# Copyright 2020 The T5 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Preprocess tensorflow.data.Dataset()."""
import collections
import math
import re
import uuid
import babel
import gin
import tensorflow.compat.v1 as tf
# pylint: disable=g-long-lambda
@gin.configurable
def num_parallel_calls(deterministic=False):
"""Number of parallel calls to tf.data.Dataset.map of stateful function.
Intended usage: gin-config deterministic to True for evaluations, so as to
make evaluation deterministic. Set deterministic to False for training
to enable parallel execution (for a faster input pipeline).
Args:
deterministic: a boolean
Returns:
a value to be passed as num_parallel_calls to tf.data.Dataset.map
"""
return None if deterministic else tf.data.experimental.AUTOTUNE
def rekey(dataset, key_map=None):
"""Replace the feature keys according to the mapping in `key_map`.
For example, if the dataset returns examples of the format:
{'foo': 'something', 'bar': 'something else'}
and key_map = {'boo': 'foo', 'spar': 'bar'} then this function will return
examples with the format
{'boo': 'something', 'spar': 'something else'}
If a mapping is to an empty key or None, set the new key to an empty string.
Args:
dataset: a tf.data.Dataset to process.
key_map: dictionary mapping new keys to original keys
Returns:
A preprocessed tf.data.Dataset with the format listed above.
"""
def my_fn(x):
if key_map:
return {new_key: x[old_key] if old_key else ''
for new_key, old_key in key_map.items()}
return x
return dataset.map(my_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
def translate(dataset, source_language, target_language):
"""Convert a translation dataset to a text2text pair.
For example, say the dataset returns examples of this format:
{'de': 'Das ist gut.', 'en': 'That is good.'}
If source_language = 'de', target_language = 'en', then the outputs will have
the format:
{'inputs': 'translate German to English: Das ist gut.',
'targets': 'That is good.'}
Args:
dataset: a tf.data.Dataset to process.
source_language: source language code (e.g. 'en') to translate from.
target_language: target language code (e.g. 'de') to translate to.
Returns:
A preprocessed tf.data.Dataset with the format listed above.
"""
# Language codes like zh-cn are not supported; use only the first 2 chars
for language in (source_language, target_language):
if language != language[:2]:
tf.logging.warn(
'Extended language code {} not supported. Falling back on {}'.format(
language, language[:2]
)
)
lang_id_to_string = {
source_language: babel.Locale(source_language[:2]).english_name,
target_language: babel.Locale(target_language[:2]).english_name,
}
def my_fn(x):
"""Add translate X to X strings to source/target language strings."""
src_str = 'translate {}'.format(lang_id_to_string[source_language])
tgt_str = ' to {}: '.format(lang_id_to_string[target_language])
return {
'inputs': tf.strings.join([src_str, tgt_str, x[source_language]]),
'targets': x[target_language],
}
return dataset.map(my_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
def summarize(dataset, article_key, summary_key):
"""Convert a summarization dataset to a text2text pair.
For example, say the dataset returns examples of this format:
{'article': <article>, 'highlights': <summary>}
If article_key = 'article', summary_key = 'highlights', then the outputs will
have the format:
{'inputs': 'summarize': <article>, 'targets': <summary>}
Args:
dataset: a tf.data.Dataset to process.
article_key: the feature key for the article to summarize.
summary_key: the feature key for the target summary.
Returns:
A preprocessed tf.data.Dataset with the format listed above.
"""
def my_fn(x):
"""Convert summarization example to a text2text example."""
strs_to_join = ['summarize:', x[article_key]]
return {
'inputs': tf.strings.join(strs_to_join, separator=' '),
'targets': x[summary_key],
}
return dataset.map(my_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
# Unicode ranges for characters in non-spaced languages.
# https://en.wikipedia.org/wiki/Category:Writing_systems_without_word_boundaries
# https://en.wikipedia.org/wiki/Han_unification#Unicode_ranges
# https://linguistics.stackexchange.com/questions/6131
NON_SPACED_LANGUAGE_RANGES = (
'\u1000-\u104f', # Burmese
'\u4e00-\u9fff', # CJK Unified Ideographs
'\u3400-\u4dbf', # CJK Unified Ideographs Extension A
'\uf900-\ufaff', # CJK Compatibility Ideographs
'\u2e80-\u2eff', # CJK Radicals Supplement
'\u31c0-\u31ef', # CJK Strokes
'\u3000-\u303f', # CJK Symbols and Punctuation
'\u3040-\u309f', # Japanese Hiragana
'\u30a0-\u30ff', # Japanese Katakana
'\ua980-\ua9df', # Javanese
'\u1780-\u17ff', # Khmer
'\u19e0-\u19ff', # Khmer Symbols
'\u0e80-\u0eff', # Lao
'\u1980-\u19df', # Tai Lue
'\u1a20-\u1aaf', # Tai Tham
'\u0e00-\u0e7f', # Thai
'\u0f00-\u0fff', # Tibetan
)
def pad_nonspaced_languages(dataset, text_key='text'):
"""Pad non-spaced languages with spaces around each character.
Args:
dataset: a tf.data.Dataset to process.
text_key: a string, the key for the text feature to preprocess in the
dataset examples.
Returns:
a tf.data.Dataset with the modified examples.
"""
def my_fn(x):
res = dict(x)
text = res[text_key]
# Add spaces around any character from a non-spaced language.
pattern = ''.join(NON_SPACED_LANGUAGE_RANGES)
text = tf.strings.regex_replace(text, u'([{}])'.format(pattern), r' \1 ')
# Collapse consecutive whitespace into one space.
text = tf.strings.regex_replace(text, r'\s+', ' ')
res[text_key] = text
return res
return dataset.map(my_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
def _pad_punctuation(text):
"""Adds spaces around punctuation."""
# Add space around punctuation.
text = tf.strings.regex_replace(text, r'(\W)', r' \1 ')
# Collapse consecutive whitespace into one space.
text = tf.strings.regex_replace(text, r'\s+', ' ')
return text
def _string_join(lst):
# Join on space, but collapse consecutive spaces.
out = tf.strings.join(lst, separator=' ')
return tf.strings.regex_replace(out, r'\s+', ' ')
def trivia_qa(dataset):
"""Convert a TriviaQA example to multiple flattened examples.
TriviaQA produces examples with this form:
{'entity_pages': {dict of wiki entities},
'search_results': <dict of web search results>,
'answer': {dict of all answers}, 'question': <question>,
'question_id': <question_id>, 'question_source': <question_source>}
This function will return flattend examples of the format:
{'inputs': 'question: <question> context: <article>'
'targets': 'answer: <sampled answer>'}
Args:
dataset: a tf.data.Dataset to process.
Returns:
A preprocessed tf.data.Dataset with the format listed above.
"""
def triviaqa_question_answer_context(x):
"""Extracts matched contexts and answers.
Returns all matched (question-context, answer) pairs.
Args:
x: A tfds sample.
Returns:
Flattened samples: (question-context, answer).
"""
contexts = []
if 'entity_pages' in x:
contexts.append(x['entity_pages']['wiki_context'])
if 'search_results' in x:
contexts.append(x['search_results']['search_context'])
contexts = tf.concat(contexts, 0)
q = _pad_punctuation(x['question'])
answers = x['answer']['normalized_aliases']
combination_size = tf.size(answers)*tf.size(contexts)
find_answers = tf.TensorArray(
tf.bool, size=combination_size, dynamic_size=True)
selected_answers = tf.TensorArray(
tf.string, size=combination_size, dynamic_size=True)
join_q_c = tf.TensorArray(
tf.string, size=combination_size, dynamic_size=True)
def cond_fn(i, find_answers, selected_answers, join_q_c):
del find_answers, selected_answers, join_q_c # Unused
return tf.less(i, combination_size)
def body_fn(i, find_answers, selected_answers, join_q_c):
"""Find answers from contexts and join."""
context_idx = tf.math.floordiv(i, tf.size(answers))
answer_idx = tf.math.mod(i, tf.size(answers))
a = _pad_punctuation(answers[answer_idx])
a_ = tf.strings.join(['.*', a, '.*'])
c = _pad_punctuation(contexts[context_idx])
find_a = tf.strings.regex_full_match(
tf.strings.lower(c),
tf.strings.lower(a_))
find_answers = find_answers.write(i, find_a)
selected_answers = selected_answers.write(i, a)
join_q_c_str = _string_join(['question:', q, 'context:', c])
join_q_c = join_q_c.write(i, join_q_c_str)
return (i + 1, find_answers, selected_answers, join_q_c)
_, find_answers, selected_answers, join_q_c = tf.while_loop(
cond_fn,
body_fn,
loop_vars=[
tf.constant(0), find_answers, selected_answers,
join_q_c
])
find_answers = find_answers.stack()
selected_answers = selected_answers.stack()
join_q_c = join_q_c.stack()
selected_answers = tf.boolean_mask(selected_answers, find_answers)
selected_join_q_c = tf.boolean_mask(join_q_c, find_answers)
return selected_join_q_c, selected_answers
def my_fn(x):
"""Create TriviaQA example."""
join_q_c, a = triviaqa_question_answer_context(x)
return {
'inputs': join_q_c,
'targets': a
}
dataset = dataset.map(my_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
return dataset.unbatch()
def squad(dataset, include_context=True):
"""Convert SQuAD examples to a text2text pair.
SQuAD produces examples with this form:
{'id': <id>, context': <article>, 'question': <question>,
'answers': { 'text': [<n answers>] }}
This function will return examples of the format:
{'inputs': 'question: <question> context: <article>',
'targets': '<answer_0>',
'id': <id>, 'question': <question>, 'context': <context>,
'answers': [<n answers>]},
Args:
dataset: a tf.data.Dataset to process.
include_context: a boolean
Returns:
A preprocessed tf.data.Dataset with the format listed above.
