Improving Pipelines by defaulting to framework='tf' when pytorch seems unavailable. (#7728)

* Improving Pipelines by defaulting to framework='tf' when

pytorch seems unavailable.

* Actually changing the default resolution order to account for model
defaults

Adding a new tests for each pipeline to check that pipeline(task) works
too without manually adding the framework too.

src/transformers/pipelines.py

@@ -85,31 +85,63 @@ if TYPE_CHECKING:
 logger = logging.get_logger(__name__)
 
 
-def get_framework(model=None):
+def get_framework(model):
     """
     Select framework (TensorFlow or PyTorch) to use.
 
     Args:
-        model (:obj:`str`, :class:`~transformers.PreTrainedModel` or :class:`~transformers.TFPreTrainedModel`, `optional`):
+        model (:obj:`str`, :class:`~transformers.PreTrainedModel` or :class:`~transformers.TFPreTrainedModel`):
             If both frameworks are installed, picks the one corresponding to the model passed (either a model class or
             the model name). If no specific model is provided, defaults to using PyTorch.
     """
-    if is_tf_available() and is_torch_available() and model is not None and not isinstance(model, str):
-        # Both framework are available but the user supplied a model class instance.
-        # Try to guess which framework to use from the model classname
-        framework = "tf" if model.__class__.__name__.startswith("TF") else "pt"
-    elif not is_tf_available() and not is_torch_available():
+    if not is_tf_available() and not is_torch_available():
         raise RuntimeError(
             "At least one of TensorFlow 2.0 or PyTorch should be installed. "
             "To install TensorFlow 2.0, read the instructions at https://www.tensorflow.org/install/ "
             "To install PyTorch, read the instructions at https://pytorch.org/."
         )
+    if isinstance(model, str):
+        if is_torch_available() and not is_tf_available():
+            model = AutoModel.from_pretrained(model)
+        elif is_tf_available() and not is_torch_available():
+            model = TFAutoModel.from_pretrained(model)
         else:
-        # framework = 'tf' if is_tf_available() else 'pt'
-        framework = "pt" if is_torch_available() else "tf"
+            try:
+                model = AutoModel.from_pretrained(model)
+            except OSError:
+                model = TFAutoModel.from_pretrained(model)
+
+    framework = "tf" if model.__class__.__name__.startswith("TF") else "pt"
     return framework
 
 
+def get_default_model(targeted_task: Dict, framework: Optional[str]) -> str:
+    """
+    Select a default model to use for a given task. Defaults to pytorch if ambiguous.
+
+    Args:
+        targeted_task (:obj:`Dict` ):
+           Dictionnary representing the given task, that should contain default models
+
+        framework (:obj:`str`, None)
+           "pt", "tf" or None, representing a specific framework if it was specified, or None if we don't know yet.
+
+    Returns
+
+        :obj:`str` The model string representing the default model for this pipeline
+    """
+    if is_torch_available() and not is_tf_available():
+        framework = "pt"
+    elif is_tf_available() and not is_torch_available():
+        framework = "tf"
+
+    default_models = targeted_task["default"]["model"]
+    if framework is None:
+        framework = "pt"
+
+    return default_models[framework]
+
+
 class PipelineException(Exception):
     """
     Raised by a :class:`~transformers.Pipeline` when handling __call__.
@@ -2685,14 +2717,16 @@ def pipeline(
     if task not in SUPPORTED_TASKS:
         raise KeyError("Unknown task {}, available tasks are {}".format(task, list(SUPPORTED_TASKS.keys())))
 
-    framework = framework or get_framework(model)
-
     targeted_task = SUPPORTED_TASKS[task]
-    task_class, model_class = targeted_task["impl"], targeted_task[framework]
 
     # Use default model/config/tokenizer for the task if no model is provided
     if model is None:
-        model = targeted_task["default"]["model"][framework]
+        # At that point framework might still be undetermined
+        model = get_default_model(targeted_task, framework)
+
+    framework = framework or get_framework(model)
+
+    task_class, model_class = targeted_task["impl"], targeted_task[framework]
 
     # Try to infer tokenizer from model or config name (if provided as str)
     if tokenizer is None:

tests/test_pipelines.py

@@ -10,6 +10,7 @@ DEFAULT_DEVICE_NUM = -1 if torch_device == "cpu" else 0
 VALID_INPUTS = ["A simple string", ["list of strings"]]
 
 NER_FINETUNED_MODELS = ["sshleifer/tiny-dbmdz-bert-large-cased-finetuned-conll03-english"]
+TF_NER_FINETUNED_MODELS = ["Narsil/small"]
 
 # xlnet-base-cased disabled for now, since it crashes TF2
 FEATURE_EXTRACT_FINETUNED_MODELS = ["sshleifer/tiny-distilbert-base-cased"]
@@ -804,6 +805,14 @@ class NerPipelineTests(unittest.TestCase):
             nlp = pipeline(task="ner", model=model_name, tokenizer=model_name, framework="tf", grouped_entities=True)
             self._test_ner_pipeline(nlp, mandatory_keys)
 
+    @require_tf
+    def test_tf_only_ner(self):
+        mandatory_keys = {"entity", "word", "score"}
+        for model_name in TF_NER_FINETUNED_MODELS:
+            # We don't specificy framework='tf' but it gets detected automatically
+            nlp = pipeline(task="ner", model=model_name, tokenizer=model_name)
+            self._test_ner_pipeline(nlp, mandatory_keys)
+
 
 class PipelineCommonTests(unittest.TestCase):
     pipelines = SUPPORTED_TASKS.keys()
@@ -815,6 +824,7 @@ class PipelineCommonTests(unittest.TestCase):
         for task in self.pipelines:
             with self.subTest(msg="Testing TF defaults with TF and {}".format(task)):
                 pipeline(task, framework="tf")
+                pipeline(task)
 
     @require_torch
     @slow
@@ -823,3 +833,4 @@ class PipelineCommonTests(unittest.TestCase):
         for task in self.pipelines:
             with self.subTest(msg="Testing Torch defaults with PyTorch and {}".format(task)):
                 pipeline(task, framework="pt")
+                pipeline(task)