d3de7333dc
## What this PR does / why we need it? pick-from:https://github.com/vllm-project/vllm-ascend/pull/7452 ### Problem Embedding models produce inconsistent outputs when prefix caching is enabled vs disabled. ### Root Cause The attention router condition was too broad: - All `model_runner_type == "pooling"` → `_forward_encoder_attention()` → uses `npu_fusion_attention` - **But `npu_fusion_attention` does NOT support prefix caching** - Result: Numerical mismatch when KV cache is managed by prefix caching ### Solution Refine the router condition to check causality: **Before**: ``` if attn_metadata.model_runner_type == "pooling": → npu_fusion_attention (no prefix caching support) ``` **After**: ``` if attn_metadata.model_runner_type == "pooling" and not attn_metadata.causal: → npu_fusion_attention (for true encoders) else: → npu_fused_infer_attention_score (prefix caching support) ``` ### Changes Made 1. **Fixed router condition** (`vllm_ascend/attention/attention_v1.py` L968) - Added `and not attn_metadata.causal` check - Effect: Non-causal embeddings now use correct operator 2. **Simplified encoder attention** (`vllm_ascend/attention/attention_v1.py` L864-877) - Removed redundant causal branch (encoders never use causal mask) - Reduced from 34 lines to 14 lines 3. **Added test** (`tests/e2e/singlecard/pooling/test_embedding.py`) - Validates embedding outputs with/without prefix caching are consistent ## Does this PR introduce _any_ user-facing change? ### Functional Changes ✅ **Yes** - Bug fix: Embedding models now produce consistent outputs with prefix caching ### API Changes ❌ **No** - All public APIs unchanged ### Configuration Changes ❌ **No** - No new configuration required ### Backward Compatibility ✅ **Fully compatible** - Only fixes incorrect behavior ## How was this patch tested? ### New Test Added `test_embed_models_using_prefix_caching_correctness()`: - Tests: `Qwen3-Embedding-0.6B` - Validates numerical consistency between runs with/without prefix caching - Uses long sequences to activate prefix caching - Tolerance: 1e-2 - vLLM version: v0.18.0 Signed-off-by: underfituu <hzhucong@163.com>
vLLM Ascend Plugin
| About Ascend | Documentation | #SIG-Ascend | Users Forum | Weekly Meeting |
English | 中文
Latest News 🔥
- [2026/02] We released the new official version v0.13.0! Please follow the official guide to start using vLLM Ascend Plugin on Ascend.
- [2025/12] We released the new official version v0.11.0! Please follow the official guide to start using vLLM Ascend Plugin on Ascend.
- [2025/09] We released the new official version v0.9.1! Please follow the official guide to start deploying 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 the 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-Experts (MoE), 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 >= 3.10, < 3.12
- CANN == 8.5.0 (Ascend HDK version refers to here)
- PyTorch == 2.9.0, torch-npu == 2.9.0
- vLLM (the same version as vllm-ascend)
Getting Started
Please use the following recommended versions to get started quickly:
| Version | Release type | Doc |
|---|---|---|
| v0.17.0rc1 | Latest release candidate | See QuickStart and Installation for more details |
| v0.13.0 | Latest stable version | See QuickStart and Installation for more details |
Contributing
See CONTRIBUTING for more details, which is a step-by-step guide to help you set up the 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 a main branch and a dev branch.
- main: main branch, corresponds to the vLLM main branch, and is continuously monitored for quality through Ascend CI.
- releases/vX.Y.Z: development branch, created alongside new releases of vLLM. For example,
releases/v0.13.0is the dev branch for vLLMv0.13.0version.
Below are the maintained branches:
| Branch | Status | Note |
|---|---|---|
| main | Maintained | CI commitment for vLLM main branch and vLLM v0.17.0 tag |
| v0.7.1-dev | Unmaintained | Only doc fixes are allowed |
| v0.7.3-dev | Maintained | CI commitment for vLLM 0.7.3 version, only bug fixes are allowed, and no new release tags anymore. |
| v0.9.1-dev | Maintained | CI commitment for vLLM 0.9.1 version |
| v0.11.0-dev | Maintained | CI commitment for vLLM 0.11.0 version |
| releases/v0.13.0 | Maintained | CI commitment for vLLM 0.13.0 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.