"""
def my_fn(x):
"""Create squad example."""
a = _pad_punctuation(x['answers']['text'])
q = _pad_punctuation(x['question'])
c = _pad_punctuation(x['context'])
if include_context:
inputs = _string_join(['question:', q, 'context:', c])
else:
inputs = _string_join(['squad trivia question:', q])
return {
'inputs': inputs,
'targets': a[0],
'id': x['id'],
'context': c,
'question': q,
'answers': a
}
return dataset.map(my_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
def _span_answer(context, answer_text):
"""Finds start/end indices of answer_text in context after space tokenization.
If answer_tokens is not a sublist of context_tokens, returns empty string.
Args:
context: 0-d string tensor
answer_text: 0-d string
Returns:
A string tensor.
"""
def space_tok(s):
"""Replace non-word chars with space then split on space."""
s = tf.strings.regex_replace(s, r'\W', ' ')
return tf.strings.split(input=[s], sep=' ').values
def find_subseq(n, h):
"""Finds index of needle subsequence inside haystack.
Args:
n: 1-d tensor
h: 1-d tensor same type as n
Returns:
Index of start of n if found found; otherwise -1.
"""
l_n = tf.size(n)
l_h = tf.size(h)
i = tf.constant(0)
end = l_h - l_n
# TODO(peterjliu): Replace with craffel@'s more efficient code
# if necessary: cr/254848350.
w = tf.while_loop(
lambda i: tf.logical_and(tf.less(i, end),
tf.reduce_any(tf.not_equal(h[i:i+l_n], n))),
lambda i: i+1,
[i])
return tf.cond(tf.equal(end, w), lambda: -1, lambda: w)
answer_tokens = space_tok(answer_text)
context_tokens = space_tok(context)
start = find_subseq(answer_tokens, context_tokens)
end = start + tf.size(answer_tokens) - 1
# Just take the first candidate that matches exactly.
return tf.cond(tf.equal(start, -1),
lambda: tf.constant(''),
lambda: tf.strings.format('start: {} end: {}', [start, end]))
def squad_span_space_tokenized(dataset):
"""Convert SQuAD examples to a text2text pair with span output.
SQuAD produces examples with this form:
{'context': <article>, 'question': <question>,
'answers': { 'text': [<all answers>] }}
This function returns examples with the format
{'inputs': 'context: <article> question: <question>',
'targets': 'start: <start_index> end: <end_index>'}
where <start_index> and <end_index> specify the space-tokenized span
start/end indices. Both <start_index> and <end_index> are included in
the answer. In the case where the tokenized answer is
not found in the tokenized context, the example is skipped.
Args:
dataset: a tf.data.Dataset to process.
Returns:
A preprocessed tf.data.Dataset with the format listed above.
"""
def my_fn(x):
"""Create squad example as in squad_span_char, but tokenized on spaces."""
res = dict(x)
res['targets'] = _span_answer(x['context'], x['targets'],)
return res
dataset = squad(dataset)
dataset = dataset.map(my_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
return dataset.filter(lambda x: tf.strings.length(x['targets']) > 0)
def random_split_text(dataset,
text_key='text',
min_words_per_segment=16,
max_words_per_segment=512,
max_words_total=8192):
"""Randomly split single-string examples into multiple examples each.
Segment lengths are chosen according to a log-uniform distribution.
Each incoming string is chopped into multiple equal-length examples
with the last one possibly being shorter.
If the input string is longer than max_words_total, then we use one random
chunk and discard the rest. This may help with model stability.
The intended use case is to break up long text examples for use in
unsupervised transfer-learning.
We don't really want to use this preprocessor for any dataset which has a
well-defined evaluation procedure. If apply this preprocessor e.g. in an MT
component, then the evaluation job will randomly split text when evaluating
and the BLEU will get funky.
Args:
dataset: a tf.data.Dataset with dictionaries containing the key text_key
text_key: a string
min_words_per_segment: an integer
max_words_per_segment: an integer
max_words_total: an integer
Returns:
a dataset
"""
def random_chunk(x, chunk_size):
"""Pick a random chunk of a 1d Tensor.
The tensor is divided into chunks of length chunk_size, with the last
chunk being potentially smaller. A random chunk is returned.
Args:
x: a 1d tf.Tensor
chunk_size: an integer
Returns:
a 1d tf.Tensor with length <= chunk_size
"""
size = tf.size(x)
num_chunks = tf.maximum(1, (size - 1) // chunk_size + 1)
chunk_num = tf.random.uniform(
[], minval=0, maxval=num_chunks, dtype=tf.int32)
return x[chunk_size * chunk_num:chunk_size * (chunk_num + 1)]
def my_fn(x):
"""Split one string into multiple strings.
Args:
x: a feature dictionary
Returns:
a feature dictionary
"""
text = x[text_key]
words = tf.strings.split([text]).values
if max_words_total:
words = random_chunk(words, max_words_total)
n_words = tf.size(words)
# first pick a length (number of words per segment)
length = tf.cast(tf.exp(tf.random_uniform(
[],
minval=math.log(min_words_per_segment),
maxval=math.log(max_words_per_segment))), tf.int32)
# Pad to a multiple of length, then use tf.reshape to split up the words
# into num_segments segments each of the given length.
num_segments = tf.cast(
tf.ceil(tf.cast(n_words, tf.float32) / tf.cast(length, tf.float32)),
tf.int32)
padding = num_segments * length - n_words
words = tf.pad(words, [[0, padding]])
words = tf.reshape(words, [-1, length])
# Finally, join with spaces and strip. The padding turns into a bunch of
# spaces that get stripped out.
words = tf.strings.reduce_join(words, axis=1, separator=' ')
return {text_key: tf.strings.strip(words)}
dataset = dataset.map(my_fn, num_parallel_calls=num_parallel_calls())
return dataset.unbatch()
def _split_text_to_words(dataset, text_key='text', min_num_words=2):
"""Split text to words and filter out examples with too few words."""
def split(x):
res = dict(x)
res['words'] = tf.strings.split([x[text_key]]).values
return res
dataset = dataset.map(split, num_parallel_calls=tf.data.experimental.AUTOTUNE)
return dataset.filter(lambda x: tf.size(x['words']) >= min_num_words)
def fill_in_the_blank(dataset,
text_key='text',
label='fill: '):
"""Create a dataset consisting of fill-in-the-blank text examples.
The input examples should have a key text_key associated with a tf.string
value.
The output examples have keys 'inputs' and 'targets'.
The input string is split on whitespace to form a sequence of words.
This sequence is chopped randomly into segments of one or more words.
Alternate segments are included in the inputs and targets, with a special
word 'X' marking a missing segment.
The given label is prepended to the inputs. Each input string produces two
examples - one the inverse of the other. Inputs with less than two words
are dropped.
EXAMPLE:
input:
{
'text': 'The fat cat sat on the mat.'
}
outputs:
{
'inputs': 'fill: The fat X the X'
'targets': 'X cat sat on X mat.'
}
{
'inputs': 'fill: X cat sat on X mat.'
'targets': 'The fat X the X'
}
Args:
dataset: a tf.data.Dataset
text_key: a string, the key for the text feature to preprocess in the
dataset examples.
label: a string, the label to prepend to the inputs.
Returns:
a tf.data.Dataset
"""
def my_fn(x):
"""Generates two preprocessed examples that are roughly inverses.
Args:
x: an example dict with text pre-split in `words` feature.
Returns:
an example dict with two inputs and two targets, one for each resulting
preprocessed example.
"""
words = x['words']
n_words = tf.size(words)
# First select the break probability. We pick this on a log-uniform
# distribution between 1/(n_words + 1) and 1/2. This means that some
# sequences will be chopped roughly and others finely.
min_log_p_break = -tf.math.log(tf.to_float(n_words) + 2.0)
max_log_p_break = -tf.math.log(2.0)
p_break = tf.exp(tf.random_uniform(
[], minval=min_log_p_break, maxval=max_log_p_break))
# craffel@ says that there may be bugs in random_uniform making it not
# really uniform. This doesn't seem horribly important here, but may
# need another look.
breaks = tf.less(tf.random_uniform([n_words - 1]), p_break)
def one_random_break():
pos = tf.random_uniform(
[], minval=0, maxval=n_words - 1, dtype=tf.int32)
return tf.one_hot(pos, n_words - 1,
dtype=tf.bool, on_value=True, off_value=False)
breaks = tf.cond(
tf.math.reduce_any(breaks), lambda: breaks, one_random_break)
breaks = tf.concat([[True], breaks], axis=0)
word_to_seq_id = tf.mod(tf.math.cumsum(tf.to_int32(breaks)), 2)
# separators:
# if in your segment: ' '
# if break to other segment: ' X'
# else: ''
results = []
for seq_id in [0, 1]:
in_my_seq = tf.equal(word_to_seq_id, seq_id)
separator_strings = tf.where(
in_my_seq,
tf.fill([n_words], ' '),
tf.where(breaks, tf.fill([n_words], ' X'),
tf.fill([n_words], '')))
word_strings = tf.where(in_my_seq, words, tf.fill([n_words], ''))
all_strings = tf.stack([separator_strings, word_strings], axis=1)
results.append(tf.strings.substr(
tf.strings.reduce_join(all_strings), 1, tf.int32.max))
inputs = tf.stack([tf.strings.join([label, results[0]]),
tf.strings.join([label, results[1]])])
targets = tf.stack([results[1], results[0]])
return {'inputs': inputs, 'targets': targets}
dataset = _split_text_to_words(dataset, text_key, min_num_words=2)
dataset = dataset.map(my_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
return dataset.unbatch()
def fill_in_the_blank_sized(
dataset,
size_bins=(1, 2, 4, 8, 16, 32, 64, 128, 256, 512),
text_key='text',
label='fill: '):
"""Fill in the blank preprocessor that labels blank with a binned size.
The actual blank size is sampled uniformly from the inclusive range of the min
and max bin. The blank is then filled in with the closest bin size to the
actual blank size.
Args:
dataset: a tf.data.Dataset, the dataset to preprocess.
size_bins: a list, a list of blank sizes to select from when labelling the
blank.
text_key: a string, the key for the text feature to preprocess in the
dataset examples.
label: a string, the label to prepend to the inputs.
