[T5Tokenizer] add prepare_seq2seq_batch method

src/transformers/tokenization_t5.py

@@ -19,8 +19,9 @@
 import os
 import re
 from shutil import copyfile
+from typing import List, Optional
 
-from .tokenization_utils import PreTrainedTokenizer
+from .tokenization_utils import BatchEncoding, PreTrainedTokenizer
 
 
 logger = logging.getLogger(__name__)
@@ -96,6 +97,8 @@ class T5Tokenizer(PreTrainedTokenizer):
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     model_input_names = ["attention_mask"]
 
+    prefix_tokens: List[int] = []
+
     def __init__(
         self,
         vocab_file,
@@ -206,3 +209,127 @@ def save_vocabulary(self, save_directory):
             copyfile(self.vocab_file, out_vocab_file)
 
         return (out_vocab_file,)
+
+    def build_inputs_with_special_tokens(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Build model inputs from a sequence or a pair of sequence for sequence classification tasks
+        by concatenating and adding special tokens. The special tokens depend on calling source text or target text.
+        A T5 sequence has the following format, where ``X`` represents the sequence:
+        - ``input_ids`` (for encoder) ``X [eos]``
+        - ``decoder_input_ids``: (for decoder) ``[pad] X [eos]``
+        Pairs of sequences are not the expected use case, but they will be handled without a separator.
+
+        Args:
+            token_ids_0 (:obj:`List[int]`):
+                List of IDs to which the special tokens will be added
+            token_ids_1 (:obj:`List[int]`, `optional`):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            :obj:`List[int]`: List of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens.
+        """
+        if token_ids_1 is None:
+            return self.prefix_tokens + token_ids_0
+        # We don't expect to process pairs, but leave the pair logic for API consistency
+        return self.prefix_tokens + token_ids_0 + token_ids_1
+
+    def prepare_seq2seq_batch(
+        self,
+        src_texts: List[str],
+        tgt_texts: Optional[List[str]] = None,
+        max_length: Optional[int] = None,
+        max_target_length: Optional[int] = None,
+        padding: str = "longest",
+        return_tensors: str = None,
+        truncation: bool = True,
+        **kwargs,
+    ) -> BatchEncoding:
+        r"""
+        Prepare a batch that can be passed directly to an instance of :class:`~transformers.T5Model`.
+        Args:
+            src_texts: (:obj:`List[str]`):
+                List of documents to summarize or source language texts.
+            tgt_texts: (:obj:`List[str]`, `optional`):
+                List of summaries or target language texts.
+            max_length (:obj:`int`, `optional`):
+                Controls the maximum length for encoder inputs (documents to summarize or source language texts).
+                If left unset or set to :obj:`None`, this will use the predefined model maximum length if a maximum
+                length is required by one of the truncation/padding parameters. If the model has no specific maximum
+                input length (like XLNet) truncation/padding to a maximum length will be deactivated.
+            max_target_length (:obj:`int`, `optional`):
+                Controls the maximum length of decoder inputs (target language texts or summaries).
+                If left unset or set to :obj:`None`, this will use the max_length value.
+            padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`False`):
+                Activates and controls padding. Accepts the following values:
+                * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a
+                  single sequence if provided).
+                * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
+                  maximum acceptable input length for the model if that argument is not provided.
+                * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
+                  different lengths).
+            return_tensors (:obj:`str` or :class:`~transformers.tokenization_utils_base.TensorType`, `optional`, defaults to "pt"):
+                If set, will return tensors instead of list of python integers. Acceptable values are:
+                * :obj:`'tf'`: Return TensorFlow :obj:`tf.constant` objects.
+                * :obj:`'pt'`: Return PyTorch :obj:`torch.Tensor` objects.
+                * :obj:`'np'`: Return Numpy :obj:`np.ndarray` objects.
+            truncation (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.TruncationStrategy`, `optional`, defaults to :obj:`True`):
+                Activates and controls truncation. Accepts the following values:
+                * :obj:`True` or :obj:`'longest_first'`: Truncate to a maximum length specified with the argument
+                  :obj:`max_length` or to the maximum acceptable input length for the model if that argument is not
+                  provided. This will truncate token by token, removing a token from the longest sequence in the pair
+                  if a pair of sequences (or a batch of pairs) is provided.
+                * :obj:`'only_first'`: Truncate to a maximum length specified with the argument :obj:`max_length` or to
+                  the maximum acceptable input length for the model if that argument is not provided. This will only
+                  truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
+                * :obj:`'only_second'`: Truncate to a maximum length specified with the argument :obj:`max_length` or
+                  to the maximum acceptable input length for the model if that argument is not provided. This will only
+                  truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
+                * :obj:`False` or :obj:`'do_not_truncate'` (default): No truncation (i.e., can output batch with
+                  sequence lengths greater than the model maximum admissible input size).
+            **kwargs:
+                Additional keyword arguments passed along to :obj:`self.__call__`.
+        Returns:
+            :class:`~transformers.BatchEncoding`: A :class:`~transformers.BatchEncoding` with the following fields:
+            - **input_ids** -- List of token ids to be fed to the encoder.
+            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model.
+            - **decoder_input_ids** -- List of token ids to be fed to the decoder.
+            - **decoder_attention_mask** -- List of indices specifying which tokens should be attended to by the decoder.
+                This does not include causal mask, which is built by the model.
+            The full set of keys ``[input_ids, attention_mask, decoder_input_ids,  decoder_attention_mask]``,
+            will only be returned if tgt_texts is passed. Otherwise, input_ids, attention_mask will be the only keys.
+        """
+        if max_length is None:
+            max_length = self.max_len
+        self.prefix_tokens = []
+        model_inputs: BatchEncoding = self(
+            src_texts,
+            add_special_tokens=True,
+            return_tensors=return_tensors,
+            max_length=max_length,
+            padding=padding,
+            truncation=truncation,
+            **kwargs,
+        )
+        if tgt_texts is None:
+            return model_inputs
+        # Process tgt_texts
+        if max_target_length is None:
+            max_target_length = max_length
+        # set prefix_tokens for target text
+        self.prefix_tokens = [self.pad_token_id]
+        decoder_inputs: BatchEncoding = self(
+            tgt_texts,
+            add_special_tokens=True,
+            return_tensors=return_tensors,
+            padding=padding,
+            max_length=max_target_length,
+            truncation=truncation,
+            **kwargs,
+        )
+        for k, v in decoder_inputs.items():
+            model_inputs[f"decoder_{k}"] = v
+
+        self.prefix_tokens = []
+        return model_inputs

