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faster by calculating R in updown and using cached R in forward

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v0xie 2023-10-18 04:56:53 -07:00
parent 853e21d98e
commit eb01d7f0e0
1 changed files with 8 additions and 7 deletions
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15
extensions-builtin/Lora/network_oft.py
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@ -58,17 +58,18 @@ class NetworkModuleOFT(network.NetworkModule):
def calc_updown(self, orig_weight): def calc_updown(self, orig_weight):
# this works # this works
R = self.R # R = self.R
self.R = self.get_weight(self.multiplier())
# this causes major deepfrying i.e. just doesn't work # 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.R.to(orig_weight.device, dtype=orig_weight.dtype)
if orig_weight.dim() == 4: # if orig_weight.dim() == 4:
weight = torch.einsum("oihw, op -> pihw", orig_weight, R) # weight = torch.einsum("oihw, op -> pihw", orig_weight, R)
else: # else:
weight = torch.einsum("oi, op -> pi", orig_weight, R) # weight = torch.einsum("oi, op -> pi", orig_weight, R)
updown = orig_weight @ R updown = orig_weight @ self.R
output_shape = self.oft_blocks.shape output_shape = self.oft_blocks.shape
## this works ## this works