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Removing duplicated code for Translation,Summarization and Text2TextGeneration pipelines (#9433)

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* Merging all duplicated codes for Text2TextPipeline while preserving
backward compat.

* Fixing TranslationPipeline Hierarchy + return_name

* torch import guard.

* Update isort version.

* Remove code from other PR disentanglement.

* Removed named example to something more agnostic.
This commit is contained in:
Nicolas Patry
2021-01-07 23:10:16 +01:00
committed by GitHub
parent f33a6f3446
commit bf9056442a
1 changed files with 154 additions and 241 deletions
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395
src/transformers/pipelines/text2text_generation.py
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@@ -6,7 +6,7 @@ from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
@@ -14,242 +14,6 @@ if is_torch_available():
logger = logging.get_logger(__name__)
@add_end_docstrings(PIPELINE_INIT_ARGS)
class SummarizationPipeline(Pipeline):
"""
Summarize news articles and other documents.
This summarizing pipeline can currently be loaded from :func:`~transformers.pipeline` using the following task
identifier: :obj:`"summarization"`.
The models that this pipeline can use are models that have been fine-tuned on a summarization task, which is
currently, '`bart-large-cnn`', '`t5-small`', '`t5-base`', '`t5-large`', '`t5-3b`', '`t5-11b`'. See the up-to-date
list of available models on `huggingface.co/models <https://huggingface.co/models?filter=summarization>`__.
Usage::
# use bart in pytorch
summarizer = pipeline("summarization")
summarizer("Sam Shleifer writes the best docstring examples in the whole world.", min_length=5, max_length=20)
# use t5 in tf
summarizer = pipeline("summarization", model="t5-base", tokenizer="t5-base", framework="tf")
summarizer("Sam Shleifer writes the best docstring examples in the whole world.", min_length=5, max_length=20)
"""
def __init__(self, *args, **kwargs):
kwargs.update(task="summarization")
super().__init__(*args, **kwargs)
self.check_model_type(
TF_MODEL_WITH_LM_HEAD_MAPPING if self.framework == "tf" else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
)
def __call__(
self, *documents, return_tensors=False, return_text=True, clean_up_tokenization_spaces=False, **generate_kwargs
):
r"""
Summarize the text(s) given as inputs.
Args:
documents (`str` or :obj:`List[str]`):
One or several articles (or one list of articles) to summarize.
return_text (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to include the decoded texts in the outputs
return_tensors (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to include the tensors of predictions (as token indices) in the outputs.
clean_up_tokenization_spaces (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to clean up the potential extra spaces in the text output.
generate_kwargs:
Additional keyword arguments to pass along to the generate method of the model (see the generate method
corresponding to your framework `here <./model.html#generative-models>`__).
Return:
A list or a list of list of :obj:`dict`: Each result comes as a dictionary with the following keys:
- **summary_text** (:obj:`str`, present when ``return_text=True``) -- The summary of the corresponding
input.
- **summary_token_ids** (:obj:`torch.Tensor` or :obj:`tf.Tensor`, present when ``return_tensors=True``) --
The token ids of the summary.
"""
assert return_tensors or return_text, "You must specify return_tensors=True or return_text=True"
assert len(documents) > 0, "Please provide a document to summarize"
prefix = self.model.config.prefix if self.model.config.prefix is not None else ""
if isinstance(documents[0], list):
assert (
self.tokenizer.pad_token_id is not None
), "Please make sure that the tokenizer has a pad_token_id when using a batch input"
documents = ([prefix + document for document in documents[0]],)
padding = True
elif isinstance(documents[0], str):
documents = (prefix + documents[0],)
padding = False
else:
raise ValueError(
" `documents[0]`: {} have the wrong format. The should be either of type `str` or type `list`".format(
documents[0]
)
)
with self.device_placement():
inputs = self._parse_and_tokenize(*documents, padding=padding)
if self.framework == "pt":
inputs = self.ensure_tensor_on_device(**inputs)
input_length = inputs["input_ids"].shape[-1]
elif self.framework == "tf":
input_length = tf.shape(inputs["input_ids"])[-1].numpy()
min_length = generate_kwargs.get("min_length", self.model.config.min_length)
if input_length < min_length // 2:
logger.warning(
"Your min_length is set to {}, but you input_length is only {}. You might consider decreasing min_length manually, e.g. summarizer('...', min_length=10)".format(
min_length, input_length
)
)
max_length = generate_kwargs.get("max_length", self.model.config.max_length)
if input_length < max_length:
logger.warning(
"Your max_length is set to {}, but you input_length is only {}. You might consider decreasing max_length manually, e.g. summarizer('...', max_length=50)".format(
max_length, input_length
)
)
summaries = self.model.generate(
inputs["input_ids"],
attention_mask=inputs["attention_mask"],
**generate_kwargs,
)
results = []
for summary in summaries:
record = {}
if return_tensors:
record["summary_token_ids"] = summary
if return_text:
record["summary_text"] = self.tokenizer.decode(
summary,
skip_special_tokens=True,
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
)
results.append(record)
return results
@add_end_docstrings(PIPELINE_INIT_ARGS)
class TranslationPipeline(Pipeline):
"""
Translates from one language to another.
This translation pipeline can currently be loaded from :func:`~transformers.pipeline` using the following task
identifier: :obj:`"translation_xx_to_yy"`.
The models that this pipeline can use are models that have been fine-tuned on a translation task. See the
up-to-date list of available models on `huggingface.co/models
<https://huggingface.co/models?filter=translation>`__.
Usage::
en_fr_translator = pipeline("translation_en_to_fr")
en_fr_translator("How old are you?")
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.check_model_type(
TF_MODEL_WITH_LM_HEAD_MAPPING if self.framework == "tf" else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
)
def __call__(
self, *args, return_tensors=False, return_text=True, clean_up_tokenization_spaces=False, **generate_kwargs
):
r"""
Translate the text(s) given as inputs.
Args:
args (:obj:`str` or :obj:`List[str]`):
Texts to be translated.
return_tensors (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to include the tensors of predictions (as token indices) in the outputs.
return_text (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to include the decoded texts in the outputs.
clean_up_tokenization_spaces (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to clean up the potential extra spaces in the text output.
generate_kwargs:
Additional keyword arguments to pass along to the generate method of the model (see the generate method
corresponding to your framework `here <./model.html#generative-models>`__).
Return:
A list or a list of list of :obj:`dict`: Each result comes as a dictionary with the following keys:
- **translation_text** (:obj:`str`, present when ``return_text=True``) -- The translation.
- **translation_token_ids** (:obj:`torch.Tensor` or :obj:`tf.Tensor`, present when ``return_tensors=True``)
-- The token ids of the translation.
"""
assert return_tensors or return_text, "You must specify return_tensors=True or return_text=True"
prefix = self.model.config.prefix if self.model.config.prefix is not None else ""
if isinstance(args[0], list):
assert (
self.tokenizer.pad_token_id is not None
), "Please make sure that the tokenizer has a pad_token_id when using a batch input"
args = ([prefix + text for text in args[0]],)
padding = True
elif isinstance(args[0], str):
args = (prefix + args[0],)
padding = False
else:
raise ValueError(
" `documents[0]`: {} have the wrong format. The should be either of type `str` or type `list`".format(
args[0]
)
)
with self.device_placement():
inputs = self._parse_and_tokenize(*args, padding=padding)
if self.framework == "pt":
inputs = self.ensure_tensor_on_device(**inputs)
input_length = inputs["input_ids"].shape[-1]
elif self.framework == "tf":
input_length = tf.shape(inputs["input_ids"])[-1].numpy()
max_length = generate_kwargs.get("max_length", self.model.config.max_length)
if input_length > 0.9 * max_length:
logger.warning(
"Your input_length: {} is bigger than 0.9 * max_length: {}. You might consider increasing your max_length manually, e.g. translator('...', max_length=400)".format(
input_length, max_length
)
)
translations = self.model.generate(
inputs["input_ids"],
attention_mask=inputs["attention_mask"],
**generate_kwargs,
)
results = []
for translation in translations:
record = {}
if return_tensors:
record["translation_token_ids"] = translation
if return_text:
record["translation_text"] = self.tokenizer.decode(
translation,
skip_special_tokens=True,
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
)
results.append(record)
return results
@add_end_docstrings(PIPELINE_INIT_ARGS)
class Text2TextGenerationPipeline(Pipeline):
"""
@@ -267,6 +31,9 @@ class Text2TextGenerationPipeline(Pipeline):
text2text_generator("question: What is 42 ? context: 42 is the answer to life, the universe and everything")
"""
