spaCy/spacy/pipeline/functions.py
Matthew Honnibal 5bebbf7550
Python 3.13 support (#13823)
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.
2025-05-22 13:47:21 +02:00

198 lines
6.5 KiB
Python

import importlib
import sys
import warnings
from typing import Any, Dict
import srsly
from .. import util
from ..errors import Warnings
from ..language import Language
from ..matcher import Matcher
from ..tokens import Doc
@Language.component(
"merge_noun_chunks",
requires=["token.dep", "token.tag", "token.pos"],
retokenizes=True,
)
def merge_noun_chunks(doc: Doc) -> Doc:
"""Merge noun chunks into a single token.
doc (Doc): The Doc object.
RETURNS (Doc): The Doc object with merged noun chunks.
DOCS: https://spacy.io/api/pipeline-functions#merge_noun_chunks
"""
if not doc.has_annotation("DEP"):
return doc
with doc.retokenize() as retokenizer:
for np in doc.noun_chunks:
attrs = {"tag": np.root.tag, "dep": np.root.dep}
retokenizer.merge(np, attrs=attrs) # type: ignore[arg-type]
return doc
@Language.component(
"merge_entities",
requires=["doc.ents", "token.ent_iob", "token.ent_type"],
retokenizes=True,
)
def merge_entities(doc: Doc):
"""Merge entities into a single token.
doc (Doc): The Doc object.
RETURNS (Doc): The Doc object with merged entities.
DOCS: https://spacy.io/api/pipeline-functions#merge_entities
"""
with doc.retokenize() as retokenizer:
for ent in doc.ents:
attrs = {"tag": ent.root.tag, "dep": ent.root.dep, "ent_type": ent.label}
retokenizer.merge(ent, attrs=attrs) # type: ignore[arg-type]
return doc
@Language.component("merge_subtokens", requires=["token.dep"], retokenizes=True)
def merge_subtokens(doc: Doc, label: str = "subtok") -> Doc:
"""Merge subtokens into a single token.
doc (Doc): The Doc object.
label (str): The subtoken dependency label.
RETURNS (Doc): The Doc object with merged subtokens.
DOCS: https://spacy.io/api/pipeline-functions#merge_subtokens
"""
# TODO: make stateful component with "label" config
merger = Matcher(doc.vocab)
merger.add("SUBTOK", [[{"DEP": label, "op": "+"}]])
matches = merger(doc)
spans = util.filter_spans([doc[start : end + 1] for _, start, end in matches]) # type: ignore[misc, operator]
with doc.retokenize() as retokenizer:
for span in spans:
retokenizer.merge(span)
return doc
class TokenSplitter:
def __init__(self, min_length: int = 0, split_length: int = 0):
self.min_length = min_length
self.split_length = split_length
def __call__(self, doc: Doc) -> Doc:
if self.min_length > 0 and self.split_length > 0:
with doc.retokenize() as retokenizer:
for t in doc:
if len(t.text) >= self.min_length:
orths = []
heads = []
attrs = {} # type: ignore[var-annotated]
for i in range(0, len(t.text), self.split_length):
orths.append(t.text[i : i + self.split_length])
heads.append((t, i / self.split_length))
retokenizer.split(t, orths, heads, attrs) # type: ignore[arg-type]
return doc
def _get_config(self) -> Dict[str, Any]:
return {
"min_length": self.min_length,
"split_length": self.split_length,
}
def _set_config(self, config: Dict[str, Any] = {}) -> None:
self.min_length = config.get("min_length", 0)
self.split_length = config.get("split_length", 0)
def to_bytes(self, **kwargs):
serializers = {
"cfg": lambda: srsly.json_dumps(self._get_config()),
}
return util.to_bytes(serializers, [])
def from_bytes(self, data, **kwargs):
deserializers = {
"cfg": lambda b: self._set_config(srsly.json_loads(b)),
}
util.from_bytes(data, deserializers, [])
return self
def to_disk(self, path, **kwargs):
path = util.ensure_path(path)
serializers = {
"cfg": lambda p: srsly.write_json(p, self._get_config()),
}
return util.to_disk(path, serializers, [])
def from_disk(self, path, **kwargs):
path = util.ensure_path(path)
serializers = {
"cfg": lambda p: self._set_config(srsly.read_json(p)),
}
util.from_disk(path, serializers, [])
class DocCleaner:
def __init__(self, attrs: Dict[str, Any], *, silent: bool = True):
self.cfg: Dict[str, Any] = {"attrs": dict(attrs), "silent": silent}
def __call__(self, doc: Doc) -> Doc:
attrs: dict = self.cfg["attrs"]
silent: bool = self.cfg["silent"]
for attr, value in attrs.items():
obj = doc
parts = attr.split(".")
skip = False
for part in parts[:-1]:
if hasattr(obj, part):
obj = getattr(obj, part)
else:
skip = True
if not silent:
warnings.warn(Warnings.W116.format(attr=attr))
if not skip:
if hasattr(obj, parts[-1]):
setattr(obj, parts[-1], value)
else:
if not silent:
warnings.warn(Warnings.W116.format(attr=attr))
return doc
def to_bytes(self, **kwargs):
serializers = {
"cfg": lambda: srsly.json_dumps(self.cfg),
}
return util.to_bytes(serializers, [])
def from_bytes(self, data, **kwargs):
deserializers = {
"cfg": lambda b: self.cfg.update(srsly.json_loads(b)),
}
util.from_bytes(data, deserializers, [])
return self
def to_disk(self, path, **kwargs):
path = util.ensure_path(path)
serializers = {
"cfg": lambda p: srsly.write_json(p, self.cfg),
}
return util.to_disk(path, serializers, [])
def from_disk(self, path, **kwargs):
path = util.ensure_path(path)
serializers = {
"cfg": lambda p: self.cfg.update(srsly.read_json(p)),
}
util.from_disk(path, serializers, [])
# Setup backwards compatibility hook for factories
def __getattr__(name):
if name == "make_doc_cleaner":
module = importlib.import_module("spacy.pipeline.factories")
return module.make_doc_cleaner
elif name == "make_token_splitter":
module = importlib.import_module("spacy.pipeline.factories")
return module.make_token_splitter
raise AttributeError(f"module {__name__} has no attribute {name}")