Fix: post-load staging regression breaks offload meta/sharded_gpu modes (#38779)

python/sglang/srt/model_loader/post_load.py

@@ -134,13 +134,16 @@ def stage_module_for_post_load(
     owner_origins: dict[int, set[torch.device]] = {}
     module_origins: set[torch.device] = set()
     tensor_states: dict[int, _TensorState] = {}
+    # Offloader-parked parameters (see sglang.srt.utils.offloader) live on the
+    # meta device through post-load processing; kernels are expected to skip
+    # them. Pass them through untouched, matching the pre-staging behaviour.
+    meta_tensor_ids: set[int] = set()
 
     # snapshot and validate all state before moving any tensor
     for owner, registry_name, name, tensor in _iter_registered_tensors(module):
         if tensor.is_meta:
-            raise RuntimeError(
-                f"Cannot post-process meta tensor {type(owner).__name__}.{name}"
-            )
+            meta_tensor_ids.add(id(tensor))
+            continue
         state = tensor_states.get(id(tensor))
         if state is None:
             state = _TensorState(tensor, tensor.data, tensor.device)
@@ -167,6 +170,9 @@ def stage_module_for_post_load(
             next(iter(module_origins)) if len(module_origins) == 1 else None
         )
         for owner, registry_name, name, tensor in _iter_registered_tensors(module):
+            if id(tensor) in meta_tensor_ids:
+                # Parked by the offloader before staging; leave it as-is.
+                continue
             key = _slot_key(owner, registry_name, name)
             original_state = original_slots.get(key)
             if original_state is None: