Clean up sgd and pipeline -> nlp

spacy/pipeline/entity_linker.py

@@ -1,5 +1,5 @@
 from itertools import islice
-from typing import Optional, Iterable, Callable, Dict, Iterator, Union, List, Tuple
+from typing import Optional, Iterable, Callable, Dict, Iterator, Union, List
 from pathlib import Path
 import srsly
 import random
@@ -144,20 +144,14 @@ class EntityLinker(Pipe):
         self,
         get_examples: Callable[[], Iterable[Example]],
         *,
-        pipeline: Optional[List[Tuple[str, Callable[[Doc], Doc]]]] = None,
+        nlp: Optional[Language] = None,
-        sgd: Optional[Optimizer] = None,
+    ):
-    ) -> Optimizer:
         """Initialize the pipe for training, using a representative set
         of data examples.
 
         get_examples (Callable[[], Iterable[Example]]): Function that
             returns a representative sample of gold-standard Example objects.
-        pipeline (List[Tuple[str, Callable]]): Optional list of pipeline
+        nlp (Language): The current nlp object the component is part of.
-            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/entitylinker#initialize
         """
@@ -174,9 +168,6 @@ class EntityLinker(Pipe):
         self.model.initialize(
             X=doc_sample, Y=self.model.ops.asarray(vector_sample, dtype="float32")
         )
-        if sgd is None:
-            sgd = self.create_optimizer()
-        return sgd
 
     def update(
         self,

spacy/pipeline/morphologizer.pyx

@@ -129,16 +129,13 @@ class Morphologizer(Tagger):
             self.cfg["labels_pos"][norm_label] = POS_IDS[pos]
         return 1
 
-    def initialize(self, get_examples, *, pipeline=None):
+    def initialize(self, get_examples, *, nlp=None):
         """Initialize the pipe for training, using a representative set
         of data examples.
 
         get_examples (Callable[[], Iterable[Example]]): Function that
             returns a representative sample of gold-standard Example objects.
-        pipeline (List[Tuple[str, Callable]]): Optional list of pipeline
+        nlp (Language): The current nlp object the component is part of.
-            components that this component is part of. Corresponds to
-            nlp.pipeline.
-        RETURNS (thinc.api.Optimizer): The optimizer.
 
         DOCS: https://nightly.spacy.io/api/morphologizer#initialize
         """

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):
+    def initialize(self, get_examples, nlp=None):
         if not hasattr(get_examples, "__call__"):
             err = Errors.E930.format(name="MultitaskObjective", obj=type(get_examples))
             raise ValueError(err)
@@ -174,7 +174,7 @@ class ClozeMultitask(Pipe):
     def set_annotations(self, docs, dep_ids):
         pass
 
-    def initialize(self, get_examples, pipeline=None):
+    def initialize(self, get_examples, nlp=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):
+    def initialize(self, get_examples, *, nlp=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
@@ -191,14 +191,11 @@ cdef class Pipe:
 
         get_examples (Callable[[], Iterable[Example]]): Function that
             returns a representative sample of gold-standard Example objects.
-        pipeline (List[Tuple[str, Callable]]): Optional list of pipeline
+        nlp (Language): The current nlp object the component is part of.
-            components that this component is part of. Corresponds to
-            nlp.pipeline.
-        RETURNS (thinc.api.Optimizer): The optimizer.
 
         DOCS: https://nightly.spacy.io/api/pipe#initialize
         """
-        raise NotImplementedError(Errors.E931.format(method="add_label", name=self.name))
+        raise NotImplementedError(Errors.E931.format(method="initialize", name=self.name))
 
     def _ensure_examples(self, get_examples):
         if get_examples is None or not hasattr(get_examples, "__call__"):

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):
+    def initialize(self, get_examples, nlp=None):
         pass
 
     def __call__(self, doc):

spacy/pipeline/senter.pyx

@@ -124,16 +124,13 @@ class SentenceRecognizer(Tagger):
             raise ValueError("nan value when computing loss")
         return float(loss), d_scores
 
-    def initialize(self, get_examples, *, pipeline=None):
+    def initialize(self, get_examples, *, nlp=None):
         """Initialize the pipe for training, using a representative set
         of data examples.
 
         get_examples (Callable[[], Iterable[Example]]): Function that
             returns a representative sample of gold-standard Example objects.
-        pipeline (List[Tuple[str, Callable]]): Optional list of pipeline
+        nlp (Language): The current nlp object the component is part of.
-            components that this component is part of. Corresponds to
-            nlp.pipeline.
-        RETURNS: None
 
         DOCS: https://nightly.spacy.io/api/sentencerecognizer#initialize
         """

spacy/pipeline/tagger.pyx

@@ -256,16 +256,13 @@ class Tagger(Pipe):
             raise ValueError("nan value when computing loss")
         return float(loss), d_scores
 
-    def initialize(self, get_examples, *, pipeline=None):
+    def initialize(self, get_examples, *, nlp=None):
         """Initialize the pipe for training, using a representative set
         of data examples.
 
         get_examples (Callable[[], Iterable[Example]]): Function that
             returns a representative sample of gold-standard Example objects..
-        pipeline (List[Tuple[str, Callable]]): Optional list of pipeline
+        nlp (Language): The current nlp object the component is part of.
-            components that this component is part of. Corresponds to
-            nlp.pipeline.
-        RETURNS (thinc.api.Optimizer): The optimizer.
 
         DOCS: https://nightly.spacy.io/api/tagger#initialize
         """

spacy/pipeline/textcat.py

@@ -338,17 +338,14 @@ class TextCategorizer(Pipe):
         self,
         get_examples: Callable[[], Iterable[Example]],
         *,
-        pipeline: Optional[List[Tuple[str, Callable[[Doc], Doc]]]] = None
+        nlp: Optional[Language] = None,
-    ) -> Optimizer:
+    ):
         """Initialize the pipe for training, using a representative set
         of data examples.
 
         get_examples (Callable[[], Iterable[Example]]): Function that
             returns a representative sample of gold-standard Example objects.
-        pipeline (List[Tuple[str, Callable]]): Optional list of pipeline
+        nlp (Language): The current nlp object the component is part of.
-            components that this component is part of. Corresponds to
-            nlp.pipeline.
-        RETURNS (thinc.api.Optimizer): The optimizer.
 
         DOCS: https://nightly.spacy.io/api/textcategorizer#initialize
         """

spacy/pipeline/tok2vec.py

@@ -1,4 +1,4 @@
-from typing import Iterator, Sequence, Iterable, Optional, Dict, Callable, List, Tuple
+from typing import Iterator, Sequence, Iterable, Optional, Dict, Callable, List
 from thinc.api import Model, set_dropout_rate, Optimizer, Config
 from itertools import islice
 
@@ -207,20 +207,14 @@ class Tok2Vec(Pipe):
         self,
         get_examples: Callable[[], Iterable[Example]],
         *,
-        pipeline: Optional[List[Tuple[str, Callable[[Doc], Doc]]]] = None,
+        nlp: Optional[Language] = None,
-        sgd: Optional[Optimizer] = None,
     ):
         """Initialize the pipe for training, using a representative set
         of data examples.
 
         get_examples (Callable[[], Iterable[Example]]): Function that
             returns a representative sample of gold-standard Example objects.
-        pipeline (List[Tuple[str, Callable]]): Optional list of pipeline
+        nlp (Language): The current nlp object the component is part of.
-            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/tok2vec#initialize
         """

spacy/pipeline/transition_parser.pyx

@@ -405,7 +405,7 @@ cdef class Parser(Pipe):
     def set_output(self, nO):
         self.model.attrs["resize_output"](self.model, nO)
 
-    def initialize(self, get_examples, pipeline=None, settings=None):
+    def initialize(self, get_examples, nlp=None):
         self._ensure_examples(get_examples)
         lexeme_norms = self.vocab.lookups.get_table("lexeme_norm", {})
         if len(lexeme_norms) == 0 and self.vocab.lang in util.LEXEME_NORM_LANGS:
@@ -425,8 +425,8 @@ cdef class Parser(Pipe):
         # make sure we resize so we have an appropriate upper layer
         self._resize()
         doc_sample = []
-        if pipeline is not None:
+        if nlp is not None:
-            for name, component in pipeline:
+            for name, component in nlp.pipeline:
                 if component is self:
                     break
                 if hasattr(component, "pipe"):
@@ -438,8 +438,8 @@ cdef class Parser(Pipe):
                 doc_sample.append(example.predicted)
         assert len(doc_sample) > 0, Errors.E923.format(name=self.name)
         self.model.initialize(doc_sample)
-        if pipeline is not None:
+        if nlp is not None:
-            self.init_multitask_objectives(get_examples, pipeline)
+            self.init_multitask_objectives(get_examples, nlp.pipeline)
 
     def to_disk(self, path, exclude=tuple()):
         serializers = {

spacy/schemas.py

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