### What this PR does / why we need it?
This PR refactors the Ascend attention implementation to align with
vLLM's core interfaces, simplifying the code and improving
maintainability.
### Key Changes:
* **Align with vLLM's Attention Interface**: The `forward` method
signature in `AscendAttentionBackendImpl` now matches the base
`AttentionImpl` in vLLM, removing the custom `trace_flag`.
* **Enable Opaque Attention Operator**: By adding `opaque_attention_op`
to `AscendPlatform`, we allow vLLM to wrap our attention kernel in its
standard `vllm.unified_attention_with_output` operator. This avoids the
need for a custom call path.
* **Remove Obsolete Code**:
* The custom op `vllm.unified_ascend_attention_with_output` has been
deleted as it is now redundant.
* The `trace_flag` and its associated logic were removed, reducing code
complexity.
* An outdated quantization branch within the attention implementation
was cleaned up.
* **Improve Readability**: Renamed output variables (`output` vs.
`intermediate_output`) and added comments to clarify the in-place nature
of the attention output.
### Does this PR introduce _any_ user-facing change?
None.
### How was this patch tested?
No extra tests needed.
- vLLM version: v0.11.0rc3
- vLLM main:
17c540a993
---------
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
### What this PR does / why we need it?
This PR boosts performance by introducing a fused kernel for the matrix
matmul and reduce scatter operations. It supports both unquantized
(e.g., BFloat16) and W8A8 quantized models.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
Signed-off-by: ZYang6263 <zy626375@gmail.com>
### What this PR does / why we need it?
In multi-Tensor Parallel (TP) scenarios, the KV pool only queries the
first GPU card. When keys on other cards are released, the query result
still returns as successful, introducing accuracy issues. This PR
modifies the KV pool's query logic to check all cards, resolving this
problem.
### 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
---------
Signed-off-by: fems14 <1804143737@qq.com>
### What this PR does / why we need it?
In certain scenarios, the performance of synchronously loading data from
the pool is better than that of asynchronously loading data. Therefore,
a control logic (or switch) for asynchronous loading from the pool has
been added.
### 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
---------
Signed-off-by: fems14 <1804143737@qq.com>
### What this PR does / why we need it?
fix bug: In the mooncake pooling scenario, when the client closes the
request, the server fails to locate the request, leading to the server
hanging.oling scenario, when the client closes the request, the server
fails to locate the request, leading to the server hanging.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
Pull up the PD separated pooling service, send requests using aisbench,
press CTRL+C twice, and check if the vllm_ascend service exit.
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
Signed-off-by: linhebiwen <linhebiwen@gmail.com>
### What this PR does / why we need it?
Check all expert maps when using muilty instance.
### Does this PR introduce _any_ user-facing change?
None.
### How was this patch tested?
Qwen 235B in double A3.
case1：master has expert map, slave has not expert map.
case2: master has expert map, slave has error expert map.
case3: master has expert map,slave has correct expert map.
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
Signed-off-by: offline0806 <3337230449@qq.com>
Co-authored-by: offline0806 <3337230449@qq.com>
### What this PR does / why we need it?
This PR adds the initial prefix cache case for nightly test for
Qwen3-32b-int8 on A3, we need test them daily.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running the test
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
Signed-off-by: jiangyunfan1 <jiangyunfan1@h-partners.com>
### What this PR does / why we need it?
`vanilla_chunked_prefill_mla` and `vanilla_decode_mla` is unused, so
remove 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
Signed-off-by: zzzzwwjj <1183291235@qq.com>
### What this PR does / why we need it?
This 'test_rejection_sampler' unit test is something wrong.
> def test_sample_recovered_tokens_pytorch_autoregressive(self):
> output_token_ids = torch.empty(2, dtype=torch.int32)
> cu_num_draft_tokens = torch.tensor([1, 1])
> draft_token_ids = torch.tensor([0, 1])
len(draft_token_ids ) = 2, cu_num_draft_tokens should be
torch.tensor([1, 2]) or torch.tensor([2, 2])
I fix it and set cu_num_draft_tokens = torch.tensor([1, 2]). The methods
before and after optimization can pass.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
NA
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
Signed-off-by: lio <1983142975@qq.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.
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
Signed-off-by: whx-sjtu <2952154980@qq.com>
### What this PR does / why we need it?
On Arm systems, os.sched_yield() does not take effect, causing the GIL
(Global Interpreter Lock) to remain unrelinquished and resulting in CPU
bound issues. This PR applies a patch to sched_yield in vLLM, making the
process execute time.sleep(0) instead to release the GIL.
### 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
---------
Signed-off-by: fems14 <1804143737@qq.com>
### What this PR does / why we need it?
The #3624 PR fix the precision of deepseek torchair, but don't consider
the limitation of torch compile which results in the recompile, This PR
fixs this problem
### 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: hust17yixuan <303660421@qq.com>
Resolves a `TypeError: got an unexpected keyword argument 'layer_type'`.
