Add onnx export of models with a multiple choice classification head (#16758)

* Add export of models with a multiple-choice classification head

src/transformers/models/albert/configuration_albert.py

@@ -159,10 +159,14 @@ class AlbertConfig(PretrainedConfig):
 class AlbertOnnxConfig(OnnxConfig):
     @property
     def inputs(self) -> Mapping[str, Mapping[int, str]]:
+        if self.task == "multiple-choice":
+            dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
+        else:
+            dynamic_axis = {0: "batch", 1: "sequence"}
         return OrderedDict(
             [
-                ("input_ids", {0: "batch", 1: "sequence"}),
-                ("attention_mask", {0: "batch", 1: "sequence"}),
-                ("token_type_ids", {0: "batch", 1: "sequence"}),
+                ("input_ids", dynamic_axis),
+                ("attention_mask", dynamic_axis),
+                ("token_type_ids", dynamic_axis),
             ]
         )

src/transformers/models/bert/configuration_bert.py

@@ -160,10 +160,14 @@ class BertConfig(PretrainedConfig):
 class BertOnnxConfig(OnnxConfig):
     @property
     def inputs(self) -> Mapping[str, Mapping[int, str]]:
+        if self.task == "multiple-choice":
+            dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
+        else:
+            dynamic_axis = {0: "batch", 1: "sequence"}
         return OrderedDict(
             [
-                ("input_ids", {0: "batch", 1: "sequence"}),
-                ("attention_mask", {0: "batch", 1: "sequence"}),
-                ("token_type_ids", {0: "batch", 1: "sequence"}),
+                ("input_ids", dynamic_axis),
+                ("attention_mask", dynamic_axis),
+                ("token_type_ids", dynamic_axis),
             ]
         )

src/transformers/models/big_bird/configuration_big_bird.py

@@ -168,9 +168,13 @@ class BigBirdConfig(PretrainedConfig):
 class BigBirdOnnxConfig(OnnxConfig):
     @property
     def inputs(self) -> Mapping[str, Mapping[int, str]]:
+        if self.task == "multiple-choice":
+            dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
+        else:
+            dynamic_axis = {0: "batch", 1: "sequence"}
         return OrderedDict(
             [
-                ("input_ids", {0: "batch", 1: "sequence"}),
-                ("attention_mask", {0: "batch", 1: "sequence"}),
+                ("input_ids", dynamic_axis),
+                ("attention_mask", dynamic_axis),
             ]
         )

src/transformers/models/camembert/configuration_camembert.py

@@ -44,9 +44,13 @@ class CamembertConfig(RobertaConfig):
 class CamembertOnnxConfig(OnnxConfig):
     @property
     def inputs(self) -> Mapping[str, Mapping[int, str]]:
+        if self.task == "multiple-choice":
+            dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
+        else:
+            dynamic_axis = {0: "batch", 1: "sequence"}
         return OrderedDict(
             [
-                ("input_ids", {0: "batch", 1: "sequence"}),
-                ("attention_mask", {0: "batch", 1: "sequence"}),
+                ("input_ids", dynamic_axis),
+                ("attention_mask", dynamic_axis),
             ]
         )

src/transformers/models/data2vec/configuration_data2vec_text.py

@@ -139,9 +139,13 @@ class Data2VecTextConfig(PretrainedConfig):
 class Data2VecTextOnnxConfig(OnnxConfig):
     @property
     def inputs(self) -> Mapping[str, Mapping[int, str]]:
+        if self.task == "multiple-choice":
+            dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
+        else:
+            dynamic_axis = {0: "batch", 1: "sequence"}
         return OrderedDict(
             [
-                ("input_ids", {0: "batch", 1: "sequence"}),
-                ("attention_mask", {0: "batch", 1: "sequence"}),
+                ("input_ids", dynamic_axis),
+                ("attention_mask", dynamic_axis),
             ]
         )

