### What this PR does / why we need it?
Port #1916 and #2157 to master branch to fuse operators in deepseek moe
layers, which can reduce scheduling overhead on devices. Note that this
feature is valid only when `tp_size = 1` and
`multistream_overlap_shared_expert` is enabled with torchair graph mode.
### Does this PR introduce _any_ user-facing change?
Users can enable this feature with `--additional-config
'{"torchair_graph_config":{"enabled":true, "enable_super_kernel":true},
"multistream_overlap_shared_expert":true}'`.
### How was this patch tested?
E2E deepseek serving with 2P1D disaggregated prefill scenarios.
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
Signed-off-by: linfeng-yuan <1102311262@qq.com>
### What this PR does / why we need it?
when using dynamic eplb, patch v1 executor to avoid create child process
failed.
### How was this patch tested?
deepseek in v3.
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
Signed-off-by: offline0806 <3337230449@qq.com>
Co-authored-by: offline0806 <3337230449@qq.com>
### What this PR does / why we need it?
Refactor the multi-machine CI use case. The purpose of this PR is to
increase the ease of adding multi-machine CI use cases, allowing
developers to add multi-machine cluster model testing use cases
(including PD separation) by simply adding a new YAML configuration
file.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
Fix dockets CI for main release.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
Signed-off-by: menogrey <1299267905@qq.com>
Pin version that can stable running 310I Duo to vllm-ascend v0.10.0rc1.
### What this PR does / why we need it?
Since PR #2614 310I Duo been broken. Although we are currently working
on fixing the issue with the 310I Duo being broken, there is no
confirmed timeline for a fix in the short term. To allow users to
quickly find a working version instead of going back and forth on trial
and error, this PR fixes the version in the 310I Duo guide.
### Does this PR introduce _any_ user-facing change?
NA
### How was this patch tested?
NA
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
Signed-off-by: leo-pony <nengjunma@outlook.com>
Optimize multi-node guide: more clearer corresponding relationship
between configuration items and nodes
### What this PR does / why we need it?
Some issues caused by misunderstandings due to unclear guidance content,
for example: #3367
### Does this PR introduce _any_ user-facing change?
NA
### How was this patch tested?
NA
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
Signed-off-by: leo-pony <nengjunma@outlook.com>
What this PR does / why we need it?
1.Record expert map without dynamic eplb.
2.Add export PYTHONOPTIMIZE=1 when using dynamic eplb.
3.change eplb doc
Does this PR introduce any user-facing change?
How was this patch tested?
Qwen3_moe in A3.
- vLLM version: v0.11.0
---------
Signed-off-by: offline0806 <3337230449@qq.com>
Co-authored-by: offline0806 <3337230449@qq.com>
### What this PR does / why we need it?
Fix ZeroDivisionError when prefill_tp_size > num_kv_head, in this
situation, num_head_replica can be 0 and used to divide another value,
this PR restricts the minimum value of a to be 1. And this PR fix
tp_resharding README.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
By CI.
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
Signed-off-by: liziyu <liziyu16@huawei.com>
Signed-off-by: nwpu-zxr <zhouxuerong2@huawei.com>
Co-authored-by: liziyu <liziyu16@huawei.com>
### What this PR does / why we need it?
Fix the wrong title of the modelrunner_prepare_inputs docs
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
pass CI
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
Signed-off-by: ChenTaoyu-SJTU <ctynb@qq.com>
### What this PR does / why we need it?
- Refacotr and integrate a unified `WeightPrefetchMethod`
- Integrate `gate_up_proj.weight` in quantized Attention modules
- Prefetching these weights ahead of matmul-like operators imporves
performance by reducing L2 cache transfer latency
### Does this PR introduce _any_ user-facing change?
Add a new config in `--additional-config` for configuration:
```json
{
"weight_prefetch_config": {
"enabled": True,
"prefetch_ratio": {
"moe": {
"gate_up": 0.8
},
},
},
}
```
This feature is enabled by default, and can be disabled through this
configuration
### How was this patch tested?
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
Signed-off-by: yuzhup <15705211260@163.com>
### What this PR does / why we need it?
Resolve the issue where, in the case of unequal TP (Tensor Parallelism),
the TP size is larger than the number of model attention kvcache heads,
causing the KV cache to generate duplicates, which leads to transmission
errors in the original code.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By ci
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
Signed-off-by: nwpu-zxr <zhouxuerong2@huawei.com>
Signed-off-by: wangxiaoteng <wangxiaoteng@huawei.com>
Co-authored-by: nwpu-zxr <zhouxuerong2@huawei.com>
we notice that torch npu 0919 doesn't work. This PR revert related
change which rely on 0919 version.
