### What this PR does / why we need it?
1. Move prepare/finalize operation from moe_comm_method to
/ops/moe/fused_moe_prepare_and_finalize
2. Adapt to token_dispatcher in moe_comm_method
3. Move
moe_comm_method/experts_selector/token_dispatcher/fused_moe_prepare_and_finalize
to /ops/moe
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
e2e & ut
- vLLM version: v0.10.1.1
- vLLM main:
f4962a6d55
Signed-off-by: weichen <calvin_zhu0210@outlook.com>
Signed-off-by: Pr0Wh1teGivee <calvin_zhu0210@outlook.com>
Co-authored-by: weijinqian0 <12153182+weijinqian0@users.noreply.github.com>
### What this PR does / why we need it?
line 408 already declared mc2_mask , remove duplicated unused code
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
CI passed with existing test.
- vLLM version: v0.10.1.1
- vLLM main:
60f0843ef8
Signed-off-by: machenglong <machenglong_yewu@cmss.chinamobile.com>
### What this PR does / why we need it?
quantization patch is unused code
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
tested by CI
- vLLM version: v0.10.1.1
- vLLM main:
f4962a6d55
Signed-off-by: 22dimensions <waitingwind@foxmail.com>
### What this PR does / why we need it?
fix https://github.com/vllm-project/vllm-ascend/issues/2702
- A2: skip graph_size update that makes it to tp_size because
dispatch/combine op support different batch size across EP ranks
- A3: add `max_num_reqs = max(new_graph_batch_sizes)` to fix graph_size
and max_num_reqs mismatch
### Does this PR introduce _any_ user-facing change?
Nope
### How was this patch tested?
- vLLM version: v0.10.1.1
- vLLM main:
e599e2c65e
---------
Signed-off-by: realliujiaxu <realliujiaxu@163.com>
### What this PR does / why we need it?
Refector prepare_inputs in model_runner_v1.py for more easy read.
### Does this PR introduce _any_ user-facing change?
NO
### How was this patch tested?
PASS CI
- vLLM version: v0.10.1.1
- vLLM main:
e599e2c65e
---------
Signed-off-by: ChenTaoyu-SJTU <ctynb@qq.com>
### What this PR does / why we need it?
Removes the condition that skips metadata synchronization when
`enforce_eager` is enabled.
This change is necessary to correctly sync the `with_prefill` and
`enable_dbo` flags across all data parallel ranks, which is not required
in the base implementation. Forcing the sync operation prevents
potential inconsistencies, albeit with a minor performance impact.
### Does this PR introduce _any_ user-facing change?
None.
### How was this patch tested?
Add a E2E online test case?
- vLLM version: v0.10.1.1
- vLLM main:
e599e2c65e
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
### What this PR does / why we need it?
- This PR adds the support for the KV connector interface in the V1
architecture, in the same way as vllm. Vllm-ascend currently lacks of
this support, required to support also layerwise management of KV
caches.
- The connector interface allows using external tools and integrate them
with vllm
### Notes:
We are aware of Issue #684 , however that issue does not modify the
attention classes as necessary to perform layerwise management of KV
caches required for connectors like LMCache.
The implementation of this PR ported the necessary code from the vanilla
vllm. The KV connector API is the same as vanilla vllm, supporting the
standard KV connector API.
EDIT: this PR was re-implementing part of the changes merged one hour
before this PR was made on the file model_runner_v1.py. I solved the
conflicts by removing any modification to the model_runner_v1 file,
which now are largely already merged in main. Now this PR is left for
the modifications to the attention_v1 file.
### Does this PR introduce _any_ user-facing change?
The PR does not modify current APIs, but it extends the behavior of
current worker runner and attention classes to save and load KV caches.
In absence of connectors, the behavior should stay untouched.
### How was this patch tested?
- No unit test implemented yet for the worker.
- Tested together with LMCache using
https://github.com/LMCache/LMCache/blob/dev/examples/kv_cache_reuse/local_backends/offload.py
with the following models:
1 Deepseek-R1-Distill-Qwen-1.5B
2 Qwen3-30B-A3B
3 Deepseek-v2-lite
4 Llama-3.1-8B
LMCache used in both layerwise and non-layerwise mode.
- Performed LMEval on LMCache integrated with vllm-ascend.
Results without LMCache on Qwen3-8B:
|Tasks|Version| Filter |n-shot| Metric | |Value | |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.8400|± |0.0101|
| | |strict-match | 5|exact_match|↑ |0.8355|± |0.0102|
Results with LMCache Layerwise:
|Tasks|Version| Filter |n-shot| Metric | |Value | |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.8385|± |0.0101|
| | |strict-match | 5|exact_match|↑ |0.8332|± |0.0103|
- vLLM version: v0.10.1.1
- vLLM main:
50fede6634
---------
Signed-off-by: marcobarlo <barlettamarco8@gmail.com>
Signed-off-by: marcobarlo <65128997+marcobarlo@users.noreply.github.com>
### What this PR does / why we need it?
vLLM now names the process with VLLM prefix after
https://github.com/vllm-project/vllm/pull/21445, we should kill the
correct process name after one iteration benchmark to avoid OOM issue
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.10.1.1
- vLLM main:
e599e2c65e
---------
Signed-off-by: wangli <wangli858794774@gmail.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?
fix prefill attention bug，not support sliding window.
npu_fused_infer_attention_score head_dim only equal 128, not support
other number.
