624 Commits

Author SHA1 Message Date
shiyuan680
7af3b880c1 support triton of mrope (#5664)
### What this PR does / why we need it?
this pr support use triton mrope like cuda_forward, which performance is
equal to ascendc ops
this triton ops should use cann 8.5.0
### Does this PR introduce _any_ user-facing change?

### How was this patch tested?
test in qwen3-vl-235b acc textvqa
native 81.82
npu triton 81.58
cuda triton 81.52
- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef

Signed-off-by: shiyuan680 <917935075@qq.com>
2026-01-13 09:13:51 +08:00
Li Wang
75c92a3640 [CI] Move nightly-a2 test to hk (#5807)
### What this PR does / why we need it?
This patch initial testing involved connecting two nodes from the HK
region to nightly A2.

- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef

---------

Signed-off-by: wangli <wangli858794774@gmail.com>
2026-01-12 22:58:35 +08:00
SILONG ZENG
7a6fde80b1 [CI]Add Kimi k2 nightly test (#5682)
### What this PR does / why we need it?
The PR add performance and accuracy tests for **Kimi-K2-Instruct-W8A8**
and **Kimi-K2-Thinking** models to the Nightly test suite.

#### Test Configuration
**Kimi-K2-Instruct-W8A8**
- model: vllm-ascend/Kimi-K2-Instruct-W8A8
- Hardware: A3, 2 Nodes (32 NPUs total, 16 NPUs per node)
- Architecture: Unified Distributed Inference
- Parallelism: **DP4 + TP8 + EP** (Data Parallel 4, Tensor Parallel 8,
Expert Parallel enabled).
  - Optimization: **torchair graph**, **no-prefix-caching**.
  - Node 0: DP Rank 0-1, Local DP 2, Tensor Parallel 8.
  - Node 1: DP Rank 2-3, Local DP 2, Tensor Parallel 8.
- Benchmarks:
  - Performance: vllm-ascend/GSM8K-in3500-bs2800.
  - Accuracy: vllm-ascend/gsm8k-lite.

**Kimi-K2-Thinking**
- Model: moonshotai/Kimi-K2-Thinking
- Hardware: A3, 1 Node (16 NPUs total)
- Architecture: Single Node Distributed Inference
- Parallelism: TP16 + EP (Tensor Parallel 16, Expert Parallel enabled).
  - Optimization: **no-prefix-caching**
- Benchmarks:
  - Performance: vllm-ascend/GSM8K-in3500-bs400.
  - Accuracy: vllm-ascend/gsm8k-lite.


### Does this PR introduce _any_ user-facing change?
**Yes.** This PR enhances the ```AisbenchRunner``` to support dynamic
configuration of the ```trust_remote_code``` flag. This allows the
AISBench client to successfully load tokenizers for models that require
custom code execution (e.g., **Kimi-K2-Thinking and
Kimi-K2-Instruct-W8A8**).

