spaCy/spacy/tests/pipeline/test_pipe_factories.py

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Refactor pipeline components, config and language data (#5759) * Update with WIP * Update with WIP * Update with pipeline serialization * Update types and pipe factories * Add deep merge, tidy up and add tests * Fix pipe creation from config * Don't validate default configs on load * Update spacy/language.py Co-authored-by: Ines Montani <ines@ines.io> * Adjust factory/component meta error * Clean up factory args and remove defaults * Add test for failing empty dict defaults * Update pipeline handling and methods * provide KB as registry function instead of as object * small change in test to make functionality more clear * update example script for EL configuration * Fix typo * Simplify test * Simplify test * splitting pipes.pyx into separate files * moving default configs to each component file * fix batch_size type * removing default values from component constructors where possible (TODO: test 4725) * skip instead of xfail * Add test for config -> nlp with multiple instances * pipeline.pipes -> pipeline.pipe * Tidy up, document, remove kwargs * small cleanup/generalization for Tok2VecListener * use DEFAULT_UPSTREAM field * revert to avoid circular imports * Fix tests * Replace deprecated arg * Make model dirs require config * fix pickling of keyword-only arguments in constructor * WIP: clean up and integrate full config * Add helper to handle function args more reliably Now also includes keyword-only args * Fix config composition and serialization * Improve config debugging and add visual diff * Remove unused defaults and fix type * Remove pipeline and factories from meta * Update spacy/default_config.cfg Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Update spacy/default_config.cfg * small UX edits * avoid printing stack trace for debug CLI commands * Add support for language-specific factories * specify the section of the config which holds the model to debug * WIP: add Language.from_config * Update with language data refactor WIP * Auto-format * Add backwards-compat handling for Language.factories * Update morphologizer.pyx * Fix morphologizer * Update and simplify lemmatizers * Fix Japanese tests * Port over tagger changes * Fix Chinese and tests * Update to latest Thinc * WIP: xfail first Russian lemmatizer test * Fix component-specific overrides * fix nO for output layers in debug_model * Fix default value * Fix tests and don't pass objects in config * Fix deep merging * Fix lemma lookup data registry Only load the lookups if an entry is available in the registry (and if spacy-lookups-data is installed) * Add types * Add Vocab.from_config * Fix typo * Fix tests * Make config copying more elegant * Fix pipe analysis * Fix lemmatizers and is_base_form * WIP: move language defaults to config * Fix morphology type * Fix vocab * Remove comment * Update to latest Thinc * Add morph rules to config * Tidy up * Remove set_morphology option from tagger factory * Hack use_gpu * Move [pipeline] to top-level block and make [nlp.pipeline] list Allows separating component blocks from component order – otherwise, ordering the config would mean a changed component order, which is bad. Also allows initial config to define more components and not use all of them * Fix use_gpu and resume in CLI * Auto-format * Remove resume from config * Fix formatting and error * [pipeline] -> [components] * Fix types * Fix tagger test: requires set_morphology? Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com> Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
2020-07-22 11:42:59 +00:00
import pytest
from spacy.language import Language
from spacy.lang.en import English
from spacy.lang.de import German
from spacy.tokens import Doc
from spacy.util import registry, SimpleFrozenDict, combine_score_weights