Returns:
a tf.data.Dataset
"""
bins = sorted(size_bins)
def my_fn(x):
"""Apply transformation."""
words = x['words']
n_words = tf.size(words)
blank_size = tf.random.uniform(
[], minval=bins[0], maxval=tf.math.minimum(n_words, bins[-1]),
dtype=tf.dtypes.int32)
bin_delta = tf.math.abs(bins - blank_size)
bin_ = tf.gather(bins, tf.argmin(bin_delta))
blank_start = tf.random.uniform(
[], minval=0, maxval=tf.math.maximum(0, n_words-blank_size) + 1,
dtype=tf.dtypes.int32)
pre_blank = tf.strings.reduce_join(words[0:blank_start], separator=' ')
post_blank = tf.strings.reduce_join(
words[blank_start+blank_size:], separator=' ')
blank = tf.strings.format('_{}_', bin_)
# We strip to handle cases where blank is at beginning or end.
input_ = tf.strings.strip(
tf.strings.join([pre_blank, blank, post_blank], ' '))
input_ = tf.strings.join([label, input_])
target = tf.strings.reduce_join(
words[blank_start:blank_start+blank_size], separator=' ')
return {
'inputs': tf.strings.strip(input_),
'targets': tf.strings.strip(target)}
dataset = _split_text_to_words(dataset, text_key, min_num_words=2)
# Filter out examples with fewer words than the minimum.
dataset = dataset.filter(lambda x: tf.size(x['words']) >= bins[0])
dataset = dataset.map(my_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
return dataset
def prefix_lm(dataset,
text_key='text',
label='prefix: '):
"""Create a dataset consisting of text prefix examples for LM-style training.
The input examples should have a key text_key associated with a tf.string
value.
The output examples have keys 'inputs' and 'targets'.
The input string is split on whitespace to form a sequence of words.
A random uniform distribution is used to select a prefix from the input text.
This prefix is the inputs and rest of the text is the targets. Inputs with
less than two words are dropped.
The given label is prepended to the inputs.
EXAMPLE:
input:
{
'text': 'The fat cat sat on the mat.'
}
output:
{
'inputs': 'prefix: The fat'
'targets': 'cat sat on the mat.'
}
Args:
dataset: a tf.data.Dataset
text_key: a string, the key for the text feature to preprocess in the
dataset examples.
label: a string, the label to prepend to the inputs.
Returns:
a tf.data.Dataset
"""
def my_fn(x):
"""Split an example into two parts for text2text models."""
words = x['words']
num_words = tf.size(words)
split = tf.random_uniform(
[], minval=0, maxval=num_words - 1, dtype=tf.int32)
input_words, target_words = tf.split(words, [split, num_words - split])
inputs = tf.strings.join(
[label,
tf.strings.reduce_join([input_words], separator=' ')])
targets = tf.strings.reduce_join([target_words], separator=' ')
return {'inputs': inputs, 'targets': targets}
dataset = _split_text_to_words(dataset, text_key, min_num_words=2)
return dataset.map(my_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
def neighboring_pairs(dataset, text_key='text', reuse_sentences=True):
"""Create a dataset consisting of neighboring sentence pairs.
The input examples should have a key text_key associated with a tf.string
value.
The output examples have keys 'first' and 'second'.
We only take sentence pairs from within the same line since lines seem to
represent paragraph-like structures in our text datasets. Empty lines and
1-sentence lines will thus be ignored.
The argument reuse_sentences determines whether a sentence can be used as both
the first and last element in the pair. For example, the input with sentences
A,B,C,D will return (A,B),(B,C),(C,D) if reuse_sentences is True and
(A,B),(C,D) if reuse_sentences is False.
Args:
dataset: a tf.data.Dataset
text_key: a string, the key for the text feature to preprocess in the
dataset examples.
reuse_sentences: a boolean
Returns:
a tf.data.Dataset
"""
def split_by_lines(dataset):
"""Splits text in dataset by line, removing empty lines."""
def my_fn(text):
lines = tf.strings.split([text], sep='\n').values
return tf.strings.strip(lines)
dataset = dataset.map(
my_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
dataset = dataset.unbatch()
return dataset.filter(lambda x: tf.strings.length(x) > 0)
def split_into_pairs(line):
"""Split a given text example into pairs of neighboring sentences."""
# TODO(mmatena): Use better sentence segmentation.
sep = str(uuid.uuid4())
sentences = tf.strings.regex_replace(line, r'((?:\.|\!|\?)+)', r'\1' + sep)
sentences = tf.strings.strip(tf.strings.split([sentences], sep).values)
if reuse_sentences:
firsts = sentences[:-1]
seconds = sentences[1:]
else:
firsts = sentences[:-1:2]
seconds = sentences[1::2]
return {
'first': firsts,
'second': seconds,
}
def example_len(x):
return tf.math.minimum(
tf.strings.length(x['first']), tf.strings.length(x['second']))
# Split by lines.
dataset = dataset.map(
lambda x: x[text_key], num_parallel_calls=tf.data.experimental.AUTOTUNE)
dataset = split_by_lines(dataset)
# Get pairs of neighboring sentences.
dataset = dataset.map(
split_into_pairs, num_parallel_calls=tf.data.experimental.AUTOTUNE)
dataset = dataset.unbatch()
# Remove examples with empty strings.
dataset = dataset.filter(lambda x: example_len(x) > 0)
return dataset
def glue(
dataset, benchmark_name, label_names, feature_names=None, id_key='idx'):
"""Convert a dataset from glue to text2text examples.
This function uses the feature names from the dataset to unpack examples into
a format amenable for a text2text problem. For example, consider the Quora
Question Pairs (QQP) benchmark, which would suggest
benchmark_name="qqp"
label_names=['not_duplicate', 'duplicate']
For QQP, a typical example might look like
{
"question1": "Why do I easily get bored of my friends?",
"question2": "Why do I get bored of friends so quickly?",
"label": 1,
"idx": 10,
}
This example would be transformed to
{
"inputs": (
"qqp question1: Why do I easily get bored of my friends? question2: "
"Why do I get bored of my friends so quickly?"
),
"targets": "duplicate",
"idx": 10,
}
Args:
dataset: a tf.data.Dataset to process.
benchmark_name: the name of the GLUE benchmark for this dataset.
label_names: a list of label names corresponding to class index.
feature_names: an optional ordered list of feature names. If provided,
features will be ordered in this way in the output. If not provided, all
features (except 'idx' and 'label') will be used, sorted by name.
id_key: str, key for id in the dataset. If not provided, 'idx' will be used.
if None, no id will be added to the dataset.
Returns:
a tf.data.Dataset
"""
def my_fn(x):
"""Collapse an example into or text2text pair."""
# If an ordering is not provided, sort feature keys to ensure a consistent
# order.
feature_keys = (
feature_names or sorted(set(x.keys()).difference(['label', 'idx'])))
# Pack keys (formatted as " key: ") and corresponding text feature
strs_to_join = []
for key in feature_keys:
strs_to_join.append('{}:'.format(key))
strs_to_join.append(x[key])
# Add benchmark name at the start
strs_to_join.insert(0, benchmark_name)
label_name = tf.cond(
# When no label is provided (label == -1), use "<unk>"
tf.equal(x['label'], -1),
lambda: tf.constant('<unk>'),
# Otherwise grab the label text from label_names
lambda: tf.gather(label_names, x['label']),
)
joined = tf.strings.join(strs_to_join, separator=' ')
ex = {}
if benchmark_name == 'multirc':
# Remove HTML markup.
joined = tf.strings.regex_replace(joined, '<br>', ' ')
joined = tf.strings.regex_replace(joined, '<(/)?b>', '')
# Store the data index in the returned example (used by eval)
ex['idx/paragraph'] = x['idx']['paragraph']
ex['idx/question'] = x['idx']['question']
ex['idx/answer'] = x['idx']['answer']
else:
# Store the data index in the returned example (used by eval)
if id_key:
ex['idx'] = x[id_key]
ex['inputs'] = joined
ex['targets'] = label_name
return ex
return dataset.map(my_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
def stsb(dataset):
"""Convert STSB examples to text2text format.
STSB maps two sentences to a floating point number between 1 and 5
representing their semantic similarity. Since we are treating all tasks as
text-to-text tasks we need to convert this floating point number to a string.
The vast majority of the similarity score labels in STSB are in the set
[0, 0.2, 0.4, ..., 4.8, 5.0]. So, we first round the number to the closest
entry in this set, and then we convert the result to a string (literally e.g.
"3.4"). This converts STSB roughly into a 26-class classification dataset.
This function uses the feature names from the dataset to unpack examples into
a format amenable for a text2text problem.
For example, a typical example from STSB might look like
{
"sentence1": "Three more US soldiers killed in Afghanistan",
"sentence2": "NATO Soldier Killed in Afghanistan",
"label": 1.8,
}
This example would be transformed to
{
"inputs": (
"stsb sentence1: Three more US soldiers killed in Afghanistan "
"sentence2: NATO Soldier Killed in Afghanistan"
),
"targets": "1.8",
}
Args:
dataset: a tf.data.Dataset to process.
Returns:
a tf.data.Dataset
"""
def my_fn(x):
"""Collapse an example into a text2text pair."""
strs_to_join = [
'stsb sentence1:', x['sentence1'], 'sentence2:', x['sentence2']
]
label_string = tf.as_string(tf.round(x['label']*5)/5, precision=1)
joined = tf.strings.join(strs_to_join, separator=' ')
return {'inputs': joined, 'targets': label_string, 'idx': x['idx']}
return dataset.map(my_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
def wsc(dataset):
"""Convert WSC examples to text2text format.
WSC includes a sentence along with 2 'spans': the first denoting a noun and
the other a pronoun. The 'label' specifies whether or not the pronoun is
referencing the noun. This preprocessor puts ' * ' around the noun and ' # '
around the pronoun.
For example, a typical example from WSC might look like
{
'text': 'This is a test sentence .',
'span1_text': 'test',
'span1_index': 3,
'span2_text': 'This',
'span2_index': 0,
'label': 0
}
This example would be transformed to
{
'inputs': 'wsc text: # This # is a * test * sentence .',
'targets': 'False'
}
Args:
dataset: a tf.data.Dataset to process.
