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Fix allocation of non-transient strings in StringStore (#13713) 

* Fix bug in memory-zone code when adding non-transient strings. The error could result in segmentation faults or other memory errors during memory zones if new labels were added to the model.
* Fix handling of new morphological labels within memory zones. Addresses second issue reported in Memory leak of MorphAnalysis object. #13684
This commit is contained in:
Matthew Honnibal 2024-12-11 13:06:53 +01:00 committed by GitHub
parent 3e30b5bef6
commit a6317b3836
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6 changed files with 64 additions and 53 deletions
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2
.github/workflows/tests.yml vendored
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@ -58,7 +58,7 @@ jobs:
fail-fast: true
matrix:
os: [ubuntu-latest, windows-latest, macos-latest]
python_version: ["3.9", "3.11", "3.12"]
python_version: ["3.9", "3.12"]
runs-on: ${{ matrix.os }}

7
setup.cfg
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@ -21,6 +21,7 @@ classifiers =
Programming Language :: Python :: 3.10
Programming Language :: Python :: 3.11
Programming Language :: Python :: 3.12
Programming Language :: Python :: 3.13
Topic :: Scientific/Engineering
project_urls =
Release notes = https://github.com/explosion/spaCy/releases
@ -29,13 +30,13 @@ project_urls =
[options]
zip_safe = false
include_package_data = true
python_requires = >=3.9
python_requires = >=3.9,<3.13
# NOTE: This section is superseded by pyproject.toml and will be removed in
# spaCy v4
setup_requires =
cython>=0.25,<3.0
numpy>=2.0.0,<2.1.0; python_version < "3.9"
numpy>=2.0.0,<2.1.0; python_version >= "3.9"
numpy>=2.0.0,<3.0.0; python_version < "3.9"
numpy>=2.0.0,<3.0.0; python_version >= "3.9"
# We also need our Cython packages here to compile against
cymem>=2.0.2,<2.1.0
preshed>=3.0.2,<3.1.0

2
spacy/about.py
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@ -1,5 +1,5 @@
# fmt: off
__title__ = "spacy"
__version__ = "3.8.2"
__version__ = "3.8.3"
__download_url__ = "https://github.com/explosion/spacy-models/releases/download"
__compatibility__ = "https://raw.githubusercontent.com/explosion/spacy-models/master/compatibility.json"

10
spacy/morphology.pyx
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@ -57,16 +57,20 @@ cdef class Morphology:
field_feature_pairs = []
for field in sorted(string_features):
values = string_features[field]
self.strings.add(field, allow_transient=False),
field_id = self.strings[field]
for value in values.split(self.VALUE_SEP):
field_sep_value = field + self.FIELD_SEP + value
self.strings.add(field_sep_value, allow_transient=False),
field_feature_pairs.append((
self.strings.add(field),
self.strings.add(field + self.FIELD_SEP + value),
field_id,
self.strings[field_sep_value]
))
cdef MorphAnalysisC tag = self.create_morph_tag(field_feature_pairs)
# the hash key for the tag is either the hash of the normalized UFEATS
# string or the hash of an empty placeholder
norm_feats_string = self.normalize_features(features)
tag.key = self.strings.add(norm_feats_string)
tag.key = self.strings.add(norm_feats_string, allow_transient=False)
self.insert(tag)
return tag.key

7
spacy/strings.pyx
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@ -222,6 +222,8 @@ cdef class StringStore:
internally should not.
RETURNS (uint64): The string's hash value.
"""
if not string:
return 0
if allow_transient is None:
allow_transient = self.mem is not self._non_temp_mem
cdef hash_t str_hash
@ -383,7 +385,10 @@ cdef class StringStore:
cdef Utf8Str* value = <Utf8Str*>self._map.get(key)
if value is not NULL:
return value
value = _allocate(self.mem, <unsigned char*>utf8_string, length)
if allow_transient:
value = _allocate(self.mem, <unsigned char*>utf8_string, length)
else:
value = _allocate(self._non_temp_mem, <unsigned char*>utf8_string, length)
self._map.set(key, value)
if allow_transient and self.mem is not self._non_temp_mem:
self._transient_keys.push_back(key)

