Fix: score final partial window in sliding window eval (openai#124)

The window_starts filter dropped windows shorter than stride,
silently skipping up to (stride-1) tokens at the end of the
validation set. Now includes all windows with >= 1 scoreable
token, and clamps the score start for short final windows.

Author: mattqlf

records/track_10min_16mb/2026-03-19_SlidingWindowEval/train_gpt.py

@@ -856,9 +856,9 @@ def eval_val_sliding(
     seq_len = args.train_seq_len
     total_tokens = val_tokens.numel() - 1
 
-    # Build windows; skip any too short to score a full stride
+    # Build windows; include final partial window if it has at least 1 token
     window_starts = [ws for ws in range(0, total_tokens, stride)
-                     if min(ws + seq_len, total_tokens) - ws >= stride]
+                     if min(ws + seq_len, total_tokens) - ws >= 1]
     total_windows = len(window_starts)
 
     # Distribute across ranks
@@ -899,7 +899,7 @@ def eval_val_sliding(
 
             for i, ws in enumerate(batch_ws):
                 wlen = wlens[i]
-                s = 0 if ws == 0 else wlen - stride
+                s = 0 if ws == 0 else max(wlen - stride, 0)
                 scored_nll = nll[i, s:wlen].to(torch.float64)
                 loss_sum += scored_nll.sum()
                 token_count += float(wlen - s)