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no idea what i'm doing, trying to support both type of OFT, kblueleaf diag_oft has MultiheadAttn which kohya's doesn't?, attempt create new module based off network_lora.py, errors about tensor dim mismatch
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v0xie
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@ -1,11 +1,12 @@
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import torch
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import network
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from einops import rearrange
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from modules import devices
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class ModuleTypeOFT(network.ModuleType):
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def create_module(self, net: network.Network, weights: network.NetworkWeights):
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if all(x in weights.w for x in ["oft_blocks"]):
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if all(x in weights.w for x in ["oft_blocks"]) or all(x in weights.w for x in ["oft_diag"]):
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return NetworkModuleOFT(net, weights)
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return None
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@ -16,66 +17,117 @@ class NetworkModuleOFT(network.NetworkModule):
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super().__init__(net, weights)
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self.oft_blocks = weights.w["oft_blocks"]
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self.alpha = weights.w["alpha"]
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self.dim = self.oft_blocks.shape[0]
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self.num_blocks = self.dim
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self.lin_module = None
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# kohya-ss
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if "oft_blocks" in weights.w.keys():
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self.is_kohya = True
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self.oft_blocks = weights.w["oft_blocks"]
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self.alpha = weights.w["alpha"]
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self.dim = self.oft_blocks.shape[0]
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elif "oft_diag" in weights.w.keys():
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self.is_kohya = False
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self.oft_blocks = weights.w["oft_diag"]
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# alpha is rank if alpha is 0 or None
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if self.alpha is None:
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pass
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self.dim = self.oft_blocks.shape[0] # FIXME: almost certainly incorrect, assumes tensor is shape [*, m, n]
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else:
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raise ValueError("oft_blocks or oft_diag must be in weights dict")
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if "Linear" in self.sd_module.__class__.__name__:
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is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear]
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is_conv = type(self.sd_module) in [torch.nn.Conv2d]
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is_other_linear = type(self.sd_module) in [ torch.nn.MultiheadAttention]
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#if "Linear" in self.sd_module.__class__.__name__ or is_linear:
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if is_linear:
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self.out_dim = self.sd_module.out_features
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elif "Conv" in self.sd_module.__class__.__name__:
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#elif hasattr(self.sd_module, "embed_dim"):
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# self.out_dim = self.sd_module.embed_dim
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#else:
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# raise ValueError("Linear sd_module must have out_features or embed_dim")
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elif is_other_linear:
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self.out_dim = self.sd_module.embed_dim
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elif is_conv:
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self.out_dim = self.sd_module.out_channels
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else:
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raise ValueError("sd_module must be Linear or Conv")
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self.constraint = self.alpha * self.out_dim
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self.block_size = self.out_dim // self.num_blocks
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if self.is_kohya:
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self.num_blocks = self.dim
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self.block_size = self.out_dim // self.num_blocks
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self.constraint = self.alpha * self.out_dim
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#elif is_linear or is_conv:
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else:
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self.num_blocks, self.block_size = factorization(self.out_dim, self.dim)
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self.constraint = None
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self.org_module: list[torch.Module] = [self.sd_module]
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# def merge_weight(self, R_weight, org_weight):
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# R_weight = R_weight.to(org_weight.device, dtype=org_weight.dtype)
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# if org_weight.dim() == 4:
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# weight = torch.einsum("oihw, op -> pihw", org_weight, R_weight)
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# else:
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# weight = torch.einsum("oi, op -> pi", org_weight, R_weight)
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# weight = torch.einsum(
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# "k n m, k n ... -> k m ...",
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# self.oft_diag * scale + torch.eye(self.block_size, device=device),
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# org_weight
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# )
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# return weight
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# if is_other_linear:
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# weight = self.oft_blocks.reshape(self.oft_blocks.shape[0], -1)
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# module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
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# with torch.no_grad():
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# if weight.shape != module.weight.shape:
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# weight = weight.reshape(module.weight.shape)
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# module.weight.copy_(weight)
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# module.to(device=devices.cpu, dtype=devices.dtype)
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# module.weight.requires_grad_(False)
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# self.lin_module = module
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#return module
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def merge_weight(self, R_weight, org_weight):
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R_weight = R_weight.to(org_weight.device, dtype=org_weight.dtype)
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if org_weight.dim() == 4:
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weight = torch.einsum("oihw, op -> pihw", org_weight, R_weight)
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else:
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weight = torch.einsum("oi, op -> pi", org_weight, R_weight)
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#weight = torch.einsum(
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# "k n m, k n ... -> k m ...",
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# self.oft_diag * scale + torch.eye(self.block_size, device=device),
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# org_weight
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#)
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return weight
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def get_weight(self, oft_blocks, multiplier=None):
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# constraint = self.constraint.to(oft_blocks.device, dtype=oft_blocks.dtype)
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if self.constraint is not None:
