### What this PR does / why we need it?
Move torchair related fused_moe section into torchair_fused_moe to make
the code clear. Next step we'll remove all torchair related code outside
of torchair_fused_moe .
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
vLLM version: v0.10.0
vLLM main:
08d5f7113a
- vLLM version: v0.10.1.1
- vLLM main:
170e8ea9ea
Signed-off-by: hust17yixuan <303660421@qq.com>
### What this PR does / why we need it?
Register RotaryEmbedding instead of overwrite forward
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
CI passed with new added/existing test.
- vLLM version: v0.10.0
- vLLM main:
808d2e9aa0
---------
Signed-off-by: Icey <1790571317@qq.com>
Signed-off-by: wxsIcey <1790571317@qq.com>
### What this PR does / why we need it?
Refactor all2all-related fused_experts (both quantized/unquantized) into
TokenDispatcherWithAll2AllV, including dispatch & combine calculation.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
E2E & UT
- vLLM version: v0.10.0
- vLLM main:
65197a5fb3
Signed-off-by: Pr0Wh1teGivee <calvin_zhu0210@outlook.com>
### What this PR does / why we need it?
Add configuration check logic for ascend scheduler: if chunked_prefill
is disabled, max_num_batched_tokens couldn't be less than max_model_len,
following vLLM;
### Does this PR introduce _any_ user-facing change?
users cannot set max_num_batched_tokens smaller than max_model_len with
ascend scheduler
### How was this patch tested?
CI and vllm serving passed
- vLLM version: v0.10.0
- vLLM main:
f77a0802b7
Signed-off-by: linfeng-yuan <1102311262@qq.com>
### What this PR does / why we need it?
Fix mtp mode ut
### Does this PR introduce _any_ user-facing change?
Nothing
### How was this patch tested?
This can be tested in the same way as a unit test.
- vLLM version: v0.10.0
- vLLM main:
53415653ff
Signed-off-by: 赵江江 <zhaojiangjiang1@h-partners.com>
Co-authored-by: 赵江江 <zhaojiangjiang1@h-partners.com>
### What this PR does / why we need it?
Fix the bug of incorrect precision
- vLLM version: v0.10.0
- vLLM main:
53415653ff
---------
Signed-off-by: weiguihua2 <weiguihua2@huawei.com>
### What this PR does / why we need it?
1. use action/checkout@v5 instead of v4
2. remove dbo test case because there is issue with it and will be
refactored later
3. make vllm-ascend compatible with vllm v0.10.1.1 and add CI for it
4. fix sampler api changes introduced by
https://github.com/vllm-project/vllm/pull/22387
6. fix qwen3 moe config changes intruoduced by
https://github.com/vllm-project/vllm/pull/20562
7. fix kvcache block changes introduced by
https://github.com/vllm-project/vllm/pull/23262
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
CI passed with existing test.
- vLLM version: v0.10.0
- vLLM main:
0c6e40bbaa
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
This PR move current unified mla backend to torchair folder and remove
torchair-related code in attention/mla_v1.py (1.3k -> 0.9k).
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Running eager mode with mla backend, and torchair mode with code before
[2445](https://github.com/vllm-project/vllm-ascend/pull/2445)
- vLLM version: v0.10.0
- vLLM main:
f571ff8eb6
Signed-off-by: linfeng-yuan <1102311262@qq.com>
### What this PR does / why we need it?
For dense models, by not applying tensor parallelism (TP) to the
attention module and applying TP to the MLP module, the allreduce
operations in the attention module can be eliminated, thereby reducing
computational overhead. However, this approach increases memory usage,
so the environment variable VLLM_ASCEND_ENABLE_MLP_OPTIMZE is used to
control this optimization.
- vLLM main:
b17109beea
Signed-off-by: wangxiaoxin-sherie <wangxiaoxin7@huawei.com>
Co-authored-by: wangxiaoxin-sherie <wangxiaoxin7@huawei.com>
refact model runner v1
### What this PR does / why we need it?
1. Separate the execute model logic from the prepare input logic
2. Disassemble the torchchair in model runner v1
- vLLM version: v0.10.0
- vLLM main:
68fcd3fa73
---------
Signed-off-by: weiguihua2 <weiguihua2@huawei.com>
### What this PR does / why we need it?
enable_shared_pert_dp is currently on by default. This optimization is
currently only valid for deepseek series models. The default opening
affects the accuracy of the qwen series models.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
use parameter --additional_config='{"enable_shared_expert_dp": true}'
- vLLM version: v0.10.0
- vLLM main:
d983769c41
Signed-off-by: Wang Kunpeng <1289706727@qq.com>
### What this PR does / why we need it?
The deepseek w4a8 weights we supported before were in mindie-format
format. It uses int8 to represent int4, so the weight size is similar to
w8a8, and we need to do a few extra steps to make vllm-ascend load it
normally.
Now we can directly use the new weight format, which uses two int4 packs
to save the weight, the weight size is reduced, and there is no need to
do many extra operations to directly use it on vllm-ascend, but we are
also compatible with the weights of the previous mindie format.
The weight changes in the new version:
1. The weight is packed （2 int4 pack to int8）
2. The bias required in the apply method is directly generated by
modelslim
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
Adding ut case in `tests/ut/quantization/test_w4a8_dynamic.py`
#### 1.How to get weights using Modelslim
##### Installation steps
we can use the branch br_release_MindStudio_8.1.RC2_TR5_20260624
git clone -b br_release_MindStudio_8.1.RC2_TR5_20260624
https://gitee.com/ascend/msit.git
cd msit/msmodelslim
bash install.sh
##### Generate w4a8 weights
cd /example/DeepSeek
Command reference: msmodelslim/example/DeepSeek/README.md Execute the
[pre-check](https://gitee.com/ascend/msit/blob/br_release_MindStudio_8.1.RC2_TR5_20260624/msmodelslim/example/DeepSeek/README.md#%E8%BF%90%E8%A1%8C%E5%89%8D%E5%BF%85%E6%A3%80)
and [DeepSeek-R1 w4a8 mix
quantization](https://gitee.com/ascend/msit/blob/br_release_MindStudio_8.1.RC2_TR5_20260624/msmodelslim/example/DeepSeek/README.md#deepseek-r1-w4a8-%E6%B7%B7%E5%90%88%E9%87%8F%E5%8C%96%E5%89%8D%E4%B8%89%E5%B1%82-mlpw8a8-dynamic-%E9%87%8F%E5%8C%96mla%E5%85%B1%E4%BA%AB%E4%B8%93%E5%AE%B6w8a8%E9%87%8F%E5%8C%96%E8%B7%AF%E7%94%B1%E4%B8%93%E5%AE%B6w4a8-dynamic%E9%87%8F%E5%8C%96)
chapter
Reference command：python3 quant_deepseek_w4a8.py --model_path {Original
weight path} --save_path {Generate weight path}
##### Adapt to vllm-ascend
Modification in `config.json`：`"model_type":deepseekv2` is changed to
`"model_type":deepseek_v3`;
#### 2.How to run w4a8
##### a.How to run eager mode
export VLLM_ASCEND_MLA_PA=1
python -m vllm.entrypoints.openai.api_server --model=$1
--trust-remote-code -tp $2 -dp $3 --enable_expert_parallel
--quantization ascend --port $4 --max-model-len $5 --max-num-seqs $6
--enforce-eager
eg: python -m vllm.entrypoints.openai.api_server
--model=/weightpath/w4a8_4_layer --trust-remote-code -tp 4 -dp 4
--enable_expert_parallel --quantization ascend --port 8002
--max-model-len 5120 --max-num-seqs 128 --enforce-eager
##### b.How to run graph mode
export HCCL_BUFFSIZE=1024
python -m vllm.entrypoints.openai.api_server --model=$1
--trust-remote-code -tp $2 -dp $3 --enable_expert_parallel
--quantization ascend --port $4 --max-model-len $5
--additional_config='{"ascend_scheduler_config":{"enabled":true},"torchair_graph_config":{"enabled":true}}'
eg: python -m vllm.entrypoints.openai.api_server
--model=/weight/dsr1_w4a8_vllm --trust-remote-code -tp 4 -dp 4
--enable_expert_parallel --quantization ascend --port 8002
--max-model-len 5120
--additional_config='{"ascend_scheduler_config":{"enabled":true},"torchair_graph_config":{"enabled":true}}'
- vLLM version: v0.10.0
- vLLM main:
103f1ec8d3
---------
Signed-off-by: Wang Kunpeng <1289706727@qq.com>
1. rename `VLLM_LLMDD_RPC_PORT` to `VLLM_ASCEND_LLMDD_RPC_PORT` to make
the prefix the same in vllm-ascend
2. enable `VLLM_ASCEND_LLMDD_RPC_IP` env for PD feature.
