NeverRaR 71de52d3a9 feat: add kv cache memory cache and skip dynamo guard (#1549)
### What this PR does / why we need it?

1、Sometimes loading torchair cache will fail because of the floating of
npu memory, so this pr add a new cache to save the old kv cache bytes to
avoid the possible crash while loading the torchair graph cache.
2、When caching is enabled and does not exist, the first compilation
introduces the overhead of Dynamo Gurad. So in this case, we will
compile them directly twice to skip them (This will bring 3-4 ms of tpot
optimization)

### Does this PR introduce _any_ user-facing change?
Add a new env `VLLM_ASCEND_KV_CACHE_MEGABYTES_FLOATING_TOLERANCE` to
control kv cache floating tolerance

### How was this patch tested?

- vLLM version: v0.9.1
- vLLM main:
1fd471e957

Signed-off-by: boying <897013703@qq.com>
2025-07-07 22:37:14 +08:00
2025-07-07 20:19:16 +08:00
2025-06-27 09:14:43 +08:00
2025-02-05 10:53:12 +08:00
2025-01-29 02:44:13 -08:00
2025-06-25 19:28:26 +08:00
2025-06-25 19:28:26 +08:00
2025-04-01 09:25:33 +08:00
2025-06-27 09:14:43 +08:00

vllm-ascend

vLLM Ascend Plugin

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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 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:

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

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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