from typing import Union, Dict, Optional, Any, 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 itertools import islice import warnings from .pretrain import get_tok2vec_ref from ..lookups import Lookups from ..vectors import Vectors from ..errors import Errors, Warnings from ..schemas import ConfigSchemaTraining from ..util import registry, load_model_from_config, resolve_dot_names, logger from ..util import load_model, ensure_path, get_sourced_components from ..util import 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 "seed" not in config["training"]: raise ValueError(Errors.E1015.format(value="[training] seed")) if "gpu_allocator" not in config["training"]: raise ValueError(Errors.E1015.format(value="[training] gpu_allocator")) 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 = 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 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) # Make sure that listeners are defined before initializing further nlp._link_components() with nlp.select_pipes(disable=[*frozen_components, *resume_components]): if T["max_epochs"] == -1: sample_size = 100 logger.debug( f"Due to streamed train corpus, using only first {sample_size} " f"examples for initialization. If necessary, provide all labels " f"in [initialize]. More info: https://spacy.io/api/cli#init_labels" ) nlp.initialize( lambda: islice(train_corpus(nlp), sample_size), sgd=optimizer ) else: nlp.initialize(lambda: train_corpus(nlp), sgd=optimizer) logger.info(f"Initialized pipeline components: {nlp.pipe_names}") # Detect components with listeners that are not frozen consistently for name, proc in nlp.pipeline: for listener in getattr( proc, "listening_components", [] ): # e.g. tok2vec/transformer # Don't warn about components not in the pipeline if listener not in nlp.pipe_names: continue if listener in frozen_components and name not in frozen_components: logger.warning(Warnings.W087.format(name=name, listener=listener)) # We always check this regardless, in case user freezes tok2vec if listener not in frozen_components and name in frozen_components: if name not in T["annotating_components"]: logger.warning(Warnings.W086.format(name=name, listener=listener)) 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}") # warn if source model vectors are not identical sourced_vectors_hashes = nlp.meta.pop("_sourced_vectors_hashes", {}) vectors_hash = hash(nlp.vocab.vectors.to_bytes()) for sourced_component, sourced_vectors_hash in sourced_vectors_hashes.items(): if vectors_hash != sourced_vectors_hash: warnings.warn(Warnings.W113.format(name=sourced_component)) 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: # Load with the same vocab, which automatically adds the vectors to # the current nlp object. Exclude lookups so they are not modified. exclude = ["lookups"] if not add_strings: exclude.append("strings") vectors_nlp = load_model(name, vocab=nlp.vocab, exclude=exclude) 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, title=title, desc=desc) raise err from None if len(vectors_nlp.vocab.vectors.keys()) == 0: logger.warning(Warnings.W112.format(name=name)) for lex in nlp.vocab: lex.rank = nlp.vocab.vectors.key2row.get(lex.orth, OOV_RANK) 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 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: layer = get_tok2vec_ref(nlp, P) layer.from_bytes(weights_data) logger.info(f"Loaded pretrained weights from {init_tok2vec}") return True return False 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 = ensure_shape(vectors_loc) 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(vectors_loc): """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. """ lines = open_file(vectors_loc) 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 length = 1 for _ in lines: length += 1 yield f"{length} {width}" # Reading the lines in again from file. This to avoid having to # store all the results in a list in memory lines2 = open_file(vectors_loc) yield from lines2