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Running on Zero
Running on Zero
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abd08dc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | import torch
def decomposite(tensor_A, tensor_B, rank):
dtype, device = tensor_A.dtype, tensor_A.device
weight = tensor_B @ tensor_A
U, S, V = torch.pca_lowrank(weight.float(), q=rank)
tensor_A = (V.T).to(dtype=dtype, device=device).contiguous()
tensor_B = (U @ torch.diag(S)).to(dtype=dtype, device=device).contiguous()
return tensor_A, tensor_B
def reset_lora_rank(lora, rank):
lora_merged = {}
keys = [i for i in lora.keys() if ".lora_A." in i]
for key in keys:
tensor_A = lora[key]
tensor_B = lora[key.replace(".lora_A.", ".lora_B.")]
tensor_A, tensor_B = decomposite(tensor_A, tensor_B, rank)
lora_merged[key] = tensor_A
lora_merged[key.replace(".lora_A.", ".lora_B.")] = tensor_B
return lora_merged |