Clean up link_vectors_to_models unused stuff

spacy/cli/project/assets.py

@@ -11,7 +11,6 @@ from ...util import ensure_path, working_dir
 from .._util import project_cli, Arg, PROJECT_FILE, load_project_config, get_checksum
 
 
-
 # TODO: find a solution for caches
 # CACHES = [
 #     Path.home() / ".torch",

spacy/language.py

@@ -21,7 +21,7 @@ from .vocab import Vocab, create_vocab
 from .pipe_analysis import analyze_pipes, analyze_all_pipes, validate_attrs
 from .gold import Example
 from .scorer import Scorer
-from .util import link_vectors_to_models, create_default_optimizer, registry
+from .util import create_default_optimizer, registry
 from .util import SimpleFrozenDict, combine_score_weights
 from .lang.tokenizer_exceptions import URL_MATCH, BASE_EXCEPTIONS
 from .lang.punctuation import TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES
@@ -1049,7 +1049,6 @@ class Language:
             if self.vocab.vectors.data.shape[1] >= 1:
                 ops = get_current_ops()
                 self.vocab.vectors.data = ops.asarray(self.vocab.vectors.data)
-        link_vectors_to_models(self.vocab)
         if sgd is None:
             sgd = create_default_optimizer()
         self._optimizer = sgd
@@ -1082,7 +1081,6 @@ class Language:
             ops = get_current_ops()
             if self.vocab.vectors.data.shape[1] >= 1:
                 self.vocab.vectors.data = ops.asarray(self.vocab.vectors.data)
-        link_vectors_to_models(self.vocab)
         if sgd is None:
             sgd = create_default_optimizer()
         self._optimizer = sgd

spacy/pipeline/morphologizer.pyx

@@ -149,7 +149,6 @@ class Morphologizer(Tagger):
                     self.cfg["labels_pos"][norm_label] = POS_IDS[pos]
         self.set_output(len(self.labels))
         self.model.initialize()
-        util.link_vectors_to_models(self.vocab)
         if sgd is None:
             sgd = self.create_optimizer()
         return sgd

spacy/pipeline/multitask.pyx

@@ -11,7 +11,6 @@ from .tagger import Tagger
 from ..language import Language
 from ..syntax import nonproj
 from ..attrs import POS, ID
-from ..util import link_vectors_to_models
 from ..errors import Errors
 
 
@@ -91,7 +90,6 @@ class MultitaskObjective(Tagger):
                 if label is not None and label not in self.labels:
                     self.labels[label] = len(self.labels)
         self.model.initialize()
-        link_vectors_to_models(self.vocab)
         if sgd is None:
             sgd = self.create_optimizer()
         return sgd
@@ -179,7 +177,6 @@ class ClozeMultitask(Pipe):
         pass
 
     def begin_training(self, get_examples=lambda: [], pipeline=None, sgd=None):
-        link_vectors_to_models(self.vocab)
         self.model.initialize()
         X = self.model.ops.alloc((5, self.model.get_ref("tok2vec").get_dim("nO")))
         self.model.output_layer.begin_training(X)

spacy/pipeline/pipe.pyx

@@ -3,7 +3,7 @@ import srsly
 
 from ..tokens.doc cimport Doc
 
-from ..util import link_vectors_to_models, create_default_optimizer
+from ..util import create_default_optimizer
 from ..errors import Errors
 from .. import util
 
@@ -145,8 +145,6 @@ class Pipe:
         DOCS: https://spacy.io/api/pipe#begin_training
         """
         self.model.initialize()
-        if hasattr(self, "vocab"):
-            link_vectors_to_models(self.vocab)
         if sgd is None:
             sgd = self.create_optimizer()
         return sgd

spacy/pipeline/senter.pyx

@@ -138,7 +138,6 @@ class SentenceRecognizer(Tagger):
         """
         self.set_output(len(self.labels))
         self.model.initialize()
-        util.link_vectors_to_models(self.vocab)
         if sgd is None:
             sgd = self.create_optimizer()
         return sgd

spacy/pipeline/simple_ner.py

@@ -168,7 +168,6 @@ class SimpleNER(Pipe):
         self.model.initialize()
         if pipeline is not None:
             self.init_multitask_objectives(get_examples, pipeline, sgd=sgd, **self.cfg)
-        util.link_vectors_to_models(self.vocab)
         self.loss_func = SequenceCategoricalCrossentropy(
             names=self.get_tag_names(), normalize=True, missing_value=None
         )

spacy/pipeline/tagger.pyx

@@ -318,7 +318,6 @@ class Tagger(Pipe):
         self.model.initialize(X=doc_sample)
         # Get batch of example docs, example outputs to call begin_training().
         # This lets the model infer shapes.
-        util.link_vectors_to_models(self.vocab)
         if sgd is None:
             sgd = self.create_optimizer()
         return sgd

spacy/pipeline/textcat.py

@@ -356,7 +356,6 @@ class TextCategorizer(Pipe):
         docs = [Doc(Vocab(), words=["hello"])]
         truths, _ = self._examples_to_truth(examples)
         self.set_output(len(self.labels))
-        util.link_vectors_to_models(self.vocab)
         self.model.initialize(X=docs, Y=truths)
         if sgd is None:
             sgd = self.create_optimizer()