A recent change (PR #3311) started passing the `layer_type` argument
when calling `get_pergroup_param()`. This specific implementation does
not use this parameter, causing the error.
This patch adds `layer_type=None` to the method signature to maintain
API compatibility and ignore the unused argument.
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
Signed-off-by: SlightwindSec <slightwindsec@gmail.com>
### What this PR does / why we need it?
This PR add a new Qwen3-32b-int8 test case for nightly test. This test
case mainly test the performance and accuracy of Qwen3-32b-int8 with a
new feature.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running the test.
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
Signed-off-by: root <root@hostname-2pbfv.foreman.pxe>
Co-authored-by: root <root@hostname-2pbfv.foreman.pxe>
### What this PR does / why we need it?
Update the Pangu Pro MoE tutorials.
### 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
Signed-off-by: menogrey <1299267905@qq.com>
### What this PR does / why we need it?
Fix mooncake connector. In scenarios where TP is not equal, when the
prefill TP size is less than the number of key-value heads,
_get_remote_tp_ranks_for_req will return a list of np.arrays. Performing
an operation like int in list of np.arrays will cause an error.
Converting the list of np.arrays into a single np.array resolves this
issue.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
qwen235B
P tp16, D tp1
P tp8, D tp1
P tp4, D tp1
P tp8, D tp2
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
Signed-off-by: liziyu <liziyu16@huawei.com>
Signed-off-by: underfituu <hzhucong@163.com>
Co-authored-by: underfituu <hzhucong@163.com>
### What this PR does / why we need it?
This PR adds the initial multi modal model for nightly test, including 3
cases for Qwen2.5-vl-7b acc/perf test on A3, we need test them daily. It
also inclues 8 cases for deepseek-r1-0528-w8a8 func, acc and perf tests
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
by running the test
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
Signed-off-by: jiangyunfan1 <jiangyunfan1@h-partners.com>
### What this PR does / why we need it?
This PR introduces a new model loader called Netloader, which leverages
high-bandwidth P2P direct transfer between NPU cards to achieve weight
loading. Netloader is implemented as a plugin through the newly added
'register_model_loader' function in vLLM 0.10. It facilitates the
process of weight loading by sending weights from a pre-loaded model
(server) to an empty model of a newly started instance (client). The
server operates concurrently with normal inference tasks through
sub-threads and the 'stateless_init_torch_distributed_process_group' in
vLLM. The client initiates a transfer request after verifying that the
model and partitioning method are the same as the server's, and uses
HCCL's collective communication (send/recv) to load the weights in the
order they are stored in the model.
Application Scenarios:
1. Significantly Reduces Inference Instance Startup Time By reusing the
weights of already loaded instances and performing high-speed transfers
directly between computing cards, this method reduces model loading
latency compared to traditional remote/local pull methods.
2. Reduces Network and Storage Pressure Avoids the need to repeatedly
download weight files from remote repositories, reducing the impact on
centralized storage and network traffic, thereby enhancing overall
system stability and service quality.
3. Improves Resource Utilization and Reduces Costs Accelerating the
loading process reduces reliance on redundant computing pools, allowing
computing resources to be elastically scaled and reclaimed as needed.
4. Enhances Business Continuity and High Availability In fault recovery
scenarios, new instances can quickly take over existing services,
avoiding prolonged business interruptions and improving the system's
high availability and user experience.
### Does this PR introduce _any_ user-facing change?
Netloader utilizes the existing --load-format=netloader and
--model-loader-extra-config to be activated. The
model-loader-extra-config needs to be input as a JSON string (as it is
now)
Afterwards, you can check whether the outputs for the same sentence are
consistent when the temperature is set to 0.
Signed-off-by: destinysky <kangrui10@126.com>
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
Signed-off-by: destinysky <kangrui10@126.com>
### What this PR does / why we need it?
The precision of deepseek torchair is broken by #3465 , which due to the
origin patch or rmsnorm in torchair. This PR fixes the precision of
deepseek torchair
### 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: hust17yixuan <303660421@qq.com>
### What this PR does / why we need it?
This PR refactors SequenceRowParallelOp forward. In order to further
expand the operator inclusion scope in dynamic judgment scenarios, this
PR customizes the entire matmul computation and communication as a
custom operator masking. With this refactor, it will support directly
writing code such as common operation fusion into the
`SequenceRowParallelOp` class's member function `matmul_and_reduce`,
without the need to register more redundant custom masking operators.
### How was this patch tested?
CI passed with existing test.
