mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-14 13:47:13 +03:00
65f6c9cd10
Support overriding registered functions in configs. Previously the registry name was parsed as a section name rather than as a registry name.
492 lines
15 KiB
Python
492 lines
15 KiB
Python
import ctypes
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import os
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from pathlib import Path
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import pytest
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from pydantic import ValidationError
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from thinc.api import (
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Config,
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ConfigValidationError,
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CupyOps,
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MPSOps,
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NumpyOps,
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Optimizer,
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get_current_ops,
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set_current_ops,
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)
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from thinc.compat import has_cupy_gpu, has_torch_mps_gpu
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from spacy import prefer_gpu, require_cpu, require_gpu, util
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from spacy.about import __version__ as spacy_version
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from spacy.lang.en import English
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from spacy.lang.nl import Dutch
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from spacy.language import DEFAULT_CONFIG_PATH
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from spacy.ml._precomputable_affine import (
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PrecomputableAffine,
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_backprop_precomputable_affine_padding,
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)
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from spacy.schemas import ConfigSchemaTraining, TokenPattern, TokenPatternSchema
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from spacy.training.batchers import minibatch_by_words
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from spacy.util import (
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SimpleFrozenList,
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dot_to_object,
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find_available_port,
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import_file,
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to_ternary_int,
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)
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from .util import get_random_doc, make_tempdir
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@pytest.fixture
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def is_admin():
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"""Determine if the tests are run as admin or not."""
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try:
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admin = os.getuid() == 0
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except AttributeError:
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admin = ctypes.windll.shell32.IsUserAnAdmin() != 0
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return admin
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@pytest.mark.issue(6207)
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def test_issue6207(en_tokenizer):
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doc = en_tokenizer("zero one two three four five six")
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# Make spans
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s1 = doc[:4]
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s2 = doc[3:6] # overlaps with s1
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s3 = doc[5:7] # overlaps with s2, not s1
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result = util.filter_spans((s1, s2, s3))
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assert s1 in result
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assert s2 not in result
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assert s3 in result
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@pytest.mark.issue(6258)
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def test_issue6258():
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"""Test that the non-empty constraint pattern field is respected"""
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# These one is valid
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TokenPatternSchema(pattern=[TokenPattern()])
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# But an empty pattern list should fail to validate
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# based on the schema's constraint
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with pytest.raises(ValidationError):
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TokenPatternSchema(pattern=[])
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@pytest.mark.parametrize("text", ["hello/world", "hello world"])
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def test_util_ensure_path_succeeds(text):
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path = util.ensure_path(text)
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assert isinstance(path, Path)
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@pytest.mark.parametrize(
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"package,result", [("numpy", True), ("sfkodskfosdkfpsdpofkspdof", False)]
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)
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def test_util_is_package(package, result):
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"""Test that an installed package via pip is recognised by util.is_package."""
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assert util.is_package(package) is result
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@pytest.mark.parametrize("package", ["thinc"])
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def test_util_get_package_path(package):
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"""Test that a Path object is returned for a package name."""
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path = util.get_package_path(package)
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assert isinstance(path, Path)
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def test_PrecomputableAffine(nO=4, nI=5, nF=3, nP=2):
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model = PrecomputableAffine(nO=nO, nI=nI, nF=nF, nP=nP).initialize()
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assert model.get_param("W").shape == (nF, nO, nP, nI)
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tensor = model.ops.alloc((10, nI))
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Y, get_dX = model.begin_update(tensor)
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assert Y.shape == (tensor.shape[0] + 1, nF, nO, nP)
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dY = model.ops.alloc((15, nO, nP))
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ids = model.ops.alloc((15, nF))
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ids[1, 2] = -1
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dY[1] = 1
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assert not model.has_grad("pad")
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d_pad = _backprop_precomputable_affine_padding(model, dY, ids)
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assert d_pad[0, 2, 0, 0] == 1.0
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ids.fill(0.0)
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dY.fill(0.0)
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dY[0] = 0
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ids[1, 2] = 0
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ids[1, 1] = -1
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ids[1, 0] = -1
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dY[1] = 1
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ids[2, 0] = -1
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dY[2] = 5
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d_pad = _backprop_precomputable_affine_padding(model, dY, ids)
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assert d_pad[0, 0, 0, 0] == 6
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assert d_pad[0, 1, 0, 0] == 1
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assert d_pad[0, 2, 0, 0] == 0
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def test_prefer_gpu():
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current_ops = get_current_ops()
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if has_cupy_gpu:
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assert prefer_gpu()
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assert isinstance(get_current_ops(), CupyOps)
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elif has_torch_mps_gpu:
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assert prefer_gpu()
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assert isinstance(get_current_ops(), MPSOps)
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else:
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assert not prefer_gpu()
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set_current_ops(current_ops)
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def test_require_gpu():
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current_ops = get_current_ops()
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if has_cupy_gpu:
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require_gpu()
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assert isinstance(get_current_ops(), CupyOps)
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elif has_torch_mps_gpu:
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require_gpu()
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assert isinstance(get_current_ops(), MPSOps)
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set_current_ops(current_ops)
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def test_require_cpu():
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current_ops = get_current_ops()
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require_cpu()
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assert isinstance(get_current_ops(), NumpyOps)
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try:
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import cupy # noqa: F401
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require_gpu()
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assert isinstance(get_current_ops(), CupyOps)
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except ImportError:
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pass
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require_cpu()
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assert isinstance(get_current_ops(), NumpyOps)
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set_current_ops(current_ops)
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def test_ascii_filenames():
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"""Test that all filenames in the project are ASCII.
