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Deprecate prepare_seq2seq_batch (#10287)

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* Deprecate prepare_seq2seq_batch

* Fix last tests

* Apply suggestions from code review

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* More review comments

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
This commit is contained in:
Sylvain Gugger
2021-02-22 12:36:16 -05:00
committed by GitHub
parent e73a3e1891
commit 9e147d31f6
31 changed files with 325 additions and 320 deletions
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11
src/transformers/models/marian/modeling_marian.py
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@@ -522,13 +522,14 @@ MARIAN_GENERATION_EXAMPLE = r"""
>>> src = 'fr' # source language
>>> trg = 'en' # target language
>>> sample_text = "où est l'arrêt de bus ?"
>>> mname = f'Helsinki-NLP/opus-mt-{src}-{trg}'
>>> model_name = f'Helsinki-NLP/opus-mt-{src}-{trg}'
>>> model = MarianMTModel.from_pretrained(mname)
>>> tok = MarianTokenizer.from_pretrained(mname)
>>> batch = tok.prepare_seq2seq_batch(src_texts=[sample_text], return_tensors="pt") # don't need tgt_text for inference
>>> model = MarianMTModel.from_pretrained(model_name)
>>> tokenizer = MarianTokenizer.from_pretrained(model_name)
>>> batch = tokenizer([sample_text], return_tensors="pt")
>>> gen = model.generate(**batch)
>>> words: List[str] = tok.batch_decode(gen, skip_special_tokens=True) # returns "Where is the bus stop ?"
>>> tokenizer.batch_decode(gen, skip_special_tokens=True)
"Where is the bus stop ?"
"""
MARIAN_INPUTS_DOCSTRING = r"""

11
src/transformers/models/marian/modeling_tf_marian.py
View File

@@ -557,13 +557,14 @@ MARIAN_GENERATION_EXAMPLE = r"""
>>> src = 'fr' # source language
>>> trg = 'en' # target language
>>> sample_text = "où est l'arrêt de bus ?"
>>> mname = f'Helsinki-NLP/opus-mt-{src}-{trg}'
>>> model_name = f'Helsinki-NLP/opus-mt-{src}-{trg}'
>>> model = MarianMTModel.from_pretrained(mname)
>>> tok = MarianTokenizer.from_pretrained(mname)
>>> batch = tok.prepare_seq2seq_batch(src_texts=[sample_text], return_tensors="tf") # don't need tgt_text for inference
>>> model = TFMarianMTModel.from_pretrained(model_name)
>>> tokenizer = MarianTokenizer.from_pretrained(model_name)
>>> batch = tokenizer([sample_text], return_tensors="tf")
>>> gen = model.generate(**batch)
>>> words: List[str] = tok.batch_decode(gen, skip_special_tokens=True) # returns "Where is the bus stop ?"
>>> tokenizer.batch_decode(gen, skip_special_tokens=True)
"Where is the bus stop ?"
"""
MARIAN_INPUTS_DOCSTRING = r"""

11
src/transformers/models/marian/tokenization_marian.py
View File

@@ -80,12 +80,15 @@ class MarianTokenizer(PreTrainedTokenizer):
Examples::
>>> from transformers import MarianTokenizer
>>> tok = MarianTokenizer.from_pretrained('Helsinki-NLP/opus-mt-en-de')
>>> tokenizer = MarianTokenizer.from_pretrained('Helsinki-NLP/opus-mt-en-de')
>>> src_texts = [ "I am a small frog.", "Tom asked his teacher for advice."]
>>> tgt_texts = ["Ich bin ein kleiner Frosch.", "Tom bat seinen Lehrer um Rat."] # optional
>>> batch_enc = tok.prepare_seq2seq_batch(src_texts, tgt_texts=tgt_texts, return_tensors="pt")
>>> # keys [input_ids, attention_mask, labels].
>>> # model(**batch) should work
>>> inputs = tokenizer(src_texts, return_tensors="pt", padding=True)
>>> with tokenizer.as_target_tokenizer():
... labels = tokenizer(tgt_texts, return_tensors="pt", padding=True)
>>> inputs["labels"] = labels["input_ids"]
# keys [input_ids, attention_mask, labels].
>>> outputs = model(**inputs) should work
"""
vocab_files_names = vocab_files_names