### Does this PR introduce _any_ user-facing change?
remove prefill phase npu_fused_infer_attention_score
### How was this patch tested?
- vLLM version: v0.10.1.1
- vLLM main:
e599e2c65e
---------
Signed-off-by: nsdie <yeyifan@huawei.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>
### What this PR does / why we need it?
Remove git .extraheader and fecth all commtis in
/vllm-workspace/vllm-ascend
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
CI passed
Closes: https://github.com/vllm-project/vllm-ascend/issues/2735
- vLLM version: v0.10.1.1
- vLLM main:
51d5e9be7d
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
### What this PR does / why we need it?
Delete redundant codes related to communication
### Does this PR introduce _any_ user-facing change?
not involve
### How was this patch tested?
not involve
- vLLM version: v0.10.1.1
- vLLM main:
6c7af8110a
---------
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?
Refactors the Mixture-of-Experts (MoE) communication method selection
logic. The choice between all-gather, all-to-all, and mc2 is now
determined by expert parallel configuration, SoC version (A2/A3), and
token count for better performance.
### Does this PR introduce _any_ user-facing change?
None.
### How was this patch tested?
Added.
- vLLM version: v0.10.1.1
- vLLM main:
eafa8dcde6
---------
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
### What this PR does / why we need it?
Remove useless PD check in deepseek
- vLLM version: v0.10.1.1
- vLLM main:
6c7af8110a
---------
Signed-off-by: liziyu <liziyu16@huawei.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?
Allow using aclgraph in ray backend, for tp + pp + aclgraph in multi
machine
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.10.1.1
- vLLM main:
4ba0c587ba
Signed-off-by: withHades <244036962@qq.com>
### What this PR does / why we need it?
AscendQuantizer/LLMQuantizer class is used to select quant method based
on quant config and some other arguments,
but it is more simple and clean replacing these classes with map. So i
remove them.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
ut and e2e test
- vLLM version: v0.10.1.1
- vLLM main:
6997a25ac6
Signed-off-by: 22dimensions <waitingwind@foxmail.com>
### What this PR does / why we need it?
Refactor spec decode
### 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:
6997a25ac6
---------
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
Signed-off-by: Icey <1790571317@qq.com>
Co-authored-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
1. Similar to #2384 , this PR add a torchair-specific modeling for
pangu.
2. Fixes a bug introduced by routed_scaling_factor in #2675 .
3. remove eager test case for pangu since there has already been a
torchair test case.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
- vLLM version: v0.10.1.1
- vLLM main:
6997a25ac6
---------
Signed-off-by: zengyanjia <z00883269@china.huawei.com>
Signed-off-by: Angazenn <supperccell@163.com>
Co-authored-by: zengyanjia <z00883269@china.huawei.com>
This PR fix a bug related to attention mask used in ring mla. Current
ring mla has supported compressed mask, so we can directly use a 512 *
512 attention mask.
- vLLM version: v0.10.1.1
- vLLM main:
b5ee1e3261
---------
Signed-off-by: whx-sjtu <2952154980@qq.com>
Refresh the news. Add meetup and official release info
- vLLM version: v0.10.1.1
- vLLM main:
b5ee1e3261
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
Fix accuracy issue on prefix caching with AscendScheduler
### How was this patch tested?
CI passed with `test_prefix_cache_with_ascend_scheduler`
- vLLM version: v0.10.1.1
- vLLM main:
6997a25ac6
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
In vllm version 0.10.1, a new KVOutputAggregator was added to the
executor, moving aggregation to the
executor(https://github.com/vllm-project/vllm/pull/19555). This caused
mooncake_connector to break. This change aims to fix this bug and also
adds a policy to forcibly release the KV cache when the prefill node
times out.
This PR is currently linked to a PR in vllm
(https://github.com/vllm-project/vllm/pull/23917). The vllm PR aims to
modify the finish and send count confirmation in heterogeneous TP
situations.
The reason for deleting many UTs is that a lot of communication codes
have been deleted, so the UT as a whole will appear more concise.
- vLLM version: v0.10.1.1
- vLLM main:
fa4311d85f
---------
Signed-off-by: baxingpiaochong <771405853@qq.com>
### What this PR does / why we need it?
The detail has been clarified in that issue :
https://github.com/vllm-project/vllm-ascend/issues/2557
### Does this PR introduce _any_ user-facing change?
NO
### How was this patch tested?
easy to test beacause we just need to echo the variable
- vLLM version: v0.10.1.1
- vLLM main:
6997a25ac6
---------
Signed-off-by: zzy-ContiLearn <1831242919@qq.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Co-authored-by: LCAIZJ <leichao139636@163.com>
### What this PR does / why we need it?
This PR ports #2312#2506#2531 to main branch.
