17 Commits

Author SHA1 Message Date
Shanshan Shen
5c0d02f689 [Bugfix] Fix multi-instance serving OOM on single card (#7427)
### What this PR does / why we need it?
Fix https://github.com/vllm-project/vllm-ascend/issues/7308.

Subtracting `init_non_torch_memory` (maybe used by the first instance)
from the total `non_torch_memory` when calculating
`available_kv_cache_memory`.

Directly use `non_torch_memory_increase` (contained in
`non_kv_cache_memory`) to calculate `available_kv_cache_memory`.

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

### How was this patch tested?
Launch tow vllm-ascend instances sequentially on single card.

```bash
# Launch first instance
vllm serve /root/.cache/modelscope/hub/models/Qwen/Qwen3-0.6B \
--port 8100 \
--host 0.0.0.0 \
--additional-config='{"enable_cpu_binding":true}'  \
--gpu-memory-utilization 0.3 \
--max-num-seqs 1 \
--max-model-len 2048 \
--max-num-batched-tokens 2048 \
--no-enable-prefix-caching \
--enforce-eager

# Launch second instance
vllm serve /root/.cache/modelscope/hub/models/Qwen/Qwen3-0.6B \
--port 8101 \
--host 0.0.0.0 \
--additional-config='{"enable_cpu_binding":true}'  \
--gpu-memory-utilization 0.3 \
--max-num-seqs 1 \
--max-model-len 2048 \
--max-num-batched-tokens 2048 \
--no-enable-prefix-caching \
--enforce-eager
```

**Before this PR:**

```bash
# First instance:
------------------------------------------------------------------
requested_memory: 18.287109375 GiB
non_kv_cache_memory: 1.2340388298034668 GiB
init_non_torch_memory: 0.3616676330566406 GiB
non_torch_memory_before_empty_cache: 0.3896217346191406 GiB
non_torch_memory_increase: 0.0279541015625 GiB
non_torch_memory_cleared_by_empty_cache: 0.3616676330566406 GiB
------------------------------------------------------------------

# Second instance:
------------------------------------------------------------------
requested_memory: 18.287109375 GiB
non_kv_cache_memory: 1.2336344718933105 GiB
init_non_torch_memory: 18.37220001220703 GiB
non_torch_memory_before_empty_cache: 18.399906158447266 GiB
non_torch_memory_increase: 0.02754974365234375 GiB
non_torch_memory_cleared_by_empty_cache: 18.372356414794922 GiB
------------------------------------------------------------------
# available_kv_cache_memory = requested_memory - non_kv_cache_memory - non_torch_memory_cleared_by_empty_cache
Available KV cache memory: -1.32 GiB
```

**After this PR:**

```bash
# First instance:
------------------------------------------------------------------
requested_memory: 18.287109375 GiB
non_kv_cache_memory: 1.2340540885925293 GiB
init_non_torch_memory: 0.36182403564453125 GiB
non_torch_memory_before_empty_cache: 0.38979339599609375 GiB
non_torch_memory_increase: 0.0279693603515625 GiB
non_torch_memory_cleared_by_empty_cache: 0.0 GiB
------------------------------------------------------------------

# Second instance:
------------------------------------------------------------------
requested_memory: 18.287109375 GiB
non_kv_cache_memory: 1.233344554901123 GiB
init_non_torch_memory: 18.74309539794922 GiB
non_torch_memory_before_empty_cache: 18.770355224609375 GiB
non_torch_memory_increase: 0.02725982666015625 GiB
non_torch_memory_cleared_by_empty_cache: 0.0 GiB
------------------------------------------------------------------
# available_kv_cache_memory = requested_memory - non_kv_cache_memory - non_torch_memory_cleared_by_empty_cache
Available KV cache memory: 17.05 GiB
```

- vLLM version: v0.17.0
- vLLM main:
4497431df6

---------

Signed-off-by: shen-shanshan <467638484@qq.com>
Signed-off-by: Shanshan Shen <87969357+shen-shanshan@users.noreply.github.com>
2026-03-23 14:22:59 +08:00
linfeng-yuan
88d03a783f [refactor] replace scattered business kwargs with typed request objects and explicit stage boundaries (#7024)
### What this PR does / why we need it?
Refactor `vllm_ascend/ops/fused_moe` to replace scattered MoE business
`**kwargs` with typed request objects and explicit stage boundaries.

- Prepare, dispatch, MLP, and quant stages now have clearer ownership.
- Main MoE path no longer depends on business `kwargs.get(...)` lookups.
- Comm and dispatcher interfaces are request-only on the main path.
- UTs can assert stage-level fields directly instead of inferring
behavior indirectly.

