tasks.py

import functools
import seqio
import tensorflow as tf
import t5.data
from datasets import load_dataset
from t5.data import postprocessors
from t5.data import preprocessors
from t5.evaluation import metrics
from seqio import FunctionDataSource, utils

from ul2_objective import ul2_objective
# values from UL2 paper https://arxiv.org/pdf/2205.05131.pdf chapter 3.1.2 table 1
R_DENOISER_SPAN_LENGTHS = [3.0, 8.0]
X_DENOISER_SPAN_LENGTHS = [3.0, 8.0, 64.0, 64.0]
R_DENOISER_CORRUPT_RATES = [0.15, 0.15]
X_DENOISER_CORRUPT_RATES = [0.5, 0.5, 0.15, 0.5]

R_DENOISER_TOKEN_PREFIX = '[NLU]'
X_DENOISER_TOKEN_PREFIX = '[NLG]'
S_DENOISER_TOKEN_PREFIX = '[S2S]'


TaskRegistry = seqio.TaskRegistry
scand_vocabulary=seqio.SentencePieceVocabulary('gs://nb-t5/t5/vocabs/wikipedia/no-da-en-sv-nn-is_32000_unigram.sp.model', extra_ids=100)
eng_vocabulary=seqio.SentencePieceVocabulary('gs://t5-data/vocabs/cc_all.32000.100extra/sentencepiece.model', extra_ids=0)
mt5_vocabulary=seqio.SentencePieceVocabulary('gs://t5-data/vocabs/mc4.250000.100extra/sentencepiece.model', extra_ids=0)

SCAND_OUTPUT_FEATURES = {
    "inputs": seqio.Feature(
        vocabulary=scand_vocabulary, add_eos=True,
        required=False),
    "targets": seqio.Feature(
        vocabulary=scand_vocabulary, add_eos=True)
}

ENG_OUTPUT_FEATURES = {
    "inputs": seqio.Feature(
        vocabulary=eng_vocabulary, add_eos=True,
        required=False),
    "targets": seqio.Feature(
        vocabulary=eng_vocabulary, add_eos=True)
}
MT5_OUTPUT_FEATURES = {
    "inputs": seqio.Feature(
        vocabulary=mt5_vocabulary, add_eos=True,
        required=False),
    "targets": seqio.Feature(
        vocabulary=mt5_vocabulary, add_eos=True)
}


def gen_dataset(split, shuffle=False, seed=None, column="text", dataset_params=None):
    dataset = load_dataset(**dataset_params)
    if shuffle:
        if seed:
            dataset = dataset.shuffle(seed=seed)
        else:
            dataset = dataset.shuffle()
    while True:
        for item in dataset[str(split)]:
            yield item[column]


def dataset_fn(split, shuffle_files, seed=None, dataset_params=None):
    return tf.data.Dataset.from_generator(
        functools.partial(gen_dataset, split, shuffle_files, seed, dataset_params=dataset_params),
        output_signature=tf.TensorSpec(shape=(), dtype=tf.string, name=dataset_name)
    )



@utils.map_over_dataset
def target_to_key(x, key_map, target_key):
    """Assign the value from the dataset to target_key in key_map"""
    return {**key_map, target_key: x}



# Final pretraining task used in Raffel et al., 2019 adaptated to NCC
dataset_name = 'NbAiLab/scandinavian'
dataset_params = {"path": dataset_name, "use_auth_token": True, "streaming": True}
dataset_shapes = None
TaskRegistry.add(
    "scandinavian_span_engvoc",
    source=seqio.FunctionDataSource(
        dataset_fn=functools.partial(dataset_fn, dataset_params=dataset_params),
        splits=("train", "validation"),
        caching_permitted=False,
        num_input_examples=dataset_shapes,
        ),
    preprocessors=[
        functools.partial(
            target_to_key, key_map={
                "inputs": None,
                "targets": None,
            }, target_key="targets"),
        seqio.preprocessors.tokenize,
        # seqio.CacheDatasetPlaceholder(),
        preprocessors.span_corruption,
        seqio.preprocessors.append_eos_after_trim,
    ],
    output_features={"targets": ENG_OUTPUT_FEATURES["targets"]},
    metric_fns=[]
)


