omic/rosettafold-all-atom
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Initial commit: RoseTTAFold-All-Atom configured for Wes with Harbor images and s3:// paths

Browse Source
This commit is contained in:
Olamide Isreal
2026-03-17 17:57:24 +01:00
commit 6eef3bb748
108 changed files with 28144 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

325
rf2aa/SE3Transformer/se3_transformer/runtime/gpu_affinity.py Normal file
View File

@@ -0,0 +1,325 @@
# 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 collections
import itertools
import math
import os
import pathlib
import re
import pynvml
class Device:
# assumes nvml returns list of 64 bit ints
_nvml_affinity_elements = math.ceil(os.cpu_count() / 64)
def __init__(self, device_idx):
super().__init__()
self.handle = pynvml.nvmlDeviceGetHandleByIndex(device_idx)
def get_name(self):
return pynvml.nvmlDeviceGetName(self.handle)
def get_uuid(self):
return pynvml.nvmlDeviceGetUUID(self.handle)
def get_cpu_affinity(self):
affinity_string = ""
for j in pynvml.nvmlDeviceGetCpuAffinity(self.handle, Device._nvml_affinity_elements):
# assume nvml returns list of 64 bit ints
affinity_string = "{:064b}".format(j) + affinity_string
affinity_list = [int(x) for x in affinity_string]
affinity_list.reverse() # so core 0 is in 0th element of list
ret = [i for i, e in enumerate(affinity_list) if e != 0]
return ret
def get_thread_siblings_list():
"""
Returns a list of 2-element integer tuples representing pairs of
hyperthreading cores.
"""
path = "/sys/devices/system/cpu/cpu*/topology/thread_siblings_list"
thread_siblings_list = []
pattern = re.compile(r"(\d+)\D(\d+)")
for fname in pathlib.Path(path[0]).glob(path[1:]):
with open(fname) as f:
content = f.read().strip()
res = pattern.findall(content)
if res:
pair = tuple(map(int, res[0]))
thread_siblings_list.append(pair)
return thread_siblings_list
def check_socket_affinities(socket_affinities):
# sets of cores should be either identical or disjoint
for i, j in itertools.product(socket_affinities, socket_affinities):
if not set(i) == set(j) and not set(i).isdisjoint(set(j)):
raise RuntimeError(f"Sets of cores should be either identical or disjoint, " f"but got {i} and {j}.")
def get_socket_affinities(nproc_per_node, exclude_unavailable_cores=True):
devices = [Device(i) for i in range(nproc_per_node)]
socket_affinities = [dev.get_cpu_affinity() for dev in devices]
if exclude_unavailable_cores:
available_cores = os.sched_getaffinity(0)
socket_affinities = [list(set(affinity) & available_cores) for affinity in socket_affinities]
check_socket_affinities(socket_affinities)
return socket_affinities
def set_socket_affinity(gpu_id):
"""
The process is assigned with all available logical CPU cores from the CPU
socket connected to the GPU with a given id.
Args:
gpu_id: index of a GPU
"""
dev = Device(gpu_id)
affinity = dev.get_cpu_affinity()
os.sched_setaffinity(0, affinity)
def set_single_affinity(gpu_id):
"""
The process is assigned with the first available logical CPU core from the
list of all CPU cores from the CPU socket connected to the GPU with a given
id.
Args:
gpu_id: index of a GPU
"""
dev = Device(gpu_id)
affinity = dev.get_cpu_affinity()
# exclude unavailable cores
available_cores = os.sched_getaffinity(0)
affinity = list(set(affinity) & available_cores)
os.sched_setaffinity(0, affinity[:1])
def set_single_unique_affinity(gpu_id, nproc_per_node):
"""
The process is assigned with a single unique available physical CPU core
from the list of all CPU cores from the CPU socket connected to the GPU with
a given id.
Args:
gpu_id: index of a GPU
"""
socket_affinities = get_socket_affinities(nproc_per_node)
siblings_list = get_thread_siblings_list()
siblings_dict = dict(siblings_list)
# remove siblings
for idx, socket_affinity in enumerate(socket_affinities):
socket_affinities[idx] = list(set(socket_affinity) - set(siblings_dict.values()))
affinities = []
assigned = []
for socket_affinity in socket_affinities:
for core in socket_affinity:
if core not in assigned:
affinities.append([core])
assigned.append(core)
break
os.sched_setaffinity(0, affinities[gpu_id])
def set_socket_unique_affinity(gpu_id, nproc_per_node, mode, balanced=True):
"""
The process is assigned with an unique subset of available physical CPU
cores from the CPU socket connected to a GPU with a given id.
Assignment automatically includes hyperthreading siblings (if siblings are
available).
Args:
gpu_id: index of a GPU
nproc_per_node: total number of processes per node
mode: mode
balanced: assign an equal number of physical cores to each process
"""
socket_affinities = get_socket_affinities(nproc_per_node)
siblings_list = get_thread_siblings_list()
siblings_dict = dict(siblings_list)
# remove hyperthreading siblings
for idx, socket_affinity in enumerate(socket_affinities):
socket_affinities[idx] = list(set(socket_affinity) - set(siblings_dict.values()))
socket_affinities_to_device_ids = collections.defaultdict(list)
for idx, socket_affinity in enumerate(socket_affinities):
socket_affinities_to_device_ids[tuple(socket_affinity)].append(idx)
# compute minimal number of physical cores per GPU across all GPUs and
# sockets, code assigns this number of cores per GPU if balanced == True
min_physical_cores_per_gpu = min(
