Update tests

spacy/tests/pipeline/test_tok2vec.py

@@ -6,12 +6,13 @@ from spacy.pipeline.tok2vec import Tok2Vec, Tok2VecListener
 from spacy.vocab import Vocab
 from spacy.tokens import Doc
 from spacy.training import Example
+from spacy.training.initialize import init_nlp
 from spacy import util
 from spacy.lang.en import English
 from thinc.api import Config
 from numpy.testing import assert_equal
 
-from ..util import get_batch
+from ..util import get_batch, make_tempdir
 
 
 def test_empty_doc():
@@ -55,17 +56,17 @@ def test_tok2vec_batch_sizes(batch_size, width, embed_size):
         assert doc_vec.shape == (len(doc), width)
 
 
-# fmt: off
 @pytest.mark.parametrize(
     "width,embed_arch,embed_config,encode_arch,encode_config",
+    # fmt: off
     [
         (8, MultiHashEmbed, {"rows": [100, 100], "attrs": ["SHAPE", "LOWER"], "include_static_vectors": False}, MaxoutWindowEncoder, {"window_size": 1, "maxout_pieces": 3, "depth": 2}),
         (8, MultiHashEmbed, {"rows": [100, 20], "attrs": ["ORTH", "PREFIX"], "include_static_vectors": False}, MishWindowEncoder, {"window_size": 1, "depth": 6}),
         (8, CharacterEmbed, {"rows": 100, "nM": 64, "nC": 8, "include_static_vectors": False}, MaxoutWindowEncoder, {"window_size": 1, "maxout_pieces": 3, "depth": 3}),
         (8, CharacterEmbed, {"rows": 100, "nM": 16, "nC": 2, "include_static_vectors": False}, MishWindowEncoder, {"window_size": 1, "depth": 3}),
     ],
-)
     # fmt: on
+)
 def test_tok2vec_configs(width, embed_arch, embed_config, encode_arch, encode_config):
     embed_config["width"] = width
     encode_config["width"] = width
@@ -196,8 +197,14 @@ def test_replace_listeners():
     tagger = nlp.get_pipe("tagger")
     assert isinstance(tagger.model.layers[0], Tok2VecListener)
     assert tok2vec.listener_map["tagger"][0] == tagger.model.layers[0]
-    assert nlp.config["components"]["tok2vec"]["model"]["@architectures"] == "spacy.Tok2Vec.v2"
+    assert (
-    assert nlp.config["components"]["tagger"]["model"]["tok2vec"]["@architectures"] == "spacy.Tok2VecListener.v1"
+        nlp.config["components"]["tok2vec"]["model"]["@architectures"]
+        == "spacy.Tok2Vec.v2"
+    )
+    assert (
+        nlp.config["components"]["tagger"]["model"]["tok2vec"]["@architectures"]
+        == "spacy.Tok2VecListener.v1"
+    )
     nlp.replace_listeners("tok2vec", "tagger", ["model.tok2vec"])
     assert not isinstance(tagger.model.layers[0], Tok2VecListener)
     t2v_cfg = nlp.config["components"]["tok2vec"]["model"]
@@ -211,3 +218,96 @@ def test_replace_listeners():
         nlp.replace_listeners("tok2vec", "tagger", ["model.yolo"])
     with pytest.raises(ValueError):
         nlp.replace_listeners("tok2vec", "tagger", ["model.tok2vec", "model.yolo"])
+
+
+cfg_string_multi = """
+    [nlp]
+    lang = "en"
+    pipeline = ["tok2vec","tagger", "ner"]
+
+    [components]
+
+    [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.ner]
+    factory = "ner"
+
+    [components.ner.model]
+    @architectures = "spacy.TransitionBasedParser.v2"
+
+    [components.ner.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_replace_listeners_from_config():
+    orig_config = Config().from_str(cfg_string_multi)
+    nlp = util.load_model_from_config(orig_config, auto_fill=True)
+    annots = {"tags": ["V", "Z"], "entities": [(0, 1, "A"), (1, 2, "B")]}
+    examples = [Example.from_dict(nlp.make_doc("x y"), annots)]
+    nlp.initialize(lambda: examples)
+    tok2vec = nlp.get_pipe("tok2vec")
+    tagger = nlp.get_pipe("tagger")
+    ner = nlp.get_pipe("ner")
+    assert tok2vec.listening_components == ["tagger", "ner"]
+    assert any(isinstance(node, Tok2VecListener) for node in ner.model.walk())
+    assert any(isinstance(node, Tok2VecListener) for node in tagger.model.walk())
+    with make_tempdir() as dir_path:
+        nlp.to_disk(dir_path)
+        base_model = str(dir_path)
+        new_config = {
+            "nlp": {"lang": "en", "pipeline": ["tok2vec", "tagger", "ner"]},
+            "components": {
+                "tok2vec": {"source": base_model},
+                "tagger": {
+                    "source": base_model,
+                    "replace_listeners": ["model.tok2vec"],
+                },
+                "ner": {"source": base_model},
+            },
+        }
+        new_nlp = util.load_model_from_config(new_config, auto_fill=True)
+    new_nlp.initialize(lambda: examples)
+    tok2vec = new_nlp.get_pipe("tok2vec")
+    tagger = new_nlp.get_pipe("tagger")
+    ner = new_nlp.get_pipe("ner")
+    assert tok2vec.listening_components == ["ner"]
+    assert any(isinstance(node, Tok2VecListener) for node in ner.model.walk())
+    assert not any(isinstance(node, Tok2VecListener) for node in tagger.model.walk())
+    t2v_cfg = new_nlp.config["components"]["tok2vec"]["model"]
+    assert t2v_cfg["@architectures"] == "spacy.Tok2Vec.v2"
+    assert new_nlp.config["components"]["tagger"]["model"]["tok2vec"] == t2v_cfg
+    assert (
+        new_nlp.config["components"]["ner"]["model"]["tok2vec"]["@architectures"]
+        == "spacy.Tok2VecListener.v1"
+    )