Move registrations

spacy/lang/ja/__init__.py

@@ -32,7 +32,6 @@ split_mode = null
 """
 
 
-@registry.tokenizers("spacy.ja.JapaneseTokenizer")
 def create_tokenizer(split_mode: Optional[str] = None):
     def japanese_tokenizer_factory(nlp):
         return JapaneseTokenizer(nlp.vocab, split_mode=split_mode)

spacy/lang/ko/__init__.py

@@ -20,7 +20,6 @@ DEFAULT_CONFIG = """
 """
 
 
-@registry.tokenizers("spacy.ko.KoreanTokenizer")
 def create_tokenizer():
     def korean_tokenizer_factory(nlp):
         return KoreanTokenizer(nlp.vocab)

spacy/lang/th/__init__.py

@@ -13,7 +13,6 @@ DEFAULT_CONFIG = """
 """
 
 
-@registry.tokenizers("spacy.th.ThaiTokenizer")
 def create_thai_tokenizer():
     def thai_tokenizer_factory(nlp):
         return ThaiTokenizer(nlp.vocab)

spacy/lang/vi/__init__.py

@@ -22,7 +22,6 @@ use_pyvi = true
 """
 
 
-@registry.tokenizers("spacy.vi.VietnameseTokenizer")
 def create_vietnamese_tokenizer(use_pyvi: bool = True):
     def vietnamese_tokenizer_factory(nlp):
         return VietnameseTokenizer(nlp.vocab, use_pyvi=use_pyvi)

spacy/lang/zh/__init__.py

@@ -46,7 +46,6 @@ class Segmenter(str, Enum):
         return list(cls.__members__.keys())
 
 
-@registry.tokenizers("spacy.zh.ChineseTokenizer")
 def create_chinese_tokenizer(segmenter: Segmenter = Segmenter.char):
     def chinese_tokenizer_factory(nlp):
         return ChineseTokenizer(nlp.vocab, segmenter=segmenter)

spacy/language.py

@@ -104,7 +104,6 @@ class BaseDefaults:
     writing_system = {"direction": "ltr", "has_case": True, "has_letters": True}
 
 
-@registry.tokenizers("spacy.Tokenizer.v1")
 def create_tokenizer() -> Callable[["Language"], Tokenizer]:
     """Registered function to create a tokenizer. Returns a factory that takes
     the nlp object and returns a Tokenizer instance using the language detaults.
@@ -130,7 +129,6 @@ def create_tokenizer() -> Callable[["Language"], Tokenizer]:
     return tokenizer_factory
 
 
-@registry.misc("spacy.LookupsDataLoader.v1")
 def load_lookups_data(lang, tables):
     util.logger.debug("Loading lookups from spacy-lookups-data: %s", tables)
     lookups = load_lookups(lang=lang, tables=tables)

spacy/ml/models/multi_task.py

@@ -30,7 +30,6 @@ if TYPE_CHECKING:
     from ...vocab import Vocab  # noqa: F401
 
 
-@registry.architectures("spacy.PretrainVectors.v1")
 def create_pretrain_vectors(
     maxout_pieces: int, hidden_size: int, loss: str
 ) -> Callable[["Vocab", Model], Model]:
@@ -57,7 +56,6 @@ def create_pretrain_vectors(
     return create_vectors_objective
 
 
-@registry.architectures("spacy.PretrainCharacters.v1")
 def create_pretrain_characters(
     maxout_pieces: int, hidden_size: int, n_characters: int
 ) -> Callable[["Vocab", Model], Model]:

spacy/ml/models/parser.py

@@ -11,7 +11,6 @@ from .._precomputable_affine import PrecomputableAffine
 from ..tb_framework import TransitionModel
 
 
-@registry.architectures("spacy.TransitionBasedParser.v2")
 def build_tb_parser_model(
     tok2vec: Model[List[Doc], List[Floats2d]],
     state_type: Literal["parser", "ner"],

spacy/ml/models/tagger.py

@@ -7,7 +7,6 @@ from ...tokens import Doc
 from ...util import registry
 
 
-@registry.architectures("spacy.Tagger.v2")
 def build_tagger_model(
     tok2vec: Model[List[Doc], List[Floats2d]], nO: Optional[int] = None, normalize=False
 ) -> Model[List[Doc], List[Floats2d]]:

spacy/ml/tb_framework.py

@@ -4,7 +4,6 @@ from ..util import registry
 from .parser_model import ParserStepModel
 
 
-@registry.layers("spacy.TransitionModel.v1")
 def TransitionModel(
     tok2vec, lower, upper, resize_output, dropout=0.2, unseen_classes=set()
 ):

spacy/pipeline/dep_parser.pyx

@@ -66,7 +66,6 @@ def parser_score(examples, **kwargs):
     return results
 
 
-@registry.scorers("spacy.parser_scorer.v1")
 def make_parser_scorer():
     return parser_score
 

spacy/pipeline/morphologizer.pyx

@@ -64,7 +64,6 @@ def morphologizer_score(examples, **kwargs):
     return results
 
