mirror of
https://github.com/vllm-project/vllm-ascend.git
synced 2025-10-20 13:43:53 +08:00
29c173ab48
## Purpose
This Pull Request enhances the EPLB (Expert Parallelism Load Balancing)
system by introducing a novel balancing algorithm: FlashLB.
## Motivation
1. The default algorithm adopts a two-stage greedy strategy:
a. Replica allotment: Determine the number of expert replicas by
minimizing the maximum load per replica (Min Max Replica, MMR).
b. Replica placement: Distribute replicas across devices by repeatedly
assigning the heaviest replica to the least loaded device (Longest
Processing Time First, LPT).
However, this sequential process lacks inter-stage collaborative
optimization, often leading to suboptimal load balancing. For example,
in the simple case shown in the figure below: given 8 logical experts
with hotness values of 600, 560, 120, 120, 20, 10, 10, 10, and 2
replicas allocated per device across 8 devices, the EPLB algorithm
yields a maximum per-device hotness of 232, while our proposed FlashLB
algorithm can reduce this value to 205.
2. The default algorithm relies on the averaged expert hotness over a
fixed time window for optimization. While this provides a coarse
approximation of the hotness distribution, it fails to capture
oscillatory deviations and temporal correlations of expert hotness
observed across iterations in real-world scenarios, limiting
optimization quality.
3. The default algorithm periodically regenerates the expert placement
table. However, it generates the table for each individual layer, and
the new table does not account for correlations with the previous one;
these two factors collectively lead to nearly full-scale expert
reassignment.
## FlashLB Algorithm Principle
1. Joint Optimization
FlashLB achieves joint optimization of replica allotment and placement
through group-based decision-making. Each group gradually determines the
replica count and placement for a subset of experts, ensuring that the
expected inter-device load balance (considering both deployed and
pending expert replicas) is holistically optimized. To attain superior
load balancing, FlashLB employs tree search to expand the solution space
while integrating pruning and precompilation techniques for
acceleration, thereby delivering load balancing that is both
high-quality and practically efficient.
2. Multi-Shot Enhancement
FlashLB partitions each profiling interval (e.g., 1024 iterations) into
consecutive smaller sub-intervals (e.g., 16 iterations), each capturing
independent hotness measurements. It then performs multi-shot
optimization to co-optimize these sub-intervals simultaneously—enabling
adaptation to time-variant expert hotness while enhancing robustness.
3. Incremental Adjustment
To reduce the overhead of frequent expert re-deployment, FlashLB
introduces an incremental adjustment scheme operating at both
inter-layer and intra-layer levels:
a. Inter-Layer: Hotness variations are tracked at the layer level. Only
layers with fluctuations exceeding a predefined threshold trigger
re-computation of expert placement, avoiding unnecessary redeployment
for stable layers；
b. Intra-Layer (Optional): A lightweight incremental LPT algorithm
(LPT-Incremental) is applied. Instead of recomputing full placement for
all experts in a layer, it selectively adjusts only the hottest experts
or those with replica count changes, further reducing migration
overhead.
This incremental strategy significantly reduces adjustment costs while
maintaining balanced performance across layers and devices.
## Co-author:
Co-authored-by: Skywalker-EP 173723846@qq.com
- vLLM version: v0.10.2
- vLLM main:
9607d5eb44
---------
Signed-off-by: sdmyzlp <lrwei2@petalmail.com>
Signed-off-by: Che Ruan <cr623@ic.ac.uk>
Signed-off-by: Shanshan Shen <87969357+shen-shanshan@users.noreply.github.com>
Signed-off-by: shen-shanshan <467638484@qq.com>
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
Signed-off-by: 22dimensions <waitingwind@foxmail.com>
Signed-off-by: zhanghaiwen <zhanghaiwen@cmss.chinamobile.com>
Signed-off-by: hfadzxy <starmoon_zhang@163.com>
Signed-off-by: Lucas Kabela <lucaskabela@meta.com>
Signed-off-by: wangli <wangli858794774@gmail.com>
Signed-off-by: MengqingCao <cmq0113@163.com>
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
Signed-off-by: Icey <1790571317@qq.com>
Signed-off-by: linfeng-yuan <1102311262@qq.com>
Signed-off-by: dependabot[bot] <support@github.com>
Signed-off-by: tangtianyi <tangtianyi4@huawei.com>
Signed-off-by: Angazenn <supperccell@163.com>
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
Signed-off-by: rjg-lyh <1318825571@qq.com>
Signed-off-by: Pr0Wh1teGivee <calvin_zhu0210@outlook.com>
Signed-off-by: fems14 <1804143737@qq.com>
Co-authored-by: sdmyzlp <117554856+sdmyzlp@users.noreply.github.com>
Co-authored-by: Che Ruan <cr623@ic.ac.uk>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Co-authored-by: Shanshan Shen <467638484@qq.com>
Co-authored-by: Yikun Jiang <yikunkero@gmail.com>
Co-authored-by: 22dimensions <waitingwind@foxmail.com>
Co-authored-by: zhanghw0354 <zhanghaiwencmss@139.com>
Co-authored-by: zhanghaiwen <zhanghaiwen@cmss.chinamobile.com>
Co-authored-by: zhangxinyuehfad <59153331+zhangxinyuehfad@users.noreply.github.com>
Co-authored-by: Lucas Kabela <lucasakabela@gmail.com>
Co-authored-by: Li Wang <wangli858794774@gmail.com>
Co-authored-by: MengqingCao <cmq0113@163.com>
Co-authored-by: wangxiyuan <wangxiyuan1007@gmail.com>
Co-authored-by: Icey <1790571317@qq.com>
Co-authored-by: linfeng-yuan <1102311262@qq.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: tianyitang <tangtianyi4@huawei.com>
Co-authored-by: Angazenn <supperccell@163.com>
Co-authored-by: Yizhou <136800916+yiz-liu@users.noreply.github.com>
Co-authored-by: rjg-lyh <83491835+rjg-lyh@users.noreply.github.com>
Co-authored-by: weichen <132029610+Pr0Wh1teGivee@users.noreply.github.com>
Co-authored-by: weijinqian0 <12153182+weijinqian0@users.noreply.github.com>
Co-authored-by: fems14 <74094523+fems14@users.noreply.github.com>
vLLM Ascend Plugin
| About Ascend | Documentation | #sig-ascend | Users Forum | Weekly Meeting |
English | 中文
Latest News 🔥
- [2025/09] We released the new official version v0.9.1! Please follow the official guide to start deploy large scale Expert Parallelism (EP) on Ascend.
