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https://github.com/explosion/spaCy.git
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In order to support Python 3.13, we had to migrate to Cython 3.0. This caused some tricky interaction with our Pydantic usage, because Cython 3 uses the from __future__ import annotations semantics, which causes type annotations to be saved as strings. The end result is that we can't have Language.factory decorated functions in Cython modules anymore, as the Language.factory decorator expects to inspect the signature of the functions and build a Pydantic model. If the function is implemented in Cython, an error is raised because the type is not resolved. To address this I've moved the factory functions into a new module, spacy.pipeline.factories. I've added __getattr__ importlib hooks to the previous locations, in case anyone was importing these functions directly. The change should have no backwards compatibility implications. Along the way I've also refactored the registration of functions for the config. Previously these ran as import-time side-effects, using the registry decorator. I've created instead a new module spacy.registrations. When the registry is accessed it calls a function ensure_populated(), which cases the registrations to occur. I've made a similar change to the Language.factory registrations in the new spacy.pipeline.factories module. I want to remove these import-time side-effects so that we can speed up the loading time of the library, which can be especially painful on the CLI. I also find that I'm often working to track down the implementations of functions referenced by strings in the config. Having the registrations all happen in one place will make this easier. With these changes I've fortunately avoided the need to migrate to Pydantic v2 properly --- we're still using the v1 compatibility shim. We might not be able to hold out forever though: Pydantic (reasonably) aren't actively supporting the v1 shims. I put a lot of work into v2 migration when investigating the 3.13 support, and it's definitely challenging. In any case, it's a relief that we don't have to do the v2 migration at the same time as the Cython 3.0/Python 3.13 support.
125 lines
4.1 KiB
Python
125 lines
4.1 KiB
Python
import warnings
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from typing import Callable, List, Optional, Sequence, Tuple, cast
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from thinc.api import Model, Ops, registry
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from thinc.initializers import glorot_uniform_init
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from thinc.types import Floats1d, Floats2d, Ints1d, Ragged
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from thinc.util import partial
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from ..attrs import ORTH
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from ..errors import Errors, Warnings
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from ..tokens import Doc
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from ..vectors import Mode, Vectors
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from ..vocab import Vocab
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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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if key_attr != "ORTH":
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warnings.warn(Warnings.W125, DeprecationWarning)
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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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vocab: Vocab = docs[0].vocab
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key_attr: int = getattr(vocab.vectors, "attr", ORTH)
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keys = model.ops.flatten([cast(Ints1d, doc.to_array(key_attr)) for doc in docs])
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W = cast(Floats2d, model.ops.as_contig(model.get_param("W")))
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if isinstance(vocab.vectors, Vectors) and vocab.vectors.mode == Mode.default:
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V = 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 isinstance(vocab.vectors, Vectors) and vocab.vectors.mode == Mode.floret:
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V = vocab.vectors.get_batch(keys)
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V = model.ops.as_contig(V)
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elif hasattr(vocab.vectors, "get_batch"):
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V = 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 isinstance(vocab.vectors, Vectors) and 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(vectors_data, model.ops.asarray1i([len(doc) for doc in docs]))
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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),
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cast(Floats2d, model.ops.as_contig(V)),
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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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