fix codex BLOCKERs: global fixed layout invariance, min_steps off-by-one, integers EOS alias, rerun guard, eval prereg-literal codes + dual-checkpoint honesty; +7 regression tests

1 files changed, 23 insertions, 5 deletions

diff --git a/src/data.py b/src/data.py
index 35682d3..e6ea57b 100644
--- a/src/data.py
+++ b/src/data.py
@@ -63,15 +63,33 @@ def build_examples(inputs: list[int], cfg: Config) -> list[tuple[list[int], list
     return out
 
 
+def _global_lengths(cfg: Config) -> tuple[int, int]:
+    """(in_max, out_max): fixed global lengths so batch layout == singleton layout (codex BLOCKER fix)."""
+    primes = sieve_primes(cfg.range_end + 100)
+    max_target = next_prime(cfg.range_end, primes)
+    if cfg.vocab_mode == "integers":
+        return 1, 2                      # [value], [value, EOS]
+    return len(str(cfg.range_end)), len(str(max_target)) + 1   # digits + EOS
+
+
+def pad_inputs(x: torch.Tensor, cfg: Config) -> torch.Tensor:
+    """LEFT-pad inputs to the global in_max so absolute positions are layout-invariant."""
+    in_max, _ = _global_lengths(cfg)
+    if x.shape[1] < in_max:
+        pad = torch.full((x.shape[0], in_max - x.shape[1]), cfg.pad_id, dtype=x.dtype, device=x.device)
+        x = torch.cat([pad, x], dim=1)
+    return x
+
+
 def make_batch(examples, cfg: Config) -> dict[str, torch.Tensor]:
+    """Fixed global layout (not batch-max): x left-padded to in_max, y right-padded to out_max."""
     xs, ys = zip(*examples)
-    T_in = max(len(x) for x in xs)
-    T_out = max(len(y) for y in ys)
+    in_max, out_max = _global_lengths(cfg)
     B = len(examples)
-    x = torch.full((B, T_in), cfg.pad_id, dtype=torch.long)
-    y = torch.full((B, T_out), cfg.pad_id, dtype=torch.long)
+    x = torch.full((B, in_max), cfg.pad_id, dtype=torch.long)
+    y = torch.full((B, out_max), cfg.pad_id, dtype=torch.long)
     for i, (xi, yi) in enumerate(examples):
-        x[i, : len(xi)] = torch.tensor(xi, dtype=torch.long)
+        x[i, in_max - len(xi):] = torch.tensor(xi, dtype=torch.long)
         y[i, : len(yi)] = torch.tensor(yi, dtype=torch.long)
     # teacher-forced decoder input: BOS(=EOS reuse) then shifted y
     y_in = torch.cat([torch.full((B, 1), cfg.eos_id, dtype=torch.long), y[:, :-1]], dim=1)