EngineX/xc-llm-ascend
3
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

[Doc][Release] Add release note skill (#6824)

Browse Source 
### What this PR does / why we need it?
This PR adds the releaseing note skills:
- `SKILL.md`: vLLM Ascend Releasing Note Writer
- `references/ref-past-release-notes-highlight.md`:
And also add a `output/v0.13.0` examples which was used by
2da476d82f

Inspired: https://github.com/simon-mo/release-notes-writing/

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?

- vLLM version: v0.15.0
- vLLM main:
83b47f67b1


Co-authored-by: esmeetu <jasonailu87@gmail.com>

---------

Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
This commit is contained in:
Yikun Jiang
2026-02-26 21:01:21 +08:00
committed by GitHub
parent 2d49f9079a
commit ed175d6d92
4 changed files with 1862 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

39
.agents/README.md
View File

@@ -8,6 +8,7 @@ Note: Please copy the skills directory `.agents/skills` to `.claude/skills` if y
- [vLLM Ascend Model Adapter Skill](#vllm-ascend-model-adapter-skill)
- [vLLM Ascend main2main Skill](#vllm-ascend-main2main-skill)
- [vLLM Ascend Release Note Writer Skill](#vllm-ascend-release-note-writer-skill)
## vLLM Ascend Model Adapter Skill
@@ -74,3 +75,41 @@ This skill facilitates the process of:
1. Open a conversation with the AI agent inside the vllm-ascend dev container.
2. Invoke the skill (e.g. `/main2main`).
3. The agent follows the playbook and produces a ready-to-merge commit.
## vLLM Ascend Release Note Writer Skill
You just need to say: `Please help me write a 0.13.0 release note based on commits from v0.11.0 and releases/v0.13.0`
### What it does
This skill guides you through a structured workflow to:
1. Fetch commits between two versions using the provided script.
2. Analyze and categorize each commit in a CSV workspace.
3. Draft highlights and write polished release notes.
4. Generate release notes organized by category (Features, Hardware Support, Performance, Dependencies, etc.).
### File layout
| File | Purpose |
| ---- | ------- |
| `SKILL.md` | Skill definition, workflow, and writing guidelines |
| `references/ref-past-release-notes-highlight.md` | Style and category reference for release notes |
| `scripts/fetch_commits-optimize.py` | Script to fetch commits between versions |
### Quick start
1. Open a conversation with the AI agent.
2. Invoke the skill (e.g. `/vllm-ascend-release-note-writer`).
3. Follow the workflow steps:
- Fetch commits between versions
- Analyze commits in CSV format
- Draft and edit highlights
4. Output files are saved to `vllm-ascend-release-note/output/$version`
### Key guidelines
- Use one-level headings (###) for sections in a specific order: Highlights, Features, Hardware and Operator Support, Performance, Dependencies, Deprecation & Breaking Changes, Documentation, Others.
- Focus on user-facing impact and include context for practical usage.
- Verify details by checking linked PRs (use GitHub API for descriptions if needed).
- Keep notes concise and avoid unnecessary technical details.

79
.agents/skills/vllm-ascend-release-note-writer/SKILL.md Normal file
View File

@@ -0,0 +1,79 @@
---
name: vLLM Ascend Release Note Writer
description: You are a release note writer for vLLM Ascend project (vllm-project/vllm-ascend). You are responsible for writing release notes for vLLM Ascend.
---
# vLLM Ascend release Note Writer Skill
## Overview
You should use the `ref-past-release-notes-highlight.md` as style and category reference. Always read these first.
## When to use this skill
When a new version of vLLM Ascend is released, you should use this skill to write the release notes.
## How to use it
0. all output files should be saved under `vllm-ascend-release-note/output/$version` folder
1. Use the `fetch_commits-optimize.py` script to fetch the commits between the previous and current version.
```bash
uv run python fetch_commits-optimize.py --base-tag $LAST_TAG --head-tag $NEW_TAG --output 0-current-raw-commits.md
```
`0-current-raw-commits.md` is your raw data input.
2. Use the `commit-analysis-draft.csv` tool to analyze the commits and put them into the correct section.
`1-commit-analysis-draft.csv` is your workspace for commit by commit analysis for which commit goes into which section, whether can be ignored, and why. You can create auxilariy files in `tmp` folder.
* You should check each commit. They are put into rows in the CSV file.
* The CSV should have headers `title`, `pr number`, `user facing impact/summary`, `category`, `decision`, `reason`. Please brainstorm other fields as you see fit.
3. Draft the highlights note, and save it to `2-highlights-note-draft.md`.
4. Edit the draft highlights note in `2-highlights-note-draft.md`, and save it to `3-highlights-note-edit.md`. You should double and triple check with the raw commits + analysis. You can leave any uncertainty and doubts in the file, and we will discuss them together.
5. Use the format `This is the $NUMBER release candidate of $VERSION for vLLM Ascend. Please follow the [official doc](https://docs.vllm.ai/projects/ascend/en/latest) to get started.`.
## Writing style
1. To keep simple, you should only save one level of headings, starting with ###, which may include the following categories follow below order:
### Highlights
### Features
### Hardware and Operator Support
### Performance
### Dependencies
### Deprecation & Breaking Changes
### Documentation
### Others
2. Additional Inclusion Criteria
* User experience improvements (CLI enhancements, better error messages, configuration flexibility)
* Core feature (PD Disaggregation, KVCaceh, Graph mode, CP/SP, quantization)
* Breaking changes and deprecations (always include with clear impact description)
* Significant infrastructure changes (elastic scaling, distributed serving, hardware support)
* Major dependency updates (CANN/torch_npu/triton-ascend/MoonCake/Ray/transformers versions, critical library updates)
* Binary/deployment improvements (size reductions, Docker enhancements)
* Default behavior changes (default models, configuration changes that affect all users)
* Hardware compatibility expansions (310P, A2, A3, A5 support)
In the end we don't want to miss any important changes. But also don't want to spam the notes with unnecessary details.
3. Section Organization Guidelines
* **Model Support first**: Most immediately visible to users, should lead the highlights
* **Group by user impact**: Hardware/performance should focus on what users experience, not internal optimizations
* **Provide usage context**: Include relevant flags, configuration options, and practical usage information
* **Technical detail level**: Explain what features enable rather than just listing technical changes
4. Writing Tips
* Look up the PR if you are not sure about the details. The PR number at the end (#12345) can be looked up via vllm-project/vllm#12345. To get the description, you just need to call <https://api.github.com/repos/vllm-project/vllm/pulls/12345> and look at the body field.
* When writing the highlights, don't be too verbose. Focus exclusively on what users should know.

198
.agents/skills/vllm-ascend-release-note-writer/references/ref-past-release-notes-highlight.md Normal file
View File

