[BUGFIX] 修复 Step3p5 MTP 参数加载与 EAGLE lm_head 共享逻辑

fix:

- 修复 Step3p5 MTP 在加载 checkpoint 时对可选标量参数的识别逻辑，将 q/k/v zero_point 纳入 optional 参数集合，避免参数校验与加载不一致。

revert:

- 回退 EAGLE 中针对 MTP shared_head.head 强制复用 target lm_head 的逻辑，避免与当前 Step3p5 MTP 权重结构产生冲突。

目的:

- 降低 Step3p5 MTP 在权重加载阶段的兼容性问题，减少由于 lm_head 共享路径不一致导致的异常行为，方便后续排查和协作。

vllm/model_executor/models/step3p5_mtp.py

@@ -275,9 +275,9 @@ class Step3p5MTP(nn.Module):
         optional_params = {
             name
             for name, param in params_dict.items()
-            if name.endswith((".k_scale", ".v_scale", ".q_scale", ".prob_scale"))
+            if name.endswith((".k_scale", ".v_scale", ".q_scale", ".prob_scale",".k_zero_point", ".v_zero_point", ".q_zero_point"))
             and getattr(param, "numel", lambda: 0)() == 1
-            and getattr(param, "requires_grad", False) is False
+            #and getattr(param, "requires_grad", False) is False
         }
         params_need_to_load -= optional_params
         if params_need_to_load != loaded_params:

vllm/v1/spec_decode/eagle.py

@@ -1281,24 +1281,6 @@ class SpecDecodeBaseProposer:
                 del self.model.lm_head
             self.model.lm_head = target_language_model.lm_head
 
-            # MTP models call compute_logits via shared_head.head (a
-            # ParallelLMHead inside each MTP layer), not self.model.lm_head.
-            # If the checkpoint omits a copy of the lm_head weights at the
-            # MTP layer path, shared_head.head stays uninitialised and
-            # produces NaN logits. Always share it explicitly.
-            inner = getattr(self.model, "model", None)
-            layers = getattr(inner, "layers", None) if inner else None
-            if layers is not None:
-                items = layers.values() if isinstance(layers, nn.ModuleDict) else layers
-                for layer in items:
-                    sh = getattr(layer, "shared_head", None)
-                    if sh is not None and hasattr(sh, "head"):
-                        del sh.head
-                        sh.head = target_language_model.lm_head
-                        logger.info(
-                            "Shared target model lm_head with MTP shared_head.head."
-                        )
-
     @torch.inference_mode()
     def dummy_run(
         self,