Returns:
a tf.data.Dataset
"""
def my_fn(x):
"""Collapse an example into a text2text pair."""
def _mark_span(text, span_str, span_idx, mark):
pattern_tmpl = r'^((?:\S+\s){N})(W)'
pattern = tf.strings.regex_replace(
pattern_tmpl, 'N', tf.as_string(span_idx))
pattern = tf.strings.regex_replace(pattern, 'W', span_str)
return tf.strings.regex_replace(
text, pattern, r'\1{0} \2 {0}'.format(mark))
text = x['text']
text = _mark_span(text, x['span1_text'], x['span1_index'], '*')
# Compensate for 2 added "words" added in previous step.
span2_index = x['span2_index'] + 2 * tf.cast(
x['span1_index'] < x['span2_index'], tf.int32)
text = _mark_span(text, x['span2_text'], span2_index, '#')
# Add benchmark name at the start
strs_to_join = ['wsc', 'text:', text]
label_name = tf.cond(
# When no label is provided (label == -1), use "<unk>"
tf.equal(x['label'], -1),
lambda: tf.constant('<unk>'),
# Otherwise use False/True.
lambda: tf.gather(['False', 'True'], x['label'])
)
joined = tf.strings.join(strs_to_join, separator=' ')
return {'inputs': joined, 'targets': label_name, 'idx': x['idx']}
return dataset.map(my_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
@gin.configurable
def record(dataset):
"""Convert ReCoRD examples to text2text examples.
ReCoRD contains a passage, query containing a '@placeholder' string, and a set
of entities that are the possible values of the placeholder. Each train and
validation example will have a list of answers, any of which would be
considered correct.
For example, a typical example from ReCoRD might look like
{
'passsage': 'This is the passage.',
'query': 'A @placeholder is a bird.',
'entities': ['penguin', 'potato', 'pigeon'],
'answers': ['penguin', 'pigeon'],
}
which this preprocessor would turn into the following two examples:
{
'inputs': 'record query: A @placeholder is a bird. entities: penguin, '
'potato, pigeon passage: This is the passage.',
'targets': 'penguin',
}
and
{
'inputs': 'record query: A @placeholder is a bird. entities: penguin, '
'potato, pigeon passage: This is the passage.',
'targets': 'potato',
}
Args:
dataset: a tf.data.Dataset to process.
Returns:
a tf.data.Dataset
"""
def process_answers(x):
"""Helper fn to get one example per answer."""
ex = x.copy()
num_answers = tf.size(ex['answers'])
def duplicate_along_first_dim(t):
n_duplicates = tf.math.maximum(num_answers, 1)
return tf.broadcast_to(
t, shape=tf.concat([[n_duplicates], tf.shape(t)], axis=0))
for k, v in x.items():
if k != 'idx':
ex[k] = duplicate_along_first_dim(v)
ex['targets'] = tf.cond(
tf.greater(num_answers, 0), lambda: x['answers'],
lambda: tf.constant(['<unk>']))
ex['idx'] = {
'passage': duplicate_along_first_dim(x['idx']['passage']),
'query': duplicate_along_first_dim(x['idx']['query']),
}
return ex
def my_fn(x):
"""Converts the processed example to text2text strings."""
passage = x['passage']
passage = tf.strings.regex_replace(passage,
r'(\.|\?|\!|\"|\')\n@highlight\n',
r'\1 ')
passage = tf.strings.regex_replace(passage, r'\n@highlight\n', '. ')
strs_to_join = [
'record query:', x['query'], 'entities:',
tf.strings.reduce_join(x['entities'], separator=', '), 'passage:',
passage
]
joined = tf.strings.join(strs_to_join, separator=' ')
ex = {}
# Store the data index in the returned example (used by eval)
ex['idx/passage'] = x['idx']['passage']
ex['idx/query'] = x['idx']['query']
ex['inputs'] = joined
# Note that "answers" has been converted to a single string by the
# process_answers function.
ex['targets'] = x['targets']
# Pass-through full list of answers for eval
ex['answers'] = x['answers']
return ex
dataset = dataset.map(
process_answers, num_parallel_calls=tf.data.experimental.AUTOTUNE)
dataset = dataset.unbatch()
return dataset.map(my_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
def multi_translate(dataset, source_language, target_language):
"""Convert a multi-translate dataset to a text2text pair.
For example, say the dataset returns examples which have a 'translations'
feature key so that examples have the following format:
{
...
'translations': {
'language': ['de', 'fr', 'en'],
'translation': ['Das ist gut.', 'Ca c'est bon', 'That is good.']
},
...
}
If source_language = 'de', target_language = 'en', then this function will
return examples of the format:
{'inputs': 'translate German to English: Das is gut.',
'targets': 'That is good.'}
Any other languages present in the dataset will be filtered out.
Args:
dataset: a tf.data.Dataset to process.
source_language: source language code (e.g. 'en') to translate from.
target_language: target language code (e.g. 'de') to translate to.
Returns:
A preprocessed tf.data.Dataset with the format listed above.
"""
def filter_fn(x):
langs = x['translations']['language']
# Test whether both source/target_language appear in the language list
source_in_langs = tf.reduce_any(tf.equal(source_language, langs))
target_in_langs = tf.reduce_any(tf.equal(target_language, langs))
return tf.logical_and(source_in_langs, target_in_langs)
def map_fn(x):
langs = x['translations']['language']
# Retrieve the index in langs where source/target_language appears
src_idx = tf.squeeze(tf.where(tf.equal(langs, source_language)))
tgt_idx = tf.squeeze(tf.where(tf.equal(langs, target_language)))
return {
source_language: x['translations']['translation'][src_idx],
target_language: x['translations']['translation'][tgt_idx],
}
dataset = dataset.filter(filter_fn)
dataset = dataset.map(
map_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE
)
return translate(dataset, source_language, target_language)
def definite_pronoun_resolution_simple(dataset,
label='wsc:'):
"""Converts DPR examples to a simple text to text format.
A typical example from the definite pronoun resolution dataset might look like
{
'sentence': 'Bob asked Tom if he can lend some money.',
'pronoun': 'he',
'candidates': ['Bob', 'Tom'],
'label': 1,
}
This will be transformed to
{
'inputs': 'wsc: Bob asked Tom if *he* can lend some money.'
'targets': 'Tom',
}
Args:
dataset: a tf.data.Dataset
label: a string, the label to prepend to the inputs.
Returns:
a tf.data.Dataset
"""
def my_fn(x):
"""Function to be called for every example."""
# If there are multiple instances of the pronoun in the sentence, the first
# one is the one that needs to be resolved.
inputs = [
label,
tf.strings.regex_replace(
x['sentence'],
tf.strings.join([r' (', x['pronoun'], r')( |\.|,)']),
r' *\1*\2',
replace_global=False,
),
]
return {
'inputs': tf.strings.join(inputs, separator=' '),
'targets': x['candidates'][x['label']],
}
return dataset.map(my_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
def next_sentence_prediction(dataset,
text_key='text',
reuse_sentences=True,
label_sentences=False,
p_neighbors=0.5,
label='nsp: ',
buffer_size=50000):
"""Create a dataset containing a next sentence prediction objective.
The input examples should have a key text_key associated with a tf.string
value.
The output examples have keys 'inputs' and 'targets'.
EXAMPLE OUTPUTS:
{
input: "nsp: sentence1: The man went to the store. sentence2: Penguins are "
"flightless birds.",
target: "not_next"
}
The "sentence1:" and "sentence2:" labels will be omitted if label_sentences is
False.
Args:
dataset: a tf.data.Dataset
text_key: a string, the key for the text feature to preprocess in the
dataset examples.
reuse_sentences: a boolean, see docs for `neighboring_pairs` for more info.
label_sentences: a boolean
p_neighbors: a float between 0 and 1, the probability that a sentence pair
will be neighbors.
label: a string, the label to prepend to the inputs.
buffer_size: an int, the size of the shuffle buffer used to get
non-neighboring sentences.
Returns:
a tf.data.Dataset
"""
sentence1_label, sentence2_label = '', ''
if label_sentences:
sentence1_label, sentence2_label = 'sentence1: ', 'sentence2: '
empty = tf.constant('', dtype=tf.string, shape=[1])
dataset = neighboring_pairs(
dataset, text_key=text_key, reuse_sentences=reuse_sentences)
dataset = dataset.shuffle(buffer_size).batch(2, drop_remainder=True)
def some_are_empty(*tensors):
"""See if at least one tensor has shape [0]."""
empty = [tf.equal(tf.size(t), 0) for t in tensors]
return tf.reduce_any(empty)
def my_fn(x):
"""Function to be applied to each example in dataset."""
use_neighbors = tf.random.uniform(shape=[]) < p_neighbors
firsts, seconds = tf.cond(
use_neighbors,
lambda: (x['first'], x['second']),
lambda: (x['first'], tf.stack([x['second'][1], x['second'][0]])),
)
relation_label = tf.cond(
use_neighbors,
lambda: 'next',
lambda: 'not_next',
)
inputs = []
for i in range(2):
first_inputs = firsts[i]
second_inputs = seconds[i]
def create_examples(first_i=first_inputs, second_i=second_inputs):
return tf.strings.join([
label,
sentence1_label,
first_i,
' ',
sentence2_label,
second_i,
])
inpt = tf.cond(
some_are_empty(first_inputs, second_inputs),
lambda: empty,
create_examples,
)
inputs.append(tf.strings.strip(inpt))
inputs = tf.reshape(inputs, [-1])
targets = tf.reshape(2 * [relation_label], [-1])
return {'inputs': inputs, 'targets': targets}
dataset = dataset.map(my_fn, num_parallel_calls=num_parallel_calls())
dataset = dataset.unbatch()
def example_len(x):
return tf.math.minimum(
tf.strings.length(x['inputs']), tf.strings.length(x['targets']))
# Remove examples with empty strings.
return dataset.filter(lambda x: example_len(x) > 0)
def lm(dataset):
"""Basic language modeling objective for text - empty inputs.
