mirror of
https://github.com/explosion/spaCy.git
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541 lines
20 KiB
Python
541 lines
20 KiB
Python
from typing import Optional, Dict, Any
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from timeit import default_timer as timer
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import srsly
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import tqdm
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from pathlib import Path
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from wasabi import msg
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import thinc
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import thinc.schedules
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from thinc.api import use_pytorch_for_gpu_memory, require_gpu, fix_random_seed
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import random
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import typer
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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
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from ..gold import Corpus, Example
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from ..lookups import Lookups
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from .. import util
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from ..errors import Errors
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from ..schemas import ConfigSchema
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# Don't remove - required to load the built-in architectures
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from ..ml import models # noqa: F401
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registry = util.registry
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@app.command(
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"train", context_settings={"allow_extra_args": True, "ignore_unknown_options": True}
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)
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def train_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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train_path: Path = Arg(..., help="Location of training data", exists=True),
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dev_path: Path = Arg(..., help="Location of development data", exists=True),
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config_path: Path = Arg(..., help="Path to config file", exists=True),
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output_path: Optional[Path] = Opt(None, "--output", "--output-path", "-o", help="Output directory to store model in"),
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code_path: Optional[Path] = Opt(None, "--code-path", "-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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"""
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Train or update a spaCy model. Requires data in spaCy's binary format. To
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convert data from other formats, use the `spacy convert` command. The
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config file includes all settings and hyperparameters used during traing.
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To override settings in the config, e.g. settings that point to local
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paths or that you want to experiment with, you can override them as
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command line options. For instance, --training.batch_size 128 overrides
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the value of "batch_size" in the block "[training]". The --code argument
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lets you pass in a Python file that's imported before training. It can be
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used to register custom functions and architectures that can then be
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referenced in the config.
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"""
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util.set_env_log(verbose)
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verify_cli_args(
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train_path=train_path, dev_path=dev_path, config_path=config_path,
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)
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overrides = parse_config_overrides(ctx.args)
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import_code(code_path)
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train(
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config_path,
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{"train": train_path, "dev": dev_path},
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output_path=output_path,
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config_overrides=overrides,
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)
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def train(
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config_path: Path,
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data_paths: Dict[str, Path],
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raw_text: Optional[Path] = None,
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output_path: Optional[Path] = None,
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config_overrides: Dict[str, Any] = {},
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) -> None:
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msg.info(f"Loading config from: {config_path}")
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# Read the config first without creating objects, to get to the original nlp_config
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with show_validation_error():
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config = util.load_config(
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config_path,
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create_objects=False,
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schema=ConfigSchema,
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overrides=config_overrides,
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)
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use_gpu = config["training"]["use_gpu"]
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if use_gpu >= 0:
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msg.info(f"Using GPU: {use_gpu}")
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require_gpu(use_gpu)
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else:
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msg.info("Using CPU")
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raw_text, tag_map, weights_data = load_from_paths(config)
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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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if config["training"].get("use_pytorch_for_gpu_memory"):
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# It feels kind of weird to not have a default for this.
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use_pytorch_for_gpu_memory()
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nlp_config = config["nlp"]
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config = util.load_config(
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config_path,
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create_objects=True,
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schema=ConfigSchema,
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overrides=config_overrides,
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)
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training = config["training"]
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msg.info("Creating nlp from config")
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nlp = util.load_model_from_config(nlp_config)
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optimizer = training["optimizer"]
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limit = training["limit"]
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corpus = Corpus(data_paths["train"], data_paths["dev"], limit=limit)
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if "textcat" in nlp_config["pipeline"]:
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verify_textcat_config(nlp, nlp_config)
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if training.get("resume", False):
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msg.info("Resuming training")
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nlp.resume_training()
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else:
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msg.info(f"Initializing the nlp pipeline: {nlp.pipe_names}")
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train_examples = list(
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corpus.train_dataset(
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nlp,
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shuffle=False,
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gold_preproc=training["gold_preproc"],
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max_length=training["max_length"],
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)
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)
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nlp.begin_training(lambda: train_examples)
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# Update tag map with provided mapping
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nlp.vocab.morphology.tag_map.update(tag_map)
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# Create empty extra lexeme tables so the data from spacy-lookups-data
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# isn't loaded if these features are accessed
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if config["training"]["omit_extra_lookups"]:
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nlp.vocab.lookups_extra = Lookups()
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nlp.vocab.lookups_extra.add_table("lexeme_cluster")
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nlp.vocab.lookups_extra.add_table("lexeme_prob")
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nlp.vocab.lookups_extra.add_table("lexeme_settings")
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# Load a pretrained tok2vec model - cf. CLI command 'pretrain'
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if weights_data is not None:
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tok2vec_path = config.get("pretraining", {}).get("tok2vec_model", None)
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if tok2vec_path is None:
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msg.fail(
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f"To use a pretrained tok2vec model, the config needs to specify which "
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f"tok2vec layer to load in the setting [pretraining.tok2vec_model].",
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exits=1,
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)
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tok2vec = config
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for subpath in tok2vec_path.split("."):
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tok2vec = tok2vec.get(subpath)
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if not tok2vec:
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msg.fail(
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f"Could not locate the tok2vec model at {tok2vec_path}.", exits=1,
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)
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tok2vec.from_bytes(weights_data)
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msg.info("Loading training corpus")
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train_batches = create_train_batches(nlp, corpus, training)
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evaluate = create_evaluation_callback(nlp, optimizer, corpus, training)
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# Create iterator, which yields out info after each optimization step.
