Update test

spacy/tests/pipeline/test_tok2vec.py

@@ -542,8 +542,48 @@ def test_tok2vec_listeners_textcat():
     assert [t.tag_ for t in docs[1]] == ["N", "V", "J", "N"]
 
 
-def test_tok2vec_distill():
+cfg_string_distillation = """
-    orig_config = Config().from_str(cfg_string_multi_textcat)
+    [nlp]
+    lang = "en"
+    pipeline = ["tok2vec","tagger"]
+
+    [components]
+
+    [components.tagger]
+    factory = "tagger"
+
+    [components.tagger.model]
+    @architectures = "spacy.Tagger.v2"
+    nO = null
+
+    [components.tagger.model.tok2vec]
+    @architectures = "spacy.Tok2VecListener.v1"
+    width = ${components.tok2vec.model.encode.width}
+
+    [components.tok2vec]
+    factory = "tok2vec"
+
+    [components.tok2vec.model]
+    @architectures = "spacy.Tok2Vec.v2"
+
+    [components.tok2vec.model.embed]
+    @architectures = "spacy.MultiHashEmbed.v2"
+    width = ${components.tok2vec.model.encode.width}
+    rows = [2000, 1000, 1000, 1000]
+    attrs = ["NORM", "PREFIX", "SUFFIX", "SHAPE"]
+    include_static_vectors = false
+
+    [components.tok2vec.model.encode]
+    @architectures = "spacy.MaxoutWindowEncoder.v2"
+    width = 96
+    depth = 4
+    window_size = 1
+    maxout_pieces = 3
+    """
+
+
+def test_tok2vec_distillation_teacher_annotations():
+    orig_config = Config().from_str(cfg_string_distillation)
     teacher_nlp = util.load_model_from_config(
         orig_config, auto_fill=True, validate=True
     )
@@ -551,10 +591,6 @@ def test_tok2vec_distill():
         orig_config, auto_fill=True, validate=True
     )
 
-    # Remove pipes that don't currently support distillation.
-    teacher_nlp.remove_pipe("textcat_multilabel")
-    student_nlp.remove_pipe("textcat_multilabel")
-
     train_examples_teacher = []
     train_examples_student = []
     for t in TRAIN_DATA:
@@ -571,39 +607,25 @@ def test_tok2vec_distill():
         teacher_nlp.update(train_examples_teacher, sgd=optimizer, losses=losses)
 
     student_nlp.initialize(lambda: train_examples_student)
-    student_tagger = student_nlp.get_pipe("tagger")
 
-    tagger_tok2vec = student_tagger.model.get_ref("tok2vec")
+    # Since Language.distill creates a copy of the examples to use as
-    tagger_tok2vec_forward = tagger_tok2vec._func
+    # its internal teacher/student docs, we'll need to monkey-patch the
-
+    # tok2vec pipe's distill method.
-    def mock_listener_forward(model: Tok2VecListener, inputs, is_train: bool):
-        model.attrs["last_input"] = inputs
-        return tagger_tok2vec_forward(model, inputs, is_train)
-
-    tagger_tok2vec._func = mock_listener_forward
-
-    # Since Language.distill creates a copy of the student docs to use as
-    # its internal teacher docs, we'll need to monkey-patch the tok2vec pipe's
-    # distill method.
     student_tok2vec = student_nlp.get_pipe("tok2vec")
     student_tok2vec._old_distill = student_tok2vec.distill
 
     def tok2vec_distill_wrapper(
         self,
         teacher_pipe,
-        teacher_docs,
+        examples,
-        student_docs,
         **kwargs,
     ):
-        assert all(not doc.tensor.any() for doc in teacher_docs)
+        assert all(not eg.reference.tensor.any() for eg in examples)
-        out = self._old_distill(teacher_pipe, teacher_docs, student_docs, **kwargs)
+        out = self._old_distill(teacher_pipe, examples, **kwargs)
-        assert all(doc.tensor.any() for doc in teacher_docs)
+        assert all(eg.reference.tensor.any() for eg in examples)
         return out
 
     student_tok2vec.distill = tok2vec_distill_wrapper.__get__(student_tok2vec, Tok2Vec)
-
-    student_docs = [eg.predicted for eg in train_examples_student]
     student_nlp.distill(
-        teacher_nlp, student_docs, sgd=optimizer, losses=losses, pipe_map={}
+        teacher_nlp, train_examples_student, sgd=optimizer, losses=losses
     )
-    assert tagger_tok2vec.attrs["last_input"] == student_docs