[Tokenizer Utils Base] Make pad function more flexible (#9928)

* change tokenizer requirement

* split line

* Correct typo from list to str

* improve style

* make other function pretty as well

* add comment

* correct typo

* add new test

* pass tests for tok without padding token

* Apply suggestions from code review

examples/text-classification/run_tf_text_classification.py

@@ -64,7 +64,7 @@ def get_tfds(
     label_name = features_name.pop(label_column_id)
     label_list = list(set(ds[list(files.keys())[0]][label_name]))
     label2id = {label: i for i, label in enumerate(label_list)}
-    input_names = ["input_ids"] + tokenizer.model_input_names
+    input_names = tokenizer.model_input_names
     transformed_ds = {}
 
     if len(features_name) == 1:

src/transformers/data/processors/glue.py

@@ -98,7 +98,7 @@ if is_tf_available():
                 label = d.pop("label")
                 yield (d, label)
 
-        input_names = ["input_ids"] + tokenizer.model_input_names
+        input_names = tokenizer.model_input_names
 
         return tf.data.Dataset.from_generator(
             gen,

src/transformers/models/barthez/tokenization_barthez.py

@@ -97,7 +97,7 @@ class BarthezTokenizer(PreTrainedTokenizer):
     vocab_files_names = VOCAB_FILES_NAMES
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["attention_mask"]
+    model_input_names = ["input_ids", "attention_mask"]
 
     def __init__(
         self,

src/transformers/models/barthez/tokenization_barthez_fast.py

@@ -106,7 +106,7 @@ class BarthezTokenizerFast(PreTrainedTokenizerFast):
     vocab_files_names = VOCAB_FILES_NAMES
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["attention_mask"]
+    model_input_names = ["input_ids", "attention_mask"]
     slow_tokenizer_class = BarthezTokenizer
 
     def __init__(

src/transformers/models/blenderbot_small/tokenization_blenderbot_small.py

@@ -92,7 +92,7 @@ class BlenderbotSmallTokenizer(PreTrainedTokenizer):
         },
     }
     max_model_input_sizes = {"facebook/blenderbot_small-90M": 512}
-    model_input_names = ["attention_mask"]
+    model_input_names = ["input_ids", "attention_mask"]
 
     def __init__(
         self,

src/transformers/models/camembert/tokenization_camembert.py

@@ -100,7 +100,7 @@ class CamembertTokenizer(PreTrainedTokenizer):
     vocab_files_names = VOCAB_FILES_NAMES
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["attention_mask"]
+    model_input_names = ["input_ids", "attention_mask"]
 
     def __init__(
         self,

src/transformers/models/camembert/tokenization_camembert_fast.py

@@ -110,7 +110,7 @@ class CamembertTokenizerFast(PreTrainedTokenizerFast):
     vocab_files_names = VOCAB_FILES_NAMES
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["attention_mask"]
+    model_input_names = ["input_ids", "attention_mask"]
     slow_tokenizer_class = CamembertTokenizer
 
     def __init__(

src/transformers/models/distilbert/tokenization_distilbert.py

@@ -68,4 +68,4 @@ class DistilBertTokenizer(BertTokenizer):
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
-    model_input_names = ["attention_mask"]
+    model_input_names = ["input_ids", "attention_mask"]

src/transformers/models/distilbert/tokenization_distilbert_fast.py

@@ -77,5 +77,5 @@ class DistilBertTokenizerFast(BertTokenizerFast):
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
-    model_input_names = ["attention_mask"]
+    model_input_names = ["input_ids", "attention_mask"]
     slow_tokenizer_class = DistilBertTokenizer

src/transformers/models/dpr/tokenization_dpr.py

@@ -385,4 +385,4 @@ class DPRReaderTokenizer(CustomDPRReaderTokenizerMixin, BertTokenizer):
     pretrained_vocab_files_map = READER_PRETRAINED_VOCAB_FILES_MAP
     max_model_input_sizes = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     pretrained_init_configuration = READER_PRETRAINED_INIT_CONFIGURATION
-    model_input_names = ["attention_mask"]
+    model_input_names = ["input_ids", "attention_mask"]

src/transformers/models/dpr/tokenization_dpr_fast.py

@@ -387,5 +387,5 @@ class DPRReaderTokenizerFast(CustomDPRReaderTokenizerMixin, BertTokenizerFast):
     pretrained_vocab_files_map = READER_PRETRAINED_VOCAB_FILES_MAP
     max_model_input_sizes = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     pretrained_init_configuration = READER_PRETRAINED_INIT_CONFIGURATION
-    model_input_names = ["attention_mask"]
+    model_input_names = ["input_ids", "attention_mask"]
     slow_tokenizer_class = DPRReaderTokenizer

src/transformers/models/fsmt/tokenization_fsmt.py

@@ -177,7 +177,7 @@ class FSMTTokenizer(PreTrainedTokenizer):
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["attention_mask"]
+    model_input_names = ["input_ids", "attention_mask"]
 
     def __init__(
         self,

src/transformers/models/gpt2/tokenization_gpt2.py

@@ -148,7 +148,7 @@ class GPT2Tokenizer(PreTrainedTokenizer):
     vocab_files_names = VOCAB_FILES_NAMES
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["attention_mask"]
+    model_input_names = ["input_ids", "attention_mask"]
 
     def __init__(
         self,

src/transformers/models/gpt2/tokenization_gpt2_fast.py

@@ -116,7 +116,7 @@ class GPT2TokenizerFast(PreTrainedTokenizerFast):
     vocab_files_names = VOCAB_FILES_NAMES
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["attention_mask"]
+    model_input_names = ["input_ids", "attention_mask"]
     slow_tokenizer_class = GPT2Tokenizer
 
     def __init__(

src/transformers/models/marian/tokenization_marian.py

@@ -92,7 +92,7 @@ class MarianTokenizer(PreTrainedTokenizer):
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["attention_mask"]
+    model_input_names = ["input_ids", "attention_mask"]
     language_code_re = re.compile(">>.+<<")  # type: re.Pattern
 
     def __init__(

src/transformers/models/mpnet/tokenization_mpnet.py

@@ -122,7 +122,7 @@ class MPNetTokenizer(PreTrainedTokenizer):
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["attention_mask"]
+    model_input_names = ["input_ids", "attention_mask"]
 
     def __init__(
         self,

src/transformers/models/mpnet/tokenization_mpnet_fast.py

@@ -102,7 +102,7 @@ class MPNetTokenizerFast(PreTrainedTokenizerFast):
     pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     slow_tokenizer_class = MPNetTokenizer
-    model_input_names = ["attention_mask"]
+    model_input_names = ["input_ids", "attention_mask"]
 
     def __init__(
         self,

src/transformers/models/openai/tokenization_openai.py

@@ -94,7 +94,7 @@ class OpenAIGPTTokenizer(PreTrainedTokenizer):
     vocab_files_names = VOCAB_FILES_NAMES
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["attention_mask"]
+    model_input_names = ["input_ids", "attention_mask"]
 
     def __init__(self, vocab_file, merges_file, unk_token="<unk>", **kwargs):
         super().__init__(unk_token=unk_token, **kwargs)

src/transformers/models/openai/tokenization_openai_fast.py

@@ -61,7 +61,7 @@ class OpenAIGPTTokenizerFast(PreTrainedTokenizerFast):
     vocab_files_names = VOCAB_FILES_NAMES
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["attention_mask"]
+    model_input_names = ["input_ids", "attention_mask"]
     slow_tokenizer_class = OpenAIGPTTokenizer
 
     def __init__(self, vocab_file, merges_file, tokenizer_file=None, unk_token="<unk>", **kwargs):

src/transformers/models/pegasus/tokenization_pegasus.py

@@ -84,7 +84,7 @@ class PegasusTokenizer(PreTrainedTokenizer):
     vocab_files_names = VOCAB_FILES_NAMES
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["attention_mask"]
+    model_input_names = ["input_ids", "attention_mask"]
 
     def __init__(
         self,

src/transformers/models/pegasus/tokenization_pegasus_fast.py

@@ -93,7 +93,7 @@ class PegasusTokenizerFast(PreTrainedTokenizerFast):
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     slow_tokenizer_class = PegasusTokenizer
-    model_input_names = ["attention_mask"]
+    model_input_names = ["input_ids", "attention_mask"]
 
     def __init__(
         self,

src/transformers/models/reformer/tokenization_reformer.py

@@ -84,7 +84,7 @@ class ReformerTokenizer(PreTrainedTokenizer):
     vocab_files_names = VOCAB_FILES_NAMES
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["attention_mask"]
+    model_input_names = ["input_ids", "attention_mask"]
 
