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DoRA support #608

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DoRA support #608

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14 changes: 10 additions & 4 deletions ldm_patched/modules/lora.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,6 +24,12 @@ def load_lora(lora, to_load):
alpha = lora[alpha_name].item()
loaded_keys.add(alpha_name)

dora_scale_name = "{}.dora_scale".format(x)
dora_scale = None
if dora_scale_name in lora.keys():
dora_scale = lora[dora_scale_name]
loaded_keys.add(dora_scale_name)

regular_lora = "{}.lora_up.weight".format(x)
diffusers_lora = "{}_lora.up.weight".format(x)
transformers_lora = "{}.lora_linear_layer.up.weight".format(x)
Expand All @@ -47,7 +53,7 @@ def load_lora(lora, to_load):
if mid_name is not None and mid_name in lora.keys():
mid = lora[mid_name]
loaded_keys.add(mid_name)
patch_dict[to_load[x]] = ("lora", (lora[A_name], lora[B_name], alpha, mid))
patch_dict[to_load[x]] = ("lora", (lora[A_name], lora[B_name], alpha, mid, dora_scale))
loaded_keys.add(A_name)
loaded_keys.add(B_name)

Expand All @@ -68,7 +74,7 @@ def load_lora(lora, to_load):
loaded_keys.add(hada_t1_name)
loaded_keys.add(hada_t2_name)

patch_dict[to_load[x]] = ("loha", (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2))
patch_dict[to_load[x]] = ("loha", (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2, dora_scale))
loaded_keys.add(hada_w1_a_name)
loaded_keys.add(hada_w1_b_name)
loaded_keys.add(hada_w2_a_name)
Expand Down Expand Up @@ -120,15 +126,15 @@ def load_lora(lora, to_load):
loaded_keys.add(lokr_t2_name)

if (lokr_w1 is not None) or (lokr_w2 is not None) or (lokr_w1_a is not None) or (lokr_w2_a is not None):
patch_dict[to_load[x]] = ("lokr", (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2))
patch_dict[to_load[x]] = ("lokr", (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2, dora_scale))

#glora
a1_name = "{}.a1.weight".format(x)
a2_name = "{}.a2.weight".format(x)
b1_name = "{}.b1.weight".format(x)
b2_name = "{}.b2.weight".format(x)
if a1_name in lora:
patch_dict[to_load[x]] = ("glora", (lora[a1_name], lora[a2_name], lora[b1_name], lora[b2_name], alpha))
patch_dict[to_load[x]] = ("glora", (lora[a1_name], lora[a2_name], lora[b1_name], lora[b2_name], alpha, dora_scale))
loaded_keys.add(a1_name)
loaded_keys.add(a2_name)
loaded_keys.add(b1_name)
Expand Down
23 changes: 23 additions & 0 deletions ldm_patched/modules/model_patcher.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,16 @@

extra_weight_calculators = {}

def apply_weight_decompose(dora_scale, weight):
weight_norm = (
weight.transpose(0, 1)
.reshape(weight.shape[1], -1)
.norm(dim=1, keepdim=True)
.reshape(weight.shape[1], *[1] * (weight.dim() - 1))
.transpose(0, 1)
)

return weight * (dora_scale / weight_norm)

class ModelPatcher:
def __init__(self, model, load_device, offload_device, size=0, current_device=None, weight_inplace_update=False):
Expand Down Expand Up @@ -264,6 +274,7 @@ def calculate_weight(self, patches, weight, key):
elif patch_type == "lora": #lora/locon
mat1 = ldm_patched.modules.model_management.cast_to_device(v[0], weight.device, torch.float32)
mat2 = ldm_patched.modules.model_management.cast_to_device(v[1], weight.device, torch.float32)
dora_scale = v[4]
if v[2] is not None:
alpha *= v[2] / mat2.shape[0]
if v[3] is not None:
Expand All @@ -273,6 +284,8 @@ def calculate_weight(self, patches, weight, key):
mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1), mat3.transpose(0, 1).flatten(start_dim=1)).reshape(final_shape).transpose(0, 1)
try:
weight += (alpha * torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1))).reshape(weight.shape).type(weight.dtype)
if dora_scale is not None:
weight = apply_weight_decompose(ldm_patched.modules.model_management.cast_to_device(dora_scale, weight.device, torch.float32), weight)
except Exception as e:
print("ERROR", key, e)
elif patch_type == "lokr":
Expand All @@ -283,6 +296,7 @@ def calculate_weight(self, patches, weight, key):
w2_a = v[5]
w2_b = v[6]
t2 = v[7]
dora_scale = v[8]
dim = None

if w1 is None:
Expand Down Expand Up @@ -312,6 +326,8 @@ def calculate_weight(self, patches, weight, key):

try:
weight += alpha * torch.kron(w1, w2).reshape(weight.shape).type(weight.dtype)
if dora_scale is not None:
weight = apply_weight_decompose(ldm_patched.modules.model_management.cast_to_device(dora_scale, weight.device, torch.float32), weight)
except Exception as e:
print("ERROR", key, e)
elif patch_type == "loha":
Expand All @@ -321,6 +337,7 @@ def calculate_weight(self, patches, weight, key):
alpha *= v[2] / w1b.shape[0]
w2a = v[3]
w2b = v[4]
dora_scale = v[7]
if v[5] is not None: #cp decomposition
t1 = v[5]
t2 = v[6]
Expand All @@ -341,18 +358,24 @@ def calculate_weight(self, patches, weight, key):

try:
weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype)
if dora_scale is not None:
weight = apply_weight_decompose(ldm_patched.modules.cast_to_device(dora_scale, weight.device, torch.float32), weight)
except Exception as e:
print("ERROR", key, e)
elif patch_type == "glora":
if v[4] is not None:
alpha *= v[4] / v[0].shape[0]

dora_scale = v[5]

a1 = ldm_patched.modules.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, torch.float32)
a2 = ldm_patched.modules.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, torch.float32)
b1 = ldm_patched.modules.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, torch.float32)
b2 = ldm_patched.modules.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, torch.float32)

weight += ((torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)) * alpha).reshape(weight.shape).type(weight.dtype)
if dora_scale is not None:
weight = apply_weight_decompose(ldm_patched.modules.model_management.cast_to_device(dora_scale, weight.device, torch.float32), weight)
elif patch_type in extra_weight_calculators:
weight = extra_weight_calculators[patch_type](weight, alpha, v)
else:
Expand Down
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