spaCy/spacy/tests/serialize/test_serialize_config.py

274 lines
8.9 KiB
Python

import pytest
from thinc.config import Config, ConfigValidationError
import spacy
from spacy.lang.en import English
from spacy.language import Language
from spacy.util import registry, deep_merge_configs, load_model_from_config
from spacy.ml.models import build_Tok2Vec_model, build_tb_parser_model
from ..util import make_tempdir
nlp_config_string = """
[training]
batch_size = 666
[nlp]
lang = "en"
pipeline = ["tok2vec", "tagger"]
[components]
[components.tok2vec]
factory = "tok2vec"
[components.tok2vec.model]
@architectures = "spacy.HashEmbedCNN.v1"
pretrained_vectors = null
width = 342
depth = 4
window_size = 1
embed_size = 2000
maxout_pieces = 3
subword_features = true
dropout = null
[components.tagger]
factory = "tagger"
[components.tagger.model]
@architectures = "spacy.Tagger.v1"
[components.tagger.model.tok2vec]
@architectures = "spacy.Tok2VecTensors.v1"
width = ${components.tok2vec.model:width}
"""
parser_config_string = """
[model]
@architectures = "spacy.TransitionBasedParser.v1"
nr_feature_tokens = 99
hidden_width = 66
maxout_pieces = 2
[model.tok2vec]
@architectures = "spacy.HashEmbedCNN.v1"
pretrained_vectors = null
width = 333
depth = 4
embed_size = 5555
window_size = 1
maxout_pieces = 7
subword_features = false
dropout = null
"""
@registry.architectures.register("my_test_parser")
def my_parser():
tok2vec = build_Tok2Vec_model(
MultiHashEmbed(
width=321,
embed_size=5432,
also_embed_subwords=True,
also_use_static_vectors=False
),
MaxoutWindowEncoder(
width=321,
window_size=3,
maxout_pieces=4,
depth=2
)
)
parser = build_tb_parser_model(
tok2vec=tok2vec, nr_feature_tokens=7, hidden_width=65, maxout_pieces=5
)
return parser
def test_create_nlp_from_config():
config = Config().from_str(nlp_config_string)
with pytest.raises(ConfigValidationError):
nlp, _ = load_model_from_config(config, auto_fill=False)
nlp, resolved = load_model_from_config(config, auto_fill=True)
assert nlp.config["training"]["batch_size"] == 666
assert len(nlp.config["training"]) > 1
assert nlp.pipe_names == ["tok2vec", "tagger"]
assert len(nlp.config["components"]) == 2
assert len(nlp.config["nlp"]["pipeline"]) == 2
nlp.remove_pipe("tagger")
assert len(nlp.config["components"]) == 1
assert len(nlp.config["nlp"]["pipeline"]) == 1
with pytest.raises(ValueError):
bad_cfg = {"yolo": {}}
load_model_from_config(Config(bad_cfg), auto_fill=True)
with pytest.raises(ValueError):
bad_cfg = {"pipeline": {"foo": "bar"}}
load_model_from_config(Config(bad_cfg), auto_fill=True)
def test_create_nlp_from_config_multiple_instances():
"""Test that the nlp object is created correctly for a config with multiple
instances of the same component."""
config = Config().from_str(nlp_config_string)
config["components"] = {
"t2v": config["components"]["tok2vec"],
"tagger1": config["components"]["tagger"],
"tagger2": config["components"]["tagger"],
}
config["nlp"]["pipeline"] = list(config["components"].keys())
nlp, _ = load_model_from_config(config, auto_fill=True)
assert nlp.pipe_names == ["t2v", "tagger1", "tagger2"]
assert nlp.get_pipe_meta("t2v").factory == "tok2vec"
assert nlp.get_pipe_meta("tagger1").factory == "tagger"
assert nlp.get_pipe_meta("tagger2").factory == "tagger"
pipeline_config = nlp.config["components"]
assert len(pipeline_config) == 3
assert list(pipeline_config.keys()) == ["t2v", "tagger1", "tagger2"]
assert nlp.config["nlp"]["pipeline"] == ["t2v", "tagger1", "tagger2"]
def test_serialize_nlp():
""" Create a custom nlp pipeline from config and ensure it serializes it correctly """
nlp_config = Config().from_str(nlp_config_string)
nlp, _ = load_model_from_config(nlp_config, auto_fill=True)
nlp.begin_training()
assert "tok2vec" in nlp.pipe_names
assert "tagger" in nlp.pipe_names
assert "parser" not in nlp.pipe_names
assert nlp.get_pipe("tagger").model.get_ref("tok2vec").get_dim("nO") == 342
with make_tempdir() as d:
nlp.to_disk(d)
nlp2 = spacy.load(d)
assert "tok2vec" in nlp2.pipe_names
assert "tagger" in nlp2.pipe_names
assert "parser" not in nlp2.pipe_names
assert nlp2.get_pipe("tagger").model.get_ref("tok2vec").get_dim("nO") == 342
