File size: 1,785 Bytes
a1427df | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | from torch import optim as optim
def build_optimizer(config, model):
"""
Build optimizer, set weight decay of normalization to 0 by default.
"""
skip = {}
skip_keywords = {}
if hasattr(model, "no_weight_decay"):
skip = model.no_weight_decay()
if hasattr(model, "no_weight_decay_keywords"):
skip_keywords = model.no_weight_decay_keywords()
parameters = set_weight_decay(model, skip, skip_keywords)
opt_lower = config.optimizer.lower()
optimizer = None
if opt_lower == "sgd":
optimizer = optim.SGD(
parameters,
momentum=config.momentum,
nesterov=True,
lr=config.lr,
weight_decay=config.weight_decay,
)
elif opt_lower == "adamw":
optimizer = optim.AdamW(
parameters,
eps=config.eps,
betas=config.betas,
lr=config.lr,
weight_decay=config.weight_decay,
)
return optimizer
def set_weight_decay(model, skip_list=(), skip_keywords=()):
has_decay = []
no_decay = []
for name, param in model.named_parameters():
if not param.requires_grad:
continue # frozen weights
if (
len(param.shape) == 1
or name.endswith(".bias")
or (name in skip_list)
or check_keywords_in_name(name, skip_keywords)
):
no_decay.append(param)
# print(f"{name} has no weight decay")
else:
has_decay.append(param)
return [{"params": has_decay}, {"params": no_decay, "weight_decay": 0.0}]
def check_keywords_in_name(name, keywords=()):
isin = False
for keyword in keywords:
if keyword in name:
isin = True
return isin
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