Add: Eagle3 support for Qwen3.5 (#36658)

Signed-off-by: Rahul-Tuli <rtuli@redhat.com>

vllm/model_executor/models/qwen3_5.py

@@ -75,6 +75,7 @@ from .interfaces import (
     IsHybrid,
     MixtureOfExperts,
     MultiModalEmbeddings,
+    SupportsEagle3,
     SupportsLoRA,
     SupportsPP,
     _require_is_multimodal,
@@ -353,6 +354,8 @@ class Qwen3_5Model(Qwen3NextModel):
         else:
             self.norm = PPMissingLayer()
 
+        self.aux_hidden_state_layers: tuple[int, ...] = ()
+
     def load_fused_expert_weights(
         self,
         name: str,
@@ -536,6 +539,7 @@ class Qwen3_5Model(Qwen3NextModel):
 class Qwen3_5ForCausalLMBase(
     nn.Module,
     HasInnerState,
+    SupportsEagle3,
     SupportsLoRA,
     SupportsPP,
 ):
@@ -592,6 +596,13 @@ class Qwen3_5ForCausalLMBase(
     def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
         return self.model.embed_input_ids(input_ids)
 
+    def set_aux_hidden_state_layers(self, layers: tuple[int, ...]) -> None:
+        self.model.aux_hidden_state_layers = layers
+
+    def get_eagle3_aux_hidden_state_layers(self) -> tuple[int, ...]:
+        num_layers = len(self.model.layers)
+        return (2, num_layers // 2, num_layers - 3)
+
     def forward(
         self,
         input_ids: torch.Tensor,

vllm/model_executor/models/qwen3_next.py

@@ -1148,6 +1148,8 @@ class Qwen3NextModel(nn.Module):
         else:
             self.norm = PPMissingLayer()
 
+        self.aux_hidden_state_layers: tuple[int, ...] = ()
+
     def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
         return self.embed_tokens(input_ids)
 
@@ -1157,7 +1159,7 @@ class Qwen3NextModel(nn.Module):
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
-    ) -> torch.Tensor:
+    ) -> torch.Tensor | IntermediateTensors | tuple[torch.Tensor, list[torch.Tensor]]:
         if get_pp_group().is_first_rank:
             if inputs_embeds is not None:
                 hidden_states = inputs_embeds
@@ -1169,7 +1171,15 @@ class Qwen3NextModel(nn.Module):
             hidden_states = intermediate_tensors["hidden_states"]
             residual = intermediate_tensors["residual"]
 
-        for layer in islice(self.layers, self.start_layer, self.end_layer):
+        aux_hidden_states = []
+        for layer_idx, layer in enumerate(
+            islice(self.layers, self.start_layer, self.end_layer),
+            start=self.start_layer,
+        ):
+            if layer_idx in self.aux_hidden_state_layers:
+                aux_hidden_states.append(
+                    hidden_states + residual if residual is not None else hidden_states
+                )
             hidden_states, residual = layer(
                 positions=positions,
                 hidden_states=hidden_states,
@@ -1181,6 +1191,8 @@ class Qwen3NextModel(nn.Module):
                 {"hidden_states": hidden_states, "residual": residual}
             )
         hidden_states, _ = self.norm(hidden_states, residual)
+        if aux_hidden_states:
+            return hidden_states, aux_hidden_states
         return hidden_states
 
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]: