<!-- 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?
To fix ops test, where `model_config` has been set to `None` and doesn't
has `hf_config` attribute, we have added a check for `model_config` to
guarantee it is not `None_Type`.
cherry-pick from main:
https://github.com/vllm-project/vllm-ascend/pull/4384.
Signed-off-by: shen-shanshan <467638484@qq.com>
### What this PR does / why we need it?
Add error log for VL models when enabling
`VLLM_ASCEND_ENABLE_FLASHCOMM1=1` or `VLLM_ASCEND_ENABLE_FLASHCOMM=1`
(for backward compatibility).
This is a temporary fix for
https://github.com/vllm-project/vllm-ascend/issues/4132.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
Signed-off-by: shen-shanshan <467638484@qq.com>
### What this PR does / why we need it?
This dev version of #4199 .
Currently, the default `cudagraph_capture_size` in vLLM is `[1, 2, 4 ,8
,16 ,24 ,... , max_capture_size]`. However, this is not always the best
choice on different situations. This PR aims to change the default
setting when running Qwen3-MoE on full dp (`dp_size > 1` && `tp_size ==
1`) setting, which is usually applied in Large-Scale EP.
old :
`[1, 2, 4 ,8 ,16 ,24 ,... , max_capture_size]`
new:
`[1, 2, 5 ,10 ,15, 16 ,24 ,... , max_capture_size]`
This is mainly because the performance of `_npu_paged_attention` op
degrades dramatically on old settings. We hope to provide better
performance if users do not set specific `cudagraph_capture_size`.
### Does this PR introduce _any_ user-facing change?
The default `cudagraph_capture_size` is modified in above cases.
However, if `cudagraph_capture_size` has already set by users, this PR
won't have any influence on this.
### How was this patch tested?
- vLLM version: v0.11.0
- vLLM main:
2918c1b49c
---------
Signed-off-by: Angazenn <supperccell@163.com>
### What this PR does / why we need it?
- Fixes Qwen3-Next enable nz accuracy problem
---------
Signed-off-by: wxsIcey <1790571317@qq.com>
Signed-off-by: Icey <1790571317@qq.com>
### What this PR does / why we need it?
Fix#3891.
The empty of `moe_comm_method` in the above issue is due to the wrong
check for MoE models. To be specific, the method `is_moe_model` only
checks whether a text-only model is a MoE model, without considering
multi-modal models, e.g., `VL` and `Omni`.
Check the config dict recursively to find if it has a key contains
"expert", without checking the model architecture.
It is worth noting that, we can't verify a model by if it contains
`FusedMoE` module because `is_moe_model` is called somewhere before the
model loading, e.g., it's called when updating the ACLGraph config in
platform initialization.
- vLLM version: v0.11.0
- vLLM main:
83f478bb19
---------
Signed-off-by: shen-shanshan <467638484@qq.com>
### What this PR does / why we need it?
1. Revert [bugfix for mtp in
fullgraph](0948483642)
and support it when vllm supports
2. raise error when cudagraph_capture_sizes can't be an integer multiple
of uniform_decode_query_len
3. bugfix when max_num_seqs=14 in mtp=2 scenario
---------
Signed-off-by: zouyida2052 <zouyida2002@gmail.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>
### What this PR does / why we need it?
bugfix for mtp in fullgraph
### Does this PR introduce _any_ user-facing change?
no
---------
Signed-off-by: zouyida2052 <zouyida2002@gmail.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 cache for MLA decode graph parameters was holding strong references
to tensors, preventing them from being garbage collected and leading to
increased memory usage.
This change wraps the cached tensors in weak references, allowing them
to be deallocated when no longer in use and reducing overall memory
pressure.
### Does this PR introduce _any_ user-facing change?
None.
### How was this patch tested?
None.
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.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?
cherry-pick Fix performance degradation when mtp>1 (#3597)
This PR aims to fix performance degradation when mtp>1. Since mtp>1 may
result in more tokens (i.e. larger batch size) than acl graph maximum
batch size, this will cause draft model to run in eager mode.
### How was this patch tested?
by ci
---------
Signed-off-by: Zetong Li <slippersss@126.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?
Fix 3 bugs in flash comm1 of Allgather
EP(https://github.com/vllm-project/vllm-ascend/pull/3334):
1. call `enable_sp()` with argument `vllm_config` trigger a lot of
warning log, this PR caches its return value.
2. `num_tokens_after_padding` should be cpu tensor as it will used as
`num_tokens_across_dp_cpu` in `DPMetadata`. It will causes may d2h copy
when running model.
3. In PD, model runner will execute `kv_connector_no_forward`，where
`num_tokens` is None
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
Signed-off-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?
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>
### What this PR does / why we need it?
Currently, users have to set `HCCL_BUFFSIZE` to 512~1024 to perform mc2
operators (dispatch and combine) while running moe models with large
`ep_size` and `batch_size`. This environmental variable not only affects
allocated VRAM for mc2 group, but also increases VRAM allocation for dp,
tp & ep groups, leading to significant kvcache and free_memory drops.
