def beam_search(model: NMT, test_iter, beam_size: int,
max_decoding_time_step: int) -> List[List[Hypothesis]]:
""" Run beam search to construct hypotheses for a list of src-language sentences.
@param model (NMT): NMT Model
@param test_data_src (List[List[str]]): List of sentences (words) in source language, from test set.
@param beam_size (int): beam_size (# of hypotheses to hold for a translation at every step)
@param max_decoding_time_step (int): maximum sentence length that Beam search can produce
@returns hypotheses (List[List[Hypothesis]]): List of Hypothesis translations for every source sentence.
"""
was_training = model.training
model.eval()
hypotheses = []
with torch.no_grad():
for _, data in enumerate(test_iter):
print(data)
(src_sents, src_lengths), (_, _) = data.abc, data.d
example_hyps = model.beam_search(
src_sents,
src_lengths,
beam_size=beam_size,
max_decoding_time_step=max_decoding_time_step)
hypotheses.append(example_hyps)
if was_training: model.train(was_training)
return hypotheses
def beam_search(model: NMT, test_data_src: List[List[str]], beam_size: int,
max_decoding_time_step: int) -> List[List[Hypothesis]]:
""" Run beam search to construct hypotheses for a list of src-language sentences.
@param model (NMT): NMT Model
@param test_data_src (List[List[str]]): List of sentences (words) in source language, from test set.
@param beam_size (int): beam_size (# of hypotheses to hold for a translation at every step)
@param max_decoding_time_step (int): maximum sentence length that Beam search can produce
@returns hypotheses (List[List[Hypothesis]]): List of Hypothesis translations for every source sentence.
"""
was_training = model.training
model.eval()
hypotheses = []
with torch.no_grad():
for src_sent in tqdm(test_data_src, desc='Decoding', file=sys.stdout):
example_hyps = model.beam_search(
src_sent,
beam_size=beam_size,
max_decoding_time_step=max_decoding_time_step)
hypotheses.append(example_hyps)
if was_training: model.train(was_training)
return hypotheses
def beam_search(model: NMT, test_data_src: List[List[str]], beam_size: int,
max_decoding_time_step: int) -> List[List[Hypothesis]]:
""" 对源句子列表使用beam search去构建假设.
@param model (NMT): NMT 模型
@param test_data_src (List[List[str]]): 源句子列表, 测试集中的.
@param beam_size (int): beam_size (每一步的候选数)
@param max_decoding_time_step (int): Beam search 能产生的最大句子长度
@returns hypotheses (List[List[Hypothesis]]): 每个源句子的beam_size个假设.
"""
was_training = model.training
model.eval()
hypotheses = [] # 所有句子的候选句列表
with torch.no_grad():
for src_sent in tqdm(test_data_src, desc='Decoding', file=sys.stdout):
example_hyps = model.beam_search(
src_sent,
beam_size=beam_size,
max_decoding_time_step=max_decoding_time_step)
hypotheses.append(example_hyps) # 把这句话的所有候选句加入列表
if was_training: model.train(was_training)
return hypotheses
def init_training(args):
from functools import partial
import pickle
pickle.load = partial(pickle.load, encoding="latin1")
pickle.Unpickler = partial(pickle.Unpickler, encoding="latin1")
# model = torch.load(model_file, map_location=lambda storage, loc: storage, pickle_module=pickle)
vocab = torch.load(args.vocab,
map_location=lambda storage, loc: storage,
pickle_module=pickle)
model = NMT(args, vocab)
model.train()
if args.uniform_init:
print('uniformly initialize parameters [-%f, +%f]' %
(args.uniform_init, args.uniform_init),
file=sys.stderr)
for p in model.parameters():
p.data.uniform_(-args.uniform_init, args.uniform_init)
vocab_mask = torch.ones(len(vocab.tgt))
vocab_mask[vocab.tgt['<pad>']] = 0
nll_loss = nn.NLLLoss(weight=vocab_mask, reduction='sum')
cross_entropy_loss = nn.CrossEntropyLoss(weight=vocab_mask,
reduction='sum')
if args.cuda:
# model = nn.DataParallel(model).cuda()
model = model.cuda()
nll_loss = nll_loss.cuda()
cross_entropy_loss = cross_entropy_loss.cuda()
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
return vocab, model, optimizer, nll_loss, cross_entropy_loss
def beam_search(model: NMT, test_iterator: BucketIterator, beam_size: int,
max_decoding_time_step: int) -> List[List[Hypothesis]]:
""" Run beam search to construct hypotheses for a list of src-language sentences.
@param model (NMT): NMT Model
@param test_iterator BucketIterator: BucketIterator in source language, from test set.
@param beam_size (int): beam_size (# of hypotheses to hold for a translation at every step)
@param max_decoding_time_step (int): maximum sentence length that Beam search can produce
@returns hypotheses (List[List[Hypothesis]]): List of Hypothesis translations for every source sentence.
"""
was_training = model.training
model.eval()
hypotheses = []
with torch.no_grad():
# for src_sent in tqdm(test_data_src, desc='Decoding', file=sys.stdout):
for i, batch in enumerate(test_iterator):
src_sents, src_sents_lens = batch.src
src_sents = src_sents.permute(1, 0)
for j in range(len(src_sents_lens)):
src_sent = src_sents[j]
example_hyps = model.beam_search(
src_sent,
src_sents_lens[j],
beam_size=beam_size,
max_decoding_time_step=max_decoding_time_step)
hypotheses.append(example_hyps)
if was_training: model.train(was_training)
return hypotheses
def train(args: Dict):
""" Train the NMT Model.
@param args (Dict): args from cmd line
"""
train_data_src = read_corpus(args['--train-src'], source='src')
train_data_tgt = read_corpus(args['--train-tgt'], source='tgt')
dev_data_src = read_corpus(args['--dev-src'], source='src')
dev_data_tgt = read_corpus(args['--dev-tgt'], source='tgt')
train_data = list(zip(train_data_src, train_data_tgt))
dev_data = list(zip(dev_data_src, dev_data_tgt))
train_batch_size = int(args['--batch-size'])
clip_grad = float(args['--clip-grad'])
valid_niter = int(args['--valid-niter'])
log_every = int(args['--log-every'])
model_save_path = args['--save-to']
vocab = Vocab.load(args['--vocab'])
model = NMT(embed_size=int(args['--embed-size']),
hidden_size=int(args['--hidden-size']),
dropout_rate=float(args['--dropout']),
vocab=vocab)
model.train()
uniform_init = float(args['--uniform-init'])
if np.abs(uniform_init) > 0.:
print('uniformly initialize parameters [-%f, +%f]' % (uniform_init, uniform_init), file=sys.stderr)
for p in model.parameters():
p.data.uniform_(-uniform_init, uniform_init)
vocab_mask = torch.ones(len(vocab.tgt))
vocab_mask[vocab.tgt['<pad>']] = 0
device = torch.device("cuda:0" if args['--cuda'] else "cpu")
def beam_search(model: NMT, test_data_src: List[List[str]], beam_size: int, max_decoding_time_step: int)\
-> List[List[Hypothesis]]:
""" Run beam search to construct hypotheses for a list of src-language sentences.
:param NMT model: NMT Model
:param List[List[str]] test_data_src: List of sentences (words) in source language, from test set
:param int beam_size: beam_size (number of hypotheses to keep for a translation at every step)
:param int max_decoding_time_step: maximum sentence length that beam search can produce
:returns List[List[Hypothesis]] hypotheses: List of Hypothesis translations for every source sentence
"""
was_training = model.training
model.eval()
hypotheses = []
with torch.no_grad():
for src_sent in tqdm(test_data_src, desc='Decoding', file=sys.stdout):
example_hyps = model.beam_search(
src_sent,
beam_size=beam_size,
max_decoding_time_step=max_decoding_time_step)
hypotheses.append(example_hyps)
if was_training: model.train(was_training)
return hypotheses
def train(args: Dict):
""" Train the NMT Model.
@param args (Dict): args from cmd line
"""
train_data_src = read_corpus(args['--train-src'], source='src')
train_data_tgt = read_corpus(args['--train-tgt'], source='tgt')
# print("train data")
# print(train_data_src)
# print(len(train_data_tgt))
dev_data_src = read_corpus(args['--dev-src'], source='src')
dev_data_tgt = read_corpus(args['--dev-tgt'], source='tgt')
#print("dev data")
#print(dev_data_src)
#rint(len(dev_data_tgt))
train_data = list(zip(train_data_src, train_data_tgt))
dev_data = list(zip(dev_data_src, dev_data_tgt))
#print(train_data)
#print(dev_data)
train_batch_size = int(args['--batch-size'])
clip_grad = float(args['--clip-grad'])
valid_niter = int(args['--valid-niter'])
log_every = int(args['--log-every'])
model_save_path = args['--save-to']
vocab = Vocab.load(args['--vocab'])
# print("vocab")
# print(vocab.src)
# print(vocab.tgt)
model = NMT(embed_size=int(args['--embed-size']),
hidden_size=int(args['--hidden-size']),
dropout_rate=float(args['--dropout']),
vocab=vocab, no_char_decoder=args['--no-char-decoder'])
model.train()
uniform_init = float(args['--uniform-init'])
if np.abs(uniform_init) > 0.:
print('uniformly initialize parameters [-%f, +%f]' % (uniform_init, uniform_init), file=sys.stderr)
for p in model.parameters():
p.data.uniform_(-uniform_init, uniform_init)
vocab_mask = torch.ones(len(vocab.tgt))
vocab_mask[vocab.tgt['<pad>']] = 0
device = torch.device("cuda:0" if args['--cuda'] else "cpu")
print('use device: %s' % device, file=sys.stderr)
model = model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=float(args['--lr']))
num_trial = 0
train_iter = patience = cum_loss = report_loss = cum_tgt_words = report_tgt_words = 0
cum_examples = report_examples = epoch = valid_num = 0
hist_valid_scores = []
train_time = begin_time = time.time()
print('begin Maximum Likelihood training')
while True:
epoch += 1
for src_sents, tgt_sents in batch_iter(train_data, batch_size=train_batch_size, shuffle=True):
train_iter += 1
#print(src_sents)
#print(src_sents.shape)
#print(tgt_sents)
#print(tgt_sents.shape)
optimizer.zero_grad()
batch_size = len(src_sents)
example_losses = -model(src_sents, tgt_sents) # (batch_size,)
batch_loss = example_losses.sum()
loss = batch_loss / batch_size
loss.backward()
# clip gradient
grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), clip_grad)
optimizer.step()
batch_losses_val = batch_loss.item()
report_loss += batch_losses_val
cum_loss += batch_losses_val
tgt_words_num_to_predict = sum(len(s[1:]) for s in tgt_sents) # omitting leading `<s>`
report_tgt_words += tgt_words_num_to_predict
cum_tgt_words += tgt_words_num_to_predict
report_examples += batch_size
cum_examples += batch_size
if train_iter % log_every == 0:
print('epoch %d, iter %d, avg. loss %.2f, avg. ppl %.2f ' \
'cum. examples %d, speed %.2f words/sec, time elapsed %.2f sec' % (epoch, train_iter,
report_loss / report_examples,
math.exp(report_loss / report_tgt_words),
cum_examples,
report_tgt_words / (time.time() - train_time),
time.time() - begin_time), file=sys.stderr)
train_time = time.time()
report_loss = report_tgt_words = report_examples = 0.
# perform validation
if train_iter % valid_niter == 0:
print('epoch %d, iter %d, cum. loss %.2f, cum. ppl %.2f cum. examples %d' % (epoch, train_iter,
cum_loss / cum_examples,
np.exp(cum_loss / cum_tgt_words),
cum_examples), file=sys.stderr)
cum_loss = cum_examples = cum_tgt_words = 0.
valid_num += 1
print('begin validation ...', file=sys.stderr)
# compute dev. ppl and bleu
dev_ppl = evaluate_ppl(model, dev_data, batch_size=128) # dev batch size can be a bit larger
valid_metric = -dev_ppl
print('validation: iter %d, dev. ppl %f' % (train_iter, dev_ppl), file=sys.stderr)
is_better = len(hist_valid_scores) == 0 or valid_metric > max(hist_valid_scores)
hist_valid_scores.append(valid_metric)
if is_better:
patience = 0
print('save currently the best model to [%s]' % model_save_path, file=sys.stderr)
model.save(model_save_path)
# also save the optimizers' state
torch.save(optimizer.state_dict(), model_save_path + '.optim')
elif patience < int(args['--patience']):
patience += 1
print('hit patience %d' % patience, file=sys.stderr)
if patience == int(args['--patience']):
num_trial += 1
print('hit #%d trial' % num_trial, file=sys.stderr)
if num_trial == int(args['--max-num-trial']):
print('early stop!', file=sys.stderr)
exit(0)
# decay lr, and restore from previously best checkpoint
lr = optimizer.param_groups[0]['lr'] * float(args['--lr-decay'])
print('load previously best model and decay learning rate to %f' % lr, file=sys.stderr)
# load model
params = torch.load(model_save_path, map_location=lambda storage, loc: storage)
model.load_state_dict(params['state_dict'])
model = model.to(device)
print('restore parameters of the optimizers', file=sys.stderr)
optimizer.load_state_dict(torch.load(model_save_path + '.optim'))
# set new lr
for param_group in optimizer.param_groups:
param_group['lr'] = lr
# reset patience
patience = 0
if epoch == int(args['--max-epoch']):
print('reached maximum number of epochs!', file=sys.stderr)
exit(0)
def train():
text = Text(config.src_corpus, config.tar_corpus)
train_data = Data(config.train_path_src, config.train_path_tar)
dev_data = Data(config.dev_path_src, config.dev_path_tar)
train_loader = DataLoader(dataset=train_data,
batch_size=config.batch_size,
shuffle=True,
collate_fn=utils.get_batch)
dev_loader = DataLoader(dataset=dev_data,
batch_size=config.dev_batch_size,
shuffle=True,
collate_fn=utils.get_batch)
parser = OptionParser()
parser.add_option("--embed_size",
dest="embed_size",
default=config.embed_size)
parser.add_option("--hidden_size",
dest="hidden_size",
default=config.hidden_size)
parser.add_option("--window_size_d",
dest="window_size_d",
default=config.window_size_d)
parser.add_option("--encoder_layer",
dest="encoder_layer",
default=config.encoder_layer)
parser.add_option("--decoder_layers",
dest="decoder_layers",
default=config.decoder_layers)
parser.add_option("--dropout_rate",
dest="dropout_rate",
default=config.dropout_rate)
(options, args) = parser.parse_args()
device = torch.device("cuda:0" if config.cuda else "cpu")
#model_path = "/home/wangshuhe/shuhelearn/ShuHeLearning/NMT_attention/result/01.31_drop0.3_54_21.46508598886769_checkpoint.pth"
#print(f"load model from {model_path}", file=sys.stderr)
#model = NMT.load(model_path)
model = NMT(text, options, device)
#model = model.cuda()
#model_path = "/home/wangshuhe/shuhelearn/ShuHeLearning/NMT_attention/result/140_164.29781984744628_checkpoint.pth"
#print(f"load model from {model_path}", file=sys.stderr)
#model = NMT.load(model_path)
#model = torch.nn.DataParallel(model)
model = model.to(device)
model = model.cuda()
model.train()
optimizer = Optim(torch.optim.Adam(model.parameters()))
#optimizer = Optim(torch.optim.Adam(model.parameters(), betas=(0.9, 0.98), eps=1e-9), config.hidden_size, config.warm_up_step)
#print(optimizer.lr)
epoch = 0
valid_num = 1
hist_valid_ppl = []
print("begin training!")
while (True):
epoch += 1
max_iter = int(math.ceil(len(train_data) / config.batch_size))
with tqdm(total=max_iter, desc="train") as pbar:
for src_sents, tar_sents, tar_words_num_to_predict in train_loader:
optimizer.zero_grad()
batch_size = len(src_sents)
now_loss = -model(src_sents, tar_sents)
now_loss = now_loss.sum()
loss = now_loss / batch_size
loss.backward()
_ = torch.nn.utils.clip_grad_norm_(model.parameters(),
config.clip_grad)
#optimizer.updata_lr()
optimizer.step_and_updata_lr()
pbar.set_postfix({
"epwwoch":
epoch,
"avg_loss":
loss.item(),
"ppl":
math.exp(now_loss.item() / tar_words_num_to_predict),
"lr":
optimizer.lr
})
#pbar.set_postfix({"epoch": epoch, "avg_loss": loss.item(), "ppl": math.exp(now_loss.item()/tar_words_num_to_predict)})
pbar.update(1)
#print(optimizer.lr)
if (epoch % config.valid_iter == 0):
#if (epoch >= config.valid_iter//2):
if (valid_num % 5 == 0):
valid_num = 0
optimizer.updata_lr()
valid_num += 1
print("now begin validation ...", file=sys.stderr)
eav_ppl = evaluate_ppl(model, dev_data, dev_loader)
print("validation ppl %.2f" % (eav_ppl), file=sys.stderr)
flag = len(hist_valid_ppl) == 0 or eav_ppl < min(hist_valid_ppl)
if (flag):
print("current model is the best!, save to [%s]" %
(config.model_save_path),
file=sys.stderr)
hist_valid_ppl.append(eav_ppl)
model.save(
os.path.join(
config.model_save_path,
f"02.08_window35drop0.2_{epoch}_{eav_ppl}_checkpoint.pth"
))
torch.save(
optimizer.optimizer.state_dict(),
os.path.join(
config.model_save_path,
f"02.08_window35drop0.2_{epoch}_{eav_ppl}_optimizer.optim"
))
if (epoch == config.max_epoch):
print("reach the maximum number of epochs!", file=sys.stderr)
return
def train(args: Dict):
""" Train the NMT Model.
@param args (Dict): args from cmd line
"""
sample_rate = 22000
resample_rate = 8000
train_records = 8
max_epoch = 10
vocab = Vocab.load('dataset/vocab_full.json')
# train_voices_files, corpus = get_voice_files_and_corpus('dataset/train/wavs', train_records)
# voices = load_voices_files(train_voices_files, sample_rate, resample_rate)
# train_data = list(zip(voices, corpus))
dev_files, dev_corpus = get_voice_files_and_corpus('dataset/dev', 2)
dev_data = list(
zip(load_voices_files(dev_files, sample_rate, resample_rate),
dev_corpus))
epoch_size = 4
train_batch_size = 2
clip_grad = 5.0
valid_niter = 100
log_every = 10
model_save_path = 'model.bin'
model = NMT(embed_size=1024, hidden_size=2048, vocab=vocab)
model.train()
vocab_mask = torch.ones(len(vocab.tgt))
vocab_mask[vocab.tgt['<pad>']] = 0
# device = torch.device("cuda:0")
device = torch.device("cpu")
print('use device: %s' % device, file=sys.stderr)
model = model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
num_trial = 0
train_iter = patience = cum_loss = report_loss = cum_tgt_words = report_tgt_words = 0
cum_examples = report_examples = epoch = valid_num = 0
hist_valid_scores = []
train_time = begin_time = time.time()
print('begin Maximum Likelihood training')
data_queue = Queue()
batch_queue = Queue(1)
loss_queue = Queue(2)
train_data_to_queue_process = Process(target=load_train_data,
args=('dataset/train', train_records,
epoch_size, data_queue))
train_data_to_queue_process.start()
batch_iter_to_queue_process = Process(target=batch_iter_to_queue2,
args=(data_queue, batch_queue,
loss_queue, max_epoch,
train_batch_size, True))
batch_iter_to_queue_process.start()
epoch, voices, tgt_sents = batch_queue.get(True)
current_epoch = -1
while voices is not None and tgt_sents is not None:
train_iter += 1
optimizer.zero_grad()
# voices = load_voices_files(voice_files, sample_rate, resample_rate)
# voices = voice_files
batch_size = len(voices)
example_losses = -model(voices, tgt_sents) # (batch_size,)
batch_loss = example_losses.sum()
loss = batch_loss / batch_size
loss.backward()
# clip gradient
grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(),
clip_grad)
optimizer.step()
batch_losses_val = batch_loss.item()
report_loss += batch_losses_val
cum_loss += batch_losses_val
tgt_words_num_to_predict = sum(
len(s[1:]) for s in tgt_sents) # omitting leading `<s>`
report_tgt_words += tgt_words_num_to_predict
cum_tgt_words += tgt_words_num_to_predict
report_examples += batch_size
cum_examples += batch_size
loss_queue.put(report_loss / report_examples)
if train_iter % log_every == 0:
print('epoch %d, iter %d, avg. loss %.2f, avg. ppl %.2f ' \
'cum. examples %d, speed %.2f words/sec, time elapsed %.2f sec' % (epoch, train_iter,
report_loss / report_examples,
math.exp(report_loss / report_tgt_words),
cum_examples,
report_tgt_words / (time.time() - train_time),
time.time() - begin_time), file=sys.stderr)
train_time = time.time()
report_loss = report_tgt_words = report_examples = 0.
# perform validation
if train_iter % valid_niter == 0:
print(
'epoch %d, iter %d, cum. loss %.2f, cum. ppl %.2f cum. examples %d'
% (epoch, train_iter, cum_loss / cum_examples,
np.exp(cum_loss / cum_tgt_words), cum_examples),
file=sys.stderr)
cum_loss = cum_examples = cum_tgt_words = 0.
valid_num += 1
print('begin validation ...', file=sys.stderr)
# compute dev. ppl and bleu
dev_ppl = evaluate_ppl(
model, dev_data,
batch_size=128) # dev batch size can be a bit larger
valid_metric = -dev_ppl
print('validation: iter %d, dev. ppl %f' % (train_iter, dev_ppl),
file=sys.stderr)
is_better = len(hist_valid_scores
) == 0 or valid_metric > max(hist_valid_scores)
hist_valid_scores.append(valid_metric)
if is_better:
patience = 0
print('save currently the best model to [%s]' %
model_save_path,
file=sys.stderr)
model.save(model_save_path)
# also save the optimizers' state
torch.save(optimizer.state_dict(), model_save_path + '.optim')
elif patience < 10:
patience += 1
print('hit patience %d' % patience, file=sys.stderr)
if patience == 5:
num_trial += 1
print('hit #%d trial' % num_trial, file=sys.stderr)
if num_trial == 3:
print('early stop!', file=sys.stderr)
exit(0)
# decay lr, and restore from previously best checkpoint
lr = optimizer.param_groups[0]['lr'] * 0.5
print(
'load previously best model and decay learning rate to %f'
% lr,
file=sys.stderr)
# load model
params = torch.load(
model_save_path,
map_location=lambda storage, loc: storage)
model.load_state_dict(params['state_dict'])
model = model.to(device)
print('restore parameters of the optimizers',
file=sys.stderr)
optimizer.load_state_dict(
torch.load(model_save_path + '.optim'))
# set new lr
for param_group in optimizer.param_groups:
param_group['lr'] = lr
# reset patience
patience = 0
epoch, voices, tgt_sents = batch_queue.get()
batch_iter_to_queue_process.join()
train_data_to_queue_process.join()
def train(args):
""" Train the NMT Model.
"""
train_data_src = read_corpus(args.train_src, source='src')
train_data_tgt = read_corpus(args.train_tgt, source='tgt')
dev_data_src = read_corpus(args.dev_src, source='src')
dev_data_tgt = read_corpus(args.dev_tgt, source='tgt')
train_data = list(zip(train_data_src, train_data_tgt))
dev_data = list(zip(dev_data_src, dev_data_tgt))
vocab = Vocab.load(args.vocab_file)
model = NMT(embed_size=args.embed_size,
hidden_size=args.hidden_size,
dropout_rate=args.dropout,
vocab=vocab)
model.train()
if np.abs(args.uniform_init) > 0.:
print('uniformly initialize parameters [-%f, +%f]' % (args.uniform_init, args.uniform_init))
for p in model.parameters():
p.data.uniform_(-args.uniform_init, args.uniform_init)
vocab_mask = torch.ones(len(vocab.tgt))
vocab_mask[vocab.tgt['<pad>']] = 0
device = torch.device("cuda:0" if args.cuda else "cpu")
print('use device: %s' % device)
model = model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
num_trial = 0
train_iter = patience = cum_loss = report_loss = cum_tgt_words = report_tgt_words = 0
cum_examples = report_examples = epoch = valid_num = 0
hist_valid_scores = []
train_time = begin_time = time.time()
print('begin Maximum Likelihood training')
while True:
epoch += 1
batch_num = math.ceil(len(train_data) / args.batch_size)
current_iter = 0
for src_sents, tgt_sents in batch_iter(train_data, batch_size=args.batch_size, shuffle=True):
current_iter += 1
train_iter += 1
optimizer.zero_grad()
batch_size = len(src_sents)
example_losses = -model(src_sents, tgt_sents)
batch_loss = example_losses.sum()
loss = batch_loss / batch_size
loss.backward()
# clip gradient
torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip_grad)
optimizer.step()
batch_losses_val = batch_loss.item()
report_loss += batch_losses_val
cum_loss += batch_losses_val
# omitting leading `<s>`
tgt_words_num_to_predict = sum(len(s[1:]) for s in tgt_sents)
report_tgt_words += tgt_words_num_to_predict
cum_tgt_words += tgt_words_num_to_predict
report_examples += batch_size
cum_examples += batch_size
if train_iter % args.log_every == 0:
print('epoch %d (%d / %d), iter %d, avg. loss %.2f, avg. ppl %.2f '
'cum. examples %d, speed %.2f words/sec, time elapsed %.2f sec' %
(epoch, current_iter, batch_num, train_iter,
report_loss / report_examples,
math.exp(report_loss / report_tgt_words),
cum_examples,
report_tgt_words / (time.time() - train_time),
time.time() - begin_time))
train_time = time.time()
report_loss = report_tgt_words = report_examples = 0.
# perform validation
if train_iter % args.valid_niter == 0:
print('epoch %d, iter %d, cum. loss %.2f, cum. ppl %.2f cum. examples %d' % (epoch, train_iter,
cum_loss / cum_examples,
np.exp(cum_loss / cum_tgt_words),
cum_examples))
cum_loss = cum_examples = cum_tgt_words = 0.
valid_num += 1
print('begin validation ...')
# compute dev. ppl and bleu
dev_ppl = evaluate_ppl(model, dev_data, batch_size=128) # dev batch size can be a bit larger
valid_metric = -dev_ppl
print('validation: iter %d, dev. ppl %f' % (train_iter, dev_ppl))
is_better = len(hist_valid_scores) == 0 or valid_metric > max(hist_valid_scores)
hist_valid_scores.append(valid_metric)
if is_better:
patience = 0
print('epoch %d, iter %d: save currently the best model to [%s]' %
(epoch, train_iter, args.model_path))
model.save(args.model_path)
torch.save(optimizer.state_dict(), args.model_path + '.optim')
elif patience < args.patience:
patience += 1
print('hit patience %d' % patience)
if patience == args.patience:
num_trial += 1
print('hit #%d trial' % num_trial)
if num_trial == args.max_num_trial:
print('early stop!')
exit(0)
# decay lr, and restore from previously best checkpoint
lr = optimizer.param_groups[0]['lr'] * args.lr_decay
print('load previously best model and decay learning rate to %f' % lr)
# load model
params = torch.load(args.model_path, map_location=lambda storage, loc: storage)
model.load_state_dict(params['state_dict'])
model = model.to(device)
print('restore parameters of the optimizers')
optimizer.load_state_dict(torch.load(args.model_path + '.optim'))
# set new lr
for param_group in optimizer.param_groups:
param_group['lr'] = lr
# reset patience
patience = 0
if epoch == args.max_epoch:
print('reached maximum number of epochs!')
return
def train(index):
torch.manual_seed(1)
if (config.cuda):
torch.cuda.manual_seed(1)
device = torch.device(f"cuda:{index}" if config.cuda else "cpu")
dist_rank = index
torch.distributed.init_process_group(backend='nccl', init_method='tcp://localhost:23456', rank=dist_rank, world_size=1)
is_master_node = (dist_rank == 0)
args = dict()
args['embed_size'] = config.embed_size
args['d_model'] = config.d_model
args['nhead'] = config.nhead
args['num_encoder_layers'] = config.num_encoder_layers
args['num_decoder_layers'] = config.num_decoder_layers
args['dim_feedforward'] = config.dim_feedforward
args['dropout'] = config.dropout
args['smoothing_eps'] = config.smoothing_eps
text = Text(config.src_corpus, config.tar_corpus)
model = NMT(text, args, device)
model = make_data_parallel(model, device)
train_data = Data(config.train_path_src, config.train_path_tar)
dev_data = Data(config.dev_path_src, config.dev_path_tar)
train_sampler = DistributedSampler(train_data)
dev_sampler = DistributedSampler(dev_data)
train_loader = DataLoader(dataset=train_data, batch_size=int(config.train_batch_size/8), shuffle=False, num_workers=9, pin_memory=True, sampler=train_sampler, collate_fn=utils.get_batch)
dev_loader = DataLoader(dataset=dev_data, batch_size=int(config.dev_batch_size/8), shuffle=False, num_workers=9, pin_memory=True, sampler=dev_sampler, collate_fn=utils.get_batch)
model.train()
optimizer = Optim(torch.optim.Adam(model.parameters(), betas=(0.9, 0.98), eps=1e-9), config.d_model, config.warm_up_step)
epoch = 0
history_valid_ppl = []
print("begin training!", file=sys.stderr)
while (True):
epoch += 1
train_loader.sampler.set_epoch(epoch)
max_iter = int(math.ceil(len(train_data)/config.train_batch_size))
with tqdm(total=max_iter, desc="train") as pbar:
for batch_src, batch_tar, tar_word_num in train_loader:
optimizer.zero_grad()
now_batch_size = len(batch_src)
batch_loss = -model(batch_src, batch_tar, smoothing=True)
batch_loss = batch_loss.sum()
loss = batch_loss / now_batch_size
loss.backward()
torch.distributed.barrier()
optimizer.step_and_updata_lr()
if (is_master_node):
pbar.set_postfix({"epoch": epoch, "avg_loss": '{%.2f}' % (loss.item()), "ppl": '{%.2f}' % (batch_loss.item()/tar_word_num)})
pbar.update(1)
if (epoch % config.valid_iter == 0):
print("now begin validation...", file=sys.stderr)
torch.distributed.barrier()
eval_ppl = evaluate_ppl(model, dev_data, dev_loader, config.dev_batch_size, is_master_node)
print(eval_ppl)
flag = len(history_valid_ppl) == 0 or eval_ppl < min(history_valid_ppl)
if (flag):
print(f"current model is the best! save to [{config.model_save_path}]", file=sys.stderr)
history_valid_ppl.append(eval_ppl)
model.save(os.path.join(config.model_save_path, f"02.19_{epoch}_{eval_ppl}_checkpoint.pth"))
torch.save(optimizer.optimizer.state_dict(), os.path.join(config.model_save_path, f"02.19_{epoch}_{eval_ppl}_optimizer.optim"))
if (epoch == config.max_epoch):
print("reach the maximum number of epochs!", file=sys.stderr)
return
def train(args: Dict):
""" Train the NMT Model.
@param args (Dict): args from cmd line
"""
train_data_src = read_corpus(args['--train-src'], source='src')
train_data_tgt = read_corpus(args['--train-tgt'], source='tgt')
dev_data_src = read_corpus(args['--dev-src'], source='src')
dev_data_tgt = read_corpus(args['--dev-tgt'], source='tgt')
train_data = list(zip(train_data_src, train_data_tgt))
dev_data = list(zip(dev_data_src, dev_data_tgt))
train_batch_size = int(args['--batch-size'])
clip_grad = float(args['--clip-grad'])
valid_niter = int(args['--valid-niter'])
log_every = int(args['--log-every'])
model_save_path = args['--save-to']
vocab = Vocab.load(args['--vocab'])
existing_model = args['--existing-model-path']
start_from_existing_model = existing_model and os.path.isfile(
existing_model)
if start_from_existing_model:
print("load model from {}".format(existing_model), file=sys.stderr)
model = NMT.load(existing_model,
no_char_decoder=args['--no-char-decoder'])
else:
print("Create a new model from hyper parameters")
model = NMT(embed_size=int(args['--embed-size']),
hidden_size=int(args['--hidden-size']),
dropout_rate=float(args['--dropout']),
vocab=vocab,
no_char_decoder=args['--no-char-decoder'])
model.train()
print_model_param_count(model)
# TODO: How to print all the parameters of this model? And is it useful?
if not start_from_existing_model:
uniform_init = float(args['--uniform-init'])
if np.abs(uniform_init) > 0.:
print('uniformly initialize parameters [-%f, +%f]' %
(uniform_init, uniform_init),
file=sys.stderr)
for p in model.parameters():
p.data.uniform_(-uniform_init, uniform_init)
vocab_mask = torch.ones(len(vocab.tgt))
vocab_mask[vocab.tgt['<pad>']] = 0
device = torch.device("cuda:0" if args['--cuda'] else "cpu")
print('use device: %s' % device, file=sys.stderr)
model = model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=float(args['--lr']))
num_trial = 0
train_iter = patience = cum_loss = report_loss = cum_tgt_words = report_tgt_words = 0
cum_examples = report_examples = epoch = valid_num = 0
hist_valid_scores = []
train_time = begin_time = time.time()
print('begin Maximum Likelihood training')
avg_train_ppls = []
avg_valid_ppls = []
# output_file_path = 'outputs/loss_%s' % datetime.datetime.now().strftime("%m-%d-%Y-%I:%M%p")
output_file_path = os.path.join(
args['--ppl-save-dir'],
'ppl.json') if args['--ppl-save-dir'] else 'ppl.json'
while True:
epoch += 1
for src_sents, tgt_sents in batch_iter(train_data,
batch_size=train_batch_size,
shuffle=True):
train_iter += 1
optimizer.zero_grad()
batch_size = len(src_sents)
example_losses = -model(src_sents, tgt_sents) # (batch_size,)
batch_loss = example_losses.sum()
loss = batch_loss / batch_size
loss.backward()
# clip gradient
grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(),
clip_grad)
optimizer.step()
batch_losses_val = batch_loss.item()
report_loss += batch_losses_val
cum_loss += batch_losses_val
tgt_words_num_to_predict = sum(
len(s[1:]) for s in tgt_sents) # omitting leading `<s>`
report_tgt_words += tgt_words_num_to_predict
cum_tgt_words += tgt_words_num_to_predict
report_examples += batch_size
cum_examples += batch_size
if train_iter % log_every == 0:
print('epoch %d, iter %d, avg. loss %.2f, avg. ppl %.2f ' \
'cum. examples %d, speed %.2f words/sec, time elapsed %.2f sec' % (epoch, train_iter,
report_loss / report_examples,
np.exp(report_loss / report_tgt_words),
cum_examples,
report_tgt_words / (time.time() - train_time),
time.time() - begin_time), file=sys.stderr)
avg_train_ppls.append(np.exp(report_loss / report_tgt_words))
train_time = time.time()
report_loss = report_tgt_words = report_examples = 0.
# perform validation
if train_iter % valid_niter == 0:
# The printed values are the train loss
print(
'epoch %d, iter %d, cum. loss %.2f, cum. ppl %.2f cum. examples %d'
% (epoch, train_iter, cum_loss / cum_examples,
np.exp(cum_loss / cum_tgt_words), cum_examples),
file=sys.stderr)
cum_loss = cum_examples = cum_tgt_words = 0.
valid_num += 1
print('begin validation ...', file=sys.stderr)
# compute dev. ppl and bleu
dev_ppl = evaluate_ppl(
model, dev_data,
batch_size=128) # dev batch size can be a bit larger
avg_valid_ppls.append(dev_ppl)
valid_metric = -dev_ppl
print('validation: iter %d, dev. ppl %f' %
(train_iter, dev_ppl),
file=sys.stderr)
is_better = len(hist_valid_scores
) == 0 or valid_metric > max(hist_valid_scores)
hist_valid_scores.append(valid_metric)
if is_better:
patience = 0
print('save currently the best model to [%s]' %
model_save_path,
file=sys.stderr)
model.save(model_save_path)
# also save the optimizers' state
torch.save(optimizer.state_dict(),
model_save_path + '.optim')
elif patience < int(args['--patience']):
patience += 1
print('hit patience %d' % patience, file=sys.stderr)
if patience == int(args['--patience']):
num_trial += 1
print('hit #%d trial' % num_trial, file=sys.stderr)
if num_trial == int(args['--max-num-trial']):
print('early stop!', file=sys.stderr)
output_losses(args, log_every, valid_niter,
avg_train_ppls, avg_valid_ppls,
output_file_path)
exit(0)
# decay lr, and restore from previously best checkpoint
lr = optimizer.param_groups[0]['lr'] * float(
args['--lr-decay'])
print(
'load previously best model and decay learning rate to %f'
% lr,
file=sys.stderr)
# load model
params = torch.load(
model_save_path,
map_location=lambda storage, loc: storage)
model.load_state_dict(params['state_dict'])
model = model.to(device)
print('restore parameters of the optimizers',
file=sys.stderr)
optimizer.load_state_dict(
torch.load(model_save_path + '.optim'))
# TODO: len(optimizer.param_groups) == 1 ? Or the below code seems odd
# set new lr
for param_group in optimizer.param_groups:
param_group['lr'] = lr
# reset patience
patience = 0
if epoch == int(args['--max-epoch']):
print('reached maximum number of epochs!', file=sys.stderr)
output_losses(args, log_every, valid_niter, avg_train_ppls,
avg_valid_ppls, output_file_path)
exit(0)
output_losses(args, log_every, valid_niter, avg_train_ppls,
avg_valid_ppls, output_file_path)
if args['--is-google-colab'] and epoch % 2 == 0 and os.path.isfile(
model_save_path):
shutil.copy(model_save_path, args['--ppl-save-dir'])
shutil.copy(model_save_path + '.optim', args['--ppl-save-dir'])
print("copied model files to google drive!")
def train():
torch.manual_seed(1)
if (config.cuda):
torch.cuda.manual_seed(1)
args = dict()
args['embed_size'] = config.embed_size
args['d_model'] = config.d_model
args['nhead'] = config.nhead
args['num_encoder_layers'] = config.num_encoder_layers
args['num_decoder_layers'] = config.num_decoder_layers
args['dim_feedforward'] = config.dim_feedforward
args['dropout'] = config.dropout
args['smoothing_eps'] = config.smoothing_eps
text = Text(config.src_corpus, config.tar_corpus)
train_data = Data(config.train_path_src, config.train_path_tar)
dev_data = Data(config.dev_path_src, config.dev_path_tar)
train_loader = DataLoader(dataset=train_data,
batch_size=config.train_batch_size,
shuffle=True,
collate_fn=utils.get_batch)
dev_loader = DataLoader(dataset=dev_data,
batch_size=config.dev_batch_size,
shuffle=True,
collate_fn=utils.get_batch)
#train_data_src, train_data_tar = utils.read_corpus(config.train_path)
#dev_data_src, dev_data_tar = utils.read_corpus(config.dev_path)
device = torch.device("cuda:0" if config.cuda else "cpu")
model = NMT(text, args, device)
#model = nn.DataParallel(model, device_ids=[0, 1])
model = model.to(device)
#model = model.module
#model_path = "/home/wangshuhe/shuhelearn/ShuHeLearning/NMT_transformer/result/02.01_1_344.6820465077113_checkpoint.pth"
#model = NMT.load(model_path)
#model = model.to(device)
model.train()
optimizer = Optim(
torch.optim.Adam(model.parameters(), betas=(0.9, 0.98), eps=1e-9),
config.d_model, config.warm_up_step)
#optimizer = Optim(torch.optim.Adam(model.parameters(), betas=(0.9, 0.98), eps=1e-9), config.warm_up_step, config.init_lr, config.lr)
#optimizer = Optim(torch.optim.Adam(model.parameters()))
epoch = 0
history_valid_ppl = []
print("begin training!", file=sys.stderr)
while (True):
epoch += 1
max_iter = int(math.ceil(len(train_data) / config.train_batch_size))
with tqdm(total=max_iter, desc="train") as pbar:
#for batch_src, batch_tar, tar_word_num in utils.batch_iter(train_data_src, train_data_tar, config.train_batch_size):
for batch_src, batch_tar, tar_word_num in train_loader:
optimizer.zero_grad()
now_batch_size = len(batch_src)
batch_loss = -model(batch_src, batch_tar, smoothing=True)
batch_loss = batch_loss.sum()
loss = batch_loss / now_batch_size
loss.backward()
#optimizer.step()
#optimizer.updata_lr()
optimizer.step_and_updata_lr()
pbar.set_postfix({
"epoch":
epoch,
"avg_loss":
'{%.2f}' % (loss.item()),
"ppl":
'{%.2f}' % (math.exp(batch_loss.item() / tar_word_num))
})
pbar.update(1)
if (epoch % config.valid_iter == 0):
print("now begin validation...", file=sys.stderr)
eval_ppl = evaluate_ppl(model, dev_data, dev_loader,
config.dev_batch_size)
print(eval_ppl)
flag = len(
history_valid_ppl) == 0 or eval_ppl < min(history_valid_ppl)
if (flag):
print(
f"current model is the best! save to [{config.model_save_path}]",
file=sys.stderr)
history_valid_ppl.append(eval_ppl)
model.save(
os.path.join(config.model_save_path,
f"02.10_{epoch}_{eval_ppl}_checkpoint.pth"))
torch.save(
optimizer.optimizer.state_dict(),
os.path.join(config.model_save_path,
f"02.10_{epoch}_{eval_ppl}_optimizer.optim"))
if (epoch == config.max_epoch):
print("reach the maximum number of epochs!", file=sys.stderr)
return
def train(args: Dict):
""" Train the NMT Model.
