refactor: fix constraint, re-use get_weight

extensions-builtin/Lora/network_oft.py

@@ -9,7 +9,7 @@ class ModuleTypeOFT(network.ModuleType):
 
         return None
 
-# adapted from https://github.com/kohya-ss/sd-scripts/blob/main/networks/oft.py
+# adapted from kohya's implementation https://github.com/kohya-ss/sd-scripts/blob/main/networks/oft.py
 class NetworkModuleOFT(network.NetworkModule):
     def __init__(self,  net: network.Network, weights: network.NetworkWeights):
 
@@ -17,7 +17,6 @@ class NetworkModuleOFT(network.NetworkModule):
 
         self.oft_blocks = weights.w["oft_blocks"]
         self.alpha = weights.w["alpha"]
-
         self.dim = self.oft_blocks.shape[0]
         self.num_blocks = self.dim
 
@@ -26,64 +25,57 @@ class NetworkModuleOFT(network.NetworkModule):
         elif "Conv" in self.sd_module.__class__.__name__:
             self.out_dim = self.sd_module.out_channels
 
-        self.constraint = self.alpha
-        #self.constraint = self.alpha * self.out_dim
+        self.constraint = self.alpha * self.out_dim
         self.block_size = self.out_dim // self.num_blocks
 
         self.org_module: list[torch.Module] = [self.sd_module]
-
-        self.R = self.get_weight()
-
+        self.R = self.get_weight(self.oft_blocks)
         self.apply_to()
 
     # replace forward method of original linear rather than replacing the module
+    # how do we revert this to unload the weights?
     def apply_to(self):
         self.org_forward = self.org_module[0].forward
         self.org_module[0].forward = self.forward
     
-    def get_weight(self, multiplier=None):
-        if not multiplier:
-            multiplier = self.multiplier()
-        block_Q = self.oft_blocks - self.oft_blocks.transpose(1, 2)
+    def get_weight(self, oft_blocks, multiplier=None):
+        block_Q = oft_blocks - oft_blocks.transpose(1, 2)
         norm_Q = torch.norm(block_Q.flatten())
         new_norm_Q = torch.clamp(norm_Q, max=self.constraint)
         block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8))
         I = torch.eye(self.block_size, device=self.oft_blocks.device).unsqueeze(0).repeat(self.num_blocks, 1, 1)
         block_R = torch.matmul(I + block_Q, (I - block_Q).inverse())
-
-        block_R_weighted = multiplier * block_R + (1 - multiplier) * I
-        R = torch.block_diag(*block_R_weighted)
+        #block_R_weighted = multiplier * block_R + (1 - multiplier) * I
+        #R = torch.block_diag(*block_R_weighted)
+        R = torch.block_diag(*block_R)
 
         return R
 
     def calc_updown(self, orig_weight):
-        # this works
-        # R = self.R
-        self.R = self.get_weight(self.multiplier())
+        oft_blocks = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
 
-        # sending R to device causes major deepfrying i.e. just doesn't work
-        # R = self.R.to(orig_weight.device, dtype=orig_weight.dtype)
+        R = self.get_weight(oft_blocks)
+        self.R = R
 
         # if orig_weight.dim() == 4:
         #     weight = torch.einsum("oihw, op -> pihw", orig_weight, R)
         # else:
         #     weight = torch.einsum("oi, op -> pi", orig_weight, R)
 
-        updown = orig_weight @ self.R
+        updown = orig_weight @ R
         output_shape = self.oft_blocks.shape
 
-        ## this works
-        # updown = orig_weight @ R
-        # output_shape = [orig_weight.size(0), R.size(1)]
-
         return self.finalize_updown(updown, orig_weight, output_shape)
     
     def forward(self, x, y=None):
         x = self.org_forward(x)
         if self.multiplier() == 0.0:
             return x
+
+        # calculating R here is excruciatingly slow
         #R = self.get_weight().to(x.device, dtype=x.dtype)
         R = self.R.to(x.device, dtype=x.dtype)
+
         if x.dim() == 4:
             x = x.permute(0, 2, 3, 1)
             x = torch.matmul(x, R)