Fix clean runs not always saving on last step

CHANGELOG.md

@@ -87,6 +87,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   - `docs/checkpointing/dcp-model.md`: updated the save/load snippets and the "shape to fill" explanation to the DCP-aware helpers.
   - Tests (fail on the pre-fix code, pass after): `tests/integration/test_checkpoint_roundtrip.py::test_manager_restores_optimizer_moments_single_gpu` and `tests/distributed/test_checkpoint.py::test_resume_restores_optimizer_moments` assert `exp_avg` / `exp_avg_sq` are restored bit-exactly into a *fresh* optimizer (single-GPU + distributed); `tests/e2e/test_training_e2e.py::test_resume_determinism_single_gpu` / `test_resume_determinism_2gpu_fsdp` assert end-to-end bit-exact loss across an interrupt-and-resume on a learnable dataset.
   - **On-disk format note:** optimizer state is now keyed by parameter fully-qualified name rather than positional index. Checkpoints written before this fix will not restore optimizer state on resume (training continues with a fresh optimizer); model state is unaffected.
+- **Always save a final checkpoint on clean completion.** A run that finished cleanly previously wrote a final checkpoint only when `max_steps` happened to land on the save schedule; otherwise the fully-trained model (including the entire WSD decay tail) was never persisted and `latest` resolved to the last scheduled step. The training-loop teardown now writes one unconditional checkpoint at `max_steps` on normal completion when that step is off-schedule, matching the emergency-checkpoint guarantee on the preemption path. Purely additive: the schedule/retention knobs (`interval`, `keep_last_n`, `dyn_ckpt_window`) are untouched.
+  - `scripts/train.py`: a `completed_normally` marker set on the final iteration, plus an off-schedule final `ckpt_mgr.save(...)` (and `on_checkpoint_save` hook) before `ckpt_mgr.wait()`.
+  - Tests: `tests/e2e/test_training_e2e.py` — a clean off-schedule completion writes `step_{max_steps}` and points `latest` at it.
 
 ## [0.1.0] — 2026-04-16
 

docs/training/training-loop.md

@@ -185,12 +185,22 @@ After the loop:
 
 ```python
 prof.stop()
+# Clean off-schedule finish: persist the fully-trained final step
+if completed_normally and not config.checkpoint.should_save(step):
+    ckpt_mgr.save(step, ...)           # final checkpoint; `latest` committed after wait
 ckpt_mgr.wait()                        # drain last async save
 hook_runner.on_train_end(step, tokens_seen)
 tracker.close()
 destroy_distributed()
 ```
 
+On a clean finish, an unconditional checkpoint is written at `max_steps`
+when that step is not already on the save schedule — so a completed run's
+fully-trained model (including the WSD decay tail) is always recoverable
+and `latest` points at it. This mirrors the emergency checkpoint the
+preemption path writes on shutdown. The `should_save` guard avoids a
+duplicate when `max_steps` already coincided with the schedule.
+
 `ckpt_mgr.wait()` is load-bearing — without it, a rank can exit before
 its async DCP write completes, corrupting the checkpoint for
 everyone else on the same save. See

scripts/train.py

@@ -672,6 +672,8 @@ def __getitem__(self, idx: int) -> dict[str, torch.Tensor]:
         )
         hook_runner.on_checkpoint_save(0, config.checkpoint.dir)
 
+    completed_normally = False
+
     while step < tc.max_steps:
         # Refresh data iterator at start / epoch boundary
         if dataloader is not None and data_iter is None:
@@ -1039,11 +1041,29 @@ def __getitem__(self, idx: int) -> dict[str, torch.Tensor]:
             shutdown_handler.finish()
             break
 
+        # Clean-completion marker for the unconditional final save after the
+        # loop. Only reached when training completes without any errors, e.g.,
+        # no NaN/NCCL/shutdown breaks. If a run encounters a NaN, the last step
+        # is intentionally *not* saved because the actual model state would be
+        # `max_steps - 1`, not `max_steps`.
+        if step >= tc.max_steps:
+            completed_normally = True
+
     if prof is not None:
         prof.stop()
         if rank == 0:
             print_profiler_summary(prof, trace_dir=config.profiling.trace_dir)
 
+    if completed_normally and not config.checkpoint.should_save(step):
+        ckpt_mgr.save(
+            step=step,
+            tokens_seen=tokens_seen,
+            scheduler=scheduler,
+            dataloader=dataloader,
+            extra=ckpt_extra,
+        )
+        hook_runner.on_checkpoint_save(step, config.checkpoint.dir)
+
     # Flush any pending async checkpoint before tearing down process group
     ckpt_mgr.wait()
 

tests/e2e/test_training_e2e.py

@@ -606,6 +606,37 @@ def test_sigterm_triggers_emergency_checkpoint(tmp_path):
     assert "Checkpoint saved" in output, f"Emergency checkpoint was not saved:\n{output[-2000:]}"
 
 
+@pytest.mark.e2e
+def test_final_checkpoint_on_clean_completion(tmp_path):
+    """A clean finish at an off-schedule step must still persist a final
+    checkpoint, with `latest` pointing at it."""
+    ckpt_dir = tmp_path / "final_ckpt"
+
+    # max_steps=10 with interval=1000 => no scheduled save lands (debug.toml
+    # configures no dyn_ckpt_window), so the only checkpoint must be the
+    # unconditional final save at step 10.
+    result = _run_training(
+        [
+            DEBUG_CONFIG,
+            "--train.max_steps=10",
+            "--metrics.log_interval=5",
+            f"--checkpoint.dir={ckpt_dir}",
+            "--checkpoint.interval=1000",  # no scheduled checkpoint
+        ],
+        nproc=1,
+    )
+    _assert_training_complete(result, expected_steps=10)
+
+    output = result.stdout + result.stderr
+    final = ckpt_dir / "step_10"
+    latest = ckpt_dir / "latest"
+    assert final.is_dir(), f"final checkpoint step_10 was not written:\n{output[-2000:]}"
+    assert latest.exists(), f"`latest` pointer is missing:\n{output[-2000:]}"
+    assert latest.resolve().name == "step_10", (
+        f"`latest` should resolve to step_10, got {latest.resolve().name!r}"
+    )
+
+
 # ============================================================================
 # MoE Training
 # ============================================================================