[feat] Support EAGLE3 for Qwen (#7745)

Co-authored-by: 纬杭 <ximing.wxm@antgroup.com>
Co-authored-by: zyksir <zyksir@outlook.com>

python/sglang/srt/models/qwen2.py

@@ -293,6 +293,9 @@ class Qwen2Model(nn.Module):
         else:
             self.norm = PPMissingLayer(return_tuple=True)
 
+        # For EAGLE3 support
+        self.layers_to_capture = []
+
     def get_input_embedding(self, input_ids: torch.Tensor) -> torch.Tensor:
         if hasattr(self.config, "scale_emb"):
             return self.get_input_embeddings()(input_ids) * self.config.scale_emb
@@ -321,7 +324,12 @@ class Qwen2Model(nn.Module):
             hidden_states = pp_proxy_tensors["hidden_states"]
             residual = pp_proxy_tensors["residual"]
 
+        aux_hidden_states = []
         for i in range(self.start_layer, self.end_layer):
+            if i in self.layers_to_capture:
+                aux_hidden_states.append(
+                    hidden_states + residual if residual is not None else hidden_states
+                )
             layer = self.layers[i]
             hidden_states, residual = layer(
                 positions,
@@ -342,7 +350,11 @@ class Qwen2Model(nn.Module):
                     hidden_states = self.norm(hidden_states)
                 else:
                     hidden_states, _ = self.norm(hidden_states, residual)
-        return hidden_states
+
+        if len(aux_hidden_states) == 0:
+            return hidden_states
+
+        return hidden_states, aux_hidden_states
 
     # If this function is called, it should always initialize KV cache scale
     # factors (or else raise an exception). Thus, handled exceptions should