Handle Docs with no entities in EntityLinker (#11640)

* Handle docs with no entities

If a whole batch contains no entities it won't make it to the model, but
it's possible for individual Docs to have no entities. Before this
commit, those Docs would cause an error when attempting to concatenate
arrays because the dimensions didn't match.

It turns out the process of preparing the Ragged at the end of the span
maker forward was a little different from list2ragged, which just uses
the flatten function directly. Letting list2ragged do the conversion
avoids the dimension issue.

This did not come up before because in NEL demo projects it's typical
for data with no entities to be discarded before it reaches the NEL
component.

This includes a simple direct test that shows the issue and checks it's
resolved. It doesn't check if there are any downstream changes, so a
more complete test could be added. A full run was tested by adding an
example with no entities to the Emerson sample project.

* Add a blank instance to default training data in tests

Rather than adding a specific test, since not failing on instances with
no entities is basic functionality, it makes sense to add it to the
default set.

* Fix without modifying architecture

If the architecture is modified this would have to be a new version, but
this change isn't big enough to merit that.

spacy/ml/models/entity_linker.py

@@ -71,11 +71,10 @@ def span_maker_forward(model, docs: List[Doc], is_train) -> Tuple[Ragged, Callab
             cands.append((start_token, end_token))
 
         candidates.append(ops.asarray2i(cands))
-    candlens = ops.asarray1i([len(cands) for cands in candidates])
-    candidates = ops.xp.concatenate(candidates)
-    outputs = Ragged(candidates, candlens)
+    lengths = model.ops.asarray1i([len(cands) for cands in candidates])
+    out = Ragged(model.ops.flatten(candidates), lengths)
     # because this is just rearranging docs, the backprop does nothing
-    return outputs, lambda x: []
+    return out, lambda x: []
 
 
 @registry.misc("spacy.KBFromFile.v1")

spacy/tests/pipeline/test_entity_linker.py

@@ -9,6 +9,7 @@ from spacy.compat import pickle
 from spacy.kb import Candidate, InMemoryLookupKB, get_candidates, KnowledgeBase
 from spacy.lang.en import English
 from spacy.ml import load_kb
+from spacy.ml.models.entity_linker import build_span_maker
 from spacy.pipeline import EntityLinker
 from spacy.pipeline.legacy import EntityLinker_v1
 from spacy.pipeline.tok2vec import DEFAULT_TOK2VEC_MODEL
@@ -715,7 +716,11 @@ TRAIN_DATA = [
     ("Russ Cochran was a member of University of Kentucky's golf team.",
         {"links": {(0, 12): {"Q7381115": 0.0, "Q2146908": 1.0}},
          "entities": [(0, 12, "PERSON"), (43, 51, "LOC")],
-         "sent_starts": [1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]})
+         "sent_starts": [1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}),
+    # having a blank instance shouldn't break things
+    ("The weather is nice today.",
+        {"links": {}, "entities": [],
+         "sent_starts": [1, -1, 0, 0, 0, 0]})
 ]
 GOLD_entities = ["Q2146908", "Q7381115", "Q7381115", "Q2146908"]
 # fmt: on
@@ -1196,3 +1201,18 @@ def test_threshold(meet_threshold: bool, config: Dict[str, Any]):
 
     assert len(doc.ents) == 1
     assert doc.ents[0].kb_id_ == entity_id if meet_threshold else EntityLinker.NIL
+
+
+def test_span_maker_forward_with_empty():
+    """The forward pass of the span maker may have a doc with no entities."""
+    nlp = English()
+    doc1 = nlp("a b c")
+    ent = doc1[0:1]
+    ent.label_ = "X"
+    doc1.ents = [ent]
+    # no entities
+    doc2 = nlp("x y z")
+
+    # just to get a model
+    span_maker = build_span_maker()
+    span_maker([doc1, doc2], False)