diff options
Diffstat (limited to 'extensions-builtin/Lora/network_oft.py')
| -rw-r--r-- | extensions-builtin/Lora/network_oft.py | 98 |
1 files changed, 81 insertions, 17 deletions
diff --git a/extensions-builtin/Lora/network_oft.py b/extensions-builtin/Lora/network_oft.py index e43c9a1d..2be67fe5 100644 --- a/extensions-builtin/Lora/network_oft.py +++ b/extensions-builtin/Lora/network_oft.py @@ -1,34 +1,62 @@ import torch import network +from lyco_helpers import factorization +from einops import rearrange class ModuleTypeOFT(network.ModuleType): def create_module(self, net: network.Network, weights: network.NetworkWeights): - if all(x in weights.w for x in ["oft_blocks"]): + if all(x in weights.w for x in ["oft_blocks"]) or all(x in weights.w for x in ["oft_diag"]): return NetworkModuleOFT(net, weights) return None -# adapted from kohya's implementation https://github.com/kohya-ss/sd-scripts/blob/main/networks/oft.py +# 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 class NetworkModuleOFT(network.NetworkModule): def __init__(self, net: network.Network, weights: network.NetworkWeights): super().__init__(net, weights) - self.oft_blocks = weights.w["oft_blocks"] - self.alpha = weights.w["alpha"] - self.dim = self.oft_blocks.shape[0] - self.num_blocks = self.dim + self.lin_module = None + self.org_module: list[torch.Module] = [self.sd_module] + + # kohya-ss + if "oft_blocks" in weights.w.keys(): + self.is_kohya = True + self.oft_blocks = weights.w["oft_blocks"] + self.alpha = weights.w["alpha"] + self.dim = self.oft_blocks.shape[0] + elif "oft_diag" in weights.w.keys(): + self.is_kohya = False + self.oft_blocks = weights.w["oft_diag"] + # 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") - if "Linear" in self.sd_module.__class__.__name__: + 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] + + if is_linear: self.out_dim = self.sd_module.out_features - elif "Conv" in self.sd_module.__class__.__name__: + 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") - self.constraint = self.alpha * self.out_dim - self.block_size = self.out_dim // self.num_blocks - - self.org_module: list[torch.Module] = [self.sd_module] + if self.is_kohya: + self.num_blocks = self.dim + self.block_size = self.out_dim // self.num_blocks + self.constraint = self.alpha * self.out_dim + else: + self.block_size, self.num_blocks = factorization(self.out_dim, self.dim) + self.constraint = None def merge_weight(self, R_weight, org_weight): R_weight = R_weight.to(org_weight.device, dtype=org_weight.dtype) @@ -39,31 +67,67 @@ class NetworkModuleOFT(network.NetworkModule): return weight def get_weight(self, oft_blocks, multiplier=None): - constraint = self.constraint.to(oft_blocks.device, dtype=oft_blocks.dtype) + 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()) - new_norm_Q = torch.clamp(norm_Q, max=constraint) + 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.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(self, orig_weight): - multiplier = self.multiplier() * self.calc_scale() + 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: + if is_other_linear and orig_weight.shape[0] != orig_weight.shape[1]: + orig_weight=orig_weight.permute(1, 0) + + R = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype) + 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 ...', + R * multiplier + torch.eye(self.block_size, device=orig_weight.device), + merged_weight + ) + merged_weight = rearrange(merged_weight, 'k m ... -> (k m) ...') + + if is_other_linear and orig_weight.shape[0] != orig_weight.shape[1]: + orig_weight=orig_weight.permute(1, 0) + + updown = merged_weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_weight + output_shape = orig_weight.shape + else: + # FIXME: skip MultiheadAttention for now + 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): + multiplier = self.multiplier() * self.calc_scale() + if self.is_kohya: + return self.calc_updown_kohya(orig_weight, multiplier) + else: + 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) |