Refactor pipeline components, config and language data (#5759) * Update with WIP * Update with WIP * Update with pipeline serialization * Update types and pipe factories * Add deep merge, tidy up and add tests * Fix pipe creation from config * Don't validate default configs on load * Update spacy/language.py Co-authored-by: Ines Montani <ines@ines.io> * Adjust factory/component meta error * Clean up factory args and remove defaults * Add test for failing empty dict defaults * Update pipeline handling and methods * provide KB as registry function instead of as object * small change in test to make functionality more clear * update example script for EL configuration * Fix typo * Simplify test * Simplify test * splitting pipes.pyx into separate files * moving default configs to each component file * fix batch_size type * removing default values from component constructors where possible (TODO: test 4725) * skip instead of xfail * Add test for config -> nlp with multiple instances * pipeline.pipes -> pipeline.pipe * Tidy up, document, remove kwargs * small cleanup/generalization for Tok2VecListener * use DEFAULT_UPSTREAM field * revert to avoid circular imports * Fix tests * Replace deprecated arg * Make model dirs require config * fix pickling of keyword-only arguments in constructor * WIP: clean up and integrate full config * Add helper to handle function args more reliably Now also includes keyword-only args * Fix config composition and serialization * Improve config debugging and add visual diff * Remove unused defaults and fix type * Remove pipeline and factories from meta * Update spacy/default_config.cfg Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Update spacy/default_config.cfg * small UX edits * avoid printing stack trace for debug CLI commands * Add support for language-specific factories * specify the section of the config which holds the model to debug * WIP: add Language.from_config * Update with language data refactor WIP * Auto-format * Add backwards-compat handling for Language.factories * Update morphologizer.pyx * Fix morphologizer * Update and simplify lemmatizers * Fix Japanese tests * Port over tagger changes * Fix Chinese and tests * Update to latest Thinc * WIP: xfail first Russian lemmatizer test * Fix component-specific overrides * fix nO for output layers in debug_model * Fix default value * Fix tests and don't pass objects in config * Fix deep merging * Fix lemma lookup data registry Only load the lookups if an entry is available in the registry (and if spacy-lookups-data is installed) * Add types * Add Vocab.from_config * Fix typo * Fix tests * Make config copying more elegant * Fix pipe analysis * Fix lemmatizers and is_base_form * WIP: move language defaults to config * Fix morphology type * Fix vocab * Remove comment * Update to latest Thinc * Add morph rules to config * Tidy up * Remove set_morphology option from tagger factory * Hack use_gpu * Move [pipeline] to top-level block and make [nlp.pipeline] list Allows separating component blocks from component order – otherwise, ordering the config would mean a changed component order, which is bad. Also allows initial config to define more components and not use all of them * Fix use_gpu and resume in CLI * Auto-format * Remove resume from config * Fix formatting and error * [pipeline] -> [components] * Fix types * Fix tagger test: requires set_morphology? Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com> Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
2020-07-22 11:42:59 +00:00
from thinc.api import Model, Linear
from thinc.config import ConfigValidationError
from pydantic import StrictInt, StrictStr
def test_pipe_function_component():
name = "test_component"
@Language.component(name)
def component(doc: Doc) -> Doc:
return doc
assert name in registry.factories
nlp = Language()
with pytest.raises(ValueError):
nlp.add_pipe(component)
nlp.add_pipe(name)
assert name in nlp.pipe_names
assert nlp.pipe_factories[name] == name
assert Language.get_factory_meta(name)
assert nlp.get_pipe_meta(name)
pipe = nlp.get_pipe(name)
assert pipe == component
pipe = nlp.create_pipe(name)
assert pipe == component
def test_pipe_class_component_init():
name1 = "test_class_component1"
name2 = "test_class_component2"
@Language.factory(name1)
class Component1:
def __init__(self, nlp: Language, name: str):
self.nlp = nlp
def __call__(self, doc: Doc) -> Doc:
return doc
class Component2:
def __init__(self, nlp: Language, name: str):
self.nlp = nlp
def __call__(self, doc: Doc) -> Doc:
return doc
@Language.factory(name2)
def factory(nlp: Language, name=name2):
return Component2(nlp, name)
nlp = Language()
for name, Component in [(name1, Component1), (name2, Component2)]:
assert name in registry.factories
with pytest.raises(ValueError):
nlp.add_pipe(Component(nlp, name))
nlp.add_pipe(name)
assert name in nlp.pipe_names
assert nlp.pipe_factories[name] == name
assert Language.get_factory_meta(name)
assert nlp.get_pipe_meta(name)
pipe = nlp.get_pipe(name)
assert isinstance(pipe, Component)
assert isinstance(pipe.nlp, Language)
pipe = nlp.create_pipe(name)
