9f5ab59e30
<!-- Thanks for sending a pull request! BEFORE SUBMITTING, PLEASE READ https://docs.vllm.ai/en/latest/contributing/overview.html --> ### What this PR does / why we need it? This PR fix accuracy issues incurred by codes that adapt to `FusedMoEParallelConfig` in vLLM 0.9.0 version. The `tp_size` used to split weights are wrongly passed. The root cause is that vLLM community and vLLM-Ascend are using different methods to decide whether to use Expert Parallel. vLLM: vLLM use a flag `enable_expert_parallel` to indicate whether to use EP and use the following codes to decide `ep_size`: ``` use_ep = (dp_size_ * tp_size_ > 1 and vllm_parallel_config.enable_expert_parallel) dp_size = dp_size_ dp_rank = get_dp_group().rank_in_group if dp_size > 1 else 0 tp_size, tp_rank = flatten_tp_across_dp(dp_rank) if not use_ep: return FusedMoEParallelConfig(tp_size=tp_size, tp_rank=tp_rank, dp_size=dp_size, dp_rank=dp_rank, ep_size=1, ep_rank=0, use_ep=False) # DP + EP / TP + EP / DP + TP + EP assert use_ep # In EP, each device owns a set of experts fully. There is no tensor # parallel update tp_size, tp_rank, ep_size and ep_rank to reflect that. ep_size = tp_size ep_rank = tp_rank return FusedMoEParallelConfig(tp_size=1, tp_rank=0, dp_size=dp_size, dp_rank=dp_rank, ep_size=ep_size, ep_rank=ep_rank, use_ep=True) ``` vLLM-Ascend: vLLM-Ascend uses `etp` to specify Tensor Parallel in MoE. ``` self.ep_size = get_ep_group().world_size self.tp_size = get_etp_group().world_size self.dp_size = (dp_size if dp_size is not None else get_dp_group().world_size) ``` So there will be conflicts if we simply combine these codes together. ### Does this PR introduce _any_ user-facing change? <!-- Note that it means *any* user-facing change including all aspects such as API, interface or other behavior changes. Documentation-only updates are not considered user-facing changes. --> ### How was this patch tested? <!-- CI passed with new added/existing test. If it was tested in a way different from regular unit tests, please clarify how you tested step by step, ideally copy and paste-able, so that other reviewers can test and check, and descendants can verify in the future. If tests were not added, please describe why they were not added and/or why it was difficult to add. --> Signed-off-by: angazenn <zengyanjia@huawei.com> Co-authored-by: angazenn <zengyanjia@huawei.com>
vLLM Ascend Plugin
| About Ascend | Documentation | #sig-ascend | Users Forum | Weekly Meeting |
English | 中文
Latest News 🔥
- [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
- OS: Linux
- Software:
- Python >= 3.9, < 3.12
- CANN >= 8.1.RC1
- PyTorch >= 2.5.1, torch-npu >= 2.5.1
- vLLM (the same version as vllm-ascend)
Getting Started
Please refer to 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 0.8.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 |
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.