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
Fix some ci issue and refactor modelrunner
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
CI passed with existing test.
- vLLM version: v0.10.0
- vLLM main:
4d9c61993a
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
Signed-off-by: MengqingCao <cmq0113@163.com>
Signed-off-by: weiguihua2 <weiguihua2@huawei.com>
Co-authored-by: wangli <wangli858794774@gmail.com>
Co-authored-by: weiguihua2 <weiguihua2@huawei.com>
### What this PR does / why we need it?
Remove redundant parentheses in pangu_moe.py.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Local.
- vLLM version: v0.10.0
- vLLM main:
099c046463
Signed-off-by: xleoken <xleoken@163.com>
### What this PR does / why we need it?
1. Move `torchair_attention` to `torchair` dir.
2. Make `AscendAttentionTorchairBackend` extend `AscendAttentionBackend`
to reduce duplicate methods.
3. Make `AscendTorchairMetadata` extend `AscendMetadata` to reduce
duplicate properties.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.10.0
- vLLM main:
0933f9d518
---------
Signed-off-by: shen-shanshan <467638484@qq.com>
### What this PR does / why we need it?
Our current code register the custom ops inside the platform
intialization phase. however, when a new process started by creating a
worker, the former patch will lose it effect on the custom ops and lead
to fallback to the native pass wrote in vllm. This PR move the patch
code to the worker to make sure the custom op patch worker as our
expected.
### Does this PR introduce _any_ user-facing change?
No
- vLLM version: v0.10.0
- vLLM main:
8ea0c2753a
Signed-off-by: ganyi <pleaplusone.gy@gmail.com>
### What this PR does / why we need it?
Add two custom operators (sgmv_shrink and sgmv_expand) to address the
performance issues of LoRA. Meanwhile, enable the graph mode for LoRA
operators to enter ACL, so as to improve the model inference
performance.
### Does this PR introduce _any_ user-facing change?
no user-facing change
### How was this patch tested?
Based on the actual test of the QWen2.5 7B model using vllm-ascend
version v0.9.2.rc1, in acl graph mode, the TTFT, TPOT and throughput
have increased by about 100%.
Signed-off-by: liuchn <909698896@qq.com>
- vLLM version: v0.10.0
- vLLM main:
1f83e7d849
---------
Signed-off-by: liuchn <909698896@qq.com>
Co-authored-by: liuchn <909698896@qq.com>
### What this PR does / why we need it?
Move torchair related model arch into torchair moduel to make the code
clear. Next step we'll remove all torchair related code outside of
torchair moduel.
### Does this PR introduce _any_ user-facing change?
No.
- vLLM version: v0.10.0
- vLLM main:
08d5f7113a
Signed-off-by: linfeng-yuan <1102311262@qq.com>
### What this PR does / why we need it?
This PR adopt Mooncake TransferEngine for kv cache register and
pull_blocks style disaggregate prefill implementation.