Revert PR: #3295#3205#3102
Related: #3353
- vLLM version: v0.11.0
### What this PR does / why we need it?
Upgrade deepseek-v3.2 doc for A2
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.11.0
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
- Refacotr and integrate a unified `WeightPrefetchMethod`
- Integrate `qkv_proj.weight` and `o_proj.weight` in quantized Attention
modules
- Prefetching these weights ahead of matmul-like operators imporves
performance by reducing L2 cache transfer latency
### Does this PR introduce _any_ user-facing change?
Add a new config in `--additional-config` for configuration:
```json
{
"weight_prefetch_config": {
"enabled": false,
"prefetch_ratio": {
"attn": {
"qkv": 1.0,
"o": 1.0,
},
},
},
}
```
This feature is enabled by default, and can be disabled through this
configuration
### How was this patch tested?
- vLLM version: v0.11.0
---------
Signed-off-by: yuzhup <15705211260@163.com>
Signed-off-by: zhoux77899 <zhouxiang100@huawei.com>
Co-authored-by: yuzhup <15705211260@163.com>
### What this PR does / why we need it?
Fix doc for A2 series and cleanup note
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
CI passed
- vLLM version: v0.11.0rc3
- vLLM main:
https://github.com/vllm-project/vllm/commit/releases/v0.11.0
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
### What this PR does / why we need it?
Upgrade torch-npu to the newest POC version
### Does this PR introduce _any_ user-facing change?
yes, user need upgrade the pta version as well.
### How was this patch tested?
- vLLM version: v0.11.0rc3
- vLLM main:
https://github.com/vllm-project/vllm/commit/releases/v0.11.0
---------
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
Bump version to v0.11.0rc2 and prepare vLLM Ascend v0.11.0rc0
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
CI passed
- vLLM version: v0.10.2
- vLLM main:
https://github.com/vllm-project/vllm/commit/releases/v0.11.0
---------
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
### What this PR does / why we need it?
add faqs:install vllm-ascend will overwrite existing torch-npu
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.10.2
- vLLM main:
https://github.com/vllm-project/vllm/commit/releases/v0.11.0
Signed-off-by: weiguihua2 <weiguihua2@huawei.com>
### What this PR does / why we need it?
This PR provides user guide documents for Qwen3-VL 4B and
Qwen3-VL-235B-A22B.
### Does this PR introduce _any_ user-facing change?
None
### How was this patch tested?
- vLLM version: v0.10.2
- vLLM main:
https://github.com/vllm-project/vllm/commit/releases/v0.11.0
---------
Signed-off-by: booker123456 <945658361@qq.com>
### What this PR does / why we need it?
1. Solved the issue where sizes capture failed for the Qwen3-32b-int8
model when aclgraph, dp1, and tp4 were enabled.
2. Added the exception thrown when sizes capture fails and provided a
solution
3. Add this common problem to the FAQ doc
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
ut
- vLLM version: v0.10.2
- vLLM main:
https://github.com/vllm-project/vllm/commit/releases/v0.11.0
Signed-off-by: lilinsiman <lilinsiman@gmail.com>
### What this PR does / why we need it?
1.Support deepseek w4a8 per-channel quantization
2.The eager mode supports converting weights to the NZ format
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
#### How to get weights using Modelslim
##### Installation steps
git clone https://gitcode.com/Ascend/msit.git
cd msit/msmodelslim
bash install.sh
##### Generate w4a8 per-channel weights
cd /example/DeepSeek
Command reference: msmodelslim/example/DeepSeek/README.md
- vLLM version: v0.10.2
- vLLM main:
f225ea7dd9
---------
Signed-off-by: Wang Kunpeng <1289706727@qq.com>
### What this PR does / why we need it?
Revise the EPLB feature guide content.Add eplb params to ascend config.
### Does this PR introduce any user-facing change?
### How was this patch tested?
- vLLM version: v0.10.2
- vLLM main:
52d0cb8458
Co-authored-by: offline0806 <3337230449@qq.com>
# What this PR does / why we need it?
When processing a mix of large and small requests, the TTFT of responses
is significantly reduc\ed. Please refer to
https://github.com/vllm-project/vllm/pull/10235, which achieves the same
effect by simply limiting the number of prompt fills for long requests.
This solution can be applied to both AscendScheduler (V0) and vLLM
Scheduler (V1). Tests show that TTFT can be significantly improved when
handling such mixed requests. However, This capability is currently
missing when Ascend Scheduler is enabled.
This benchmark used the Qwen3-8B model, with a context length of 128K,
running on a single card.
