from typing import Optional, Dict, Any, Tuple, Union, Callable, List import srsly import tqdm from pathlib import Path from wasabi import msg import thinc import thinc.schedules from thinc.api import use_pytorch_for_gpu_memory, require_gpu, fix_random_seed from thinc.api import Config, Optimizer import random import typer from ._util import app, Arg, Opt, parse_config_overrides, show_validation_error from ._util import import_code from ..gold import Corpus, Example from ..language import Language from .. import util from ..errors import Errors # Don't remove - required to load the built-in architectures from ..ml import models # noqa: F401 @app.command( "train", context_settings={"allow_extra_args": True, "ignore_unknown_options": True} ) def train_cli( # fmt: off ctx: typer.Context, # This is only used to read additional arguments train_path: Path = Arg(..., help="Location of training data", exists=True), dev_path: Path = Arg(..., help="Location of development data", exists=True), config_path: Path = Arg(..., help="Path to config file", exists=True), output_path: Optional[Path] = Opt(None, "--output", "--output-path", "-o", help="Output directory to store model in"), code_path: Optional[Path] = Opt(None, "--code-path", "-c", help="Path to Python file with additional code (registered functions) to be imported"), verbose: bool = Opt(False, "--verbose", "-V", "-VV", help="Display more information for debugging purposes"), use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU"), resume: bool = Opt(False, "--resume", "-R", help="Resume training"), # fmt: on ): """ Train or update a spaCy model. Requires data in spaCy's binary format. To convert data from other formats, use the `spacy convert` command. The config file includes all settings and hyperparameters used during traing. To override settings in the config, e.g. settings that point to local paths or that you want to experiment with, you can override them as command line options. For instance, --training.batch_size 128 overrides the value of "batch_size" in the block "[training]". The --code argument lets you pass in a Python file that's imported before training. It can be used to register custom functions and architectures that can then be referenced in the config. """ util.set_env_log(verbose) verify_cli_args(train_path, dev_path, config_path) overrides = parse_config_overrides(ctx.args) import_code(code_path) train( config_path, {"train": train_path, "dev": dev_path}, output_path=output_path, config_overrides=overrides, use_gpu=use_gpu, resume_training=resume, ) def train( config_path: Path, data_paths: Dict[str, Path], raw_text: Optional[Path] = None, output_path: Optional[Path] = None, config_overrides: Dict[str, Any] = {}, use_gpu: int = -1, resume_training: bool = False, ) -> None: if use_gpu >= 0: msg.info(f"Using GPU: {use_gpu}") require_gpu(use_gpu) else: msg.info("Using CPU") msg.info(f"Loading config and nlp from: {config_path}") config = Config().from_disk(config_path) if config.get("training", {}).get("seed") is not None: fix_random_seed(config["training"]["seed"]) with show_validation_error(): nlp, config = util.load_model_from_config(config, overrides=config_overrides) if config["training"]["base_model"]: # TODO: do something to check base_nlp against regular nlp described in config? # If everything matches it will look something like: # base_nlp = util.load_model(config["training"]["base_model"]) # nlp = base_nlp raise NotImplementedError("base_model not supported yet.") if config["training"]["vectors"] is not None: util.load_vectors_into_model(nlp, config["training"]["vectors"]) verify_config(nlp) raw_text, tag_map, morph_rules, weights_data = load_from_paths(config) if config["training"]["use_pytorch_for_gpu_memory"]: # It feels kind of weird to not have a default for this. use_pytorch_for_gpu_memory() training = config["training"] optimizer = training["optimizer"] limit = training["limit"] corpus = Corpus(data_paths["train"], data_paths["dev"], limit=limit) if resume_training: msg.info("Resuming training") nlp.resume_training() else: msg.info(f"Initializing the nlp pipeline: {nlp.pipe_names}") train_examples = corpus.train_dataset( nlp, shuffle=False, gold_preproc=training["gold_preproc"], max_length=training["max_length"], ) train_examples = list(train_examples) nlp.begin_training(lambda: train_examples) if tag_map: # Replace tag map with provided