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

### How was this patch tested?
CI passed.

---------

Signed-off-by: linfeng-yuan <1102311262@qq.com>
2026-03-20 23:23:57 +08:00
Nengjun Ma
ee804ce23e Main2main upgrade vllm to 0318 commit (#7412)
### What this PR does / why we need it?
Upgrade vllm commit to 0318. 

Main content: Added a pre-operation for cleaning up and waiting(default
max 50s) for the completion of the clean up of the NPU memory to some
test cases that failed due to the failure to release the NPU memory in a
timely manner when the previous test cases were executed.

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

### How was this patch tested?
NA

- vLLM version: v0.17.0
- vLLM main:
4497431df6

---------

Signed-off-by: leo-pony <nengjunma@outlook.com>
2026-03-19 17:17:36 +08:00
rjg-lyh
4d443b9228 [bugfix] restore pr-7029 and fix patch error (#7294)
### What this PR does / why we need it?
This PR restores #7029, which adds W8A8C8 support for dsv3.2/glm5 using
the `lightning_indexer_quant` ops in the pd-mix stage.

The original PR was reverted by #7288 because the patch did not work
with the recompute scheduler.

This PR also fixes the patching issue so that it works correctly with
the recompute scheduler.

### Does this PR introduce _any_ user-facing change?
Yes. To enable LI C8, users need to set the `enable_sparse_c8` option to
`"true"` in `additional_config`.

- vLLM version: v0.17.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: rjg-lyh <1318825571@qq.com>
2026-03-16 15:39:42 +08:00
Mengqing Cao
0c299f79b9 Revert "[Perf][1/N] w8a8c8 support in dsv3.2/glm5 (#7029)" (#7288)
### What this PR does / why we need it?
This reverts commit 7ed9e9de69, which
introduces an issue that the patch doesn't work with recompute scheduler
enabled.
- vLLM version: v0.17.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
2026-03-15 20:19:09 +08:00
rjg-lyh
7ed9e9de69 [Perf][1/N] w8a8c8 support in dsv3.2/glm5 (#7029)
### What this PR does / why we need it?
This PR supports W8A8C8 in dsv3.2/glm5 with lightning_indexer_quant ops
in pd-mix stage mainly.

Because the code for the current PD-disaggregated scenario is still
under refactoring and cleanup, this PR prioritizes ensuring the C8
functionality in the pd-mix scenario.

The next steps are planned in two parts:
① Once the optimized scatter operator is updated, we will replace the
original operator to improve the performance of storing k_scale.
② Once the code logic for the PD-disaggregated scenario becomes stable,
we will carry out more comprehensive validation and make appropriate
adaptations.
③ Because enabling C8 currently introduces several new operators whose
performance still needs improvement, performance may regress in some
scenarios. Therefore, only after all the operators are fully ready can
we ensure that this feature does not cause any performance degradation.
At that point, we will enable this feature by default and remove the
switch in `additional_config`.


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

### How was this patch tested?
CI passed with new added/existing test.

- vLLM version: v0.16.0
- vLLM main:
4034c3d32e

---------

Signed-off-by: rjg-lyh <1318825571@qq.com>
2026-03-13 14:47:42 +08:00
SILONG ZENG
43df2cb2fc [Lint]Style: Convert test/ to ruff format(Batch #1) (#6738)
### What this PR does / why we need it?
**Scope of Changes**:
| File Path |
| :--- |
| `tests/e2e/310p/multicard/test_vl_model_multicard.py` |
| `tests/e2e/310p/singlecard/test_vl_model_singlecard.py` |
| `tests/e2e/310p/test_utils.py` |
| `tests/e2e/conftest.py` |
| `tests/e2e/model_utils.py` |
| `tests/e2e/models/conftest.py` |
| `tests/e2e/models/test_lm_eval_correctness.py` |
| `tests/e2e/multicard/2-cards/spec_decode/test_spec_decode.py` |
| `tests/e2e/multicard/2-cards/test_aclgraph_capture_replay.py` |
| `tests/e2e/multicard/2-cards/test_data_parallel.py` |
| `tests/e2e/multicard/2-cards/test_disaggregated_encoder.py` |
| `tests/e2e/multicard/2-cards/test_expert_parallel.py` |
| `tests/e2e/multicard/2-cards/test_external_launcher.py` |
| `tests/e2e/multicard/2-cards/test_full_graph_mode.py` |
| `tests/e2e/multicard/2-cards/test_ilama_lora_tp2.py` |
| `tests/e2e/multicard/2-cards/test_offline_inference_distributed.py` |
| `tests/e2e/multicard/2-cards/test_offline_weight_load.py` |
| `tests/e2e/multicard/2-cards/test_pipeline_parallel.py` |
| `tests/e2e/multicard/2-cards/test_prefix_caching.py` |
| `tests/e2e/multicard/2-cards/test_quantization.py` |
| `tests/e2e/multicard/2-cards/test_qwen3_moe.py` |
| `tests/e2e/multicard/2-cards/test_qwen3_moe_routing_replay.py` |
| `tests/e2e/multicard/2-cards/test_qwen3_performance.py` |
| `tests/e2e/multicard/2-cards/test_shared_expert_dp.py` |
| `tests/e2e/multicard/2-cards/test_single_request_aclgraph.py` |
| `tests/e2e/multicard/2-cards/test_sp_pass.py` |