@param args (Dict): args from cmd line
"""
train_data_src = read_corpus(args['--train-src'], source='src')
train_data_tgt = read_corpus(args['--train-tgt'], source='tgt')
dev_data_src = read_corpus(args['--dev-src'], source='src')
dev_data_tgt = read_corpus(args['--dev-tgt'], source='tgt')
train_data = list(zip(train_data_src, train_data_tgt))
dev_data = list(zip(dev_data_src, dev_data_tgt))
train_batch_size = int(args['--batch-size'])
clip_grad = float(args['--clip-grad'])
valid_niter = int(args['--valid-niter'])
log_every = int(args['--log-every'])
model_save_path = args['--save-to']
vocab = Vocab.load(args['--vocab'])
model = NMT(embed_size=int(args['--embed-size']),
hidden_size=int(args['--hidden-size']),
dropout_rate=float(args['--dropout']),
vocab=vocab)
model.train()
uniform_init = float(args['--uniform-init'])
if np.abs(uniform_init) > 0.:
print('uniformly initialize parameters [-%f, +%f]' %
(uniform_init, uniform_init),
file=sys.stderr)
for p in model.parameters():
p.data.uniform_(-uniform_init, uniform_init)
vocab_mask = torch.ones(len(vocab.tgt))
vocab_mask[vocab.tgt['<pad>']] = 0
device = torch.device("cuda:0" if args['--cuda'] else "cpu")
print('use device: %s' % device, file=sys.stderr)
model = model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=float(args['--lr']))
# Set counters
num_trial = 0
train_iter = patience = cum_loss = report_loss = cum_tgt_words = report_tgt_words = 0
cum_examples = report_examples = epoch = valid_num = 0
hist_valid_scores = []
fwd_time = train_time = begin_time = time.time()
# Begin training
print('begin Maximum Likelihood training')
while True:
epoch += 1
# Loop over all data in selection batches
for src_sents, tgt_sents in batch_iter(train_data,
batch_size=train_batch_size,
shuffle=True):
# Sentences must be sorted in length (that is number of words)
src_sents = sorted(src_sents, key=lambda e: len(e), reverse=True)
tgt_sents = sorted(tgt_sents, key=lambda e: len(e), reverse=True)
train_iter += 1
# Zero out gradients, pytorch accumulates them
optimizer.zero_grad()
# Get loss
train_batch_losses = (-model.forward(src_sents, tgt_sents))
batch_loss = train_batch_losses.sum()
loss = batch_loss / train_batch_size
# Get gradients
loss.backward()
# clip gradient
grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(),
clip_grad)
# step
optimizer.step()
# Report progress
batch_losses_val = batch_loss.item()
report_loss += batch_losses_val
cum_loss += batch_losses_val
# Get some report metric
tgt_words_num_to_predict = sum(
len(s[1:]) for s in tgt_sents) # omitting leading `<s>`
report_tgt_words += tgt_words_num_to_predict
cum_tgt_words += tgt_words_num_to_predict
report_examples += train_batch_size
cum_examples += train_batch_size
if train_iter % log_every == 0:
print('epoch %d, iter %d, avg. loss %.2f, avg. ppl %.2f ' \
'cum. examples %d, speed %.2f words/sec, time elapsed %.2f sec' % (epoch, train_iter,
report_loss / report_examples,
math.exp(report_loss / report_tgt_words),
cum_examples,
report_tgt_words / (time.time() - train_time),
time.time() - begin_time), file=sys.stderr)
train_time = time.time()
report_loss = report_tgt_words = report_examples = 0.
# Test saving and loading the model
# test_save_load_model(model=model,optimizer=optimizer)
# perform validation
if train_iter % valid_niter == 0:
print(
'epoch %d, iter %d, cum. loss %.2f, cum. ppl %.2f, cum. examples %d'
% (epoch, train_iter, cum_loss / cum_examples,
np.exp(cum_loss / cum_tgt_words), cum_examples),
file=sys.stderr)
cum_loss = cum_examples = cum_tgt_words = 0.
valid_num += 1
print('begin validation ...', file=sys.stderr)
# compute dev. ppl and bleu
#dev_ppl = evaluate_ppl(model, dev_data, batch_size=128) # dev batch size can be a bit larger
dev_ppl = evaluate_ppl(model,
dev_data,
batch_size=train_batch_size *
2) # dev batch size can be a bit larger
valid_metric = -dev_ppl
print('validation: iter %d, dev. ppl %f' %
(train_iter, dev_ppl),
file=sys.stderr)
is_better = len(hist_valid_scores
) == 0 or valid_metric > max(hist_valid_scores)
hist_valid_scores.append(valid_metric)
if is_better:
patience = 0
print('save currently the best model to [%s]' %
model_save_path,
file=sys.stderr)
model.save(model_save_path)
# also save the optimizers' state
torch.save(optimizer.state_dict(),
model_save_path + '.optim')
elif patience < int(args['--patience']):
patience += 1
print('hit patience %d' % patience, file=sys.stderr)
if patience == int(args['--patience']):
num_trial += 1
print('hit #%d trial' % num_trial, file=sys.stderr)
if num_trial == int(args['--max-num-trial']):
print('early stop!', file=sys.stderr)
exit(0)
# decay lr, and restore from previously best checkpoint
lr = optimizer.param_groups[0]['lr'] * float(
args['--lr-decay'])
print(
'load previously best model and decay learning rate to %f'
% lr,
file=sys.stderr)
# load model
#params = torch.load(model_save_path, map_location=lambda storage, loc: storage)
# See https://github.com/pytorch/pytorch/issues/7415 and
# https://discuss.pytorch.org/t/on-a-cpu-device-how-to-load-checkpoint-saved-on-gpu-device/349 and
# https://github.com/pytorch/pytorch/issues/9139
params = torch.load(model_save_path,
map_location='cpu')
model.load_state_dict(params['state_dict'])
model = model.to(device)
print('restore parameters of the optimizers',
file=sys.stderr)
# optimizer.load_state_dict(torch.load(model_save_path + '.optim')
optimizer.load_state_dict(
torch.load(model_save_path + '.optim',
map_location='cpu'))
optimizer_to(optimizer, device)
# set new lr
for param_group in optimizer.param_groups:
param_group['lr'] = lr
# reset patience
patience = 0
if epoch == int(args['--max-epoch']):
print('reached maximum number of epochs!', file=sys.stderr)
exit(0)
def experiement(args: Dict, test_only, device):
""" Train and Test the NMT Model.
@param args (Dict): args from cmd line
"""
# train_data_src = read_corpus(args['--train-src'], source='src')
# train_data_tgt = read_corpus(args['--train-tgt'], source='tgt')
#
# dev_data_src = read_corpus(args['--dev-src'], source='src')
# dev_data_tgt = read_corpus(args['--dev-tgt'], source='tgt')
#
# train_data = list(zip(train_data_src, train_data_tgt))
# dev_data = list(zip(dev_data_src, dev_data_tgt))
train_batch_size = int(args['--batch-size'])
clip_grad = float(args['--clip-grad'])
valid_niter = int(args['--valid-niter'])
log_every = int(args['--log-every'])
model_save_path = args['--save-to']
use_pos_embed = False
if args['--use-pos-embed']:
use_pos_embed = True
use_copy = False
if args['--use-copy']:
use_copy = True
SRC, TRG, train_iterator, dev_iterator, test_iterator = load_data(
args['--train-data'], args['--dev-data'], args['--test-data'], device,
train_batch_size, (use_pos_embed or use_copy))
vocab = Vocab(SRC, TRG)
model = NMT(src_embed_size=int(args['--src-embed-size']),
dst_embed_size=int(args['--dst-embed-size']),
hidden_size=int(args['--hidden-size']),
dropout_rate=float(args['--dropout']),
vocab=vocab,
use_pos_embed=use_pos_embed,
use_copy=use_copy)
model.load_pretrained_embeddings(vocab)
# print("args: {}".format(args))
uniform_init = float(args['--uniform-init'])
if np.abs(uniform_init) > 0.:
print('uniformly initialize parameters [-%f, +%f]' %
(uniform_init, uniform_init),
file=sys.stderr)
for p in model.parameters():
p.data.uniform_(-uniform_init, uniform_init)
# def init_weights(m):
# for name, param in m.named_parameters():
# if 'weight' in name:
# nn.init.normal_(param.data, mean=0, std=0.01)
# else:
# nn.init.constant_(param.data, 0)
#
# model.apply(init_weights)
# vocab_mask = torch.ones(len(vocab.tgt))
# vocab_mask[vocab.tgt['<pad>']] = 0
print('use device: %s' % device, file=sys.stderr)
print(model)
para_count = sum(p.numel() for p in model.parameters() if p.requires_grad)
print(f'The model has {para_count:,} trainable parameters')
print("file path: {}".format(model_save_path))
if test_only:
model.eval()
decode(args, test_iterator, vocab, device)
exit(0)
# perform training
model.train()
model = model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=float(args['--lr']))
num_trial = 0
train_iter = patience = cum_loss = report_loss = cum_tgt_words = report_tgt_words = 0
cum_examples = report_examples = epoch = valid_num = 0
hist_valid_scores = []
train_time = begin_time = time.time()
print('begin Maximum Likelihood training')
while True:
epoch += 1
#perform training
model.train()
# for src_sents, tgt_sents in batch_iter(train_data, batch_size=train_batch_size, shuffle=True):
for i, batch in enumerate(train_iterator):
train_iter += 1
optimizer.zero_grad()
src_sents, src_sents_lens = batch.src
tgt_sents = batch.trg
batch_size = src_sents.shape[1]
example_losses = -model(src_sents, src_sents_lens,
tgt_sents) # (batch_size,)
batch_loss = example_losses.sum()
loss = batch_loss / batch_size
loss.backward()
# clip gradient
grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(),
clip_grad)
optimizer.step()
batch_losses_val = batch_loss.item()
report_loss += batch_losses_val
cum_loss += batch_losses_val
tgt_words_num_to_predict = sum(
len(s[1:]) for s in tgt_sents) # omitting leading `<s>`
report_tgt_words += tgt_words_num_to_predict
cum_tgt_words += tgt_words_num_to_predict
report_examples += batch_size
cum_examples += batch_size
# if train_iter % log_every == 0:
# print("")
print('epoch %d, iter %d, avg. loss %.2f, avg. ppl %.2f ' \
'cum. examples %d, speed %.2f words/sec, time elapsed %.2f sec' % (epoch, train_iter, report_loss / report_examples,
math.exp(report_loss / report_tgt_words),
cum_examples,
report_tgt_words / (time.time() - train_time),
time.time() - begin_time), file=sys.stderr)
train_time = time.time()
report_loss = report_tgt_words = report_examples = 0.
# perform validation
# model.eval()
# if train_iter % valid_niter == 0:
# print('epoch %d, iter %d, cum. loss %.2f, cum. ppl %.2f cum. examples %d' % (epoch, train_iter,
# cum_loss / cum_examples,
# np.exp(cum_loss / cum_tgt_words),
# cum_examples), file=sys.stderr)
cum_loss = cum_examples = cum_tgt_words = 0.
valid_num += 1
# print('begin validation ...', file=sys.stderr)
# compute dev. ppl and bleu
dev_ppl = evaluate_ppl(
model, dev_iterator,
batch_size=128) # dev batch size can be a bit larger
valid_metric = -dev_ppl
print('validation: iter %d, dev. ppl %f' % (train_iter, dev_ppl),
file=sys.stderr)
is_better = len(
hist_valid_scores) == 0 or valid_metric > max(hist_valid_scores)
hist_valid_scores.append(valid_metric)
if is_better:
patience = 0
# print('save currently the best model to [%s]' % model_save_path, file=sys.stderr)
model.save(model_save_path)
# also save the optimizers' state
torch.save(optimizer.state_dict(), model_save_path + '.optim')
elif patience < int(args['--patience']):
patience += 1
print('hit patience %d' % patience, file=sys.stderr)
if patience == int(args['--patience']):
num_trial += 1
print('hit #%d trial' % num_trial, file=sys.stderr)
if num_trial == int(args['--max-num-trial']):
print('early stop!', file=sys.stderr)
# exit(0)
break
# decay lr, and restore from previously best checkpoint
lr = optimizer.param_groups[0]['lr'] * float(
args['--lr-decay'])
print(
'load previously best model and decay learning rate to %f'
% lr,
file=sys.stderr)
# load model
params = torch.load(model_save_path,
map_location=lambda storage, loc: storage)
model.load_state_dict(params['state_dict'])
model = model.to(device)
print('restore parameters of the optimizers', file=sys.stderr)
optimizer.load_state_dict(
torch.load(model_save_path + '.optim'))
# set new lr
for param_group in optimizer.param_groups:
param_group['lr'] = lr
# reset patience
patience = 0
if epoch == int(args['--max-epoch']):
print('reached maximum number of epochs!', file=sys.stderr)
break
# perform testing
model.eval()
decode(args, test_iterator, vocab, device)
def train(args: Dict):
""" Train the NMT Model.
