[Feature] Enhance EAGLE Architecture with Proper RMS Norms (#14990)

Signed-off-by: Bryan Lu <yuzhelu@amazon.com>
Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com>

vllm/config.py

@@ -800,10 +800,18 @@ class ModelConfig:
 
     @property
     def is_deepseek_mla(self) -> bool:
-        return (hasattr(self.hf_text_config, "model_type")) \
-                and (self.hf_text_config.model_type in \
-                    ('deepseek_v2', 'deepseek_v3', 'deepseek_mtp'))\
-                and (self.hf_text_config.kv_lora_rank is not None)
+        if not hasattr(self.hf_text_config, "model_type"):
+            return False
+        elif self.hf_text_config.model_type in \
+            ('deepseek_v2', 'deepseek_v3', 'deepseek_mtp'):
+            return self.hf_text_config.kv_lora_rank is not None
+        elif self.hf_text_config.model_type == 'eagle':
+            # if the model is an EAGLE module, check for the
+            # underlying architecture
+            return self.hf_text_config.model.model_type in \
+                    ('deepseek_v2', 'deepseek_v3') \
+                and self.hf_text_config.kv_lora_rank is not None
+        return False
 
     def get_head_size(self) -> int:
         # TODO remove hard code

vllm/model_executor/models/eagle.py

@@ -7,6 +7,7 @@ import torch.nn as nn
 
 from vllm.config import VllmConfig
 from vllm.logger import init_logger
+from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.sampler import SamplerOutput
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -59,7 +60,15 @@ class EAGLE(nn.Module):
        truncated_vocab_size < vocab_size. To use this technique, one has to find
        the top-k most frequent tokens in target dataset and add that as a tensor
        in the draft checkpoint (using key token_map). Also, the draft config
-       needs to have truncated_vocab_size (=k) as an attribute."""
+       needs to have truncated_vocab_size (=k) as an attribute.
+    4. We allow an enhanced EAGLE architecture similar to the DeepSeek MTP 
+       module with regards to the use of additional RMS norms. The original 
+       EAGLE architecture 1) skips the pre-attention norm in its first 
+       transformer block, and 2) skips the final output norm, both of which we 
+       found to be suboptimal. We also add the support for separate norms
+       applying to both the token embedding and hidden states before projection
+       as in DeepSeek MTP, which we found to improve performance as well.
+    """
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
@@ -81,10 +90,23 @@ class EAGLE(nn.Module):
         # While weights and biases are generally not needed,
         # they are retained here to support certain unit tests
         # (e.g., spec_decode/e2e/test_eagle_correctness.py).
+        if not hasattr(self.config.model,
+                       "skip_prenorm") or self.config.model.skip_prenorm:
             self.model.model.layers[0].input_layernorm = DummyInputLayerNorm(
                 weight=self.model.model.layers[0].input_layernorm.weight)
+
+        if not hasattr(
+                self.config.model,
+                "skip_output_norm") or self.config.model.skip_output_norm:
             self.model.model.norm = DummyOutputNorm()
 
+        self.add_para_norm = False
+        if hasattr(self.config.model,
+                   "add_para_norm") and self.config.model.add_para_norm:
+            self.enorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+            self.hnorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+            self.add_para_norm = True
+
         self.orig_vocab_size = config.vocab_size
         self.truncated_vocab_size = config.truncated_vocab_size
         self.unpadded_vocab_size = self.truncated_vocab_size
@@ -128,8 +150,17 @@ class EAGLE(nn.Module):
         if inputs_embeds is None:
             inputs_embeds = self.get_input_embeddings(input_ids)
 
-        inputs_embeds = self.fc(
-            torch.cat([inputs_embeds, previous_hidden_states], dim=-1))
+        if self.add_para_norm:
+            inputs_embeds = torch.cat([
+                self.enorm(inputs_embeds),
+                self.hnorm(previous_hidden_states)
+            ],
+                                      dim=-1)
+        else:
+            inputs_embeds = torch.cat([inputs_embeds, previous_hidden_states],
+                                      dim=-1)
+
+        inputs_embeds = self.fc(inputs_embeds)
 
         inputs_embeds[positions == 0] = 0  # masking inputs at position=0
 
@@ -190,6 +221,14 @@ class EAGLE(nn.Module):
                 else:
                     logger.warning_once("Found bias in the loaded weights but "
                                         "the model config doesn't have bias.")
+            elif name.startswith("enorm.weight"):
+                weight_loader = getattr(self.enorm.weight, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(self.enorm.weight, loaded_weight)
+            elif name.startswith("hnorm.weight"):
+                weight_loader = getattr(self.hnorm.weight, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(self.hnorm.weight, loaded_weight)
             elif name.startswith("model.lm_head.") or name.startswith(
                     "model.model."):
                 model_weights[name.split("model.", 1)[-1]] = loaded_weight

vllm/transformers_utils/configs/eagle.py

@@ -5,6 +5,8 @@ from typing import Optional, Union
 
 from transformers import AutoConfig, PretrainedConfig
 
+from vllm.transformers_utils.configs.deepseek_vl2 import DeepseekV2Config
+
 
 class EAGLEConfig(PretrainedConfig):
     model_type = "eagle"
@@ -14,8 +16,17 @@ class EAGLEConfig(PretrainedConfig):
                  truncated_vocab_size: Optional[int] = None,
                  **kwargs):
 
-        model_config = None if model is None else (AutoConfig.for_model(
-            **model) if isinstance(model, dict) else model)
+        model_config: Union[PretrainedConfig, DeepseekV2Config, None]
+        if isinstance(model, dict):
+            archs = model.get("architectures", [])
+            target_archs = ["DeepseekV2ForCausalLM", "DeepseekV3ForCausalLM"]
+            if any(target_arch in archs for target_arch in target_archs):
+                # AutoConfig does not support DeepSeek MoE models yet
+                model_config = DeepseekV2Config(**model)
+            else:
+                model_config = AutoConfig.for_model(**model)
+        else:
+            model_config = model
 
         for k, v in kwargs.items():
             if k != "architectures" and k != "model_type" and hasattr(