@@ -0,0 +1,198 @@
## v0.14.0rc1 - 2026.01.26
This is the first release candidate of v0.14.0 for vLLM Ascend. Please follow the [official doc](https://docs.vllm.ai/projects/ascend/en/latest) to get started. This release includes all the changes in v0.13.0rc2. So We just list the differences from v0.13.0rc2. If you are upgrading from v0.13.0rc1, please read both v0.14.0rc1 and v0.13.0rc2 release notes.
### Highlights
- 310P support is back now. In this release, only basic dense and vl models are supported with eager mode. We'll keep improving and maintaining the support for 310P. [#5776](https://github.com/vllm-project/vllm-ascend/pull/5776)
- Support compressed tensors moe w8a8-int8 quantization. [#5718](https://github.com/vllm-project/vllm-ascend/pull/5718)
- Support Medusa speculative decoding. [#5668](https://github.com/vllm-project/vllm-ascend/pull/5668)
- Support Eagle3 speculative decoding for Qwen3vl. [#4848](https://github.com/vllm-project/vllm-ascend/pull/4848)
### Features
- Xlite Backend supports Qwen3 MoE now. [#5951](https://github.com/vllm-project/vllm-ascend/pull/5951)
- Support DSA-CP for PD-mix deployment case. [#5702](https://github.com/vllm-project/vllm-ascend/pull/5702)
- Add support of new W4A4_LAOS_DYNAMIC quantization method. [#5143](https://github.com/vllm-project/vllm-ascend/pull/5143)
### Performance
- The performance of Qwen3-next has been improved. [#5664](https://github.com/vllm-project/vllm-ascend/pull/5664) [#5984](https://github.com/vllm-project/vllm-ascend/pull/5984) [#5765](https://github.com/vllm-project/vllm-ascend/pull/5765)
- The CPU bind logic and performance has been improved. [#5555](https://github.com/vllm-project/vllm-ascend/pull/5555)
- Merge Q/K split to simplify AscendApplyRotaryEmb for better performance. [#5799](https://github.com/vllm-project/vllm-ascend/pull/5799)
- Add Matmul Allreduce Rmsnorm fusion Pass. It's disabled by default. Set `fuse_allreduce_rms=True` in `--additional_config` to enable it. [#5034](https://github.com/vllm-project/vllm-ascend/pull/5034)
- Optimize rope embedding with triton kernel for huge performance gain. [#5918](https://github.com/vllm-project/vllm-ascend/pull/5918)
- support advanced apply_top_k_top_p without top_k constraint. [#6098](https://github.com/vllm-project/vllm-ascend/pull/6098)
- Parallelize Q/K/V padding in AscendMMEncoderAttention for better performance. [#6204](https://github.com/vllm-project/vllm-ascend/pull/6204)
### Others
- model runner v2 support triton of penalty. [#5854](https://github.com/vllm-project/vllm-ascend/pull/5854)
- model runner v2 support eagle spec decoding. [#5840](https://github.com/vllm-project/vllm-ascend/pull/5840)
- Fix multi-modal inference OOM issues by setting `expandable_segments:True` by default. [#5855](https://github.com/vllm-project/vllm-ascend/pull/5855)
- `VLLM_ASCEND_ENABLE_MLAPO` is set to `True` by default. It's enabled automatically on decode node in PD deployment case. Please note that this feature will cost more memory. If you are memory sensitive, please set it to False. [#5952](https://github.com/vllm-project/vllm-ascend/pull/5952)
- SSL config can be set to kv_extra_config for PD deployment with mooncake layerwise connector. [#5875](https://github.com/vllm-project/vllm-ascend/pull/5875)
- support `--max_model_len=auto`. [#6193](https://github.com/vllm-project/vllm-ascend/pull/6193)
### Dependencies
- torch-npu is upgraded to 2.9.0 [#6112](https://github.com/vllm-project/vllm-ascend/pull/6112)
### Deprecation & Breaking Changes
- EPLB config options is moved to `eplb_config` in [additional config](https://docs.vllm.ai/projects/ascend/en/latest/user_guide/configuration/additional_config.html). The old ones are removed in this release.
- The profiler envs, such as `VLLM_TORCH_PROFILER_DIR` and `VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY` do not work with vLLM Ascend now. Please use vLLM `--profiler-config` parameters instead. [#5928](https://github.com/vllm-project/vllm-ascend/pull/5928)
### Known Issues
- If you hit the pickle error from `EngineCore` process sometimes, please cherry-pick the [PR](https://github.com/vllm-project/vllm/pull/32022) into your local vLLM code. This known issue will be fixed in vLLM in the next release.
