def assert_packed_msg_equal(b1, b2):
"""Assert that two packed msgpack messages are equal."""
msg1 = srsly.msgpack_loads(b1)
msg2 = srsly.msgpack_loads(b2)
assert sorted(msg1.keys()) == sorted(msg2.keys())
for (k1, v1), (k2, v2) in zip(sorted(msg1.items()), sorted(msg2.items())):
assert k1 == k2
assert v1 == v2
def from_bytes(bytes_data, setters, exclude):
msg = srsly.msgpack_loads(bytes_data)
for key, setter in setters.items():
# Split to support file names like meta.json
if key.split(".")[0] not in exclude and key in msg:
setter(msg[key])
return msg
def from_bytes(self, bytes_data):
data = srsly.msgpack_loads(bytes_data)
weights = data[b"weights"]
queue = [self]
i = 0
for layer in queue:
# Hack to support saving/loading PyTorch models. TODO: Improve
if hasattr(layer, "_model") and not isinstance(layer._model, Model):
layer.from_bytes(weights[i])
i += 1
elif hasattr(layer, "_mem"):
if b"seed" in weights[i]:
layer.seed = weights[i][b"seed"]
for dim, value in weights[i][b"dims"].items():
if isinstance(dim, bytes):
dim = dim.decode("utf8")
setattr(layer, dim, value)
for param in weights[i][b"params"]:
name = param[b"name"]
if isinstance(name, bytes):
name = name.decode("utf8")
dest = getattr(layer, name)
copy_array(dest, param[b"value"])
i += 1
if hasattr(layer, "_layers"):
queue.extend(layer._layers)
return self
def from_bytes(self, bytes_data: bytes) -> "Model":
"""Deserialize the model from a bytes representation. Models are usually
serialized using msgpack, so you should be able to call msgpack.loads()
on the data and get back a dictionary with the contents.
Serialization should round-trip identically, i.e. the same bytes should
result from loading and serializing a model.
"""
msg = srsly.msgpack_loads(bytes_data)
msg = convert_recursive(is_xp_array, self.ops.asarray, msg)
return self.from_dict(msg)
def from_bytes(self, patterns_bytes, **kwargs):
"""Load the entity ruler from a bytestring.
patterns_bytes (bytes): The bytestring to load.
**kwargs: Other config paramters, mostly for consistency.
RETURNS (EntityRuler): The loaded entity ruler.
DOCS: https://spacy.io/api/entityruler#from_bytes
"""
patterns = srsly.msgpack_loads(patterns_bytes)
self.add_patterns(patterns)
return self
def from_bytes(self, bytes_data: bytes, **kwargs) -> "Lookups":
"""Load the lookups from a bytestring.
bytes_data (bytes): The data to load.
RETURNS (Lookups): The loaded Lookups.
DOCS: https://spacy.io/api/lookups#from_bytes
"""
self._tables = {}
for key, value in srsly.msgpack_loads(bytes_data).items():
self._tables[key] = Table(key, value)
return self
def from_bytes(self, patterns_bytes, **kwargs):
"""Load the entity ruler from a bytestring.
patterns_bytes (bytes): The bytestring to load.
**kwargs: Other config paramters, mostly for consistency.
RETURNS (EntityRuler): The loaded entity ruler.
DOCS: https://spacy.io/api/entityruler#from_bytes
"""
patterns = srsly.msgpack_loads(patterns_bytes)
self.add_patterns(patterns)
return self
def can_from_bytes(self,
bytes_data: bytes,
*,
strict: bool = True) -> bool:
"""Check whether the bytes data is compatible with the model. If 'strict',
the function returns False if the model has an attribute already loaded
that would be changed.
"""
try:
msg = srsly.msgpack_loads(bytes_data)
except ValueError:
return False
return self.can_from_dict(msg, strict=strict)
def get_docs(self, vocab):
"""Recover Doc objects from the annotations, using the given vocab."""
for string in self.strings:
vocab[string]
orth_col = self.attrs.index(ORTH)
for i in range(len(self.tokens)):
tokens = self.tokens[i]
spaces = self.spaces[i]
words = [vocab.strings[orth] for orth in tokens[:, orth_col]]
doc = Doc(vocab, words=words, spaces=spaces)
doc = doc.from_array(self.attrs, tokens)
if self.store_user_data:
doc.user_data.update(srsly.msgpack_loads(self.user_data[i]))
yield doc
def from_bytes(self, bytes_data):
ops = get_current_ops()
msg = srsly.msgpack_loads(bytes_data)
self.cfg = msg["config"]
filelike = BytesIO(msg["state"])
filelike.seek(0)
if ops.device_type == "cpu":
map_location = "cpu"
else: # pragma: no cover
device_id = torch.cuda.current_device()
map_location = "cuda:%d" % device_id
self._model.load_state_dict(torch.load(filelike, map_location=map_location))
self._model.to(map_location)
return self
def from_bytes(self, bytes_data: bytes, exclude: Sequence[str] = tuple()):
"""Load a Sense2Vec object from a bytestring.
bytes_data (bytes): The data to load.
exclude (list): Names of serialization fields to exclude.
