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refactor: remove used OFT functions

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v0xie 2023-10-22 08:54:24 -07:00
parent de8ee92ed8
commit 4a50c9638c
1 changed files with 10 additions and 72 deletions
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82
extensions-builtin/Lora/network_oft.py
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@ -29,98 +29,36 @@ class NetworkModuleOFT(network.NetworkModule):
self.block_size = self.out_dim // self.num_blocks self.block_size = self.out_dim // self.num_blocks
self.org_module: list[torch.Module] = [self.sd_module] self.org_module: list[torch.Module] = [self.sd_module]
#self.org_weight = self.org_module[0].weight.to(self.org_module[0].weight.device, copy=True)
init_multiplier = self.multiplier() * self.calc_scale() def merge_weight(self, R_weight, org_weight):
self.last_multiplier = init_multiplier R_weight = R_weight.to(org_weight.device, dtype=org_weight.dtype)
self.R = self.get_weight(self.oft_blocks, init_multiplier)
self.hooks = []
self.merged_weight = self.merge_weight()
#self.apply_to()
self.applied = False
self.merged = False
def merge_weight(self):
org_weight = self.org_module[0].weight
R = self.R.to(org_weight.device, dtype=org_weight.dtype)
if org_weight.dim() == 4: if org_weight.dim() == 4:
weight = torch.einsum("oihw, op -> pihw", org_weight, R) weight = torch.einsum("oihw, op -> pihw", org_weight, R_weight)
else: else:
weight = torch.einsum("oi, op -> pi", org_weight, R) weight = torch.einsum("oi, op -> pi", org_weight, R_weight)
return weight return weight
def replace_weight(self, new_weight):
org_sd = self.org_module[0].state_dict()
org_sd['weight'] = new_weight
self.org_module[0].load_state_dict(org_sd)
self.merged = True
def restore_weight(self):
pass
#org_sd = self.org_module[0].state_dict()
#org_sd['weight'] = self.org_weight
#self.org_module[0].load_state_dict(org_sd)
#self.merged = False
# FIXME: hook forward method of original linear, but how do we undo the hook when we are done?
def apply_to(self):
if not self.applied:
self.org_forward = self.org_module[0].forward
#self.org_module[0].forward = self.forward
prehook = self.org_module[0].register_forward_pre_hook(self.pre_forward_hook)
hook = self.org_module[0].register_forward_hook(self.forward_hook)
self.hooks.append(prehook)
self.hooks.append(hook)
self.applied = True
def remove_from(self):
if self.applied:
for hook in self.hooks:
hook.remove()
self.hooks = []
self.applied = False
def get_weight(self, oft_blocks, multiplier=None): def get_weight(self, oft_blocks, multiplier=None):
multiplier = multiplier.to(oft_blocks.device, dtype=oft_blocks.dtype)
constraint = self.constraint.to(oft_blocks.device, dtype=oft_blocks.dtype) constraint = self.constraint.to(oft_blocks.device, dtype=oft_blocks.dtype)
block_Q = oft_blocks - oft_blocks.transpose(1, 2) block_Q = oft_blocks - oft_blocks.transpose(1, 2)
norm_Q = torch.norm(block_Q.flatten()) norm_Q = torch.norm(block_Q.flatten())
new_norm_Q = torch.clamp(norm_Q, max=constraint) new_norm_Q = torch.clamp(norm_Q, max=constraint)
block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8)) block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8))
m_I = torch.eye(self.block_size, device=oft_blocks.device).unsqueeze(0).repeat(self.num_blocks, 1, 1) m_I = torch.eye(self.block_size, device=oft_blocks.device).unsqueeze(0).repeat(self.num_blocks, 1, 1)
block_R = torch.matmul(m_I + block_Q, (m_I - block_Q).inverse()) block_R = torch.matmul(m_I + block_Q, (m_I - block_Q).inverse())
block_R_weighted = multiplier * block_R + (1 - multiplier) * m_I block_R_weighted = multiplier * block_R + (1 - multiplier) * m_I
R = torch.block_diag(*block_R_weighted) R = torch.block_diag(*block_R_weighted)
return R return R
def calc_updown(self, orig_weight): def calc_updown(self, orig_weight):
if not self.applied: R = self.get_weight(self.oft_blocks, self.multiplier())
self.apply_to() merged_weight = self.merge_weight(R, orig_weight)
self.merged_weight = self.merged_weight.to(orig_weight.device, dtype=orig_weight.dtype) updown = merged_weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_weight
updown = torch.zeros_like(orig_weight, device=orig_weight.device, dtype=orig_weight.dtype)
output_shape = orig_weight.shape output_shape = orig_weight.shape
orig_weight = self.merged_weight orig_weight = orig_weight
#output_shape = self.oft_blocks.shape
return self.finalize_updown(updown, orig_weight, output_shape) return self.finalize_updown(updown, orig_weight, output_shape)
def pre_forward_hook(self, module, input):
#if not self.applied:
# self.apply_to()
multiplier = self.multiplier() * self.calc_scale()
if not multiplier==self.last_multiplier or not self.merged:
self.R = self.get_weight(self.oft_blocks, multiplier)
self.last_multiplier = multiplier
self.merged_weight = self.merge_weight()
self.replace_weight(self.merged_weight)
def forward_hook(self, module, args, output):
pass