mirror of https://github.com/explosion/spaCy.git
264 lines
10 KiB
Python
264 lines
10 KiB
Python
from typing import Union, Dict, Optional, Any, List, IO, TYPE_CHECKING
|
|
from thinc.api import Config, fix_random_seed, set_gpu_allocator
|
|
from thinc.api import ConfigValidationError
|
|
from pathlib import Path
|
|
import srsly
|
|
import numpy
|
|
import tarfile
|
|
import gzip
|
|
import zipfile
|
|
import tqdm
|
|
|
|
from ..lookups import Lookups
|
|
from ..vectors import Vectors
|
|
from ..errors import Errors
|
|
from ..schemas import ConfigSchemaTraining
|
|
from ..util import registry, load_model_from_config, resolve_dot_names, logger
|
|
from ..util import load_model, ensure_path, OOV_RANK, DEFAULT_OOV_PROB
|
|
|
|
if TYPE_CHECKING:
|
|
from ..language import Language # noqa: F401
|
|
|
|
|
|
def init_nlp(config: Config, *, use_gpu: int = -1) -> "Language":
|
|
raw_config = config
|
|
config = raw_config.interpolate()
|
|
if config["training"]["seed"] is not None:
|
|
fix_random_seed(config["training"]["seed"])
|
|
allocator = config["training"]["gpu_allocator"]
|
|
if use_gpu >= 0 and allocator:
|
|
set_gpu_allocator(allocator)
|
|
# Use original config here before it's resolved to functions
|
|
sourced_components = get_sourced_components(config)
|
|
nlp = load_model_from_config(raw_config, auto_fill=True)
|
|
logger.info("Set up nlp object from config")
|
|
config = nlp.config.interpolate()
|
|
# Resolve all training-relevant sections using the filled nlp config
|
|
T = registry.resolve(config["training"], schema=ConfigSchemaTraining)
|
|
dot_names = [T["train_corpus"], T["dev_corpus"]]
|
|
if not isinstance(T["train_corpus"], str):
|
|
raise ConfigValidationError(desc=Errors.E897.format(field="training.train_corpus", type=type(T["train_corpus"])))
|
|
if not isinstance(T["dev_corpus"], str):
|
|
raise ConfigValidationError(desc=Errors.E897.format(field="training.dev_corpus", type=type(T["dev_corpus"])))
|
|
train_corpus, dev_corpus = resolve_dot_names(config, dot_names)
|
|
optimizer = T["optimizer"]
|
|
# Components that shouldn't be updated during training
|
|
frozen_components = T["frozen_components"]
|
|
# Sourced components that require resume_training
|
|
resume_components = [p for p in sourced_components if p not in frozen_components]
|
|
logger.info(f"Pipeline: {nlp.pipe_names}")
|
|
if resume_components:
|
|
with nlp.select_pipes(enable=resume_components):
|
|
logger.info(f"Resuming training for: {resume_components}")
|
|
nlp.resume_training(sgd=optimizer)
|
|
with nlp.select_pipes(disable=[*frozen_components, *resume_components]):
|
|
nlp.initialize(lambda: train_corpus(nlp), sgd=optimizer)
|
|
logger.info(f"Initialized pipeline components: {nlp.pipe_names}")
|
|
return nlp
|
|
|
|
|
|
def init_vocab(
|
|
nlp: "Language",
|
|
*,
|
|
data: Optional[Path] = None,
|
|
lookups: Optional[Lookups] = None,
|
|
vectors: Optional[str] = None,
|
|
) -> "Language":
|
|
if lookups:
|
|
nlp.vocab.lookups = lookups
|
|
logger.info(f"Added vocab lookups: {', '.join(lookups.tables)}")
|
|
data_path = ensure_path(data)
|
|
if data_path is not None:
|
|
lex_attrs = srsly.read_jsonl(data_path)
|
|
for lexeme in nlp.vocab:
|
|
lexeme.rank = OOV_RANK
|
|
for attrs in lex_attrs:
|
|
if "settings" in attrs:
|
|
continue
|
|
lexeme = nlp.vocab[attrs["orth"]]
|
|
lexeme.set_attrs(**attrs)
|
|
if len(nlp.vocab):
|
|
oov_prob = min(lex.prob for lex in nlp.vocab) - 1
|
|
else:
|
|
oov_prob = DEFAULT_OOV_PROB
|
|
nlp.vocab.cfg.update({"oov_prob": oov_prob})
|
|
logger.info(f"Added {len(nlp.vocab)} lexical entries to the vocab")
|
|
logger.info("Created vocabulary")
|
|
if vectors is not None:
|
|
load_vectors_into_model(nlp, vectors)
|
|
logger.info(f"Added vectors: {vectors}")
|
|
logger.info("Finished initializing nlp object")
|
|
|
|
|
|
def load_vectors_into_model(
|
|
nlp: "Language", name: Union[str, Path], *, add_strings: bool = True
|
|
) -> None:
|
|
"""Load word vectors from an installed model or path into a model instance."""