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

### How was this patch tested?

- vLLM version: v0.15.0
- vLLM main:
9562912cea

Signed-off-by: MrZ20 <2609716663@qq.com>
Co-authored-by: wangxiyuan <wangxiyuan1007@gmail.com>
2026-03-10 09:52:50 +08:00
starmountain1997
bc1622338c [CI] Add long and short prompt tests for DeepSeek-V3.2 (#6536)
### What this PR does / why we need it?

This version has no divisibility constraint between tp and mtp+1.
However, cudagraph_capture_sizes must be a common multiple of tp and
mtp+1, with a maximum of tp * (mtp+1). Therefore, we fixed
cudagraph_capture_sizes.

We added a long-sequence test (64k input, 3k output) for the two-node
mixed deployment scenario. Due to the excessive time required for
performance benchmarking, we are only verifying functionality. The
single-node scenario is skipped because VRAM limitations prevent
launching the model with a max-model-len of 68,000.

and we also add aime2025 test for dual-node deepseek 3.2 nightly test.

### How was this patch tested?

test at nightly environment.

- vLLM version: v0.15.0
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.15.0

Signed-off-by: guozr <guozr1997@hotmail.com>
Co-authored-by: guozr <guozr1997@hotmail.com>
2026-02-26 10:58:50 +08:00
jiahao.quan
7221045777 [Attention] add gpt-oss support (#5901)
### What this PR does / why we need it?
Please refer to the following link for the historical conversation
https://github.com/vllm-project/vllm-ascend/pull/4467. We have made
updates in light of the comments from the prior PR review. Given the
refactoring of the attention_v1 component, we have carried out necessary
adjustments to fit the newly revised code.

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

1. Modified the code in the Attention section to adapt to the SWA and
Sink features required by gpt-oss.
2. Modified the code in the MoE section to add support for bias and
swigluoai.

### How was this patch tested?
Please refer to the
https://github.com/vllm-project/vllm-ascend/pull/4467 for performance
tests, on the basis of which the accuracy tests from AIME2024 have been
newly added.

![img_v3_02tu_501e88e3-2217-4565-8edf-b9acf4f43f2g](https://github.com/user-attachments/assets/024f8283-18ab-4d4d-ab12-27917b5d7d06)


- vLLM version: v0.13.0
- vLLM main:
bde38c11df

---------

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
Signed-off-by: mikequan0425 <mikequan0425@foxmail.com>
Signed-off-by: hfadzxy <starmoon_zhang@163.com>
Signed-off-by: shenchuxiaofugui <1311027364@qq.com>
Signed-off-by: jiangyunfan1 <jiangyunfan1@h-partners.com>
Signed-off-by: pu-zhe <zpuaa@outlook.com>
Signed-off-by: liziyu <liziyu16@huawei.com>
Signed-off-by: wangxiaoteng <wangxiaoteng@huawei.com>
Signed-off-by: luomin2005 <luomin2005@huawei.com>
Signed-off-by: whx-sjtu <2952154980@qq.com>
Signed-off-by: SlightwindSec <slightwindsec@gmail.com>
Signed-off-by: wxsIcey <1790571317@qq.com>
Signed-off-by: MrZ20 <2609716663@qq.com>
Co-authored-by: wangxiyuan <wangxiyuan1007@gmail.com>
Co-authored-by: leon_tao <taoyao2@huawei.com>
Co-authored-by: nurxat <738457498@qq.com>
Co-authored-by: hfadzxy <starmoon_zhang@163.com>
Co-authored-by: mikequan <199741451@qq.com>
Co-authored-by: LI SHENGYONG <49200266+shenchuxiaofugui@users.noreply.github.com>
Co-authored-by: jiangyunfan1 <jiangyunfan1@h-partners.com>
Co-authored-by: pu-zhe <zpuaa@outlook.com>
Co-authored-by: luomin2005 <luomin2005@huawei.com>
Co-authored-by: liziyu <56102866+liziyu179@users.noreply.github.com>
Co-authored-by: wangxiaoteng <wangxiaoteng@huawei.com>
Co-authored-by: whx <56632993+whx-sjtu@users.noreply.github.com>
Co-authored-by: Cao Yi <slightwindsec@gmail.com>
Co-authored-by: Icey <1790571317@qq.com>
Co-authored-by: SILONG ZENG <2609716663@qq.com>
2026-02-12 10:55:34 +08:00
starmountain1997
bfcc372f75 [CI] Add long and short prompt tests for DeepSeek-V3.2 (#6499)
### What this PR does / why we need it?