42
src/transformers/models/mbart/tokenization_mbart.py
View File

@@ -59,30 +59,23 @@ class MBartTokenizer(XLMRobertaTokenizer):
"""
Construct an MBART tokenizer.
:class:`~transformers.MBartTokenizer` is a subclass of :class:`~transformers.XLMRobertaTokenizer` and adds a new
:meth:`~transformers.MBartTokenizer.prepare_seq2seq_batch`
Refer to superclass :class:`~transformers.XLMRobertaTokenizer` for usage examples and documentation concerning the
:class:`~transformers.MBartTokenizer` is a subclass of :class:`~transformers.XLMRobertaTokenizer`. Refer to
superclass :class:`~transformers.XLMRobertaTokenizer` for usage examples and documentation concerning the
initialization parameters and other methods.
.. warning::
``prepare_seq2seq_batch`` should be used to encode inputs. Other tokenizer methods like ``encode`` do not work
properly.
The tokenization method is ``<tokens> <eos> <language code>`` for source language documents, and ``<language code>
<tokens> <eos>``` for target language documents.
Examples::
>>> from transformers import MBartTokenizer
>>> tokenizer = MBartTokenizer.from_pretrained('facebook/mbart-large-en-ro')
>>> tokenizer = MBartTokenizer.from_pretrained('facebook/mbart-large-en-ro', src_lang="en_XX", tgt_lang="ro_RO")
>>> example_english_phrase = " UN Chief Says There Is No Military Solution in Syria"
>>> expected_translation_romanian = "Şeful ONU declară că nu există o soluţie militară în Siria"
>>> batch: dict = tokenizer.prepare_seq2seq_batch(
... example_english_phrase, src_lang="en_XX", tgt_lang="ro_RO", tgt_texts=expected_translation_romanian, return_tensors="pt"
... )
>>> inputs = tokenizer(example_english_phrase, return_tensors="pt)
>>> with tokenizer.as_target_tokenizer():
... labels = tokenizer(expected_translation_romanian, return_tensors="pt")
>>> inputs["labels"] = labels["input_ids"]
"""
vocab_files_names = {"vocab_file": "sentencepiece.bpe.model"}
@@ -92,26 +85,38 @@ class MBartTokenizer(XLMRobertaTokenizer):
prefix_tokens: List[int] = []
suffix_tokens: List[int] = []
def __init__(self, *args, tokenizer_file=None, **kwargs):
super().__init__(*args, tokenizer_file=tokenizer_file, **kwargs)
def __init__(self, *args, tokenizer_file=None, src_lang=None, tgt_lang=None, **kwargs):
super().__init__(*args, tokenizer_file=tokenizer_file, src_lang=src_lang, tgt_lang=tgt_lang, **kwargs)
self.sp_model_size = len(self.sp_model)
self.lang_code_to_id = {
code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(FAIRSEQ_LANGUAGE_CODES)
}
self.id_to_lang_code = {v: k for k, v in self.lang_code_to_id.items()}
self.cur_lang_code = self.lang_code_to_id["en_XX"]
self.fairseq_tokens_to_ids["<mask>"] = len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset
self.fairseq_tokens_to_ids.update(self.lang_code_to_id)
self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
self._additional_special_tokens = list(self.lang_code_to_id.keys())
self.set_src_lang_special_tokens(kwargs.get("src_lang", "en_XX"))
self._src_lang = src_lang if src_lang is not None else "en_XX"
self.cur_lang_code_id = self.lang_code_to_id[self._src_lang]
self.tgt_lang = tgt_lang
self.set_src_lang_special_tokens(self._src_lang)
@property
def vocab_size(self):
return len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset + 1 # Plus 1 for the mask token
@property
def src_lang(self) -> str:
return self._src_lang
@src_lang.setter
def src_lang(self, new_src_lang: str) -> None:
self._src_lang = new_src_lang
self.set_src_lang_special_tokens(self._src_lang)
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
@@ -181,7 +186,6 @@ class MBartTokenizer(XLMRobertaTokenizer):
) -> BatchEncoding:
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)
@contextmanager

