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https://github.com/explosion/spaCy.git
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💫 Feature/improve pretraining (#2971)
* Improve spacy pretrain script * Implement BERT-style 'masked language model' objective. Much better results. * Improve logging. * Add length cap for documents, to avoid memory errors. * Require thinc 7.0.0.dev1 * Require thinc 7.0.0.dev1 * Add argument for using pretrained vectors * Fix defaults * Fix syntax error * Improve spacy pretrain script * Implement BERT-style 'masked language model' objective. Much better results. * Improve logging. * Add length cap for documents, to avoid memory errors. * Require thinc 7.0.0.dev1 * Require thinc 7.0.0.dev1 * Add argument for using pretrained vectors * Fix defaults * Fix syntax error * Tweak pretraining script * Fix data limits in spacy.gold * Fix pretrain script
This commit is contained in:
Matthew Honnibal
GitHub
parent
0fdb25b958
commit
61e435610e
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@ -2,7 +2,7 @@ cython>=0.25
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numpy>=1.15.0
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cymem>=2.0.2,<2.1.0
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preshed>=2.0.1,<2.1.0
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thinc==7.0.0.dev0
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thinc==7.0.0.dev1
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blis>=0.2.2,<0.3.0
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murmurhash>=0.28.0,<1.1.0
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cytoolz>=0.9.0,<0.10.0
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2
setup.py
2
setup.py
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@ -200,7 +200,7 @@ def setup_package():
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"murmurhash>=0.28.0,<1.1.0",
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"cymem>=2.0.2,<2.1.0",
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"preshed>=2.0.1,<2.1.0",
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"thinc==7.0.0.dev0",
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"thinc==7.0.0.dev1",
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"blis>=0.2.2,<0.3.0",
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"plac<1.0.0,>=0.9.6",
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"ujson>=1.35",
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@ -24,10 +24,12 @@ import sys
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from collections import Counter
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import spacy
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from spacy.attrs import ID
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from spacy.tokens import Doc
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from spacy.attrs import ID, HEAD
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from spacy.util import minibatch, minibatch_by_words, use_gpu, compounding, ensure_path
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from spacy._ml import Tok2Vec, flatten, chain, zero_init, create_default_optimizer
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from thinc.v2v import Affine
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from thinc.api import wrap
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def prefer_gpu():
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@ -47,13 +49,14 @@ def load_texts(path):
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'''
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path = ensure_path(path)
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with path.open('r', encoding='utf8') as file_:
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texts = [json.loads(line)['text'] for line in file_]
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texts = [json.loads(line) for line in file_]
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random.shuffle(texts)
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return texts
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def stream_texts():
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for line in sys.stdin:
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yield json.loads(line)['text']
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yield json.loads(line)
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def make_update(model, docs, optimizer, drop=0.):
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@ -65,11 +68,33 @@ def make_update(model, docs, optimizer, drop=0.):
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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)
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gradients = get_vectors_loss(model.ops, docs, predictions)
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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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loss = float((gradients**2).mean())
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return loss
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def make_docs(nlp, batch):
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docs = []
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for record in batch:
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text = record["text"]
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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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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) >= 1 and len(doc) < 200:
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docs.append(doc)
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return docs
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def get_vectors_loss(ops, docs, prediction):
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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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@ -84,10 +109,8 @@ def get_vectors_loss(ops, docs, prediction):
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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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d_scores = (prediction - target) / prediction.shape[0]
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# Don't want to return a cupy object here
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loss = float((d_scores**2).sum())
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return loss, d_scores
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d_scores = prediction - target
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return d_scores
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def create_pretraining_model(nlp, tok2vec):
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@ -107,15 +130,77 @@ def create_pretraining_model(nlp, tok2vec):
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tok2vec,
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output_layer
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)
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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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def masked_language_model(vocab, model, mask_prob=0.15):
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'''Convert a model into a BERT-style masked language model'''
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vocab_words = [lex.text for lex in vocab if lex.prob != 0.0]
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vocab_probs = [lex.prob for lex in vocab if lex.prob != 0.0]
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vocab_words = vocab_words[:10000]
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vocab_probs = vocab_probs[:10000]
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vocab_probs = numpy.exp(numpy.array(vocab_probs, dtype='f'))
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vocab_probs /= vocab_probs.sum()
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def mlm_forward(docs, drop=0.):
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mask, docs = apply_mask(docs, vocab_words, vocab_probs,
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mask_prob=mask_prob)
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mask = model.ops.asarray(mask).reshape((mask.shape[0], 1))
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output, backprop = model.begin_update(docs, drop=drop)
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def mlm_backward(d_output, sgd=None):
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d_output *= 1-mask
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return backprop(d_output, sgd=sgd)
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return output, mlm_backward
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return wrap(mlm_forward, model)
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def apply_mask(docs, vocab_texts, vocab_probs, mask_prob=0.15):
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N = sum(len(doc) for doc in docs)
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mask = numpy.random.uniform(0., 1.0, (N,))
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mask = mask >= mask_prob
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i = 0
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masked_docs = []
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for doc in docs:
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words = []
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for token in doc:
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if not mask[i]:
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word = replace_word(token.text, vocab_texts, vocab_probs)
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else:
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word = token.text
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words.append(word)
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i += 1
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spaces = [bool(w.whitespace_) for w in doc]
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# NB: If you change this implementation to instead modify
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# the docs in place, take care that the IDs reflect the original
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# words. Currently we use the original docs to make the vectors
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# for the target, so we don't lose the original tokens. But if
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# you modified the docs in place here, you would.
