mirror of
https://github.com/explosion/spaCy.git
synced 2025-11-04 01:48:04 +03:00
f98b41c390
* Add vector deduplication * Add `Vocab.deduplicate_vectors()` * Always run deduplication in `spacy init vectors` * Clean up a few vector-related error messages and docs examples * Always unique with numpy * Fix types
340 lines
13 KiB
Python
340 lines
13 KiB
Python
from typing import Union, Dict, Optional, Any, IO, TYPE_CHECKING
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from thinc.api import Config, fix_random_seed, set_gpu_allocator
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from thinc.api import ConfigValidationError
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from pathlib import Path
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import srsly
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import numpy
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import tarfile
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import gzip
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import zipfile
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import tqdm
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from itertools import islice
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import warnings
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from .pretrain import get_tok2vec_ref
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from ..lookups import Lookups
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from ..vectors import Vectors, Mode as VectorsMode
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from ..errors import Errors, Warnings
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from ..schemas import ConfigSchemaTraining
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from ..util import registry, load_model_from_config, resolve_dot_names, logger
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from ..util import load_model, ensure_path, get_sourced_components
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from ..util import OOV_RANK, DEFAULT_OOV_PROB
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if TYPE_CHECKING:
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from ..language import Language # noqa: F401
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def init_nlp(config: Config, *, use_gpu: int = -1) -> "Language":
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raw_config = config
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config = raw_config.interpolate()
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if "seed" not in config["training"]:
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raise ValueError(Errors.E1015.format(value="[training] seed"))
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if "gpu_allocator" not in config["training"]:
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raise ValueError(Errors.E1015.format(value="[training] gpu_allocator"))
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if config["training"]["seed"] is not None:
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fix_random_seed(config["training"]["seed"])
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allocator = config["training"]["gpu_allocator"]
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if use_gpu >= 0 and allocator:
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set_gpu_allocator(allocator)
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# Use original config here before it's resolved to functions
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sourced = get_sourced_components(config)
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nlp = load_model_from_config(raw_config, auto_fill=True)
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logger.info("Set up nlp object from config")
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config = nlp.config.interpolate()
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# Resolve all training-relevant sections using the filled nlp config
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T = registry.resolve(config["training"], schema=ConfigSchemaTraining)
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dot_names = [T["train_corpus"], T["dev_corpus"]]
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if not isinstance(T["train_corpus"], str):
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raise ConfigValidationError(
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desc=Errors.E897.format(
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field="training.train_corpus", type=type(T["train_corpus"])
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)
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)
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if not isinstance(T["dev_corpus"], str):
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raise ConfigValidationError(
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desc=Errors.E897.format(
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field="training.dev_corpus", type=type(T["dev_corpus"])
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)
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)
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train_corpus, dev_corpus = resolve_dot_names(config, dot_names)
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optimizer = T["optimizer"]
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# Components that shouldn't be updated during training
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frozen_components = T["frozen_components"]
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# Sourced components that require resume_training
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resume_components = [p for p in sourced if p not in frozen_components]
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logger.info(f"Pipeline: {nlp.pipe_names}")
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if resume_components:
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with nlp.select_pipes(enable=resume_components):
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logger.info(f"Resuming training for: {resume_components}")
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nlp.resume_training(sgd=optimizer)
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# Make sure that listeners are defined before initializing further
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nlp._link_components()
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with nlp.select_pipes(disable=[*frozen_components, *resume_components]):
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if T["max_epochs"] == -1:
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sample_size = 100
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logger.debug(
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f"Due to streamed train corpus, using only first {sample_size} "
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f"examples for initialization. If necessary, provide all labels "
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f"in [initialize]. More info: https://spacy.io/api/cli#init_labels"
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)
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nlp.initialize(
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lambda: islice(train_corpus(nlp), sample_size), sgd=optimizer
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)
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else:
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nlp.initialize(lambda: train_corpus(nlp), sgd=optimizer)
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logger.info(f"Initialized pipeline components: {nlp.pipe_names}")
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# Detect components with listeners that are not frozen consistently
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for name, proc in nlp.pipeline:
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for listener in getattr(
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proc, "listening_components", []
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): # e.g. tok2vec/transformer
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# Don't warn about components not in the pipeline
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if listener not in nlp.pipe_names:
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continue
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if listener in frozen_components and name not in frozen_components:
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logger.warning(Warnings.W087.format(name=name, listener=listener))
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# We always check this regardless, in case user freezes tok2vec
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if listener not in frozen_components and name in frozen_components:
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if name not in T["annotating_components"]:
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logger.warning(Warnings.W086.format(name=name, listener=listener))
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return nlp
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def init_vocab(
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nlp: "Language",
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*,
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data: Optional[Path] = None,
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lookups: Optional[Lookups] = None,
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vectors: Optional[str] = None,
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) -> None:
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if lookups:
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nlp.vocab.lookups = lookups
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logger.info(f"Added vocab lookups: {', '.join(lookups.tables)}")
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data_path = ensure_path(data)
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if data_path is not None:
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lex_attrs = srsly.read_jsonl(data_path)
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for lexeme in nlp.vocab:
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lexeme.rank = OOV_RANK
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for attrs in lex_attrs:
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if "settings" in attrs:
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continue
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lexeme = nlp.vocab[attrs["orth"]]
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lexeme.set_attrs(**attrs)
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if len(nlp.vocab):
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oov_prob = min(lex.prob for lex in nlp.vocab) - 1
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else:
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oov_prob = DEFAULT_OOV_PROB
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nlp.vocab.cfg.update({"oov_prob": oov_prob})
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logger.info(f"Added {len(nlp.vocab)} lexical entries to the vocab")
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logger.info("Created vocabulary")
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if vectors is not None:
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load_vectors_into_model(nlp, vectors)
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logger.info(f"Added vectors: {vectors}")
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# warn if source model vectors are not identical
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sourced_vectors_hashes = nlp.meta.pop("_sourced_vectors_hashes", {})
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vectors_hash = hash(nlp.vocab.vectors.to_bytes(exclude=["strings"]))
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for sourced_component, sourced_vectors_hash in sourced_vectors_hashes.items():
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if vectors_hash != sourced_vectors_hash:
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warnings.warn(Warnings.W113.format(name=sourced_component))
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logger.info("Finished initializing nlp object")
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def load_vectors_into_model(
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nlp: "Language", name: Union[str, Path], *, add_strings: bool = True
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) -> None:
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"""Load word vectors from an installed model or path into a model instance."""
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try:
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# Load with the same vocab, which automatically adds the vectors to
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# the current nlp object. Exclude lookups so they are not modified.
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exclude = ["lookups"]
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if not add_strings:
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exclude.append("strings")
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vectors_nlp = load_model(name, vocab=nlp.vocab, exclude=exclude)
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except ConfigValidationError as e:
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title = f"Config validation error for vectors {name}"
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desc = (
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"This typically means that there's a problem in the config.cfg included "
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"with the packaged vectors. Make sure that the vectors package you're "
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"loading is compatible with the current version of spaCy."
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)
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err = ConfigValidationError.from_error(e, title=title, desc=desc)
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raise err from None
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if (
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len(vectors_nlp.vocab.vectors.keys()) == 0
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and vectors_nlp.vocab.vectors.mode != VectorsMode.floret
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) or (
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vectors_nlp.vocab.vectors.shape[0] == 0
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and vectors_nlp.vocab.vectors.mode == VectorsMode.floret
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):
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logger.warning(Warnings.W112.format(name=name))
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for lex in nlp.vocab:
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lex.rank = nlp.vocab.vectors.key2row.get(lex.orth, OOV_RANK) # type: ignore[attr-defined]
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def init_tok2vec(
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nlp: "Language", pretrain_config: Dict[str, Any], init_config: Dict[str, Any]
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) -> bool:
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# Load pretrained tok2vec weights - cf. CLI command 'pretrain'
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P = pretrain_config
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I = init_config
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weights_data = None
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init_tok2vec = ensure_path(I["init_tok2vec"])
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if init_tok2vec is not None:
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if not init_tok2vec.exists():
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err = f"can't find pretrained tok2vec: {init_tok2vec}"
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errors = [{"loc": ["initialize", "init_tok2vec"], "msg": err}]
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raise ConfigValidationError(config=nlp.config, errors=errors)
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with init_tok2vec.open("rb") as file_:
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weights_data = file_.read()
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if weights_data is not None:
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layer = get_tok2vec_ref(nlp, P)
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layer.from_bytes(weights_data)
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logger.info(f"Loaded pretrained weights from {init_tok2vec}")
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return True
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return False
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def convert_vectors(
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nlp: "Language",
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vectors_loc: Optional[Path],
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*,
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truncate: int,
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prune: int,
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name: Optional[str] = None,
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mode: str = VectorsMode.default,
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) -> None:
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vectors_loc = ensure_path(vectors_loc)
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if vectors_loc and vectors_loc.parts[-1].endswith(".npz"):
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nlp.vocab.vectors = Vectors(
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strings=nlp.vocab.strings, data=numpy.load(vectors_loc.open("rb"))
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)
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for lex in nlp.vocab:
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if lex.rank and lex.rank != OOV_RANK:
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nlp.vocab.vectors.add(lex.orth, row=lex.rank) # type: ignore[attr-defined]
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nlp.vocab.deduplicate_vectors()
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else:
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if vectors_loc:
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logger.info(f"Reading vectors from {vectors_loc}")
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vectors_data, vector_keys, floret_settings = read_vectors(
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vectors_loc,
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truncate,
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mode=mode,
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)
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logger.info(f"Loaded vectors from {vectors_loc}")
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else:
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vectors_data, vector_keys = (None, None)
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if vector_keys is not None and mode != VectorsMode.floret:
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for word in vector_keys:
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if word not in nlp.vocab:
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nlp.vocab[word]
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if vectors_data is not None:
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if mode == VectorsMode.floret:
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nlp.vocab.vectors = Vectors(
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strings=nlp.vocab.strings,
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data=vectors_data,
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**floret_settings,
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)
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else:
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nlp.vocab.vectors = Vectors(
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strings=nlp.vocab.strings, data=vectors_data, keys=vector_keys
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)
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nlp.vocab.deduplicate_vectors()
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if name is None:
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# TODO: Is this correct? Does this matter?
