spaCy/spacy/gold/batchers.py

from typing import Union, Iterator, Iterable, Sequence, TypeVar, List, Callable
from typing import Optional, Any
from functools import partial
import itertools

from .example import Example
from ..util import registry, minibatch


Sizing = Union[Iterable[int], int]
ItemT = TypeVar("ItemT")
BatcherT = Callable[[Iterable[ItemT]], Iterable[List[ItemT]]]


@registry.batchers("batch_by_padded.v1")
def configure_minibatch_by_padded_size(
    *,
    size: Sizing,
    buffer: int,
    discard_oversize: bool,
    get_length: Optional[Callable[[ItemT], int]] = None
) -> BatcherT:
    # Avoid displacing optional values from the underlying function.
    optionals = {"get_length": get_length} if get_length is not None else {}
    return partial(
        minibatch_by_padded_size,
        size=size,
        buffer=buffer,
        discard_oversize=discard_oversize,
        **optionals
    )


@registry.batchers("batch_by_words.v1")
def configure_minibatch_by_words(
    *,
    size: Sizing,
    tolerance: float,
    discard_oversize: bool,
    get_length: Optional[Callable[[ItemT], int]] = None
) -> BatcherT:
    optionals = {"get_length": get_length} if get_length is not None else {}
    return partial(
        minibatch_by_words,
        size=size,
        discard_oversize=discard_oversize,
        **optionals
    )


@registry.batchers("batch_by_sequence.v1")
def configure_minibatch(size: Sizing, get_length=None) -> BatcherT:
    optionals = ({"get_length": get_length} if get_length is not None else {})
    return partial(minibatch, size=size, **optionals)


def minibatch_by_padded_size(
    docs: Iterator["Doc"],
    size: Sizing,
    buffer: int = 256,
    discard_oversize: bool = False,
    get_length: Callable = len,
) -> Iterator[Iterator["Doc"]]:
    if isinstance(size, int):
        size_ = itertools.repeat(size)
    else:
        size_ = size
    for outer_batch in minibatch(docs, size=buffer):
        outer_batch = list(outer_batch)
        target_size = next(size_)
        for indices in _batch_by_length(outer_batch, target_size, get_length):
            subbatch = [outer_batch[i] for i in indices]
            padded_size = max(len(seq) for seq in subbatch) * len(subbatch)
            if discard_oversize and padded_size >= target_size:
                pass
            else:
                yield subbatch


def minibatch_by_words(
    docs, size, tolerance=0.2, discard_oversize=False, get_length=len
):
    """Create minibatches of roughly a given number of words. If any examples
    are longer than the specified batch length, they will appear in a batch by
    themselves, or be discarded if discard_oversize=True.
    The argument 'docs' can be a list of strings, Doc's or Example's. """
    if isinstance(size, int):
        size_ = itertools.repeat(size)
    elif isinstance(size, List):
        size_ = iter(size)
    else:
        size_ = size
    target_size = next(size_)
    tol_size = target_size * tolerance
    batch = []
    overflow = []
    batch_size = 0
    overflow_size = 0
    for doc in docs:
        n_words = get_length(doc)
        # if the current example exceeds the maximum batch size, it is returned separately
        # but only if discard_oversize=False.
        if n_words > target_size + tol_size:
            if not discard_oversize:
                yield [doc]
        # add the example to the current batch if there's no overflow yet and it still fits
        elif overflow_size == 0 and (batch_size + n_words) <= target_size:
            batch.append(doc)
            batch_size += n_words
        # add the example to the overflow buffer if it fits in the tolerance margin
        elif (batch_size + overflow_size + n_words) <= (target_size + tol_size):
            overflow.append(doc)
            overflow_size += n_words
        # yield the previous batch and start a new one. The new one gets the overflow examples.
        else:
            if batch:
                yield batch
            target_size = next(size_)
            tol_size = target_size * tolerance
            batch = overflow
            batch_size = overflow_size
            overflow = []
            overflow_size = 0
            # this example still fits
            if (batch_size + n_words) <= target_size:
                batch.append(doc)
                batch_size += n_words
            # this example fits in overflow
            elif (batch_size + n_words) <= (target_size + tol_size):
                overflow.append(doc)
                overflow_size += n_words
            # this example does not fit with the previous overflow: start another new batch
            else:
                if batch:
                    yield batch
                target_size = next(size_)
                tol_size = target_size * tolerance
                batch = [doc]
                batch_size = n_words
    batch.extend(overflow)
    if batch:
        yield batch


def _batch_by_length(
    seqs: Sequence[Any], max_words: int, get_length=len
) -> List[List[Any]]:
    """Given a list of sequences, return a batched list of indices into the
    list, where the batches are grouped by length, in descending order.

    Batches may be at most max_words in size, defined as max sequence length * size.
    """
    # Use negative index so we can get sort by position ascending.
    lengths_indices = [(get_length(seq), i) for i, seq in enumerate(seqs)]
    lengths_indices.sort()
    batches = []
    batch = []
    for length, i in lengths_indices:
        if not batch:
            batch.append(i)
        elif length * (len(batch) + 1) <= max_words:
            batch.append(i)
        else:
            batches.append(batch)
            batch = [i]
    if batch:
        batches.append(batch)
    # Check lengths match
    assert sum(len(b) for b in batches) == len(seqs)
    batches = [list(sorted(batch)) for batch in batches]
    batches.reverse()
    return batches