mirror of https://github.com/explosion/spaCy.git
120 lines
3.8 KiB
Python
120 lines
3.8 KiB
Python
from typing import List, Tuple, Callable, Optional, Sequence, cast
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from thinc.initializers import glorot_uniform_init
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from thinc.util import partial
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from thinc.types import Ragged, Floats2d, Floats1d, Ints1d
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from thinc.api import Model, Ops, registry
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from ..tokens import Doc
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from ..errors import Errors
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from ..vectors import Mode
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from ..vocab import Vocab
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@registry.layers("spacy.StaticVectors.v2")
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def StaticVectors(
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nO: Optional[int] = None,
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nM: Optional[int] = None,
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*,
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dropout: Optional[float] = None,
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init_W: Callable = glorot_uniform_init,
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key_attr: str = "ORTH"
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) -> Model[List[Doc], Ragged]:
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"""Embed Doc objects with their vocab's vectors table, applying a learned
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linear projection to control the dimensionality. If a dropout rate is
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specified, the dropout is applied per dimension over the whole batch.
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"""
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return Model(
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"static_vectors",
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forward,
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init=partial(init, init_W),
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params={"W": None},
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attrs={"key_attr": key_attr, "dropout_rate": dropout},
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dims={"nO": nO, "nM": nM},
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)
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def forward(
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model: Model[List[Doc], Ragged], docs: List[Doc], is_train: bool
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) -> Tuple[Ragged, Callable]:
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token_count = sum(len(doc) for doc in docs)
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if not token_count:
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return _handle_empty(model.ops, model.get_dim("nO"))
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key_attr: int = model.attrs["key_attr"]
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keys: Ints1d = model.ops.flatten(
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cast(Sequence, [doc.to_array(key_attr) for doc in docs])
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)
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vocab: Vocab = docs[0].vocab
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W = cast(Floats2d, model.ops.as_contig(model.get_param("W")))
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if vocab.vectors.mode == Mode.default:
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V = cast(Floats2d, model.ops.asarray(vocab.vectors.data))
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rows = vocab.vectors.find(keys=keys)
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V = model.ops.as_contig(V[rows])
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elif vocab.vectors.mode == Mode.floret:
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V = cast(Floats2d, vocab.vectors.get_batch(keys))
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V = model.ops.as_contig(V)
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else:
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raise RuntimeError(Errors.E896)
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try:
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vectors_data = model.ops.gemm(V, W, trans2=True)
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except ValueError:
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raise RuntimeError(Errors.E896)
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if vocab.vectors.mode == Mode.default:
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# Convert negative indices to 0-vectors
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# TODO: more options for UNK tokens
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vectors_data[rows < 0] = 0
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output = Ragged(
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vectors_data, model.ops.asarray([len(doc) for doc in docs], dtype="i") # type: ignore
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)
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mask = None
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if is_train:
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mask = _get_drop_mask(model.ops, W.shape[0], model.attrs.get("dropout_rate"))
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if mask is not None:
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output.data *= mask
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def backprop(d_output: Ragged) -> List[Doc]:
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if mask is not None:
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d_output.data *= mask
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model.inc_grad(
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"W",
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model.ops.gemm(
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cast(Floats2d, d_output.data), model.ops.as_contig(V), trans1=True
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),
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)
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return []
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return output, backprop
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def init(
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init_W: Callable,
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model: Model[List[Doc], Ragged],
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X: Optional[List[Doc]] = None,
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Y: Optional[Ragged] = None,
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) -> Model[List[Doc], Ragged]:
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nM = model.get_dim("nM") if model.has_dim("nM") else None
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nO = model.get_dim("nO") if model.has_dim("nO") else None
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if X is not None and len(X):
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nM = X[0].vocab.vectors.shape[1]
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if Y is not None:
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nO = Y.data.shape[1]
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if nM is None:
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raise ValueError(Errors.E905)
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if nO is None:
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raise ValueError(Errors.E904)
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model.set_dim("nM", nM)
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model.set_dim("nO", nO)
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model.set_param("W", init_W(model.ops, (nO, nM)))
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return model
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def _handle_empty(ops: Ops, nO: int):
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return Ragged(ops.alloc2f(0, nO), ops.alloc1i(0)), lambda d_ragged: []
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def _get_drop_mask(ops: Ops, nO: int, rate: Optional[float]) -> Optional[Floats1d]:
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if rate is not None:
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mask = ops.get_dropout_mask((nO,), rate)
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return mask # type: ignore
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return None
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