Add: `SupportsEagle3` interface for explicit EAGLE3 support (#22642)

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

tests/speculative_decoding/speculators/test_eagle3.py

@@ -3,12 +3,20 @@
 import pytest
 import torch
 
+from vllm.model_executor.models.interfaces import supports_eagle3
+
 
 @pytest.mark.parametrize(
     "model_path",
     [("nm-testing/SpeculatorLlama3-1-8B-Eagle3-converted-0717-quantized")])
-def test_llama(vllm_runner, example_prompts, model_path):
+def test_llama(vllm_runner, example_prompts, model_path, monkeypatch):
+    # Set environment variable for V1 engine serialization
+    monkeypatch.setenv("VLLM_ALLOW_INSECURE_SERIALIZATION", "1")
+
     with vllm_runner(model_path, dtype=torch.bfloat16) as vllm_model:
+        eagle3_supported = vllm_model.apply_model(supports_eagle3)
+        assert eagle3_supported
+
         vllm_outputs = vllm_model.generate_greedy(example_prompts,
                                                   max_tokens=20)
         print(vllm_outputs)
@@ -18,8 +26,14 @@ def test_llama(vllm_runner, example_prompts, model_path):
 @pytest.mark.parametrize(
     "model_path",
     [("nm-testing/Speculator-Qwen3-8B-Eagle3-converted-071-quantized")])
-def test_qwen(vllm_runner, example_prompts, model_path):
+def test_qwen(vllm_runner, example_prompts, model_path, monkeypatch):
+    # Set environment variable for V1 engine serialization
+    monkeypatch.setenv("VLLM_ALLOW_INSECURE_SERIALIZATION", "1")
+
     with vllm_runner(model_path, dtype=torch.bfloat16) as vllm_model:
+        eagle3_supported = vllm_model.apply_model(supports_eagle3)
+        assert eagle3_supported
+
         vllm_outputs = vllm_model.generate_greedy(example_prompts,
                                                   max_tokens=20)
         print(vllm_outputs)

vllm/model_executor/models/interfaces.py

@@ -823,3 +823,56 @@ def supports_v0_only(
     model: Union[type[object], object],
 ) -> Union[TypeIs[type[SupportsV0Only]], TypeIs[SupportsV0Only]]:
     return getattr(model, "supports_v0_only", False)
+
+
+@runtime_checkable
+class SupportsEagle3(Protocol):
+    """The interface required for models that support 
+    EAGLE3 speculative decoding."""
+
+    supports_eagle3: ClassVar[Literal[True]] = True
+    """
+    A flag that indicates this model supports EAGLE3 
+    speculative decoding.
+
+    Note:
+        There is no need to redefine this flag if this class is in the
+        MRO of your model class.
+    """
+
+    def set_aux_hidden_state_layers(self, layers: tuple[int, ...]) -> None:
+        """
+        Set which layers should output auxiliary
+        hidden states for EAGLE3.
+        
+        Args:
+            layers: Tuple of layer indices that should output auxiliary
+              hidden states.
+        """
+        ...
+
+    def get_eagle3_aux_hidden_state_layers(self) -> tuple[int, ...]:
+        """
+        Get the layer indices that should output auxiliary hidden states
+        for EAGLE3.
+        
+        Returns:
+            Tuple of layer indices for auxiliary hidden state outputs.
+        """
+        ...
+
+
+@overload
+def supports_eagle3(model: type[object]) -> TypeIs[type[SupportsEagle3]]:
+    ...
+
+
+@overload
+def supports_eagle3(model: object) -> TypeIs[SupportsEagle3]:
+    ...
+
+
+def supports_eagle3(
+    model: Union[type[object], object],
+) -> Union[TypeIs[type[SupportsEagle3]], TypeIs[SupportsEagle3]]:
+    return isinstance(model, SupportsEagle3)

vllm/model_executor/models/llama.py

@@ -49,7 +49,7 @@ from vllm.model_executor.model_loader.weight_utils import (
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
 
-from .interfaces import SupportsLoRA, SupportsPP
+from .interfaces import SupportsEagle3, SupportsLoRA, SupportsPP
 from .utils import (AutoWeightsLoader, PPMissingLayer, extract_layer_index,
                     is_pp_missing_parameter,
                     make_empty_intermediate_tensors_factory, make_layers,
@@ -463,7 +463,7 @@ class LlamaModel(nn.Module):
         return loaded_params
 
 
-class LlamaForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
+class LlamaForCausalLM(nn.Module, SupportsLoRA, SupportsPP, SupportsEagle3):
     packed_modules_mapping = {
         "qkv_proj": ["q_proj", "k_proj", "v_proj"],
         "gate_up_proj": ["gate_proj", "up_proj"]

vllm/model_executor/models/qwen3.py

@@ -44,7 +44,7 @@ from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
 
-from .interfaces import SupportsLoRA, SupportsPP
+from .interfaces import SupportsEagle3, SupportsLoRA, SupportsPP
 from .qwen2 import Qwen2MLP as Qwen3MLP
 from .qwen2 import Qwen2Model
 from .utils import (AutoWeightsLoader, PPMissingLayer, extract_layer_index,
@@ -261,7 +261,7 @@ class Qwen3Model(Qwen2Model):
                          decoder_layer_type=Qwen3DecoderLayer)
 
 
-class Qwen3ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
+class Qwen3ForCausalLM(nn.Module, SupportsLoRA, SupportsPP, SupportsEagle3):
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",

vllm/v1/worker/gpu_model_runner.py

@@ -35,6 +35,7 @@ from vllm.model_executor.layers.mamba.mamba_mixer2 import MambaBase
 from vllm.model_executor.layers.rotary_embedding import MRotaryEmbedding
 from vllm.model_executor.model_loader import TensorizerLoader, get_model_loader
 from vllm.model_executor.models.interfaces import (is_mixture_of_experts,
+                                                   supports_eagle3,
                                                    supports_transcription)
 from vllm.model_executor.models.interfaces_base import (
     VllmModelForPooling, is_pooling_model, is_text_generation_model)
@@ -1981,8 +1982,13 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
                 logger.info("Loading drafter model...")
                 self.drafter.load_model(self.model)
             if self.use_aux_hidden_state_outputs:
+                if supports_eagle3(self.model):
                     self.model.set_aux_hidden_state_layers(
                         self.model.get_eagle3_aux_hidden_state_layers())
+                else:
+                    raise RuntimeError(
+                        "Model does not support EAGLE3 interface but "
+                        "aux_hidden_state_outputs was requested")
             time_after_load = time.perf_counter()
         self.model_memory_usage = m.consumed_memory
         logger.info("Model loading took %.4f GiB and %.6f seconds",