Given inputs with the format:
{"text": "Here is some text."}
This preprocess produces examples with the format
{"inputs": "", "targets": "Here is some text."}
Args:
dataset: A tf.data.Dataset to process.
Returns:
A preprocessed tf.data.Dataset.
"""
return dataset.map(
lambda x: {'inputs': '', 'targets': x['text']},
num_parallel_calls=tf.data.experimental.AUTOTUNE,
)
def _wsc_inputs(x):
"""Given an example from SuperGLUE WSC, compute the 'inputs' value.
The output will look like a fill in the blank with the pronoun blanked out.
For example, the text
'Mitchell asked Tom if he could lend some money.'
would be transformed to
'Mitchell asked Tom if X could lend some money.'
Args:
x: A dict that is an example from the WSC task of SuperGLUE.
Returns:
A scalar string tensor.
"""
words = tf.strings.split([x['text']], sep=' ').values
# We would need some special logic to handle the case where the pronoun is the
# first or last word in the text. None of the examples in WSC seem to have
# this, so we are ignoring these cases.
with tf.control_dependencies([
tf.assert_greater(x['span2_index'], 0),
tf.assert_less(x['span2_index'], tf.size(words)),
]):
pronoun_index = tf.identity(x['span2_index'])
def create_input():
with tf.control_dependencies(
[tf.assert_equal(words[pronoun_index], x['span2_text'])]):
return tf.strings.join(
[
tf.strings.reduce_join(words[:pronoun_index], separator=' '),
'X',
tf.strings.reduce_join(
words[pronoun_index + 1:], separator=' '),
],
separator=' ',
)
# Handle some special cases.
return tf.case(
{
# The issue here is that the pronoun is 'him,"' in the text.
tf.equal(
x['text'],
'The boy continued to whip the pony , and eventually the pony threw him over. John laughed out quite loud. \"Good for him,\" he said. '
):
lambda:
'The boy continued to whip the pony , and eventually the pony threw him over. John laughed out quite loud. "Good for X ," he said.',
# Using the span2_index, we get 'use' instead of 'it'.
tf.equal(
x['text'],
'When they had eventually calmed down a bit , and had gotten home, Mr. Farley put the magic pebble in an iron safe . Some day they might want to use it , but really for now, what more could they wish for?'
):
lambda:
'When they had eventually calmed down a bit , and had gotten home, Mr. Farley put the magic pebble in an iron safe . Some day they might want to use X , but really for now, what more could they wish for?'
},
default=create_input,
exclusive=True)
def wsc_simple(dataset,
label='wsc:',
correct_referent_only=False):
"""Converts SuperGLUE WSC examples to a simple text to text format.
A typical example from SuperGLUE WSC might look like
{
'text': 'Mitchell asked Tom if he could lend some money.',
'span1_text': 'Tom',
'span2_text': 'he',
'span2_index': 4,
}
This will be transformed to
{
'inputs': 'wsc: Bob asked Tom if *he* can lend some money.'
'targets': 'Tom',
}
The targets will always be the text of the referent regardless of whether it
is the correct referrent of the pronoun. Thus for training purposes, please
set `correct_referent_only` to be True.
Args:
dataset: a tf.data.Dataset
label: a string, the label to prepend to the inputs.
correct_referent_only: a bool, whether to filter out examples for which the
targets is not the correct referent of the pronoun.
Returns:
a tf.data.Dataset
"""
def map_fn(x):
"""Function to be called for every example in dataset."""
inputs = [
label,
tf.strings.regex_replace(
_wsc_inputs(x), r' X ', ' *' + x['span2_text'] + '* '),
]
referent = x['span1_text']
return {
'inputs': tf.strings.join(inputs, separator=' '),
# The reshape is necessary as otherwise the tensor has unknown rank.
'targets': tf.reshape(referent, shape=[]),
'label': x.get('label', 0),
'idx': x['idx'],
}
if correct_referent_only:
dataset = dataset.filter(lambda x: tf.cast(x.get('label', False), tf.bool))
return dataset.map(map_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
def wnli_simple(dataset, label='wsc:'):
"""Converts GLUE WNLI examples to a simple text to text format.
A typical example from WNLI might look like:
{
'sentence1': 'The fish ate the worm. It was tasty.',
'sentence2': 'The worm was tasty.',
'label': 1,
}
This will be transformed to:
{
'inputs': 'wsc: The fish ate the worm. *It* was tasty.',
'targets': 'The worm',
'premise': 'The fish ate the worm. It was tasty.,
'hypothesis': 'The worm was tasty.',
'label': 1,
}
This preprocessor has been manually verified to produce reasonable WSC
examples for the dev and test sets. Tasks using this preprocessor should only
be used eval and not train.
Args:
dataset: a tf.data.Dataset
label: a string, the label to prepend to the inputs.
Returns:
a tf.data.Dataset
"""
pronouns = ['he', 'she', 'they', 'it', 'her', 'his', 'their', 'them', 'him']
PronounMatch = collections.namedtuple( # pylint: disable=invalid-name
'PronounMatch', ['score', 'index_in_premise', 'candidate'])
def split_clean(s):
"""Returns array of words with punctuation and capitalization removed."""
words = [
re.sub(r'(\.|,|\?|\!)$', '', w) for w in s.strip().lower().split(' ')
]
return [w for w in words if w]
def get_all_pronoun_indices(s):
return [i for i, w in enumerate(s) if w in pronouns]
def get_post_match_size(hypothesis, words):
"""Returns len of largest prefix of words that is substr of hypothesis."""
hypothesis = ' '.join(hypothesis)
for i in range(len(words)):
if ' '.join(words[:i + 1]) not in hypothesis:
return i
return len(words)
def get_pre_match_size(hypothesis, words):
"""Returns len of largest suffix of words that is substr of hypothesis."""
return get_post_match_size(hypothesis[::-1], words[::-1])
def get_pronoun_match(premise, hypothesis, index):
"""Return the PronounMatch for the pronoun at `index` in premise."""
pre, post = premise[:index], premise[index + 1:]
pre_match_size = get_pre_match_size(hypothesis, pre)
post_match_size = get_post_match_size(hypothesis, post)
score = pre_match_size + post_match_size
candidate = ''
if score:
pre_match = pre[-pre_match_size or len(pre):]
post_match = post[:post_match_size]
m = re.search(' '.join(pre_match + [r'(.+)'] + post_match),
' '.join(hypothesis))
if not m:
# Handle cases where the candidate is at the start of the hypthesis.
m = re.search(' '.join([r'^(.+)'] + post_match), ' '.join(hypothesis))
if not m:
# Handle cases where the candidate is at the end of the hypthesis.
m = re.search(' '.join(pre_match + [r'(.+)$']), ' '.join(hypothesis))
if m:
candidate = m.group(1)
return PronounMatch(
score=score, index_in_premise=index, candidate=candidate)
def get_best_pronoun_match(premise, hypothesis):
"""Returns the match for the pronoun in the premise to disambiguate."""
pronoun_indices = get_all_pronoun_indices(premise)
scoredpronouns = [
get_pronoun_match(premise, hypothesis, index)
for index in pronoun_indices
]
return max(scoredpronouns, key=lambda x: x.score)
def highlight(sentence, index):
words = sentence.split(' ')
word = words[index]
if word[-1] in ['.', ',', '!', '?']:
highlighted = '*{}* {}'.format(word[:-1], word[-1])
else:
highlighted = '*{}*'.format(word)
return ' '.join(words[:index] + [highlighted] + words[index + 1:])
def make_nonpossessive(word):
# WSC simple targets will never be possessive, even when the pronoun is
# possesive.
if word.endswith("'"):
return word[:-1]
elif word.endswith("'s"):
return word[:-2]
else:
return word
def clean_up(candidate):
words = candidate.split(' ')
# Sometimes the candidate extraction messes up, and the candidate will start
# with the start of the hypothesis and extend to the correct candidate. We
# can try to clean up the candidate in some cases by removing everything up
# to the last article in the sentence.
article_index = max(
[words.index(art) for art in {'a', 'an', 'the'} if art in words] or [0])
return ' '.join(words[article_index:])
def process_candidate(candidate, hypothesis):
"""Handles special cases and adds proper punctuation/capitalization."""
candidate = clean_up(candidate)
pattern = '({})'.format(' '.join([
r'{}(?:\.|,|\?|\!)?'.format(re.escape(c)) for c in candidate.split(' ')
]))
m = re.search(pattern, hypothesis, re.IGNORECASE)
if not m:
raise ValueError(
'Unable to find candidate "{}" in hypothesis "{}".'.format(
candidate, hypothesis))
candidate = m.group(1)
if candidate and candidate[-1] in ['.', ',', '!', '?']:
candidate = candidate[:-1]
return make_nonpossessive(candidate)
def compute_inputs_and_targets(premise, hypothesis):
"""Compute inputs and targets for WNLI simple."""
premise = tf.compat.as_text(premise.numpy())
hypothesis = tf.compat.as_text(hypothesis.numpy())
match = get_best_pronoun_match(
split_clean(premise), split_clean(hypothesis))
targets = process_candidate(match.candidate, hypothesis)
inputs = '{} {}'.format(label, highlight(premise, match.index_in_premise))
return inputs, targets
def map_fn(x):
inputs, targets = tf.py_function(
compute_inputs_and_targets,
inp=[x['sentence1'], x['sentence2']],
Tout=[tf.string, tf.string])
return {
# The reshape is necessary as otherwise the tensor has unknown rank.
'inputs': tf.reshape(inputs, shape=[]),
'targets': tf.reshape(targets, shape=[]),
'premise': x['sentence1'],
'hypothesis': x['sentence2'],
'label': x.get('label', 0),
'idx': x['idx'],
}
return dataset.map(map_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
# ======================Token Preprocessors=====================================
@gin.configurable
def select_random_chunk(dataset,
max_length=gin.REQUIRED,
feature_key='targets',
**unused_kwargs):
"""Token-preprocessor to extract one span of at most `max_length` tokens.
If the token sequence is longer than `max_length`, then we return a random
subsequence. Otherwise, we return the full sequence.
