mirror of
https://github.com/explosion/spaCy.git
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429 lines
17 KiB
Python
429 lines
17 KiB
Python
from typing import Optional, Dict, Any, Tuple, Union, Callable, List
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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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from thinc.api import Config, Optimizer
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import random
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import typer
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import logging
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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, get_sourced_components
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from ..language import Language
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from .. import util
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from ..gold.example import Example
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from ..errors import Errors
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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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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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use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU"),
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resume: bool = Opt(False, "--resume", "-R", help="Resume training"),
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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.logger.setLevel(logging.DEBUG if verbose else logging.ERROR)
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verify_cli_args(config_path, output_path)
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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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output_path=output_path,
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config_overrides=overrides,
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use_gpu=use_gpu,
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resume_training=resume,
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)
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def train(
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config_path: Path,
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output_path: Optional[Path] = None,
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config_overrides: Dict[str, Any] = {},
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use_gpu: int = -1,
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resume_training: bool = False,
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) -> None:
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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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msg.info(f"Loading config and nlp from: {config_path}")
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with show_validation_error(config_path):
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config = util.load_config(
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config_path, overrides=config_overrides, interpolate=True
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)
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if config.get("training", {}).get("seed") is not None:
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fix_random_seed(config["training"]["seed"])
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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(config_path):
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nlp, config = util.load_model_from_config(config)
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if config["training"]["vectors"] is not None:
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util.load_vectors_into_model(nlp, config["training"]["vectors"])
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verify_config(nlp)
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raw_text, tag_map, morph_rules, weights_data = load_from_paths(config)
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if config.get("system", {}).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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T_cfg = config["training"]
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optimizer = T_cfg["optimizer"]
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train_corpus = T_cfg["train_corpus"]
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dev_corpus = T_cfg["dev_corpus"]
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batcher = T_cfg["batcher"]
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train_logger = T_cfg["logger"]
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# Components that shouldn't be updated during training
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frozen_components = T_cfg["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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if tag_map:
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# Replace tag map with provided mapping
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nlp.vocab.morphology.load_tag_map(tag_map)
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if morph_rules:
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# Load morph rules
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nlp.vocab.morphology.load_morph_exceptions(morph_rules)
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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["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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err = f"Could not locate the tok2vec model at {tok2vec_path}"
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msg.fail(err, exits=1)
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tok2vec.from_bytes(weights_data)
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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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score_weights = T_cfg["score_weights"]
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training_step_iterator = train_while_improving(
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nlp,
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optimizer,
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create_train_batches(train_corpus(nlp), batcher, T_cfg["max_epochs"]),
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create_evaluation_callback(nlp, dev_corpus, score_weights),
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dropout=T_cfg["dropout"],
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accumulate_gradient=T_cfg["accumulate_gradient"],
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patience=T_cfg["patience"],
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max_steps=T_cfg["max_steps"],
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eval_frequency=T_cfg["eval_frequency"],
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raw_text=None,
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exclude=frozen_components,
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)
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msg.info(f"Training. Initial learn rate: {optimizer.learn_rate}")
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print_row, finalize_logger = train_logger(nlp)
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try:
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progress = tqdm.tqdm(total=T_cfg["eval_frequency"], leave=False)
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progress.set_description(f"Epoch 1")
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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(T_cfg, nlp, info)
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nlp.to_disk(output_path / "model-best")
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progress = tqdm.tqdm(total=T_cfg["eval_frequency"], leave=False)
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progress.set_description(f"Epoch {info['epoch']}")
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except Exception as e:
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finalize_logger()
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if output_path is not None:
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# We don't want to swallow the traceback if we don't have a
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# specific error.
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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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)
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nlp.to_disk(output_path / "model-final")
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raise e
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finally:
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finalize_logger()
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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(iterator, batcher, max_epochs: int):
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epoch = 1
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examples = []
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# Stream the first epoch, so we start training faster and support
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# infinite streams.
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for batch in batcher(iterator):
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yield epoch, batch
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if max_epochs != 1:
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examples.extend(batch)
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if not examples:
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# Raise error if no data
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raise ValueError(Errors.E986)
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while epoch != max_epochs:
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random.shuffle(examples)
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for batch in batcher(examples):
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yield epoch, batch
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epoch += 1
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def create_evaluation_callback(
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nlp: Language, dev_corpus: Callable, weights: Dict[str, float]
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) -> Callable[[], Tuple[float, Dict[str, float]]]:
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def evaluate() -> Tuple[float, Dict[str, float]]:
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dev_examples = list(dev_corpus(nlp))
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scores = nlp.evaluate(dev_examples)
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# Calculate a weighted sum based on score_weights for the main score
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try:
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weighted_score = sum(
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scores.get(s, 0.0) * weights.get(s, 0.0) for s in weights
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)
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except KeyError as e:
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keys = list(scores.keys())
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err = Errors.E983.format(dict="score_weights", key=str(e), keys=keys)
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raise KeyError(err) from None
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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: Language,
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optimizer: Optimizer,
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train_data,
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evaluate,
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*,
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dropout: float,
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eval_frequency: int,
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accumulate_gradient: int,
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patience: int,
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max_steps: int,
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raw_text: List[Dict[str, str]],
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exclude: List[str],
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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.
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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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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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for subbatch in subdivide_batch(batch, accumulate_gradient):
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nlp.update(
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subbatch, drop=dropout, losses=losses, sgd=False, exclude=exclude
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)
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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, exclude=exclude)
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# TODO: refactor this so we don't have to run it separately in here
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for name, proc in nlp.pipeline:
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if (
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name not in exclude
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and hasattr(proc, "model")
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and proc.model not in (True, False, None)
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):
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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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if optimizer.averages:
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with nlp.use_params(optimizer.averages):
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score, other_scores = evaluate()
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else:
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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 update_meta(
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training: Union[Dict[str, Any], Config], nlp: Language, info: Dict[str, Any]
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) -> None:
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nlp.meta["performance"] = {}
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for metric in training["score_weights"]:
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nlp.meta["performance"][metric] = info["other_scores"].get(metric, 0.0)
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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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def load_from_paths(
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config: Config,
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) -> Tuple[List[Dict[str, str]], Dict[str, dict], bytes]:
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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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morph_rules = {}
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weights_data = None
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init_tok2vec = util.ensure_path(config["training"]["init_tok2vec"])
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if init_tok2vec is not None:
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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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return raw_text, tag_map, morph_rules, weights_data
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def verify_cli_args(config_path: Path, output_path: Optional[Path] = None) -> None:
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# Make sure all files and paths exists if they are needed
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if not config_path or not config_path.exists():
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msg.fail("Config file not found", config_path, exits=1)
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if output_path is not None:
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if not output_path.exists():
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output_path.mkdir()
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msg.good(f"Created output directory: {output_path}")
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def verify_config(nlp: Language) -> None:
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"""Perform additional checks based on the config and loaded nlp object."""
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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").cfg.get("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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msg.fail(
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f"The textcat's 'positive_label' config setting '{pos_label}' "
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f"does not match any label in the training data.",
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exits=1,
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)
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if len(textcat_labels) != 2:
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msg.fail(
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f"A textcat 'positive_label' '{pos_label}' was "
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f"provided for training data that does not appear to be a "
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f"binary classification problem with two labels.",
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exits=1,
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)
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