Regarding dataset selection, the sharegpt_clean dataset is used, with
its content concatenated and cropped. Small requests with token=50 and
medium requests with token=10240 were constructed (there were also large
requests with token=102400, but these were ignored because when using
the Prefill First scheduling strategy, max_num_batched_tokens will not
be set to such a large value). When loading vLLM, set
max_num_batched_tokens=22000. This length can accommodate two
medium-sized requests and some short requests, reflecting an extreme
scenario where the budget is almost entirely occupied by longer
requests.
Next, we mix 990 small requests and 100 medium requests into one type of
load scenario (hereinafter referred to as 10%), and similarly generate
load scenarios with 5% medium requests and 1% load scenarios.
Performance tests were conducted separately for enabling vLLMScheduler,
AscendScheduler, and AscendScheduler (long prompt concurrency set to 1).
- vLLM version: v0.10.2
- vLLM main:
1dfea5f4a9
---------
Signed-off-by: Csrayz <jover@cmbchina.com>
This PR puts the calculation of shared experts into a separate stream,
overlaping with routing experts.
- vLLM version: v0.10.2
- vLLM main:
fbd6523ac0
---------
Signed-off-by: whx-sjtu <2952154980@qq.com>
### What this PR does / why we need it?
Remove chunked prefill for mla branch in mla , and change dtype of
prefill_mask to avoid accuracy problem
### Does this PR introduce _any_ user-facing change?
NO
### How was this patch tested?
- vLLM version: v0.10.2
- vLLM main:
ef7eefe17a
---------
Signed-off-by: SunnyLee219 <3294305115@qq.com>
### What this PR does / why we need it?
Add multi-node ray backend tutorial for Qwen235B-A3B
### How was this patch tested?
- vLLM version: v0.10.2
- vLLM main:
f4cd80f944
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
Add an option of enable frozen parameter
### How was this patch tested?
- vLLM version: v0.10.2
- vLLM main:
68dbde5dbb
Signed-off-by: 1Fire4 <wangdingyi2@huawei.com>
### Motivation
Currently dynamically experts balancing would stop-the-world.
Asynchronously expert load balancing would be better without flowing
problems:
Host-bound latency:
There are many cpu operations during EPLB such as
eplb-algorithm、creating p2p ops、and log2phy expert converting would
spend long cpu time, as ~1s.
Communication latency: The transfer time would cost much in the
situation without nvlink. As the weight of an expert maybe transfer to
multiple new positions, thus N times send/recv for one expert, with
result long latency. We had tested that batch_isend_irecv cost more
100ms for 16 experts weight transmission in A2 server of ascend.
SwiftBalancer would not stop-the-world anymore, in out test on NPU 1~2ms
cost for each layer while benefit 5ms-8ms decode latency with ep_size =
64.
The following updates have been made:
1、expert distribution recording with lower cost.
2、async cpu computing for eplb algo and other python operator.
3、new eplb algo with less expert rebalancing while almost the same
effect.
### Proposed Change
We will gradually migrate the EPLB logic to the VLLM community and
implement a generalized design. Relevant RFC:
https://github.com/vllm-project/vllm/issues/22246
The overall workflow involves:
<img width="801" height="302"
alt="474430541-23b06f58-23bc-44a3-a1be-00f268aeb15c"
src="https://github.com/user-attachments/assets/1d73a459-1b23-4b0a-812a-bf0a75debfed"
/>
1. Record experts distribution during forward. We using expert_token_num
after disptach instead of topk_ids, thus we got much smaller tensor
shape to reduce cost of hbm recording and add-operator.
2. Do all-gather for experts distribution. Using all-gather instead of
all-reduce as less traffic volume.
3. Wake up eplb worker process with experts distribution when
num_iterations comes. Run eplb algorithm in eplb worker.
4. Generate p2p send/recv ops and other operator such as log2phy would
cost long cpu time.
5. Lanch ibatch_send_recv in async_stream before forward.
6. After forward, wait for the ibatch_send_recv finish, then do uapte
expert map and expert weights.
### Co-author
Co-authored-by: raindaywhu raindaywhu@raindaywhu@ 163.con
Co-authored-by: njuyuan yuanjl19@smail.nju.edu.cn
Co-authored-by: qmkakaxi wjh1594260677@qq.com
Co-authored-by: Skywalker-EP 173723846@qq.com
- vLLM version: v0.10.2
- vLLM main:
