mirror of https://github.com/explosion/spaCy.git
305 lines
11 KiB
Python
305 lines
11 KiB
Python
# coding: utf8
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from __future__ import print_function, unicode_literals
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import plac
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import random
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import numpy
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import time
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from collections import Counter
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from pathlib import Path
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from thinc.v2v import Affine, Maxout
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from thinc.misc import LayerNorm as LN
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from thinc.neural.util import prefer_gpu, get_array_module
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from wasabi import Printer
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import srsly
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from ..tokens import Doc
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from ..attrs import ID, HEAD
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from .._ml import Tok2Vec, flatten, chain, create_default_optimizer
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from .._ml import masked_language_model
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from .. import util
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@plac.annotations(
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texts_loc=("Path to jsonl file with texts to learn from", "positional", None, str),
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vectors_model=("Name or path to vectors model to learn from"),
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output_dir=("Directory to write models each epoch", "positional", None, str),
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width=("Width of CNN layers", "option", "cw", int),
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depth=("Depth of CNN layers", "option", "cd", int),
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embed_rows=("Embedding rows", "option", "er", int),
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loss_func=("Loss to use for the objective. L2 or cosine", "option", "L", str),
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use_vectors=("Whether to use the static vectors as input features", "flag", "uv"),
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dropout=("Dropout", "option", "d", float),
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batch_size=("Number of words per training batch", "option", "bs", int),
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max_length=("Max words per example.", "option", "xw", int),
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min_length=("Min words per example.", "option", "nw", int),
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seed=("Seed for random number generators", "option", "s", float),
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n_iter=("Number of iterations to pretrain", "option", "i", int),
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n_save_every=("Save model every X batches.", "option", "se", int),
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)
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def pretrain(
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texts_loc,
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vectors_model,
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output_dir,
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width=96,
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depth=4,
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embed_rows=2000,
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loss_func="cosine",
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use_vectors=False,
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dropout=0.2,
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n_iter=1000,
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batch_size=3000,
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max_length=500,
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min_length=5,
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seed=0,
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n_save_every=None,
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):
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"""
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Pre-train the 'token-to-vector' (tok2vec) layer of pipeline components,
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using an approximate language-modelling objective. Specifically, we load
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pre-trained vectors, and train a component like a CNN, BiLSTM, etc to predict
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vectors which match the pre-trained ones. The weights are saved to a directory
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after each epoch. You can then pass a path to one of these pre-trained weights
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files to the 'spacy train' command.
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This technique may be especially helpful if you have little labelled data.
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However, it's still quite experimental, so your mileage may vary.
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To load the weights back in during 'spacy train', you need to ensure
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all settings are the same between pretraining and training. The API and
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errors around this need some improvement.
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"""
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config = dict(locals())
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msg = Printer()
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util.fix_random_seed(seed)
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has_gpu = prefer_gpu()
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msg.info("Using GPU" if has_gpu else "Not using GPU")
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output_dir = Path(output_dir)
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if not output_dir.exists():
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output_dir.mkdir()
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msg.good("Created output directory")
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srsly.write_json(output_dir / "config.json", config)
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msg.good("Saved settings to config.json")
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# Load texts from file or stdin
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if texts_loc != "-": # reading from a file
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texts_loc = Path(texts_loc)
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if not texts_loc.exists():
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msg.fail("Input text file doesn't exist", texts_loc, exits=1)
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with msg.loading("Loading input texts..."):
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texts = list(srsly.read_jsonl(texts_loc))
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msg.good("Loaded input texts")
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random.shuffle(texts)
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else: # reading from stdin
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msg.text("Reading input text from stdin...")
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texts = srsly.read_jsonl("-")
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with msg.loading("Loading model '{}'...".format(vectors_model)):
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nlp = util.load_model(vectors_model)
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msg.good("Loaded model '{}'".format(vectors_model))
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pretrained_vectors = None if not use_vectors else nlp.vocab.vectors.name
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model = create_pretraining_model(
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nlp,
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Tok2Vec(
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width,
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embed_rows,
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conv_depth=depth,
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pretrained_vectors=pretrained_vectors,
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bilstm_depth=0, # Requires PyTorch. Experimental.
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cnn_maxout_pieces=3, # You can try setting this higher
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subword_features=True, # Set to False for Chinese etc
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),
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)
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optimizer = create_default_optimizer(model.ops)
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tracker = ProgressTracker(frequency=10000)
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msg.divider("Pre-training tok2vec layer")
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row_settings = {"widths": (3, 10, 10, 6, 4), "aligns": ("r", "r", "r", "r", "r")}
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msg.row(("#", "# Words", "Total Loss", "Loss", "w/s"), **row_settings)
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def _save_model(epoch, is_temp=False):
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is_temp_str = ".temp" if is_temp else ""
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with model.use_params(optimizer.averages):
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with (output_dir / ("model%d%s.bin" % (epoch, is_temp_str))).open(
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"wb"
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) as file_:
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file_.write(model.tok2vec.to_bytes())
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log = {
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"nr_word": tracker.nr_word,
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"loss": tracker.loss,
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"epoch_loss": tracker.epoch_loss,
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"epoch": epoch,
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}
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with (output_dir / "log.jsonl").open("a") as file_:
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file_.write(srsly.json_dumps(log) + "\n")
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for epoch in range(n_iter):
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for batch_id, batch in enumerate(
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util.minibatch_by_words(((text, None) for text in texts), size=batch_size)
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):
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docs = make_docs(
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nlp,
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[text for (text, _) in batch],
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max_length=max_length,
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min_length=min_length,
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)
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loss = make_update(
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model, docs, optimizer, objective=loss_func, drop=dropout
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)
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progress = tracker.update(epoch, loss, docs)
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if progress:
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msg.row(progress, **row_settings)
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if texts_loc == "-" and tracker.words_per_epoch[epoch] >= 10 ** 7:
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break
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if n_save_every and (batch_id % n_save_every == 0):
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_save_model(epoch, is_temp=True)
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_save_model(epoch)
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tracker.epoch_loss = 0.0
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if texts_loc != "-":
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# Reshuffle the texts if texts were loaded from a file
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random.shuffle(texts)
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def make_update(model, docs, optimizer, drop=0.0, objective="L2"):
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"""Perform an update over a single batch of documents.
