refactor: use same updown for both kohya OFT and LyCORIS diag-oft

import network

from lyco_helpers import factorization

from einops import rearrange

from modules import devices

class ModuleTypeOFT(network.ModuleType):

        # kohya-ss

        if "oft_blocks" in weights.w.keys():

            self.is_kohya = True

            self.oft_blocks = weights.w["oft_blocks"]

            self.oft_blocks = weights.w["oft_blocks"] # (num_blocks, block_size, block_size)

            self.alpha = weights.w["alpha"]

            self.dim = self.oft_blocks.shape[0]

            self.dim = self.oft_blocks.shape[0] # lora dim

            #self.oft_blocks = rearrange(self.oft_blocks, 'k m ... -> (k m) ...')

        elif "oft_diag" in weights.w.keys():

            self.is_kohya = False

            self.oft_blocks = weights.w["oft_diag"]

            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

            raise ValueError("sd_module must be Linear or Conv")

        if self.is_kohya:

            self.num_blocks = self.dim

            self.block_size = self.out_dim // self.num_blocks

            #self.num_blocks = self.dim

            #self.block_size = self.out_dim // self.num_blocks

            #self.block_size = self.dim

            #self.num_blocks = self.out_dim // self.block_size

            self.constraint = self.alpha * self.out_dim

            self.num_blocks, self.block_size = factorization(self.out_dim, self.dim)

        else:

            self.block_size, self.num_blocks = factorization(self.out_dim, self.dim)

            self.constraint = None

            self.block_size, self.num_blocks = factorization(self.out_dim, self.dim)

        if is_other_linear:

            self.lin_module = self.create_module(weights.w, "oft_diag", none_ok=True)

    def create_module(self, weights, key, none_ok=False):

        weight = weights.get(key)

        if weight is None and none_ok:

            return None

        is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear, torch.nn.MultiheadAttention]

        is_conv = type(self.sd_module) in [torch.nn.Conv2d]

        if is_linear:

            weight = weight.reshape(weight.shape[0], -1)

            module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)

        elif is_conv and key == "lora_down.weight" or key == "dyn_up":

            if len(weight.shape) == 2:

                weight = weight.reshape(weight.shape[0], -1, 1, 1)

            if weight.shape[2] != 1 or weight.shape[3] != 1:

                module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], self.sd_module.kernel_size, self.sd_module.stride, self.sd_module.padding, bias=False)

            else:

                module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)

        elif is_conv and key == "lora_mid.weight":

            module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], self.sd_module.kernel_size, self.sd_module.stride, self.sd_module.padding, bias=False)

        elif is_conv and key == "lora_up.weight" or key == "dyn_down":

            module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)

        else:

            raise AssertionError(f'Lora layer {self.network_key} matched a layer with unsupported type: {type(self.sd_module).__name__}')

        with torch.no_grad():

            if weight.shape != module.weight.shape:

                weight = weight.reshape(module.weight.shape)

            module.weight.copy_(weight)

        module.to(device=devices.cpu, dtype=devices.dtype)

        module.weight.requires_grad_(False)

        return module

    def merge_weight(self, R_weight, org_weight):

        R_weight = R_weight.to(org_weight.device, dtype=org_weight.dtype)

        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)

        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

        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)

            #if is_other_linear and orig_weight.shape[0] != orig_weight.shape[1]:

            #    orig_weight=orig_weight.permute(1, 0)

            oft_blocks = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)

            # without this line the results are significantly worse / less accurate

            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)

            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),

                R,

                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)

            #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

            #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])

    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:

        #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