mirror of
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116 lines
5.0 KiB
Python
116 lines
5.0 KiB
Python
from typing import Optional
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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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import re
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from ._util import app, Arg, Opt, parse_config_overrides, show_validation_error
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from ._util import import_code, setup_gpu
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from ..training.pretrain import pretrain
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from ..util import load_config
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@app.command(
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"pretrain",
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context_settings={"allow_extra_args": True, "ignore_unknown_options": True},
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)
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def pretrain_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, dir_okay=False, allow_dash=True),
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output_dir: Path = Arg(..., help="Directory to write weights to on each epoch"),
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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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resume_path: Optional[Path] = Opt(None, "--resume-path", "-r", help="Path to pretrained weights from which to resume pretraining"),
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epoch_resume: Optional[int] = Opt(None, "--epoch-resume", "-er", help="The epoch to resume counting from when using --resume-path. Prevents unintended overwriting of existing weight files."),
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use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU"),
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# fmt: on
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):
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"""
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Pre-train the 'token-to-vector' (tok2vec) layer of pipeline components,
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using an approximate language-modelling objective. Two objective types
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are available, vector-based and character-based.
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In the vector-based objective, we load word vectors that have been trained
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using a word2vec-style distributional similarity algorithm, and train a
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component like a CNN, BiLSTM, etc to predict vectors which match the
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pretrained ones. The weights are saved to a directory after each epoch. You
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can then pass a path to one of these pretrained weights files to the
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'spacy train' command.
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This technique may be especially helpful if you have little labelled data.
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However, it's still quite experimental, so your mileage may vary.
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To load the weights back in during 'spacy train', you need to ensure
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all settings are the same between pretraining and training. Ideally,
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this is done by using the same config file for both commands.
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DOCS: https://spacy.io/api/cli#pretrain
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"""
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config_overrides = parse_config_overrides(ctx.args)
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import_code(code_path)
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verify_cli_args(config_path, output_dir, resume_path, epoch_resume)
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setup_gpu(use_gpu)
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msg.info(f"Loading config from: {config_path}")
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with show_validation_error(config_path):
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raw_config = load_config(
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config_path, overrides=config_overrides, interpolate=False
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)
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config = raw_config.interpolate()
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if not config.get("pretraining"):
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# TODO: What's the solution here? How do we handle optional blocks?
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msg.fail("The [pretraining] block in your config is empty", exits=1)
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if not output_dir.exists():
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output_dir.mkdir(parents=True)
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msg.good(f"Created output directory: {output_dir}")
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# Save non-interpolated config
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raw_config.to_disk(output_dir / "config.cfg")
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msg.good("Saved config file in the output directory")
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pretrain(
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config,
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output_dir,
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resume_path=resume_path,
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epoch_resume=epoch_resume,
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use_gpu=use_gpu,
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silent=False,
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)
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msg.good("Successfully finished pretrain")
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def verify_cli_args(config_path, output_dir, resume_path, epoch_resume):
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if not config_path or (str(config_path) != "-" and not config_path.exists()):
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msg.fail("Config file not found", config_path, exits=1)
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if output_dir.exists() and [p for p in output_dir.iterdir()]:
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if resume_path:
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msg.warn(
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"Output directory is not empty.",
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"If you're resuming a run in this directory, the old weights "
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"for the consecutive epochs will be overwritten with the new ones.",
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)
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else:
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msg.warn(
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"Output directory is not empty. ",
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"It is better to use an empty directory or refer to a new output path, "
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"then the new directory will be created for you.",
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)
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if resume_path is not None:
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if resume_path.is_dir():
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# This is necessary because Windows gives a Permission Denied when we
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# try to open the directory later, which is confusing. See #7878
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msg.fail(
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"--resume-path should be a weights file, but {resume_path} is a directory.",
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exits=True,
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)
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model_name = re.search(r"model\d+\.bin", str(resume_path))
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if not model_name and not epoch_resume:
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msg.fail(
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"You have to use the --epoch-resume setting when using a renamed weight file for --resume-path",
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exits=True,
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)
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elif not model_name and epoch_resume < 0:
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msg.fail(
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f"The argument --epoch-resume has to be greater or equal to 0. {epoch_resume} is invalid",
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exits=True,
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)
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