diff --git a/P3.py b/P3.py deleted file mode 100644 index ef0a55bc2e7b63d68e3d3c545a1e817814bbf79f..0000000000000000000000000000000000000000 --- a/P3.py +++ /dev/null @@ -1,202 +0,0 @@ -# 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 os - -import google.protobuf as _protobuf # From: protobuf - -import datasets - -from ._tfrecord_example_pb2 import SequenceExample -from .io_utils import iterate_tfrecord_file, parse_tfrecord_sequence_example -from .tasks_splits_and_features import _TASK_SPLITS_AND_FEATURES_DICT - - -_CITATION = """@misc{sanh2021multitask, - title={Multitask Prompted Training Enables Zero-Shot Task Generalization}, - author={Victor Sanh and Albert Webson and Colin Raffel and Stephen H. Bach and Lintang Sutawika and Zaid Alyafeai and Antoine Chaffin and Arnaud Stiegler and Teven Le Scao and Arun Raja and Manan Dey and M Saiful Bari and Canwen Xu and Urmish Thakker and Shanya Sharma Sharma and Eliza Szczechla and Taewoon Kim and Gunjan Chhablani and Nihal Nayak and Debajyoti Datta and Jonathan Chang and Mike Tian-Jian Jiang and Han Wang and Matteo Manica and Sheng Shen and Zheng Xin Yong and Harshit Pandey and Rachel Bawden and Thomas Wang and Trishala Neeraj and Jos Rozen and Abheesht Sharma and Andrea Santilli and Thibault Fevry and Jason Alan Fries and Ryan Teehan and Stella Biderman and Leo Gao and Tali Bers and Thomas Wolf and Alexander M. Rush}, - year={2021}, - eprint={2110.08207}, - archivePrefix={arXiv}, - primaryClass={cs.LG} -}""" - -_DESCRIPTION = """\ -P3 (Public 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 of P3 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 [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://arxiv.org/abs/2110.08207) which represent only a subset of the datasets for which there is at least one prompt in Promptsource.** -""" - -_LICENSE = "Apache License 2.0" - -_HOMEPAGE = "https://github.com/bigscience-workshop/promptsource" - -_DATA_PATH = "data" - -logger = datasets.logging.get_logger(__name__) - -_URLs = { - task_name: { - split_name: [ - os.path.join( - _DATA_PATH, task_name, split_name + ".tfrecord-00000-of-00001" - ), # TODO -> handle multiple shards - ] - for split_name in splits_and_features_dict["splits"] - } - for task_name, splits_and_features_dict in _TASK_SPLITS_AND_FEATURES_DICT.items() -} - - -class P3Config(datasets.BuilderConfig): - """BuilderConfig for P3.""" - - def __init__(self, splits, features_dict, score_eval, **kwargs): - """BuilderConfig for P3. - - Args: - splits: `List[str]`, the lists of splits which are available for this task - features_dict: `dict`, the dict 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_dict = features_dict - 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_dict["splits"], - features_dict=splits_and_features_dict["features_dict"], - score_eval=task_name.endswith("score_eval"), - ) - for task_name, splits_and_features_dict in _TASK_SPLITS_AND_FEATURES_DICT.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 = {feat_name: _FEAT_MAPPING[feat_name] for feat_name in self.config.features_dict.keys()} - 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(_URLs[self.config.name]) - if "train" in self.config.splits: - split_name = "train" - split_generators.append( - datasets.SplitGenerator( - name=datasets.Split.TRAIN, - gen_kwargs={ - "tfrecord_files": data_dir[split_name], - }, - ) - ) - if "validation" in self.config.splits: - split_name = "validation" - split_generators.append( - datasets.SplitGenerator( - name=datasets.Split.VALIDATION, - gen_kwargs={ - "tfrecord_files": data_dir[split_name], - }, - ) - ) - if "test" in self.config.splits: - split_name = "test" - split_generators.append( - datasets.SplitGenerator( - name=datasets.Split.TEST, - gen_kwargs={ - "tfrecord_files": data_dir[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={ - "tfrecord_files": data_dir[special_split_name], - }, - ) - ) - return split_generators - - def _generate_examples(self, tfrecord_files): - """This function returns the examples in the raw (text) form.""" - _POST_PROC_FUNCTIONS = { - "answer_choices": lambda x: [choice.decode("utf-8") for choice in x], - "inputs": lambda x: x.tolist(), - "inputs_pretokenized": lambda x: x[0].decode("utf-8"), - "targets": lambda x: x.tolist(), - "targets_pretokenized": lambda x: x[0].decode("utf-8"), - "idx": lambda x: x.tolist(), - "weight": lambda x: float(x), - "is_correct": lambda x: x, - } - - def _prepare_col_spec(shape, dtype): - if dtype in ("int32", "bool"): - # int32 and bool are stored as int64 in the tf.train.Example protobuf. - dtype = "int64" - elif dtype == "string": - dtype = "str" - if shape and shape[0] is None: - shape = (-1, *shape[1:]) - return (shape, dtype) - - spec = {k: _prepare_col_spec(**v) for k, v in self.config.features_dict.items()} - idx = 0 - for tfrecord_file in tfrecord_files: - with open(tfrecord_file, "rb") as f: - for example_bytes in iterate_tfrecord_file(f): - example = SequenceExample() - example.ParseFromString(example_bytes) - example = parse_tfrecord_sequence_example(example, spec) - example = {k: _POST_PROC_FUNCTIONS[k](v) for k, v in example.items()} - yield idx, example - idx += 1 diff --git a/_tfrecord_example_pb2.py b/_tfrecord_example_pb2.py deleted file mode 100644 index cd36059c9c44af82a374155ac9cf1739573917be..0000000000000000000000000000000000000000 --- a/_tfrecord_example_pb2.py +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:50e227d1c6e389901c2ec71b36b8d73b0b7711b14c42962a837f01c197056f2c -size 21378 diff --git a/data/adversarial_qa_dbert_answer_the_following_q/COMPLETED b/data/adversarial_qa_dbert_answer_the_following_q/COMPLETED deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/data/adversarial_qa_dbert_answer_the_following_q/info.test.json b/data/adversarial_qa_dbert_answer_the_following_q/info.test.json deleted file mode 100644 index 0967ef424bce6791893e9a57bb952f80fd536e93..0000000000000000000000000000000000000000 --- a/data/adversarial_qa_dbert_answer_the_following_q/info.test.json +++ /dev/null @@ -1 +0,0 @@ -{} diff --git a/data/adversarial_qa_dbert_answer_the_following_q/info.train.json b/data/adversarial_qa_dbert_answer_the_following_q/info.train.json deleted file mode 100644 index 2e0955791e1f4c0d502ceec1de751826f781e469..0000000000000000000000000000000000000000 --- a/data/adversarial_qa_dbert_answer_the_following_q/info.train.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - 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"targets_tokens": 1300000 -} diff --git a/data/yelp_review_full_format_star/test.tfrecord-00000-of-00001 b/data/yelp_review_full_format_star/test.tfrecord-00000-of-00001 deleted file mode 100644 index 0e29e17dfe2038ac33aa4a95f848319b209ddf94..0000000000000000000000000000000000000000 --- a/data/yelp_review_full_format_star/test.tfrecord-00000-of-00001 +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:7e10793a206cda6d6b00ae09bf15d2493e045edf1da74f72b322cc92285ba2ef -size 59694259 diff --git a/data/yelp_review_full_format_star/train.tfrecord-00000-of-00001 b/data/yelp_review_full_format_star/train.tfrecord-00000-of-00001 deleted file mode 100644 index 67cecdb46386ae7f6f15e8a85e390f206d479590..0000000000000000000000000000000000000000 --- a/data/yelp_review_full_format_star/train.tfrecord-00000-of-00001 +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:4d40eee0a7170e3ea81fddf78a21e9dd1a9c0ebc8166a5a2ddc01a4acc715956 -size 775354691 diff --git a/data/yelp_review_full_on_a_scale/COMPLETED b/data/yelp_review_full_on_a_scale/COMPLETED deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/data/yelp_review_full_on_a_scale/info.test.json b/data/yelp_review_full_on_a_scale/info.test.json deleted file mode 100644 index aab54b75718b834c19692fed4d6c9ef9ff7d5926..0000000000000000000000000000000000000000 --- a/data/yelp_review_full_on_a_scale/info.test.json +++ /dev/null @@ -1,32 +0,0 @@ -{ - 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"targets_pretokenized": { - "dtype": "string", - "shape": [] - } - }, - "num_shards": 1, - "seqio_version": "0.0.6" -} diff --git a/data/yelp_review_full_on_a_scale/stats.test.json b/data/yelp_review_full_on_a_scale/stats.test.json deleted file mode 100644 index 6bb0ac21972e374fe38a3d2a8ec9c749205ff95e..0000000000000000000000000000000000000000 --- a/data/yelp_review_full_on_a_scale/stats.test.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "examples": 50000, - "inputs_max_tokens": 898, - "inputs_tokens": 9819779, - "targets_max_tokens": 1, - "targets_tokens": 50000 -} diff --git a/data/yelp_review_full_on_a_scale/stats.train.json b/data/yelp_review_full_on_a_scale/stats.train.json deleted file mode 100644 index 3a63d469db94871b424e4d2271d9fd4598fb3c0c..0000000000000000000000000000000000000000 --- a/data/yelp_review_full_on_a_scale/stats.train.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "examples": 650000, - "inputs_max_tokens": 1247, - "inputs_tokens": 127608321, - "targets_max_tokens": 1, - "targets_tokens": 650000 -} diff --git a/data/yelp_review_full_on_a_scale/test.tfrecord-00000-of-00001 b/data/yelp_review_full_on_a_scale/test.tfrecord-00000-of-00001 deleted file mode 100644 index 8e55ee2c1ad578a34af0c1cc14376e9fcd6bd17c..0000000000000000000000000000000000000000 --- a/data/yelp_review_full_on_a_scale/test.tfrecord-00000-of-00001 +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:cdab1b9fffc73ec2eae2312553cc96d981e1d0f3fd7b13681225233816529b13 -size 60317482 diff --git a/data/yelp_review_full_on_a_scale/train.tfrecord-00000-of-00001 b/data/yelp_review_full_on_a_scale/train.tfrecord-00000-of-00001 deleted file mode 100644 index 414e9950bc69d47685c9eb2bf75dd7360df32105..0000000000000000000000000000000000000000 --- a/data/yelp_review_full_on_a_scale/train.tfrecord-00000-of-00001 +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:34667232a0268961b32ba03f9f6b7f409f3d08ef2addfd7a645913ecfa665f43 -size 783454804 diff --git a/data/yelp_review_full_so_i_would/COMPLETED b/data/yelp_review_full_so_i_would/COMPLETED deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/data/yelp_review_full_so_i_would/info.test.json b/data/yelp_review_full_so_i_would/info.test.json deleted file mode 100644 index aab54b75718b834c19692fed4d6c9ef9ff7d5926..0000000000000000000000000000000000000000 --- a/data/yelp_review_full_so_i_would/info.test.json +++ /dev/null @@ -1,32 +0,0 @@ -{ - 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"targets_pretokenized": { - "dtype": "string", - "shape": [] - } - }, - "num_shards": 1, - "seqio_version": "0.0.6" -} diff --git a/data/yelp_review_full_so_i_would/stats.test.json b/data/yelp_review_full_so_i_would/stats.test.json deleted file mode 100644 index 947e74c3e233ee0a6ac4a7fb984d7bf69b765fd9..0000000000000000000000000000000000000000 --- a/data/yelp_review_full_so_i_would/stats.test.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "examples": 50000, - "inputs_max_tokens": 890, - "inputs_tokens": 9419779, - "targets_max_tokens": 2, - "targets_tokens": 100000 -} diff --git a/data/yelp_review_full_so_i_would/stats.train.json b/data/yelp_review_full_so_i_would/stats.train.json deleted file mode 100644 index 25b7dcfa775e3218d025b0a9e959acca31000360..0000000000000000000000000000000000000000 --- a/data/yelp_review_full_so_i_would/stats.train.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "examples": 650000, - "inputs_max_tokens": 1239, - "inputs_tokens": 122408321, - "targets_max_tokens": 2, - "targets_tokens": 1300000 -} diff --git a/data/yelp_review_full_so_i_would/test.tfrecord-00000-of-00001 b/data/yelp_review_full_so_i_would/test.tfrecord-00000-of-00001 deleted file mode 100644 index 103841a653795e0cb9b575dc2d6d9d3c4659aff7..0000000000000000000000000000000000000000 --- a/data/yelp_review_full_so_i_would/test.tfrecord-00000-of-00001 +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:fc3b1afac1f15357608729f5c22aa2c23ce09c607a5891144526c89175d4719b -size 60045873 diff --git a/data/yelp_review_full_so_i_would/train.tfrecord-00000-of-00001 b/data/yelp_review_full_so_i_would/train.tfrecord-00000-of-00001 deleted file mode 100644 index 767bfdd6380de1e39dce99deddc04c65eca378f0..0000000000000000000000000000000000000000 --- a/data/yelp_review_full_so_i_would/train.tfrecord-00000-of-00001 +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:531326a962f6745a20217173b912ead205fdba17abec8d00b65e4089a53b9cdc -size 779925253 diff --git a/data/yelp_review_full_this_place/COMPLETED b/data/yelp_review_full_this_place/COMPLETED deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/data/yelp_review_full_this_place/info.test.json b/data/yelp_review_full_this_place/info.test.json deleted file mode 100644 index aab54b75718b834c19692fed4d6c9ef9ff7d5926..0000000000000000000000000000000000000000 --- a/data/yelp_review_full_this_place/info.test.json +++ /dev/null @@ -1,32 +0,0 @@ -{ - "features": { - "answer_choices": { - "dtype": "string", - "shape": [ - null - ] - }, - "inputs": { - "dtype": "int32", - "shape": [ - null - ] - }, - "inputs_pretokenized": { - "dtype": "string", - "shape": [] - }, - "targets": { - "dtype": "int32", - "shape": [ - null - ] - }, - "targets_pretokenized": { - "dtype": "string", - "shape": [] - } - }, - "num_shards": 1, - "seqio_version": "0.0.6" -} diff --git a/data/yelp_review_full_this_place/info.train.json b/data/yelp_review_full_this_place/info.train.json deleted file mode 100644 index aab54b75718b834c19692fed4d6c9ef9ff7d5926..0000000000000000000000000000000000000000 --- a/data/yelp_review_full_this_place/info.train.json +++ /dev/null @@ -1,32 +0,0 @@ -{ - "features": { - "answer_choices": { - "dtype": "string", - "shape": [ - null - ] - }, - "inputs": { - "dtype": "int32", - "shape": [ - null - ] - }, - "inputs_pretokenized": { - "dtype": "string", - "shape": [] - }, - "targets": { - "dtype": "int32", - "shape": [ - null - ] - }, - "targets_pretokenized": { - "dtype": "string", - "shape": [] - } - }, - "num_shards": 1, - "seqio_version": "0.0.6" -} diff --git a/data/yelp_review_full_this_place/stats.test.json b/data/yelp_review_full_this_place/stats.test.json deleted file mode 100644 index 53c5f0818a0383136d26048c41de6861bccd7c3d..0000000000000000000000000000000000000000 --- a/data/yelp_review_full_this_place/stats.test.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "examples": 50000, - "inputs_max_tokens": 889, - "inputs_tokens": 9369779, - "targets_max_tokens": 2, - "targets_tokens": 100000 -} diff --git a/data/yelp_review_full_this_place/stats.train.json b/data/yelp_review_full_this_place/stats.train.json deleted file mode 100644 index 8e7bf203fdc1f864dcb2ea7effc5b2892b922628..0000000000000000000000000000000000000000 --- a/data/yelp_review_full_this_place/stats.train.