[to #42322933] change star3 to space_T_cn

1. 合并star和star3框架
2. 修改star和star3的model type
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10492793

modelscope/metainfo.py

@@ -67,8 +67,9 @@ class Models(object):
     space_dst = 'space-dst'
     space_intent = 'space-intent'
     space_modeling = 'space-modeling'
-    star = 'star'
-    star3 = 'star3'
+    space_T_en = 'space-T-en'
+    space_T_cn = 'space-T-cn'
+
     tcrf = 'transformer-crf'
     transformer_softmax = 'transformer-softmax'
     lcrf = 'lstm-crf'

modelscope/models/nlp/star3/configuration_star3.py → modelscope/models/nlp/space_T_cn/configuration_space_T_cn.py

@@ -24,8 +24,8 @@ import json
 logger = logging.getLogger(__name__)
 
 
-class Star3Config(object):
-    """Configuration class to store the configuration of a `Star3Model`.
+class SpaceTCnConfig(object):
+    """Configuration class to store the configuration of a `SpaceTCnModel`.
     """
 
     def __init__(self,
@@ -40,10 +40,10 @@ class Star3Config(object):
                  max_position_embeddings=512,
                  type_vocab_size=2,
                  initializer_range=0.02):
-        """Constructs Star3Config.
+        """Constructs SpaceTCnConfig.
 
         Args:
-            vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `Star3Model`.
+            vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `SpaceTCnConfig`.
             hidden_size: Size of the encoder layers and the pooler layer.
             num_hidden_layers: Number of hidden layers in the Transformer encoder.
             num_attention_heads: Number of attention heads for each attention layer in
@@ -59,7 +59,7 @@ class Star3Config(object):
             max_position_embeddings: The maximum sequence length that this model might
                 ever be used with. Typically set this to something large just in case
                 (e.g., 512 or 1024 or 2048).
-            type_vocab_size: The vocabulary size of the `token_type_ids` passed into `Star3Model`.
+            type_vocab_size: The vocabulary size of the `token_type_ids` passed into `SpaceTCnConfig`.
             initializer_range: The sttdev of the truncated_normal_initializer for
                 initializing all weight matrices.
         """
@@ -89,15 +89,15 @@ class Star3Config(object):
 
     @classmethod
     def from_dict(cls, json_object):
-        """Constructs a `Star3Config` from a Python dictionary of parameters."""
-        config = Star3Config(vocab_size_or_config_json_file=-1)
+        """Constructs a `SpaceTCnConfig` from a Python dictionary of parameters."""
+        config = SpaceTCnConfig(vocab_size_or_config_json_file=-1)
         for key, value in json_object.items():
             config.__dict__[key] = value
         return config
 
     @classmethod
     def from_json_file(cls, json_file):
-        """Constructs a `Star3Config` from a json file of parameters."""
+        """Constructs a `SpaceTCnConfig` from a json file of parameters."""
         with open(json_file, 'r', encoding='utf-8') as reader:
             text = reader.read()
         return cls.from_dict(json.loads(text))

modelscope/models/nlp/star3/modeling_star3.py → modelscope/models/nlp/space_T_cn/modeling_space_T_cn.py

@@ -27,7 +27,8 @@ import numpy as np
 import torch
 from torch import nn
 
-from modelscope.models.nlp.star3.configuration_star3 import Star3Config
+from modelscope.models.nlp.space_T_cn.configuration_space_T_cn import \
+    SpaceTCnConfig
 from modelscope.utils.constant import ModelFile
 from modelscope.utils.logger import get_logger
 
@@ -609,9 +610,9 @@ class PreTrainedBertModel(nn.Module):
 
     def __init__(self, config, *inputs, **kwargs):
         super(PreTrainedBertModel, self).__init__()
-        if not isinstance(config, Star3Config):
+        if not isinstance(config, SpaceTCnConfig):
             raise ValueError(
-                'Parameter config in `{}(config)` should be an instance of class `Star3Config`. '
+                'Parameter config in `{}(config)` should be an instance of class `SpaceTCnConfig`. '
                 'To create a model from a Google pretrained model use '
                 '`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`'.format(
                     self.__class__.__name__, self.__class__.__name__))
@@ -676,7 +677,7 @@ class PreTrainedBertModel(nn.Module):
             serialization_dir = tempdir
         # Load config
         config_file = os.path.join(serialization_dir, CONFIG_NAME)
-        config = Star3Config.from_json_file(config_file)
+        config = SpaceTCnConfig.from_json_file(config_file)
         logger.info('Model config {}'.format(config))
         # Instantiate model.
         model = cls(config, *inputs, **kwargs)
@@ -742,11 +743,11 @@ class PreTrainedBertModel(nn.Module):
         return model
 
 
-class Star3Model(PreTrainedBertModel):
-    """Star3Model model ("Bidirectional Embedding Representations from a Transformer pretrained on STAR3.0").
+class SpaceTCnModel(PreTrainedBertModel):
+    """SpaceTCnModel model ("Bidirectional Embedding Representations from a Transformer pretrained on STAR-T-CN").
 
