def run(options):
logger = get_logger()
experiment_logger = ExperimentLogger()
train_dataset, validation_dataset = get_train_and_validation(options)
train_iterator = get_train_iterator(options, train_dataset)
validation_iterator = get_validation_iterator(options, validation_dataset)
embeddings = train_dataset['embeddings']
logger.info('Initializing model.')
trainer = build_net(options, embeddings, validation_iterator)
logger.info('Model:')
for name, p in trainer.net.named_parameters():
logger.info('{} {}'.format(name, p.shape))
if options.save_init:
logger.info('Saving model (init).')
trainer.save_model(
os.path.join(options.experiment_path, 'model_init.pt'))
if options.parse_only:
run_parse(options, train_iterator, trainer, validation_iterator)
sys.exit()
run_train(options, train_iterator, trainer, validation_iterator)
def run(options):
logger = get_logger()
validation_dataset = get_validation_dataset(options)
validation_iterator = get_validation_iterator(options, validation_dataset)
word2idx = validation_dataset['word2idx']
embeddings = validation_dataset['embeddings']
idx2word = {v: k for k, v in word2idx.items()}
logger.info('Initializing model.')
trainer = build_net(options, embeddings, validation_iterator)
# Parse
diora = trainer.net.diora
## Monkey patch parsing specific methods.
override_init_with_batch(diora)
override_inside_hook(diora)
## Turn off outside pass.
trainer.net.diora.outside = False
## Eval mode.
trainer.net.eval()
return trainer, word2idx
def run_parse(options, train_iterator, trainer, validation_iterator):
logger = get_logger()
validation_dataset = get_validation_dataset(options)
validation_iterator = get_validation_iterator(options, validation_dataset)
word2idx = validation_dataset['word2idx']
embeddings = validation_dataset['embeddings']
idx2word = {v: k for k, v in word2idx.items()}
logger.info('Initializing model.')
trainer = build_net(options, embeddings, validation_iterator)
# Parse
diora = trainer.net.diora
## Turn off outside pass.
trainer.net.diora.outside = False
## Eval mode.
trainer.net.eval()
## Topk predictor.
parse_predictor = CKY(net=diora, word2idx=word2idx)
batches = validation_iterator.get_iterator(random_seed=options.seed)
logger.info('Beginning to parse.')
with torch.no_grad():
for i, batch_map in enumerate(batches):
sentences = batch_map['sentences']
batch_size = sentences.shape[0]
length = sentences.shape[1]
# Rather than skipping, just log the trees (they are trivially easy to find).
if length <= 2:
for i in range(batch_size):
example_id = batch_map['example_ids'][i]
tokens = sentences[i].tolist()
words = [idx2word[idx] for idx in tokens]
if length == 2:
o = dict(example_id=example_id, tree=(words[0], words[1]))
elif length == 1:
o = dict(example_id=example_id, tree=words[0])
print(json.dumps(o))
continue
_ = trainer.step(batch_map, train=False, compute_loss=False)
trees = parse_predictor.parse_batch(batch_map)
for ii, tr in enumerate(trees):
example_id = batch_map['example_ids'][ii]
s = [idx2word[idx] for idx in sentences[ii].tolist()]
tr = replace_leaves(tr, s)
o = dict(example_id=example_id, tree=tr)
print(json.dumps(o))
def build_net(options, embeddings, batch_iterator=None):
from diora.net.trainer import build_net
trainer = build_net(options, embeddings, batch_iterator, random_seed=options.seed)
logger = get_logger()
logger.info('# of params = {}'.format(count_params(trainer.net)))
return trainer
def configure(options):
# Configure output paths for this experiment.
configure_experiment(options.experiment_path, rank=options.local_rank)
# Get logger.
logger = get_logger()
# Print flags.
logger.info(stringify_flags(options))
save_flags(options, options.experiment_path)
def __init__(self, data_source, batch_size, include_partial=False, rng=None, maxlen=None,
length_to_size=None):
self.data_source = data_source
self.active = False
if rng is None:
rng = np.random.RandomState(seed=11)
self.rng = rng
self.batch_size = batch_size
self.maxlen = maxlen
self.include_partial = include_partial
self.length_to_size = length_to_size
self._batch_size_cache = { 0: self.batch_size }
self.logger = get_logger()
def context_insensitive_elmo(weights_path,
options_path,
word2idx,
cuda=False,
cache_dir=None):
logger = get_logger()
vocab = [w for w, i in sorted(word2idx.items(), key=lambda x: x[1])]
validate_word2idx(word2idx)
if cache_dir is not None:
key = hash_vocab(vocab)
cache_path = os.path.join(cache_dir, 'elmo_{}.npy'.format(key))
if os.path.exists(cache_path):
logger.info('Loading cached elmo vectors: {}'.format(cache_path))
return load_elmo_cache(cache_path)
if cuda:
device = 0
else:
device = -1
batch_size = 256
nbatches = len(vocab) // batch_size + 1
logger.info('Begin caching vectors. nbatches={} device={}'.format(
nbatches, device))
logger.info('Initialize ELMo Model.')
# TODO: Does not support padding.
elmo = ElmoEmbedder(options_file=options_path,
weight_file=weights_path,
cuda_device=device)
vec_lst = []
for i in tqdm(range(nbatches), desc='elmo'):
start = i * batch_size
batch = vocab[start:start + batch_size]
if len(batch) == 0:
continue
vec = elmo.embed_sentence(batch)
vec_lst.append(vec)
vectors = np.concatenate([x[0] for x in vec_lst], axis=0)
if cache_dir is not None:
logger.info('Saving cached elmo vectors: {}'.format(cache_path))
save_elmo_cache(cache_path, vectors)
return vectors
def run(options):
logger = get_logger()
validation_dataset = get_validation_dataset(options)
#print(validation_dataset['sentence1'][0],validation_dataset['example_ids'][0])
validation_iterator = get_validation_iterator(options, validation_dataset)
word2idx = validation_dataset['word2idx']
embeddings = validation_dataset['embeddings']
idx2word = {v: k for k, v in word2idx.items()}
logger.info('Initializing model.')
trainer = build_net(options, embeddings, validation_iterator)
# Parse
diora = trainer.net.encoder
## Monkey patch parsing specific methods.
override_init_with_batch(diora)
override_inside_hook(diora)
## Turn off outside pass.
#trainer.net.encoder.outside = False
## Eval mode.
trainer.net.eval()
## Parse predictor.
parse_predictor = CKY(net=diora, word2idx=word2idx)
batches = validation_iterator.get_iterator(random_seed=options.seed)
output_path1 = os.path.abspath(os.path.join(options.experiment_path, 'parse_mnli1.jsonl'))
output_path2 = os.path.abspath(os.path.join(options.experiment_path, 'parse_mnli2.jsonl'))
logger.info('Beginning.')
logger.info('Writing output to = {}'.format(output_path1))
logger.info('Writing output to = {}'.format(output_path2))
f = open(output_path1, 'w')
with torch.no_grad():
for i, batch_map in tqdm(enumerate(batches)):
#print(batch_map.keys())
sentences1 = batch_map['sentences_1']
sentences2 = batch_map['sentences_2']
#print(sentences.shape)
batch_size = sentences1.shape[0]
length = sentences1.shape[1]
# Skip very short sentences.
if length <= 2:
continue
_ = trainer.step(batch_map, train=False, compute_loss=False)
trees1 = parse_predictor.parse_batch(sentences1)
trees2 = parse_predictor.parse_batch(sentences2)
#print(list(zip(trees1,trees2)))
for ii,tree in enumerate(list(zip(trees1,trees2))):
tr1,tr2 = tree[0],tree[1]
example_id = batch_map['example_ids'][ii]
#print(batch_map['example_ids'])
s1 = [idx2word[idx] for idx in sentences1[ii].tolist()]
s2 = [idx2word[idx] for idx in sentences2[ii].tolist()]
tr1 = replace_leaves(tr1, s1)
tr2 = replace_leaves(tr2, s2)
if options.postprocess:
tr = postprocess(tr, s1)
o = collections.OrderedDict(example_id=example_id, sentence1=tr1,sentence2=tr2)
#print(o)
#exit()
f.write(json.dumps(o) + '\n')
f.close()
def run(options):
logger = get_logger()
validation_dataset = get_validation_dataset(options)
validation_iterator = get_validation_iterator(options, validation_dataset)
word2idx = validation_dataset['word2idx']
embeddings = validation_dataset['embeddings']
idx2word = {v: k for k, v in word2idx.items()}
logger.info('Initializing model.')
trainer = build_net(options, embeddings, validation_iterator)
diora = trainer.net.diora
tree_helper = TreeHelper(diora, word2idx)
tree_helper.init(options)
csv_helper = CSVHelper()
## Eval mode.
trainer.net.eval()
batches = validation_iterator.get_iterator(random_seed=options.seed)
meta_output_path = os.path.abspath(os.path.join(options.experiment_path, 'vectors.csv'))
vec_output_path = os.path.abspath(os.path.join(options.experiment_path, 'vectors.npy'))
logger.info('Beginning.')
logger.info('Writing vectors to = {}'.format(vec_output_path))
logger.info('Writing metadata to = {}'.format(meta_output_path))
f_csv = open(meta_output_path, 'w')
f_vec = open(vec_output_path, 'ab')
csv_helper.write_header(f_csv)
with torch.no_grad():
for i, batch_map in tqdm(enumerate(batches)):
sentences = batch_map['sentences']
batch_size = sentences.shape[0]
length = sentences.shape[1]
# Skip very short sentences.
if length <= 2:
continue
_ = trainer.step(batch_map, train=False, compute_loss=False)
if options.parse_mode == 'all-spans':
for ii in range(batch_size):
example_id = batch_map['example_ids'][ii]
for level in range(length):
size = level + 1
for pos in range(length - level):
# metadata
csv_helper.write_row(f_csv,
collections.OrderedDict(
example_id=example_id,
position=str(pos),
size=str(size)
))
inside_vectors = diora.inside_h.view(-1, options.hidden_dim)
outside_vectors = diora.outside_h.view(-1, options.hidden_dim)
else:
trees, spans = tree_helper.get_trees_for_batch(batch_map, options)
batch_index = []
cell_index = []
offset_cache = diora.index.get_offset(length)
for ii, sp_lst in enumerate(spans):
example_id = batch_map['example_ids'][ii]
for pos, size in sp_lst:
# metadata
csv_helper.write_row(f_csv,
collections.OrderedDict(
example_id=example_id,
position=str(pos),
size=str(size)
))
# for vectors
level = size - 1
cell = offset_cache[level] + pos
batch_index.append(ii)
cell_index.append(cell)
inside_vectors = diora.inside_h[batch_index, cell_index]
assert inside_vectors.shape == (len(batch_index), options.hidden_dim)
outside_vectors = diora.outside_h[batch_index, cell_index]
assert outside_vectors.shape == (len(batch_index), options.hidden_dim)
vectors = np.concatenate([inside_vectors, outside_vectors], axis=1)
np.savetxt(f_vec, vectors)
f_csv.close()
f_vec.close()
def run(options):
logger = get_logger()
validation_dataset = get_validation_dataset(options)
validation_iterator = get_validation_iterator(options, validation_dataset)
word2idx = validation_dataset['word2idx']
embeddings = validation_dataset['embeddings']
idx2word = {v: k for k, v in word2idx.items()}
logger.info('Initializing model.')
trainer = build_net(options, embeddings, validation_iterator)
# Parse
diora = trainer.net.diora
## Monkey patch parsing specific methods.
override_init_with_batch(diora)
override_inside_hook(diora)
## Turn off outside pass.
trainer.net.diora.outside = False
## Eval mode.
trainer.net.eval()
## Parse predictor.
parse_predictor = CKY(net=diora, word2idx=word2idx)
batches = validation_iterator.get_iterator(random_seed=options.seed)
output_path = os.path.abspath(os.path.join(options.experiment_path, 'parse.jsonl'))
logger.info('Beginning.')