src/transformers/models/distilbert/configuration_distilbert.py

@@ -134,9 +134,13 @@ class DistilBertConfig(PretrainedConfig):
 class DistilBertOnnxConfig(OnnxConfig):
     @property
     def inputs(self) -> Mapping[str, Mapping[int, str]]:
+        if self.task == "multiple-choice":
+            dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
+        else:
+            dynamic_axis = {0: "batch", 1: "sequence"}
         return OrderedDict(
             [
-                ("input_ids", {0: "batch", 1: "sequence"}),
-                ("attention_mask", {0: "batch", 1: "sequence"}),
+                ("input_ids", dynamic_axis),
+                ("attention_mask", dynamic_axis),
             ]
         )

src/transformers/models/electra/configuration_electra.py

@@ -179,10 +179,14 @@ class ElectraConfig(PretrainedConfig):
 class ElectraOnnxConfig(OnnxConfig):
     @property
     def inputs(self) -> Mapping[str, Mapping[int, str]]:
+        if self.task == "multiple-choice":
+            dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
+        else:
+            dynamic_axis = {0: "batch", 1: "sequence"}
         return OrderedDict(
             [
-                ("input_ids", {0: "batch", 1: "sequence"}),
-                ("attention_mask", {0: "batch", 1: "sequence"}),
-                ("token_type_ids", {0: "batch", 1: "sequence"}),
+                ("input_ids", dynamic_axis),
+                ("attention_mask", dynamic_axis),
+                ("token_type_ids", dynamic_axis),
             ]
         )

src/transformers/models/flaubert/configuration_flaubert.py

@@ -146,9 +146,13 @@ class FlaubertConfig(XLMConfig):
 class FlaubertOnnxConfig(OnnxConfig):
     @property
     def inputs(self) -> Mapping[str, Mapping[int, str]]:
+        if self.task == "multiple-choice":
+            dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
+        else:
+            dynamic_axis = {0: "batch", 1: "sequence"}
         return OrderedDict(
             [
-                ("input_ids", {0: "batch", 1: "sequence"}),
-                ("attention_mask", {0: "batch", 1: "sequence"}),
+                ("input_ids", dynamic_axis),
+                ("attention_mask", dynamic_axis),
             ]
         )

src/transformers/models/gpt2/configuration_gpt2.py

@@ -234,7 +234,7 @@ class GPT2OnnxConfig(OnnxConfigWithPast):
         framework: Optional[TensorType] = None,
     ) -> Mapping[str, Any]:
         common_inputs = super(OnnxConfigWithPast, self).generate_dummy_inputs(
-            tokenizer, batch_size, seq_length, is_pair, framework
+            tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
         )
 
         # We need to order the input in the way they appears in the forward()

src/transformers/models/gpt_neo/configuration_gpt_neo.py

@@ -233,7 +233,7 @@ class GPTNeoOnnxConfig(OnnxConfigWithPast):
     ) -> Mapping[str, Any]:
 
         common_inputs = super(OnnxConfigWithPast, self).generate_dummy_inputs(
-            tokenizer, batch_size, seq_length, is_pair, framework
+            tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
         )
 
         # We need to order the input in the way they appears in the forward()

src/transformers/models/gptj/configuration_gptj.py

@@ -183,7 +183,7 @@ class GPTJOnnxConfig(OnnxConfigWithPast):
         framework: Optional[TensorType] = None,
     ) -> Mapping[str, Any]:
         common_inputs = super(OnnxConfigWithPast, self).generate_dummy_inputs(
-            tokenizer, batch_size, seq_length, is_pair, framework
+            tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
         )
 