# Final pretraining task used in Tay et al., 2022 adaptated by @beats
dataset_name = 'NbAiLab/scandinavian'
dataset_params = {"path": dataset_name, "use_auth_token": True, "streaming": True}
dataset_shapes = None
TaskRegistry.add(
    "scandinavian_span_scandvoc",
    source=seqio.FunctionDataSource(
        dataset_fn=functools.partial(dataset_fn, dataset_params=dataset_params),
        splits=("train", "validation"),
        caching_permitted=False,
        num_input_examples=dataset_shapes,
        ),
    preprocessors=[
        functools.partial(
            target_to_key, key_map={
                "inputs": None,
                "targets": None,
            }, target_key="targets"),
        seqio.preprocessors.tokenize,
        # seqio.CacheDatasetPlaceholder(),
        preprocessors.span_corruption,
        seqio.preprocessors.append_eos_after_trim,
    ],
    output_features={"targets": SCAND_OUTPUT_FEATURES["targets"]},
    metric_fns=[]
)


# Final pretraining task used in Tay et al., 2022 adaptated by @beats
dataset_name = 'NbAiLab/scandinavian'
dataset_params = {"path": dataset_name, "use_auth_token": True, "streaming": True}
dataset_shapes = None
TaskRegistry.add(
    "scandinavian_ul2_engvoc",
    source=seqio.FunctionDataSource(
        dataset_fn=functools.partial(dataset_fn, dataset_params=dataset_params),
        splits=("train", "validation"),
        caching_permitted=False,
        num_input_examples=dataset_shapes,
        ),
    preprocessors=[
        functools.partial(
            target_to_key, key_map={
                "inputs": None,
                "targets": None,
            }, target_key="targets"),
        seqio.preprocessors.tokenize,
        functools.partial(
            ul2_objective,
            shard_ds=False,
            use_prefix_lm_task=True,  # use S-denoising
            rates=[0.4 / len(R_DENOISER_SPAN_LENGTHS)]*len(R_DENOISER_SPAN_LENGTHS) + [
                0.4 / len(X_DENOISER_SPAN_LENGTHS)]*len(X_DENOISER_SPAN_LENGTHS) + [0.2],  # equal total 40% rate for both R- and X-denoisers + 20% for S-denoising (suggested at the paper chapter 4.5)
            mean_noise_span_lengths=R_DENOISER_SPAN_LENGTHS + X_DENOISER_SPAN_LENGTHS,
            noise_densities=R_DENOISER_CORRUPT_RATES + X_DENOISER_CORRUPT_RATES,
            optional_task_prefixes=[R_DENOISER_TOKEN_PREFIX]*len(R_DENOISER_SPAN_LENGTHS) + [
                X_DENOISER_TOKEN_PREFIX]*len(X_DENOISER_SPAN_LENGTHS) + [S_DENOISER_TOKEN_PREFIX],
            reserved_for_packing=5,  # make room for task prefix token - Depends on the tokenizer
        ),
        seqio.preprocessors.append_eos_after_trim,
    ],
    output_features={"targets": ENG_OUTPUT_FEATURES["targets"]},
    metric_fns=[]
)


