spaCy/spacy/tests/test_tok2vec.py
Adriane Boyd f3db3f6fe0
Add vectors option to CharacterEmbed (#6069)
* Add vectors option to CharacterEmbed

* Update spacy/pipeline/morphologizer.pyx

* Adjust default morphologizer config

Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
2020-09-16 17:45:04 +02:00

172 lines
5.4 KiB
Python

import pytest
from spacy.ml.models.tok2vec import build_Tok2Vec_model
from spacy.ml.models.tok2vec import MultiHashEmbed, CharacterEmbed
from spacy.ml.models.tok2vec import MishWindowEncoder, MaxoutWindowEncoder
from spacy.pipeline.tok2vec import Tok2Vec, Tok2VecListener
from spacy.vocab import Vocab
from spacy.tokens import Doc
from spacy.training import Example
from spacy import util
from spacy.lang.en import English
from .util import get_batch
from thinc.api import Config
from numpy.testing import assert_equal
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,
also_use_static_vectors=False,
also_embed_subwords=True,
),
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,
also_use_static_vectors=False,
also_embed_subwords=True,
),
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)
# fmt: off
@pytest.mark.parametrize(
"width,embed_arch,embed_config,encode_arch,encode_config",
[
(8, MultiHashEmbed, {"rows": 100, "also_embed_subwords": True, "also_use_static_vectors": False}, MaxoutWindowEncoder, {"window_size": 1, "maxout_pieces": 3, "depth": 2}),
(8, MultiHashEmbed, {"rows": 100, "also_embed_subwords": True, "also_use_static_vectors": False}, MishWindowEncoder, {"window_size": 1, "depth": 6}),
(8, CharacterEmbed, {"rows": 100, "nM": 64, "nC": 8, "also_use_static_vectors": False}, MaxoutWindowEncoder, {"window_size": 1, "maxout_pieces": 3, "depth": 3}),
(8, CharacterEmbed, {"rows": 100, "nM": 16, "nC": 2, "also_use_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
docs = get_batch(3)
tok2vec = build_Tok2Vec_model(
embed_arch(**embed_config),
encode_arch(**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.begin_training()
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.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.v1"
[components.tok2vec.model.embed]
@architectures = "spacy.MultiHashEmbed.v1"
width = ${components.tok2vec.model.encode.width}
rows = 2000
also_embed_subwords = true
also_use_static_vectors = false
[components.tok2vec.model.encode]
@architectures = "spacy.MaxoutWindowEncoder.v1"
width = 96
depth = 4
window_size = 1
maxout_pieces = 3
"""
TRAIN_DATA = [
("I like green eggs", {"tags": ["N", "V", "J", "N"]}),
("Eat blue ham", {"tags": ["V", "J", "N"]}),
]
def test_tok2vec_listener():
orig_config = Config().from_str(cfg_string)
nlp, config = 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")
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 it listeners
assert tok2vec.listeners == []
optimizer = nlp.begin_training(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]
assert_equal(doc.tensor, doc_tensor)
# 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.")