diff options
| author | v0xie <28695009+v0xie@users.noreply.github.com> | 2023-10-21 13:43:31 -0700 |
|---|---|---|
| committer | v0xie <28695009+v0xie@users.noreply.github.com> | 2023-10-21 13:43:31 -0700 |
| commit | 2d8c894b274d60a3e3563a2ace23c4ebcea9e652 (patch) | |
| tree | d812648a1cf56624fe9fcb6b6e6f2975f79aca28 /extensions-builtin | |
| parent | 0550659ce6e1c37d1ab05cb8a2cb31d499fa552f (diff) | |
refactor: use forward hook instead of custom forward
Diffstat (limited to 'extensions-builtin')
| -rw-r--r-- | extensions-builtin/Lora/network_oft.py | 33 |
1 files changed, 24 insertions, 9 deletions
diff --git a/extensions-builtin/Lora/network_oft.py b/extensions-builtin/Lora/network_oft.py index 4e8382c1..8e561ab0 100644 --- a/extensions-builtin/Lora/network_oft.py +++ b/extensions-builtin/Lora/network_oft.py @@ -36,9 +36,11 @@ class NetworkModuleOFT(network.NetworkModule): # 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 + #self.org_module[0].forward = self.forward + self.org_module[0].register_forward_hook(self.forward_hook) def get_weight(self, oft_blocks, multiplier=None): + self.constraint = self.constraint.to(oft_blocks.device, dtype=oft_blocks.dtype) 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) @@ -66,14 +68,10 @@ class NetworkModuleOFT(network.NetworkModule): output_shape = self.oft_blocks.shape 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) + + def forward_hook(self, module, args, output): + #print(f'Forward hook in {self.network_key} called') + x = output R = self.R.to(x.device, dtype=x.dtype) if x.dim() == 4: @@ -83,3 +81,20 @@ class NetworkModuleOFT(network.NetworkModule): else: x = torch.matmul(x, R) return x + + # 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) + # x = x.permute(0, 3, 1, 2) + # else: + # x = torch.matmul(x, R) + # return x |