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nlp.vocab.vectors.name = f"{nlp.meta['lang']}_{nlp.meta['name']}.vectors"
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else:
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nlp.vocab.vectors.name = name
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nlp.meta["vectors"]["name"] = nlp.vocab.vectors.name
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if prune >= 1 and mode != VectorsMode.floret:
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nlp.vocab.prune_vectors(prune)
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def read_vectors(
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vectors_loc: Path, truncate_vectors: int, *, mode: str = VectorsMode.default
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):
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f = ensure_shape(vectors_loc)
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header_parts = next(f).split()
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shape = tuple(int(size) for size in header_parts[:2])
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floret_settings = {}
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if mode == VectorsMode.floret:
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if len(header_parts) != 8:
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raise ValueError(
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"Invalid header for floret vectors. "
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"Expected: bucket dim minn maxn hash_count hash_seed BOW EOW"
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)
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floret_settings = {
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"mode": "floret",
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"minn": int(header_parts[2]),
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"maxn": int(header_parts[3]),
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"hash_count": int(header_parts[4]),
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"hash_seed": int(header_parts[5]),
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"bow": header_parts[6],
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"eow": header_parts[7],
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}
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if truncate_vectors >= 1:
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raise ValueError(Errors.E860)
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else:
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assert len(header_parts) == 2
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if truncate_vectors >= 1:
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shape = (truncate_vectors, shape[1])
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vectors_data = numpy.zeros(shape=shape, dtype="f")
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vectors_keys = []
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for i, line in enumerate(tqdm.tqdm(f)):
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line = line.rstrip()
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pieces = line.rsplit(" ", vectors_data.shape[1])
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word = pieces.pop(0)
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if len(pieces) != vectors_data.shape[1]:
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raise ValueError(Errors.E094.format(line_num=i, loc=vectors_loc))
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vectors_data[i] = numpy.asarray(pieces, dtype="f")
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vectors_keys.append(word)
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if i == truncate_vectors - 1:
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break
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return vectors_data, vectors_keys, floret_settings
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def open_file(loc: Union[str, Path]) -> IO:
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"""Handle .gz, .tar.gz or unzipped files"""
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loc = ensure_path(loc)
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if tarfile.is_tarfile(str(loc)):
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return tarfile.open(str(loc), "r:gz") # type: ignore[return-value]
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elif loc.parts[-1].endswith("gz"):
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return (line.decode("utf8") for line in gzip.open(str(loc), "r")) # type: ignore[return-value]
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elif loc.parts[-1].endswith("zip"):
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zip_file = zipfile.ZipFile(str(loc))
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names = zip_file.namelist()
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file_ = zip_file.open(names[0])
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return (line.decode("utf8") for line in file_) # type: ignore[return-value]
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else:
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return loc.open("r", encoding="utf8")
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def ensure_shape(vectors_loc):
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"""Ensure that the first line of the data is the vectors shape.
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If it's not, we read in the data and output the shape as the first result,
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so that the reader doesn't have to deal with the problem.
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"""
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lines = open_file(vectors_loc)
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first_line = next(lines)
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try:
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shape = tuple(int(size) for size in first_line.split()[:2])
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except ValueError:
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shape = None
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if shape is not None:
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# All good, give the data
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yield first_line
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yield from lines
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else:
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# Figure out the shape, make it the first value, and then give the
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# rest of the data.
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width = len(first_line.split()) - 1
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length = 1
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for _ in lines:
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length += 1
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yield f"{length} {width}"
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# Reading the lines in again from file. This to avoid having to
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# store all the results in a list in memory
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lines2 = open_file(vectors_loc)
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yield from lines2
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