deuglify kb deserializer

spacy/language.py

@@ -118,7 +118,7 @@ class Language(object):
         "tagger": lambda nlp, **cfg: Tagger(nlp.vocab, **cfg),
         "parser": lambda nlp, **cfg: DependencyParser(nlp.vocab, **cfg),
         "ner": lambda nlp, **cfg: EntityRecognizer(nlp.vocab, **cfg),
-        "entity_linker": lambda nlp, **cfg: EntityLinker(**cfg),
+        "entity_linker": lambda nlp, **cfg: EntityLinker(nlp.vocab, **cfg),
         "similarity": lambda nlp, **cfg: SimilarityHook(nlp.vocab, **cfg),
         "textcat": lambda nlp, **cfg: TextCategorizer(nlp.vocab, **cfg),
         "sentencizer": lambda nlp, **cfg: Sentencizer(**cfg),
@@ -811,13 +811,6 @@ class Language(object):
             exclude = list(exclude) + ["vocab"]
         util.from_disk(path, deserializers, exclude)
 
-        # download the KB for the entity linking component - requires the vocab
-        for pipe_name, pipe in self.pipeline:
-            if pipe_name == "entity_linker":
-                kb = KnowledgeBase(vocab=self.vocab, entity_vector_length=pipe.cfg["entity_width"])
-                kb.load_bulk(path / pipe_name / "kb")
-                pipe.set_kb(kb)
-
         self._path = path
         return self
 

spacy/pipeline/pipes.pyx

@@ -13,6 +13,7 @@ from thinc.misc import LayerNorm
 from thinc.neural.util import to_categorical
 from thinc.neural.util import get_array_module
 
+from spacy.kb import KnowledgeBase
 from ..cli.pretrain import get_cossim_loss
 from .functions import merge_subtokens
 from ..tokens.doc cimport Doc
@@ -1079,7 +1080,8 @@ class EntityLinker(Pipe):
         model = build_nel_encoder(embed_width=embed_width, hidden_width=hidden_width, ner_types=len(type_to_int), **cfg)
         return model
 
-    def __init__(self, **cfg):
+    def __init__(self, vocab, **cfg):
+        self.vocab = vocab
         self.model = True
         self.kb = None
         self.cfg = dict(cfg)
@@ -1277,6 +1279,7 @@ class EntityLinker(Pipe):
     def to_disk(self, path, exclude=tuple(), **kwargs):
         serialize = OrderedDict()
         serialize["cfg"] = lambda p: srsly.write_json(p, self.cfg)
+        serialize["vocab"] = lambda p: self.vocab.to_disk(p)
         serialize["kb"] = lambda p: self.kb.dump(p)
         if self.model not in (None, True, False):
             serialize["model"] = lambda p: p.open("wb").write(self.model.to_bytes())
@@ -1289,8 +1292,15 @@ class EntityLinker(Pipe):
                 self.model = self.Model(**self.cfg)
              self.model.from_bytes(p.open("rb").read())
 
+        def load_kb(p):
+            kb = KnowledgeBase(vocab=self.vocab, entity_vector_length=self.cfg["entity_width"])
+            kb.load_bulk(p)
+            self.set_kb(kb)
+
         deserialize = OrderedDict()
         deserialize["cfg"] = lambda p: self.cfg.update(_load_cfg(p))
+        deserialize["vocab"] = lambda p: self.vocab.from_disk(p)
+        deserialize["kb"] = load_kb
         deserialize["model"] = load_model
         exclude = util.get_serialization_exclude(deserialize, exclude, kwargs)
         util.from_disk(path, deserialize, exclude)