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