mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-11 04:08:09 +03:00
298 lines
12 KiB
Python
298 lines
12 KiB
Python
from typing import Union, Dict, Optional, Any, List, 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 .loop import create_before_to_disk_callback
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from ..lookups import Lookups
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from ..vectors import Vectors
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from ..errors import Errors
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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, 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 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_components = 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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train_corpus, dev_corpus = resolve_dot_names(config, dot_names)
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optimizer = T["optimizer"]
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before_to_disk = create_before_to_disk_callback(T["before_to_disk"])
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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_components 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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with nlp.select_pipes(disable=[*frozen_components, *resume_components]):
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nlp.initialize(lambda: train_corpus(nlp), sgd=optimizer)
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logger.info("Initialized pipeline components")
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# Verify the config after calling 'initialize' to ensure labels
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# are properly initialized
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verify_config(nlp)
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nlp = before_to_disk(nlp)
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return nlp
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def must_reinitialize(train_config: Config, init_config: Config) -> bool:
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# TODO: do this better and more fine-grained
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return train_config.interpolate().to_str() == init_config.interpolate().to_str()
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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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) -> "Language":
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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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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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vectors_nlp = load_model(name)
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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(config=None, title=title, desc=desc)
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raise err from None
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nlp.vocab.vectors = vectors_nlp.vocab.vectors
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if add_strings:
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# I guess we should add the strings from the vectors_nlp model?
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# E.g. if someone does a similarity query, they might expect the strings.
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for key in nlp.vocab.vectors.key2row:
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if key in vectors_nlp.vocab.strings:
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nlp.vocab.strings.add(vectors_nlp.vocab.strings[key])
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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 P["objective"].get("type") == "vectors" and not I["vectors"]:
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err = 'need initialize.vectors if pretraining.objective.type is "vectors"'
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errors = [{"loc": ["initialize"], "msg": err}]
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raise ConfigValidationError(config=nlp.config, errors=errors)
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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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tok2vec_component = P["component"]
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if tok2vec_component is None:
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desc = (
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f"To use pretrained tok2vec weights, [pretraining.component] "
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f"needs to specify the component that should load them."
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)
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err = "component can't be null"
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errors = [{"loc": ["pretraining", "component"], "msg": err}]
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raise ConfigValidationError(
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config=nlp.config["pretraining"], errors=errors, desc=desc
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)
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layer = nlp.get_pipe(tok2vec_component).model
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if P["layer"]:
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layer = layer.get_ref(P["layer"])
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layer.from_bytes(weights_data)
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return True
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return False
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def verify_config(nlp: "Language") -> None:
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"""Perform additional checks based on the config, loaded nlp object and training data."""
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# TODO: maybe we should validate based on the actual components, the list
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# in config["nlp"]["pipeline"] instead?
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for pipe_config in nlp.config["components"].values():
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# We can't assume that the component name == the factory
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factory = pipe_config["factory"]
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if factory == "textcat":
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verify_textcat_config(nlp, pipe_config)
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def verify_textcat_config(nlp: "Language", pipe_config: Dict[str, Any]) -> None:
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# if 'positive_label' is provided: double check whether it's in the data and
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# the task is binary
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if pipe_config.get("positive_label"):
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textcat_labels = nlp.get_pipe("textcat").labels
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pos_label = pipe_config.get("positive_label")
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if pos_label not in textcat_labels:
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raise ValueError(
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Errors.E920.format(pos_label=pos_label, labels=textcat_labels)
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)
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if len(list(textcat_labels)) != 2:
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raise ValueError(
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Errors.E919.format(pos_label=pos_label, labels=textcat_labels)
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)
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def get_sourced_components(config: Union[Dict[str, Any], Config]) -> List[str]:
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"""RETURNS (List[str]): All sourced components in the original config,
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e.g. {"source": "en_core_web_sm"}. If the config contains a key
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"factory", we assume it refers to a component factory.
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"""
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return [
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name
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for name, cfg in config.get("components", {}).items()
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if "factory" not in cfg and "source" in cfg
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]
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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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) -> 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(data=numpy.load(vectors_loc.open("rb")))
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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)
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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 = read_vectors(vectors_loc, truncate)
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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:
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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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nlp.vocab.vectors = Vectors(data=vectors_data, keys=vector_keys)
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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:
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nlp.vocab.prune_vectors(prune)
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def read_vectors(vectors_loc: Path, truncate_vectors: int):
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f = open_file(vectors_loc)
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f = ensure_shape(f)
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shape = tuple(int(size) for size in next(f).split())
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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
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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")
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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"))
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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_)
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else:
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return loc.open("r", encoding="utf8")
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def ensure_shape(lines):
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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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first_line = next(lines)
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try:
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shape = tuple(int(size) for size in first_line.split())
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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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captured = [first_line] + list(lines)
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length = len(captured)
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yield f"{length} {width}"
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yield from captured
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