mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-27 18:36:36 +03:00
252 lines
9.5 KiB
Python
252 lines
9.5 KiB
Python
# coding: utf8
|
|
from __future__ import print_function, unicode_literals
|
|
|
|
import plac
|
|
import random
|
|
import numpy
|
|
import time
|
|
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 Printer
|
|
import srsly
|
|
|
|
from ..tokens import Doc
|
|
from ..attrs import ID, HEAD
|
|
from .._ml import Tok2Vec, flatten, chain, create_default_optimizer
|
|
from .._ml import masked_language_model
|
|
from .. import util
|
|
|
|
|
|
@plac.annotations(
|
|
texts_loc=("Path to jsonl file with texts to learn from", "positional", None, str),
|
|
vectors_model=("Name or path to vectors model to learn from"),
|
|
output_dir=("Directory to write models each epoch", "positional", None, str),
|
|
width=("Width of CNN layers", "option", "cw", int),
|
|
depth=("Depth of CNN layers", "option", "cd", int),
|
|
embed_rows=("Embedding rows", "option", "er", int),
|
|
use_vectors=("Whether to use the static vectors as input features", "flag", "uv"),
|
|
dropout=("Dropout", "option", "d", float),
|
|
seed=("Seed for random number generators", "option", "s", float),
|
|
nr_iter=("Number of iterations to pretrain", "option", "i", int),
|
|
)
|
|
def pretrain(
|
|
texts_loc,
|
|
vectors_model,
|
|
output_dir,
|
|
width=96,
|
|
depth=4,
|
|
embed_rows=2000,
|
|
use_vectors=False,
|
|
dropout=0.2,
|
|
nr_iter=1000,
|
|
seed=0,
|
|
):
|
|
"""
|
|
Pre-train the 'token-to-vector' (tok2vec) layer of pipeline components,
|
|
using an approximate language-modelling objective. Specifically, we load
|
|
pre-trained vectors, and train a component like a CNN, BiLSTM, etc to predict
|
|
vectors which match the pre-trained ones. The weights are saved to a directory
|
|
after each epoch. You can then pass a path to one of these pre-trained 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())
|
|
msg = Printer()
|
|
util.fix_random_seed(seed)
|
|
|
|
has_gpu = prefer_gpu()
|
|
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))
|
|
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("Loading model '{}'...".format(vectors_model)):
|
|
nlp = util.load_model(vectors_model)
|
|
msg.good("Loaded model '{}'".format(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=0, # Requires PyTorch. Experimental.
|
|
cnn_maxout_pieces=3, # You can try setting this higher
|
|
subword_features=True, # Set to False for Chinese etc
|
|
),
|
|
)
|
|
optimizer = create_default_optimizer(model.ops)
|
|
tracker = ProgressTracker(frequency=10000)
|
|
msg.divider("Pre-training tok2vec layer")
|
|
row_settings = {"widths": (3, 10, 10, 6, 4), "aligns": ("r", "r", "r", "r", "r")}
|
|
msg.row(("#", "# Words", "Total Loss", "Loss", "w/s"), **row_settings)
|
|
for epoch in range(nr_iter):
|
|
for batch in util.minibatch_by_words(
|
|
((text, None) for text in texts), size=3000
|
|
):
|
|
docs = make_docs(nlp, [text for (text, _) in batch])
|
|
loss = make_update(model, docs, optimizer, 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
|
|
with model.use_params(optimizer.averages):
|
|
with (output_dir / ("model%d.bin" % epoch)).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")
|
|
tracker.epoch_loss = 0.0
|
|
if texts_loc != "-":
|
|
# Reshuffle the texts if texts were loaded from a file
|
|
random.shuffle(texts)
|
|
|
|
|
|
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 droput 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=1, max_length=500):
|
|
docs = []
|
|
for record in batch:
|
|
text = record["text"]
|
|
if "tokens" in record:
|
|
doc = Doc(nlp.vocab, words=record["tokens"])
|
|
else:
|
|
doc = nlp.make_doc(text)
|
|
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
|
|
|
|
|
|
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_scores = prediction - target
|
|
loss = (d_scores ** 2).sum()
|
|
else:
|
|
raise NotImplementedError(objective)
|
|
return loss, d_scores
|
|
|
|
|
|
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,
|
|
"%.8f" % self.loss,
|
|
"%.8f" % loss_per_word,
|
|
int(wps),
|
|
)
|
|
self.prev_loss = float(self.loss)
|
|
return status
|
|
else:
|
|
return None
|