Always truncate argument in the encode method

transformers/tests/tokenization_tests_commons.py

@@ -232,6 +232,23 @@ class CommonTestCases:
             assert len(truncated_sequence) == total_length - 2
             assert truncated_sequence == tokenizer.add_special_tokens_single_sequence(sequence[:-2])
 
+        def test_always_truncate(self):
+            tokenizer = self.get_tokenizer()
+
+            seq_0 = "This is a sentence to be encoded."
+            length_single_sequence = len(tokenizer.encode(seq_0))
+            length = len(tokenizer.encode(seq_0, seq_0, add_special_tokens=True))
+
+            not_truncated = tokenizer.encode(seq_0, seq_0, add_special_tokens=True, max_length=length_single_sequence)
+            truncated = tokenizer.encode(
+                seq_0, seq_0,
+                max_length=length_single_sequence,
+                add_special_tokens=True,
+                always_truncate=True
+            )
+
+            assert truncated == not_truncated[:length_single_sequence - length]
+
         def test_maximum_encoding_length_pair_input(self):
             tokenizer = self.get_tokenizer()
 

transformers/tokenization_utils.py

@@ -693,14 +693,15 @@ class PreTrainedTokenizer(object):
         raise NotImplementedError
 
     def encode(self,
-                text,
-                text_pair=None,
-                add_special_tokens=False,
-                max_length=None,
-                stride=0,
-                truncate_first_sequence=True,
-                return_tensors=None,
-                **kwargs):
+               text,
+               text_pair=None,
+               add_special_tokens=False,
+               max_length=None,
+               stride=0,
+               truncate_first_sequence=True,
+               return_tensors=None,
+               always_truncate=False,
+               **kwargs):
         """
         Converts a string in a sequence of ids (integer), using the tokenizer and vocabulary.
 
@@ -721,6 +722,8 @@ class PreTrainedTokenizer(object):
                 from the main sequence returned. The value of this argument defined the number of additional tokens.
             truncate_first_sequence: if there is a specified max_length, this flag will choose which sequence
                 will be truncated.
+            always_truncate: if set to True, will always truncate the sequences when overflowing, even if one of the
+                sequences may be lost in the process.
             return_tensors: (optional) can be set to 'tf' or 'pt' to return respectively TensorFlow tf.constant
                 or PyTorch torch.Tensor instead of a list of python integers.
             **kwargs: passed to the `self.tokenize()` method
@@ -732,6 +735,7 @@ class PreTrainedTokenizer(object):
                                           stride=stride,
                                           truncate_first_sequence=truncate_first_sequence,
                                           return_tensors=return_tensors,
+                                          always_truncate=always_truncate,
                                           **kwargs)
 
         return encoded_inputs["input_ids"]
@@ -744,6 +748,7 @@ class PreTrainedTokenizer(object):
                     stride=0,
                     truncate_first_sequence=True,
                     return_tensors=None,
+                    always_truncate=False,
                     **kwargs):
         """
         Returns a dictionary containing the encoded sequence or sequence pair and additional informations:
@@ -764,6 +769,8 @@ class PreTrainedTokenizer(object):
                 from the main sequence returned. The value of this argument defined the number of additional tokens.
             truncate_first_sequence: if there is a specified max_length, this flag will choose which sequence
                 will be truncated.
+            always_truncate: if set to True, will always truncate the sequences when overflowing, even if one of the
+                sequences may be lost in the process.
             return_tensors: (optional) can be set to 'tf' or 'pt' to return respectively TensorFlow tf.constant
                 or PyTorch torch.Tensor instead of a list of python integers.
             **kwargs: passed to the `self.tokenize()` method
@@ -788,11 +795,12 @@ class PreTrainedTokenizer(object):
                                       add_special_tokens=add_special_tokens,
                                       stride=stride,
                                       truncate_first_sequence=truncate_first_sequence,
+                                      always_truncate=always_truncate,
                                       return_tensors=return_tensors)
 
 
     def prepare_for_model(self, ids, pair_ids=None, max_length=None, add_special_tokens=False, stride=0,
-                          truncate_first_sequence=True, return_tensors=None):
+                          truncate_first_sequence=True, always_truncate=False, return_tensors=None):
         """
         Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model.
         It adds special tokens, truncates
@@ -812,6 +820,8 @@ class PreTrainedTokenizer(object):
             truncate_first_sequence: if set to `True` and an optional second list of input ids is provided,
                 alongside a specified `max_length`, will truncate the first sequence if the total size is superior
                 than the specified `max_length`. If set to `False`, will truncate the second sequence instead.
+            always_truncate: if set to True, will always truncate the sequences when overflowing, even if one of the
+                sequences may be lost in the process.
             return_tensors: (optional) can be set to 'tf' or 'pt' to return respectively TensorFlow tf.constant
                 or PyTorch torch.Tensor instead of a list of python integers.
 
@@ -826,9 +836,14 @@ class PreTrainedTokenizer(object):
         if max_length:
             n_added_tokens = self.num_added_tokens(pair=pair) if add_special_tokens else 0
             if pair and n_added_tokens + (len_pair_ids if truncate_first_sequence else len_ids) >= max_length:
-                logger.warning(
-                    "You supplied a pair of sequence in which the sequence that will not be truncated is longer than the maximum specified length."
-                    "This pair of sequences will not be truncated.")
+                if always_truncate:
+                    logger.warning(
+                        "You supplied a pair of sequence in which the sequence that will not be truncated is longer than the maximum specified length. "
+                        "This pair of sequences will be truncated but one of the sequences may not be present in the resulting list of ids.")
+                else:
+                    logger.warning(
+                        "You supplied a pair of sequence in which the sequence that will not be truncated is longer than the maximum specified length. "
+                        "This pair of sequences will not be truncated.")
             else:
                 if n_added_tokens + len_ids + len_pair_ids > max_length:
                     if truncate_first_sequence or not pair:
@@ -860,6 +875,10 @@ class PreTrainedTokenizer(object):
         encoded_inputs["input_ids"] = sequence
         encoded_inputs["token_type_ids"] = token_type_ids
 
+        if always_truncate and len(encoded_inputs["input_ids"]) > max_length:
+            encoded_inputs["input_ids"] = encoded_inputs["input_ids"][:max_length]
+            encoded_inputs["token_type_ids"] = encoded_inputs["token_type_ids"][:max_length]
+
         return encoded_inputs
 
     def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1):