43
src/transformers/models/mbart/tokenization_mbart_fast.py
View File

@@ -70,15 +70,9 @@ class MBartTokenizerFast(XLMRobertaTokenizerFast):
Construct a "fast" MBART tokenizer (backed by HuggingFace's `tokenizers` library). Based on `BPE
<https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=BPE#models>`__.
:class:`~transformers.MBartTokenizerFast` is a subclass of :class:`~transformers.XLMRobertaTokenizerFast` and adds
a new :meth:`~transformers.MBartTokenizerFast.prepare_seq2seq_batch`.
Refer to superclass :class:`~transformers.XLMRobertaTokenizerFast` for usage examples and documentation concerning
the initialization parameters and other methods.
.. warning::
``prepare_seq2seq_batch`` should be used to encode inputs. Other tokenizer methods like ``encode`` do not work
properly.
:class:`~transformers.MBartTokenizerFast` is a subclass of :class:`~transformers.XLMRobertaTokenizerFast`. Refer to
superclass :class:`~transformers.XLMRobertaTokenizerFast` for usage examples and documentation concerning the
initialization parameters and other methods.
The tokenization method is ``<tokens> <eos> <language code>`` for source language documents, and ``<language code>
<tokens> <eos>``` for target language documents.
@@ -86,12 +80,13 @@ class MBartTokenizerFast(XLMRobertaTokenizerFast):
Examples::
>>> from transformers import MBartTokenizerFast
>>> tokenizer = MBartTokenizerFast.from_pretrained('facebook/mbart-large-en-ro')
>>> tokenizer = MBartTokenizerFast.from_pretrained('facebook/mbart-large-en-ro', src_lang="en_XX", tgt_lang="ro_RO")
>>> example_english_phrase = " UN Chief Says There Is No Military Solution in Syria"
>>> expected_translation_romanian = "Şeful ONU declară că nu există o soluţie militară în Siria"
>>> batch: dict = tokenizer.prepare_seq2seq_batch(
... example_english_phrase, src_lang="en_XX", tgt_lang="ro_RO", tgt_texts=expected_translation_romanian, return_tensors="pt"
... )
>>> inputs = tokenizer(example_english_phrase, return_tensors="pt)
>>> with tokenizer.as_target_tokenizer():
... labels = tokenizer(expected_translation_romanian, return_tensors="pt")
>>> inputs["labels"] = labels["input_ids"]
"""
vocab_files_names = {"vocab_file": "sentencepiece.bpe.model"}
@@ -102,14 +97,25 @@ class MBartTokenizerFast(XLMRobertaTokenizerFast):
prefix_tokens: List[int] = []
suffix_tokens: List[int] = []
def __init__(self, *args, tokenizer_file=None, **kwargs):
super().__init__(*args, tokenizer_file=tokenizer_file, **kwargs)
self.cur_lang_code = self.convert_tokens_to_ids("en_XX")
self.set_src_lang_special_tokens(kwargs.get("src_lang", "en_XX"))
def __init__(self, *args, tokenizer_file=None, src_lang=None, tgt_lang=None, **kwargs):
super().__init__(*args, tokenizer_file=tokenizer_file, src_lang=src_lang, tgt_lang=tgt_lang, **kwargs)
self.add_special_tokens({"additional_special_tokens": FAIRSEQ_LANGUAGE_CODES})
self._src_lang = src_lang if src_lang is not None else "en_XX"
self.cur_lang_code = self.convert_tokens_to_ids(self._src_lang)
self.tgt_lang = tgt_lang
self.set_src_lang_special_tokens(self._src_lang)
@property
def src_lang(self) -> str:
return self._src_lang
@src_lang.setter
def src_lang(self, new_src_lang: str) -> None:
self._src_lang = new_src_lang
self.set_src_lang_special_tokens(self._src_lang)
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
@@ -181,7 +187,6 @@ class MBartTokenizerFast(XLMRobertaTokenizerFast):
) -> BatchEncoding:
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)
@contextmanager

16
src/transformers/models/mt5/modeling_mt5.py
View File

@@ -31,13 +31,17 @@ class MT5Model(T5Model):
alongside usage examples.
Examples::
>>> from transformers import MT5Model, T5Tokenizer
>>> model = MT5Model.from_pretrained("google/mt5-small")
>>> tokenizer = T5Tokenizer.from_pretrained("google/mt5-small")
>>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien."
>>> summary = "Weiter Verhandlung in Syrien."
>>> batch = tokenizer.prepare_seq2seq_batch(src_texts=[article], tgt_texts=[summary], return_tensors="pt")
>>> outputs = model(input_ids=batch.input_ids, decoder_input_ids=batch.labels)
>>> inputs = tokenizer(article, return_tensors="pt")
>>> with tokenizer.as_target_tokenizer():
... labels = tokenizer(summary, return_tensors="pt")
>>> outputs = model(input_ids=inputs["input_ids"], decoder_input_ids=labels["input_ids"])
>>> hidden_states = outputs.last_hidden_state
"""
model_type = "mt5"
@@ -59,13 +63,17 @@ class MT5ForConditionalGeneration(T5ForConditionalGeneration):
appropriate documentation alongside usage examples.
Examples::
>>> from transformers import MT5ForConditionalGeneration, T5Tokenizer
>>> model = MT5ForConditionalGeneration.from_pretrained("google/mt5-small")
>>> tokenizer = T5Tokenizer.from_pretrained("google/mt5-small")
>>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien."
>>> summary = "Weiter Verhandlung in Syrien."
>>> batch = tokenizer.prepare_seq2seq_batch(src_texts=[article], tgt_texts=[summary], return_tensors="pt")
>>> outputs = model(**batch)
>>> inputs = tokenizer(article, return_tensors="pt")
>>> with tokenizer.as_target_tokenizer():
... labels = tokenizer(summary, return_tensors="pt")
>>> outputs = model(**inputs,labels=labels["input_ids"])
>>> loss = outputs.loss
"""