**Changes:**
1. ```AisbenchRunner.__init__ ```Added the ability to capture the
```trust_remote_code``` parameter from the case configuration.
``` python
         self.batch_size = aisbench_config["batch_size"]
         self.request_rate = aisbench_config.get("request_rate", 0)
+        self.trust_remote_code = aisbench_config.get("trust_remote_code", False)
         self.temperature = aisbench_config.get("temperature")
         self.top_k = aisbench_config.get("top_k")
```
2. ```AisbenchRunner._init_request_conf``` Added regex substitution to
inject the parameter into the generated dynamic configuration file.
``` python
         content = re.sub(r'batch_size.*', f'batch_size = {self.batch_size},',
                          content)
+        content = re.sub(r'trust_remote_code=.*',
+                         f'trust_remote_code={self.trust_remote_code},',
+                         content)
         content = content.replace("top_k", "#top_k")
         content = content.replace("seed", "#seed")
```

**Details:**
- New Config Key: Users can add ```"trust_remote_code": True``` to any
dictionary within the ```aisbench_cases``` list.
- Default Value: Defaults to ```False``` to maintain existing security
protocols for standard models.
- Impact: Resolves ```ValueError``` when benchmarking reasoning models
or models with custom tokenizers that previously failed during the
AISBench local initialization phase.

**User Example:**
Users can now enable custom code execution for specific models (like
Kimi-K2-Thinking) directly in their test suite:
```
# Now supported in test scripts:
aisbench_cases = [{
    "case_type": "performance",
    "request_conf": "vllm_api_stream_chat",
    "trust_remote_code": True,  # New user-facing parameter
    ...
}]
```
### How was this patch tested?
Actions:
- https://github.com/vllm-project/vllm-ascend/actions/runs/20849768433

Result as following:

- **Kimi-K2-Instruct-W8A8**(25m25s)
1. Accuracy test
```
dataset    version    metric    mode      vllm-api-general-chat
---------  ---------  --------  ------  -----------------------
gsm8k      7cd45e     accuracy  gen                       96.88
```
2. Perf test
```
╒══════════════════════════╤═════════╤════════════════╤════════════════╤═══════════════╤════════════════╤════════════════╤════════════════╤════════════════╤═════╕
│ Performance Parameters   │ Stage   │ Average        │ Min            │ Max           │ Median         │ P75            │ P90            │ P99            │  N  │
╞══════════════════════════╪═════════╪════════════════╪════════════════╪═══════════════╪════════════════╪════════════════╪════════════════╪════════════════╪═════╡
│ E2EL                     │ total   │ 34571.489 ms   │ 28657.8054 ms  │ 36294.1788 ms │ 34714.7329 ms  │ 35247.2724 ms  │ 35526.6758 ms  │ 36146.4314 ms  │ 512 │
├──────────────────────────┼─────────┼────────────────┼────────────────┼───────────────┼────────────────┼────────────────┼────────────────┼────────────────┼─────┤
│ TTFT                     │ total   │ 2043.9136 ms   │ 627.4718 ms    │ 3532.3978 ms  │ 1906.0194 ms   │ 2307.7979 ms   │ 2883.8528 ms   │ 3283.7012 ms   │ 512 │
├──────────────────────────┼─────────┼────────────────┼────────────────┼───────────────┼────────────────┼────────────────┼────────────────┼────────────────┼─────┤
│ TPOT                     │ total   │ 127.5591 ms    │ 106.4937 ms    │ 137.107 ms    │ 128.3135 ms    │ 129.5704 ms    │ 131.1332 ms    │ 134.1087 ms    │ 512 │
├──────────────────────────┼─────────┼────────────────┼────────────────┼───────────────┼────────────────┼────────────────┼────────────────┼────────────────┼─────┤
│ ITL                      │ total   │ 126.5571 ms    │ 0.0095 ms      │ 1340.783 ms   │ 104.1398 ms    │ 110.1272 ms    │ 119.6124 ms    │ 950.2924 ms    │ 512 │
├──────────────────────────┼─────────┼────────────────┼────────────────┼───────────────┼────────────────┼────────────────┼────────────────┼────────────────┼─────┤
│ InputTokens              │ total   │ 3516.6055      │ 3014.0         │ 3985.0        │ 3525.0         │ 3525.0         │ 3586.8         │ 3800.67        │ 512 │
├──────────────────────────┼─────────┼────────────────┼────────────────┼───────────────┼────────────────┼────────────────┼────────────────┼────────────────┼─────┤
│ OutputTokens             │ total   │ 256.0          │ 256.0          │ 256.0         │ 256.0          │ 256.0          │ 256.0          │ 256.0          │ 512 │
├──────────────────────────┼─────────┼────────────────┼────────────────┼───────────────┼────────────────┼────────────────┼────────────────┼────────────────┼─────┤
│ OutputTokenThroughput    │ total   │ 7.4143 token/s │ 7.0535 token/s │ 8.933 token/s │ 7.3744 token/s │ 7.4118 token/s │ 7.5608 token/s │ 8.7051 token/s │ 512 │
╘══════════════════════════╧═════════╧════════════════╧════════════════╧═══════════════╧════════════════╧════════════════╧════════════════╧════════════════╧═════╛
╒══════════════════════════╤═════════╤═══════════════════╕
│ Common Metric            │ Stage   │ Value             │
╞══════════════════════════╪═════════╪═══════════════════╡
│ Benchmark Duration       │ total   │ 279430.9375 ms    │
├──────────────────────────┼─────────┼───────────────────┤
│ Total Requests           │ total   │ 512               │
├──────────────────────────┼─────────┼───────────────────┤
│ Failed Requests          │ total   │ 0                 │
├──────────────────────────┼─────────┼───────────────────┤
│ Success Requests         │ total   │ 512               │
├──────────────────────────┼─────────┼───────────────────┤
│ Concurrency              │ total   │ 63.3452           │
├──────────────────────────┼─────────┼───────────────────┤
│ Max Concurrency          │ total   │ 64                │
├──────────────────────────┼─────────┼───────────────────┤
│ Request Throughput       │ total   │ 1.8323 req/s      │
├──────────────────────────┼─────────┼───────────────────┤
│ Total Input Tokens       │ total   │ 1800502           │
├──────────────────────────┼─────────┼───────────────────┤
│ Prefill Token Throughput │ total   │ 1720.5255 token/s │
├──────────────────────────┼─────────┼───────────────────┤
│ Total generated tokens   │ total   │ 131072            │
├──────────────────────────┼─────────┼───────────────────┤
│ Input Token Throughput   │ total   │ 6443.4598 token/s │
├──────────────────────────┼─────────┼───────────────────┤
│ Output Token Throughput  │ total   │ 469.0676 token/s  │
├──────────────────────────┼─────────┼───────────────────┤
