Ensemble textcat with listener (#8012)

* add unit test for two listeners, with a textcat ensemble in the middle

* return zero gradients instead of None in accumulate_gradient

spacy/pipeline/tok2vec.py

@@ -173,6 +173,7 @@ class Tok2Vec(TrainablePipe):
             for i in range(len(one_d_tokvecs)):
                 d_tokvecs[i] += one_d_tokvecs[i]
                 losses[self.name] += float((one_d_tokvecs[i] ** 2).sum())
+            return [self.model.ops.alloc2f(*t2v.shape) for t2v in tokvecs]
 
         def backprop(one_d_tokvecs):
             """Callback to actually do the backprop. Passed to last listener."""

spacy/tests/pipeline/test_tok2vec.py

@@ -129,8 +129,8 @@ cfg_string = """
     """
 
 TRAIN_DATA = [
-    ("I like green eggs", {"tags": ["N", "V", "J", "N"]}),
-    ("Eat blue ham", {"tags": ["V", "J", "N"]}),
+    ("I like green eggs", {"tags": ["N", "V", "J", "N"], "cats": {"preference": 1.0, "imperative": 0.0}}),
+    ("Eat blue ham", {"tags": ["V", "J", "N"], "cats": {"preference": 0.0, "imperative": 1.0}}),
 ]
 
 
@@ -318,3 +318,92 @@ def test_replace_listeners_from_config():
         new_nlp.config["components"]["ner"]["model"]["tok2vec"]["@architectures"]
         == "spacy.Tok2VecListener.v1"
     )
+
+
+cfg_string_multi_textcat = """
+    [nlp]
+    lang = "en"
+    pipeline = ["tok2vec","textcat_multilabel","tagger"]
+
+    [components]
+
+    [components.textcat_multilabel]
+    factory = "textcat_multilabel"
+
+    [components.textcat_multilabel.model]
+    @architectures = "spacy.TextCatEnsemble.v2"
+    nO = null
+
+    [components.textcat_multilabel.model.tok2vec]
+    @architectures = "spacy.Tok2VecListener.v1"
+    width = ${components.tok2vec.model.encode.width}
+
+    [components.textcat_multilabel.model.linear_model]
+    @architectures = "spacy.TextCatBOW.v1"
+    exclusive_classes = false
+    ngram_size = 1
+    no_output_layer = false
+
+    [components.tagger]
+    factory = "tagger"
+
+    [components.tagger.model]
+    @architectures = "spacy.Tagger.v1"
+    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.v1"
+    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_listeners_textcat():
+    orig_config = Config().from_str(cfg_string_multi_textcat)
+    nlp = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
+    assert nlp.pipe_names == ["tok2vec", "textcat_multilabel", "tagger"]
+    tagger = nlp.get_pipe("tagger")
+    textcat = nlp.get_pipe("textcat_multilabel")
+    tok2vec = nlp.get_pipe("tok2vec")
+    tagger_tok2vec = tagger.model.get_ref("tok2vec")
+    textcat_tok2vec = textcat.model.get_ref("tok2vec")
+    assert isinstance(tok2vec, Tok2Vec)
+    assert isinstance(tagger_tok2vec, Tok2VecListener)
+    assert isinstance(textcat_tok2vec, Tok2VecListener)
+    train_examples = []
+    for t in TRAIN_DATA:
+        train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
+
+    optimizer = nlp.initialize(lambda: train_examples)
+    for i in range(50):
+        losses = {}
+        nlp.update(train_examples, sgd=optimizer, losses=losses)
+
+    docs = list(nlp.pipe(["Eat blue ham", "I like green eggs"]))
+    cats0 = docs[0].cats
+    assert cats0["preference"] < 0.1
+    assert cats0["imperative"] > 0.9
+    cats1 = docs[1].cats
+    assert cats1["preference"] > 0.1
+    assert cats1["imperative"] < 0.9
+    assert([t.tag_ for t in docs[0]] == ["V", "J", "N"])
+    assert([t.tag_ for t in docs[1]] == ["N", "V", "J", "N"])