Update app.py

app.py

@@ -587,6 +587,7 @@ def task_resize(hf_token, lora_input, new_rank, dynamic_method, dynamic_param, o
         if "down" in g and "up" in g:
             down, up = g["down"].float(), g["up"].float()
             
+            # 1. Merge Up/Down
             if len(down.shape) == 4:
                 merged = (up.squeeze() @ down.squeeze()).reshape(up.shape[0], down.shape[1], down.shape[2], down.shape[3])
                 flat = merged.flatten(1)
@@ -594,37 +595,45 @@ def task_resize(hf_token, lora_input, new_rank, dynamic_method, dynamic_param, o
                 merged = up @ down
                 flat = merged
             
-            # FAST SVD (svd_lowrank)
+            # 2. FAST SVD (svd_lowrank)
             target_rank = int(new_rank)
-            # Add buffer to q to ensure convergence, then slice
+            # Add buffer to q to ensure convergence
             q = min(target_rank + 10, min(flat.shape))
             
             U, S, V = torch.svd_lowrank(flat, q=q)
-            # V from svd_lowrank is (N, q), we need Vh (q, N)
-            Vh = V.t()
+            Vh = V.t() 
             
-            # Exact truncation
+            # 3. Dynamic Rank Selection
+            if dynamic_method == "sv_ratio":
+                target_rank = index_sv_ratio(S, dynamic_param)
+            
+            # Hard limit by user's max rank
+            target_rank = min(target_rank, int(new_rank), S.shape[0])
+            
+            # 4. Truncate
             U = U[:, :target_rank]
             S = S[:target_rank]
             Vh = Vh[:target_rank, :]
             
-            # Reconstruct
+            # 5. Reconstruct Up Matrix
             U = U @ torch.diag(S)
             
             if len(down.shape) == 4:
                 U = U.reshape(up.shape[0], target_rank, 1, 1)
                 Vh = Vh.reshape(target_rank, down.shape[1], down.shape[2], down.shape[3])
                 
-            new_state[f"{stem}.lora_down.weight"] = Vh
-            new_state[f"{stem}.lora_up.weight"] = U
+            # 6. Save (FIX: Enforce contiguous memory layout)
+            new_state[f"{stem}.lora_down.weight"] = Vh.contiguous()
+            new_state[f"{stem}.lora_up.weight"] = U.contiguous()
             new_state[f"{stem}.alpha"] = torch.tensor(target_rank).float()
 
     out = TempDir / "resized.safetensors"
+    # safetensors requires contiguous tensors
     save_file(new_state, out)
+    
     api.create_repo(repo_id=out_repo, exist_ok=True, token=hf_token)
     api.upload_file(path_or_fileobj=out, path_in_repo="resized.safetensors", repo_id=out_repo, token=hf_token)
     return "Done"
-
 # =================================================================================
 # UI
 # =================================================================================