│ Total Token Throughput   │ total   │ 6912.5274 token/s │
╘══════════════════════════╧═════════╧═══════════════════╛
```

- **Kimi-K2-Thinking**(43m51s)
1. Accuracy test
```
dataset    version    metric    mode      vllm-api-general-chat
---------  ---------  --------  ------  -----------------------
gsm8k      7cd45e     accuracy  gen                      100.00
```
2. Perf test
```
╒══════════════════════════╤═════════╤════════════════╤════════════════╤════════════════╤════════════════╤════════════════╤════════════════╤════════════════╤═════╕
│ Performance Parameters   │ Stage   │ Average        │ Min            │ Max            │ Median         │ P75            │ P90            │ P99            │  N  │
╞══════════════════════════╪═════════╪════════════════╪════════════════╪════════════════╪════════════════╪════════════════╪════════════════╪════════════════╪═════╡
│ E2EL                     │ total   │ 172384.3573 ms │ 34456.5517 ms  │ 205922.9407 ms │ 174844.2216 ms │ 202656.092 ms  │ 204428.9502 ms │ 205468.6776 ms │ 400 │
├──────────────────────────┼─────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼─────┤
│ TTFT                     │ total   │ 138740.3228 ms │ 655.1066 ms    │ 171777.3003 ms │ 141088.0561 ms │ 169237.5599 ms │ 170716.4954 ms │ 171393.1278 ms │ 400 │
├──────────────────────────┼─────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼─────┤
│ TPOT                     │ total   │ 131.9374 ms    │ 90.6331 ms     │ 135.4144 ms    │ 132.405 ms     │ 132.948 ms     │ 133.7549 ms    │ 135.2543 ms    │ 400 │
├──────────────────────────┼─────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼─────┤
│ ITL                      │ total   │ 130.9028 ms    │ 0.0099 ms      │ 960.3683 ms    │ 116.9623 ms    │ 122.3127 ms    │ 132.0522 ms    │ 886.4662 ms    │ 400 │
├──────────────────────────┼─────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼─────┤
│ InputTokens              │ total   │ 3514.575       │ 3014.0         │ 3843.0         │ 3525.0         │ 3525.0         │ 3588.0         │ 3801.08        │ 400 │
├──────────────────────────┼─────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼─────┤
│ OutputTokens             │ total   │ 256.0          │ 256.0          │ 256.0          │ 256.0          │ 256.0          │ 256.0          │ 256.0          │ 400 │
├──────────────────────────┼─────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼─────┤
│ OutputTokenThroughput    │ total   │ 1.6799 token/s │ 1.2432 token/s │ 7.4296 token/s │ 1.4642 token/s │ 1.4737 token/s │ 1.8754 token/s │ 7.125 token/s  │ 400 │
╘══════════════════════════╧═════════╧════════════════╧════════════════╧════════════════╧════════════════╧════════════════╧════════════════╧════════════════╧═════╛
╒══════════════════════════╤═════════╤═══════════════════╕
│ Common Metric            │ Stage   │ Value             │
╞══════════════════════════╪═════════╪═══════════════════╡
│ Benchmark Duration       │ total   │ 1166795.568 ms    │
├──────────────────────────┼─────────┼───────────────────┤
│ Total Requests           │ total   │ 400               │
├──────────────────────────┼─────────┼───────────────────┤
│ Failed Requests          │ total   │ 0                 │
├──────────────────────────┼─────────┼───────────────────┤
│ Success Requests         │ total   │ 400               │
├──────────────────────────┼─────────┼───────────────────┤
│ Concurrency              │ total   │ 59.0967           │
├──────────────────────────┼─────────┼───────────────────┤
│ Max Concurrency          │ total   │ 64                │
├──────────────────────────┼─────────┼───────────────────┤
│ Request Throughput       │ total   │ 0.3428 req/s      │
├──────────────────────────┼─────────┼───────────────────┤
│ Total Input Tokens       │ total   │ 1405830           │
├──────────────────────────┼─────────┼───────────────────┤
│ Prefill Token Throughput │ total   │ 25.332 token/s    │
├──────────────────────────┼─────────┼───────────────────┤
│ Total generated tokens   │ total   │ 102400            │
├──────────────────────────┼─────────┼───────────────────┤
│ Input Token Throughput   │ total   │ 1204.864 token/s  │
├──────────────────────────┼─────────┼───────────────────┤
│ Output Token Throughput  │ total   │ 87.7617 token/s   │
├──────────────────────────┼─────────┼───────────────────┤
│ Total Token Throughput   │ total   │ 1292.6258 token/s │
╘══════════════════════════╧═════════╧═══════════════════╛
```

- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef

---------

Signed-off-by: MrZ20 <2609716663@qq.com>
Signed-off-by: root <root@LAPTOP-VQKDDVMG.localdomain>
2026-01-12 15:56:07 +08:00
Nengjun Ma
297f6deb09 [CI] Align multi-node nightly test paramter with corresponding tutorials document (#5756)
### What this PR does / why we need it?
Align multi-node nightly test paramter with tutorials documents.

### Does this PR introduce _any_ user-facing change?
NA

### How was this patch tested?
Test locally and nighly e2e multi-node test cases.

- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef

---------

Signed-off-by: leo-pony <nengjunma@outlook.com>
2026-01-12 09:00:31 +08:00
gh924
6880c1b383 [Feature] Support for cross-attention and whisper model (#5592)
### What this PR does / why we need it?
To solve the problem of the
issue:https://github.com/vllm-project/vllm-ascend/issues/2262

- support for cross-attention when the model is encoder-decoder
- support for whisper model

- vLLM version: v0.13.0
- vLLM main:
7157596103

Signed-off-by: gh924 <guihao2@huawei.com>
Co-authored-by: Aoxuan Chen <43376869+chenaoxuan@users.noreply.github.com>
2026-01-11 11:38:45 +08:00
Levi
ecd4232698 [Feat] flashcomm2+oshard Generalized (#4723)
### What this PR does / why we need it?
[FlashComm2](https://gitcode.com/ascend-tribe/ascend-inference-cluster/blob/main/FlashComm/FlashComm2%E5%A4%A7%E6%A8%A1%E5%9E%8B%E6%8E%A8%E7%90%86%E4%B8%AD%E4%BB%A5%E5%AD%98%E6%8D%A2%E4%BC%A0%E7%9A%84%E9%80%9A%E4%BF%A1%E4%BC%98%E5%8C%96%E6%8A%80%E6%9C%AF.pdf)
introduces redundant storage of the o_proj matrix, which imposes
pressure on GPU memory. We propose the FlashComm2+Oshard approach by
integrating the shared linear layer feature (#2931). This approach
distributes weights layer-by-layer to each GPU and accesses the o_proj
of each layer via asynchronous broadcast operations, thereby alleviating
memory pressure while achieving nearly lossless performance compared to
the original FlashComm2. This PR implements a generalized
FlashComm2+Oshard solution.

Using following env to support flashcomm2 with oshard

```shell
export VLLM_ASCEND_FLASHCOMM2_PARALLEL_SIZE=1
--additional-config '{
  "layer_sharding": ["o_proj"]
}'
```

### How was this patch tested?

- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c

---------

Signed-off-by: Levi-JQ <yujinqi2@huawei.com>
Co-authored-by: Levi-JQ <yujinqi2@huawei.com>
2026-01-10 22:57:57 +08:00
SILONG ZENG
09b3f9d91b [CI]Add Disaggregated PD Nightly Test for Qwen3-235B and Qwen3-VL-235B (#5502)
### What this PR does / why we need it?
This PR adds online **Disaggregated Prefill/Decode** performance and
accuracy tests for the **Qwen3-235B-A22B** and
**Qwen3-VL-235B-A22B-Instruct** models to the Nightly test suite.

These test configurations simulate the deployment of massive MoE and
Vision-Language models in **a dual-node (32 NPU)** environment,
utilizing Mooncake (KVCache Transfer) technology to achieve efficient KV
cache transfer between the Prefill node and the Decode node.

#### Test Configuration
**Qwen3-235B-A22B**
- Model: Qwen/Qwen3-235B-A22B
- Hardware: A3, 2 Nodes (32 NPUs total, 16 NPUs per node)
- Architecture: Disaggregated Prefill & Decode
- Node 0 (Producer/Prefill): **DP2 + TP8 + EP + FLASHCOMM1 +
FUSED_MC2**.
- Node 1 (Consumer/Decode): **DP4 + TP4 + EP + FLASHCOMM1 + FUSED_MC2 +
FULL_DECODE_ONLY**.
- Benchmarks:
  - Performance: vllm-ascend/GSM8K-in3500-bs2800.
  - Accuracy: vllm-ascend/gsm8k-lite.

**Qwen3-VL-235B-A22B-Instruct**
- Model: Qwen/Qwen3-VL-235B-A22B-Instruct
- Hardware: A3, 2 Nodes (32 NPUs total, 16 NPUs per node)
- Architecture: Disaggregated Prefill & Decode
  - Node 0 (Producer/Prefill): **DP2 + TP8 + EP**.
  - Node 1 (Consumer/Decode): **DP4 + TP4 + EP + FULL_DECODE_ONLY**.
- Benchmarks:
  - Performance: vllm-ascend/textvqa-perf-1080p.
  - Accuracy: vllm-ascend/textvqa-lite.

### How was this patch tested?
Nightly test action on CI

- vLLM version: v0.13.0
- vLLM main:
45c1ca1ca1

---------

Signed-off-by: MrZ20 <2609716663@qq.com>
2026-01-09 16:25:20 +08:00
1092626063
f63c1341d9 [Feature] GLM4.6 support mtp with fullgraph (#5460)
### What this PR does / why we need it?
GLM4.6 support mtp with fullgraph to improve performance

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?
`
export HCCL_BUFFSIZE=1024
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export HCCL_OP_EXPANSION_MODE=AIV

vllm serve /weight/glm4.6_w8a8_with_float_mtp \
  --data-parallel-size 1 \
  --tensor-parallel-size 16 \
  --seed 1024 \
  --served-model-name glm \
  --max-model-len 35000 \
  --max-num-batched-tokens 16384 \
  --max-num-seqs 16 \
  --trust-remote-code \
  --gpu-memory-utilization 0.9 \
--speculative-config '{"num_speculative_tokens": 1,
"model":"/weight/glm4.6_w8a8_with_float_mtp", "method":"mtp"}' \
--compilation-config '{"cudagraph_capture_sizes": [1,2,4,8,16,32],
"cudagraph_mode": "FULL_DECODE_ONLY"}' \
  --async-scheduling \
`

test case:
`
vllm bench serve \
  --backend vllm \
  --dataset-name prefix_repetition \
  --prefix-repetition-prefix-len 22400 \
  --prefix-repetition-suffix-len 9600 \
  --prefix-repetition-output-len 1024 \
  --num-prompts 1 \
  --prefix-repetition-num-prefixes 1 \
  --ignore-eos \
  --model glm \
  --tokenizer /weight/glm4.6_w8a8_with_float_mtp \
  --seed 1000 \
  --host 0.0.0.0 \
  --port 8000 \
  --endpoint /v1/completions \
  --max-concurrency 1 \
  --request-rate 1

`
- vLLM version: v0.13.0
- vLLM main:
5326c89803

Signed-off-by: 1092626063 <1092626063@qq.com>
2026-01-09 16:07:42 +08:00
ZT-AIA
e11ff8e535 [BufFix]Fix the error when using Ascend custom operators with rank=128 (#5394)
### What this PR does / why we need it?
The customized ascend operator sgmv_expand and sgmv_shrink applies only
to the scenario where rank is 8,16,32,64. When rank >= 128, the operator
is out of range, causing the model to report an error.
### Does this PR introduce _any_ user-facing change?

### How was this patch tested?
Depends on this commit https://github.com/vllm-project/vllm/pull/31408 
- vLLM version: release/v0.13.0
- vLLM main:
254f6b9867

---------

Signed-off-by: ZT-AIA <1028681969@qq.com>
Signed-off-by: ZT-AIA <63220130+ZT-AIA@users.noreply.github.com>
2026-01-09 15:57:43 +08:00
lhchg
dc99cfdc15 [CustomOp] support TensorList for dispatchFFNCombine (#5665)
### What this PR does / why we need it?
To support tensorList for dispatch_ffn_combine, to adjust eplb

### Does this PR introduce _any_ user-facing change?
N/A

### How was this patch tested?
Single Operator Testing

- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef

---------

Signed-off-by: lhchg <lhao_cheng@163.com>
Co-authored-by: lihaocheng <lihaosheng1@h-partners.com>
2026-01-09 15:56:29 +08:00
InSec
2d713fee93 [CI] Accuracy issue of qwen3-next-w8a8 nightly test fix. (#5746)
### What this PR does / why we need it?
Close the **Full Graph** mode to temporarily avoid accuracy issue for
**Qwen3-Next-80B-A3B-Instruct-W8A8**.
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?

- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef

---------

Signed-off-by: InSec <1790766300@qq.com>
2026-01-09 15:55:13 +08:00
LeeWenquan
a3a74d6984 [CI] Add qwen3 next ci (#5395)
### What this PR does / why we need it?
Add Qwen3Next CI 

### Does this PR introduce _any_ user-facing change?

NO

### How was this patch tested?

- vLLM version: release/v0.13.0
- vLLM main:
254f6b9867

---------

Signed-off-by: SunnyLee219 <3294305115@qq.com>
2026-01-09 10:29:09 +08:00
Chenxi Qian
40eb3e1836 [OP] Enable custom op aclnnMoeInitRoutingCustom (#5332)
### What this PR does / why we need it?
This PR enables custom op `aclnnMoeInitRoutingCustom` introduced in PR
#5251

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?

- vLLM version: release/v0.13.0
- vLLM main:
bc0a5a0c08

---------

Signed-off-by: QianChenxi <chenxi.qian.cq@outlook.com>
Signed-off-by: zzzzwwjj <1183291235@qq.com>
Co-authored-by: zzzzwwjj <1183291235@qq.com>
2026-01-09 09:35:18 +08:00
Li Wang
595d3484c4 [Nightly] Move ops to the correct path (#5642)
### What this PR does / why we need it?
Move ops to the correct path where they belong

- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef

Signed-off-by: wangli <wangli858794774@gmail.com>
2026-01-09 09:23:36 +08:00
zhenwenqi2024
97f6be8108 [feature]dcp&pcp support mlapo (#5672)
### What this PR does / why we need it?
mlapo in deepseek is a huge performance improvement in decode, this pr
support pcp & dcp with mlapo

### Does this PR introduce _any_ user-facing change?
NO

### How was this patch tested?

- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef

---------

Signed-off-by: zhenwenqi2024 <zhenwenqi_2022@qq.com>
2026-01-08 23:49:23 +08:00
meihanc
6315a31399 [CI] Add triton ascend in nightly CI (#5716)
### What this PR does / why we need it?
Add triton ascend in nightly
### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef

---------

Signed-off-by: Meihan-chen <jcccx.cmh@gmail.com>
2026-01-08 21:17:32 +08:00
drslark
ccbc5e2ba1 [Feat][Bugfix][main] Adapted SP to eagle3 (#5562)
### What this PR does / why we need it?

Adapted sp to eagle3.

There may still be some problems, e.g., accuracy in some scenes,
`sp`+`dp`...

We will fix them later.

### Does this PR introduce _any_ user-facing change?

N/A

### How was this patch tested?

We tested it mainly in a new `e2e`.

```shell
pytest -s tests/e2e/singlecard/spec_decode/test_v1_spec_decode.py::test_llama_qwen_eagle_acceptance
```

```text
.

=============================== warnings summary ===============================
<frozen importlib._bootstrap>:241
  <frozen importlib._bootstrap>:241: DeprecationWarning: builtin type SwigPyPacked has no __module__ attribute

<frozen importlib._bootstrap>:241
  <frozen importlib._bootstrap>:241: DeprecationWarning: builtin type SwigPyObject has no __module__ attribute

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
============= 3 passed, 1 skipped, 2 warnings in 142.05s (0:02:22) =============
```

It passed.

- vLLM version: v0.13.0
- vLLM main:
7157596103

Signed-off-by: drslark <slarksblood@qq.com>
2026-01-08 15:33:52 +08:00
LI SHENGYONG
b69db4ce55 [EPLB][CI] EPLB add aclgraph and redundant expert ci (#5625)
### What this PR does / why we need it?
EPLB currently does not have CI related to aclgraph and redundancy
experts; this PR adds them.
release on #5529

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?
Tested the use cases to be added in this PR.

PASSED

====================================================== warnings summary
==========================================================
<frozen importlib._bootstrap>:241
<frozen importlib._bootstrap>:241: DeprecationWarning: builtin type
SwigPyPacked has no __module__ attribute

<frozen importlib._bootstrap>:241
<frozen importlib._bootstrap>:241: DeprecationWarning: builtin type
SwigPyObject has no __module__ attribute

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
====================================================== 1 passed, 2
warnings in 272.24s (0:04:32)
=====================================================

- vLLM version: v0.13.0
- vLLM main:
8be6432bda

Signed-off-by: shenchuxiaofugui <1311027364@qq.com>
2026-01-08 09:51:48 +08:00
Aoxuan Chen
8763953f56 [Feature] add the magicmtp speculative decoding acceleration algorithm (#5542)
### What this PR does / why we need it?

1. MagicMTP (paper: "Block Verification Accelerates Speculative
Decoding") was introduced to consider the influence among multiple draft
tokens, improving the acceptance rate without compromising accuracy.
2. Added Triton and PyTorch implementations, and added E2E test cases.

### Does this PR introduce _any_ user-facing change?
MagicMTP will automatically take effect when the parameter
"num_speculative_tokens" >= 3.
- vLLM version: v0.13.0
- vLLM main:
7157596103

Signed-off-by: chenaoxuan <cax1165@163.com>
2026-01-08 09:15:55 +08:00
ZCG12345
3be8e33fe9 [Kernel] Add moe_gating_top_k operator support for Ascend NPU (#5579)
### What this PR does / why we need it?

1.replace moe_gating_top_k from torch_npu with custom op
2.enable the  renorm function of moe_gating_top_k in softmax scenerio

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
No need test

- vLLM version: v0.13.0
- vLLM main:
7157596103

---------

Signed-off-by: ZCG12345 <2097562023@qq.com>
2026-01-07 21:42:31 +08:00
Li Wang
1165b2c863 [1/N][CI] Refactor accuracy test (#5400)
### What this PR does / why we need it?
1. Accuracy testing no longer compares eager and graph modes; instead,
it directly extracts the golden result under the graph mode
configuration (the implicit purpose of this case is to verify whether
modifications affect existing results)
2. Next step: finer-grained supervision of logits/sampler results
### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: release/v0.13.0
- vLLM main:
254f6b9867

Signed-off-by: wangli <wangli858794774@gmail.com>
2026-01-07 20:58:15 +08:00
Icey
137f28341d [Tests] Add qwen3-8b nightly test (#5597)
### What this PR does / why we need it?
Add qwen3-8b nightly test 

- vLLM version: v0.13.0
- vLLM main:
7157596103
---------
Signed-off-by: wxsIcey <1790571317@qq.com>
2026-01-07 18:42:05 +08:00
wangxiyuan
6f7a81cd9f [CI] cleanup single/multi-card test (#5623)
1. speed up e2e light test.
2. create `2-cards` and `4-cards` folder in multicard
3. move ops to nightly
4. run test in Alphabetical Order

- vLLM version: v0.13.0
- vLLM main:
8be6432bda

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
2026-01-07 14:13:34 +08:00
wangyibo1005
25baf6df09 [Feature]EPLB:Adapt DispatchGmmCombineDecode operator to eplb tensor list and expert token numbers (#5552)
#### What this PR does / why we need it?
This PR adapt DispatchGmmCombineDecode operator to eplb tensor list and
expert token numbers.

This operator support gmm1, gmm2, gmm1Scale and gmm2Scale in format of
list.
This operator support couting how many token each local expert recieves
by expertTokensNum .


- vLLM version: v0.13.0
- vLLM main:
7157596103

More info about this operator, please refer to RFC: issue
https://github.com/vllm-project/vllm-ascend/issues/5476
2026-01-07 11:23:42 +08:00
starmountain1997
086c093347 [CI] Add DeepSeek-V3.2-W8A8 nightly ci test (#5371)
# What this PR does / why we need it?

Add DeepSeek-V3.2-W8A8 dual-node nightly CI test and update A3 nightly
test configuration:

1. Add DeepSeek-V3.2-W8A8 dual-node test:
tests/e2e/nightly/multi_node/config/DeepSeek-V3_2-W8A8-A3-dual-nodes.yaml
    - 2 nodes, 16 NPUs per node (32 NPUs total)
- Configuration: 2P+1D (data-parallel-size=4, tensor-parallel-size=8,
data-parallel-size-local=2)
    - Includes performance and accuracy benchmarks with GSM8K dataset
  2. Update A3 nightly workflow: .github/workflows/nightly_test_a3.yaml
- Added DeepSeek-V3.2-W8A8 dual-node test to the A3 nightly test matrix
    - Test name: multi-node-dpsk3.2-2node
3. Improve test scripts: Updated
.github/workflows/_e2e_nightly_multi_node.yaml and related scripts for
better multi-node testing support

test on A3 instances
  - Performance baseline: 1 (threshold: 0.97)
  - Accuracy baseline: 95% (threshold: 5%)
- Test dataset: GSM8K with 512 prompts for performance, gsm8k-lite for
accuracy
---------
Signed-off-by: guozr <guozr1997@hotmail.com>
Co-authored-by: guozr <guozr1997@hotmail.com>
2026-01-07 10:02:02 +08:00
Ronald
6ea2afe5fa [Feature] implement basic framework for batch invariant (#5517)
### What this PR does / why we need it?
This PR implement the basic framework for batch invariant, please see
https://github.com/vllm-project/vllm-ascend/issues/5487.
### Does this PR introduce _any_ user-facing change?
we reuse the function `vllm_is_batch_invariant` in vllm to judge if
batch invariant is enabled.

- vLLM version: v0.13.0
- vLLM main:
45c1ca1ca1
---------
Signed-off-by: Ronald1995 <ronaldautomobile@163.com>
Signed-off-by: Lord_of_Ironhill <suiweiyi@huawei.com>
Signed-off-by: zjchenn <zjchenn@gmail.com>
Signed-off-by: wangx700 <wangxin700@huawei.com>
Co-authored-by: Lord_of_Ironhill <suiweiyi@huawei.com>
Co-authored-by: zjchenn <zjchenn@gmail.com>
Co-authored-by: wangx700 <wangxin700@huawei.com>
2026-01-07 09:11:26 +08:00
zhenwenqi2024
ad9b711f89 [Bugfix] fix dcp_only bug and add e2e accuracy test for dcp only and pcp only (#5565)
### What this PR does / why we need it?
[Bugfix] fix dcp_only bug and add e2e accuracy test for dcp only and pcp
only
this pr fix the bug of accuracy test when decode_parallel_size>1 and
prefill_context_parallel_size=1.
### Does this PR introduce _any_ user-facing change?
NO

### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
7157596103

---------

Signed-off-by: zhenwenqi2024 <zhenwenqi_2022@qq.com>
2026-01-06 22:48:21 +08:00
InSec
089ca2ddcc [Nightly][Test] Add Qwen3-Next-80B-A3B-Instruct-W8A8 nightly test (#5616)
### What this PR does / why we need it?
There was an accuracy issue with **Qwen3-Next-80B-A3B-Instruct-W8A8**
model in the old version of **Triton-Ascend**, so, we are now adding one
nightly test to maintain it.
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?

- vLLM version: v0.13.0
- vLLM main:
7157596103

Signed-off-by: IncSec <1790766300@qq.com>
2026-01-06 17:36:00 +08:00
Shanshan Shen
b94d589769 [MM][Bugfix] Update hf_config to hf_text_config (#5319)
### What this PR does / why we need it?

Following https://github.com/vllm-project/vllm-ascend/pull/5205, update
`hf_config` to `hf_text_config`.

Find more details at
https://github.com/vllm-project/vllm-ascend/pull/5205#issuecomment-3675417534
and
https://github.com/vllm-project/vllm-ascend/pull/5205#issuecomment-3677920872.

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: release/v0.13.0
- vLLM main:
5fbfa8d9ef

Signed-off-by: shen-shanshan <467638484@qq.com>
2026-01-06 16:41:39 +08:00
wjunLu
3cf059a72b [Main2Main] Upgrade vllm commit to 0105 (#5595)
### What this PR does / why we need it?

Upgrade vllm commit to 0105 (8be6432bdaf6275664d857b1e5e9bf8ed1ce299e)

1. Remove `maybe_padded_num_tokens` arg in `model_runner_v1.py` since
https://github.com/vllm-project/vllm/pull/31517 deleted unused arg

2. Remove dense `Qwen/Qwen3-0.6B` in
`tests/e2e/multicard/test_aclgraph_capture_replay.py` and
`tests/e2e/multicard/test_data_parallel.py` due to
https://github.com/vllm-project/vllm/pull/30739
where offline data parallel mode will not be supported/useful for dense
models

3. Adapt `vllm_ascend/worker/worker.py` due to
https://github.com/vllm-project/vllm/pull/31584

4. Adapt `self.block_size` calling due to
https://github.com/vllm-project/vllm/pull/31540

5. Modify `test_mla_v1.py` due to
https://github.com/vllm-project/vllm/pull/28454 , which refactorred
`get_head_size()`

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: v0.13.0
- vLLM main:
7157596103

Signed-off-by: wjunLu <wjunlu217@gmail.com>
2026-01-06 08:44:29 +08:00
Li Wang
c5e2f48510 [CI] mv ops to correct path (#5615)
### What this PR does / why we need it?
mv ops to correct path
:`tests/e2e/nightly/single_node/ops/singlecard_ops/triton`

Signed-off-by: wangli <wangli858794774@gmail.com>
2026-01-05 23:17:07 +08:00
dsxsteven
129ba9fe1b [BugFix] Fix Smoke Testing Bug for DSR1 longseq (#5613)
### What this PR does / why we need it?
Fix Smoke Testing Bug for DSR1 longseq
We need to make this change because the daily smoke test case is
throwing an error: "max_tokens or max_completion_tokens is too large:
32768.This model's maximum context length is 32768 tokens and your
request has 128 input tokens". We encounter this error due to
max-out-len equals to max-model-len. We can fix this error by increasing
max-model-len argument in the script.
### Does this PR introduce _any_ user-facing change?

### How was this patch tested?


- vLLM version: v0.13.0
- vLLM main:
7157596103

Signed-off-by: daishixun <dsxsteven@sina.com>
2026-01-05 22:40:28 +08:00
Angazenn
11e75494b1 [TRITON][TEST]Add nightly test for triton split_qkv_rmsnorm_rope (#5267)
### What this PR does / why we need it?
Add nightly test for triton split_rmsnorm_rope

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c

---------

Signed-off-by: Angazenn <supperccell@163.com>
2026-01-05 21:35:37 +08:00
ZT-AIA
58e8d19c35 [UT]add triton ops ut : test_fused_qkvzba_split_reshape_cat (#5474)
### What this PR does / why we need it?
[UT]add triton ops ut :  test_fused_qkvzba_split_reshape_cat
