### What this PR does / why we need it?
Redundant experts bugfix
The calculation logic for redundant experts has been fixed, allowing the
correct number of redundant experts to be calculated using the map.
Therefore, there is no longer a need to set the redundant expert
parameter when passing the map.
### Does this PR introduce _any_ user-facing change?
After configuring the path for experts_map, users do not need to
configure iinit_redundancy_expert.
### How was this patch tested?
The accuracy of EPLB was tested with and without the use of redundant
experts.
---------
Signed-off-by: shenchuxiaofugui <1311027364@qq.com>
### What this PR does / why we need it?
The expert mapping table and weights of the dynamic EPLB were not
updated, causing the accuracy to be correct but not effective. This bug
has now been fixed.
If it was tested in a way different from regular unit tests, please
clarify how you tested step by step, ideally copy and paste-able, so
that other reviewers can test and check, and descendants can verify in
the future.
Signed-off-by: shenchuxiaofugui <1311027364@qq.com>
<!-- Thanks for sending a pull request!
BEFORE SUBMITTING, PLEASE READ
https://docs.vllm.ai/en/latest/contributing/overview.html
-->
### What this PR does / why we need it?
disable NZ for float weight case. This is only a quick fix for dev
branch.
For main branch, we'll consider more case to make it more common.
### Does this PR introduce _any_ user-facing change?
<!--
Note that it means *any* user-facing change including all aspects such
as API, interface or other behavior changes.
Documentation-only updates are not considered user-facing changes.
-->
### How was this patch tested?
qwen2.5 32B
<img width="441" height="221" alt="image"
src="https://github.com/user-attachments/assets/7ae18ffd-1ce2-43d9-9960-be45250ad0da"
/>
---------
Signed-off-by: 刘哲续 <liuzhexu1@huawei.com>
Co-authored-by: 刘哲续 <liuzhexu1@huawei.com>
### What this PR does / why we need it?
Enable force_load_balance in aclgraph, solving OOM issues.
pick from https://github.com/vllm-project/vllm-ascend/pull/4366
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
e2e & ut
Signed-off-by: Pr0Wh1teGivee <calvin_zhu0210@outlook.com>
### What this PR does / why we need it?
Eplb Verify Fix
---------
Signed-off-by: shenchuxiaofugui <1311027364@qq.com>
Signed-off-by: LI SHENGYONG <49200266+shenchuxiaofugui@users.noreply.github.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
### What this PR does / why we need it?
avoid mrope fusion op when running qwen25vl on x86 machine
---------
Signed-off-by: 李少鹏 <lishaopeng21@huawei.com>
### What this PR does / why we need it?
pick from : https://github.com/vllm-project/vllm-ascend/pull/2958
Past：
npu_moe_gating_top_k can only support 'group_count=256' pattern
Now：
1、npu_moe_gating_top_k support all size of group_count
2、the functionality of `torch_npu.npu_moe_gating_top_k_softmax` are
included in `torch_npu.npu_moe_gating_top_k`
CANN: depends on 8.3.RC1
Performance：
1. GLM4.5-w8a8, TPS improve 6%
2. Qwen3, the same as before
Signed-off-by: 1092626063 <1092626063@qq.com>
### What this PR does / why we need it?
Quick fix for missing log2phy conversion in MC2 token_dispatcher, which
has been already fixed in main branch
https://github.com/vllm-project/vllm-ascend/pull/3512.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
e2e & ut
Signed-off-by: Pr0Wh1teGivee <calvin_zhu0210@outlook.com>
This PR upgrade CANN from 8.2rc1 to 8.3rc1 and remove the CANN version
check logic.
TODO: we notice that UT runs failed with CANN 8.3 image. So the base
image for UT is still 8.2. We'll fix it later.
- vLLM version: v0.11.0
- vLLM main:
83f478bb19
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
This PR temporarily bypasses the scenario where some models in vLLM
trigger a `ValueError` during the process of storing values in
`static_forward_context` when no `prefix` is specified for the linear
layers, which is a bug in some models in vLLM. The official fix will be
addressed by submitting a PR to the vLLM community that specifies a
prefix for the linear layers in each model.
- vLLM version: v0.11.0
- vLLM main:
83f478bb19
### How was this patch tested?
CI passed with new added/existing test.
Signed-off-by: rjg-lyh <1318825571@qq.com>
### What this PR does / why we need it?
Fix the issue of MTP being enabled and setting
Imhead_tensor_parallel_size=16 causing the inference to hang.
Signed-off-by: wyh145 <1987244901@qq.com>
### What this PR does / why we need it?
Fix the precision issue caused by the inconsistency between the group
list type used by mc2 and that of eplb.
