data.py

import random
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path

import jax
import jax.numpy as jnp
import numpy as np
from braceexpand import braceexpand
from datasets import Dataset, load_dataset

from .model.text import TextNormalizer


@dataclass
class Dataset:
    dataset_repo_or_path: str
    train_file: str = None
    validation_file: str = None
    streaming: bool = True
    use_auth_token: bool = False
    text_column: str = "caption"
    encoding_column: str = "encoding"
    max_train_samples: int = None
    max_eval_samples: int = None
    preprocessing_num_workers: int = None
    overwrite_cache: bool = False
    do_train: bool = False
    do_eval: bool = True
    seed_dataset: int = None
    shard_by_host: bool = False
    blank_caption_prob: float = 0.0
    clip_score_column: str = "clip_score"
    min_clip_score: float = None
    max_clip_score: float = None
    filter_column: str = None
    filter_value: str = None
    multi_eval_ds: bool = False
    train_dataset: Dataset = field(init=False)
    eval_dataset: Dataset = field(init=False)
    other_eval_datasets: list = field(init=False)
    rng_dataset: jnp.ndarray = field(init=False)
    multi_hosts: bool = field(init=False)

    def __post_init__(self):
        if self.seed_dataset is None:
            # create a random seed
            self.seed_dataset = random.randint(0, 2**32 - 1)
        # set numpy rng
        self.np_rng = np.random.default_rng(self.seed_dataset)
        self.multi_hosts = jax.process_count() > 1
        # feed blank captions only in streaming mode for now
        # otherwise dataset could be cached with same blanked captions
        if self.blank_caption_prob:
            assert (
                self.streaming is True
            ), "blank_caption_prob can only be used in streaming mode"
        # define data_files
        if self.train_file is not None or self.validation_file is not None:
            # accept braceexpand notation
            for k in ["train_file", "validation_file"]:
                f = getattr(self, k)
                if isinstance(f, str):
                    setattr(self, k, list(braceexpand(f)))
            # for list of files, split training data shards by host
            if (
                isinstance(self.train_file, list)
                and self.multi_hosts
                and self.shard_by_host
            ):
                self.train_file = self.train_file[
                    jax.process_index() :: jax.process_count()
                ]
            data_files = {
                "train": self.train_file,
                "validation": self.validation_file,
            }
        else:
            data_files = None

        # multiple validation datasets
        if self.multi_eval_ds:
            assert Path(
                self.dataset_repo_or_path
            ).is_dir(), f"{self.dataset_repo_or_path} is not a directory, required for multi_eval_ds"
            data_files = {
                split.name: [str(f) for f in split.glob("*.parquet")]
                for split in Path(self.dataset_repo_or_path).glob("*")
            }
            # rename "valid" to "validation" if present for consistency
            if "valid" in data_files:
                data_files["validation"] = data_files["valid"]
                del data_files["valid"]
            self.dataset_repo_or_path = "parquet"

        # load dataset
        dataset = load_dataset(
            self.dataset_repo_or_path,
            data_files=data_files,
            streaming=self.streaming,
            use_auth_token=self.use_auth_token,
        )
        if self.do_train:
            if "train" not in dataset:
                raise ValueError("Training requires a training dataset")
            self.train_dataset = dataset["train"]
            if self.max_train_samples is not None:
                self.train_dataset = (
                    self.train_dataset.take(self.max_train_samples)
                    if self.streaming
                    else self.train_dataset.select(range(self.max_train_samples))
                )
        if self.do_eval:
            if "validation" not in dataset:
                raise ValueError("Evaluating requires a validation dataset")
            self.eval_dataset = dataset["validation"]
            if self.max_eval_samples is not None:
                self.eval_dataset = (
                    self.eval_dataset.take(self.max_eval_samples)
                    if self.streaming
                    else self.eval_dataset.select(range(self.max_eval_samples))
                )
            # other eval datasets
            other_eval_splits = dataset.keys() - {"train", "validation"}
            self.other_eval_datasets = {
                split: dataset[split] for split in other_eval_splits
            }

    def preprocess(self, tokenizer, config):
        # get required config variables
        decoder_start_token_id = config.decoder_start_token_id
        normalize_text = config.normalize_text
        max_length = config.max_text_length

        if self.streaming:
            # we need to shuffle early in streaming mode
            if hasattr(self, "train_dataset"):
                self.train_dataset = self.train_dataset.shuffle(
                    buffer_size=5000, seed=self.seed_dataset
                )
        else:
            self.rng_dataset = jax.random.PRNGKey(self.seed_dataset)