RETURNS (Sense2Vec): The loaded object.
"""
data = srsly.msgpack_loads(bytes_data)
self.vectors = Vectors().from_bytes(data["vectors"])
self.freqs = dict(data.get("freqs", []))
self.cfg.update(data.get("cfg", {}))
if "strings" not in exclude and "strings" in data:
self.strings = StringStore().from_bytes(data["strings"])
return self
def from_bytes(self, bytes_data: bytes) -> "Table":
"""Load a table from a bytestring.
bytes_data (bytes): The data to load.
RETURNS (Table): The loaded table.
DOCS: https://spacy.io/api/lookups#table.from_bytes
"""
loaded = srsly.msgpack_loads(bytes_data)
data = loaded.get("dict", {})
self.name = loaded["name"]
self.bloom = BloomFilter().from_bytes(loaded["bloom"])
self.clear()
self.update(data)
return self
def from_bytes(self, string):
"""Deserialize the binder's annotations from a byte string."""
msg = srsly.msgpack_loads(gzip.decompress(string))
self.attrs = msg["attrs"]
self.strings = set(msg["strings"])
lengths = numpy.fromstring(msg["lengths"], dtype="int32")
flat_spaces = numpy.fromstring(msg["spaces"], dtype=bool)
flat_tokens = numpy.fromstring(msg["tokens"], dtype="uint64")
shape = (flat_tokens.size // len(self.attrs), len(self.attrs))
flat_tokens = flat_tokens.reshape(shape)
flat_spaces = flat_spaces.reshape((flat_spaces.size, 1))
self.tokens = NumpyOps().unflatten(flat_tokens, lengths)
self.spaces = NumpyOps().unflatten(flat_spaces, lengths)
for tokens in self.tokens:
assert len(tokens.shape) == 2, tokens.shape
return self
def from_bytes(self, string):
"""Deserialize the binder's annotations from a byte string."""
msg = srsly.msgpack_loads(gzip.decompress(string))
self.attrs = msg["attrs"]
self.strings = set(msg["strings"])
lengths = numpy.fromstring(msg["lengths"], dtype="int32")
flat_spaces = numpy.fromstring(msg["spaces"], dtype=bool)
flat_tokens = numpy.fromstring(msg["tokens"], dtype="uint64")
shape = (flat_tokens.size // len(self.attrs), len(self.attrs))
flat_tokens = flat_tokens.reshape(shape)
flat_spaces = flat_spaces.reshape((flat_spaces.size, 1))
self.tokens = NumpyOps().unflatten(flat_tokens, lengths)
self.spaces = NumpyOps().unflatten(flat_spaces, lengths)
for tokens in self.tokens:
assert len(tokens.shape) == 2, tokens.shape
return self
def from_bytes(self, bytes_data):
msg = srsly.msgpack_loads(bytes_data)
config_dict = msg["config"]
tok_dict = msg["tokenizer"]
if config_dict:
with make_tempdir() as temp_dir:
config_file = temp_dir / "config.json"
srsly.write_json(config_file, config_dict)
config = AutoConfig.from_pretrained(config_file)
for x, x_bytes in tok_dict.items():
Path(temp_dir / x).write_bytes(x_bytes)
tokenizer = AutoTokenizer.from_pretrained(str(temp_dir.absolute()))
vocab_file_contents = None
if hasattr(tokenizer, "vocab_file"):
vocab_file_name = tokenizer.vocab_files_names["vocab_file"]
vocab_file_path = str((temp_dir / vocab_file_name).absolute())
with open(vocab_file_path, "rb") as fileh:
vocab_file_contents = fileh.read()
transformer = AutoModel.from_config(config)
self._hfmodel = HFObjects(
tokenizer,
transformer,
vocab_file_contents,
SimpleFrozenDict(),
SimpleFrozenDict(),
)
self._model = transformer
filelike = BytesIO(msg["state"])
filelike.seek(0)
ops = get_current_ops()
if ops.device_type == "cpu":
map_location = "cpu"
else: # pragma: no cover
device_id = torch.cuda.current_device()
map_location = f"cuda:{device_id}"
self._model.load_state_dict(torch.load(filelike, map_location=map_location))
self._model.to(map_location)
else:
self._hfmodel = HFObjects(
None,
None,
None,
msg["_init_tokenizer_config"],
msg["_init_transformer_config"],
)
return self
def load(cls, lang, filepath):
"""
Load previously saved :class:`Corpus` binary data, reproduce the original
`:class:`spacy.tokens.Doc`s tokens and annotations, and instantiate
a new :class:`Corpus` from them.
