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support fused_back_pass for prodigy-plus-schedule-free #1867

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support fused_back_pass for prodigy-plus-schedule-free #1867

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8cee727
support fused_back_pass for prodigy-plus-schedule-free
michP247 Jan 6, 2025
d0eba37
fix
michP247 Jan 6, 2025
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42 changes: 24 additions & 18 deletions flux_train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -381,6 +381,8 @@ def train(args):
optimizer_train_fn = lambda: None # dummy function
optimizer_eval_fn = lambda: None # dummy function
else:
if args.optimizer_type == "prodigyplus.ProdigyPlusScheduleFree" and args.fused_backward_pass:
args.optimizer_args.append("fused_back_pass=True")
_, _, optimizer = train_util.get_optimizer(args, trainable_params=params_to_optimize)
optimizer_train_fn, optimizer_eval_fn = train_util.get_optimizer_train_eval_fn(optimizer, args)

Expand Down Expand Up @@ -473,25 +475,29 @@ def train(args):
train_util.resume_from_local_or_hf_if_specified(accelerator, args)

if args.fused_backward_pass:
# use fused optimizer for backward pass: other optimizers will be supported in the future
import library.adafactor_fused

library.adafactor_fused.patch_adafactor_fused(optimizer)

for param_group, param_name_group in zip(optimizer.param_groups, param_names):
for parameter, param_name in zip(param_group["params"], param_name_group):
if parameter.requires_grad:

def create_grad_hook(p_name, p_group):
def grad_hook(tensor: torch.Tensor):
if accelerator.sync_gradients and args.max_grad_norm != 0.0:
accelerator.clip_grad_norm_(tensor, args.max_grad_norm)
optimizer.step_param(tensor, p_group)
tensor.grad = None

return grad_hook
if args.optimizer_type == "AdaFactor":
import library.adafactor_fused
library.adafactor_fused.patch_adafactor_fused(optimizer)
for param_group, param_name_group in zip(optimizer.param_groups, param_names):
for parameter, param_name in zip(param_group["params"], param_name_group):
if parameter.requires_grad:

parameter.register_post_accumulate_grad_hook(create_grad_hook(param_name, param_group))
def create_grad_hook(p_name, p_group):
def grad_hook(tensor: torch.Tensor):
if accelerator.sync_gradients and args.max_grad_norm != 0.0:
accelerator.clip_grad_norm_(tensor, args.max_grad_norm)
optimizer.step_param(tensor, p_group)
tensor.grad = None

return grad_hook
parameter.register_post_accumulate_grad_hook(create_grad_hook(param_name, param_group))
elif args.optimizer_type == "prodigyplus.ProdigyPlusScheduleFree":
# ProdigyPlus uses its internal fused_back_pass mechanism, pass for now
pass
else:
logger.warning(
f"Fused backward pass is not supported for optimizer type: {args.optimizer_type}. Ignoring."
)

elif args.blockwise_fused_optimizers:
# prepare for additional optimizers and lr schedulers
Expand Down
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