### Does this PR introduce any user-facing change?
No
### Dependencies
1. Cann Dependencies
Using Mooncake TransferEngine with Ascend Transport requires CANN
version 8.2.RC1 or higher.（see detail
Mooncake[#502](https://github.com/kvcache-ai/Mooncake/pull/502)）
2. vllm-ascend
This PR depends on changes introduced by #950 (modifications to
`model_runner_v1`) and #1361 (updates to `schedule`), both of which have
been merged into the `v0.9.1-dev` branch and are expected to land in
`main` shortly.
### How was this patch tested?
- vLLM version: v0.10.0
- vLLM main:
1c859a1387
---------
Signed-off-by: leichao.lc <leichao139636@163.com>
Co-authored-by: jianzs <zheng.shoujian@outlook.com>
Co-authored-by: zzy-ContiLearn <1831242919@qq.com>
Co-authored-by: fems14 <1804143737@qq.com>
Co-authored-by: Dreamerleader <2270923832@qq.com>
Co-authored-by: chris668899 <15105191595@126.com>
Co-authored-by: Pz1116 <zpbzpb123123@gmail.com>
### What this PR does / why we need it?
Mainline vLLM fixes its disaggregated prefill in
https://github.com/vllm-project/vllm/pull/22598 . But it is still not
working in vllm-ascend.
To be concrete, decoder instances crash before vllm's fix and hang after
vllm's fix in ascend devices.
This patch allows disaggregated prefill to work.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Qwen3-0.6B 1P1D tp=1 dp=1
- vLLM version: v0.10.0
- vLLM main:
0fe85087a9
---------
Signed-off-by: CaveNightingale <cavenightingale@foxmail.com>
### What this PR does / why we need it?
cache.py only contains a function that will never be called, so remove
it.
### Does this PR introduce _any_ user-facing change?
No
- vLLM version: v0.10.0
- vLLM main:
f1f0d2fab8
Signed-off-by: 22dimensions <waitingwind@foxmail.com>
### What this PR does / why we need it?
1. update `CachedRequestState` as `NewRequestData` changed in
https://github.com/vllm-project/vllm/pull/22570
2. drop maintenance of vllm v0.10.0 in the branch main
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
CI passed with existing test.
- vLLM version: v0.10.0
- vLLM main:
92ff41abea
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
Use function CustomOp.register_oot to achieve the customop registery
```
from vllm.model_executor.custom_op import CustomOp
CustomOp.register_oot(_decorated_op_cls=AscendRMSNorm, name="RMSNorm")
```
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
CI passed with new added/existing test.
- vLLM version: v0.10.0
- vLLM main:
afa5b7ca0b
---------
Signed-off-by: Icey <1790571317@qq.com>
### What this PR does / why we need it?
this pr refactor select_experts of moe module
i merge implementations of quantitative and non-quantitative method in a
new class
use such as vllm like ExpertsSelector.select_experts
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
test in qwen3-moe and all ut.
- vLLM version: v0.10.0
- vLLM main:
e18859298d
Signed-off-by: yangcheng <yangcheng104@huawei.com>
Co-authored-by: yangcheng (AJ) <y00806874@china.huawei.com>
### What this PR does / why we need it?
Remove redundant imported `envs`, using `envs_ascend` instead.
```python
import vllm.envs as envs_vllm
import vllm_ascend.envs as envs_ascend
```
- vLLM version: v0.10.0
- vLLM main:
71683ca6f6
---------
Signed-off-by: shen-shanshan <467638484@qq.com>
### What this PR does / why we need it?
Refactor V1 Attention for better extensibility (prepared for torchair
attention refactor).
**Main changes:**
- Move different kinds of foward into their method respectively, e.g.,
`_forward_prefill_no_cache()`, `_forward_prefill_cache_hit()`,
`_forward_decode_only()`, `_forward_v1_style()`.
### Does this PR introduce _any_ user-facing change?
No.
- vLLM version: v0.10.0
- vLLM main:
14a5d903ab
Signed-off-by: shen-shanshan <467638484@qq.com>
### What this PR does / why we need it?
Add the missing `apply_router_weight_on_input` in FusedMoE init
Quick fix on
https://github.com/vllm-project/vllm-ascend/pull/2268#discussion_r2265828849
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
CI passed with existing test.
