class Solver(BaseSolver):
"""
Solver for training language models
"""
def __init__(self, config, paras, mode):
super().__init__(config, paras, mode)
# Logger settings
self.best_loss = 10
def fetch_data(self, data):
''' Move data to device, insert <sos> and compute text seq. length'''
txt = torch.cat((torch.zeros(
(data.shape[0], 1), dtype=torch.long), data),
dim=1).to(self.device)
txt_len = torch.sum(data != 0, dim=-1)
return txt, txt_len
def load_data(self):
"""
Load data for training/validation, store tokenizer and input/output shape
:return:
"""
self.tr_set, self.dv_set, self.vocab_size, self.tokenizer, msg = \
load_textset(self.paras.njobs, self.paras.gpu, self.paras.pin_memory, **self.config['data'])
self.verbose(msg)
def set_model(self):
"""
Setup ASR model and optimizer
:return:
"""
# Model
self.model = RNNLM(self.vocab_size,
**self.config['model']).to(self.device)
self.verbose(self.model.create_msg())
# Losses
self.seq_loss = torch.nn.CrossEntropyLoss(ignore_index=0)
# Optimizer
self.optimizer = Optimizer(self.model.parameters(),
**self.config['hparas'])
# Enable AMP if needed
self.enable_apex()
# load pre-trained model
if self.paras.load:
self.load_ckpt()
ckpt = torch.load(self.paras.load, map_location=self.device)
self.model.load_state_dict(ckpt['model'])
self.optimizer.load_opt_state_dict(ckpt['optimizer'])
self.step = ckpt['global_step']
self.verbose('Load ckpt from {}, restarting at step {}'.format(
self.paras.load, self.step))
def exec(self):
"""
Training End-to-end ASR system
:return:
"""
self.verbose('Total training steps {}.'.format(
human_format(self.max_step)))
self.timer.set()
while self.step < self.max_step:
for data in self.tr_set:
# Pre-step : update tf_rate/lr_rate and do zero_grad
self.optimizer.pre_step(self.step)
# Fetch data
txt, txt_len = self.fetch_data(data)
self.timer.cnt('rd')
# Forward model
pred, _ = self.model(txt[:, :-1], txt_len)
# Compute all objectives
lm_loss = self.seq_loss(pred.view(-1, self.vocab_size),
txt[:, 1:].reshape(-1))
self.timer.cnt('fw')
# Backprop
grad_norm = self.backward(lm_loss)
self.step += 1
# Logger
if self.step % self.PROGRESS_STEP == 0:
self.progress(
'Tr stat | Loss - {:.2f} | Grad. Norm - {:.2f} | {}'.
format(lm_loss.cpu().item(), grad_norm,
self.timer.show()))
self.write_log('entropy', {'tr': lm_loss})
self.write_log('perplexity',
{'tr': torch.exp(lm_loss).cpu().item()})
# Validation
if (self.step == 1) or (self.step % self.valid_step == 0):
self.validate()
# End of step
self.timer.set()
if self.step > self.max_step:
break
self.log.close()
def validate(self):
# Eval mode
self.model.eval()
dev_loss = []
for i, data in enumerate(self.dv_set):
self.progress('Valid step - {}/{}'.format(i + 1, len(self.dv_set)))
# Fetch data
txt, txt_len = self.fetch_data(data)
# Forward model
with torch.no_grad():
pred, _ = self.model(txt[:, :-1], txt_len)
lm_loss = self.seq_loss(pred.view(-1, self.vocab_size),
txt[:, 1:].reshape(-1))
dev_loss.append(lm_loss)
# Ckpt if performance improves
dev_loss = sum(dev_loss) / len(dev_loss)
dev_ppx = torch.exp(dev_loss).cpu().item()
if dev_loss < self.best_loss:
self.best_loss = dev_loss
self.save_checkpoint('best_ppx.pth', 'perplexity', dev_ppx)
self.write_log('entropy', {'dv': dev_loss})
self.write_log('perplexity', {'dv': dev_ppx})
# Show some example of last batch on tensorboard
for i in range(min(len(txt), self.DEV_N_EXAMPLE)):
if self.step == 1:
self.write_log('true_text{}'.format(i),
self.tokenizer.decode(txt[i].tolist()))
self.write_log(
'pred_text{}'.format(i),
self.tokenizer.decode(pred[i].argmax(dim=-1).tolist()))
# Resume training
self.model.train()
class Solver(BaseSolver):
''' Solver for training'''
def __init__(self, config, paras, mode):
super().__init__(config, paras, mode)
# Logger settings
self.best_wer = {'att': 3.0, 'ctc': 3.0}
# Curriculum learning affects data loader
self.curriculum = self.config['hparas']['curriculum']