## v0.13.0rc2 - 2026.01.24
This is the second release candidate of v0.13.0 for vLLM Ascend. In this rc release, we fixed lots of bugs and improved the performance of many models. Please follow the [official doc](https://docs.vllm.ai/projects/ascend/en/v0.13.0/) to get started. Any feedback is welcome to help us to improve the final version of v0.13.0.
### Highlights
We mainly focus on quality and performance improvement in this release. The spec decode, graph mode, context parallel and EPLB have been improved significantly. A lot of bugs have been fixed and the performance has been improved for DeepSeek3.1/3.2, Qwen3 Dense/MOE models.
### Features
- implement basic framework for batch invariant [#5517](https://github.com/vllm-project/vllm-ascend/pull/5517)
- Eagle spec decode feature now works with full graph mode. [#5118](https://github.com/vllm-project/vllm-ascend/pull/5118)
- Context Parallel(PCP&DCP) feature is more stable now. And it works for most case. Please try it out.
- MTP and eagle spec decode feature now works in most cases. And it's suggested to use them in most cases.
- EPLB feature more stable now. Many bugs have been fixed. Mix placement works now [#6086](https://github.com/vllm-project/vllm-ascend/pull/6086)
- Support kv nz feature for DeepSeek decode node in disagg-prefill scenario [#3072](https://github.com/vllm-project/vllm-ascend/pull/3072)
### Model Support
- LongCat-Flash is supported now.[#3833](https://github.com/vllm-project/vllm-ascend/pull/3833)
- minimax_m2 is supported now. [#5624](https://github.com/vllm-project/vllm-ascend/pull/5624)
- Support for cross-attention and whisper models [#5592](https://github.com/vllm-project/vllm-ascend/pull/5592)
### Performance
- Many custom ops and triton kernels are added in this release to speed up the performance of models. Such as `RejectSampler`, `MoeInitRoutingCustom`, `DispatchFFNCombine` and so on.
- Improved the performance of Layerwise Connector [#5303](https://github.com/vllm-project/vllm-ascend/pull/5303)
### Others
- Basic support Model Runner v2. Model Runner V2 is the next generation of vLLM. It will be used by default in the future release. [#5210](https://github.com/vllm-project/vllm-ascend/pull/5210)
- Fixed a bug that the zmq send/receive may failed [#5503](https://github.com/vllm-project/vllm-ascend/pull/5503)
- Supported to use full-graph with Qwen3-Next-MTP [#5477](https://github.com/vllm-project/vllm-ascend/pull/5477)
- Fix weight transpose in RL scenarios [#5567](https://github.com/vllm-project/vllm-ascend/pull/5567)
- Adapted SP to eagle3 [#5562](https://github.com/vllm-project/vllm-ascend/pull/5562)
- Context Parallel(PCP&DCP) support mlapo [#5672](https://github.com/vllm-project/vllm-ascend/pull/5672)
- GLM4.6 support mtp with fullgraph [#5460](https://github.com/vllm-project/vllm-ascend/pull/5460)
- Flashcomm2 now works with oshard generalized feature [#4723](https://github.com/vllm-project/vllm-ascend/pull/4723)
- Support setting tp=1 for the Eagle draft model [#5804](https://github.com/vllm-project/vllm-ascend/pull/5804)
- Flashcomm1 feature now works with qwen3-vl [#5848](https://github.com/vllm-project/vllm-ascend/pull/5848)
- Support fine-grained shared expert overlap [#5962](https://github.com/vllm-project/vllm-ascend/pull/5962)
### Dependencies
- CANN is upgraded to 8.5.0
- torch-npu is upgraded to 2.8.0.post1. Please note that the post version will not be installed by default. Please install it by hand from [pypi mirror](https://mirrors.huaweicloud.com/ascend/repos/pypi/torch-npu/).
- triton-ascend is upgraded to 3.2.0
### Deprecation & Breaking Changes
- `CPUOffloadingConnector` is deprecated. We'll remove it in the next release. It'll be replaced by CPUOffload feature from vLLM in the future.
- eplb config options is moved to `eplb_config` in [additional config](https://docs.vllm.ai/projects/ascend/en/latest/user_guide/configuration/additional_config.html). The old ones will be removed in the next release.
