AutoModelForTableQuestionAnswering (#9154)

* AutoModelForTableQuestionAnswering

* Update src/transformers/models/auto/modeling_auto.py

* Style

docs/source/model_doc/auto.rst

@@ -114,6 +114,13 @@ AutoModelForQuestionAnswering
     :members:
 
 
+AutoModelForTableQuestionAnswering
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.AutoModelForTableQuestionAnswering
+    :members:
+
+
 TFAutoModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

src/transformers/__init__.py

@@ -358,6 +358,7 @@ if is_torch_available():
         MODEL_FOR_QUESTION_ANSWERING_MAPPING,
         MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
         MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
+        MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
         MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
         MODEL_MAPPING,
         MODEL_WITH_LM_HEAD_MAPPING,
@@ -370,6 +371,7 @@ if is_torch_available():
         AutoModelForQuestionAnswering,
         AutoModelForSeq2SeqLM,
         AutoModelForSequenceClassification,
+        AutoModelForTableQuestionAnswering,
         AutoModelForTokenClassification,
         AutoModelWithLMHead,
     )

src/transformers/dependency_versions_table.py

@@ -40,8 +40,8 @@ deps = {
     "sphinx-rtd-theme": "sphinx-rtd-theme==0.4.3",
     "sphinx": "sphinx==3.2.1",
     "starlette": "starlette",
-    "tensorflow-cpu": "tensorflow-cpu>=2.0,<2.4",
-    "tensorflow": "tensorflow>=2.0,<2.4",
+    "tensorflow-cpu": "tensorflow-cpu>=2.0",
+    "tensorflow": "tensorflow>=2.0",
     "timeout-decorator": "timeout-decorator",
     "tokenizers": "tokenizers==0.9.4",
     "torch": "torch>=1.0",

src/transformers/models/auto/__init__.py

@@ -31,6 +31,7 @@ if is_torch_available():
         MODEL_FOR_QUESTION_ANSWERING_MAPPING,
         MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
         MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
+        MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
         MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
         MODEL_MAPPING,
         MODEL_WITH_LM_HEAD_MAPPING,
@@ -43,6 +44,7 @@ if is_torch_available():
         AutoModelForQuestionAnswering,
         AutoModelForSeq2SeqLM,
         AutoModelForSequenceClassification,
+        AutoModelForTableQuestionAnswering,
         AutoModelForTokenClassification,
         AutoModelWithLMHead,
     )

src/transformers/models/auto/modeling_auto.py

@@ -467,6 +467,12 @@ MODEL_FOR_QUESTION_ANSWERING_MAPPING = OrderedDict(
         (FunnelConfig, FunnelForQuestionAnswering),
         (LxmertConfig, LxmertForQuestionAnswering),
         (MPNetConfig, MPNetForQuestionAnswering),
+    ]
+)
+
+MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING = OrderedDict(
+    [
+        # Model for Table Question Answering mapping
         (TapasConfig, TapasForQuestionAnswering),
     ]
 )
@@ -1384,6 +1390,106 @@ class AutoModelForQuestionAnswering:
         )
 
 
+class AutoModelForTableQuestionAnswering:
+    r"""
+    This is a generic model class that will be instantiated as one of the model classes of the library---with a table
+    question answering head---when created with the when created with the
+    :meth:`~transformers.AutoModeForTableQuestionAnswering.from_pretrained` class method or the
+    :meth:`~transformers.AutoModelForTableQuestionAnswering.from_config` class method.
+
+    This class cannot be instantiated directly using ``__init__()`` (throws an error).
+    """
+
+    def __init__(self):
+        raise EnvironmentError(
+            "AutoModelForQuestionAnswering is designed to be instantiated "
+            "using the `AutoModelForTableQuestionAnswering.from_pretrained(pretrained_model_name_or_path)` or "
+            "`AutoModelForTableQuestionAnswering.from_config(config)` methods."
+        )
+
+    @classmethod
+    @replace_list_option_in_docstrings(MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING, use_model_types=False)
+    def from_config(cls, config):
+        r"""
+        Instantiates one of the model classes of the library---with a table question answering head---from a
+        configuration.
+
+        Note:
+            Loading a model from its configuration file does **not** load the model weights. It only affects the
+            model's configuration. Use :meth:`~transformers.AutoModelForTableQuestionAnswering.from_pretrained` to load
+            the model weights.
+
+        Args:
+            config (:class:`~transformers.PretrainedConfig`):
+                The model class to instantiate is selected based on the configuration class:
+
+                List options
+
+        Examples::
+
+            >>> from transformers import AutoConfig, AutoModelForTableQuestionAnswering
+            >>> # Download configuration from huggingface.co and cache.
+            >>> config = AutoConfig.from_pretrained('google/tapas-base-finetuned-wtq')
+            >>> model = AutoModelForTableQuestionAnswering.from_config(config)
+        """
+        if type(config) in MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.keys():
+            return MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING[type(config)](config)
+
+        raise ValueError(
+            "Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
+            "Model type should be one of {}.".format(
+                config.__class__,
+                cls.__name__,
+                ", ".join(c.__name__ for c in MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.keys()),
+            )
+        )
+
+    @classmethod
+    @replace_list_option_in_docstrings(MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING)
+    @add_start_docstrings(
+        "Instantiate one of the model classes of the library---with a table question answering head---from a "
+        "pretrained model.",
+        AUTO_MODEL_PRETRAINED_DOCSTRING,
+    )
+    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
+        r"""
+        Examples::
+
+            >>> from transformers import AutoConfig, AutoModelForTableQuestionAnswering
+
+            >>> # Download model and configuration from huggingface.co and cache.
+            >>> model = AutoModelForTableQuestionAnswering.from_pretrained('google/tapas-base-finetuned-wtq')
+
+            >>> # Update configuration during loading
+            >>> model = AutoModelForTableQuestionAnswering.from_pretrained('google/tapas-base-finetuned-wtq', output_attentions=True)
+            >>> model.config.output_attentions
+            True
+
+            >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
+            >>> config = AutoConfig.from_json_file('./tf_model/tapas_tf_checkpoint.json')
+            >>> model = AutoModelForQuestionAnswering.from_pretrained('./tf_model/tapas_tf_checkpoint.ckpt.index', from_tf=True, config=config)
+        """
+        config = kwargs.pop("config", None)
+        if not isinstance(config, PretrainedConfig):
+            config, kwargs = AutoConfig.from_pretrained(
+                pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
+            )
+
+        if type(config) in MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.keys():
+            return MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING[type(config)].from_pretrained(
+                pretrained_model_name_or_path, *model_args, config=config, **kwargs
+            )
+
+        raise ValueError(
+            "Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
+            "Model type should be one of {}.".format(
+                config.__class__,
+                cls.__name__,
+                ", ".join(c.__name__ for c in MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.keys()),
+            )
+        )
+
+
 class AutoModelForTokenClassification:
     r"""
     This is a generic model class that will be instantiated as one of the model classes of the library---with a token

src/transformers/utils/dummy_pt_objects.py

@@ -303,6 +303,9 @@ MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING = None
 MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = None
 
 
+MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING = None
+
+
 MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = None
 
 
@@ -393,6 +396,15 @@ class AutoModelForSequenceClassification:
         requires_pytorch(self)
 
 
+class AutoModelForTableQuestionAnswering:
+    def __init__(self, *args, **kwargs):
+        requires_pytorch(self)
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_pytorch(self)
+
+
 class AutoModelForTokenClassification:
     def __init__(self, *args, **kwargs):
         requires_pytorch(self)

tests/test_modeling_auto.py

@@ -17,7 +17,13 @@
 import unittest
 
 from transformers import is_torch_available
-from transformers.testing_utils import DUMMY_UNKWOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, require_torch, slow
+from transformers.testing_utils import (
+    DUMMY_UNKWOWN_IDENTIFIER,
+    SMALL_MODEL_IDENTIFIER,
+    require_scatter,
+    require_torch,
+    slow,
+)
 
 
 if is_torch_available():
@@ -30,6 +36,7 @@ if is_torch_available():
         AutoModelForQuestionAnswering,
         AutoModelForSeq2SeqLM,
         AutoModelForSequenceClassification,
+        AutoModelForTableQuestionAnswering,
         AutoModelForTokenClassification,
         AutoModelWithLMHead,
         BertConfig,
@@ -44,6 +51,8 @@ if is_torch_available():
         RobertaForMaskedLM,
         T5Config,
         T5ForConditionalGeneration,
+        TapasConfig,
+        TapasForQuestionAnswering,
     )
     from transformers.models.auto.modeling_auto import (
         MODEL_FOR_CAUSAL_LM_MAPPING,
@@ -52,6 +61,7 @@ if is_torch_available():
         MODEL_FOR_QUESTION_ANSWERING_MAPPING,
         MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
         MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
+        MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
         MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
         MODEL_MAPPING,
         MODEL_WITH_LM_HEAD_MAPPING,
@@ -59,6 +69,7 @@ if is_torch_available():
     from transformers.models.bert.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_LIST
     from transformers.models.gpt2.modeling_gpt2 import GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
     from transformers.models.t5.modeling_t5 import T5_PRETRAINED_MODEL_ARCHIVE_LIST
+    from transformers.models.tapas.modeling_tapas import TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST
 
 
 @require_torch
@@ -168,6 +179,21 @@ class AutoModelTest(unittest.TestCase):
             self.assertIsNotNone(model)
             self.assertIsInstance(model, BertForQuestionAnswering)
 
+    @slow
+    @require_scatter
+    def test_table_question_answering_model_from_pretrained(self):
+        for model_name in TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
+            config = AutoConfig.from_pretrained(model_name)
+            self.assertIsNotNone(config)
+            self.assertIsInstance(config, TapasConfig)
+
+            model = AutoModelForTableQuestionAnswering.from_pretrained(model_name)
+            model, loading_info = AutoModelForTableQuestionAnswering.from_pretrained(
+                model_name, output_loading_info=True
+            )
+            self.assertIsNotNone(model)
+            self.assertIsInstance(model, TapasForQuestionAnswering)
+
     @slow
     def test_token_classification_model_from_pretrained(self):
         for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
@@ -200,6 +226,7 @@ class AutoModelTest(unittest.TestCase):
             MODEL_MAPPING,
             MODEL_FOR_PRETRAINING_MAPPING,
             MODEL_FOR_QUESTION_ANSWERING_MAPPING,
+            MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
             MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
             MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
             MODEL_WITH_LM_HEAD_MAPPING,

tests/test_modeling_tapas.py

@@ -25,9 +25,9 @@ from transformers import (
     MODEL_FOR_MASKED_LM_MAPPING,
     MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
     MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
-    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
     MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
     MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
+    MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
     MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
     is_torch_available,
 )
@@ -436,7 +436,7 @@ class TapasModelTest(ModelTesterMixin, unittest.TestCase):
         if return_labels:
             if model_class in MODEL_FOR_MULTIPLE_CHOICE_MAPPING.values():
                 inputs_dict["labels"] = torch.ones(self.model_tester.batch_size, dtype=torch.long, device=torch_device)
-            elif model_class in MODEL_FOR_QUESTION_ANSWERING_MAPPING.values():
+            elif model_class in MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.values():
                 inputs_dict["labels"] = torch.zeros(
                     (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
                 )