explosion/spaCy
1
1
Fork 0
mirror of https://github.com/explosion/spaCy.git synced 2024-11-14 05:37:03 +03:00
Code Issues Packages Projects Releases Wiki Activity
82ef6783a8
spaCy/spacy/tests/pipeline/test_tok2vec.py
Daniël de Kok e5ec45cb7e Revert "Merge the parser refactor into v4 (#10940)" 
This reverts commit a183db3cef.
2023-12-08 20:23:08 +01:00

699 lines
24 KiB
Python
Raw Blame History

import pytest
from numpy.testing import assert_array_equal
from thinc.api import Config, get_current_ops
from spacy import util
from spacy.lang.en import English
from spacy.ml.models.tok2vec import (
MaxoutWindowEncoder,
MultiHashEmbed,
build_Tok2Vec_model,
)
from spacy.pipeline.tok2vec import Tok2Vec, Tok2VecListener
from spacy.tokens import Doc
from spacy.training import Example
from spacy.util import registry
from spacy.vocab import Vocab
from ..util import add_vecs_to_vocab, get_batch, make_tempdir
def test_empty_doc():
width = 128
embed_size = 2000
vocab = Vocab()
doc = Doc(vocab, words=[])
tok2vec = build_Tok2Vec_model(
MultiHashEmbed(
width=width,
rows=[embed_size, embed_size, embed_size, embed_size],
include_static_vectors=False,
attrs=["NORM", "PREFIX", "SUFFIX", "SHAPE"],
),
MaxoutWindowEncoder(width=width, depth=4, window_size=1, maxout_pieces=3),
)
tok2vec.initialize()
vectors, backprop = tok2vec.begin_update([doc])
assert len(vectors) == 1
assert vectors[0].shape == (0, width)
@pytest.mark.parametrize(
"batch_size,width,embed_size", [[1, 128, 2000], [2, 128, 2000], [3, 8, 63]]
)
def test_tok2vec_batch_sizes(batch_size, width, embed_size):
batch = get_batch(batch_size)
tok2vec = build_Tok2Vec_model(
MultiHashEmbed(
width=width,
rows=[embed_size] * 4,
include_static_vectors=False,
attrs=["NORM", "PREFIX", "SUFFIX", "SHAPE"],
),
MaxoutWindowEncoder(width=width, depth=4, window_size=1, maxout_pieces=3),
)
tok2vec.initialize()
vectors, backprop = tok2vec.begin_update(batch)
assert len(vectors) == len(batch)
for doc_vec, doc in zip(vectors, batch):
assert doc_vec.shape == (len(doc), width)
@pytest.mark.slow
@pytest.mark.parametrize("width", [8])
@pytest.mark.parametrize(
"embed_arch,embed_config",
# fmt: off
[
("spacy.MultiHashEmbed.v1", {"rows": [100, 100], "attrs": ["SHAPE", "LOWER"], "include_static_vectors": False}),
("spacy.MultiHashEmbed.v1", {"rows": [100, 20], "attrs": ["ORTH", "PREFIX"], "include_static_vectors": False}),
("spacy.CharacterEmbed.v1", {"rows": 100, "nM": 64, "nC": 8, "include_static_vectors": False}),
("spacy.CharacterEmbed.v1", {"rows": 100, "nM": 16, "nC": 2, "include_static_vectors": False}),
],
# fmt: on
)
@pytest.mark.parametrize(
"tok2vec_arch,encode_arch,encode_config",
# fmt: off
[
("spacy.Tok2Vec.v1", "spacy.MaxoutWindowEncoder.v1", {"window_size": 1, "maxout_pieces": 3, "depth": 2}),
("spacy.Tok2Vec.v2", "spacy.MaxoutWindowEncoder.v2", {"window_size": 1, "maxout_pieces": 3, "depth": 2}),
("spacy.Tok2Vec.v1", "spacy.MishWindowEncoder.v1", {"window_size": 1, "depth": 6}),
("spacy.Tok2Vec.v2", "spacy.MishWindowEncoder.v2", {"window_size": 1, "depth": 6}),
],
# fmt: on
)
def test_tok2vec_configs(
width, tok2vec_arch, embed_arch, embed_config, encode_arch, encode_config
):
embed = registry.get("architectures", embed_arch)
encode = registry.get("architectures", encode_arch)
tok2vec_model = registry.get("architectures", tok2vec_arch)
embed_config["width"] = width
encode_config["width"] = width
docs = get_batch(3)
tok2vec = tok2vec_model(embed(**embed_config), encode(**encode_config))
tok2vec.initialize(docs)
vectors, backprop = tok2vec.begin_update(docs)
assert len(vectors) == len(docs)
assert vectors[0].shape == (len(docs[0]), width)
backprop(vectors)
def test_init_tok2vec():
