def test_perplexity(self):
nll = NLLLoss()
ppl = Perplexity()
nll.eval_batch(self.outputs, self.batch)
ppl.eval_batch(self.outputs, self.batch)
nll_loss = nll.get_loss()
ppl_loss = ppl.get_loss()
self.assertAlmostEqual(ppl_loss, math.exp(nll_loss))
def test_perplexity(self):
nll = NLLLoss()
ppl = Perplexity()
for output, target in zip(self.outputs, self.targets):
nll.eval_batch(output, target)
ppl.eval_batch(output, target)
nll_loss = nll.get_loss()
ppl_loss = ppl.get_loss()
self.assertAlmostEqual(ppl_loss, math.exp(nll_loss))
def test_nllloss_WITH_OUT_SIZE_AVERAGE(self):
loss = NLLLoss(size_average=False)
pytorch_loss = 0
pytorch_criterion = torch.nn.NLLLoss(size_average=False)
for output, target in zip(self.outputs, self.targets):
loss.eval_batch(output, target)
pytorch_loss += pytorch_criterion(output, target)
loss_val = loss.get_loss()
self.assertAlmostEqual(loss_val, pytorch_loss.data[0])
def test_nllloss_WITH_OUT_SIZE_AVERAGE(self):
loss = NLLLoss(reduction='sum')
pytorch_loss = 0
pytorch_criterion = torch.nn.NLLLoss(reduction='sum')
for output, target in zip(self.outputs, self.targets):
loss.eval_batch(output, target)
pytorch_loss += pytorch_criterion(output, target)
loss_val = loss.get_loss()
self.assertAlmostEqual(loss_val, pytorch_loss.item())
def test_nllloss(self):
loss = NLLLoss()
pytorch_loss = 0
pytorch_criterion = torch.nn.NLLLoss()
for output, target in zip(self.outputs, self.targets):
loss.eval_batch(output, target)
pytorch_loss += pytorch_criterion(output, target)
loss_val = loss.get_loss()
pytorch_loss /= self.num_batch
self.assertAlmostEqual(loss_val, pytorch_loss.data[0])
def test_nllloss_WITH_OUT_SIZE_AVERAGE(self):
num_repeat = 10
loss = NLLLoss(reduction='sum')
pytorch_loss = 0
pytorch_criterion = torch.nn.NLLLoss(reduction='sum')
for _ in range(num_repeat):
for step, output in enumerate(self.outputs):
pytorch_loss += pytorch_criterion(output,
self.targets[:, step + 1])
loss.eval_batch(self.outputs, self.batch)
loss_val = loss.get_loss()
self.assertAlmostEqual(loss_val, pytorch_loss.item())
def test_nllloss(self):
num_batch = 10
loss = NLLLoss()
pytorch_loss = 0
pytorch_criterion = torch.nn.NLLLoss()
for _ in range(num_batch):
for step, output in enumerate(self.outputs):
pytorch_loss += pytorch_criterion(output,
self.targets[:, step + 1])
loss.eval_batch(self.outputs, self.batch)
loss_val = loss.get_loss()
pytorch_loss /= (num_batch * len(self.outputs))
self.assertAlmostEqual(loss_val, pytorch_loss.item())
def test_perplexity(self):
num_class = 5
num_batch = 10
batch_size = 5
outputs = [F.softmax(Variable(torch.randn(batch_size, num_class)))
for _ in range(num_batch)]
targets = [Variable(torch.LongTensor([random.randint(0, num_class - 1)
for _ in range(batch_size)]))
for _ in range(num_batch)]
nll = NLLLoss()
ppl = Perplexity()
for output, target in zip(outputs, targets):
nll.eval_batch(output, target)
ppl.eval_batch(output, target)
nll_loss = nll.get_loss()
ppl_loss = ppl.get_loss()
self.assertAlmostEqual(ppl_loss, math.exp(nll_loss))
def test_nllloss(self):
num_class = 5
num_batch = 10
batch_size = 5
outputs = [F.softmax(Variable(torch.randn(batch_size, num_class)))
for _ in range(num_batch)]
targets = [Variable(torch.LongTensor([random.randint(0, num_class - 1)
for _ in range(batch_size)]))
for _ in range(num_batch)]
loss = NLLLoss()
pytorch_loss = 0
pytorch_criterion = torch.nn.NLLLoss()
for output, target in zip(outputs, targets):
loss.eval_batch(output, target)
pytorch_loss += pytorch_criterion(output, target)
loss_val = loss.get_loss()
pytorch_loss /= num_batch
self.assertAlmostEqual(loss_val, pytorch_loss.data[0])
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
train_iter = data.BucketIterator(dataset=train,
batch_size=16,
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.clone() # a -> b' -> a
decoder_outputs, decoder_hiddens, other = G.forward(
src_seq, src_length.tolist(), target_variable=None)
crit_G.reset()
for i, step_output in enumerate(decoder_outputs):
batch_size = tgt_seq.size(0)
crit_G.eval_batch(step_output.contiguous().view(batch_size, -1),
tgt_seq[:, i + 1])
optim_G.zero_grad()
crit_G.backward()
optim_G.step()
if step % 100 == 0:
pred = torch.cat([x for x in other['sequence']], dim=1)
print('[step %d] loss %.4f' %
(epoch * len(train_iter) + step, crit_G.get_loss()))
print('%s -> %s' %
