mirror of
https://github.com/explosion/spaCy.git
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Merge pre-trained vectors and noshare patches
This commit is contained in:
Matthew Honnibal
commit
4348c479fc
86
spacy/_ml.py
86
spacy/_ml.py
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@ -4,6 +4,7 @@ from thinc.neural import Model, Maxout, Softmax, Affine
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from thinc.neural._classes.hash_embed import HashEmbed
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from thinc.neural.ops import NumpyOps, CupyOps
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from thinc.neural.util import get_array_module
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import thinc.extra.load_nlp
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import random
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import cytoolz
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@ -31,6 +32,7 @@ from . import util
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import numpy
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import io
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VECTORS_KEY = 'spacy_pretrained_vectors'
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@layerize
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def _flatten_add_lengths(seqs, pad=0, drop=0.):
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@ -225,45 +227,51 @@ def drop_layer(layer, factor=2.):
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model.predict = layer
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return model
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def link_vectors_to_models(vocab):
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vectors = vocab.vectors
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ops = Model.ops
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for word in vocab:
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if word.orth in vectors.key2row:
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word.rank = vectors.key2row[word.orth]
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else:
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word.rank = 0
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data = ops.asarray(vectors.data)
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# Set an entry here, so that vectors are accessed by StaticVectors
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# (unideal, I know)
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thinc.extra.load_nlp.VECTORS[(ops.device, VECTORS_KEY)] = data
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def Tok2Vec(width, embed_size, **kwargs):
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pretrained_dims = kwargs.get('pretrained_dims', 0)
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cnn_maxout_pieces = kwargs.get('cnn_maxout_pieces', 3)
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cols = [ID, NORM, PREFIX, SUFFIX, SHAPE, ORTH]
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with Model.define_operators({'>>': chain, '|': concatenate, '**': clone, '+': add}):
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with Model.define_operators({'>>': chain, '|': concatenate, '**': clone, '+': add,
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'*': reapply}):
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norm = HashEmbed(width, embed_size, column=cols.index(NORM), name='embed_norm')
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prefix = HashEmbed(width, embed_size//2, column=cols.index(PREFIX), name='embed_prefix')
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suffix = HashEmbed(width, embed_size//2, column=cols.index(SUFFIX), name='embed_suffix')
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shape = HashEmbed(width, embed_size//2, column=cols.index(SHAPE), name='embed_shape')
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if pretrained_dims is not None and pretrained_dims >= 1:
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glove = StaticVectors(VECTORS_KEY, width, column=cols.index(ID))
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embed = uniqued(
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(glove | norm | prefix | suffix | shape)
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>> LN(Maxout(width, width*5, pieces=3)), column=5)
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else:
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embed = uniqued(
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(norm | prefix | suffix | shape)
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>> LN(Maxout(width, width*4, pieces=3)), column=5)
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trained_vectors = (
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FeatureExtracter(cols)
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>> with_flatten(
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uniqued(
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(norm | prefix | suffix | shape)
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>> LN(Maxout(width, width*4, pieces=3)), column=5)
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)
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)
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convolution = Residual(
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ExtractWindow(nW=1)
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>> LN(Maxout(width, width*3, pieces=cnn_maxout_pieces))
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)
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if pretrained_dims >= 1:
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embed = concatenate_lists(trained_vectors, SpacyVectors)
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tok2vec = (
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embed
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>> with_flatten(
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Affine(width, width+pretrained_dims)
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>> convolution ** 4,
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pad=4)
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)
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else:
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embed = trained_vectors
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tok2vec = (
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embed
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>> with_flatten(convolution ** 4, pad=4)
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)
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tok2vec = (
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FeatureExtracter(cols)
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>> with_flatten(
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embed >> (convolution * 4), pad=4)
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)
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# Work around thinc API limitations :(. TODO: Revise in Thinc 7
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tok2vec.nO = width
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@ -271,6 +279,28 @@ def Tok2Vec(width, embed_size, **kwargs):
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return tok2vec
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def reapply(layer, n_times):
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def reapply_fwd(X, drop=0.):
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backprops = []
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for i in range(n_times):
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Y, backprop = layer.begin_update(X, drop=drop)
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X = Y
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backprops.append(backprop)
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def reapply_bwd(dY, sgd=None):
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dX = None
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for backprop in reversed(backprops):
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dY = backprop(dY, sgd=sgd)
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if dX is None:
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dX = dY
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else:
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dX += dY
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return dX
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return Y, reapply_bwd
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return wrap(reapply_fwd, layer)
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def asarray(ops, dtype):
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def forward(X, drop=0.):
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return ops.asarray(X, dtype=dtype), None
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@ -474,11 +504,13 @@ def getitem(i):
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return X[i], None
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return layerize(getitem_fwd)
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def build_tagger_model(nr_class, pretrained_dims=0, **cfg):
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def build_tagger_model(nr_class, **cfg):
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embed_size = util.env_opt('embed_size', 4000)
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if 'token_vector_width' not in cfg:
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if 'token_vector_width' in cfg:
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token_vector_width = cfg['token_vector_width']
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else:
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token_vector_width = util.env_opt('token_vector_width', 128)
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pretrained_dims = cfg.get('pretrained_dims', 0)
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with Model.define_operators({'>>': chain, '+': add}):
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tok2vec = Tok2Vec(token_vector_width, embed_size,
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pretrained_dims=pretrained_dims)
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@ -35,14 +35,14 @@ numpy.random.seed(0)
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n_iter=("number of iterations", "option", "n", int),
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n_sents=("number of sentences", "option", "ns", int),
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use_gpu=("Use GPU", "option", "g", int),
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resume=("Whether to resume training", "flag", "R", bool),
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vectors=("Model to load vectors from", "option", "v"),
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no_tagger=("Don't train tagger", "flag", "T", bool),
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no_parser=("Don't train parser", "flag", "P", bool),
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no_entities=("Don't train NER", "flag", "N", bool),
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gold_preproc=("Use gold preprocessing", "flag", "G", bool),
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)
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def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
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use_gpu=-1, resume=False, no_tagger=False, no_parser=False, no_entities=False,
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use_gpu=-1, vectors=None, no_tagger=False, no_parser=False, no_entities=False,
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gold_preproc=False):
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"""
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Train a model. Expects data in spaCy's JSON format.
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@ -78,25 +78,20 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
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corpus = GoldCorpus(train_path, dev_path, limit=n_sents)
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n_train_words = corpus.count_train()
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if not resume:
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lang_class = util.get_lang_class(lang)
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nlp = lang_class(pipeline=pipeline)
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optimizer = nlp.begin_training(lambda: corpus.train_tuples, device=use_gpu)
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else:
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print("Load resume")
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util.use_gpu(use_gpu)
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nlp = _resume_model(lang, pipeline, corpus)
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optimizer = nlp.resume_training(device=use_gpu)
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lang_class = nlp.__class__
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lang_class = util.get_lang_class(lang)
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nlp = lang_class(pipeline=pipeline)
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if vectors:
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util.load_model(vectors, vocab=nlp.vocab)
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optimizer = nlp.begin_training(lambda: corpus.train_tuples, device=use_gpu)
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nlp._optimizer = None
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print("Itn.\tLoss\tUAS\tNER P.\tNER R.\tNER F.\tTag %\tToken %")
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try:
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train_docs = corpus.train_docs(nlp, projectivize=True, noise_level=0.0,
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gold_preproc=gold_preproc, max_length=0)
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train_docs = list(train_docs)
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for i in range(n_iter):
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with tqdm.tqdm(total=n_train_words, leave=False) as pbar:
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train_docs = corpus.train_docs(nlp, projectivize=True, noise_level=0.0,
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gold_preproc=gold_preproc, max_length=0)
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losses = {}
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for batch in minibatch(train_docs, size=batch_sizes):
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docs, golds = zip(*batch)
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@ -109,8 +104,8 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
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util.set_env_log(False)
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epoch_model_path = output_path / ('model%d' % i)
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nlp.to_disk(epoch_model_path)
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#nlp_loaded = lang_class(pipeline=pipeline)
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#nlp_loaded = nlp_loaded.from_disk(epoch_model_path)
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nlp_loaded = lang_class(pipeline=pipeline)
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nlp_loaded = nlp_loaded.from_disk(epoch_model_path)
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scorer = nlp.evaluate(
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corpus.dev_docs(
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nlp,
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@ -129,26 +124,6 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
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except:
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pass
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def _resume_model(lang, pipeline, corpus):
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nlp = util.load_model(lang)
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pipes = {getattr(pipe, 'name', None) for pipe in nlp.pipeline}
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for name in pipeline:
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if name not in pipes:
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factory = nlp.Defaults.factories[name]
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for pipe in factory(nlp):
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if hasattr(pipe, 'begin_training'):
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pipe.begin_training(corpus.train_tuples,
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pipeline=nlp.pipeline)
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nlp.pipeline.append(pipe)
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nlp.meta['pipeline'] = pipeline
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if nlp.vocab.vectors.data.shape[1] >= 1:
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nlp.vocab.vectors.data = Model.ops.asarray(
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nlp.vocab.vectors.data)
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return nlp
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def _render_parses(i, to_render):
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to_render[0].user_data['title'] = "Batch %d" % i
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with Path('/tmp/entities.html').open('w') as file_:
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@ -348,7 +348,6 @@ class Language(object):
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self._optimizer.device = device
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return self._optimizer
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def begin_training(self, get_gold_tuples=None, **cfg):
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"""Allocate models, pre-process training data and acquire a trainer and
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optimizer. Used as a contextmanager.
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@ -43,6 +43,7 @@ from .compat import json_dumps
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from .attrs import ID, LOWER, PREFIX, SUFFIX, SHAPE, TAG, DEP, POS
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from ._ml import rebatch, Tok2Vec, flatten
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from ._ml import build_text_classifier, build_tagger_model
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from ._ml import link_vectors_to_models
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from .parts_of_speech import X
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@ -121,6 +122,7 @@ class BaseThincComponent(object):
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token_vector_width = pipeline[0].model.nO
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if self.model is True:
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self.model = self.Model(1, token_vector_width)
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link_vectors_to_models(self.vocab)
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def use_params(self, params):
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with self.model.use_params(params):
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@ -143,8 +145,8 @@ class BaseThincComponent(object):
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deserialize = OrderedDict((
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('cfg', lambda b: self.cfg.update(ujson.loads(b))),
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('model', load_model),
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('vocab', lambda b: self.vocab.from_bytes(b))
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('model', load_model),
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))
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util.from_bytes(bytes_data, deserialize, exclude)
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return self
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@ -152,8 +154,8 @@ class BaseThincComponent(object):
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def to_disk(self, path, **exclude):
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serialize = OrderedDict((
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('cfg', lambda p: p.open('w').write(json_dumps(self.cfg))),
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('vocab', lambda p: self.vocab.to_disk(p)),
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('model', lambda p: p.open('wb').write(self.model.to_bytes())),
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('vocab', lambda p: self.vocab.to_disk(p))
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))
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util.to_disk(path, serialize, exclude)
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@ -166,8 +168,8 @@ class BaseThincComponent(object):
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deserialize = OrderedDict((
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('cfg', lambda p: self.cfg.update(_load_cfg(p))),
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('model', load_model),
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('vocab', lambda p: self.vocab.from_disk(p)),
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('model', load_model),
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))
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util.from_disk(path, deserialize, exclude)
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return self
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@ -215,7 +217,7 @@ class TokenVectorEncoder(BaseThincComponent):
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self.model = model
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self.cfg = dict(cfg)
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self.cfg['pretrained_dims'] = self.vocab.vectors.data.shape[1]
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self.cfg.setdefault('cnn_maxout_pieces', 2)
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self.cfg.setdefault('cnn_maxout_pieces', 3)
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def __call__(self, doc):
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"""Add context-sensitive vectors to a `Doc`, e.g. from a CNN or LSTM
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@ -287,7 +289,9 @@ class TokenVectorEncoder(BaseThincComponent):
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pipeline (list): The pipeline the model is part of.
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"""
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if self.model is True:
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self.cfg['pretrained_dims'] = self.vocab.vectors_length
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self.model = self.Model(**self.cfg)
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link_vectors_to_models(self.vocab)
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class NeuralTagger(BaseThincComponent):
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@ -297,6 +301,7 @@ class NeuralTagger(BaseThincComponent):
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self.model = model
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self.cfg = dict(cfg)
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self.cfg.setdefault('cnn_maxout_pieces', 2)
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self.cfg.setdefault('pretrained_dims', self.vocab.vectors.data.shape[1])
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def __call__(self, doc):
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tags = self.predict([doc])
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@ -388,8 +393,9 @@ class NeuralTagger(BaseThincComponent):
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vocab.morphology.lemmatizer,
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exc=vocab.morphology.exc)
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if self.model is True:
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self.model = self.Model(self.vocab.morphology.n_tags,
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pretrained_dims=self.vocab.vectors_length)
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self.cfg['pretrained_dims'] = self.vocab.vectors.data.shape[1]
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self.model = self.Model(self.vocab.morphology.n_tags, **self.cfg)
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link_vectors_to_models(self.vocab)
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@classmethod
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def Model(cls, n_tags, **cfg):
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@ -414,8 +420,7 @@ class NeuralTagger(BaseThincComponent):
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if self.model is True:
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token_vector_width = util.env_opt('token_vector_width',
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self.cfg.get('token_vector_width', 128))
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self.model = self.Model(self.vocab.morphology.n_tags, token_vector_width,
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pretrained_dims=self.vocab.vectors_length)
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self.model = self.Model(self.vocab.morphology.n_tags, **self.cfg)
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self.model.from_bytes(b)
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def load_tag_map(b):
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@ -449,10 +454,7 @@ class NeuralTagger(BaseThincComponent):
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def from_disk(self, path, **exclude):
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def load_model(p):
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if self.model is True:
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token_vector_width = util.env_opt('token_vector_width',
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self.cfg.get('token_vector_width', 128))
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self.model = self.Model(self.vocab.morphology.n_tags, token_vector_width,
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**self.cfg)
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self.model = self.Model(self.vocab.morphology.n_tags, **self.cfg)
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self.model.from_bytes(p.open('rb').read())
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def load_tag_map(p):
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@ -480,6 +482,7 @@ class NeuralLabeller(NeuralTagger):
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self.model = model
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self.cfg = dict(cfg)
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self.cfg.setdefault('cnn_maxout_pieces', 2)
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self.cfg.setdefault('pretrained_dims', self.vocab.vectors.data.shape[1])
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@property
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def labels(self):
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@ -502,13 +505,13 @@ class NeuralLabeller(NeuralTagger):
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self.labels[dep] = len(self.labels)
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token_vector_width = pipeline[0].model.nO
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if self.model is True:
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self.model = self.Model(len(self.labels), token_vector_width,
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pretrained_dims=self.vocab.vectors_length)
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self.cfg['pretrained_dims'] = self.vocab.vectors.data.shape[1]
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self.model = self.Model(len(self.labels), **self.cfg)
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link_vectors_to_models(self.vocab)
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@classmethod
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def Model(cls, n_tags, token_vector_width, pretrained_dims=0):
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return build_tagger_model(n_tags, token_vector_width,
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pretrained_dims)
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def Model(cls, n_tags, **cfg):
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return build_tagger_model(n_tags, **cfg)
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def get_loss(self, docs, golds, scores):
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scores = self.model.ops.flatten(scores)
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@ -579,6 +582,7 @@ class SimilarityHook(BaseThincComponent):
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"""
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if self.model is True:
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self.model = self.Model(pipeline[0].model.nO)
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link_vectors_to_models(self.vocab)
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class TextCategorizer(BaseThincComponent):
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@ -650,6 +654,7 @@ class TextCategorizer(BaseThincComponent):
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self.cfg['pretrained_dims'] = self.vocab.vectors_length
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self.model = self.Model(len(self.labels), token_vector_width,
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**self.cfg)
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link_vectors_to_models(self.vocab)
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cdef class EntityRecognizer(LinearParser):
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|
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@ -49,6 +49,7 @@ from ..util import get_async, get_cuda_stream
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from .._ml import zero_init, PrecomputableAffine, PrecomputableMaxouts
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from .._ml import Tok2Vec, doc2feats, rebatch, fine_tune
|
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from .._ml import Residual, drop_layer, flatten
|
||||
from .._ml import link_vectors_to_models
|
||||
from ..compat import json_dumps
|
||||
|
||||
from . import _parse_features
|
||||
|
|
@ -310,6 +311,7 @@ cdef class Parser:
|
|||
cfg['beam_density'] = util.env_opt('beam_density', 0.0)
|
||||
if 'pretrained_dims' not in cfg:
|
||||
cfg['pretrained_dims'] = self.vocab.vectors.data.shape[1]
|
||||
cfg.setdefault('cnn_maxout_pieces', 3)
|
||||
self.cfg = cfg
|
||||
if 'actions' in self.cfg:
|
||||
for action, labels in self.cfg.get('actions', {}).items():
|
||||
|
|
@ -339,7 +341,7 @@ cdef class Parser:
|
|||
self.set_annotations([doc], states)
|
||||
return doc
|
||||
else:
|
||||
beam = self.beam_parse([doc],
|
||||
beam = self.beam_parse([doc],
|
||||
beam_width=beam_width, beam_density=beam_density)[0]
|
||||
output = self.moves.get_beam_annot(beam)
|
||||
state = <StateClass>beam.at(0)
|
||||
|
|
@ -754,6 +756,7 @@ cdef class Parser:
|
|||
if self.model is True:
|
||||
cfg['pretrained_dims'] = self.vocab.vectors_length
|
||||
self.model, cfg = self.Model(self.moves.n_moves, **cfg)
|
||||
link_vectors_to_models(self.vocab)
|
||||
self.cfg.update(cfg)
|
||||
|
||||
def preprocess_gold(self, docs_golds):
|
||||
|
|
@ -835,8 +838,7 @@ cdef class Parser:
|
|||
msg = util.from_bytes(bytes_data, deserializers, exclude)
|
||||
if 'model' not in exclude:
|
||||
if self.model is True:
|
||||
self.model, cfg = self.Model(self.moves.n_moves,
|
||||
pretrained_dims=self.vocab.vectors_length)
|
||||
self.model, cfg = self.Model(**self.cfg)
|
||||
cfg['pretrained_dims'] = self.vocab.vectors_length
|
||||
else:
|
||||
cfg = {}
|
||||
|
|
|
|||
|
|
@ -27,6 +27,7 @@ from .vectors import Vectors
|
|||
from . import util
|
||||
from . import attrs
|
||||
from . import symbols
|
||||
from ._ml import link_vectors_to_models
|
||||
|
||||
|
||||
cdef class Vocab:
|
||||
|
|
@ -323,6 +324,7 @@ cdef class Vocab:
|
|||
self.lexemes_from_bytes(file_.read())
|
||||
if self.vectors is not None:
|
||||
self.vectors.from_disk(path, exclude='strings.json')
|
||||
link_vectors_to_models(self)
|
||||
return self
|
||||
|
||||
def to_bytes(self, **exclude):
|
||||
|
|
@ -362,6 +364,7 @@ cdef class Vocab:
|
|||
('vectors', lambda b: serialize_vectors(b))
|
||||
))
|
||||
util.from_bytes(bytes_data, setters, exclude)
|
||||
link_vectors_to_models(self)
|
||||
return self
|
||||
|
||||
def lexemes_to_bytes(self):
|
||||
|
|
@ -436,6 +439,7 @@ def unpickle_vocab(sstore, morphology, data_dir,
|
|||
vocab.lex_attr_getters = lex_attr_getters
|
||||
vocab.lexemes_from_bytes(lexemes_data)
|
||||
vocab.length = length
|
||||
link_vectors_to_models(vocab)
|
||||
return vocab
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user