Move more layers

spacy/matcher/levenshtein.pyx

@@ -27,6 +27,5 @@ cpdef bint levenshtein_compare(input_text: str, pattern_text: str, fuzzy: int =
     return levenshtein(input_text, pattern_text, max_edits) <= max_edits
 
 
-@registry.misc("spacy.levenshtein_compare.v1")
 def make_levenshtein_compare():
     return levenshtein_compare

spacy/ml/extract_ngrams.py

@@ -4,7 +4,6 @@ from ..attrs import LOWER
 from ..util import registry
 
 
-@registry.layers("spacy.extract_ngrams.v1")
 def extract_ngrams(ngram_size: int, attr: int = LOWER) -> Model:
     model: Model = Model("extract_ngrams", forward)
     model.attrs["ngram_size"] = ngram_size

spacy/ml/extract_spans.py

@@ -6,7 +6,6 @@ from thinc.types import Ints1d, Ragged
 from ..util import registry
 
 
-@registry.layers("spacy.extract_spans.v1")
 def extract_spans() -> Model[Tuple[Ragged, Ragged], Ragged]:
     """Extract spans from a sequence of source arrays, as specified by an array
     of (start, end) indices. The output is a ragged array of the

spacy/ml/featureextractor.py

@@ -6,7 +6,6 @@ from thinc.types import Ints2d
 from ..tokens import Doc
 
 
-@registry.layers("spacy.FeatureExtractor.v1")
 def FeatureExtractor(
     columns: Union[List[str], List[int], List[Union[int, str]]]
 ) -> Model[List[Doc], List[Ints2d]]:

spacy/ml/models/entity_linker.py

@@ -28,7 +28,6 @@ from ...vocab import Vocab
 from ..extract_spans import extract_spans
 
 
-@registry.architectures("spacy.EntityLinker.v2")
 def build_nel_encoder(
     tok2vec: Model, nO: Optional[int] = None
 ) -> Model[List[Doc], Floats2d]:
@@ -92,7 +91,6 @@ def span_maker_forward(model, docs: List[Doc], is_train) -> Tuple[Ragged, Callab
     return out, lambda x: []
 
 
-@registry.misc("spacy.KBFromFile.v1")
 def load_kb(
     kb_path: Path,
 ) -> Callable[[Vocab], KnowledgeBase]:
@@ -104,7 +102,6 @@ def load_kb(
     return kb_from_file
 
 
-@registry.misc("spacy.EmptyKB.v2")
 def empty_kb_for_config() -> Callable[[Vocab, int], KnowledgeBase]:
     def empty_kb_factory(vocab: Vocab, entity_vector_length: int):
         return InMemoryLookupKB(vocab=vocab, entity_vector_length=entity_vector_length)
@@ -112,7 +109,6 @@ def empty_kb_for_config() -> Callable[[Vocab, int], KnowledgeBase]:
     return empty_kb_factory
 
 
-@registry.misc("spacy.EmptyKB.v1")
 def empty_kb(
     entity_vector_length: int,
 ) -> Callable[[Vocab], KnowledgeBase]:
@@ -122,12 +118,10 @@ def empty_kb(
     return empty_kb_factory
 
 
-@registry.misc("spacy.CandidateGenerator.v1")
 def create_candidates() -> Callable[[KnowledgeBase, Span], Iterable[Candidate]]:
     return get_candidates
 
 
-@registry.misc("spacy.CandidateBatchGenerator.v1")
 def create_candidates_batch() -> Callable[
     [KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]
 ]:

spacy/registrations.py

@@ -59,6 +59,12 @@ def populate_registry() -> None:
     registry.misc("spacy.preset_spans_suggester.v1")(build_preset_spans_suggester)
     registry.misc("spacy.prioritize_new_ents_filter.v1")(make_prioritize_new_ents_filter)
     registry.misc("spacy.prioritize_existing_ents_filter.v1")(make_preserve_existing_ents_filter)
+    registry.misc("spacy.levenshtein_compare.v1")(make_levenshtein_compare)
+    registry.misc("spacy.KBFromFile.v1")(load_kb)
+    registry.misc("spacy.EmptyKB.v2")(empty_kb_for_config)
+    registry.misc("spacy.EmptyKB.v1")(empty_kb)
+    registry.misc("spacy.CandidateGenerator.v1")(create_candidates)
+    registry.misc("spacy.CandidateBatchGenerator.v1")(create_candidates_batch)
 
     # Need to get references to the existing functions in registry by importing the function that is there
     # For the registry that was previously decorated
@@ -74,6 +80,20 @@ def populate_registry() -> None:
         build_Tok2Vec_model,
         tok2vec_listener_v1,
     )
+    
+    # Import decorator-removed ML components
+    from .ml.featureextractor import FeatureExtractor
+    from .ml.extract_spans import extract_spans
+    from .ml.extract_ngrams import extract_ngrams
+    from .ml.models.entity_linker import (
+        build_nel_encoder, 
+        load_kb, 
+        empty_kb_for_config, 
+        empty_kb, 
+        create_candidates, 
+        create_candidates_batch
+    )
+    from .matcher.levenshtein import make_levenshtein_compare
 
     # Register scorers
     registry.scorers("spacy.tagger_scorer.v1")(make_tagger_scorer)
@@ -106,6 +126,12 @@ def populate_registry() -> None:
     registry.architectures("spacy.MaxoutWindowEncoder.v2")(MaxoutWindowEncoder)
     registry.architectures("spacy.MishWindowEncoder.v2")(MishWindowEncoder)
     registry.architectures("spacy.TorchBiLSTMEncoder.v1")(BiLSTMEncoder)
+    registry.architectures("spacy.EntityLinker.v2")(build_nel_encoder)
+    
+    # Register layers
+    registry.layers("spacy.FeatureExtractor.v1")(FeatureExtractor)
+    registry.layers("spacy.extract_spans.v1")(extract_spans)
+    registry.layers("spacy.extract_ngrams.v1")(extract_ngrams)
 
     # Set the flag to indicate that the registry has been populated
     REGISTRY_POPULATED = True