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https://github.com/explosion/spaCy.git
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91ccacea12
* Auto-format code with black * add black requirement to dev dependencies and pin to 22.x * ignore black dependency for comparison with setup.cfg Co-authored-by: explosion-bot <explosion-bot@users.noreply.github.com> Co-authored-by: svlandeg <svlandeg@github.com>
252 lines
8.5 KiB
Python
252 lines
8.5 KiB
Python
from typing import Any, Optional, Iterable, Tuple, List, Callable, TYPE_CHECKING, cast
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from thinc.types import Floats2d
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from thinc.api import chain, Maxout, LayerNorm, Softmax, Linear, zero_init, Model
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from thinc.api import MultiSoftmax, list2array
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from thinc.api import to_categorical, CosineDistance, L2Distance
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from thinc.loss import Loss
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from ...util import registry, OOV_RANK
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from ...errors import Errors
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from ...attrs import ID
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import numpy
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from functools import partial
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if TYPE_CHECKING:
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# This lets us add type hints for mypy etc. without causing circular imports
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from ...vocab import Vocab # noqa: F401
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from ...tokens.doc import Doc # noqa: F401
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@registry.architectures("spacy.PretrainVectors.v1")
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def create_pretrain_vectors(
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maxout_pieces: int, hidden_size: int, loss: str
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) -> Callable[["Vocab", Model], Model]:
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def create_vectors_objective(vocab: "Vocab", tok2vec: Model) -> Model:
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if vocab.vectors.shape[1] == 0:
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raise ValueError(Errors.E875)
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model = build_cloze_multi_task_model(
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vocab, tok2vec, hidden_size=hidden_size, maxout_pieces=maxout_pieces
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)
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model.attrs["loss"] = create_vectors_loss()
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return model
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def create_vectors_loss() -> Callable:
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distance: Loss
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if loss == "cosine":
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distance = CosineDistance(normalize=True, ignore_zeros=True)
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return partial(get_vectors_loss, distance=distance)
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elif loss == "L2":
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distance = L2Distance(normalize=True)
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return partial(get_vectors_loss, distance=distance)
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else:
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raise ValueError(Errors.E906.format(found=loss, supported="'cosine', 'L2'"))
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return create_vectors_objective
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@registry.architectures("spacy.PretrainCharacters.v1")
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def create_pretrain_characters(
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maxout_pieces: int, hidden_size: int, n_characters: int
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) -> Callable[["Vocab", Model], Model]:
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def create_characters_objective(vocab: "Vocab", tok2vec: Model) -> Model:
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model = build_cloze_characters_multi_task_model(
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vocab,
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tok2vec,
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hidden_size=hidden_size,
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maxout_pieces=maxout_pieces,
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nr_char=n_characters,
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)
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model.attrs["loss"] = partial(get_characters_loss, nr_char=n_characters)
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return model
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return create_characters_objective
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def get_vectors_loss(ops, docs, prediction, distance):
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"""Compute a loss based on a distance between the documents' vectors and
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the prediction.
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"""
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# The simplest way to implement this would be to vstack the
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# token.vector values, but that's a bit inefficient, especially on GPU.
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# Instead we fetch the index into the vectors table for each of our tokens,
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# and look them up all at once. This prevents data copying.
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ids = ops.flatten([doc.to_array(ID).ravel() for doc in docs])
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target = docs[0].vocab.vectors.data[ids]
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target[ids == OOV_RANK] = 0
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d_target, loss = distance(prediction, target)
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return loss, d_target
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def get_characters_loss(ops, docs, prediction, nr_char):
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"""Compute a loss based on a number of characters predicted from the docs."""
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target_ids = numpy.vstack([doc.to_utf8_array(nr_char=nr_char) for doc in docs])
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target_ids = target_ids.reshape((-1,))
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target = ops.asarray(to_categorical(target_ids, n_classes=256), dtype="f")
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target = target.reshape((-1, 256 * nr_char))
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diff = prediction - target
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loss = (diff**2).sum()
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d_target = diff / float(prediction.shape[0])
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return loss, d_target
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def build_multi_task_model(
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tok2vec: Model,
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maxout_pieces: int,
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token_vector_width: int,
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nO: Optional[int] = None,
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) -> Model:
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softmax = Softmax(nO=nO, nI=token_vector_width * 2)
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model = chain(
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tok2vec,
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Maxout(
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nO=token_vector_width * 2,
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nI=token_vector_width,
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nP=maxout_pieces,
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dropout=0.0,
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),
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LayerNorm(token_vector_width * 2),
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softmax,
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)
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model.set_ref("tok2vec", tok2vec)
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model.set_ref("output_layer", softmax)
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return model
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def build_cloze_multi_task_model(
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vocab: "Vocab", tok2vec: Model, maxout_pieces: int, hidden_size: int
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) -> Model:
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nO = vocab.vectors.shape[1]
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output_layer = chain(
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cast(Model[List["Floats2d"], Floats2d], list2array()),
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Maxout(
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nO=hidden_size,
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nI=tok2vec.get_dim("nO"),
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nP=maxout_pieces,
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normalize=True,
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dropout=0.0,
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),
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Linear(nO=nO, nI=hidden_size, init_W=zero_init),
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)
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model = chain(tok2vec, output_layer)
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model = build_masked_language_model(vocab, model)
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model.set_ref("tok2vec", tok2vec)
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model.set_ref("output_layer", output_layer)
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return model
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def build_cloze_characters_multi_task_model(
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vocab: "Vocab", tok2vec: Model, maxout_pieces: int, hidden_size: int, nr_char: int
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) -> Model:
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output_layer = chain(
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cast(Model[List["Floats2d"], Floats2d], list2array()),
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Maxout(nO=hidden_size, nP=maxout_pieces),
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LayerNorm(nI=hidden_size),
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MultiSoftmax([256] * nr_char, nI=hidden_size), # type: ignore[arg-type]
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)
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model = build_masked_language_model(vocab, chain(tok2vec, output_layer))
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model.set_ref("tok2vec", tok2vec)
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model.set_ref("output_layer", output_layer)
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return model
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def build_masked_language_model(
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vocab: "Vocab", wrapped_model: Model, mask_prob: float = 0.15
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) -> Model:
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"""Convert a model into a BERT-style masked language model"""
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random_words = _RandomWords(vocab)
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def mlm_forward(model, docs, is_train):
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mask, docs = _apply_mask(docs, random_words, mask_prob=mask_prob)
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mask = model.ops.asarray(mask).reshape((mask.shape[0], 1))
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output, backprop = model.layers[0](docs, is_train)
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def mlm_backward(d_output):
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d_output *= 1 - mask
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return backprop(d_output)
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return output, mlm_backward
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def mlm_initialize(model: Model, X=None, Y=None):
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wrapped = model.layers[0]
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wrapped.initialize(X=X, Y=Y)
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for dim in wrapped.dim_names:
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if wrapped.has_dim(dim):
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model.set_dim(dim, wrapped.get_dim(dim))
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mlm_model: Model = Model(
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"masked-language-model",
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mlm_forward,
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layers=[wrapped_model],
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init=mlm_initialize,
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refs={"wrapped": wrapped_model},
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dims={dim: None for dim in wrapped_model.dim_names},
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)
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mlm_model.set_ref("wrapped", wrapped_model)
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return mlm_model
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class _RandomWords:
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def __init__(self, vocab: "Vocab") -> None:
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# Extract lexeme representations
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self.words = [lex.text for lex in vocab if lex.prob != 0.0]
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self.words = self.words[:10000]
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# Compute normalized lexeme probabilities
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probs = [lex.prob for lex in vocab if lex.prob != 0.0]
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probs = probs[:10000]
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probs: numpy.ndarray = numpy.exp(numpy.array(probs, dtype="f"))
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probs /= probs.sum()
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self.probs = probs
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# Initialize cache
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self._cache: List[int] = []
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def next(self) -> str:
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if not self._cache:
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self._cache.extend(
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numpy.random.choice(len(self.words), 10000, p=self.probs)
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)
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index = self._cache.pop()
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return self.words[index]
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def _apply_mask(
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docs: Iterable["Doc"], random_words: _RandomWords, mask_prob: float = 0.15
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) -> Tuple[numpy.ndarray, List["Doc"]]:
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# This needs to be here to avoid circular imports
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from ...tokens.doc import Doc # noqa: F811
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N = sum(len(doc) for doc in docs)
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mask = numpy.random.uniform(0.0, 1.0, (N,))
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mask = mask >= mask_prob
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i = 0
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masked_docs = []
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for doc in docs:
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words = []
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for token in doc:
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if not mask[i]:
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word = _replace_word(token.text, random_words)
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else:
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word = token.text
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words.append(word)
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i += 1
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spaces = [bool(w.whitespace_) for w in doc]
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# NB: If you change this implementation to instead modify
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# the docs in place, take care that the IDs reflect the original
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# words. Currently we use the original docs to make the vectors
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# for the target, so we don't lose the original tokens. But if
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# you modified the docs in place here, you would.
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masked_docs.append(Doc(doc.vocab, words=words, spaces=spaces))
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return mask, masked_docs
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def _replace_word(word: str, random_words: _RandomWords, mask: str = "[MASK]") -> str:
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roll = numpy.random.random()
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if roll < 0.8:
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return mask
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elif roll < 0.9:
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return random_words.next()
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else:
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return word
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