File size: 3,492 Bytes
7e3773e | 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 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 | from torch import optim
import logging
class EmptyOptimizer:
def __init__(self):
self.param_groups = []
def step(self, *args, **kwargs):
pass
def state_dict(self):
return dict()
def load_state_dict(self, *args, **kwargs):
pass
def zero_grad(self):
pass
def build_optimizer(args, model):
def exclude(
n, p): return p.ndim < 2 or "bn" in n or "ln" in n or "bias" in n or 'logit_scale' in n
def include(n, p): return not exclude(n, p)
named_parameters = list(model.named_parameters())
# we create three optimizer for image encode, text encoder, and jointly part
model_parts = [
list(model.image_named_params()),
list(model.text_named_params()),
list(model.joint_named_params()),
]
cnt1 = sum(v.numel() for k, v in named_parameters if v.requires_grad)
cnt2 = sum(sum(v.numel() for k, v in part if v.requires_grad)
for part in model_parts)
assert cnt1 == cnt2, f"cnt1 {cnt1} != cnt2 {cnt2}"
optimizer = []
part_names = ['image', 'text', 'joint']
assert len(model_parts) == len(part_names)
for name, named_parameters in zip(part_names, model_parts):
gain_or_bias_params = [p for n, p in named_parameters if exclude(
n, p) and p.requires_grad and "l0_module" not in n]
rest_params = [p for n, p in named_parameters if include(
n, p) and p.requires_grad and "l0_module" not in n]
params_groups = [
{"params": gain_or_bias_params, "weight_decay": 0.},
{"params": rest_params, "weight_decay": args.wd},
]
num_opt_params = 0
for pg in params_groups:
num_opt_params += sum(p.numel() for p in pg['params'])
logging.info(f'number of optimizer ({name}) params: {num_opt_params}')
if num_opt_params > 0:
optimizer_i = optim.AdamW(
params_groups,
lr=args.lr,
betas=(args.beta1, args.beta2),
eps=args.eps,
)
else:
optimizer_i = EmptyOptimizer()
optimizer.append(optimizer_i)
if args.prune_image or args.prune_text:
lr_l0 = 0.02
lr_lamda = args.l0lr
l0_params = []
# add l0 optimizer
if args.prune_image:
l0_params.extend([
{
"params": [p for n, p in model.image_named_params() if p.requires_grad and "lambda" not in n and "l0_module" in n],
"weight_decay": 0.0,
"lr": lr_l0
}, {
"params": [p for n, p in model.image_named_params() if p.requires_grad and "lambda" in n and "l0_module" in n],
"weight_decay": 0.0,
"lr": lr_lamda
}])
if args.prune_text:
l0_params.extend([
{
"params": [p for n, p in model.text_named_params() if p.requires_grad and "lambda" not in n and "l0_module" in n],
"weight_decay": 0.0,
"lr": lr_l0
}, {
"params": [p for n, p in model.text_named_params() if p.requires_grad and "lambda" in n and "l0_module" in n],
"weight_decay": 0.0,
"lr": lr_lamda
}])
l0_optimizer = optim.AdamW(l0_params)
optimizer.append(l0_optimizer)
return optimizer
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