oneCCL
oneAPI Collective Communications Library (oneCCL) provides an efficient implementation of communication patterns used in deep learning. oneCCL is governed by the UXL Foundation and is an implementation of the oneAPI specification.
oneCCL can be used through
- native C++ SYCL mode
- Horovod
- PyTorch Distributed Data Parallel (DDP)
Aurora oneCCL environment
kaushikvelusamy@aurora-uan-0012:~> module load frameworks
(/opt/aurora/24.180.0/frameworks/aurora_nre_models_frameworks-2024.2.1_u1) kaushikvelusamy@aurora-uan-0012:~> echo $CCL_ROOT
/opt/aurora/24.180.0/CNDA/oneapi/ccl/2021.13.1_20240808.145507
OneCCL mandatory environment variables
module load frameworks
echo $CCL_ROOT
export LD_LIBRARY_PATH=$CCL_ROOT/lib:$LD_LIBRARY_PATH
export CPATH=$CCL_ROOT/include:$CPATH
export LIBRARY_PATH=$CCL_ROOT/lib:$LIBRARY_PATH
export CCL_PROCESS_LAUNCHER=pmix
export CCL_ATL_TRANSPORT=mpi
export CCL_ALLREDUCE=topo
export CCL_ALLREDUCE_SCALEOUT=rabenseifner # currently best allreduce algorithm at large scale
export CCL_BCAST=double_tree # currently best bcast algorithm at large scale
export CCL_KVS_MODE=mpi
export CCL_CONFIGURATION_PATH=""
export CCL_CONFIGURATION=cpu_gpu_dpcpp
export CCL_KVS_CONNECTION_TIMEOUT=600
export CCL_ZE_CACHE_OPEN_IPC_HANDLES_THRESHOLD=1024
export CCL_KVS_USE_MPI_RANKS=1
OneCCL optional environment variables
ulimit -c unlimited
export FI_MR_ZE_CACHE_MONITOR_ENABLED=0
export FI_MR_CACHE_MONITOR=disabled
export FI_CXI_RX_MATCH_MODE=hybrid
export FI_CXI_OFLOW_BUF_SIZE=8388608
export FI_CXI_DEFAULT_CQ_SIZE=1048576
export FI_CXI_CQ_FILL_PERCENT=30
export MPI_PROVIDER=$FI_PROVIDER
unset MPIR_CVAR_CH4_COLL_SELECTION_TUNING_JSON_FILE
unset MPIR_CVAR_COLL_SELECTION_TUNING_JSON_FILE
export INTELGT_AUTO_ATTACH_DISABLE=1
export PALS_PING_PERIOD=240
export PALS_RPC_TIMEOUT=240
export MPIR_CVAR_GATHERV_INTER_SSEND_MIN_PROCS=-1 #to solve the sync send issue in Horovod seg fault
export CCL_ATL_SYNC_COLL=1 #to avoid potential hang at large scale
export CCL_OP_SYNC=1 #to avoid potential hang at large scale
Algorithm selection
More info on Algorithm selection: https://oneapi-src.github.io/oneCCL/env-variables.htmlnative C++ SYCL mode
You can compile examples from the oneCCL gitrepository and use the library from the system default instead of local builds. More information at : https://www.intel.com/content/www/us/en/docs/oneccl/benchmark-user-guide/2021-12/overview.html
To build the C++ benchmark examples
cd oneccl
mkdir build
cd build
module load cmake
cmake .. -DCMAKE_C_COMPILER=icx-cc -DCMAKE_CXX_COMPILER=icpx -DCOMPUTE_BACKEND=dpcpp -DCMAKE_INSTALL_PREFIX=/lus/flare/projects/Aurora_deployment/kaushik/all_reduce_frameworks/gitrepos/oneCCL/build/
make -j install
rm -rf _install/bin/* _install/lib/*mpi* _install/lib/*fabric* _install/opt/
To run from a jobscript
#!/bin/bash -x