     def __init__(self, vocab_file, eos_token="</s>", unk_token="<unk>", additional_special_tokens=[], **kwargs):
         super().__init__(

src/transformers/models/reformer/tokenization_reformer_fast.py

@@ -93,7 +93,7 @@ class ReformerTokenizerFast(PreTrainedTokenizerFast):
     vocab_files_names = VOCAB_FILES_NAMES
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["attention_mask"]
+    model_input_names = ["input_ids", "attention_mask"]
     slow_tokenizer_class = ReformerTokenizer
 
     def __init__(

src/transformers/models/retribert/tokenization_retribert.py

@@ -53,4 +53,4 @@ class RetriBertTokenizer(BertTokenizer):
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
-    model_input_names = ["attention_mask"]
+    model_input_names = ["input_ids", "attention_mask"]

src/transformers/models/retribert/tokenization_retribert_fast.py

@@ -58,4 +58,4 @@ class RetriBertTokenizerFast(BertTokenizerFast):
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
     slow_tokenizer_class = RetriBertTokenizer
-    model_input_names = ["attention_mask"]
+    model_input_names = ["input_ids", "attention_mask"]

src/transformers/models/roberta/tokenization_roberta.py

@@ -129,7 +129,7 @@ class RobertaTokenizer(GPT2Tokenizer):
     vocab_files_names = VOCAB_FILES_NAMES
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["attention_mask"]
+    model_input_names = ["input_ids", "attention_mask"]
 
     def __init__(
         self,

src/transformers/models/roberta/tokenization_roberta_fast.py

@@ -138,7 +138,7 @@ class RobertaTokenizerFast(GPT2TokenizerFast):
     vocab_files_names = VOCAB_FILES_NAMES
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["attention_mask"]
+    model_input_names = ["input_ids", "attention_mask"]
     slow_tokenizer_class = RobertaTokenizer
 
     def __init__(

src/transformers/models/t5/tokenization_t5.py

@@ -97,7 +97,7 @@ class T5Tokenizer(PreTrainedTokenizer):
     vocab_files_names = VOCAB_FILES_NAMES
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["attention_mask"]
+    model_input_names = ["input_ids", "attention_mask"]
 
     def __init__(
         self,

src/transformers/models/t5/tokenization_t5_fast.py

@@ -108,7 +108,7 @@ class T5TokenizerFast(PreTrainedTokenizerFast):
     vocab_files_names = VOCAB_FILES_NAMES
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["attention_mask"]
+    model_input_names = ["input_ids", "attention_mask"]
     slow_tokenizer_class = T5Tokenizer
 
     prefix_tokens: List[int] = []

src/transformers/models/transfo_xl/tokenization_transfo_xl.py

@@ -151,7 +151,7 @@ class TransfoXLTokenizer(PreTrainedTokenizer):
     vocab_files_names = VOCAB_FILES_NAMES
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = []
+    model_input_names = ["input_ids"]
 
     def __init__(
         self,

src/transformers/models/xlm_prophetnet/tokenization_xlm_prophetnet.py

@@ -104,7 +104,7 @@ class XLMProphetNetTokenizer(PreTrainedTokenizer):
     vocab_files_names = VOCAB_FILES_NAMES
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["attention_mask"]
+    model_input_names = ["input_ids", "attention_mask"]
 
     def __init__(
         self,

src/transformers/models/xlm_roberta/tokenization_xlm_roberta.py

@@ -102,7 +102,7 @@ class XLMRobertaTokenizer(PreTrainedTokenizer):
     vocab_files_names = VOCAB_FILES_NAMES
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["attention_mask"]
+    model_input_names = ["input_ids", "attention_mask"]
 
     def __init__(
         self,

src/transformers/models/xlm_roberta/tokenization_xlm_roberta_fast.py

@@ -114,7 +114,7 @@ class XLMRobertaTokenizerFast(PreTrainedTokenizerFast):
     vocab_files_names = VOCAB_FILES_NAMES
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["attention_mask"]
+    model_input_names = ["input_ids", "attention_mask"]
     slow_tokenizer_class = XLMRobertaTokenizer
 
     def __init__(

src/transformers/pipelines/question_answering.py

@@ -300,7 +300,7 @@ class QuestionAnsweringPipeline(Pipeline):
 
         all_answers = []
         for features, example in zip(features_list, examples):
-            model_input_names = self.tokenizer.model_input_names + ["input_ids"]
+            model_input_names = self.tokenizer.model_input_names
             fw_args = {k: [feature.__dict__[k] for feature in features] for k in model_input_names}
 