### Does this PR introduce _any_ user-facing change?

### How was this patch tested?
pytest -sv tests/ut/ops/test_fused_qkvzba_split_reshape_cat.py
- vLLM version: v0.13.0
- vLLM main:
5326c89803

---------

Signed-off-by: ZT-AIA <1028681969@qq.com>
2026-01-05 20:05:07 +08:00
Yizhou
755caeb06e [Feat][Spec] Optimize token index calculation in spec decode with Triton kernel (#5356)
### What this PR does / why we need it?
Replace multiple PyTorch operations with a fused Triton kernel to
determine token indices for sampling during speculative decoding. This
reduces kernel launch overhead and memory traffic, improving overall
performance on Ascend hardware.

---------

Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
2026-01-05 16:51:29 +08:00
daniel
8ffe3f5d78 feat: implement high-performance Triton kernels for rejection sampling: optimization for rejection_random_sample_kernel (#5259)
### What this PR does / why we need it?

This PR introduces optimized Triton implementations for the
rejection_random_sample_kernel delivering superior performance compared
to the existing Triton implementations. The new Triton kernels maintain
full functional accuracy while delivering significant performance
improvements across various batch sizes and MTP configurations.

### Does this PR introduce _any_ user-facing change?

Yes, this PR modifies rejection_sampler.py to use optimized Triton
kernels:
rejection_random_sample_kernel is modified and optimized

### How was this patch tested?
performance benchmark results:
<html xmlns:v="urn:schemas-microsoft-com:vml"
xmlns:o="urn:schemas-microsoft-com:office:office"
xmlns:x="urn:schemas-microsoft-com:office:excel"
xmlns="http://www.w3.org/TR/REC-html40">
<head>

<meta name=Generator content="Microsoft Excel">
<!--[if !mso]>
</head>
<body>
<!--StartFragment-->

Batch Size | MTP | origin implementation(us) | optimized version(us)
-- | -- | -- | --
1 | 1 | 2.934 | 3.64
8 | 1 | 4.467 | 4
32 | 1 | 6.98 | 4.54
64 | 1 | 11.087 | 6.42
128 | 1 | 13.414 | 7.84
256 | 1 | 19.66 | 8.487
512 | 1 | 39.908 | 11.62
1024 | 1 | 81.781 | 18.16
2048 | 1 | 137.923 | 32.934
1 | 2 | 3.4 | 4.02
8 | 2 | 3.74 | 4.24
32 | 2 | 6.373 | 7.394
64 | 2 | 9.747 | 6.46
128 | 2 | 12.98 | 7.76
256 | 2 | 20.834 | 9.787
512 | 2 | 39.314 | 13.56
1024 | 2 | 83.135 | 22.387
2048 | 2 | 157.563 | 40.607


<!--EndFragment-->
</body>

</html>


- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c

Signed-off-by: 1024daniel <xxltju324@gmail.com>
2026-01-05 16:03:02 +08:00
Trunrain
91bf524364 [BugFix][kernel] fix matmul_allreduce_add_rmsnorm_kernel (#5335)
### What this PR does / why we need it?
fix matmul_allreduce_add_rmsnorm_kernel, add hccl Init, SetCcTiling
interface
test case use multicard-4 
### Does this PR introduce _any_ user-facing change?
NO
### How was this patch tested?
pytest -sv tests/e2e/nightly/ops/test_matmul_allreduce_add_rmsnorm.py
multicard-4 pass

https://github.com/vllm-project/vllm-ascend/actions/runs/20502630658/job/58914474652?pr=5335



- vLLM version: release/v0.13.0
- vLLM main:
bc0a5a0c08

Signed-off-by: tongrunze <t00574058@china.huawei.com>
Co-authored-by: tongrunze <t00574058@china.huawei.com>
2026-01-05 15:19:54 +08:00
weiguihua2
549be94397 [Bugfix] fix pcp + eplb error (#5561)
### What this PR does / why we need it?
Fix the bug in the PCP overlay feature

1、Fix the bug related to PCP and EPLB overlap by including PCP size in
the word_size calculation.
2、In the PCP pooling scenario, a prompt has been added for setting the
cp_kv_cache_interleave_size.

- vLLM version: v0.13.0
- vLLM main:
7157596103

Signed-off-by: weiguihua2 <weiguihua2@huawei.com>
2026-01-05 14:08:11 +08:00
Magnus
2b5536362a [CI] skip xlite-decode-only e2e test (#5407)
### What this PR does / why we need it?
skip xlite-decode-only e2e test, since it's unstable

- vLLM version: release/v0.13.0
- vLLM main:
81786c8774

Signed-off-by: changdawei1 <changdawei3@huawei.com>
2026-01-05 11:05:26 +08:00
LookAround0301
d25a2c20c5 [Bugfix] Fix chunk prefill bug for long_sequence feature (#5444)
### What this PR does / why we need it?
Fix chunk prefill bug for long_sequence feature

When there are two requests with chunk prefill enabled in the
long-sequence scenario, if one request has only 1 token during
scheduling, it will be identified as a decode request and trigger an
error. This PR fixes the issue.
Closes: https://github.com/vllm-project/vllm-ascend/issues/5445

- vLLM version: release/v0.13.0
- vLLM main:
81786c8774
---------
Signed-off-by: LookAround <lixushi@huawei.com>
2026-01-05 09:16:36 +08:00
dsxsteven
37fd48bee5 [CI] Move longseq Nightly CI (#5577)
### What this PR does / why we need it?
move longseq nightly CI to correct path due to #5479 [1/N] Refactor
nightly test structure

Signed-off-by: daishixun <dsxsteven@sina.com>
2026-01-04 15:42:43 +08:00
drslark
363ac1b80f [Feat][main] Supported to use full-graph with Qwen3-Next-MTP (#5477)
### What this PR does / why we need it?

Supported to use full-graph with Qwen3-Next-MTP.

In detail, we adatpted `AscendAttentionState.ChunkedPrefill` in main
model, and also adapted `AscendAttentionState.ChunkedPrefill` in mtp
model.

### Does this PR introduce _any_ user-facing change?

N/A

### How was this patch tested?

We changed the test of Qwen3-Next-MTP in
`tests/e2e/multicard/test_qwen3_next.py` to make it a test of
`FULL_DECODE_ONLY`. Then run `pytest -s
tests/e2e/multicard/test_qwen3_next.py::test_qwen3_next_distributed_mp_eager_mtp_similarity_tp4`.

And this test passed.

