Loading extensions-builtin/Lora/network_oft.py +26 −82 Original line number Diff line number Diff line Loading @@ -11,8 +11,8 @@ class ModuleTypeOFT(network.ModuleType): return None # adapted from kohya-ss' implementation https://github.com/kohya-ss/sd-scripts/blob/main/networks/oft.py # and KohakuBlueleaf's implementation https://github.com/KohakuBlueleaf/LyCORIS/blob/dev/lycoris/modules/diag_oft.py # Supports both kohya-ss' implementation of COFT https://github.com/kohya-ss/sd-scripts/blob/main/networks/oft.py # and KohakuBlueleaf's implementation of OFT/COFT https://github.com/KohakuBlueleaf/LyCORIS/blob/dev/lycoris/modules/diag_oft.py class NetworkModuleOFT(network.NetworkModule): def __init__(self, net: network.Network, weights: network.NetworkWeights): Loading @@ -25,88 +25,42 @@ class NetworkModuleOFT(network.NetworkModule): if "oft_blocks" in weights.w.keys(): self.is_kohya = True self.oft_blocks = weights.w["oft_blocks"] # (num_blocks, block_size, block_size) self.alpha = weights.w["alpha"] self.alpha = weights.w["alpha"] # alpha is constraint self.dim = self.oft_blocks.shape[0] # lora dim #self.oft_blocks = rearrange(self.oft_blocks, 'k m ... -> (k m) ...') # LyCORIS elif "oft_diag" in weights.w.keys(): self.is_kohya = False self.oft_blocks = weights.w["oft_diag"] # (num_blocks, block_size, block_size) # alpha is rank if alpha is 0 or None if self.alpha is None: pass self.dim = self.oft_blocks.shape[1] # FIXME: almost certainly incorrect, assumes tensor is shape [*, m, n] else: raise ValueError("oft_blocks or oft_diag must be in weights dict") self.oft_blocks = weights.w["oft_diag"] # self.alpha is unused self.dim = self.oft_blocks.shape[1] # (num_blocks, block_size, block_size) is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear] is_conv = type(self.sd_module) in [torch.nn.Conv2d] is_other_linear = type(self.sd_module) in [torch.nn.MultiheadAttention] is_other_linear = type(self.sd_module) in [torch.nn.MultiheadAttention] # unsupported if is_linear: self.out_dim = self.sd_module.out_features elif is_other_linear: self.out_dim = self.sd_module.embed_dim elif is_conv: self.out_dim = self.sd_module.out_channels else: raise ValueError("sd_module must be Linear or Conv") elif is_other_linear: self.out_dim = self.sd_module.embed_dim if self.is_kohya: self.constraint = self.alpha * self.out_dim self.num_blocks, self.block_size = factorization(self.out_dim, self.dim) self.num_blocks = self.dim self.block_size = self.out_dim // self.dim else: self.constraint = None self.block_size, self.num_blocks = factorization(self.out_dim, self.dim) def merge_weight(self, R_weight, org_weight): R_weight = R_weight.to(org_weight.device, dtype=org_weight.dtype) if org_weight.dim() == 4: weight = torch.einsum("oihw, op -> pihw", org_weight, R_weight) else: weight = torch.einsum("oi, op -> pi", org_weight, R_weight) return weight def get_weight(self, oft_blocks, multiplier=None): if self.constraint is not None: 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()) if self.constraint is not None: new_norm_Q = torch.clamp(norm_Q, max=constraint) else: new_norm_Q = norm_Q block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8)) m_I = torch.eye(self.num_blocks, device=oft_blocks.device).unsqueeze(0).repeat(self.block_size, 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_weighted = multiplier * block_R + (1 - multiplier) * m_I R = torch.block_diag(*block_R_weighted) return R def calc_updown_kohya(self, orig_weight, multiplier): R = self.get_weight(self.oft_blocks, multiplier) merged_weight = self.merge_weight(R, orig_weight) updown = merged_weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_weight output_shape = orig_weight.shape orig_weight = orig_weight return self.finalize_updown(updown, orig_weight, output_shape) def calc_updown_kb(self, orig_weight, multiplier): is_other_linear = type(self.sd_module) in [torch.nn.MultiheadAttention] if not