             # Manage tensor allocation on correct device

src/transformers/tokenization_utils_base.py

@@ -1492,7 +1492,10 @@ class PreTrainedTokenizerBase(SpecialTokensMixin):
     pretrained_vocab_files_map: Dict[str, Dict[str, str]] = {}
     pretrained_init_configuration: Dict[str, Dict[str, Any]] = {}
     max_model_input_sizes: Dict[str, Optional[int]] = {}
-    model_input_names: List[str] = ["token_type_ids", "attention_mask"]
+
+    # first name has to correspond to main model input name
+    # to make sure `tokenizer.pad(...)` works correctly
+    model_input_names: List[str] = ["input_ids", "token_type_ids", "attention_mask"]
     padding_side: str = "right"
     slow_tokenizer_class = None
 
@@ -2633,13 +2636,16 @@ class PreTrainedTokenizerBase(SpecialTokensMixin):
         if isinstance(encoded_inputs, (list, tuple)) and isinstance(encoded_inputs[0], (dict, BatchEncoding)):
             encoded_inputs = {key: [example[key] for example in encoded_inputs] for key in encoded_inputs[0].keys()}
 
-        assert "input_ids" in encoded_inputs, (
-            "You should supply an encoding or a list of encodings to this method. "
-            "An encoding is the output of one the encoding methods of the tokenizer, i.e. "
-            "__call__/encode_plus/batch_encode_plus. "
-        )
+        # The model's main input name, usually `input_ids`, has be passed for padding
+        if self.model_input_names[0] not in encoded_inputs:
+            raise ValueError(
+                "You should supply an encoding or a list of encodings to this method"
+                f"that includes {self.model_input_names[0]}, but you provided {list(encoded_inputs.keys())}"
+            )
 
-        if not encoded_inputs["input_ids"]:
+        required_input = encoded_inputs[self.model_input_names[0]]
+
+        if not required_input:
             if return_attention_mask:
                 encoded_inputs["attention_mask"] = []
             return encoded_inputs
@@ -2648,14 +2654,14 @@ class PreTrainedTokenizerBase(SpecialTokensMixin):
         # and rebuild them afterwards if no return_tensors is specified
         # Note that we lose the specific device the tensor may be on for PyTorch
 
-        first_element = encoded_inputs["input_ids"][0]
+        first_element = required_input[0]
         if isinstance(first_element, (list, tuple)):
             # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
             index = 0
-            while len(encoded_inputs["input_ids"][index]) == 0:
+            while len(required_input[index]) == 0:
                 index += 1
-            if index < len(encoded_inputs["input_ids"]):
-                first_element = encoded_inputs["input_ids"][index][0]
+            if index < len(required_input):
+                first_element = required_input[index][0]
         # At this state, if `first_element` is still a list/tuple, it's an empty one so there is nothing to do.
         if not isinstance(first_element, (int, list, tuple)):
             if is_tf_available() and _is_tensorflow(first_element):
@@ -2678,7 +2684,8 @@ class PreTrainedTokenizerBase(SpecialTokensMixin):
             padding=padding, max_length=max_length, verbose=verbose
         )
 
-        if encoded_inputs["input_ids"] and not isinstance(encoded_inputs["input_ids"][0], (list, tuple)):
+        required_input = encoded_inputs[self.model_input_names[0]]
+        if required_input and not isinstance(required_input[0], (list, tuple)):
             encoded_inputs = self._pad(
                 encoded_inputs,
                 max_length=max_length,
@@ -2688,13 +2695,13 @@ class PreTrainedTokenizerBase(SpecialTokensMixin):
             )
             return BatchEncoding(encoded_inputs, tensor_type=return_tensors)
 
-        batch_size = len(encoded_inputs["input_ids"])
+        batch_size = len(required_input)
         assert all(
             len(v) == batch_size for v in encoded_inputs.values()
         ), "Some items in the output dictionary have a different batch size than others."
 
         if padding_strategy == PaddingStrategy.LONGEST:
-            max_length = max(len(inputs) for inputs in encoded_inputs["input_ids"])
+            max_length = max(len(inputs) for inputs in required_input)
             padding_strategy = PaddingStrategy.MAX_LENGTH
 
         batch_outputs = {}
@@ -3004,42 +3011,42 @@ class PreTrainedTokenizerBase(SpecialTokensMixin):
         if return_attention_mask is None:
             return_attention_mask = "attention_mask" in self.model_input_names
 
+        required_input = encoded_inputs[self.model_input_names[0]]
+
         if padding_strategy == PaddingStrategy.LONGEST:
-            max_length = len(encoded_inputs["input_ids"])
+            max_length = len(required_input)
 
         if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
             max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
 
-        needs_to_be_padded = (
-            padding_strategy != PaddingStrategy.DO_NOT_PAD and len(encoded_inputs["input_ids"]) != max_length
-        )
+        needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
 
         if needs_to_be_padded:
-            difference = max_length - len(encoded_inputs["input_ids"])
+            difference = max_length - len(required_input)
             if self.padding_side == "right":
                 if return_attention_mask:
-                    encoded_inputs["attention_mask"] = [1] * len(encoded_inputs["input_ids"]) + [0] * difference
+                    encoded_inputs["attention_mask"] = [1] * len(required_input) + [0] * difference
                 if "token_type_ids" in encoded_inputs:
                     encoded_inputs["token_type_ids"] = (
                         encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference
                     )
                 if "special_tokens_mask" in encoded_inputs:
                     encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference
-                encoded_inputs["input_ids"] = encoded_inputs["input_ids"] + [self.pad_token_id] * difference
+                encoded_inputs[self.model_input_names[0]] = required_input + [self.pad_token_id] * difference
             elif self.padding_side == "left":
                 if return_attention_mask:
-                    encoded_inputs["attention_mask"] = [0] * difference + [1] * len(encoded_inputs["input_ids"])
+                    encoded_inputs["attention_mask"] = [0] * difference + [1] * len(required_input)
                 if "token_type_ids" in encoded_inputs:
                     encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[
                         "token_type_ids"
                     ]
                 if "special_tokens_mask" in encoded_inputs:
                     encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
-                encoded_inputs["input_ids"] = [self.pad_token_id] * difference + encoded_inputs["input_ids"]
+                encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
             else:
                 raise ValueError("Invalid padding strategy:" + str(self.padding_side))
         elif return_attention_mask and "attention_mask" not in encoded_inputs:
-            encoded_inputs["attention_mask"] = [1] * len(encoded_inputs["input_ids"])
+            encoded_inputs["attention_mask"] = [1] * len(required_input)
 
         return encoded_inputs
 

templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/tokenization_{{cookiecutter.lowercase_modelname}}.py

@@ -125,7 +125,7 @@ class {{cookiecutter.camelcase_modelname}}Tokenizer(PreTrainedTokenizer):
     vocab_files_names = VOCAB_FILES_NAMES
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["attention_mask"]
+    model_input_names = ["input_ids", "attention_mask"]
 
     def __init__(
             self,
@@ -270,7 +270,7 @@ class {{cookiecutter.camelcase_modelname}}TokenizerFast(PreTrainedTokenizerFast)
     vocab_files_names = VOCAB_FILES_NAMES
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["attention_mask"]
+    model_input_names = ["input_ids", "attention_mask"]
 
     def __init__(
             self,

tests/test_tokenization_common.py

@@ -160,6 +160,38 @@ class TokenizerTesterMixin:
     #         "This is a test",
     #     )
 
+    def assert_padded_input_match(self, input_r: list, input_p: list, max_length: int, pad_token_id: int):
+        # Ensure we match max_length
+        self.assertEqual(len(input_r), max_length)
+        self.assertEqual(len(input_p), max_length)
+
+        # Ensure the number of padded tokens is the same
+        padded_tokens_r = list(takewhile(lambda i: i == pad_token_id, reversed(input_r)))
+        padded_tokens_p = list(takewhile(lambda i: i == pad_token_id, reversed(input_p)))
+        self.assertSequenceEqual(padded_tokens_r, padded_tokens_p)
+
+    def assert_batch_padded_input_match(
+        self,
+        input_r: dict,
+        input_p: dict,
+        max_length: int,
+        pad_token_id: int,
+        model_main_input_name: str = "input_ids",
+    ):
+        for i_r in input_r.values():
+            self.assertEqual(len(i_r), 2), self.assertEqual(len(i_r[0]), max_length), self.assertEqual(
+                len(i_r[1]), max_length
+            )
+            self.assertEqual(len(i_r), 2), self.assertEqual(len(i_r[0]), max_length), self.assertEqual(
+                len(i_r[1]), max_length
+            )
+
+        for i_r, i_p in zip(input_r[model_main_input_name], input_p[model_main_input_name]):
+            self.assert_padded_input_match(i_r, i_p, max_length, pad_token_id)
+
+        for i_r, i_p in zip(input_r["attention_mask"], input_p["attention_mask"]):
+            self.assertSequenceEqual(i_r, i_p)
+
     @staticmethod
     def convert_batch_encode_plus_format_to_encode_plus(batch_encode_plus_sequences):
         # Switch from batch_encode_plus format:   {'input_ids': [[...], [...]], ...}
@@ -169,6 +201,18 @@ class TokenizerTesterMixin:
             for i in range(len(batch_encode_plus_sequences["input_ids"]))
         ]
 
+    def test_model_input_names_signature(self):
+        accepted_model_main_input_names = [
+            "input_ids",  # nlp models
+            "input_values",  # speech models
+        ]
+
+        tokenizers = self.get_tokenizers()
+        for tokenizer in tokenizers:
+            # first name of model_input_names has to correspond to main model input name
+            # to make sure `tokenizer.pad(...)` works correctly
+            self.assertTrue(tokenizer.model_input_names[0] in accepted_model_main_input_names)
+
     def test_rust_tokenizer_signature(self):
         if not self.test_rust_tokenizer:
             return
@@ -2419,43 +2463,20 @@ class TokenizerTesterMixin:
                 tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
                 tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
 
-                def assert_padded_input_match(input_r: list, input_p: list, max_length: int):
-
-                    # Ensure we match max_length
-                    self.assertEqual(len(input_r), max_length)
-                    self.assertEqual(len(input_p), max_length)
-
-                    # Ensure the number of padded tokens is the same
-                    padded_tokens_r = list(takewhile(lambda i: i == tokenizer_r.pad_token_id, reversed(input_r)))
-                    padded_tokens_p = list(takewhile(lambda i: i == tokenizer_p.pad_token_id, reversed(input_p)))
-                    self.assertSequenceEqual(padded_tokens_r, padded_tokens_p)
-
-                def assert_batch_padded_input_match(input_r: dict, input_p: dict, max_length: int):
-                    for i_r in input_r.values():
-                        self.assertEqual(len(i_r), 2), self.assertEqual(len(i_r[0]), max_length), self.assertEqual(
-                            len(i_r[1]), max_length
-                        )
-                        self.assertEqual(len(i_r), 2), self.assertEqual(len(i_r[0]), max_length), self.assertEqual(
-                            len(i_r[1]), max_length
-                        )
-
-                    for i_r, i_p in zip(input_r["input_ids"], input_p["input_ids"]):
-                        assert_padded_input_match(i_r, i_p, max_length)
-
-                    for i_r, i_p in zip(input_r["attention_mask"], input_p["attention_mask"]):
-                        self.assertSequenceEqual(i_r, i_p)
+                self.assertEqual(tokenizer_p.pad_token_id, tokenizer_r.pad_token_id)
+                pad_token_id = tokenizer_p.pad_token_id
 
                 # Encode - Simple input
                 input_r = tokenizer_r.encode("This is a simple input", max_length=max_length, pad_to_max_length=True)
                 input_p = tokenizer_p.encode("This is a simple input", max_length=max_length, pad_to_max_length=True)
-                assert_padded_input_match(input_r, input_p, max_length)
+                self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
                 input_r = tokenizer_r.encode("This is a simple input", max_length=max_length, padding="max_length")
                 input_p = tokenizer_p.encode("This is a simple input", max_length=max_length, padding="max_length")
-                assert_padded_input_match(input_r, input_p, max_length)
+                self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
 
                 input_r = tokenizer_r.encode("This is a simple input", padding="longest")
                 input_p = tokenizer_p.encode("This is a simple input", padding=True)
-                assert_padded_input_match(input_r, input_p, len(input_r))
+                self.assert_padded_input_match(input_r, input_p, len(input_r), pad_token_id)
 
