Clean up sgd

spacy/language.py

@@ -1298,7 +1298,8 @@ class Language:
     
     def create_optimizer(self):
         """Create an optimizer, usually using the [training.optimizer] config."""
-        return registry.resolve(self.config["training"]["optimizer"])
+        subconfig = {"optimizer": self.config["training"]["optimizer"]}
+        return registry.resolve(subconfig)["optimizer"]
 
     @contextmanager
     def use_params(self, params: Optional[dict]):

spacy/pipeline/multitask.pyx

@@ -91,9 +91,6 @@ class MultitaskObjective(Tagger):
                 if label is not None and label not in self.labels:
                     self.labels[label] = len(self.labels)
         self.model.initialize()   # TODO: fix initialization by defining X and Y
-        if sgd is None:
-            sgd = self.create_optimizer()
-        return sgd
 
     def predict(self, docs):
         tokvecs = self.model.get_ref("tok2vec")(docs)
@@ -181,9 +178,6 @@ class ClozeMultitask(Pipe):
         self.model.initialize()  # TODO: fix initialization by defining X and Y
         X = self.model.ops.alloc((5, self.model.get_ref("tok2vec").get_dim("nO")))
         self.model.output_layer.initialize(X)
-        if sgd is None:
-            sgd = self.create_optimizer()
-        return sgd
 
     def predict(self, docs):
         tokvecs = self.model.get_ref("tok2vec")(docs)

spacy/pipeline/senter.pyx

@@ -149,9 +149,6 @@ class SentenceRecognizer(Tagger):
         assert len(doc_sample) > 0, Errors.E923.format(name=self.name)
         assert len(label_sample) > 0, Errors.E923.format(name=self.name)
         self.model.initialize(X=doc_sample, Y=label_sample)
-        if sgd is None:
-            sgd = self.create_optimizer()
-        return sgd
 
     def add_label(self, label, values=None):
         raise NotImplementedError

spacy/pipeline/textcat.py

@@ -348,8 +348,6 @@ class TextCategorizer(Pipe):
         pipeline (List[Tuple[str, Callable]]): Optional list of pipeline
             components that this component is part of. Corresponds to
             nlp.pipeline.
-        sgd (thinc.api.Optimizer): Optional optimizer. Will be created with
-            create_optimizer if it doesn't exist.
         RETURNS (thinc.api.Optimizer): The optimizer.
 
         DOCS: https://nightly.spacy.io/api/textcategorizer#initialize
@@ -367,9 +365,6 @@ class TextCategorizer(Pipe):
         assert len(doc_sample) > 0, Errors.E923.format(name=self.name)
         assert len(label_sample) > 0, Errors.E923.format(name=self.name)
         self.model.initialize(X=doc_sample, Y=label_sample)
-        if sgd is None:
-            sgd = self.create_optimizer()
-        return sgd
 
     def score(self, examples: Iterable[Example], **kwargs) -> Dict[str, Any]:
         """Score a batch of examples.

spacy/pipeline/transition_parser.pyx

@@ -440,7 +440,6 @@ cdef class Parser(Pipe):
         self.model.initialize(doc_sample)
         if pipeline is not None:
             self.init_multitask_objectives(get_examples, pipeline)
-        return sgd
 
     def to_disk(self, path, exclude=tuple()):
         serializers = {