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|
| """Determinism helpers for reproducible evaluation runs. |
| |
| Seeding is opt-in. If no seed is supplied and the ``GR00T_EVAL_SEED`` |
| environment variable is unset, these helpers are no-ops, so training and |
| production code paths are unaffected. |
| |
| When enabled, :func:`seed_everything` seeds Python, NumPy, torch CPU and |
| torch CUDA RNGs, sets cuDNN to deterministic mode, and (optionally) asks |
| torch to use deterministic algorithm implementations. This is required |
| before any historical metric recording so that run-to-run variance on the |
| same checkpoint is driven by hardware/library noise only, not by unseeded |
| RNGs. |
| """ |
|
|
| from __future__ import annotations |
|
|
| import logging |
| import os |
| import random |
|
|
| import numpy as np |
| import torch |
|
|
|
|
| EVAL_SEED_ENV_VAR = "GR00T_EVAL_SEED" |
|
|
|
|
| logger = logging.getLogger(__name__) |
|
|
|
|
| def get_eval_seed(default: int | None = None) -> int | None: |
| """Read the eval seed from the environment, or return ``default``. |
| |
| Returns ``None`` if the env var is unset / empty and no default is given. |
| Raises ``ValueError`` if the env var is set but not a valid integer. |
| """ |
| raw = os.environ.get(EVAL_SEED_ENV_VAR) |
| if raw is None or raw == "": |
| return default |
| try: |
| return int(raw) |
| except ValueError as exc: |
| raise ValueError(f"{EVAL_SEED_ENV_VAR}={raw!r} is not a valid integer") from exc |
|
|
|
|
| def seed_everything(seed: int | None = None, *, warn_only: bool = True) -> int | None: |
| """Seed all standard RNGs and enable deterministic cuDNN / CUDA kernels. |
| |
| Args: |
| seed: Seed to apply. If ``None``, reads ``GR00T_EVAL_SEED``; if that |
| is also unset, the function returns ``None`` without changing |
| any global state. |
| warn_only: Forwarded to ``torch.use_deterministic_algorithms``. When |
| ``True`` (default), ops without a deterministic implementation |
| warn instead of raising. |
| |
| Returns: |
| The effective seed that was applied, or ``None`` if no seeding was |
| done (so callers can pass it downstream unconditionally). |
| """ |
| if seed is None: |
| seed = get_eval_seed() |
| if seed is None: |
| return None |
|
|
| random.seed(seed) |
| np.random.seed(seed) |
| torch.manual_seed(seed) |
| torch.cuda.manual_seed_all(seed) |
| torch.use_deterministic_algorithms(True, warn_only=warn_only) |
| torch.backends.cudnn.deterministic = True |
| torch.backends.cudnn.benchmark = False |
| |
| os.environ.setdefault("CUBLAS_WORKSPACE_CONFIG", ":4096:8") |
|
|
| logger.info("Determinism enabled: seed=%d warn_only=%s", seed, warn_only) |
| return seed |
|
|