                 # Encode - Pair input
                 input_r = tokenizer_r.encode(
@@ -2464,17 +2485,17 @@ class TokenizerTesterMixin:
                 input_p = tokenizer_p.encode(
                     "This is a simple input", "This is a pair", max_length=max_length, pad_to_max_length=True
                 )
-                assert_padded_input_match(input_r, input_p, max_length)
+                self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
                 input_r = tokenizer_r.encode(
                     "This is a simple input", "This is a pair", max_length=max_length, padding="max_length"
                 )
                 input_p = tokenizer_p.encode(
                     "This is a simple input", "This is a pair", max_length=max_length, padding="max_length"
                 )
-                assert_padded_input_match(input_r, input_p, max_length)
+                self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
                 input_r = tokenizer_r.encode("This is a simple input", "This is a pair", padding=True)
                 input_p = tokenizer_p.encode("This is a simple input", "This is a pair", padding="longest")
-                assert_padded_input_match(input_r, input_p, len(input_r))
+                self.assert_padded_input_match(input_r, input_p, len(input_r), pad_token_id)
 
                 # Encode_plus - Simple input
                 input_r = tokenizer_r.encode_plus(
@@ -2483,7 +2504,7 @@ class TokenizerTesterMixin:
                 input_p = tokenizer_p.encode_plus(
                     "This is a simple input", max_length=max_length, pad_to_max_length=True
                 )
-                assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length)
+                self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
                 self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
                 input_r = tokenizer_r.encode_plus(
                     "This is a simple input", max_length=max_length, padding="max_length"
@@ -2491,12 +2512,14 @@ class TokenizerTesterMixin:
                 input_p = tokenizer_p.encode_plus(
                     "This is a simple input", max_length=max_length, padding="max_length"
                 )
-                assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length)
+                self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
                 self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
 
                 input_r = tokenizer_r.encode_plus("This is a simple input", padding="longest")
                 input_p = tokenizer_p.encode_plus("This is a simple input", padding=True)
-                assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]))
+                self.assert_padded_input_match(
+                    input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id
+                )
 
                 self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
 
@@ -2507,7 +2530,7 @@ class TokenizerTesterMixin:
                 input_p = tokenizer_p.encode_plus(
                     "This is a simple input", "This is a pair", max_length=max_length, pad_to_max_length=True
                 )
-                assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length)
+                self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
                 self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
                 input_r = tokenizer_r.encode_plus(
                     "This is a simple input", "This is a pair", max_length=max_length, padding="max_length"
@@ -2515,11 +2538,13 @@ class TokenizerTesterMixin:
                 input_p = tokenizer_p.encode_plus(
                     "This is a simple input", "This is a pair", max_length=max_length, padding="max_length"
                 )
-                assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length)
+                self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
                 self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
                 input_r = tokenizer_r.encode_plus("This is a simple input", "This is a pair", padding="longest")
                 input_p = tokenizer_p.encode_plus("This is a simple input", "This is a pair", padding=True)
-                assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]))
+                self.assert_padded_input_match(
+                    input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id
+                )
                 self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
 
                 # Batch_encode_plus - Simple input
@@ -2533,7 +2558,7 @@ class TokenizerTesterMixin:
                     max_length=max_length,
                     pad_to_max_length=True,
                 )
-                assert_batch_padded_input_match(input_r, input_p, max_length)
+                self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
 
                 input_r = tokenizer_r.batch_encode_plus(
                     ["This is a simple input 1", "This is a simple input 2"],
@@ -2545,7 +2570,7 @@ class TokenizerTesterMixin:
                     max_length=max_length,
                     padding="max_length",
                 )
-                assert_batch_padded_input_match(input_r, input_p, max_length)
+                self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
 
                 input_r = tokenizer_r.batch_encode_plus(
                     ["This is a simple input 1", "This is a simple input 2"],
@@ -2557,7 +2582,7 @@ class TokenizerTesterMixin:
                     max_length=max_length,
                     padding=True,
                 )
-                assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]))
+                self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)
 
                 input_r = tokenizer_r.batch_encode_plus(
                     ["This is a simple input 1", "This is a simple input 2"], padding="longest"
@@ -2565,7 +2590,7 @@ class TokenizerTesterMixin:
                 input_p = tokenizer_p.batch_encode_plus(
                     ["This is a simple input 1", "This is a simple input 2"], padding=True
                 )
-                assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]))
+                self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)
 
