Move more functions

spacy/ml/models/spancat.py

@@ -22,7 +22,6 @@ from ...util import registry
 from ..extract_spans import extract_spans
 
 
-@registry.layers("spacy.LinearLogistic.v1")
 def build_linear_logistic(nO=None, nI=None) -> Model[Floats2d, Floats2d]:
     """An output layer for multi-label classification. It uses a linear layer
     followed by a logistic activation.
@@ -30,7 +29,6 @@ def build_linear_logistic(nO=None, nI=None) -> Model[Floats2d, Floats2d]:
     return chain(Linear(nO=nO, nI=nI, init_W=glorot_uniform_init), Logistic())
 
 
-@registry.layers("spacy.mean_max_reducer.v1")
 def build_mean_max_reducer(hidden_size: int) -> Model[Ragged, Floats2d]:
     """Reduce sequences by concatenating their mean and max pooled vectors,
     and then combine the concatenated vectors with a hidden layer.
@@ -46,7 +44,6 @@ def build_mean_max_reducer(hidden_size: int) -> Model[Ragged, Floats2d]:
     )
 
 
-@registry.architectures("spacy.SpanCategorizer.v1")
 def build_spancat_model(
     tok2vec: Model[List[Doc], List[Floats2d]],
     reducer: Model[Ragged, Floats2d],

spacy/ml/models/textcat.py

@@ -44,7 +44,6 @@ from .tok2vec import get_tok2vec_width
 NEG_VALUE = -5000
 
 
-@registry.architectures("spacy.TextCatCNN.v2")
 def build_simple_cnn_text_classifier(
     tok2vec: Model, exclusive_classes: bool, nO: Optional[int] = None
 ) -> Model[List[Doc], Floats2d]:
@@ -72,7 +71,6 @@ def resize_and_set_ref(model, new_nO, resizable_layer):
     return model
 
 
-@registry.architectures("spacy.TextCatBOW.v2")
 def build_bow_text_classifier(
     exclusive_classes: bool,
     ngram_size: int,
@@ -88,7 +86,6 @@ def build_bow_text_classifier(
     )
 
 
-@registry.architectures("spacy.TextCatBOW.v3")
 def build_bow_text_classifier_v3(
     exclusive_classes: bool,
     ngram_size: int,
@@ -142,7 +139,6 @@ def _build_bow_text_classifier(
     return model
 
 
-@registry.architectures("spacy.TextCatEnsemble.v2")
 def build_text_classifier_v2(
     tok2vec: Model[List[Doc], List[Floats2d]],
     linear_model: Model[List[Doc], Floats2d],
@@ -200,7 +196,6 @@ def init_ensemble_textcat(model, X, Y) -> Model:
     return model
 
 
-@registry.architectures("spacy.TextCatLowData.v1")
 def build_text_classifier_lowdata(
     width: int, dropout: Optional[float], nO: Optional[int] = None
 ) -> Model[List[Doc], Floats2d]:
@@ -221,7 +216,6 @@ def build_text_classifier_lowdata(
     return model
 
 
-@registry.architectures("spacy.TextCatParametricAttention.v1")
 def build_textcat_parametric_attention_v1(
     tok2vec: Model[List[Doc], List[Floats2d]],
     exclusive_classes: bool,
@@ -294,7 +288,6 @@ def _init_parametric_attention_with_residual_nonlinear(model, X, Y) -> Model:
     return model
 
 
-@registry.architectures("spacy.TextCatReduce.v1")
 def build_reduce_text_classifier(
     tok2vec: Model,
     exclusive_classes: bool,

spacy/registrations.py

@@ -93,7 +93,28 @@ def populate_registry() -> None:
         create_candidates, 
         create_candidates_batch
     )
+    from .ml.models.textcat import (
+        build_simple_cnn_text_classifier,
+        build_bow_text_classifier,
+        build_bow_text_classifier_v3,
+        build_text_classifier_v2,
+        build_text_classifier_lowdata,
+        build_textcat_parametric_attention_v1,
+        build_reduce_text_classifier
+    )
+    from .ml.models.spancat import (
+        build_linear_logistic,
+        build_mean_max_reducer,
+        build_spancat_model
+    )
     from .matcher.levenshtein import make_levenshtein_compare
+    from .training.callbacks import create_copy_from_base_model
+    from .training.loggers import console_logger, console_logger_v3
+    from .training.batchers import (
+        configure_minibatch_by_padded_size,
+        configure_minibatch_by_words,
+        configure_minibatch
+    )
 