This is generally followed by split_tokens.
Args:
dataset: a tf.data.Dataset with dictionaries containing the key feature_key.
max_length: an integer
feature_key: an string
Returns:
a dataset
"""
def _my_fn(x):
"""Select a random chunk of tokens.
Args:
x: a 1d Tensor
Returns:
a 1d Tensor
"""
tokens = x[feature_key]
n_tokens = tf.size(tokens)
num_segments = tf.cast(
tf.ceil(tf.cast(n_tokens, tf.float32)
/ tf.cast(max_length, tf.float32)),
tf.int32)
start = max_length * tf.random_uniform(
[], maxval=num_segments, dtype=tf.int32)
end = tf.minimum(start + max_length, n_tokens)
return {feature_key: tokens[start:end]}
# Filter empty examples.
dataset = dataset.filter(lambda x: tf.not_equal(tf.size(x[feature_key]), 0))
return dataset.map(_my_fn, num_parallel_calls=num_parallel_calls())
@gin.configurable
def reduce_concat_tokens(dataset,
feature_key='targets',
batch_size=128,
**unused_kwargs):
"""Token-preprocessor to concatenate multiple unrelated documents.
If we want to generate examples of exactly the right length,
(to avoid wasting space on padding), then we use this function, folowed by
split_tokens.
Args:
dataset: a tf.data.Dataset with dictionaries containing the key feature_key.
feature_key: an string
batch_size: an integer - how many documents to concatenate into one
Returns:
a dataset
"""
dataset = dataset.map(lambda x: {feature_key: x[feature_key]},
num_parallel_calls=tf.data.experimental.AUTOTUNE)
dataset = dataset.padded_batch(batch_size, padded_shapes={feature_key: [-1]})
def _my_fn(x):
tokens = tf.reshape(x[feature_key], [-1])
# strip padding
tokens = tf.boolean_mask(tokens, tf.cast(tokens, tf.bool))
return {feature_key: tokens}
return dataset.map(_my_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
@gin.configurable
def split_tokens(dataset,
min_tokens_per_segment=None,
max_tokens_per_segment=gin.REQUIRED,
feature_key='targets',
**unused_kwargs):
"""Split examples into multiple examples each.
The intended use case is to break up long examples for use in unsupervised
transfer-learning.
This function is generally preceded by select_random_chunk.
If min_tokens_per_segment is provided, the segment length is chosen randomly
per document from a log-uniform distribution. If min_tokens_per_segment is
None, then the segment length is max_tokens_per_segment (except for a possibly
shorter last segment in each document).
Args:
dataset: a tf.data.Dataset with dictionaries containing the key feature_key.
min_tokens_per_segment: an optional integer
max_tokens_per_segment: an integer, the maximum number of tokens in each
segment. Only the final segment may be shorter.
feature_key: a string, the feature to split
Returns:
a dataset
"""
def _split_tokens(x):
"""Split one token sequence into multiple multiple."""
tokens = x[feature_key]
n_tokens = tf.size(tokens)
if min_tokens_per_segment is None:
length = max_tokens_per_segment
else:
# pick a length - log-uniformly distributed
length = tf.cast(tf.exp(tf.random_uniform(
[],
minval=math.log(min_tokens_per_segment),
maxval=math.log(max_tokens_per_segment))), tf.int32)
# Pad to a multiple of length, then use tf.reshape to split up the tokens
# into num_segments segments each of the given length.
num_segments = tf.cast(
tf.ceil(tf.cast(n_tokens, tf.float32) / tf.cast(length, tf.float32)),
tf.int32)
padding = num_segments * length - tf.size(tokens)
tokens = tf.pad(tokens, [[0, padding]])
return tf.reshape(tokens, [-1, length])
def _strip_padding(x):
return {feature_key: tf.boolean_mask(x, tf.cast(x, tf.bool))}
# Filter empty examples.
dataset = dataset.filter(lambda x: tf.not_equal(tf.size(x[feature_key]), 0))
dataset = dataset.map(_split_tokens, num_parallel_calls=num_parallel_calls())
dataset = dataset.unbatch()
return dataset.map(
_strip_padding, num_parallel_calls=tf.data.experimental.AUTOTUNE)
@gin.configurable
def split_tokens_to_inputs_length(dataset, sequence_length, **unused_kwargs):
return split_tokens(dataset,
max_tokens_per_segment=sequence_length['inputs'])
@gin.configurable
def split_tokens_to_targets_length(dataset, sequence_length, **unused_kwargs):
return split_tokens(dataset,
max_tokens_per_segment=sequence_length['targets'])
@gin.configurable
def split_tokens_to_random_length(dataset, sequence_length, **unused_kwargs):
return split_tokens(dataset,
min_tokens_per_segment=8,
max_tokens_per_segment=sequence_length['inputs'])
@gin.configurable()
def denoise(dataset,
output_features,
noise_density=gin.REQUIRED,
noise_mask_fn=gin.REQUIRED,
inputs_fn=gin.REQUIRED,
targets_fn=None,
**unused_kwargs):
"""Gin-configurable token preprocessor for self-supervised denoising tasks.
This function takes a dataset containing "targets" sequences,
and turns each sequence into a dictionary containing:
{
"inputs": noisy version of the original sequence
"targets": the full original sequence or missing parts of original sequence
}
In particular, for each sequence, we choose a boolean noise_mask identifying
which tokens in the sequence to corrupt, as defined by the given
noise_mask_fn.
Given the sequence and the noise mask, we generate the inputs and targets
using the given inputs_fn and targets_fn respectively.
The self-supervised tasks vary along these axes:
- noise_density: What fraction of the tokens to select as noise
- noise_mask_fn: What pattern should the noise mask follow
(iid, regular segments, etc.)
- inputs_fn: How to apply the noise
(drop noise tokens, replace with sentinels, etc.)
- targets_fn: How to represent the output
(full sequence, only non-noise tokens, etc.)
Note: Some functionality has been deleted, which we may or may not want to
restore at a later date. The code for this functionality can be found in
the deleted code for this CL. In particular:
- mixture of masking and random replacement
- task labels prepended to the inputs
Args:
dataset: A tf.data.Dataset to process.
output_features: a dict mapping feature name to t5.data.Feature.
noise_density: a float
noise_mask_fn: a function from (length, noise_density) -> boolean mask
inputs_fn: a function from (tokens, noise_mask, vocabulary) -> tokens
targets_fn: a function from (tokens, noise_mask, vocabulary) -> tokens
Returns:
A preprocessed tf.data.Dataset.
"""
def my_fn(features):
"""Map function."""
tokens = features['targets']
vocabulary = output_features['targets'].vocabulary
if ('inputs' in output_features and
vocabulary != output_features['inputs'].vocabulary):
raise ValueError(
'denoise creates inputs based on tokenized targets but was applied '
'to a task that uses different vocabularies for inputs and targets.'
)
noise_mask = noise_mask_fn(tf.size(tokens), noise_density)
inputs = inputs_fn(tokens, noise_mask, vocabulary)
if targets_fn:
targets = targets_fn(tokens, noise_mask, vocabulary)
else:
targets = tokens
return {'inputs': inputs, 'targets': targets}
return dataset.map(my_fn, num_parallel_calls=num_parallel_calls())
def trivia_qa_truncate_inputs(dataset, output_features, sequence_length):
"""Gin configurable token preprocessor for the trivia QA dataset.
This function takes a dataset containing "targets" and "inputs". It searches
for the "targets" in the "inputs" and truncates the "inputs" to
`sequence_length` while ensuring that the "targets" are present in the
"inputs". The function will randomly select a subset of "inputs".
If "targets" are not found in the "inputs", then the example is
is dropped from the dataset.
E.g.
Input dataset
{
"inputs": [0, 3, 5, 7, 9, 11, 13, 15, 17, 18]
"targets": [5, 7, 9]
}
Output dataset (assuming sequence_length['inputs'] = 4)
{
"inputs": [3, 5, 7, 9]
"targets": [5, 7, 9]
}
or
{
"inputs": [5, 7, 9, 11]
"targets": [5, 7, 9]
}
Args:
dataset: a tf.data.Dataset with dictionaries containing the "inputs" and
"targets".
output_features: unused by this function.
sequence_length: a dict, with keys as "inputs" and "targets" indicating the
maximum number of tokens in each of the sequences.
Returns:
a dataset
"""
del output_features
def my_fn(features):
"""Function to map original dataset to the new dataset."""
inputs = features['inputs']
targets = features['targets']
ans_len = tf.shape(targets)[0]
max_input_tokens = sequence_length['inputs']
def truncate_inputs():
"""Helper function to truncate the inputs."""
def answer_in_context(context, answer):
"""Helper function that checks if the answer is present in the context.
Args:
context: Tensor, tokenized representation of the context
answer: Tensor, tokenized representation of the answer
Returns:
result: boolean, indicates if the answer was present in the context.
pos_mask: boolean mask, a mask for every possible start position of
the answer in the context. Indicates whether the answer starts at
the particular position.