Args:
lang (str or :class:`spacy.language.Language`)
filepath (str): Full path to file on disk where :class:`Corpus` data
was previously saved as a binary file.
Returns:
:class:`Corpus`
See Also:
:meth:`Corpus.save()`
"""
spacy_lang = _get_spacy_lang(lang)
with tio.open_sesame(filepath, mode="rb") as f:
msg = srsly.msgpack_loads(f.read())
if spacy_lang.meta != msg["meta"]:
LOGGER.warning("the spacy langs are different!")
for string in msg["strings"]:
spacy_lang.vocab[string]
attrs = msg["attrs"]
lengths = np.frombuffer(msg["lengths"], dtype="int32")
flat_tokens = np.frombuffer(msg["tokens"], dtype="uint64")
flat_tokens = flat_tokens.reshape(
(flat_tokens.size // len(attrs), len(attrs)))
tokens = np.asarray(NumpyOps().unflatten(flat_tokens, lengths))
user_datas = msg["user_datas"]
def _make_spacy_docs(tokens, user_datas):
for toks, user_data in compat.zip_(tokens, user_datas):
doc = spacy.tokens.Doc(
spacy_lang.vocab,
words=[
spacy_lang.vocab.strings[orth] for orth in toks[:, 0]
],
spaces=np.ndarray.tolist(toks[:, 1]),
)
doc = doc.from_array(attrs[2:], toks[:, 2:])
doc.user_data = user_data
yield doc
return cls(spacy_lang, data=_make_spacy_docs(tokens, user_datas))
def from_bytes(self, patterns_bytes, **kwargs):
"""Load the entity ruler from a bytestring.
patterns_bytes (bytes): The bytestring to load.
**kwargs: Other config paramters, mostly for consistency.
RETURNS (EntityRuler): The loaded entity ruler.
DOCS: https://spacy.io/api/entityruler#from_bytes
"""
cfg = srsly.msgpack_loads(patterns_bytes)
if isinstance(cfg, dict):
self.add_patterns(cfg.get('patterns', cfg))
self.overwrite = cfg.get('overwrite', False)
self.ent_id_sep = cfg.get('ent_id_sep', DEFAULT_ENT_ID_SEP)
else:
self.add_patterns(cfg)
return self
def from_bytes(self, patterns_bytes, **kwargs):
"""Load the entity ruler from a bytestring.
patterns_bytes (bytes): The bytestring to load.
**kwargs: Other config paramters, mostly for consistency.
RETURNS (EntityRuler): The loaded entity ruler.
DOCS: https://spacy.io/api/entityruler#from_bytes
"""
cfg = srsly.msgpack_loads(patterns_bytes)
if isinstance(cfg, dict):
self.add_patterns(cfg.get("patterns", cfg))
self.overwrite = cfg.get("overwrite", False)
self.phrase_matcher_attr = cfg.get("phrase_matcher_attr", None)
if self.phrase_matcher_attr is not None:
self.phrase_matcher = PhraseMatcher(
self.nlp.vocab, attr=self.phrase_matcher_attr)
self.ent_id_sep = cfg.get("ent_id_sep", DEFAULT_ENT_ID_SEP)
else:
self.add_patterns(cfg)
return self
def get_docs(self, vocab):
"""Recover Doc objects from the annotations, using the given vocab.
vocab (Vocab): The shared vocab.
YIELDS (Doc): The Doc objects.
DOCS: https://spacy.io/api/docbin#get_docs
"""
for string in self.strings:
vocab[string]
orth_col = self.attrs.index(ORTH)
for i in range(len(self.tokens)):
tokens = self.tokens[i]
spaces = self.spaces[i]
words = [vocab.strings[orth] for orth in tokens[:, orth_col]]
doc = Doc(vocab, words=words, spaces=spaces)
doc = doc.from_array(self.attrs, tokens)
if self.store_user_data:
user_data = srsly.msgpack_loads(self.user_data[i],
use_list=False)
doc.user_data.update(user_data)
yield doc
def from_bytes(self, serial: bytes, **kwargs):
"""Load waterwheel from a bytestring.
Parameters
----------
serial : bytes
The serialized bytes data.
Returns
-------
self : WaterWheel
The loaded WaterWheel object.