---------
Signed-off-by: offline0806 <3337230449@qq.com>
### What this PR does / why we need it?
After refactoring vllm_ascend/models and FusedMoE, we are unable to pass
`gate` from deepseekv2.py to `AscendFusedMoE.forward`, which will result
in error when running deepseek v3/r1 with allgather.
Hence, this pr removes `gate` related computations from FusedMoE module
in eager/aclgraph mode.
### Does this PR introduce _any_ user-facing change?
`rm_router_logits` is deprecated in eager/aclgraph.
### How was this patch tested?
e2e & ut
Signed-off-by: Pr0Wh1teGivee <calvin_zhu0210@outlook.com>
<!-- Thanks for sending a pull request!
BEFORE SUBMITTING, PLEASE READ
https://docs.vllm.ai/en/latest/contributing/overview.html
-->
cherry pick https://github.com/vllm-project/vllm-ascend/pull/3677
Remove redundant operations from `model_runner` and `forward_context`.
This optimization can significantly reduce the idle time (bubble) before
decoding when running models with small parameter counts (e.g.,
Qwen/Qwen2.5-0.5B).
Testing on 800I A2, bubble is reduced from 3.8ms to 2.8ms :
Before
<img width="1655" height="696" alt="image"
src="https://github.com/user-attachments/assets/d7608e52-2438-46dd-8fc9-391fd6274495"
/>
After
<img width="1607" height="774" alt="image"
src="https://github.com/user-attachments/assets/56daf081-2dba-4d2e-99d4-e055187d9806"
/>
### What this PR does / why we need it?
<!--
- Please clarify what changes you are proposing. The purpose of this
section is to outline the changes and how this PR fixes the issue.
If possible, please consider writing useful notes for better and faster
reviews in your PR.
- Please clarify why the changes are needed. For instance, the use case
and bug description.
- Fixes #
-->
### Does this PR introduce _any_ user-facing change?
<!--
Note that it means *any* user-facing change including all aspects such
as API, interface or other behavior changes.
Documentation-only updates are not considered user-facing changes.
-->
No
### How was this patch tested?
<!--
CI passed with new added/existing test.
If it was tested in a way different from regular unit tests, please
clarify how you tested step by step, ideally copy and paste-able, so
that other reviewers can test and check, and descendants can verify in
the future.
If tests were not added, please describe why they were not added and/or
why it was difficult to add.
-->
---------
Signed-off-by: realliujiaxu <realliujiaxu@163.com>
### What this PR does / why we need it?
The current MatmulReduceScatter operator experiences performance
degradation in small-shape scenarios, so it determines whether to use
this operator by judging the size of the shape.
---------
Signed-off-by: ZYang6263 <zy626375@gmail.com>
### What this PR does / why we need it?
- `qkv_proj.weight` prefetching has been implemented with `Quant` op,
when `AddRmsNormQuant` is enabled (#3465) `qkv_proj.weight` prefetching
won't work
- Implement `qkv_proj.weight` prefetching with `AddRmsNormQuant`, which
has been merged on `main` branch (#3517)
### Does this PR introduce _any_ user-facing change?
None.
### How was this patch tested?
Tested on `Qwen3-235B-A22B-W8A8`
<img width="1868" height="109" alt="image"
src="https://github.com/user-attachments/assets/0bc28082-0287-4d5c-b8f6-f907c3134d36"
/>
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
<!-- Thanks for sending a pull request!
BEFORE SUBMITTING, PLEASE READ
https://docs.vllm.ai/en/latest/contributing/overview.html
-->
### What this PR does / why we need it?
<!--
- Please clarify what changes you are proposing. The purpose of this
section is to outline the changes and how this PR fixes the issue.
If possible, please consider writing useful notes for better and faster
reviews in your PR.
- Please clarify why the changes are needed. For instance, the use case
and bug description.
- Fixes #
-->
### Does this PR introduce _any_ user-facing change?
<!--
Note that it means *any* user-facing change including all aspects such
as API, interface or other behavior changes.
Documentation-only updates are not considered user-facing changes.
-->
### How was this patch tested?
<!--
CI passed with new added/existing test.
If it was tested in a way different from regular unit tests, please
clarify how you tested step by step, ideally copy and paste-able, so
that other reviewers can test and check, and descendants can verify in
the future.
If tests were not added, please describe why they were not added and/or
why it was difficult to add.
-->
Signed-off-by: zhoux77899 <zhouxiang100@huawei.com>
This PR moves the communication operation of shared experts out of extra
stream because I found that this might cause rtMemcpy related errors
when running shared experts multistream with aclgraph.
Furthermore, I utilize a global variable as extra stream object to avoid
allocating streams for each layer in full-graph mode.
Signed-off-by: whx-sjtu <2952154980@qq.com>
### What this PR does / why we need it?
Add mrope fusion op for qwen2.5-vl. This mrope operator dosen't
support Qwen3-VL currently. Thus could only take affect in qwen2.5-vl
cherry pick from 39b994a987888f7ba78df28b1ccb41a5e8d6eaf5
CI passed with existing test
Signed-off-by: shaopeng666 <shaopeng666@noreply.gitcode.com>
Co-authored-by: shaopeng666 <shaopeng666@noreply.gitcode.com>
### What this PR does / why we need it?
This PR boosts performance by introducing a fused kernel for the matrix
matmul and reduce scatter operations. It supports both unquantized
(e.g., BFloat16) and W8A8 quantized models.
### 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
<!-- Thanks for sending a pull request!
BEFORE SUBMITTING, PLEASE READ
https://docs.vllm.ai/en/latest/contributing/overview.html
-->
### What this PR does / why we need it?
<!--
- Please clarify what changes you are proposing. The purpose of this
section is to outline the changes and how this PR fixes the issue.
If possible, please consider writing useful notes for better and faster
reviews in your PR.
- Please clarify why the changes are needed. For instance, the use case
and bug description.
- Fixes #
-->
### Does this PR introduce _any_ user-facing change?
<!--
Note that it means *any* user-facing change including all aspects such
as API, interface or other behavior changes.
Documentation-only updates are not considered user-facing changes.
-->
### How was this patch tested?
<!--
CI passed with new added/existing test.
If it was tested in a way different from regular unit tests, please
clarify how you tested step by step, ideally copy and paste-able, so
that other reviewers can test and check, and descendants can verify in
the future.
If tests were not added, please describe why they were not added and/or
why it was difficult to add.
-->
Signed-off-by: ZYang6263 <zy626375@gmail.com>
### What this PR does / why we need it?
Check all expert maps when using muilty instance.
### Does this PR introduce _any_ user-facing change?
None.
### How was this patch tested?
Qwen 235B in double A3.
case1：master has expert map, slave has not expert map.
case2: master has expert map, slave has error expert map.
case3: master has expert map,slave has correct expert map.
- 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?
This PR refactors SequenceRowParallelOp forward. In order to further
expand the operator inclusion scope in dynamic judgment scenarios, this
PR customizes the entire matmul computation and communication as a
custom operator masking. With this refactor, it will support directly
writing code such as common operation fusion into the
SequenceRowParallelOp class's member function matmul_and_reduce, without
the need to register more redundant custom masking operators.
### How was this patch tested?
CI passed with new added/existing test.
Signed-off-by: rjg-lyh <1318825571@qq.com>
### What this PR does / why we need it?
This reverts commit
bf87606932.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
E2E vllm serving with `enable_shared_expert_dp: true` in eager mode as
before.
- 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>
This reverts commit 646c1db5d7.
this new ops may lead accuracy problem
### What this PR does / why we need it?
### 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
### 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>
This PR deletes model codes of deepseek_v2 and deepseek_v3 to reuse the
model file from vLLM.
vLLM Ascend now uses custom ops register way instead of model file
hard-coding.
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
Signed-off-by: whx-sjtu <2952154980@qq.com>
This PR adds support for redundant experts in the EPLB.
Key points:
- Use global_num_experts = num_experts + num_redundant_experts
consistently.
- Backward compatible when num_redundant_experts=0.
Tested
On a 16-rank setup (W8A8) with static EPLB and expert_map_path,
verifying router logits shape and successful requests.
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
Signed-off-by: yechao237 <yechao20180411@gmail.com>
### What this PR does / why we need it?
shared expert dp for deepseek and deepseek_mtp, could be combined with
sp to improve performance.
### 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: zhaozx-cn <zhaozx2116@163.com>
Co-authored-by: realliujiaxu <realliujiaxu@163.com>
### What this PR does / why we need it?
1.qwen3 moe uses add_rms_norm_quant op instead of 'add_rms_norm op and
quant op' during quantization scene.
2.torch_npu.add_rms_norm_quant op fixed accuracy while model weights is
quantized by anti_method m4, m4 quantization is asymmetric outlier
suppression method, it will generate none-zero norm bias,
add_rms_norm_quant op updated to add this parameter to calculate.
3. add torch-npu check
### Does this PR introduce _any_ user-facing change?
new feature works if torch_npu version >= torch_npu-2.7.1.dev20250919
### How was this patch tested?
1.no special parameters to set, no new envs to set. new feature works if
torch_npu version >= torch_npu-2.7.1.dev20250919
2.use qwen3 moe quantization model to test ,such as
Qwen3-235B-A22B-W8A8, Qwen3-30B-A3B-W8A8,
Qwen3-235B-A22B-Instruct-2507-m4 (anti_method m4)
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
Signed-off-by: h30027576 <huangdong51@huawei.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?
