faster by using cached R in forward

1 files changed, 14 insertions, 3 deletions

diff --git a/extensions-builtin/Lora/network_oft.py b/extensions-builtin/Lora/network_oft.py
index f085eca5..68efb1db 100644
--- a/extensions-builtin/Lora/network_oft.py
+++ b/extensions-builtin/Lora/network_oft.py
@@ -57,21 +57,32 @@ class NetworkModuleOFT(network.NetworkModule):
         return R
 
     def calc_updown(self, orig_weight):
+        # this works
         R = self.R
+
+        # this causes major deepfrying i.e. just doesn't work
+        # R = self.R.to(orig_weight.device, dtype=orig_weight.dtype)
+
         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 @ R
-        output_shape = [orig_weight.size(0), R.size(1)]
-        #output_shape = [R.size(0), orig_weight.size(1)]
+        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
-        R = self.get_weight().to(x.device, dtype=x.dtype)
+        #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)