SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

[BartTokenizerFast] add prepare_seq2seq_batch (#6543)

Browse Source
This commit is contained in:
Suraj Patil
2020-08-19 20:07:48 +05:30
committed by GitHub
parent 8bcceaceff
commit 7581884dee
2 changed files with 157 additions and 41 deletions
Download Patch File Download Diff File Expand all files Collapse all files

103
src/transformers/tokenization_bart.py
View File

@@ -155,3 +155,106 @@ class BartTokenizerFast(RobertaTokenizerFast):
"vocab_file": {m: vocab_url for m in _all_bart_models},
"merges_file": {m: merges_url for m in _all_bart_models},
}
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=True,
**kwargs,
) -> BatchEncoding:
r"""
Prepare a batch that can be passed directly to an instance of :class:`~transformers.BartModel`.
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.model_max_length
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
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
return model_inputs

25
tests/test_modeling_bart.py
View File

@@ -38,6 +38,7 @@ if is_torch_available():
BartForQuestionAnswering,
BartConfig,
BartTokenizer,
BartTokenizerFast,
pipeline,
)
from transformers.modeling_bart import (
@@ -421,6 +422,10 @@ class BartModelIntegrationTests(unittest.TestCase):
def default_tokenizer(self):
return BartTokenizer.from_pretrained("facebook/bart-large")
@cached_property
def default_tokenizer_fast(self):
return BartTokenizerFast.from_pretrained("facebook/bart-large")
@slow
def test_inference_no_head(self):
model = BartModel.from_pretrained("facebook/bart-large").to(torch_device)
@@ -564,13 +569,15 @@ class BartModelIntegrationTests(unittest.TestCase):
# TODO(SS): add test case that hits max_length
def test_prepare_seq2seq_batch(self):
tokenizer = self.default_tokenizer
tokenizers = [self.default_tokenizer, self.default_tokenizer_fast]
src_text = ["A long paragraph for summrization.", "Another paragraph for summrization."]
tgt_text = [
"Summary of the text.",
"Another summary.",
]
expected_src_tokens = [0, 250, 251, 17818, 13, 32933, 21645, 1258, 4, 2]
for tokenizer in tokenizers:
batch = tokenizer.prepare_seq2seq_batch(
src_text, tgt_texts=tgt_text, max_length=len(expected_src_tokens), return_tensors="pt"
)
@@ -583,8 +590,9 @@ class BartModelIntegrationTests(unittest.TestCase):
# Test that special tokens are reset
def test_empty_target_text(self):
tokenizer = self.default_tokenizer
tokenizers = [self.default_tokenizer, self.default_tokenizer_fast]
src_text = ["A long paragraph for summrization.", "Another paragraph for summrization."]
for tokenizer in tokenizers:
batch = tokenizer.prepare_seq2seq_batch(src_text, return_tensors="pt")
# check if input_ids are returned and no decoder_input_ids
self.assertIn("input_ids", batch)
@@ -593,12 +601,13 @@ class BartModelIntegrationTests(unittest.TestCase):
self.assertNotIn("decoder_attention_mask", batch)
def test_max_target_length(self):
tokenizer = self.default_tokenizer
tokenizers = [self.default_tokenizer, self.default_tokenizer_fast]
src_text = ["A long paragraph for summrization.", "Another paragraph for summrization."]
tgt_text = [
"Summary of the text.",
"Another summary.",
]
for tokenizer in tokenizers:
batch = tokenizer.prepare_seq2seq_batch(
src_text, tgt_texts=tgt_text, max_target_length=32, padding="max_length", return_tensors="pt"
)
@@ -613,18 +622,22 @@ class BartModelIntegrationTests(unittest.TestCase):
self.assertEqual(32, batch["decoder_attention_mask"].shape[1])
def test_outputs_not_longer_than_maxlen(self):
tokenizer = self.default_tokenizer
tokenizers = [self.default_tokenizer, self.default_tokenizer_fast]
batch = tokenizer.prepare_seq2seq_batch(["I am a small frog" * 1024, "I am a small frog"], return_tensors="pt")
for tokenizer in tokenizers:
batch = tokenizer.prepare_seq2seq_batch(
["I am a small frog" * 1024, "I am a small frog"], return_tensors="pt"
)
self.assertIsInstance(batch, BatchEncoding)
self.assertEqual(batch.input_ids.shape, (2, 1024))
def test_special_tokens(self):
tokenizer = self.default_tokenizer
tokenizers = [self.default_tokenizer, self.default_tokenizer_fast]
src_text = ["A long paragraph for summrization."]
tgt_text = [
"Summary of the text.",
]
for tokenizer in tokenizers:
batch = tokenizer.prepare_seq2seq_batch(src_text, tgt_texts=tgt_text, return_tensors="pt")
input_ids = batch["input_ids"]
decoder_input_ids = batch["decoder_input_ids"]