logger.info('Writing output to = {}'.format(output_path))
f = open(output_path, 'w')
with torch.no_grad():
for i, batch_map in tqdm(enumerate(batches)):
sentences = batch_map['sentences']
batch_size = sentences.shape[0]
length = sentences.shape[1]
# Skip very short sentences.
if length <= 2:
continue
_ = trainer.step(batch_map, train=False, compute_loss=False)
trees = parse_predictor.parse_batch(batch_map)
for ii, tr in enumerate(trees):
example_id = batch_map['example_ids'][ii]
s = [idx2word[idx] for idx in sentences[ii].tolist()]
tr = replace_leaves(tr, s)
if options.postprocess:
tr = postprocess(tr, s)
o = collections.OrderedDict(example_id=example_id, tree=tr)
f.write(json.dumps(o) + '\n')
f.close()
def run_train(options, train_iterator, trainer, validation_iterator):
logger = get_logger()
experiment_logger = ExperimentLogger()
logger.info('Running train.')
seeds = generate_seeds(options.max_epoch, options.seed)
step = 0
for epoch, seed in zip(range(options.max_epoch), seeds):
# --- Train--- #
seed = seeds[epoch]
logger.info('epoch={} seed={}'.format(epoch, seed))
def myiterator():
it = train_iterator.get_iterator(random_seed=seed)
count = 0
for batch_map in it:
# TODO: Skip short examples (optionally).
if batch_map['length'] <= 2:
continue
yield count, batch_map
count += 1
for batch_idx, batch_map in myiterator():
if options.finetune and step >= options.finetune_after:
trainer.freeze_diora()
result = trainer.step(batch_map)
experiment_logger.record(result)
if step % options.log_every_batch == 0:
experiment_logger.log_batch(epoch,
step,
batch_idx,
batch_size=options.batch_size)
# -- Periodic Checkpoints -- #
if not options.multigpu or options.local_rank == 0:
if step % options.save_latest == 0 and step >= options.save_after:
logger.info('Saving model (periodic).')
trainer.save_model(
os.path.join(options.experiment_path,
'model_periodic.pt'))
save_experiment(
os.path.join(options.experiment_path,
'experiment_periodic.json'), step)
if step % options.save_distinct == 0 and step >= options.save_after:
logger.info('Saving model (distinct).')
trainer.save_model(
os.path.join(options.experiment_path,
'model.step_{}.pt'.format(step)))
save_experiment(
os.path.join(options.experiment_path,
'experiment.step_{}.json'.format(step)),
step)
del result
step += 1
experiment_logger.log_epoch(epoch, step)
if options.max_step is not None and step >= options.max_step:
logger.info('Max-Step={} Quitting.'.format(options.max_step))
sys.exit()
def __init__(self):
super(ExperimentLogger, self).__init__()
self.logger = get_logger()
self.A = None
self.c = Counter()
def read_glove(filename, word2idx):
"""
Two cases:
1. The word2idx has already been filtered according to embedding vocabulary.
2. The word2idx is derived solely from the raw text data.
"""
logger = get_logger()
glove_vocab = set()
size = None
validate_word2idx(word2idx)
logger.info('Reading Glove Vocab.')
with open(filename) as f:
for i, line in enumerate(f):
word, vec = line.split(' ', 1)
glove_vocab.add(word)
if i == 0:
size = len(vec.strip().split(' '))
new_vocab = set.intersection(set(word2idx.keys()), glove_vocab)
new_vocab.discard(PADDING_TOKEN)
new_vocab.discard(UNK_TOKEN)
if word2idx.get(EXISTING_VOCAB_TOKEN, None) == 2:
new_word2idx = word2idx.copy()
logger.info('Using existing vocab mapping.')
else:
new_word2idx = OrderedDict()
new_word2idx[PADDING_TOKEN] = len(new_word2idx)
new_word2idx[UNK_TOKEN] = len(new_word2idx)
new_word2idx[EXISTING_VOCAB_TOKEN] = len(new_word2idx)
for w, _ in word2idx.items():
if w in new_word2idx:
continue
new_word2idx[w] = len(new_word2idx)
logger.info('Creating new mapping.')
logger.info(
'glove-vocab-size={} vocab-size={} intersection-size={} (-{})'.format(
len(glove_vocab), len(word2idx), len(new_vocab),
len(word2idx) - len(new_vocab)))
embeddings = np.zeros((len(new_word2idx), size), dtype=np.float32)
logger.info('Reading Glove Embeddings.')
with open(filename) as f:
for line in f:
word, vec = line.strip().split(' ', 1)
if word is PADDING_TOKEN or word is UNK_TOKEN:
continue
if word in new_vocab and word not in new_word2idx:
raise ValueError
if word not in new_word2idx:
continue
word_id = new_word2idx[word]
vec = np.fromstring(vec, dtype=float, sep=' ')
embeddings[word_id] = vec
validate_word2idx(new_word2idx)
return embeddings, new_word2idx
def __init__(self, reader):
super(ReaderManager, self).__init__()
self.reader = reader
self.logger = get_logger()
def run(options):
logger = get_logger()
validation_dataset = get_validation_dataset(options)
validation_iterator = get_validation_iterator(options, validation_dataset)
word2idx = validation_dataset['word2idx']
embeddings = validation_dataset['embeddings']
idx2word = {v: k for k, v in word2idx.items()}
logger.info('Initializing model.')
trainer = build_net(options, embeddings, validation_iterator)
diora = trainer.net.diora
# 1. Get all relevant phrase vectors.
dtype = {
'example_ids': 'list',
'labels': 'list',
'positions': 'list',
'sizes': 'list',
'phrases': 'list',
'inside': 'torch',
'outside': 'torch',
}
batch_recorder = BatchRecorder(dtype=dtype)
## Eval mode.
trainer.net.eval()
batches = validation_iterator.get_iterator(random_seed=options.seed)
logger.info('Beginning to embed phrases.')
with torch.no_grad():
for i, batch_map in enumerate(batches):
sentences = batch_map['sentences']
batch_size = sentences.shape[0]
length = sentences.shape[1]
# Skips very short examples.
if length <= 2:
continue
_ = trainer.step(batch_map, train=False, compute_loss=False)
entity_labels = batch_map['entity_labels']
if len(entity_labels) == 0:
continue
try:
batch_index, positions, sizes, labels = get_cell_index(entity_labels)
except:
continue
# Skip short phrases.
batch_index = [x for x, y in zip(batch_index, sizes) if y >= 2]
positions = [x for x, y in zip(positions, sizes) if y >= 2]
labels = [x for x, y in zip(labels, sizes) if y >= 2]
sizes = [y for y in sizes if y >= 2]
cell_index = (batch_index, positions, sizes)
batch_result = {}
batch_result['example_ids'] = [batch_map['example_ids'][idx] for idx in cell_index[0]]
batch_result['labels'] = labels
batch_result['positions'] = cell_index[1]
batch_result['sizes'] = cell_index[2]
batch_result['phrases'] = get_many_phrases(sentences, *cell_index)
batch_result['inside'] = get_many_cells(diora, diora.inside_h, *cell_index)
batch_result['outside'] = get_many_cells(diora, diora.outside_h, *cell_index)
batch_recorder.record(**batch_result)
result = batch_recorder.get_flattened_result()
# 2. Build an index of nearest neighbors.
vectors = np.concatenate([result['inside'], result['outside']], axis=1)
normalize_L2(vectors)
index = Index(dim=vectors.shape[1])
index.add(vectors)
index.cache(vectors, options.k_candidates)
# 3. Print a summary.
example_ids = result['example_ids']
phrases = result['phrases']
labels = result['labels']
assert len(example_ids) == len(phrases)
assert len(example_ids) == vectors.shape[0]
def stringify(phrase):
return ' '.join([idx2word[idx] for idx in phrase])
prec_1 = []
prec_10 = []
prec_100 = []
for i in range(vectors.shape[0]):
topk = []
corr_lab = 0
for j, score in index.topk(i, options.k_candidates):
# Skip same example.
if example_ids[i] == example_ids[j]:
continue
# Skip string match.
if phrases[i] == phrases[j]:
continue
topk.append((j, score))
corr_lab += 1. * (labels[i] == labels[j])
if len(topk) == 1:
prec_1.append(corr_lab)
elif len(topk) == 10:
prec_10.append(corr_lab)
elif len(topk) == 100:
prec_100.append(corr_lab)
if len(topk) == options.k_top:
break
assert len(topk) == options.k_top, 'Did not find enough valid candidates.'