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docs (iterable): A batch of `Doc` objects.
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drop (float): The droput rate.
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optimizer (callable): An optimizer.
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RETURNS loss: A float for the loss.
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"""
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predictions, backprop = model.begin_update(docs, drop=drop)
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loss, gradients = get_vectors_loss(model.ops, docs, predictions, objective)
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backprop(gradients, sgd=optimizer)
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# Don't want to return a cupy object here
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# The gradients are modified in-place by the BERT MLM,
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# so we get an accurate loss
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return float(loss)
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def make_docs(nlp, batch, min_length, max_length):
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docs = []
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for record in batch:
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if "tokens" in record:
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doc = Doc(nlp.vocab, words=record["tokens"])
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else:
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text = record["text"]
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doc = nlp.make_doc(text)
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if "heads" in record:
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heads = record["heads"]
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heads = numpy.asarray(heads, dtype="uint64")
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heads = heads.reshape((len(doc), 1))
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doc = doc.from_array([HEAD], heads)
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if len(doc) >= min_length and len(doc) < max_length:
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docs.append(doc)
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return docs
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def get_vectors_loss(ops, docs, prediction, objective="L2"):
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"""Compute a mean-squared error loss between the documents' vectors and
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the prediction.
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Note that this is ripe for customization! We could compute the vectors
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in some other word, e.g. with an LSTM language model, or use some other
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type of objective.
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"""
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# The simplest way to implement this would be to vstack the
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# token.vector values, but that's a bit inefficient, especially on GPU.
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# Instead we fetch the index into the vectors table for each of our tokens,
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# and look them up all at once. This prevents data copying.
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ids = ops.flatten([doc.to_array(ID).ravel() for doc in docs])
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target = docs[0].vocab.vectors.data[ids]
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if objective == "L2":
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d_target = prediction - target
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loss = (d_target ** 2).sum()
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elif objective == "cosine":
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loss, d_target = get_cossim_loss(prediction, target)
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return loss, d_target
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def get_cossim_loss(yh, y):
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# Add a small constant to avoid 0 vectors
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yh = yh + 1e-8
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y = y + 1e-8
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# https://math.stackexchange.com/questions/1923613/partial-derivative-of-cosine-similarity
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xp = get_array_module(yh)
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norm_yh = xp.linalg.norm(yh, axis=1, keepdims=True)
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norm_y = xp.linalg.norm(y, axis=1, keepdims=True)
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mul_norms = norm_yh * norm_y
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cosine = (yh * y).sum(axis=1, keepdims=True) / mul_norms
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d_yh = (y / mul_norms) - (cosine * (yh / norm_yh ** 2))
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loss = xp.abs(cosine - 1).sum()
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return loss, -d_yh
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def create_pretraining_model(nlp, tok2vec):
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"""Define a network for the pretraining. We simply add an output layer onto
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the tok2vec input model. The tok2vec input model needs to be a model that
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takes a batch of Doc objects (as a list), and returns a list of arrays.
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Each array in the output needs to have one row per token in the doc.
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"""
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output_size = nlp.vocab.vectors.data.shape[1]
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output_layer = chain(
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LN(Maxout(300, pieces=3)), Affine(output_size, drop_factor=0.0)
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)
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# This is annoying, but the parser etc have the flatten step after
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# the tok2vec. To load the weights in cleanly, we need to match
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# the shape of the models' components exactly. So what we cann
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# "tok2vec" has to be the same set of processes as what the components do.
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tok2vec = chain(tok2vec, flatten)
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model = chain(tok2vec, output_layer)
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model = masked_language_model(nlp.vocab, model)
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model.tok2vec = tok2vec
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model.output_layer = output_layer
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model.begin_training([nlp.make_doc("Give it a doc to infer shapes")])
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return model
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class ProgressTracker(object):
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def __init__(self, frequency=1000000):
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self.loss = 0.0
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self.prev_loss = 0.0
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self.nr_word = 0
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self.words_per_epoch = Counter()
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self.frequency = frequency
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self.last_time = time.time()
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self.last_update = 0
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self.epoch_loss = 0.0
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def update(self, epoch, loss, docs):
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self.loss += loss
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self.epoch_loss += loss
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words_in_batch = sum(len(doc) for doc in docs)
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self.words_per_epoch[epoch] += words_in_batch
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self.nr_word += words_in_batch
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words_since_update = self.nr_word - self.last_update
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if words_since_update >= self.frequency:
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wps = words_since_update / (time.time() - self.last_time)
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self.last_update = self.nr_word
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self.last_time = time.time()
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loss_per_word = self.loss - self.prev_loss
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status = (
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epoch,
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self.nr_word,
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_smart_round(self.loss, width=10),
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_smart_round(loss_per_word, width=6),
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int(wps),
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)
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self.prev_loss = float(self.loss)
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return status
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else:
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return None
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def _smart_round(figure, width=10, max_decimal=4):
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"""Round large numbers as integers, smaller numbers as decimals."""
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n_digits = len(str(int(figure)))
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n_decimal = width - (n_digits + 1)
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if n_decimal <= 1:
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return str(int(figure))
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else:
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n_decimal = min(n_decimal, max_decimal)
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format_str = "%." + str(n_decimal) + "f"
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return format_str % figure
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