- `ProfileExecuteDuration` [feature](https://docs.vllm.ai/projects/ascend/en/latest/developer_guide/performance_and_debug/profile_execute_duration.html) is deprecated. It's replaced by `ObservabilityConfig` from vLLM.
- The value of `VLLM_ASCEND_ENABLE_MLAPO` env will be set to True by default in the next release. It'll be enabled in decode node by default. Please note that this feature will cost more memory. If you are memory sensitive, please set it to False.
## v0.13.0rc1 - 2025.12.27
This is the first release candidate of v0.13.0 for vLLM Ascend. We landed lots of bug fix, performance improvement and feature support in this release. Any feedback is welcome to help us to improve vLLM Ascend. Please follow the [official doc](https://docs.vllm.ai/projects/ascend/en/latest) to get started.
### Highlights
- Improved the performance of DeepSeek V3.2, please refer to [tutorials](https://docs.vllm.ai/projects/ascend/en/latest/tutorials/DeepSeek-V3.2.html)
- Qwen3-Next MTP with chunked prefill is supported now [#4770](https://github.com/vllm-project/vllm-ascend/pull/4770), please refer to [tutorials](https://docs.vllm.ai/projects/ascend/en/latest/tutorials/Qwen3-Next.html)
- [Experimental] Prefill Context Parallel and Decode Context Parallel are supported, but notice that it is an experimental feature now, welcome any feedback. please refer to [context parallel feature guide](https://docs.vllm.ai/projects/ascend/en/latest/user_guide/feature_guide/context_parallel.html)
### Features
- Support openPangu Ultra MoE [4615](https://github.com/vllm-project/vllm-ascend/pull/4615)
- A new quantization method W8A16 is supported now. [#4541](https://github.com/vllm-project/vllm-ascend/pull/4541)
- Cross-machine Disaggregated Prefill is supported now. [#5008](https://github.com/vllm-project/vllm-ascend/pull/5008)
- Add UCMConnector for KV Cache Offloading. [#4411](https://github.com/vllm-project/vllm-ascend/pull/4411)
- Support async_scheduler and disable_padded_drafter_batch in eagle. [#4893](https://github.com/vllm-project/vllm-ascend/pull/4893)
- Support pcp + mtp in full graph mode. [#4572](https://github.com/vllm-project/vllm-ascend/pull/4572)
- Enhance all-reduce skipping logic for MoE models in NPUModelRunner [#5329](https://github.com/vllm-project/vllm-ascend/pull/5329)
### Performance
Some general performance improvement:
- Add l2norm triton kernel [#4595](https://github.com/vllm-project/vllm-ascend/pull/4595)
- Add new pattern for AddRmsnormQuant with SP, which could only take effect in graph mode. [#5077](https://github.com/vllm-project/vllm-ascend/pull/5077)
- Add async exponential while model executing. [#4501](https://github.com/vllm-project/vllm-ascend/pull/4501)
- Remove the transpose step after attention and switch to transpose_batchmatmul [#5390](https://github.com/vllm-project/vllm-ascend/pull/5390)
- To optimize the performance in small batch size scenario, an attention operator with flash decoding function is offered, please refer to item 22 in [FAQs](https://docs.vllm.ai/projects/ascend/en/latest/faqs.html) to enable it.
### Other
- OOM error on VL models is fixed now. We're keeping observing it, if you hit OOM problem again, please submit an issue. [#5136](https://github.com/vllm-project/vllm-ascend/pull/5136)
- Fixed an accuracy bug of Qwen3-Next-MTP when batched inferring. [#4932](https://github.com/vllm-project/vllm-ascend/pull/4932)
- Fix npu-cpu offloading interface change bug. [#5290](https://github.com/vllm-project/vllm-ascend/pull/5290)
- Fix MHA model runtime error in aclgraph mode [#5397](https://github.com/vllm-project/vllm-ascend/pull/5397)
- Fix unsuitable moe_comm_type under ep=1 scenario [#5388](https://github.com/vllm-project/vllm-ascend/pull/5388)
### Deprecation & Breaking Changes
- `VLLM_ASCEND_ENABLE_DENSE_OPTIMIZE` is removed and `VLLM_ASCEND_ENABLE_PREFETCH_MLP` is recommend to replace as they always be enabled together. [#5272](https://github.com/vllm-project/vllm-ascend/pull/5272)
- `VLLM_ENABLE_FUSED_EXPERTS_ALLGATHER_EP` is dropped now. [#5270](https://github.com/vllm-project/vllm-ascend/pull/5270)
