8f45f9ce29
## Description This PR addresses two key issues in the MoE module when redundant experts are enabled, and fixes a calculation precision bug in the forward inference of quantized MLP: ### 1. Shape Mismatch in EPLB Expert Map Update - **Root Cause**: When redundant experts are turned on, a shape inconsistency occurs during the expert map update in `Vllm_apaptor`: - The shape of `self.expert_map_per_layer[layer_id]` is `[num_physical_experts,]` (aligned with physical expert count). - The shape of `updated_expert_map` is `[num_logical_experts,]` (aligned with logical expert count). - Indices in `self.expert_map_per_layer[layer_id]` that exceed the logical expert count cannot be properly mapped, leading to tensor shape mismatch errors. - The same shape mismatch exists in the `log2phy` map update (between `self.log2phy_map_per_layer[layer_id]` and `updated_log2phy_map`). - **Fix**: - Fix the shape initialization of `expert_map_per_layer` and `log2phy_map_per_layer` to be consistently set to `[num_physical_experts,]` across the module lifecycle. - Align the shape of `updated_expert_map` and `updated_log2phy_map` with the pre-initialized physical-expert-sized tensors during update operations, ensuring shape consistency for index mapping. ### 2. Calculation Precision Issue in Quantized MoE MLP Forward Inference - **Root Cause**: In the forward pass of `moe_mlp`, the `torch_npu.npu_dequant_swiglu_quant` operator only accepts group lists in **Count format** as input. However, the group list provided by `quant_apply_mlp` was in **Cumsum format**, which caused operator input format mismatch and degraded calculation precision. - **Fix**: - Convert the cumsum-formatted group list from `quant_apply_mlp` to Count format before passing it to `torch_npu.npu_dequant_swiglu_quant`. - Ensure the input format of the dequantization operator meets its requirements, restoring the expected calculation precision for quantized MoE MLP layers. ## Impact - Resolves shape mismatch errors in EPLB expert/log2phy map updates when redundant experts are enabled, ensuring stable expert routing. - Fixes quantized MoE MLP forward precision issues on NPU, aligning operator input formats with NPU kernel requirements. - No breaking changes to existing interfaces; the fixes are backward-compatible for scenarios without redundant experts enabled. --------- Signed-off-by: Che Ruan <cr623@ic.ac.uk> Signed-off-by: Mercykid-bash <ruanche0218@gmail.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>
vLLM Ascend Plugin
| About Ascend | Documentation | #sig-ascend | Users Forum | Weekly Meeting |
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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 >= 8.3.rc1 (Ascend HDK version refers to here)
- PyTorch == 2.7.1, torch-npu == 2.7.1
- vLLM (the same version as vllm-ascend)
Getting Started
Please use the following recommended versions to get started quickly:
| Version | Release type | Doc |
|---|---|---|
| v0.11.0rc0 | 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 v0.11.0 tag |
| 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.