[to #42322515]support plain pipeline for bert

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/8945177

    * support plain pipeline for bert

maas_lib/models/nlp/sequence_classification_model.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, Optional, Union
+from typing import Any, Dict
 
 import numpy as np
 

maas_lib/pipelines/nlp/sequence_classification_pipeline.py

@@ -1,6 +1,6 @@
 import os
 import uuid
-from typing import Any, Dict
+from typing import Any, Dict, Union
 
 import json
 import numpy as np
@@ -8,6 +8,7 @@ import numpy as np
 from maas_lib.models.nlp import SequenceClassificationModel
 from maas_lib.preprocessors import SequenceClassificationPreprocessor
 from maas_lib.utils.constant import Tasks
+from ...models import Model
 from ..base import Input, Pipeline
 from ..builder import PIPELINES
 
@@ -18,19 +19,29 @@ __all__ = ['SequenceClassificationPipeline']
     Tasks.text_classification, module_name=r'bert-sentiment-analysis')
 class SequenceClassificationPipeline(Pipeline):
 
-    def __init__(self, model: SequenceClassificationModel,
-                 preprocessor: SequenceClassificationPreprocessor, **kwargs):
+    def __init__(self,
+                 model: Union[SequenceClassificationModel, str],
+                 preprocessor: SequenceClassificationPreprocessor = None,
+                 **kwargs):
         """use `model` and `preprocessor` to create a nlp text classification pipeline for prediction
 
         Args:
             model (SequenceClassificationModel): a model instance
             preprocessor (SequenceClassificationPreprocessor): a preprocessor instance
         """
-
-        super().__init__(model=model, preprocessor=preprocessor, **kwargs)
+        sc_model = model if isinstance(
+            model,
+            SequenceClassificationModel) else Model.from_pretrained(model)
+        if preprocessor is None:
+            preprocessor = SequenceClassificationPreprocessor(
+                sc_model.model_dir,
+                first_sequence='sentence',
+                second_sequence=None)
+        super().__init__(model=sc_model, preprocessor=preprocessor, **kwargs)
 
         from easynlp.utils import io
-        self.label_path = os.path.join(model.model_dir, 'label_mapping.json')
+        self.label_path = os.path.join(sc_model.model_dir,
+                                       'label_mapping.json')
         with io.open(self.label_path) as f:
             self.label_mapping = json.load(f)
         self.label_id_to_name = {

tests/pipelines/test_text_classification.py

@@ -29,6 +29,12 @@ class SequenceClassificationTest(unittest.TestCase):
 
         print(data)
 
+    def printDataset(self, dataset: PyDataset):
+        for i, r in enumerate(dataset):
+            if i > 10:
+                break
+            print(r)
+
     def test_run(self):
         model_url = 'https://atp-modelzoo-sh.oss-cn-shanghai.aliyuncs.com' \
                     '/release/easynlp_modelzoo/alibaba-pai/bert-base-sst2.zip'
@@ -53,7 +59,7 @@ class SequenceClassificationTest(unittest.TestCase):
             Tasks.text_classification, model=model, preprocessor=preprocessor)
         print(pipeline2('Hello world!'))
 
-    def test_run_modelhub(self):
+    def test_run_with_model_from_modelhub(self):
         model = Model.from_pretrained('damo/bert-base-sst2')
         preprocessor = SequenceClassificationPreprocessor(
             model.model_dir, first_sequence='sentence', second_sequence=None)
@@ -63,6 +69,13 @@ class SequenceClassificationTest(unittest.TestCase):
             preprocessor=preprocessor)
         self.predict(pipeline_ins)
 
+    def test_run_with_model_name(self):
+        text_classification = pipeline(
+            task=Tasks.text_classification, model='damo/bert-base-sst2')
+        result = text_classification(
+            PyDataset.load('glue', name='sst2', target='sentence'))
+        self.printDataset(result)
+
     def test_run_with_dataset(self):
         model = Model.from_pretrained('damo/bert-base-sst2')
         preprocessor = SequenceClassificationPreprocessor(
@@ -74,10 +87,7 @@ class SequenceClassificationTest(unittest.TestCase):
         # TODO: rename parameter as dataset_name and subset_name
         dataset = PyDataset.load('glue', name='sst2', target='sentence')
         result = text_classification(dataset)
-        for i, r in enumerate(result):
-            if i > 10:
-                break
-            print(r)
+        self.printDataset(result)
 
 
 if __name__ == '__main__':