This PR supports to automatically calculate and set `hccl_buffer_size`
for each process group **(except mc2 group)** separately when users set
`HCCL_BUFFSIZE` for mc2 group. This can significantly reduce wasted
buffer_size set for dp, tp & ep groups.
Note that current mc2 operators can only perform communication space
partitioning based on `HCCL_BUFFSIZE` configuration. Once they support
`hccl_buffer_size` configuration with `pg_options` while initializing
process group, we'll caculate the required buffer size and users would
avoid set `HCCL_BUFFSIZE` themselves.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
We performed E2E serving with deepseek_r1 initializing DP/TP/EP/MC2
process group and observed significant kv_cache and free_memory
increase!
- 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?
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?
- 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?
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.
### Does this PR introduce _any_ user-facing change?
please use a 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.
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: huangdong2022 <huangdong51@huawei.com>
Signed-off-by: h30027576 <huangdong51@huawei.com>
### What this PR does / why we need it?
Before optimizing，the rmsnorm time in one decoding is 531.5us. After
optimizing，the rmsnorm time in one decoding is 105us.
I closed the previous
PR（https://github.com/vllm-project/vllm-ascend/pull/2456） by mistake and
resubmitted it now
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- vLLM version: v0.10.2
- vLLM main:
b1068903fd
---------
Signed-off-by: socrahow <suzihao4@h-partners.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?
there are two sets of sp implementations for moe and dense models. One
is called sequence_parallelism, and the other is flashcomm_v1.
We did the following things：
Merge two sets of code with the same implementation into one.
Remove the implementation of sequence_parallelism, as this solution
cannot support aclgraph.
Does this PR introduce any user-facing change?
No
How was this patch tested?
e2e&ut
- vLLM version: v0.10.2
- vLLM main:
f225ea7dd9
---------
Signed-off-by: weijinqian_v1 <weijinqian@huawei.com>
Co-authored-by: weijinqian_v1 <weijinqian@huawei.com>
### What this PR does / why we need it?
This is the follow-up PR of #2128 .
Moves graph parameter management components, including `GraphParams`,
`get_graph_params`, and `set_graph_params`, from the generic `utils.py`
to the more specific `compilation/acl_graph.py`.
Additionally, extracts the `update_attn_params` logic from the
`NPUModelRunner` class into a standalone function within the `acl_graph`
module.
This refactoring improves code organization by centralizing ACL
graph-related logic into its own dedicated module, enhancing modularity
and clarity.
### Does this PR introduce _any_ user-facing change?
None.
### How was this patch tested?
None needed.
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
Note: This depends on [vLLM
#25161](https://github.com/vllm-project/vllm/pull/25161) and the
torch\_npu release from September 30.
### What this PR does / why we need it?
This pull request adds `FULL_DECODE_ONLY` mode for GQA/MHA models (MLA
models like DeepSeek V3/R1 are not included). Key improvements include:
* **Reduced dispatch latency:** By replaying the entire model execution
graph at once, we cut overhead compared with multiple smaller replays.
* **Stabilized multi-device performance:** Captureing the whole model as
one static graph also mitigates the dispatch fluctuations across
devices.
* **Stream/resource savings:** Consolidating graph captures frees up
streams, allowing more graphs to be captured.
**Known issues:**
1. `_npu_paged_attention` currently manages its own workspace in
`torch_npu`, which can deadlock when synchronizing during graph replay —
we’re working on a fix.
There may be other corner cases. This PR is the first in a planned
series; we’ll continue to iterate and address remaining issues in
follow-ups.
This is essentially a port of #1503 and #1677, but includes two major
changes:
1. Let `graph_dispatcher` decide the graph mode instead of hard-coding
it in the backend, which decouples Full Graph and Piecewise Graph and
could make it possible to remove dynamo.
2. Adapt to the new `attn_group` logic, but leave a small hack in
`update_graph_params`; multi-attention models may or may not be fully
supported yet.
### Does this PR introduce _any_ user-facing change?
```python
compilation_config={
"cudagraph_mode": "FULL_DECODE_ONLY",
},
```
### How was this patch tested?
Tests included.
- vLLM version: v0.10.2
- vLLM main:
9607d5eb44
---------
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.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>
**Background:**
There are two principles about operator registration in PyTorch
- The same namespace can be only registered once by `TORCH_LIBRARY`
- The operator signatures can be only registered once by `def`
Considering that all custom operators defined in the current repo are
only used by Ascend, instead of defining a common operator schema by
vLLM, all accelerators then follow this operator schema and complete the
implementation based on their respective hardware, which is conducive to
functional abstraction.
Therefore, we can rename the operator registration namespace to an
Ascend-specific namespace(**_C_ascend**).
