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Refactor prepare_seq2seq_batch (#9524)

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* Add target contextmanager and rework prepare_seq2seq_batch

* Fix tests, treat BART and Barthez

* Add last tokenizers

* Fix test

* Set src token before calling the superclass

* Remove special behavior for T5

* Remove needless imports

* Remove needless asserts
This commit is contained in:
Sylvain Gugger
2021-01-12 18:19:38 -05:00
committed by GitHub
parent e6ecef711e
commit 063d8d27f4
24 changed files with 169 additions and 700 deletions
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46
src/transformers/models/bart/tokenization_bart.py
View File

@@ -13,10 +13,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List, Optional
from ...file_utils import add_start_docstrings
from ...tokenization_utils_base import PREPARE_SEQ2SEQ_BATCH_DOCSTRING, BatchEncoding
from ...utils import logging
from ..roberta.tokenization_roberta import RobertaTokenizer
@@ -54,45 +50,3 @@ class BartTokenizer(RobertaTokenizer):
"vocab_file": {m: vocab_url for m in _all_bart_models},
"merges_file": {m: merges_url for m in _all_bart_models},
}
@add_start_docstrings(PREPARE_SEQ2SEQ_BATCH_DOCSTRING)
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:
kwargs.pop("src_lang", None)
kwargs.pop("tgt_lang", None)
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
labels = self(
tgt_texts,
add_special_tokens=True,
return_tensors=return_tensors,
padding=padding,
max_length=max_target_length,
truncation=truncation,
**kwargs,
)["input_ids"]
model_inputs["labels"] = labels
return model_inputs

44
src/transformers/models/bart/tokenization_bart_fast.py
View File

@@ -13,10 +13,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List, Optional
from ...file_utils import add_start_docstrings
from ...tokenization_utils_base import PREPARE_SEQ2SEQ_BATCH_DOCSTRING, BatchEncoding
from ...utils import logging
from ..roberta.tokenization_roberta_fast import RobertaTokenizerFast
from .tokenization_bart import BartTokenizer
@@ -49,43 +45,3 @@ class BartTokenizerFast(RobertaTokenizerFast):
"tokenizer_file": {m: tokenizer_url for m in _all_bart_models},
}
slow_tokenizer_class = BartTokenizer
@add_start_docstrings(PREPARE_SEQ2SEQ_BATCH_DOCSTRING)
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: Optional[str] = None,
truncation=True,
**kwargs,
) -> BatchEncoding:
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
labels = self(
tgt_texts,
add_special_tokens=True,
return_tensors=return_tensors,
padding=padding,
max_length=max_target_length,
truncation=truncation,
**kwargs,
)["input_ids"]
model_inputs["labels"] = labels
return model_inputs

44
src/transformers/models/barthez/tokenization_barthez.py
View File

@@ -21,9 +21,7 @@ from typing import List, Optional, Tuple
import sentencepiece as spm
from ...file_utils import add_start_docstrings
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import PREPARE_SEQ2SEQ_BATCH_DOCSTRING, BatchEncoding
from ...utils import logging
@@ -264,45 +262,3 @@ class BarthezTokenizer(PreTrainedTokenizer):
copyfile(self.vocab_file, out_vocab_file)
return (out_vocab_file,)
@add_start_docstrings(PREPARE_SEQ2SEQ_BATCH_DOCSTRING)
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:
kwargs.pop("src_lang", None)
kwargs.pop("tgt_lang", None)
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
labels = self(
tgt_texts,
add_special_tokens=True,
return_tensors=return_tensors,
padding=padding,
max_length=max_target_length,
truncation=truncation,
**kwargs,
)["input_ids"]
model_inputs["labels"] = labels
return model_inputs

45
src/transformers/models/barthez/tokenization_barthez_fast.py
View File

@@ -19,8 +19,7 @@ import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...file_utils import add_start_docstrings, is_sentencepiece_available
from ...tokenization_utils_base import PREPARE_SEQ2SEQ_BATCH_DOCSTRING, BatchEncoding
from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
@@ -228,45 +227,3 @@ class BarthezTokenizerFast(PreTrainedTokenizerFast):
copyfile(self.vocab_file, out_vocab_file)
return (out_vocab_file,)
@add_start_docstrings(PREPARE_SEQ2SEQ_BATCH_DOCSTRING)
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:
kwargs.pop("src_lang", None)
kwargs.pop("tgt_lang", None)
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
labels = self(
tgt_texts,
add_special_tokens=True,
return_tensors=return_tensors,
padding=padding,
max_length=max_target_length,
truncation=truncation,
**kwargs,
)["input_ids"]
model_inputs["labels"] = labels
return model_inputs

38
src/transformers/models/fsmt/tokenization_fsmt.py
View File

@@ -23,9 +23,7 @@ from typing import Dict, List, Optional, Tuple
import sacremoses as sm
from ...file_utils import add_start_docstrings
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...tokenization_utils_base import PREPARE_SEQ2SEQ_BATCH_DOCSTRING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
@@ -484,40 +482,6 @@ class FSMTTokenizer(PreTrainedTokenizer):
return len(token_ids_0 + sep) * [0]
return len(token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
@add_start_docstrings(PREPARE_SEQ2SEQ_BATCH_DOCSTRING)
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,
return_tensors: Optional[str] = None,
truncation=True,
padding="longest",
**unused,
) -> BatchEncoding:
if type(src_texts) is not list:
raise ValueError("src_texts is expected to be a list")
if "" in src_texts:
raise ValueError(f"found empty string in src_texts: {src_texts}")
tokenizer_kwargs = dict(
add_special_tokens=True,
return_tensors=return_tensors,
max_length=max_length,
truncation=truncation,
padding=padding,
)
model_inputs: BatchEncoding = self(src_texts, **tokenizer_kwargs)
if tgt_texts is None:
return model_inputs
if max_target_length is not None:
tokenizer_kwargs["max_length"] = max_target_length
model_inputs["labels"] = self(tgt_texts, **tokenizer_kwargs)["input_ids"]
return model_inputs
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error("Vocabulary path ({}) should be a directory".format(save_directory))

44
src/transformers/models/marian/tokenization_marian.py
View File

@@ -15,15 +15,14 @@
import json
import re
import warnings
from contextlib import contextmanager
from pathlib import Path
from shutil import copyfile
from typing import Dict, List, Optional, Tuple, Union
import sentencepiece
from ...file_utils import add_start_docstrings
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...tokenization_utils_base import PREPARE_SEQ2SEQ_BATCH_DOCSTRING
from ...tokenization_utils import PreTrainedTokenizer
vocab_files_names = {
@@ -182,40 +181,15 @@ class MarianTokenizer(PreTrainedTokenizer):
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_0 + token_ids_1 + [self.eos_token_id]
@add_start_docstrings(PREPARE_SEQ2SEQ_BATCH_DOCSTRING)
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,
return_tensors: Optional[str] = None,
truncation=True,
padding="longest",
**unused,
) -> BatchEncoding:
if "" in src_texts:
raise ValueError(f"found empty string in src_texts: {src_texts}")
self.current_spm = self.spm_source
src_texts = [self.normalize(t) for t in src_texts] # this does not appear to do much
tokenizer_kwargs = dict(
add_special_tokens=True,
return_tensors=return_tensors,
max_length=max_length,
truncation=truncation,
padding=padding,
)
model_inputs: BatchEncoding = self(src_texts, **tokenizer_kwargs)
if tgt_texts is None:
return model_inputs
if max_target_length is not None:
tokenizer_kwargs["max_length"] = max_target_length
@contextmanager
def as_target_tokenizer(self):
"""
Temporarily sets the tokenizer for encoding the targets. Useful for tokenizer associated to
sequence-to-sequence models that need a slightly different processing for the labels.
"""
self.current_spm = self.spm_target
model_inputs["labels"] = self(tgt_texts, **tokenizer_kwargs)["input_ids"]
yield
self.current_spm = self.spm_source
return model_inputs
@property
def vocab_size(self) -> int:

53
src/transformers/models/mbart/tokenization_mbart.py
View File

@@ -13,11 +13,10 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from contextlib import contextmanager
from typing import List, Optional
from ...file_utils import add_start_docstrings
from ...tokenization_utils import BatchEncoding
from ...tokenization_utils_base import PREPARE_SEQ2SEQ_BATCH_DOCSTRING
from ...utils import logging
from ..xlm_roberta.tokenization_xlm_roberta import XLMRobertaTokenizer
@@ -172,52 +171,28 @@ class MBartTokenizer(XLMRobertaTokenizer):
# 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 + self.suffix_tokens
@add_start_docstrings(PREPARE_SEQ2SEQ_BATCH_DOCSTRING)
def prepare_seq2seq_batch(
self,
src_texts: List[str],
src_lang: str = "en_XX",
tgt_texts: Optional[List[str]] = None,
tgt_lang: str = "ro_RO",
max_length: Optional[int] = None,
max_target_length: Optional[int] = None,
truncation: bool = True,
padding: str = "longest",
return_tensors: Optional[str] = None,
add_prefix_space: bool = False, # ignored
**kwargs,
) -> BatchEncoding:
if max_length is None:
max_length = self.model_max_length
self.set_src_lang_special_tokens(src_lang)
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
self.set_tgt_lang_special_tokens(tgt_lang)
self.src_lang = src_lang
self.tgt_lang = tgt_lang
self.set_src_lang_special_tokens(self.src_lang)
return super().prepare_seq2seq_batch(src_texts, tgt_texts, **kwargs)
labels = self(
tgt_texts,
add_special_tokens=True,
return_tensors=return_tensors,
padding=padding,
max_length=max_target_length,
truncation=True,
**kwargs,
)["input_ids"]
model_inputs["labels"] = labels
self.set_src_lang_special_tokens(src_lang) # sets to src_lang
return model_inputs
@contextmanager
def as_target_tokenizer(self):
"""
Temporarily sets the tokenizer for encoding the targets. Useful for tokenizer associated to
sequence-to-sequence models that need a slightly different processing for the labels.
"""
self.set_tgt_lang_special_tokens(self.tgt_lang)
yield
self.set_src_lang_special_tokens(self.src_lang)
def set_src_lang_special_tokens(self, src_lang) -> None:
"""Reset the special tokens to the source lang setting. No prefix and suffix=[eos, src_lang_code]."""

53
src/transformers/models/mbart/tokenization_mbart_fast.py
View File

@@ -13,13 +13,13 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from contextlib import contextmanager
from typing import List, Optional
from tokenizers import processors
from ...file_utils import add_start_docstrings, is_sentencepiece_available
from ...file_utils import is_sentencepiece_available
from ...tokenization_utils import BatchEncoding
from ...tokenization_utils_base import PREPARE_SEQ2SEQ_BATCH_DOCSTRING
from ...utils import logging
from ..xlm_roberta.tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast
@@ -171,51 +171,28 @@ class MBartTokenizerFast(XLMRobertaTokenizerFast):
# 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 + self.suffix_tokens
@add_start_docstrings(PREPARE_SEQ2SEQ_BATCH_DOCSTRING)
def prepare_seq2seq_batch(
self,
src_texts: List[str],
src_lang: str = "en_XX",
tgt_texts: Optional[List[str]] = None,
tgt_lang: str = "ro_RO",
max_length: Optional[int] = None,
max_target_length: Optional[int] = None,
truncation: bool = True,
padding: str = "longest",
return_tensors: str = None,
**kwargs,
) -> BatchEncoding:
if max_length is None:
max_length = self.model_max_length
self.set_src_lang_special_tokens(src_lang)
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
self.set_tgt_lang_special_tokens(tgt_lang)
self.src_lang = src_lang
self.tgt_lang = tgt_lang
self.set_src_lang_special_tokens(self.src_lang)
return super().prepare_seq2seq_batch(src_texts, tgt_texts, **kwargs)
labels = self(
tgt_texts,
add_special_tokens=True,
return_tensors=return_tensors,
padding=padding,
max_length=max_target_length,
truncation=True,
**kwargs,
)["input_ids"]
model_inputs["labels"] = labels
self.set_src_lang_special_tokens(src_lang) # sets to src_lang
return model_inputs
@contextmanager
def as_target_tokenizer(self):
"""
Temporarily sets the tokenizer for encoding the targets. Useful for tokenizer associated to
sequence-to-sequence models that need a slightly different processing for the labels.
"""
self.set_tgt_lang_special_tokens(self.tgt_lang)
yield
self.set_src_lang_special_tokens(self.src_lang)
def set_src_lang_special_tokens(self, src_lang) -> None:
"""Reset the special tokens to the source lang setting. No prefix and suffix=[eos, src_lang_code]."""

32
src/transformers/models/pegasus/tokenization_pegasus.py
View File

@@ -18,9 +18,7 @@ from typing import Dict, List, Optional, Tuple
import sentencepiece as spm
from ...file_utils import add_start_docstrings
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import PREPARE_SEQ2SEQ_BATCH_DOCSTRING, BatchEncoding
from ...utils import logging
@@ -250,36 +248,6 @@ class PegasusTokenizer(PreTrainedTokenizer):
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_0 + token_ids_1 + [self.eos_token_id]
@add_start_docstrings(PREPARE_SEQ2SEQ_BATCH_DOCSTRING)
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,
return_tensors: str = None,
truncation=True,
padding="longest",
**unused,
) -> BatchEncoding:
if "" in src_texts:
raise ValueError(f"found empty string in src_texts: {src_texts}")
tokenizer_kwargs = dict(
add_special_tokens=True,
return_tensors=return_tensors,
max_length=max_length,
truncation=truncation,
padding=padding,
)
model_inputs: BatchEncoding = self(src_texts, **tokenizer_kwargs)
if tgt_texts is None:
return model_inputs
if max_target_length is not None:
tokenizer_kwargs["max_length"] = max_target_length
labels: BatchEncoding = self(tgt_texts, **tokenizer_kwargs)["input_ids"]
model_inputs["labels"] = labels
return model_inputs
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error("Vocabulary path ({}) should be a directory".format(save_directory))

33
src/transformers/models/pegasus/tokenization_pegasus_fast.py
View File

@@ -19,8 +19,7 @@ import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...file_utils import add_start_docstrings, is_sentencepiece_available
from ...tokenization_utils_base import PREPARE_SEQ2SEQ_BATCH_DOCSTRING, BatchEncoding
from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
@@ -188,36 +187,6 @@ class PegasusTokenizerFast(PreTrainedTokenizerFast):
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_0 + token_ids_1 + [self.eos_token_id]
@add_start_docstrings(PREPARE_SEQ2SEQ_BATCH_DOCSTRING)
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,
return_tensors: str = None,
truncation=True,
padding="longest",
**unused,
) -> BatchEncoding:
if "" in src_texts:
raise ValueError(f"found empty string in src_texts: {src_texts}")
tokenizer_kwargs = dict(
add_special_tokens=True,
return_tensors=return_tensors,
max_length=max_length,
truncation=truncation,
padding=padding,
)
model_inputs: BatchEncoding = self(src_texts, **tokenizer_kwargs)
if tgt_texts is None:
return model_inputs
if max_target_length is not None:
tokenizer_kwargs["max_length"] = max_target_length
labels: BatchEncoding = self(tgt_texts, **tokenizer_kwargs)["input_ids"]
model_inputs["labels"] = labels
return model_inputs
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error("Vocabulary path ({}) should be a directory".format(save_directory))

44
src/transformers/models/prophetnet/tokenization_prophetnet.py
View File

@@ -17,9 +17,7 @@ import collections
import os
from typing import List, Optional, Tuple
from ...file_utils import add_start_docstrings
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...tokenization_utils_base import PREPARE_SEQ2SEQ_BATCH_DOCSTRING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..bert.tokenization_bert import BasicTokenizer, WordpieceTokenizer
@@ -288,43 +286,3 @@ class ProphetNetTokenizer(PreTrainedTokenizer):
return token_ids_0 + [self.sep_token_id]
sep = [self.sep_token_id]
return token_ids_0 + sep + token_ids_1 + sep
@add_start_docstrings(PREPARE_SEQ2SEQ_BATCH_DOCSTRING)
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:
if max_length is None:
max_length = self.model_max_length
model_inputs = 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
labels_and_decoder_mask = self(
tgt_texts,
add_special_tokens=True,
return_tensors=return_tensors,
padding=padding,
max_length=max_target_length,
truncation=truncation,
**kwargs,
)
model_inputs["labels"] = labels_and_decoder_mask["input_ids"]
return model_inputs

33
src/transformers/models/rag/tokenization_rag.py
View File

@@ -16,8 +16,7 @@
import os
from typing import List, Optional
from ...file_utils import add_start_docstrings
from ...tokenization_utils_base import PREPARE_SEQ2SEQ_BATCH_DOCSTRING, BatchEncoding
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
@@ -63,42 +62,18 @@ class RagTokenizer:
def batch_decode(self, *args, **kwargs):
return self.generator.batch_decode(*args, **kwargs)
@add_start_docstrings(PREPARE_SEQ2SEQ_BATCH_DOCSTRING)
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:
if max_length is None:
max_length = self.question_encoder.model_max_length
model_inputs: BatchEncoding = self.question_encoder(
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 = self.generator.model_max_length
labels = self.generator(
tgt_texts,
add_special_tokens=True,
return_tensors=return_tensors,
padding=padding,
max_length=max_target_length,
truncation=truncation,
**kwargs,
)["input_ids"]
model_inputs["labels"] = labels
return model_inputs
return super().prepare_seq2seq_batch(
src_texts, tgt_texts, max_length=max_length, max_target_length=max_target_length, **kwargs
)

44
src/transformers/models/t5/tokenization_t5.py
View File

@@ -23,9 +23,7 @@ from typing import List, Optional, Tuple
import sentencepiece as spm
from ...file_utils import add_start_docstrings
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...tokenization_utils_base import PREPARE_SEQ2SEQ_BATCH_DOCSTRING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
@@ -295,43 +293,3 @@ class T5Tokenizer(PreTrainedTokenizer):
copyfile(self.vocab_file, out_vocab_file)
return (out_vocab_file,)
@add_start_docstrings(PREPARE_SEQ2SEQ_BATCH_DOCSTRING)
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:
if max_length is None:
max_length = self.model_max_length
model_inputs = 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
labels_and_decoder_mask = self(
tgt_texts,
add_special_tokens=True,
return_tensors=return_tensors,
padding=padding,
max_length=max_target_length,
truncation=truncation,
**kwargs,
)
model_inputs["labels"] = labels_and_decoder_mask["input_ids"]
return model_inputs

48
src/transformers/models/t5/tokenization_t5_fast.py
View File

@@ -19,9 +19,7 @@ import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...file_utils import add_start_docstrings, is_sentencepiece_available
from ...tokenization_utils import BatchEncoding
from ...tokenization_utils_base import PREPARE_SEQ2SEQ_BATCH_DOCSTRING
from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
@@ -212,47 +210,3 @@ class T5TokenizerFast(PreTrainedTokenizerFast):
if token_ids_1 is None:
return len(token_ids_0 + eos) * [0]
return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
@add_start_docstrings(PREPARE_SEQ2SEQ_BATCH_DOCSTRING)
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:
if max_length is None:
max_length = self.model_max_length
self.prefix_tokens = []
model_inputs = 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]
labels_and_decoder_mask = self(
tgt_texts,
add_special_tokens=True,
return_tensors=return_tensors,
padding=padding,
max_length=max_target_length,
truncation=truncation,
**kwargs,
)
model_inputs["labels"] = labels_and_decoder_mask["input_ids"]
self.prefix_tokens = []
return model_inputs

77
src/transformers/tokenization_utils.py
View File

@@ -738,80 +738,3 @@ class PreTrainedTokenizer(PreTrainedTokenizerBase):
return clean_text
else:
return text
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.AutoModelForSeq2SeqLM`.
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`):
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.
- **labels** -- List of token ids for tgt_texts
The full set of keys ``[input_ids, attention_mask, labels]``, will only be returned if tgt_texts is passed.
Otherwise, input_ids, attention_mask will be the only keys.
"""
raise NotImplementedError(
"If your model requires more than input_ids for a typical forward pass, you should implement this method. "
"Returned keys should be [input_ids, attention_mask, labels]. See MarianTokenizer or T5Tokenizer for a "
"reference implementation."
)

173
src/transformers/tokenization_utils_base.py
View File

@@ -23,6 +23,7 @@ import json
import os
import warnings
from collections import OrderedDict, UserDict
from contextlib import contextmanager
from dataclasses import dataclass, field
from enum import Enum
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Sequence, Tuple, Union
@@ -1473,68 +1474,6 @@ INIT_TOKENIZER_DOCSTRING = r"""
"""
PREPARE_SEQ2SEQ_BATCH_DOCSTRING = """
Prepare model inputs for translation. For best performance, translate one sentence at a time.
Arguments:
src_texts (:obj:`List[str]`):
List of documents to summarize or source language texts.
tgt_texts (:obj:`list`, `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`):
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__`.
Return:
: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.
- **labels** -- List of token ids for tgt_texts.
The full set of keys ``[input_ids, attention_mask, labels]``, will only be returned if tgt_texts is passed.
Otherwise, input_ids, attention_mask will be the only keys.
"""
@add_end_docstrings(INIT_TOKENIZER_DOCSTRING)
class PreTrainedTokenizerBase(SpecialTokensMixin):
"""
@@ -3252,3 +3191,113 @@ class PreTrainedTokenizerBase(SpecialTokensMixin):
"indexing errors".format(len(ids), self.model_max_length)
)
self.deprecation_warnings["sequence-length-is-longer-than-the-specified-maximum"] = True
@contextmanager
def as_target_tokenizer(self):
"""
Temporarily sets the tokenizer for encoding the targets. Useful for tokenizer associated to
sequence-to-sequence models that need a slightly different processing for the labels.
"""
yield
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:
"""
Prepare model inputs for translation. For best performance, translate one sentence at a time.
Arguments:
src_texts (:obj:`List[str]`):
List of documents to summarize or source language texts.
tgt_texts (:obj:`list`, `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`):
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__`.
Return:
: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.
- **labels** -- List of token ids for tgt_texts.
The full set of keys ``[input_ids, attention_mask, labels]``, will only be returned if tgt_texts is passed.
Otherwise, input_ids, attention_mask will be the only keys.
"""
# mBART-specific kwargs that should be ignored by other models.
kwargs.pop("src_lang", None)
kwargs.pop("tgt_lang", None)
if max_length is None:
max_length = self.model_max_length
model_inputs = 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
with self.as_target_tokenizer():
labels = self(
tgt_texts,
add_special_tokens=True,
return_tensors=return_tensors,
padding=padding,
max_length=max_target_length,
truncation=truncation,
**kwargs,
)
model_inputs["labels"] = labels["input_ids"]
return model_inputs