Source code for pytorch_lightning.utilities.seed

# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
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"""Helper functions to help with reproducibility of models. """

import logging
import os
import random
from typing import Optional

import numpy as np
import torch

from pytorch_lightning.utilities import _TORCH_GREATER_EQUAL_1_7, rank_zero_warn
from pytorch_lightning.utilities.distributed import rank_zero_only

log = logging.getLogger(__name__)


def seed_everything(seed: Optional[int] = None, workers: bool = False) -> int:
    """
    Function that sets seed for pseudo-random number generators in:
    pytorch, numpy, python.random
    In addition, sets the following environment variables:

    - `PL_GLOBAL_SEED`: will be passed to spawned subprocesses (e.g. ddp_spawn backend).
    - `PL_SEED_WORKERS`: (optional) is set to 1 if ``workers=True``.

    Args:
        seed: the integer value seed for global random state in Lightning.
            If `None`, will read seed from `PL_GLOBAL_SEED` env variable
            or select it randomly.
        workers: if set to ``True``, will properly configure all dataloaders passed to the
            Trainer with a ``worker_init_fn``. If the user already provides such a function
            for their dataloaders, setting this argument will have no influence. See also:
            :func:`~pytorch_lightning.utilities.seed.pl_worker_init_function`.
    """
    max_seed_value = np.iinfo(np.uint32).max
    min_seed_value = np.iinfo(np.uint32).min

    try:
        if seed is None:
            seed = os.environ.get("PL_GLOBAL_SEED")
        seed = int(seed)
    except (TypeError, ValueError):
        seed = _select_seed_randomly(min_seed_value, max_seed_value)
        rank_zero_warn(f"No correct seed found, seed set to {seed}")

    if not (min_seed_value <= seed <= max_seed_value):
        rank_zero_warn(f"{seed} is not in bounds, numpy accepts from {min_seed_value} to {max_seed_value}")
        seed = _select_seed_randomly(min_seed_value, max_seed_value)

    # using `log.info` instead of `rank_zero_info`,
    # so users can verify the seed is properly set in distributed training.
    log.info(f"Global seed set to {seed}")
    os.environ["PL_GLOBAL_SEED"] = str(seed)
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)

    os.environ["PL_SEED_WORKERS"] = f"{int(workers)}"

    return seed


def _select_seed_randomly(min_seed_value: int = 0, max_seed_value: int = 255) -> int:
    return random.randint(min_seed_value, max_seed_value)


def reset_seed() -> None:
    """
    Reset the seed to the value that :func:`pytorch_lightning.utilities.seed.seed_everything` previously set.
    If :func:`pytorch_lightning.utilities.seed.seed_everything` is unused, this function will do nothing.
    """
    seed = os.environ.get("PL_GLOBAL_SEED", None)
    workers = os.environ.get("PL_SEED_WORKERS", False)
    if seed is not None:
        seed_everything(int(seed), workers=bool(workers))


def pl_worker_init_function(worker_id: int, rank: Optional = None) -> None:  # pragma: no cover
    """
    The worker_init_fn that Lightning automatically adds to your dataloader if you previously set
    set the seed with ``seed_everything(seed, workers=True)``.
    See also the PyTorch documentation on
    `randomness in DataLoaders <https://pytorch.org/docs/stable/notes/randomness.html#dataloader>`_.
    """
    # implementation notes: https://github.com/pytorch/pytorch/issues/5059#issuecomment-817392562
    global_rank = rank if rank is not None else rank_zero_only.rank
    process_seed = torch.initial_seed()
    # back out the base seed so we can use all the bits
    base_seed = process_seed - worker_id
    log.debug(
        f"Initializing random number generators of process {global_rank} worker {worker_id} with base seed {base_seed}"
    )
    ss = np.random.SeedSequence([base_seed, worker_id, global_rank])
    # use 128 bits (4 x 32-bit words)
    np.random.seed(ss.generate_state(4))
    # Spawn distinct SeedSequences for the PyTorch PRNG and the stdlib random module
    torch_ss, stdlib_ss = ss.spawn(2)
    # PyTorch 1.7 and above takes a 64-bit seed
    dtype = np.uint64 if _TORCH_GREATER_EQUAL_1_7 else np.uint32
    torch.manual_seed(torch_ss.generate_state(1, dtype=dtype)[0])
    # use 128 bits expressed as an integer
    stdlib_seed = (stdlib_ss.generate_state(2, dtype=np.uint64).astype(object) * [1 << 64, 1]).sum()
    random.seed(stdlib_seed)