[Feature] minicpm eagle support (#18943)

Signed-off-by: huangyuxiang03 <huangyx0321@gmail.com>
Co-authored-by: huangyuxiang03 <huangyx0321@gmail.com>

vllm/model_executor/models/minicpm.py

@@ -242,6 +242,7 @@ class MiniCPMAttention(nn.Module):
             base=rope_theta,
             rope_scaling=rope_scaling,
         )
+
         self.attn = Attention(self.num_heads,
                               self.head_dim,
                               self.scaling,
@@ -444,6 +445,7 @@ class MiniCPMModel(nn.Module):
             for weight_name in ["w1", "w2", "w3"]
         ]
         params_dict = dict(self.named_parameters())
+
         loaded_params: set[str] = set()
         for name, loaded_weight in weights:
             if "rotary_emb.inv_freq" in name:
@@ -567,7 +569,7 @@ class MiniCPMForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
         inputs_embeds: Optional[torch.Tensor] = None,
     ) -> Union[torch.Tensor, IntermediateTensors]:
         hidden_states = self.model(input_ids, positions, intermediate_tensors,
-                                   inputs_embeds)
+                                   inputs_embeds) / self.scale_width
         return hidden_states
 
     def compute_logits(
@@ -575,7 +577,6 @@ class MiniCPMForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
         hidden_states: torch.Tensor,
         sampling_metadata: SamplingMetadata,
     ) -> Optional[torch.Tensor]:
-        hidden_states = hidden_states / self.scale_width
         logits = self.logits_processor(self.lm_head, hidden_states,
                                        sampling_metadata)
         return logits