spacy/pipeline/tok2vec.py

@@ -7,7 +7,7 @@ from ..tokens import Doc
 from ..vocab import Vocab
 from ..language import Language
 from ..errors import Errors
-from ..util import link_vectors_to_models, minibatch
+from ..util import minibatch
 
 
 default_model_config = """
@@ -198,7 +198,6 @@ class Tok2Vec(Pipe):
         """
         docs = [Doc(self.vocab, words=["hello"])]
         self.model.initialize(X=docs)
-        link_vectors_to_models(self.vocab)
 
 
 class Tok2VecListener(Model):

spacy/syntax/_parser_model.pyx

@@ -21,7 +21,7 @@ from .transition_system cimport Transition
 
 from ..compat import copy_array
 from ..errors import Errors, TempErrors
-from ..util import link_vectors_to_models, create_default_optimizer
+from ..util import create_default_optimizer
 from .. import util
 from . import nonproj
 

spacy/syntax/nn_parser.pyx

@@ -29,7 +29,7 @@ from .stateclass cimport StateClass
 from ._state cimport StateC
 from .transition_system cimport Transition
 
-from ..util import link_vectors_to_models, create_default_optimizer, registry
+from ..util import create_default_optimizer, registry
 from ..compat import copy_array
 from ..errors import Errors, Warnings
 from .. import util
@@ -456,7 +456,6 @@ cdef class Parser:
             self.model.initialize()
         if pipeline is not None:
             self.init_multitask_objectives(get_examples, pipeline, sgd=sgd, **self.cfg)
-        link_vectors_to_models(self.vocab)
         return sgd
 
     def to_disk(self, path, exclude=tuple()):

spacy/tests/regression/test_issue2501-3000.py

@@ -9,7 +9,6 @@ from spacy.matcher import Matcher
 from spacy.tokens import Doc, Span
 from spacy.vocab import Vocab
 from spacy.compat import pickle
-from spacy.util import link_vectors_to_models
 import numpy
 import random
 
@@ -190,7 +189,6 @@ def test_issue2871():
         _ = vocab[word]  # noqa: F841
         vocab.set_vector(word, vector_data[0])
     vocab.vectors.name = "dummy_vectors"
-    link_vectors_to_models(vocab)
     assert vocab["dog"].rank == 0
     assert vocab["cat"].rank == 1
     assert vocab["SUFFIX"].rank == 2

spacy/util.py

@@ -1200,29 +1200,6 @@ class DummyTokenizer:
         return self
 
 
-def link_vectors_to_models(
-    vocab: "Vocab",
-    models: List[Model] = [],
-    *,
-    vectors_name_attr="vectors_name",
-    vectors_attr="vectors",
-    key2row_attr="key2row",
-    default_vectors_name="spacy_pretrained_vectors",
-) -> None:
-    """Supply vectors data to models."""
-    vectors = vocab.vectors
-    if vectors.name is None:
-        vectors.name = default_vectors_name
-        if vectors.data.size != 0:
-            warnings.warn(Warnings.W020.format(shape=vectors.data.shape))
-
-    for model in models:
-        for node in model.walk():
-            if node.attrs.get(vectors_name_attr) == vectors.name:
-                node.attrs[vectors_attr] = Unserializable(vectors.data)
-                node.attrs[key2row_attr] = Unserializable(vectors.key2row)
-
-
 def create_default_optimizer() -> Optimizer:
     # TODO: Do we still want to allow env_opt?
     learn_rate = env_opt("learn_rate", 0.001)

spacy/vocab.pyx

@@ -16,7 +16,7 @@ from .errors import Errors
 from .lemmatizer import Lemmatizer
 from .attrs import intify_attrs, NORM, IS_STOP
 from .vectors import Vectors
-from .util import link_vectors_to_models, registry
+from .util import registry
 from .lookups import Lookups, load_lookups
 from . import util
 from .lang.norm_exceptions import BASE_NORMS
@@ -344,7 +344,6 @@ cdef class Vocab:
             synonym = self.strings[syn_keys[i][0]]
             score = scores[i][0]
             remap[word] = (synonym, score)
-        link_vectors_to_models(self)
         return remap
 
     def get_vector(self, orth, minn=None, maxn=None):
@@ -476,8 +475,6 @@ cdef class Vocab:
         if "vectors" not in exclude:
             if self.vectors is not None:
                 self.vectors.from_disk(path, exclude=["strings"])
-            if self.vectors.name is not None:
-                link_vectors_to_models(self)
         if "lookups" not in exclude:
             self.lookups.from_disk(path)
         if "lexeme_norm" in self.lookups:
@@ -537,8 +534,6 @@ cdef class Vocab:
             )
         self.length = 0
         self._by_orth = PreshMap()
-        if self.vectors.name is not None:
-            link_vectors_to_models(self)
         return self
 
     def _reset_cache(self, keys, strings):