def test_serialize_custom_nlp():
""" Create a custom nlp pipeline and ensure it serializes it correctly"""
nlp = English()
parser_cfg = dict()
parser_cfg["model"] = {"@architectures": "my_test_parser"}
nlp.add_pipe("parser", config=parser_cfg)
nlp.begin_training()
with make_tempdir() as d:
nlp.to_disk(d)
nlp2 = spacy.load(d)
model = nlp2.get_pipe("parser").model
model.get_ref("tok2vec")
upper = model.get_ref("upper")
# check that we have the correct settings, not the default ones
assert upper.get_dim("nI") == 65
def test_serialize_parser():
""" Create a non-default parser config to check nlp serializes it correctly """
nlp = English()
model_config = Config().from_str(parser_config_string)
parser = nlp.add_pipe("parser", config=model_config)
parser.add_label("nsubj")
nlp.begin_training()
with make_tempdir() as d:
nlp.to_disk(d)
nlp2 = spacy.load(d)
model = nlp2.get_pipe("parser").model
model.get_ref("tok2vec")
upper = model.get_ref("upper")
# check that we have the correct settings, not the default ones
assert upper.get_dim("nI") == 66
def test_deep_merge_configs():
config = {"a": "hello", "b": {"c": "d"}}
defaults = {"a": "world", "b": {"c": "e", "f": "g"}}
merged = deep_merge_configs(config, defaults)
assert len(merged) == 2
assert merged["a"] == "hello"
assert merged["b"] == {"c": "d", "f": "g"}
config = {"a": "hello", "b": {"@test": "x", "foo": 1}}
defaults = {"a": "world", "b": {"@test": "x", "foo": 100, "bar": 2}, "c": 100}
merged = deep_merge_configs(config, defaults)
assert len(merged) == 3
assert merged["a"] == "hello"
assert merged["b"] == {"@test": "x", "foo": 1, "bar": 2}
assert merged["c"] == 100
config = {"a": "hello", "b": {"@test": "x", "foo": 1}, "c": 100}
defaults = {"a": "world", "b": {"@test": "y", "foo": 100, "bar": 2}}
merged = deep_merge_configs(config, defaults)
assert len(merged) == 3
assert merged["a"] == "hello"
assert merged["b"] == {"@test": "x", "foo": 1}
assert merged["c"] == 100
# Test that leaving out the factory just adds to existing
config = {"a": "hello", "b": {"foo": 1}, "c": 100}
defaults = {"a": "world", "b": {"@test": "y", "foo": 100, "bar": 2}}
merged = deep_merge_configs(config, defaults)
assert len(merged) == 3
assert merged["a"] == "hello"
assert merged["b"] == {"@test": "y", "foo": 1, "bar": 2}
assert merged["c"] == 100
def test_config_nlp_roundtrip():
"""Test that a config prduced by the nlp object passes training config
validation."""
nlp = English()
nlp.add_pipe("entity_ruler")
nlp.add_pipe("ner")
new_nlp, new_config = load_model_from_config(nlp.config, auto_fill=False)
assert new_nlp.config == nlp.config
assert new_nlp.pipe_names == nlp.pipe_names
assert new_nlp._pipe_configs == nlp._pipe_configs
assert new_nlp._pipe_meta == nlp._pipe_meta
assert new_nlp._factory_meta == nlp._factory_meta
def test_serialize_config_language_specific():
"""Test that config serialization works as expected with language-specific
factories."""
name = "test_serialize_config_language_specific"
@English.factory(name, default_config={"foo": 20})
def custom_factory(nlp: Language, name: str, foo: int):
return lambda doc: doc
nlp = Language()
assert not nlp.has_factory(name)
nlp = English()
assert nlp.has_factory(name)
nlp.add_pipe(name, config={"foo": 100}, name="bar")
pipe_config = nlp.config["components"]["bar"]
assert pipe_config["foo"] == 100
assert pipe_config["factory"] == name
with make_tempdir() as d:
nlp.to_disk(d)
nlp2 = spacy.load(d)
assert nlp2.has_factory(name)
assert nlp2.pipe_names == ["bar"]
assert nlp2.get_pipe_meta("bar").factory == name
pipe_config = nlp2.config["components"]["bar"]
assert pipe_config["foo"] == 100
assert pipe_config["factory"] == name
config = Config().from_str(nlp2.config.to_str())
config["nlp"]["lang"] = "de"
with pytest.raises(ValueError):
# German doesn't have a factory, only English does
load_model_from_config(config)
def test_serialize_config_missing_pipes():
config = Config().from_str(nlp_config_string)
config["components"].pop("tok2vec")
assert "tok2vec" in config["nlp"]["pipeline"]
assert "tok2vec" not in config["components"]
with pytest.raises(ValueError):
load_model_from_config(config, auto_fill=True)