is_other_linear: oft_blocks = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype) # ensure skew-symmetric matrix oft_blocks = oft_blocks - oft_blocks.transpose(1, 2) oft_blocks = oft_blocks - oft_blocks.transpose(1, 2) # ensure skew-symmetric orthogonal matrix R = oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype) R = R * multiplier + torch.eye(self.block_size, device=orig_weight.device) # This errors out for MultiheadAttention, might need to be handled up-stream merged_weight = rearrange(orig_weight, '(k n) ... -> k n ...', k=self.num_blocks, n=self.block_size) merged_weight = torch.einsum( 'k n m, k n ... -> k m ...', Loading @@ -117,25 +71,15 @@ class NetworkModuleOFT(network.NetworkModule): updown = merged_weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_weight output_shape = orig_weight.shape else: # FIXME: skip MultiheadAttention for now #up = self.lin_module.weight.to(orig_weight.device, dtype=orig_weight.dtype) updown = torch.zeros([orig_weight.shape[1], orig_weight.shape[1]], device=orig_weight.device, dtype=orig_weight.dtype) output_shape = (orig_weight.shape[1], orig_weight.shape[1]) return self.finalize_updown(updown, orig_weight, output_shape) def calc_updown(self, orig_weight): # if alpha is a very small number as in coft, calc_scale will return a almost zero number so we ignore it #multiplier = self.multiplier() * self.calc_scale() # if alpha is a very small number as in coft, calc_scale() will return a almost zero number so we ignore it multiplier = self.multiplier() return self.calc_updown_kb(orig_weight, multiplier) # override to remove the multiplier/scale factor; it's already multiplied in get_weight def finalize_updown(self, updown, orig_weight, output_shape, ex_bias=None): #return super().finalize_updown(updown, orig_weight, output_shape, ex_bias) if self.bias is not None: updown = updown.reshape(self.bias.shape) updown += self.bias.to(orig_weight.device, dtype=orig_weight.dtype) Loading Loading
extensions-builtin/Lora/network_oft.py +26 −82 Original line number Diff line number Diff line Loading @@ -11,8 +11,8 @@ class ModuleTypeOFT(network.ModuleType): return None # adapted from kohya-ss' implementation https://github.com/kohya-ss/sd-scripts/blob/main/networks/oft.py # and KohakuBlueleaf's implementation https://github.com/KohakuBlueleaf/LyCORIS/blob/dev/lycoris/modules/diag_oft.py # Supports both kohya-ss' implementation of COFT https://github.com/kohya-ss/sd-scripts/blob/main/networks/oft.py # and KohakuBlueleaf's implementation of OFT/COFT https://github.com/KohakuBlueleaf/LyCORIS/blob/dev/lycoris/modules/diag_oft.py class NetworkModuleOFT(network.NetworkModule): def __init__(self, net: network.Network, weights: network.NetworkWeights): Loading @@ -25,88 +25,42 @@ class NetworkModuleOFT(network.NetworkModule): if "oft_blocks" in weights.w.keys(): self.is_kohya = True self.oft_blocks = weights.w["oft_blocks"] # (num_blocks, block_size, block_size) self.alpha = weights.w["alpha"] self.alpha = weights.w["alpha"] # alpha is constraint self.dim = self.oft_blocks.shape[0] # lora dim #self.oft_blocks = rearrange(self.oft_blocks, 'k m ... -> (k m) ...') # LyCORIS elif "oft_diag" in weights.w.keys(): self.is_kohya = False self.oft_blocks = weights.w["oft_diag"] # (num_blocks, block_size, block_size) # alpha is rank if alpha is 0 or None if self.alpha is None: pass self.dim = self.oft_blocks.shape[1] # FIXME: almost certainly incorrect, assumes tensor is shape [*, m, n] else: raise ValueError("oft_blocks or oft_diag must be in weights dict") self.oft_blocks = weights.w["oft_diag"] # self.alpha is unused self.dim = self.oft_blocks.shape[1] # (num_blocks, block_size, block_size) is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear] is_conv = type(self.sd_module) in [torch.nn.Conv2d] is_other_linear = type(self.sd_module) in [torch.nn.MultiheadAttention] is_other_linear = type(self.sd_module) in [torch.nn.MultiheadAttention] # unsupported if is_linear: self.out_dim = self.sd_module.out_features elif is_other_linear: self.out_dim = self.sd_module.embed_dim elif is_conv: self.out_dim = self.sd_module.out_channels else: raise ValueError("sd_module must be Linear or Conv") elif is_other_linear: self.out_dim = self.sd_module.embed_dim if self.is_kohya: self.constraint = self.alpha * self.out_dim self.num_blocks, self.block_size = factorization(self.out_dim, self.dim) self.num_blocks = self.dim self.block_size = self.out_dim // self.dim else: self.constraint = None self.block_size, self.num_blocks = factorization(self.out_dim, self.dim) def merge_weight(self, R_weight, org_weight): R_weight = R_weight.to(org_weight.device, dtype=org_weight.dtype) if org_weight.dim() == 4: weight = torch.einsum("oihw, op -> pihw", org_weight, R_weight) else: weight = torch.einsum("oi, op -> pi", org_weight, R_weight) return weight def get_weight(self, oft_blocks, multiplier=None): if self.constraint is not None: 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()) if self.constraint is not None: new_norm_Q = torch.clamp(norm_Q, max=constraint) else: new_norm_Q = norm_Q block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8)) m_I = torch.eye(self.num_blocks, device=oft_blocks.device).unsqueeze(0).repeat(self.block_size, 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_weighted = multiplier * block_R + (1 - multiplier) * m_I R = torch.block_diag(*block_R_weighted) return R def calc_updown_kohya(self, orig_weight, multiplier): R = self.get_weight(self.oft_blocks, multiplier) merged_weight = self.merge_weight(R, orig_weight) updown = merged_weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_weight output_shape = orig_weight.shape orig_weight = orig_weight return self.finalize_updown(updown, orig_weight, output_shape) def calc_updown_kb(self, orig_weight, multiplier): is_other_linear = type(self.sd_module) in [torch.nn.MultiheadAttention] if not is_other_linear: oft_blocks = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype) # ensure skew-symmetric matrix oft_blocks = oft_blocks - oft_blocks.transpose(1, 2) oft_blocks = oft_blocks - oft_blocks.transpose(1, 2) # ensure skew-symmetric orthogonal matrix R = oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype) R = R * multiplier + torch.eye(self.block_size, device=orig_weight.device) # This errors out for MultiheadAttention, might need to be handled up-stream merged_weight = rearrange(orig_weight, '(k n) ... -> k n ...', k=self.num_blocks, n=self.block_size) merged_weight = torch.einsum( 'k n m, k n ... -> k m ...', Loading @@ -117,25 +71,15 @@ class NetworkModuleOFT(network.NetworkModule): updown = merged_weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_weight output_shape = orig_weight.shape else: # FIXME: skip MultiheadAttention for now #up = self.lin_module.weight.to(orig_weight.device, dtype=orig_weight.dtype) updown = torch.zeros([orig_weight.shape[1], orig_weight.shape[1]], device=orig_weight.device, dtype=orig_weight.dtype) output_shape = (orig_weight.shape[1], orig_weight.shape[1]) return self.finalize_updown(updown, orig_weight, output_shape) def calc_updown(self, orig_weight): # if alpha is a very small number as in coft, calc_scale will return a almost zero number so we ignore it #multiplier = self.multiplier() * self.calc_scale() # if alpha is a very small number as in coft, calc_scale() will return a almost zero number so we ignore it multiplier = self.multiplier() return self.calc_updown_kb(orig_weight, multiplier) # override to remove the multiplier/scale factor; it's already multiplied in get_weight def finalize_updown(self, updown, orig_weight, output_shape, ex_bias=None): #return super().finalize_updown(updown, orig_weight, output_shape, ex_bias) if self.bias is not None: updown = updown.reshape(self.bias.shape) updown += self.bias.to(orig_weight.device, dtype=orig_weight.dtype) Loading
