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tasks.py
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tasks.py
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"""
This file defines 8 mixtures that we used in the T-Zero paper:
- t0_train: T0 training mixture
- t0+_train: T0+ training mixture
- t0++_train: T0++ training mixture
- t0_eval_score_eval: T0 main evaluation mixture (Figure 4 for instance)
- t0_train_score_eval: Evaluation mixture for checkpoint selection on T0 (validation splits of the training sets)
- t0_train_one_og_prompt: T0 (p=1) training mixture for - one original-task prompt per dataset. Figure 6
- t0_train_all_og_prompts: T0 (p=5.7) training mixture for - all original-task prompts for all datasets. Figure 6
- bias_fairness_eval_score_eval: Bias & fairness evaluation mixture. Appendix B3
"""
import csv
import functools
from typing import Dict, List, Optional, Tuple
import datasets
import pkg_resources
from promptsource import templates
import seqio
import t5
from t5.data.glue_utils import get_glue_metric, get_super_glue_metric
from t5.evaluation import metrics as mt
import tensorflow as tf
from t0.seqio_tasks import utils
GET_METRICS = {
"BLEU": mt.bleu,
"ROUGE": mt.rouge,
"Span Squad": mt.span_squad,
"Squad": mt.squad,
"Trivia QA": mt.trivia_qa,
"Accuracy": mt.accuracy,
"Sequence Accuracy": mt.sequence_accuracy,
"Pearson Correlation": mt.pearson_corrcoef,
"Spearman Correlation": mt.spearman_corrcoef,
"MultiRC": mt.multirc_f1_over_all_answers,
"AUC": mt.auc,
"COQA F1": mt.coqa_f1,
"Edit Distance": mt.edit_distance,
# "Mean Reciprocal Rank": mt.accuracy, # NOTE not in T5?
"Other": mt.accuracy,
# Missing support for mean_multiclass_f1 etc. which need a num_classes parameter
}
MAX_EXAMPLES_PER_DATASET = 500_000
def strip_whitespace(output_or_target, example=None, is_target=False):
"""Cached tasks from promptsource all have a leading space on the ground-truth targets."""
return output_or_target.strip()
def maybe_get_class_id_postprocessor(template):
if template.get_fixed_answer_choices_list():
def postprocess_fn(output_or_target, example=None, is_target=False):
output_or_target = strip_whitespace(output_or_target)
return t5.data.postprocessors.string_label_to_class_id(
output_or_target, label_classes=template.get_fixed_answer_choices_list()
)
return postprocess_fn
else:
return strip_whitespace
def get_tf_dataset(split, shuffle_files, seed, dataset_name, subset_name, template, split_mapping):
# HF datasets does not support file-level shuffling
del shuffle_files, seed
dataset = datasets.load_dataset(dataset_name, subset_name)
dataset = dataset[split_mapping[split]]
dataset = utils.apply_template(dataset, template)
return utils.hf_dataset_to_tf_dataset(dataset)
def add_task(dataset_name, subset_name, template_name, task_name=None, split_mapping=None):
template = all_templates.get_dataset(dataset_name, subset_name)[template_name]
task_name = task_name or utils.get_task_name(dataset_name, subset_name, template_name)
if dataset_name == "glue":
metrics = get_glue_metric(subset_name)
elif dataset_name == "super_glue":
if subset_name in ("wsc.fixed", "multirc"):
# TODO: WSC and MultiRC need special pre/postprocesing
metrics = [mt.accuracy]
else:
metrics = get_super_glue_metric(subset_name)
else:
# TODO what if metric is null?
metrics = [GET_METRICS[m] for m in template.metadata.metrics]
dataset_splits = utils.get_dataset_splits(dataset_name, subset_name)
split_mapping = split_mapping or {k: k for k in dataset_splits.keys()}
dataset_fn = functools.partial(
get_tf_dataset,
seed=None,
dataset_name=dataset_name,
subset_name=subset_name,
template=template,
split_mapping=split_mapping,
)
data_source = seqio.FunctionDataSource(
dataset_fn,
splits=list(split_mapping.keys()),
num_input_examples={s: dataset_splits[split_mapping[s]].num_examples for s in split_mapping.keys()},
)
output_features = {
"inputs": seqio.Feature(t5.data.get_default_vocabulary(), add_eos=False, dtype=tf.int32),
"targets": seqio.Feature(t5.data.get_default_vocabulary(), add_eos=True, dtype=tf.int32),
}
preprocessors = [
seqio.preprocessors.tokenize,
seqio.preprocessors.append_eos,
seqio.CacheDatasetPlaceholder(required=False),
]
# Add train and normal eval tasks
seqio.TaskRegistry.add(
task_name,
data_source,
preprocessors=preprocessors,
output_features=output_features,
metric_fns=metrics,
postprocess_fn=maybe_get_class_id_postprocessor(template),
)
# Add rank classification eval task
if template.answer_choices:
rank_classification_preprocessor = functools.partial(
t5.data.preprocessors.rank_classification,
inputs_fn=lambda ex: tf.fill((len(ex["answer_choices"]),), ex["inputs"]),
targets_fn=lambda ex: ex["answer_choices"],
is_correct_fn=lambda ex: tf.equal(ex["answer_choices"], tf.strings.strip(ex["targets"])),
weight_fn=lambda ex: 1.0,
)
fixed_choices = template.get_fixed_answer_choices_list()
num_classes = len(fixed_choices) if fixed_choices else None
seqio.TaskRegistry.add(
task_name + "_score_eval",
data_source,
preprocessors=[rank_classification_preprocessor] + preprocessors,
output_features=output_features,
metric_fns=[functools.partial(t5.evaluation.metrics.rank_classification, num_classes=num_classes)],
postprocess_fn=t5.data.postprocessors.rank_classification,
)
datatset_subset_tuple = Tuple[str, Optional[str]]
t0_eval: Dict[str, List[datatset_subset_tuple]] = {
"BASE": [],
"BIAS_FAIRNESS": []
}
t0_train: Dict[str, List[datatset_subset_tuple]] = {
"BASE": [],
# GPT3 evaluation set
"GPT_EVAL": [],
# SuperGLUE (except RTE and CB)
"SGLUE": []
}
gsheet: Dict[datatset_subset_tuple, Dict] = {}
experiment_path = pkg_resources.resource_filename(__name__, "../datasets.csv")
with open(experiment_path) as exp_file:
reader = csv.DictReader(exp_file)
for row in reader:
if row["subset"] == "":
row["subset"] = None # to match promptsource.Template object
dataset_subset = (row["HF_name"], row["subset"])
if row["do_train"] != "":
do_train_source = row["do_train"]
# sanity checks
if do_train_source == "SGLUE":
assert dataset_subset[0] == "super_glue"
t0_train[do_train_source].append(dataset_subset)
if row["do_eval"] != "":
do_eval_source = row["do_eval"]
# sanity checks
if do_eval_source == "BIAS_FAIRNESS":
assert row["task_by_convention"] == "bias_and_fairness"
t0_eval[do_eval_source].append(dataset_subset)
gsheet[dataset_subset] = row
all_datasets = sum(t0_train.values(), []) + sum(t0_eval.values(), [])
all_templates = templates.TemplateCollection()
all_templates.remove("anli") # Need to special-case ANLI due to weird split conventions
# 3 stages of training/ablation: D4 -> GPT -> SuperGLUE
t0_train_mixture: Dict[str,List[str]] = {key: [] for key in t0_train}
t0_eval_mixture: Dict[str,List[str]] = {key: [] for key in t0_eval}
mixture_cap: Dict[str, int] = {}
single_original_task: Dict[Tuple[str, str], str] = {}
all_original_tasks: List[str] = []
for dataset_name, subset_name in all_templates.keys:
if (dataset_name, subset_name) not in all_datasets:
all_templates.remove(dataset_name, subset_name)
continue
dataset = all_templates.get_dataset(dataset_name, subset_name)
num_templates = len(dataset.all_template_names)
train_size = gsheet[(dataset_name, subset_name)]["train_size"]
if train_size == "":
train_size = 0
else:
train_size = int(train_size)
if train_size > MAX_EXAMPLES_PER_DATASET:
cap = MAX_EXAMPLES_PER_DATASET // num_templates
else:
cap = train_size
for template_name in dataset.all_template_names:
add_task(dataset_name, subset_name, template_name)
template = dataset[template_name]
task_name = utils.get_task_name(dataset_name, subset_name, template_name)
if (dataset_name, subset_name) not in single_original_task and template.metadata.original_task:
single_original_task[(dataset_name, subset_name)] = task_name
if template.metadata.original_task:
all_original_tasks.append(task_name)
# Check that the dataset_subset_tuple is in t0_train
for key, dataset_subset_tuples in t0_train.items():
if (dataset_name, subset_name) in dataset_subset_tuples:
t0_train_mixture[key].append(task_name)
mixture_cap[task_name] = cap
# Check that the dataset_subset_tuple is in t0_eval
if (dataset_name, subset_name) in t0_eval["BASE"]:
if template.metadata.original_task:
t0_eval_mixture["BASE"].append(task_name)
# TODO use template.metadata.answer_choices here for rank eval
if (dataset_name, subset_name) in t0_eval["BIAS_FAIRNESS"]:
t0_eval_mixture["BIAS_FAIRNESS"].append(task_name)
# Special case for ANLI, which has weirdly-named splits and rounds that should be subsets
dataset_name, subset_name = ("anli", None)
dataset = all_templates.get_dataset(dataset_name, subset_name)
for anli_round in ("r1", "r2", "r3"):
for template_name in all_templates.get_dataset(dataset_name, subset_name).all_template_names:
task_name = utils.get_task_name(dataset_name, subset_name, template_name) + f"_{anli_round}"
split_mapping = {
"train": f"train_{anli_round}",
"validation": f"dev_{anli_round}",
"test": f"test_{anli_round}",
}
add_task(dataset_name, subset_name, template_name, task_name, split_mapping)
template = dataset[template_name]
if template.metadata.original_task:
t0_eval_mixture["BASE"].append(task_name) # TODO or add to ANLI special mixture
# TODO use template.metadata.answer_choices here for rank eval
TASK_BLACKLIST = [
# Tasks which often tokenize to > 1024 tokens currently
"hotpot_qa_distractor_Generate_Explanations",
"hotpot_qa_fullwiki_Generate_Explanations",
"hotpot_qa_distractor_Generate_Answer_and_Explanations",
"hotpot_qa_fullwiki_Generate_Answer_and_Explanations",
"hotpot_qa_fullwiki_Generate_Answer",
"hotpot_qa_distractor_Generate_Answer",
"hotpot_qa_distractor_Generate_Title_2",
"hotpot_qa_fullwiki_Generate_Title_2",
"hotpot_qa_fullwiki_Generate_Title_1",
"hotpot_qa_distractor_Generate_Title_1",
"hotpot_qa_distractor_Generate_Question",
"hotpot_qa_fullwiki_Generate_Question",
"tab_fact_tab_fact_tab_fact_3",
"tab_fact_tab_fact_tab_fact_2",
"tab_fact_tab_fact_tab_fact_1",
"tab_fact_tab_fact_tab_fact_7",
"tab_fact_tab_fact_tab_fact_4",
"tab_fact_tab_fact_tab_fact_5",
"tab_fact_tab_fact_tab_fact_6",
"wiki_hop_masked_Choose_Best_Object_Candidate",
"wiki_hop_masked_Indirect_Question_about_Birthplace_Citizenship_Place_of_Death",
"narrativeqa_Template_05",
"ecthr_cases_alleged_violation_prediction_silver_rationales",
# Tasks with broken cached files
"gigaword_summarize_",
]
