Merge branch 'master-github' into release/1.10

modelscope/__init__.py

@@ -2,6 +2,7 @@
 from typing import TYPE_CHECKING
 
 from modelscope.utils.import_utils import LazyImportModule
+from .utils.automodel_utils import fix_transformers_upgrade
 
 if TYPE_CHECKING:
     from .exporters import Exporter, TfModelExporter, TorchModelExporter
@@ -33,7 +34,8 @@ if TYPE_CHECKING:
                                 AutoModelForSeq2SeqLM,
                                 AutoModelForSequenceClassification,
                                 AutoModelForTokenClassification, AutoTokenizer,
-                                GenerationConfig)
+                                GenerationConfig, AutoImageProcessor,
+                                BatchFeature)
     from .utils.hub import create_model_if_not_exist, read_config
     from .utils.logger import get_logger
     from .version import __release_datetime__, __version__
@@ -81,7 +83,8 @@ else:
             'BitsAndBytesConfig', 'AutoModelForCausalLM',
             'AutoModelForSeq2SeqLM', 'AutoTokenizer',
             'AutoModelForSequenceClassification',
-            'AutoModelForTokenClassification'
+            'AutoModelForTokenClassification', 'AutoImageProcessor',
+            'BatchFeature'
         ],
         'msdatasets': ['MsDataset']
     }
@@ -95,3 +98,5 @@ else:
         module_spec=__spec__,
         extra_objects={},
     )
+
+fix_transformers_upgrade()

modelscope/models/base/base_model.py

@@ -7,7 +7,7 @@ from typing import Any, Dict, List, Optional, Union
 from modelscope.hub.snapshot_download import snapshot_download
 from modelscope.metainfo import Tasks
 from modelscope.models.builder import build_backbone, build_model
-from modelscope.utils.automodel_utils import (can_load_by_ms, fix_upgrade,
+from modelscope.utils.automodel_utils import (can_load_by_ms,
                                               try_to_load_hf_model)
 from modelscope.utils.config import Config, ConfigDict
 from modelscope.utils.constant import DEFAULT_MODEL_REVISION, Invoke, ModelFile
@@ -192,7 +192,6 @@ class Model(ABC):
         model_cfg.pop('model_dir', None)
         model.name = model_name_or_path
         model.model_dir = local_model_dir
-        fix_upgrade(model)
         return model
 
     def save_pretrained(self,

modelscope/models/nlp/dgds/document_grounded_dialog_retrieval.py

@@ -28,6 +28,8 @@ class DocumentGroundedDialogRetrievalModel(TorchModel):
             map_location='cpu')
         compatible_position_ids(state_dict,
                                 'ctx_encoder.encoder.embeddings.position_ids')
+        compatible_position_ids(state_dict,
+                                'qry_encoder.encoder.embeddings.position_ids')
         self.model.load_state_dict(state_dict)
 
     def forward(self, input: Dict[str, Tensor], gck_segment=32):

modelscope/utils/automodel_utils.py

@@ -25,15 +25,27 @@ def can_load_by_ms(model_dir: str, task_name: Optional[str],
 
 
 def fix_upgrade(module_obj: Any):
+    from transformers import PreTrainedModel
+    if hasattr(module_obj, '_set_gradient_checkpointing') \
+            and 'value' in inspect.signature(module_obj._set_gradient_checkpointing).parameters.keys():
+        module_obj._set_gradient_checkpointing = MethodType(
+            PreTrainedModel._set_gradient_checkpointing, module_obj)
+
+
+def post_init(self, *args, **kwargs):
+    fix_upgrade(self)
+    self.post_init_origin(*args, **kwargs)
+
+
+def fix_transformers_upgrade():
     if is_transformers_available():
         # from 4.35.0, transformers changes its arguments of _set_gradient_checkpointing
         import transformers
         from transformers import PreTrainedModel
-        if version.parse(transformers.__version__) >= version.parse('4.35.0'):
-            if isinstance(module_obj, PreTrainedModel) and hasattr(module_obj, '_set_gradient_checkpointing') \
-                    and 'value' in inspect.signature(module_obj._set_gradient_checkpointing).parameters.keys():
-                module_obj._set_gradient_checkpointing = MethodType(
-                    PreTrainedModel._set_gradient_checkpointing, module_obj)
+        if version.parse(transformers.__version__) >= version.parse('4.35.0') \
+                and not hasattr(PreTrainedModel, 'post_init_origin'):
+            PreTrainedModel.post_init_origin = PreTrainedModel.post_init
+            PreTrainedModel.post_init = post_init
 
 
 def _can_load_by_hf_automodel(automodel_class: type, config) -> bool:

modelscope/utils/hf_util.py

@@ -2,6 +2,7 @@
 import os
 
 from transformers import AutoConfig as AutoConfigHF
+from transformers import AutoImageProcessor as AutoImageProcessorHF
 from transformers import AutoModel as AutoModelHF
 from transformers import AutoModelForCausalLM as AutoModelForCausalLMHF
 from transformers import AutoModelForSeq2SeqLM as AutoModelForSeq2SeqLMHF
@@ -10,12 +11,12 @@ from transformers import \
 from transformers import \
     AutoModelForTokenClassification as AutoModelForTokenClassificationHF
 from transformers import AutoTokenizer as AutoTokenizerHF
+from transformers import BatchFeature as BatchFeatureHF
 from transformers import BitsAndBytesConfig as BitsAndBytesConfigHF
 from transformers import GenerationConfig as GenerationConfigHF
 from transformers import PreTrainedModel, PreTrainedTokenizerBase
 
 from modelscope import snapshot_download
-from modelscope.utils.automodel_utils import fix_upgrade
 from modelscope.utils.constant import DEFAULT_MODEL_REVISION, Invoke
 
 try:
@@ -112,7 +113,6 @@ def get_wrapped_class(module_class, ignore_file_pattern=[], **kwargs):
 
             if module_class.__name__.startswith('AutoModel'):
                 module_obj.model_dir = model_dir
-            fix_upgrade(module_obj)
             return module_obj
 
     ClassWrapper.__name__ = module_class.__name__
@@ -136,3 +136,5 @@ GenerationConfig = get_wrapped_class(
     GenerationConfigHF, ignore_file_pattern=[r'\w+\.bin', r'\w+\.safetensors'])
 GPTQConfig = GPTQConfigHF
 BitsAndBytesConfig = BitsAndBytesConfigHF
+AutoImageProcessor = get_wrapped_class(AutoImageProcessorHF)
+BatchFeature = get_wrapped_class(BatchFeatureHF)

modelscope/utils/test_utils.py

@@ -115,6 +115,9 @@ def get_case_model_info():
         elements = line.split(':')
         test_file = elements[0]
         model_pos = line.find('damo')
+        if model_pos == -1 or (model_pos - 1) > len(line):
+            print('Processing line: %s failed' % line)
+            continue
         left_quote = line[model_pos - 1]
         rquote_idx = line.rfind(left_quote)
         model_name = line[model_pos:rquote_idx]