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[Doc] [1/N] Reorganize Getting Started section (#11645)

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(installation-arm)=
# Installation for ARM CPUs
vLLM has been adapted to work on ARM64 CPUs with NEON support, leveraging the CPU backend initially developed for the x86 platform. This guide provides installation instructions specific to ARM. For additional details on supported features, refer to the [x86 CPU documentation](#installation-x86) covering:
- CPU backend inference capabilities
- Relevant runtime environment variables
- Performance optimization tips
ARM CPU backend currently supports Float32, FP16 and BFloat16 datatypes.
Contents:
1. [Requirements](#arm-backend-requirements)
2. [Quick Start with Dockerfile](#arm-backend-quick-start-dockerfile)
3. [Building from Source](#build-arm-backend-from-source)
(arm-backend-requirements)=
## Requirements
- **Operating System**: Linux or macOS
- **Compiler**: `gcc/g++ >= 12.3.0` (optional, but recommended)
- **Instruction Set Architecture (ISA)**: NEON support is required
(arm-backend-quick-start-dockerfile)=
## Quick Start with Dockerfile
You can quickly set up vLLM on ARM using Docker:
```console
$ docker build -f Dockerfile.arm -t vllm-cpu-env --shm-size=4g .
$ docker run -it \
--rm \
--network=host \
--cpuset-cpus=<cpu-id-list, optional> \
--cpuset-mems=<memory-node, optional> \
vllm-cpu-env
```
(build-arm-backend-from-source)=
## Building from Source
To build vLLM from source on Ubuntu 22.04 or other Linux distributions, follow a similar process as with x86. Testing has been conducted on AWS Graviton3 instances for compatibility.

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(installation-x86)=
# Installation for x86 CPUs
vLLM initially supports basic model inferencing and serving on x86 CPU platform, with data types FP32, FP16 and BF16. vLLM CPU backend supports the following vLLM features:
- Tensor Parallel
- Model Quantization (`INT8 W8A8, AWQ`)
- Chunked-prefill
- Prefix-caching
- FP8-E5M2 KV-Caching (TODO)
Table of contents:
1. [Requirements](#cpu-backend-requirements)
2. [Quick start using Dockerfile](#cpu-backend-quick-start-dockerfile)
3. [Build from source](#build-cpu-backend-from-source)
4. [Related runtime environment variables](#env-intro)
5. [Intel Extension for PyTorch](#ipex-guidance)
6. [Performance tips](#cpu-backend-performance-tips)
(cpu-backend-requirements)=
## Requirements
- OS: Linux
- Compiler: `gcc/g++>=12.3.0` (optional, recommended)
- Instruction set architecture (ISA) requirement: AVX512 (optional, recommended)
(cpu-backend-quick-start-dockerfile)=
## Quick start using Dockerfile
```console
$ docker build -f Dockerfile.cpu -t vllm-cpu-env --shm-size=4g .
$ docker run -it \
--rm \
--network=host \
--cpuset-cpus=<cpu-id-list, optional> \
--cpuset-mems=<memory-node, optional> \
vllm-cpu-env
```
(build-cpu-backend-from-source)=
## Build from source
- First, install recommended compiler. We recommend to use `gcc/g++ >= 12.3.0` as the default compiler to avoid potential problems. For example, on Ubuntu 22.4, you can run:
```console
$ sudo apt-get update -y
$ sudo apt-get install -y gcc-12 g++-12 libnuma-dev
$ sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-12 10 --slave /usr/bin/g++ g++ /usr/bin/g++-12
```
- Second, install Python packages for vLLM CPU backend building:
```console
$ pip install --upgrade pip
$ pip install cmake>=3.26 wheel packaging ninja "setuptools-scm>=8" numpy
$ pip install -v -r requirements-cpu.txt --extra-index-url https://download.pytorch.org/whl/cpu
```
- Finally, build and install vLLM CPU backend:
```console
$ VLLM_TARGET_DEVICE=cpu python setup.py install
```
```{note}
- AVX512_BF16 is an extension ISA provides native BF16 data type conversion and vector product instructions, will brings some performance improvement compared with pure AVX512. The CPU backend build script will check the host CPU flags to determine whether to enable AVX512_BF16.
- If you want to force enable AVX512_BF16 for the cross-compilation, please set environment variable `VLLM_CPU_AVX512BF16=1` before the building.
```
(env-intro)=
## Related runtime environment variables
- `VLLM_CPU_KVCACHE_SPACE`: specify the KV Cache size (e.g, `VLLM_CPU_KVCACHE_SPACE=40` means 40 GB space for KV cache), larger setting will allow vLLM running more requests in parallel. This parameter should be set based on the hardware configuration and memory management pattern of users.
- `VLLM_CPU_OMP_THREADS_BIND`: specify the CPU cores dedicated to the OpenMP threads. For example, `VLLM_CPU_OMP_THREADS_BIND=0-31` means there will be 32 OpenMP threads bound on 0-31 CPU cores. `VLLM_CPU_OMP_THREADS_BIND=0-31|32-63` means there will be 2 tensor parallel processes, 32 OpenMP threads of rank0 are bound on 0-31 CPU cores, and the OpenMP threads of rank1 are bound on 32-63 CPU cores.
(ipex-guidance)=
## Intel Extension for PyTorch
- [Intel Extension for PyTorch (IPEX)](https://github.com/intel/intel-extension-for-pytorch) extends PyTorch with up-to-date features optimizations for an extra performance boost on Intel hardware.
(cpu-backend-performance-tips)=
## Performance tips
- We highly recommend to use TCMalloc for high performance memory allocation and better cache locality. For example, on Ubuntu 22.4, you can run:
```console
$ sudo apt-get install libtcmalloc-minimal4 # install TCMalloc library
$ find / -name *libtcmalloc* # find the dynamic link library path
$ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libtcmalloc_minimal.so.4:$LD_PRELOAD # prepend the library to LD_PRELOAD
$ python examples/offline_inference.py # run vLLM
```
- When using the online serving, it is recommended to reserve 1-2 CPU cores for the serving framework to avoid CPU oversubscription. For example, on a platform with 32 physical CPU cores, reserving CPU 30 and 31 for the framework and using CPU 0-29 for OpenMP:
```console
$ export VLLM_CPU_KVCACHE_SPACE=40
$ export VLLM_CPU_OMP_THREADS_BIND=0-29
$ vllm serve facebook/opt-125m
```
- If using vLLM CPU backend on a machine with hyper-threading, it is recommended to bind only one OpenMP thread on each physical CPU core using `VLLM_CPU_OMP_THREADS_BIND`. On a hyper-threading enabled platform with 16 logical CPU cores / 8 physical CPU cores:
```console
$ lscpu -e # check the mapping between logical CPU cores and physical CPU cores
# The "CPU" column means the logical CPU core IDs, and the "CORE" column means the physical core IDs. On this platform, two logical cores are sharing one physical core.
CPU NODE SOCKET CORE L1d:L1i:L2:L3 ONLINE MAXMHZ MINMHZ MHZ
0 0 0 0 0:0:0:0 yes 2401.0000 800.0000 800.000
1 0 0 1 1:1:1:0 yes 2401.0000 800.0000 800.000
2 0 0 2 2:2:2:0 yes 2401.0000 800.0000 800.000
3 0 0 3 3:3:3:0 yes 2401.0000 800.0000 800.000
4 0 0 4 4:4:4:0 yes 2401.0000 800.0000 800.000
5 0 0 5 5:5:5:0 yes 2401.0000 800.0000 800.000
6 0 0 6 6:6:6:0 yes 2401.0000 800.0000 800.000
7 0 0 7 7:7:7:0 yes 2401.0000 800.0000 800.000
8 0 0 0 0:0:0:0 yes 2401.0000 800.0000 800.000
9 0 0 1 1:1:1:0 yes 2401.0000 800.0000 800.000
10 0 0 2 2:2:2:0 yes 2401.0000 800.0000 800.000
11 0 0 3 3:3:3:0 yes 2401.0000 800.0000 800.000
12 0 0 4 4:4:4:0 yes 2401.0000 800.0000 800.000
13 0 0 5 5:5:5:0 yes 2401.0000 800.0000 800.000
14 0 0 6 6:6:6:0 yes 2401.0000 800.0000 800.000
15 0 0 7 7:7:7:0 yes 2401.0000 800.0000 800.000
# On this platform, it is recommend to only bind openMP threads on logical CPU cores 0-7 or 8-15
$ export VLLM_CPU_OMP_THREADS_BIND=0-7
$ python examples/offline_inference.py
```
- If using vLLM CPU backend on a multi-socket machine with NUMA, be aware to set CPU cores using `VLLM_CPU_OMP_THREADS_BIND` to avoid cross NUMA node memory access.
## CPU Backend Considerations
- The CPU backend significantly differs from the GPU backend since the vLLM architecture was originally optimized for GPU use. A number of optimizations are needed to enhance its performance.
- Decouple the HTTP serving components from the inference components. In a GPU backend configuration, the HTTP serving and tokenization tasks operate on the CPU, while inference runs on the GPU, which typically does not pose a problem. However, in a CPU-based setup, the HTTP serving and tokenization can cause significant context switching and reduced cache efficiency. Therefore, it is strongly recommended to segregate these two components for improved performance.
- On CPU based setup with NUMA enabled, the memory access performance may be largely impacted by the [topology](https://github.com/intel/intel-extension-for-pytorch/blob/main/docs/tutorials/performance_tuning/tuning_guide.md#non-uniform-memory-access-numa). For NUMA architecture, two optimizations are to recommended: Tensor Parallel or Data Parallel.
