diff --git a/modelscope/models/nlp/T5/text2text_generation.py b/modelscope/models/nlp/T5/text2text_generation.py
index 4c6fd295..aa2826a8 100644
--- a/modelscope/models/nlp/T5/text2text_generation.py
+++ b/modelscope/models/nlp/T5/text2text_generation.py
@@ -57,7 +57,7 @@ class T5ForConditionalGeneration(T5PreTrainedModel):
         r'decoder\.block\.0\.layer\.1\.EncDecAttention\.relative_attention_bias\.weight',
     ]
 
-    def __init__(self, config: T5Config, **kwargs):
+    def __init__(self, config: T5Config, device_map=None, **kwargs):
         super().__init__(config)
         self.model_dim = config.d_model
 
@@ -82,7 +82,8 @@ class T5ForConditionalGeneration(T5PreTrainedModel):
 
         # Model parallel
         self.model_parallel = False
-        self.device_map = None
+        if device_map == 'auto':
+            self.parallelize()
 
     def parallelize(self, device_map=None):
         self.device_map = (
diff --git a/modelscope/models/nlp/use/user_satisfaction_estimation.py b/modelscope/models/nlp/use/user_satisfaction_estimation.py
index f006f544..77b12414 100644
--- a/modelscope/models/nlp/use/user_satisfaction_estimation.py
+++ b/modelscope/models/nlp/use/user_satisfaction_estimation.py
@@ -27,7 +27,8 @@ class UserSatisfactionEstimation(TorchModel):
     def __init__(self,
                  model_dir: str,
                  bert_name: str = None,
-                 device: str = None):
+                 device: str = None,
+                 **kwargs):
         """initialize the user satisfaction estimation model from the `model_dir` path. The default preprocessor
         for this task is DialogueClassificationUsePreprocessor.
 
@@ -36,7 +37,7 @@ class UserSatisfactionEstimation(TorchModel):
             bert_name: The pretrained model, default bert-base-chinese
             device: The device of running model, default cpu
         """
-        super().__init__(model_dir)
+        super().__init__(model_dir, **kwargs)
         self.model_dir = model_dir
         self.bert_name = bert_name if bert_name is not None else 'bert-base-chinese'
         self.device = 'cpu'
diff --git a/modelscope/pipelines/base.py b/modelscope/pipelines/base.py
index ac244421..48d328d9 100644
--- a/modelscope/pipelines/base.py
+++ b/modelscope/pipelines/base.py
@@ -54,7 +54,8 @@ class Pipeline(ABC):
                 model,
                 device=self.device_name,
                 model_prefetched=True,
-                invoked_by=Invoke.PIPELINE) if is_model(model) else model
+                invoked_by=Invoke.PIPELINE,
+                device_map=self.device_map) if is_model(model) else model
         else:
             return model
 
@@ -70,6 +71,7 @@ class Pipeline(ABC):
                  preprocessor: Union[Preprocessor, List[Preprocessor]] = None,
                  device: str = 'gpu',
                  auto_collate=True,
+                 device_map=None,
                  **kwargs):
         """ Base class for pipeline.
 
@@ -87,6 +89,9 @@ class Pipeline(ABC):
             compile_options (dict, optional): The compile options if compile=True,
                 default None to use the default params of 'TorchModel.compile'.
         """
+        if device_map is not None:
+            assert device == 'gpu', '`device` and `device_map` cannot be input at the same time!'
+        self.device_map = device_map
         verify_device(device)
         self.device_name = device
 
@@ -133,13 +138,14 @@ class Pipeline(ABC):
         self._model_prepare_lock.acquire(timeout=600)
 
         def _prepare_single(model):
-            if isinstance(model, torch.nn.Module):
+            if not isinstance(model, torch.nn.Module) and hasattr(
+                    model, 'model'):
+                model = model.model
+            if not isinstance(model, torch.nn.Module):
+                return
+            model.eval()
+            if self.device_map is None:
                 model.to(self.device)
-                model.eval()
-            elif hasattr(model, 'model') and isinstance(
-                    model.model, torch.nn.Module):
-                model.model.to(self.device)
-                model.model.eval()
 
         if not self._model_prepare:
             # prepare model for pytorch