# Final pretraining task used in Raffel et al., 2019 adaptated to NCC
dataset_name = 'NbAiLab/scandinavian'
dataset_params = {"path": dataset_name, "use_auth_token": True, "streaming": True}
dataset_shapes = None
TaskRegistry.add(
    "scandinavian_ul2_scandvoc",
    source=seqio.FunctionDataSource(
        dataset_fn=functools.partial(dataset_fn, dataset_params=dataset_params),
        splits=("train", "validation"),
        caching_permitted=False,
        num_input_examples=dataset_shapes,
        ),
    preprocessors=[
        functools.partial(
            target_to_key, key_map={
                "inputs": None,
                "targets": None,
            }, target_key="targets"),
        seqio.preprocessors.tokenize,
        functools.partial(
            ul2_objective,
            shard_ds=False,
            use_prefix_lm_task=True,  # use S-denoising
            rates=[0.4 / len(R_DENOISER_SPAN_LENGTHS)]*len(R_DENOISER_SPAN_LENGTHS) + [
                0.4 / len(X_DENOISER_SPAN_LENGTHS)]*len(X_DENOISER_SPAN_LENGTHS) + [0.2],  # equal total 40% rate for both R- and X-denoisers + 20% for S-denoising (suggested at the paper chapter 4.5)
            mean_noise_span_lengths=R_DENOISER_SPAN_LENGTHS + X_DENOISER_SPAN_LENGTHS,
            noise_densities=R_DENOISER_CORRUPT_RATES + X_DENOISER_CORRUPT_RATES,
            optional_task_prefixes=[R_DENOISER_TOKEN_PREFIX]*len(R_DENOISER_SPAN_LENGTHS) + [
                X_DENOISER_TOKEN_PREFIX]*len(X_DENOISER_SPAN_LENGTHS) + [S_DENOISER_TOKEN_PREFIX],
            reserved_for_packing=5,  # make room for task prefix token - depends on the tokenizer
        ),
        seqio.preprocessors.append_eos_after_trim,
    ],
    output_features={"targets": SCAND_OUTPUT_FEATURES["targets"]},
    metric_fns=[]
)


# Final pretraining task used in Tay et al., 2022 adaptated by @beats
dataset_name = 'NbAiLab/scandinavian'
dataset_params = {"path": dataset_name, "use_auth_token": True, "streaming": True}
dataset_shapes = None
TaskRegistry.add(
    "scandinavian_ul2_mt5voc",
    source=seqio.FunctionDataSource(
        dataset_fn=functools.partial(dataset_fn, dataset_params=dataset_params),
        splits=("train", "validation"),
        caching_permitted=False,
        num_input_examples=dataset_shapes,
        ),
    preprocessors=[
        functools.partial(
            target_to_key, key_map={
                "inputs": None,
                "targets": None,
            }, target_key="targets"),
        seqio.preprocessors.tokenize,
        functools.partial(
            ul2_objective,
            shard_ds=False,
            use_prefix_lm_task=True,  # use S-denoising
            rates=[0.4 / len(R_DENOISER_SPAN_LENGTHS)]*len(R_DENOISER_SPAN_LENGTHS) + [
                0.4 / len(X_DENOISER_SPAN_LENGTHS)]*len(X_DENOISER_SPAN_LENGTHS) + [0.2],  # equal total 40% rate for both R- and X-denoisers + 20% for S-denoising (suggested at the paper chapter 4.5)
            mean_noise_span_lengths=R_DENOISER_SPAN_LENGTHS + X_DENOISER_SPAN_LENGTHS,
            noise_densities=R_DENOISER_CORRUPT_RATES + X_DENOISER_CORRUPT_RATES,
            optional_task_prefixes=[R_DENOISER_TOKEN_PREFIX]*len(R_DENOISER_SPAN_LENGTHS) + [
                X_DENOISER_TOKEN_PREFIX]*len(X_DENOISER_SPAN_LENGTHS) + [S_DENOISER_TOKEN_PREFIX],
            reserved_for_packing=5,  # make room for task prefix token - Depends on the tokenizer
        ),
        seqio.preprocessors.append_eos_after_trim,
    ],
    output_features={"targets": MT5_OUTPUT_FEATURES["targets"]},
    metric_fns=[]
)



# Final pretraining task used in Tay et al., 2022 adaptated by @beats
dataset_name = 'NbAiLab/scandinavian'
dataset_params = {"path": dataset_name, "use_auth_token": True, "streaming": True}
dataset_shapes = None
TaskRegistry.add(
    "scandinavian_span_mt5voc",
    source=seqio.FunctionDataSource(
        dataset_fn=functools.partial(dataset_fn, dataset_params=dataset_params),
        splits=("train", "validation"),
        caching_permitted=False,
        num_input_examples=dataset_shapes,
        ),
    preprocessors=[
        functools.partial(
            target_to_key, key_map={
                "inputs": None,
                "targets": None,
            }, target_key="targets"),
        seqio.preprocessors.tokenize,
        # seqio.CacheDatasetPlaceholder(),
        preprocessors.span_corruption,
        seqio.preprocessors.append_eos_after_trim,
    ],
    output_features={"targets": MT5_OUTPUT_FEATURES["targets"]},
    metric_fns=[]
)