|
|
try:
|
|
vectors_nlp = load_model(name)
|
|
except ConfigValidationError as e:
|
|
title = f"Config validation error for vectors {name}"
|
|
desc = (
|
|
"This typically means that there's a problem in the config.cfg included "
|
|
"with the packaged vectors. Make sure that the vectors package you're "
|
|
"loading is compatible with the current version of spaCy."
|
|
)
|
|
err = ConfigValidationError.from_error(e, config=None, title=title, desc=desc)
|
|
raise err from None
|
|
nlp.vocab.vectors = vectors_nlp.vocab.vectors
|
|
if add_strings:
|
|
# I guess we should add the strings from the vectors_nlp model?
|
|
# E.g. if someone does a similarity query, they might expect the strings.
|
|
for key in nlp.vocab.vectors.key2row:
|
|
if key in vectors_nlp.vocab.strings:
|
|
nlp.vocab.strings.add(vectors_nlp.vocab.strings[key])
|
|
|
|
|
|
def init_tok2vec(
|
|
nlp: "Language", pretrain_config: Dict[str, Any], init_config: Dict[str, Any]
|
|
) -> bool:
|
|
# Load pretrained tok2vec weights - cf. CLI command 'pretrain'
|
|
P = pretrain_config
|
|
I = init_config
|
|
weights_data = None
|
|
init_tok2vec = ensure_path(I["init_tok2vec"])
|
|
if init_tok2vec is not None:
|
|
if P["objective"].get("type") == "vectors" and not I["vectors"]:
|
|
err = 'need initialize.vectors if pretraining.objective.type is "vectors"'
|
|
errors = [{"loc": ["initialize"], "msg": err}]
|
|
raise ConfigValidationError(config=nlp.config, errors=errors)
|
|
if not init_tok2vec.exists():
|
|
err = f"can't find pretrained tok2vec: {init_tok2vec}"
|
|
errors = [{"loc": ["initialize", "init_tok2vec"], "msg": err}]
|
|
raise ConfigValidationError(config=nlp.config, errors=errors)
|
|
with init_tok2vec.open("rb") as file_:
|
|
weights_data = file_.read()
|
|
if weights_data is not None:
|
|
tok2vec_component = P["component"]
|
|
if tok2vec_component is None:
|
|
desc = (
|
|
f"To use pretrained tok2vec weights, [pretraining.component] "
|
|
f"needs to specify the component that should load them."
|
|
)
|
|
err = "component can't be null"
|
|
errors = [{"loc": ["pretraining", "component"], "msg": err}]
|
|
raise ConfigValidationError(
|
|
config=nlp.config["pretraining"], errors=errors, desc=desc
|
|
)
|
|
layer = nlp.get_pipe(tok2vec_component).model
|
|
if P["layer"]:
|
|
layer = layer.get_ref(P["layer"])
|
|
layer.from_bytes(weights_data)
|
|
return True
|
|
return False
|
|
|
|
|
|
def get_sourced_components(config: Union[Dict[str, Any], Config]) -> List[str]:
|
|
"""RETURNS (List[str]): All sourced components in the original config,
|
|
e.g. {"source": "en_core_web_sm"}. If the config contains a key
|
|
"factory", we assume it refers to a component factory.
|
|
"""
|
|
return [
|
|
name
|
|
for name, cfg in config.get("components", {}).items()
|
|
if "factory" not in cfg and "source" in cfg
|
|
]
|
|
|
|
|
|
def convert_vectors(
|
|
nlp: "Language",
|
|
vectors_loc: Optional[Path],
|
|
*,
|
|
truncate: int,
|
|
prune: int,
|
|
name: Optional[str] = None,
|
|
) -> None:
|
|
vectors_loc = ensure_path(vectors_loc)
|
|
if vectors_loc and vectors_loc.parts[-1].endswith(".npz"):
|
|
nlp.vocab.vectors = Vectors(data=numpy.load(vectors_loc.open("rb")))
|
|
for lex in nlp.vocab:
|
|
if lex.rank and lex.rank != OOV_RANK:
|
|
nlp.vocab.vectors.add(lex.orth, row=lex.rank)
|
|
else:
|
|
if vectors_loc:
|
|
logger.info(f"Reading vectors from {vectors_loc}")
|
|
vectors_data, vector_keys = read_vectors(vectors_loc, truncate)
|
|
logger.info(f"Loaded vectors from {vectors_loc}")
|
|
else:
|
|
vectors_data, vector_keys = (None, None)
|
|
if vector_keys is not None:
|
|
for word in vector_keys:
|
|
if word not in nlp.vocab:
|
|
nlp.vocab[word]
|
|
if vectors_data is not None:
|
|
nlp.vocab.vectors = Vectors(data=vectors_data, keys=vector_keys)
|
|
if name is None:
|
|
# TODO: Is this correct? Does this matter?
|
|
nlp.vocab.vectors.name = f"{nlp.meta['lang']}_{nlp.meta['name']}.vectors"
|
|
else:
|
|
nlp.vocab.vectors.name = name
|
|
nlp.meta["vectors"]["name"] = nlp.vocab.vectors.name
|
|
if prune >= 1:
|
|
nlp.vocab.prune_vectors(prune)
|
|
|
|
|
|
def read_vectors(vectors_loc: Path, truncate_vectors: int):
|
|
f = open_file(vectors_loc)
|
|
f = ensure_shape(f)
|
|
shape = tuple(int(size) for size in next(f).split())
|
|
if truncate_vectors >= 1:
|
|
shape = (truncate_vectors, shape[1])
|
|
vectors_data = numpy.zeros(shape=shape, dtype="f")
|
|
vectors_keys = []
|
|
for i, line in enumerate(tqdm.tqdm(f)):
|
|
line = line.rstrip()
|
|
pieces = line.rsplit(" ", vectors_data.shape[1])
|
|
word = pieces.pop(0)
|
|
if len(pieces) != vectors_data.shape[1]:
|
|
raise ValueError(Errors.E094.format(line_num=i, loc=vectors_loc))
|
|
vectors_data[i] = numpy.asarray(pieces, dtype="f")
|
|
vectors_keys.append(word)
|
|
if i == truncate_vectors - 1:
|
|
break
|
|
return vectors_data, vectors_keys
|
|
|
|
|
|
def open_file(loc: Union[str, Path]) -> IO:
|
|
"""Handle .gz, .tar.gz or unzipped files"""
|
|
loc = ensure_path(loc)
|
|
if tarfile.is_tarfile(str(loc)):
|
|
return tarfile.open(str(loc), "r:gz")
|
|
elif loc.parts[-1].endswith("gz"):
|
|
return (line.decode("utf8") for line in gzip.open(str(loc), "r"))
|
|
elif loc.parts[-1].endswith("zip"):
|
|
zip_file = zipfile.ZipFile(str(loc))
|
|
names = zip_file.namelist()
|
|
file_ = zip_file.open(names[0])
|
|
return (line.decode("utf8") for line in file_)
|
|
else:
|
|
return loc.open("r", encoding="utf8")
|
|
|
|
|
|
def ensure_shape(lines):
|
|
"""Ensure that the first line of the data is the vectors shape.
|
|
If it's not, we read in the data and output the shape as the first result,
|
|
so that the reader doesn't have to deal with the problem.
|
|
"""
|
|
first_line = next(lines)
|
|
try:
|
|
shape = tuple(int(size) for size in first_line.split())
|
|
except ValueError:
|
|
shape = None
|
|
if shape is not None:
|
|
# All good, give the data
|
|
yield first_line
|
|
yield from lines
|
|
else:
|
|
# Figure out the shape, make it the first value, and then give the
|
|
# rest of the data.
|
|
width = len(first_line.split()) - 1
|
|
captured = [first_line] + list(lines)
|
|
length = len(captured)
|
|
yield f"{length} {width}"
|
|
yield from captured
|