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "examples": 650000, - "inputs_max_tokens": 1238, - "inputs_tokens": 121758321, - "targets_max_tokens": 2, - "targets_tokens": 1300000 -} diff --git a/data/yelp_review_full_this_place/test.tfrecord-00000-of-00001 b/data/yelp_review_full_this_place/test.tfrecord-00000-of-00001 deleted file mode 100644 index 4d42587110748421f28b6ba786a9b573251f1c62..0000000000000000000000000000000000000000 --- a/data/yelp_review_full_this_place/test.tfrecord-00000-of-00001 +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:a5c2a1dff8413d7b79f304924cd7330c0f042cc9beacbd0ea288d29f38571f58 -size 60045465 diff --git a/data/yelp_review_full_this_place/train.tfrecord-00000-of-00001 b/data/yelp_review_full_this_place/train.tfrecord-00000-of-00001 deleted file mode 100644 index 51c5282cb18e14948477173150f21e6b12c88d3a..0000000000000000000000000000000000000000 --- a/data/yelp_review_full_this_place/train.tfrecord-00000-of-00001 +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:4c69fe15510eed4bb6eea0184165d8320cd7239218aa942f8acdb82bada3b419 -size 779919642 diff --git a/io_utils.py b/io_utils.py deleted file mode 100644 index 28873de38bac076ba374d030d4f76e28925301ea..0000000000000000000000000000000000000000 --- a/io_utils.py +++ /dev/null @@ -1,166 +0,0 @@ -# Code copied from: https://github.com/pytorch/data/blob/d9bbbecf64d0149795dc65ba390b50bc9e176e95/torchdata/datapipes/iter/util/tfrecordloader.py - -import struct -from functools import partial -from io import BufferedIOBase -from typing import Any, Dict, Iterator, List, NamedTuple, Optional, Tuple, Union, cast - -import numpy as np - - -try: - from math import prod -except ImportError: - import operator - from functools import reduce - - def prod(xs): - return reduce(operator.mul, xs, 1) - - -U = Union[bytes, bytearray, str] -TFRecordFeatureSpec = Tuple[Tuple[int, ...], Union[str, np.dtype]] -TFRecordExampleSpec = Dict[str, TFRecordFeatureSpec] - -# Note, reccursive types not supported by mypy at the moment -# TODO(640): uncomment as soon as it becomes supported -# https://github.com/python/mypy/issues/731 -# BinaryData = Union[str, List['BinaryData']] -TFRecordBinaryData = Union[str, List[str], List[List[str]], List[List[List[Any]]]] -TFRecordExampleFeature = Union[np.ndarray, List[np.ndarray], TFRecordBinaryData] -TFRecordExample = Dict[str, TFRecordExampleFeature] - - -class SequenceExampleSpec(NamedTuple): - context: TFRecordExampleSpec - feature_lists: TFRecordExampleSpec - - -def iterate_tfrecord_file(data: BufferedIOBase) -> Iterator[memoryview]: - length_bytes = bytearray(8) - crc_bytes = bytearray(4) - data_bytes = bytearray(1024) - - while True: - bytes_read = data.readinto(length_bytes) - if bytes_read == 0: - break - elif bytes_read != 8: - raise RuntimeError("Invalid tfrecord file: failed to read the record size.") - if data.readinto(crc_bytes) != 4: - raise RuntimeError("Invalid tfrecord file: failed to read the start token.") - (length,) = struct.unpack(" len(data_bytes): - data_bytes = data_bytes.zfill(int(length * 1.5)) - data_bytes_view = memoryview(data_bytes)[:length] - if data.readinto(data_bytes_view) != length: - raise RuntimeError("Invalid tfrecord file: failed to read the record.") - if data.readinto(crc_bytes) != 4: - raise RuntimeError("Invalid tfrecord file: failed to read the end token.") - - # TODO(641): check CRC - yield data_bytes_view - - -def process_feature(feature) -> np.ndarray: - # NOTE: We assume that each key in the example has only one field - # (either "bytes_list", "float_list", or "int64_list")! - field = feature.ListFields()[0] - inferred_typename, value = field[0].name, field[1].value - if inferred_typename == "bytes_list": - pass - elif inferred_typename == "float_list": - value = np.array(value, dtype=np.float32) - elif inferred_typename == "int64_list": - value = np.array(value, dtype=np.int64) - return value - - -def _reshape_list(value, shape): - # Flatten list - flat_list = [] - - def flatten(value): - if isinstance(value, (str, bytes)): - flat_list.append(value) - else: - for x in value: - flatten(x) - - flatten(value) - - # Compute correct shape - common_divisor = prod(x for x in shape