         # We need to order the input in the way they appears in the forward()

src/transformers/models/ibert/configuration_ibert.py

@@ -131,9 +131,13 @@ class IBertConfig(PretrainedConfig):
 class IBertOnnxConfig(OnnxConfig):
     @property
     def inputs(self) -> Mapping[str, Mapping[int, str]]:
+        if self.task == "multiple-choice":
+            dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
+        else:
+            dynamic_axis = {0: "batch", 1: "sequence"}
         return OrderedDict(
             [
-                ("input_ids", {0: "batch", 1: "sequence"}),
-                ("attention_mask", {0: "batch", 1: "sequence"}),
+                ("input_ids", dynamic_axis),
+                ("attention_mask", dynamic_axis),
             ]
         )

src/transformers/models/layoutlm/configuration_layoutlm.py

@@ -171,7 +171,9 @@ class LayoutLMOnnxConfig(OnnxConfig):
             Mapping[str, Tensor] holding the kwargs to provide to the model's forward function
         """
 
-        input_dict = super().generate_dummy_inputs(tokenizer, batch_size, seq_length, is_pair, framework)
+        input_dict = super().generate_dummy_inputs(
+            tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
+        )
 
         # Generate a dummy bbox
         box = [48, 84, 73, 128]

src/transformers/models/roberta/configuration_roberta.py

@@ -70,9 +70,13 @@ class RobertaConfig(BertConfig):
 class RobertaOnnxConfig(OnnxConfig):
     @property
     def inputs(self) -> Mapping[str, Mapping[int, str]]:
+        if self.task == "multiple-choice":
+            dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
+        else:
+            dynamic_axis = {0: "batch", 1: "sequence"}
         return OrderedDict(
             [
-                ("input_ids", {0: "batch", 1: "sequence"}),
-                ("attention_mask", {0: "batch", 1: "sequence"}),
+                ("input_ids", dynamic_axis),
+                ("attention_mask", dynamic_axis),
             ]
         )

src/transformers/models/xlm_roberta/configuration_xlm_roberta.py

@@ -47,9 +47,13 @@ class XLMRobertaConfig(RobertaConfig):
 class XLMRobertaOnnxConfig(OnnxConfig):
     @property
     def inputs(self) -> Mapping[str, Mapping[int, str]]:
+        if self.task == "multiple-choice":
+            dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
+        else:
+            dynamic_axis = {0: "batch", 1: "sequence"}
         return OrderedDict(
             [
-                ("input_ids", {0: "batch", 1: "sequence"}),
-                ("attention_mask", {0: "batch", 1: "sequence"}),
+                ("input_ids", dynamic_axis),
+                ("attention_mask", dynamic_axis),
             ]
         )

src/transformers/models/xlm_roberta_xl/configuration_xlm_roberta_xl.py

@@ -143,9 +143,13 @@ class XLMRobertaXLConfig(PretrainedConfig):
 class XLMRobertaXLOnnxConfig(OnnxConfig):
     @property
     def inputs(self) -> Mapping[str, Mapping[int, str]]:
+        if self.task == "multiple-choice":
+            dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
+        else:
+            dynamic_axis = {0: "batch", 1: "sequence"}
         return OrderedDict(
             [
-                ("input_ids", {0: "batch", 1: "sequence"}),
-                ("attention_mask", {0: "batch", 1: "sequence"}),
+                ("input_ids", dynamic_axis),
+                ("attention_mask", dynamic_axis),
             ]
         )

src/transformers/onnx/config.py

@@ -71,6 +71,7 @@ class OnnxConfig(ABC):
 
     default_fixed_batch = 2
     default_fixed_sequence = 8
+    default_fixed_num_choices = 4
     torch_onnx_minimum_version = version.parse("1.8")
     _tasks_to_common_outputs = {
         "default": OrderedDict({"last_hidden_state": {0: "batch", 1: "sequence"}}),
@@ -174,6 +175,16 @@ class OnnxConfig(ABC):
         """
         return OnnxConfig.default_fixed_sequence
 
