from typing import Optional, Dict, List, Union, Sequence from pydantic import BaseModel, FilePath import plac import tqdm from pathlib import Path from wasabi import msg import thinc import thinc.schedules from thinc.api import Model from ..gold import GoldCorpus from .. import util registry = util.registry CONFIG_STR = """ [training] patience = 10 eval_frequency = 10 dropout = 0.2 init_tok2vec = null vectors = null max_epochs = 100 orth_variant_level = 0.0 gold_preproc = false max_length = 0 use_gpu = 0 scores = ["ents_p", "ents_r", "ents_f"] score_weights = {"ents_f": 1.0} limit = 0 [training.batch_size] @schedules = "compounding.v1" start = 100 stop = 1000 compound = 1.001 [optimizer] @optimizers = "Adam.v1" learn_rate = 0.001 beta1 = 0.9 beta2 = 0.999 [nlp] lang = "en" vectors = ${training:vectors} [nlp.pipeline.tok2vec] factory = "tok2vec" [nlp.pipeline.ner] factory = "ner" [nlp.pipeline.ner.model] @architectures = "spacy.TransitionBasedParser.v1" nr_feature_tokens = 3 hidden_width = 64 maxout_pieces = 3 [nlp.pipeline.ner.model.tok2vec] @architectures = "spacy.Tok2VecTensors.v1" width = ${nlp.pipeline.tok2vec.model:width} [nlp.pipeline.tok2vec.model] @architectures = "spacy.HashEmbedCNN.v1" pretrained_vectors = ${nlp:vectors} width = 128 depth = 4 window_size = 1 embed_size = 10000 maxout_pieces = 3 subword_features = true """ class PipelineComponent(BaseModel): factory: str model: Model class Config: arbitrary_types_allowed = True class ConfigSchema(BaseModel): optimizer: Optional["Optimizer"] class training(BaseModel): patience: int = 10 eval_frequency: int = 100 dropout: float = 0.2 init_tok2vec: Optional[FilePath] = None vectors: Optional[str] = None max_epochs: int = 100 orth_variant_level: float = 0.0 gold_preproc: bool = False max_length: int = 0 use_gpu: int = 0 scores: List[str] = ["ents_p", "ents_r", "ents_f"] score_weights: Dict[str, Union[int, float]] = {"ents_f": 1.0} limit: int = 0 batch_size: Union[Sequence[int], int] class nlp(BaseModel): lang: str vectors: Optional[str] pipeline: Optional[Dict[str, PipelineComponent]] class Config: extra = "allow" @plac.annotations( # fmt: off train_path=("Location of JSON-formatted training data", "positional", None, Path), dev_path=("Location of JSON-formatted development data", "positional", None, Path), config_path=("Path to config file", "positional", None, Path), output_path=("Output directory to store model in", "option", "o", Path), meta_path=("Optional path to meta.json to use as base.", "option", "m", Path), raw_text=("Path to jsonl file with unlabelled text documents.", "option", "rt", Path), # fmt: on ) def train_from_config_cli( train_path, dev_path, config_path, output_path=None, meta_path=None, raw_text=None, debug=False, verbose=False, ): """ Train or update a spaCy model. Requires data to be formatted in spaCy's JSON format. To convert data from other formats, use the `spacy convert` command. """ 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 meta_path is not None and not meta_path.exists(): msg.fail("Can't find model meta.json", meta_path, exits=1) if output_path is not None and not output_path.exists(): output_path.mkdir() try: train_from_config( config_path, {"train": train_path, "dev": dev_path}, output_path=output_path, meta_path=meta_path, raw_text=raw_text, ) except KeyboardInterrupt: msg.warn("Cancelled.") def train_from_config( config_path, data_paths, raw_text=None, meta_path=None, output_path=None, ): msg.info(f"Loading config from: {config_path}") config = util.load_config(config_path, create_objects=True) use_gpu = config["training"]["use_gpu"] if use_gpu >= 0: msg.info("Using GPU") else: msg.info("Using CPU") msg.info("Creating nlp from config") nlp_config = util.load_config(config_path, create_objects=False)["nlp"] nlp = util.load_model_from_config(nlp_config) optimizer = config["optimizer"] training = config["training"] limit = training["limit"] msg.info("Loading training corpus") corpus = GoldCorpus(data_paths["train"], data_paths["dev"], limit=limit) msg.info("Initializing the nlp pipeline") nlp.begin_training(lambda: corpus.train_examples, device=use_gpu) train_batches = create_train_batches(nlp, corpus, training) evaluate = create_evaluation_callback(nlp, optimizer, corpus, training) # Create iterator, which yields out info after each optimization step. msg.info("Start training") training_step_iterator = train_while_improving( nlp, optimizer, train_batches, evaluate, training["dropout"], training["patience"], training["eval_frequency"], ) msg.info(f"Training. Initial learn rate: {optimizer.learn_rate}") print_row = setup_printer(training, nlp) try: progress = tqdm.tqdm(total=training["eval_frequency"], leave=False) for batch, info, is_best_checkpoint in training_step_iterator: progress.update(1) if is_best_checkpoint is not None: progress.close() print_row(info) if is_best_checkpoint and output_path is not None: nlp.to_disk(output_path) progress = tqdm.tqdm(total=training["eval_frequency"], leave=False) finally: if