This PR enhances the test_deepseek3_2_w8a8_pruning_mtp_tp2_ep E2E test
by adding both short and long prompt test cases:
- Short test: Validates basic functionality with minimal input ("Hello
")
- Long test: Validates the model can handle prompts near its maximum
context length (~163K tokens, approaching the max_position_embeddings
limit of 163,840)
Additionally, explicitly sets max_model_len=163840 to ensure the test
properly exercises the model's full context window capability.
### Does this PR introduce _any_ user-facing change?

No. This change only affects internal E2E testing infrastructure.  

### How was this patch tested?

The modified test case will be executed as part of the E2E test suite
and has been validated
[here](https://github.com/vllm-project/vllm-ascend/actions/runs/21620195055/job/62308026205?pr=6499).



- vLLM version: v0.15.0
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.15.0

Signed-off-by: guozr <guozr1997@hotmail.com>
Co-authored-by: guozr <guozr1997@hotmail.com>
2026-02-04 09:10:50 +08:00
Nengjun Ma
78fad4e348 [Refactor] MLP weight prefetch to consistency with MoE Model's prefetching in terms of code and usage (#6442)
### What this PR does / why we need it?
Refactor MLP weight prefetch to consistency with MoE Model's prefetching
in terms of code and usage.
Environments VLLM_ASCEND_ENABLE_PREFETCH_MLP,
VLLM_ASCEND_MLP_DOWN_PREFETCH_SIZE and
VLLM_ASCEND_MLP_GATE_UP_PREFETCH_SIZE is removed, usage as following:

--additional-config '{"weight_prefetch_config": { "enabled": true,
"prefetch_ratio": {"mlp": { "gate_up": 1.0, "down": 1.0} }}}'

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

### How was this patch tested?

- vLLM version: v0.14.1
- vLLM main:
dc917cceb8

---------

Signed-off-by: leo-pony <nengjunma@outlook.com>
2026-02-04 09:08:18 +08:00
wangxiyuan
f8e76a49fa [CI] Upgrade trasnformers version (#6307)
Upgrade transformers to >=4.56.4

- vLLM version: v0.14.1
- vLLM main:
dc917cceb8

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
2026-01-28 14:06:39 +08:00
starmountain1997
6c73b88dd6 [CI] Enable FLASHCOMM1 with layer_sharding and FULL_DECODE_ONLY in ds32 testing (#6115)
### What this PR does / why we need it?

This PR enables FLASHCOMM1 communication optimization with layer
sharding for DeepSeek-V3.2 W8A8 model testing to
  validate PR #5702. The changes include:

  1. Enable FLASHCOMM1: Set VLLM_ASCEND_ENABLE_FLASHCOMM1=1
  improves performance for distributed inference
2. Add layer sharding: Configure layer_sharding: ["q_b_proj", "o_proj"]
4. Update baselines: Adjust performance baselines to reflect the
improvements from FLASHCOMM1 and layer sharding

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

No. This is a CI/test-only change that enables new communication
optimization features for testing purposes.

### How was this patch tested?

- vLLM version: v0.13.0
- vLLM main:
d68209402d

Signed-off-by: guozr <guozr1997@hotmail.com>
Co-authored-by: guozr <guozr1997@hotmail.com>
2026-01-23 19:48:37 +08:00
maxmgrdv
ef9d8367f5 [Feature] Add support of new W4A4_LAOS_DYNAMIC quantization method (#5143)
Introduce W4A4 LAOS Quantization for better model compression and
inference efficiency on Ascend devices.

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

Signed-off-by: hfadzxy <starmoon_zhang@163.com>
2026-01-22 10:34:58 +08:00
zhangxinyuehfad
4f446aec4c [CI] Add DeepSeek-V3.2-W8A8-Pruning e2e test (#5922)
### What this PR does / why we need it?
1. Fix DeepSeek-V3.2-W8A8-Pruning mtp
2. Add DeepSeek-V3.2-W8A8-Pruning e2e test

### How was this patch tested?

- vLLM version: v0.13.0
- vLLM main:
11b6af5280

Signed-off-by: hfadzxy <starmoon_zhang@163.com>
2026-01-16 15:49:57 +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
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