src.vocab.load_vectors(wv_type=opt.pre_emb, wv_dim=opt.word_dim)
# src.vocab.load_vectors(wv_type='fasttext.en.300d', wv_dim=300)
# src.vocab.load_vectors(wv_type='charngram.100d', wv_dim=100)
# NOTE: If the source field name and the target field name
# are different from 'src' and 'tgt' respectively, they have
# to be set explicitly before any training or inference
# seq2seq.src_field_name = 'src'
# seq2seq.tgt_field_name = 'tgt'
# Prepare loss
weight = torch.ones(len(tgt.vocab))
pad = tgt.vocab.stoi[tgt.pad_token]
# loss = Perplexity(weight, pad)
loss = NLLLoss(weight=weight, mask=pad, size_average=True)
if torch.cuda.is_available():
loss.cuda()
seq2seq = None
optimizer = None
if not opt.resume:
# Initialize model
hidden_size = opt.word_lstm_dim
bidirectional = opt.word_bidirect
encoder = EncoderRNN(vocab_size=len(src.vocab),
max_len=max_len,
word_dim=opt.word_dim,
hidden_size=hidden_size,
input_dropout_p=opt.input_dropout,
bidirectional=bidirectional,
n_layers=1,
rnn_cell='gru',
# optim_G = ScheduledOptim(optim.Adam(
# G.get_trainable_parameters(),
# betas=(0.9, 0.98), eps=1e-09),
# opt.d_model, opt.n_warmup_steps)
optim_G = optim.Adam(G.parameters(), lr=1e-4,
betas=(0.9, 0.98), eps=1e-09)
optim_D = torch.optim.Adam(D.parameters(), lr=1e-4)
crit_G = NLLLoss(size_average=False)
crit_D = nn.BCELoss()
if opt.cuda:
G.cuda()
D.cuda()
crit_G.cuda()
crit_D.cuda()
# ---------- train ----------
trainer_D = trainers.DiscriminatorTrainer()
# pre-train D
if not hasattr(opt, 'load_D_from'):
pool = helper.DiscriminatorDataPool(opt.max_len, D.min_len, Constants.PAD)
for epoch in range(1):
logging.info('[Pretrain D Epoch %d]' % epoch)
D.train()
def main():
parser = argparse.ArgumentParser()
opt = options.train_options(parser)
opt = parser.parse_args()
opt.cuda = torch.cuda.is_available()
opt.device = None if opt.cuda else -1
# 快速變更設定
opt.exp_dir = './experiment/transformer-reinforce/use_billion'
opt.load_vocab_from = './experiment/transformer/lang8-cor2err/vocab.pt'
opt.build_vocab_from = './data/billion/billion.30m.model.vocab'
opt.load_D_from = opt.exp_dir
# opt.load_D_from = None
# dataset params
opt.max_len = 20
# G params
# opt.load_G_a_from = './experiment/transformer/lang8-err2cor/'
# opt.load_G_b_from = './experiment/transformer/lang8-cor2err/'
opt.d_word_vec = 300
opt.d_model = 300
opt.d_inner_hid = 600
opt.n_head = 6
opt.n_layers = 3
opt.embs_share_weight = False
opt.beam_size = 1
opt.max_token_seq_len = opt.max_len + 2 # 包含<BOS>, <EOS>
opt.n_warmup_steps = 4000
# D params
opt.embed_dim = opt.d_model
opt.num_kernel = 100
opt.kernel_sizes = [3, 4, 5, 6, 7]
opt.dropout_p = 0.25
# train params
opt.batch_size = 1
opt.n_epoch = 10
if not os.path.exists(opt.exp_dir):
os.makedirs(opt.exp_dir)
logging.basicConfig(filename=opt.exp_dir + '/.log',
format=LOG_FORMAT,
level=logging.DEBUG)
logging.getLogger().addHandler(logging.StreamHandler())
logging.info('Use CUDA? ' + str(opt.cuda))
logging.info(opt)
# ---------- prepare dataset ----------
def len_filter(example):
return len(example.src) <= opt.max_len and len(
example.tgt) <= opt.max_len
EN = SentencePieceField(init_token=Constants.BOS_WORD,
eos_token=Constants.EOS_WORD,
batch_first=True,
include_lengths=True)
train = datasets.TranslationDataset(path='./data/dualgan/train',
exts=('.billion.sp', '.use.sp'),
fields=[('src', EN), ('tgt', EN)],
filter_pred=len_filter)
val = datasets.TranslationDataset(path='./data/dualgan/val',
exts=('.billion.sp', '.use.sp'),
fields=[('src', EN), ('tgt', EN)],
filter_pred=len_filter)
train_lang8, val_lang8 = Lang8.splits(exts=('.err.sp', '.cor.sp'),
fields=[('src', EN), ('tgt', EN)],
train='test',
validation='test',
test=None,
filter_pred=len_filter)
# 讀取 vocabulary（確保一致）
try:
logging.info('Load voab from %s' % opt.load_vocab_from)
EN.load_vocab(opt.load_vocab_from)
except FileNotFoundError:
EN.build_vocab_from(opt.build_vocab_from)
EN.save_vocab(opt.load_vocab_from)
logging.info('Vocab len: %d' % len(EN.vocab))
# 檢查Constants是否有誤
assert EN.vocab.stoi[Constants.BOS_WORD] == Constants.BOS
assert EN.vocab.stoi[Constants.EOS_WORD] == Constants.EOS
assert EN.vocab.stoi[Constants.PAD_WORD] == Constants.PAD
assert EN.vocab.stoi[Constants.UNK_WORD] == Constants.UNK
# ---------- init model ----------
# G = build_G(opt, EN, EN)
hidden_size = 512
bidirectional = True
encoder = EncoderRNN(len(EN.vocab),
opt.max_len,
hidden_size,
n_layers=1,
bidirectional=bidirectional)
decoder = DecoderRNN(len(EN.vocab),
opt.max_len,
hidden_size * 2 if bidirectional else 1,
n_layers=1,
dropout_p=0.2,
use_attention=True,
bidirectional=bidirectional,
eos_id=Constants.EOS,
sos_id=Constants.BOS)
G = Seq2seq(encoder, decoder)
for param in G.parameters():
param.data.uniform_(-0.08, 0.08)
# optim_G = ScheduledOptim(optim.Adam(
# G.get_trainable_parameters(),
# betas=(0.9, 0.98), eps=1e-09),
# opt.d_model, opt.n_warmup_steps)
optim_G = optim.Adam(G.parameters(), lr=1e-4, betas=(0.9, 0.98), eps=1e-09)
loss_G = NLLLoss(size_average=False)
if torch.cuda.is_available():
loss_G.cuda()
# # 預先訓練D
if opt.load_D_from:
D = load_model(opt.load_D_from)
else:
D = build_D(opt, EN)
optim_D = torch.optim.Adam(D.parameters(), lr=1e-4)
def get_criterion(vocab_size):
''' With PAD token zero weight '''
weight = torch.ones(vocab_size)
weight[Constants.PAD] = 0
return nn.CrossEntropyLoss(weight, size_average=False)
crit_G = get_criterion(len(EN.vocab))
crit_D = nn.BCELoss()
if opt.cuda:
G.cuda()
D.cuda()
crit_G.cuda()
crit_D.cuda()
# ---------- train ----------
trainer_D = trainers.DiscriminatorTrainer()
if not opt.load_D_from:
for epoch in range(1):
logging.info('[Pretrain D Epoch %d]' % epoch)
pool = helper.DiscriminatorDataPool(opt.max_len, D.min_len,
Constants.PAD)
# 將資料塞進pool中
train_iter = data.BucketIterator(dataset=train,
batch_size=opt.batch_size,
device=opt.device,
sort_key=lambda x: len(x.src),
repeat=False)
pool.fill(train_iter)
# train D
trainer_D.train(D,
train_iter=pool.batch_gen(),
crit=crit_D,
optimizer=optim_D)
pool.reset()
Checkpoint(model=D,
optimizer=optim_D,
epoch=0,
step=0,
input_vocab=EN.vocab,
output_vocab=EN.vocab).save(opt.exp_dir)
def eval_D():
pool = helper.DiscriminatorDataPool(opt.max_len, D.min_len,
Constants.PAD)
val_iter = data.BucketIterator(dataset=val,
batch_size=opt.batch_size,
device=opt.device,
sort_key=lambda x: len(x.src),
repeat=False)
pool.fill(val_iter)
trainer_D.evaluate(D, val_iter=pool.batch_gen(), crit=crit_D)
# eval_D()
# Train G
ALPHA = 0
for epoch in range(100):
logging.info('[Epoch %d]' % epoch)
train_iter = data.BucketIterator(dataset=train,
batch_size=1,
device=opt.device,
sort_within_batch=True,
sort_key=lambda x: len(x.src),
repeat=False)
for step, batch in enumerate(train_iter):
src_seq = batch.src[0]
src_length = batch.src[1]
tgt_seq = src_seq[0].clone()
# gold = tgt_seq[:, 1:]
optim_G.zero_grad()
