fix the test_hotswapping_compiled_model_xxx case failure in xpu

src/diffusers/loaders/peft.py

@@ -311,12 +311,28 @@ def map_state_dict_for_hotswap(sd):
                     state_dict = map_state_dict_for_hotswap(state_dict)
                     check_hotswap_configs_compatible(self.peft_config[adapter_name], lora_config)
                     try:
-                        hotswap_adapter_from_state_dict(
-                            model=self,
-                            state_dict=state_dict,
-                            adapter_name=adapter_name,
-                            config=lora_config,
-                        )
+                        # When enable_lora_hotswap was called, weights were padded to target_rank so that
+                        # torch.compile does not recompile after hotswapping. peft's hotswap_adapter_from_state_dict
+                        # uses swap_tensors for un-compiled models, which replaces the parameter tensor object.
+                        # On backends without CUDA Graphs (e.g. XPU), the compiled inductor code re-fetches
+                        # the real model parameters on every call and its assert_size_stride checks fail because
+                        # the swapped-in tensor has the smaller (un-padded) rank shape.
+                        # Fix: temporarily signal to peft that this model should be treated as compiled so
+                        # it uses the in-place data copy path (which preserves both the tensor object and the
+                        # padded shape).
+                        _supports_compiled_lora_hotswap = getattr(self, "_supports_compiled_lora_hotswap", False)
+                        _orig_compiled_call_impl = getattr(self, "_compiled_call_impl", None)
+                        if _supports_compiled_lora_hotswap and not hasattr(self, "_orig_mod"):
+                            self._compiled_call_impl = True
+                        try:
+                            hotswap_adapter_from_state_dict(
+                                model=self,
+                                state_dict=state_dict,
+                                adapter_name=adapter_name,
+                                config=lora_config,
+                            )
+                        finally:
+                            self._compiled_call_impl = _orig_compiled_call_impl
                     except Exception as e:
                         logger.error(f"Hotswapping {adapter_name} was unsuccessful with the following error: \n{e}")
                         raise
@@ -830,3 +846,6 @@ def enable_lora_hotswap(
                 )
 
         self._prepare_lora_hotswap_kwargs = {"target_rank": target_rank, "check_compiled": check_compiled}
+        # Remember that this model was prepared for compiled LoRA hotswapping even after the
+        # one-shot prepare kwargs are consumed when the first adapter is loaded.
+        self._supports_compiled_lora_hotswap = True