                 # Batch_encode_plus - Pair input
                 input_r = tokenizer_r.batch_encode_plus(
@@ -2586,7 +2611,7 @@ class TokenizerTesterMixin:
                     truncation=True,
                     padding="max_length",
                 )
-                assert_batch_padded_input_match(input_r, input_p, max_length)
+                self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
 
                 input_r = tokenizer_r.batch_encode_plus(
                     [
@@ -2602,7 +2627,7 @@ class TokenizerTesterMixin:
                     ],
                     padding="longest",
                 )
-                assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]))
+                self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)
 
                 # Using pad on single examples after tokenization
                 input_r = tokenizer_r.encode_plus("This is a input 1")
@@ -2611,7 +2636,9 @@ class TokenizerTesterMixin:
                 input_p = tokenizer_r.encode_plus("This is a input 1")
                 input_p = tokenizer_r.pad(input_p)
 
-                assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]))
+                self.assert_padded_input_match(
+                    input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id
+                )
 
                 # Using pad on single examples after tokenization
                 input_r = tokenizer_r.encode_plus("This is a input 1")
@@ -2620,7 +2647,7 @@ class TokenizerTesterMixin:
                 input_p = tokenizer_r.encode_plus("This is a input 1")
                 input_p = tokenizer_r.pad(input_p, max_length=max_length, padding="max_length")
 
-                assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length)
+                self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
 
                 # Using pad after tokenization
                 input_r = tokenizer_r.batch_encode_plus(
@@ -2633,7 +2660,7 @@ class TokenizerTesterMixin:
                 )
                 input_p = tokenizer_r.pad(input_p)
 
-                assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]))
+                self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)
 
                 # Using pad after tokenization
                 input_r = tokenizer_r.batch_encode_plus(
@@ -2646,7 +2673,41 @@ class TokenizerTesterMixin:
                 )
                 input_p = tokenizer_r.pad(input_p, max_length=max_length, padding="max_length")
 
-                assert_batch_padded_input_match(input_r, input_p, max_length)
+                self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
+
+    def test_padding_different_model_input_name(self):
+        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
+            with self.subTest("{} ({})".format(tokenizer.__class__.__name__, pretrained_name)):
+                tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
+                tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
+                self.assertEqual(tokenizer_p.pad_token_id, tokenizer_r.pad_token_id)
+                pad_token_id = tokenizer_p.pad_token_id
+
+                input_r = tokenizer_r.batch_encode_plus(
+                    ["This is a input 1", "This is a much longer input whilch should be padded"]
+                )
+                input_p = tokenizer_r.batch_encode_plus(
+                    ["This is a input 1", "This is a much longer input whilch should be padded"]
+                )
+
+                # rename encoded batch to "inputs"
+                input_r["inputs"] = input_r[tokenizer_r.model_input_names[0]]
+                del input_r[tokenizer_r.model_input_names[0]]
+
+                input_p["inputs"] = input_p[tokenizer_p.model_input_names[0]]
+                del input_p[tokenizer_p.model_input_names[0]]
+
+                # Renaming `input_ids` to `inputs`
+                tokenizer_r.model_input_names = ["inputs"] + tokenizer_r.model_input_names[1:]
+                tokenizer_p.model_input_names = ["inputs"] + tokenizer_p.model_input_names[1:]
+
+                input_r = tokenizer_r.pad(input_r, padding="longest")
+                input_p = tokenizer_r.pad(input_p, padding="longest")
+
+                max_length = len(input_p["inputs"][0])
+                self.assert_batch_padded_input_match(
+                    input_r, input_p, max_length, pad_token_id, model_main_input_name="inputs"
+                )
 
     def test_save_pretrained(self):
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:

tests/test_tokenization_gpt2.py

@@ -174,3 +174,7 @@ class GPT2TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
                     max_length=max_length,
                     padding="max_length",
                 )
+
+    # tokenizer has no padding token
+    def test_padding_different_model_input_name(self):
+        pass

tests/test_tokenization_openai.py

@@ -128,3 +128,7 @@ class OpenAIGPTTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
                     max_length=max_length,
                     padding="max_length",
                 )
+
+    # tokenizer has no padding token
+    def test_padding_different_model_input_name(self):
+        pass

tests/test_tokenization_reformer.py

@@ -107,6 +107,10 @@ class ReformerTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
                     padding="max_length",
                 )
 
+    # tokenizer has no padding token
+    def test_padding_different_model_input_name(self):
+        pass
+
     def test_full_tokenizer(self):
         tokenizer = ReformerTokenizer(SAMPLE_VOCAB, keep_accents=True)