mirror of
https://github.com/explosion/spaCy.git
synced 2025-10-30 23:47:31 +03:00
94 lines
3.0 KiB
Python
94 lines
3.0 KiB
Python
from typing import List, Tuple, Callable, Optional, cast
|
|
from thinc.initializers import glorot_uniform_init
|
|
from thinc.util import partial
|
|
from thinc.types import Ragged, Floats2d, Floats1d
|
|
from thinc.api import Model, Ops, registry
|
|
|
|
from ..tokens import Doc
|
|
from ..errors import Errors
|
|
|
|
|
|
@registry.layers("spacy.StaticVectors.v1")
|
|
def StaticVectors(
|
|
nO: Optional[int] = None,
|
|
nM: Optional[int] = None,
|
|
*,
|
|
dropout: Optional[float] = None,
|
|
init_W: Callable = glorot_uniform_init,
|
|
key_attr: str = "ORTH"
|
|
) -> Model[List[Doc], Ragged]:
|
|
"""Embed Doc objects with their vocab's vectors table, applying a learned
|
|
linear projection to control the dimensionality. If a dropout rate is
|
|
specified, the dropout is applied per dimension over the whole batch.
|
|
"""
|
|
return Model(
|
|
"static_vectors",
|
|
forward,
|
|
init=partial(init, init_W),
|
|
params={"W": None},
|
|
attrs={"key_attr": key_attr, "dropout_rate": dropout},
|
|
dims={"nO": nO, "nM": nM},
|
|
)
|
|
|
|
|
|
def forward(
|
|
model: Model[List[Doc], Ragged], docs: List[Doc], is_train: bool
|
|
) -> Tuple[Ragged, Callable]:
|
|
if not len(docs):
|
|
return _handle_empty(model.ops, model.get_dim("nO"))
|
|
key_attr = model.attrs["key_attr"]
|
|
W = cast(Floats2d, model.ops.as_contig(model.get_param("W")))
|
|
V = cast(Floats2d, docs[0].vocab.vectors.data)
|
|
mask = _get_drop_mask(model.ops, W.shape[0], model.attrs.get("dropout_rate"))
|
|
rows = model.ops.flatten(
|
|
[doc.vocab.vectors.find(keys=doc.to_array(key_attr)) for doc in docs]
|
|
)
|
|
output = Ragged(
|
|
model.ops.gemm(model.ops.as_contig(V[rows]), W, trans2=True),
|
|
model.ops.asarray([len(doc) for doc in docs], dtype="i"),
|
|
)
|
|
if mask is not None:
|
|
output.data *= mask
|
|
|
|
def backprop(d_output: Ragged) -> List[Doc]:
|
|
if mask is not None:
|
|
d_output.data *= mask
|
|
model.inc_grad(
|
|
"W",
|
|
model.ops.gemm(d_output.data, model.ops.as_contig(V[rows]), trans1=True),
|
|
)
|
|
return []
|
|
|
|
return output, backprop
|
|
|
|
|
|
def init(
|
|
init_W: Callable,
|
|
model: Model[List[Doc], Ragged],
|
|
X: Optional[List[Doc]] = None,
|
|
Y: Optional[Ragged] = None,
|
|
) -> Model[List[Doc], Ragged]:
|
|
nM = model.get_dim("nM") if model.has_dim("nM") else None
|
|
nO = model.get_dim("nO") if model.has_dim("nO") else None
|
|
if X is not None and len(X):
|
|
nM = X[0].vocab.vectors.data.shape[1]
|
|
if Y is not None:
|
|
nO = Y.data.shape[1]
|
|
|
|
if nM is None:
|
|
raise ValueError(Errors.E905)
|
|
if nO is None:
|
|
raise ValueError(Errors.E904)
|
|
model.set_dim("nM", nM)
|
|
model.set_dim("nO", nO)
|
|
model.set_param("W", init_W(model.ops, (nO, nM)))
|
|
return model
|
|
|
|
|
|
def _handle_empty(ops: Ops, nO: int):
|
|
return Ragged(ops.alloc2f(0, nO), ops.alloc1i(0)), lambda d_ragged: []
|
|
|
|
|
|
def _get_drop_mask(ops: Ops, nO: int, rate: Optional[float]) -> Optional[Floats1d]:
|
|
return ops.get_dropout_mask((nO,), rate) if rate is not None else None
|