[features]support split qkv rmsnorm rmope for qwen3.5 (#7368)

### What this PR does / why we need it?
Qwen3.5 full attention supports enabling the split_qkv_rmsnorm_mrope
fusion operator.

### How was this patch tested?
vLLM version: v0.16.0
vLLM-Ascend main: https://github.com/vllm-project/vllm-ascend/pull/6730
- vLLM version: v0.17.0
- vLLM main:
4034c3d32e

---------

Signed-off-by: ZhuQi-seu <zhuqi12@huawei.com>

vllm_ascend/patch/worker/patch_qwen3_5.py

@@ -22,6 +22,7 @@ from vllm.forward_context import get_forward_context
 from vllm.model_executor.layers.fla.ops import chunk_gated_delta_rule, fused_recurrent_gated_delta_rule
 from vllm.model_executor.layers.mamba.ops.causal_conv1d import causal_conv1d_update
 from vllm.model_executor.models.qwen3_5 import Qwen3_5GatedDeltaNet
+from vllm.model_executor.models.qwen3_next import Qwen3NextAttention
 from vllm.v1.attention.backend import AttentionMetadata  # type: ignore
 from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadata
 from vllm.v1.attention.backends.utils import PAD_SLOT_ID
@@ -260,4 +261,54 @@ class AscendQwen3_5GatedDeltaNet(Qwen3_5GatedDeltaNet):
         maybe_save_kv_layer_to_connector("", [])
 
 
+class AscendQwen3NextAttention(Qwen3NextAttention):
+    def forward(self, positions: torch.Tensor, output: torch.Tensor, hidden_states: torch.Tensor):
+        qkv, _ = self.qkv_proj(hidden_states)
+        if "qwen3_5" in self.config.model_type:
+            cos_sin = self.rotary_emb.cos_sin_cache[positions]
+            if cos_sin.device != qkv.device:
+                cos_sin = cos_sin.to(qkv.device)
+            if cos_sin.dtype != qkv.dtype:
+                cos_sin = cos_sin.to(qkv.dtype)
+
+            q, k, v, gate = torch.ops.vllm.triton_split_qkv_rmsnorm_mrope(
+                qkv=qkv,
+                q_weight=1.0 + self.q_norm.weight,
+                k_weight=1.0 + self.k_norm.weight,
+                cos_sin=cos_sin,
+                num_q_heads=self.num_heads,
+                num_kv_heads=self.num_kv_heads,
+                head_size=self.head_dim,
+                eps=self.config.rms_norm_eps,
+                mrope_section=self.rotary_emb.mrope_section,
+                is_interleaved=self.rotary_emb.mrope_interleaved,
+                rope_dim=self.rotary_emb.rotary_dim,
+                has_gate=self.attn_output_gate,
+            )
+        else:
+            if self.attn_output_gate:
+                q_gate, k, v = qkv.split([self.q_size * 2, self.kv_size, self.kv_size], dim=-1)
+                orig_shape = q_gate.shape[:-1]
+                q_gate = q_gate.view(*orig_shape, self.num_heads, -1)
+                q, gate = torch.chunk(q_gate, 2, dim=-1)
+                q = q.reshape(*orig_shape, -1)
+                gate = gate.reshape(*orig_shape, -1)
+            else:
+                q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+
+            q = self.q_norm(q.view(-1, self.num_heads, self.head_dim)).view(-1, self.num_heads * self.head_dim)
+            k = self.k_norm(k.view(-1, self.num_kv_heads, self.head_dim)).view(-1, self.num_kv_heads * self.head_dim)
+
+            q, k = self.rotary_emb(positions, q, k)
+
+        attn_output = self.attn(q, k, v)
+
+        if self.attn_output_gate:
+            gate = torch.sigmoid(gate)
+            attn_output = attn_output * gate
+
+        output[:], _ = self.o_proj(attn_output)
+
+
 Qwen3_5GatedDeltaNet._forward_core = AscendQwen3_5GatedDeltaNet._forward_core
+Qwen3NextAttention.forward = AscendQwen3NextAttention.forward