     Params:
-        config: a Star3Config class instance with the configuration to build a new model
+        config: a SpaceTCnConfig class instance with the configuration to build a new model
 
     Inputs:
         `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
@@ -780,16 +781,16 @@ class Star3Model(PreTrainedBertModel):
     input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
     token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
 
-    config = modeling.Star3Config(vocab_size_or_config_json_file=32000, hidden_size=768,
+    config = modeling.SpaceTCnConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
         num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
 
-    model = modeling.Star3Model(config=config)
+    model = modeling.SpaceTCnModel(config=config)
     all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
     ```
     """
 
     def __init__(self, config, schema_link_module='none'):
-        super(Star3Model, self).__init__(config)
+        super(SpaceTCnModel, self).__init__(config)
         self.embeddings = BertEmbeddings(config)
         self.encoder = BertEncoder(
             config, schema_link_module=schema_link_module)

modelscope/models/nlp/star_text_to_sql.py

@@ -20,7 +20,7 @@ __all__ = ['StarForTextToSql']
 
 
 @MODELS.register_module(
-    Tasks.conversational_text_to_sql, module_name=Models.star)
+    Tasks.table_question_answering, module_name=Models.space_T_en)
 class StarForTextToSql(Model):
 
     def __init__(self, model_dir: str, *args, **kwargs):

modelscope/models/nlp/table_question_answering.py

@@ -3,27 +3,25 @@
 import os
 from typing import Dict
 
-import json
 import numpy
 import torch
 import torch.nn.functional as F
-import tqdm
 from transformers import BertTokenizer
 
 from modelscope.metainfo import Models
 from modelscope.models.base import Model, Tensor
 from modelscope.models.builder import MODELS
-from modelscope.models.nlp.star3.configuration_star3 import Star3Config
-from modelscope.models.nlp.star3.modeling_star3 import Seq2SQL, Star3Model
-from modelscope.preprocessors.star3.fields.struct import Constant
+from modelscope.preprocessors.space_T_cn.fields.struct import Constant
 from modelscope.utils.constant import ModelFile, Tasks
 from modelscope.utils.device import verify_device
+from .space_T_cn.configuration_space_T_cn import SpaceTCnConfig
+from .space_T_cn.modeling_space_T_cn import Seq2SQL, SpaceTCnModel
 
 __all__ = ['TableQuestionAnswering']
 
 
 @MODELS.register_module(
-    Tasks.table_question_answering, module_name=Models.star3)
+    Tasks.table_question_answering, module_name=Models.space_T_cn)
 class TableQuestionAnswering(Model):
 
     def __init__(self, model_dir: str, *args, **kwargs):
@@ -43,9 +41,9 @@ class TableQuestionAnswering(Model):
             os.path.join(self.model_dir, ModelFile.TORCH_MODEL_BIN_FILE),
             map_location='cpu')
 
-        self.backbone_config = Star3Config.from_json_file(
+        self.backbone_config = SpaceTCnConfig.from_json_file(
             os.path.join(self.model_dir, ModelFile.CONFIGURATION))
-        self.backbone_model = Star3Model(
+        self.backbone_model = SpaceTCnModel(
             config=self.backbone_config, schema_link_module='rat')
         self.backbone_model.load_state_dict(state_dict['backbone_model'])
 

modelscope/outputs.py

@@ -606,21 +606,12 @@ TASK_OUTPUTS = {
     # }
     Tasks.task_oriented_conversation: [OutputKeys.OUTPUT],
 
-    # conversational text-to-sql result for single sample
-    # {
-    #   "text": "SELECT shop.Name FROM shop."
-    # }
-    Tasks.conversational_text_to_sql: [OutputKeys.TEXT],
-
     # table-question-answering result for single sample
     # {
     #   "sql": "SELECT shop.Name FROM shop."
     #   "sql_history": {sel: 0, agg: 0, conds: [[0, 0, 'val']]}
     # }
-    Tasks.table_question_answering: [
-        OutputKeys.SQL_STRING, OutputKeys.SQL_QUERY, OutputKeys.HISTORY,
-        OutputKeys.QUERT_RESULT
-    ],
+    Tasks.table_question_answering: [OutputKeys.OUTPUT],
 
