[to #42322933]add default mapping for preprocessors

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10536603

modelscope/pipelines/nlp/faq_question_answering_pipeline.py

@@ -26,10 +26,6 @@ class FaqQuestionAnsweringPipeline(Pipeline):
         if preprocessor is None:
             preprocessor = Preprocessor.from_pretrained(
                 model.model_dir, **kwargs)
-            if preprocessor is None:
-                from modelscope.preprocessors import FaqQuestionAnsweringPreprocessor
-                preprocessor = FaqQuestionAnsweringPreprocessor(
-                    model.model_dir, **kwargs)
         super().__init__(model=model, preprocessor=preprocessor, **kwargs)
 
     def _sanitize_parameters(self, **pipeline_parameters):

modelscope/preprocessors/base.py

@@ -4,7 +4,8 @@ from abc import ABC, abstractmethod
 from copy import deepcopy
 from typing import Any, Dict, Optional, Sequence
 
-from modelscope.utils.config import Config
+from modelscope.metainfo import Models, Preprocessors
+from modelscope.utils.config import Config, ConfigDict
 from modelscope.utils.constant import DEFAULT_MODEL_REVISION, ModeKeys, Tasks
 from modelscope.utils.hub import read_config, snapshot_download
 from modelscope.utils.logger import get_logger
@@ -12,6 +13,112 @@ from .builder import build_preprocessor
 
 logger = get_logger(__name__)
 
+PREPROCESSOR_MAP = {
+    # nlp
+    # bart
+    (Models.bart, Tasks.text_error_correction):
+    Preprocessors.text_error_correction,
+
+    # bert
+    (Models.bert, Tasks.backbone):
+    Preprocessors.sen_cls_tokenizer,
+    (Models.bert, Tasks.document_segmentation):
+    Preprocessors.document_segmentation,
+    (Models.bert, Tasks.fill_mask):
+    Preprocessors.fill_mask,
+    (Models.bert, Tasks.sentence_embedding):
+    Preprocessors.sentence_embedding,
+    (Models.bert, Tasks.text_classification):
+    Preprocessors.sen_cls_tokenizer,
+    (Models.bert, Tasks.nli):
+    Preprocessors.sen_cls_tokenizer,
+    (Models.bert, Tasks.sentiment_classification):
+    Preprocessors.sen_cls_tokenizer,
+    (Models.bert, Tasks.sentence_similarity):
+    Preprocessors.sen_cls_tokenizer,
+    (Models.bert, Tasks.zero_shot_classification):
+    Preprocessors.sen_cls_tokenizer,
+    (Models.bert, Tasks.text_ranking):
+    Preprocessors.text_ranking,
+    (Models.bert, Tasks.part_of_speech):
+    Preprocessors.token_cls_tokenizer,
+    (Models.bert, Tasks.token_classification):
+    Preprocessors.token_cls_tokenizer,
+    (Models.bert, Tasks.word_segmentation):
+    Preprocessors.token_cls_tokenizer,
+
+    # bloom
+    (Models.bloom, Tasks.backbone):
+    Preprocessors.text_gen_tokenizer,
+
+    # gpt_neo
+    # gpt_neo may have different preprocessors, but now only one
+    (Models.gpt_neo, Tasks.backbone):
+    Preprocessors.sentence_piece,
+
+    # gpt3 has different preprocessors by different sizes of models, so they are not listed here.
+
+    # palm_v2
+    (Models.palm, Tasks.backbone):
+    Preprocessors.text_gen_tokenizer,
+
+    # T5
+    (Models.T5, Tasks.backbone):
+    Preprocessors.text2text_gen_preprocessor,
+    (Models.T5, Tasks.text2text_generation):
+    Preprocessors.text2text_gen_preprocessor,
+
+    # deberta_v2
+    (Models.deberta_v2, Tasks.backbone):
+    Preprocessors.sen_cls_tokenizer,
+    (Models.deberta_v2, Tasks.fill_mask):
+    Preprocessors.fill_mask,
+
+    # ponet
+    (Models.ponet, Tasks.fill_mask):
+    Preprocessors.fill_mask_ponet,
+
+    # structbert
+    (Models.structbert, Tasks.backbone):
+    Preprocessors.sen_cls_tokenizer,
+    (Models.structbert, Tasks.fill_mask):
+    Preprocessors.fill_mask,
+    (Models.structbert, Tasks.faq_question_answering):
+    Preprocessors.faq_question_answering_preprocessor,
+    (Models.structbert, Tasks.text_classification):
+    Preprocessors.sen_cls_tokenizer,
+    (Models.structbert, Tasks.nli):
+    Preprocessors.sen_cls_tokenizer,
+    (Models.structbert, Tasks.sentiment_classification):
+    Preprocessors.sen_cls_tokenizer,
+    (Models.structbert, Tasks.sentence_similarity):
+    Preprocessors.sen_cls_tokenizer,
+    (Models.structbert, Tasks.zero_shot_classification):
+    Preprocessors.sen_cls_tokenizer,
+    (Models.structbert, Tasks.part_of_speech):
+    Preprocessors.token_cls_tokenizer,
+    (Models.structbert, Tasks.token_classification):
+    Preprocessors.token_cls_tokenizer,
+    (Models.structbert, Tasks.word_segmentation):
+    Preprocessors.token_cls_tokenizer,
+
+    # veco
+    (Models.veco, Tasks.backbone):
+    Preprocessors.sen_cls_tokenizer,
+    (Models.veco, Tasks.fill_mask):
+    Preprocessors.fill_mask,
+    (Models.veco, Tasks.text_classification):
+    Preprocessors.sen_cls_tokenizer,
+    (Models.veco, Tasks.nli):
+    Preprocessors.sen_cls_tokenizer,
+    (Models.veco, Tasks.sentiment_classification):
+    Preprocessors.sen_cls_tokenizer,
+    (Models.veco, Tasks.sentence_similarity):
+    Preprocessors.sen_cls_tokenizer,
+
+    # space
+}
+
 
 class Preprocessor(ABC):
 
@@ -56,37 +163,59 @@ class Preprocessor(ABC):
         if 'task' in kwargs:
             task = kwargs.pop('task')
         field_name = Tasks.find_field_by_task(task)
+        sub_key = 'train' if preprocessor_mode == ModeKeys.TRAIN else 'val'
+
         if not hasattr(cfg, 'preprocessor'):
             logger.error('No preprocessor field found in cfg.')
-            return None
-
-        sub_key = 'train' if preprocessor_mode == ModeKeys.TRAIN else 'val'
+            preprocessor_cfg = ConfigDict()
+        else:
+            preprocessor_cfg = cfg.preprocessor
 
-        if 'type' not in cfg.preprocessor:
-            if sub_key in cfg.preprocessor:
-                sub_cfg = getattr(cfg.preprocessor, sub_key)
+        if 'type' not in preprocessor_cfg:
+            if sub_key in preprocessor_cfg:
+                sub_cfg = getattr(preprocessor_cfg, sub_key)
             else:
                 logger.error(
                     f'No {sub_key} key and type key found in '
                     f'preprocessor domain of configuration.json file.')
-                return None
+                sub_cfg = preprocessor_cfg
         else:
-            sub_cfg = cfg.preprocessor
+            sub_cfg = preprocessor_cfg
 
-        if len(sub_cfg):
+        sub_cfg.update({'model_dir': model_dir})
+        sub_cfg.update(kwargs)
+        if 'type' in sub_cfg:
             if isinstance(sub_cfg, Sequence):
                 # TODO: for Sequence, need adapt to `mode` and `mode_dir` args,
                 # and add mode for Compose or other plans
                 raise NotImplementedError('Not supported yet!')
             sub_cfg = deepcopy(sub_cfg)
-            sub_cfg.update({'model_dir': model_dir})
-            sub_cfg.update(kwargs)
+
             preprocessor = build_preprocessor(sub_cfg, field_name)
         else:
             logger.error(
                 f'Cannot find available config to build preprocessor at mode {preprocessor_mode}, '
-                f'please check the preprocessor field in the configuration.json file.'
+                f'current config: {sub_cfg}. trying to build by task and model information.'
             )
-            return None
+            model_cfg = getattr(cfg, 'model', ConfigDict())
+            model_type = model_cfg.type if hasattr(
+                model_cfg, 'type') else getattr(model_cfg, 'model_type', None)
+            if task is None or model_type is None:
+                logger.error(
+                    f'Find task: {task}, model type: {model_type}. '
+                    f'Insufficient information to build preprocessor, skip building preprocessor'
+                )
+                return None
+            if (model_type, task) not in PREPROCESSOR_MAP:
+                logger.error(
+                    f'No preprocessor key {(model_type, task)} found in PREPROCESSOR_MAP, '
+                    f'skip building preprocessor.')
+                return None
+
+            sub_cfg = ConfigDict({
+                'type': PREPROCESSOR_MAP[(model_type, task)],
+                **sub_cfg
+            })
+            preprocessor = build_preprocessor(sub_cfg, field_name)
         preprocessor.mode = preprocessor_mode
         return preprocessor