Clean up

spacy/language.py

@@ -8,7 +8,7 @@ from contextlib import contextmanager
 from copy import deepcopy
 from pathlib import Path
 import warnings
-from thinc.api import Model, get_current_ops, Config, require_gpu, Optimizer
+from thinc.api import Model, get_current_ops, Config, Optimizer
 import srsly
 import multiprocessing as mp
 from itertools import chain, cycle
@@ -1153,10 +1153,9 @@ class Language:
         get_examples: Optional[Callable[[], Iterable[Example]]] = None,
         *,
         sgd: Optional[Optimizer] = None,
-        device: int = -1,
     ) -> Optimizer:
         warnings.warn(Warnings.W089, DeprecationWarning)
-        return self.initialize(get_examples, sgd=sgd, device=device)
+        return self.initialize(get_examples, sgd=sgd)
 
     def initialize(
         self,
@@ -1169,7 +1168,7 @@ class Language:
 
         get_examples (Callable[[], Iterable[Example]]): Optional function that
             returns gold-standard Example objects.
-        sgd (Optional[Optimizer]): An optimizer to use for updates. If not 
+        sgd (Optional[Optimizer]): An optimizer to use for updates. If not
             provided, will be created using the .create_optimizer() method.
         RETURNS (thinc.api.Optimizer): The optimizer.
 
@@ -1220,7 +1219,6 @@ class Language:
                     proc.initialize, p_settings, section="components", name=name
                 )
                 proc.initialize(
-                    get_examples, pipeline=self.pipeline
                     get_examples,
                     pipeline=self.pipeline,
                     **p_settings,
@@ -1315,7 +1313,7 @@ class Language:
         n_words = sum(len(doc) for doc in docs)
         results["speed"] = n_words / (end_time - start_time)
         return results
-    
+
     def create_optimizer(self):
         """Create an optimizer, usually using the [training.optimizer] config."""
         subconfig = {"optimizer": self.config["training"]["optimizer"]}

spacy/pipeline/dep_parser.pyx

@@ -132,7 +132,7 @@ cdef class DependencyParser(Parser):
             labeller.model.set_dim("nO", len(self.labels))
             if labeller.model.has_ref("output_layer"):
                 labeller.model.get_ref("output_layer").set_dim("nO", len(self.labels))
-            labeller.initialize(get_examples, pipeline=pipeline, sgd=sgd)
+            labeller.initialize(get_examples, pipeline=pipeline)
 
     @property
     def labels(self):

spacy/pipeline/sentencizer.pyx

@@ -58,7 +58,7 @@ class Sentencizer(Pipe):
         else:
             self.punct_chars = set(self.default_punct_chars)
 
-    def initialize(self, get_examples, pipeline=None, sgd=None):
+    def initialize(self, get_examples, pipeline=None):
         pass
 
     def __call__(self, doc):

spacy/schemas.py

@@ -107,7 +107,7 @@ def validate_init_settings(
     *,
     section: Optional[str] = None,
     name: str = "",
-    exclude: Iterable[str] = ("get_examples", "nlp", "pipeline", "sgd"),
+    exclude: Iterable[str] = ("get_examples", "nlp", "pipeline"),
 ) -> Dict[str, Any]:
     """Validate initialization settings against the expected arguments in
     the method signature. Will parse values if possible (e.g. int to string)

spacy/training/initialize.py

@@ -55,7 +55,7 @@ def init_nlp(config: Config, *, use_gpu: int = -1, silent: bool = True) -> Langu
             msg.info(f"Resuming training for: {resume_components}")
             nlp.resume_training(sgd=optimizer)
     with nlp.select_pipes(disable=[*frozen_components, *resume_components]):
-        nlp.initialize(lambda: train_corpus(nlp), sgd=optimizer, settings=I)
+        nlp.initialize(lambda: train_corpus(nlp), settings=I)
         msg.good("Initialized pipeline components")
     # Verify the config after calling 'initialize' to ensure labels
     # are properly initialized