to [Andreessen Horowitz](https://a16z.com/2023/08/30/supporting-the-open-source-ai-community/) (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](https://github.com/skypilot-org/skypilot/blob/master/llm/vllm) to start the vLLM demo, and the [blog post](https://blog.skypilot.co/serving-llm-24x-faster-on-the-cloud-with-vllm-and-skypilot/) 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](https://chat.lmsys.org) since mid-April. Check out our [blog post](https://vllm.ai).
 
 ---
@@ -58,14 +39,16 @@ vLLM is fast with:
 - Quantization: [GPTQ](https://arxiv.org/abs/2210.17323), [AWQ](https://arxiv.org/abs/2306.00978), [SqueezeLLM](https://arxiv.org/abs/2306.07629), FP8 KV Cache
 - Optimized CUDA kernels
 
+**Performance benchmark**: We include a [performance benchmark](https://buildkite.com/vllm/performance-benchmark/builds/4068) that compares the performance of vllm against other LLM serving engines ([TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM), [text-generation-inference](https://github.com/huggingface/text-generation-inference) and [lmdeploy](https://github.com/InternLM/lmdeploy)).
+
 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
+- Tensor parallelism and pipeline parallelism support for distributed inference
 - Streaming outputs
 - OpenAI-compatible API server
-- Support NVIDIA GPUs and AMD GPUs
+- Support NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs and GPUs, PowerPC CPUs
 - (Experimental) Prefix caching support
 - (Experimental) Multi-lora support
 
@@ -109,6 +92,7 @@ vLLM is a community project. Our compute resources for development and testing a
 - Databricks
 - DeepInfra
 - Dropbox
+- Google Cloud
 - Lambda Lab
 - NVIDIA
 - Replicate
@@ -118,6 +102,7 @@ vLLM is a community project. Our compute resources for development and testing a
 - Trainy
 - UC Berkeley
 - UC San Diego
+- ZhenFund
 
 We also have an official fundraising venue through [OpenCollective](https://opencollective.com/vllm). We plan to use the fund to support the development, maintenance, and adoption of vLLM.