Related ISSUE: https://github.com/vllm-project/vllm-ascend/issues/2742
- vLLM version: main
- vLLM main:
f592b3174b
Signed-off-by: FFFrog <ljw1101.vip@gmail.com>
### What this PR does / why we need it?
modelslim will generate self.bias for rms norm in quantization, since
RMSNorm in vllm has no this parameter, so its nesscesary
to create a AscendQuantRmsNorm.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
tested by deepseek-v3.1-w8a8
<img width="2496" height="592" alt="image"
src="https://github.com/user-attachments/assets/004c6e76-3d7a-4a1f-b59f-a14304012663"
/>
- vLLM version: main
- vLLM main:
d6249d0699
Signed-off-by: 22dimensions <waitingwind@foxmail.com>
### What this PR does / why we need it?
Fix UTs on register customop and warm up model
### How was this patch tested?
CI passed with existing test.
Co-authored-by: Icey <1790571317@qq.com>
- vLLM version: main
- vLLM main:
cc99baf14d
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
1. Solved the problem that in the Qwen3 Moe model case, opening DP would
use an extra stream, causing ACLgraph sizes capture error
2. After experimentation, it was found that in many cases, some
operators would occupy more streams than expected. Therefore, the buffer
area for streams in ACLgraph was not large enough. After discussion,
extra 120 streams were added as buffer.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
ut
- vLLM version: main
- vLLM main:
0ae43dbf8c
Signed-off-by: lilinsiman <lilinsiman@gmail.com>
### What this PR does / why we need it?
Register AscendMultiHeadLatentAttention as CustomOP, following vllm changes
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
CI passed with new added/existing test.
- vLLM version: main
- vLLM main:
b23fb78623
---------
Signed-off-by: Icey <1790571317@qq.com>
### What this PR does / why we need it?
Flashcomm_v1 optim in Qwen Dense Models.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
CI passed with new added/existing test.
- vLLM version: v0.10.1.1
- vLLM main:
5e537f45b4
Co-authored-by: 1024daniel <xxltju324@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>
### What this PR does / why we need it?
When both speculative decoding and aclgraph are applied, and
cudagraph_capture_sizes uses the default value, it will report that the
stream resources are insufficient.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.10.1.1
- vLLM main:
9c99e4871f
Signed-off-by: withHades <244036962@qq.com>
### What this PR does / why we need it?
This PR introduces LMhead tensor model parallel to achieve decreasing of
memory consumption, and TPOT performance improvement. It support both
eager mode and graph mode.
In deepseek r1 w8a8 PD disagregated Decode instance, using pure DP, with
lmhead_tensor_parallel_size = 8, we have 1 ms TPOT optimization, saved
1.48 GB NPU memory per RANK.
performance data:
<img width="1444" height="438" alt="image"
src="https://github.com/user-attachments/assets/3c5ef0d3-a7c7-46fd-9797-4de728eb0cb0"
/>
### Does this PR introduce _any_ user-facing change?
This PR introduces one new config in `additional_config`.
| Name | Effect | Required | Type | Constraints |
| :---------------------------- |
:--------------------------------------- | :------- | :--- |
:----------------- |
| lmhead_tensor_parallel_size | Split the lm_head matrix along the
column dimension (vocab_size) into lmhead_tensor_parallel_size pieces |
No | int | default value is None, once this value is set, the feature
will be enabled, vocab_size must be divisible by this value. |
example
`--additional_config={"lmhead_tensor_parallel_size": 8}`
### How was this patch tested?
- vLLM version: v0.10.1.1
- vLLM main:
de533ab2a1
---------
Signed-off-by: zzhx1 <zzh_201018@outlook.com>
Co-authored-by: zhangzihang <zzh_201018@outlook.com>
### What this PR does / why we need it?
Register VocabParallelEmbedding 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: v0.10.1.1
- vLLM main:
644d57d531
---------
Signed-off-by: Icey <1790571317@qq.com>
### What this PR does / why we need it?
This method replaces the previous all-gather approach for small numbers
of tokens.
The key changes include:
- A new `AscendFusedMoE` layer that handles token splitting, local
computation, and final aggregation via all-gather.
- Logic in the model runner to dynamically select between the new MC2
method and the existing all-gather method based on the number of input
tokens.
- Sharding the MoE communication mask across tensor-parallel ranks.
### Does this PR introduce _any_ user-facing change?
None.
### How was this patch tested?
Test case fixed.
- vLLM version: v0.10.1.1
- vLLM main:
b00e69f8ca
---------
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
[Bugfix]Support Qwen3-MOE on aclgraph mode in sizes capture and add new
ut
What this PR does / why we need it?
This PR solves the problem of sizes capture and stream error caused by
using ACLgraph on the Qwen3-30B MOE model.
Add new ut.
Does this PR introduce any user-facing change?
no
How was this patch tested?
ut
- vLLM version: v0.10.1.1
- vLLM main:
6fad29b11b
Signed-off-by: lilinsiman <lilinsiman@gmail.com>
### What this PR does / why we need it?
The constant ASCEND_QUATIZATION_METHOD in vllm_ascend/utils.py is
misspelled and should be corrected to ASCEND_QUANTIZATION_METHOD.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
CI passed with new added/existing test.
- vLLM version: v0.10.1.1
- vLLM main:
c9abb10489
Signed-off-by: zhanghaiwen <zhanghaiwen@cmss.chinamobile.com>
Co-authored-by: zhanghaiwen <zhanghaiwen@cmss.chinamobile.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>