loss_G.reset()
decoder_outputs, decoder_hidden, other = G.rollout(src_seq,
None,
None,
n_rollout=1)
for i, step_output in enumerate(decoder_outputs):
batch_size = tgt_seq.size(0)
# print(step_output)
# loss_G.eval_batch(step_output.contiguous().view(batch_size, -1), tgt_seq[:, i + 1])
softmax_output = torch.exp(
torch.cat([x for x in decoder_outputs], dim=0)).unsqueeze(0)
softmax_output = helper.stack(softmax_output, 8)
print(softmax_output)
rollout = softmax_output.multinomial(1)
print(rollout)
tgt_seq = helper.pad_seq(tgt_seq.data,
max_len=len(decoder_outputs) + 1,
pad_value=Constants.PAD)
tgt_seq = autograd.Variable(tgt_seq)
for i, step_output in enumerate(decoder_outputs):
batch_size = tgt_seq.size(0)
loss_G.eval_batch(
step_output.contiguous().view(batch_size, -1),
tgt_seq[:, i + 1])
G.zero_grad()
loss_G.backward()
optim_G.step()
if step % 100 == 0:
pred = torch.cat([x for x in other['sequence']], dim=1)
print('[step %d] loss_rest %.4f' %
(epoch * len(train_iter) + step, loss_G.get_loss()))
print('%s -> %s' %
(EN.reverse(tgt_seq.data)[0], EN.reverse(pred.data)[0]))
# Reinforce Train G
for p in D.parameters():
p.requires_grad = False
bidirectional=bidirectional,
rnn_cell='lstm',
eos_id=Constants.EOS,
sos_id=Constants.BOS)
G = Seq2seq(encoder, decoder)
for param in G.parameters():
param.data.uniform_(-0.08, 0.08)
# optim_G = ScheduledOptim(optim.Adam(
# G.get_trainable_parameters(),
# betas=(0.9, 0.98), eps=1e-09),
# opt.d_model, opt.n_warmup_steps)
optim_G = optim.Adam(G.parameters(), lr=1e-4, betas=(0.9, 0.98), eps=1e-09)
loss_G = NLLLoss(size_average=False)
if torch.cuda.is_available():
loss_G.cuda()
# init D
if opt.load_D_from:
D = load_model(opt.load_D_from)
else:
D = build_D(opt, EN)
optim_D = torch.optim.Adam(D.parameters(), lr=1e-4)
def get_criterion(vocab_size):
''' With PAD token zero weight '''
weight = torch.ones(vocab_size)
weight[Constants.PAD] = 0
return nn.CrossEntropyLoss(weight, size_average=False)
cpt.build_vocab(train, max_size=50000)
input_vocab = src.vocab
output_vocab = tgt.vocab
# NOTE: If the source field name and the target field name
# are different from 'src' and 'tgt' respectively, they have
# to be set explicitly before any training or inference
# seq2seq.src_field_name = 'src'
# seq2seq.tgt_field_name = 'tgt'
# Prepare loss
weight = torch.ones(len(tgt.vocab))
pad = tgt.vocab.stoi[tgt.pad_token]
loss = NLLLoss(weight, pad)
if torch.cuda.is_available():
loss.cuda()
if opt.concept:
state_weight = torch.ones(len(cpt.vocab))
state_pad = cpt.vocab.stoi['<pad>']
state_loss = NLLLoss(state_weight, state_pad)
if torch.cuda.is_available():
state_loss.cuda()
seq2seq = None
optimizer = None
if not opt.resume:
# Initialize model
hidden_size = 128
dialog_hidden_size = 128
dropout = 0.5
bidirectional = True
def train(opt):
LOG_FORMAT = '%(asctime)s %(levelname)-8s %(message)s'
logging.basicConfig(format=LOG_FORMAT,
level=getattr(logging, opt.log_level.upper()))
logging.info(opt)
if int(opt.GPU) >= 0:
torch.cuda.set_device(int(opt.GPU))
if opt.load_checkpoint is not None:
logging.info("loading checkpoint from {}".format(
os.path.join(opt.expt_dir, Checkpoint.CHECKPOINT_DIR_NAME,
opt.load_checkpoint)))
checkpoint_path = os.path.join(opt.expt_dir,
Checkpoint.CHECKPOINT_DIR_NAME,
opt.load_checkpoint)
checkpoint = Checkpoint.load(checkpoint_path)
seq2tree = checkpoint.model
