extend moe padding to DUMMY weights

vllm/model_executor/model_loader/loader.py

@@ -407,6 +407,17 @@ def load_model(self, *, model_config: ModelConfig,
             # NOTE(woosuk): For accurate performance evaluation, we assign
             # random values to the weights.
             initialize_dummy_weights(model)
+
+            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, torch.device(device_config.device)):
+                        quant_method.process_weights_after_loading(module)
         return model.eval()