import torch
import numpy as np
def check_update(model, grad_clip, ignore_stopnet=False):
r'''Check model gradient against unexpected jumps and failures'''
skip_flag = False
if ignore_stopnet:
grad_norm = torch.nn.utils.clip_grad_norm_([param for name, param in model.named_parameters() if 'stopnet' not in name], grad_clip)
else:
grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip)
# compatibility with different torch versions
if isinstance(grad_norm, float):
if np.isinf(grad_norm):
print(" | > Gradient is INF !!")
skip_flag = True
else:
if torch.isinf(grad_norm):
print(" | > Gradient is INF !!")
skip_flag = True
return grad_norm, skip_flag
def lr_decay(init_lr, global_step, warmup_steps):
r'''from https://github.com/r9y9/tacotron_pytorch/blob/master/train.py'''
warmup_steps = float(warmup_steps)
step = global_step + 1.
lr = init_lr * warmup_steps**0.5 * np.minimum(step * warmup_steps**-1.5,
step**-0.5)
return lr
def adam_weight_decay(optimizer):
"""
Custom weight decay operation, not effecting grad values.
"""
for group in optimizer.param_groups:
for param in group['params']:
current_lr = group['lr']
weight_decay = group['weight_decay']
factor = -weight_decay * group['lr']
param.data = param.data.add(param.data,
alpha=factor)
return optimizer, current_lr
# pylint: disable=dangerous-default-value
def set_weight_decay(model, weight_decay, skip_list={"decoder.attention.v", "rnn", "lstm", "gru", "embedding"}):
"""
Skip biases, BatchNorm parameters, rnns.
and attention projection layer v
"""
decay = []
no_decay = []
for name, param in model.named_parameters():
if not param.requires_grad:
continue
if len(param.shape) == 1 or any([skip_name in name for skip_name in skip_list]):
no_decay.append(param)
else:
decay.append(param)
return [{
'params': no_decay,
'weight_decay': 0.
}, {
'params': decay,
'weight_decay': weight_decay
}]
# pylint: disable=protected-access
class NoamLR(torch.optim.lr_scheduler._LRScheduler):
def __init__(self, optimizer, warmup_steps=0.1, last_epoch=-1):
self.warmup_steps = float(warmup_steps)
super(NoamLR, self).__init__(optimizer, last_epoch)
def get_lr(self):
step = max(self.last_epoch, 1)
return [
base_lr * self.warmup_steps**0.5 *
min(step * self.warmup_steps**-1.5, step**-0.5)
for base_lr in self.base_lrs
]
def gradual_training_scheduler(global_step, config):
"""Setup the gradual training schedule wrt number
of active GPUs"""
num_gpus = torch.cuda.device_count()
if num_gpus == 0:
num_gpus = 1
new_values = None
# we set the scheduling wrt num_gpus
for values in config.gradual_training:
if global_step * num_gpus >= values[0]:
new_values = values
return new_values[1], new_values[2]
