fix: get boft params from weight shape

extensions-builtin/Lora/network_oft.py

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