From cb9654e98c6d2fe34cedd7d8dc43e233d133ba84 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Tue, 28 Jul 2020 00:52:50 +0200
Subject: [PATCH] WIP on new StaticVectors

---
 spacy/ml/models/tok2vec.py | 46 ++++++++++++++++++++------------------
 spacy/util.py              | 27 ++++++++++++++--------
 2 files changed, 42 insertions(+), 31 deletions(-)

diff --git a/spacy/ml/models/tok2vec.py b/spacy/ml/models/tok2vec.py
index 1766fa80e..caa9c467c 100644
--- a/spacy/ml/models/tok2vec.py
+++ b/spacy/ml/models/tok2vec.py
@@ -1,7 +1,9 @@
+from typing import Optional, List
 from thinc.api import chain, clone, concatenate, with_array, uniqued
 from thinc.api import Model, noop, with_padded, Maxout, expand_window
 from thinc.api import HashEmbed, StaticVectors, PyTorchLSTM
 from thinc.api import residual, LayerNorm, FeatureExtractor, Mish
+from thinc.types import Floats2d
 
 from ... import util
 from ...util import registry
@@ -42,15 +44,15 @@ def Doc2Feats(columns):
 
 @registry.architectures.register("spacy.HashEmbedCNN.v1")
 def hash_embed_cnn(
-    pretrained_vectors,
-    width,
-    depth,
-    embed_size,
-    maxout_pieces,
-    window_size,
-    subword_features,
-    dropout,
-):
+    pretrained_vectors: str,
+    width: int,
+    depth: int,
+    embed_size: int,
+    maxout_pieces: int,
+    window_size: int,
+    subword_features: bool,
+    dropout: float,
+) -> Model[List[Doc], List[Floats2d]:
     # Does not use character embeddings: set to False by default
     return build_Tok2Vec_model(
         width=width,
@@ -182,7 +184,7 @@ def MultiHashEmbed(
 
     if pretrained_vectors:
         glove = StaticVectors(
-            vectors=pretrained_vectors.data,
+            vectors_name=pretrained_vectors,
             nO=width,
             column=columns.index(ID),
             dropout=dropout,
@@ -261,18 +263,18 @@ def TorchBiLSTMEncoder(width, depth):
 
 
 def build_Tok2Vec_model(
-    width,
-    embed_size,
-    pretrained_vectors,
-    window_size,
-    maxout_pieces,
-    subword_features,
-    char_embed,
-    nM,
-    nC,
-    conv_depth,
-    bilstm_depth,
-    dropout,
+    width: int,
+    embed_size: int,
+    pretrained_vectors: Optional[str],
+    window_size: int,
+    maxout_pieces: int,
+    subword_features: bool,
+    char_embed: bool,
+    nM: int,
+    nC: int,
+    conv_depth: int,
+    bilstm_depth: int,
+    dropout: float,
 ) -> Model:
     if char_embed:
         subword_features = False
diff --git a/spacy/util.py b/spacy/util.py
index d1951145f..de6d9831b 100644
--- a/spacy/util.py
+++ b/spacy/util.py
@@ -24,6 +24,8 @@ import tempfile
 import shutil
 import shlex
 import inspect
+from thinc.types import Unserializable
+
 
 try:
     import cupy.random
@@ -1184,20 +1186,27 @@ class DummyTokenizer:
         return self
 
 
-def link_vectors_to_models(vocab: "Vocab") -> None:
+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 = VECTORS_KEY
+        vectors.name = default_vectors_name
         if vectors.data.size != 0:
             warnings.warn(Warnings.W020.format(shape=vectors.data.shape))
-    for word in vocab:
-        if word.orth in vectors.key2row:
-            word.rank = vectors.key2row[word.orth]
-        else:
-            word.rank = 0
 
-
-VECTORS_KEY = "spacy_pretrained_vectors"
+    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: