added eh proj option to eagle draft model

tensorrt_llm/_torch/models/modeling_speculative.py

@@ -30,8 +30,10 @@ def __init__(
         self,
         model_config: ModelConfig[LlamaConfig],
         layer_idx: Optional[int] = None,
+        next_layer_regular: bool = False,
     ):
         config = model_config.pretrained_config
+        self._next_layer_regular = next_layer_regular
         super().__init__(
             hidden_size=config.hidden_size,
             num_attention_heads=config.num_attention_heads,
@@ -78,8 +80,9 @@ def __init__(
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         super().__init__()
         config = model_config.pretrained_config
+        eagle_config = config.eagle_config if hasattr(config, "eagle_config") else {}
         self.layer_idx = layer_idx
-        self._next_layer_regular = config.eagle_config.get("next_layer_regular", True) and not is_first_layer
+        self._next_layer_regular = (eagle_config.get("next_layer_regular", True) and not is_first_layer) or eagle_config.get("eh_proj_before_attn", False)
         self.self_attn = Eagle3Attention(model_config, layer_idx, self._next_layer_regular)
 
         if config.model_type == "llama4_text":
@@ -153,18 +156,20 @@ def __init__(
         super().__init__(model_config)
 
         config = model_config.pretrained_config
+        eagle_config = config.eagle_config if hasattr(config, "eagle_config") else {}
         self.spec_config = model_config.spec_config
         self.dtype = config.torch_dtype
         self.hidden_size = config.hidden_size
         self.mapping = model_config.mapping
         self.num_layers = model_config.pretrained_config.num_hidden_layers
+        self._eh_proj_before_attn = eagle_config.get("eh_proj_before_attn", False)
 
         if hasattr(config, "target_hidden_size"):
             self.hidden_size_in = config.target_hidden_size
         else:
             self.hidden_size_in = config.hidden_size
 
-        self._return_hidden_post_norm = config.eagle_config.get("return_hidden_post_norm", False)
+        self._return_hidden_post_norm = eagle_config.get("return_hidden_post_norm", False)
 
         if self.spec_config.num_capture_layers > 1:
             self.fc = Linear(self.hidden_size_in *
@@ -189,6 +194,14 @@ def __init__(
             self.d2t = nn.Parameter(torch.empty((config.draft_vocab_size, ),
                                                 dtype=torch.int32),
                                     requires_grad=False)
+        if self._eh_proj_before_attn:
+            self.enorm = RMSNorm(hidden_size=config.hidden_size,
+                                        eps=config.rms_norm_eps,
+                                        dtype=config.torch_dtype)
+            self.eh_proj = nn.Linear(config.hidden_size * 2,
+                                        config.hidden_size,
+                                        bias=eagle_config.get("eh_proj_bias", False),
+                                        dtype=config.torch_dtype)
 
         if self.hidden_size_in != config.hidden_size:
             if model_config.mapping.enable_attention_dp:
@@ -230,11 +243,15 @@ def forward(
             inputs_embeds = self.embed_tokens(input_ids).to(self.dtype)
 
         assert hidden_states is not None
-
         # NOTE: If hidden states from the target model have to be concatenated,
-        # we expect that to happen outside the model definition. This helps us
-        # avoid data-dependent control flow and gives us better CUDA graph
-        # coverage.
+        # ideally,we expect that to happen outside the model definition. This
+        # helps usavoid data-dependent control flow and gives us better CUDA
+        # graph coverage.
+        if self._eh_proj_before_attn:
+            input_embeds = self.enorm(inputs_embeds)
+            hidden_states = torch.cat([input_embeds, hidden_states], dim=-1)
+            hidden_states = self.eh_proj(hidden_states)
+
         residual = None
         if self.num_layers > 1:
             for layer in self.midlayer: