spaCy/spacy/tests/serialize/test_serialize_pipeline.py
Raphael Mitsch aea16719be
Simplify and clarify enable/disable behavior of spacy.load() (#11459)
* Change enable/disable behavior so that arguments take precedence over config options. Extend error message on conflict. Add warning message in case of overwriting config option with arguments.

* Fix tests in test_serialize_pipeline.py to reflect changes to handling of enable/disable.

* Fix type issue.

* Move comment.

* Move comment.

* Issue UserWarning instead of printing wasabi message. Adjust test.

* Added pytest.warns(UserWarning) for expected warning to fix tests.

* Update warning message.

* Move type handling out of fetch_pipes_status().

* Add global variable for default value. Use id() to determine whether used values are default value.

* Fix default value for disable.

* Rename DEFAULT_PIPE_STATUS to _DEFAULT_EMPTY_PIPES.
2022-09-27 14:22:36 +02:00

473 lines
16 KiB
Python

import pickle
import pytest
import srsly
from thinc.api import Linear
import spacy
from spacy import Vocab, load, registry
from spacy.lang.en import English
from spacy.language import Language
from spacy.pipeline import DependencyParser, EntityRecognizer, EntityRuler
from spacy.pipeline import SentenceRecognizer, Tagger, TextCategorizer
from spacy.pipeline import TrainablePipe
from spacy.pipeline.dep_parser import DEFAULT_PARSER_MODEL
from spacy.pipeline.senter import DEFAULT_SENTER_MODEL
from spacy.pipeline.tagger import DEFAULT_TAGGER_MODEL
from spacy.pipeline.textcat import DEFAULT_SINGLE_TEXTCAT_MODEL
from spacy.util import ensure_path, load_model
from spacy.tokens import Span
from ..util import make_tempdir
test_parsers = [DependencyParser, EntityRecognizer]
@pytest.fixture
def parser(en_vocab):
config = {
"learn_tokens": False,
"min_action_freq": 30,
"update_with_oracle_cut_size": 100,
"beam_width": 1,
"beam_update_prob": 1.0,
"beam_density": 0.0,
}
cfg = {"model": DEFAULT_PARSER_MODEL}
model = registry.resolve(cfg, validate=True)["model"]
parser = DependencyParser(en_vocab, model, **config)
parser.add_label("nsubj")
return parser
@pytest.fixture
def blank_parser(en_vocab):
config = {
"learn_tokens": False,
"min_action_freq": 30,
"update_with_oracle_cut_size": 100,
"beam_width": 1,
"beam_update_prob": 1.0,
"beam_density": 0.0,
}
cfg = {"model": DEFAULT_PARSER_MODEL}
model = registry.resolve(cfg, validate=True)["model"]
parser = DependencyParser(en_vocab, model, **config)
return parser
@pytest.fixture
def taggers(en_vocab):
cfg = {"model": DEFAULT_TAGGER_MODEL}
model = registry.resolve(cfg, validate=True)["model"]
tagger1 = Tagger(en_vocab, model)
tagger2 = Tagger(en_vocab, model)
return tagger1, tagger2
@pytest.mark.issue(3456)
def test_issue3456():
# this crashed because of a padding error in layer.ops.unflatten in thinc
nlp = English()
tagger = nlp.add_pipe("tagger")
tagger.add_label("A")
nlp.initialize()
list(nlp.pipe(["hi", ""]))
@pytest.mark.issue(3526)
def test_issue_3526_1(en_vocab):
patterns = [
{"label": "HELLO", "pattern": "hello world"},
{"label": "BYE", "pattern": [{"LOWER": "bye"}, {"LOWER": "bye"}]},
{"label": "HELLO", "pattern": [{"ORTH": "HELLO"}]},
{"label": "COMPLEX", "pattern": [{"ORTH": "foo", "OP": "*"}]},
{"label": "TECH_ORG", "pattern": "Apple", "id": "a1"},
]
nlp = Language(vocab=en_vocab)
ruler = EntityRuler(nlp, patterns=patterns, overwrite_ents=True)
ruler_bytes = ruler.to_bytes()
assert len(ruler) == len(patterns)
assert len(ruler.labels) == 4
assert ruler.overwrite
new_ruler = EntityRuler(nlp)
new_ruler = new_ruler.from_bytes(ruler_bytes)
assert len(new_ruler) == len(ruler)
assert len(new_ruler.labels) == 4
assert new_ruler.overwrite == ruler.overwrite
assert new_ruler.ent_id_sep == ruler.ent_id_sep
@pytest.mark.issue(3526)
def test_issue_3526_2(en_vocab):
patterns = [
{"label": "HELLO", "pattern": "hello world"},
{"label": "BYE", "pattern": [{"LOWER": "bye"}, {"LOWER": "bye"}]},
{"label": "HELLO", "pattern": [{"ORTH": "HELLO"}]},
{"label": "COMPLEX", "pattern": [{"ORTH": "foo", "OP": "*"}]},
{"label": "TECH_ORG", "pattern": "Apple", "id": "a1"},
]
nlp = Language(vocab=en_vocab)
ruler = EntityRuler(nlp, patterns=patterns, overwrite_ents=True)
bytes_old_style = srsly.msgpack_dumps(ruler.patterns)
new_ruler = EntityRuler(nlp)
new_ruler = new_ruler.from_bytes(bytes_old_style)
assert len(new_ruler) == len(ruler)
for pattern in ruler.patterns:
assert pattern in new_ruler.patterns
assert new_ruler.overwrite is not ruler.overwrite
@pytest.mark.issue(3526)
def test_issue_3526_3(en_vocab):
patterns = [
{"label": "HELLO", "pattern": "hello world"},
{"label": "BYE", "pattern": [{"LOWER": "bye"}, {"LOWER": "bye"}]},
{"label": "HELLO", "pattern": [{"ORTH": "HELLO"}]},
{"label": "COMPLEX", "pattern": [{"ORTH": "foo", "OP": "*"}]},
{"label": "TECH_ORG", "pattern": "Apple", "id": "a1"},
]
nlp = Language(vocab=en_vocab)
ruler = EntityRuler(nlp, patterns=patterns, overwrite_ents=True)
with make_tempdir() as tmpdir:
out_file = tmpdir / "entity_ruler"
srsly.write_jsonl(out_file.with_suffix(".jsonl"), ruler.patterns)
new_ruler = EntityRuler(nlp).from_disk(out_file)
for pattern in ruler.patterns:
assert pattern in new_ruler.patterns
assert len(new_ruler) == len(ruler)
assert new_ruler.overwrite is not ruler.overwrite
@pytest.mark.issue(3526)
def test_issue_3526_4(en_vocab):
nlp = Language(vocab=en_vocab)
patterns = [{"label": "ORG", "pattern": "Apple"}]
config = {"overwrite_ents": True}
ruler = nlp.add_pipe("entity_ruler", config=config)
ruler.add_patterns(patterns)
with make_tempdir() as tmpdir:
nlp.to_disk(tmpdir)
ruler = nlp.get_pipe("entity_ruler")
assert ruler.patterns == [{"label": "ORG", "pattern": "Apple"}]
assert ruler.overwrite is True
nlp2 = load(tmpdir)
new_ruler = nlp2.get_pipe("entity_ruler")
assert new_ruler.patterns == [{"label": "ORG", "pattern": "Apple"}]
assert new_ruler.overwrite is True
@pytest.mark.issue(4042)
def test_issue4042():
"""Test that serialization of an EntityRuler before NER works fine."""
nlp = English()
# add ner pipe
ner = nlp.add_pipe("ner")
ner.add_label("SOME_LABEL")
nlp.initialize()
# Add entity ruler
patterns = [
{"label": "MY_ORG", "pattern": "Apple"},
{"label": "MY_GPE", "pattern": [{"lower": "san"}, {"lower": "francisco"}]},
]
# works fine with "after"
ruler = nlp.add_pipe("entity_ruler", before="ner")
ruler.add_patterns(patterns)
doc1 = nlp("What do you think about Apple ?")
assert doc1.ents[0].label_ == "MY_ORG"
with make_tempdir() as d:
output_dir = ensure_path(d)
if not output_dir.exists():
output_dir.mkdir()
nlp.to_disk(output_dir)
nlp2 = load_model(output_dir)
doc2 = nlp2("What do you think about Apple ?")
assert doc2.ents[0].label_ == "MY_ORG"
@pytest.mark.issue(4042)
def test_issue4042_bug2():
"""
Test that serialization of an NER works fine when new labels were added.
This is the second bug of two bugs underlying the issue 4042.
"""
nlp1 = English()
# add ner pipe
ner1 = nlp1.add_pipe("ner")
ner1.add_label("SOME_LABEL")
nlp1.initialize()
# add a new label to the doc
doc1 = nlp1("What do you think about Apple ?")
assert len(ner1.labels) == 1
assert "SOME_LABEL" in ner1.labels
apple_ent = Span(doc1, 5, 6, label="MY_ORG")
doc1.ents = list(doc1.ents) + [apple_ent]
# Add the label explicitly. Previously we didn't require this.
ner1.add_label("MY_ORG")
ner1(doc1)
assert len(ner1.labels) == 2
assert "SOME_LABEL" in ner1.labels
assert "MY_ORG" in ner1.labels
with make_tempdir() as d:
# assert IO goes fine
output_dir = ensure_path(d)
if not output_dir.exists():
output_dir.mkdir()
ner1.to_disk(output_dir)
config = {}
ner2 = nlp1.create_pipe("ner", config=config)
ner2.from_disk(output_dir)
assert len(ner2.labels) == 2
@pytest.mark.issue(4725)
def test_issue4725_1():
"""Ensure the pickling of the NER goes well"""
vocab = Vocab(vectors_name="test_vocab_add_vector")
nlp = English(vocab=vocab)
config = {
"update_with_oracle_cut_size": 111,
}
ner = nlp.create_pipe("ner", config=config)
with make_tempdir() as tmp_path:
with (tmp_path / "ner.pkl").open("wb") as file_:
pickle.dump(ner, file_)
assert ner.cfg["update_with_oracle_cut_size"] == 111
with (tmp_path / "ner.pkl").open("rb") as file_:
ner2 = pickle.load(file_)
assert ner2.cfg["update_with_oracle_cut_size"] == 111
@pytest.mark.parametrize("Parser", test_parsers)
def test_serialize_parser_roundtrip_bytes(en_vocab, Parser):
cfg = {"model": DEFAULT_PARSER_MODEL}
model = registry.resolve(cfg, validate=True)["model"]
parser = Parser(en_vocab, model)
new_parser = Parser(en_vocab, model)
new_parser = new_parser.from_bytes(parser.to_bytes(exclude=["vocab"]))
bytes_2 = new_parser.to_bytes(exclude=["vocab"])
bytes_3 = parser.to_bytes(exclude=["vocab"])
assert len(bytes_2) == len(bytes_3)
assert bytes_2 == bytes_3
@pytest.mark.parametrize("Parser", test_parsers)
def test_serialize_parser_strings(Parser):
vocab1 = Vocab()
label = "FunnyLabel"
assert label not in vocab1.strings
cfg = {"model": DEFAULT_PARSER_MODEL}
model = registry.resolve(cfg, validate=True)["model"]
parser1 = Parser(vocab1, model)
parser1.add_label(label)
assert label in parser1.vocab.strings
vocab2 = Vocab()
assert label not in vocab2.strings
parser2 = Parser(vocab2, model)
parser2 = parser2.from_bytes(parser1.to_bytes(exclude=["vocab"]))
assert label in parser2.vocab.strings
@pytest.mark.parametrize("Parser", test_parsers)
def test_serialize_parser_roundtrip_disk(en_vocab, Parser):
cfg = {"model": DEFAULT_PARSER_MODEL}
model = registry.resolve(cfg, validate=True)["model"]
parser = Parser(en_vocab, model)
with make_tempdir() as d:
file_path = d / "parser"
parser.to_disk(file_path)
parser_d = Parser(en_vocab, model)
parser_d = parser_d.from_disk(file_path)
parser_bytes = parser.to_bytes(exclude=["model", "vocab"])
parser_d_bytes = parser_d.to_bytes(exclude=["model", "vocab"])
assert len(parser_bytes) == len(parser_d_bytes)
assert parser_bytes == parser_d_bytes
def test_to_from_bytes(parser, blank_parser):
assert parser.model is not True
assert blank_parser.model is not True
assert blank_parser.moves.n_moves != parser.moves.n_moves
bytes_data = parser.to_bytes(exclude=["vocab"])
# the blank parser needs to be resized before we can call from_bytes
blank_parser.model.attrs["resize_output"](blank_parser.model, parser.moves.n_moves)
blank_parser.from_bytes(bytes_data)
assert blank_parser.model is not True
assert blank_parser.moves.n_moves == parser.moves.n_moves
def test_serialize_tagger_roundtrip_bytes(en_vocab, taggers):
tagger1 = taggers[0]
tagger1_b = tagger1.to_bytes()
tagger1 = tagger1.from_bytes(tagger1_b)
assert tagger1.to_bytes() == tagger1_b
cfg = {"model": DEFAULT_TAGGER_MODEL}
model = registry.resolve(cfg, validate=True)["model"]
new_tagger1 = Tagger(en_vocab, model).from_bytes(tagger1_b)
new_tagger1_b = new_tagger1.to_bytes()
assert len(new_tagger1_b) == len(tagger1_b)
assert new_tagger1_b == tagger1_b
def test_serialize_tagger_roundtrip_disk(en_vocab, taggers):
tagger1, tagger2 = taggers
with make_tempdir() as d:
file_path1 = d / "tagger1"
file_path2 = d / "tagger2"
tagger1.to_disk(file_path1)
tagger2.to_disk(file_path2)
cfg = {"model": DEFAULT_TAGGER_MODEL}
model = registry.resolve(cfg, validate=True)["model"]
tagger1_d = Tagger(en_vocab, model).from_disk(file_path1)
tagger2_d = Tagger(en_vocab, model).from_disk(file_path2)
assert tagger1_d.to_bytes() == tagger2_d.to_bytes()
def test_serialize_tagger_strings(en_vocab, de_vocab, taggers):
label = "SomeWeirdLabel"
assert label not in en_vocab.strings
assert label not in de_vocab.strings
tagger = taggers[0]
assert label not in tagger.vocab.strings
with make_tempdir() as d:
# check that custom labels are serialized as part of the component's strings.jsonl
tagger.add_label(label)
assert label in tagger.vocab.strings
file_path = d / "tagger1"
tagger.to_disk(file_path)
# ensure that the custom strings are loaded back in when using the tagger in another pipeline
cfg = {"model": DEFAULT_TAGGER_MODEL}
model = registry.resolve(cfg, validate=True)["model"]
tagger2 = Tagger(de_vocab, model).from_disk(file_path)
assert label in tagger2.vocab.strings
@pytest.mark.issue(1105)
def test_serialize_textcat_empty(en_vocab):
# See issue #1105
cfg = {"model": DEFAULT_SINGLE_TEXTCAT_MODEL}
model = registry.resolve(cfg, validate=True)["model"]
textcat = TextCategorizer(en_vocab, model, threshold=0.5)
textcat.to_bytes(exclude=["vocab"])
@pytest.mark.parametrize("Parser", test_parsers)
def test_serialize_pipe_exclude(en_vocab, Parser):
cfg = {"model": DEFAULT_PARSER_MODEL}
model = registry.resolve(cfg, validate=True)["model"]
def get_new_parser():
new_parser = Parser(en_vocab, model)
return new_parser
parser = Parser(en_vocab, model)
parser.cfg["foo"] = "bar"
new_parser = get_new_parser().from_bytes(parser.to_bytes(exclude=["vocab"]))
assert "foo" in new_parser.cfg
new_parser = get_new_parser().from_bytes(
parser.to_bytes(exclude=["vocab"]), exclude=["cfg"]
)
assert "foo" not in new_parser.cfg
new_parser = get_new_parser().from_bytes(
parser.to_bytes(exclude=["cfg"]), exclude=["vocab"]
)
assert "foo" not in new_parser.cfg
def test_serialize_sentencerecognizer(en_vocab):
cfg = {"model": DEFAULT_SENTER_MODEL}
model = registry.resolve(cfg, validate=True)["model"]
sr = SentenceRecognizer(en_vocab, model)
sr_b = sr.to_bytes()
sr_d = SentenceRecognizer(en_vocab, model).from_bytes(sr_b)
assert sr.to_bytes() == sr_d.to_bytes()
def test_serialize_pipeline_disable_enable():
nlp = English()
nlp.add_pipe("ner")
nlp.add_pipe("tagger")
nlp.disable_pipe("tagger")
assert nlp.config["nlp"]["disabled"] == ["tagger"]
config = nlp.config.copy()
nlp2 = English.from_config(config)
assert nlp2.pipe_names == ["ner"]
assert nlp2.component_names == ["ner", "tagger"]
assert nlp2.disabled == ["tagger"]
assert nlp2.config["nlp"]["disabled"] == ["tagger"]
with make_tempdir() as d:
nlp2.to_disk(d)
nlp3 = spacy.load(d)
assert nlp3.pipe_names == ["ner"]
assert nlp3.component_names == ["ner", "tagger"]
with make_tempdir() as d:
nlp3.to_disk(d)
with pytest.warns(UserWarning):
nlp4 = spacy.load(d, disable=["ner"])
assert nlp4.pipe_names == ["tagger"]
assert nlp4.component_names == ["ner", "tagger"]
assert nlp4.disabled == ["ner"]
with make_tempdir() as d:
nlp.to_disk(d)
nlp5 = spacy.load(d, exclude=["tagger"])
assert nlp5.pipe_names == ["ner"]
assert nlp5.component_names == ["ner"]
assert nlp5.disabled == []
def test_serialize_custom_trainable_pipe():
class BadCustomPipe1(TrainablePipe):
def __init__(self, vocab):
pass
class BadCustomPipe2(TrainablePipe):
def __init__(self, vocab):
self.vocab = vocab
self.model = None
class CustomPipe(TrainablePipe):
def __init__(self, vocab, model):
self.vocab = vocab
self.model = model
pipe = BadCustomPipe1(Vocab())
with pytest.raises(ValueError):
pipe.to_bytes()
with make_tempdir() as d:
with pytest.raises(ValueError):
pipe.to_disk(d)
pipe = BadCustomPipe2(Vocab())
with pytest.raises(ValueError):
pipe.to_bytes()
with make_tempdir() as d:
with pytest.raises(ValueError):
pipe.to_disk(d)
pipe = CustomPipe(Vocab(), Linear())
pipe_bytes = pipe.to_bytes()
new_pipe = CustomPipe(Vocab(), Linear()).from_bytes(pipe_bytes)
assert new_pipe.to_bytes() == pipe_bytes
with make_tempdir() as d:
pipe.to_disk(d)
new_pipe = CustomPipe(Vocab(), Linear()).from_disk(d)
assert new_pipe.to_bytes() == pipe_bytes
def test_load_without_strings():
nlp = spacy.blank("en")
orig_strings_length = len(nlp.vocab.strings)
word = "unlikely_word_" * 20
nlp.vocab.strings.add(word)
assert len(nlp.vocab.strings) == orig_strings_length + 1
with make_tempdir() as d:
nlp.to_disk(d)
# reload with strings
reloaded_nlp = load(d)
assert len(nlp.vocab.strings) == len(reloaded_nlp.vocab.strings)
assert word in reloaded_nlp.vocab.strings
# reload without strings
reloaded_nlp = load(d, exclude=["strings"])
assert orig_strings_length == len(reloaded_nlp.vocab.strings)
assert word not in reloaded_nlp.vocab.strings