[len(cores) // len(gpus) for cores, gpus in socket_affinities_to_device_ids.items()]
)
for socket_affinity, device_ids in socket_affinities_to_device_ids.items():
devices_per_group = len(device_ids)
if balanced:
cores_per_device = min_physical_cores_per_gpu
socket_affinity = socket_affinity[: devices_per_group * min_physical_cores_per_gpu]
else:
cores_per_device = len(socket_affinity) // devices_per_group
for group_id, device_id in enumerate(device_ids):
if device_id == gpu_id:
# In theory there should be no difference in performance between
# 'interleaved' and 'continuous' pattern on Intel-based DGX-1,
# but 'continuous' should be better for DGX A100 because on AMD
# Rome 4 consecutive cores are sharing L3 cache.
# TODO: code doesn't attempt to automatically detect layout of
# L3 cache, also external environment may already exclude some
# cores, this code makes no attempt to detect it and to align
# mapping to multiples of 4.
if mode == "interleaved":
affinity = list(socket_affinity[group_id::devices_per_group])
elif mode == "continuous":
affinity = list(socket_affinity[group_id * cores_per_device: (group_id + 1) * cores_per_device])
else:
raise RuntimeError("Unknown set_socket_unique_affinity mode")
# unconditionally reintroduce hyperthreading siblings, this step
# may result in a different numbers of logical cores assigned to
# each GPU even if balanced == True (if hyperthreading siblings
# aren't available for a subset of cores due to some external
# constraints, siblings are re-added unconditionally, in the
# worst case unavailable logical core will be ignored by
# os.sched_setaffinity().
affinity += [siblings_dict[aff] for aff in affinity if aff in siblings_dict]
os.sched_setaffinity(0, affinity)
def set_affinity(gpu_id, nproc_per_node, mode="socket_unique_continuous", balanced=True):
"""
The process is assigned with a proper CPU affinity which matches hardware
architecture on a given platform. Usually it improves and stabilizes
performance of deep learning training workloads.
This function assumes that the workload is running in multi-process
single-device mode (there are multiple training processes and each process
is running on a single GPU), which is typical for multi-GPU training
workloads using `torch.nn.parallel.DistributedDataParallel`.
Available affinity modes:
* 'socket' - the process is assigned with all available logical CPU cores
from the CPU socket connected to the GPU with a given id.
* 'single' - the process is assigned with the first available logical CPU
core from the list of all CPU cores from the CPU socket connected to the GPU
with a given id (multiple GPUs could be assigned with the same CPU core).
* 'single_unique' - the process is assigned with a single unique available
physical CPU core from the list of all CPU cores from the CPU socket
connected to the GPU with a given id.
* 'socket_unique_interleaved' - the process is assigned with an unique
subset of available physical CPU cores from the CPU socket connected to a
GPU with a given id, hyperthreading siblings are included automatically,
cores are assigned with interleaved indexing pattern
* 'socket_unique_continuous' - (the default) the process is assigned with an
unique subset of available physical CPU cores from the CPU socket connected
to a GPU with a given id, hyperthreading siblings are included
automatically, cores are assigned with continuous indexing pattern
'socket_unique_continuous' is the recommended mode for deep learning
training workloads on NVIDIA DGX machines.
Args:
gpu_id: integer index of a GPU
nproc_per_node: number of processes per node
mode: affinity mode
balanced: assign an equal number of physical cores to each process,
affects only 'socket_unique_interleaved' and
'socket_unique_continuous' affinity modes
Returns a set of logical CPU cores on which the process is eligible to run.
Example:
import argparse
import os
import gpu_affinity
import torch
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'--local_rank',
type=int,
default=os.getenv('LOCAL_RANK', 0),
)
args = parser.parse_args()
nproc_per_node = torch.cuda.device_count()
affinity = gpu_affinity.set_affinity(args.local_rank, nproc_per_node)
print(f'{args.local_rank}: core affinity: {affinity}')
if __name__ == "__main__":
main()
Launch the example with:
python -m torch.distributed.launch --nproc_per_node <#GPUs> example.py
WARNING: On DGX A100 only a half of CPU cores have direct access to GPUs.
This function restricts execution only to the CPU cores directly connected
to GPUs, so on DGX A100 it will limit the code to half of CPU cores and half
of CPU memory bandwidth (which may be fine for many DL models).
"""
pynvml.nvmlInit()
if mode == "socket":
set_socket_affinity(gpu_id)
elif mode == "single":
set_single_affinity(gpu_id)
elif mode == "single_unique":
set_single_unique_affinity(gpu_id, nproc_per_node)
elif mode == "socket_unique_interleaved":
set_socket_unique_affinity(gpu_id, nproc_per_node, "interleaved", balanced)
elif mode == "socket_unique_continuous":
set_socket_unique_affinity(gpu_id, nproc_per_node, "continuous", balanced)
else:
raise RuntimeError("Unknown affinity mode")
affinity = os.sched_getaffinity(0)
return affinity