- [2025/08] We hosted the vLLM Beijing Meetup with vLLM and Tencent! Please find the meetup slides here.
- [2025/06] User stories page is now live! It kicks off with LLaMA-Factory/verl//TRL/GPUStack to demonstrate how vLLM Ascend assists Ascend users in enhancing their experience across fine-tuning, evaluation, reinforcement learning (RL), and deployment scenarios.
- [2025/06] Contributors page is now live! All contributions deserve to be recorded, thanks for all contributors.
- [2025/05] We've released first official version v0.7.3! We collaborated with the vLLM community to publish a blog post sharing our practice: Introducing vLLM Hardware Plugin, Best Practice from Ascend NPU.
- [2025/03] We hosted the vLLM Beijing Meetup with vLLM team! Please find the meetup slides here.
- [2025/02] vLLM community officially created vllm-project/vllm-ascend repo for running vLLM seamlessly on the Ascend NPU.
- [2024/12] We are working with the vLLM community to support [RFC]: Hardware pluggable.
Overview
vLLM Ascend (vllm-ascend) is a community maintained hardware plugin for running vLLM seamlessly on the Ascend NPU.
It is the recommended approach for supporting the Ascend backend within the vLLM community. It adheres to the principles outlined in the [RFC]: Hardware pluggable, providing a hardware-pluggable interface that decouples the integration of the Ascend NPU with vLLM.
By using vLLM Ascend plugin, popular open-source models, including Transformer-like, Mixture-of-Expert, Embedding, Multi-modal LLMs can run seamlessly on the Ascend NPU.
Prerequisites
- Hardware: Atlas 800I A2 Inference series, Atlas A2 Training series, Atlas 800I A3 Inference series, Atlas A3 Training series, Atlas 300I Duo (Experimental)
- OS: Linux
- Software:
- Python >= 3.9, < 3.12
- CANN >= 8.2.rc1 (Ascend HDK version refers to here)
- PyTorch >= 2.7.1, torch-npu >= 2.7.1.dev20250724
- vLLM (the same version as vllm-ascend)
Getting Started
Please use the following recommended versions to get started quickly:
| Version | Release type | Doc |
|---|---|---|
| v0.10.2rc1 | Latest release candidate | QuickStart and Installation for more details |
| v0.9.1 | Latest stable version | QuickStart and Installation for more details |
Contributing
See CONTRIBUTING for more details, which is a step-by-step guide to help you set up development environment, build and test.
We welcome and value any contributions and collaborations:
- Please let us know if you encounter a bug by filing an issue
- Please use User forum for usage questions and help.
Branch
vllm-ascend has main branch and dev branch.
- main: main branch，corresponds to the vLLM main branch, and is continuously monitored for quality through Ascend CI.
- vX.Y.Z-dev: development branch, created with part of new releases of vLLM. For example,
v0.7.3-devis the dev branch for vLLMv0.7.3version.
Below is maintained branches:
| Branch | Status | Note |
|---|---|---|
| main | Maintained | CI commitment for vLLM main branch and vLLM 0.10.x branch |
| v0.7.1-dev | Unmaintained | Only doc fixed is allowed |
| v0.7.3-dev | Maintained | CI commitment for vLLM 0.7.3 version, only bug fix is allowed and no new release tag any more. |
| v0.9.1-dev | Maintained | CI commitment for vLLM 0.9.1 version |
| rfc/feature-name | Maintained | Feature branches for collaboration |
Please refer to Versioning policy for more details.
Weekly Meeting
- vLLM Ascend Weekly Meeting: https://tinyurl.com/vllm-ascend-meeting
- Wednesday, 15:00 - 16:00 (UTC+8, Convert to your timezone)
License
Apache License 2.0, as found in the LICENSE file.