More automodel (#1098)

* add more hf alias

---------

Co-authored-by: Yingda Chen <yingda.chen@alibaba-inc.com>

modelscope/__init__.py

@@ -36,9 +36,12 @@ if TYPE_CHECKING:
         from .utils.hf_util import (
             AutoModel, AutoModelForCausalLM, AutoModelForSeq2SeqLM,
             AutoModelForSequenceClassification,
-            AutoModelForTokenClassification, AutoModelForImageSegmentation,
-            AutoTokenizer, GenerationConfig, AutoImageProcessor, BatchFeature,
-            T5EncoderModel)
+            AutoModelForTokenClassification, AutoModelForImageClassification,
+            AutoModelForImageToImage, AutoModelForImageSegmentation,
+            AutoModelForQuestionAnswering, AutoModelForMaskedLM, AutoTokenizer,
+            AutoModelForMaskGeneration, AutoModelForPreTraining,
+            AutoModelForTextEncoding, GenerationConfig, AutoImageProcessor,
+            BatchFeature, T5EncoderModel)
     else:
         print(
             'transformer is not installed, please install it if you want to use related modules'
@@ -96,8 +99,13 @@ else:
             'AwqConfig', 'BitsAndBytesConfig', 'AutoModelForCausalLM',
             'AutoModelForSeq2SeqLM', 'AutoTokenizer',
             'AutoModelForSequenceClassification',
-            'AutoModelForTokenClassification', 'AutoModelForImageSegmentation',
-            'AutoImageProcessor', 'BatchFeature', 'T5EncoderModel'
+            'AutoModelForTokenClassification',
+            'AutoModelForImageClassification', 'AutoModelForImageToImage',
+            'AutoModelForQuestionAnswering', 'AutoModelForMaskedLM',
+            'AutoModelForMaskGeneration', 'AutoModelForPreTraining',
+            'AutoModelForTextEncoding', 'AutoModelForTokenClassification',
+            'AutoModelForImageSegmentation', 'AutoImageProcessor',
+            'BatchFeature', 'T5EncoderModel'
         ]
 
     import sys

modelscope/utils/hf_util.py

@@ -9,11 +9,21 @@ from transformers import AutoFeatureExtractor as AutoFeatureExtractorHF
 from transformers import AutoImageProcessor as AutoImageProcessorHF
 from transformers import AutoModel as AutoModelHF
 from transformers import AutoModelForCausalLM as AutoModelForCausalLMHF
+from transformers import \
+    AutoModelForImageClassification as AutoModelForImageClassificationHF
 from transformers import \
     AutoModelForImageSegmentation as AutoModelForImageSegmentationHF
+from transformers import AutoModelForImageToImage as AutoModelForImageToImageHF
+from transformers import AutoModelForMaskedLM as AutoModelForMaskedLMHF
+from transformers import \
+    AutoModelForMaskGeneration as AutoModelForMaskGenerationHF
+from transformers import AutoModelForPreTraining as AutoModelForPreTrainingHF
+from transformers import \
+    AutoModelForQuestionAnswering as AutoModelForQuestionAnsweringHF
 from transformers import AutoModelForSeq2SeqLM as AutoModelForSeq2SeqLMHF
 from transformers import \
     AutoModelForSequenceClassification as AutoModelForSequenceClassificationHF
+from transformers import AutoModelForTextEncoding as AutoModelForTextEncodingHF
 from transformers import \
     AutoModelForTokenClassification as AutoModelForTokenClassificationHF
 from transformers import AutoProcessor as AutoProcessorHF
@@ -272,7 +282,7 @@ def get_wrapped_class(module_class,
             ignore_file_pattern = kwargs.pop('ignore_file_pattern',
                                              default_ignore_file_pattern)
             subfolder = kwargs.pop('subfolder', default_file_filter)
-
+            file_filter = None
             if subfolder:
                 file_filter = f'{subfolder}/*'
             if not os.path.exists(pretrained_model_name_or_path):
@@ -315,25 +325,48 @@ AutoModelForTokenClassification = get_wrapped_class(
     AutoModelForTokenClassificationHF)
 AutoModelForImageSegmentation = get_wrapped_class(
     AutoModelForImageSegmentationHF)
+AutoModelForImageClassification = get_wrapped_class(
+    AutoModelForImageClassificationHF)
+AutoModelForImageToImage = get_wrapped_class(AutoModelForImageToImageHF)
+AutoModelForQuestionAnswering = get_wrapped_class(
+    AutoModelForQuestionAnsweringHF)
+AutoModelForMaskedLM = get_wrapped_class(AutoModelForMaskedLMHF)
+AutoModelForMaskGeneration = get_wrapped_class(AutoModelForMaskGenerationHF)
+AutoModelForPreTraining = get_wrapped_class(AutoModelForPreTrainingHF)
+AutoModelForTextEncoding = get_wrapped_class(AutoModelForTextEncodingHF)
 T5EncoderModel = get_wrapped_class(T5EncoderModelHF)
 
 AutoTokenizer = get_wrapped_class(
     AutoTokenizerHF,
     ignore_file_pattern=[
-        r'\w+\.bin', r'\w+\.safetensors', r'\w+\.pth', r'\w+\.pt'
+        r'\w+\.bin', r'\w+\.safetensors', r'\w+\.pth', r'\w+\.pt', r'\w+\.h5'
+    ])
+AutoProcessor = get_wrapped_class(
+    AutoProcessorHF,
+    ignore_file_pattern=[
+        r'\w+\.bin', r'\w+\.safetensors', r'\w+\.pth', r'\w+\.pt', r'\w+\.h5'
     ])
 AutoConfig = get_wrapped_class(
     AutoConfigHF,
     ignore_file_pattern=[
-        r'\w+\.bin', r'\w+\.safetensors', r'\w+\.pth', r'\w+\.pt'
+        r'\w+\.bin', r'\w+\.safetensors', r'\w+\.pth', r'\w+\.pt', r'\w+\.h5'
     ])
 GenerationConfig = get_wrapped_class(
     GenerationConfigHF,
     ignore_file_pattern=[
-        r'\w+\.bin', r'\w+\.safetensors', r'\w+\.pth', r'\w+\.pt'
+        r'\w+\.bin', r'\w+\.safetensors', r'\w+\.pth', r'\w+\.pt', r'\w+\.h5'
     ])
+BitsAndBytesConfig = get_wrapped_class(
+    BitsAndBytesConfigHF,
+    ignore_file_pattern=[
+        r'\w+\.bin', r'\w+\.safetensors', r'\w+\.pth', r'\w+\.pt', r'\w+\.h5'
+    ])
+AutoImageProcessor = get_wrapped_class(
+    AutoImageProcessorHF,
+    ignore_file_pattern=[
+        r'\w+\.bin', r'\w+\.safetensors', r'\w+\.pth', r'\w+\.pt', r'\w+\.h5'
+    ])
+
 GPTQConfig = GPTQConfigHF
 AwqConfig = AwqConfigHF
-BitsAndBytesConfig = BitsAndBytesConfigHF
-AutoImageProcessor = get_wrapped_class(AutoImageProcessorHF)
 BatchFeature = get_wrapped_class(BatchFeatureHF)