Format

spacy/ml/models/tok2vec.py

@@ -23,7 +23,7 @@ def tok2vec_listener_v1(width, upstream="*"):
 @registry.architectures.register("spacy.Tok2Vec.v1")
 def Tok2Vec(
     embed: Model[List[Doc], List[Floats2d]],
-    encode: Model[List[Floats2d], List[Floats2d]]
+    encode: Model[List[Floats2d], List[Floats2d]],
 ) -> Model[List[Doc], List[Floats2d]]:
 
     receptive_field = encode.attrs.get("receptive_field", 0)
@@ -36,14 +36,12 @@ def Tok2Vec(
 
 @registry.architectures.register("spacy.MultiHashEmbed.v1")
 def MultiHashEmbed(
-    width: int,
+    width: int, rows: int, also_embed_subwords: bool, also_use_static_vectors: bool
-    rows: int,
-    also_embed_subwords: bool,
-    also_use_static_vectors: bool
 ):
     cols = [NORM, PREFIX, SUFFIX, SHAPE, ORTH]
 
     seed = 7
+
     def make_hash_embed(feature):
         nonlocal seed
         seed += 1
@@ -52,7 +50,7 @@ def MultiHashEmbed(
             rows if feature == NORM else rows // 2,
             column=cols.index(feature),
             seed=seed,
-            dropout=0.0
+            dropout=0.0,
         )
 
     if also_embed_subwords:
@@ -60,7 +58,7 @@ def MultiHashEmbed(
             make_hash_embed(NORM),
             make_hash_embed(PREFIX),
             make_hash_embed(SUFFIX),
-            make_hash_embed(SHAPE)
+            make_hash_embed(SHAPE),
         ]
     else:
         embeddings = [make_hash_embed(NORM)]
@@ -71,22 +69,22 @@ def MultiHashEmbed(
                 chain(
                     FeatureExtractor(cols),
                     list2ragged(),
-                    with_array(concatenate(*embeddings))
+                    with_array(concatenate(*embeddings)),
                 ),
-                StaticVectors(width, dropout=0.0)
+                StaticVectors(width, dropout=0.0),
             ),
             with_array(Maxout(width, nP=3, dropout=0.0, normalize=True)),
-            ragged2list()
+            ragged2list(),
         )
     else:
         model = chain(
             chain(
                 FeatureExtractor(cols),
                 list2ragged(),
-                with_array(concatenate(*embeddings))
+                with_array(concatenate(*embeddings)),
             ),
             with_array(Maxout(width, nP=3, dropout=0.0, normalize=True)),
-            ragged2list()
+            ragged2list(),
         )
     return model
 
@@ -137,6 +135,4 @@ def MishWindowEncoder(width, window_size, depth):
 def BiLSTMEncoder(width, depth, dropout):
     if depth == 0:
         return noop()
-    return with_padded(
+    return with_padded(PyTorchLSTM(width, width, bi=True, depth=depth, dropout=dropout))
-        PyTorchLSTM(width, width, bi=True, depth=depth, dropout=dropout)
-    )

spacy/ml/staticvectors.py

@@ -15,7 +15,7 @@ def StaticVectors(
     *,
     dropout: Optional[float] = None,
     init_W: Callable = glorot_uniform_init,
-    key_attr: str="ORTH"
+    key_attr: str = "ORTH"
 ) -> Model[List[Doc], Ragged]:
     """Embed Doc objects with their vocab's vectors table, applying a learned
     linear projection to control the dimensionality. If a dropout rate is
@@ -45,7 +45,7 @@ def forward(
     )
     output = Ragged(
         model.ops.gemm(model.ops.as_contig(V[rows]), W, trans2=True),
-        model.ops.asarray([len(doc) for doc in docs], dtype="i")
+        model.ops.asarray([len(doc) for doc in docs], dtype="i"),
     )
     if mask is not None:
         output.data *= mask
@@ -55,11 +55,7 @@ def forward(
             d_output.data *= mask
         model.inc_grad(
             "W",
-            model.ops.gemm(
+            model.ops.gemm(d_output.data, model.ops.as_contig(V[rows]), trans1=True),
-                d_output.data,
-                model.ops.as_contig(V[rows]),
-                trans1=True
-            )
         )
         return []
 

spacy/util.py

@@ -190,10 +190,7 @@ def get_module_path(module: ModuleType) -> Path:
 
 
 def load_vectors_into_model(
-    nlp: "Language",
+    nlp: "Language", name: Union[str, Path], *, add_strings=True
-    name: Union[str, Path],
-    *,
-    add_strings=True
 ) -> None:
     """Load word vectors from an installed model or path into a model instance."""
     vectors_nlp = load_model(name)
@@ -1205,12 +1202,12 @@ class DummyTokenizer:
 
 def link_vectors_to_models(
     vocab: "Vocab",
-    models: List[Model]=[],
+    models: List[Model] = [],
     *,
     vectors_name_attr="vectors_name",
     vectors_attr="vectors",
     key2row_attr="key2row",
-    default_vectors_name="spacy_pretrained_vectors"
+    default_vectors_name="spacy_pretrained_vectors",
 ) -> None:
     """Supply vectors data to models."""
     vectors = vocab.vectors