spaCy/spacy/cli/pretrain.py

363 lines
13 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, get_array_module
from wasabi import Printer
import srsly
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
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),
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,
),
)
def pretrain(
texts_loc,
vectors_model,
output_dir,
width=96,
depth=4,
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,
):
"""
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())
for key in config:
if isinstance(config[key], Path):
config[key] = str(config[key])
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))
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("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
),
)
# Load in pre-trained weights
if init_tok2vec is not None:
components = _load_pretrained_tok2vec(nlp, init_tok2vec)
msg.text("Loaded pretrained tok2vec for: {}".format(components))
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)
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(n_iter):
for batch_id, batch in enumerate(
util.minibatch_by_words(((text, None) 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("Skipped {count} empty values".format(count=str(skip_counter)))
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 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, 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.E139.format(loss_func=objective))
return loss, d_target
def get_cossim_loss(yh, y):
# Add a small constant to avoid 0 vectors
yh = yh + 1e-8
y = y + 1e-8
# https://math.stackexchange.com/questions/1923613/partial-derivative-of-cosine-similarity
xp = get_array_module(yh)
norm_yh = xp.linalg.norm(yh, axis=1, keepdims=True)
norm_y = xp.linalg.norm(y, axis=1, keepdims=True)
mul_norms = norm_yh * norm_y
cosine = (yh * y).sum(axis=1, keepdims=True) / mul_norms
d_yh = (y / mul_norms) - (cosine * (yh / norm_yh ** 2))
loss = xp.abs(cosine - 1).sum()
return loss, -d_yh
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