remove unicode (#955)
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@@ -26,7 +26,7 @@ def factorization(dimension: int, factor: int = -1) -> tuple[int, int]:
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In LoRA with Kroneckor Product, first value is a value for weight scale.
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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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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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Becuase of non-commutative property, A(kron)B != B(kron)A. Meaning of two matrices is slightly different.
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examples)
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examples)
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factor
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factor
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@@ -151,7 +151,7 @@ class LokrModule(ToolkitModuleMixin, nn.Module):
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torch.empty(shape[0][1], lora_dim))
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torch.empty(shape[0][1], lora_dim))
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self.lokr_w2_b = nn.Parameter(torch.empty(
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self.lokr_w2_b = nn.Parameter(torch.empty(
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lora_dim, shape[1][1]*shape[2]*shape[3]))
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lora_dim, shape[1][1]*shape[2]*shape[3]))
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# w1 ⊗ (w2_a x w2_b) = (a, b)⊗((c, dim)x(dim, d*k1*k2)) = (a, b)⊗(c, d*k1*k2) = (ac, bd*k1*k2)
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# w1 (kron) (w2_a x w2_b) = (a, b)(kron)((c, dim)x(dim, d*k1*k2)) = (a, b)(kron)(c, d*k1*k2) = (ac, bd*k1*k2)
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self.op = F.conv2d
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self.op = F.conv2d
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self.extra_args = {
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self.extra_args = {
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@@ -191,7 +191,7 @@ class LokrModule(ToolkitModuleMixin, nn.Module):
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torch.empty(shape[0][1], lora_dim))
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torch.empty(shape[0][1], lora_dim))
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self.lokr_w2_b = nn.Parameter(
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self.lokr_w2_b = nn.Parameter(
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torch.empty(lora_dim, shape[1][1]))
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torch.empty(lora_dim, shape[1][1]))
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# w1 ⊗ (w2_a x w2_b) = (a, b)⊗((c, dim)x(dim, d)) = (a, b)⊗(c, d) = (ac, bd)
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# w1 (kron) (w2_a x w2_b) = (a, b)(kron)((c, dim)x(dim, d)) = (a, b)(kron)(c, d) = (ac, bd)
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else:
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else:
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self.use_w2 = True
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self.use_w2 = True
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self.lokr_w2 = nn.Parameter(
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self.lokr_w2 = nn.Parameter(
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