     # ============ audio tasks ===================
     # asr result for single sample

modelscope/pipelines/builder.py

@@ -69,9 +69,6 @@ DEFAULT_MODEL_FOR_PIPELINE = {
                                        'damo/nlp_space_dialog-modeling'),
     Tasks.dialog_state_tracking: (Pipelines.dialog_state_tracking,
                                   'damo/nlp_space_dialog-state-tracking'),
-    Tasks.conversational_text_to_sql:
-    (Pipelines.conversational_text_to_sql,
-     'damo/nlp_star_conversational-text-to-sql'),
     Tasks.table_question_answering:
     (Pipelines.table_question_answering_pipeline,
      'damo/nlp-convai-text2sql-pretrain-cn'),

modelscope/pipelines/nlp/conversational_text_to_sql_pipeline.py

@@ -19,7 +19,7 @@ __all__ = ['ConversationalTextToSqlPipeline']
 
 
 @PIPELINES.register_module(
-    Tasks.conversational_text_to_sql,
+    Tasks.table_question_answering,
     module_name=Pipelines.conversational_text_to_sql)
 class ConversationalTextToSqlPipeline(Pipeline):
 
@@ -62,7 +62,7 @@ class ConversationalTextToSqlPipeline(Pipeline):
             Dict[str, str]: the prediction results
         """
         sql = Example.evaluator.obtain_sql(inputs['predict'][0], inputs['db'])
-        result = {OutputKeys.TEXT: sql}
+        result = {OutputKeys.OUTPUT: {OutputKeys.TEXT: sql}}
         return result
 
     def _collate_fn(self, data):

modelscope/pipelines/nlp/table_question_answering_pipeline.py

@@ -13,8 +13,9 @@ from modelscope.outputs import OutputKeys
 from modelscope.pipelines.base import Pipeline
 from modelscope.pipelines.builder import PIPELINES
 from modelscope.preprocessors import TableQuestionAnsweringPreprocessor
-from modelscope.preprocessors.star3.fields.database import Database
-from modelscope.preprocessors.star3.fields.struct import Constant, SQLQuery
+from modelscope.preprocessors.space_T_cn.fields.database import Database
+from modelscope.preprocessors.space_T_cn.fields.struct import (Constant,
+                                                               SQLQuery)
 from modelscope.utils.constant import ModelFile, Tasks
 
 __all__ = ['TableQuestionAnsweringPipeline']
@@ -320,7 +321,7 @@ class TableQuestionAnsweringPipeline(Pipeline):
             OutputKeys.QUERT_RESULT: tabledata,
         }
 
-        return output
+        return {OutputKeys.OUTPUT: output}
 
     def _collate_fn(self, data):
         return data

modelscope/preprocessors/__init__.py

@@ -40,7 +40,7 @@ if TYPE_CHECKING:
                         DialogStateTrackingPreprocessor)
     from .video import ReadVideoData, MovieSceneSegmentationPreprocessor
     from .star import ConversationalTextToSqlPreprocessor
-    from .star3 import TableQuestionAnsweringPreprocessor
+    from .space_T_cn import TableQuestionAnsweringPreprocessor
 
 else:
     _import_structure = {
@@ -81,7 +81,7 @@ else:
             'DialogStateTrackingPreprocessor', 'InputFeatures'
         ],
         'star': ['ConversationalTextToSqlPreprocessor'],
-        'star3': ['TableQuestionAnsweringPreprocessor'],
+        'space_T_cn': ['TableQuestionAnsweringPreprocessor'],
     }
 
     import sys

modelscope/preprocessors/star3/fields/database.py → modelscope/preprocessors/space_T_cn/fields/database.py

@@ -4,7 +4,7 @@ import sqlite3
 import json
 import tqdm
 
-from modelscope.preprocessors.star3.fields.struct import Trie
+from modelscope.preprocessors.space_T_cn.fields.struct import Trie
 
 
 class Database:

modelscope/preprocessors/star3/fields/schema_link.py → modelscope/preprocessors/space_T_cn/fields/schema_link.py

@@ -1,7 +1,7 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 import re
 
-from modelscope.preprocessors.star3.fields.struct import TypeInfo
+from modelscope.preprocessors.space_T_cn.fields.struct import TypeInfo
 
 
 class SchemaLinker:

modelscope/preprocessors/star3/table_question_answering_preprocessor.py → modelscope/preprocessors/space_T_cn/table_question_answering_preprocessor.py