assert isinstance(pipe, Component)
assert isinstance(pipe.nlp, Language)
def test_pipe_class_component_config():
name = "test_class_component_config"
@Language.factory(name)
class Component:
def __init__(
self, nlp: Language, name: str, value1: StrictInt, value2: StrictStr
):
self.nlp = nlp
self.value1 = value1
self.value2 = value2
self.is_base = True
def __call__(self, doc: Doc) -> Doc:
return doc
@English.factory(name)
class ComponentEN:
def __init__(
self, nlp: Language, name: str, value1: StrictInt, value2: StrictStr
):
self.nlp = nlp
self.value1 = value1
self.value2 = value2
self.is_base = False
def __call__(self, doc: Doc) -> Doc:
return doc
nlp = Language()
with pytest.raises(ConfigValidationError): # no config provided
nlp.add_pipe(name)
with pytest.raises(ConfigValidationError): # invalid config
nlp.add_pipe(name, config={"value1": "10", "value2": "hello"})
nlp.add_pipe(name, config={"value1": 10, "value2": "hello"})
pipe = nlp.get_pipe(name)
assert isinstance(pipe.nlp, Language)
assert pipe.value1 == 10
assert pipe.value2 == "hello"
assert pipe.is_base is True
nlp_en = English()
with pytest.raises(ConfigValidationError): # invalid config
nlp_en.add_pipe(name, config={"value1": "10", "value2": "hello"})
nlp_en.add_pipe(name, config={"value1": 10, "value2": "hello"})
pipe = nlp_en.get_pipe(name)
assert isinstance(pipe.nlp, English)
assert pipe.value1 == 10
assert pipe.value2 == "hello"
assert pipe.is_base is False
def test_pipe_class_component_defaults():
name = "test_class_component_defaults"
@Language.factory(name)
class Component:
def __init__(
self,
nlp: Language,
name: str,
value1: StrictInt = 10,
value2: StrictStr = "hello",
):
self.nlp = nlp
self.value1 = value1
self.value2 = value2
def __call__(self, doc: Doc) -> Doc:
return doc
nlp = Language()
nlp.add_pipe(name)
pipe = nlp.get_pipe(name)
assert isinstance(pipe.nlp, Language)
assert pipe.value1 == 10
assert pipe.value2 == "hello"
def test_pipe_class_component_model():
name = "test_class_component_model"
default_config = {
"model": {
"@architectures": "spacy.TextCatEnsemble.v1",
Refactor pipeline components, config and language data (#5759) * Update with WIP * Update with WIP * Update with pipeline serialization * Update types and pipe factories * Add deep merge, tidy up and add tests * Fix pipe creation from config * Don't validate default configs on load * Update spacy/language.py Co-authored-by: Ines Montani <ines@ines.io> * Adjust factory/component meta error * Clean up factory args and remove defaults * Add test for failing empty dict defaults * Update pipeline handling and methods * provide KB as registry function instead of as object * small change in test to make functionality more clear * update example script for EL configuration * Fix typo * Simplify test * Simplify test * splitting pipes.pyx into separate files * moving default configs to each component file * fix batch_size type * removing default values from component constructors where possible (TODO: test 4725) * skip instead of xfail * Add test for config -> nlp with multiple instances * pipeline.pipes -> pipeline.pipe * Tidy up, document, remove kwargs * small cleanup/generalization for Tok2VecListener * use DEFAULT_UPSTREAM field * revert to avoid circular imports * Fix tests * Replace deprecated arg * Make model dirs require config * fix pickling of keyword-only arguments in constructor * WIP: clean up and integrate full config * Add helper to handle function args more reliably Now also includes keyword-only args * Fix config composition and serialization * Improve config debugging and add visual diff * Remove unused defaults and fix type * Remove pipeline and factories from meta * Update spacy/default_config.cfg Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Update spacy/default_config.cfg * small UX edits * avoid printing stack trace for debug CLI commands * Add support for language-specific factories * specify the section of the config which holds the model to debug * WIP: add Language.from_config * Update with language data refactor WIP * Auto-format * Add backwards-compat handling for Language.factories * Update morphologizer.pyx * Fix morphologizer * Update and simplify lemmatizers * Fix Japanese tests * Port over tagger changes * Fix Chinese and tests * Update to latest Thinc * WIP: xfail first Russian lemmatizer test * Fix component-specific overrides * fix nO for output layers in debug_model * Fix default value * Fix tests and don't pass objects in config * Fix deep merging * Fix