# qsub -l nodes=2:ncpus=208 -q workq -l walltime=02:00:00 -l filesystems=lustre_scaling -A Aurora_deployment ./pbs_job_
#PBS -A Aurora_deployment
#PBS -k doe
module load frameworks
cd $PBS_O_WORKDIR
echo Jobid: $PBS_JOBID
echo Running on nodes `cat $PBS_NODEFILE`
NNODES=`wc -l < $PBS_NODEFILE`
RANKS_PER_NODE=12 # Number of MPI ranks per node
NRANKS=$(( NNODES * RANKS_PER_NODE ))
echo "NUM_OF_NODES=${NNODES} TOTAL_NUM_RANKS=${NRANKS} RANKS_PER_NODE=${RANKS_PER_NODE}"
CPU_BINDING1=list:4:9:14:19:20:25:56:61:66:71:74:79
EXT_ENV="--env FI_CXI_DEFAULT_CQ_SIZE=1048576"
APP1=/lus/flare/projects/Aurora_deployment/kaushik/all_reduce_frameworks/gitrepos/oneCCL/build/_install/examples/benchmark/benchmark
echo $CCL_ROOT
export LD_LIBRARY_PATH=$CCL_ROOT/lib:$LD_LIBRARY_PATH
export CPATH=$CCL_ROOT/include:$CPATH
export LIBRARY_PATH=$CCL_ROOT/lib:$LIBRARY_PATH
export CCL_PROCESS_LAUNCHER=pmix
export CCL_ATL_TRANSPORT=mpi
export CCL_ALLREDUCE=topo
export CCL_ALLREDUCE_SCALEOUT=rabenseifner
export CCL_KVS_MODE=mpi
export CCL_CONFIGURATION_PATH=""
export CCL_CONFIGURATION=cpu_gpu_dpcpp
export CCL_KVS_CONNECTION_TIMEOUT=600
which python
mkdir -p ./out_${PBS_JOBID}/c_oneccl_gpu
for NNODES in 4 8 16 32 64
do
RANKS_PER_NODE=12 # Number of MPI ranks per node
NRANKS=$(( NNODES * RANKS_PER_NODE ))
for BUF_SIZE in 1 2 4 8 16 32 64 128 256 512 1024 2048 4096 8192 16384 32768 65536 131072 262144 524288 1048576 2097152 4194304 8388608 16777216 33554432 67108864 134217728 268435456
do
date
mpiexec ${EXT_ENV} --env CCL_LOG_LEVEL=info --env CCL_PROCESS_LAUNCHER=pmix --env CCL_ATL_TRANSPORT=mpi \
--np ${NRANKS} -ppn ${RANKS_PER_NODE} --cpu-bind $CPU_BINDING1 $APP1 \
--elem_counts ${BUF_SIZE},${BUF_SIZE},${BUF_SIZE} \
--coll allreduce -j off -i 1 -w 0 --backend sycl --sycl_dev_type gpu > ./out_${PBS_JOBID}/c_oneccl_gpu/${PBS_JOBID}_${NNODES}_${NRANKS}_${RANKS_PER_NODE}_${BUF_SIZE}_sycl_ccl_gpu_out_w1.txt
date
echo ${BUF_SIZE}
done
done
# For CPU only, change benchmark options to : --backend host --sycl_dev_type host
Horovod
Tensorflow horovod example
import datetime
from time import perf_counter_ns
import sys
import tensorflow as tf
import horovod.tensorflow as hvd
import intel_extension_for_tensorflow as itex
print(itex.__version__)
hvd.init()
hvd_local_rank = hvd.local_rank()
hvd_size = hvd.size()
print("hvd_local_rank = %d hvd_size = %d" % (hvd_local_rank, hvd_size))
xpus = tf.config.experimental.list_physical_devices('XPU')
logical_gpus = tf.config.experimental.set_visible_devices(xpus[hvd.local_rank()], 'XPU')
print(xpus)
tf.debugging.set_log_device_placement(True)
dim_size=int(int(sys.argv[1])/4)
elapsed1=[]
for _ in range(5):
with tf.device(f"XPU:{hvd_local_rank%12}"):
x = tf.ones([1, dim_size],dtype=tf.float32)
# print(x)
t5 = perf_counter_ns()
y = hvd.allreduce(x, average=False)
t6 = perf_counter_ns()
elapsed1.append(t6 - t5)
if hvd.rank() == 0:
for e in elapsed1:
print(e)
Pytorch horovod example
from time import perf_counter_ns
import sys
import intel_extension_for_pytorch # Added Extra
import torch.nn.parallel
import horovod.torch as hvd
hvd.init()
hvd_local_rank = hvd.local_rank()
hvd_size = hvd.size()
# print("hvd_local_rank = %d hvd_size = %d" % (hvd_local_rank, hvd_size))
def get_default_device():
if torch.xpu.is_available():
return torch.device(f"xpu:{hvd_local_rank%12}")
else:
return torch.device('cpu')
device = get_default_device()
dim_size=int(int(sys.argv[1])/4)
elapsed1=[]
for _ in range(50):
x = torch.ones([1, dim_size],dtype=torch.float32).to(device, non_blocking=True)
# print(x)
t5 = perf_counter_ns()
y = hvd.allreduce(x, average=False)
t6 = perf_counter_ns()
elapsed1.append(t6 - t5)
if hvd.rank() == 0:
for e in elapsed1:
print(e)
Pytorch DDP
import datetime
from time import perf_counter_ns
import sys
import os
import socket
from mpi4py import MPI
import intel_extension_for_pytorch # Added Extra
import torch.nn.parallel
import torch.distributed as dist
import oneccl_bindings_for_pytorch
MPI.COMM_WORLD.Barrier()
os.environ['RANK'] = str(os.environ.get('PMI_RANK', 0))
os.environ['WORLD_SIZE'] = str(os.environ.get('PMI_SIZE', 1))
mpi_world_size = MPI.COMM_WORLD.Get_size()
mpi_my_rank = MPI.COMM_WORLD.Get_rank()
if mpi_my_rank == 0:
master_addr = socket.gethostname()
sock = socket.socket()
sock.bind(('',0))
# master_port = sock.getsockname()[1]
master_port = 2345
else:
master_addr = None
master_port = None
master_addr = MPI.COMM_WORLD.bcast(master_addr, root=0)
master_port = MPI.COMM_WORLD.bcast(master_port, root=0)
os.environ["MASTER_ADDR"] = master_addr
os.environ["MASTER_PORT"] = str(master_port)
MPI.COMM_WORLD.Barrier()
dist.init_process_group(backend = "ccl", init_method = 'env://', world_size = mpi_world_size, rank = mpi_my_rank, timeout = datetime.timedelta(seconds=3600))
MPI.COMM_WORLD.Barrier()
dist_my_rank = dist.get_rank()
dist_world_size = dist.get_world_size()
def get_default_device():
if torch.xpu.is_available():
return torch.device(f"xpu:{dist_my_rank%12}")
else:
return torch.device('cpu')
device = get_default_device()
dim_size=int(int(sys.argv[1])/4)
MPI.COMM_WORLD.Barrier()
elapsed1=[]
for _ in range(50):
x = torch.ones([1, dim_size],dtype=torch.float32).to(device, non_blocking=True)
# print(x)
t5 = perf_counter_ns()
dist.all_reduce(x, op=dist.ReduceOp.SUM) # Added Extra op
MPI.COMM_WORLD.Barrier()
t6 = perf_counter_ns()
elapsed1.append(t6 - t5)
if mpi_my_rank == 0:
for e in elapsed1:
print(e)
References
- https://oneapi-src.github.io/oneCCL/env-variables.html
- https://github.com/oneapi-src/oneCCL
- https://github.com/intel/torch-ccl
- https://github.com/argonne-lcf/dl_scaling
- https://www.intel.com/content/www/us/en/docs/oneccl/benchmark-user-guide/2021-12/overview.html