fix compatible with npu. (#8409)

Author: ZHUI

paddlenlp/trainer/plugins/unified_checkpoint.py

@@ -93,6 +93,11 @@
 async_save_queue = []
 
 
+DEST_PLACE = paddle.CPUPlace()
+if paddle.device.is_compiled_with_cuda():
+    DEST_PLACE = paddle.CUDAPinnedPlace()
+
+
 class UnifiedCheckpointOption(ExplicitEnum):
     """
     "- skip_save_model_weight: do not save model weights when the masters weight exist\n"
@@ -1746,7 +1751,7 @@ def merge_tensor_parallel_with_shard(state_dict, tp_actions, all_filter_keys):
                 action = tp_actions.pop(key)
                 tensor = action(ret) if is_dst else None
             else:
-                tensor = tensor._copy_to(paddle.CUDAPinnedPlace(), False) if is_dst else None
+                tensor = tensor._copy_to(DEST_PLACE, False) if is_dst else None
 
             if is_dst:
                 state_dict_to_save[key] = tensor
@@ -1777,15 +1782,13 @@ def merge_tensor_parallel_for_optimizer(state_dict, tp_actions, all_filter_keys)
             if model_key in tp_actions:
                 # for example: beta1, beta2
                 if tensor.numel().item() == 1:
-                    tensor = (
-                        tensor._copy_to(paddle.CUDAPinnedPlace(), False) if is_dst else None
-                    )  # Need broadcast when loaded
+                    tensor = tensor._copy_to(DEST_PLACE, False) if is_dst else None  # Need broadcast when loaded
                 else:
                     ret = distributed_gather(tensor, dst=j, group=tp_group, offload=False)
                     action = tp_actions[model_key]
                     tensor = action(ret) if is_dst else None
             else:
-                tensor = tensor._copy_to(paddle.CUDAPinnedPlace(), False) if is_dst else None
+                tensor = tensor._copy_to(DEST_PLACE, False) if is_dst else None
 
             if is_dst:
                 state_dict_to_save[filter_keys[i]] = tensor