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See: https://twitter.com/_inesmontani/status/1177941471632211968
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"""
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root = Path(__file__).parent.parent
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for path in root.glob("**/*"):
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assert all(ord(c) < 128 for c in path.name), path.name
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def test_load_model_blank_shortcut():
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"""Test that using a model name like "blank:en" works as a shortcut for
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spacy.blank("en").
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"""
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nlp = util.load_model("blank:en")
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assert nlp.lang == "en"
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assert nlp.pipeline == []
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# ImportError for loading an unsupported language
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with pytest.raises(ImportError):
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util.load_model("blank:zxx")
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# ImportError for requesting an invalid language code that isn't registered
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with pytest.raises(ImportError):
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util.load_model("blank:fjsfijsdof")
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@pytest.mark.parametrize(
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"version,constraint,compatible",
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[
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(spacy_version, spacy_version, True),
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(spacy_version, f">={spacy_version}", True),
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("3.0.0", "2.0.0", False),
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("3.2.1", ">=2.0.0", True),
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("2.2.10a1", ">=1.0.0,<2.1.1", False),
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("3.0.0.dev3", ">=1.2.3,<4.5.6", True),
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("n/a", ">=1.2.3,<4.5.6", None),
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("1.2.3", "n/a", None),
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("n/a", "n/a", None),
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],
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)
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def test_is_compatible_version(version, constraint, compatible):
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assert util.is_compatible_version(version, constraint) is compatible
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@pytest.mark.parametrize(
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"constraint,expected",
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[
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("3.0.0", False),
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("==3.0.0", False),
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(">=2.3.0", True),
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(">2.0.0", True),
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("<=2.0.0", True),
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(">2.0.0,<3.0.0", False),
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(">=2.0.0,<3.0.0", False),
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("!=1.1,>=1.0,~=1.0", True),
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("n/a", None),
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],
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)
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def test_is_unconstrained_version(constraint, expected):
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assert util.is_unconstrained_version(constraint) is expected
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@pytest.mark.parametrize(
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"a1,a2,b1,b2,is_match",
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[
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("3.0.0", "3.0", "3.0.1", "3.0", True),
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("3.1.0", "3.1", "3.2.1", "3.2", False),
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("xxx", None, "1.2.3.dev0", "1.2", False),
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],
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)
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def test_minor_version(a1, a2, b1, b2, is_match):
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assert util.get_minor_version(a1) == a2
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assert util.get_minor_version(b1) == b2
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assert util.is_minor_version_match(a1, b1) is is_match
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assert util.is_minor_version_match(a2, b2) is is_match
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@pytest.mark.parametrize(
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"dot_notation,expected",
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[
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(
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{"token.pos": True, "token._.xyz": True},
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{"token": {"pos": True, "_": {"xyz": True}}},
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),
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(
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{"training.batch_size": 128, "training.optimizer.learn_rate": 0.01},
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{"training": {"batch_size": 128, "optimizer": {"learn_rate": 0.01}}},
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),
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(
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{"attribute_ruler.scorer.@scorers": "spacy.tagger_scorer.v1"},
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{"attribute_ruler": {"scorer": {"@scorers": "spacy.tagger_scorer.v1"}}},
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),
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],
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)
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def test_dot_to_dict(dot_notation, expected):
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result = util.dot_to_dict(dot_notation)
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assert result == expected
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assert util.dict_to_dot(result) == dot_notation
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@pytest.mark.parametrize(
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"dot_notation,expected",
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[
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(
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{"token.pos": True, "token._.xyz": True},
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{"token": {"pos": True, "_": {"xyz": True}}},
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),
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(
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{"training.batch_size": 128, "training.optimizer.learn_rate": 0.01},
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{"training": {"batch_size": 128, "optimizer": {"learn_rate": 0.01}}},
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),
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(
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{"attribute_ruler.scorer": {"@scorers": "spacy.tagger_scorer.v1"}},
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{"attribute_ruler": {"scorer": {"@scorers": "spacy.tagger_scorer.v1"}}},
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),
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],
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)
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def test_dot_to_dict_overrides(dot_notation, expected):
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result = util.dot_to_dict(dot_notation)
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assert result == expected
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assert util.dict_to_dot(result, for_overrides=True) == dot_notation
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def test_set_dot_to_object():
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config = {"foo": {"bar": 1, "baz": {"x": "y"}}, "test": {"a": {"b": "c"}}}
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with pytest.raises(KeyError):
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util.set_dot_to_object(config, "foo.bar.baz", 100)
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with pytest.raises(KeyError):
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util.set_dot_to_object(config, "hello.world", 100)
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with pytest.raises(KeyError):
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util.set_dot_to_object(config, "test.a.b.c", 100)
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util.set_dot_to_object(config, "foo.bar", 100)
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assert config["foo"]["bar"] == 100
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util.set_dot_to_object(config, "foo.baz.x", {"hello": "world"})
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assert config["foo"]["baz"]["x"]["hello"] == "world"
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assert config["test"]["a"]["b"] == "c"
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util.set_dot_to_object(config, "foo", 123)
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assert config["foo"] == 123
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util.set_dot_to_object(config, "test", "hello")
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assert dict(config) == {"foo": 123, "test": "hello"}
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@pytest.mark.parametrize(
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"doc_sizes, expected_batches",
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[
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([400, 400, 199], [3]),
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([400, 400, 199, 3], [4]),
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([400, 400, 199, 3, 200], [3, 2]),
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([400, 400, 199, 3, 1], [5]),
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([400, 400, 199, 3, 1, 1500], [5]), # 1500 will be discarded
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([400, 400, 199, 3, 1, 200], [3, 3]),
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([400, 400, 199, 3, 1, 999], [3, 3]),
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([400, 400, 199, 3, 1, 999, 999], [3, 2, 1, 1]),
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([1, 2, 999], [3]),
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([1, 2, 999, 1], [4]),
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([1, 200, 999, 1], [2, 2]),
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([1, 999, 200, 1], [2, 2]),
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],
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)
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def test_util_minibatch(doc_sizes, expected_batches):
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docs = [get_random_doc(doc_size) for doc_size in doc_sizes]
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tol = 0.2
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batch_size = 1000
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batches = list(
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minibatch_by_words(docs, size=batch_size, tolerance=tol, discard_oversize=True)
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)
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assert [len(batch) for batch in batches] == expected_batches
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max_size = batch_size + batch_size * tol
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for batch in batches:
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assert sum([len(doc) for doc in batch]) < max_size
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@pytest.mark.parametrize(
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"doc_sizes, expected_batches",
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[
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([400, 4000, 199], [1, 2]),
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([400, 400, 199, 3000, 200], [1, 4]),
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([400, 400, 199, 3, 1, 1500], [1, 5]),
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([400, 400, 199, 3000, 2000, 200, 200], [1, 1, 3, 2]),
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([1, 2, 9999], [1, 2]),
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([2000, 1, 2000, 1, 1, 1, 2000], [1, 1, 1, 4]),
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],
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)
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def test_util_minibatch_oversize(doc_sizes, expected_batches):
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"""Test that oversized documents are returned in their own batch"""
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docs = [get_random_doc(doc_size) for doc_size in doc_sizes]
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tol = 0.2
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batch_size = 1000
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batches = list(
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minibatch_by_words(docs, size=batch_size, tolerance=tol, discard_oversize=False)
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)
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assert [len(batch) for batch in batches] == expected_batches
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def test_util_dot_section():
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cfg_string = """
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[nlp]
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lang = "en"
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pipeline = ["textcat"]
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[components]
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[components.textcat]
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factory = "textcat"
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[components.textcat.model]
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@architectures = "spacy.TextCatBOW.v2"
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exclusive_classes = true
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ngram_size = 1
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no_output_layer = false
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"""
