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https://github.com/explosion/spaCy.git
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502 lines
15 KiB
Python
502 lines
15 KiB
Python
import pytest
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from thinc.api import Config, ConfigValidationError
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import spacy
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from spacy.lang.en import English
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from spacy.lang.de import German
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from spacy.language import Language, DEFAULT_CONFIG, DEFAULT_CONFIG_PRETRAIN_PATH
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from spacy.util import (
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registry,
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load_model_from_config,
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load_config,
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load_config_from_str,
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)
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from spacy.ml.models import build_Tok2Vec_model, build_tb_parser_model
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from spacy.ml.models import MultiHashEmbed, MaxoutWindowEncoder
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from spacy.schemas import ConfigSchema, ConfigSchemaPretrain
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from catalogue import RegistryError
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from ..util import make_tempdir
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nlp_config_string = """
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[paths]
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train = null
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dev = null
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[corpora]
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[corpora.train]
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@readers = "spacy.Corpus.v1"
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path = ${paths.train}
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[corpora.dev]
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@readers = "spacy.Corpus.v1"
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path = ${paths.dev}
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[training]
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[training.batcher]
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@batchers = "spacy.batch_by_words.v1"
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size = 666
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[nlp]
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lang = "en"
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pipeline = ["tok2vec", "tagger"]
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[components]
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[components.tok2vec]
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factory = "tok2vec"
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[components.tok2vec.model]
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@architectures = "spacy.HashEmbedCNN.v1"
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pretrained_vectors = null
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width = 342
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depth = 4
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window_size = 1
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embed_size = 2000
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maxout_pieces = 3
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subword_features = true
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[components.tagger]
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factory = "tagger"
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[components.tagger.model]
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@architectures = "spacy.Tagger.v1"
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[components.tagger.model.tok2vec]
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@architectures = "spacy.Tok2VecListener.v1"
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width = ${components.tok2vec.model.width}
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"""
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pretrain_config_string = """
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[paths]
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train = null
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dev = null
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[corpora]
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[corpora.train]
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@readers = "spacy.Corpus.v1"
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path = ${paths.train}
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[corpora.dev]
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@readers = "spacy.Corpus.v1"
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path = ${paths.dev}
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[training]
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[training.batcher]
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@batchers = "spacy.batch_by_words.v1"
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size = 666
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[nlp]
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lang = "en"
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pipeline = ["tok2vec", "tagger"]
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[components]
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[components.tok2vec]
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factory = "tok2vec"
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[components.tok2vec.model]
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@architectures = "spacy.HashEmbedCNN.v1"
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pretrained_vectors = null
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width = 342
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depth = 4
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window_size = 1
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embed_size = 2000
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maxout_pieces = 3
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subword_features = true
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[components.tagger]
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factory = "tagger"
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[components.tagger.model]
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@architectures = "spacy.Tagger.v1"
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[components.tagger.model.tok2vec]
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@architectures = "spacy.Tok2VecListener.v1"
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width = ${components.tok2vec.model.width}
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[pretraining]
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"""
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parser_config_string_upper = """
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[model]
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@architectures = "spacy.TransitionBasedParser.v2"
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state_type = "parser"
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extra_state_tokens = false
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hidden_width = 66
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maxout_pieces = 2
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use_upper = true
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[model.tok2vec]
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@architectures = "spacy.HashEmbedCNN.v1"
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pretrained_vectors = null
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width = 333
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depth = 4
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embed_size = 5555
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window_size = 1
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maxout_pieces = 7
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subword_features = false
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"""
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parser_config_string_no_upper = """
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[model]
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@architectures = "spacy.TransitionBasedParser.v2"
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state_type = "parser"
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extra_state_tokens = false
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hidden_width = 66
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maxout_pieces = 2
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use_upper = false
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[model.tok2vec]
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@architectures = "spacy.HashEmbedCNN.v1"
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pretrained_vectors = null
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width = 333
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depth = 4
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embed_size = 5555
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window_size = 1
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maxout_pieces = 7
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subword_features = false
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"""
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@registry.architectures("my_test_parser")
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def my_parser():
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tok2vec = build_Tok2Vec_model(
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MultiHashEmbed(
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width=321,
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attrs=["LOWER", "SHAPE"],
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rows=[5432, 5432],
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include_static_vectors=False,
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),
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MaxoutWindowEncoder(width=321, window_size=3, maxout_pieces=4, depth=2),
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)
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parser = build_tb_parser_model(
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tok2vec=tok2vec,
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state_type="parser",
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extra_state_tokens=True,
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hidden_width=65,
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maxout_pieces=5,
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use_upper=True,
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)
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return parser
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def test_create_nlp_from_config():
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config = Config().from_str(nlp_config_string)
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with pytest.raises(ConfigValidationError):
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load_model_from_config(config, auto_fill=False)
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nlp = load_model_from_config(config, auto_fill=True)
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assert nlp.config["training"]["batcher"]["size"] == 666
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assert len(nlp.config["training"]) > 1
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assert nlp.pipe_names == ["tok2vec", "tagger"]
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assert len(nlp.config["components"]) == 2
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assert len(nlp.config["nlp"]["pipeline"]) == 2
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nlp.remove_pipe("tagger")
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assert len(nlp.config["components"]) == 1
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assert len(nlp.config["nlp"]["pipeline"]) == 1
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with pytest.raises(ValueError):
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bad_cfg = {"yolo": {}}
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load_model_from_config(Config(bad_cfg), auto_fill=True)
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with pytest.raises(ValueError):
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bad_cfg = {"pipeline": {"foo": "bar"}}
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load_model_from_config(Config(bad_cfg), auto_fill=True)
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def test_create_nlp_from_pretraining_config():
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"""Test that the default pretraining config validates properly"""
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config = Config().from_str(pretrain_config_string)
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pretrain_config = load_config(DEFAULT_CONFIG_PRETRAIN_PATH)
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filled = config.merge(pretrain_config)
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registry.resolve(filled["pretraining"], schema=ConfigSchemaPretrain)
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def test_create_nlp_from_config_multiple_instances():
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"""Test that the nlp object is created correctly for a config with multiple
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instances of the same component."""
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config = Config().from_str(nlp_config_string)
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config["components"] = {
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"t2v": config["components"]["tok2vec"],
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"tagger1": config["components"]["tagger"],
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"tagger2": config["components"]["tagger"],
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}
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config["nlp"]["pipeline"] = list(config["components"].keys())
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nlp = load_model_from_config(config, auto_fill=True)
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assert nlp.pipe_names == ["t2v", "tagger1", "tagger2"]
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assert nlp.get_pipe_meta("t2v").factory == "tok2vec"
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assert nlp.get_pipe_meta("tagger1").factory == "tagger"
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assert nlp.get_pipe_meta("tagger2").factory == "tagger"
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pipeline_config = nlp.config["components"]
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assert len(pipeline_config) == 3
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assert list(pipeline_config.keys()) == ["t2v", "tagger1", "tagger2"]
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assert nlp.config["nlp"]["pipeline"] == ["t2v", "tagger1", "tagger2"]
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def test_serialize_nlp():
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"""Create a custom nlp pipeline from config and ensure it serializes it correctly"""
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nlp_config = Config().from_str(nlp_config_string)
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nlp = load_model_from_config(nlp_config, auto_fill=True)
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nlp.get_pipe("tagger").add_label("A")
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nlp.initialize()
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assert "tok2vec" in nlp.pipe_names
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assert "tagger" in nlp.pipe_names
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assert "parser" not in nlp.pipe_names
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assert nlp.get_pipe("tagger").model.get_ref("tok2vec").get_dim("nO") == 342
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with make_tempdir() as d:
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nlp.to_disk(d)
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nlp2 = spacy.load(d)
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assert "tok2vec" in nlp2.pipe_names
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assert "tagger" in nlp2.pipe_names
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assert "parser" not in nlp2.pipe_names
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assert nlp2.get_pipe("tagger").model.get_ref("tok2vec").get_dim("nO") == 342
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def test_serialize_custom_nlp():
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"""Create a custom nlp pipeline and ensure it serializes it correctly"""
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nlp = English()
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parser_cfg = dict()
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parser_cfg["model"] = {"@architectures": "my_test_parser"}
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nlp.add_pipe("parser", config=parser_cfg)
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nlp.initialize()
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with make_tempdir() as d:
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nlp.to_disk(d)
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nlp2 = spacy.load(d)
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model = nlp2.get_pipe("parser").model
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model.get_ref("tok2vec")
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# check that we have the correct settings, not the default ones
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assert model.get_ref("upper").get_dim("nI") == 65
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assert model.get_ref("lower").get_dim("nI") == 65
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@pytest.mark.parametrize(
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"parser_config_string", [parser_config_string_upper, parser_config_string_no_upper]
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)
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def test_serialize_parser(parser_config_string):
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"""Create a non-default parser config to check nlp serializes it correctly"""
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nlp = English()
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model_config = Config().from_str(parser_config_string)
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parser = nlp.add_pipe("parser", config=model_config)
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parser.add_label("nsubj")
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nlp.initialize()
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with make_tempdir() as d:
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nlp.to_disk(d)
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nlp2 = spacy.load(d)
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model = nlp2.get_pipe("parser").model
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model.get_ref("tok2vec")
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# check that we have the correct settings, not the default ones
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if model.attrs["has_upper"]:
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assert model.get_ref("upper").get_dim("nI") == 66
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assert model.get_ref("lower").get_dim("nI") == 66
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def test_config_nlp_roundtrip():
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"""Test that a config produced by the nlp object passes training config
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validation."""
