mirror of
https://github.com/explosion/spaCy.git
synced 2024-09-21 11:29:13 +03:00
112 lines
4.5 KiB
Python
112 lines
4.5 KiB
Python
from typing import Optional, Dict, Any, Tuple, Union, Callable, List
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import logging
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import srsly
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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
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from .train import create_before_to_disk_callback
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from .. import util
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from ..util import registry
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from ..schemas import ConfigSchemaTraining
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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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from ..util import resolve_dot_names
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@init_cli.command(
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"pipeline",
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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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config = util.load_config(config_path, overrides=overrides)
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with show_validation_error(config_path):
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nlp = init_pipeline(config)
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nlp.to_disk(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=-1):
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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 = util.load_model_from_config(raw_config)
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# Resolve all training-relevant sections using the filled nlp config
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T = registry.resolve(
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config["training"],
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schema=ConfigSchemaTraining,
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validate=True,
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)
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dot_names = [T["train_corpus"], T["dev_corpus"], T["raw_text"]]
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train_corpus, dev_corpus, raw_text = resolve_dot_names(config, dot_names)
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util.load_vocab_data_into_model(nlp, lookups=T["lookups"])
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if T["vectors"] is not None:
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add_vectors(nlp, T["vectors"])
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score_weights = T["score_weights"]
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optimizer = T["optimizer"]
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batcher = T["batcher"]
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train_logger = T["logger"]
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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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# 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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# Load pretrained tok2vec weights - cf. CLI command 'pretrain'
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if weights_data is not None:
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tok2vec_component = C["pretraining"]["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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tok2vec_layer = C["pretraining"]["layer"]
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if tok2vec_layer:
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layer = layer.get_ref(tok2vec_layer)
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layer.from_bytes(weights_data)
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msg.info(f"Loaded pretrained weights into component '{tok2vec_component}'")
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return nlp
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