 
-@registry.scorers("spacy.morphologizer_scorer.v1")
 def make_morphologizer_scorer():
     return morphologizer_score
 

spacy/registrations.py

@@ -43,6 +43,13 @@ def populate_registry() -> None:
         make_preserve_existing_ents_filter,
     )
     from .pipeline.attributeruler import make_attribute_ruler_scorer
+    from .pipeline.dep_parser import make_parser_scorer
+    from .pipeline.morphologizer import make_morphologizer_scorer
+    from .lang.ja import create_tokenizer as create_japanese_tokenizer
+    from .lang.zh import create_chinese_tokenizer
+    from .lang.ko import create_tokenizer as create_korean_tokenizer
+    from .lang.vi import create_vietnamese_tokenizer
+    from .lang.th import create_thai_tokenizer
 
     # Import all pipeline components that were using registry decorators
     from .pipeline.tagger import make_tagger_scorer
@@ -65,6 +72,7 @@ def populate_registry() -> None:
     registry.misc("spacy.EmptyKB.v1")(empty_kb)
     registry.misc("spacy.CandidateGenerator.v1")(create_candidates)
     registry.misc("spacy.CandidateBatchGenerator.v1")(create_candidates_batch)
+    registry.misc("spacy.LookupsDataLoader.v1")(load_lookups_data)
 
     # Need to get references to the existing functions in registry by importing the function that is there
     # For the registry that was previously decorated
@@ -109,11 +117,13 @@ def populate_registry() -> None:
     )
     from .ml.models.span_finder import build_finder_model
     from .ml.models.parser import build_tb_parser_model
-    from .ml.models.multi_task import create_pretrain_vectors
+    from .ml.models.multi_task import create_pretrain_vectors, create_pretrain_characters
     from .ml.models.tagger import build_tagger_model
     from .ml.staticvectors import StaticVectors
     from .ml._precomputable_affine import PrecomputableAffine
     from .ml._character_embed import CharacterEmbed
+    from .ml.tb_framework import TransitionModel
+    from .language import create_tokenizer, load_lookups_data
     from .matcher.levenshtein import make_levenshtein_compare
     from .training.callbacks import create_copy_from_base_model
     from .ml.callbacks import create_models_with_nvtx_range, create_models_and_pipes_with_nvtx_range
@@ -150,6 +160,15 @@ def populate_registry() -> None:
     registry.scorers("spacy.entity_linker_scorer.v1")(make_entity_linker_scorer)
     registry.scorers("spacy.overlapping_labeled_spans_scorer.v1")(make_overlapping_labeled_spans_scorer)
     registry.scorers("spacy.attribute_ruler_scorer.v1")(make_attribute_ruler_scorer)
+    registry.scorers("spacy.parser_scorer.v1")(make_parser_scorer)
+    registry.scorers("spacy.morphologizer_scorer.v1")(make_morphologizer_scorer)
+    
+    # Register tokenizers
+    registry.tokenizers("spacy.ja.JapaneseTokenizer")(create_japanese_tokenizer)
+    registry.tokenizers("spacy.zh.ChineseTokenizer")(create_chinese_tokenizer)
+    registry.tokenizers("spacy.ko.KoreanTokenizer")(create_korean_tokenizer)
+    registry.tokenizers("spacy.vi.VietnameseTokenizer")(create_vietnamese_tokenizer)
+    registry.tokenizers("spacy.th.ThaiTokenizer")(create_thai_tokenizer)
 
     # Register tok2vec architectures we've modified
     registry.architectures("spacy.Tok2VecListener.v1")(tok2vec_listener_v1)
@@ -172,6 +191,7 @@ def populate_registry() -> None:
     registry.architectures("spacy.SpanFinder.v1")(build_finder_model) 
     registry.architectures("spacy.TransitionBasedParser.v2")(build_tb_parser_model)
     registry.architectures("spacy.PretrainVectors.v1")(create_pretrain_vectors)
+    registry.architectures("spacy.PretrainCharacters.v1")(create_pretrain_characters)
     registry.architectures("spacy.Tagger.v2")(build_tagger_model)
     
     # Register layers
@@ -183,6 +203,7 @@ def populate_registry() -> None:
     registry.layers("spacy.StaticVectors.v2")(StaticVectors)
     registry.layers("spacy.PrecomputableAffine.v1")(PrecomputableAffine)
     registry.layers("spacy.CharEmbed.v1")(CharacterEmbed)
+    registry.layers("spacy.TransitionModel.v1")(TransitionModel)
 
     # Register callbacks
     registry.callbacks("spacy.copy_from_base_model.v1")(create_copy_from_base_model)