if x != -1) - if sum(1 for x in shape if x == -1) > 1: - raise RuntimeError("Shape can contain at most one dynamic dimension (-1).") - if len(flat_list) % max(common_divisor, 1) != 0: - raise RuntimeError(f"Cannot reshape {len(flat_list)} values into shape {shape}") - shape = [x if x != -1 else (len(flat_list) // common_divisor) for x in shape] - - # Reshape list into the correct shape - def _reshape(value, shape): - if len(shape) == 0: - assert len(value) == 1 - return value[0] - elif len(shape) == 1: # To make the reccursion faster - assert len(value) == shape[0] - return value - dim_size = len(value) // shape[0] - return [_reshape(value[i * dim_size : (i + 1) * dim_size], shape[1:]) for i in range(dim_size)] - - return _reshape(flat_list, shape) - - -def _apply_feature_spec(value, feature_spec): - if isinstance(value, np.ndarray): - if feature_spec is not None: - shape, dtype = feature_spec - if isinstance(dtype, (str, np.dtype)): - if shape: - value = value.reshape(shape) - value = value.astype(dtype) - elif shape: - # Manual list reshape - value = _reshape_list(value, shape) - return value - - -def _parse_tfrecord_features(features, spec: Optional[TFRecordExampleSpec]) -> Dict[str, np.ndarray]: - result = {} - features = features.feature - for key in features.keys(): - if spec is not None and key not in spec: - continue - feature_spec = None if spec is None else spec[key] - feature = features[key] - result[key] = _apply_feature_spec(process_feature(feature), feature_spec) - return result - - -def parse_tfrecord_sequence_example(example, spec: Optional[TFRecordExampleSpec]) -> TFRecordExample: - # Parse context features - result = cast(TFRecordExample, _parse_tfrecord_features(example.context, spec)) - - # Parse feature lists - feature_lists_keys = None if spec is None else set(spec.keys()) - set(result.keys()) - features = example.feature_lists.feature_list - for key in features.keys(): - if feature_lists_keys is not None and key not in feature_lists_keys: - continue - feature_spec = None if spec is None else spec[key] - feature = features[key].feature - if key in result: - raise RuntimeError( - "TFRecord example's key {key} is contained in both the context and feature lists. This is not supported." - ) - - value: Union[np.ndarray, List[Any]] = list(map(partial(process_feature), feature)) - - # For known numpy dtypes, we stack the list features - if feature_spec is not None and isinstance(feature_spec[1], (str, np.dtype)): - value = np.stack(cast(List[np.ndarray], value), 0) - value = _apply_feature_spec(value, feature_spec) - result[key] = value - if spec is not None and len(result.keys()) != len(spec.keys()): - raise RuntimeError(f"Example is missing some required keys: {sorted(result.keys())} != {sorted(spec.keys())}") - return result diff --git a/print_data_split_sizes.py b/print_data_split_sizes.py deleted file mode 100644 index eab26a490e1261c9df7be2fe729699858c1e4d75..0000000000000000000000000000000000000000 --- a/print_data_split_sizes.py +++ /dev/null @@ -1,31 +0,0 @@ -import glob -import json -import os -from collections import defaultdict - - -_DATA_PATH = "data" - -data_split_sizes = defaultdict(dict) - -for stats in glob.glob(f"{_DATA_PATH}/*/stats.*.json"): - folder_path = os.path.dirname(stats) - task_name = folder_path.split("/")[-1] - 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: - data_split_sizes[task_name][split_name] = nb_examples - -with open("data_split_sizes.csv", "w", encoding="utf=8") as f: - # The file is now merged into `tasks_splits_and_features.py` - f.write("Data(sub)set|Number of examples per splits\n") - for task_name in sorted(list(data_split_sizes.keys())): - split_sizes_dict = json.dumps(data_split_sizes[task_name]) - f.write(f"{task_name}|{split_sizes_dict}\n")