# Used in the return key of the pipeline.
return_name = "generated"
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
@@ -276,6 +43,12 @@ class Text2TextGenerationPipeline(Pipeline):
else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
)
def check_inputs(self, input_length: int, min_length: int, max_length: int):
"""
Checks wether there might be something wrong with given input with regard to the model.
"""
return True
def __call__(
self, *args, return_tensors=False, return_text=True, clean_up_tokenization_spaces=False, **generate_kwargs
):
@@ -304,26 +77,39 @@ class Text2TextGenerationPipeline(Pipeline):
"""
assert return_tensors or return_text, "You must specify return_tensors=True or return_text=True"
prefix = self.model.config.prefix if self.model.config.prefix is not None else ""
if isinstance(args[0], list):
assert (
self.tokenizer.pad_token_id is not None
), "Please make sure that the tokenizer has a pad_token_id when using a batch input"
args = ([prefix + arg for arg in args[0]],)
padding = True
elif isinstance(args[0], str):
args = (prefix + args[0],)
padding = False
else:
raise ValueError(
" `documents[0]`: {} have the wrong format. The should be either of type `str` or type `list`".format(
" `args[0]`: {} have the wrong format. The should be either of type `str` or type `list`".format(
args[0]
)
)
with self.device_placement():
inputs = self._parse_and_tokenize(*args, padding=padding)
inputs = self._parse_and_tokenize(*args, padding=padding, **generate_kwargs)
if self.framework == "pt":
inputs = self.ensure_tensor_on_device(**inputs)
input_length = inputs["input_ids"].shape[-1]
elif self.framework == "tf":
input_length = tf.shape(inputs["input_ids"])[-1].numpy()
min_length = generate_kwargs.get("min_length", self.model.config.min_length)
max_length = generate_kwargs.get("max_length", self.model.config.max_length)
self.check_inputs(input_length, min_length, max_length)
# truncation should be used by _parse_and_tokenize
generate_kwargs.pop("truncation", None)
generations = self.model.generate(
inputs["input_ids"],
@@ -334,12 +120,139 @@ class Text2TextGenerationPipeline(Pipeline):
for generation in generations:
record = {}
if return_tensors:
record["generated_token_ids"] = generation
record[f"{self.return_name}_token_ids"] = generation
if return_text:
record["generated_text"] = self.tokenizer.decode(
record[f"{self.return_name}_text"] = self.tokenizer.decode(
generation,
skip_special_tokens=True,
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
)
results.append(record)
return results
@add_end_docstrings(PIPELINE_INIT_ARGS)
class SummarizationPipeline(Text2TextGenerationPipeline):
"""
Summarize news articles and other documents.
This summarizing pipeline can currently be loaded from :func:`~transformers.pipeline` using the following task
identifier: :obj:`"summarization"`.
The models that this pipeline can use are models that have been fine-tuned on a summarization task, which is
currently, '`bart-large-cnn`', '`t5-small`', '`t5-base`', '`t5-large`', '`t5-3b`', '`t5-11b`'. See the up-to-date
list of available models on `huggingface.co/models <https://huggingface.co/models?filter=summarization>`__.
