[Autotuner] Auto-checkpoint feature and ability to resume from checkpoint

docs/api/settings.md

@@ -209,6 +209,14 @@ def my_kernel(x: torch.Tensor) -> torch.Tensor:
    Each preset also sets a default initial population strategy (see :doc:`../deployment_autotuning` for details).
    Users can still override individual ``autotune_*`` settings; explicit values win over the preset. Controlled by ``HELION_AUTOTUNE_EFFORT``.
 
+.. autoattribute:: Settings.autotune_checkpoint_dir
+
+   Directory path for saving and resuming autotuning checkpoints. When set, the autotuner
+   saves in-progress state to ``{dir}/{stable_hash}.pt`` and auto-discovers matching
+   checkpoints on subsequent runs. The checkpoint file is deleted on successful completion.
+   When unset (default), no checkpoints are saved or loaded (opt-in).
+   Controlled by ``HELION_AUTOTUNE_CHECKPOINT_DIR``.
+
 .. autoattribute:: Settings.autotune_best_available_max_configs
 
    Maximum number of cached configs to use when seeding the initial population with the ``from_best_available`` strategy.
@@ -323,6 +331,7 @@ Built-in values for ``HELION_AUTOTUNER`` include ``"LFBOTreeSearch"`` (default),
 | ``HELION_AUTOTUNE_PROGRESS_BAR`` | ``autotune_progress_bar`` | Enable or disable the progress bar UI during autotuning. |
 | ``HELION_AUTOTUNE_IGNORE_ERRORS`` | ``autotune_ignore_errors`` | Continue autotuning even when recoverable runtime errors occur. |
 | ``HELION_AUTOTUNE_CONFIG_OVERRIDES`` | ``autotune_config_overrides`` | Supply JSON forcing particular autotuner config key/value pairs. |
+| ``HELION_AUTOTUNE_CHECKPOINT_DIR`` | ``autotune_checkpoint_dir`` | Directory path for saving/resuming autotuning checkpoints (opt-in). |
 | ``TRITON_STORE_BINARY_ONLY`` | Triton (autotuning) | Set to ``1`` during autotuning to skip Triton intermediate IRs, reducing cache size ~40%. Set to ``0`` to retain IRs for debugging. |
 | ``HELION_CACHE_DIR`` | ``LocalAutotuneCache`` | Override the on-disk directory used for cached autotuning artifacts. |
 | ``HELION_SKIP_CACHE`` | ``LocalAutotuneCache`` | When set to ``1``, skip both reading and writing the autotuning cache entirely. |

docs/deployment_autotuning.md

@@ -183,6 +183,29 @@ Related settings for `from_best_available` (see {doc}`api/settings`):
 | `autotune_best_available_max_configs` | `HELION_BEST_AVAILABLE_MAX_CONFIGS` | 20 | Maximum cached configs to seed |
 | `autotune_best_available_max_cache_scan` | `HELION_BEST_AVAILABLE_MAX_CACHE_SCAN` | 500 | Maximum cache files to scan |
 
+### Checkpointing Long-Running Autotuning
+
+For very long autotuning sessions, you can save and resume state using
+checkpoints. This is useful when tuning might be interrupted (e.g., preemptible
+instances) or when you want to continue tuning from a previous unfinished run.
+
+Set the `HELION_AUTOTUNE_CHECKPOINT_DIR` environment variable to a directory
+path. The autotuner will periodically save checkpoints there, keyed by the
+kernel's stable hash. If interrupted, re-run with the same directory to resume
+automatically. On successful completion, the checkpoint file is cleaned up.
+
+```bash
+# Enable checkpointing to a directory:
+HELION_AUTOTUNE_CHECKPOINT_DIR=/tmp/helion_checkpoints python run_kernel.py
+
+# If interrupted, just re-run with the same directory to resume:
+HELION_AUTOTUNE_CHECKPOINT_DIR=/tmp/helion_checkpoints python run_kernel.py
+```
+
+Without `HELION_AUTOTUNE_CHECKPOINT_DIR`, no checkpoints are saved (opt-in).
+Multiple kernels can safely use the same directory — each kernel writes to a
+file named by its unique stable hash.
+
 ## Deploy a Single Config
 
 If one configuration wins for every production call, bake it into the decorator:

helion/_testing.py

@@ -10,6 +10,7 @@
 import operator
 import os
 from pathlib import Path
+import random
 import re
 import sys
 from typing import TYPE_CHECKING
@@ -19,6 +20,7 @@
 from typing import cast
 import unittest
 
+import numpy as np
 import pytest
 import torch
 import torch.distributed as dist
@@ -55,6 +57,26 @@
     from .runtime.kernel import Kernel
 
 
+def seed_rng(seed: int) -> None:
+    random.seed(seed)
+    np.random.seed(seed)  # noqa: NPY002
+    torch.manual_seed(seed)
+
+
+@contextlib.contextmanager
+def fork_rng() -> Generator[None, None, None]:
+    """Context manager that forks all RNGs and restores original state on exit."""
+    python_state = random.getstate()
+    numpy_state = np.random.get_state()  # noqa: NPY002
+
+    with torch.random.fork_rng():
+        try:
+            yield
+        finally:
+            random.setstate(python_state)
+            np.random.set_state(numpy_state)  # noqa: NPY002
+
+
 def _strip_launcher_args(value: str) -> str:
     strip_pairs = []
     if supports_amd_cdna_tunables():