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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
#
# SPDX-FileCopyrightText: Copyright (c) 2021 NVIDIA CORPORATION & AFFILIATES
# SPDX-License-Identifier: MIT
import argparse
import ctypes
import logging
import os
import random
from functools import wraps
from typing import Union, List, Dict
import numpy as np
import torch
import torch.distributed as dist
from torch import Tensor
def aggregate_residual(feats1, feats2, method: str):
""" Add or concatenate two fiber features together. If degrees don't match, will use the ones of feats2. """
if method in ['add', 'sum']:
return {k: (v + feats1[k]) if k in feats1 else v for k, v in feats2.items()}
elif method in ['cat', 'concat']:
return {k: torch.cat([v, feats1[k]], dim=1) if k in feats1 else v for k, v in feats2.items()}
else:
raise ValueError('Method must be add/sum or cat/concat')
def degree_to_dim(degree: int) -> int:
return 2 * degree + 1
def unfuse_features(features: Tensor, degrees: List[int]) -> Dict[str, Tensor]:
return dict(zip(map(str, degrees), features.split([degree_to_dim(deg) for deg in degrees], dim=-1)))
def str2bool(v: Union[bool, str]) -> bool:
if isinstance(v, bool):
return v
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected.')
def to_cuda(x):
""" Try to convert a Tensor, a collection of Tensors or a DGLGraph to CUDA """
if isinstance(x, Tensor):
return x.cuda(non_blocking=True)
elif isinstance(x, tuple):
return (to_cuda(v) for v in x)
elif isinstance(x, list):
return [to_cuda(v) for v in x]
elif isinstance(x, dict):
return {k: to_cuda(v) for k, v in x.items()}
else:
# DGLGraph or other objects
return x.to(device=torch.cuda.current_device())
def get_local_rank() -> int:
return int(os.environ.get('LOCAL_RANK', 0))
def init_distributed() -> bool:
world_size = int(os.environ.get('WORLD_SIZE', 1))
distributed = world_size > 1
if distributed:
backend = 'nccl' if torch.cuda.is_available() else 'gloo'
dist.init_process_group(backend=backend, init_method='env://')
if backend == 'nccl':
torch.cuda.set_device(get_local_rank())
else:
logging.warning('Running on CPU only!')
assert torch.distributed.is_initialized()
return distributed
def increase_l2_fetch_granularity():
# maximum fetch granularity of L2: 128 bytes
_libcudart = ctypes.CDLL('libcudart.so')
# set device limit on the current device
# cudaLimitMaxL2FetchGranularity = 0x05
pValue = ctypes.cast((ctypes.c_int * 1)(), ctypes.POINTER(ctypes.c_int))
_libcudart.cudaDeviceSetLimit(ctypes.c_int(0x05), ctypes.c_int(128))
_libcudart.cudaDeviceGetLimit(pValue, ctypes.c_int(0x05))
assert pValue.contents.value == 128
def seed_everything(seed):
seed = int(seed)
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
def rank_zero_only(fn):
@wraps(fn)
def wrapped_fn(*args, **kwargs):
if not dist.is_initialized() or dist.get_rank() == 0:
return fn(*args, **kwargs)
return wrapped_fn
def using_tensor_cores(amp: bool) -> bool:
major_cc, minor_cc = torch.cuda.get_device_capability()
return (amp and major_cc >= 7) or major_cc >= 8
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