output_path is not None: with nlp.use_params(optimizer.averages): final_model_path = output_path / "model-final" nlp.to_disk(final_model_path) msg.good("Saved model to output directory", final_model_path) # with msg.loading("Creating best model..."): # best_model_path = _collate_best_model(meta, output_path, nlp.pipe_names) # msg.good("Created best model", best_model_path) def create_train_batches(nlp, corpus, cfg): while True: train_examples = corpus.train_dataset( nlp, noise_level=0.0, orth_variant_level=cfg["orth_variant_level"], gold_preproc=cfg["gold_preproc"], max_length=cfg["max_length"], ignore_misaligned=True, ) for batch in util.minibatch_by_words(train_examples, size=cfg["batch_size"]): yield batch def create_evaluation_callback(nlp, optimizer, corpus, cfg): def evaluate(): with nlp.use_params(optimizer.averages): dev_examples = list( corpus.dev_dataset( nlp, gold_preproc=cfg["gold_preproc"], ignore_misaligned=True ) ) scorer = nlp.evaluate(dev_examples) scores = scorer.scores # Calculate a weighted sum based on score_weights for the main score weights = cfg["score_weights"] weighted_score = sum(scores[s] * weights.get(s, 0.0) for s in weights) return weighted_score, scorer.scores return evaluate def train_while_improving( nlp, optimizer, train_data, evaluate, dropout, patience, eval_frequency ): """Train until an evaluation stops improving. Works as a generator, with each iteration yielding a tuple `(batch, info, is_best_checkpoint)`, where info is a dict, and is_best_checkpoint is in [True, False, None] -- None indicating that the iteration was not evaluated as a checkpoint. The evaluation is conducted by calling the evaluate callback, which should Positional arguments: nlp: The spaCy pipeline to evaluate. train_data (Iterable[Batch]): A generator of batches, with the training data. Each batch should be a Sized[Tuple[Input, Annot]]. The training data iterable needs to take care of iterating over the epochs and shuffling. evaluate (Callable[[], Tuple[float, Any]]): A callback to perform evaluation. The callback should take no arguments and return a tuple `(main_score, other_scores)`. The main_score should be a float where higher is better. other_scores can be any object. Every iteration, the function yields out a tuple with: * batch: A zipped sequence of Tuple[Doc, GoldParse] pairs. * info: A dict with various information about the last update (see below). * is_best_checkpoint: A value in None, False, True, indicating whether this was the best evaluation so far. You should use this to save the model checkpoints during training. If None, evaluation was not conducted on that iteration. False means evaluation was conducted, but a previous evaluation was better. The info dict provides the following information: epoch (int): How many passes over the data have been completed. step (int): How many steps have been completed. score (float): The main score form the last evaluation. other_scores: : The other scores from the last evaluation. loss: The accumulated losses throughout training. checkpoints: A list of previous results, where each result is a (score, step, epoch) tuple. """ if isinstance(dropout, float): dropouts = thinc.schedules.constant(dropout) else: dropouts = dropout results = [] losses = {} for step, batch in enumerate(train_data): dropout = next(dropouts) for subbatch in subdivide_batch(batch): nlp.update(subbatch, drop=dropout, losses=losses, sgd=False) for name, proc in nlp.pipeline: if hasattr(proc, "model"): proc.model.finish_update(optimizer) optimizer.step_schedules() if not (step % eval_frequency): score, other_scores = evaluate() results.append((score, step)) is_best_checkpoint = score == max(results)[0] else: score, other_scores = (None, None) is_best_checkpoint = None info = { "step": step, "score": score, "other_scores": other_scores, "losses": losses, "checkpoints": results, } yield batch, info, is_best_checkpoint if is_best_checkpoint is not None: losses = {} # Stop if no improvement in `patience` updates best_score, best_step = max(results) if (step - best_step) >= patience: break def subdivide_batch(batch): return [batch] def setup_printer(training, nlp): score_cols = training["scores"] score_widths = [max(len(col), 6) for col in score_cols] loss_cols = [f"Loss {pipe}" for pipe in nlp.pipe_names] loss_widths = [max(len(col), 8) for col in loss_cols] table_header = ["#"] + loss_cols + score_cols + ["Score"] table_header = [col.upper() for col in table_header] table_widths = [6] + loss_widths + score_widths + [6] table_aligns = ["r" for _ in table_widths] msg.row(table_header, widths=table_widths) msg.row(["-" * width for width in table_widths]) def print_row(info): losses = [ "{0:.2f}".format(info["losses"].get(pipe_name, 0.0)) for pipe_name in nlp.pipe_names ] scores = [ "{0:.2f}".format(info["other_scores"].get(col, 0.0)) for col in score_cols ] data = [info["step"]] + losses + scores + ["{0:.2f}".format(info["score"])] msg.row(data, widths=table_widths, aligns=table_aligns) return print_row