import plac import random import numpy import time import re from collections import Counter from pathlib import Path from thinc.v2v import Affine, Maxout from thinc.misc import LayerNorm as LN from thinc.neural.util import prefer_gpu from wasabi import msg import srsly from spacy.gold import Example from ..errors import Errors from ..tokens import Doc from ..attrs import ID, HEAD from .._ml import Tok2Vec, flatten, chain, create_default_optimizer from .._ml import masked_language_model, get_cossim_loss from .. import util from .train import _load_pretrained_tok2vec @plac.annotations( texts_loc=( "Path to JSONL file with raw texts to learn from, with text provided as the key 'text' or tokens as the " "key 'tokens'", "positional", None, str, ), vectors_model=("Name or path to spaCy model with vectors to learn from"), output_dir=("Directory to write models to on each epoch", "positional", None, str), width=("Width of CNN layers", "option", "cw", int), depth=("Depth of CNN layers", "option", "cd", int), cnn_window=("Window size for CNN layers", "option", "cW", int), cnn_pieces=("Maxout size for CNN layers. 1 for Mish", "option", "cP", int), use_chars=("Whether to use character-based embedding", "flag", "chr", bool), sa_depth=("Depth of self-attention layers", "option", "sa", int), bilstm_depth=("Depth of BiLSTM layers (requires PyTorch)", "option", "lstm", int), embed_rows=("Number of embedding rows", "option", "er", int), loss_func=( "Loss function to use for the objective. Either 'L2' or 'cosine'", "option", "L", str, ), use_vectors=("Whether to use the static vectors as input features", "flag", "uv"), dropout=("Dropout rate", "option", "d", float), batch_size=("Number of words per training batch", "option", "bs", int), max_length=( "Max words per example. Longer examples are discarded", "option", "xw", int, ), min_length=( "Min words per example. Shorter examples are discarded", "option", "nw", int, ), seed=("Seed for random number generators", "option", "s", int), n_iter=("Number of iterations to pretrain", "option", "i", int), n_save_every=("Save model every X batches.", "option", "se", int), init_tok2vec=( "Path to pretrained weights for the token-to-vector parts of the models. See 'spacy pretrain'. Experimental.", "option", "t2v", Path, ), epoch_start=( "The epoch to start counting at. Only relevant when using '--init-tok2vec' and the given weight file has been " "renamed. Prevents unintended overwriting of existing weight files.", "option", "es", int, ), ) def pretrain( texts_loc, vectors_model, output_dir, width=96, depth=4, bilstm_depth=0, cnn_pieces=3, sa_depth=0, use_chars=False, cnn_window=1, embed_rows=2000, loss_func="cosine", use_vectors=False, dropout=0.2, n_iter=1000, batch_size=3000, max_length=500, min_length=5, seed=0, n_save_every=None, init_tok2vec=None, epoch_start=None, ): """ Pre-train the 'token-to-vector' (tok2vec) layer of pipeline components, using an approximate language-modelling objective. Specifically, we load pretrained vectors, 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. The API and errors around this need some improvement. """ config = dict(locals()) for key in config: if isinstance(config[key], Path): config[key] = str(config[key]) util.fix_random_seed(seed) has_gpu = prefer_gpu() if has_gpu: import torch torch.set_default_tensor_type("torch.cuda.FloatTensor") msg.info("Using GPU" if has_gpu else "Not using GPU") output_dir = Path(output_dir) if not output_dir.exists(): output_dir.mkdir() msg.good("Created output directory") srsly.write_json(output_dir / "config.json", config) msg.good("Saved settings to config.json") # Load texts from file or stdin 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) with msg.loading("Loading input texts..."): texts = list(srsly.read_jsonl(texts_loc)) if not texts: msg.fail("Input file is empty", texts_loc, exits=1) msg.good("Loaded input texts") random.shuffle(texts) else: # reading from stdin msg.text("Reading input text from stdin...") texts = srsly.read_jsonl("-") with msg.loading(f"Loading model '{vectors_model}'..."): nlp = util.load_model(vectors_model) msg.good(f"Loaded