Remove 'device' argument from Language, clean up 'sgd' arg

spacy/language.py

@@ -19,7 +19,7 @@ from .vocab import Vocab, create_vocab
 from .pipe_analysis import validate_attrs, analyze_pipes, print_pipe_analysis
 from .training import Example, validate_examples
 from .scorer import Scorer
-from .util import create_default_optimizer, registry, SimpleFrozenList
+from .util import registry, SimpleFrozenList
 from .util import SimpleFrozenDict, combine_score_weights, CONFIG_SECTION_ORDER
 from .lang.tokenizer_exceptions import URL_MATCH, BASE_EXCEPTIONS
 from .lang.punctuation import TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES
@@ -1065,7 +1065,7 @@ class Language:
         validate_examples(examples, "Language.update")
         if sgd is None:
             if self._optimizer is None:
-                self._optimizer = create_default_optimizer()
+                self._optimizer = self.create_optimizer()
             sgd = self._optimizer
         if component_cfg is None:
             component_cfg = {}
@@ -1123,7 +1123,7 @@ class Language:
         validate_examples(examples, "Language.rehearse")
         if sgd is None:
             if self._optimizer is None:
-                self._optimizer = create_default_optimizer()
+                self._optimizer = self.create_optimizer()
             sgd = self._optimizer
         pipes = list(self.pipeline)
         random.shuffle(pipes)
@@ -1161,16 +1161,14 @@ class Language:
     def initialize(
         self,
         get_examples: Optional[Callable[[], Iterable[Example]]] = None,
-        *,
+        sgd: Optional[Optimizer]=None
-        sgd: Optional[Optimizer] = None,
+    ) -> None:
-        device: int = -1,
-    ) -> Optimizer:
         """Initialize the pipe for training, using data examples if available.
 
         get_examples (Callable[[], Iterable[Example]]): Optional function that
             returns gold-standard Example objects.
-        sgd (thinc.api.Optimizer): Optional optimizer. Will be created with
+        sgd (Optional[Optimizer]): An optimizer to use for updates. If not 
-            create_optimizer if it doesn't exist.
+            provided, will be created using the .create_optimizer() method.
         RETURNS (thinc.api.Optimizer): The optimizer.
 
         DOCS: https://nightly.spacy.io/api/language#initialize
@@ -1199,25 +1197,22 @@ class Language:
         if not valid_examples:
             err = Errors.E930.format(name="Language", obj="empty list")
             raise ValueError(err)
-        if device >= 0:  # TODO: do we need this here?
+        if self.vocab.vectors.data.shape[1] >= 1:
-            require_gpu(device)
+            ops = get_current_ops()
-            if self.vocab.vectors.data.shape[1] >= 1:
+            self.vocab.vectors.data = ops.asarray(self.vocab.vectors.data)
-                ops = get_current_ops()
-                self.vocab.vectors.data = ops.asarray(self.vocab.vectors.data)
-        if sgd is None:
-            sgd = create_default_optimizer()
-        self._optimizer = sgd
         for name, proc in self.pipeline:
             if hasattr(proc, "initialize"):
                 proc.initialize(
-                    get_examples, pipeline=self.pipeline, sgd=self._optimizer
+                    get_examples, pipeline=self.pipeline
                 )
         self._link_components()
+        if sgd is not None:
+            self._optimizer = sgd
+        elif self._optimizer is None:
+            self._optimizer = self.create_optimizer()
         return self._optimizer
 
-    def resume_training(
+    def resume_training(self, *, sgd: Optional[Optimizer] = None) -> Optimizer:
-        self, *, sgd: Optional[Optimizer] = None, device: int = -1
-    ) -> Optimizer:
         """Continue training a pretrained model.
 
         Create and return an optimizer, and initialize "rehearsal" for any pipeline
@@ -1226,22 +1221,20 @@ class Language:
         rehearsal, collect samples of text you want the models to retain performance
         on, and call nlp.rehearse() with a batch of Example objects.
 
-        sgd (Optional[Optimizer]): An optimizer.
         RETURNS (Optimizer): The optimizer.
 
         DOCS: https://nightly.spacy.io/api/language#resume_training
         """
-        if device >= 0:  # TODO: do we need this here?
+        ops = get_current_ops()
-            require_gpu(device)
+        if self.vocab.vectors.data.shape[1] >= 1:
-            ops = get_current_ops()
+            self.vocab.vectors.data = ops.asarray(self.vocab.vectors.data)
-            if self.vocab.vectors.data.shape[1] >= 1:
-                self.vocab.vectors.data = ops.asarray(self.vocab.vectors.data)
-        if sgd is None:
-            sgd = create_default_optimizer()
-        self._optimizer = sgd
         for name, proc in self.pipeline:
             if hasattr(proc, "_rehearsal_model"):
                 proc._rehearsal_model = deepcopy(proc.model)
+        if sgd is not None:
+            self._optimizer = sgd
+        elif self._optimizer is None:
+            self._optimizer = self.create_optimizer()
         return self._optimizer
 
     def evaluate(
@@ -1302,6 +1295,10 @@ 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."""
+        return registry.resolve(self.config["training"]["optimizer"])
 
     @contextmanager
     def use_params(self, params: Optional[dict]):