(EN.reverse(tgt_seq.data)[0], EN.reverse(pred.data)[0]))
def forward(self,
batch_x,
batch_y=None,
teacher_forcing_ratio=0,
output_token=False,
pad_token='<PAD>',
visualize=False):
# input_var = batch_x if type(batch_x) == Variable else Variable(batch_x)
# input_var = input_var.float()
if batch_y is not None:
if type(batch_y) == list:
batch_y = torch.LongTensor(batch_y).cuda(
) if self.use_cuda else torch.LongTensor(batch_y)
batch_y = batch_y if type(batch_y) == Variable else Variable(
batch_y)
''' input will be changed to variable by state_encoder's forward function '''
decoder_outputs, decoder_hidden, ret_dict = self.seq2seq(
input_variable=batch_x,
target_variable=batch_y,
teacher_forcing_ratio=teacher_forcing_ratio)
''' decode prediction vector as result '''
pred_tokens = []
preds = []
attentions = []
for ind in range(len(batch_x)):
length = ret_dict['length'][ind]
tgt_id_seq = [
ret_dict['sequence'][di][ind].data[0] for di in range(length)
]
tgt_seq = [self.id2token[int(tok)] for tok in tgt_id_seq]
preds.append(tgt_id_seq)
pred_tokens.append(tgt_seq)
''' Visualization '''
attentions = ret_dict[
'attention_score'] # shape = output_len * batch * 1 * input_len
if visualize and self.use_attention and not self.training:
# print('Bug detect', attentions.shape, len(pred_tokens))
for j in range(len(tgt_seq)):
# print('===', tgt_seq[j], 'BD', len(attentions), attentions[j].shape)
print('===', tgt_seq[j])
for k in zip(self.state_v_component,
attentions[j][ind][0].tolist()):
print(k)
if self.training:
'''computing loss '''
# loss = Perplexity()
weight = torch.FloatTensor([1 for i in range(len(self.token2id))])
if self.use_cuda:
weight = weight.cuda()
loss = NLLLoss(weight=weight,
mask=self.token2id[pad_token],
size_average=True)
# loss = nn.NLLLoss(weight=weight, size_average=size_average)
for step, step_output in enumerate(decoder_outputs):
try:
batch_size = batch_x.size(0)
except AttributeError:
batch_size = len(batch_x)
''' Select out current step's token. EOS is not included in step_output, so + 1 step for target '''
pred_token_distribute_batch = step_output.contiguous().view(
batch_size, -1)
target_token_id_v_batch = batch_y[:, step + 1]
loss.eval_batch(pred_token_distribute_batch,
target_token_id_v_batch)
if output_token:
return preds, loss.acc_loss, pred_tokens
else:
return preds, loss.acc_loss
else:
if output_token:
return preds, pred_tokens
else:
return preds
def forward(self,
batch_x,
batch_y=None,
teacher_forcing_ratio=0,
output_token=False,
pad_token='<PAD>'):
input_var = batch_x if type(batch_x) == Variable else Variable(batch_x)
input_var = input_var.float()
if batch_y is not None:
batch_y = batch_y if type(batch_y) == Variable else Variable(
batch_y)
decoder_outputs, decoder_hidden, other = self.seq2seq(
input_variable=input_var,
target_variable=batch_y,
teacher_forcing_ratio=teacher_forcing_ratio)
# ''' prepare var for loss computing '''
# target_var = []
# for label in batch_y:
# tmp_var = []
# for id in label:
# token_v = [0 for i in range(len(self.token2id))]
# token_v[id] = 1
# tmp_var.append(token_v)
# target_var.append(tmp_var)
# target_var = torch.LongTensor(target_var)
''' decode prediction vector as result '''
pred_tokens = []
preds = []
for ind in range(len(batch_x)):
length = other['length'][ind]
tgt_id_seq = [
other['sequence'][di][ind].data[0] for di in range(length)
]
tgt_seq = [self.id2token[int(tok)] for tok in tgt_id_seq]
preds.append(tgt_id_seq)
pred_tokens.append(tgt_seq)
if self.training:
'''computing loss '''
# loss = Perplexity()
weight = torch.FloatTensor([1 for i in range(len(self.token2id))])
if self.use_cuda:
weight = weight.cuda()
loss = NLLLoss(weight=weight,
mask=self.token2id[pad_token],
size_average=True)
# loss = nn.NLLLoss(weight=weight, size_average=size_average)
for step, step_output in enumerate(decoder_outputs):
batch_size = batch_x.size(0)
''' Select out current step's token. EOS is not included in step_output, so + 1 step for target '''
pred_token_distribute_batch = step_output.contiguous().view(
batch_size, -1)
target_token_id_v_batch = batch_y[:, step + 1]
loss.eval_batch(pred_token_distribute_batch,
target_token_id_v_batch)
if output_token:
return preds, loss.acc_loss, pred_tokens
else:
return preds, loss.acc_loss
else:
if output_token:
return preds, pred_tokens
else:
return preds
