Fix wrong weight reference in dynamic EPLB (#6818)

python/sglang/srt/models/deepseek_v2.py

@@ -91,6 +91,7 @@ from sglang.srt.two_batch_overlap import (
 from sglang.srt.utils import (
     BumpAllocator,
     DeepEPMode,
+    LazyValue,
     add_prefix,
     bind_or_assign,
     get_bool_env_var,
@@ -1661,6 +1662,18 @@ class DeepseekV2ForCausalLM(nn.Module):
         self.logits_processor = LogitsProcessor(config)
         self.dp_size = get_local_attention_dp_size()
 
+        self._routed_experts_weights_of_layer = LazyValue(
+            lambda: {
+                layer_id: layer.mlp.get_moe_weights()
+                for layer_id, layer in enumerate(self.model.layers)
+                if isinstance(layer.mlp, DeepseekV2MoE)
+            }
+        )
+
+    @property
+    def routed_experts_weights_of_layer(self):
+        return self._routed_experts_weights_of_layer.value
+
     def determine_n_share_experts_fusion(
         self, architecture: str = "DeepseekV3ForCausalLM"
     ):
@@ -1873,14 +1886,6 @@ class DeepseekV2ForCausalLM(nn.Module):
                 self_attn.w_vc = bind_or_assign(self_attn.w_vc, w_vc.contiguous())
                 self_attn.use_deep_gemm_bmm = True
 
-        # TODO support nextn later
-        if not is_nextn:
-            self.routed_experts_weights_of_layer = {
-                layer_id: layer.mlp.get_moe_weights()
-                for layer_id, layer in enumerate(self.model.layers)
-                if isinstance(layer.mlp, DeepseekV2MoE)
-            }
-
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]], is_nextn=False):
         if is_nextn:
             if hasattr(self.config, "num_nextn_predict_layers"):

python/sglang/srt/models/qwen3_moe.py

@@ -18,15 +18,10 @@
 """Inference-only Qwen3MoE model compatible with HuggingFace weights."""
 
 import logging
-from dataclasses import dataclass
-from enum import Enum, auto
-from functools import partial
 from typing import Any, Dict, Iterable, Optional, Tuple
 
 import torch
-import torch.nn.functional as F
 from torch import nn
-from transformers.configuration_utils import PretrainedConfig
 
 from sglang.srt.distributed import (
     get_pp_group,
@@ -811,6 +806,7 @@ class Qwen3MoeForCausalLM(nn.Module):
                     else:
                         logger.warning(f"Parameter {name} not found in params_dict")
 
+        # TODO mimic deepseek
         self.routed_experts_weights_of_layer = {
             layer_id: self.model.layers[layer_id].mlp.get_moe_weights()
             for layer_id in range(self.start_layer, self.end_layer)

python/sglang/srt/utils.py

@@ -2257,3 +2257,16 @@ except:
 
 def cpu_has_amx_support():
     return torch._C._cpu._is_amx_tile_supported() and is_intel_amx_backend_available
+
+
+class LazyValue:
+    def __init__(self, creator: Callable):
+        self._creator = creator
+        self._value = None
+
+    @property
+    def value(self):
+        if self._creator is not None:
+            self._value = self._creator()
+            self._creator = None
+        return self._value