This document teaches you how to add a new modality to vLLM.
Each modality in vLLM is represented by a {class}`~vllm.multimodal.MultiModalPlugin` and registered to {data}`~vllm.multimodal.MULTIMODAL_REGISTRY`.
For vLLM to recognize a new modality type, you have to create a new plugin and then pass it to {meth}`~vllm.multimodal.MultiModalRegistry.register_plugin`.
The remainder of this document details how to define custom {class}`~vllm.multimodal.MultiModalPlugin` s.
```{note}
This article is a work in progress.
```
% TODO: Add more instructions on how to add new plugins once embeddings is in.
This document teaches you how to add a new modality to vLLM.
Each modality in vLLM is represented by a :class:`~vllm.multimodal.MultiModalPlugin` and registered to :data:`~vllm.multimodal.MULTIMODAL_REGISTRY`.
For vLLM to recognize a new modality type, you have to create a new plugin and then pass it to :meth:`~vllm.multimodal.MultiModalRegistry.register_plugin`.
The remainder of this document details how to define custom :class:`~vllm.multimodal.MultiModalPlugin` s.
.. note::
This article is a work in progress.
..
TODO: Add more instructions on how to add new plugins once embeddings is in.
The community frequently requests the ability to extend vLLM with custom features. To facilitate this, vLLM includes a plugin system that allows users to add custom features without modifying the vLLM codebase. This document explains how plugins work in vLLM and how to create a plugin for vLLM.
## How Plugins Work in vLLM
Plugins are user-registered code that vLLM executes. Given vLLM's architecture (see [](#arch-overview)), multiple processes may be involved, especially when using distributed inference with various parallelism techniques. To enable plugins successfully, every process created by vLLM needs to load the plugin. This is done by the [load_general_plugins](https://github.com/vllm-project/vllm/blob/c76ac49d266e27aa3fea84ef2df1f813d24c91c7/vllm/plugins/__init__.py#L16) function in the `vllm.plugins` module. This function is called for every process created by vLLM before it starts any work.
## How vLLM Discovers Plugins
vLLM's plugin system uses the standard Python `entry_points` mechanism. This mechanism allows developers to register functions in their Python packages for use by other packages. An example of a plugin:
For more information on adding entry points to your package, please check the [official documentation](https://setuptools.pypa.io/en/latest/userguide/entry_point.html).
Every plugin has three parts:
1.**Plugin group**: The name of the entry point group. vLLM uses the entry point group `vllm.general_plugins` to register general plugins. This is the key of `entry_points` in the `setup.py` file. Always use `vllm.general_plugins` for vLLM's general plugins.
2.**Plugin name**: The name of the plugin. This is the value in the dictionary of the `entry_points` dictionary. In the example above, the plugin name is `register_dummy_model`. Plugins can be filtered by their names using the `VLLM_PLUGINS` environment variable. To load only a specific plugin, set `VLLM_PLUGINS` to the plugin name.
3.**Plugin value**: The fully qualified name of the function to register in the plugin system. In the example above, the plugin value is `vllm_add_dummy_model:register`, which refers to a function named `register` in the `vllm_add_dummy_model` module.
## What Can Plugins Do?
Currently, the primary use case for plugins is to register custom, out-of-the-tree models into vLLM. This is done by calling `ModelRegistry.register_model` to register the model. In the future, the plugin system may be extended to support more features, such as swapping in custom implementations for certain classes in vLLM.
## Guidelines for Writing Plugins
-**Being re-entrant**: The function specified in the entry point should be re-entrant, meaning it can be called multiple times without causing issues. This is necessary because the function might be called multiple times in some processes.
## Compatibility Guarantee
vLLM guarantees the interface of documented plugins, such as `ModelRegistry.register_model`, will always be available for plugins to register models. However, it is the responsibility of plugin developers to ensure their plugins are compatible with the version of vLLM they are targeting. For example, `"vllm_add_dummy_model.my_llava:MyLlava"` should be compatible with the version of vLLM that the plugin targets. The interface for the model may change during vLLM's development.
The community frequently requests the ability to extend vLLM with custom features. To facilitate this, vLLM includes a plugin system that allows users to add custom features without modifying the vLLM codebase. This document explains how plugins work in vLLM and how to create a plugin for vLLM.
How Plugins Work in vLLM
------------------------
Plugins are user-registered code that vLLM executes. Given vLLM's architecture (see :ref:`arch_overview`), multiple processes may be involved, especially when using distributed inference with various parallelism techniques. To enable plugins successfully, every process created by vLLM needs to load the plugin. This is done by the `load_general_plugins <https://github.com/vllm-project/vllm/blob/c76ac49d266e27aa3fea84ef2df1f813d24c91c7/vllm/plugins/__init__.py#L16>`__ function in the ``vllm.plugins`` module. This function is called for every process created by vLLM before it starts any work.
How vLLM Discovers Plugins
--------------------------
vLLM's plugin system uses the standard Python ``entry_points`` mechanism. This mechanism allows developers to register functions in their Python packages for use by other packages. An example of a plugin:
if "MyLlava" not in ModelRegistry.get_supported_archs():
ModelRegistry.register_model("MyLlava",
"vllm_add_dummy_model.my_llava:MyLlava")
For more information on adding entry points to your package, please check the `official documentation <https://setuptools.pypa.io/en/latest/userguide/entry_point.html>`__.
Every plugin has three parts:
1. **Plugin group**: The name of the entry point group. vLLM uses the entry point group ``vllm.general_plugins`` to register general plugins. This is the key of ``entry_points`` in the ``setup.py`` file. Always use ``vllm.general_plugins`` for vLLM's general plugins.
2. **Plugin name**: The name of the plugin. This is the value in the dictionary of the ``entry_points`` dictionary. In the example above, the plugin name is ``register_dummy_model``. Plugins can be filtered by their names using the ``VLLM_PLUGINS`` environment variable. To load only a specific plugin, set ``VLLM_PLUGINS`` to the plugin name.
3. **Plugin value**: The fully qualified name of the function to register in the plugin system. In the example above, the plugin value is ``vllm_add_dummy_model:register``, which refers to a function named ``register`` in the ``vllm_add_dummy_model`` module.
What Can Plugins Do?
--------------------
Currently, the primary use case for plugins is to register custom, out-of-the-tree models into vLLM. This is done by calling ``ModelRegistry.register_model`` to register the model. In the future, the plugin system may be extended to support more features, such as swapping in custom implementations for certain classes in vLLM.
Guidelines for Writing Plugins
------------------------------
- **Being re-entrant**: The function specified in the entry point should be re-entrant, meaning it can be called multiple times without causing issues. This is necessary because the function might be called multiple times in some processes.
Compatibility Guarantee
-----------------------
vLLM guarantees the interface of documented plugins, such as ``ModelRegistry.register_model``, will always be available for plugins to register models. However, it is the responsibility of plugin developers to ensure their plugins are compatible with the version of vLLM they are targeting. For example, ``"vllm_add_dummy_model.my_llava:MyLlava"`` should be compatible with the version of vLLM that the plugin targets. The interface for the model may change during vLLM's development.
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 [Dockerfile.rocm](https://github.com/vllm-project/vllm/blob/main/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
}
}
```
[Dockerfile.rocm](https://github.com/vllm-project/vllm/blob/main/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:
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)
- 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`.
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).
First, build a docker image from `Dockerfile.rocm <https://github.com/vllm-project/vllm/blob/main/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:
.. code-block:: console
{
"features": {
"buildkit": true
}
}
`Dockerfile.rocm <https://github.com/vllm-project/vllm/blob/main/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:
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>`_
- 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`.
Note to get your gfx architecture, run `rocminfo |grep gfx`.
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>`_.
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 platform documentation covering:
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 platform documentation covering:
* CPU backend inference capabilities
- CPU backend inference capabilities
* Relevant runtime environment variables
- Relevant runtime environment variables
* Performance optimization tips
- Performance optimization tips
ARM CPU backend currently supports Float32, FP16 and BFloat16 datatypes.
ARM CPU backend currently supports Float32, FP16 and BFloat16 datatypes.
Contents:
Contents:
1. :ref:`Requirements <arm_backend_requirements>`
1.[Requirements](#arm-backend-requirements)
2. :ref:`Quick Start with Dockerfile <arm_backend_quick_start_dockerfile>`
2.[Quick Start with Dockerfile](#arm-backend-quick-start-dockerfile)
3. :ref:`Building from Source <build_arm_backend_from_source>`
3.[Building from Source](#build-arm-backend-from-source)
.. _arm_backend_requirements:
(arm-backend-requirements)=
Requirements
## Requirements
------------
* **Operating System**: Linux or macOS
-**Operating System**: Linux or macOS
* **Compiler**: gcc/g++ >= 12.3.0 (optional, but recommended)
-**Compiler**: gcc/g++ >= 12.3.0 (optional, but recommended)
* **Instruction Set Architecture (ISA)**: NEON support is required
-**Instruction Set Architecture (ISA)**: NEON support is required
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.
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.
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)
- 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:
- 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:
$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](https://github.com/vllm-project/vllm/pull/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:
- 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](../serving/deploying_with_nginx) 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).
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:
#. :ref:`Requirements <cpu_backend_requirements>`
#. :ref:`Quick start using Dockerfile <cpu_backend_quick_start_dockerfile>`
#. :ref:`Build from source <build_cpu_backend_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:
- 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:
$ 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:
.. code-block:: 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:
.. code-block:: 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 <https://github.com/vllm-project/vllm/pull/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:
* 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 <../serving/deploying_with_nginx.html>`_ 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>`_.
Rafael Vasquez <rafvasq21@gmail.com>