Usage::
# use bart in pytorch
summarizer = pipeline("summarization")
summarizer("An apple a day, keeps the doctor away", min_length=5, max_length=20)
# use t5 in tf
summarizer = pipeline("summarization", model="t5-base", tokenizer="t5-base", framework="tf")
summarizer("An apple a day, keeps the doctor away", min_length=5, max_length=20)
"""
# Used in the return key of the pipeline.
return_name = "summary"
def __call__(self, *args, **kwargs):
r"""
Summarize the text(s) given as inputs.
Args:
documents (`str` or :obj:`List[str]`):
One or several articles (or one list of articles) to summarize.
return_text (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to include the decoded texts in the outputs
return_tensors (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to include the tensors of predictions (as token indices) in the outputs.
clean_up_tokenization_spaces (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to clean up the potential extra spaces in the text output.
generate_kwargs:
Additional keyword arguments to pass along to the generate method of the model (see the generate method
corresponding to your framework `here <./model.html#generative-models>`__).
Return:
A list or a list of list of :obj:`dict`: Each result comes as a dictionary with the following keys:
- **summary_text** (:obj:`str`, present when ``return_text=True``) -- The summary of the corresponding
input.
- **summary_token_ids** (:obj:`torch.Tensor` or :obj:`tf.Tensor`, present when ``return_tensors=True``) --
The token ids of the summary.
"""
return super().__call__(*args, **kwargs)
def check_inputs(self, input_length: int, min_length: int, max_length: int) -> bool:
"""
Checks wether there might be something wrong with given input with regard to the model.
"""
if input_length < min_length // 2:
logger.warning(
"Your min_length is set to {}, but you input_length is only {}. You might consider decreasing min_length manually, e.g. summarizer('...', min_length=10)".format(
min_length, input_length
)
)
if input_length < max_length:
logger.warning(
"Your max_length is set to {}, but you input_length is only {}. You might consider decreasing max_length manually, e.g. summarizer('...', max_length=50)".format(
max_length, input_length
)
)
@add_end_docstrings(PIPELINE_INIT_ARGS)
class TranslationPipeline(Text2TextGenerationPipeline):
"""
Translates from one language to another.
This translation pipeline can currently be loaded from :func:`~transformers.pipeline` using the following task
identifier: :obj:`"translation_xx_to_yy"`.
The models that this pipeline can use are models that have been fine-tuned on a translation task. See the
up-to-date list of available models on `huggingface.co/models
<https://huggingface.co/models?filter=translation>`__.
Usage::
en_fr_translator = pipeline("translation_en_to_fr")
en_fr_translator("How old are you?")
"""
# Used in the return key of the pipeline.
return_name = "translation"
def check_inputs(self, input_length: int, min_length: int, max_length: int):
if input_length > 0.9 * max_length:
logger.warning(
"Your input_length: {} is bigger than 0.9 * max_length: {}. You might consider increasing your max_length manually, e.g. translator('...', max_length=400)".format(
input_length, max_length
)
)
def __call__(self, *args, **kwargs):
r"""
Translate the text(s) given as inputs.
Args:
args (:obj:`str` or :obj:`List[str]`):
Texts to be translated.
return_tensors (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to include the tensors of predictions (as token indices) in the outputs.
return_text (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to include the decoded texts in the outputs.
clean_up_tokenization_spaces (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to clean up the potential extra spaces in the text output.
generate_kwargs:
Additional keyword arguments to pass along to the generate method of the model (see the generate method
corresponding to your framework `here <./model.html#generative-models>`__).
Return:
A list or a list of list of :obj:`dict`: Each result comes as a dictionary with the following keys:
- **translation_text** (:obj:`str`, present when ``return_text=True``) -- The translation.
- **translation_token_ids** (:obj:`torch.Tensor` or :obj:`tf.Tensor`, present when ``return_tensors=True``)
-- The token ids of the translation.
"""
return super().__call__(*args, **kwargs)