"""
conv_inp = tf.reshape(tf.cast(context, tf.float32), [1, -1, 1])
ans_len = tf.shape(answer)[0]
filters = tf.eye(ans_len, dtype=tf.float32)
# Assume context len is N and answer len is M.
# Use a convolution to create a matrix of (N-M) x M elements where
# each row of the matrix is a sequence of len M. This matrix contains
# all possible contiguous sequences of length M from the context.
# Every row of this matrix is compared with the answer to check if the
# answer exists in the context.
strided = tf.nn.conv1d(conv_inp,
tf.reshape(filters, [ans_len, 1, ans_len]), 1,
'VALID')
strided = tf.cast(strided[0], answer.dtype)
pos_mask = tf.reduce_all(
tf.equal(strided, tf.reshape(answer, [1, -1])), 1)
result = tf.reduce_any(pos_mask)
return result, pos_mask
def slice_inputs(inputs, answer_len, pos_mask):
"""Helper function to slice inputs while keeping the answer."""
ans_start_pos = tf.to_int32(tf.where(pos_mask)[0][0])
inputs_len = tf.shape(inputs)[0]
start_range_min = tf.maximum(
0, ans_start_pos - (max_input_tokens - answer_len))
start_range_max = tf.minimum(ans_start_pos,
inputs_len - max_input_tokens) + 1
start_pos = tf.random.uniform([],
minval=start_range_min,
maxval=start_range_max,
dtype=tf.int32)
return inputs[start_pos:start_pos + max_input_tokens]
result, pos_mask = answer_in_context(inputs, targets)
truncated_inputs = tf.cond(
result, lambda: slice_inputs(inputs, ans_len, pos_mask),
lambda: tf.constant([], dtype=inputs.dtype))
return truncated_inputs
inputs = tf.cond(
tf.shape(inputs)[0] > max_input_tokens, truncate_inputs, lambda: inputs)
return {'inputs': inputs, 'targets': features['targets']}
dataset = dataset.map(my_fn, num_parallel_calls=num_parallel_calls())
return dataset.filter(lambda x: tf.size(x['inputs']) > 0)
@gin.configurable()
def unsupervised(dataset, preprocessors=None, **kwargs):
"""Configure this to point at unsupervised preprocessors.
This function creates an extra level of indirection in case we want
different unsupervised pretraining functions in the future which do not
fit into the denoise() framework.
Args:
dataset: A tf.data.Dataset to process.
preprocessors: a list of token-preprocessor functions
**kwargs: passthrough keyword arguments for token preprocessors
Returns:
A preprocessed tf.data.Dataset.
"""
if preprocessors is None:
tf.logging.warn(
'unsupervised preprocessor got preprocessors=None; no preprocessing '
'will be applied.'
)
return dataset
for p in preprocessors:
dataset = p(dataset, **kwargs)
return dataset
@gin.configurable()
def iid_noise_mask(length, noise_density):
"""Independent and identically distributed token noise.
Args:
length: an int32 scalar
noise_density: a float - approximate density of output mask
Returns:
a boolean tensor with shape [length]
"""
return tf.random.uniform([length]) < noise_density
@gin.configurable()
def regular_noise_mask(length,
noise_density,
min_span_length=1,
max_span_length=5):
"""Noise mask consisting of equally spaced spans of equal length.
The span length and the offset are chosen randomly per-example.
The beginning and end of the sequence may be part of shorter spans of noise.
For example, if noise_density=0.25 and a span length of 2 is chosen,
then the output might be:
[T F F F F F F T T F F F F F F T T F F F F F F T T F F]
Args:
length: an int32 scalar
noise_density: a float - approximate density of output mask
min_span_length: an integer
max_span_length: an integer
Returns:
a boolean tensor with shape [length]
"""
span_length = tf.random.uniform([],
minval=min_span_length,
maxval=max_span_length + 1,
dtype=tf.int32)
period = tf.cast(
tf.round(tf.cast(span_length, tf.float32) / noise_density), tf.int32)
offset = tf.random.uniform([], maxval=period, dtype=tf.int32)
return (tf.range(length, dtype=tf.int32) + offset) % period < span_length
@gin.configurable()
def random_spans_noise_mask(length,
noise_density,
mean_noise_span_length=3.0):
"""Noise mask consisting of random spans of noise tokens.
The number of noise tokens and the number of noise spans and non-noise spans
are determined deterministically as follows:
num_noise_tokens = round(length * noise_density)
num_nonnoise_spans = num_noise_spans = round(
num_noise_tokens / mean_noise_span_length)
Spans alternate between non-noise and noise, beginning with non-noise.
Subject to the above restrictions, all masks are equally likely.
Args:
length: an int32 scalar (length of the incoming token sequence)
noise_density: a float - approximate density of output mask
mean_noise_span_length: a number
Returns:
a boolean tensor with shape [length]
"""
orig_length = length
# increase length to avoid degeneracy
length = tf.maximum(length, 2)
def to_int(x):
return tf.cast(x, tf.int32)
def to_float(x):
return tf.cast(x, tf.float32)
num_noise_tokens = to_int(tf.round(to_float(length) * noise_density))
# avoid degeneracy by ensuring positive numbers of noise and nonnoise tokens.
num_noise_tokens = tf.minimum(tf.maximum(num_noise_tokens, 1), length - 1)
num_noise_spans = to_int(
tf.round(to_float(num_noise_tokens) / mean_noise_span_length))
# avoid degeneracy by ensuring positive number of noise spans
num_noise_spans = tf.maximum(num_noise_spans, 1)
num_nonnoise_tokens = length - num_noise_tokens
# pick the lengths of the noise spans and the non-noise spans
def _random_segmentation(num_items, num_segments):
"""Partition a sequence of items randomly into non-empty segments.
Args:
num_items: an integer scalar > 0
num_segments: an integer scalar in [1, num_items]
Returns:
a Tensor with shape [num_segments] containing positive integers that add
up to num_items
"""
first_in_segment = tf.pad(
tf.random.shuffle(to_int(tf.range(num_items - 1) < num_segments - 1),
seed=123),
[[1, 0]])
segment_id = tf.cumsum(first_in_segment)
segment_length = tf.segment_sum(tf.ones_like(segment_id), segment_id)
return segment_length
noise_span_lengths = _random_segmentation(num_noise_tokens, num_noise_spans)
nonnoise_span_lengths = _random_segmentation(
num_nonnoise_tokens, num_noise_spans)
interleaved_span_lengths = tf.reshape(
tf.stack([nonnoise_span_lengths, noise_span_lengths], axis=1),
[num_noise_spans * 2])
span_starts = tf.cumsum(interleaved_span_lengths)[:-1]
span_start_indicator = tf.unsorted_segment_sum(
tf.ones_like(span_starts), span_starts, length)
span_num = tf.cumsum(span_start_indicator)
is_noise = tf.equal(span_num % 2, 1)
return is_noise[:orig_length]
@gin.configurable
def random_spans_helper(inputs_length=gin.REQUIRED,
noise_density=gin.REQUIRED,
mean_noise_span_length=gin.REQUIRED,
extra_tokens_per_span_inputs=gin.REQUIRED,
extra_tokens_per_span_targets=gin.REQUIRED):
"""Training parameters to avoid padding with random_spans_noise_mask.
When training a model with random_spans_noise_mask, we would like to set the
other training hyperparmeters in a way that avoids padding. This function
helps us compute these hyperparameters.
We assume that each noise span in the input is replaced by
extra_tokens_per_span_inputs sentinel tokens, and each non-noise span in the
targets is replaced by extra_tokens_per_span_targets sentinel tokens.
This function tells us the required number of tokens in the raw example (for
split_tokens()) as well as the length of the encoded targets.
Args:
inputs_length: an integer - desired length of the tokenized inputs sequence
noise_density: a float
mean_noise_span_length: a float
extra_tokens_per_span_inputs: an integer
extra_tokens_per_span_targets: an integer
Returns:
tokens_length: length of original text in tokens
targets_length: an integer - length in tokens of encoded targets sequence
"""
def _tokens_length_to_inputs_length_targets_length(tokens_length):
num_noise_tokens = int(round(tokens_length * noise_density))
num_nonnoise_tokens = tokens_length - num_noise_tokens
num_noise_spans = int(round(num_noise_tokens / mean_noise_span_length))
# inputs contain all nonnoise tokens, sentinels for all noise spans
# and one EOS token.
return (
num_nonnoise_tokens +
num_noise_spans * extra_tokens_per_span_inputs + 1,
num_noise_tokens +
num_noise_spans * extra_tokens_per_span_targets + 1)
tokens_length = inputs_length
while (_tokens_length_to_inputs_length_targets_length(tokens_length + 1)[0]
<= inputs_length):
tokens_length += 1
inputs_length, targets_length = (
_tokens_length_to_inputs_length_targets_length(tokens_length))
# minor hack to get the targets length to be equal to inputs length
# which is more likely to have been set to a nice round number.
if noise_density == 0.5 and targets_length > inputs_length:
tokens_length -= 1
targets_length -= 1
tf.logging.info(
'tokens_length=%s inputs_length=%s targets_length=%s '
'noise_density=%s mean_noise_span_length=%s ' %
(tokens_length, inputs_length, targets_length,
noise_density, mean_noise_span_length))
return tokens_length, targets_length
@gin.configurable
def random_spans_tokens_length():
"""Helper for gin-configuring split_tokens with random_spans_noise_mask."""
return random_spans_helper()[0]
@gin.configurable
def random_spans_targets_length():
"""Helper for gin-configuring the targets sequence length."""
return random_spans_helper()[1]
@gin.configurable()
def random_prefix_noise_mask(length, noise_density):
"""First part of the sequence is noise (for prefix_lm).
The length of the prefix is chosen uniformly between [1, length)
noise_density must be 0.5
TODO(noam): figure out some distribution to use if noise_density != 0.5
Args:
length: an int32 scalar
noise_density: a float - must equal 0.5
Returns:
a boolean tensor with shape [length]
"""
if noise_density != 0.5:
raise NotImplementedError(
'noise density must equal 0.5 for random_prefix_noise_mask')
max_input_tokens = length - 1
min_input_tokens = tf.minimum(max_input_tokens, 1)
num_input_tokens = tf.random.uniform(
[], minval=min_input_tokens, maxval=max_input_tokens + 1, dtype=tf.int32)
return tf.range(length, dtype=tf.int32) < num_input_tokens
@gin.configurable()
def sentinel_id(vocabulary, return_value=None):
"""Token ID to use as a sentinel.
By default, we use the last token in the vocabulary.
Args:
vocabulary: a t5.data.vocabularies.Vocabulary
return_value: an optional integer
Returns:
an integer
"""
if return_value is not None:
return return_value
return vocabulary.vocab_size - 1
@gin.configurable()
def noise_token_to_sentinel(tokens, noise_mask, vocabulary):
"""Replace each noise token with the given sentinel.