# Simple test to initialize the default tok2vec
nlp = English()
tok2vec = nlp.add_pipe("tok2vec")
assert tok2vec.listeners == []
nlp.initialize()
assert tok2vec.model.get_dim("nO")
cfg_string = """
[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.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
"""
TRAIN_DATA = [
(
"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}},
),
]
@pytest.mark.parametrize("with_vectors", (False, True))
def test_tok2vec_listener(with_vectors):
orig_config = Config().from_str(cfg_string)
orig_config["components"]["tok2vec"]["model"]["embed"][
"include_static_vectors"
] = with_vectors
nlp = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
if with_vectors:
ops = get_current_ops()
vectors = [
("apple", ops.asarray([1, 2, 3])),
("orange", ops.asarray([-1, -2, -3])),
("and", ops.asarray([-1, -1, -1])),
("juice", ops.asarray([5, 5, 10])),
("pie", ops.asarray([7, 6.3, 8.9])),
]
add_vecs_to_vocab(nlp.vocab, vectors)
assert nlp.pipe_names == ["tok2vec", "tagger"]
tagger = nlp.get_pipe("tagger")
tok2vec = nlp.get_pipe("tok2vec")
tagger_tok2vec = tagger.model.get_ref("tok2vec")
assert isinstance(tok2vec, Tok2Vec)
assert isinstance(tagger_tok2vec, Tok2VecListener)
train_examples = []
for t in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
for tag in t[1]["tags"]:
tagger.add_label(tag)
# Check that the Tok2Vec component finds its listeners
optimizer = nlp.initialize(lambda: train_examples)
assert tok2vec.listeners == [tagger_tok2vec]
for i in range(5):
losses = {}
nlp.update(train_examples, sgd=optimizer, losses=losses)
doc = nlp("Running the pipeline as a whole.")
doc_tensor = tagger_tok2vec.predict([doc])[0]
ops = get_current_ops()
assert_array_equal(ops.to_numpy(doc.tensor), ops.to_numpy(doc_tensor))
# test with empty doc
doc = nlp("")
# TODO: should this warn or error?
nlp.select_pipes(disable="tok2vec")
assert nlp.pipe_names == ["tagger"]
nlp("Running the pipeline with the Tok2Vec component disabled.")
def test_tok2vec_listener_callback():
orig_config = Config().from_str(cfg_string)
nlp = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
assert nlp.pipe_names == ["tok2vec", "tagger"]
tagger = nlp.get_pipe("tagger")
tok2vec = nlp.get_pipe("tok2vec")
docs = [nlp.make_doc("A random sentence")]
tok2vec.model.initialize(X=docs)
gold_array = [[1.0 for tag in ["V", "Z"]] for word in docs]
label_sample = [tagger.model.ops.asarray(gold_array, dtype="float32")]
tagger.model.initialize(X=docs, Y=label_sample)
docs = [nlp.make_doc("Another entirely random sentence")]
tok2vec.update([Example.from_dict(x, {}) for x in docs])
Y, get_dX = tagger.model.begin_update(docs)
# assure that the backprop call works (and doesn't hit a 'None' callback)
assert get_dX(Y) is not None
def test_tok2vec_listener_overfitting():
"""Test that a pipeline with a listener properly overfits, even if 'tok2vec' is in the annotating components"""
orig_config = Config().from_str(cfg_string)
nlp = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
train_examples = []
for t in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
optimizer = nlp.initialize(get_examples=lambda: train_examples)
for i in range(50):
losses = {}
nlp.update(train_examples, sgd=optimizer, losses=losses, annotates=["tok2vec"])
assert losses["tagger"] < 0.00001
# test the trained model
test_text = "I like blue eggs"
doc = nlp(test_text)
assert doc[0].tag_ == "N"
assert doc[1].tag_ == "V"
assert doc[2].tag_ == "J"
assert doc[3].tag_ == "N"
# Also test the results are still the same after IO
with make_tempdir() as tmp_dir:
nlp.to_disk(tmp_dir)
nlp2 = util.load_model_from_path(tmp_dir)
doc2 = nlp2(test_text)
assert doc2[0].tag_ == "N"
assert doc2[1].tag_ == "V"
assert doc2[2].tag_ == "J"
assert doc2[3].tag_ == "N"
def test_tok2vec_frozen_not_annotating():
"""Test that a pipeline with a frozen tok2vec raises an error when the tok2vec is not annotating"""
orig_config = Config().from_str(cfg_string)
nlp = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
train_examples = []
for t in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
optimizer = nlp.initialize(get_examples=lambda: train_examples)
for i in range(2):
losses = {}
with pytest.raises(
ValueError, match=r"the tok2vec embedding layer is not updated"
):
nlp.update(
train_examples, sgd=optimizer, losses=losses, exclude=["tok2vec"]
)
def test_tok2vec_frozen_overfitting():
"""Test that a pipeline with a frozen & annotating tok2vec can still overfit"""
orig_config = Config().from_str(cfg_string)
nlp = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
train_examples = []
for t in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
optimizer = nlp.initialize(get_examples=lambda: train_examples)
for i in range(100):
losses = {}
nlp.update(
train_examples,
sgd=optimizer,
losses=losses,
exclude=["tok2vec"],
annotates=["tok2vec"],
)
assert losses["tagger"] < 0.0001
# test the trained model
test_text = "I like blue eggs"
doc = nlp(test_text)
assert doc[0].tag_ == "N"
assert doc[1].tag_ == "V"
assert doc[2].tag_ == "J"
assert doc[3].tag_ == "N"
# Also test the results are still the same after IO
with make_tempdir() as tmp_dir:
nlp.to_disk(tmp_dir)
nlp2 = util.load_model_from_path(tmp_dir)
doc2 = nlp2(test_text)
assert doc2[0].tag_ == "N"
assert doc2[1].tag_ == "V"
assert doc2[2].tag_ == "J"
assert doc2[3].tag_ == "N"
def test_replace_listeners():
orig_config = Config().from_str(cfg_string)
nlp = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
examples = [Example.from_dict(nlp.make_doc("x y"), {"tags": ["V", "Z"]})]
nlp.initialize(lambda: examples)
tok2vec = nlp.get_pipe("tok2vec")
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 (
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"]
assert t2v_cfg["@architectures"] == "spacy.Tok2Vec.v2"
assert nlp.config["components"]["tagger"]["model"]["tok2vec"] == t2v_cfg
with pytest.raises(ValueError):
nlp.replace_listeners("invalid", "tagger", ["model.tok2vec"])
with pytest.raises(ValueError):
nlp.replace_listeners("tok2vec", "parser", ["model.tok2vec"])
with pytest.raises(ValueError):
nlp.replace_listeners("tok2vec", "tagger", ["model.yolo"])
with pytest.raises(ValueError):
nlp.replace_listeners("tok2vec", "tagger", ["model.tok2vec", "model.yolo"])
# attempt training with the new pipeline
optimizer = nlp.initialize(lambda: examples)
for i in range(2):
losses = {}
nlp.update(examples, sgd=optimizer, losses=losses)
assert losses["tok2vec"] == 0.0
assert losses["tagger"] > 0.0
cfg_string_multi = """
[nlp]
lang = "en"
pipeline = ["tok2vec","tagger", "ner"]
[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.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", "tagger2", "ner3", "tagger4"],
},
"components": {
"tok2vec": {"source": base_model},
"tagger2": {
"source": base_model,
"component": "tagger",
"replace_listeners": ["model.tok2vec"],
},
"ner3": {
"source": base_model,
"component": "ner",
},
"tagger4": {
"source": base_model,
"component": "tagger",
},
},
}
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("tagger2")
ner = new_nlp.get_pipe("ner3")
assert "ner" not in new_nlp.pipe_names
assert "tagger" not in new_nlp.pipe_names
assert tok2vec.listening_components == ["ner3", "tagger4"]
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"]["tagger2"]["model"]["tok2vec"] == t2v_cfg
assert (
new_nlp.config["components"]["ner3"]["model"]["tok2vec"]["@architectures"]
== "spacy.Tok2VecListener.v1"
)
assert (
new_nlp.config["components"]["tagger4"]["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.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.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"]
cfg_string_distillation = """
[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
)
student_nlp = util.load_model_from_config(
orig_config, auto_fill=True, validate=True
)
train_examples_teacher = []
train_examples_student = []
for t in TRAIN_DATA:
train_examples_teacher.append(
Example.from_dict(teacher_nlp.make_doc(t[0]), t[1])
)
train_examples_student.append(
Example.from_dict(student_nlp.make_doc(t[0]), t[1])
)
optimizer = teacher_nlp.initialize(lambda: train_examples_teacher)
student_nlp.initialize(lambda: train_examples_student)
# Since Language.distill creates a copy of the examples to use as
# its internal teacher/student 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,
examples,
**kwargs,
):
assert all(not eg.reference.tensor.any() for eg in examples)
out = self._old_distill(teacher_pipe, examples, **kwargs)
assert all(eg.reference.tensor.any() for eg in examples)
return out
student_tok2vec.distill = tok2vec_distill_wrapper.__get__(student_tok2vec, Tok2Vec)
student_nlp.distill(teacher_nlp, train_examples_student, sgd=optimizer, losses={})
def test_tok2vec_listener_source_link_name():
"""The component's internal name and the tok2vec listener map correspond
to the most recently modified pipeline.
"""
orig_config = Config().from_str(cfg_string_multi)
nlp1 = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
assert nlp1.get_pipe("tok2vec").listening_components == ["tagger", "ner"]
nlp2 = English()
nlp2.add_pipe("tok2vec", source=nlp1)
nlp2.add_pipe("tagger", name="tagger2", source=nlp1)
# there is no way to have the component have the right name for both
# pipelines, right now the most recently modified pipeline is prioritized
assert nlp1.get_pipe("tagger").name == nlp2.get_pipe("tagger2").name == "tagger2"
# there is no way to have the tok2vec have the right listener map for both
# pipelines, right now the most recently modified pipeline is prioritized
assert nlp2.get_pipe("tok2vec").listening_components == ["tagger2"]
nlp2.add_pipe("ner", name="ner3", source=nlp1)
assert nlp2.get_pipe("tok2vec").listening_components == ["tagger2", "ner3"]
nlp2.remove_pipe("ner3")
assert nlp2.get_pipe("tok2vec").listening_components == ["tagger2"]
nlp2.remove_pipe("tagger2")
assert nlp2.get_pipe("tok2vec").listening_components == []
# at this point the tok2vec component corresponds to nlp2
assert nlp1.get_pipe("tok2vec").listening_components == []
# modifying the nlp1 pipeline syncs the tok2vec listener map back to nlp1
nlp1.add_pipe("sentencizer")
assert nlp1.get_pipe("tok2vec").listening_components == ["tagger", "ner"]
# modifying nlp2 syncs it back to nlp2
nlp2.add_pipe("sentencizer")
assert nlp1.get_pipe("tok2vec").listening_components == []
def test_tok2vec_listener_source_replace_listeners():
orig_config = Config().from_str(cfg_string_multi)
nlp1 = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
assert nlp1.get_pipe("tok2vec").listening_components == ["tagger", "ner"]
nlp1.replace_listeners("tok2vec", "tagger", ["model.tok2vec"])
assert nlp1.get_pipe("tok2vec").listening_components == ["ner"]
nlp2 = English()
nlp2.add_pipe("tok2vec", source=nlp1)
assert nlp2.get_pipe("tok2vec").listening_components == []
nlp2.add_pipe("tagger", source=nlp1)
assert nlp2.get_pipe("tok2vec").listening_components == []
nlp2.add_pipe("ner", name="ner2", source=nlp1)
assert nlp2.get_pipe("tok2vec").listening_components == ["ner2"]
Reference in New Issue View Git Blame Copy Permalink