[V1][Spec Decode] Make Eagle model arch config driven (#17323)

vllm/config.py

@@ -2401,7 +2401,8 @@ class SpeculativeConfig:
                         pass
                     else:
                         eagle_config = EAGLEConfig(
-                            self.draft_model_config.hf_config)
+                            self.draft_model_config.hf_config,
+                            method=self.method)
                         self.draft_model_config.hf_config = eagle_config
 
                 if (self.num_speculative_tokens is not None

vllm/transformers_utils/configs/eagle.py

@@ -15,6 +15,7 @@ class EAGLEConfig(PretrainedConfig):
     def __init__(self,
                  model: Union[PretrainedConfig, dict, None] = None,
                  truncated_vocab_size: Optional[int] = None,
+                 method: Optional[str] = 'eagle',
                  **kwargs):
 
         model_config: Union[PretrainedConfig, DeepseekV2Config, None]
@@ -45,7 +46,23 @@ class EAGLEConfig(PretrainedConfig):
         if not envs.VLLM_USE_V1:
             kwargs["architectures"] = ["EAGLEModel"]
         else:
-            kwargs["architectures"] = ["EagleLlamaForCausalLM"]
+            # Eagle model name should follow naming convention of
+            # LlamaForCausalLM -> EagleLlamaForCausalLM
+            if method == "eagle":
+                assert self.model is not None, \
+                    "model should not be None when method is eagle"
+                kwargs["architectures"] = [
+                    f"Eagle{arch}" for arch in self.model.architectures
+                ]
+            elif method == "eagle3":
+                assert self.model is not None, \
+                    "model should not be None when method is eagle3"
+                kwargs["architectures"] = [
+                    f"Eagle3{arch}" for arch in self.model.architectures
+                ]
+            else:
+                raise ValueError(f"Invalid method {method}. \
+                    Supported methods are eagle and eagle3.")
 
         super().__init__(**kwargs)
 

vllm/v1/spec_decode/eagle.py

@@ -9,8 +9,7 @@ from vllm.forward_context import set_forward_context
 from vllm.logger import init_logger
 from vllm.model_executor.model_loader.loader import get_model_loader
 from vllm.model_executor.model_loader.utils import set_default_torch_dtype
-from vllm.model_executor.models.llama_eagle import EagleLlamaForCausalLM
-from vllm.model_executor.models.llama_eagle3 import Eagle3LlamaForCausalLM
+from vllm.model_executor.models import ModelRegistry
 from vllm.v1.attention.backends.flash_attn import FlashAttentionMetadata
 from vllm.v1.sample.metadata import SamplingMetadata
 
@@ -225,15 +224,11 @@ class EagleProposer:
         with set_default_torch_dtype(
                 draft_model_config.dtype), set_current_vllm_config(
                     self.vllm_config):
-            if self.vllm_config.speculative_config.method == "eagle":
-                self.model = EagleLlamaForCausalLM(
-                    model_config=draft_model_config,
-                    start_layer_id=target_layer_num).to(target_device)
-            else:
-                assert self.vllm_config.speculative_config.method == "eagle3"
-                self.model = Eagle3LlamaForCausalLM(
-                    model_config=draft_model_config,
-                    start_layer_id=target_layer_num).to(target_device)
+            draft_model_cls, arch = ModelRegistry.resolve_model_cls(
+                draft_model_config.architectures)
+            self.model = draft_model_cls(
+                model_config=draft_model_config,
+                start_layer_id=target_layer_num).to(target_device)
 
         loaded_weights = self.model.load_weights(
             loader.get_all_weights(