spaCy/spacy/pipeline/tok2vec.py

243 lines
8.4 KiB
Python

from typing import Iterator, Sequence, Iterable, Optional, Dict, Callable, List, Tuple
from thinc.api import Model, set_dropout_rate, Optimizer, Config
from .pipe import Pipe
from ..gold import Example
from ..tokens import Doc
from ..vocab import Vocab
from ..language import Language
from ..errors import Errors
from ..util import minibatch
default_model_config = """
[model]
@architectures = "spacy.HashEmbedCNN.v1"
pretrained_vectors = null
width = 96
depth = 4
embed_size = 2000
window_size = 1
maxout_pieces = 3
subword_features = true
dropout = null
"""
DEFAULT_TOK2VEC_MODEL = Config().from_str(default_model_config)["model"]
@Language.factory(
"tok2vec", assigns=["doc.tensor"], default_config={"model": DEFAULT_TOK2VEC_MODEL}
)
def make_tok2vec(nlp: Language, name: str, model: Model) -> "Tok2Vec":
return Tok2Vec(nlp.vocab, model, name)
class Tok2Vec(Pipe):
def __init__(self, vocab: Vocab, model: Model, name: str = "tok2vec") -> None:
"""Initialize a tok2vec component.
vocab (Vocab): The shared vocabulary.
model (thinc.api.Model): The Thinc Model powering the pipeline component.
name (str): The component instance name.
DOCS: https://spacy.io/api/tok2vec#init
"""
self.vocab = vocab
self.model = model
self.name = name
self.listeners = []
self.cfg = {}
def add_listener(self, listener: "Tok2VecListener") -> None:
self.listeners.append(listener)
def find_listeners(self, model: Model) -> None:
for node in model.walk():
if isinstance(node, Tok2VecListener) and node.upstream_name in (
"*",
self.name,
):
self.add_listener(node)
def __call__(self, doc: Doc) -> Doc:
"""Add context-sensitive embeddings to the Doc.tensor attribute.
docs (Doc): The Doc to preocess.
RETURNS (Doc): The processed Doc.
DOCS: https://spacy.io/api/tok2vec#call
"""
tokvecses = self.predict([doc])
self.set_annotations([doc], tokvecses)
return doc
def pipe(self, stream: Iterator[Doc], *, batch_size: int = 128) -> Iterator[Doc]:
"""Apply the pipe to a stream of documents. This usually happens under
the hood when the nlp object is called on a text and all components are
applied to the Doc.
stream (Iterable[Doc]): A stream of documents.
batch_size (int): The number of documents to buffer.
YIELDS (Doc): Processed documents in order.
DOCS: https://spacy.io/api/tok2vec#pipe
"""
for docs in minibatch(stream, batch_size):
docs = list(docs)
tokvecses = self.predict(docs)
self.set_annotations(docs, tokvecses)
yield from docs
def predict(self, docs: Iterable[Doc]):
"""Apply the pipeline's model to a batch of docs, without modifying them.
Returns a single tensor for a batch of documents.
docs (Iterable[Doc]): The documents to predict.
RETURNS: Vector representations for each token in the documents.
DOCS: https://spacy.io/api/tok2vec#predict
"""
tokvecs = self.model.predict(docs)
batch_id = Tok2VecListener.get_batch_id(docs)
for listener in self.listeners:
listener.receive(batch_id, tokvecs, None)
return tokvecs
def set_annotations(self, docs: Sequence[Doc], tokvecses) -> None:
"""Modify a batch of documents, using pre-computed scores.
docs (Iterable[Doc]): The documents to modify.
tokvecses: The tensors to set, produced by Tok2Vec.predict.
DOCS: https://spacy.io/api/tok2vec#set_annotations
"""
for doc, tokvecs in zip(docs, tokvecses):
assert tokvecs.shape[0] == len(doc)
doc.tensor = tokvecs
def update(
self,
examples: Iterable[Example],
*,
drop: float = 0.0,
sgd: Optional[Optimizer] = None,
losses: Optional[Dict[str, float]] = None,
set_annotations: bool = False,
):
"""Learn from a batch of documents and gold-standard information,
updating the pipe's model.
examples (Iterable[Example]): A batch of Example objects.
drop (float): The dropout rate.
set_annotations (bool): Whether or not to update the Example objects
with the predictions.
sgd (thinc.api.Optimizer): The optimizer.
losses (Dict[str, float]): Optional record of the loss during training.
Updated using the component name as the key.
RETURNS (Dict[str, float]): The updated losses dictionary.
DOCS: https://spacy.io/api/tok2vec#update
"""
if losses is None:
losses = {}
docs = [eg.predicted for eg in examples]
if isinstance(docs, Doc):
docs = [docs]
set_dropout_rate(self.model, drop)
tokvecs, bp_tokvecs = self.model.begin_update(docs)
d_tokvecs = [self.model.ops.alloc2f(*t2v.shape) for t2v in tokvecs]
losses.setdefault(self.name, 0.0)
def accumulate_gradient(one_d_tokvecs):
"""Accumulate tok2vec loss and gradient. This is passed as a callback
to all but the last listener. Only the last one does the backprop.
"""
nonlocal d_tokvecs
for i in range(len(one_d_tokvecs)):
d_tokvecs[i] += one_d_tokvecs[i]
losses[self.name] += float((one_d_tokvecs[i] ** 2).sum())
def backprop(one_d_tokvecs):
"""Callback to actually do the backprop. Passed to last listener."""
accumulate_gradient(one_d_tokvecs)
d_docs = bp_tokvecs(d_tokvecs)
if sgd is not None:
self.model.finish_update(sgd)
return d_docs
batch_id = Tok2VecListener.get_batch_id(docs)
for listener in self.listeners[:-1]:
listener.receive(batch_id, tokvecs, accumulate_gradient)
self.listeners[-1].receive(batch_id, tokvecs, backprop)
if set_annotations:
self.set_annotations(docs, tokvecs)
return losses
def get_loss(self, examples, scores) -> None:
pass
def begin_training(
self,
get_examples: Callable[[], Iterable[Example]] = lambda: [],
*,
pipeline: Optional[List[Tuple[str, Callable[[Doc], Doc]]]] = None,
sgd: Optional[Optimizer] = None,
):
"""Initialize the pipe for training, using data examples if available.
get_examples (Callable[[], Iterable[Example]]): Optional function that
returns gold-standard Example objects.
pipeline (List[Tuple[str, Callable]]): Optional list of pipeline
components that this component is part of. Corresponds to
nlp.pipeline.
sgd (thinc.api.Optimizer): Optional optimizer. Will be created with
create_optimizer if it doesn't exist.
RETURNS (thinc.api.Optimizer): The optimizer.
DOCS: https://spacy.io/api/tok2vec#begin_training
"""
docs = [Doc(self.vocab, words=["hello"])]
self.model.initialize(X=docs)
class Tok2VecListener(Model):
"""A layer that gets fed its answers from an upstream connection,
for instance from a component earlier in the pipeline.
"""
name = "tok2vec-listener"
def __init__(self, upstream_name: str, width: int) -> None:
Model.__init__(self, name=self.name, forward=forward, dims={"nO": width})
self.upstream_name = upstream_name
self._batch_id = None
self._outputs = None
self._backprop = None
@classmethod
def get_batch_id(cls, inputs) -> int:
return sum(sum(token.orth for token in doc) for doc in inputs)
def receive(self, batch_id: int, outputs, backprop) -> None:
self._batch_id = batch_id
self._outputs = outputs
self._backprop = backprop
def verify_inputs(self, inputs) -> bool:
if self._batch_id is None and self._outputs is None:
raise ValueError(Errors.E954)
else:
batch_id = self.get_batch_id(inputs)
if batch_id != self._batch_id:
raise ValueError(Errors.E953.format(id1=batch_id, id2=self._batch_id))
else:
return True
def forward(model: Tok2VecListener, inputs, is_train: bool):
if is_train:
model.verify_inputs(inputs)
return model._outputs, model._backprop
else:
return [doc.tensor for doc in inputs], lambda dX: []