tests/test_tokenization_t5.py

@@ -17,6 +17,8 @@
 import os
 import unittest
 
+from transformers import BatchEncoding
+from transformers.testing_utils import _torch_available
 from transformers.tokenization_t5 import T5Tokenizer
 from transformers.tokenization_xlnet import SPIECE_UNDERLINE
 
@@ -25,6 +27,8 @@
 
 SAMPLE_VOCAB = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures/test_sentencepiece.model")
 
+FRAMEWORK = "pt" if _torch_available else "tf"
+
 
 class T5TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
 
@@ -102,3 +106,77 @@ def test_full_tokenizer(self):
                 ".",
             ],
         )
+
+    def test_prepare_seq2seq_batch(self):
+        tokenizer = T5Tokenizer.from_pretrained("t5-small")
+        src_text = ["A long paragraph for summrization.", "Another paragraph for summrization."]
+        tgt_text = [
+            "Summary of the text.",
+            "Another summary.",
+        ]
+        expected_src_tokens = [71, 307, 8986, 21, 4505, 51, 52, 1707, 5]
+        batch = tokenizer.prepare_seq2seq_batch(
+            src_text, tgt_texts=tgt_text, max_length=len(expected_src_tokens), return_tensors=FRAMEWORK
+        )
+        self.assertIsInstance(batch, BatchEncoding)
+
+        self.assertEqual((2, 9), batch.input_ids.shape)
+        self.assertEqual((2, 9), batch.attention_mask.shape)
+        result = list(batch.input_ids.numpy()[0])
+        self.assertListEqual(expected_src_tokens, result)
+        # Test that special tokens are reset
+        self.assertEqual(tokenizer.prefix_tokens, [])
+
+    def test_empty_target_text(self):
+        tokenizer = T5Tokenizer.from_pretrained("t5-small")
+        src_text = ["A long paragraph for summrization.", "Another paragraph for summrization."]
+        batch = tokenizer.prepare_seq2seq_batch(src_text, return_tensors=FRAMEWORK)
+        # check if input_ids are returned and no decoder_input_ids
+        self.assertIn("input_ids", batch)
+        self.assertIn("attention_mask", batch)
+        self.assertNotIn("decoder_input_ids", batch)
+        self.assertNotIn("decoder_attention_mask", batch)
+
+    def test_max_target_length(self):
+        tokenizer = T5Tokenizer.from_pretrained("t5-small")
+        src_text = ["A long paragraph for summrization.", "Another paragraph for summrization."]
+        tgt_text = [
+            "Summary of the text.",
+            "Another summary.",
+        ]
+        batch = tokenizer.prepare_seq2seq_batch(
+            src_text, tgt_texts=tgt_text, max_target_length=32, padding="max_length", return_tensors=FRAMEWORK
+        )
+        self.assertEqual(32, batch["decoder_input_ids"].shape[1])
+        self.assertEqual(32, batch["decoder_attention_mask"].shape[1])
+
+        # test None max_target_length
+        batch = tokenizer.prepare_seq2seq_batch(
+            src_text, tgt_texts=tgt_text, max_length=32, padding="max_length", return_tensors=FRAMEWORK
+        )
+        self.assertEqual(32, batch["decoder_input_ids"].shape[1])
+        self.assertEqual(32, batch["decoder_attention_mask"].shape[1])
+
+    def test_outputs_not_longer_than_maxlen(self):
+        tokenizer = T5Tokenizer.from_pretrained("t5-small")
+
+        batch = tokenizer.prepare_seq2seq_batch(
+            ["I am a small frog" * 1000, "I am a small frog"], return_tensors=FRAMEWORK
+        )
+        self.assertIsInstance(batch, BatchEncoding)
+        self.assertEqual(batch.input_ids.shape, (2, 512))
+
+    def test_eos_in_input(self):
+        tokenizer = T5Tokenizer.from_pretrained("t5-small")
+        src_text = ["A long paragraph for summrization. </s>"]
+        tgt_text = ["Summary of the text. </s>"]
+        expected_src_tokens = [71, 307, 8986, 21, 4505, 51, 52, 1707, 5, 1]
+        expected_tgt_tokens = [0, 20698, 13, 8, 1499, 5, 1]
+
+        batch = tokenizer.prepare_seq2seq_batch(src_text, tgt_texts=tgt_text, return_tensors=FRAMEWORK)
+
+        src_ids = list(batch.input_ids.numpy()[0])
+        tgt_ids = list(batch.decoder_input_ids.numpy()[0])
+
+        self.assertEqual(expected_src_tokens, src_ids)
+        self.assertEqual(expected_tgt_tokens, tgt_ids)