18
src/transformers/models/mt5/modeling_tf_mt5.py
View File

@@ -31,15 +31,17 @@ class TFMT5Model(TFT5Model):
documentation alongside usage examples.
Examples::
>>> from transformers import TFMT5Model, T5Tokenizer
>>> model = TFMT5Model.from_pretrained("google/mt5-small")
>>> tokenizer = T5Tokenizer.from_pretrained("google/mt5-small")
>>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien."
>>> summary = "Weiter Verhandlung in Syrien."
>>> batch = tokenizer.prepare_seq2seq_batch(src_texts=[article], tgt_texts=[summary], return_tensors="tf")
>>> batch["decoder_input_ids"] = batch["labels"]
>>> del batch["labels"]
>>> outputs = model(batch)
>>> inputs = tokenizer(article, return_tensors="tf")
>>> with tokenizer.as_target_tokenizer():
... labels = tokenizer(summary, return_tensors="tf")
>>> outputs = model(input_ids=inputs["input_ids"], decoder_input_ids=labels["input_ids"])
>>> hidden_states = outputs.last_hidden_state
"""
model_type = "mt5"
@@ -52,13 +54,17 @@ class TFMT5ForConditionalGeneration(TFT5ForConditionalGeneration):
appropriate documentation alongside usage examples.
Examples::
>>> from transformers import TFMT5ForConditionalGeneration, T5Tokenizer
>>> model = TFMT5ForConditionalGeneration.from_pretrained("google/mt5-small")
>>> tokenizer = T5Tokenizer.from_pretrained("google/mt5-small")
>>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien."
>>> summary = "Weiter Verhandlung in Syrien."
>>> batch = tokenizer.prepare_seq2seq_batch(src_texts=[article], tgt_texts=[summary], return_tensors="tf")
>>> outputs = model(batch)
>>> inputs = tokenizer(article, return_tensors="tf")
>>> with tokenizer.as_target_tokenizer():
... labels = tokenizer(summary, return_tensors="tf")
>>> outputs = model(**inputs,labels=labels["input_ids"])
>>> loss = outputs.loss
"""

