Add previous HashEmbedCNN tok2vec to make transition easier

spacy/ml/models/tok2vec.py

@@ -20,8 +20,37 @@ def tok2vec_listener_v1(width, upstream="*"):
     return tok2vec
 
 
+@registry.architectures.register("spacy.HashEmbedCNN.v1")
+def build_hash_embed_cnn_tok2vec(
+    *,
+    width: int,
+    depth: int,
+    embed_size: int,
+    window_size: int,
+    maxout_pieces: int,
+    subword_features: bool,
+    dropout: Optional[float],
+    pretrained_vectors: Optional[bool]
+) -> Model[List[Doc], List[Floats2d]]:
+    """Build spaCy's 'standard' tok2vec layer, which uses hash embedding
+    with subword features and a CNN with layer-normalized maxout."""
+    return build_Tok2Vec_model(
+        embed=MultiHashEmbed(
+            width=width,
+            rows=embed_size,
+            also_embed_subwords=subword_features,
+            also_use_static_vectors=bool(pretrained_vectors),
+        ),
+        encode=MaxoutWindowEncoder(
+            width=width,
+            depth=depth,
+            window_size=window_size,
+            maxout_pieces=maxout_pieces
+        )
+    )
+
 @registry.architectures.register("spacy.Tok2Vec.v1")
-def Tok2Vec(
+def build_Tok2Vec_model(
     embed: Model[List[Doc], List[Floats2d]],
     encode: Model[List[Floats2d], List[Floats2d]],
 ) -> Model[List[Doc], List[Floats2d]]:
@@ -62,7 +91,7 @@ def MultiHashEmbed(
         ]
     else:
         embeddings = [make_hash_embed(NORM)]
-
+    concat_size = width * (len(embeddings) + also_use_static_vectors)
     if also_use_static_vectors:
         model = chain(
             concatenate(
@@ -73,7 +102,7 @@ def MultiHashEmbed(
                 ),
                 StaticVectors(width, dropout=0.0),
             ),
-            with_array(Maxout(width, nP=3, dropout=0.0, normalize=True)),
+            with_array(Maxout(width, concat_size, nP=3, dropout=0.0, normalize=True)),
             ragged2list(),
         )
     else:
@@ -83,7 +112,7 @@ def MultiHashEmbed(
                 list2ragged(),
                 with_array(concatenate(*embeddings)),
             ),
-            with_array(Maxout(width, nP=3, dropout=0.0, normalize=True)),
+            with_array(Maxout(width, concat_size, nP=3, dropout=0.0, normalize=True)),
             ragged2list(),
         )
     return model