spaCy/spacy/cli/pretrain.py

from typing import Optional
import random
import numpy
import time
import re
from collections import Counter
from pathlib import Path
from thinc.api import use_pytorch_for_gpu_memory, require_gpu
from thinc.api import set_dropout_rate, to_categorical, fix_random_seed
from thinc.api import CosineDistance, L2Distance
from wasabi import msg
import srsly
from functools import partial

from ._app import app, Arg, Opt
from ..errors import Errors
from ..ml.models.multi_task import build_cloze_multi_task_model
from ..ml.models.multi_task import build_cloze_characters_multi_task_model
from ..tokens import Doc
from ..attrs import ID, HEAD
from .. import util


@app.command("pretrain")
def pretrain_cli(
    # fmt: off
    texts_loc: Path = Arg(..., help="Path to JSONL file with raw texts to learn from, with text provided as the key 'text' or tokens as the key 'tokens'", exists=True),
    output_dir: Path = Arg(..., help="Directory to write models to on each epoch"),
    config_path: Path = Arg(..., help="Path to config file", exists=True, dir_okay=False),
    use_gpu: int = Opt(-1, "--use-gpu", "-g", help="Use GPU"),
    resume_path: Optional[Path] = Opt(None, "--resume-path", "-r", help="Path to pretrained weights from which to resume pretraining"),
    epoch_resume: Optional[int] = Opt(None, "--epoch-resume", "-er", help="The epoch to resume counting from when using '--resume_path'. Prevents unintended overwriting of existing weight files."),
    # fmt: on
):
    """
    Pre-train the 'token-to-vector' (tok2vec) layer of pipeline components,
    using an approximate language-modelling objective. Two objective types
    are available, vector-based and character-based.

    In the vector-based objective, we load word vectors that have been trained
    using a word2vec-style distributional similarity algorithm, and train a
    component like a CNN, BiLSTM, etc to predict vectors which match the
    pretrained ones. The weights are saved to a directory after each epoch. You
    can then pass a path to one of these pretrained weights files to the
    'spacy train' command.

    This technique may be especially helpful if you have little labelled data.
    However, it's still quite experimental, so your mileage may vary.

    To load the weights back in during 'spacy train', you need to ensure
    all settings are the same between pretraining and training. Ideally,
    this is done by using the same config file for both commands.
    """
    pretrain(
        texts_loc,
        output_dir,
        config_path,
        use_gpu=use_gpu,
        resume_path=resume_path,
        epoch_resume=epoch_resume,
    )


def pretrain(
    texts_loc: Path,
    output_dir: Path,
    config_path: Path,
    use_gpu: int = -1,
    resume_path: Optional[Path] = None,
    epoch_resume: Optional[int] = None,
):
    verify_cli_args(**locals())
    if not output_dir.exists():
        output_dir.mkdir()
        msg.good(f"Created output directory: {output_dir}")

    if use_gpu >= 0:
        msg.info("Using GPU")
        require_gpu(use_gpu)
    else:
        msg.info("Using CPU")

    msg.info(f"Loading config from: {config_path}")
    config = util.load_config(config_path, create_objects=False)
    fix_random_seed(config["pretraining"]["seed"])
    if use_gpu >= 0 and config["pretraining"]["use_pytorch_for_gpu_memory"]:
        use_pytorch_for_gpu_memory()

    nlp_config = config["nlp"]
    srsly.write_json(output_dir / "config.json", config)
    msg.good("Saved config file in the output directory")

    config = util.load_config(config_path, create_objects=True)
    nlp = util.load_model_from_config(nlp_config)
    pretrain_config = config["pretraining"]

    if texts_loc != "-":  # reading from a file
        with msg.loading("Loading input texts..."):
            texts = list(srsly.read_jsonl(texts_loc))
        random.shuffle(texts)
    else:  # reading from stdin
        msg.info("Reading input text from stdin...")
        texts = srsly.read_jsonl("-")

    tok2vec_path = pretrain_config["tok2vec_model"]
    tok2vec = config
    for subpath in tok2vec_path.split("."):
        tok2vec = tok2vec.get(subpath)
    model = create_pretraining_model(nlp, tok2vec, pretrain_config)
    optimizer = pretrain_config["optimizer"]

    # Load in pretrained weights to resume from
    if resume_path is not None:
        _resume_model(model, resume_path, epoch_resume)
    else:
        # Without '--resume-path' the '--epoch-resume' argument is ignored
        epoch_resume = 0

    tracker = ProgressTracker(frequency=10000)
    msg.divider(f"Pre-training tok2vec layer - starting at epoch {epoch_resume}")
    row_settings = {"widths": (3, 10, 10, 6, 4), "aligns": ("r", "r", "r", "r", "r")}
    msg.row(("#", "# Words", "Total Loss", "Loss", "w/s"), **row_settings)

    def _save_model(epoch, is_temp=False):
        is_temp_str = ".temp" if is_temp else ""
        with model.use_params(optimizer.averages):
            with (output_dir / f"model{epoch}{is_temp_str}.bin").open("wb") as file_:
                file_.write(model.get_ref("tok2vec").to_bytes())
            log = {
                "nr_word": tracker.nr_word,
                "loss": tracker.loss,
                "epoch_loss": tracker.epoch_loss,
                "epoch": epoch,
            }
            with (output_dir / "log.jsonl").open("a") as file_:
                file_.write(srsly.json_dumps(log) + "\n")

    skip_counter = 0
    objective = create_objective(pretrain_config["objective"])
    for epoch in range(epoch_resume, pretrain_config["max_epochs"]):
        batches = util.minibatch_by_words(texts, size=pretrain_config["batch_size"])
        for batch_id, batch in enumerate(batches):
            docs, count = make_docs(
                nlp,
                batch,
                max_length=pretrain_config["max_length"],
                min_length=pretrain_config["min_length"],
            )
            skip_counter += count
            loss = make_update(model, docs, optimizer, objective)
            progress = tracker.update(epoch, loss, docs)
            if progress:
                msg.row(progress, **row_settings)
                if texts_loc == "-" and tracker.words_per_epoch[epoch] >= 10 ** 7:
                    break
            if pretrain_config["n_save_every"] and (
                batch_id % pretrain_config["n_save_every"] == 0
            ):
                _save_model(epoch, is_temp=True)
        _save_model(epoch)
        tracker.epoch_loss = 0.0
        if texts_loc != "-":
            # Reshuffle the texts if texts were loaded from a file
            random.shuffle(texts)
    if skip_counter > 0:
        msg.warn(f"Skipped {skip_counter} empty values")
    msg.good("Successfully finished pretrain")


def _resume_model(model, resume_path, epoch_resume):
    msg.info(f"Resume training tok2vec from: {resume_path}")
    with resume_path.open("rb") as file_:
        weights_data = file_.read()
        model.get_ref("tok2vec").from_bytes(weights_data)
    # Parse the epoch number from the given weight file
    model_name = re.search(r"model\d+\.bin", str(resume_path))
    if model_name:
        # Default weight file name so read epoch_start from it by cutting off 'model' and '.bin'
        epoch_resume = int(model_name.group(0)[5:][:-4]) + 1
        msg.info(f"Resuming from epoch: {epoch_resume}")
    else:
        msg.info(f"Resuming from epoch: {epoch_resume}")


def make_update(model, docs, optimizer, objective_func):
    """Perform an update over a single batch of documents.

    docs (iterable): A batch of `Doc` objects.
    optimizer (callable): An optimizer.
    RETURNS loss: A float for the loss.
    """
    predictions, backprop = model.begin_update(docs)
    loss, gradients = objective_func(model.ops, docs, predictions)
    backprop(gradients)
    model.finish_update(optimizer)
    # Don't want to return a cupy object here
    # The gradients are modified in-place by the BERT MLM,
    # so we get an accurate loss
    return float(loss)


def make_docs(nlp, batch, min_length, max_length):
    docs = []
    skip_count = 0
    for record in batch:
        if not isinstance(record, dict):
            raise TypeError(Errors.E137.format(type=type(record), line=record))
        if "tokens" in record:
            words = record["tokens"]
            if not words:
                skip_count += 1
                continue
            doc = Doc(nlp.vocab, words=words)
        elif "text" in record:
            text = record["text"]
            if not text:
                skip_count += 1
                continue
            doc = nlp.make_doc(text)
        else:
            raise ValueError(Errors.E138.format(text=record))
        if "heads" in record:
            heads = record["heads"]
            heads = numpy.asarray(heads, dtype="uint64")
            heads = heads.reshape((len(doc), 1))
            doc = doc.from_array([HEAD], heads)
        if min_length <= len(doc) < max_length:
            docs.append(doc)
    return docs, skip_count


def create_objective(config):
    """Create the objective for pretraining.

    We'd like to replace this with a registry function but it's tricky because
    we're also making a model choice based on this. For now we hard-code support
    for two types (characters, vectors). For characters you can specify
    n_characters, for vectors you can specify the loss.

    Bleh.
    """
    objective_type = config["type"]
    if objective_type == "characters":
        return partial(get_characters_loss, nr_char=config["n_characters"])
    elif objective_type == "vectors":
        if config["loss"] == "cosine":
            return partial(
                get_vectors_loss,
                distance=CosineDistance(normalize=True, ignore_zeros=True),
            )
        elif config["loss"] == "L2":
            return partial(
                get_vectors_loss, distance=L2Distance(normalize=True, ignore_zeros=True)
            )
        else:
            raise ValueError("Unexpected loss type", config["loss"])
    else:
        raise ValueError("Unexpected objective_type", objective_type)


def get_vectors_loss(ops, docs, prediction, distance):
    """Compute a loss based on a distance between the documents' vectors and
    the prediction.
    """
    # The simplest way to implement this would be to vstack the
    # token.vector values, but that's a bit inefficient, especially on GPU.
    # Instead we fetch the index into the vectors table for each of our tokens,
    # and look them up all at once. This prevents data copying.
    ids = ops.flatten([doc.to_array(ID).ravel() for doc in docs])
    target = docs[0].vocab.vectors.data[ids]
    d_target, loss = distance(prediction, target)
    return loss, d_target


