mirror of https://github.com/explosion/spaCy.git
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
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@ -58,7 +58,7 @@ jobs:
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fail-fast: true
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matrix:
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os: [ubuntu-latest, windows-latest, macos-latest]
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python_version: ["3.9", "3.11", "3.12"]
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python_version: ["3.9", "3.12"]
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runs-on: ${{ matrix.os }}
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@ -21,6 +21,7 @@ classifiers =
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Programming Language :: Python :: 3.10
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Programming Language :: Python :: 3.11
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Programming Language :: Python :: 3.12
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Programming Language :: Python :: 3.13
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Topic :: Scientific/Engineering
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project_urls =
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Release notes = https://github.com/explosion/spaCy/releases
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@ -29,13 +30,13 @@ project_urls =
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[options]
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zip_safe = false
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include_package_data = true
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python_requires = >=3.9
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python_requires = >=3.9,<3.13
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# NOTE: This section is superseded by pyproject.toml and will be removed in
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# spaCy v4
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setup_requires =
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cython>=0.25,<3.0
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numpy>=2.0.0,<2.1.0; python_version < "3.9"
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numpy>=2.0.0,<2.1.0; python_version >= "3.9"
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numpy>=2.0.0,<3.0.0; python_version < "3.9"
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numpy>=2.0.0,<3.0.0; python_version >= "3.9"
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# We also need our Cython packages here to compile against
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cymem>=2.0.2,<2.1.0
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preshed>=3.0.2,<3.1.0
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@ -1,5 +1,5 @@
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# fmt: off
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__title__ = "spacy"
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__version__ = "3.8.2"
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__version__ = "3.8.3"
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__download_url__ = "https://github.com/explosion/spacy-models/releases/download"
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__compatibility__ = "https://raw.githubusercontent.com/explosion/spacy-models/master/compatibility.json"
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@ -57,16 +57,20 @@ cdef class Morphology:
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field_feature_pairs = []
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for field in sorted(string_features):
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values = string_features[field]
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self.strings.add(field, allow_transient=False),
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field_id = self.strings[field]
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for value in values.split(self.VALUE_SEP):
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field_sep_value = field + self.FIELD_SEP + value
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self.strings.add(field_sep_value, allow_transient=False),
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field_feature_pairs.append((
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self.strings.add(field),
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self.strings.add(field + self.FIELD_SEP + value),
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field_id,
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self.strings[field_sep_value]
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))
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cdef MorphAnalysisC tag = self.create_morph_tag(field_feature_pairs)
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# the hash key for the tag is either the hash of the normalized UFEATS
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# string or the hash of an empty placeholder
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norm_feats_string = self.normalize_features(features)
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tag.key = self.strings.add(norm_feats_string)
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tag.key = self.strings.add(norm_feats_string, allow_transient=False)
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self.insert(tag)
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return tag.key
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@ -222,6 +222,8 @@ cdef class StringStore:
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internally should not.
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RETURNS (uint64): The string's hash value.
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"""
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if not string:
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return 0
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if allow_transient is None:
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allow_transient = self.mem is not self._non_temp_mem
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cdef hash_t str_hash
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@ -383,7 +385,10 @@ cdef class StringStore:
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cdef Utf8Str* value = <Utf8Str*>self._map.get(key)
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if value is not NULL:
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return value
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if allow_transient:
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value = _allocate(self.mem, <unsigned char*>utf8_string, length)
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else:
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value = _allocate(self._non_temp_mem, <unsigned char*>utf8_string, length)
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self._map.set(key, value)
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if allow_transient and self.mem is not self._non_temp_mem:
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self._transient_keys.push_back(key)
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@ -264,50 +264,51 @@ def test_pretraining_tagger():
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pretrain(filled, tmp_dir)
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def test_pretraining_training():
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"""Test that training can use a pretrained Tok2Vec model"""
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config = Config().from_str(pretrain_string_internal)
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nlp = util.load_model_from_config(config, auto_fill=True, validate=False)
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filled = nlp.config
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pretrain_config = util.load_config(DEFAULT_CONFIG_PRETRAIN_PATH)
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filled = pretrain_config.merge(filled)
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train_config = util.load_config(DEFAULT_CONFIG_PATH)
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filled = train_config.merge(filled)
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with make_tempdir() as tmp_dir:
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pretrain_dir = tmp_dir / "pretrain"
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pretrain_dir.mkdir()
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file_path = write_sample_jsonl(pretrain_dir)
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filled["paths"]["raw_text"] = file_path
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filled["pretraining"]["component"] = "tagger"
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filled["pretraining"]["layer"] = "tok2vec"
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train_dir = tmp_dir / "train"
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train_dir.mkdir()
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train_path, dev_path = write_sample_training(train_dir)
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filled["paths"]["train"] = train_path
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filled["paths"]["dev"] = dev_path
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filled = filled.interpolate()
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P = filled["pretraining"]
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nlp_base = init_nlp(filled)
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model_base = (
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nlp_base.get_pipe(P["component"]).model.get_ref(P["layer"]).get_ref("embed")
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)
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embed_base = None
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for node in model_base.walk():
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if node.name == "hashembed":
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embed_base = node
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pretrain(filled, pretrain_dir)
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pretrained_model = Path(pretrain_dir / "model3.bin")
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assert pretrained_model.exists()
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filled["initialize"]["init_tok2vec"] = str(pretrained_model)
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nlp = init_nlp(filled)
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model = nlp.get_pipe(P["component"]).model.get_ref(P["layer"]).get_ref("embed")
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embed = None
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for node in model.walk():
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if node.name == "hashembed":
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embed = node
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# ensure that the tok2vec weights are actually changed by the pretraining
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assert np.any(np.not_equal(embed.get_param("E"), embed_base.get_param("E")))
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train(nlp, train_dir)
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# Try to debug segfault on windows
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#def test_pretraining_training():
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# """Test that training can use a pretrained Tok2Vec model"""
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# config = Config().from_str(pretrain_string_internal)
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# nlp = util.load_model_from_config(config, auto_fill=True, validate=False)
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# filled = nlp.config
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# pretrain_config = util.load_config(DEFAULT_CONFIG_PRETRAIN_PATH)
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# filled = pretrain_config.merge(filled)
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# train_config = util.load_config(DEFAULT_CONFIG_PATH)
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# filled = train_config.merge(filled)
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# with make_tempdir() as tmp_dir:
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# pretrain_dir = tmp_dir / "pretrain"
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# pretrain_dir.mkdir()
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# file_path = write_sample_jsonl(pretrain_dir)
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# filled["paths"]["raw_text"] = file_path
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# filled["pretraining"]["component"] = "tagger"
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# filled["pretraining"]["layer"] = "tok2vec"
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# train_dir = tmp_dir / "train"
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# train_dir.mkdir()
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# train_path, dev_path = write_sample_training(train_dir)
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# filled["paths"]["train"] = train_path
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# filled["paths"]["dev"] = dev_path
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# filled = filled.interpolate()
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# P = filled["pretraining"]
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# nlp_base = init_nlp(filled)
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# model_base = (
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# nlp_base.get_pipe(P["component"]).model.get_ref(P["layer"]).get_ref("embed")
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# )
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# embed_base = None
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# for node in model_base.walk():
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# if node.name == "hashembed":
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# embed_base = node
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# pretrain(filled, pretrain_dir)
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# pretrained_model = Path(pretrain_dir / "model3.bin")
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# assert pretrained_model.exists()
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# filled["initialize"]["init_tok2vec"] = str(pretrained_model)
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# nlp = init_nlp(filled)
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# model = nlp.get_pipe(P["component"]).model.get_ref(P["layer"]).get_ref("embed")
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# embed = None
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# for node in model.walk():
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# if node.name == "hashembed":
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# embed = node
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# # ensure that the tok2vec weights are actually changed by the pretraining
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# assert np.any(np.not_equal(embed.get_param("E"), embed_base.get_param("E")))
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# train(nlp, train_dir)
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def write_sample_jsonl(tmp_dir):
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