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faster by using cached R in forward

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This commit is contained in:
v0xie 2023-10-18 04:27:44 -07:00
parent 1c6efdbba7
commit 853e21d98e
1 changed files with 14 additions and 3 deletions
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17
extensions-builtin/Lora/network_oft.py
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@ -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)