# Print.
# print('[query] example_id={} phrase={} lab={}'.format(
# example_ids[i], stringify(phrases[i]),labels[i]))
# for rank, (j, score) in enumerate(topk[:2]):
# print('rank={} score={:.3f} example_id={} phrase={} lab={}'.format(
# rank, score, example_ids[j], stringify(phrases[j]), labels[j]))
print(np.mean(prec_1), np.mean(prec_10)/10)
def run_train(options, train_iterator, trainer, validation_iterator):
logger = get_logger()
experiment_logger = ExperimentLogger()
logger.info('Running train.')
seeds = generate_seeds(options.max_epoch, options.seed)
step = 0
# Added now
idx2word = {v: k for k, v in train_iterator.word2idx.items()}
parse_predictor = CKY(net=trainer.net.diora,
word2idx=train_iterator.word2idx)
# Added now
for epoch, seed in zip(range(options.max_epoch), seeds):
# --- Train--- #
# Added now
precision = 0
recall = 0
total_len = 0
count_des = 0
# Added now
seed = seeds[epoch]
logger.info('epoch={} seed={}'.format(epoch, seed))
def myiterator():
it = train_iterator.get_iterator(random_seed=seed)
count = 0
for batch_map in it:
# TODO: Skip short examples (optionally).
if batch_map['length'] <= 2:
continue
yield count, batch_map
count += 1
for batch_idx, batch_map in myiterator():
if options.finetune and step >= options.finetune_after:
trainer.freeze_diora()
result = trainer.step(batch_map)
# Added now
trainer.net.eval()
sentences = batch_map['sentences']
trees = parse_predictor.parse_batch(batch_map)
o_list = []
for ii, tr in enumerate(trees):
example_id = batch_map['example_ids'][ii]
s = [idx2word[idx] for idx in sentences[ii].tolist()]
tr = replace_leaves(tr, s)
o = dict(example_id=example_id, tree=tr)
o_list.append(o["tree"])
# print(json.dumps(o))
# print(o["tree"])
# print(batch_map["parse_tree"][ii])
if isinstance(batch_map["parse_tree"][ii], str):
parse_tree_tuple = str_to_tuple(
batch_map["parse_tree"][ii])
else:
parse_tree_tuple = batch_map["parse_tree"][ii]
o_spans = tree_to_spans(o["tree"])
batch_spans = tree_to_spans(parse_tree_tuple[0])
p, r, t = precision_and_recall(batch_spans, o_spans)
precision += p
recall += r
total_len += t
# print(precision, recall, total_len)
# print(precision / total_len, recall / total_len)
# print((2*precision*recall)/(total_len*(precision+recall)))
trainer.net.train()
# Added now
experiment_logger.record(result)
if step % options.log_every_batch == 0:
experiment_logger.log_batch(epoch,
step,
batch_idx,
batch_size=options.batch_size)
# -- Periodic Checkpoints -- #
if not options.multigpu or options.local_rank == 0:
if step % options.save_latest == 0 and step >= options.save_after:
logger.info('Saving model (periodic).')
trainer.save_model(
os.path.join(options.experiment_path,
'model_periodic.pt'))
save_experiment(
os.path.join(options.experiment_path,
'experiment_periodic.json'), step)
if step % options.save_distinct == 0 and step >= options.save_after:
logger.info('Saving model (distinct).')
trainer.save_model(
os.path.join(options.experiment_path,
'model.step_{}.pt'.format(step)))
save_experiment(
os.path.join(options.experiment_path,
'experiment.step_{}.json'.format(step)),
step)
del result
step += 1
# Added now
print(precision, recall, total_len)
print(precision / total_len, recall / total_len)
print(count_des)
# Added now
experiment_logger.log_epoch(epoch, step)
if options.max_step is not None and step >= options.max_step:
logger.info('Max-Step={} Quitting.'.format(options.max_step))
sys.exit()
def run(options):
logger = get_logger()
validation_dataset = get_validation_dataset(options)
validation_iterator = get_validation_iterator(options, validation_dataset)
word2idx = validation_dataset['word2idx']
embeddings = validation_dataset['embeddings']
idx2word = {v: k for k, v in word2idx.items()}
logger.info('Initializing model.')
