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 link_vectors_to_models, 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: """Construct a new statistical model. Weights are not allocated on initialisation. vocab (Vocab): A `Vocab` instance. The model must share the same `Vocab` instance with the `Doc` objects it will process. """ 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 vectors to a `Doc`, e.g. from a CNN or LSTM model. Vectors are set to the `Doc.tensor` attribute. docs (Doc or iterable): One or more documents to add vectors to. RETURNS (dict or None): Intermediate computations. """ tokvecses = self.predict([doc]) self.set_annotations([doc], tokvecses) return doc def pipe(self, stream: Iterator[Doc], batch_size: int = 128) -> Iterator[Doc]: """Process `Doc` objects as a stream. stream (iterator): A sequence of `Doc` objects to process. batch_size (int): Number of `Doc` objects to group. YIELDS (iterator): A sequence of `Doc` objects, in order of input. """ 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: Sequence[Doc]): """Return a single tensor for a batch of documents. docs (iterable): A sequence of `Doc` objects. RETURNS (object): Vector representations for each token in the documents. """ 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: """Set the tensor attribute for a batch of documents. docs (iterable): A sequence of `Doc` objects. tokvecs (object): Vector representation for each token in the documents. """ 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, ): """Update the model. examples (Iterable[Example]): A batch of examples drop (float): The droput rate. sgd (Optimizer): An optimizer. losses (Dict[str, float]): Dictionary to update with the loss, keyed by component. set_annotations (bool): whether or not to update the examples with the predictions RETURNS (Dict[str, float]): The updated losses dictionary """ 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 = lambda: [], pipeline: Optional[List[Tuple[str, Callable[[Doc], Doc]]]] = None, sgd: Optional[Optimizer] = None, ): """Allocate models and pre-process training data get_examples (function): Function returning example training data. pipeline (list): The pipeline the model is part of. """ docs = [Doc(Vocab(), words=["hello"])] self.model.initialize(X=docs) link_vectors_to_models(self.vocab) 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: []