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constraint = self.constraint.to(oft_blocks.device, dtype=oft_blocks.dtype)
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# block_Q = oft_blocks - oft_blocks.transpose(1, 2)
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# norm_Q = torch.norm(block_Q.flatten())
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# new_norm_Q = torch.clamp(norm_Q, max=constraint)
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# block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8))
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# m_I = torch.eye(self.block_size, device=oft_blocks.device).unsqueeze(0).repeat(self.num_blocks, 1, 1)
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# block_R = torch.matmul(m_I + block_Q, (m_I - block_Q).inverse())
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block_Q = oft_blocks - oft_blocks.transpose(1, 2)
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norm_Q = torch.norm(block_Q.flatten())
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if self.constraint is not None:
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new_norm_Q = torch.clamp(norm_Q, max=constraint)
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else:
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new_norm_Q = norm_Q
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block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8))
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m_I = torch.eye(self.block_size, device=oft_blocks.device).unsqueeze(0).repeat(self.num_blocks, 1, 1)
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block_R = torch.matmul(m_I + block_Q, (m_I - block_Q).inverse())
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# block_R_weighted = multiplier * block_R + (1 - multiplier) * m_I
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# R = torch.block_diag(*block_R_weighted)
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#return R
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return self.oft_blocks
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block_R_weighted = multiplier * block_R + (1 - multiplier) * m_I
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R = torch.block_diag(*block_R_weighted)
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return R
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#return self.oft_blocks
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def calc_updown(self, orig_weight):
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multiplier = self.multiplier() * self.calc_scale()
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#R = self.get_weight(self.oft_blocks, multiplier)
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R = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
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#merged_weight = self.merge_weight(R, orig_weight)
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R = self.get_weight(self.oft_blocks, multiplier)
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#R = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
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merged_weight = self.merge_weight(R, orig_weight)
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orig_weight = rearrange(orig_weight, '(k n) ... -> k n ...', k=self.num_blocks, n=self.block_size)
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weight = torch.einsum(
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'k n m, k n ... -> k m ...',
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R * multiplier + torch.eye(self.block_size, device=orig_weight.device),
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orig_weight
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)
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weight = rearrange(weight, 'k m ... -> (k m) ...')
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#if self.lin_module is not None:
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# R = self.lin_module.weight.to(orig_weight.device, dtype=orig_weight.dtype)
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# weight = torch.mul(torch.mul(R, multiplier), orig_weight)
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#else:
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# orig_weight = rearrange(orig_weight, '(k n) ... -> k n ...', k=self.num_blocks, n=self.block_size)
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# weight = torch.einsum(
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# 'k n m, k n ... -> k m ...',
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# R * multiplier + torch.eye(self.block_size, device=orig_weight.device),
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# orig_weight
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# )
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# weight = rearrange(weight, 'k m ... -> (k m) ...')
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#updown = merged_weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_weight
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updown = weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_weight
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updown = merged_weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_weight
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#updown = weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_weight
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output_shape = orig_weight.shape
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orig_weight = orig_weight
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@ -100,3 +152,49 @@ class NetworkModuleOFT(network.NetworkModule):
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ex_bias = ex_bias * self.multiplier()
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return updown, ex_bias
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# copied from https://github.com/KohakuBlueleaf/LyCORIS/blob/dev/lycoris/modules/lokr.py
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def factorization(dimension: int, factor:int=-1) -> tuple[int, int]:
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'''
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return a tuple of two value of input dimension decomposed by the number closest to factor
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second value is higher or equal than first value.
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In LoRA with Kroneckor Product, first value is a value for weight scale.
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secon value is a value for weight.
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Becuase of non-commutative property, A⊗B ≠ B⊗A. Meaning of two matrices is slightly different.
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examples)
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factor
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-1 2 4 8 16 ...
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127 -> 1, 127 127 -> 1, 127 127 -> 1, 127 127 -> 1, 127 127 -> 1, 127
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128 -> 8, 16 128 -> 2, 64 128 -> 4, 32 128 -> 8, 16 128 -> 8, 16
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250 -> 10, 25 250 -> 2, 125 250 -> 2, 125 250 -> 5, 50 250 -> 10, 25
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360 -> 8, 45 360 -> 2, 180 360 -> 4, 90 360 -> 8, 45 360 -> 12, 30
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512 -> 16, 32 512 -> 2, 256 512 -> 4, 128 512 -> 8, 64 512 -> 16, 32
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1024 -> 32, 32 1024 -> 2, 512 1024 -> 4, 256 1024 -> 8, 128 1024 -> 16, 64
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'''
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if factor > 0 and (dimension % factor) == 0:
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m = factor
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n = dimension // factor
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if m > n:
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n, m = m, n
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return m, n
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if factor < 0:
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factor = dimension
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m, n = 1, dimension
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length = m + n
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while m<n:
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new_m = m + 1
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while dimension%new_m != 0:
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new_m += 1
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new_n = dimension // new_m
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if new_m + new_n > length or new_m>factor:
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break
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else:
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m, n = new_m, new_n
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if m > n:
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n, m = m, n
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return m, n
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