- vLLM version: v0.10.0
- vLLM main:
6807af8f46
Signed-off-by: MengqingCao <cmq0113@163.com>
The attn mask was declared in the mla.py，we don't need the splitfuse
mask when mla chunkprefill, and this mask will cause memory problem when
long context like 64k or 128k
- vLLM version: v0.10.0
- vLLM main:
14a5d903ab
---------
Signed-off-by: haojiangzheng <justineric096@gmail.com>
### What this PR does / why we need it?
This PR refactors the MoE (Mixture of Experts) communication logic by
introducing a strategy pattern. It defines an abstract base class,
`MoECommMethod`, which encapsulates different communication strategies
for MoE layers. By decoupling the MoE implementation from any single
communication method, this change makes it simpler to add, replace, or
optimize communication strategies in the future.
Plan / Roadmap
1. Introduce `MoECommMethod`, implement `AllGatherImpl`, and adapt ACL
Graph handling to cover all scenarios (this PR).
2. Implement `MC2CommImpl` and `AllToAllCommImpl` to optimize
performance in specific scenarios.
3. Enable W8A8 / Int8 models to use `unified_fused_experts`.
Other notes
* Data-parallel (DP) communication currently does not work with vLLM's
dispatch/combine mechanisms; an alternative approach is required to
resolve this incompatibility.
- vLLM version: v0.10.0
- vLLM main:
f7ad6a1eb3
---------
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
There is lot of torchair code in model runner leading the code hard for
maintenance. We'll create new torchair_model_runner to split torchair
related logic. Following the workflow #2203
What's this PR do:
create common function `_capture_model` for capture_model
- vLLM version: v0.10.0
- vLLM main:
1891a265d3
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
1.Shared Expert Sharding Strategy Update: Switched from TP-aligned to
pure DP for shared experts, enabling more efficient execution.
2.O_Proj AllReduce → ReduceScatter: Reduced communication overhead by
using ReduceScatter, made possible by pure DP sharding.
3.AllGather Postponed: Delayed to after QKV down projection to reduce
synchronization impact during prefill.
### How was this patch tested?
Adding ut case in `tests/ut/attention/test_mla_v1.py`
#### How to run
use parameter `--additional_config='{"enable_shared_expert_dp": true}'`
##### a.How to run eager mode
eg:
python -m vllm.entrypoints.openai.api_server --model=/model_path
--trust-remote-code -tp 8 -dp 2 --enable_expert_parallel --port 8002
--max-model-len 5120 --max-num-batched-tokens 16384 --enforce-eager
--disable-log-requests
--additional_config='{"ascend_scheduler_config":{"enabled":true},"enable_shared_expert_dp":
true,"chunked_prefill_for_mla":true}'
##### b.How to run graph mode
eg:
python -m vllm.entrypoints.openai.api_server --model=/model_path
--trust-remote-code -tp 8 -dp 2 --enable_expert_parallel --port 8002
--max-model-len 5120 --max-num-batched-tokens 16384
--disable-log-requests
--additional_config='{"ascend_scheduler_config":{"enabled":true},"enable_shared_expert_dp":
true,"chunked_prefill_for_mla":true,"torchair_graph_config":{"enabled":true}}'
- vLLM version: v0.10.0
- vLLM main:
9edd1db02b
---------
Signed-off-by: Wang Kunpeng <1289706727@qq.com>
Signed-off-by: SlightwindSec <slightwindsec@gmail.com>
Co-authored-by: SlightwindSec <slightwindsec@gmail.com>
There is lot of torchair code in model runner leading the code hard for
maintenance. We'll create new torchair_model_runner to split torchair
related logic. Following the workflow #2203, this is the first PR.