def fetch_data(self, data):
''' Move data to device and compute text seq. length'''
_, feat, feat_len, txt = data
feat = feat.to(self.device)
feat_len = feat_len.to(self.device)
txt = txt.to(self.device)
txt_len = torch.sum(txt != 0, dim=-1)
return feat, feat_len, txt, txt_len
def load_data(self):
print(
"Load data for training/validation, store tokenizer and input/output shape"
)
self.tr_set, self.dv_set, self.feat_dim, self.vocab_size, self.tokenizer, msg = \
load_dataset(self.paras.njobs, self.paras.gpu, self.paras.pin_memory,
self.curriculum > 0, **self.config['data'])
self.verbose(msg)
def set_model(self):
print("Setup ASR model and optimizer ")
# Model
self.model = ASR(self.feat_dim, self.vocab_size,
**self.config['model']).to(self.device)
self.verbose(self.model.create_msg())
model_paras = [{'params': self.model.parameters()}]
print("# Losses")
self.seq_loss = torch.nn.CrossEntropyLoss(ignore_index=0)
print("# Note: zero_infinity=False is unstable?")
self.ctc_loss = torch.nn.CTCLoss(blank=0, zero_infinity=False)
print("Plug-ins")
self.emb_fuse = False
self.emb_reg = ('emb'
in self.config) and (self.config['emb']['enable'])
if self.emb_reg:
from core.plugin import EmbeddingRegularizer
self.emb_decoder = EmbeddingRegularizer(
self.tokenizer, self.model.dec_dim,
**self.config['emb']).to(self.device)
model_paras.append({'params': self.emb_decoder.parameters()})
self.emb_fuse = self.emb_decoder.apply_fuse
if self.emb_fuse:
self.seq_loss = torch.nn.NLLLoss(ignore_index=0)
self.verbose(self.emb_decoder.create_msg())
print("# Optimizer")
self.optimizer = Optimizer(model_paras, **self.config['hparas'])
self.verbose(self.optimizer.create_msg())
print("# Enable AMP if needed")
self.enable_apex()
# Automatically load pre-trained model if self.paras.load is given
self.load_ckpt()
# ToDo: other training methods
def exec(self):
print("Training End-to-end ASR system")
self.verbose('Total training steps {}.'.format(
human_format(self.max_step)))
ctc_loss, att_loss, emb_loss = None, None, None
n_epochs = 0
self.timer.set()
while self.step < self.max_step:
# Renew dataloader to enable random sampling
if self.curriculum > 0 and n_epochs == self.curriculum:
self.verbose(
'Curriculum learning ends after {} epochs, starting random sampling.'
.format(n_epochs))
self.tr_set, _, _, _, _, _ = \
load_dataset(self.paras.njobs, self.paras.gpu, self.paras.pin_memory,
False, **self.config['data'])
print("self.tr_set: {}".format(len(self.tr_set)))
for data in self.tr_set:
# print("Pre-step : update tf_rate/lr_rate and do zero_grad")
tf_rate = self.optimizer.pre_step(self.step)
total_loss = 0
# Fetch data
feat, feat_len, txt, txt_len = self.fetch_data(data)
self.timer.cnt('rd')
# Forward model
# Note: txt should NOT start w/ <sos>
ctc_output, encode_len, att_output, att_align, dec_state = \
self.model(feat, feat_len, max(txt_len), tf_rate=tf_rate,
teacher=txt, get_dec_state=self.emb_reg)
# Plugins
if self.emb_reg:
emb_loss, fuse_output = self.emb_decoder(dec_state,
att_output,
label=txt)
total_loss += self.emb_decoder.weight * emb_loss
# Compute all objectives
if ctc_output is not None:
if self.paras.cudnn_ctc:
ctc_loss = self.ctc_loss(
ctc_output.transpose(0, 1),
txt.to_sparse().values().to(device='cpu',
dtype=torch.int32),
[ctc_output.shape[1]] * len(ctc_output),
txt_len.cpu().tolist())
else:
ctc_loss = self.ctc_loss(ctc_output.transpose(0, 1),
txt, encode_len, txt_len)
total_loss += ctc_loss * self.model.ctc_weight
if att_output is not None:
b, t, _ = att_output.shape
att_output = fuse_output if self.emb_fuse else att_output
att_loss = self.seq_loss(att_output.view(b * t, -1),
txt.view(-1))
total_loss += att_loss * (1 - self.model.ctc_weight)
self.timer.cnt('fw')
# Backprop
grad_norm = self.backward(total_loss)
self.step += 1
# Logger
if (self.step == 1) or (self.step % self.PROGRESS_STEP == 0):
self.progress(
'Tr stat | Loss - {:.2f} | Grad. Norm - {:.2f} | {}'.
