# 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, 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"] with tf.io.gfile.GFile(features_file) as f: features = json.load(f) # 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([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"], "split": split_name, } ) ) 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"], "split": split_name, } ) ) 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"], "split": split_name, } ) ) # 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"], "split": special_split_name, } ) ) return split_generators def _generate_examples(self, features_file, tfrecord, split): """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, split) 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