1dbb888275
### What this PR does / why we need it? This PR fixes a tensor shape mismatch in `add_lora_logits`. Previously, `lora_a_stacked` was passed as shape `[num_loras, in_dim, rank]`, which does not match the expected einsum pattern `"bi, boi -> bo"` used in `bgmv_shrink`. This causes runtime errors like: RuntimeError: einsum(): subscript i has size 3 for operand 1 which does not broadcast with previously seen size 4  This fix transposes `lora_a_stacked` and `lora_b_stacked` to match the expected shapes: - `lora_a`: `[num_loras, rank, in_dim]` - `lora_b`: `[num_loras, out_dim, rank]` All unit tests pass after this fix. ### Does this PR introduce _any_ user-facing change? N/A ### How was this patch tested? ``` import torch import pytest from unittest.mock import patch, PropertyMock, ANY from vllm_ascend.lora.punica_wrapper.punica_npu import PunicaWrapperNPU @pytest.fixture def wrapper_cpu(): cfg = {"max_num_batched_tokens": 10, "max_batches": 2, "device": "cpu"} w = PunicaWrapperNPU(**cfg) w.is_prefill = True w.no_lora = False return w def test_add_lora_logits(wrapper_cpu): batch_size = 2 hidden_size = 4 lora_rank = 3 vocab_size = 5 y = torch.zeros(batch_size, vocab_size) x = torch.randn(batch_size, hidden_size) num_loras = 1 lora_a = torch.randn(num_loras, hidden_size, lora_rank) lora_b = torch.randn(num_loras, lora_rank, vocab_size) with patch.object(wrapper_cpu.__class__, "sampler_indices", new_callable=PropertyMock) as mock_idx: mock_idx.return_value = torch.zeros(batch_size, dtype=torch.long) wrapper_cpu.add_lora_logits(y, x, lora_a, lora_b, scale=1.0) assert y.shape == (batch_size, vocab_size) assert not torch.allclose(y, torch.zeros_like(y)) Signed-off-by: hongfugui <hongfugui_yewu@cmss.chinamobile.com>
vLLM Ascend Plugin
| About Ascend | Documentation | #sig-ascend | Users Forum | Weekly Meeting |
English | 中文
Latest News 🔥
- [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
- OS: Linux
- Software:
- Python >= 3.9, < 3.12
- CANN >= 8.2.rc1
- PyTorch >= 2.5.1, torch-npu >= 2.5.1.post1.dev20250619
- vLLM (the same version as vllm-ascend)
Getting Started
Please use the following recommended versions to get started quickly:
| Version | Release type | Doc |
|---|---|---|
| v0.9.2rc1 | Latest release candidate | QuickStart and Installation for more details |
| v0.7.3.post1 | 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 0.9.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 |
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.