format(total_loss.cpu().item(), grad_norm,
self.timer.show()))
self.write_log('loss', {
'tr_ctc': ctc_loss,
'tr_att': att_loss
})
self.write_log('emb_loss', {'tr': emb_loss})
self.write_log(
'wer', {
'tr_att':
cal_er(self.tokenizer, att_output, txt),
'tr_ctc':
cal_er(self.tokenizer, ctc_output, txt, ctc=True)
})
if self.emb_fuse:
if self.emb_decoder.fuse_learnable:
self.write_log(
'fuse_lambda',
{'emb': self.emb_decoder.get_weight()})
self.write_log('fuse_temp',
{'temp': self.emb_decoder.get_temp()})
# Validation
if (self.step == 1) or (self.step % self.valid_step == 0):
self.validate()
# End of step
# https://github.com/pytorch/pytorch/issues/13246#issuecomment-529185354
torch.cuda.empty_cache()
self.timer.set()
if self.step > self.max_step:
break
n_epochs += 1
self.log.close()
def validate(self):
# Eval mode
self.model.eval()
if self.emb_decoder is not None:
self.emb_decoder.eval()
dev_wer = {'att': [], 'ctc': []}
for i, data in enumerate(self.dv_set):
self.progress('Valid step - {}/{}'.format(i + 1, len(self.dv_set)))
# Fetch data
feat, feat_len, txt, txt_len = self.fetch_data(data)
# Forward model
with torch.no_grad():
ctc_output, encode_len, att_output, att_align, dec_state = \
self.model(feat, feat_len, int(max(txt_len) * self.DEV_STEP_RATIO),
emb_decoder=self.emb_decoder)
dev_wer['att'].append(cal_er(self.tokenizer, att_output, txt))
dev_wer['ctc'].append(
cal_er(self.tokenizer, ctc_output, txt, ctc=True))
# Show some example on tensorboard
if i == len(self.dv_set) // 2:
for i in range(min(len(txt), self.DEV_N_EXAMPLE)):
if self.step == 1:
self.write_log('true_text{}'.format(i),
self.tokenizer.decode(txt[i].tolist()))
if att_output is not None:
self.write_log(
'att_align{}'.format(i),
feat_to_fig(att_align[i, 0, :, :].cpu().detach()))
self.write_log(
'att_text{}'.format(i),
self.tokenizer.decode(
att_output[i].argmax(dim=-1).tolist()))
if ctc_output is not None:
self.write_log(
'ctc_text{}'.format(i),
self.tokenizer.decode(
ctc_output[i].argmax(dim=-1).tolist(),
ignore_repeat=True))
# Ckpt if performance improves
self.save_checkpoint('latest.pth',
'wer',
dev_wer['att'],
show_msg=False)
for task in ['att', 'ctc']:
dev_wer[task] = sum(dev_wer[task]) / len(dev_wer[task])
if dev_wer[task] < self.best_wer[task]:
self.best_wer[task] = dev_wer[task]
self.save_checkpoint('best_{}.pth'.format(task), 'wer',
dev_wer[task])
self.write_log('wer', {'dv_' + task: dev_wer[task]})
# Resume training
self.model.train()
if self.emb_decoder is not None:
self.emb_decoder.train()