89
spacy/tests/training/test_pretraining.py.disabled
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@ -264,50 +264,51 @@ def test_pretraining_tagger():
pretrain(filled, tmp_dir)
def test_pretraining_training():
"""Test that training can use a pretrained Tok2Vec model"""
config = Config().from_str(pretrain_string_internal)
nlp = util.load_model_from_config(config, auto_fill=True, validate=False)
filled = nlp.config
pretrain_config = util.load_config(DEFAULT_CONFIG_PRETRAIN_PATH)
filled = pretrain_config.merge(filled)
train_config = util.load_config(DEFAULT_CONFIG_PATH)
filled = train_config.merge(filled)
with make_tempdir() as tmp_dir:
pretrain_dir = tmp_dir / "pretrain"
pretrain_dir.mkdir()
file_path = write_sample_jsonl(pretrain_dir)
filled["paths"]["raw_text"] = file_path
filled["pretraining"]["component"] = "tagger"
filled["pretraining"]["layer"] = "tok2vec"
train_dir = tmp_dir / "train"
train_dir.mkdir()
train_path, dev_path = write_sample_training(train_dir)
filled["paths"]["train"] = train_path
filled["paths"]["dev"] = dev_path
filled = filled.interpolate()
P = filled["pretraining"]
nlp_base = init_nlp(filled)
model_base = (
nlp_base.get_pipe(P["component"]).model.get_ref(P["layer"]).get_ref("embed")
)
embed_base = None
for node in model_base.walk():
if node.name == "hashembed":
embed_base = node
pretrain(filled, pretrain_dir)
pretrained_model = Path(pretrain_dir / "model3.bin")
assert pretrained_model.exists()
filled["initialize"]["init_tok2vec"] = str(pretrained_model)
nlp = init_nlp(filled)
model = nlp.get_pipe(P["component"]).model.get_ref(P["layer"]).get_ref("embed")
embed = None
for node in model.walk():
if node.name == "hashembed":
embed = node
# ensure that the tok2vec weights are actually changed by the pretraining
assert np.any(np.not_equal(embed.get_param("E"), embed_base.get_param("E")))
train(nlp, train_dir)
# Try to debug segfault on windows
#def test_pretraining_training():
# """Test that training can use a pretrained Tok2Vec model"""
# config = Config().from_str(pretrain_string_internal)
# nlp = util.load_model_from_config(config, auto_fill=True, validate=False)
# filled = nlp.config
# pretrain_config = util.load_config(DEFAULT_CONFIG_PRETRAIN_PATH)
# filled = pretrain_config.merge(filled)
# train_config = util.load_config(DEFAULT_CONFIG_PATH)
# filled = train_config.merge(filled)
# with make_tempdir() as tmp_dir:
# pretrain_dir = tmp_dir / "pretrain"
# pretrain_dir.mkdir()
# file_path = write_sample_jsonl(pretrain_dir)
# filled["paths"]["raw_text"] = file_path
# filled["pretraining"]["component"] = "tagger"
# filled["pretraining"]["layer"] = "tok2vec"
# train_dir = tmp_dir / "train"
# train_dir.mkdir()
# train_path, dev_path = write_sample_training(train_dir)
# filled["paths"]["train"] = train_path
# filled["paths"]["dev"] = dev_path
# filled = filled.interpolate()
# P = filled["pretraining"]
# nlp_base = init_nlp(filled)
# model_base = (
# nlp_base.get_pipe(P["component"]).model.get_ref(P["layer"]).get_ref("embed")
# )
# embed_base = None
# for node in model_base.walk():
# if node.name == "hashembed":
# embed_base = node
# pretrain(filled, pretrain_dir)
# pretrained_model = Path(pretrain_dir / "model3.bin")
# assert pretrained_model.exists()
# filled["initialize"]["init_tok2vec"] = str(pretrained_model)
# nlp = init_nlp(filled)
# model = nlp.get_pipe(P["component"]).model.get_ref(P["layer"]).get_ref("embed")
# embed = None
# for node in model.walk():
# if node.name == "hashembed":
# embed = node
# # ensure that the tok2vec weights are actually changed by the pretraining
# assert np.any(np.not_equal(embed.get_param("E"), embed_base.get_param("E")))
# train(nlp, train_dir)
def write_sample_jsonl(tmp_dir):