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import numpy as np
import torch
def check_update(model, grad_clip, ignore_stopnet=False, amp_opt_params=None):
r"""Check model gradient against unexpected jumps and failures"""
skip_flag = False
if ignore_stopnet:
if not amp_opt_params:
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_(amp_opt_params, grad_clip)
else:
if not amp_opt_params:
grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip)
else:
grad_norm = torch.nn.utils.clip_grad_norm_(amp_opt_params, 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 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]
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