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masked_docs.append(Doc(doc.vocab, words=words, spaces=spaces))
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return mask, masked_docs
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def replace_word(word, vocab_texts, vocab_probs, mask='[MASK]'):
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roll = random.random()
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if roll < 0.8:
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return mask
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elif roll < 0.9:
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index = numpy.random.choice(len(vocab_texts), p=vocab_probs)
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return vocab_texts[index]
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else:
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return word
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class ProgressTracker(object):
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def __init__(self, frequency=100000):
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self.loss = 0.
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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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@ -132,7 +217,15 @@ class ProgressTracker(object):
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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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status = (epoch, self.nr_word, '%.5f' % self.loss, int(wps))
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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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"%.5f" % self.loss,
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"%.4f" % loss_per_word,
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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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@ -145,12 +238,13 @@ class ProgressTracker(object):
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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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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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seed=("Seed for random number generators", "option", "s", float),
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nr_iter=("Number of iterations to pretrain", "option", "i", int),
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)
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def pretrain(texts_loc, vectors_model, output_dir, width=128, depth=4,
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embed_rows=1000, dropout=0.2, nr_iter=10, seed=0):
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embed_rows=5000, use_vectors=False, dropout=0.2, nr_iter=100, seed=0):
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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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@ -175,11 +269,13 @@ def pretrain(texts_loc, vectors_model, output_dir, width=128, depth=4,
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with (output_dir / 'config.json').open('w') as file_:
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file_.write(json.dumps(config))
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has_gpu = prefer_gpu()
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print("Use GPU?", has_gpu)
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nlp = spacy.load(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(nlp,
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Tok2Vec(width, embed_rows,
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conv_depth=depth,
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pretrained_vectors=nlp.vocab.vectors.name,
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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=2, # You can try setting this higher
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subword_features=True)) # Set to False for character models, e.g. Chinese
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@ -188,19 +284,19 @@ def pretrain(texts_loc, vectors_model, output_dir, width=128, depth=4,
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print('Epoch', '#Words', 'Loss', 'w/s')
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texts = stream_texts() if texts_loc == '-' else load_texts(texts_loc)
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for epoch in range(nr_iter):
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for batch in minibatch(texts, size=64):
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docs = [nlp.make_doc(text) for text in batch]
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for batch in minibatch(texts, size=256):
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docs = make_docs(nlp, batch)
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loss = make_update(model, docs, optimizer, drop=dropout)
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progress = tracker.update(epoch, loss, docs)
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if progress:
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print(*progress)
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if texts_loc == '-' and tracker.words_per_epoch[epoch] >= 10**6:
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if texts_loc == '-' and tracker.words_per_epoch[epoch] >= 10**7:
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break
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with model.use_params(optimizer.averages):
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with (output_dir / ('model%d.bin' % epoch)).open('wb') as file_:
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file_.write(model.tok2vec.to_bytes())
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with (output_dir / 'log.jsonl').open('a') as file_:
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file_.write(json.dumps({'nr_word': tracker.nr_word,
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'loss': tracker.loss, 'epoch': epoch}))
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'loss': tracker.loss, 'epoch': epoch}) + '\n')
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if texts_loc != '-':
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texts = load_texts(texts_loc)
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@ -25,6 +25,7 @@ from .compat import json_dumps
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from libc.stdio cimport FILE, fopen, fclose, fread, fwrite, feof, fseek
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def tags_to_entities(tags):
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entities = []
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start = None
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@ -110,19 +111,23 @@ class GoldCorpus(object):
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# Write temp directory with one doc per file, so we can shuffle
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# and stream
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self.tmp_dir = Path(tempfile.mkdtemp())
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self.write_msgpack(self.tmp_dir / 'train', train)
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self.write_msgpack(self.tmp_dir / 'dev', dev)
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self.write_msgpack(self.tmp_dir / 'train', train, limit=self.limit)
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self.write_msgpack(self.tmp_dir / 'dev', dev, limit=self.limit)
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def __del__(self):
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shutil.rmtree(self.tmp_dir)
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@staticmethod
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def write_msgpack(directory, doc_tuples):
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def write_msgpack(directory, doc_tuples, limit=0):
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if not directory.exists():
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directory.mkdir()
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n = 0
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for i, doc_tuple in enumerate(doc_tuples):
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with open(directory / '{}.msg'.format(i), 'wb') as file_:
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msgpack.dump([doc_tuple], file_, use_bin_type=True)
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n += len(doc_tuple[1])
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if limit and n >= limit:
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break
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@staticmethod
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def walk_corpus(path):
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@ -350,7 +355,7 @@ def _json_iterate(loc):
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py_str = py_raw[start : i+1].decode('utf8')
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try:
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yield json.loads(py_str)
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except:
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except Exception:
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print(py_str)
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raise
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start = -1
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