Original implementation of torchair caching forces users to make
everything prepared, fix all the configuration and enable
`use_cached_npu_graph`, and it might cause some problems confusing to
understand and tackle for users. It is better to compile the graph twice
instead of reusing the old kvcaches and cached torchair graph. And the
extra duration time is acceptable. Additionally, this pr fixes a
recompilation problem of torchair graph mode caused by
`running_in_graph` variable in `AscendMLATorchairImpl`.
### Does this PR introduce _any_ user-facing change?
If users want to enabling torchair.cache_compile with high compilation
speed, it is recommended to enable both `use_cached_kv_cache_bytes` and
`use_cached_graph` in `torchair_graph_config`. Without
`use_cached_kv_cache_bytes`, we'll compile torchair computation graph
twice to avoid runtime error caused by configuration mismtaches (the
second compilation will be much faster). Additionally, we've made a
change to how the TORCHAIR_CACHE_HOME enviroment variable is utilized to
enhance safety and prevent accidental file deletion by adding a suffix
directory.
### How was this patch tested?
CI and e2e vllm serving pass.
- vLLM version: v0.10.1.1
- vLLM main:
70549c1245
---------
Signed-off-by: linfeng-yuan <1102311262@qq.com>
### What this PR does / why we need it?
bugfix ascend schedule encountered an incorrect req block length in the
check_watermark_for_prefill function
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.10.1.1
- vLLM main:
426cc8629f
Signed-off-by: liziyu <liziyu16@huawei.com>
This PR re-implements external-dp starter based on vllm's support for
external dp.
- vLLM version: v0.10.1.1
- vLLM main:
f38035c123
---------
Signed-off-by: whx-sjtu <2952154980@qq.com>
See #2687 we notice that test_platform and test_vocab_parallel_embedding
is unstable, let's skip them first.
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
Correct `AscendQwen2_5_VLForConditionalGeneration_Without_Padding`
override methods
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.10.1.1
- vLLM main:
42dc59dbac
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
Disable per-PR triggering for A3 for now, we trigger the dist test in
the label `dist-test` rather than
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.10.1.1
- vLLM main:
136d853e65
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
Fix long context seq accuracy problem for `GLM4.5`.
When `max_tokens=1000`, there is cyclic output problem like:
```bash
00 00 00 00 00 00 00 00 00 00 00 00 00 00
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
```python
import os
os.environ["VLLM_USE_MODELSCOPE"] = "True"
os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
from vllm import LLM, SamplingParams
def main():
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=1000, temperature=0.0)
# Create an LLM.
llm = LLM(model="/root/.cache/modelscope/hub/models/ZhipuAI/GLM-4___5",
tensor_parallel_size=8,
enforce_eager=True,
trust_remote_code=True,
max_model_len=1024)
# Generate texts from the prompts.
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
if __name__ == "__main__":
main()
```
- vLLM version: v0.10.1.1
- vLLM main:
0235103cbb
---------
Signed-off-by: Shanshan Shen <87969357+shen-shanshan@users.noreply.github.com>
Signed-off-by: shen-shanshan <467638484@qq.com>
### What this PR does / why we need it?
This PR enables `npu_moe_gating_top_k_softmax` when running quantized
MoE (such as W8A8). This op in fact makes no distinction between
quantized and non-quantized scenarios. Introducing this op reduces 3~4ms
for TPOT.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
- vLLM version: v0.10.1.1
- vLLM main:
ce30dca5c4
Signed-off-by: Angazenn <supperccell@163.com>
### What this PR does / why we need it?
Upgrade openEuler to 24.03
### 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:
4071c76cf3
---------
Signed-off-by: Icey <1790571317@qq.com>
Clean up useless code which is only used for torchair in rotary_embedding
- vLLM version: v0.10.1.1
- vLLM main:
a344a5aa0a
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
Due to the registration mechanism, torchair ops can not take effect， so
have to patch the Ascend ops to adapt torchair
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
vLLM version: main
vLLM main:
7ea22e42d5
- vLLM version: main
- vLLM main:
7ea22e42d5
Signed-off-by: hust17yixuan <303660421@qq.com>
### What this PR does / why we need it?
Fix the LoRA accuracy issue that introduced by custom AscendC operator
"bgmv_shrink, sgmv_shrink, bgmv_expand, sgmv_epand".
The bug details are:
- In the kernel function, if you want to call GlobalTensor.GetSize
method, you have to pass the second parameter of bufferSize when you
call GlobalTensor.SetGlobalBuffer first.
- Or GlobalTensor.GetSize method will return a random value.
- You can refer to [this
doc](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/81RC1alpha002/apiref/ascendcopapi/atlasascendc_api_07_00024.html).
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
pytest -sv tests/e2e/singlecard/test_ilama_lora.py
pytest -sv tests/e2e/multicard/test_ilama_lora_tp2.py
- vLLM version: v0.10.1.1
- vLLM main:
a344a5aa0a
---------
Signed-off-by: paulyu12 <paulyu0307@gmail.com>
Signed-off-by: paulyu12 <507435917@qq.com>
Co-authored-by: paulyu12 <paulyu0307@gmail.com>
