[Feature] qwen3_moe qk norm support multi_stream.

Signed-off-by: weijinqian_v1 <weijinqian@huawei.com>

Author: weijinqian_v1

vllm_ascend/models/qwen3_moe.py

@@ -17,7 +17,7 @@
 # Adapted from vllm/model_executor/models/qwen3_moe.py
 # This file is a part of the vllm-ascend project.
 
-from typing import Optional, Union
+from typing import Any, Optional, Union
 
 import torch
 from torch import nn
@@ -50,6 +50,7 @@
 from vllm_ascend.ops.fused_moe import AscendFusedMoE
 from vllm_ascend.ops.sequence_parallel import (MetadataForPadding,
                                                init_metadata_for_sp)
+from vllm_ascend.utils import npu_stream_switch
 
 
 class CustomSparseMoeBlock(Qwen3MoeSparseMoeBlock):
@@ -125,6 +126,58 @@ def forward(
         return hidden_states
 
 
+class CustomQwen3MoeAttention(Qwen3MoeAttention):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        rope_theta: float = 10000,
+        rope_scaling: Optional[dict[str, Any]] = None,
+        max_position_embeddings: int = 8192,
+        head_dim: Optional[int] = None,
+        rms_norm_eps: float = 1e-06,
+        qkv_bias: bool = False,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+        dual_chunk_attention_config: Optional[dict[str, Any]] = None,
+    ) -> None:
+        super().__init__(hidden_size, num_heads, num_kv_heads, rope_theta,
+                         rope_scaling, max_position_embeddings, head_dim,
+                         rms_norm_eps, qkv_bias, cache_config, quant_config,
+                         prefix, dual_chunk_attention_config)
+        self.alt_stream = torch.npu.Stream()
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+
+        self.alt_stream.wait_stream(torch.npu.current_stream())
+        with npu_stream_switch(self.alt_stream):
+            # Add qk-norm
+            q_by_head = q.view(*q.shape[:-1], q.shape[-1] // self.head_dim,
+                               self.head_dim)
+            q_by_head = self.q_norm(q_by_head)
+            q = q_by_head.view(q.shape)
+
+        k_by_head = k.view(*k.shape[:-1], k.shape[-1] // self.head_dim,
+                           self.head_dim)
+        k_by_head = self.k_norm(k_by_head)
+        k = k_by_head.view(k.shape)
+
+        torch.npu.current_stream().wait_stream(self.alt_stream)
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
 class CustomQwen3MoeDecoderLayer(Qwen3MoeDecoderLayer):
 
     def __init__(
@@ -142,7 +195,7 @@ def __init__(
         rope_scaling = getattr(config, "rope_scaling", None)
         max_position_embeddings = getattr(config, "max_position_embeddings",
                                           8192)
-        self.self_attn = Qwen3MoeAttention(
+        self.self_attn = CustomQwen3MoeAttention(
             hidden_size=self.hidden_size,
             num_heads=config.num_attention_heads,
             num_kv_heads=config.num_key_value_heads,