This repository holds PyTorch bindings maintained by Intel for the Intel® oneAPI Collective Communications Library (oneCCL).
PyTorch is an open-source machine learning framework.
Intel® oneCCL (collective communications library) is a library for efficient distributed deep learning training implementing such collectives like allreduce, allgather, alltoall. For more information on oneCCL, please refer to the oneCCL documentation and oneCCL specification.
oneccl_bindings_for_pytorch module implements PyTorch C10D ProcessGroup API and can be dynamically loaded as external ProcessGroup and only works on Linux platform now.
We recommend Anaconda as Python package management system. The following is the corresponding branches (tags) of oneccl_bindings_for_pytorch and supported Pytorch.
torch |
oneccl_bindings_for_pytorch |
|---|---|
master |
master |
| v1.12.0 | ccl_torch1.12 |
| v1.11.0 | ccl_torch1.11 |
| v1.10.0 | ccl_torch1.10 |
| v1.9.0 | ccl_torch1.9 |
| v1.8.1 | ccl_torch1.8 |
| v1.7.1 | ccl_torch1.7 |
| v1.6.0 | ccl_torch1.6 |
| v1.5-rc3 | beta09 |
The usage details can be found in the README of corresponding branch. The following part is about the usage of v1.9 tag. if you want to use other version of torch-ccl please checkout to that branch(tag). For pytorch-1.5.0-rc3, the #PR28068 and #PR32361 are need to dynamicall register external ProcessGroup and enable alltoall collective communication primitive. The patch file about these two PRs is in patches directory and you can use it directly.
-
Python 3.6 or later and a C++17 compiler
-
PyTorch v1.12.0
-
clone the
oneccl_bindings_for_pytorch.git clone https://github.com/intel/torch-ccl.git && cd torch-ccl git submodule sync git submodule update --init --recursive
-
Install
oneccl_bindings_for_pytorchpython setup.py install
Wheel files are avaiable for the following Python versions.
| Extension Version | Python 3.6 | Python 3.7 | Python 3.8 | Python 3.9 | Python 3.10 |
|---|---|---|---|---|---|
| 1.12.0 | √ | √ | √ | √ | |
| 1.11.0 | √ | √ | √ | √ | |
| 1.10.0 | √ | √ | √ | √ |
python -m pip install oneccl_bind_pt==1.12.0 -f https://developer.intel.com/ipex-whl-stableNote: oneccl_bindings_for_pytorch 1.12.0 prebuilt wheel does not work with PyTorch 1.12.1 (it is for PyTorch 1.12.0)
example.py
import torch.nn.parallel
import torch.distributed as dist
import oneccl_bindings_for_pytorch
...
os.environ['MASTER_ADDR'] = '127.0.0.1'
os.environ['MASTER_PORT'] = '29500'
os.environ['RANK'] = str(os.environ.get('PMI_RANK', 0))
os.environ['WORLD_SIZE'] = str(os.environ.get('PMI_SIZE', 1))
backend = 'ccl'
dist.init_process_group(backend, ...)
my_rank = dist.get_rank()
my_size = dist.get_world_size()
print("my rank = %d my size = %d" % (my_rank, my_size))
...
model = torch.nn.parallel.DistributedDataParallel(model, ...)
...(oneccl_bindings_for_pytorch is installed along with the MPI tool set.)
source <oneccl_bindings_for_pytorch_path>/env/setvars.sh
# eg:
# $ oneccl_bindings_for_pytorch_path=$(python -c "from oneccl_bindings_for_pytorch import cwd; print(cwd)")
# $ source $oneccl_bindings_for_pytorch_path/env/setvars.sh
mpirun -n <N> -ppn <PPN> -f <hostfile> python example.pyFor debugging performance of communication primitives PyTorch's Autograd profiler can be used to inspect time spent inside oneCCL calls.
Example:
profiling.py
import torch.nn.parallel
import torch.distributed as dist
import oneccl_bindings_for_pytorch
import os
os.environ['MASTER_ADDR'] = '127.0.0.1'
os.environ['MASTER_PORT'] = '29500'
os.environ['RANK'] = str(os.environ.get('PMI_RANK', 0))
os.environ['WORLD_SIZE'] = str(os.environ.get('PMI_SIZE', 1))
backend = 'ccl'
dist.init_process_group(backend)
my_rank = dist.get_rank()
my_size = dist.get_world_size()
print("my rank = %d my size = %d" % (my_rank, my_size))
x = torch.ones([2, 2])
y = torch.ones([4, 4])
with torch.autograd.profiler.profile(record_shapes=True) as prof:
for _ in range(10):
dist.all_reduce(x)
dist.all_reduce(y)
dist.barrier()
print(prof.key_averages(group_by_input_shape=True).table(sort_by="self_cpu_time_total"))mpirun -n 2 -l python profiling.py[0] my rank = 0 my size = 2
[0] ----------------------------------------------------- ------------ ------------ ------------ ------------ ------------ ------------ --------------------
[0] Name Self CPU % Self CPU CPU total % CPU total CPU time avg # of Calls Input Shapes
[0] ----------------------------------------------------- ------------ ------------ ------------ ------------ ------------ ------------ --------------------
[0] oneccl_bindings_for_pytorch::allreduce 91.41% 297.900ms 91.41% 297.900ms 29.790ms 10 [[2, 2]]
[0] oneccl_bindings_for_pytorch::wait::cpu::allreduce 8.24% 26.845ms 8.24% 26.845ms 2.684ms 10 [[2, 2], [2, 2]]
[0] oneccl_bindings_for_pytorch::wait::cpu::allreduce 0.30% 973.651us 0.30% 973.651us 97.365us 10 [[4, 4], [4, 4]]
[0] oneccl_bindings_for_pytorch::allreduce 0.06% 190.254us 0.06% 190.254us 19.025us 10 [[4, 4]]
[0] ----------------------------------------------------- ------------ ------------ ------------ ------------ ------------ ------------ --------------------
[0] Self CPU time total: 325.909ms
[0]
[1] my rank = 1 my size = 2
[1] ----------------------------------------------------- ------------ ------------ ------------ ------------ ------------ ------------ --------------------
[1] Name Self CPU % Self CPU CPU total % CPU total CPU time avg # of Calls Input Shapes
[1] ----------------------------------------------------- ------------ ------------ ------------ ------------ ------------ ------------ --------------------
[1] oneccl_bindings_for_pytorch::allreduce 96.03% 318.551ms 96.03% 318.551ms 31.855ms 10 [[2, 2]]
[1] oneccl_bindings_for_pytorch::wait::cpu::allreduce 3.62% 12.019ms 3.62% 12.019ms 1.202ms 10 [[2, 2], [2, 2]]
[1] oneccl_bindings_for_pytorch::allreduce 0.33% 1.082ms 0.33% 1.082ms 108.157us 10 [[4, 4]]
[1] oneccl_bindings_for_pytorch::wait::cpu::allreduce 0.02% 56.505us 0.02% 56.505us 5.651us 10 [[4, 4], [4, 4]]
[1] ----------------------------------------------------- ------------ ------------ ------------ ------------ ------------ ------------ --------------------
[1] Self CPU time total: 331.708ms
[1]