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nlp_config = Config().from_str(cfg_string)
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en_nlp = util.load_model_from_config(nlp_config, auto_fill=True)
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default_config = Config().from_disk(DEFAULT_CONFIG_PATH)
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default_config["nlp"]["lang"] = "nl"
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nl_nlp = util.load_model_from_config(default_config, auto_fill=True)
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# Test that creation went OK
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assert isinstance(en_nlp, English)
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assert isinstance(nl_nlp, Dutch)
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assert nl_nlp.pipe_names == []
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assert en_nlp.pipe_names == ["textcat"]
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# not exclusive_classes
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assert en_nlp.get_pipe("textcat").model.attrs["multi_label"] is False
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# Test that default values got overwritten
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assert en_nlp.config["nlp"]["pipeline"] == ["textcat"]
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assert nl_nlp.config["nlp"]["pipeline"] == [] # default value []
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# Test proper functioning of 'dot_to_object'
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with pytest.raises(KeyError):
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dot_to_object(en_nlp.config, "nlp.pipeline.tagger")
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with pytest.raises(KeyError):
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dot_to_object(en_nlp.config, "nlp.unknownattribute")
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T = util.registry.resolve(nl_nlp.config["training"], schema=ConfigSchemaTraining)
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assert isinstance(dot_to_object({"training": T}, "training.optimizer"), Optimizer)
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def test_simple_frozen_list():
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t = SimpleFrozenList(["foo", "bar"])
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assert t == ["foo", "bar"]
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assert t.index("bar") == 1 # okay method
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with pytest.raises(NotImplementedError):
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t.append("baz")
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with pytest.raises(NotImplementedError):
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t.sort()
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with pytest.raises(NotImplementedError):
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t.extend(["baz"])
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with pytest.raises(NotImplementedError):
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t.pop()
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t = SimpleFrozenList(["foo", "bar"], error="Error!")
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with pytest.raises(NotImplementedError):
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t.append("baz")
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def test_resolve_dot_names():
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config = {
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"training": {"optimizer": {"@optimizers": "Adam.v1"}},
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"foo": {"bar": "training.optimizer", "baz": "training.xyz"},
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}
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result = util.resolve_dot_names(config, ["training.optimizer"])
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assert isinstance(result[0], Optimizer)
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with pytest.raises(ConfigValidationError) as e:
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util.resolve_dot_names(config, ["training.xyz", "training.optimizer"])
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errors = e.value.errors
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assert len(errors) == 1
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assert errors[0]["loc"] == ["training", "xyz"]
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def test_import_code():
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code_str = """
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from spacy import Language
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class DummyComponent:
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def __init__(self, vocab, name):
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pass
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def initialize(self, get_examples, *, nlp, dummy_param: int):
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pass
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@Language.factory(
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"dummy_component",
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)
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def make_dummy_component(
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nlp: Language, name: str
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):
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return DummyComponent(nlp.vocab, name)
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"""
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with make_tempdir() as temp_dir:
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code_path = os.path.join(temp_dir, "code.py")
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with open(code_path, "w") as fileh:
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fileh.write(code_str)
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import_file("python_code", code_path)
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config = {"initialize": {"components": {"dummy_component": {"dummy_param": 1}}}}
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nlp = English.from_config(config)
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nlp.add_pipe("dummy_component")
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nlp.initialize()
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def test_to_ternary_int():
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assert to_ternary_int(True) == 1
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assert to_ternary_int(None) == 0
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assert to_ternary_int(False) == -1
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assert to_ternary_int(1) == 1
|
|
assert to_ternary_int(1.0) == 1
|
|
assert to_ternary_int(0) == 0
|
|
assert to_ternary_int(0.0) == 0
|
|
assert to_ternary_int(-1) == -1
|
|
assert to_ternary_int(5) == -1
|
|
assert to_ternary_int(-10) == -1
|
|
assert to_ternary_int("string") == -1
|
|
assert to_ternary_int([0, "string"]) == -1
|
|
|
|
|
|
def test_find_available_port():
|
|
host = "0.0.0.0"
|
|
port = 5000
|
|
assert find_available_port(port, host) == port, "Port 5000 isn't free"
|
|
|
|
from wsgiref.simple_server import demo_app, make_server
|
|
|
|
with make_server(host, port, demo_app) as httpd:
|
|
with pytest.warns(UserWarning, match="already in use"):
|
|
found_port = find_available_port(port, host, auto_select=True)
|
|
assert found_port == port + 1, "Didn't find next port"
|