From eb667e715ad3eea981f6263c143ab0422e5340c9 Mon Sep 17 00:00:00 2001 From: v0xie <28695009+v0xie@users.noreply.github.com> Date: Wed, 15 Nov 2023 18:28:48 -0800 Subject: [PATCH] feat: LyCORIS/kohya OFT network support --- extensions-builtin/Lora/network_oft.py | 108 ++++++------------------- 1 file changed, 26 insertions(+), 82 deletions(-) diff --git a/extensions-builtin/Lora/network_oft.py b/extensions-builtin/Lora/network_oft.py index c45a8d23a..05c378118 100644 --- a/extensions-builtin/Lora/network_oft.py +++ b/extensions-builtin/Lora/network_oft.py @@ -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): @@ -25,117 +25,61 @@ 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 calc_updown_kb(self, orig_weight, multiplier): + oft_blocks = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype) + oft_blocks = oft_blocks - oft_blocks.transpose(1, 2) # ensure skew-symmetric orthogonal matrix - 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) + R = oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype) + R = R * multiplier + torch.eye(self.block_size, device=orig_weight.device) - 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) + # 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 ...', + R, + merged_weight + ) + merged_weight = rearrange(merged_weight, 'k m ... -> (k m) ...') 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) - - R = oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype) - R = R * multiplier + torch.eye(self.block_size, device=orig_weight.device) - - 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, - merged_weight - ) - merged_weight = rearrange(merged_weight, 'k m ... -> (k m) ...') - - 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)