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msg.info("Start training")
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training_step_iterator = train_while_improving(
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nlp,
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optimizer,
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train_batches,
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evaluate,
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dropout=training["dropout"],
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accumulate_gradient=training["accumulate_gradient"],
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patience=training.get("patience", 0),
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max_steps=training.get("max_steps", 0),
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eval_frequency=training["eval_frequency"],
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raw_text=raw_text,
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)
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msg.info(f"Training. Initial learn rate: {optimizer.learn_rate}")
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print_row = setup_printer(training, nlp)
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try:
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progress = tqdm.tqdm(total=training["eval_frequency"], leave=False)
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for batch, info, is_best_checkpoint in training_step_iterator:
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progress.update(1)
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if is_best_checkpoint is not None:
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progress.close()
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print_row(info)
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if is_best_checkpoint and output_path is not None:
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update_meta(training, nlp, info)
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nlp.to_disk(output_path / "model-best")
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progress = tqdm.tqdm(total=training["eval_frequency"], leave=False)
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except Exception as e:
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if output_path is not None:
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msg.warn(
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f"Aborting and saving the final best model. "
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f"Encountered exception: {str(e)}",
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exits=1,
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)
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else:
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raise e
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finally:
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if output_path is not None:
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final_model_path = output_path / "model-final"
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if optimizer.averages:
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with nlp.use_params(optimizer.averages):
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nlp.to_disk(final_model_path)
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else:
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nlp.to_disk(final_model_path)
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msg.good(f"Saved model to output directory {final_model_path}")
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def create_train_batches(nlp, corpus, cfg):
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max_epochs = cfg.get("max_epochs", 0)
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train_examples = list(
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corpus.train_dataset(
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nlp,
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shuffle=True,
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gold_preproc=cfg["gold_preproc"],
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max_length=cfg["max_length"],
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)
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)
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epoch = 0
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batch_strategy = cfg.get("batch_by", "sequences")
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while True:
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if len(train_examples) == 0:
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raise ValueError(Errors.E988)
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epoch += 1
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if batch_strategy == "padded":
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batches = util.minibatch_by_padded_size(
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train_examples,
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size=cfg["batch_size"],
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buffer=256,
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discard_oversize=cfg["discard_oversize"],
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)
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elif batch_strategy == "words":
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batches = util.minibatch_by_words(
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train_examples,
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size=cfg["batch_size"],
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discard_oversize=cfg["discard_oversize"],
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)
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else:
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batches = util.minibatch(train_examples, size=cfg["batch_size"])
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# make sure the minibatch_by_words result is not empty, or we'll have an infinite training loop
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try:
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first = next(batches)
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yield epoch, first
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except StopIteration:
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raise ValueError(Errors.E986)
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for batch in batches:
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yield epoch, batch
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if max_epochs >= 1 and epoch >= max_epochs:
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break
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random.shuffle(train_examples)
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def create_evaluation_callback(nlp, optimizer, corpus, cfg):
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def evaluate():
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dev_examples = list(
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corpus.dev_dataset(
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nlp, gold_preproc=cfg["gold_preproc"], ignore_misaligned=True
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)
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)
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n_words = sum(len(ex.predicted) for ex in dev_examples)
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batch_size = cfg.get("evaluation_batch_size", 128)
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start_time = timer()
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if optimizer.averages:
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with nlp.use_params(optimizer.averages):
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scorer = nlp.evaluate(dev_examples, batch_size=batch_size)
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else:
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scorer = nlp.evaluate(dev_examples, batch_size=batch_size)
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end_time = timer()
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wps = n_words / (end_time - start_time)
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scores = scorer.scores
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# Calculate a weighted sum based on score_weights for the main score
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weights = cfg["score_weights"]
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try:
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weighted_score = sum(scores[s] * weights.get(s, 0.0) for s in weights)
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except KeyError as e:
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raise KeyError(
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Errors.E983.format(
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dict="score_weights", key=str(e), keys=list(scores.keys())
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)
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)
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scores["speed"] = wps
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return weighted_score, scores
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return evaluate
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def train_while_improving(
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nlp,
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optimizer,
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train_data,
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evaluate,
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*,
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dropout,
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eval_frequency,
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accumulate_gradient=1,
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patience=0,
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max_steps=0,
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raw_text=None,
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):
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"""Train until an evaluation stops improving. Works as a generator,
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with each iteration yielding a tuple `(batch, info, is_best_checkpoint)`,
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where info is a dict, and is_best_checkpoint is in [True, False, None] --
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None indicating that the iteration was not evaluated as a checkpoint.
