### What this PR does / why we need it?
torch_npu.npu_grouped_matmul:
https://www.hiascend.com/document/detail/zh/Pytorch/710/apiref/torchnpuCustomsapi/context/torch_npu-npu_grouped_matmul.md
According to the document, when `split_item` is 2 or 3,
`torch_npu.npu_grouped_matmul` will return a list which has one element.
Therefore, the `torch.cat` after `torch_npu.npu_grouped_matmul` is
unnecessary.
### Does this PR introduce _any_ user-facing change?
not involved
### How was this patch tested?
ut and e2e covered: `tests/ut/ops/test_fused_ops.py`,
`tests/e2e/singlecard/ops/test_fused_moe.py`
**performance**:
(qwen3 30B, 2k->20k)
base:
Total Token throughput (tok/s): 667.76
remove cat:
Total Token throughput (tok/s): 680.82
- vLLM version: v0.10.0
- vLLM main:
fa00c5d75b
Signed-off-by: huangxialu <huangxialu1@huawei.com>
we recently added disaggregated_prefill and ascend_forward_context
feature by
ba3dfbd59e
and
df0ec55162.
This PR fix some nit introduced by them to make the code clear.
1. drop `current_platform` usage. It'll lead unknown circular import
error in some case
2. update `set_ascend_forward_context` function to make the logic clear.
for example, remove V0 support in this function.
3. Remove useless `self.local_rank_across_dp` in worker
4. Remove `soc_info.py` to use `get_ascend_soc_version` instead.
- vLLM version: v0.10.0
- vLLM main:
02f82fe438
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
test vllm_ascend/ops/vocab_parallel_embedding.py contains vocab parallel
embedding forward
CI passed with new added test.
vLLM version: v0.10.0
vLLM main:
2cc571199b
- vLLM version: v0.10.0
- vLLM main:
05cbbe20c5
Signed-off-by: chengyuan <chengyuan27@huawei.com>
Co-authored-by: chengyuan <chengyuan27@huawei.com>
### What this PR does / why we need it?
A refactoring of forward_context and model_runner_v1, add some context
which is necessary in model inference into forward_context, and refactor
dummy_run logic, make it more reasonable.
Some details for this PR:
Add `ascend_forward_context`;
Update mc2_v2 op, and support `active_mask` param;
Update scripts in examples dir;
refactor `dummy_run` logic;
Add soc_version for A2 and A3;
### Does this PR introduce _any_ user-facing change?
No change at user-facing.
### How was this patch tested?
- vLLM version: v0.10.0
- vLLM main:
57c22e57f9
Signed-off-by: zzzzwwjj <1183291235@qq.com>
What this PR does / why we need it?
Add uts for deepseek_v2
Does this PR introduce any user-facing change?
No
How was this patch tested?
- vLLM version: v0.9.2
- vLLM main:
f3137cdd81
---------
Signed-off-by: 张帮政 <zhangbangzheng@huawei.com>
### What this PR does / why we need it?
Use base test to avoid patch everwhere.
Followup here: https://github.com/vllm-project/vllm-ascend/pull/1566
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
ut ci passed
- vLLM version: v0.9.2
- vLLM main:
8d0a01a5f2
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
### What this PR does / why we need it?
We'll refator `CustomOp` in vllm-ascend from this pr on.
Use function `CustomOp.register_oot` to achieve the customop registery,
taking `AscendQuickGELU` as an example:
```python
from vllm_ascend.ops.activation import AscendQuickGELU
CustomOp.register_oot(_decorated_op_cls=AscendQuickGELU, name="QuickGELU")
```
This is a quick adapt for `CustomOp.register_oot` mechanism from vllm
0.9.2. For further step, we can remove inherit from `QuickGELU` can
write our own `QuickGELU` at all.
Part of https://github.com/vllm-project/vllm-ascend/pull/1647
- vLLM version: v0.9.2
- vLLM main:
8dfb45ca33
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
Use Base test and cleanup all manaul patch code
- Cleanup EPLB config to avoid tmp test file
- Use BaseTest with global cache
- Add license
- Add a doc to setup unit test in local env
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
CI passed
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
### What this PR does / why we need it?
This PR supports torchair graph mode with non-mla backend on both 800IA2
and 300I Duo platforms. The main change is to add
`attention_v1_torchair.py` to support specific attention related
operations that are required by torchair.
### Does this PR introduce _any_ user-facing change?
Before this PR, vLLM-Ascend only allows deepseek to use torchair. Now we
can also use it with pangu. Besides, we add a support model list to
control which type of models that can use torchair.
### How was this patch tested?
We have test it with PanguProMoE on both 800IA2 and 300I Duo platforms,
and model generates answer normally.
---------
Signed-off-by: angazenn <zengyanjia@huawei.com>
Signed-off-by: tianyitang <tangtianyi4@huawei.com>
Co-authored-by: angazenn <zengyanjia@huawei.com>
Co-authored-by: tianyitang <tangtianyi4@huawei.com>
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### What this PR does / why we need it?
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1.add static EPLB unit test
2.fix bug: Tensor cannot be directly judged by if statements
### Does this PR introduce _any_ user-facing change?
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as API, interface or other behavior changes.
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### How was this patch tested?
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Run the unit test.
---------
Signed-off-by: songshanhu07 <1763685535@qq.com>