     # Register scorers
     registry.scorers("spacy.tagger_scorer.v1")(make_tagger_scorer)
@@ -127,11 +148,33 @@ def populate_registry() -> None:
     registry.architectures("spacy.MishWindowEncoder.v2")(MishWindowEncoder)
     registry.architectures("spacy.TorchBiLSTMEncoder.v1")(BiLSTMEncoder)
     registry.architectures("spacy.EntityLinker.v2")(build_nel_encoder)
+    registry.architectures("spacy.TextCatCNN.v2")(build_simple_cnn_text_classifier)
+    registry.architectures("spacy.TextCatBOW.v2")(build_bow_text_classifier)
+    registry.architectures("spacy.TextCatBOW.v3")(build_bow_text_classifier_v3)
+    registry.architectures("spacy.TextCatEnsemble.v2")(build_text_classifier_v2)
+    registry.architectures("spacy.TextCatLowData.v1")(build_text_classifier_lowdata)
+    registry.architectures("spacy.TextCatParametricAttention.v1")(build_textcat_parametric_attention_v1)
+    registry.architectures("spacy.TextCatReduce.v1")(build_reduce_text_classifier)
+    registry.architectures("spacy.SpanCategorizer.v1")(build_spancat_model)
     
     # Register layers
     registry.layers("spacy.FeatureExtractor.v1")(FeatureExtractor)
     registry.layers("spacy.extract_spans.v1")(extract_spans)
     registry.layers("spacy.extract_ngrams.v1")(extract_ngrams)
+    registry.layers("spacy.LinearLogistic.v1")(build_linear_logistic)
+    registry.layers("spacy.mean_max_reducer.v1")(build_mean_max_reducer)
 
+    # Register callbacks
+    registry.callbacks("spacy.copy_from_base_model.v1")(create_copy_from_base_model)
+    
+    # Register loggers
+    registry.loggers("spacy.ConsoleLogger.v2")(console_logger)
+    registry.loggers("spacy.ConsoleLogger.v3")(console_logger_v3)
+    
+    # Register batchers
+    registry.batchers("spacy.batch_by_padded.v1")(configure_minibatch_by_padded_size)
+    registry.batchers("spacy.batch_by_words.v1")(configure_minibatch_by_words)
+    registry.batchers("spacy.batch_by_sequence.v1")(configure_minibatch)
+    
     # Set the flag to indicate that the registry has been populated
     REGISTRY_POPULATED = True

spacy/training/batchers.py

@@ -19,7 +19,6 @@ ItemT = TypeVar("ItemT")
 BatcherT = Callable[[Iterable[ItemT]], Iterable[List[ItemT]]]
 
 
-@registry.batchers("spacy.batch_by_padded.v1")
 def configure_minibatch_by_padded_size(
     *,
     size: Sizing,
@@ -54,7 +53,6 @@ def configure_minibatch_by_padded_size(
     )
 
 
-@registry.batchers("spacy.batch_by_words.v1")
 def configure_minibatch_by_words(
     *,
     size: Sizing,
@@ -82,7 +80,6 @@ def configure_minibatch_by_words(
     )
 
 
-@registry.batchers("spacy.batch_by_sequence.v1")
 def configure_minibatch(
     size: Sizing, get_length: Optional[Callable[[ItemT], int]] = None
 ) -> BatcherT:

spacy/training/callbacks.py

@@ -7,7 +7,6 @@ if TYPE_CHECKING:
     from ..language import Language
 
 
-@registry.callbacks("spacy.copy_from_base_model.v1")
 def create_copy_from_base_model(
     tokenizer: Optional[str] = None,
     vocab: Optional[str] = None,

spacy/training/loggers.py

@@ -29,7 +29,6 @@ def setup_table(
 
 # We cannot rename this method as it's directly imported
 # and used by external packages such as spacy-loggers.
-@registry.loggers("spacy.ConsoleLogger.v2")
 def console_logger(
     progress_bar: bool = False,
     console_output: bool = True,
@@ -47,7 +46,6 @@ def console_logger(
     )
 
 
-@registry.loggers("spacy.ConsoleLogger.v3")
 def console_logger_v3(
     progress_bar: Optional[str] = None,
     console_output: bool = True,