Tiny refactor weight loading logic (#5232)

python/sglang/srt/model_executor/model_runner.py

@@ -557,12 +557,7 @@ class ModelRunner:
             return iter
 
         def model_load_weights(model, iter):
-            model.load_weights(iter)
-            for _, module in self.model.named_modules():
-                quant_method = getattr(module, "quant_method", None)
-                if quant_method is not None:
-                    with device_loading_context(module, target_device):
-                        quant_method.process_weights_after_loading(module)
+            DefaultModelLoader.load_weights_and_postprocess(model, iter, target_device)
             return model
 
         with set_default_torch_dtype(self.model_config.dtype):

python/sglang/srt/model_loader/loader.py

@@ -374,20 +374,27 @@ class DefaultModelLoader(BaseModelLoader):
                     self.load_config,
                 )
 
-            model.load_weights(self._get_all_weights(model_config, model))
+            self.load_weights_and_postprocess(
+                model, self._get_all_weights(model_config, model), target_device
+            )
 
-            for _, module in model.named_modules():
-                quant_method = getattr(module, "quant_method", None)
-                if quant_method is not None:
-                    # When quant methods need to process weights after loading
-                    # (for repacking, quantizing, etc), they expect parameters
-                    # to be on the global target device. This scope is for the
-                    # case where cpu offloading is used, where we will move the
-                    # parameters onto device for processing and back off after.
-                    with device_loading_context(module, target_device):
-                        quant_method.process_weights_after_loading(module)
         return model.eval()
 
+    @staticmethod
+    def load_weights_and_postprocess(model, weights, target_device):
+        model.load_weights(weights)
+
+        for _, module in model.named_modules():
+            quant_method = getattr(module, "quant_method", None)
+            if quant_method is not None:
+                # When quant methods need to process weights after loading
+                # (for repacking, quantizing, etc), they expect parameters
+                # to be on the global target device. This scope is for the
+                # case where cpu offloading is used, where we will move the
+                # parameters onto device for processing and back off after.
+                with device_loading_context(module, target_device):
+                    quant_method.process_weights_after_loading(module)
+
 
 class LayeredModelLoader(DefaultModelLoader):
     """Model loader that loads weights layer by layer so that one can quantize a