input_vocab = checkpoint.input_vocab
else:
# Prepare dataset
src = SourceField()
nt = NTField()
pos = PosField()
tgt_tree = TreeField()
comp = CompField()
max_len = opt.max_len
def len_filter(example):
return len(example.src) <= max_len
train = torchtext.data.TabularDataset(path=opt.train_path,
format='tsv',
fields=[('src', src), ('nt', nt),
('pos', pos),
('tree', tgt_tree)],
filter_pred=len_filter)
dev = torchtext.data.TabularDataset(path=opt.dev_path,
format='tsv',
fields=[('src', src), ('nt', nt),
('pos', pos),
('tree', tgt_tree)],
filter_pred=len_filter)
src.build_vocab(train, max_size=50000)
comp.build_vocab(train, max_size=50000)
nt.build_vocab(train, max_size=50000)
pos.build_vocab(train, max_size=50000)
# src_tree.build_vocab(train, max_size=50000)
pos_in_nt = set()
for Pos in pos.vocab.stoi:
if nt.vocab.stoi[Pos] > 1:
pos_in_nt.add(nt.vocab.stoi[Pos])
hidden_size = opt.hidden_size
input_vocab = src.vocab
nt_vocab = nt.vocab
def tree_to_id(tree):
tree.set_label(nt_vocab.stoi[tree.label()])
if len(tree) == 1 and str(tree[0])[0] is not '(':
tree[0] = input_vocab.stoi[tree[0]]
return
else:
for subtree in tree:
tree_to_id(subtree)
tree.append(Tree(nt_vocab.stoi['<eos>'], []))
return tree
# train.examples = [str(tree_to_id(ex.tree)) for ex in train.examples]
# dev.examples = [str(tree_to_id(ex.tree)) for ex in dev.examples]
for ex in train.examples:
ex.tree = str(tree_to_id(Tree.fromstring(ex.tree)))
for ex in dev.examples:
ex.tree = str(tree_to_id(Tree.fromstring(ex.tree)))
# train.examples = [tree_to_id(Tree.fromstring(ex.tree)) for ex in train.examples]
# dev.examples = [str(tree_to_id(Tree.fromstring(ex.tree))) for ex in dev.examples]
if opt.word_embedding is not None:
input_vocab.load_vectors([opt.word_embedding])
loss = NLLLoss()
if torch.cuda.is_available():
loss.cuda()
loss.reset()
seq2tree = None
optimizer = None
if not opt.resume:
# Initialize model
bidirectional = opt.bidirectional_encoder
encoder = EncoderRNN(len(src.vocab),
opt.word_embedding_size,
max_len,
hidden_size,
bidirectional=bidirectional,
variable_lengths=True)
decoder = DecoderTree(len(src.vocab),
opt.word_embedding_size,
opt.nt_embedding_size,
len(nt.vocab),
max_len,
hidden_size *
2 if bidirectional else hidden_size,
sos_id=nt_vocab.stoi['<sos>'],
eos_id=nt_vocab.stoi['<eos>'],
dropout_p=0.2,
use_attention=True,
bidirectional=bidirectional,
pos_in_nt=pos_in_nt)
seq2tree = Seq2tree(encoder, decoder)
if torch.cuda.is_available():
seq2tree.cuda()
for param in seq2tree.parameters():
param.data.uniform_(-0.08, 0.08)
# encoder.embedding.weight.data.set_(input_vocab.vectors)
# encoder.embedding.weight.data.set_(output_vocab.vectors)
# Optimizer and learning rate scheduler can be customized by
# explicitly constructing the objects and pass to the trainer.
#
# optimizer = Optimizer(torch.optim.Adam(seq2seq.parameters()), max_grad_norm=5)
# scheduler = StepLR(optimizer.optimizer, 1)
# optimizer.set_scheduler(scheduler)
optimizer = Optimizer(optim.Adam(seq2tree.parameters(), lr=opt.lr),
max_grad_norm=5)
# train
t = SupervisedTrainer(loss=loss,
batch_size=opt.batch_size,
checkpoint_every=opt.checkpoint_every,
print_every=10,
expt_dir=opt.expt_dir,
lr=opt.lr)
seq2tree = t.train(seq2tree,
train,
num_epochs=opt.epoch,
dev_data=dev,
optimizer=optimizer,
teacher_forcing_ratio=0,
resume=opt.resume)
predictor = Predictor(seq2tree, input_vocab, nt_vocab)
return predictor, dev, train
