[AMD] Fix Qwen3.5 MTP dropping fused shared-expert weights (#35719)

python/sglang/srt/models/qwen3_5_mtp.py

@@ -39,10 +39,13 @@ from sglang.srt.runtime_context import (
     get_parallel,
     get_spec,
 )
-from sglang.srt.utils import add_prefix, is_npu
+from sglang.srt.utils import add_prefix, get_bool_env_var, is_hip, is_npu
 
 logger = logging.getLogger(__name__)
 
+_is_hip = is_hip()
+_use_aiter = get_bool_env_var("SGLANG_USE_AITER") and _is_hip
+
 
 def _mtp_quant_config(quant_config):
     """The quantization the MTP module itself is built with.
@@ -244,12 +247,20 @@ class Qwen3_5ForCausalLMMTP(nn.Module):
 
         # Params for MoE experts (non-fused/fused)
         num_experts = getattr(self.config, "num_experts", None)
+        # A fused shared expert lives in routed slot `num_experts`.
+        num_fused_shared_experts = 0
+        if _use_aiter:
+            for module in self.modules():
+                fused = getattr(module, "num_fused_shared_experts", 0)
+                if fused:
+                    num_fused_shared_experts = fused
+                    break
         if num_experts is not None:
             expert_params_mapping = FusedMoE.make_expert_params_mapping(
                 ckpt_gate_proj_name="gate_proj",
                 ckpt_down_proj_name="down_proj",
                 ckpt_up_proj_name="up_proj",
-                num_experts=num_experts,
+                num_experts=num_experts + num_fused_shared_experts,
             )
         else:
             expert_params_mapping = []
@@ -317,6 +328,17 @@ class Qwen3_5ForCausalLMMTP(nn.Module):
             if ".self_attn." in name:
                 name = name.replace(".self_attn", "")
 
+            if (
+                _use_aiter
+                and num_fused_shared_experts > 0
+                and "mlp.shared_expert." in name
+            ):
+                # Map mlp.shared_expert.xx_proj to mlp.experts.{num_experts}.xx_proj
+                name = name.replace(
+                    "mlp.shared_expert.",
+                    f"mlp.experts.{num_experts}.",
+                )
+
             # 1) Process stacked parameters (q_proj/k_proj/v_proj & gate_proj/up_proj)
             for param_name, weight_name, shard_id in stacked_params_mapping:
                 # Check if this is a fused expert weight