model '{vectors_model}'") pretrained_vectors = None if not use_vectors else nlp.vocab.vectors.name model = create_pretraining_model( nlp, Tok2Vec( width, embed_rows, conv_depth=depth, pretrained_vectors=pretrained_vectors, bilstm_depth=bilstm_depth, # Requires PyTorch. Experimental. subword_features=not use_chars, # Set to False for Chinese etc cnn_maxout_pieces=cnn_pieces, # If set to 1, use Mish activation. ), ) # Load in pretrained weights if init_tok2vec is not None: components = _load_pretrained_tok2vec(nlp, init_tok2vec) msg.text(f"Loaded pretrained tok2vec for: {components}") # Parse the epoch number from the given weight file model_name = re.search(r"model\d+\.bin", str(init_tok2vec)) if model_name: # Default weight file name so read epoch_start from it by cutting off 'model' and '.bin' epoch_start = int(model_name.group(0)[5:][:-4]) + 1 else: if not epoch_start: msg.fail( "You have to use the '--epoch-start' argument when using a renamed weight file for " "'--init-tok2vec'", exits=True, ) elif epoch_start < 0: msg.fail( "The argument '--epoch-start' has to be greater or equal to 0. '%d' is invalid" % epoch_start, exits=True, ) else: # Without '--init-tok2vec' the '--epoch-start' argument is ignored epoch_start = 0 optimizer = create_default_optimizer(model.ops) tracker = ProgressTracker(frequency=10000) msg.divider("Pre-training tok2vec layer - starting at epoch %d" % epoch_start) 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 / ("model%d%s.bin" % (epoch, is_temp_str))).open( "wb" ) as file_: file_.write(model.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 for epoch in range(epoch_start, n_iter + epoch_start): for batch_id, batch in enumerate( util.minibatch_by_words( (Example(doc=text) for text in texts), size=batch_size ) ): docs, count = make_docs( nlp, [text for (text, _) in batch], max_length=max_length, min_length=min_length, ) skip_counter += count loss = make_update( model, docs, optimizer, objective=loss_func, drop=dropout ) 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 n_save_every and (batch_id % 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 make_update(model, docs, optimizer, drop=0.0, objective="L2"): """Perform an update over a single batch of documents. docs (iterable): A batch of `Doc` objects. drop (float): The dropout rate. optimizer (callable): An optimizer. RETURNS loss: A float for the loss. """ predictions, backprop = model.begin_update(docs, drop=drop) loss, gradients = get_vectors_loss(model.ops, docs, predictions, objective) backprop(gradients, sgd=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 len(doc) >= min_length and len(doc) < max_length: docs.append(doc) return docs, skip_count def get_vectors_loss(ops, docs, prediction, objective="L2"): """Compute a mean-squared error loss between the documents' vectors and the prediction. Note that this is ripe for customization! We could compute the vectors in some other word, e.g. with an LSTM language model, or use some other type of objective. """ # 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] if objective == "L2": d_target = prediction - target loss = (d_target ** 2).sum() elif objective == "cosine": loss, d_target = get_cossim_loss(prediction, target) else: raise ValueError(Errors.E142.format(loss_func=objective)) return loss, d_target def create_pretraining_model(nlp, tok2vec): """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. """ output_size = nlp.vocab.vectors.data.shape[1] output_layer = chain( LN(Maxout(300, pieces=3)), Affine(output_size, drop_factor=0.0) ) # This is annoying, but the parser etc have the flatten step after # the tok2vec. To load the weights in cleanly, we need to match # the shape of the models' components exactly. So what we cann # "tok2vec" has to be the same set of processes as what the components do. tok2vec = chain(tok2vec, flatten) model = chain(tok2vec, output_layer) model = masked_language_model(nlp.vocab, model) model.tok2vec = tok2vec model.output_layer = output_layer model.begin_training([nlp.make_doc("Give it a doc to infer shapes")]) 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