Remove 'sgd' arg from component initialize

spacy/pipeline/morphologizer.pyx

@@ -129,7 +129,7 @@ class Morphologizer(Tagger):
             self.cfg["labels_pos"][norm_label] = POS_IDS[pos]
         return 1
 
-    def initialize(self, get_examples, *, pipeline=None, sgd=None):
+    def initialize(self, get_examples, *, pipeline=None):
         """Initialize the pipe for training, using a representative set
         of data examples.
 
@@ -138,8 +138,6 @@ class Morphologizer(Tagger):
         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/morphologizer#initialize
@@ -178,9 +176,6 @@ class Morphologizer(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 set_annotations(self, docs, batch_tag_ids):
         """Modify a batch of documents, using pre-computed scores.

spacy/pipeline/multitask.pyx

@@ -81,7 +81,7 @@ class MultitaskObjective(Tagger):
     def set_annotations(self, docs, dep_ids):
         pass
 
-    def initialize(self, get_examples, pipeline=None, sgd=None):
+    def initialize(self, get_examples, pipeline=None):
         if not hasattr(get_examples, "__call__"):
             err = Errors.E930.format(name="MultitaskObjective", obj=type(get_examples))
             raise ValueError(err)
@@ -177,7 +177,7 @@ class ClozeMultitask(Pipe):
     def set_annotations(self, docs, dep_ids):
         pass
 
-    def initialize(self, get_examples, pipeline=None, sgd=None):
+    def initialize(self, get_examples, pipeline=None):
         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)

spacy/pipeline/pipe.pyx

@@ -183,7 +183,7 @@ cdef class Pipe:
         """
         return util.create_default_optimizer()
 
-    def initialize(self, get_examples, *, pipeline=None, sgd=None):
+    def initialize(self, get_examples, *, pipeline=None):
         """Initialize the pipe for training, using data examples if available.
         This method needs to be implemented by each Pipe component,
         ensuring the internal model (if available) is initialized properly
@@ -194,8 +194,6 @@ cdef class 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/pipe#initialize

spacy/pipeline/senter.pyx

@@ -124,7 +124,7 @@ class SentenceRecognizer(Tagger):
             raise ValueError("nan value when computing loss")
         return float(loss), d_scores
 
-    def initialize(self, get_examples, *, pipeline=None, sgd=None):
+    def initialize(self, get_examples, *, pipeline=None):
         """Initialize the pipe for training, using a representative set
         of data examples.
 
@@ -133,9 +133,7 @@ class SentenceRecognizer(Tagger):
         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
+        RETURNS: None
-            create_optimizer if it doesn't exist.
-        RETURNS (thinc.api.Optimizer): The optimizer.
 
         DOCS: https://nightly.spacy.io/api/sentencerecognizer#initialize
         """

spacy/pipeline/tagger.pyx

@@ -256,7 +256,7 @@ class Tagger(Pipe):
             raise ValueError("nan value when computing loss")
         return float(loss), d_scores
 
-    def initialize(self, get_examples, *, pipeline=None, sgd=None):
+    def initialize(self, get_examples, *, pipeline=None):
         """Initialize the pipe for training, using a representative set
         of data examples.
 
@@ -265,8 +265,6 @@ class Tagger(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/tagger#initialize
@@ -289,9 +287,6 @@ class Tagger(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 add_label(self, label):
         """Add a new label to the pipe.

spacy/pipeline/textcat.py

@@ -338,8 +338,7 @@ class TextCategorizer(Pipe):
         self,
         get_examples: Callable[[], Iterable[Example]],
         *,
-        pipeline: Optional[List[Tuple[str, Callable[[Doc], Doc]]]] = None,
+        pipeline: Optional[List[Tuple[str, Callable[[Doc], Doc]]]] = None
-        sgd: Optional[Optimizer] = None,
     ) -> Optimizer:
         """Initialize the pipe for training, using a representative set
         of data examples.

spacy/pipeline/transition_parser.pyx

@@ -354,7 +354,7 @@ cdef class Parser(Pipe):
             # If all weights for an output are 0 in the original model, don't
             # supervise that output. This allows us to add classes.
             loss += (d_scores**2).sum()
-            backprop(d_scores, sgd=sgd)
+            backprop(d_scores)
             # Follow the predicted action
             self.transition_states(states, guesses)
             states = [state for state in states if not state.is_final()]
@@ -405,9 +405,8 @@ cdef class Parser(Pipe):
     def set_output(self, nO):
         self.model.attrs["resize_output"](self.model, nO)
 
-    def initialize(self, get_examples, pipeline=None, sgd=None, **kwargs):
+    def initialize(self, get_examples, pipeline=None, settings=None):
         self._ensure_examples(get_examples)
-        self.cfg.update(kwargs)
         lexeme_norms = self.vocab.lookups.get_table("lexeme_norm", {})
         if len(lexeme_norms) == 0 and self.vocab.lang in util.LEXEME_NORM_LANGS:
             langs = ", ".join(util.LEXEME_NORM_LANGS)
@@ -425,8 +424,6 @@ cdef class Parser(Pipe):
         self.moves.initialize_actions(actions)
         # make sure we resize so we have an appropriate upper layer
         self._resize()
-        if sgd is None:
-            sgd = self.create_optimizer()
         doc_sample = []
         if pipeline is not None:
             for name, component in pipeline:
@@ -442,7 +439,7 @@ cdef class Parser(Pipe):
         assert len(doc_sample) > 0, Errors.E923.format(name=self.name)
         self.model.initialize(doc_sample)
         if pipeline is not None:
-            self.init_multitask_objectives(get_examples, pipeline, sgd=sgd, **self.cfg)
+            self.init_multitask_objectives(get_examples, pipeline)
         return sgd
 
     def to_disk(self, path, exclude=tuple()):