- Using Tensor Parallel for a latency constraints deployment: following GPU backend design, a Megatron-LM's parallel algorithm will be used to shard the model, based on the number of NUMA nodes (e.g. TP = 2 for a two NUMA node system). With [TP feature on CPU](gh-pr:6125) merged, Tensor Parallel is supported for serving and offline inferencing. In general each NUMA node is treated as one GPU card. Below is the example script to enable Tensor Parallel = 2 for serving:
```console
$ VLLM_CPU_KVCACHE_SPACE=40 VLLM_CPU_OMP_THREADS_BIND="0-31|32-63" vllm serve meta-llama/Llama-2-7b-chat-hf -tp=2 --distributed-executor-backend mp
```
- Using Data Parallel for maximum throughput: to launch an LLM serving endpoint on each NUMA node along with one additional load balancer to dispatch the requests to those endpoints. Common solutions like [Nginx](#nginxloadbalancer) or HAProxy are recommended. Anyscale Ray project provides the feature on LLM [serving](https://docs.ray.io/en/latest/serve/index.html). Here is the example to setup a scalable LLM serving with [Ray Serve](https://github.com/intel/llm-on-ray/blob/main/docs/setup.md).

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(installation-cuda)=
# Installation for CUDA
vLLM is a Python library that also contains pre-compiled C++ and CUDA (12.1) binaries.
## Requirements
- OS: Linux
- Python: 3.9 -- 3.12
- GPU: compute capability 7.0 or higher (e.g., V100, T4, RTX20xx, A100, L4, H100, etc.)
## Install released versions
You can install vLLM using pip:
```console
$ # (Recommended) Create a new conda environment.
$ conda create -n myenv python=3.12 -y
$ conda activate myenv
$ # Install vLLM with CUDA 12.1.
$ pip install vllm
```
```{note}
Although we recommend using `conda` to create and manage Python environments, it is highly recommended to use `pip` to install vLLM. This is because `pip` can install `torch` with separate library packages like `NCCL`, while `conda` installs `torch` with statically linked `NCCL`. This can cause issues when vLLM tries to use `NCCL`. See <gh-issue:8420> for more details.
```
````{note}
As of now, vLLM's binaries are compiled with CUDA 12.1 and public PyTorch release versions by default.
We also provide vLLM binaries compiled with CUDA 11.8 and public PyTorch release versions:
```console
$ # Install vLLM with CUDA 11.8.
$ export VLLM_VERSION=0.6.1.post1
$ export PYTHON_VERSION=310
$ pip install https://github.com/vllm-project/vllm/releases/download/v${VLLM_VERSION}/vllm-${VLLM_VERSION}+cu118-cp${PYTHON_VERSION}-cp${PYTHON_VERSION}-manylinux1_x86_64.whl --extra-index-url https://download.pytorch.org/whl/cu118
```
In order to be performant, vLLM has to compile many cuda kernels. The compilation unfortunately introduces binary incompatibility with other CUDA versions and PyTorch versions, even for the same PyTorch version with different building configurations.
Therefore, it is recommended to install vLLM with a **fresh new** conda environment. If either you have a different CUDA version or you want to use an existing PyTorch installation, you need to build vLLM from source. See below for instructions.
````
(install-the-latest-code)=
## Install the latest code
LLM inference is a fast-evolving field, and the latest code may contain bug fixes, performance improvements, and new features that are not released yet. To allow users to try the latest code without waiting for the next release, vLLM provides wheels for Linux running on a x86 platform with CUDA 12 for every commit since `v0.5.3`. You can download and install it with the following command:
```console
$ pip install https://vllm-wheels.s3.us-west-2.amazonaws.com/nightly/vllm-1.0.0.dev-cp38-abi3-manylinux1_x86_64.whl
```
If you want to access the wheels for previous commits, you can specify the commit hash in the URL:
```console
$ export VLLM_COMMIT=33f460b17a54acb3b6cc0b03f4a17876cff5eafd # use full commit hash from the main branch
$ pip install https://vllm-wheels.s3.us-west-2.amazonaws.com/${VLLM_COMMIT}/vllm-1.0.0.dev-cp38-abi3-manylinux1_x86_64.whl
```
Note that the wheels are built with Python 3.8 ABI (see [PEP 425](https://peps.python.org/pep-0425/) for more details about ABI), so **they are compatible with Python 3.8 and later**. The version string in the wheel file name (`1.0.0.dev`) is just a placeholder to have a unified URL for the wheels. The actual versions of wheels are contained in the wheel metadata. Although we don't support Python 3.8 any more (because PyTorch 2.5 dropped support for Python 3.8), the wheels are still built with Python 3.8 ABI to keep the same wheel name as before.
Another way to access the latest code is to use the docker images:
```console
$ export VLLM_COMMIT=33f460b17a54acb3b6cc0b03f4a17876cff5eafd # use full commit hash from the main branch
$ docker pull public.ecr.aws/q9t5s3a7/vllm-ci-postmerge-repo:${VLLM_COMMIT}
```
These docker images are used for CI and testing only, and they are not intended for production use. They will be expired after several days.
The latest code can contain bugs and may not be stable. Please use it with caution.
(build-from-source)=
## Build from source
(python-only-build)=
### Python-only build (without compilation)
If you only need to change Python code, you can build and install vLLM without compilation. Using `pip`'s [`--editable` flag](https://pip.pypa.io/en/stable/topics/local-project-installs/#editable-installs), changes you make to the code will be reflected when you run vLLM:
```console
$ git clone https://github.com/vllm-project/vllm.git
$ cd vllm
$ VLLM_USE_PRECOMPILED=1 pip install --editable .
```
This will download the latest nightly wheel and use the compiled libraries from there in the install.
The `VLLM_PRECOMPILED_WHEEL_LOCATION` environment variable can be used instead of `VLLM_USE_PRECOMPILED` to specify a custom path or URL to the wheel file. For example, to use the [0.6.1.post1 PyPi wheel](https://pypi.org/project/vllm/#files):
```console
$ export VLLM_PRECOMPILED_WHEEL_LOCATION=https://files.pythonhosted.org/packages/4a/4c/ee65ba33467a4c0de350ce29fbae39b9d0e7fcd887cc756fa993654d1228/vllm-0.6.3.post1-cp38-abi3-manylinux1_x86_64.whl
$ pip install --editable .
```
You can find more information about vLLM's wheels [above](#install-the-latest-code).
```{note}
There is a possibility that your source code may have a different commit ID compared to the latest vLLM wheel, which could potentially lead to unknown errors.
It is recommended to use the same commit ID for the source code as the vLLM wheel you have installed. Please refer to [the section above](#install-the-latest-code) for instructions on how to install a specified wheel.
```
### Full build (with compilation)
If you want to modify C++ or CUDA code, you'll need to build vLLM from source. This can take several minutes:
```console
$ git clone https://github.com/vllm-project/vllm.git
$ cd vllm
$ pip install -e .
```
```{tip}
Building from source requires a lot of compilation. If you are building from source repeatedly, it's more efficient to cache the compilation results.
For example, you can install [ccache](https://github.com/ccache/ccache) using `conda install ccache` or `apt install ccache` .
As long as `which ccache` command can find the `ccache` binary, it will be used automatically by the build system. After the first build, subsequent builds will be much faster.
[sccache](https://github.com/mozilla/sccache) works similarly to `ccache`, but has the capability to utilize caching in remote storage environments.
The following environment variables can be set to configure the vLLM `sccache` remote: `SCCACHE_BUCKET=vllm-build-sccache SCCACHE_REGION=us-west-2 SCCACHE_S3_NO_CREDENTIALS=1`. We also recommend setting `SCCACHE_IDLE_TIMEOUT=0`.
```
#### Use an existing PyTorch installation
There are scenarios where the PyTorch dependency cannot be easily installed via pip, e.g.:
- Building vLLM with PyTorch nightly or a custom PyTorch build.
- Building vLLM with aarch64 and CUDA (GH200), where the PyTorch wheels are not available on PyPI. Currently, only the PyTorch nightly has wheels for aarch64 with CUDA. You can run `pip3 install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu124` to [install PyTorch nightly](https://pytorch.org/get-started/locally/), and then build vLLM on top of it.
To build vLLM using an existing PyTorch installation:
```console
$ git clone https://github.com/vllm-project/vllm.git
$ cd vllm
$ python use_existing_torch.py
$ pip install -r requirements-build.txt
$ pip install -e . --no-build-isolation
```
#### Use the local cutlass for compilation
Currently, before starting the build process, vLLM fetches cutlass code from GitHub. However, there may be scenarios where you want to use a local version of cutlass instead.
To achieve this, you can set the environment variable VLLM_CUTLASS_SRC_DIR to point to your local cutlass directory.
```console
$ git clone https://github.com/vllm-project/vllm.git
$ cd vllm
$ VLLM_CUTLASS_SRC_DIR=/path/to/cutlass pip install -e .
```
#### Troubleshooting
To avoid your system being overloaded, you can limit the number of compilation jobs
to be run simultaneously, via the environment variable `MAX_JOBS`. For example:
```console
$ export MAX_JOBS=6
$ pip install -e .
```
This is especially useful when you are building on less powerful machines. For example, when you use WSL it only [assigns 50% of the total memory by default](https://learn.microsoft.com/en-us/windows/wsl/wsl-config#main-wsl-settings), so using `export MAX_JOBS=1` can avoid compiling multiple files simultaneously and running out of memory.
A side effect is a much slower build process.
Additionally, if you have trouble building vLLM, we recommend using the NVIDIA PyTorch Docker image.
```console
$ # Use `--ipc=host` to make sure the shared memory is large enough.
