There is a lot hack code for v0.11.0, which makes the code hard to
upgrade to newer vLLM version. Since v0.11.0 will release soon. Let's
drop v0.11.0 support first. Then we'll upgrade to v0.11.2 soon.
- vLLM version: v0.11.0
- vLLM main:
2918c1b49c
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
remove get_metadata_cls. It's only used for V0 engine and has been removed from vLLM already.
- vLLM version: v0.11.0
- vLLM main:
83f478bb19
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
Remove codes of dbo.
Currently, vLLM has supported dbo with pr:
https://github.com/vllm-project/vllm/pull/23693.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.11.0rc3
- vLLM main:
17c540a993
Signed-off-by: zzzzwwjj <1183291235@qq.com>
### 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* An outdated quantization branch within the attention implementation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>
Note: This depends on [vLLM
#25161](https://github.com/vllm-project/vllm/pull/25161) and the
torch\_npu release from September 30.
### What this PR does / why we need it?
This pull request adds `FULL_DECODE_ONLY` mode for GQA/MHA models (MLA
models like DeepSeek V3/R1 are not included). Key improvements include:
* **Reduced dispatch latency:** By replaying the entire model execution
graph at once, we cut overhead compared with multiple smaller replays.
* **Stabilized multi-device performance:** Captureing the whole model as
one static graph also mitigates the dispatch fluctuations across
devices.
* **Stream/resource savings:** Consolidating graph captures frees up
streams, allowing more graphs to be captured.
**Known issues:**
1. `_npu_paged_attention` currently manages its own workspace in
`torch_npu`, which can deadlock when synchronizing during graph replay —
we’re working on a fix.
There may be other corner cases. This PR is the first in a planned
series; we’ll continue to iterate and address remaining issues in
follow-ups.
This is essentially a port of #1503 and #1677, but includes two major
changes:
1. Let `graph_dispatcher` decide the graph mode instead of hard-coding
it in the backend, which decouples Full Graph and Piecewise Graph and
could make it possible to remove dynamo.
2. Adapt to the new `attn_group` logic, but leave a small hack in
`update_graph_params`; multi-attention models may or may not be fully
supported yet.
### Does this PR introduce _any_ user-facing change?
```python
compilation_config={
"cudagraph_mode": "FULL_DECODE_ONLY",
},
```
### How was this patch tested?
Tests included.
- vLLM version: v0.10.2
- vLLM main:
9607d5eb44
---------
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
### What this PR does / why we need it?
[Bugfix]:replace npu_incre_flash_attention with
npu_fused_infer_attention_score in order to be able to tiling update
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- vLLM version: v0.10.2
- vLLM main:
2b85697031
Signed-off-by: p00465316 <panchao13@huawei.com>
Co-authored-by: p00465316 <panchao13@huawei.com>
### What this PR does / why we need it?
Fix qwen torchair attention PrefillCacheHit
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
vLLM version: v0.10.1.1
vLLM main:
e599e2c65e
- vLLM version: main
- vLLM main:
0b9a612fa3
Signed-off-by: zhaozixin <zhaozixin1@huawei.com>
Co-authored-by: zhaozixin <zhaozixin1@huawei.com>
### What this PR does / why we need it?
remove aicpu op for torchair mode
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
vLLM version: v0.10.1.1
vLLM main:
05d839c19e
- vLLM version: v0.10.1.1
- vLLM main:
67c14906aa
Signed-off-by: zhangdepeng <zhangdepeng2@huawei.com>
Co-authored-by: zhangdepeng <zhangdepeng2@huawei.com>
### What this PR does / why we need it?
Fix some ci issue and refactor modelrunner
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
CI passed with existing test.
- vLLM version: v0.10.0
- vLLM main:
4d9c61993a
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
Signed-off-by: MengqingCao <cmq0113@163.com>
Signed-off-by: weiguihua2 <weiguihua2@huawei.com>
Co-authored-by: wangli <wangli858794774@gmail.com>
Co-authored-by: weiguihua2 <weiguihua2@huawei.com>
### What this PR does / why we need it?
1. Move `torchair_attention` to `torchair` dir.
2. Make `AscendAttentionTorchairBackend` extend `AscendAttentionBackend`
to reduce duplicate methods.
3. Make `AscendTorchairMetadata` extend `AscendMetadata` to reduce
duplicate properties.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.10.0
- vLLM main:
0933f9d518
---------
Signed-off-by: shen-shanshan <467638484@qq.com>
