Skip to content

Add DoRA (weight-decompose) support for LoRA/LoHa/LoKr #15160

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Mar 8, 2024
+15 −0
Merged

Add DoRA (weight-decompose) support for LoRA/LoHa/LoKr #15160

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
15 changes: 15 additions & 0 deletions extensions-builtin/Lora/network.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -146,6 +146,9 @@ def __init__(self, net: Network, weights: NetworkWeights):
self.alpha = weights.w["alpha"].item() if "alpha" in weights.w else None
self.scale = weights.w["scale"].item() if "scale" in weights.w else None

self.dora_scale = weights.w["dora_scale"] if "dora_scale" in weights.w else None
self.dora_mean_dim = tuple(i for i in range(len(self.shape)) if i != 1)

def multiplier(self):
if 'transformer' in self.sd_key[:20]:
return self.network.te_multiplier
Expand All @@ -160,6 +163,15 @@ def calc_scale(self):

return 1.0

def apply_weight_decompose(self, updown, orig_weight):
orig_weight = orig_weight.to(updown)
merged_scale1 = updown + orig_weight
dora_merged = (
merged_scale1 / merged_scale1(dim=self.dora_mean_dim, keepdim=True) * self.dora_scale
)
final_updown = dora_merged - orig_weight
return final_updown

def finalize_updown(self, updown, orig_weight, output_shape, ex_bias=None):
if self.bias is not None:
updown = updown.reshape(self.bias.shape)
Expand All @@ -175,6 +187,9 @@ def finalize_updown(self, updown, orig_weight, output_shape, ex_bias=None):
if ex_bias is not None:
ex_bias = ex_bias * self.multiplier()

if self.dora_scale is not None:
updown = self.apply_weight_decompose(updown, orig_weight)

return updown * self.calc_scale() * self.multiplier(), ex_bias

def calc_updown(self, target):
Expand Down