"""
cfg = srsly.msgpack_loads(serial)
if isinstance(cfg, dict):
vocab = cfg.get('vocab', {})
for hash, label in vocab.items():
self._ent_ids[int(hash)] = label
self._qualifiers[label] = [label.lower(), label.lower() + 's']
self._qualifiers['MOUNTAIN'].extend(['mount', 'mounts', 'mt.'])
self._stop_words = cfg.get('stop_words', [])
self._stop_words = set(self._stop_words)
self._wikidata = cfg.get('wikidata', {})
doc_bins_bytes = cfg.get('doc_bins', {})
self._doc_bins = {
key: DocBin().from_bytes(value)
for key, value in doc_bins_bytes.items()
}
phrases_bin = {
key: list(bin.get_docs(self.nlp.vocab))
for key, bin in self._doc_bins.items()
}
for key, phrases in phrases_bin.items():
self.phrase_matcher.add(key.upper(), phrases)
return self
def from_bytes(
self, patterns_bytes: bytes, *, exclude: Iterable[str] = SimpleFrozenList()
) -> "EntityRuler":
"""Load the entity ruler from a bytestring.
patterns_bytes (bytes): The bytestring to load.
RETURNS (EntityRuler): The loaded entity ruler.
DOCS: https://spacy.io/api/entityruler#from_bytes
"""
cfg = srsly.msgpack_loads(patterns_bytes)
self.clear()
if isinstance(cfg, dict):
self.add_patterns(cfg.get("patterns", cfg))
self.overwrite = cfg.get("overwrite", False)
self.phrase_matcher_attr = cfg.get("phrase_matcher_attr", None)
if self.phrase_matcher_attr is not None:
self.phrase_matcher = PhraseMatcher(
self.nlp.vocab, attr=self.phrase_matcher_attr
)
self.ent_id_sep = cfg.get("ent_id_sep", DEFAULT_ENT_ID_SEP)
else:
self.add_patterns(cfg)
return self
def from_bytes(self, bytes_data):
"""Deserialize the DocBin's annotations from a bytestring.
bytes_data (bytes): The data to load from.
RETURNS (DocBin): The loaded DocBin.
DOCS: https://spacy.io/api/docbin#from_bytes
"""
msg = srsly.msgpack_loads(zlib.decompress(bytes_data))
self.attrs = msg["attrs"]
self.strings = set(msg["strings"])
lengths = numpy.fromstring(msg["lengths"], dtype="int32")
flat_spaces = numpy.fromstring(msg["spaces"], dtype=bool)
flat_tokens = numpy.fromstring(msg["tokens"], dtype="uint64")
shape = (flat_tokens.size // len(self.attrs), len(self.attrs))
flat_tokens = flat_tokens.reshape(shape)
flat_spaces = flat_spaces.reshape((flat_spaces.size, 1))
self.tokens = NumpyOps().unflatten(flat_tokens, lengths)
self.spaces = NumpyOps().unflatten(flat_spaces, lengths)
if self.store_user_data and "user_data" in msg:
self.user_data = list(msg["user_data"])
for tokens in self.tokens:
assert len(tokens.shape) == 2, tokens.shape # this should never happen
return self
def deserialize_pkuseg_processors(b):
nonlocal pkuseg_processors_data
pkuseg_processors_data = srsly.msgpack_loads(b)
def load_patterns(b):
self.add_patterns(srsly.msgpack_loads(b))
def from_bytes(self, bytes_data, exclude=tuple(), **kwargs):
msg = srsly.msgpack_loads(bytes_data)
for field in self.serialization_fields:
setattr(self, field, msg[field])
self.finish_deserializing()
return self
def test_serialize_transformer_data():
data = {"x": TransformerData.empty()}
bytes_data = srsly.msgpack_dumps(data)
new_data = srsly.msgpack_loads(bytes_data)
assert isinstance(new_data["x"], TransformerData)
def deserialize_attr(_: Any, value: Any, name: str, model: Model) -> Any:
"""Deserialize an attribute value (defaults to msgpack). You can register
custom deserializers using the @deserialize_attr.register decorator with the
type to deserialize, e.g.: @deserialize_attr.register(MyCustomObject).
"""
return srsly.msgpack_loads(value)
def from_bytes(self, byte_string: bytes) -> "TransformerData":
msg = srsly.msgpack_loads(byte_string)
self.from_dict(msg)
return self
def deserialize_pkuseg_processors(b):
pkuseg_data["processors_data"] = srsly.msgpack_loads(b)
def from_bytes(self, bytes_data):
msg = srsly.msgpack_loads(bytes_data)
self.cfg = msg["config"]
self._load_params(msg["state"])
return self