$ docker run --gpus all -it --rm --ipc=host nvcr.io/nvidia/pytorch:23.10-py3
```
If you don't want to use docker, it is recommended to have a full installation of CUDA Toolkit. You can download and install it from [the official website](https://developer.nvidia.com/cuda-toolkit-archive). After installation, set the environment variable `CUDA_HOME` to the installation path of CUDA Toolkit, and make sure that the `nvcc` compiler is in your `PATH`, e.g.:
```console
$ export CUDA_HOME=/usr/local/cuda
$ export PATH="${CUDA_HOME}/bin:$PATH"
```
Here is a sanity check to verify that the CUDA Toolkit is correctly installed:
```console
$ nvcc --version # verify that nvcc is in your PATH
$ ${CUDA_HOME}/bin/nvcc --version # verify that nvcc is in your CUDA_HOME
```
### Unsupported OS build
vLLM can fully run only on Linux but for development purposes, you can still build it on other systems (for example, macOS), allowing for imports and a more convenient development environment. The binaries will not be compiled and won't work on non-Linux systems.
Simply disable the `VLLM_TARGET_DEVICE` environment variable before installing:
```console
$ export VLLM_TARGET_DEVICE=empty
$ pip install -e .
```

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(installation-rocm)=
# Installation for ROCm
vLLM supports AMD GPUs with ROCm 6.2.
## Requirements
- OS: Linux
- Python: 3.9 -- 3.12
- GPU: MI200s (gfx90a), MI300 (gfx942), Radeon RX 7900 series (gfx1100)
- ROCm 6.2
Installation options:
1. [Build from source with docker](#build-from-source-docker-rocm)
2. [Build from source](#build-from-source-rocm)
(build-from-source-docker-rocm)=
## Option 1: Build from source with docker (recommended)
You can build and install vLLM from source.
First, build a docker image from <gh-file:Dockerfile.rocm> and launch a docker container from the image.
It is important that the user kicks off the docker build using buildkit. Either the user put DOCKER_BUILDKIT=1 as environment variable when calling docker build command, or the user needs to setup buildkit in the docker daemon configuration /etc/docker/daemon.json as follows and restart the daemon:
```console
{
"features": {
"buildkit": true
}
}
```
<gh-file:Dockerfile.rocm> uses ROCm 6.2 by default, but also supports ROCm 5.7, 6.0 and 6.1 in older vLLM branches.
It provides flexibility to customize the build of docker image using the following arguments:
- `BASE_IMAGE`: specifies the base image used when running `docker build`, specifically the PyTorch on ROCm base image.
- `BUILD_FA`: specifies whether to build CK flash-attention. The default is 1. For [Radeon RX 7900 series (gfx1100)](https://rocm.docs.amd.com/projects/radeon/en/latest/index.html), this should be set to 0 before flash-attention supports this target.
- `FX_GFX_ARCHS`: specifies the GFX architecture that is used to build CK flash-attention, for example, `gfx90a;gfx942` for MI200 and MI300. The default is `gfx90a;gfx942`
- `FA_BRANCH`: specifies the branch used to build the CK flash-attention in [ROCm's flash-attention repo](https://github.com/ROCmSoftwarePlatform/flash-attention). The default is `ae7928c`
- `BUILD_TRITON`: specifies whether to build triton flash-attention. The default value is 1.
Their values can be passed in when running `docker build` with `--build-arg` options.
To build vllm on ROCm 6.2 for MI200 and MI300 series, you can use the default:
```console
$ DOCKER_BUILDKIT=1 docker build -f Dockerfile.rocm -t vllm-rocm .
```
To build vllm on ROCm 6.2 for Radeon RX7900 series (gfx1100), you should specify `BUILD_FA` as below:
```console
$ DOCKER_BUILDKIT=1 docker build --build-arg BUILD_FA="0" -f Dockerfile.rocm -t vllm-rocm .
```
To run the above docker image `vllm-rocm`, use the below command:
```console
$ docker run -it \
--network=host \
--group-add=video \
--ipc=host \
--cap-add=SYS_PTRACE \
--security-opt seccomp=unconfined \
--device /dev/kfd \
--device /dev/dri \
-v <path/to/model>:/app/model \
vllm-rocm \
bash
```
Where the `<path/to/model>` is the location where the model is stored, for example, the weights for llama2 or llama3 models.
(build-from-source-rocm)=
## Option 2: Build from source
0. Install prerequisites (skip if you are already in an environment/docker with the following installed):
- [ROCm](https://rocm.docs.amd.com/en/latest/deploy/linux/index.html)
- [PyTorch](https://pytorch.org/)
For installing PyTorch, you can start from a fresh docker image, e.g, `rocm/pytorch:rocm6.2_ubuntu20.04_py3.9_pytorch_release_2.3.0`, `rocm/pytorch-nightly`.
Alternatively, you can install PyTorch using PyTorch wheels. You can check PyTorch installation guide in PyTorch [Getting Started](https://pytorch.org/get-started/locally/)
1. Install [Triton flash attention for ROCm](https://github.com/ROCm/triton)
Install ROCm's Triton flash attention (the default triton-mlir branch) following the instructions from [ROCm/triton](https://github.com/ROCm/triton/blob/triton-mlir/README.md)
```console
$ python3 -m pip install ninja cmake wheel pybind11
$ pip uninstall -y triton
$ git clone https://github.com/OpenAI/triton.git
$ cd triton
$ git checkout e192dba
$ cd python
$ pip3 install .
$ cd ../..
```
```{note}
- If you see HTTP issue related to downloading packages during building triton, please try again as the HTTP error is intermittent.
```
2. Optionally, if you choose to use CK flash attention, you can install [flash attention for ROCm](https://github.com/ROCm/flash-attention/tree/ck_tile)
Install ROCm's flash attention (v2.5.9.post1) following the instructions from [ROCm/flash-attention](https://github.com/ROCm/flash-attention/tree/ck_tile#amd-gpurocm-support)
Alternatively, wheels intended for vLLM use can be accessed under the releases.
For example, for ROCm 6.2, suppose your gfx arch is `gfx90a`. To get your gfx architecture, run `rocminfo |grep gfx`.
```console
$ git clone https://github.com/ROCm/flash-attention.git
$ cd flash-attention
$ git checkout 3cea2fb
$ git submodule update --init
$ GPU_ARCHS="gfx90a" python3 setup.py install
$ cd ..
```
```{note}
- You might need to downgrade the "ninja" version to 1.10 it is not used when compiling flash-attention-2 (e.g. `pip install ninja==1.10.2.4`)
```
3. Build vLLM. For example, vLLM on ROCM 6.2 can be built with the following steps:
```bash
$ pip install --upgrade pip
# Install PyTorch
$ pip uninstall torch -y
$ pip install --no-cache-dir --pre torch==2.6.0.dev20241024 --index-url https://download.pytorch.org/whl/nightly/rocm6.2
# Build & install AMD SMI
$ pip install /opt/rocm/share/amd_smi
# Install dependencies
$ pip install --upgrade numba scipy huggingface-hub[cli]
$ pip install "numpy<2"
$ pip install -r requirements-rocm.txt
# Build vLLM for MI210/MI250/MI300.
$ export PYTORCH_ROCM_ARCH="gfx90a;gfx942"
$ python3 setup.py develop
```
This may take 5-10 minutes. Currently, {code}`pip install .` does not work for ROCm installation.
```{tip}
- Triton flash attention is used by default. For benchmarking purposes, it is recommended to run a warm up step before collecting perf numbers.
- Triton flash attention does not currently support sliding window attention. If using half precision, please use CK flash-attention for sliding window support.
- To use CK flash-attention or PyTorch naive attention, please use this flag `export VLLM_USE_TRITON_FLASH_ATTN=0` to turn off triton flash attention.
- The ROCm version of PyTorch, ideally, should match the ROCm driver version.
```
```{tip}
- For MI300x (gfx942) users, to achieve optimal performance, please refer to [MI300x tuning guide](https://rocm.docs.amd.com/en/latest/how-to/tuning-guides/mi300x/index.html) for performance optimization and tuning tips on system and workflow level.
For vLLM, please refer to [vLLM performance optimization](https://rocm.docs.amd.com/en/latest/how-to/tuning-guides/mi300x/workload.html#vllm-performance-optimization).
```

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(installation-gaudi)=
# Installation for Intel® Gaudi®
This README provides instructions on running vLLM with Intel Gaudi devices.
## Requirements and Installation
Please follow the instructions provided in the [Gaudi Installation
Guide](https://docs.habana.ai/en/latest/Installation_Guide/index.html)
to set up the execution environment. To achieve the best performance,
please follow the methods outlined in the [Optimizing Training Platform
Guide](https://docs.habana.ai/en/latest/PyTorch/Model_Optimization_PyTorch/Optimization_in_Training_Platform.html).
### Requirements
- OS: Ubuntu 22.04 LTS
- Python: 3.10
- Intel Gaudi accelerator
- Intel Gaudi software version 1.18.0
### Quick start using Dockerfile
```console
$ docker build -f Dockerfile.hpu -t vllm-hpu-env .
$ docker run -it --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --net=host --rm vllm-hpu-env
```
```{tip}
If you're observing the following error: `docker: Error response from daemon: Unknown runtime specified habana.`, please refer to "Install Using Containers" section of [Intel Gaudi Software Stack and Driver Installation](https://docs.habana.ai/en/v1.18.0/Installation_Guide/Bare_Metal_Fresh_OS.html). Make sure you have `habana-container-runtime` package installed and that `habana` container runtime is registered.
```
### Build from source
#### Environment verification
To verify that the Intel Gaudi software was correctly installed, run:
```console
$ hl-smi # verify that hl-smi is in your PATH and each Gaudi accelerator is visible
$ apt list --installed | grep habana # verify that habanalabs-firmware-tools, habanalabs-graph, habanalabs-rdma-core, habanalabs-thunk and habanalabs-container-runtime are installed
$ pip list | grep habana # verify that habana-torch-plugin, habana-torch-dataloader, habana-pyhlml and habana-media-loader are installed
$ pip list | grep neural # verify that neural_compressor is installed
```
Refer to [Intel Gaudi Software Stack
Verification](https://docs.habana.ai/en/latest/Installation_Guide/SW_Verification.html#platform-upgrade)
for more details.
