default models defined in component decorator (#5452)

* move defaults to pipeline and use in component decorator

* black formatting

* relative import

spacy/language.py

@@ -184,33 +184,6 @@ class Language(object):
         self.max_length = max_length
         self._optimizer = None
 
-        # TODO: de-uglify (incorporating into component decorator didn't work because of circular imports)
-        from .ml.models.defaults import (
-            default_tagger_config,
-            default_parser_config,
-            default_ner_config,
-            default_textcat_config,
-            default_nel_config,
-            default_morphologizer_config,
-            default_senter_config,
-            default_tensorizer_config,
-            default_tok2vec_config,
-            default_simple_ner_config
-        )
-
-        self.defaults = {
-            "tagger": default_tagger_config(),
-            "parser": default_parser_config(),
-            "ner": default_ner_config(),
-            "textcat": default_textcat_config(),
-            "entity_linker": default_nel_config(),
-            "morphologizer": default_morphologizer_config(),
-            "senter": default_senter_config(),
-            "simple_ner": default_simple_ner_config(),
-            "tensorizer": default_tensorizer_config(),
-            "tok2vec": default_tok2vec_config(),
-        }
-
     @property
     def path(self):
         return self._path
@@ -338,7 +311,6 @@ class Language(object):
             else:
                 raise KeyError(Errors.E002.format(name=name))
         factory = self.factories[name]
-        default_config = self.defaults.get(name, None)
 
         # transform the model's config to an actual Model
         factory_cfg = dict(config)
@@ -349,11 +321,6 @@ class Language(object):
                 warnings.warn(Warnings.W099.format(type=type(model_cfg), pipe=name))
                 model_cfg = None
             del factory_cfg["model"]
-        if model_cfg is None and default_config is not None:
-            warnings.warn(Warnings.W098.format(name=name))
-            model_cfg = default_config["model"]
-        if model_cfg is None:
-            warnings.warn(Warnings.W097.format(name=name))
         model = None
         if model_cfg is not None:
             self.config[name] = {"model": model_cfg}
@@ -539,7 +506,11 @@ class Language(object):
             to_disable = [pipe for pipe in self.pipe_names if pipe not in enable]
             # raise an error if the enable and disable keywords are not consistent
             if disable is not None and disable != to_disable:
-                raise ValueError(Errors.E992.format(enable=enable, disable=disable, names=self.pipe_names))
+                raise ValueError(
+                    Errors.E992.format(
+                        enable=enable, disable=disable, names=self.pipe_names
+                    )
+                )
             disable = to_disable
         return DisabledPipes(self, disable)
 
@@ -1085,7 +1056,14 @@ class component(object):
     # NB: This decorator needs to live here, because it needs to write to
     # Language.factories. All other solutions would cause circular import.
 
-    def __init__(self, name=None, assigns=tuple(), requires=tuple(), retokenizes=False):
+    def __init__(
+        self,
+        name=None,
+        assigns=tuple(),
+        requires=tuple(),
+        retokenizes=False,
+        default_model=lambda: None,
+    ):
         """Decorate a pipeline component.
 
         name (unicode): Default component and factory name.
@@ -1097,6 +1075,7 @@ class component(object):
         self.assigns = validate_attrs(assigns)
         self.requires = validate_attrs(requires)
         self.retokenizes = retokenizes
+        self.default_model = default_model
 
     def __call__(self, *args, **kwargs):
         obj = args[0]
@@ -1109,6 +1088,11 @@ class component(object):
         obj.retokenizes = self.retokenizes
 
         def factory(nlp, model, **cfg):
+            if model is None:
+                model = self.default_model()
+                warnings.warn(Warnings.W098.format(name=self.name))
+            if model is None:
+                warnings.warn(Warnings.W097.format(name=self.name))
             if hasattr(obj, "from_nlp"):
                 return obj.from_nlp(nlp, model, **cfg)
             elif isinstance(obj, type):

spacy/pipeline/defaults/__init__.py

@@ -1,6 +1,6 @@
 from pathlib import Path
 
-from .... import util
+from ... import util
 
 
 def default_nel_config():

spacy/pipeline/morphologizer.pyx

@@ -17,9 +17,10 @@ from ..util import link_vectors_to_models, create_default_optimizer
 from ..errors import Errors, TempErrors
 from .pipes import Tagger, _load_cfg
 from .. import util
+from .defaults import default_morphologizer
 