lemma lookup data registry Only load the lookups if an entry is available in the registry (and if spacy-lookups-data is installed) * Add types * Add Vocab.from_config * Fix typo * Fix tests * Make config copying more elegant * Fix pipe analysis * Fix lemmatizers and is_base_form * WIP: move language defaults to config * Fix morphology type * Fix vocab * Remove comment * Update to latest Thinc * Add morph rules to config * Tidy up * Remove set_morphology option from tagger factory * Hack use_gpu * Move [pipeline] to top-level block and make [nlp.pipeline] list Allows separating component blocks from component order – otherwise, ordering the config would mean a changed component order, which is bad. Also allows initial config to define more components and not use all of them * Fix use_gpu and resume in CLI * Auto-format * Remove resume from config * Fix formatting and error * [pipeline] -> [components] * Fix types * Fix tagger test: requires set_morphology? Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com> Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
2020-07-22 11:42:59 +00:00
"exclusive_classes": False,
"pretrained_vectors": None,
"width": 64,
"embed_size": 2000,
"window_size": 1,
"conv_depth": 2,
"ngram_size": 1,
"dropout": None,
},
"value1": 10,
}
@Language.factory(name, default_config=default_config)
class Component:
def __init__(self, nlp: Language, model: Model, name: str, value1: StrictInt):
self.nlp = nlp
self.model = model
self.value1 = value1
self.name = name
def __call__(self, doc: Doc) -> Doc:
return doc
nlp = Language()
nlp.add_pipe(name)
pipe = nlp.get_pipe(name)
assert isinstance(pipe.nlp, Language)
assert pipe.value1 == 10
assert isinstance(pipe.model, Model)
def test_pipe_class_component_model_custom():
name = "test_class_component_model_custom"
arch = f"{name}.arch"
default_config = {"value1": 1, "model": {"@architectures": arch, "nO": 0, "nI": 0}}
@Language.factory(name, default_config=default_config)
class Component:
def __init__(
self, nlp: Language, model: Model, name: str, value1: StrictInt = 10
):
self.nlp = nlp
self.model = model
self.value1 = value1
self.name = name
def __call__(self, doc: Doc) -> Doc:
return doc
@registry.architectures(arch)
def make_custom_arch(nO: StrictInt, nI: StrictInt):
return Linear(nO, nI)
nlp = Language()
config = {"value1": 20, "model": {"@architectures": arch, "nO": 1, "nI": 2}}
nlp.add_pipe(name, config=config)
pipe = nlp.get_pipe(name)
assert isinstance(pipe.nlp, Language)
assert pipe.value1 == 20
assert isinstance(pipe.model, Model)
assert pipe.model.name == "linear"
nlp = Language()
with pytest.raises(ConfigValidationError):
config = {"value1": "20", "model": {"@architectures": arch, "nO": 1, "nI": 2}}
nlp.add_pipe(name, config=config)
with pytest.raises(ConfigValidationError):
config = {"value1": 20, "model": {"@architectures": arch, "nO": 1.0, "nI": 2.0}}
nlp.add_pipe(name, config=config)
def test_pipe_factories_wrong_formats():
with pytest.raises(ValueError):
# Decorator is not called
@Language.component
def component(foo: int, bar: str):
...
with pytest.raises(ValueError):
# Decorator is not called
@Language.factory
def factory1(foo: int, bar: str):
...
with pytest.raises(ValueError):
# Factory function is missing "nlp" and "name" arguments
@Language.factory("test_pipe_factories_missing_args")
def factory2(foo: int, bar: str):
...
def test_pipe_factory_meta_config_cleanup():
"""Test that component-specific meta and config entries are represented
correctly and cleaned up when pipes are removed, replaced or renamed."""
nlp = Language()
nlp.add_pipe("ner", name="ner_component")
nlp.add_pipe("textcat")
assert nlp.get_factory_meta("ner")
assert nlp.get_pipe_meta("ner_component")
assert nlp.get_pipe_config("ner_component")
assert nlp.get_factory_meta("textcat")
assert nlp.get_pipe_meta("textcat")
assert nlp.get_pipe_config("textcat")
nlp.rename_pipe("textcat", "tc")
assert nlp.get_pipe_meta("tc")
assert nlp.get_pipe_config("tc")
with pytest.raises(ValueError):
nlp.remove_pipe("ner")
nlp.remove_pipe("ner_component")
assert "ner_component" not in nlp._pipe_meta
assert "ner_component" not in nlp._pipe_configs
with pytest.raises(ValueError):
nlp.replace_pipe("textcat", "parser")
nlp.replace_pipe("tc", "parser")
assert nlp.get_factory_meta("parser")
assert nlp.get_pipe_meta("tc").factory == "parser"
def test_pipe_factories_empty_dict_default():
"""Test that default config values can be empty dicts and that no config
validation error is raised."""