mapping nlp.vocab.morphology.load_tag_map(tag_map) if morph_rules: # Load morph rules nlp.vocab.morphology.load_morph_exceptions(morph_rules) # Load a pretrained tok2vec model - cf. CLI command 'pretrain' if weights_data is not None: tok2vec_path = config.get("pretraining", {}).get("tok2vec_model", None) if tok2vec_path is None: msg.fail( f"To use a pretrained tok2vec model, the config needs to specify which " f"tok2vec layer to load in the setting [pretraining.tok2vec_model].", exits=1, ) tok2vec = config for subpath in tok2vec_path.split("."): tok2vec = tok2vec.get(subpath) if not tok2vec: err = f"Could not locate the tok2vec model at {tok2vec_path}" msg.fail(err, exits=1) tok2vec.from_bytes(weights_data) msg.info("Loading training corpus") 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, dropout=training["dropout"], accumulate_gradient=training["accumulate_gradient"], patience=training["patience"], max_steps=training["max_steps"], eval_frequency=training["eval_frequency"], raw_text=raw_text, ) 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: update_meta(training, nlp, info) nlp.to_disk(output_path / "model-best") progress = tqdm.tqdm(total=training["eval_frequency"], leave=False) except Exception as e: if output_path is not None: raise e msg.warn( f"Aborting and saving the final best model. " f"Encountered exception: {str(e)}", exits=1, ) else: raise e finally: if output_path is not None: final_model_path = output_path / "model-final" if optimizer.averages: with nlp.use_params(optimizer.averages): nlp.to_disk(final_model_path) else: nlp.to_disk(final_model_path) msg.good(f"Saved model to output directory {final_model_path}") def create_train_batches( nlp: Language, corpus: Corpus, cfg: Union[Config, Dict[str, Any]] ): max_epochs = cfg["max_epochs"] train_examples = list( corpus.train_dataset( nlp, shuffle=True, gold_preproc=cfg["gold_preproc"], max_length=cfg["max_length"], ) ) epoch = 0 batch_strategy = cfg["batch_by"] while True: if len(train_examples) == 0: raise ValueError(Errors.E988) epoch += 1 if batch_strategy == "padded": batches = util.minibatch_by_padded_size( train_examples, size=cfg["batch_size"], buffer=256, discard_oversize=cfg["discard_oversize"], ) elif batch_strategy == "words": batches = util.minibatch_by_words( train_examples, size=cfg["batch_size"], discard_oversize=cfg["discard_oversize"], ) else: batches = util.minibatch(train_examples, size=cfg["batch_size"]) # make sure the minibatch_by_words result is not empty, or we'll have an infinite training loop try: first = next(batches) yield epoch, first except StopIteration: raise ValueError(Errors.E986) for batch in batches: yield epoch, batch if max_epochs >= 1 and epoch >= max_epochs: break random.shuffle(train_examples) def create_evaluation_callback( nlp: Language, optimizer: Optimizer, corpus: Corpus, cfg: Union[Config, Dict[str, Any]], ) -> Callable[[], Tuple[float, Dict[str, float]]]: def evaluate() -> Tuple[float, Dict[str, float]]: dev_examples = corpus.dev_dataset( nlp, gold_preproc=cfg["gold_preproc"] ) dev_examples = list(dev_examples) n_words = sum(len(ex.predicted) for ex in dev_examples) batch_size = cfg["eval_batch_size"] if optimizer.averages: with nlp.use_params(optimizer.averages): scores = nlp.evaluate(dev_examples, batch_size=batch_size) else: scores = nlp.evaluate(dev_examples, batch_size=batch_size) # Calculate a weighted sum based on score_weights for the main score weights = cfg["score_weights"] try: weighted_score = sum(scores[s] * weights.get(s, 0.0) for s in weights) except KeyError as e: keys = list(scores.keys()) err = Errors.E983.format(dict="score_weights", key=str(e), keys=keys) raise KeyError(err) return weighted_score, scores return evaluate def train_while_improving( nlp: Language, optimizer: Optimizer, train_data, evaluate, *, dropout: float, eval_frequency: int, accumulate_gradient: int, patience: int, max_steps: int, raw_text: List[Dict[str, str]], ): """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. optimizer: The optimizer callable. 