Enable ability to resize lora dim based off ratios

networks/resize_lora.py

@@ -38,10 +38,11 @@ def save_to_file(file_name, model, state_dict, dtype, metadata):
     torch.save(model, file_name)
 
 
-def resize_lora_model(lora_sd, new_rank, save_dtype, device, verbose):
+def resize_lora_model(lora_sd, new_rank, save_dtype, device, sv_ratio, verbose):
   network_alpha = None
   network_dim = None
   verbose_str = "\n"
+  ratio_flag = False
 
   CLAMP_QUANTILE = 0.99
 
@@ -57,9 +58,12 @@ def resize_lora_model(lora_sd, new_rank, save_dtype, device, verbose):
       network_alpha = network_dim
 
   scale = network_alpha/network_dim
+  if not sv_ratio:
     new_alpha = float(scale*new_rank)  # calculate new alpha from scale
-
+    print(f"old dimension: {network_dim}, old alpha: {network_alpha}, new dim: {new_rank}, new alpha: {new_alpha}")
-  print(f"old dimension: {network_dim}, old alpha: {network_alpha}, new alpha: {new_alpha}")
+  else:
+    print(f"Dynamically determining new alphas and dims based off sv ratio: {sv_ratio}")
+    ratio_flag = True
 
   lora_down_weight = None
   lora_up_weight = None
@@ -97,11 +101,24 @@ def resize_lora_model(lora_sd, new_rank, save_dtype, device, verbose):
 
         U, S, Vh = torch.linalg.svd(full_weight_matrix)
 
+        if ratio_flag:
+          # Calculate new dim and alpha for dynamic sizing
+          max_sv = S[0]
+          min_sv = max_sv/sv_ratio
+          new_rank = torch.sum(S > min_sv).item()
+          new_rank = max(new_rank, 1)
+          new_alpha = float(scale*new_rank)
+
         if verbose:
           s_sum = torch.sum(torch.abs(S))
           s_rank = torch.sum(torch.abs(S[:new_rank]))
-          verbose_str+=f"{block_down_name:76} | "
+          verbose_str+=f"{block_down_name:75} | "
-          verbose_str+=f"sum(S) retained: {(s_rank)/s_sum:.1%}, max(S) ratio: {S[0]/S[new_rank]:0.1f}\n"
+          verbose_str+=f"sum(S) retained: {(s_rank)/s_sum:.1%}, max(S) ratio: {S[0]/S[new_rank]:0.1f}"
+
+        if verbose and ratio_flag:
+          verbose_str+=f", dynamic| dim: {new_rank}, alpha: {new_alpha}\n"
+        else:
+          verbose_str+=f"\n"
 
         U = U[:, :new_rank]
         S = S[:new_rank]
@@ -160,16 +177,21 @@ def resize(args):
   lora_sd, metadata = load_state_dict(args.model, merge_dtype)
 
   print("resizing rank...")
-  state_dict, old_dim, new_alpha = resize_lora_model(lora_sd, args.new_rank, save_dtype, args.device, args.verbose)
+  state_dict, old_dim, new_alpha = resize_lora_model(lora_sd, args.new_rank, save_dtype, args.device, args.sv_ratio, args.verbose)
 
   # update metadata
   if metadata is None:
     metadata = {}
 
   comment = metadata.get("ss_training_comment", "")
+  if not args.sv_ratio:
     metadata["ss_training_comment"] = f"dimension is resized from {old_dim} to {args.new_rank}; {comment}"
     metadata["ss_network_dim"] = str(args.new_rank)
     metadata["ss_network_alpha"] = str(new_alpha)
+  else:
+    metadata["ss_training_comment"] = f"Dynamic resize from {old_dim} with ratio {args.sv_ratio}; {comment}"
+    metadata["ss_network_dim"] = 'Dynamic'
+    metadata["ss_network_alpha"] = 'Dynamic'
 
   model_hash, legacy_hash = train_util.precalculate_safetensors_hashes(state_dict, metadata)
   metadata["sshs_model_hash"] = model_hash
@@ -193,6 +215,8 @@ if __name__ == '__main__':
   parser.add_argument("--device", type=str, default=None, help="device to use, cuda for GPU / 計算を行うデバイス、cuda でGPUを使う")
   parser.add_argument("--verbose", action="store_true", 
                       help="Display verbose resizing information / rank変更時の詳細情報を出力する")
+  parser.add_argument("--sv_ratio", type=float, default=None,
+                      help="Specify svd ratio for dim calcs. Will override --new_rank")
 
   args = parser.parse_args()
   resize(args)