#### Run Docker Image
It is highly recommended to use the latest Docker image from Intel Gaudi
vault. Refer to the [Intel Gaudi
documentation](https://docs.habana.ai/en/latest/Installation_Guide/Bare_Metal_Fresh_OS.html#pull-prebuilt-containers)
for more details.
Use the following commands to run a Docker image:
```console
$ docker pull vault.habana.ai/gaudi-docker/1.18.0/ubuntu22.04/habanalabs/pytorch-installer-2.4.0:latest
$ docker run -it --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --net=host --ipc=host vault.habana.ai/gaudi-docker/1.18.0/ubuntu22.04/habanalabs/pytorch-installer-2.4.0:latest
```
#### Build and Install vLLM
To build and install vLLM from source, run:
```console
$ git clone https://github.com/vllm-project/vllm.git
$ cd vllm
$ python setup.py develop
```
Currently, the latest features and performance optimizations are developed in Gaudi's [vLLM-fork](https://github.com/HabanaAI/vllm-fork) and we periodically upstream them to vLLM main repo. To install latest [HabanaAI/vLLM-fork](https://github.com/HabanaAI/vllm-fork), run the following:
```console
$ git clone https://github.com/HabanaAI/vllm-fork.git
$ cd vllm-fork
$ git checkout habana_main
$ python setup.py develop
```
## Supported Features
- [Offline batched inference](#offline-batched-inference)
- Online inference via [OpenAI-Compatible Server](#openai-compatible-server)
- HPU autodetection - no need to manually select device within vLLM
- Paged KV cache with algorithms enabled for Intel Gaudi accelerators
- Custom Intel Gaudi implementations of Paged Attention, KV cache ops,
prefill attention, Root Mean Square Layer Normalization, Rotary
Positional Encoding
- Tensor parallelism support for multi-card inference
- Inference with [HPU Graphs](https://docs.habana.ai/en/latest/PyTorch/Inference_on_PyTorch/Inference_Using_HPU_Graphs.html)
for accelerating low-batch latency and throughput
- Attention with Linear Biases (ALiBi)
## Unsupported Features
- Beam search
- LoRA adapters
- Quantization
- Prefill chunking (mixed-batch inferencing)
## Supported Configurations
The following configurations have been validated to be function with
Gaudi2 devices. Configurations that are not listed may or may not work.
- [meta-llama/Llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b)
on single HPU, or with tensor parallelism on 2x and 8x HPU, BF16
datatype with random or greedy sampling
- [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)
on single HPU, or with tensor parallelism on 2x and 8x HPU, BF16
datatype with random or greedy sampling
- [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B)
on single HPU, or with tensor parallelism on 2x and 8x HPU, BF16
datatype with random or greedy sampling
- [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
on single HPU, or with tensor parallelism on 2x and 8x HPU, BF16
datatype with random or greedy sampling
- [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B)
on single HPU, or with tensor parallelism on 2x and 8x HPU, BF16
datatype with random or greedy sampling
- [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct)
on single HPU, or with tensor parallelism on 2x and 8x HPU, BF16
datatype with random or greedy sampling
- [meta-llama/Llama-2-70b](https://huggingface.co/meta-llama/Llama-2-70b)
with tensor parallelism on 8x HPU, BF16 datatype with random or greedy sampling
- [meta-llama/Llama-2-70b-chat-hf](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf)
with tensor parallelism on 8x HPU, BF16 datatype with random or greedy sampling
- [meta-llama/Meta-Llama-3-70B](https://huggingface.co/meta-llama/Meta-Llama-3-70B)
with tensor parallelism on 8x HPU, BF16 datatype with random or greedy sampling
- [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct)
with tensor parallelism on 8x HPU, BF16 datatype with random or greedy sampling
- [meta-llama/Meta-Llama-3.1-70B](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B)
with tensor parallelism on 8x HPU, BF16 datatype with random or greedy sampling
- [meta-llama/Meta-Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct)
with tensor parallelism on 8x HPU, BF16 datatype with random or greedy sampling
## Performance Tuning
### Execution modes
Currently in vLLM for HPU we support four execution modes, depending on selected HPU PyTorch Bridge backend (via `PT_HPU_LAZY_MODE` environment variable), and `--enforce-eager` flag.
```{list-table} vLLM execution modes
:widths: 25 25 50
:header-rows: 1
* - `PT_HPU_LAZY_MODE`
- `enforce_eager`
- execution mode
* - 0
- 0
- torch.compile
* - 0
- 1
- PyTorch eager mode
* - 1
- 0
- HPU Graphs
* - 1
- 1
- PyTorch lazy mode
```
```{warning}
In 1.18.0, all modes utilizing `PT_HPU_LAZY_MODE=0` are highly experimental and should be only used for validating functional correctness. Their performance will be improved in the next releases. For obtaining the best performance in 1.18.0, please use HPU Graphs, or PyTorch lazy mode.
```
(gaudi-bucketing-mechanism)=
### Bucketing mechanism
Intel Gaudi accelerators work best when operating on models with fixed tensor shapes. [Intel Gaudi Graph Compiler](https://docs.habana.ai/en/latest/Gaudi_Overview/Intel_Gaudi_Software_Suite.html#graph-compiler-and-runtime) is responsible for generating optimized binary code that implements the given model topology on Gaudi. In its default configuration, the produced binary code may be heavily dependent on input and output tensor shapes, and can require graph recompilation when encountering differently shaped tensors within the same topology. While the resulting binaries utilize Gaudi efficiently, the compilation itself may introduce a noticeable overhead in end-to-end execution.
In a dynamic inference serving scenario, there is a need to minimize the number of graph compilations and reduce the risk of graph compilation occurring during server runtime. Currently it is achieved by "bucketing" model's forward pass across two dimensions - `batch_size` and `sequence_length`.
```{note}
Bucketing allows us to reduce the number of required graphs significantly, but it does not handle any graph compilation and device code generation - this is done in warmup and HPUGraph capture phase.
```
Bucketing ranges are determined with 3 parameters - `min`, `step` and `max`. They can be set separately for prompt and decode phase, and for batch size and sequence length dimension. These parameters can be observed in logs during vLLM startup:
```
INFO 08-01 21:37:59 hpu_model_runner.py:493] Prompt bucket config (min, step, max_warmup) bs:[1, 32, 4], seq:[128, 128, 1024]
INFO 08-01 21:37:59 hpu_model_runner.py:499] Generated 24 prompt buckets: [(1, 128), (1, 256), (1, 384), (1, 512), (1, 640), (1, 768), (1, 896), (1, 1024), (2, 128), (2, 256), (2, 384), (2, 512), (2, 640), (2, 768), (2, 896), (2, 1024), (4, 128), (4, 256), (4, 384), (4, 512), (4, 640), (4, 768), (4, 896), (4, 1024)]
INFO 08-01 21:37:59 hpu_model_runner.py:504] Decode bucket config (min, step, max_warmup) bs:[1, 128, 4], seq:[128, 128, 2048]
INFO 08-01 21:37:59 hpu_model_runner.py:509] Generated 48 decode buckets: [(1, 128), (1, 256), (1, 384), (1, 512), (1, 640), (1, 768), (1, 896), (1, 1024), (1, 1152), (1, 1280), (1, 1408), (1, 1536), (1, 1664), (1, 1792), (1, 1920), (1, 2048), (2, 128), (2, 256), (2, 384), (2, 512), (2, 640), (2, 768), (2, 896), (2, 1024), (2, 1152), (2, 1280), (2, 1408), (2, 1536), (2, 1664), (2, 1792), (2, 1920), (2, 2048), (4, 128), (4, 256), (4, 384), (4, 512), (4, 640), (4, 768), (4, 896), (4, 1024), (4, 1152), (4, 1280), (4, 1408), (4, 1536), (4, 1664), (4, 1792), (4, 1920), (4, 2048)]
```
`min` determines the lowest value of the bucket. `step` determines the interval between buckets, and `max` determines the upper bound of the bucket. Furthermore, interval between `min` and `step` has special handling -- `min` gets multiplied by consecutive powers of two, until `step` gets reached. We call this the ramp-up phase and it is used for handling lower batch sizes with minimum wastage, while allowing larger padding on larger batch sizes.
Example (with ramp-up)
```
min = 2, step = 32, max = 64
=> ramp_up = (2, 4, 8, 16)
=> stable = (32, 64)
=> buckets = ramp_up + stable => (2, 4, 8, 16, 32, 64)
```
Example (without ramp-up)
```
min = 128, step = 128, max = 512
=> ramp_up = ()
=> stable = (128, 256, 384, 512)
=> buckets = ramp_up + stable => (128, 256, 384, 512)
```
In the logged scenario, 24 buckets were generated for prompt (prefill) runs, and 48 buckets for decode runs. Each bucket corresponds to a separate optimized device binary for a given model with specified tensor shapes. Whenever a batch of requests is processed, it is padded across batch and sequence length dimension to the smallest possible bucket.
```{warning}
If a request exceeds maximum bucket size in any dimension, it will be processed without padding, and its processing may require a graph compilation, potentially significantly increasing end-to-end latency. The boundaries of the buckets are user-configurable via environment variables, and upper bucket boundaries can be increased to avoid such scenario.
```
As an example, if a request of 3 sequences, with max sequence length of 412 comes in to an idle vLLM server, it will be padded executed as `(4, 512)` prefill bucket, as `batch_size` (number of sequences) will be padded to 4 (closest batch_size dimension higher than 3), and max sequence length will be padded to 512 (closest sequence length dimension higher than 412). After prefill stage, it will be executed as `(4, 512)` decode bucket and will continue as that bucket until either batch dimension changes (due to request being finished) - in which case it will become a `(2, 512)` bucket, or context length increases above 512 tokens, in which case it will become `(4, 640)` bucket.
```{note}
Bucketing is transparent to a client -- padding in sequence length dimension is never returned to the client, and padding in batch dimension does not create new requests.
```
### Warmup
Warmup is an optional, but highly recommended step occurring before vLLM server starts listening. It executes a forward pass for each bucket with dummy data. The goal is to pre-compile all graphs and not incur any graph compilation overheads within bucket boundaries during server runtime. Each warmup step is logged during vLLM startup:
```
INFO 08-01 22:26:47 hpu_model_runner.py:1066] [Warmup][Prompt][1/24] batch_size:4 seq_len:1024 free_mem:79.16 GiB
INFO 08-01 22:26:47 hpu_model_runner.py:1066] [Warmup][Prompt][2/24] batch_size:4 seq_len:896 free_mem:55.43 GiB
INFO 08-01 22:26:48 hpu_model_runner.py:1066] [Warmup][Prompt][3/24] batch_size:4 seq_len:768 free_mem:55.43 GiB
...
INFO 08-01 22:26:59 hpu_model_runner.py:1066] [Warmup][Prompt][24/24] batch_size:1 seq_len:128 free_mem:55.43 GiB
INFO 08-01 22:27:00 hpu_model_runner.py:1066] [Warmup][Decode][1/48] batch_size:4 seq_len:2048 free_mem:55.43 GiB
INFO 08-01 22:27:00 hpu_model_runner.py:1066] [Warmup][Decode][2/48] batch_size:4 seq_len:1920 free_mem:55.43 GiB
INFO 08-01 22:27:01 hpu_model_runner.py:1066] [Warmup][Decode][3/48] batch_size:4 seq_len:1792 free_mem:55.43 GiB
...
INFO 08-01 22:27:16 hpu_model_runner.py:1066] [Warmup][Decode][47/48] batch_size:2 seq_len:128 free_mem:55.43 GiB
INFO 08-01 22:27:16 hpu_model_runner.py:1066] [Warmup][Decode][48/48] batch_size:1 seq_len:128 free_mem:55.43 GiB
```
This example uses the same buckets as in the [Bucketing Mechanism](#gaudi-bucketing-mechanism) section. Each output line corresponds to execution of a single bucket. When bucket is executed for the first time, its graph is compiled and can be reused later on, skipping further graph compilations.
```{tip}
Compiling all the buckets might take some time and can be turned off with `VLLM_SKIP_WARMUP=true` environment variable. Keep in mind that if you do that, you may face graph compilations once executing a given bucket for the first time. It is fine to disable warmup for development, but it's highly recommended to enable it in deployment.
```
### HPU Graph capture
[HPU Graphs](https://docs.habana.ai/en/latest/PyTorch/Inference_on_PyTorch/Inference_Using_HPU_Graphs.html) are currently the most performant execution method of vLLM on Intel Gaudi. When HPU Graphs are enabled, execution graphs will be traced (recorded) ahead of time (after performing warmup), to be later replayed during inference, significantly reducing host overheads. Recording can take large amounts of memory, which needs to be taken into account when allocating KV cache. Enabling HPU Graphs will impact the number of available KV cache blocks, but vLLM provides user-configurable variables to control memory management.
When HPU Graphs are being used, they share the common memory pool ("usable memory") as KV cache, determined by `gpu_memory_utilization` flag (`0.9` by default).
Before KV cache gets allocated, model weights are loaded onto the device, and a forward pass of the model is executed on dummy data, to estimate memory usage.
Only after that, `gpu_memory_utilization` flag is utilized - at its default value, will mark 90% of free device memory at that point as usable.
Next, KV cache gets allocated, model is warmed up, and HPU Graphs are captured.
Environment variable `VLLM_GRAPH_RESERVED_MEM` defines the ratio of memory reserved for HPU Graphs capture.
With its default value (`VLLM_GRAPH_RESERVED_MEM=0.1`), 10% of usable memory will be reserved for graph capture (later referred to as "usable graph memory"), and the remaining 90% will be utilized for KV cache.
Environment variable `VLLM_GRAPH_PROMPT_RATIO` determines the ratio of usable graph memory reserved for prefill and decode graphs. By default (`VLLM_GRAPH_PROMPT_RATIO=0.3`), both stages have equal memory constraints.
Lower value corresponds to less usable graph memory reserved for prefill stage, e.g. `VLLM_GRAPH_PROMPT_RATIO=0.2` will reserve 20% of usable graph memory for prefill graphs, and 80% of usable graph memory for decode graphs.
```{note}
`gpu_memory_utilization` does not correspond to the absolute memory usage across HPU. It specifies the memory margin after loading the model and performing a profile run. If device has 100 GiB of total memory, and 50 GiB of free memory after loading model weights and executing profiling run, `gpu_memory_utilization` at its default value will mark 90% of 50 GiB as usable, leaving 5 GiB of margin, regardless of total device memory.
```
User can also configure the strategy for capturing HPU Graphs for prompt and decode stages separately. Strategy affects the order of capturing graphs. There are two strategies implemented:
\- `max_bs` - graph capture queue will sorted in descending order by their batch sizes. Buckets with equal batch sizes are sorted by sequence length in ascending order (e.g. `(64, 128)`, `(64, 256)`, `(32, 128)`, `(32, 256)`, `(1, 128)`, `(1,256)`), default strategy for decode
\- `min_tokens` - graph capture queue will be sorted in ascending order by the number of tokens each graph processes (`batch_size*sequence_length`), default strategy for prompt
When there's large amount of requests pending, vLLM scheduler will attempt to fill the maximum batch size for decode as soon as possible. When a request is finished, decode batch size decreases. When that happens, vLLM will attempt to schedule a prefill iteration for requests in the waiting queue, to fill the decode batch size to its previous state. This means that in a full load scenario, decode batch size is often at its maximum, which makes large batch size HPU Graphs crucial to capture, as reflected by `max_bs` strategy. On the other hand, prefills will be executed most frequently with very low batch sizes (1-4), which is reflected in `min_tokens` strategy.
```{note}
`VLLM_GRAPH_PROMPT_RATIO` does not set a hard limit on memory taken by graphs for each stage (prefill and decode). vLLM will first attempt to use up entirety of usable prefill graph memory (usable graph memory * `VLLM_GRAPH_PROMPT_RATIO`) for capturing prefill HPU Graphs, next it will attempt do the same for decode graphs and usable decode graph memory pool. If one stage is fully captured, and there is unused memory left within usable graph memory pool, vLLM will attempt further graph capture for the other stage, until no more HPU Graphs can be captured without exceeding reserved memory pool. The behavior on that mechanism can be observed in the example below.
```
Each described step is logged by vLLM server, as follows (negative values correspond to memory being released):
```
INFO 08-02 17:37:44 hpu_model_runner.py:493] Prompt bucket config (min, step, max_warmup) bs:[1, 32, 4], seq:[128, 128, 1024]
INFO 08-02 17:37:44 hpu_model_runner.py:499] Generated 24 prompt buckets: [(1, 128), (1, 256), (1, 384), (1, 512), (1, 640), (1, 768), (1, 896), (1, 1024), (2, 128), (2, 256), (2, 384), (2, 512), (2, 640), (2, 768), (2, 896), (2, 1024), (4, 128), (4, 256), (4, 384), (4, 512), (4, 640), (4, 768), (4, 896), (4, 1024)]
INFO 08-02 17:37:44 hpu_model_runner.py:504] Decode bucket config (min, step, max_warmup) bs:[1, 128, 4], seq:[128, 128, 2048]
INFO 08-02 17:37:44 hpu_model_runner.py:509] Generated 48 decode buckets: [(1, 128), (1, 256), (1, 384), (1, 512), (1, 640), (1, 768), (1, 896), (1, 1024), (1, 1152), (1, 1280), (1, 1408), (1, 1536), (1, 1664), (1, 1792), (1, 1920), (1, 2048), (2, 128), (2, 256), (2, 384), (2, 512), (2, 640), (2, 768), (2, 896), (2, 1024), (2, 1152), (2, 1280), (2, 1408), (2, 1536), (2, 1664), (2, 1792), (2, 1920), (2, 2048), (4, 128), (4, 256), (4, 384), (4, 512), (4, 640), (4, 768), (4, 896), (4, 1024), (4, 1152), (4, 1280), (4, 1408), (4, 1536), (4, 1664), (4, 1792), (4, 1920), (4, 2048)]
INFO 08-02 17:37:52 hpu_model_runner.py:430] Pre-loading model weights on hpu:0 took 14.97 GiB of device memory (14.97 GiB/94.62 GiB used) and 2.95 GiB of host memory (475.2 GiB/1007 GiB used)
INFO 08-02 17:37:52 hpu_model_runner.py:438] Wrapping in HPU Graph took 0 B of device memory (14.97 GiB/94.62 GiB used) and -252 KiB of host memory (475.2 GiB/1007 GiB used)
INFO 08-02 17:37:52 hpu_model_runner.py:442] Loading model weights took in total 14.97 GiB of device memory (14.97 GiB/94.62 GiB used) and 2.95 GiB of host memory (475.2 GiB/1007 GiB used)
INFO 08-02 17:37:54 hpu_worker.py:134] Model profiling run took 504 MiB of device memory (15.46 GiB/94.62 GiB used) and 180.9 MiB of host memory (475.4 GiB/1007 GiB used)
INFO 08-02 17:37:54 hpu_worker.py:158] Free device memory: 79.16 GiB, 39.58 GiB usable (gpu_memory_utilization=0.5), 15.83 GiB reserved for HPUGraphs (VLLM_GRAPH_RESERVED_MEM=0.4), 23.75 GiB reserved for KV cache
INFO 08-02 17:37:54 hpu_executor.py:85] # HPU blocks: 1519, # CPU blocks: 0
INFO 08-02 17:37:54 hpu_worker.py:190] Initializing cache engine took 23.73 GiB of device memory (39.2 GiB/94.62 GiB used) and -1.238 MiB of host memory (475.4 GiB/1007 GiB used)
INFO 08-02 17:37:54 hpu_model_runner.py:1066] [Warmup][Prompt][1/24] batch_size:4 seq_len:1024 free_mem:55.43 GiB
...
INFO 08-02 17:38:22 hpu_model_runner.py:1066] [Warmup][Decode][48/48] batch_size:1 seq_len:128 free_mem:55.43 GiB
INFO 08-02 17:38:22 hpu_model_runner.py:1159] Using 15.85 GiB/55.43 GiB of free device memory for HPUGraphs, 7.923 GiB for prompt and 7.923 GiB for decode (VLLM_GRAPH_PROMPT_RATIO=0.3)
INFO 08-02 17:38:22 hpu_model_runner.py:1066] [Warmup][Graph/Prompt][1/24] batch_size:1 seq_len:128 free_mem:55.43 GiB
...
INFO 08-02 17:38:26 hpu_model_runner.py:1066] [Warmup][Graph/Prompt][11/24] batch_size:1 seq_len:896 free_mem:48.77 GiB
INFO 08-02 17:38:27 hpu_model_runner.py:1066] [Warmup][Graph/Decode][1/48] batch_size:4 seq_len:128 free_mem:47.51 GiB
...
INFO 08-02 17:38:41 hpu_model_runner.py:1066] [Warmup][Graph/Decode][48/48] batch_size:1 seq_len:2048 free_mem:47.35 GiB
INFO 08-02 17:38:41 hpu_model_runner.py:1066] [Warmup][Graph/Prompt][12/24] batch_size:4 seq_len:256 free_mem:47.35 GiB
INFO 08-02 17:38:42 hpu_model_runner.py:1066] [Warmup][Graph/Prompt][13/24] batch_size:2 seq_len:512 free_mem:45.91 GiB
INFO 08-02 17:38:42 hpu_model_runner.py:1066] [Warmup][Graph/Prompt][14/24] batch_size:1 seq_len:1024 free_mem:44.48 GiB
INFO 08-02 17:38:43 hpu_model_runner.py:1066] [Warmup][Graph/Prompt][15/24] batch_size:2 seq_len:640 free_mem:43.03 GiB
INFO 08-02 17:38:43 hpu_model_runner.py:1128] Graph/Prompt captured:15 (62.5%) used_mem:14.03 GiB buckets:[(1, 128), (1, 256), (1, 384), (1, 512), (1, 640), (1, 768), (1, 896), (1, 1024), (2, 128), (2, 256), (2, 384), (2, 512), (2, 640), (4, 128), (4, 256)]
INFO 08-02 17:38:43 hpu_model_runner.py:1128] Graph/Decode captured:48 (100.0%) used_mem:161.9 MiB buckets:[(1, 128), (1, 256), (1, 384), (1, 512), (1, 640), (1, 768), (1, 896), (1, 1024), (1, 1152), (1, 1280), (1, 1408), (1, 1536), (1, 1664), (1, 1792), (1, 1920), (1, 2048), (2, 128), (2, 256), (2, 384), (2, 512), (2, 640), (2, 768), (2, 896), (2, 1024), (2, 1152), (2, 1280), (2, 1408), (2, 1536), (2, 1664), (2, 1792), (2, 1920), (2, 2048), (4, 128), (4, 256), (4, 384), (4, 512), (4, 640), (4, 768), (4, 896), (4, 1024), (4, 1152), (4, 1280), (4, 1408), (4, 1536), (4, 1664), (4, 1792), (4, 1920), (4, 2048)]
INFO 08-02 17:38:43 hpu_model_runner.py:1206] Warmup finished in 49 secs, allocated 14.19 GiB of device memory
INFO 08-02 17:38:43 hpu_executor.py:91] init_cache_engine took 37.92 GiB of device memory (53.39 GiB/94.62 GiB used) and 57.86 MiB of host memory (475.4 GiB/1007 GiB used)
```
### Recommended vLLM Parameters
- We recommend running inference on Gaudi 2 with `block_size` of 128
for BF16 data type. Using default values (16, 32) might lead to
sub-optimal performance due to Matrix Multiplication Engine
under-utilization (see [Gaudi
Architecture](https://docs.habana.ai/en/latest/Gaudi_Overview/Gaudi_Architecture.html)).
- For max throughput on Llama 7B, we recommend running with batch size
of 128 or 256 and max context length of 2048 with HPU Graphs enabled.
If you encounter out-of-memory issues, see troubleshooting section.
### Environment variables
**Diagnostic and profiling knobs:**
- `VLLM_PROFILER_ENABLED`: if `true`, high level profiler will be enabled. Resulting JSON traces can be viewed in [perfetto.habana.ai](https://perfetto.habana.ai/#!/viewer). Disabled by default.
- `VLLM_HPU_LOG_STEP_GRAPH_COMPILATION`: if `true`, will log graph compilations per each vLLM engine step, only when there was any - highly recommended to use alongside `PT_HPU_METRICS_GC_DETAILS=1`. Disabled by default.
- `VLLM_HPU_LOG_STEP_GRAPH_COMPILATION_ALL`: if `true`, will log graph compilations per each vLLM engine step, always, even if there were none. Disabled by default.
- `VLLM_HPU_LOG_STEP_CPU_FALLBACKS`: if `true`, will log cpu fallbacks per each vLLM engine step, only when there was any. Disabled by default.
- `VLLM_HPU_LOG_STEP_CPU_FALLBACKS_ALL`: if `true`, will log cpu fallbacks per each vLLM engine step, always, even if there were none. Disabled by default.
**Performance tuning knobs:**
- `VLLM_SKIP_WARMUP`: if `true`, warmup will be skipped, `false` by default
- `VLLM_GRAPH_RESERVED_MEM`: percentage of memory dedicated for HPUGraph capture, `0.1` by default
- `VLLM_GRAPH_PROMPT_RATIO`: percentage of reserved graph memory dedicated for prompt graphs, `0.3` by default
- `VLLM_GRAPH_PROMPT_STRATEGY`: strategy determining order of prompt graph capture, `min_tokens` or `max_bs`, `min_tokens` by default
- `VLLM_GRAPH_DECODE_STRATEGY`: strategy determining order of decode graph capture, `min_tokens` or `max_bs`, `max_bs` by default
- `VLLM_{phase}_{dim}_BUCKET_{param}` - collection of 12 environment variables configuring ranges of bucketing mechanism
- `{phase}` is either `PROMPT` or `DECODE`
- `{dim}` is either `BS`, `SEQ` or `BLOCK`
- `{param}` is either `MIN`, `STEP` or `MAX`
- Default values:
- Prompt:
: - batch size min (`VLLM_PROMPT_BS_BUCKET_MIN`): `1`
- batch size step (`VLLM_PROMPT_BS_BUCKET_STEP`): `min(max_num_seqs, 32)`
- batch size max (`VLLM_PROMPT_BS_BUCKET_MAX`): `min(max_num_seqs, 64)`
- sequence length min (`VLLM_PROMPT_SEQ_BUCKET_MIN`): `block_size`
- sequence length step (`VLLM_PROMPT_SEQ_BUCKET_STEP`): `block_size`
- sequence length max (`VLLM_PROMPT_SEQ_BUCKET_MAX`): `max_model_len`
- Decode:
: - batch size min (`VLLM_DECODE_BS_BUCKET_MIN`): `1`
- batch size step (`VLLM_DECODE_BS_BUCKET_STEP`): `min(max_num_seqs, 32)`
- batch size max (`VLLM_DECODE_BS_BUCKET_MAX`): `max_num_seqs`
- sequence length min (`VLLM_DECODE_BLOCK_BUCKET_MIN`): `block_size`
- sequence length step (`VLLM_DECODE_BLOCK_BUCKET_STEP`): `block_size`
- sequence length max (`VLLM_DECODE_BLOCK_BUCKET_MAX`): `max(128, (max_num_seqs*max_model_len)/block_size)`
Additionally, there are HPU PyTorch Bridge environment variables impacting vLLM execution:
- `PT_HPU_LAZY_MODE`: if `0`, PyTorch Eager backend for Gaudi will be used, if `1` PyTorch Lazy backend for Gaudi will be used, `1` is default
- `PT_HPU_ENABLE_LAZY_COLLECTIVES`: required to be `true` for tensor parallel inference with HPU Graphs
## Troubleshooting: Tweaking HPU Graphs
If you experience device out-of-memory issues or want to attempt
inference at higher batch sizes, try tweaking HPU Graphs by following
the below:
- Tweak `gpu_memory_utilization` knob. It will decrease the
allocation of KV cache, leaving some headroom for capturing graphs
with larger batch size. By default `gpu_memory_utilization` is set
to 0.9. It attempts to allocate ~90% of HBM left for KV cache after
short profiling run. Note that decreasing reduces the number of KV
cache blocks you have available, and therefore reduces the effective
maximum number of tokens you can handle at a given time.
- If this method is not efficient, you can disable `HPUGraph`
completely. With HPU Graphs disabled, you are trading latency and
throughput at lower batches for potentially higher throughput on
higher batches. You can do that by adding `--enforce-eager` flag to
server (for online inference), or by passing `enforce_eager=True`
argument to LLM constructor (for offline inference).

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(installation-index)=
# Installation
vLLM supports the following hardware platforms:
```{toctree}
:maxdepth: 1
gpu-cuda
gpu-rocm
cpu-x86
cpu-arm
hpu-gaudi
tpu
xpu
openvino
neuron
```

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(installation-neuron)=
# Installation for Neuron
vLLM 0.3.3 onwards supports model inferencing and serving on AWS Trainium/Inferentia with Neuron SDK with continuous batching.