@param args (Dict): args from cmd line
"""
train_data_src = read_corpus(args['--train-src'], source='src')
train_data_tgt = read_corpus(args['--train-tgt'], source='tgt')
dev_data_src = read_corpus(args['--dev-src'], source='src')
dev_data_tgt = read_corpus(args['--dev-tgt'], source='tgt')
train_data = list(zip(train_data_src, train_data_tgt))
dev_data = list(zip(dev_data_src, dev_data_tgt))
train_batch_size = int(args['--batch-size'])
clip_grad = float(args['--clip-grad'])
valid_niter = int(args['--valid-niter'])
log_every = int(args['--log-every'])
model_save_path = args['--save-to']
#prefer to do our entire train,test,val split in the code itself as opposed to our previous script
# remove these comments
#data preprocessing for Qs and As.
spacy_en = spacy.load('en')
def tokenizer(text): # create a tokenizer function
return [tok.text for tok in spacy_en.tokenizer(text)]
TEXT = Field(sequential=True,
tokenize=tokenizer,
lower=True,
include_lengths=True,
init_token='<s>',
eos_token='</s>')
analogies_datafields = [("abc", TEXT), ("d", TEXT)]
train, val, test = TabularDataset.splits(
path="data", # the root directory where the data lies
train='ngram_train.csv',
validation="ngram_val.csv",
test='ngram_test.csv',
format='csv',
skip_header=
False, # if your csv header has a header, make sure to pass this to ensure it doesn't get proceesed as data!
fields=analogies_datafields)
pretrained_vecs = torchtext.vocab.Vectors('../GloVe-1.2/life_vectors.txt')
TEXT.build_vocab(
vectors=pretrained_vecs) # specials=['<pad>', '<s>', '</s>']
if args['--cuda'] == 'cpu':
torch_text_device = -1
else:
torch_text_device = 0
training_iter, val_iter, test_iter = Iterator.splits(
(train, val, test),
sort_key=lambda x: len(x.abc),
batch_sizes=(100, 20, 1),
device=torch_text_device,
sort_within_batch=True)
model = NMT(embed_size=int(args['--embed-size']),
hidden_size=int(args['--hidden-size']),
dropout_rate=float(args['--dropout']),
vocab=TEXT.vocab)
model.train() #sets training = True
uniform_init = float(args['--uniform-init'])
if np.abs(uniform_init) > 0.:
print('uniformly initialize parameters [-%f, +%f]' %
(uniform_init, uniform_init),
file=sys.stderr)
for p in model.parameters():
p.data.uniform_(-uniform_init, uniform_init)
device = torch.device("cuda:0" if args['--cuda'] else "cpu")
print('use device: %s' % device, file=sys.stderr)
model = model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=float(args['--lr']))
num_trial = 0
train_iter = patience = cum_loss = report_loss = cum_tgt_words = report_tgt_words = 0
cum_examples = report_examples = epoch = valid_num = 0
hist_valid_scores = []
train_time = begin_time = time.time()
print('begin Maximum Likelihood training')
writer = SummaryWriter('logs')
is_better_count = 0 #TODO: Remove this and debug the nonstopping part
while True:
epoch += 1
for _, data in enumerate(training_iter):
(src_sents, src_lengths), (tgt_sents, _) = data.abc, data.d
train_iter += 1
optimizer.zero_grad()
batch_size = src_sents.shape[1]
example_losses = model(src_sents, src_lengths,
tgt_sents) # (batch_size,)
batch_loss = example_losses.sum()
loss = batch_loss / batch_size
loss.backward()
# clip gradient
grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(),
clip_grad)
optimizer.step()
batch_losses_val = batch_loss.item()
report_loss += batch_losses_val
cum_loss += batch_losses_val
tgt_words_num_to_predict = sum(
len(s[1:]) for s in tgt_sents) # omitting leading `<s>`
report_tgt_words += tgt_words_num_to_predict
cum_tgt_words += tgt_words_num_to_predict
report_examples += batch_size
cum_examples += batch_size
if train_iter % log_every == 0:
print('epoch %d, iter %d, avg. loss %.2f, avg. ppl %.2f ' \
'cum. examples %d, speed %.2f words/sec, time elapsed %.2f sec' % (epoch, train_iter,
report_loss / report_examples,
math.exp(report_loss / report_tgt_words),
cum_examples,
report_tgt_words / (time.time() - train_time),
time.time() - begin_time), file=sys.stderr)
writer.add_scalar('Train/AvgLoss',
report_loss / report_examples, epoch)
writer.add_scalar('Train/AvgPPL',
math.exp(report_loss / report_tgt_words),
epoch)
train_time = time.time()
report_loss = report_tgt_words = report_examples = 0.
# perform validation
if train_iter % valid_niter == 0:
print(
'epoch %d, iter %d, cum. loss %.2f, cum. ppl %.2f cum. examples %d'
% (epoch, train_iter, cum_loss / cum_examples,
np.exp(cum_loss / cum_tgt_words), cum_examples),
file=sys.stderr)
cum_loss = cum_examples = cum_tgt_words = 0.
valid_num += 1
print('begin validation ...', file=sys.stderr)
# compute dev. ppl and bleu
dev_ppl, val_loss = evaluate_ppl(
model, val_iter) # dev batch size can be a bit larger
valid_metric = -dev_ppl
print('validation: iter %d, dev. ppl %f, dev loss %f' %
(train_iter, dev_ppl, val_loss),
file=sys.stderr)
writer.add_scalar('Val/AvgPPL', dev_ppl, epoch)
writer.add_scalar('Val/AvgLoss', val_loss, epoch)
is_better = len(hist_valid_scores
) == 0 or valid_metric > max(hist_valid_scores)
print(hist_valid_scores)
print(valid_metric)
hist_valid_scores.append(valid_metric)
if is_better:
patience = 0
print('save currently the best model to [%s]' %
model_save_path,
file=sys.stderr)
model.save(model_save_path)
is_better_count = is_better_count + 1
print(is_better_count)
# also save the optimizers' state
torch.save(optimizer.state_dict(),
model_save_path + '.optim')
if is_better_count > 3:
print('reached maximum number of epochs!',
file=sys.stderr)
writer.close()
exit(0)
elif patience < int(args['--patience']):
patience += 1
print('hit patience %d' % patience, file=sys.stderr)
if patience == int(args['--patience']):
num_trial += 1
print('hit #%d trial' % num_trial, file=sys.stderr)
if num_trial == int(args['--max-num-trial']):
print('early stop!', file=sys.stderr)
exit(0)
# decay lr, and restore from previously best checkpoint
lr = optimizer.param_groups[0]['lr'] * float(
args['--lr-decay'])
print(
'load previously best model and decay learning rate to %f'
% lr,
file=sys.stderr)
# load model
params = torch.load(
model_save_path,
map_location=lambda storage, loc: storage)
model.load_state_dict(params['state_dict'])
model = model.to(device)
print('restore parameters of the optimizers',
file=sys.stderr)
optimizer.load_state_dict(
torch.load(model_save_path + '.optim'))
# set new lr
for param_group in optimizer.param_groups:
param_group['lr'] = lr
# reset patience
patience = 0
if epoch == int(args['--max-epoch']):
print('reached maximum number of epochs!', file=sys.stderr)
writer.close()
exit(0)
def train(args: Dict):
""" Train the NMT Model.
@param args (Dict): args from cmd line
"""
long_logfile = "long_logfiles/" + str(time.time()) + "long.txt"
train_logfile = "train_logfiles/" + str(time.time()) + "train.txt"
dev_logfile = "dev_logfiles/" + str(time.time()) + "dev.txt"
f_long = open(long_logfile, "w")
f_train = open(train_logfile, "w")
# TODO: add hyperparameters
args_tuples = [(arg, args[arg]) for arg in args]
f_train.write("#args_tuples: %s\n" % args_tuples)
for (arg, val) in args_tuples:
f_train.write("#%s: %s\n" % (arg, val))
f_train.write("#epoch, train iter, train score\n")
f_dev = open(dev_logfile, "w")
f_dev.write("#epoch, train iter, dev score, dev accuracy\n")
binary = int(args["--num-classes"]) == 2
train_data = load_train_data(perct=float(args["--train-perct"]),
binary=binary)
dev_data = load_dev_data(dev_perct=float(args["--dev-perct"]),
binary=binary)
train_batch_size = int(args["--batch-size"])
clip_grad = float(args["--clip-grad"])
valid_niter = int(args["--valid-niter"])
log_every = int(args["--log-every"])
model_save_path = args["--save-to"]
embed_size = int(args["--embed-size"])
# TODO: load train data_augmenter based on args
data_augmenter = str(args["--data-aug"]).lower()
print_and_write("Using data augmentation method: %s" % data_augmenter,
f_long)
if data_augmenter == "gaussian":
data_augmenter = GaussianNoiseDataAugmenter(
float(args["--data-aug-amount"]), int(args["--data-aug-nx"]))
elif data_augmenter == "identity":
data_augmenter = NoisyIdentityDataAugmenter(
float(args["--data-aug-amount"]), int(args["--data-aug-nx"]))
elif data_augmenter == "swapdim":
data_augmenter = EmbedDimensionSwapDataAugmenter(
int(args["--data-aug-amount"]), int(args["--data-aug-nx"]))
else:
data_augmenter = BaseDataAugmenter()
# perform augmentation
train_data_aug = data_augmenter.augment(train_data)
print_and_write(
"train size: %d, after aug %d" %
(len(train_data[0]), len(train_data_aug)),
f_long,
)
model = NMT(embed_size=embed_size,
hidden_size=int(args["--hidden-size"]),
num_classes=int(args["--num-classes"]),
dropout_rate=float(args["--dropout"]))
model.train()
uniform_init = float(args["--uniform-init"])
if np.abs(uniform_init) > 0.0:
print_and_write(
"uniformly initialize parameters [-%f, +%f]" %
(uniform_init, uniform_init),
f_long,
)
for p in model.parameters():
p.data.uniform_(-uniform_init, uniform_init)
device = torch.device("cuda:0" if args["--cuda"] else "cpu")
print_and_write("use device: %s" % device, f_long)
model = model.to(device)
print_and_write("confirming model device %s" % model.device, f_long)
optimizer = torch.optim.Adam(model.parameters(), lr=float(args["--lr"]))
num_trial = 0
train_iter = patience = cum_loss = report_loss = 0
cum_examples = report_examples = epoch = valid_num = 0
hist_valid_scores = []
train_time = begin_time = time.time()
print_and_write("begin Maximum Likelihood training", f_long)
while True:
epoch += 1
for sentences, sentiments in batch_iter(train_data_aug,
batch_size=train_batch_size,
shuffle=True):
train_iter += 1
optimizer.zero_grad()
example_losses = -model(sentences, sentiments) # (batch_size,)
batch_size = len(
example_losses) # in case data augmentation makes returned
# number of examples > input batch size
batch_loss = example_losses.sum()
loss = batch_loss / batch_size
loss.backward()
# clip gradient
grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(),
clip_grad)
optimizer.step()
batch_losses_val = batch_loss.item()
report_loss += batch_losses_val
cum_loss += batch_losses_val
report_examples += batch_size
cum_examples += batch_size
if train_iter % log_every == 0:
# train_accuracy = model.compute_accuracy(sentences, sentiments)
print_and_write(
"epoch %d, iter %d, avg. loss %.2f, "
"cum. examples %d, time elapsed %.2f sec" % (
epoch,
train_iter,
report_loss / report_examples,
cum_examples,
time.time() - begin_time,
),
f_long,
)
f_train.write(
"%d, %d, %.2f\n" %
(epoch, train_iter, report_loss / report_examples))
train_time = time.time()
report_loss = report_examples = 0.0
# perform validation
if train_iter % valid_niter == 0:
cum_loss = cum_examples = 0.0
valid_num += 1
print_and_write("begin validation ...", f_long)
# compute dev
dev_score, dev_accuracy = evaluate_dev(
model, dev_data,
batch_size=5000) # dev batch size can be a bit larger
valid_metric = -dev_score # maybe use accuracy instead?
print_and_write(
"validation: iter %d, dev. score %f, dev. accuracy %f" %
(train_iter, dev_score, dev_accuracy),
f_long,
)
f_dev.write("%d, %d, %f, %f\n" %
(epoch, train_iter, dev_score, dev_accuracy))
is_better = len(hist_valid_scores
) == 0 or valid_metric > max(hist_valid_scores)
hist_valid_scores.append(valid_metric)
# train_score = evaluate_dev(model, train_data, batch_size=100000)
if is_better:
patience = 0
print_and_write(
"save currently the best model to [%s]" %
model_save_path,
f_long,
)
model.save(model_save_path)
# also save the optimizers' state
torch.save(optimizer.state_dict(),
model_save_path + ".optim")
elif patience < int(args["--patience"]):
patience += 1
print_and_write("hit patience %d" % patience, f_long)
if patience == int(args["--patience"]):
num_trial += 1
print_and_write("hit #%d trial" % num_trial, f_long)
if num_trial == int(args["--max-num-trial"]):
print_and_write("early stop!", f_long)
exit(0)
# decay lr, and restore from previously best checkpoint
lr = optimizer.param_groups[0]["lr"] * float(
args["--lr-decay"])
print_and_write(
"load previously best model and decay learning rate to %f"
% lr,
f_long,
)
# load model
params = torch.load(
model_save_path,
map_location=lambda storage, loc: storage)
model.load_state_dict(params["state_dict"])
model = model.to(device)
print_and_write("restore parameters of the optimizers",
f_long)
optimizer.load_state_dict(
torch.load(model_save_path + ".optim"))
# set new lr
for param_group in optimizer.param_groups:
param_group["lr"] = lr
# reset patience
patience = 0
if epoch == int(args["--max-epoch"]):
print_and_write("reached maximum number of epochs!",
f_long)
exit(0)
def train(args: Dict):
""" Train the NMT Model.
:param Dict args: arguments from command line
"""
train_data_src = read_corpus(args['--train-src'], source='src')
train_data_tgt = read_corpus(args['--train-tgt'], source='tgt')
dev_data_src = read_corpus(args['--dev-src'], source='src')
dev_data_tgt = read_corpus(args['--dev-tgt'], source='tgt')
# Lists of (src_sent, tgt_sent) tuples
train_data = list(zip(train_data_src, train_data_tgt))
dev_data = list(zip(dev_data_src, dev_data_tgt))
train_batch_size = int(args['--batch-size'])
clip_grad = float(args['--clip-grad'])
valid_niter = int(args['--valid-niter'])
log_every = int(args['--log-every'])
model_save_path = args['--save-to']
vocab = Vocab.load(args['--vocab'])
model = NMT(embed_size=int(args['--embed-size']),
hidden_size=int(args['--hidden-size']),
dropout_rate=float(args['--dropout']),
vocab=vocab,
no_char_decoder=args['--no-char-decoder'])
model.train() # Set to train mode
uniform_init = float(args['--uniform-init'])
if np.abs(uniform_init) > 0.:
print('uniformly initialize parameters [-%f, +%f]' %
(uniform_init, uniform_init),
file=sys.stderr)
for p in model.parameters():
p.data.uniform_(-uniform_init, uniform_init) # Initialize in-place
# vocab_mask = torch.ones(len(vocab.tgt))
# vocab_mask[vocab.tgt['<pad>']] = 0
device = torch.device("cuda:0" if args['--cuda'] else "cpu")
print('use device: %s' % device, file=sys.stderr)
print('device name: ', torch.cuda.get_device_name(device))
print('device available: ', torch.cuda.is_available())
model = model.to(device) # Send model parameters to the chosen device
optimizer = torch.optim.Adam(model.parameters(), lr=float(args['--lr']))
# Initialize necessary variables
num_trial = 0
train_iter = patience = cum_loss = report_loss = cum_tgt_words = report_tgt_words = 0
cum_examples = report_examples = epoch = valid_num = 0
# To keep track of previous scores
hist_valid_scores = []
train_time = begin_time = time.time()
print('begin Maximum Likelihood training')
# Load the previous model parameters if they exist
if os.path.isfile('model.bin'):
print('Load previous best model...', file=sys.stderr)
# load model
params = torch.load(model_save_path,
map_location=lambda storage, loc: storage)
model.load_state_dict(params['state_dict'])
model = model.to(device)
print('restore parameters of the optimizers', file=sys.stderr)
optimizer.load_state_dict(torch.load(model_save_path + '.optim'))
# save the loaded previous best model as current best - otherwise they will be overwritten
dev_ppl = evaluate_ppl(
model, dev_data,
batch_size=64) # dev batch size can be a bit larger
valid_metric = -dev_ppl
hist_valid_scores.append(valid_metric)
while True:
epoch += 1
# Iterate over lazily generated batches (lists) of sentences (each sentence is a list of words)
for src_sents, tgt_sents in batch_iter(train_data,
batch_size=train_batch_size,
shuffle=True):
train_iter += 1
# Zero gradients, otherwise they would be accumulated across batches
optimizer.zero_grad()
batch_size = len(src_sents)
# Calculate the losses for each example in the batch (i.e. forward propagation)
example_losses = -model(src_sents, tgt_sents) # Dim: (batch_size,)
# Average losses over the entire batch
batch_loss = example_losses.sum()
loss = batch_loss / batch_size
# Compute gradients
loss.backward()
# Clip gradients
grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(),
clip_grad)
# Update parameters
optimizer.step()
# Get a number from a tensor containing a single scalar
batch_losses_val = batch_loss.item()
# Add to the ''report_loss'' (zeroed every ''log_every'' iterations - for logging)
report_loss += batch_losses_val
# Add to the ''cum_loss'' (zeroed every ''valid_niter'' iterations - for validation)
cum_loss += batch_losses_val
tgt_words_num_to_predict = sum(
len(s[1:]) for s in tgt_sents) # omitting leading `<s>`
report_tgt_words += tgt_words_num_to_predict
cum_tgt_words += tgt_words_num_to_predict
report_examples += batch_size
cum_examples += batch_size
if train_iter % log_every == 0:
print('epoch %d, iter %d, avg. loss %.2f, avg. ppl %.2f ' \
'cum. examples %d, speed %.2f words/sec, time elapsed %.2f sec' % (epoch, train_iter,
report_loss / report_examples,
math.exp(report_loss / report_tgt_words),
cum_examples,
report_tgt_words / (time.time() - train_time),
time.time() - begin_time), file=sys.stderr)
train_time = time.time()
report_loss = report_tgt_words = report_examples = 0.
# Perform validation
if train_iter % valid_niter == 0:
print(
'epoch %d, iter %d, cum. loss %.2f, cum. ppl %.2f cum. examples %d'
% (epoch, train_iter, cum_loss / cum_examples,
np.exp(cum_loss / cum_tgt_words), cum_examples),
file=sys.stderr)
cum_loss = cum_examples = cum_tgt_words = 0.
valid_num += 1
print('begin validation ...', file=sys.stderr)
# compute dev. ppl
dev_ppl = evaluate_ppl(
model, dev_data,
batch_size=64) # dev batch size can be a bit larger
valid_metric = -dev_ppl
print('validation: iter %d, dev. ppl %f' %
(train_iter, dev_ppl),
file=sys.stderr)
# Lower perplexity is better
is_better = len(hist_valid_scores
) == 0 or valid_metric > max(hist_valid_scores)
hist_valid_scores.append(valid_metric)
if is_better:
patience = 0
print('save currently the best model to [%s]' %
model_save_path,
file=sys.stderr)
model.save(model_save_path)
# also save the optimizers' state
torch.save(optimizer.state_dict(),
model_save_path + '.optim')
elif patience < int(args['--patience']):
patience += 1
print('hit patience %d' % patience, file=sys.stderr)
if patience == int(args['--patience']):
num_trial += 1
print('hit #%d trial' % num_trial, file=sys.stderr)
if num_trial == int(args['--max-num-trial']):
print('early stop!', file=sys.stderr)
exit(0)
# decay lr, and restore from previously best checkpoint
lr = optimizer.param_groups[0]['lr'] * float(
args['--lr-decay'])
print(
'load previously best model and decay learning rate to %f'
% lr,
file=sys.stderr)
# load model
params = torch.load(
model_save_path,
map_location=lambda storage, loc: storage)
model.load_state_dict(params['state_dict'])
model = model.to(device)
print('restore parameters of the optimizers',
file=sys.stderr)
optimizer.load_state_dict(
torch.load(model_save_path + '.optim'))
# set new lr
for param_group in optimizer.param_groups:
param_group['lr'] = lr
# reset patience
patience = 0
if epoch == int(args['--max-epoch']):
print('reached maximum number of epochs!', file=sys.stderr)
exit(0)
def train(model_config,
data_config,
output_path,
device,
epoch_size,
max_epoch,
batch_size,
repeats,
decade_rate,
clip_grad,
log_every,
valid_every,
learning_rate=0.0005):
print('use device: %s' % device, file=sys.stderr)
vocab = Vocab.load(data_config["vacab_file"])
model = NMT(vocab=vocab, **model_config)
model = model.to(torch.device(device))
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
data_config.pop("vacab_file", None)
data_loader = DataLoader(**data_config)
batch_queue, loss_queue = data_loader.load_train_data(
epoch_size, max_epoch, batch_size, repeats, decade_rate)
dev_data = data_loader.load_dev_data()
hist_valid_scores = []
train_losses = []
train_iter = cum_loss = report_loss = cum_tgt_words = report_tgt_words = 0
cum_examples = report_examples = epoch = valid_num = 0
if os.path.isfile(output_path + '/speech-to-text.model'):
print('loading saved model...')
