lightning/pytorch_lightning/utilities/seed.py

# 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
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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"""Helper functions to help with reproducibility of models. """

import os
import random
from typing import Optional

import numpy as np
import torch

from pytorch_lightning import _logger as log
from pytorch_lightning.utilities import rank_zero_warn


def seed_everything(seed: Optional[int] = None) -> int:
    """
    Function that sets seed for pseudo-random number generators in:
    pytorch, numpy, python.random
    In addition, sets the env variable `PL_GLOBAL_SEED` which will be passed to
    spawned subprocesses (e.g. ddp_spawn backend).

    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.
    """
    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)

    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)
    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)