Support llama3 eagle3 head with llama4 verifier (#25961)

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

vllm/model_executor/models/mllama4.py

@@ -64,7 +64,12 @@ from vllm.multimodal.profiling import BaseDummyInputsBuilder
 from vllm.sequence import IntermediateTensors
 from vllm.utils.tensor_schema import TensorSchema, TensorShape
 
-from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
+from .interfaces import (
+    MultiModalEmbeddings,
+    SupportsEagle3,
+    SupportsMultiModal,
+    SupportsPP,
+)
 from .llama4 import Llama4ForCausalLM
 from .utils import AutoWeightsLoader, flatten_bn, maybe_prefix
 from .vision import run_dp_sharded_vision_model
@@ -717,7 +722,9 @@ class Mllama4DummyInputsBuilder(BaseDummyInputsBuilder[Mllama4ProcessingInfo]):
     info=Mllama4ProcessingInfo,
     dummy_inputs=Mllama4DummyInputsBuilder,
 )
-class Llama4ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
+class Llama4ForConditionalGeneration(
+    nn.Module, SupportsMultiModal, SupportsPP, SupportsEagle3
+):
     packed_modules_mapping = {
         "qkv_proj": ["q_proj", "k_proj", "v_proj"],
         "gate_up_proj": ["gate_proj", "up_proj"],
@@ -767,6 +774,22 @@ class Llama4ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
             self.language_model.make_empty_intermediate_tensors
         )
 
+    def set_aux_hidden_state_layers(self, layers: tuple[int, ...]) -> None:
+        """Set which layers should output auxiliary hidden states for EAGLE3."""
+        # Delegate to underlying language model (Llama4ForCausalLM)
+        assert hasattr(self.language_model, "set_aux_hidden_state_layers")
+        self.language_model.set_aux_hidden_state_layers(layers)
+
+    def get_eagle3_aux_hidden_state_layers(self) -> tuple[int, ...]:
+        """Get the layer indices for auxiliary hidden state outputs.
+
+        Note: The GPU model runner will override this with layers from
+        the speculative config if available, providing dynamic configuration.
+        """
+        # Delegate to underlying language model (Llama4ForCausalLM)
+        assert hasattr(self.language_model, "get_eagle3_aux_hidden_state_layers")
+        return self.language_model.get_eagle3_aux_hidden_state_layers()
+
     def _parse_and_validate_image_input(
         self, **kwargs: object
     ) -> Optional[Llama4ImagePatchInputs]: