spaCy/spacy/cli/init_pipeline.py

207 lines
8.3 KiB
Python

from typing import Optional, Dict, Callable, Any
import logging
from pathlib import Path
from wasabi import msg
import typer
from thinc.api import Config, fix_random_seed, set_gpu_allocator
import srsly
from .. import util
from ..util import registry, resolve_dot_names, OOV_RANK
from ..schemas import ConfigSchemaTraining, ConfigSchemaPretrain
from ..language import Language
from ..lookups import Lookups
from ..errors import Errors
from ._util import init_cli, Arg, Opt, parse_config_overrides, show_validation_error
from ._util import import_code, get_sourced_components, load_from_paths
DEFAULT_OOV_PROB = -20
@init_cli.command(
"nlp", context_settings={"allow_extra_args": True, "ignore_unknown_options": True},
)
def init_pipeline_cli(
# fmt: off
ctx: typer.Context, # This is only used to read additional arguments
config_path: Path = Arg(..., help="Path to config file", exists=True),
output_path: Path = Arg(..., help="Output directory for the prepared data"),
code_path: Optional[Path] = Opt(None, "--code", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
verbose: bool = Opt(False, "--verbose", "-V", "-VV", help="Display more information for debugging purposes"),
# fmt: on
):
util.logger.setLevel(logging.DEBUG if verbose else logging.ERROR)
overrides = parse_config_overrides(ctx.args)
import_code(code_path)
with show_validation_error(config_path):
config = util.load_config(config_path, overrides=overrides)
nlp = init_pipeline(config)
nlp.to_disk(output_path)
# TODO: add more instructions
msg.good(f"Saved initialized pipeline to {output_path}")
def must_initialize(init_path: Path, config_path: Path, overrides: Dict) -> bool:
config = util.load_config(config_path, overrides=overrides)
if not init_path.exists():
return True
elif not (init_path / "config.cfg").exists():
return True
else:
init_cfg = util.load_config(init_path / "config.cfg", interpolate=True)
if config.to_str() != init_cfg.to_str():
return True
else:
return False
def init_pipeline(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)
with show_validation_error():
nlp = util.load_model_from_config(raw_config)
msg.good("Set up nlp object from config")
# 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"], T["raw_text"]]
train_corpus, dev_corpus, raw_text = resolve_dot_names(config, dot_names)
# TODO: move lookups to [initialize], add vocab data
init_vocab(nlp, lookups=T["lookups"])
msg.good("Created vocabulary")
if T["vectors"] is not None:
add_vectors(nlp, T["vectors"])
msg.good(f"Added vectors: {T['vectors']}")
optimizer = T["optimizer"]
before_to_disk = create_before_to_disk_callback(T["before_to_disk"])
# 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]
msg.info(f"Pipeline: {nlp.pipe_names}")
if resume_components:
with nlp.select_pipes(enable=resume_components):
msg.info(f"Resuming training for: {resume_components}")
nlp.resume_training(sgd=optimizer)
with nlp.select_pipes(disable=[*frozen_components, *resume_components]):
nlp.begin_training(lambda: train_corpus(nlp), sgd=optimizer)
msg.good(f"Initialized pipeline components")
# Verify the config after calling 'begin_training' to ensure labels
# are properly initialized
verify_config(nlp)
if "pretraining" in config and config["pretraining"]:
P = registry.resolve(config["pretraining"], schema=ConfigSchemaPretrain)
add_tok2vec_weights({"training": T, "pretraining": P}, nlp)
# TODO: this should be handled better?
nlp = before_to_disk(nlp)
return nlp
def init_vocab(
nlp: Language,
*,
vocab_data: Optional[Path] = None,
lookups: Optional[Lookups] = None,
) -> Language:
if lookups:
nlp.vocab.lookups = lookups
msg.good(f"Added vocab lookups: {', '.join(lookups.tables)}")
data_path = util.ensure_path(vocab_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})
msg.good(f"Added {len(nlp.vocab)} lexical entries to the vocab")
def add_tok2vec_weights(config: Config, nlp: Language) -> None:
# Load pretrained tok2vec weights - cf. CLI command 'pretrain'
weights_data = load_from_paths(config)
if weights_data is not None:
tok2vec_component = config["pretraining"]["component"]
if tok2vec_component is None:
msg.fail(
f"To use pretrained tok2vec weights, [pretraining.component] "
f"needs to specify the component that should load them.",
exits=1,
)
layer = nlp.get_pipe(tok2vec_component).model
tok2vec_layer = config["pretraining"]["layer"]
if tok2vec_layer:
layer = layer.get_ref(tok2vec_layer)
layer.from_bytes(weights_data)
msg.good(f"Loaded pretrained weights into component '{tok2vec_component}'")
def add_vectors(nlp: Language, vectors: str) -> None:
title = f"Config validation error for vectors {vectors}"
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."
)
with show_validation_error(
title=title, desc=desc, hint_fill=False, show_config=False
):
util.load_vectors_into_model(nlp, vectors)
msg(f"Added {len(nlp.vocab.vectors)} vectors from {vectors}")
def verify_config(nlp: Language) -> None:
"""Perform additional checks based on the config, loaded nlp object and training data."""
# TODO: maybe we should validate based on the actual components, the list
# in config["nlp"]["pipeline"] instead?
for pipe_config in nlp.config["components"].values():
# We can't assume that the component name == the factory
factory = pipe_config["factory"]
if factory == "textcat":
verify_textcat_config(nlp, pipe_config)
def verify_textcat_config(nlp: Language, pipe_config: Dict[str, Any]) -> None:
# if 'positive_label' is provided: double check whether it's in the data and
# the task is binary
if pipe_config.get("positive_label"):
textcat_labels = nlp.get_pipe("textcat").labels
pos_label = pipe_config.get("positive_label")
if pos_label not in textcat_labels:
raise ValueError(
Errors.E920.format(pos_label=pos_label, labels=textcat_labels)
)
if len(list(textcat_labels)) != 2:
raise ValueError(
Errors.E919.format(pos_label=pos_label, labels=textcat_labels)
)
def create_before_to_disk_callback(
callback: Optional[Callable[[Language], Language]]
) -> Callable[[Language], Language]:
def before_to_disk(nlp: Language) -> Language:
if not callback:
return nlp
modified_nlp = callback(nlp)
if not isinstance(modified_nlp, Language):
err = Errors.E914.format(name="before_to_disk", value=type(modified_nlp))
raise ValueError(err)
return modified_nlp
return before_to_disk