add eval() to pipeline call

modelscope/pipelines/nlp/fill_mask_pipeline.py

@@ -1,4 +1,6 @@
-from typing import Dict, Optional, Union
+from typing import Dict, Optional, Union, Any
+
+import torch
 
 from ...models import Model
 from ...models.nlp.masked_language_model import \
@@ -35,6 +37,7 @@ class FillMaskPipeline(Pipeline):
                 fill_mask_model.model_dir,
                 first_sequence=first_sequence,
                 second_sequence=None)
+        fill_mask_model.eval()
         super().__init__(model=fill_mask_model, preprocessor=preprocessor, **kwargs)
         self.preprocessor = preprocessor
         self.tokenizer = preprocessor.tokenizer
@@ -61,6 +64,11 @@ class FillMaskPipeline(Pipeline):
             }
         }
 
+    def forward(self, inputs: Dict[str, Any],
+                **forward_params) -> Dict[str, Any]:
+        with torch.no_grad():
+            return super().forward(inputs, **forward_params)
+
     def postprocess(self, inputs: Dict[str, Tensor]) -> Dict[str, Tensor]:
         """process the prediction results
 

modelscope/pipelines/nlp/nli_pipeline.py

@@ -1,6 +1,6 @@
 import uuid
 from typing import Any, Dict, Union
-
+import torch
 import uuid
 from typing import Any, Dict, Union
 
@@ -42,9 +42,15 @@ class NLIPipeline(Pipeline):
                 sc_model.model_dir,
                 first_sequence=first_sequence,
                 second_sequence=second_sequence)
+        sc_model.eval()
         super().__init__(model=sc_model, preprocessor=preprocessor, **kwargs)
         assert len(sc_model.id2label) > 0
 
+    def forward(self, inputs: Dict[str, Any],
+                **forward_params) -> Dict[str, Any]:
+        with torch.no_grad():
+            return super().forward(inputs, **forward_params)
+
     def postprocess(self, inputs: Dict[str, Any], **postprocess_params) -> Dict[str, str]:
         """process the prediction results
 

modelscope/pipelines/nlp/sentence_similarity_pipeline.py

@@ -1,7 +1,7 @@
 from typing import Any, Dict, Union
 
 import numpy as np
-
+import torch
 from ...metainfo import Pipelines
 from ...models.nlp import SbertForSentenceSimilarity
 from ...preprocessors import SequenceClassificationPreprocessor
@@ -39,11 +39,17 @@ class SentenceSimilarityPipeline(Pipeline):
                 sc_model.model_dir,
                 first_sequence=first_sequence,
                 second_sequence=second_sequence)
+        sc_model.eval()
         super().__init__(model=sc_model, preprocessor=preprocessor, **kwargs)
 
         assert hasattr(self.model, 'id2label'), \
             'id2label map should be initalizaed in init function.'
 
+    def forward(self, inputs: Dict[str, Any],
+                **forward_params) -> Dict[str, Any]:
+        with torch.no_grad():
+            return super().forward(inputs, **forward_params)
+
     def postprocess(self, inputs: Dict[str, Any], **postprocess_params) -> Dict[str, str]:
         """process the prediction results
 

modelscope/pipelines/nlp/sentiment_classification_pipeline.py

@@ -1,7 +1,7 @@
 import os
 import uuid
 from typing import Any, Dict, Union
-
+import torch
 import json
 import numpy as np
 
@@ -43,9 +43,15 @@ class SentimentClassificationPipeline(Pipeline):
                 sc_model.model_dir,
                 first_sequence=first_sequence,
                 second_sequence=second_sequence)
+        sc_model.eval()
         super().__init__(model=sc_model, preprocessor=preprocessor, **kwargs)
         assert len(sc_model.id2label) > 0
 
+    def forward(self, inputs: Dict[str, Any],
+                **forward_params) -> Dict[str, Any]:
+        with torch.no_grad():
+            return super().forward(inputs, **forward_params)
+
     def postprocess(self, inputs: Dict[str, Any], **postprocess_params) -> Dict[str, str]:
         """process the prediction results
 

modelscope/pipelines/nlp/text_generation_pipeline.py

@@ -1,5 +1,5 @@
-from typing import Dict, Optional, Union
-
+from typing import Dict, Optional, Union, Any
+import torch
 from ...metainfo import Pipelines
 from ...models import Model
 from ...models.nlp import PalmForTextGeneration
@@ -33,9 +33,15 @@ class TextGenerationPipeline(Pipeline):
                 model.tokenizer,
                 first_sequence='sentence',
                 second_sequence=None)
+        model.eval()
         super().__init__(model=model, preprocessor=preprocessor, **kwargs)
         self.tokenizer = model.tokenizer
 
+    def forward(self, inputs: Dict[str, Any],
+                **forward_params) -> Dict[str, Any]:
+        with torch.no_grad():
+            return super().forward(inputs, **forward_params)
+
     def postprocess(self, inputs: Dict[str, Tensor], **postprocess_params) -> Dict[str, str]:
         """process the prediction results
 

modelscope/pipelines/nlp/word_segmentation_pipeline.py

@@ -1,5 +1,5 @@
 from typing import Any, Dict, Optional, Union
-
+import torch
 from ...metainfo import Pipelines
 from ...models import Model
 from ...models.nlp import SbertForTokenClassification
@@ -30,12 +30,18 @@ class WordSegmentationPipeline(Pipeline):
             SbertForTokenClassification) else Model.from_pretrained(model)
         if preprocessor is None:
             preprocessor = TokenClassifcationPreprocessor(model.model_dir)
+        model.eval()
         super().__init__(model=model, preprocessor=preprocessor, **kwargs)
         self.tokenizer = preprocessor.tokenizer
         self.config = model.config
         assert len(self.config.id2label) > 0
         self.id2label = self.config.id2label
 
+    def forward(self, inputs: Dict[str, Any],
+                **forward_params) -> Dict[str, Any]:
+        with torch.no_grad():
+            return super().forward(inputs, **forward_params)
+
     def postprocess(self, inputs: Dict[str, Any], **postprocess_params) -> Dict[str, str]:
         """process the prediction results
 

modelscope/pipelines/nlp/zero_shot_classification_pipeline.py

@@ -1,7 +1,7 @@
 import os
 import uuid
 from typing import Any, Dict, Union
-
+import torch
 import json
 import numpy as np
 from scipy.special import softmax
@@ -44,6 +44,7 @@ class ZeroShotClassificationPipeline(Pipeline):
         if preprocessor is None:
             preprocessor = ZeroShotClassificationPreprocessor(
                 sc_model.model_dir)
+        model.eval()
         super().__init__(model=sc_model, preprocessor=preprocessor, **kwargs)
 
     def _sanitize_parameters(self, **kwargs):
@@ -62,6 +63,11 @@ class ZeroShotClassificationPipeline(Pipeline):
         postprocess_params['multi_label'] = kwargs.pop('multi_label', False)
         return preprocess_params, {}, postprocess_params
 
+    def forward(self, inputs: Dict[str, Any],
+                **forward_params) -> Dict[str, Any]:
+        with torch.no_grad():
+            return super().forward(inputs, **forward_params)
+
     def postprocess(self,
                     inputs: Dict[str, Any],
                     candidate_labels,