tmp

modelscope/__init__.py

@@ -54,7 +54,7 @@ if TYPE_CHECKING:
             AutoModelForMaskedLM, AutoTokenizer, AutoModelForMaskGeneration,
             AutoModelForPreTraining, AutoModelForTextEncoding,
             AutoImageProcessor, BatchFeature, Qwen2VLForConditionalGeneration,
-            T5EncoderModel)
+            T5EncoderModel, hf_pipeline)
     else:
         print(
             'transformer is not installed, please install it if you want to use related modules'
@@ -131,7 +131,8 @@ else:
             'AutoModelForMaskedLM', 'AutoTokenizer',
             'AutoModelForMaskGeneration', 'AutoModelForPreTraining',
             'AutoModelForTextEncoding', 'AutoImageProcessor', 'BatchFeature',
-            'Qwen2VLForConditionalGeneration', 'T5EncoderModel'
+            'Qwen2VLForConditionalGeneration', 'T5EncoderModel',
+            'hf_pipeline'
         ]
 
     import sys

modelscope/pipelines/builder.py

@@ -13,10 +13,14 @@ from modelscope.utils.hub import read_config
 from modelscope.utils.plugins import (register_modelhub_repo,
                                       register_plugins_repo)
 from modelscope.utils.registry import Registry, build_from_cfg
+from modelscope.utils.logger import get_logger
+from modelscope.utils.import_utils import is_transformers_available
+
 from .base import Pipeline
 from .util import is_official_hub_path
 
 PIPELINES = Registry('pipelines')
+logger = get_logger()
 
 
 def normalize_model_input(model,
@@ -109,6 +113,7 @@ def pipeline(task: str = None,
     if task is None and pipeline_name is None:
         raise ValueError('task or pipeline_name is required')
 
+    pipeline_props = None
     if pipeline_name is None:
         # get default pipeline for this task
         if isinstance(model, str) \
@@ -157,8 +162,11 @@ def pipeline(task: str = None,
                 if pipeline_name:
                     pipeline_props = {'type': pipeline_name}
                 else:
-                    check_config(cfg)
-                    pipeline_props = cfg.pipeline
+                    try:
+                        check_config(cfg)
+                        pipeline_props = cfg.pipeline
+                    except AssertionError as e:
+                        logger.info(str(e))
 
         elif model is not None:
             # get pipeline info from Model object
@@ -176,6 +184,14 @@ def pipeline(task: str = None,
     else:
         pipeline_props = {'type': pipeline_name}
 
+    if not pipeline_props and is_transformers_available():
+        from modelscope.utils.hf_util import hf_pipeline
+        return hf_pipeline(task=task,
+             model=model,
+             framework=framework,
+             device=device,
+             **kwargs)
+
     pipeline_props['model'] = model
     pipeline_props['device'] = device
     cfg = ConfigDict(pipeline_props)

modelscope/utils/hf_util.py

@@ -63,6 +63,7 @@ from transformers import (PretrainedConfig, PreTrainedModel,
                           PreTrainedTokenizerBase)
 from transformers import T5EncoderModel as T5EncoderModelHF
 from transformers import __version__ as transformers_version
+from transformers import pipeline as hf_pipeline
 
 from modelscope import snapshot_download
 from modelscope.utils.constant import DEFAULT_MODEL_REVISION, Invoke