def get_characters_loss(ops, docs, prediction, nr_char):
    """Compute a loss based on a number of characters predicted from the docs."""
    target_ids = numpy.vstack([doc.to_utf8_array(nr_char=nr_char) for doc in docs])
    target_ids = target_ids.reshape((-1,))
    target = ops.asarray(to_categorical(target_ids, n_classes=256), dtype="f")
    target = target.reshape((-1, 256 * nr_char))
    diff = prediction - target
    loss = (diff ** 2).sum()
    d_target = diff / float(prediction.shape[0])
    return loss, d_target


def create_pretraining_model(nlp, tok2vec, pretrain_config):
    """Define a network for the pretraining. We simply add an output layer onto
    the tok2vec input model. The tok2vec input model needs to be a model that
    takes a batch of Doc objects (as a list), and returns a list of arrays.
    Each array in the output needs to have one row per token in the doc.
    The actual tok2vec layer is stored as a reference, and only this bit will be
    serialized to file and read back in when calling the 'train' command.
    """
    # TODO
    maxout_pieces = 3
    hidden_size = 300
    if pretrain_config["objective"]["type"] == "vectors":
        model = build_cloze_multi_task_model(
            nlp.vocab, tok2vec, hidden_size=hidden_size, maxout_pieces=maxout_pieces
        )
    elif pretrain_config["objective"]["type"] == "characters":
        model = build_cloze_characters_multi_task_model(
            nlp.vocab,
            tok2vec,
            hidden_size=hidden_size,
            maxout_pieces=maxout_pieces,
            nr_char=pretrain_config["objective"]["n_characters"],
        )
    model.initialize(X=[nlp.make_doc("Give it a doc to infer shapes")])
    set_dropout_rate(model, pretrain_config["dropout"])
    return model


class ProgressTracker(object):
    def __init__(self, frequency=1000000):
        self.loss = 0.0
        self.prev_loss = 0.0
        self.nr_word = 0
        self.words_per_epoch = Counter()
        self.frequency = frequency
        self.last_time = time.time()
        self.last_update = 0
        self.epoch_loss = 0.0

    def update(self, epoch, loss, docs):
        self.loss += loss
        self.epoch_loss += loss
        words_in_batch = sum(len(doc) for doc in docs)
        self.words_per_epoch[epoch] += words_in_batch
        self.nr_word += words_in_batch
        words_since_update = self.nr_word - self.last_update
        if words_since_update >= self.frequency:
            wps = words_since_update / (time.time() - self.last_time)
            self.last_update = self.nr_word
            self.last_time = time.time()
            loss_per_word = self.loss - self.prev_loss
            status = (
                epoch,
                self.nr_word,
                _smart_round(self.loss, width=10),
                _smart_round(loss_per_word, width=6),
                int(wps),
            )
            self.prev_loss = float(self.loss)
            return status
        else:
            return None


def _smart_round(figure, width=10, max_decimal=4):
    """Round large numbers as integers, smaller numbers as decimals."""
    n_digits = len(str(int(figure)))
    n_decimal = width - (n_digits + 1)
    if n_decimal <= 1:
        return str(int(figure))
    else:
        n_decimal = min(n_decimal, max_decimal)
        format_str = "%." + str(n_decimal) + "f"
        return format_str % figure


def verify_cli_args(
    texts_loc, output_dir, config_path, use_gpu, resume_path, epoch_resume
):
    if not config_path or not config_path.exists():
        msg.fail("Config file not found", config_path, exits=1)
    if output_dir.exists() and [p for p in output_dir.iterdir()]:
        if resume_path:
            msg.warn(
                "Output directory is not empty. ",
                "If you're resuming a run from a previous model in this directory, "
                "the old models for the consecutive epochs will be overwritten "
                "with the new ones.",
            )
        else:
            msg.warn(
                "Output directory is not empty. ",
                "It is better to use an empty directory or refer to a new output path, "
                "then the new directory will be created for you.",
            )
    if texts_loc != "-":  # reading from a file
        texts_loc = Path(texts_loc)
        if not texts_loc.exists():
            msg.fail("Input text file doesn't exist", texts_loc, exits=1)

        for text in srsly.read_jsonl(texts_loc):
            break
        else:
            msg.fail("Input file is empty", texts_loc, exits=1)

    if resume_path is not None:
        model_name = re.search(r"model\d+\.bin", str(resume_path))
        if not model_name and not epoch_resume:
            msg.fail(
                "You have to use the --epoch-resume setting when using a renamed weight file for --resume-path",
                exits=True,
            )
        elif not model_name and epoch_resume < 0:
            msg.fail(
                f"The argument --epoch-resume has to be greater or equal to 0. {epoch_resume} is invalid",
                exits=True,
            )
    config = util.load_config(config_path, create_objects=False)
    if config["pretraining"]["objective"]["type"] == "vectors":
        if not config["nlp"]["vectors"]:
            msg.fail(
                "Must specify nlp.vectors if pretraining.objective.type is vectors",
                exits=True,
            )