Paged Attention and Chunked Prefill are currently in development and will be available soon.
Data types currently supported in Neuron SDK are FP16 and BF16.
## Requirements
- OS: Linux
- Python: 3.9 -- 3.11
- Accelerator: NeuronCore_v2 (in trn1/inf2 instances)
- Pytorch 2.0.1/2.1.1
- AWS Neuron SDK 2.16/2.17 (Verified on python 3.8)
Installation steps:
- [Build from source](#build-from-source-neuron)
- [Step 0. Launch Trn1/Inf2 instances](#launch-instances)
- [Step 1. Install drivers and tools](#install-drivers)
- [Step 2. Install transformers-neuronx and its dependencies](#install-tnx)
- [Step 3. Install vLLM from source](#install-vllm)
(build-from-source-neuron)=
```{note}
The currently supported version of Pytorch for Neuron installs `triton` version `2.1.0`. This is incompatible with `vllm >= 0.5.3`. You may see an error `cannot import name 'default_dump_dir...`. To work around this, run a `pip install --upgrade triton==3.0.0` after installing the vLLM wheel.
```
## Build from source
Following instructions are applicable to Neuron SDK 2.16 and beyond.
(launch-instances)=
### Step 0. Launch Trn1/Inf2 instances
Here are the steps to launch trn1/inf2 instances, in order to install [PyTorch Neuron ("torch-neuronx") Setup on Ubuntu 22.04 LTS](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/general/setup/neuron-setup/pytorch/neuronx/ubuntu/torch-neuronx-ubuntu22.html).
- Please follow the instructions at [launch an Amazon EC2 Instance](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/EC2_GetStarted.html#ec2-launch-instance) to launch an instance. When choosing the instance type at the EC2 console, please make sure to select the correct instance type.
- To get more information about instances sizes and pricing see: [Trn1 web page](https://aws.amazon.com/ec2/instance-types/trn1/), [Inf2 web page](https://aws.amazon.com/ec2/instance-types/inf2/)
- Select Ubuntu Server 22.04 TLS AMI
- When launching a Trn1/Inf2, please adjust your primary EBS volume size to a minimum of 512GB.
- After launching the instance, follow the instructions in [Connect to your instance](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/AccessingInstancesLinux.html) to connect to the instance
(install-drivers)=
### Step 1. Install drivers and tools
The installation of drivers and tools wouldn't be necessary, if [Deep Learning AMI Neuron](https://docs.aws.amazon.com/dlami/latest/devguide/appendix-ami-release-notes.html) is installed. In case the drivers and tools are not installed on the operating system, follow the steps below:
```console
# Configure Linux for Neuron repository updates
. /etc/os-release
sudo tee /etc/apt/sources.list.d/neuron.list > /dev/null <<EOF
deb https://apt.repos.neuron.amazonaws.com ${VERSION_CODENAME} main
EOF
wget -qO - https://apt.repos.neuron.amazonaws.com/GPG-PUB-KEY-AMAZON-AWS-NEURON.PUB | sudo apt-key add -
# Update OS packages
sudo apt-get update -y
# Install OS headers
sudo apt-get install linux-headers-$(uname -r) -y
# Install git
sudo apt-get install git -y
# install Neuron Driver
sudo apt-get install aws-neuronx-dkms=2.* -y
# Install Neuron Runtime
sudo apt-get install aws-neuronx-collectives=2.* -y
sudo apt-get install aws-neuronx-runtime-lib=2.* -y
# Install Neuron Tools
sudo apt-get install aws-neuronx-tools=2.* -y
# Add PATH
export PATH=/opt/aws/neuron/bin:$PATH
```
(install-tnx)=
### Step 2. Install transformers-neuronx and its dependencies
[transformers-neuronx](https://github.com/aws-neuron/transformers-neuronx) will be the backend to support inference on trn1/inf2 instances.
Follow the steps below to install transformer-neuronx package and its dependencies.
```console
# Install Python venv
sudo apt-get install -y python3.10-venv g++
# Create Python venv
python3.10 -m venv aws_neuron_venv_pytorch
# Activate Python venv
source aws_neuron_venv_pytorch/bin/activate
# Install Jupyter notebook kernel
pip install ipykernel
python3.10 -m ipykernel install --user --name aws_neuron_venv_pytorch --display-name "Python (torch-neuronx)"
pip install jupyter notebook
pip install environment_kernels
# Set pip repository pointing to the Neuron repository
python -m pip config set global.extra-index-url https://pip.repos.neuron.amazonaws.com
# Install wget, awscli
python -m pip install wget
python -m pip install awscli
# Update Neuron Compiler and Framework
python -m pip install --upgrade neuronx-cc==2.* --pre torch-neuronx==2.1.* torchvision transformers-neuronx
```
(install-vllm)=
### Step 3. Install vLLM from source
Once neuronx-cc and transformers-neuronx packages are installed, we will be able to install vllm as follows:
```console
$ git clone https://github.com/vllm-project/vllm.git
$ cd vllm
$ pip install -U -r requirements-neuron.txt
$ VLLM_TARGET_DEVICE="neuron" pip install .
```
If neuron packages are detected correctly in the installation process, `vllm-0.3.0+neuron212` will be installed.

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(installation-openvino)=
# Installation for OpenVINO
vLLM powered by OpenVINO supports all LLM models from [vLLM supported models list](#supported-models) and can perform optimal model serving on all x86-64 CPUs with, at least, AVX2 support, as well as on both integrated and discrete Intel® GPUs ([the list of supported GPUs](https://docs.openvino.ai/2024/about-openvino/release-notes-openvino/system-requirements.html#gpu)). OpenVINO vLLM backend supports the following advanced vLLM features:
- Prefix caching (`--enable-prefix-caching`)
- Chunked prefill (`--enable-chunked-prefill`)
**Table of contents**:
- [Requirements](#openvino-backend-requirements)
- [Quick start using Dockerfile](#openvino-backend-quick-start-dockerfile)
- [Build from source](#install-openvino-backend-from-source)
- [Performance tips](#openvino-backend-performance-tips)
- [Limitations](#openvino-backend-limitations)
(openvino-backend-requirements)=
## Requirements
- OS: Linux
- Instruction set architecture (ISA) requirement: at least AVX2.
(openvino-backend-quick-start-dockerfile)=
## Quick start using Dockerfile
```console
$ docker build -f Dockerfile.openvino -t vllm-openvino-env .
$ docker run -it --rm vllm-openvino-env
```
(install-openvino-backend-from-source)=
## Install from source
- First, install Python. For example, on Ubuntu 22.04, you can run:
```console
$ sudo apt-get update -y
$ sudo apt-get install python3
```
- Second, install prerequisites vLLM OpenVINO backend installation:
```console
$ pip install --upgrade pip
$ pip install -r requirements-build.txt --extra-index-url https://download.pytorch.org/whl/cpu
```
- Finally, install vLLM with OpenVINO backend:
```console
$ PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu" VLLM_TARGET_DEVICE=openvino python -m pip install -v .
```
- [Optional] To use vLLM OpenVINO backend with a GPU device, ensure your system is properly set up. Follow the instructions provided here: [https://docs.openvino.ai/2024/get-started/configurations/configurations-intel-gpu.html](https://docs.openvino.ai/2024/get-started/configurations/configurations-intel-gpu.html).
(openvino-backend-performance-tips)=
## Performance tips
### vLLM OpenVINO backend environment variables
- `VLLM_OPENVINO_DEVICE` to specify which device utilize for the inference. If there are multiple GPUs in the system, additional indexes can be used to choose the proper one (e.g, `VLLM_OPENVINO_DEVICE=GPU.1`). If the value is not specified, CPU device is used by default.
- `VLLM_OPENVINO_ENABLE_QUANTIZED_WEIGHTS=ON` to enable U8 weights compression during model loading stage. By default, compression is turned off. You can also export model with different compression techniques using `optimum-cli` and pass exported folder as `<model_id>`
### CPU performance tips
CPU uses the following environment variables to control behavior:
- `VLLM_OPENVINO_KVCACHE_SPACE` to specify the KV Cache size (e.g, `VLLM_OPENVINO_KVCACHE_SPACE=40` means 40 GB space for KV cache), larger setting will allow vLLM running more requests in parallel. This parameter should be set based on the hardware configuration and memory management pattern of users.
- `VLLM_OPENVINO_CPU_KV_CACHE_PRECISION=u8` to control KV cache precision. By default, FP16 / BF16 is used depending on platform.
To enable better TPOT / TTFT latency, you can use vLLM's chunked prefill feature (`--enable-chunked-prefill`). Based on the experiments, the recommended batch size is `256` (`--max-num-batched-tokens`)
OpenVINO best known configuration for CPU is:
```console
$ VLLM_OPENVINO_KVCACHE_SPACE=100 VLLM_OPENVINO_CPU_KV_CACHE_PRECISION=u8 VLLM_OPENVINO_ENABLE_QUANTIZED_WEIGHTS=ON \
python3 vllm/benchmarks/benchmark_throughput.py --model meta-llama/Llama-2-7b-chat-hf --dataset vllm/benchmarks/ShareGPT_V3_unfiltered_cleaned_split.json --enable-chunked-prefill --max-num-batched-tokens 256
```
### GPU performance tips
GPU device implements the logic for automatic detection of available GPU memory and, by default, tries to reserve as much memory as possible for the KV cache (taking into account `gpu_memory_utilization` option). However, this behavior can be overridden by explicitly specifying the desired amount of memory for the KV cache using `VLLM_OPENVINO_KVCACHE_SPACE` environment variable (e.g, `VLLM_OPENVINO_KVCACHE_SPACE=8` means 8 GB space for KV cache).
Currently, the best performance using GPU can be achieved with the default vLLM execution parameters for models with quantized weights (8 and 4-bit integer data types are supported) and `preemption-mode=swap`.
OpenVINO best known configuration for GPU is:
```console
$ VLLM_OPENVINO_DEVICE=GPU VLLM_OPENVINO_ENABLE_QUANTIZED_WEIGHTS=ON \
python3 vllm/benchmarks/benchmark_throughput.py --model meta-llama/Llama-2-7b-chat-hf --dataset vllm/benchmarks/ShareGPT_V3_unfiltered_cleaned_split.json
```
(openvino-backend-limitations)=
## Limitations
- LoRA serving is not supported.
- Only LLM models are currently supported. LLaVa and encoder-decoder models are not currently enabled in vLLM OpenVINO integration.
- Tensor and pipeline parallelism are not currently enabled in vLLM integration.

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(installation-tpu)=
# Installation for TPUs
Tensor Processing Units (TPUs) are Google's custom-developed application-specific
integrated circuits (ASICs) used to accelerate machine learning workloads. TPUs
are available in different versions each with different hardware specifications.
For more information about TPUs, see [TPU System Architecture](https://cloud.google.com/tpu/docs/system-architecture-tpu-vm).
For more information on the TPU versions supported with vLLM, see:
- [TPU v6e](https://cloud.google.com/tpu/docs/v6e)
- [TPU v5e](https://cloud.google.com/tpu/docs/v5e)
- [TPU v5p](https://cloud.google.com/tpu/docs/v5p)
- [TPU v4](https://cloud.google.com/tpu/docs/v4)
These TPU versions allow you to configure the physical arrangements of the TPU
chips. This can improve throughput and networking performance. For more
information see:
- [TPU v6e topologies](https://cloud.google.com/tpu/docs/v6e#configurations)
- [TPU v5e topologies](https://cloud.google.com/tpu/docs/v5e#tpu-v5e-config)
- [TPU v5p topologies](https://cloud.google.com/tpu/docs/v5p#tpu-v5p-config)
- [TPU v4 topologies](https://cloud.google.com/tpu/docs/v4#tpu-v4-config)
In order for you to use Cloud TPUs you need to have TPU quota granted to your
Google Cloud Platform project. TPU quotas specify how many TPUs you can use in a
GPC project and are specified in terms of TPU version, the number of TPU you
want to use, and quota type. For more information, see [TPU quota](https://cloud.google.com/tpu/docs/quota#tpu_quota).
For TPU pricing information, see [Cloud TPU pricing](https://cloud.google.com/tpu/pricing).
You may need additional persistent storage for your TPU VMs. For more
information, see [Storage options for Cloud TPU data](https://cloud.devsite.corp.google.com/tpu/docs/storage-options).
## Requirements
- Google Cloud TPU VM
- TPU versions: v6e, v5e, v5p, v4
- Python: 3.10 or newer
### Provision Cloud TPUs
You can provision Cloud TPUs using the [Cloud TPU API](https://cloud.google.com/tpu/docs/reference/rest)
or the [queued resources](https://cloud.google.com/tpu/docs/queued-resources)
API. This section shows how to create TPUs using the queued resource API. For
more information about using the Cloud TPU API, see [Create a Cloud TPU using the Create Node API](https://cloud.google.com/tpu/docs/managing-tpus-tpu-vm#create-node-api).
Queued resources enable you to request Cloud TPU resources in a queued manner.
When you request queued resources, the request is added to a queue maintained by
the Cloud TPU service. When the requested resource becomes available, it's
assigned to your Google Cloud project for your immediate exclusive use.
```{note}
In all of the following commands, replace the ALL CAPS parameter names with
appropriate values. See the parameter descriptions table for more information.
```
## Provision a Cloud TPU with the queued resource API
Create a TPU v5e with 4 TPU chips:
```console
gcloud alpha compute tpus queued-resources create QUEUED_RESOURCE_ID \
--node-id TPU_NAME \
--project PROJECT_ID \
--zone ZONE \
--accelerator-type ACCELERATOR_TYPE \
--runtime-version RUNTIME_VERSION \
--service-account SERVICE_ACCOUNT
```
```{list-table} Parameter descriptions
:header-rows: 1
* - Parameter name
- Description
* - QUEUED_RESOURCE_ID
- The user-assigned ID of the queued resource request.
* - TPU_NAME
- The user-assigned name of the TPU which is created when the queued
resource request is allocated.
* - PROJECT_ID
- Your Google Cloud project
* - ZONE
- The GCP zone where you want to create your Cloud TPU. The value you use
depends on the version of TPUs you are using. For more information, see
`TPU regions and zones <https://cloud.google.com/tpu/docs/regions-zones>`_
* - ACCELERATOR_TYPE
- The TPU version you want to use. Specify the TPU version, for example
`v5litepod-4` specifies a v5e TPU with 4 cores. For more information,
see `TPU versions <https://cloud.devsite.corp.google.com/tpu/docs/system-architecture-tpu-vm#versions>`_.
* - RUNTIME_VERSION
- The TPU VM runtime version to use. For more information see `TPU VM images <https://cloud.google.com/tpu/docs/runtimes>`_.
* - SERVICE_ACCOUNT
- The email address for your service account. You can find it in the IAM
Cloud Console under *Service Accounts*. For example:
`tpu-service-account@<your_project_ID>.iam.gserviceaccount.com`
```
Connect to your TPU using SSH:
```bash
gcloud compute tpus tpu-vm ssh TPU_NAME --zone ZONE
```
Install Miniconda:
```bash
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh
source ~/.bashrc
```
Create and activate a Conda environment for vLLM:
```bash
conda create -n vllm python=3.10 -y
conda activate vllm
```
Clone the vLLM repository and go to the vLLM directory:
```bash
git clone https://github.com/vllm-project/vllm.git && cd vllm
```
Uninstall the existing `torch` and `torch_xla` packages:
```bash
pip uninstall torch torch-xla -y
```
Install build dependencies:
```bash
pip install -r requirements-tpu.txt
sudo apt-get install libopenblas-base libopenmpi-dev libomp-dev
```
Run the setup script:
```bash
VLLM_TARGET_DEVICE="tpu" python setup.py develop
```
## Provision Cloud TPUs with GKE
For more information about using TPUs with GKE, see
<https://cloud.google.com/kubernetes-engine/docs/how-to/tpus>
<https://cloud.google.com/kubernetes-engine/docs/concepts/tpus>
<https://cloud.google.com/kubernetes-engine/docs/concepts/plan-tpus>
(build-docker-tpu)=
## Build a docker image with {code}`Dockerfile.tpu`
You can use <gh-file:Dockerfile.tpu> to build a Docker image with TPU support.
```console
$ docker build -f Dockerfile.tpu -t vllm-tpu .
```
Run the Docker image with the following command:
```console
$ # Make sure to add `--privileged --net host --shm-size=16G`.
$ docker run --privileged --net host --shm-size=16G -it vllm-tpu
```
```{note}
Since TPU relies on XLA which requires static shapes, vLLM bucketizes the
possible input shapes and compiles an XLA graph for each shape. The
compilation time may take 20~30 minutes in the first run. However, the
compilation time reduces to ~5 minutes afterwards because the XLA graphs are
cached in the disk (in {code}`VLLM_XLA_CACHE_PATH` or {code}`~/.cache/vllm/xla_cache` by default).
```
````{tip}
If you encounter the following error:
```console
from torch._C import * # noqa: F403
ImportError: libopenblas.so.0: cannot open shared object file: No such
file or directory
```
Install OpenBLAS with the following command:
```console
$ sudo apt-get install libopenblas-base libopenmpi-dev libomp-dev
```
````

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(installation-xpu)=
# Installation for XPUs
vLLM initially supports basic model inferencing and serving on Intel GPU platform.
Table of contents:
1. [Requirements](#xpu-backend-requirements)
2. [Quick start using Dockerfile](#xpu-backend-quick-start-dockerfile)
3. [Build from source](#build-xpu-backend-from-source)
(xpu-backend-requirements)=
## Requirements
- OS: Linux
- Supported Hardware: Intel Data Center GPU, Intel ARC GPU
- OneAPI requirements: oneAPI 2024.2
(xpu-backend-quick-start-dockerfile)=
## Quick start using Dockerfile
```console
$ docker build -f Dockerfile.xpu -t vllm-xpu-env --shm-size=4g .
$ docker run -it \
--rm \
--network=host \
--device /dev/dri \
-v /dev/dri/by-path:/dev/dri/by-path \
vllm-xpu-env
```
(build-xpu-backend-from-source)=
## Build from source
- First, install required driver and intel OneAPI 2024.2 or later.
- Second, install Python packages for vLLM XPU backend building:
```console
$ source /opt/intel/oneapi/setvars.sh
$ pip install --upgrade pip
$ pip install -v -r requirements-xpu.txt
```
- Finally, build and install vLLM XPU backend:
```console
$ VLLM_TARGET_DEVICE=xpu python setup.py install
```
```{note}
- FP16 is the default data type in the current XPU backend. The BF16 data
type will be supported in the future.
```
## Distributed inference and serving
XPU platform supports tensor-parallel inference/serving and also supports pipeline parallel as a beta feature for online serving. We requires Ray as the distributed runtime backend. For example, a reference execution likes following:
```console
$ python -m vllm.entrypoints.openai.api_server \
$ --model=facebook/opt-13b \
$ --dtype=bfloat16 \
$ --device=xpu \
$ --max_model_len=1024 \
$ --distributed-executor-backend=ray \
$ --pipeline-parallel-size=2 \
$ -tp=8
```
By default, a ray instance will be launched automatically if no existing one is detected in system, with `num-gpus` equals to `parallel_config.world_size`. We recommend properly starting a ray cluster before execution, referring to the <gh-file:examples/run_cluster.sh> helper script.