+    @property
+    def default_num_choices(self) -> int:
+        """
+        The default number of choices to use if no other indication
+
+        Returns:
+            Integer > 0
+        """
+        return OnnxConfig.default_fixed_num_choices
+
     @property
     def default_onnx_opset(self) -> int:
         """
@@ -240,6 +251,7 @@ class OnnxConfig(ABC):
         preprocessor: Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"],
         batch_size: int = -1,
         seq_length: int = -1,
+        num_choices: int = -1,
         is_pair: bool = False,
         framework: Optional[TensorType] = None,
         num_channels: int = 3,
@@ -255,6 +267,8 @@ class OnnxConfig(ABC):
                 The preprocessor associated with this model configuration.
             batch_size (`int`, *optional*, defaults to -1):
                 The batch size to export the model for (-1 means dynamic axis).
+            num_choices (`int`, *optional*, defaults to -1):
+                The number of candidate answers provided for multiple choice task (-1 means dynamic axis).
             seq_length (`int`, *optional*, defaults to -1):
                 The sequence length to export the model for (-1 means dynamic axis).
             is_pair (`bool`, *optional*, defaults to `False`):
@@ -295,6 +309,19 @@ class OnnxConfig(ABC):
             )
             # Generate dummy inputs according to compute batch and sequence
             dummy_input = [" ".join([preprocessor.unk_token]) * seq_length] * batch_size
+            if self.task == "multiple-choice":
+                # If dynamic axis (-1) we forward with a fixed dimension of 4 candidate answers to avoid optimizations
+                # made by ONNX
+                num_choices = compute_effective_axis_dimension(
+                    num_choices, fixed_dimension=OnnxConfig.default_fixed_num_choices, num_token_to_add=0
+                )
+                dummy_input = dummy_input * num_choices
+                # The shape of the tokenized inputs values is [batch_size * num_choices, seq_length]
+                tokenized_input = preprocessor(dummy_input, text_pair=dummy_input)
+                # Unflatten the tokenized inputs values expanding it to the shape [batch_size, num_choices, seq_length]
+                for k, v in tokenized_input.items():
+                    tokenized_input[k] = [v[i : i + num_choices] for i in range(0, len(v), num_choices)]
+                return dict(tokenized_input.convert_to_tensors(tensor_type=framework))
             return dict(preprocessor(dummy_input, return_tensors=framework))
         elif isinstance(preprocessor, FeatureExtractionMixin) and preprocessor.model_input_names[0] == "pixel_values":
             # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
@@ -408,7 +435,9 @@ class OnnxConfigWithPast(OnnxConfig, ABC):
     ) -> Mapping[str, Any]:
 
         # TODO: should we set seq_length = 1 when self.use_past = True?
-        common_inputs = super().generate_dummy_inputs(tokenizer, batch_size, seq_length, is_pair, framework)
+        common_inputs = super().generate_dummy_inputs(
+            tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
+        )
 
         if self.use_past:
             if not is_torch_available():
@@ -527,13 +556,13 @@ class OnnxSeq2SeqConfigWithPast(OnnxConfigWithPast):
     ) -> Mapping[str, Any]:
 
         encoder_inputs = super(OnnxConfigWithPast, self).generate_dummy_inputs(
-            tokenizer, batch_size, seq_length, is_pair, framework
+            tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
         )
 
         # Generate decoder inputs
         decoder_seq_length = seq_length if not self.use_past else 1
         decoder_inputs = super(OnnxConfigWithPast, self).generate_dummy_inputs(
-            tokenizer, batch_size, decoder_seq_length, is_pair, framework
+            tokenizer, batch_size=batch_size, seq_length=decoder_seq_length, is_pair=is_pair, framework=framework
         )
         decoder_inputs = {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
         common_inputs = dict(**encoder_inputs, **decoder_inputs)