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nlp = English()
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nlp.add_pipe("entity_ruler")
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nlp.add_pipe("ner")
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new_nlp = load_model_from_config(nlp.config, auto_fill=False)
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assert new_nlp.config == nlp.config
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assert new_nlp.pipe_names == nlp.pipe_names
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assert new_nlp._pipe_configs == nlp._pipe_configs
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assert new_nlp._pipe_meta == nlp._pipe_meta
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assert new_nlp._factory_meta == nlp._factory_meta
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def test_config_nlp_roundtrip_bytes_disk():
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"""Test that the config is serialized correctly and not interpolated
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by mistake."""
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nlp = English()
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nlp_bytes = nlp.to_bytes()
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new_nlp = English().from_bytes(nlp_bytes)
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assert new_nlp.config == nlp.config
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nlp = English()
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with make_tempdir() as d:
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nlp.to_disk(d)
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new_nlp = spacy.load(d)
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assert new_nlp.config == nlp.config
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def test_serialize_config_language_specific():
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"""Test that config serialization works as expected with language-specific
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factories."""
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name = "test_serialize_config_language_specific"
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@English.factory(name, default_config={"foo": 20})
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def custom_factory(nlp: Language, name: str, foo: int):
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return lambda doc: doc
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nlp = Language()
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assert not nlp.has_factory(name)
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nlp = English()
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assert nlp.has_factory(name)
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nlp.add_pipe(name, config={"foo": 100}, name="bar")
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pipe_config = nlp.config["components"]["bar"]
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assert pipe_config["foo"] == 100
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assert pipe_config["factory"] == name
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with make_tempdir() as d:
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nlp.to_disk(d)
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nlp2 = spacy.load(d)
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assert nlp2.has_factory(name)
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assert nlp2.pipe_names == ["bar"]
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assert nlp2.get_pipe_meta("bar").factory == name
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pipe_config = nlp2.config["components"]["bar"]
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assert pipe_config["foo"] == 100
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assert pipe_config["factory"] == name
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config = Config().from_str(nlp2.config.to_str())
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config["nlp"]["lang"] = "de"
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with pytest.raises(ValueError):
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# German doesn't have a factory, only English does
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load_model_from_config(config)
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def test_serialize_config_missing_pipes():
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config = Config().from_str(nlp_config_string)
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config["components"].pop("tok2vec")
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assert "tok2vec" in config["nlp"]["pipeline"]
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assert "tok2vec" not in config["components"]
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with pytest.raises(ValueError):
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load_model_from_config(config, auto_fill=True)
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def test_config_overrides():
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overrides_nested = {"nlp": {"lang": "de", "pipeline": ["tagger"]}}
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overrides_dot = {"nlp.lang": "de", "nlp.pipeline": ["tagger"]}
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# load_model from config with overrides passed directly to Config
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config = Config().from_str(nlp_config_string, overrides=overrides_dot)
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nlp = load_model_from_config(config, auto_fill=True)
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assert isinstance(nlp, German)
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assert nlp.pipe_names == ["tagger"]
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# Serialized roundtrip with config passed in
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base_config = Config().from_str(nlp_config_string)
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base_nlp = load_model_from_config(base_config, auto_fill=True)
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assert isinstance(base_nlp, English)
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assert base_nlp.pipe_names == ["tok2vec", "tagger"]
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with make_tempdir() as d:
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base_nlp.to_disk(d)
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nlp = spacy.load(d, config=overrides_nested)
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assert isinstance(nlp, German)
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assert nlp.pipe_names == ["tagger"]
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with make_tempdir() as d:
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base_nlp.to_disk(d)
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nlp = spacy.load(d, config=overrides_dot)
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assert isinstance(nlp, German)
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assert nlp.pipe_names == ["tagger"]
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with make_tempdir() as d:
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base_nlp.to_disk(d)
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nlp = spacy.load(d)
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assert isinstance(nlp, English)
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assert nlp.pipe_names == ["tok2vec", "tagger"]
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def test_config_interpolation():
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config = Config().from_str(nlp_config_string, interpolate=False)
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assert config["corpora"]["train"]["path"] == "${paths.train}"
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interpolated = config.interpolate()
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assert interpolated["corpora"]["train"]["path"] is None
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nlp = English.from_config(config)
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assert nlp.config["corpora"]["train"]["path"] == "${paths.train}"
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# Ensure that variables are preserved in nlp config
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width = "${components.tok2vec.model.width}"
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assert config["components"]["tagger"]["model"]["tok2vec"]["width"] == width
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assert nlp.config["components"]["tagger"]["model"]["tok2vec"]["width"] == width
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interpolated2 = nlp.config.interpolate()
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assert interpolated2["corpora"]["train"]["path"] is None
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assert interpolated2["components"]["tagger"]["model"]["tok2vec"]["width"] == 342
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nlp2 = English.from_config(interpolated)
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assert nlp2.config["corpora"]["train"]["path"] is None
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assert nlp2.config["components"]["tagger"]["model"]["tok2vec"]["width"] == 342
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def test_config_optional_sections():
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config = Config().from_str(nlp_config_string)
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config = DEFAULT_CONFIG.merge(config)
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assert "pretraining" not in config
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filled = registry.fill(config, schema=ConfigSchema, validate=False)
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# Make sure that optional "pretraining" block doesn't default to None,
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# which would (rightly) cause error because it'd result in a top-level
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# key that's not a section (dict). Note that the following roundtrip is
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# also how Config.interpolate works under the hood.
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new_config = Config().from_str(filled.to_str())
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assert new_config["pretraining"] == {}
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def test_config_auto_fill_extra_fields():
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config = Config({"nlp": {"lang": "en"}, "training": {}})
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assert load_model_from_config(config, auto_fill=True)
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config = Config({"nlp": {"lang": "en"}, "training": {"extra": "hello"}})
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nlp = load_model_from_config(config, auto_fill=True, validate=False)
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assert "extra" not in nlp.config["training"]
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# Make sure the config generated is valid
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load_model_from_config(nlp.config)
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@pytest.mark.parametrize(
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"parser_config_string", [parser_config_string_upper, parser_config_string_no_upper]
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)
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def test_config_validate_literal(parser_config_string):
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nlp = English()
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config = Config().from_str(parser_config_string)
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config["model"]["state_type"] = "nonsense"
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with pytest.raises(ConfigValidationError):
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nlp.add_pipe("parser", config=config)
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config["model"]["state_type"] = "ner"
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nlp.add_pipe("parser", config=config)
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def test_config_only_resolve_relevant_blocks():
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"""Test that only the relevant blocks are resolved in the different methods
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and that invalid blocks are ignored if needed. For instance, the [initialize]
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shouldn't be resolved at runtime.
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"""
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nlp = English()
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config = nlp.config
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config["training"]["before_to_disk"] = {"@misc": "nonexistent"}
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config["initialize"]["lookups"] = {"@misc": "nonexistent"}
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# This shouldn't resolve [training] or [initialize]
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nlp = load_model_from_config(config, auto_fill=True)
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# This will raise for nonexistent value
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with pytest.raises(RegistryError):
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nlp.initialize()
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nlp.config["initialize"]["lookups"] = None
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nlp.initialize()
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def test_hyphen_in_config():
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hyphen_config_str = """
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[nlp]
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lang = "en"
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pipeline = ["my_punctual_component"]
|
|
|
|
[components]
|
|
|
|
[components.my_punctual_component]
|
|
factory = "my_punctual_component"
|
|
punctuation = ["?","-"]
|
|
"""
|
|
|
|
@spacy.Language.factory("my_punctual_component")
|
|
class MyPunctualComponent(object):
|
|
name = "my_punctual_component"
|
|
|
|
def __init__(
|
|
self,
|
|
nlp,
|
|
name,
|
|
punctuation,
|
|
):
|
|
self.punctuation = punctuation
|
|
|
|
nlp = English.from_config(load_config_from_str(hyphen_config_str))
|
|
assert nlp.get_pipe("my_punctual_component").punctuation == ["?", "-"]
|