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fix: get boft params from weight shape
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@ -1,6 +1,6 @@
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import torch
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import network
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from lyco_helpers import factorization, butterfly_factor
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from lyco_helpers import factorization
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from einops import rearrange
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@ -37,10 +37,8 @@ class NetworkModuleOFT(network.NetworkModule):
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self.dim = self.oft_blocks.shape[1] # (num_blocks, block_size, block_size)
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self.is_boft = False
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if "boft" in weights.w.keys():
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if weights.w["oft_diag"].dim() == 4:
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self.is_boft = True
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self.boft_b = weights.w["boft_b"]
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self.boft_m = weights.w["boft_m"]
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is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear]
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is_conv = type(self.sd_module) in [torch.nn.Conv2d]
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@ -59,7 +57,11 @@ class NetworkModuleOFT(network.NetworkModule):
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self.block_size = self.out_dim // self.dim
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elif self.is_boft:
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self.constraint = None
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self.block_size, self.block_num = butterfly_factor(self.out_dim, self.dim)
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self.boft_m = weights.w["oft_diag"].shape[0]
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self.block_num = weights.w["oft_diag"].shape[1]
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self.block_size = weights.w["oft_diag"].shape[2]
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self.boft_b = self.block_size
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#self.block_size, self.block_num = butterfly_factor(self.out_dim, self.dim)
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else:
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self.constraint = None
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self.block_size, self.num_blocks = factorization(self.out_dim, self.dim)
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@ -88,8 +90,8 @@ class NetworkModuleOFT(network.NetworkModule):
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merged_weight = rearrange(merged_weight, 'k m ... -> (k m) ...')
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else:
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scale = 1.0
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m = self.boft_m.to(device=oft_blocks.device, dtype=oft_blocks.dtype)
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b = self.boft_b.to(device=oft_blocks.device, dtype=oft_blocks.dtype)
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m = self.boft_m
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b = self.boft_b
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r_b = b // 2
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inp = orig_weight
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for i in range(m):
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