# TODO: fix this
name = "test_pipe_factories_empty_dict_default"
@Language.factory(name, default_config={"foo": {}})
def factory(nlp: Language, name: str, foo: dict):
...
nlp = Language()
nlp.create_pipe(name)
def test_pipe_factories_language_specific():
"""Test that language sub-classes can have their own factories, with
fallbacks to the base factories."""
name1 = "specific_component1"
name2 = "specific_component2"
Language.component(name1, func=lambda: "base")
English.component(name1, func=lambda: "en")
German.component(name2, func=lambda: "de")
assert Language.has_factory(name1)
assert not Language.has_factory(name2)
assert English.has_factory(name1)
assert not English.has_factory(name2)
assert German.has_factory(name1)
assert German.has_factory(name2)
nlp = Language()
assert nlp.create_pipe(name1)() == "base"
with pytest.raises(ValueError):
nlp.create_pipe(name2)
nlp_en = English()
assert nlp_en.create_pipe(name1)() == "en"
with pytest.raises(ValueError):
nlp_en.create_pipe(name2)
nlp_de = German()
assert nlp_de.create_pipe(name1)() == "base"
assert nlp_de.create_pipe(name2)() == "de"
def test_language_factories_invalid():
"""Test that assigning directly to Language.factories is now invalid and
raises a custom error."""
assert isinstance(Language.factories, SimpleFrozenDict)
with pytest.raises(NotImplementedError):
Language.factories["foo"] = "bar"
nlp = Language()
assert isinstance(nlp.factories, SimpleFrozenDict)
assert len(nlp.factories)
with pytest.raises(NotImplementedError):
nlp.factories["foo"] = "bar"
@pytest.mark.parametrize(
"weights,expected",
[
([{"a": 1.0}, {"b": 1.0}, {"c": 1.0}], {"a": 0.33, "b": 0.33, "c": 0.33}),
([{"a": 1.0}, {"b": 50}, {"c": 123}], {"a": 0.33, "b": 0.33, "c": 0.33}),
(
[{"a": 0.7, "b": 0.3}, {"c": 1.0}, {"d": 0.5, "e": 0.5}],
{"a": 0.23, "b": 0.1, "c": 0.33, "d": 0.17, "e": 0.17},
),
(
[{"a": 100, "b": 400}, {"c": 0.5, "d": 0.5}],
{"a": 0.1, "b": 0.4, "c": 0.25, "d": 0.25},
),
(
[{"a": 0.5, "b": 0.5}, {"b": 1.0}],
{"a": 0.25, "b": 0.75},
),
],
)
def test_language_factories_combine_score_weights(weights, expected):
result = combine_score_weights(weights)
assert sum(result.values()) in (0.99, 1.0)
assert result == expected
def test_language_factories_scores():
name = "test_language_factories_scores"
func = lambda doc: doc
weights1 = {"a1": 0.5, "a2": 0.5}
weights2 = {"b1": 0.2, "b2": 0.7, "b3": 0.1}
Language.component(
f"{name}1", scores=list(weights1), default_score_weights=weights1, func=func,
)
Language.component(
f"{name}2", scores=list(weights2), default_score_weights=weights2, func=func,
)
meta1 = Language.get_factory_meta(f"{name}1")
assert meta1.default_score_weights == weights1
meta2 = Language.get_factory_meta(f"{name}2")
assert meta2.default_score_weights == weights2
2020-07-26 11:40:19 +00:00
nlp = Language()
nlp._config["training"]["scores"] = ["speed"]
nlp._config["training"]["score_weights"] = {}
nlp.add_pipe(f"{name}1")
nlp.add_pipe(f"{name}2")
cfg = nlp.config["training"]
assert cfg["scores"] == sorted(["speed", *list(weights1.keys()), *list(weights2.keys())])
expected_weights = {"a1": 0.25, "a2": 0.25, "b1": 0.1, "b2": 0.35, "b3": 0.05}
assert cfg["score_weights"] == expected_weights