        # filter data
        partial_filter_function = partial(
            filter_function,
            filter_column=self.filter_column,
            filter_value=self.filter_value,
            clip_score_column=self.clip_score_column,
            min_clip_score=self.min_clip_score,
            max_clip_score=self.max_clip_score,
        )
        for ds in ["train_dataset", "eval_dataset"]:
            if hasattr(self, ds):
                setattr(
                    self,
                    ds,
                    (
                        getattr(self, ds).filter(partial_filter_function)
                        if self.streaming
                        else getattr(self, ds).filter(
                            partial_filter_function,
                            num_proc=self.preprocessing_num_workers,
                            load_from_cache_file=not self.overwrite_cache,
                            desc="Filtering datasets",
                        )
                    ),
                )
        if hasattr(self, "other_eval_datasets"):
            self.other_eval_datasets = {
                split: (
                    ds.filter(partial_filter_function)
                    if self.streaming
                    else ds.filter(
                        partial_filter_function,
                        num_proc=self.preprocessing_num_workers,
                        load_from_cache_file=not self.overwrite_cache,
                        desc="Filtering datasets",
                    )
                )
                for split, ds in self.other_eval_datasets.items()
            }

        # normalize text
        if normalize_text:
            text_normalizer = TextNormalizer()
            partial_normalize_function = partial(
                normalize_function,
                text_column=self.text_column,
                text_normalizer=text_normalizer,
            )
            for ds in ["train_dataset", "eval_dataset"]:
                if hasattr(self, ds):
                    setattr(
                        self,
                        ds,
                        (
                            getattr(self, ds).map(partial_normalize_function)
                            if self.streaming
                            else getattr(self, ds).map(
                                partial_normalize_function,
                                num_proc=self.preprocessing_num_workers,
                                load_from_cache_file=not self.overwrite_cache,
                                desc="Normalizing datasets",
                            )
                        ),
                    )
            if hasattr(self, "other_eval_datasets"):
                self.other_eval_datasets = {
                    split: (
                        ds.map(partial_normalize_function)
                        if self.streaming
                        else ds.map(
                            partial_normalize_function,
                            num_proc=self.preprocessing_num_workers,
                            load_from_cache_file=not self.overwrite_cache,
                            desc="Normalizing datasets",
                        )
                    )
                    for split, ds in self.other_eval_datasets.items()
                }

        # blank captions
        if self.blank_caption_prob:
            partial_blank_caption_function = partial(
                blank_caption_function,
                text_column=self.text_column,
                blank_caption_prob=self.blank_caption_prob,
                rng=self.np_rng,
            )
            if hasattr(self, "train_dataset"):
                self.train_dataset = (
                    self.train_dataset.map(partial_blank_caption_function)
                    if self.streaming
                    else self.train_dataset.map(
                        partial_blank_caption_function,
                        num_proc=None
                        if self.seed_dataset
                        else self.preprocessing_num_workers,
                        load_from_cache_file=False,
                        desc="Blanking some captions",
                    )
                )

        # preprocess
        partial_preprocess_function = partial(
            preprocess_function,
            tokenizer=tokenizer,
            text_column=self.text_column,
            encoding_column=self.encoding_column,
            max_length=max_length,
            decoder_start_token_id=decoder_start_token_id,
        )
        for ds in ["train_dataset", "eval_dataset"]:
            if hasattr(self, ds):
                setattr(
                    self,
                    ds,
                    (
                        getattr(self, ds).map(
                            partial_preprocess_function,
                            batched=True,
                            remove_columns=[
                                self.text_column,
                                self.encoding_column,
                            ],
                        )
                        if self.streaming
                        else getattr(self, ds).map(
                            partial_preprocess_function,
                            batched=True,
                            remove_columns=getattr(ds, "column_names"),
                            num_proc=self.preprocessing_num_workers,
                            load_from_cache_file=not self.overwrite_cache,
                            desc="Preprocessing datasets",
                        )
                    ),
                )
        if hasattr(self, "other_eval_datasets"):
            self.other_eval_datasets = {
                split: (
                    ds.map(
                        partial_preprocess_function,
                        batched=True,
                        remove_columns=[
                            self.text_column,
                            self.encoding_column,
                        ],
                    )
                    if self.streaming
                    else ds.map(
                        partial_preprocess_function,
                        batched=True,
                        remove_columns=getattr(ds, "column_names"),
                        num_proc=self.preprocessing_num_workers,
                        load_from_cache_file=not self.overwrite_cache,
                        desc="Preprocessing datasets",
                    )
                )
                for split, ds in self.other_eval_datasets.items()
            }

    def dataloader(self, split, batch_size, epoch=None):
        def _dataloader_datasets_non_streaming(
            dataset: Dataset,
            rng: jax.random.PRNGKey = None,
        ):
            """
            Returns batches of size `batch_size` from truncated `dataset`, sharded over all local devices.
            Shuffle batches if rng is set.
            """
            steps_per_epoch = len(dataset) // batch_size

            if rng is not None:
                batch_idx = jax.random.permutation(rng, len(dataset))
            else:
                batch_idx = jnp.arange(len(dataset))

            batch_idx = batch_idx[
                : steps_per_epoch * batch_size
            ]  # Skip incomplete batch.
            batch_idx = batch_idx.reshape((steps_per_epoch, batch_size))

            for idx in batch_idx:
                batch = dataset[idx]
                batch = {k: jnp.array(v) for k, v in batch.items()}
                yield batch

        def _dataloader_datasets_streaming(
            dataset: Dataset,
            epoch: int,
        ):
            keys = ["input_ids", "attention_mask", "labels", "decoder_input_ids"]
            batch = {k: [] for k in keys}
            first_loop = True  # stop after one loop in some cases
            while (self.multi_hosts and split == "train") or first_loop:
                # in multi-host, we run forever (no epoch) as hosts need to stop
                # at the same time and training data may not be split equally
                # For validation data we put the entire batch on each host and then
                # keep only the one specific to each host (could be improved but not necessary)
                if epoch is not None:
                    assert split == "train"
                    # reshuffle training data at each epoch
                    dataset.set_epoch(epoch)
                    epoch += 1
                for item in dataset:
                    for k in keys:
                        batch[k].append(item[k])
                    if len(batch[keys[0]]) == batch_size:
                        batch = {k: jnp.array(v) for k, v in batch.items()}
                        yield batch
                        batch = {k: [] for k in keys}
                first_loop = False

        if split == "train":
            ds = self.train_dataset
        elif split == "eval":
            ds = self.eval_dataset
        else:
            ds = self.other_eval_datasets[split]

        if self.streaming:
            return _dataloader_datasets_streaming(ds, epoch)
        else:
            if split == "train":
                self.rng_dataset, input_rng = jax.random.split(self.rng_dataset)
            return _dataloader_datasets_non_streaming(ds, input_rng)

    @property
    def length(self):
        len_train_dataset, len_eval_dataset = None, None
        if self.streaming:
            # we don't know the length, let's just assume max_samples if defined
            if self.max_train_samples is not None:
                len_train_dataset = self.max_train_samples
            if self.max_eval_samples is not None:
                len_eval_dataset = self.max_eval_samples
        else:
            len_train_dataset = (
                len(self.train_dataset) if hasattr(self, "train_dataset") else None
            )
            len_eval_dataset = (
                len(self.eval_dataset) if hasattr(self, "eval_dataset") else None
            )
        return len_train_dataset, len_eval_dataset


def shift_tokens_right(input_ids: np.array, decoder_start_token_id: int):
    """
    Shift input ids one token to the right.
    """
    shifted_input_ids = np.zeros(input_ids.shape)
    shifted_input_ids[:, 1:] = input_ids[:, :-1]
    shifted_input_ids[:, 0] = decoder_start_token_id
    return shifted_input_ids


def blank_caption_function(example, text_column, blank_caption_prob, rng=None):
    if (
        blank_caption_prob
        and (rng.random() if rng is not None else np.random.random())
        < blank_caption_prob
    ):
        example[text_column] = ""
    return example


def normalize_function(example, text_column, text_normalizer):
    example[text_column] = text_normalizer(example[text_column])
    return example


def filter_function(
    example,
    min_clip_score,
    max_clip_score,
    clip_score_column,
    filter_column,
    filter_value,
):
    if min_clip_score is not None and example[clip_score_column] < min_clip_score:
        return False
    if max_clip_score is not None and example[clip_score_column] > max_clip_score:
        return False
    if filter_column is not None and example[filter_column] != filter_value:
        return False
    return True


def preprocess_function(
    examples,
    tokenizer,
    text_column,
    encoding_column,
    max_length,
    decoder_start_token_id,
):
    inputs = examples[text_column]
    # Setting padding="max_length" as we need fixed length inputs for jitted functions
    model_inputs = tokenizer(
        inputs,
        max_length=max_length,
        padding="max_length",
        truncation=True,
        return_tensors="np",
    )

    # set up targets
    # Note: labels correspond to our target indices
    # decoder input ids are the same but shifted to the right with bos at the beginning (and without last token)
    labels = examples[encoding_column]
    labels = np.asarray(labels)

    # We need the labels, in addition to the decoder_input_ids, for the compute_loss function
    model_inputs["labels"] = labels

    # In our case, this prepends the bos token and removes the last one
    decoder_input_ids = shift_tokens_right(labels, decoder_start_token_id)
    model_inputs["decoder_input_ids"] = decoder_input_ids

    return model_inputs