zhangxinyuehfad d1c640841b [Bugfix] Fix num_hidden_layers when Qwen2-Audio 7B (#1803)
### What this PR does / why we need it?
Fix num_hidden_layers when Qwen2-Audio 7B and #1760 :
```
INFO 07-15 04:38:53 [platform.py:174] PIECEWISE compilation enabled on NPU. use_inductor not supported - using only ACL Graph mode
Traceback (most recent call last):
  File "/workspace/test1.py", line 58, in <module>
    main(audio_count)
  File "/workspace/test1.py", line 38, in main
    llm = LLM(model="Qwen/Qwen2-Audio-7B-Instruct",
  File "/vllm-workspace/vllm/vllm/entrypoints/llm.py", line 271, in __init__
    self.llm_engine = LLMEngine.from_engine_args(
  File "/vllm-workspace/vllm/vllm/engine/llm_engine.py", line 494, in from_engine_args
    vllm_config = engine_args.create_engine_config(usage_context)
  File "/vllm-workspace/vllm/vllm/engine/arg_utils.py", line 1286, in create_engine_config
    config = VllmConfig(
  File "/usr/local/python3.10.17/lib/python3.10/site-packages/pydantic/_internal/_dataclasses.py", line 123, in __init__
    s.__pydantic_validator__.validate_python(ArgsKwargs(args, kwargs), self_instance=s)
  File "/vllm-workspace/vllm/vllm/config.py", line 4624, in __post_init__
    current_platform.check_and_update_config(self)
  File "/vllm-workspace/vllm-ascend/vllm_ascend/platform.py", line 180, in check_and_update_config
    update_aclgraph_sizes(vllm_config)
  File "/vllm-workspace/vllm-ascend/vllm_ascend/utils.py", line 307, in update_aclgraph_sizes
    num_hidden_layers = vllm_config.model_config.hf_config.num_hidden_layers
  File "/usr/local/python3.10.17/lib/python3.10/site-packages/transformers/configuration_utils.py", line 211, in __getattribute__
    return super().__getattribute__(key)
AttributeError: 'Qwen2AudioConfig' object has no attribute 'num_hidden_layers'
```

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

Closes: https://github.com/vllm-project/vllm-ascend/issues/1780
https://github.com/vllm-project/vllm-ascend/issues/1760
https://github.com/vllm-project/vllm-ascend/issues/1276
https://github.com/vllm-project/vllm-ascend/issues/359

- vLLM version: v0.10.0
- vLLM main:
7728dd77bb

Signed-off-by: hfadzxy <starmoon_zhang@163.com>
2025-07-26 20:13:00 +08:00
2025-02-05 10:53:12 +08:00
2025-07-26 15:43:29 +08:00
2025-07-26 15:43:29 +08:00
2025-01-29 02:44:13 -08:00
2025-07-26 15:43:29 +08:00
2025-06-27 09:14:43 +08:00

vllm-ascend

vLLM Ascend Plugin

| About Ascend | Documentation | #sig-ascend | Users Forum | Weekly Meeting |

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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 >= 8.1.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:

Branch

vllm-ascend has main branch and dev branch.

  • main: main branchcorresponds 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-dev is the dev branch for vLLM v0.7.3 version.

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

License

Apache License 2.0, as found in the LICENSE file.

Description
XC-LLM: A Specially Optimized LLM Inference Engine for ModelHub XC
Readme Apache-2.0 8.6 MiB
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