 
-@component("morphologizer", assigns=["token.morph", "token.pos"])
+@component("morphologizer", assigns=["token.morph", "token.pos"], default_model=default_morphologizer)
 class Morphologizer(Tagger):
 
     def __init__(self, vocab, model, **cfg):

spacy/pipeline/pipes.pyx

@@ -2,6 +2,7 @@
 import numpy
 import srsly
 import random
+
 from thinc.api import CosineDistance, to_categorical, get_array_module
 from thinc.api import set_dropout_rate, SequenceCategoricalCrossentropy
 import warnings
@@ -13,6 +14,8 @@ from ..syntax.arc_eager cimport ArcEager
 from ..morphology cimport Morphology
 from ..vocab cimport Vocab
 
+from .defaults import default_tagger, default_parser,  default_ner,  default_textcat
+from .defaults import default_nel, default_senter, default_tensorizer
 from .functions import merge_subtokens
 from ..language import Language, component
 from ..syntax import nonproj
@@ -234,7 +237,7 @@ class Pipe(object):
         return self
 
 
-@component("tensorizer", assigns=["doc.tensor"])
+@component("tensorizer", assigns=["doc.tensor"], default_model=default_tensorizer)
 class Tensorizer(Pipe):
     """Pre-train position-sensitive vectors for tokens."""
 
@@ -366,7 +369,7 @@ class Tensorizer(Pipe):
         return sgd
 
 
-@component("tagger", assigns=["token.tag", "token.pos", "token.lemma"])
+@component("tagger", assigns=["token.tag", "token.pos", "token.lemma"], default_model=default_tagger)
 class Tagger(Pipe):
     """Pipeline component for part-of-speech tagging.
 
@@ -636,7 +639,7 @@ class Tagger(Pipe):
         return self
 
 
-@component("senter", assigns=["token.is_sent_start"])
+@component("senter", assigns=["token.is_sent_start"], default_model=default_senter)
 class SentenceRecognizer(Tagger):
     """Pipeline component for sentence segmentation.
 
@@ -976,7 +979,7 @@ class ClozeMultitask(Pipe):
             losses[self.name] += loss
 
 
-@component("textcat", assigns=["doc.cats"])
+@component("textcat", assigns=["doc.cats"], default_model=default_textcat)
 class TextCategorizer(Pipe):
     """Pipeline component for text classification.
 
@@ -1227,7 +1230,8 @@ cdef class EntityRecognizer(Parser):
 @component(
     "entity_linker",
     requires=["doc.ents", "doc.sents", "token.ent_iob", "token.ent_type"],
-    assigns=["token.ent_kb_id"]
+    assigns=["token.ent_kb_id"],
+    default_model=default_nel,
 )
 class EntityLinker(Pipe):
     """Pipeline component for named entity linking.
@@ -1673,8 +1677,19 @@ class Sentencizer(Pipe):
 
 
 # Cython classes can't be decorated, so we need to add the factories here
-Language.factories["parser"] = lambda nlp, model, **cfg: DependencyParser.from_nlp(nlp, model, **cfg)
+Language.factories["parser"] = lambda nlp, model, **cfg: parser_factory(nlp, model, **cfg)
-Language.factories["ner"] = lambda nlp, model, **cfg: EntityRecognizer.from_nlp(nlp, model, **cfg)
+Language.factories["ner"] = lambda nlp, model, **cfg: ner_factory(nlp, model, **cfg)
 
+def parser_factory(nlp, model, **cfg):
+    if model is None:
+        model = default_parser()
+        warnings.warn(Warnings.W098.format(name="parser"))
+    return DependencyParser.from_nlp(nlp, model, **cfg)
+
+def ner_factory(nlp, model, **cfg):
+    if model is None:
+        model = default_ner()
+        warnings.warn(Warnings.W098.format(name="ner"))
+    return EntityRecognizer.from_nlp(nlp, model, **cfg)
 
 __all__ = ["Tagger", "DependencyParser", "EntityRecognizer", "Tensorizer", "TextCategorizer", "EntityLinker", "Sentencizer", "SentenceRecognizer"]

spacy/pipeline/simple_ner.py

@@ -2,6 +2,8 @@ from typing import List
 from thinc.types import Floats2d
 from thinc.api import SequenceCategoricalCrossentropy, set_dropout_rate
 from thinc.util import to_numpy
+
+from .defaults import default_simple_ner
 from ..gold import Example, spans_from_biluo_tags, iob_to_biluo, biluo_to_iob
 from ..tokens import Doc
 from ..language import component
@@ -9,7 +11,7 @@ from ..util import link_vectors_to_models
 from .pipes import Pipe
 
 
-@component("simple_ner", assigns=["doc.ents"])
+@component("simple_ner", assigns=["doc.ents"], default_model=default_simple_ner)
 class SimpleNER(Pipe):
     """Named entity recognition with a tagging model. The model should include
     validity constraints to ensure that only valid tag sequences are returned."""

spacy/pipeline/tok2vec.py

@@ -6,9 +6,10 @@ from ..tokens import Doc
 from ..vocab import Vocab
 from ..language import component
 from ..util import link_vectors_to_models, minibatch, eg2doc
+from .defaults import default_tok2vec
 
 
-@component("tok2vec", assigns=["doc.tensor"])
+@component("tok2vec", assigns=["doc.tensor"], default_model=default_tok2vec)
 class Tok2Vec(Pipe):
     @classmethod
     def from_nlp(cls, nlp, model, **cfg):

spacy/tests/doc/test_add_entities.py

@@ -3,7 +3,7 @@ from spacy.tokens import Span
 import pytest
 
 from ..util import get_doc
-from ...ml.models.defaults import default_ner
+from spacy.pipeline.defaults import default_ner
 
 
 def test_doc_add_entities_set_ents_iob(en_vocab):

spacy/tests/parser/test_add_label.py

@@ -4,7 +4,7 @@ from spacy.attrs import NORM
 from spacy.gold import GoldParse
 from spacy.vocab import Vocab
 
-from spacy.ml.models.defaults import default_parser, default_ner
+from spacy.pipeline.defaults import default_parser, default_ner
 from spacy.tokens import Doc
 from spacy.pipeline import DependencyParser, EntityRecognizer
 from spacy.util import fix_random_seed

spacy/tests/parser/test_arc_eager_oracle.py

@@ -1,7 +1,7 @@
 import pytest
 from spacy.vocab import Vocab
 
-from spacy.ml.models.defaults import default_parser
+from spacy.pipeline.defaults import default_parser
 from spacy.pipeline import DependencyParser
 from spacy.tokens import Doc
 from spacy.gold import GoldParse

spacy/tests/parser/test_ner.py

@@ -2,7 +2,7 @@ import pytest
 
 from spacy import util
 from spacy.lang.en import English
-from spacy.ml.models.defaults import default_ner
+from spacy.pipeline.defaults import default_ner
 
 from spacy.pipeline import EntityRecognizer, EntityRuler
 from spacy.vocab import Vocab

spacy/tests/parser/test_neural_parser.py

@@ -1,5 +1,5 @@
 import pytest
-from spacy.ml.models.defaults import default_parser, default_tok2vec
+from spacy.pipeline.defaults import default_parser, default_tok2vec
 from spacy.vocab import Vocab
 from spacy.syntax.arc_eager import ArcEager
 from spacy.syntax.nn_parser import Parser

spacy/tests/parser/test_nn_beam.py

@@ -2,7 +2,7 @@ import pytest
 import numpy
 from spacy.vocab import Vocab
 from spacy.language import Language
-from spacy.ml.models.defaults import default_parser
+from spacy.pipeline.defaults import default_parser
 from spacy.pipeline import DependencyParser
 from spacy.syntax.arc_eager import ArcEager
 from spacy.tokens import Doc

spacy/tests/parser/test_preset_sbd.py

@@ -4,7 +4,7 @@ from spacy.attrs import NORM
 from spacy.gold import GoldParse
 from spacy.vocab import Vocab
 
-from spacy.ml.models.defaults import default_parser
+from spacy.pipeline.defaults import default_parser
 from spacy.tokens import Doc
 from spacy.pipeline import DependencyParser
 

spacy/tests/pipeline/test_textcat.py

@@ -11,7 +11,7 @@ from spacy.gold import GoldParse
 from spacy.util import fix_random_seed
 
 from ..util import make_tempdir
-from ...ml.models.defaults import default_tok2vec
+from spacy.pipeline.defaults import default_tok2vec
 
 TRAIN_DATA = [
     ("I'm so happy.", {"cats": {"POSITIVE": 1.0, "NEGATIVE": 0.0}}),

spacy/tests/regression/test_issue1501-2000.py

@@ -10,7 +10,7 @@ from spacy.lang.lex_attrs import is_stop
 from spacy.vectors import Vectors
 from spacy.vocab import Vocab
 from spacy.language import Language
-from spacy.ml.models.defaults import default_ner, default_tagger
+from spacy.pipeline.defaults import default_ner, default_tagger
 from spacy.tokens import Doc, Span, Token
 from spacy.pipeline import Tagger, EntityRecognizer
 from spacy.attrs import HEAD, DEP

spacy/tests/regression/test_issue3001-3500.py

@@ -1,7 +1,7 @@
 import pytest
 from spacy.lang.en import English
 from spacy.lang.de import German
-from spacy.ml.models.defaults import default_ner
+from spacy.pipeline.defaults import default_ner
 from spacy.pipeline import EntityRuler, EntityRecognizer
 from spacy.matcher import Matcher, PhraseMatcher
 from spacy.tokens import Doc

spacy/tests/regression/test_issue3830.py

@@ -1,7 +1,7 @@
 from spacy.pipeline.pipes import DependencyParser
 from spacy.vocab import Vocab
 
-from spacy.ml.models.defaults import default_parser
+from spacy.pipeline.defaults import default_parser
 
 
 def test_issue3830_no_subtok():

spacy/tests/regression/test_issue4042.py

@@ -3,7 +3,7 @@ from spacy.pipeline import EntityRecognizer, EntityRuler
 from spacy.lang.en import English
 from spacy.tokens import Span
 from spacy.util import ensure_path
-from spacy.ml.models.defaults import default_ner
+from spacy.pipeline.defaults import default_ner
 
 from ..util import make_tempdir
 

spacy/tests/regression/test_issue4313.py

@@ -1,6 +1,6 @@
 from collections import defaultdict
 
-from spacy.ml.models.defaults import default_ner
+from spacy.pipeline.defaults import default_ner
 from spacy.pipeline import EntityRecognizer
 
 from spacy.lang.en import English

spacy/tests/serialize/test_serialize_pipeline.py

@@ -1,8 +1,8 @@
 import pytest
 from spacy.pipeline import Tagger, DependencyParser, EntityRecognizer
 from spacy.pipeline import Tensorizer, TextCategorizer, SentenceRecognizer
-from spacy.ml.models.defaults import default_parser, default_tensorizer, default_tagger
+from spacy.pipeline.defaults import default_parser, default_tensorizer, default_tagger
-from spacy.ml.models.defaults import default_textcat, default_senter
+from spacy.pipeline.defaults import default_textcat, default_senter
 
 from ..util import make_tempdir