Signed-off-by: rjg-lyh <1318825571@qq.com>
### What this PR does / why we need it?
Update the docker run command, specifically: add --shm-size=1g
### Does this PR introduce _any_ user-facing change?
users/developers using docker to pull vllm-ascend, the shared memory of
the container will be increased from the default 64MB to 1G
### 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: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
- `qkv_proj.weight` prefetching has been implemented with `Quant` op,
when `AddRmsNormQuant` is enabled (#3465) `qkv_proj.weight` prefetching
won't work
- Implement `qkv_proj.weight` prefetching with `AddRmsNormQuant`
### Does this PR introduce _any_ user-facing change?
None.
### How was this patch tested?
Tested on `Qwen3-235B-A22B-W8A8`
<img width="1868" height="109" alt="image"
src="https://github.com/user-attachments/assets/0bc28082-0287-4d5c-b8f6-f907c3134d36"
/>
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
Signed-off-by: zhoux77899 <zhouxiang100@huawei.com>
### What this PR does / why we need it?
Move the creation of dummy attention metadata to occur after the ACL
graph runtime mode is determined. This ensures the metadata is
initialized with the correct configuration during a profile run.
Additionally, remove the `attn_metadata` existence check before updating
MLA attention parameters. This change prevents the update from being
skipped when metadata is not yet available, ensuring parameters are set
correctly.
### Does this PR introduce _any_ user-facing change?
None.
### How was this patch tested?
None.
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
### What this PR does / why we need it?
Corrects the attribute access for retrieving the device from `q_a_proj`
to `q_proj`. This prevents an `AttributeError` as `q_a_proj` does not
exist on the class instance.
### Does this PR introduce _any_ user-facing change?
None.
### How was this patch tested?
Need MLAPO tests.
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
### What this PR does / why we need it?
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.
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
Signed-off-by: Zetong Li <slippersss@126.com>
### What this PR does / why we need it?
This PR adds a jinja template for the k8s configuration file, prepare
for the upcoming 4-node CI
### 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
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
This pr is for https://github.com/vllm-project/vllm-ascend/issues/3241 ,
which is in-house solution for offloading KV cache data from the GPU
memory to other medium (in particular, CPU memory)。Previous solutions
required reliance on third-party components, which had issues with
compatibility between different versions.
### How was this patch tested?
use the following script for testing:
export CUDA_VISIBLE_DEVICES=0
export TP=1
export MODEL_PATH=/model/Qwen3-14B
export MODEL_NAME=Qwen3-14B
export PORT=10000
#export ASCEND_LAUNCH_BLOCKING=1
#export ASCEND_SLOG_PRINT_TO_STDOUT=1
python3 -m vllm.entrypoints.openai.api_server --host 0.0.0.0 --port
${PORT} --dtype bfloat16 --model ${MODEL_PATH} --served-model-name
${MODEL_NAME} --tensor-parallel-size ${TP} --gpu-memory-utilization 0.7
--max-model-len 32768 --trust-remote-code --disable-log-requests \
--block-size 128 \
--kv-transfer-config
'{"kv_connector":"OffloadingConnector","kv_role":"kv_both","kv_connector_extra_config":{"block_size":
128, "num_cpu_blocks": 1000, "spec_name":"NPUOffloadingSpec",
"spec_module_path": "vllm_ascend.kv_offload.npu"}}'
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
Signed-off-by: HF-001 <1670186653@qq.com>
### What this PR does / why we need it?
1. Replace manual memory cleanup with passing parameter.
2. FusedMoEPrepareAndFinalizeWithMC2 inherits All2All avoid duplicated
code.
3. Fix MC2 bug introduced in
https://github.com/vllm-project/vllm-ascend/pull/3365
4. Unify aclgraph & eager in W8A8_dynamic.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
e2e & ut
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
Signed-off-by: Pr0Wh1teGivee <calvin_zhu0210@outlook.com>
### What this PR does / why we need it?
1.Add eplb ci to check the change of eplb feature.
2.Add param checking of eplb params.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
Qwen in A3.
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
Signed-off-by: offline0806 <3337230449@qq.com>
Co-authored-by: offline0806 <3337230449@qq.com>
### What this PR does / why we need it?
This reverts commit
bf87606932.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
E2E vllm serving with `enable_shared_expert_dp: true` in eager mode as
before.
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
Signed-off-by: linfeng-yuan <1102311262@qq.com>
### What this PR does / why we need it?
Upgrade manylinux image
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
CI passed with new added/existing test.
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
Signed-off-by: Icey <1790571317@qq.com>
### What this PR does / why we need it?
Fix mtp tochair bug cuased by #2719
Since FIA need extra space for padding, we need to enforce
`self.max_num_seqs > self.scheduler_config.max_num_seqs` in KV consumer
+ MTP
This means that, `self.max_num_seqs` **>** the actual maximum requests
(`self.scheduler_config.max_num_seqs`)
### 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
---------
Signed-off-by: xuyexiong <xuyexiong@huawei.com>
