no idea what i'm doing, trying to support both type of OFT, kblueleaf diag_oft...

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

extensions-builtin/Lora/network_oft.py

 import torch
 import network
 from einops import rearrange
+from modules import devices
 
 
 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
@@ -16,66 +17,117 @@ class NetworkModuleOFT(network.NetworkModule):
 
         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
-
-        if "Linear" in self.sd_module.__class__.__name__:
+        self.lin_module = None
+        # 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[0] # FIXME: almost certainly incorrect, assumes tensor is shape [*, m, n]
+        else:
+            raise ValueError("oft_blocks or oft_diag must be in weights dict")
+
+        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 "Linear" in self.sd_module.__class__.__name__ or is_linear:
+        if is_linear:
             self.out_dim = self.sd_module.out_features
-        elif "Conv" in self.sd_module.__class__.__name__:
+            #elif hasattr(self.sd_module, "embed_dim"):
+            #    self.out_dim = self.sd_module.embed_dim
+            #else:
+            #    raise ValueError("Linear sd_module must have out_features or embed_dim")
+        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
+        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
+        #elif is_linear or is_conv:
+        else:
+            self.num_blocks, self.block_size = factorization(self.out_dim, self.dim)
+            self.constraint = None
 
         self.org_module: list[torch.Module] = [self.sd_module]
 
-    # 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)
-    #     weight = torch.einsum(
-    #         "k n m, k n ... -> k m ...", 
-    #         self.oft_diag * scale + torch.eye(self.block_size, device=device), 
-    #         org_weight
-    #     )
-    #     return weight
+        # if is_other_linear:
+        #     weight = self.oft_blocks.reshape(self.oft_blocks.shape[0], -1)
+        #     module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
+        #     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)
+        #     self.lin_module = module
+            #return module
+
+    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)
+        #weight = torch.einsum(
+        #    "k n m, k n ... -> k m ...", 
+        #    self.oft_diag * scale + torch.eye(self.block_size, device=device), 
+        #    org_weight
+        #)
+        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)
-        # 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_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.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
-        return self.oft_blocks
+        block_R_weighted = multiplier * block_R + (1 - multiplier) * m_I
+        R = torch.block_diag(*block_R_weighted)
+        return R
+        #return self.oft_blocks
 
 
     def calc_updown(self, orig_weight):
         multiplier = self.multiplier() * self.calc_scale()
-        #R = self.get_weight(self.oft_blocks, multiplier)
-        R = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
-        #merged_weight = self.merge_weight(R, orig_weight)
-
-        orig_weight = rearrange(orig_weight, '(k n) ... -> k n ...', k=self.num_blocks, n=self.block_size)
-        weight = torch.einsum(
-            'k n m, k n ... -> k m ...',
-            R * multiplier + torch.eye(self.block_size, device=orig_weight.device),
-            orig_weight
-        )
-        weight = rearrange(weight, 'k m ... -> (k m) ...')
-
-        #updown = merged_weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_weight
-        updown = weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_weight
+        R = self.get_weight(self.oft_blocks, multiplier)
+        #R = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
+        merged_weight = self.merge_weight(R, orig_weight)
+
+        #if self.lin_module is not None:
+        #    R = self.lin_module.weight.to(orig_weight.device, dtype=orig_weight.dtype)
+        #    weight = torch.mul(torch.mul(R, multiplier), orig_weight)
+        #else:
+        #    orig_weight = rearrange(orig_weight, '(k n) ... -> k n ...', k=self.num_blocks, n=self.block_size)
+        #    weight = torch.einsum(
+        #        'k n m, k n ... -> k m ...',
+        #        R * multiplier + torch.eye(self.block_size, device=orig_weight.device),
+        #        orig_weight
+        #    )
+        #    weight = rearrange(weight, 'k m ... -> (k m) ...')
+
+        updown = merged_weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_weight
+        #updown = weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_weight
         output_shape = orig_weight.shape
         orig_weight = orig_weight
 
@@ -100,3 +152,49 @@ class NetworkModuleOFT(network.NetworkModule):
             ex_bias = ex_bias * self.multiplier()
 
         return updown, ex_bias
+    
+# copied from https://github.com/KohakuBlueleaf/LyCORIS/blob/dev/lycoris/modules/lokr.py
+def factorization(dimension: int, factor:int=-1) -> tuple[int, int]:
+    '''
+    return a tuple of two value of input dimension decomposed by the number closest to factor
+    second value is higher or equal than first value.
+    
+    In LoRA with Kroneckor Product, first value is a value for weight scale.
+    secon value is a value for weight.
+    
+    Becuase of non-commutative property, A⊗B ≠ B⊗A. Meaning of two matrices is slightly different.
+    
+    examples)
+    factor
+        -1               2                4               8               16               ...
+    127 -> 1, 127   127 -> 1, 127    127 -> 1, 127   127 -> 1, 127   127 -> 1, 127
+    128 -> 8, 16    128 -> 2, 64     128 -> 4, 32    128 -> 8, 16    128 -> 8, 16
+    250 -> 10, 25   250 -> 2, 125    250 -> 2, 125   250 -> 5, 50    250 -> 10, 25
+    360 -> 8, 45    360 -> 2, 180    360 -> 4, 90    360 -> 8, 45    360 -> 12, 30
+    512 -> 16, 32   512 -> 2, 256    512 -> 4, 128   512 -> 8, 64    512 -> 16, 32
+    1024 -> 32, 32  1024 -> 2, 512   1024 -> 4, 256  1024 -> 8, 128  1024 -> 16, 64
+    '''
+    
+    if factor > 0 and (dimension % factor) == 0:
+        m = factor
+        n = dimension // factor
+        if m > n:
+            n, m = m, n
+        return m, n
+    if factor < 0:
+        factor = dimension
+    m, n = 1, dimension
+    length = m + n
+    while m<n:
+        new_m = m + 1
+        while dimension%new_m != 0:
+            new_m += 1
+        new_n = dimension // new_m
+        if new_m + new_n > length or new_m>factor:
+            break
+        else:
+            m, n = new_m, new_n
+    if m > n:
+        n, m = m, n
+    return m, n
+