[main][BugFix] Fixed an accuracy bug of Qwen3-next-MTP when batched inferring (#4932)

### What this PR does / why we need it?
Fixes an accuracy bug of Qwen3-next-MTP when batched inferring.
It is descibed in
https://github.com/vllm-project/vllm-ascend/issues/4930.

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

Signed-off-by: drslark <slarksblood@qq.com>

vllm_ascend/patch/__init__.py

@@ -237,7 +237,7 @@
 #       Replace with a new bind_kv_cache.
 #       Skip the raise.
 #    Related PR (if no, explain why):
-#       https://github.com/vllm-project/vllm/pull/4770
+#       It need discuss.
 #    Future Plan:
 #       Remove this patch after discussing with vllm community and adapting bind_kv_cache to npu.
 #
@@ -245,11 +245,15 @@
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 #   1. `vllm.v1.attention.backends.gdn_attn.torch.argsort`
 #    Why:
-#       'torch.argsort' func of npu does not support bool.
+#       1. 'torch.argsort' func of npu does not support bool.
+#       2. Without `stable=True`, the output will have a lot of redundant tokens.
 #    How：
-#       Replace with a new torch.argsort that will cast the input to torch.int32.
+#       Replace with a new torch.argsort that will cast the input to torch.int32
+#       and do stable sort.
 #    Related PR (if no, explain why):
-#       https://github.com/vllm-project/vllm/pull/4770
+#       1. It depends on torch_npu.
+#       2. https://github.com/vllm-project/vllm/pull/30632
 #    Future Plan:
 #       Remove this patch when bool is supported in 'torch.argsort' func of npu.
+#       Make 'torch.argsort' in `vllm.v1.attention.backends.gdn_attn` be stable.
 #

vllm_ascend/patch/worker/patch_module.py

@@ -5,6 +5,8 @@ import torch
 # TODO When the operator of argsort is ready, this patch must be removed.
 def _argsort(tensor, *args, **kwargs):
     if tensor.dtype == torch.bool:
+        # If it is not stable, it will have redundant outputs.
+        kwargs["stable"] = True
         return torch.argsort(tensor.to(torch.int32), *args, **kwargs)
     else:
         return torch.argsort(tensor, *args, **kwargs)