Warn and reset chunk_index when resume changes batch or seq_len

Trying a larger micro-batch on an existing 0.5b run re-packs wiki
chunks; keep tokens/opt_step and restart the data cursor.

train_k3.py

@@ -133,6 +133,9 @@ def _payload(
         "tokens": tokens,
         "chunk_index": chunk_index,
         "best_heldout": best_heldout,
+        "batch": args.batch,
+        "seq_len": args.seq_len,
+        "grad_acc": args.grad_acc,
     }
 
 
@@ -392,10 +395,23 @@ def main() -> None:
     )
     print(
         f"packed tokens {n_ids:,} -> {train_chunks.size(0)} train / "
-        f"{held_chunks.size(0)} held-out chunks of [{args.batch}, {args.seq_len}]"
+        f"{held_chunks.size(0)} held-out chunks of [{args.batch}, {args.seq_len}]  "
+        f"{tpm} tok/micro"
     )
     if train_chunks.size(0) == 0:
         raise SystemExit("no training chunks; raise --limit or lower --batch/--seq-len")
+    if args.resume:
+        old_batch = payload.get("batch")
+        old_seq = payload.get("seq_len")
+        if old_batch is not None and (
+            int(old_batch) != args.batch or int(old_seq or args.seq_len) != args.seq_len
+        ):
+            print(
+                f"warning: resume pack [{old_batch}, {old_seq}] -> "
+                f"[{args.batch}, {args.seq_len}]; reset chunk_index 0 "
+                f"(tokens/opt_step kept)"
+            )
+            chunk_index = 0
 
     optim = torch.optim.AdamW(
         model.parameters(),