From 8a2de46fcb08ef1105a2b433f22631342fd963c1 Mon Sep 17 00:00:00 2001 From: Matthew Honnibal Date: Sat, 19 Nov 2016 10:58:00 -0600 Subject: [PATCH] Fix GPU usage in chainer example --- examples/chainer_sentiment.py | 49 +++++++++++++++++++++-------------- 1 file changed, 30 insertions(+), 19 deletions(-) diff --git a/examples/chainer_sentiment.py b/examples/chainer_sentiment.py index 84075a231..251eadef3 100644 --- a/examples/chainer_sentiment.py +++ b/examples/chainer_sentiment.py @@ -1,6 +1,7 @@ '''WIP --- Doesn't work well yet''' import plac import random +import six import pathlib import cPickle as pickle @@ -9,7 +10,10 @@ from itertools import izip import spacy import cytoolz -import numpy as np +import cupy as xp +import cupy.cuda +import chainer.cuda + import chainer.links as L import chainer.functions as F from chainer import Chain, Variable, report @@ -17,7 +21,7 @@ import chainer.training import chainer.optimizers from chainer.training import extensions from chainer.iterators import SerialIterator -from chainer.datasets.tuple_dataset import TupleDataset +from chainer.datasets import TupleDataset class SentimentAnalyser(object): @@ -79,6 +83,7 @@ class SentimentModel(Chain): encode=_Encode(shape['nr_hidden'], shape['nr_hidden']), attend=_Attend(shape['nr_hidden'], shape['nr_hidden']), predict=_Predict(shape['nr_hidden'], shape['nr_class'])) + self.to_gpu(0) def __call__(self, sentence): return self.predict( @@ -145,6 +150,16 @@ class SentenceDataset(TupleDataset): get_features(sents, max_length), labels) + def __getitem__(self, index): + batches = [dataset[index] for dataset in self._datasets] + if isinstance(index, slice): + length = len(batches[0]) + returns = [tuple([batch[i] for batch in batches]) + for i in six.moves.range(length)] + return returns + else: + return tuple(batches) + def _get_labelled_sentences(self, docs, doc_labels): labels = [] sentences = [] @@ -152,19 +167,17 @@ class SentenceDataset(TupleDataset): for sent in doc.sents: sentences.append(sent) labels.append(y) - return sentences, labels + return sentences, xp.asarray(labels, dtype='i') class DocDataset(TupleDataset): def __init__(self, nlp, texts, labels): self.max_length = max_length - TupleDataset.__init__(self, + DatasetMixin.__init__(self, get_features( nlp.pipe(texts, batch_size=5000, n_threads=3), self.max_length), labels) - - def read_data(data_dir, limit=0): examples = [] for subdir, label in (('pos', 1), ('neg', 0)): @@ -180,7 +193,7 @@ def read_data(data_dir, limit=0): def get_features(docs, max_length): docs = list(docs) - Xs = np.zeros((len(docs), max_length), dtype='int32') + Xs = xp.zeros((len(docs), max_length), dtype='i') for i, doc in enumerate(docs): j = 0 for token in doc: @@ -195,7 +208,7 @@ def get_features(docs, max_length): def get_embeddings(vocab, max_rank=1000): if max_rank is None: max_rank = max(lex.rank+1 for lex in vocab if lex.has_vector) - vectors = np.ndarray((max_rank+1, vocab.vectors_length), dtype='float32') + vectors = xp.ndarray((max_rank+1, vocab.vectors_length), dtype='f') for lex in vocab: if lex.has_vector and lex.rank < max_rank: lex.norm = lex.rank+1 @@ -208,15 +221,12 @@ def get_embeddings(vocab, max_rank=1000): def train(train_texts, train_labels, dev_texts, dev_labels, lstm_shape, lstm_settings, lstm_optimizer, batch_size=100, nb_epoch=5, by_sentence=True): - print("Loading spaCy") nlp = spacy.load('en', entity=False) for lex in nlp.vocab: if lex.rank >= (lstm_shape['nr_vector'] - 1): lex.norm = 0 else: lex.norm = lex.rank+1 - #print("Get embeddings") - #embeddings = get_embeddings(nlp.vocab) print("Make model") model = Classifier(SentimentModel(lstm_shape, **lstm_settings)) print("Parsing texts...") @@ -230,13 +240,12 @@ def train(train_texts, train_labels, dev_texts, dev_labels, shuffle=True, repeat=True) dev_iter = SerialIterator(dev_data, batch_size=batch_size, shuffle=False, repeat=False) - optimizer = chainer.optimizers.Adam() optimizer.setup(model) - updater = chainer.training.StandardUpdater(train_iter, optimizer) + updater = chainer.training.StandardUpdater(train_iter, optimizer, device=0) trainer = chainer.training.Trainer(updater, (20, 'epoch'), out='result') - trainer.extend(extensions.Evaluator(dev_iter, model)) + trainer.extend(extensions.Evaluator(dev_iter, model, device=0)) trainer.extend(extensions.LogReport()) trainer.extend(extensions.PrintReport([ 'epoch', 'main/accuracy', 'validation/main/accuracy'])) @@ -293,14 +302,16 @@ def main(model_dir, train_dir, dev_dir, print("Read data") train_texts, train_labels = read_data(train_dir, limit=nr_examples) dev_texts, dev_labels = read_data(dev_dir, limit=nr_examples) - train_labels = np.asarray(train_labels, dtype='int32') - dev_labels = np.asarray(dev_labels, dtype='int32') + print("Using GPU 0") + #chainer.cuda.get_device(0).use() + train_labels = xp.asarray(train_labels, dtype='i') + dev_labels = xp.asarray(dev_labels, dtype='i') lstm = train(train_texts, train_labels, dev_texts, dev_labels, {'nr_hidden': nr_hidden, 'max_length': max_length, 'nr_class': 2, 'nr_vector': 2000, 'nr_dim': 32}, - {'dropout': 0.5, 'lr': learn_rate}, - {}, - nb_epoch=nb_epoch, batch_size=batch_size) + {'dropout': 0.5, 'lr': learn_rate}, + {}, + nb_epoch=nb_epoch, batch_size=batch_size) if __name__ == '__main__':