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 list of Example objects. * 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 = {} to_enable = [name for name, proc in nlp.pipeline if hasattr(proc, "model")] if raw_text: random.shuffle(raw_text) raw_examples = [ Example.from_dict(nlp.make_doc(rt["text"]), {}) for rt in raw_text ] raw_batches = util.minibatch(raw_examples, size=8) for step, (epoch, batch) in enumerate(train_data): dropout = next(dropouts) with nlp.select_pipes(enable=to_enable): for subbatch in subdivide_batch(batch, accumulate_gradient): nlp.update(subbatch, drop=dropout, losses=losses, sgd=False) if raw_text: # If raw text is available, perform 'rehearsal' updates, # which use unlabelled data to reduce overfitting. raw_batch = list(next(raw_batches)) nlp.rehearse(raw_batch, sgd=optimizer, losses=losses) 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 = { "epoch": epoch, "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 (if specified) best_score, best_step = max(results) if patience and (step - best_step) >= patience: break # Stop if we've exhausted our max steps (if specified) if max_steps and step >= max_steps: break def subdivide_batch(batch, accumulate_gradient): batch = list(batch) batch.sort(key=lambda eg: len(eg.predicted)) sub_len = len(batch) // accumulate_gradient start = 0 for i in range(accumulate_gradient): subbatch = batch[start : start + sub_len] if subbatch: yield subbatch start += len(subbatch) subbatch = batch[start:] if subbatch: yield subbatch def setup_printer( training: Union[Dict[str, Any], Config], nlp: Language ) -> Callable[[Dict[str, Any]], None]: score_cols = list(training["score_weights"]) 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 = ["E", "#"] + loss_cols + score_cols + ["Score"] table_header = [col.upper() for col in table_header] table_widths = [3, 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: Dict[str, Any]) -> None: try: losses = [ "{0:.2f}".format(float(info["losses"][pipe_name])) for pipe_name in nlp.pipe_names ] except KeyError as e: raise KeyError( Errors.E983.format( dict="scores (losses)", key=str(e), keys=list(info["losses"].keys()) ) ) try: scores = [ "{0:.2f}".format(float(info["other_scores"][col])) for col in score_cols ] except KeyError as e: raise KeyError( Errors.E983.format( dict="scores (other)", key=str(e), keys=list(info["other_scores"].keys()), ) ) data = ( [info["epoch"], info["step"]] + losses + scores + ["{0:.2f}".format(float(info["score"]))] ) msg.row(data, widths=table_widths, aligns=table_aligns) return print_row def update_meta( training: Union[Dict[str, Any], Config], nlp: Language, info: Dict[str, Any] ) -> None: nlp.meta["performance"] = {} for metric in training["score_weights"]: nlp.meta["performance"][metric] = info["other_scores"][metric] for pipe_name in nlp.pipe_names: nlp.meta["performance"][f"{pipe_name}_loss"] = info["losses"][pipe_name] def load_from_paths( config: Config, ) -> Tuple[List[Dict[str, str]], Dict[str, dict], bytes]: # TODO: separate checks from loading raw_text = util.ensure_path(config["training"]["raw_text"]) if raw_text is not None: if not raw_text.exists(): msg.fail("Can't find raw text", raw_text, exits=1) raw_text = list(srsly.read_jsonl(config["training"]["raw_text"])) tag_map = {} tag_map_path = util.ensure_path(config["training"]["tag_map"]) if tag_map_path is not None: 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"]) morph_rules = {} morph_rules_path = util.ensure_path(config["training"]["morph_rules"]) if morph_rules_path is not None: if not morph_rules_path.exists(): msg.fail("Can't find tag map path", morph_rules_path, exits=1) morph_rules = srsly.read_json(config["training"]["morph_rules"]) 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, morph_rules, weights_data def verify_cli_args( train_path: Path, dev_path: Path, config_path: Path, output_path: Optional[Path] = None, ) -> 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_config(nlp: Language) -> None: """Perform additional checks based on the config and loaded nlp object.""" # TODO: maybe we should validate based on the actual components, the list # in config["nlp"]["pipeline"] instead? for pipe_config in nlp.config["components"].values(): # We can't assume that the component name == the factory factory = pipe_config["factory"] if factory == "textcat": verify_textcat_config(nlp, pipe_config) def verify_textcat_config(nlp: Language, pipe_config: Dict[str, Any]) -> None: # if 'positive_label' is provided: double check whether it's in the data and # the task is binary if pipe_config.get("positive_label"): textcat_labels = nlp.get_pipe("textcat").cfg.get("labels", []) pos_label = pipe_config.get("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, )