import torch def make_weight_cp(t, wa, wb): temp = torch.einsum('i j k l, j r -> i r k l', t, wb) return torch.einsum('i j k l, i r -> r j k l', temp, wa) def rebuild_conventional(up, down, shape, dyn_dim=None): up = up.reshape(up.size(0), -1) down = down.reshape(down.size(0), -1) if dyn_dim is not None: up = up[:, :dyn_dim] down = down[:dyn_dim, :] return (up @ down).reshape(shape) def rebuild_cp_decomposition(up, down, mid): up = up.reshape(up.size(0), -1) down = down.reshape(down.size(0), -1) return torch.einsum('n m k l, i n, m j -> i j k l', mid, up, down) # copied from https://github.com/KohakuBlueleaf/LyCORIS/blob/dev/lycoris/modules/lokr.py def factorization(dimension: int, factor:int=-1) -> tuple[int, int]: ''' return a tuple of two value of input dimension decomposed by the number closest to factor second value is higher or equal than first value. In LoRA with Kroneckor Product, first value is a value for weight scale. secon value is a value for weight. Because of non-commutative property, A⊗B ≠ B⊗A. Meaning of two matrices is slightly different. examples) factor -1 2 4 8 16 ... 127 -> 1, 127 127 -> 1, 127 127 -> 1, 127 127 -> 1, 127 127 -> 1, 127 128 -> 8, 16 128 -> 2, 64 128 -> 4, 32 128 -> 8, 16 128 -> 8, 16 250 -> 10, 25 250 -> 2, 125 250 -> 2, 125 250 -> 5, 50 250 -> 10, 25 360 -> 8, 45 360 -> 2, 180 360 -> 4, 90 360 -> 8, 45 360 -> 12, 30 512 -> 16, 32 512 -> 2, 256 512 -> 4, 128 512 -> 8, 64 512 -> 16, 32 1024 -> 32, 32 1024 -> 2, 512 1024 -> 4, 256 1024 -> 8, 128 1024 -> 16, 64 ''' if factor > 0 and (dimension % factor) == 0: m = factor n = dimension // factor if m > n: n, m = m, n return m, n if factor < 0: factor = dimension m, n = 1, dimension length = m + n while m length or new_m>factor: break else: m, n = new_m, new_n if m > n: n, m = m, n return m, n