1. Replace manual memory cleanup with passing parameter.
2. FusedMoEPrepareAndFinalizeWithMC2 inherits All2All avoid duplicated
code.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
e2e & ut
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
Signed-off-by: Pr0Wh1teGivee <calvin_zhu0210@outlook.com>
### What this PR does / why we need it?
The `row_idx` parameter is no longer used since
PR[#2689](https://github.com/vllm-project/vllm-ascend/pull/2689), so
remove it across multiple files to remove unnecessary calculations and
parameter passing.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
accuracy test passed for Qwen3 235B and DeepSeek V3 671B after this PR.
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
Signed-off-by: CaranLic <740821011@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?
Currently, when executing to the Linear layer of models in vLLM-Ascend,
the weights format is ND in unquantized case and skipped ascend case.
This PR supplements the execution logic for Linear layer. We use a new
global variable: VLLM_ASCEND_ENABLE_NZ. When VLLM_ASCEND_ENABLE_NZ=1 and
CANN version is 8.3, the weights of the Linear layer will be converted
to FRACTAL_NZ, in both unquantized case and skipped ascend case. We also
use VLLM_ASCEND_ENABLE_NZ to control the existing NZ conversion, such as
w8a8-quantized case.
### Does this PR introduce _any_ user-facing change?
Add a new global variable VLLM_ASCEND_ENABLE_NZ. If you want to use NZ
format, you should set VLLM_ASCEND_ENABLE_NZ=1.
### 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: anon189Ty <Stari_Falcon@outlook.com>
This PR will accomplish the following tasks:
**optimize SP**
In the old version implementation, the first layer was all_reduce, which
used rms to split chunks. We changed it to perform reduce_scatter on the
embedding side, replace one all_reduce operation and one chunk with one
reduce_scatter operation.
**Support qwen3 next**
Since Qwen3 Next includes a linear attention module, the prefix name of
this module cannot take effect directly.
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
Signed-off-by: weijinqian_v1 <weijinqian@huawei.com>
Co-authored-by: weijinqian_v1 <weijinqian@huawei.com>
### What this PR does / why we need it?
When using dynamic eplb,it will be blocking by nz tensor.We fix these
prolems by clone src tensor and recv tensor.
### Does this PR introduce any user-facing change?
### How was this patch tested?
Qwen3_moe in A3.
- 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?
YaRN scaling is used to improve long seq accuracy for models like Qwen3.
In vLLM, YaRN scaling refers to `YaRNScalingRotaryEmbedding` class which
inherits from original `RotaryEmbedding`. Although
`YaRNScalingRotaryEmbedding` does not rewrite the `forward` function of
`RotaryEmbedding` , using YaRN on npu still run into the native
implementation of foward in `RotaryEmbedding`, rather than forward_oot
in vLLM-Ascend. Thus I register another custom op here to enable the oot
implementation for YaRN in vLLM-Ascend, similar to #3151 .
### 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: Angazenn <supperccell@163.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?
Due to the special input data during the dummy run, the majority of
tokens are distributed on DP0TP0, which results in insufficient
available KV cache on DP0TP0.
This PR changes the `topk_ids` of the dummy_run input from all zeros to
random values.
This is a naive implementation for experts load balance so as to avoid
accumulating too much tokens on a single rank.
### How was this patch tested?
model: DeepSeek-v3-w8a8
```bash
vllm serve DeepSeek-v3-w8a8 \
--host 0.0.0.0 \
--port 8004 \
--data-parallel-size 2 \
--tensor-parallel-size 8 \
--quantization ascend \
--seed 1024 \
--enforce-eager \
--served-model-name deepseek_v3 \
--enable-expert-parallel \
--disable-log-stats \
--max-num-seqs 18 \
--max-model-len 8192 \
--max-num-batched-tokens 8192 \
--trust-remote-code \
--no-enable-prefix-caching \
--gpu-memory-utilization 0.9 \
--speculative-config '{"num_speculative_tokens": 1, "method":"deepseek_mtp"}' \
--additional-config \
'{"ascend_scheduler_config":{"enabled":false},"torchair_graph_config":{"enabled":false}}'
```
The Available memory: **2728672256** -> **6771544064**
KV Cache size: **38144** -> **95232** tokens
After enabling load balance
- vLLM version: v0.11.0
---------
Signed-off-by: chenmenglong <chenmenglong1@huawei.com>
### What this PR does / why we need it?
Resolved the issue of EPLB failure caused by changes in the log2phy map
due to device type modifications when using MTP rotation position
encoding.
### Does this PR introduce any user-facing change?
### How was this patch tested?
https://github.com/vllm-project/vllm/commit/releases/v0.11.0
- 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?
- 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>
