File size: 13,249 Bytes
da0475a f52f478 da0475a f52f478 e37c8bf da0475a 210a627 da0475a 856a31b b6b3cf8 856a31b d4f54c6 da0475a 856a31b da0475a b6b3cf8 da0475a 2b2a2db a4fe76a da0475a 856a31b da0475a a4fe76a 856a31b da0475a a4fe76a da0475a a4fe76a a587173 da0475a 0b6cd18 da0475a 0b6cd18 da0475a 0b6cd18 da0475a 0b6cd18 da0475a 0b6cd18 da0475a 0b6cd18 da0475a 0b6cd18 da0475a b6b3cf8 da0475a b6b3cf8 da0475a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 |
# coding=utf-8
# Copyright 2020 BigScience Contributors.
#
# 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.
"""P3 (Public Pool of Prompts)"""
import datasets
import glob
import json
import os
from collections import defaultdict
import tensorflow as tf
_CITATION = """\
TODO"""
_DESCRIPTION = """\
P3 (Pubic Pool of Prompts)is a collection of prompted English datasets covering a diverse set of NLP tasks. A prompt is the combination of an input template and a target template. The templates are functions mapping a data example into natural language for the input and target sequences. For example, in the case of an NLI dataset, the data example would include fields for *Premise, Hypothesis, Label*. An input template would be *If {Premise} is true, is it also true that {Hypothesis}?*, whereas a target template can be defined with the label choices *Choices[label]*. Here *Choices* is prompt-specific metadata that consists of the options *yes, maybe, no* corresponding to *label* being entailment (0), neutral (1) or contradiction (2).
Prompts are collected using [Promptsource](https://github.com/bigscience-workshop/promptsource), an interface to interactively write prompts on datasets, and collect prompt-specific metadata such as evaluation metrics. As of October 13th, there are 2'000 prompts collected for 270+ data(sub)sets. The collection of prompts is publicly available on [Promptsource](https://github.com/bigscience-workshop/promptsource).
To train [T0*](https://huggingface.co/bigscience/T0pp), we used a subset of the prompts available in Promptsource (see details [here](https://huggingface.co/bigscience/T0pp#training-data)). However, some of the prompts use `random.choice`, a method that selects uniformly at random an option in a list of valid possibilities. For reproducibility purposes, we release the collection of prompted examples used to train T0*. **The data available here are the materialized version of the prompted datasets used in [Multi-task enables task zero-shot generalization](TODO) which represent only a subset datasets for which there is at least one prompt on Promptsource.**
"""
_LICENSE = "Apache License 2.0"
_HOMEPAGE = "https://github.com/bigscience-workshop/promptsource"
_DATA_PATH = "data"
# def load_cached_task(cache_dir, split):
# # TODO(Victor): this info.*.json is actually done twice... -> factorize
# with tf.io.gfile.GFile(os.path.join(cache_dir, f"info.{split}.json")) as f:
# split_info = json.load(f)
# features = split_info["features"]
# # Use `FixedLenSequenceFeature` for sequences with variable length.
# def _feature_config(shape, dtype):
# if dtype in ("int32", "bool"):
# # int32 and bool are stored as int64 in the tf.train.Example protobuf.
# dtype = "int64"
# if shape and shape[0] is None:
# return tf.io.FixedLenSequenceFeature(
# shape[1:], dtype, allow_missing=True
# )
# return tf.io.FixedLenFeature(shape, dtype)
# feature_description = {
# feat: _feature_config(**desc) for feat, desc in features.items()
# }
# tfrecords = os.path.join(
# cache_dir, f"{split}.tfrecord-*-of-*{split_info['num_shards']}"
# )
# ds = tf.data.TFRecordDataset(tf.io.gfile.glob(tfrecords))
# ds = ds.map(
# lambda pb: tf.io.parse_single_example(pb, feature_description),
# num_parallel_calls=tf.data.experimental.AUTOTUNE
# )
# # Cast features back to the types from the info JSON since some features
# # must be cast for storage (e.g., in32 is stored as int64).
# ds = ds.map(
# lambda x: {k: tf.cast(v, features[k]["dtype"]) for k, v in x.items()},
# num_parallel_calls=tf.data.experimental.AUTOTUNE
# )
# return ds
def load_cached_task(features_file, tfrecord):
# # TODO(Victor): this info.*.json is actually done twice... -> factorize
# with tf.io.gfile.GFile(os.path.join(cache_dir, f"info.{split}.json")) as f:
with tf.io.gfile.GFile(features_file) as f:
split_info = json.load(f)
features = split_info["features"]
# Use `FixedLenSequenceFeature` for sequences with variable length.
def _feature_config(shape, dtype):
if dtype in ("int32", "bool"):
# int32 and bool are stored as int64 in the tf.train.Example protobuf.
dtype = "int64"
if shape and shape[0] is None:
return tf.io.FixedLenSequenceFeature(
shape[1:], dtype, allow_missing=True
)
return tf.io.FixedLenFeature(shape, dtype)
feature_description = {
feat: _feature_config(**desc) for feat, desc in features.items()
}
ds = tf.data.TFRecordDataset(tf.io.gfile.glob([tfrecord]))
ds = ds.map(
lambda pb: tf.io.parse_single_example(pb, feature_description),
num_parallel_calls=tf.data.experimental.AUTOTUNE
)
# Cast features back to the types from the info JSON since some features
# must be cast for storage (e.g., in32 is stored as int64).
ds = ds.map(
lambda x: {k: tf.cast(v, features[k]["dtype"]) for k, v in x.items()},
num_parallel_calls=tf.data.experimental.AUTOTUNE
)
return ds
def find_task_splits_and_features():
"""Find the available tasks under ./data and their available splits and features."""
task_and_their_splits = defaultdict(dict)
for stats in glob.glob(f"{_DATA_PATH}/*/stats.*.json"):
folder_path = os.path.dirname(stats)
task_name = folder_path.split("/")[-1]
if "adversarial_qa" not in task_name:
continue
split_name = os.path.basename(stats).split(".")[1]
if not os.path.exists(f"{folder_path}/COMPLETED"):
continue
with open(stats, "r") as f:
split_stats = json.load(f)
nb_examples = split_stats["examples"]
if nb_examples > 0:
with open(os.path.join(folder_path, f"info.{split_name}.json")) as f:
split_info = json.load(f)
features = split_info["features"]
assert split_info["num_shards"] == 1
# All splits under the same task have the same features dictionary (and thus the same features list)
if task_and_their_splits[task_name] == {}:
task_and_their_splits[task_name] = {
"splits": [],
"features": [],
}
task_and_their_splits[task_name]["splits"].append(split_name)
if task_and_their_splits[task_name]["features"] == []:
task_and_their_splits[task_name]["features"] = sorted(list(features.keys()))
else:
assert task_and_their_splits[task_name]["features"] == sorted(list(features.keys()))
return task_and_their_splits
_TASK_SPLITS_AND_FEATURES = find_task_splits_and_features()
_URLs = {
task_name: {
split_name: {
"tfrecord": f"{_DATA_PATH}/{task_name}/{split_name}.tfrecord-00000-of-00001",
"features_file": f"{_DATA_PATH}/{task_name}/info.{split_name}.json",
}
for split_name in splits_and_features["splits"]
}
for task_name, splits_and_features in _TASK_SPLITS_AND_FEATURES.items()
}
class P3Config(datasets.BuilderConfig):
"""BuilderConfig for P3."""
def __init__(self, splits, features, score_eval, **kwargs):
"""BuilderConfig for P3.
Args:
splits: `List[str]`, the lists of splits which are available for this task
features: `List[str]`, the list of features for this task
score_eval: `bool`, whether this is task formulated as a rank classification problem
**kwargs: keyword arguments forwarded to super.
"""
# Version history:
# 0.1 initial commit
super(P3Config, self).__init__(version=datasets.Version("0.1.0"), **kwargs)
self.splits = splits
self.features = features
self.score_eval = score_eval
class P3(datasets.GeneratorBasedBuilder):
"""Subset of P3 used in `Multitask Prompted Training Enables Zero-Shot Task Generalization`"""
BUILDER_CONFIGS = [
P3Config(
name=task_name,
splits=splits_and_features["splits"],
features=splits_and_features["features"],
score_eval=task_name.endswith("score_eval")
)
for task_name, splits_and_features in _TASK_SPLITS_AND_FEATURES.items()
]
def _info(self):
# All features available are: 'inputs', 'inputs_pretokenized', 'targets',
# 'targets_pretokenized', 'idx', 'is_correct', 'weight', and 'answer_choices'
_FEAT_MAPPING = {
"answer_choices": datasets.Sequence(datasets.Value("string")),
"inputs": datasets.Sequence(datasets.Value("int32")),
"inputs_pretokenized": datasets.Value("string"),
"targets": datasets.Sequence(datasets.Value("int32")),
"targets_pretokenized": datasets.Value("string"),
"idx": datasets.Sequence(datasets.Value("int32")),
"weight": datasets.Value("float32"),
"is_correct": datasets.Value("bool"),
}
features = {}
for feat_name in self.config.features:
features[feat_name] = _FEAT_MAPPING[feat_name]
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(features),
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
license=_LICENSE,
)
def _split_generators(self, dl_manager):
split_generators = []
data_dir = dl_manager.download_and_extract(_URLs)
task_name = self.config.name
if "train" in self.config.splits:
split_name = "train"
split_generators.append(
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"features_file": data_dir[task_name][split_name]["features_file"],
"tfrecord": data_dir[task_name][split_name]["tfrecord"],
}
)
)
if "validation" in self.config.splits:
split_name = "validation"
split_generators.append(
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"features_file": data_dir[task_name][split_name]["features_file"],
"tfrecord": data_dir[task_name][split_name]["tfrecord"],
}
)
)
if "test" in self.config.splits:
split_name = "test"
split_generators.append(
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"features_file": data_dir[task_name][split_name]["features_file"],
"tfrecord": data_dir[task_name][split_name]["tfrecord"],
}
)
)
# Handle splits that are not train, validation or test
special_splits = set(self.config.splits) - set(["train", "validation", "test"])
for special_split_name in special_splits:
split_generators.append(
datasets.SplitGenerator(
name=datasets.Split(special_split_name),
gen_kwargs={
"features_file": data_dir[task_name][special_split_name]["features_file"],
"tfrecord": data_dir[task_name][special_split_name]["tfrecord"],
}
)
)
return split_generators
def _generate_examples(self, features_file, tfrecord):
"""This function returns the examples in the raw (text) form."""
_FEAT_MAPPING_FUNCTIONS = {
"answer_choices": lambda x: [choice.decode("utf-8") for choice in x],
"inputs": lambda x: x.tolist(),
"inputs_pretokenized": lambda x: x.decode("utf-8"),
"targets": lambda x: x.tolist(),
"targets_pretokenized": lambda x: x.decode("utf-8"),
"idx": lambda x: x.tolist(),
"weight": lambda x: float(x),
"is_correct": lambda x: x,
}
key = 0
ds = load_cached_task(features_file, tfrecord)
for ex in ds.as_numpy_iterator():
ex_dict = {}
for feat_name, feat_value in ex.items():
ex_dict[feat_name] = _FEAT_MAPPING_FUNCTIONS[feat_name](feat_value)
yield key, ex_dict
key += 1
|