trainer = build_net(options, embeddings, validation_iterator)
diora = trainer.net.diora
# 1. Get all relevant phrase vectors.
dtype = {
'example_ids': 'list',
'labels': 'list',
'positions': 'list',
'sizes': 'list',
'phrases': 'list',
'inside': 'torch',
'outside': 'torch',
}
batch_recorder = BatchRecorder(dtype=dtype)
# Eval mode.
trainer.net.eval()
batches = validation_iterator.get_iterator(random_seed=options.seed)
logger.info('Beginning to embed phrases.')
strings = []
with torch.no_grad():
for i, batch_map in enumerate(batches):
sentences = batch_map['sentences']
length = sentences.shape[1]
# Skips very short examples.
if length <= 2:
continue
strings.extend([
"".join([idx2word[idx] for idx in x])
for x in sentences.numpy()
])
trainer.step(batch_map, train=False, compute_loss=False)
batch_result = {}
batch_result['inside'] = diora.inside_h[:, -1]
batch_result['outside'] = diora.outside_h[:, -1]
batch_recorder.record(**batch_result)
result = batch_recorder.get_flattened_result()
# 2. Build an index of nearest neighbors.
vectors = np.concatenate([result['inside'], result['outside']], axis=1)
print(len(strings), vectors.shape)
r = Reach(vectors, strings)
for s in strings:
print(s)
print(r.most_similar(s))
def build_net(options, embeddings=None, batch_iterator=None, random_seed=None):
logger = get_logger()
lr = options.lr
size = options.hidden_dim
k_neg = options.k_neg
margin = options.margin
normalize = options.normalize
input_dim = embeddings.shape[1]
cuda = options.cuda
rank = options.local_rank
ngpus = 1
if cuda and options.multigpu:
ngpus = torch.cuda.device_count()
os.environ['MASTER_ADDR'] = options.master_addr
os.environ['MASTER_PORT'] = options.master_port
torch.distributed.init_process_group(backend='nccl',
init_method='env://')
# Embed
embedding_layer = nn.Embedding.from_pretrained(
torch.from_numpy(embeddings), freeze=True)
embed = Embed(embedding_layer, input_size=input_dim, size=size)
# Diora
if options.arch == 'treelstm':
diora = DioraTreeLSTM(size,
outside=True,
normalize=normalize,
compress=False)
elif options.arch == 'mlp':
diora = DioraMLP(size,
outside=True,
normalize=normalize,
compress=False)
elif options.arch == 'mlp-shared':
diora = DioraMLPShared(size,
outside=True,
normalize=normalize,
compress=False)
# Loss
loss_funcs = get_loss_funcs(options, batch_iterator, embedding_layer)
# Net
net = Net(embed, diora, loss_funcs=loss_funcs)
# Load model.
if options.load_model_path is not None:
logger.info('Loading model: {}'.format(options.load_model_path))
Trainer.load_model(net, options.load_model_path)
# CUDA-support
if cuda:
if options.multigpu:
torch.cuda.set_device(options.local_rank)
net.cuda()
diora.cuda()
if cuda and options.multigpu:
net = torch.nn.parallel.DistributedDataParallel(net,
device_ids=[rank],
output_device=rank)
# Trainer
trainer = Trainer(net, k_neg=k_neg, ngpus=ngpus, cuda=cuda)
trainer.rank = rank
trainer.experiment_name = options.experiment_name # for multigpu cleanup
trainer.init_optimizer(optim.Adam, dict(lr=lr,
betas=(0.9, 0.999),
eps=1e-8))
return trainer
def __init__(self, net, word2idx):
super(ParsePredictor, self).__init__()
self.net = net
self.word2idx = word2idx
self.idx2word = {v: k for k, v in word2idx.items()}
self.logger = get_logger()