```text
.

================================================================================================================================= warnings summary =================================================================================================================================
<frozen importlib._bootstrap>:241
  <frozen importlib._bootstrap>:241: DeprecationWarning: builtin type SwigPyPacked has no __module__ attribute

<frozen importlib._bootstrap>:241
  <frozen importlib._bootstrap>:241: DeprecationWarning: builtin type SwigPyObject has no __module__ attribute

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
==================================================================================================================== 1 passed, 2 warnings in 271.89s (0:04:31) =====================================================================================================================
```
- vLLM version: v0.13.0
- vLLM main:
5326c89803

Signed-off-by: drslark <slarksblood@qq.com>
2026-01-04 12:03:21 +08:00
dsxsteven
3c7e6c6817 [CI] Add multi-nodes longseq configs of DeepSeek-R1-W8A8 & Qwen3-235B-W8A8 (#5381)
### What this PR does / why we need it?
add DeepSeek-R1-W8A8 and Qwen3-235B-W8A8 configs in multi-nodes and
longseq (PCP&DCP) scenario

- vLLM version: release/v0.13.0
- vLLM main:
bc0a5a0c08
---------
Signed-off-by: daishixun <dsxsteven@sina.com>
2026-01-04 10:38:40 +08:00
Jade Zheng
7d5242faca [Refactor] Formatting output types related to FuseMoE (#5481)
Currently in the Fused MoE module, functions of classes like
MoECommMethod and MoETokenDispatcher output data in dictionary or tuple
format, which hampers code maintainability, readability, and
extensibility. This PR introduces dataclasses for these key output types
to address these issues.

- vLLM version: v0.13.0
- vLLM main:
5326c89803

---------

Signed-off-by: Jade Zheng <zheng.shoujian@outlook.com>
2025-12-31 14:24:37 +08:00
Jade Zheng
38570cfeb6 [Feature] Support kv nz feature for DeepSeek decode node in disagg-prefill scenario (#3072)
By converting the KV cache from ND to NZ format when the decode node
receives it, this PR ensures that the KV NZ feature works correctly
during the decoding phase in disagg-prefill scenario.

- vLLM version: v0.11.0
- vLLM main:
83f478bb19

---------

Signed-off-by: Jade Zheng <zheng.shoujian@outlook.com>
Co-authored-by: ghphotoframe <854746559@qq.com>
Co-authored-by: alex101-ops <alex1015718386@gmail.com>
2025-12-31 14:24:04 +08:00
wjunLu
3c2d3e52e5 [Main2Main] Upgrade vllm commit to 1230 (#5495)
### What this PR does / why we need it?

Upgrade vllm commit to 1230

Affected by https://github.com/vllm-project/vllm/pull/27614 (and the
core PR https://github.com/vllm-project/vllm/pull/26866), we have to
make the following changes:

1. Modify `tests/e2e/multicard/test_aclgraph_capture_replay.py` to keep
compatible with both vllm version of `v0.13.0` and latest main commitID,
while vllm enables async scheduling by default
2. Skip `test_guided_decoding.py` due to xgrammar errors
(https://github.com/vllm-project/vllm-ascend/issues/5524)

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: v0.13.0
- vLLM main:
45c1ca1ca1

---------

Signed-off-by: wjunLu <wjunlu217@gmail.com>
2025-12-31 09:44:35 +08:00
lilinsiman
46862ce1af [main][test] Refactor the mtp and eagle test case (#5326)
### What this PR does / why we need it?
1. Refactor the current test with mtp and eagle cases
2. Add new necessary cases with mtp and eagle

### Does this PR introduce _any_ user-facing change?
no

### How was this patch tested?
ut

- vLLM version: release/v0.13.0
- vLLM main:
5fbfa8d9ef

---------

Signed-off-by: lilinsiman <lilinsiman@gmail.com>
2025-12-31 09:22:58 +08:00
Li Wang
2ee17e50a1 [2/N] Upgrade nightly doc (#5534)
### What this PR does / why we need it?
Follow up https://github.com/vllm-project/vllm-ascend/pull/5479, upgrade
the corresponding doc for developers

- vLLM version: v0.13.0
- vLLM main:
45c1ca1ca1

Signed-off-by: wangli <wangli858794774@gmail.com>
2025-12-31 09:11:42 +08:00
Li Wang
e760aae1df [1/N] Refactor nightly test structure (#5479)
### What this PR does / why we need it?
This patch is a series of refactoring actions, including clarifying the
directory structure of nightly tests, refactoring the config retrieval
logic, and optimizing the workflow, etc. This is the first step:
refactoring the directory structure of nightly to make it more readable
and logical.

- vLLM version: v0.13.0
- vLLM main:
5326c89803

Signed-off-by: wangli <wangli858794774@gmail.com>
2025-12-30 19:03:02 +08:00
zzzzwwjj
71f729a661 Revert "moe_gating_top_k" (#5512)
Reverts vllm-project/vllm-ascend#5271

It breaks e2e test

- vLLM version: v0.13.0
- vLLM main:
45c1ca1ca1
2025-12-30 15:05:47 +08:00