src/transformers/onnx/features.py

@@ -10,6 +10,7 @@ from ..models.big_bird import BigBirdOnnxConfig
 from ..models.blenderbot import BlenderbotOnnxConfig
 from ..models.blenderbot_small import BlenderbotSmallOnnxConfig
 from ..models.camembert import CamembertOnnxConfig
+from ..models.data2vec import Data2VecTextOnnxConfig
 from ..models.distilbert import DistilBertOnnxConfig
 from ..models.electra import ElectraOnnxConfig
 from ..models.flaubert import FlaubertOnnxConfig
@@ -120,7 +121,7 @@ class FeaturesManager:
             "default",
             "masked-lm",
             "sequence-classification",
-            # "multiple-choice",
+            "multiple-choice",
             "token-classification",
             "question-answering",
             onnx_config_cls=AlbertOnnxConfig,
@@ -152,7 +153,7 @@ class FeaturesManager:
             "masked-lm",
             "causal-lm",
             "sequence-classification",
-            # "multiple-choice",
+            "multiple-choice",
             "token-classification",
             "question-answering",
             onnx_config_cls=BertOnnxConfig,
@@ -162,6 +163,7 @@ class FeaturesManager:
             "masked-lm",
             "causal-lm",
             "sequence-classification",
+            "multiple-choice",
             "token-classification",
             "question-answering",
             onnx_config_cls=BigBirdOnnxConfig,
@@ -170,7 +172,7 @@ class FeaturesManager:
             "default",
             "masked-lm",
             "sequence-classification",
-            # "multiple-choice",
+            "multiple-choice",
             "token-classification",
             "question-answering",
             onnx_config_cls=IBertOnnxConfig,
@@ -180,7 +182,7 @@ class FeaturesManager:
             "masked-lm",
             "causal-lm",
             "sequence-classification",
-            # "multiple-choice",
+            "multiple-choice",
             "token-classification",
             "question-answering",
             onnx_config_cls=CamembertOnnxConfig,
@@ -189,7 +191,7 @@ class FeaturesManager:
             "default",
             "masked-lm",
             "sequence-classification",
-            # "multiple-choice",
+            "multiple-choice",
             "token-classification",
             "question-answering",
             onnx_config_cls=DistilBertOnnxConfig,
@@ -199,6 +201,7 @@ class FeaturesManager:
             "masked-lm",
             "causal-lm",
             "sequence-classification",
+            "multiple-choice",
             "token-classification",
             "question-answering",
             onnx_config_cls=FlaubertOnnxConfig,
@@ -220,7 +223,7 @@ class FeaturesManager:
             "masked-lm",
             "causal-lm",
             "sequence-classification",
-            # "multiple-choice",
+            "multiple-choice",
             "token-classification",
             "question-answering",
             onnx_config_cls=RobertaOnnxConfig,
@@ -233,7 +236,7 @@ class FeaturesManager:
             "masked-lm",
             "causal-lm",
             "sequence-classification",
-            # "multiple-choice",
+            "multiple-choice",
             "token-classification",
             "question-answering",
             onnx_config_cls=XLMRobertaOnnxConfig,
@@ -276,6 +279,7 @@ class FeaturesManager:
             "masked-lm",
             "causal-lm",
             "sequence-classification",
+            "multiple-choice",
             "token-classification",
             "question-answering",
             onnx_config_cls=ElectraOnnxConfig,
@@ -300,6 +304,15 @@ class FeaturesManager:
             "seq2seq-lm-with-past",
             onnx_config_cls=BlenderbotSmallOnnxConfig,
         ),
+        "data2vec-text": supported_features_mapping(
+            "default",
+            "masked-lm",
+            "sequence-classification",
+            "multiple-choice",
+            "token-classification",
+            "question-answering",
+            onnx_config_cls=Data2VecTextOnnxConfig,
+        ),
     }
 
     AVAILABLE_FEATURES = sorted(reduce(lambda s1, s2: s1 | s2, (v.keys() for v in _SUPPORTED_MODEL_TYPE.values())))

tests/onnx/test_onnx_v2.py

@@ -182,6 +182,7 @@ PYTORCH_EXPORT_MODELS = {
     ("layoutlm", "microsoft/layoutlm-base-uncased"),
     ("vit", "google/vit-base-patch16-224"),
     ("beit", "microsoft/beit-base-patch16-224"),
+    ("data2vec-text", "facebook/data2vec-text-base"),
 }
 
 PYTORCH_EXPORT_WITH_PAST_MODELS = {