28
src/transformers/models/rag/modeling_rag.py
View File

@@ -550,10 +550,8 @@ class RagModel(RagPreTrainedModel):
>>> # initialize with RagRetriever to do everything in one forward call
>>> model = RagModel.from_pretrained("facebook/rag-token-base", retriever=retriever)
>>> input_dict = tokenizer.prepare_seq2seq_batch("How many people live in Paris?", "In Paris, there are 10 million people.", return_tensors="pt")
>>> input_ids = input_dict["input_ids"]
>>> outputs = model(input_ids=input_ids)
>>> inputs = tokenizer("How many people live in Paris?", return_tensors="pt")
>>> outputs = model(input_ids=inputs["input_ids"])
"""
n_docs = n_docs if n_docs is not None else self.config.n_docs
use_cache = use_cache if use_cache is not None else self.config.use_cache
@@ -752,9 +750,12 @@ class RagSequenceForGeneration(RagPreTrainedModel):
>>> # initialize with RagRetriever to do everything in one forward call
>>> model = RagSequenceForGeneration.from_pretrained("facebook/rag-token-nq", retriever=retriever)
>>> input_dict = tokenizer.prepare_seq2seq_batch("How many people live in Paris?", "In Paris, there are 10 million people.", return_tensors="pt")
>>> input_ids = input_dict["input_ids"]
>>> outputs = model(input_ids=input_ids, labels=input_dict["labels"])
>>> inputs = tokenizer("How many people live in Paris?", return_tensors="pt")
>>> with tokenizer.as_target_tokenizer():
... targets = tokenizer("In Paris, there are 10 million people.", return_tensors="pt")
>>> input_ids = inputs["input_ids"]
>>> labels = targets["input_ids"]
>>> outputs = model(input_ids=input_ids, labels=labels)
>>> # or use retriever separately
>>> model = RagSequenceForGeneration.from_pretrained("facebook/rag-sequence-nq", use_dummy_dataset=True)
@@ -764,7 +765,7 @@ class RagSequenceForGeneration(RagPreTrainedModel):
>>> docs_dict = retriever(input_ids.numpy(), question_hidden_states.detach().numpy(), return_tensors="pt")
>>> doc_scores = torch.bmm(question_hidden_states.unsqueeze(1), docs_dict["retrieved_doc_embeds"].float().transpose(1, 2)).squeeze(1)
>>> # 3. Forward to generator
>>> outputs = model(context_input_ids=docs_dict["context_input_ids"], context_attention_mask=docs_dict["context_attention_mask"], doc_scores=doc_scores, decoder_input_ids=input_dict["labels"])
>>> outputs = model(context_input_ids=docs_dict["context_input_ids"], context_attention_mask=docs_dict["context_attention_mask"], doc_scores=doc_scores, decoder_input_ids=labels)
"""
n_docs = n_docs if n_docs is not None else self.config.n_docs
exclude_bos_score = exclude_bos_score if exclude_bos_score is not None else self.config.exclude_bos_score
@@ -1203,9 +1204,12 @@ class RagTokenForGeneration(RagPreTrainedModel):
>>> # initialize with RagRetriever to do everything in one forward call
>>> model = RagTokenForGeneration.from_pretrained("facebook/rag-token-nq", retriever=retriever)
>>> input_dict = tokenizer.prepare_seq2seq_batch("How many people live in Paris?", "In Paris, there are 10 million people.", return_tensors="pt")
>>> input_ids = input_dict["input_ids"]
>>> outputs = model(input_ids=input_ids, labels=input_dict["labels"])
>>> inputs = tokenizer("How many people live in Paris?", return_tensors="pt")
>>> with tokenizer.as_target_tokenizer():
... targets = tokenizer("In Paris, there are 10 million people.", return_tensors="pt")
>>> input_ids = inputs["input_ids"]
>>> labels = targets["input_ids"]
>>> outputs = model(input_ids=input_ids, labels=labels)
>>> # or use retriever separately
>>> model = RagTokenForGeneration.from_pretrained("facebook/rag-token-nq", use_dummy_dataset=True)
@@ -1215,7 +1219,7 @@ class RagTokenForGeneration(RagPreTrainedModel):
>>> docs_dict = retriever(input_ids.numpy(), question_hidden_states.detach().numpy(), return_tensors="pt")
>>> doc_scores = torch.bmm(question_hidden_states.unsqueeze(1), docs_dict["retrieved_doc_embeds"].float().transpose(1, 2)).squeeze(1)
>>> # 3. Forward to generator
>>> outputs = model(context_input_ids=docs_dict["context_input_ids"], context_attention_mask=docs_dict["context_attention_mask"], doc_scores=doc_scores, decoder_input_ids=input_dict["labels"])
>>> outputs = model(context_input_ids=docs_dict["context_input_ids"], context_attention_mask=docs_dict["context_attention_mask"], doc_scores=doc_scores, decoder_input_ids=labels)
>>> # or directly generate
>>> generated = model.generate(context_input_ids=docs_dict["context_input_ids"], context_attention_mask=docs_dict["context_attention_mask"], doc_scores=doc_scores)

8
src/transformers/models/rag/tokenization_rag.py
View File

@@ -14,6 +14,7 @@
# limitations under the License.
"""Tokenization classes for RAG."""
import os
import warnings
from contextlib import contextmanager
from typing import List, Optional
@@ -88,6 +89,13 @@ class RagTokenizer:
truncation: bool = True,
**kwargs,
) -> BatchEncoding:
warnings.warn(
"`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the "
"regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` "
"context manager to prepare your targets. See the documentation of your specific tokenizer for more "
"details",
FutureWarning,
)
if max_length is None:
max_length = self.current_tokenizer.model_max_length
model_inputs = self(

7
src/transformers/tokenization_utils_base.py
View File

@@ -3303,6 +3303,13 @@ class PreTrainedTokenizerBase(SpecialTokensMixin):
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.
"""
warnings.warn(
"`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the "
"regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` "
"context manager to prepare your targets. See the documentation of your specific tokenizer for more "
"details",
FutureWarning,
)
# mBART-specific kwargs that should be ignored by other models.
kwargs.pop("src_lang", None)
kwargs.pop("tgt_lang", None)