What's this PR do:
create common function `_convert_torch_foramt` for initialize_kv_cache
- vLLM version: v0.10.0
- vLLM main:
14a5d903ab
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
chunkprefill mla only support eager mode now，we want to optimaze it by
support torchair graph, the idea is simple, when all the request is
running in decode, use torchair graph to deal with it, else when
chunkprefill or prefill only, use the eager mode
- vLLM version: v0.10.0
- vLLM main:
ebf7605b0d
Signed-off-by: haojiangzheng <justineric096@gmail.com>
Co-authored-by: haojiangzheng <justineric096@gmail.com>
There is lot of torchair code in model runner leading the code hard for
maintenance. We'll create new torchair_model_runner to split torchair
related logic. Following the workflow #2203, this is the first PR.
What's this PR do:
create common function `_build_attention_metadata` and
`_generate_dummy_run_hidden_states` for dummy_run
- vLLM version: v0.10.0
- vLLM main:
ebf7605b0d
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
This PR port optimization in PR #2002 to main and makes it cleaner.
- vLLM version: v0.10.0
- vLLM main:
afa5b7ca0b
---------
Signed-off-by: whx-sjtu <2952154980@qq.com>
### What this PR does / why we need it?
Thanks to the PR https://github.com/vllm-project/vllm-ascend/pull/426
make vllm-ascend support the aclgraph inference to reduce the host
overhead. However, the capability of aclgraph strongly relies on the
functionality provided by `torch.compile`, which is the key feature
supported in torch 2.x . Therefore, capture custom op into aclgraph is
only possible when it can be recognize and captured by `torch.compile`.
In this PR, we register the meta implementation of current custom ops to
enable the fx graph capture. And by doing that, insert those custom ops
into aclgraph become a natural thing to the ascend runtime.
### Does this PR introduce _any_ user-facing change?
No user face change.
### How was this patch tested?
Tested in unittest, we will integrate the `rotary_embedding` op into a
small custom model and use `torch.compile` and aclgraph to capture and
replay it to verify its functionality.
- vLLM version: v0.10.0
- vLLM main:
1b99028069
---------
Signed-off-by: ganyi <pleaplusone.gy@gmail.com>
There is lot of torchair code in model runner leading the code hard for
maintenance. We'll create new torchair_model_runner to split torchair
related logic. Following the workflow #2203
What's this PR do:
move `torchair` related logic into `_get_forward_metadata_across_dp` and
override it in torchair model runner
- vLLM version: v0.10.0
- vLLM main:
1b99028069
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
Fix AscendFusedMoE init error. Use `super().__init__()` instead of
`super(FusedMoE, self).__init__()` to ensure the member variables in
base class could be called by the children class
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
CI passed with new existing test.
- vLLM version: v0.10.0
- vLLM main:
766bc8162c
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
torch_npu.npu_grouped_matmul:
https://www.hiascend.com/document/detail/zh/Pytorch/710/apiref/torchnpuCustomsapi/context/torch_npu-npu_grouped_matmul.md
According to the document, when `split_item` is 2 or 3,
`torch_npu.npu_grouped_matmul` will return a list which has one element.
Therefore, the `torch.cat` after `torch_npu.npu_grouped_matmul` is
unnecessary.
### Does this PR introduce _any_ user-facing change?
not involved
### How was this patch tested?
ut and e2e covered: `tests/ut/ops/test_fused_ops.py`,
`tests/e2e/singlecard/ops/test_fused_moe.py`
**performance**:
(qwen3 30B, 2k->20k)
base:
Total Token throughput (tok/s): 667.76
remove cat:
Total Token throughput (tok/s): 680.82
- vLLM version: v0.10.0
- vLLM main:
fa00c5d75b
Signed-off-by: huangxialu <huangxialu1@huawei.com>
### What this PR does / why we need it?
This PR is to add e2e test for using npu_mm_all_reduce_base fusion
kernel.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
not involved
- vLLM version: v0.10.0
- vLLM main:
5d5d419ca6
Signed-off-by: Ronald1995 <ronaldautomobile@163.com>
### What this PR does / why we need it?
Qwen3 MoE supports SP. In scenarios like AlltoAll, AlltoAllv, and MC2,
replacing AllReduce with Reduce-Scatter and AllGather achieves
computational benefits in norm operations while saving one AllGather
communication. This feature is enabled during the P-phase and delivers
notable gains in long-sequence scenarios (e.g., 16k–25k), with
performance improvements reaching 5%–10%.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
```
compilation_config={
"pass_config":{
"enable_sequence_parallelism": True
}
},
enable_expert_parallel=True,
```
- vLLM version: v0.10.0
- vLLM main:
9edd1db02b
---------
Signed-off-by: libaokui <libaokui@huawei.com>
Co-authored-by: libaokui <libaokui@huawei.com>
### What this PR does / why we need it?
Fix `ascend_env has no attr VLLM_ASCEND_ENABLE_CHUNK_MC2`, remove
useless lines
- vLLM version: v0.10.0
- vLLM main:
9edd1db02b
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
Support MTP with：
- [x] V0 Scheduler
- [x] TorchAir
- [x] Single DP
- [x] Multi DP
- [x] Disaggregate PD
Known issues：
- [ ] Not support V1 Scheduler (chunked prefill), will be supported in a
few weeks
- [ ] vllm v0.10.0 does not support metrics with `DP > 1` right now,
need to comment out the line 171-175 in file
`vllm/vllm/v1/metrics/loggers.py`
```
if (len(self.engine_indexes) > 1
and vllm_config.speculative_config is not None):
raise NotImplementedError("Prometheus metrics with Spec Decoding "
"with >1 EngineCore per AsyncLLM is not "
"supported yet.")
```
To start an online server with torchair enabled, here is an example:
```
python -m vllm.entrypoints.openai.api_server \
--model="/weights/DeepSeek-R1_w8a8/" \
--trust-remote-code \
--max-model-len 40000 \
--tensor-parallel-size 4 \
--data_parallel_size 4 \
--max-num-seqs 16 \
--no-enable-prefix-caching \
--enable_expert_parallel \
--served-model-name deepseekr1 \
--speculative-config '{"num_speculative_tokens": 1, "method":"deepseek_mtp"}' \
--quantization ascend \
--host 0.0.0.0 \
--port 1234 \
--additional-config '{"ascend_scheduler_config":{"enabled":true,"enable_chunked_prefill":false},"torchair_graph_config":{"enabled":true,"graph_batch_sizes":[16]},"enable_weight_nz_layout":true}' \
--gpu_memory_utilization 0.9
```
offline example with torchair enabled
```
from vllm import LLM, SamplingParams
prompts = [
"Hello, my name is",
"The president of the United States is",
"The capital of France is",
"The future of AI is",
]
# Create a sampling params object.
sampling_params = SamplingParams(max_tokens=16, temperature=0)
# Create an LLM.
llm = LLM(
model="/home/data/DeepSeek-R1_w8a8/",
tensor_parallel_size=16,
max_num_seqs=16,
gpu_memory_utilization=0.9,
distributed_executor_backend="mp",
enable_expert_parallel=True,
speculative_config={
"method": "deepseek_mtp",
"num_speculative_tokens": 1,
},
trust_remote_code=True,
enforce_eager=False,
max_model_len=2000,
additional_config = {
'torchair_graph_config': {
'enabled': True,
"graph_batch_sizes": [16],
'enable_multistream_shared_expert': False,
},
"ascend_scheduler_config": {
"enabled": True
},
# 'expert_tensor_parallel_size': 16,
}
)
# Generate texts from the prompts.
# llm.start_profile()
outputs = llm.generate(prompts, sampling_params)
# llm.stop_profile()
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
- vLLM version: v0.10.0
- vLLM main:
302962e806
---------
Signed-off-by: xuyexiong <xuyexiong@huawei.com>
### What this PR does / why we need it?
In fact, the kimi-k2 model is similar to the deepseek model, and we only
need to make a few changes to support it. what does this pr do:
1. Add kimi-k2-w8a8 deployment doc
2. Update quantization doc
3. Upgrade torchair support list
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.10.0
- vLLM main:
9edd1db02b
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
