refactor: move factorization to lyco_helpers, separate calc_updown fo…

…r kohya and kb

extensions-builtin/Lora/lyco_helpers.py

@@ -19,3 +19,50 @@ def rebuild_cp_decomposition(up, down, mid):
     up = up.reshape(up.size(0), -1)
     down = down.reshape(down.size(0), -1)
     return torch.einsum('n m k l, i n, m j -> i j k l', mid, up, down)
+
+
+# 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
+

extensions-builtin/Lora/network_oft.py

@@ -1,7 +1,7 @@
 import torch
 import network
+from lyco_helpers import factorization
 from einops import rearrange
-from modules import devices
 
 
 class ModuleTypeOFT(network.ModuleType):
@@ -11,14 +11,16 @@ def create_module(self, net: network.Network, weights: network.NetworkWeights):
 
         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.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
@@ -37,61 +39,31 @@ def __init__(self,  net: network.Network, weights: network.NetworkWeights):
 
         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:
+        is_other_linear = type(self.sd_module) in [torch.nn.MultiheadAttention]
+
         if is_linear:
             self.out_dim = self.sd_module.out_features
-            #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
-            #self.org_weight = self.org_module[0].weight
-#            if hasattr(self.sd_module, "in_proj_weight"):
-#                self.in_proj_dim = self.sd_module.in_proj_weight.shape[1]
-#            if hasattr(self.sd_module, "out_proj_weight"):
-#                self.out_proj_dim = self.sd_module.out_proj_weight.shape[0]
-#            self.in_proj_dim = self.sd_module.in_proj_weight.shape[1]
         elif is_conv:
             self.out_dim = self.sd_module.out_channels
         else:
             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.constraint = self.alpha * self.out_dim
-        #elif is_linear or is_conv:
         else:
             self.block_size, self.num_blocks = factorization(self.out_dim, self.dim)
             self.constraint = None
 
-
-        # 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):
@@ -111,48 +83,51 @@ def get_weight(self, oft_blocks, multiplier=None):
         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_kohya(self, orig_weight, multiplier):
+        R = self.get_weight(self.oft_blocks, multiplier)
+        merged_weight = self.merge_weight(R, orig_weight)
 
-    def calc_updown(self, orig_weight):
-        multiplier = self.multiplier() * self.calc_scale()
-        is_other_linear = type(self.sd_module) in [ torch.nn.MultiheadAttention]
-        if self.is_kohya and not is_other_linear:
-            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)
-        elif not self.is_kohya and not is_other_linear:
+        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)
-            #orig_weight = rearrange(orig_weight, '(k n) ... -> k n ...', k=self.block_size, n=self.num_blocks)
             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
             )
             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)
-                #merged_weight=merged_weight.permute(1, 0)
+
             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
         else:
-            # skip for now
+            # 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])
 
-        #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 = orig_weight
-
         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)
@@ -172,49 +147,3 @@ def finalize_updown(self, updown, orig_weight, output_shape, ex_bias=None):
             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
-