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The evaluation is conducted by calling the evaluate callback, which should
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Positional arguments:
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nlp: The spaCy pipeline to evaluate.
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optimizer: The optimizer callable.
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train_data (Iterable[Batch]): A generator of batches, with the training
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data. Each batch should be a Sized[Tuple[Input, Annot]]. The training
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data iterable needs to take care of iterating over the epochs and
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shuffling.
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evaluate (Callable[[], Tuple[float, Any]]): A callback to perform evaluation.
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The callback should take no arguments and return a tuple
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`(main_score, other_scores)`. The main_score should be a float where
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higher is better. other_scores can be any object.
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Every iteration, the function yields out a tuple with:
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* batch: A list of Example objects.
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* info: A dict with various information about the last update (see below).
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* is_best_checkpoint: A value in None, False, True, indicating whether this
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was the best evaluation so far. You should use this to save the model
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checkpoints during training. If None, evaluation was not conducted on
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that iteration. False means evaluation was conducted, but a previous
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evaluation was better.
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The info dict provides the following information:
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epoch (int): How many passes over the data have been completed.
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step (int): How many steps have been completed.
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score (float): The main score form the last evaluation.
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other_scores: : The other scores from the last evaluation.
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loss: The accumulated losses throughout training.
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checkpoints: A list of previous results, where each result is a
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(score, step, epoch) tuple.
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"""
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if isinstance(dropout, float):
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dropouts = thinc.schedules.constant(dropout)
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else:
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dropouts = dropout
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results = []
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losses = {}
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to_enable = [name for name, proc in nlp.pipeline if hasattr(proc, "model")]
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if raw_text:
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random.shuffle(raw_text)
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raw_examples = [
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Example.from_dict(nlp.make_doc(rt["text"]), {}) for rt in raw_text
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]
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raw_batches = util.minibatch(raw_examples, size=8)
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for step, (epoch, batch) in enumerate(train_data):
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dropout = next(dropouts)
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with nlp.select_pipes(enable=to_enable):
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for subbatch in subdivide_batch(batch, accumulate_gradient):
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nlp.update(subbatch, drop=dropout, losses=losses, sgd=False)
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if raw_text:
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# If raw text is available, perform 'rehearsal' updates,
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# which use unlabelled data to reduce overfitting.
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raw_batch = list(next(raw_batches))
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nlp.rehearse(raw_batch, sgd=optimizer, losses=losses)
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for name, proc in nlp.pipeline:
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if hasattr(proc, "model"):
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proc.model.finish_update(optimizer)
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optimizer.step_schedules()
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if not (step % eval_frequency):
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score, other_scores = evaluate()
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results.append((score, step))
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is_best_checkpoint = score == max(results)[0]
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else:
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score, other_scores = (None, None)
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is_best_checkpoint = None
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info = {
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"epoch": epoch,
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"step": step,
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"score": score,
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"other_scores": other_scores,
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"losses": losses,
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"checkpoints": results,
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}
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yield batch, info, is_best_checkpoint
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if is_best_checkpoint is not None:
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losses = {}
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# Stop if no improvement in `patience` updates (if specified)
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best_score, best_step = max(results)
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if patience and (step - best_step) >= patience:
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break
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# Stop if we've exhausted our max steps (if specified)
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if max_steps and step >= max_steps:
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break
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def subdivide_batch(batch, accumulate_gradient):
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batch = list(batch)
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batch.sort(key=lambda eg: len(eg.predicted))
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sub_len = len(batch) // accumulate_gradient
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start = 0
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for i in range(accumulate_gradient):
