fix: save adapter and loss curves on cancel, not only on normal completion

Wraps training loop in try/finally so adapter_final.pt and loss PNGs are
always written. On cancellation the adapter is named
adapter_cancelled_stepXXXXX.pt so it can be used with --resume.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

nodes/selva_lora_trainer.py

@@ -489,6 +489,9 @@ class SelvaLoraTrainer:
         print(f"\n[LoRA Trainer] Training {remaining} steps "
               f"(step {start_step + 1} → {steps}, batch_size={batch_size})\n", flush=True)
 
+        last_step = start_step
+        completed = False
+        try:
             for step in range(start_step + 1, steps + 1):
                 batch = random.choices(dataset, k=batch_size)
                 x1_list, clip_list, sync_list, text_list = zip(*batch)
@@ -553,32 +556,45 @@ class SelvaLoraTrainer:
                             sf.write(str(wav_path), wav.squeeze(0).numpy(), sr)
                         print(f"[LoRA Trainer] Sample saved: {wav_path}", flush=True)
 
+                last_step = step
                 pbar_train.update(1)
 
-        # Save inference adapter (state_dict + meta only — SelvaLoraLoader compatible)
-        # Increment filename if a previous final already exists (resume case)
+            completed = True
+
+        finally:
+            # Save adapter and loss curves whether training completed or was cancelled.
+            # Skip if we never completed a single step (nothing useful to save).
+            if loss_history:
+                if completed:
+                    # Normal completion — use adapter_final.pt (increment if exists)
                     final_path = output_dir / "adapter_final.pt"
                     if final_path.exists():
                         i = 1
                         while (output_dir / f"adapter_final_{i:03d}.pt").exists():
                             i += 1
                         final_path = output_dir / f"adapter_final_{i:03d}.pt"
+                    label = "Done"
+                else:
+                    # Cancelled — include the step number so the file is useful for resume
+                    final_path = output_dir / f"adapter_cancelled_step{last_step:05d}.pt"
+                    label = f"Cancelled at step {last_step}"
+
                 torch.save({"state_dict": get_lora_state_dict(generator), "meta": meta}, final_path)
                 (output_dir / "meta.json").write_text(json.dumps(meta, indent=2))
-        print(f"\n[LoRA Trainer] Done. Adapter saved to {final_path}", flush=True)
-
-        # --- Return patched model ---
-        generator.eval()
-        generator.to(next(model["generator"].parameters()).device)
-        patched = {**model, "generator": generator}
+                print(f"\n[LoRA Trainer] {label}. Adapter saved to {final_path}", flush=True)
 
                 smoothed     = _smooth_losses(loss_history)
                 raw_img      = _draw_loss_curve(loss_history, log_interval, start_step)
-        smoothed_img = _draw_loss_curve(loss_history, log_interval, start_step, smoothed=smoothed)
+                smoothed_img = _draw_loss_curve(loss_history, log_interval, start_step,
+                                                smoothed=smoothed)
                 raw_img.save(str(output_dir / "loss_raw.png"))
                 smoothed_img.save(str(output_dir / "loss_smoothed.png"))
                 print(f"[LoRA Trainer] Loss curves saved to {output_dir}", flush=True)
 
-        loss_curve = _pil_to_tensor(smoothed_img)
+        # Reached only on normal completion (exception re-raises past this point)
+        generator.eval()
+        generator.to(next(model["generator"].parameters()).device)
+        patched = {**model, "generator": generator}
 
+        loss_curve = _pil_to_tensor(smoothed_img)
         return (patched, str(final_path), loss_curve)