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920fd91 | 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 | import torch
class GeneralLoRALoader:
def __init__(self, device="cpu", torch_dtype=torch.float32):
self.device = device
self.torch_dtype = torch_dtype
def get_name_dict(self, lora_state_dict):
lora_name_dict = {}
for key in lora_state_dict:
if ".lora_up." in key:
lora_A_key = "lora_down"
lora_B_key = "lora_up"
else:
lora_A_key = "lora_A"
lora_B_key = "lora_B"
if lora_B_key not in key:
continue
keys = key.split(".")
if len(keys) > keys.index(lora_B_key) + 2:
keys.pop(keys.index(lora_B_key) + 1)
keys.pop(keys.index(lora_B_key))
if keys[0] == "diffusion_model":
keys.pop(0)
keys.pop(-1)
target_name = ".".join(keys)
lora_name_dict[target_name] = (key, key.replace(lora_B_key, lora_A_key))
return lora_name_dict
def convert_state_dict(self, state_dict, suffix=".weight"):
name_dict = self.get_name_dict(state_dict)
state_dict_ = {}
for name in name_dict:
weight_up = state_dict[name_dict[name][0]]
weight_down = state_dict[name_dict[name][1]]
state_dict_[name + f".lora_B{suffix}"] = weight_up
state_dict_[name + f".lora_A{suffix}"] = weight_down
return state_dict_
def fuse_lora_to_base_model(self, model: torch.nn.Module, state_dict, alpha=1.0):
updated_num = 0
state_dict = self.convert_state_dict(state_dict)
lora_layer_names = set([i.replace(".lora_B.weight", "") for i in state_dict if i.endswith(".lora_B.weight")])
for name, module in model.named_modules():
if name in lora_layer_names:
weight_up = state_dict[name + ".lora_B.weight"].to(device=self.device, dtype=self.torch_dtype)
weight_down = state_dict[name + ".lora_A.weight"].to(device=self.device, dtype=self.torch_dtype)
if len(weight_up.shape) == 4:
weight_up = weight_up.squeeze(3).squeeze(2)
weight_down = weight_down.squeeze(3).squeeze(2)
weight_lora = alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3)
else:
weight_lora = alpha * torch.mm(weight_up, weight_down)
state_dict_base = module.state_dict()
state_dict_base["weight"] = state_dict_base["weight"].to(device=self.device, dtype=self.torch_dtype) + weight_lora
module.load_state_dict(state_dict_base)
updated_num += 1
print(f"{updated_num} tensors are fused by LoRA. Fused LoRA layers cannot be cleared by `pipe.clear_lora()`.")
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