# Tasks that failed caching (won't try to fix them for now) - remove when we are done
D4_TRAIN_SCORE_EVAL_TASK_BLACKLIST = [
"amazon_polarity_Is_this_product_review_positive_score_eval",
"amazon_polarity_Is_this_review_negative_score_eval",
"amazon_polarity_Is_this_review_score_eval",
"amazon_polarity_User_recommend_this_product_score_eval",
"amazon_polarity_convey_negative_or_positive_sentiment_score_eval",
"amazon_polarity_flattering_or_not_score_eval",
"amazon_polarity_negative_or_positive_tone_score_eval",
"amazon_polarity_user_satisfied_score_eval",
"amazon_polarity_would_you_buy_score_eval",
"dbpedia_14_given_a_choice_of_categories__score_eval",
"dbpedia_14_given_list_what_category_does_the_paragraph_belong_to_score_eval",
"dbpedia_14_pick_one_category_for_the_following_text_score_eval",
"wiki_hop_original_choose_best_object_affirmative_1_score_eval",
"wiki_hop_original_choose_best_object_affirmative_2_score_eval",
"wiki_hop_original_choose_best_object_affirmative_3_score_eval",
"wiki_hop_original_choose_best_object_interrogative_1_score_eval",
"wiki_hop_original_choose_best_object_interrogative_2_score_eval",
]
seqio.MixtureRegistry.add(
"t0_train",
[task for task in t0_train_mixture["BASE"] if task not in TASK_BLACKLIST],
default_rate=lambda t: mixture_cap[t.name],
)
seqio.MixtureRegistry.add(
"t0+_train",
[task for task in t0_train_mixture["BASE"] + t0_train_mixture["GPT_EVAL"] if task not in TASK_BLACKLIST],
default_rate=lambda t: mixture_cap[t.name],
)
seqio.MixtureRegistry.add(
"t0++_train",
[task for task in t0_train_mixture["BASE"] + t0_train_mixture["GPT_EVAL"] + t0_train_mixture["SGLUE"] if task not in TASK_BLACKLIST],
default_rate=lambda t: mixture_cap[t.name],
)
seqio.MixtureRegistry.add(
"t0_eval_score_eval",
[
task
for task in seqio.TaskRegistry.names()
if task.endswith("_score_eval")
and task.split("_score_eval")[0] in t0_eval_mixture["BASE"]
and task.split("_score_eval")[0] not in TASK_BLACKLIST
],
default_rate=functools.partial(seqio.mixing_rate_num_examples, maximum=500_000),
)
# Train tasks we don't care about evaluating on
D4_TRAIN_SKIP_EVAL = [
"paws_labeled_final",
"adversarial_qa_dbidaf",
"adversarial_qa_dbert",
"duorc_ParaphraseRC",
"dream",
"amazon_polarity",
"app_reviews",
"imdb",
"wiki_bio",
"gigaword",
"multi_news",
"samsum",
"dbpedia_14",
"trec",
]
seqio.MixtureRegistry.add(
"t0_train_score_eval",
[
task
for task in seqio.TaskRegistry.names()
if task.endswith("_score_eval")
and task.split("_score_eval")[0] in t0_train_mixture["BASE"]
and task.split("_score_eval")[0] not in TASK_BLACKLIST
and task not in D4_TRAIN_SCORE_EVAL_TASK_BLACKLIST
and not any([skip in task for skip in D4_TRAIN_SKIP_EVAL])
and task.split("_score_eval")[0] in all_original_tasks
],
default_rate=functools.partial(seqio.mixing_rate_num_examples, maximum=500_000),
)
seqio.MixtureRegistry.add(
"t0_train_one_og_prompt",
[task for task in single_original_task.values() if task in t0_train_mixture["BASE"] and task not in TASK_BLACKLIST],
default_rate=lambda t: mixture_cap[t.name],
)
seqio.MixtureRegistry.add(
"t0_train_all_og_prompts",
[task for task in all_original_tasks if task in t0_train_mixture["BASE"] and task not in TASK_BLACKLIST],
default_rate=lambda t: mixture_cap[t.name],
)
seqio.MixtureRegistry.add(
"bias_fairness_eval_score_eval",
[
task
for task in seqio.TaskRegistry.names()
if task.endswith("_score_eval") and task.split("_score_eval")[0] in t0_eval_mixture["BIAS_FAIRNESS"]
],
default_rate=functools.partial(seqio.mixing_rate_num_examples, maximum=500_000),
)