params = torch.load(output_path + '/speech-to-text.model',
map_location=lambda storage, loc: storage)
model.load_state_dict(params['state_dict'])
print('restoring parameters of the optimizers', file=sys.stderr)
optimizer.load_state_dict(
torch.load(output_path + '/speech-to-text.optim'))
dev_ppl = evaluate_ppl(
model, dev_data,
batch_size=128) # dev batch size can be a bit larger
valid_metric = -dev_ppl
hist_valid_scores.append(valid_metric)
print("saved model ppl: ", dev_ppl)
model.train()
train_time = begin_time = time.time()
epoch, voices, tgt_sents = batch_queue.get(True)
while voices is not None and tgt_sents is not None:
train_iter += 1
optimizer.zero_grad()
# print("received voices:", len(voices))
# print("tgt_sents[0]:", len(tgt_sents[0]), tgt_sents[0])
# print("tgt_sents[1]:", len(tgt_sents[1]), tgt_sents[1])
optimizer.zero_grad()
batch_size = len(voices)
sample_losses = -model(voices, tgt_sents)
batch_loss = sample_losses.sum()
loss = batch_loss / batch_size
loss.backward()
# clip gradient
grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(),
clip_grad)
optimizer.step()
batch_losses_val = batch_loss.item()
report_loss += batch_losses_val
cum_loss += batch_losses_val
tgt_words_num_to_predict = sum(
len(s[1:]) for s in tgt_sents) # omitting leading `<s>`
report_tgt_words += tgt_words_num_to_predict
cum_tgt_words += tgt_words_num_to_predict
report_examples += batch_size
cum_examples += batch_size
loss_queue.put(report_loss / report_examples)
train_losses.append({
'epoch':
epoch,
'iter':
train_iter,
'loss':
report_loss / report_examples,
'ppl':
math.exp(report_loss / report_tgt_words),
'cum':
cum_examples,
'speed':
report_tgt_words / (time.time() - train_time)
})
if train_iter % log_every == 0:
print(
'epoch %d, iter %d, avg. loss %.2f, avg. ppl %.2f '
'cum. examples %d, speed %.2f words/sec, time elapsed %.2f sec'
% (epoch, train_iter, report_loss / report_examples,
math.exp(report_loss / report_tgt_words), cum_examples,
report_tgt_words /
(time.time() - train_time), time.time() - begin_time),
file=sys.stderr)
train_time = time.time()
report_loss = report_tgt_words = report_examples = 0.
# perform validation
if train_iter % valid_every == 0:
print(
'epoch %d, iter %d, cum. loss %.2f, cum. ppl %.2f cum. examples %d'
% (epoch, train_iter, cum_loss / cum_examples,
np.exp(cum_loss / cum_tgt_words), cum_examples),
file=sys.stderr)
cum_loss = cum_examples = cum_tgt_words = 0.
valid_num += 1
print('begin validation ...', file=sys.stderr)
# compute dev. ppl and bleu
dev_ppl = evaluate_ppl(
model, dev_data,
batch_size=128) # dev batch size can be a bit larger
valid_metric = -dev_ppl
print('validation: iter %d, dev. ppl %f' % (train_iter, dev_ppl),
file=sys.stderr)
is_better = len(hist_valid_scores
) == 0 or valid_metric > max(hist_valid_scores)
hist_valid_scores.append(valid_metric)
if is_better:
patience = 0
print('save currently the best model to [%s]' % output_path,
file=sys.stderr)
model.save(output_path + '/speech-to-text.model')
torch.save(optimizer.state_dict(),
output_path + '/speech-to-text.optim')
epoch, voices, tgt_sents = batch_queue.get(True)
def train(train_data,
dev_data,
vocab,
embed_size=256,
hidden_size=256,
dropout_rate=0.2,
uniform_init=0.1,
device='cpu',
lr=0.001,
batch_size=32,
clip_grad=5.0,
log_every=10,
valid_niter=2000,
save_path='model.bin',
patience=5,
lr_decay=0.5,
max_trials=5,
max_epochs=30):
""" Train the NMT model
Params:
train_data (list of (src_sent, tgt_sent)): list of tuples containing source and target
sentences for training
dev_data (list of (src_sent, tgt_sent)): list of tuples containing source and target
sentences for dev
vocab (Vocab): Vocab object for source and target
embed_size (int): Embedding dimensionality. Default = 256
hidden_size (int): Dimensionality for hidden states. Default = 256
dropout_rate (float): Dropout probability. Default: 0.2
uniform_init (float): If > 0: uniformly initialize all parameters
device (str): device to perform the calc on. Default = 'cpu'
lr (float): learning rate. Default = 0.001
batch_size (int): batch size. Default = 32
clip_grad (float): used in gradient clipping. Default = 5.0
log_every (int): number of iterations to print stats. Default = 10
valid_niter (int): number of iterations to perform validation. Default = 2000
save_path (str): path to save the best model. Default: 'model.bin' in current dir
patience (int): number of iterations to decay learning rate. Default = 5
lr_decay (float): learning rate decay. Default = 0.5
max_trials (int): terminate training after how many trials. Default = 5
max_epochs (int): max number of epochs. Default = 30
Return:
"""
# Create NMT model and put it in train mode
model = NMT(embed_size, hidden_size, vocab, dropout_rate)
model.train()
# Uniformely initialize model parameters if required
if np.abs(uniform_init) > 0.:
print(f'uniformly init parameters [-{uniform_init}, +{uniform_init}]',
file=sys.stderr)
for p in model.parameters():
p.data.uniform_(-uniform_init, uniform_init)
# Create target vocab mask with 0 for 'padding' index and 1 otherwise
vocab_mask = torch.ones(len(vocab.tgt))
vocab_mask[vocab.tgt['<pad>']] = 0
# Set model device
device = torch.device(device)
model = model.to(device)
print(f'Using device: {device}', file=sys.stderr)
# Choose optimizer
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
# Initializations
num_trial = 0
train_iter = 0
current_patience = 0
cum_loss = 0
report_loss = 0
cum_tgt_words = 0
report_tgt_words = 0
cum_examples = 0
report_examples = 0
epoch = 0
valid_num = 0
hist_valid_scores = []
train_time = time.time()
begin_time = time.time()
print('begin Maximum Likelihood training')
while True:
epoch += 1
# Iterate over the batches in the training data
for src_sents, tgt_sents in batch_iter(train_data,
batch_size,
shuffle=True):
train_iter += 1
optimizer.zero_grad()
current_batch_size = len(src_sents)
# Calculate loss and backpropagate
example_losses = -model(src_sents,
tgt_sents) # (current_batch_size,)
batch_loss = example_losses.sum()
loss = batch_loss / current_batch_size # average loss
loss.backward()
# clip gradient and update parameters
_ = torch.nn.utils.clip_grad_norm_(model.parameters(), clip_grad)
optimizer.step()
batch_losses_val = batch_loss.item()
report_loss += batch_losses_val
cum_loss += batch_losses_val
tgt_words_num_to_predict = sum(
len(s[1:]) for s in tgt_sents) # omitting leading `<s>`
report_tgt_words += tgt_words_num_to_predict
cum_tgt_words += tgt_words_num_to_predict
report_examples += batch_size
cum_examples += batch_size
if train_iter % log_every == 0:
print(f'epoch {epoch}, iter {train_iter}, ' \
f'avg. loss {report_loss / report_examples:.2f}, '\
f'avg. ppl {math.exp(report_loss / report_tgt_words):.2f}, ' \
f'cum. examples {cum_examples}, ' \
f'speed {report_tgt_words / (time.time() - train_time):.2f} words/sec, ' \
f'time elapsed {(time.time() - begin_time):.2f} sec', file=sys.stderr)
train_time = time.time()
report_loss = report_tgt_words = report_examples = 0.
# perform validation
if train_iter % valid_niter == 0:
print(f'epoch {epoch}, iter {train_iter}, cum. loss {cum_loss / cum_examples:.2f}, '\
f'cum. ppl {np.exp(cum_loss / cum_tgt_words):.2f} cum. examples {cum_examples}',
file=sys.stderr)
cum_loss = cum_examples = cum_tgt_words = 0.
valid_num += 1
print('begin validation ...', file=sys.stderr)
# compute dev. ppl and bleu
dev_ppl = evaluate_ppl(
model, dev_data,
batch_size=128) # dev batch size can be a bit larger
valid_metric = -dev_ppl
print(f'validation: iter {train_iter}, dev. ppl {dev_ppl}',
file=sys.stderr)
is_better = len(hist_valid_scores
) == 0 or valid_metric > max(hist_valid_scores)
hist_valid_scores.append(valid_metric)
if is_better:
# save model and optimizer state
print(f'save the best model to [{save_path}]',
file=sys.stderr)
model.save(save_path)
torch.save(optimizer.state_dict(), save_path + '.optim')
current_patience = 0
elif current_patience < patience:
current_patience += 1
print(f'hit patience {current_patience}', file=sys.stderr)
if current_patience == patience:
num_trial += 1
print(f'hit #{num_trial} trial', file=sys.stderr)
if num_trial == max_trials:
print('early stop!', file=sys.stderr)
exit(0)
# decay lr, and restore from previously best checkpoint
lr = optimizer.param_groups[0]['lr'] * lr_decay
print(
f'load previously best model and decay learning rate to {lr}',
file=sys.stderr)
# load model
params = torch.load(
save_path,
map_location=lambda storage, loc: storage)
model.load_state_dict(params['state_dict'])
model = model.to(device)
print('restore parameters of the optimizers',
file=sys.stderr)
optimizer.load_state_dict(
torch.load(save_path + '.optim'))
# set new lr
for param_group in optimizer.param_groups:
param_group['lr'] = lr
# reset patience
current_patience = 0
if epoch == max_epochs:
print('reached maximum number of epochs!', file=sys.stderr)
exit(0)
