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1b2d73a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | # SPDX-FileCopyrightText: Copyright (c) 2025 The Self-Forcing Authors. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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
# limitations under the License.
import numpy as np
import random
import torch
def set_seed(seed: int, deterministic: bool = False):
"""
Helper function for reproducible behavior to set the seed in `random`, `numpy`, `torch`.
Args:
seed (`int`):
The seed to set.
deterministic (`bool`, *optional*, defaults to `False`):
Whether to use deterministic algorithms where available. Can slow down training.
"""
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
if deterministic:
torch.use_deterministic_algorithms(True)
def merge_dict_list(dict_list):
if len(dict_list) == 1:
return dict_list[0]
merged_dict = {}
for k, v in dict_list[0].items():
if isinstance(v, torch.Tensor):
if v.ndim == 0:
merged_dict[k] = torch.stack([d[k] for d in dict_list], dim=0)
else:
merged_dict[k] = torch.cat([d[k] for d in dict_list], dim=0)
else:
# for non-tensor values, we just copy the value from the first item
merged_dict[k] = v
return merged_dict
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