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subbatch = batch[start : start + sub_len]
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if subbatch:
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yield subbatch
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start += len(subbatch)
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subbatch = batch[start:]
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if subbatch:
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yield subbatch
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def setup_printer(training, nlp):
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score_cols = training["scores"]
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score_widths = [max(len(col), 6) for col in score_cols]
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loss_cols = [f"Loss {pipe}" for pipe in nlp.pipe_names]
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loss_widths = [max(len(col), 8) for col in loss_cols]
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table_header = ["E", "#"] + loss_cols + score_cols + ["Score"]
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table_header = [col.upper() for col in table_header]
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table_widths = [3, 6] + loss_widths + score_widths + [6]
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table_aligns = ["r" for _ in table_widths]
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msg.row(table_header, widths=table_widths)
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msg.row(["-" * width for width in table_widths])
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def print_row(info):
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try:
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losses = [
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"{0:.2f}".format(float(info["losses"][pipe_name]))
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for pipe_name in nlp.pipe_names
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]
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except KeyError as e:
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raise KeyError(
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Errors.E983.format(
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dict="scores (losses)", key=str(e), keys=list(info["losses"].keys())
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)
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)
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try:
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scores = [
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"{0:.2f}".format(float(info["other_scores"][col])) for col in score_cols
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]
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except KeyError as e:
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raise KeyError(
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Errors.E983.format(
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dict="scores (other)",
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key=str(e),
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keys=list(info["other_scores"].keys()),
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)
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)
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data = (
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[info["epoch"], info["step"]]
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+ losses
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+ scores
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+ ["{0:.2f}".format(float(info["score"]))]
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)
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msg.row(data, widths=table_widths, aligns=table_aligns)
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return print_row
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def update_meta(training, nlp, info):
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score_cols = training["scores"]
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nlp.meta["performance"] = {}
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for metric in score_cols:
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nlp.meta["performance"][metric] = info["other_scores"][metric]
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for pipe_name in nlp.pipe_names:
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nlp.meta["performance"][f"{pipe_name}_loss"] = info["losses"][pipe_name]
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|
|
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def load_from_paths(config):
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# TODO: separate checks from loading
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raw_text = util.ensure_path(config["training"]["raw_text"])
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if raw_text is not None:
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if not raw_text.exists():
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msg.fail("Can't find raw text", raw_text, exits=1)
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raw_text = list(srsly.read_jsonl(config["training"]["raw_text"]))
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tag_map = {}
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tag_map_path = util.ensure_path(config["training"]["tag_map"])
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if tag_map_path is not None:
|
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if not tag_map_path.exists():
|
|
msg.fail("Can't find tag map path", tag_map_path, exits=1)
|
|
tag_map = srsly.read_json(config["training"]["tag_map"])
|
|
weights_data = None
|
|
init_tok2vec = util.ensure_path(config["training"]["init_tok2vec"])
|
|
if init_tok2vec is not None:
|
|
if not init_tok2vec.exists():
|
|
msg.fail("Can't find pretrained tok2vec", init_tok2vec, exits=1)
|
|
with init_tok2vec.open("rb") as file_:
|
|
weights_data = file_.read()
|
|
return raw_text, tag_map, weights_data
|
|
|
|
|
|
def verify_cli_args(
|
|
train_path: Path,
|
|
dev_path: Path,
|
|
config_path: Path,
|
|
output_path: Optional[Path] = None,
|
|
):
|
|
# Make sure all files and paths exists if they are needed
|
|
if not config_path or not config_path.exists():
|
|
msg.fail("Config file not found", config_path, exits=1)
|
|
if not train_path or not train_path.exists():
|
|
msg.fail("Training data not found", train_path, exits=1)
|
|
if not dev_path or not dev_path.exists():
|
|
msg.fail("Development data not found", dev_path, exits=1)
|
|
if output_path is not None:
|
|
if not output_path.exists():
|
|
output_path.mkdir()
|
|
msg.good(f"Created output directory: {output_path}")
|
|
elif output_path.exists() and [p for p in output_path.iterdir() if p.is_dir()]:
|
|
msg.warn(
|
|
"Output directory is not empty.",
|
|
"This can lead to unintended side effects when saving the model. "
|
|
"Please use an empty directory or a different path instead. If "
|
|
"the specified output path doesn't exist, the directory will be "
|
|
"created for you.",
|
|
)
|
|
|
|
|
|
def verify_textcat_config(nlp, nlp_config):
|
|
# if 'positive_label' is provided: double check whether it's in the data and
|
|
# the task is binary
|
|
if nlp_config["pipeline"]["textcat"].get("positive_label", None):
|
|
textcat_labels = nlp.get_pipe("textcat").cfg.get("labels", [])
|
|
pos_label = nlp_config["pipeline"]["textcat"]["positive_label"]
|
|
if pos_label not in textcat_labels:
|
|
msg.fail(
|
|
f"The textcat's 'positive_label' config setting '{pos_label}' "
|
|
f"does not match any label in the training data.",
|
|
exits=1,
|
|
)
|
|
if len(textcat_labels) != 2:
|
|
msg.fail(
|
|
f"A textcat 'positive_label' '{pos_label}' was "
|
|
f"provided for training data that does not appear to be a "
|
|
f"binary classification problem with two labels.",
|
|
exits=1,
|
|
)
|