mirror of
https://github.com/explosion/spaCy.git
synced 2024-09-21 11:29:13 +03:00
218 lines
8.8 KiB
Python
218 lines
8.8 KiB
Python
from typing import Optional, Dict, Callable, Any
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import logging
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from pathlib import Path
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from wasabi import msg
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import typer
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from thinc.api import Config, fix_random_seed, set_gpu_allocator
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import srsly
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from .. import util
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from ..util import registry, resolve_dot_names, OOV_RANK
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from ..schemas import ConfigSchemaTraining, ConfigSchemaPretrain, ConfigSchemaInit
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from ..language import Language
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from ..lookups import Lookups
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from ..errors import Errors
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from ._util import init_cli, Arg, Opt, parse_config_overrides, show_validation_error
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from ._util import import_code, get_sourced_components
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DEFAULT_OOV_PROB = -20
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@init_cli.command(
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"nlp", context_settings={"allow_extra_args": True, "ignore_unknown_options": True},
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)
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def init_pipeline_cli(
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# fmt: off
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ctx: typer.Context, # This is only used to read additional arguments
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config_path: Path = Arg(..., help="Path to config file", exists=True),
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output_path: Path = Arg(..., help="Output directory for the prepared data"),
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code_path: Optional[Path] = Opt(None, "--code", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
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verbose: bool = Opt(False, "--verbose", "-V", "-VV", help="Display more information for debugging purposes"),
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# fmt: on
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):
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util.logger.setLevel(logging.DEBUG if verbose else logging.ERROR)
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overrides = parse_config_overrides(ctx.args)
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import_code(code_path)
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with show_validation_error(config_path):
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config = util.load_config(config_path, overrides=overrides)
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nlp = init_pipeline(config)
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nlp.to_disk(output_path)
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# TODO: add more instructions
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msg.good(f"Saved initialized pipeline to {output_path}")
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def must_initialize(init_path: Path, config_path: Path, overrides: Dict) -> bool:
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config = util.load_config(config_path, overrides=overrides)
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if not init_path.exists():
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return True
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elif not (init_path / "config.cfg").exists():
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return True
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else:
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init_cfg = util.load_config(init_path / "config.cfg", interpolate=True)
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if config.to_str() != init_cfg.to_str():
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return True
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else:
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return False
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def init_pipeline(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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with show_validation_error():
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nlp = util.load_model_from_config(raw_config, auto_fill=True)
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msg.good("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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I = registry.resolve(config["initialize"], schema=ConfigSchemaInit)
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V = I["vocab"]
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init_vocab(nlp, data=V["data"], lookups=V["lookups"])
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msg.good("Created vocabulary")
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if V["vectors"] is not None:
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add_vectors(nlp, V["vectors"])
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msg.good(f"Added vectors: {V['vectors']}")
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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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msg.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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msg.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.begin_training(lambda: train_corpus(nlp), sgd=optimizer)
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msg.good(f"Initialized pipeline components")
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# Verify the config after calling 'begin_training' to ensure labels
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# are properly initialized
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verify_config(nlp)
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if "pretraining" in config and config["pretraining"]:
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P = registry.resolve(config["pretraining"], schema=ConfigSchemaPretrain)
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add_tok2vec_weights(nlp, P, I)
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# TODO: this should be handled better?
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nlp = before_to_disk(nlp)
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return nlp
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def init_vocab(
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nlp: Language, *, data: Optional[Path] = None, lookups: Optional[Lookups] = None,
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) -> Language:
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if lookups:
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nlp.vocab.lookups = lookups
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msg.good(f"Added vocab lookups: {', '.join(lookups.tables)}")
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data_path = util.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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msg.good(f"Added {len(nlp.vocab)} lexical entries to the vocab")
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def add_tok2vec_weights(
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nlp: Language, pretrain_config: Dict[str, Any], vocab_config: Dict[str, Any]
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) -> None:
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# Load pretrained tok2vec weights - cf. CLI command 'pretrain'
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P = pretrain_config
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V = vocab_config
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weights_data = None
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init_tok2vec = util.ensure_path(V["init_tok2vec"])
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if init_tok2vec is not None:
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if P["objective"].get("type") == "vectors" and not V["vectors"]:
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err = "Need initialize.vectors if pretraining.objective.type is vectors"
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msg.fail(err, exits=1)
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if not init_tok2vec.exists():
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msg.fail("Can't find pretrained tok2vec", init_tok2vec, exits=1)
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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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msg.fail(
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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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exits=1,
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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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msg.good(f"Loaded pretrained weights into component '{tok2vec_component}'")
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def add_vectors(nlp: Language, vectors: str) -> None:
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title = f"Config validation error for vectors {vectors}"
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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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with show_validation_error(
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title=title, desc=desc, hint_fill=False, show_config=False
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):
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util.load_vectors_into_model(nlp, vectors)
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msg(f"Added {len(nlp.vocab.vectors)} vectors from {vectors}")
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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 create_before_to_disk_callback(
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callback: Optional[Callable[[Language], Language]]
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) -> Callable[[Language], Language]:
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def before_to_disk(nlp: Language) -> Language:
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if not callback:
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return nlp
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modified_nlp = callback(nlp)
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if not isinstance(modified_nlp, Language):
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err = Errors.E914.format(name="before_to_disk", value=type(modified_nlp))
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raise ValueError(err)
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return modified_nlp
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return before_to_disk
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