@@ -8,8 +8,8 @@ from transformers import BertTokenizer
 from modelscope.metainfo import Preprocessors
 from modelscope.preprocessors.base import Preprocessor
 from modelscope.preprocessors.builder import PREPROCESSORS
-from modelscope.preprocessors.star3.fields.database import Database
-from modelscope.preprocessors.star3.fields.schema_link import SchemaLinker
+from modelscope.preprocessors.space_T_cn.fields.database import Database
+from modelscope.preprocessors.space_T_cn.fields.schema_link import SchemaLinker
 from modelscope.utils.config import Config
 from modelscope.utils.constant import Fields, ModelFile
 from modelscope.utils.type_assert import type_assert

modelscope/utils/constant.py

@@ -123,7 +123,6 @@ class NLPTasks(object):
     backbone = 'backbone'
     text_error_correction = 'text-error-correction'
     faq_question_answering = 'faq-question-answering'
-    conversational_text_to_sql = 'conversational-text-to-sql'
     information_extraction = 'information-extraction'
     document_segmentation = 'document-segmentation'
     feature_extraction = 'feature-extraction'

modelscope/utils/nlp/nlp_utils.py

@@ -20,7 +20,7 @@ def text2sql_tracking_and_print_results(
             results = p(case)
             print({'question': item})
             print(results)
-            last_sql = results['text']
+            last_sql = results[OutputKeys.OUTPUT][OutputKeys.TEXT]
             history.append(item)
 
 

tests/pipelines/test_conversational_text_to_sql.py

@@ -16,7 +16,7 @@ from modelscope.utils.test_utils import test_level
 class ConversationalTextToSql(unittest.TestCase, DemoCompatibilityCheck):
 
     def setUp(self) -> None:
-        self.task = Tasks.conversational_text_to_sql
+        self.task = Tasks.table_question_answering
         self.model_id = 'damo/nlp_star_conversational-text-to-sql'
 
     model_id = 'damo/nlp_star_conversational-text-to-sql'
@@ -66,11 +66,6 @@ class ConversationalTextToSql(unittest.TestCase, DemoCompatibilityCheck):
         pipelines = [pipeline(task=self.task, model=self.model_id)]
         text2sql_tracking_and_print_results(self.test_case, pipelines)
 
-    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
-    def test_run_with_default_model(self):
-        pipelines = [pipeline(task=self.task)]
-        text2sql_tracking_and_print_results(self.test_case, pipelines)
-
     @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
     def test_demo_compatibility(self):
         self.compatibility_check()

tests/pipelines/test_table_question_answering.py

@@ -12,7 +12,7 @@ from modelscope.outputs import OutputKeys
 from modelscope.pipelines import pipeline
 from modelscope.pipelines.nlp import TableQuestionAnsweringPipeline
 from modelscope.preprocessors import TableQuestionAnsweringPreprocessor
-from modelscope.preprocessors.star3.fields.database import Database
+from modelscope.preprocessors.space_T_cn.fields.database import Database
 from modelscope.utils.constant import ModelFile, Tasks
 from modelscope.utils.test_utils import test_level
 
@@ -38,7 +38,7 @@ def tableqa_tracking_and_print_results_with_history(
             output_dict = p({
                 'question': question,
                 'history_sql': historical_queries
-            })
+            })[OutputKeys.OUTPUT]
             print('question', question)
             print('sql text:', output_dict[OutputKeys.SQL_STRING])
             print('sql query:', output_dict[OutputKeys.SQL_QUERY])
@@ -61,7 +61,7 @@ def tableqa_tracking_and_print_results_without_history(
     }
     for p in pipelines:
         for question in test_case['utterance']:
-            output_dict = p({'question': question})
+            output_dict = p({'question': question})[OutputKeys.OUTPUT]
             print('question', question)
             print('sql text:', output_dict[OutputKeys.SQL_STRING])
             print('sql query:', output_dict[OutputKeys.SQL_QUERY])
@@ -92,7 +92,7 @@ def tableqa_tracking_and_print_results_with_tableid(
                 'question': question,
                 'table_id': table_id,
                 'history_sql': historical_queries
-            })
+            })[OutputKeys.OUTPUT]
             print('question', question)
             print('sql text:', output_dict[OutputKeys.SQL_STRING])
             print('sql query:', output_dict[OutputKeys.SQL_QUERY])
@@ -147,11 +147,6 @@ class TableQuestionAnswering(unittest.TestCase):
         ]
         tableqa_tracking_and_print_results_with_tableid(pipelines)
 
-    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
-    def test_run_with_model_from_task(self):
-        pipelines = [pipeline(Tasks.table_question_answering, self.model_id)]
-        tableqa_tracking_and_print_results_with_history(pipelines)
-
     @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
     def test_run_with_model_from_modelhub_with_other_classes(self):
         model = Model.from_pretrained(self.model_id)