Fix empty input into StaticVectors layer (#6211)

* Add test for empty doc(s)

* Fix empty check in staticvectors

* Remove xfail

* Update spacy/ml/staticvectors.py

spacy/ml/staticvectors.py

@@ -34,7 +34,7 @@ def StaticVectors(
 def forward(
     model: Model[List[Doc], Ragged], docs: List[Doc], is_train: bool
 ) -> Tuple[Ragged, Callable]:
-    if not len(docs):
+    if not sum(len(doc) for doc in docs):
         return _handle_empty(model.ops, model.get_dim("nO"))
     key_attr = model.attrs["key_attr"]
     W = cast(Floats2d, model.ops.as_contig(model.get_param("W")))

spacy/tests/test_models.py

@@ -7,6 +7,7 @@ import numpy
 
 from spacy.ml.models import build_Tok2Vec_model, MultiHashEmbed, MaxoutWindowEncoder
 from spacy.ml.models import build_text_classifier, build_simple_cnn_text_classifier
+from spacy.ml.staticvectors import StaticVectors
 from spacy.lang.en import English
 from spacy.lang.en.examples import sentences as EN_SENTENCES
 
@@ -185,3 +186,22 @@ def test_models_update_consistently(seed, dropout, model_func, kwargs, get_X):
     model1 = get_updated_model()
     model2 = get_updated_model()
     assert_array_equal(get_all_params(model1), get_all_params(model2))
+
+
+@pytest.mark.parametrize(
+    "model_func,kwargs",
+    [
+        (StaticVectors, {"nO": 128, "nM": 300}),
+    ]
+)
+def test_empty_docs(model_func, kwargs):
+    nlp = English()
+    model = model_func(**kwargs).initialize()
+    # Test the layer can be called successfully with 0, 1 and 2 empty docs.
+    for n_docs in range(3):
+        docs = [nlp("") for _ in range(n_docs)]
+        # Test predict
+        _ = model.predict(docs)
+        # Test backprop
+        output, backprop = model.begin_update(docs)
+        _ = backprop(output)