567939953b
---------
Signed-off-by: offline0806 <z00858301@china.huawei.com>
Co-authored-by: offline0806 <z00858301@china.huawei.com>
### What this PR does / why we need it?
Update max_tokens and prompt in qwen3 online doc
Before:
```
"'max_tokens' or 'max_completion_tokens' is too large: 4096. This model's maximum context length is 4096 tokens and your request has 18 input tokens (4096 > 4096 - 18). None"
```
After:
```
curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{
"model": "/root/.cache/modelscope/hub/models/Qwen-SGlang/Qwen3-Next-80B-A3B-Instruct",
"messages": [
{"role": "user", "content": "Who are you?"}
],
"temperature": 0.6,
"top_p": 0.95,
"top_k": 20,
"max_tokens": 32
}'
.{"id":"chatcmpl-8ddbd65c9ddc405397219a6792feb9a0","object":"chat.completion","created":1757985049,"model":"/root/.cache/modelscope/hub/models/Qwen-SGlang/Qwen3-Next-80B-A3B-Instruct","choices":[{"index":0,"message":{"role":"assistant","content":"Hello! I am Qwen, a large-scale language model independently developed by the Tongyi Lab under Alibaba Group. I am designed to assist you in generating various","refusal":null,"annotations":null,"audio":null,"function_call":null,"tool_calls":[],"reasoning_content":null},"logprobs":null,"finish_reason":"length","stop_reason":null,"token_ids":null}],"service_tier":null,"system_fingerprint":null,"usage":{"prompt_tokens":12,"total_tokens":44,"completion_tokens":32,"prompt_tokens_details":null},"prompt_logprobs":null,"prompt_token_ids":null,"kv_transfer_params":null}
```
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- Manually test on my local env
- CI passed
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
### What this PR does / why we need it?
Bump vLLM version to v0.10.2
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
CI passed
- vLLM version: v0.10.2rc3
- vLLM main:
15b8fef453
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
### What this PR does / why we need it?
This reverts commit 339fceb89c1571eb8751157bf2d19a9b8479aed0.
### Does this PR introduce _any_ user-facing change?
Yes, use 8.2rc1 image by default
### How was this patch tested?
CI passed
- vLLM version: v0.10.2rc2
- vLLM main:
cfa3234a5b
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
### What this PR does / why we need it?
Upgrade CANN version to 8.3.rc1.alpha001
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- vLLM version: v0.10.2rc2
- vLLM main:
89e08d6d18
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
### What this PR does / why we need it?
Upgrade vLLM version to 0.10.2rc2
### Does this PR introduce _any_ user-facing change?
Yes, image will use 0.10.2rc2 vLLM
### How was this patch tested?
- vLLM version: main
- vLLM main:
f17c075884
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
### What this PR does / why we need it?
Remove compatibility maintenance for vllm v0.10.1 and v0.10.1.1
### Does this PR introduce _any_ user-facing change?
branch main of vllm-ascend will not be compatible with vllm v0.10.1 and
v0.10.1.1
### How was this patch tested?
CI passed with existing test.
- vLLM version: v0.10.1.1
- vLLM main:
6fb2788163
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
The PR is for the document of the prefiller&decoder disaggregation
deloyment guide.
The scenario of the guide is:
- Use 3 nodes totally and 2 NPUs on each node
- Qwen3-30B-A3B
- 1P2D
- Expert Parallel
The deployment can be used to verify PD Disggregation / Expert Parallel
features with a slightly less resources.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
No.
- vLLM version: v0.10.1.1
- vLLM main:
e599e2c65e
---------
Signed-off-by: paulyu12 <507435917@qq.com>
### What this PR does / why we need it?
Add note for Ascend HDK version
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
CI passed
- vLLM version: v0.10.1.1
- vLLM main:
e599e2c65e
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
### What this PR does / why we need it?
This PR introduces Oproj matrix tensor model parallel to achieve
decreasing of memory consumption. It only support graph mode in pure DP
scenario.
In deepseek r1 w8a8 PD disagregated Decode instance, using pure DP, with
oproj_tensor_parallel_size = 8, we have 1 ms TPOT increasing, saved 5.8
GB NPU memory per RANK. We got best performance when
oproj_tensor_parallel_size=4 without TPOT increasing.
performance data:
<img width="1442" height="442" alt="image"
src="https://github.com/user-attachments/assets/83270fc5-868a-4387-b0a9-fac29b4a376d"
/>
### Does this PR introduce _any_ user-facing change?
This PR introduces one new config in `additional_config`.
| Name | Effect | Required | Type | Constraints |
| :---------------------------- |
:--------------------------------------- | :------- | :--- |
:----------------- |
| oproj_tensor_parallel_size | Split the o_proj matrix along the row
dimension (head num * head dim) into oproj_tensor_parallel_size pieces.
| No | int | default value is None, once this value is set, the feature
will be enabled, head num * head dim must be divisible by this value. |
example
`--additional_config={"oproj_tensor_parallel_size": 8}`
### How was this patch tested?
- vLLM version: v0.10.1.1
- vLLM main:
eddaafc1c7
---------
Signed-off-by: zzhx1 <zzh_201018@outlook.com>
Co-authored-by: zzh <zzh_201018@outlook.com>