- `VLLM_ASCEND_ENABLE_NZ` is disabled for float weight case, since we notice that the performance is not good in some float case. Feel free to set it to 2 if you make sure it works for your case. [#4878](https://github.com/vllm-project/vllm-ascend/pull/4878)
- `chunked_prefill_for_mla` in `additional_config` is dropped now. [#5296](https://github.com/vllm-project/vllm-ascend/pull/5296)
- `dump_config` in `additional_config` is renamed to `dump_config_path` and the type is change from `dict` to `string`. [#5296](https://github.com/vllm-project/vllm-ascend/pull/5296)
### Dependencies
- vLLM version has been upgraded to 0.13.0 and drop 0.12.0 support. [#5146](https://github.com/vllm-project/vllm-ascend/pull/5146)
- Transformer version has been upgraded >= 4.57.3 [#5250](https://github.com/vllm-project/vllm-ascend/pull/5250)
### Known Issues
- Qwen3-Next doesn't support long sequence scenario, and we should limit `gpu-memory-utilization` according to the doc to run Qwen3-Next. We'll improve it in the next release
- The functional break on Qwen3-Next when the input/output is around 3.5k/1.5k is fixed, but it introduces a regression on performance. We'll fix it in next release. [#5357](https://github.com/vllm-project/vllm-ascend/issues/5357)
- There is a precision issue with curl on ultra-short sequences in DeepSeek-V3.2. We'll fix it in next release. [#5370](https://github.com/vllm-project/vllm-ascend/issues/5370)
## v0.11.0 - 2025.12.16
We're excited to announce the release of v0.11.0 for vLLM Ascend. This is the official release for v0.11.0. Please follow the [official doc](https://docs.vllm.ai/projects/ascend/en/v0.11.0) to get started. We'll consider to release post version in the future if needed. This release note will only contain the important change and note from v0.11.0rc3.
### Highlights
- Improved the performance for deepseek 3/3.1. [#3995](https://github.com/vllm-project/vllm-ascend/pull/3995)
- Fixed the accuracy bug for qwen3-vl. [#4811](https://github.com/vllm-project/vllm-ascend/pull/4811)
- Improved the performance of sample. [#4153](https://github.com/vllm-project/vllm-ascend/pull/4153)
- Eagle3 is back now. [#4721](https://github.com/vllm-project/vllm-ascend/pull/4721)
### Other
- Improved the performance for kimi-k2. [#4555](https://github.com/vllm-project/vllm-ascend/pull/4555)
- Fixed a quantization bug for deepseek3.2-exp. [#4797](https://github.com/vllm-project/vllm-ascend/pull/4797)
- Fixed qwen3-vl-moe bug under high concurrency. [#4658](https://github.com/vllm-project/vllm-ascend/pull/4658)
- Fixed an accuracy bug for Prefill Decode disaggregation case. [#4437](https://github.com/vllm-project/vllm-ascend/pull/4437)
- Fixed some bugs for EPLB [#4576](https://github.com/vllm-project/vllm-ascend/pull/4576) [#4777](https://github.com/vllm-project/vllm-ascend/pull/4777)
- Fixed the version incompatibility issue for openEuler docker image. [#4745](https://github.com/vllm-project/vllm-ascend/pull/4745)
### Deprecation announcement
- LLMdatadist connector has been deprecated, it'll be removed in v0.12.0rc1
- Torchair graph has been deprecated, it'll be removed in v0.12.0rc1
- Ascend scheduler has been deprecated, it'll be removed in v0.12.0rc1
### Upgrade notice
- torch-npu is upgraded to 2.7.1.post1. Please note that the package is pushed to [pypi mirror](https://mirrors.huaweicloud.com/ascend/repos/pypi/torch-npu/). So it's hard to add it to auto dependence. Please install it by yourself.
- CANN is upgraded to 8.3.rc2.
### Known Issues
- Qwen3-Next doesn't support expert parallel and MTP features in this release. And it'll be oom if the input is too long. We'll improve it in the next release
- Deepseek 3.2 only work with torchair graph mode in this release. We'll make it work with aclgraph mode in the next release.
- Qwen2-audio doesn't work by default. Temporary solution is to set `--gpu-memory-utilization` to a suitable value, such as 0.8.
- CPU bind feature doesn't work if more than one vLLM instance is running on the same node.

1546
.agents/skills/vllm-ascend-release-note-writer/scripts/fetch_commits-optimize.py Normal file
View File

File diff suppressed because it is too large Load Diff