Args:
tokens: a 1d integer Tensor
noise_mask: a boolean Tensor with the same shape as tokens
vocabulary: a vocabulary.Vocabulary
Returns:
a Tensor with the same shape and dtype as tokens
"""
return tf.where_v2(noise_mask,
tf.cast(sentinel_id(vocabulary), tokens.dtype),
tokens)
@gin.configurable()
def noise_span_to_sentinel(tokens, noise_mask, vocabulary):
"""Replace each run of consecutive noise tokens with a single sentinel.
Args:
tokens: a 1d integer Tensor
noise_mask: a boolean Tensor with the same shape as tokens
vocabulary: a vocabulary.Vocabulary
Returns:
a Tensor with the same shape and dtype as tokens
"""
tokens = tf.where_v2(noise_mask,
tf.cast(sentinel_id(vocabulary), tokens.dtype),
tokens)
prev_token_is_noise = tf.pad(noise_mask[:-1], [[1, 0]])
subsequent_noise_tokens = tf.logical_and(noise_mask, prev_token_is_noise)
return tf.boolean_mask(tokens, tf.logical_not(subsequent_noise_tokens))
@gin.configurable()
def nonnoise_span_to_sentinel(tokens, noise_mask, vocabulary):
return noise_span_to_sentinel(
tokens, tf.logical_not(noise_mask), vocabulary)
@gin.configurable()
def noise_span_to_unique_sentinel(tokens, noise_mask, vocabulary):
"""Replace each run of consecutive noise tokens with a different sentinel.
The idea here is to be able to align the dropped spans in the inputs
with the markers in the targets.
We want to generate training examples like
"We hold X to be Y that" -> "X these truths Y self evident Z"
Sentinels assigned in decreasing order within the sequence starting at
vocabulary.size - 1. That is, we appropriate the last tokens in the
vocabulary for additional use as sentinels.
TODO(noam): we may want to try enlarging the vocabulary and leaving room
for the sentinels instead. However, this requires enlarging the embedding
tables in the model, so that is a bigger change.
Args:
tokens: a 1d integer Tensor
noise_mask: a boolean Tensor with the same shape as tokens
vocabulary: a vocabulary.Vocabulary
Returns:
a Tensor with the same shape and dtype as tokens
"""
vocab_size = vocabulary.vocab_size
prev_token_is_noise = tf.pad(noise_mask[:-1], [[1, 0]])
first_noise_tokens = tf.logical_and(
noise_mask, tf.logical_not(prev_token_is_noise))
subsequent_noise_tokens = tf.logical_and(noise_mask, prev_token_is_noise)
sentinel = vocab_size - tf.cumsum(tf.cast(first_noise_tokens, tokens.dtype))
tokens = tf.where_v2(first_noise_tokens, sentinel, tokens)
return tf.boolean_mask(tokens, tf.logical_not(subsequent_noise_tokens))
@gin.configurable()
def nonnoise_span_to_unique_sentinel(tokens, noise_mask, vocabulary):
return noise_span_to_unique_sentinel(
tokens, tf.logical_not(noise_mask), vocabulary)
@gin.configurable()
def drop_noise_tokens(tokens, noise_mask, unused_vocabulary):
"""Drop noise tokens without inserting a sentinel.
Args:
tokens: a 1d integer Tensor
noise_mask: a boolean Tensor with the same shape as tokens
unused_vocabulary: a vocabulary.Vocabulary
Returns:
a Tensor with the same shape and dtype as tokens
"""
return tf.boolean_mask(tokens, tf.logical_not(noise_mask))
@gin.configurable()
def drop_nonnoise_tokens(tokens, noise_mask, unused_vocabulary):
"""Drop non-noise tokens without inserting a sentinel.
Args:
tokens: a 1d integer Tensor
noise_mask: a boolean Tensor with the same shape as tokens
unused_vocabulary: a vocabulary.Vocabulary
Returns:
a Tensor with the same shape and dtype as tokens
"""
return tf.boolean_mask(tokens, noise_mask)
@gin.configurable()
def permute_noise_tokens(tokens, noise_mask, unused_vocabulary):
"""Permute the noise tokens, keeping the non-noise tokens where they are.
Args:
tokens: a 1d integer Tensor
noise_mask: a boolean Tensor with the same shape as tokens
unused_vocabulary: a vocabulary.Vocabulary
Returns:
a Tensor with the same shape and dtype as tokens
"""
masked_only = tf.boolean_mask(tokens, noise_mask)
permuted = tf.random.shuffle(masked_only)
# pad to avoid errors when it has size 0
permuted = tf.pad(permuted, [[0, 1]])
indices = tf.cumsum(tf.cast(noise_mask, tf.int32), exclusive=True)
return tf.where_v2(noise_mask,
tf.gather(permuted, indices),
tokens)
@gin.configurable()
def noise_token_to_gathered_token(tokens, noise_mask, unused_vocabulary):
"""Replace each noise token with a random token from the sequence.
Args:
tokens: a 1d integer Tensor
noise_mask: a boolean Tensor with the same shape as tokens
unused_vocabulary: a vocabulary.Vocabulary
Returns:
a Tensor with the same shape and dtype as tokens
"""
indices = tf.random_uniform(
shape=tf.shape(tokens), maxval=tf.size(tokens), dtype=tf.int32)
return tf.where_v2(noise_mask,
tf.gather(tokens, indices),
tokens)
@gin.configurable()
def noise_token_to_random_token(tokens, noise_mask, vocabulary,
num_reserved_tokens=3):
"""Replace each noise token with a random token from the vocabulary.
Args:
tokens: a 1d integer Tensor
noise_mask: a boolean Tensor with the same shape as tokens
vocabulary: a vocabulary.Vocabulary
num_reserved_tokens: an integer
Returns:
a Tensor with the same shape and dtype as tokens
"""
return tf.where_v2(noise_mask,
tf.random.uniform(
tf.shape(tokens),
minval=num_reserved_tokens,
maxval=vocabulary.vocab_size,
dtype=tokens.dtype),
tokens)
@gin.configurable()
def noise_token_to_random_token_or_sentinel(tokens, noise_mask, vocabulary,
random_prob=0.1):
"""Replace each noise token with a random token or a sentinel.
For each masked token, with probability random_prob, we replace it by a
random token from the vocabulary. Otherwise, we replace it with a sentinel.
Args:
tokens: a 1d integer Tensor
noise_mask: a boolean Tensor with the same shape as tokens
vocabulary: a vocabulary.Vocabulary
random_prob: a float
Returns:
a Tensor with the same shape and dtype as tokens
"""
use_random = tf.random.uniform(tf.shape(tokens)) < random_prob
return tf.where_v2(
use_random,
noise_token_to_random_token(tokens, noise_mask, vocabulary),
noise_token_to_sentinel(tokens, noise_mask, vocabulary))
@gin.configurable
def take(dataset, num_examples=-1, **unused_kwargs):
"""Takes the first `num_examples` examples from the dataset.
This is done to simulate that the dataset is smaller than it actually is. The
result will be cached via `tf.data.Dataset.cache`. This ensures that the same
examples get repeated if `dataset` is stochastic and `repeat()` is called on
the dataset after `take`. To use this preprocessor, make sure your resulting
dataset can fit in memory.
Args:
dataset: tf.data.Dataset, dataset to process.
num_examples: int, the number of examples to take from the dataset. If
`num_examples == -1`, the new dataset will have as many examples as the
original dataset (i.e., no truncation will occur).
Returns:
A tf.data.Dataset with at most `num_examples` examples.
"""
if num_examples == -1:
return dataset
else:
return dataset.take(num_examples).cache()
def parse_tsv(dataset,
field_names,
field_delim='\t'):
"""Splits TSV lines into dict examples mapping field name to string value.
Args:
dataset: a `tf.data.Dataset` containing comma/tab-delimited strings.
field_names: a list of strings, the ordered names of the TSV fields.
field_delim: a string, the delimiter to split on e.g. ',' for csv.
Returns:
A `tf.data.Dataset` containing dict examples mapping field name to string
value.
"""
def parse_line(line):
return dict(zip(
field_names,
tf.io.decode_csv(
line, record_defaults=[''] * len(field_names),
field_delim=field_delim, use_quote_delim=False)
))
return dataset.map(
parse_line, num_parallel_calls=tf.data.experimental.AUTOTUNE)
def preprocess_tsv(dataset,
field_delim='\t',
num_fields=2,
inputs_format='{0}',
targets_format='{1}'):
r"""Parse tab-delimited strings into inputs and targets.
This function takes a tf.data.Dataset of strings, each of which contains
tab-delimited fields. The function returns a tf.data.Dataset of feature
dictionaries of the form {"inputs": string, "targets": string}.
inputs_format contains a template string and field numbers used to produce
the "inputs" string.
targets_format contains a template string and field numbers used to produce
the "targets" string.
Example:
The input dataset contains the lines:
"6,7,42"
"2,9,18"
preprocess_tsv(dataset,
field_delim=',',
inputs_format='numerator: {2} denominator: {1}',
targets_format='quotient: {0}'
would produce a dataset containing the dictionaries:
{"inputs": "numerator: 42 denomnator: 7", "targets": "quotient: 6"}
{"inputs": "numerator: 18 denomnator: 9", "targets": "quotient: 2"}
Args:
dataset: a tf.data.Dataset containing comma/tab-delimited strings.
field_delim: a string, the delimiter to split on e.g. ',' for csv.
num_fields: an integer
inputs_format: a string, the desired output format with placeholders for
field values.
targets_format: a string, the desired output format with placeholders for
field values.
Returns:
a tf.data.Dataset of feature dictionaries with 'inputs' and
'targets' features.
"""
def _format_part(part, field_values):
found = re.findall(r'{(\d)}', part)
if found:
return field_values[int(found[0])]
else:
return part
def _format(format_string, field_values):
parts = [_format_part(p, field_values)
for p in re.split(r'({\d})', format_string)]
return tf.strings.join(parts)
def _parse_fn(line):
"""Function to process a line."""
field_values = tf.io.decode_csv(
line, record_defaults=[''] * num_fields,
field_delim=field_delim, use_quote_delim=False)
return {'inputs': _format(inputs_format, field_values),
'targets': _format(targets_format, field_values)}
return dataset.map(
_parse_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
