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 collectives like allreduce, allgather, alltoall. For more information on oneCCL, please refer to the oneCCL documentation.
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.
The table below shows which functions are available for use with CPU / Intel dGPU tensors.
| CPU | GPU | |
|---|---|---|
send |
× | √ |
recv |
× | √ |
broadcast |
√ | √ |
all_reduce |
√ | √ |
reduce |
√ | √ |
all_gather |
√ | √ |
gather |
√ | √ |
scatter |
√ | √ |
reduce_scatter |
√ | √ |
all_to_all |
√ | √ |
barrier |
√ | √ |
We recommend using Anaconda as Python package management system. The followings are the corresponding branches (tags) of oneccl_bindings_for_pytorch and supported PyTorch.
The usage details can be found in the README of corresponding branch.
-
Python 3.8 or later and a C++17 compiler
-
PyTorch v2.3.1
The following build options are supported in Intel® oneCCL Bindings for PyTorch*.
| Build Option | Default Value | Description |
|---|---|---|
| COMPUTE_BACKEND | N/A | Set oneCCL COMPUTE_BACKEND, set to dpcpp and use DPC++ compiler to enable support for Intel XPU |
| USE_SYSTEM_ONECCL | OFF | Use oneCCL library in system |
| CCL_PACKAGE_NAME | oneccl-bind-pt | Set wheel name |
| ONECCL_BINDINGS_FOR_PYTORCH_BACKEND | cpu | Set backend |
| CCL_SHA_VERSION | False | Add git head sha version into wheel name |
The following launch options are supported in Intel® oneCCL Bindings for PyTorch*.
| Launch Option | Default Value | Description |
|---|---|---|
| ONECCL_BINDINGS_FOR_PYTORCH_ENV_VERBOSE | 0 | Set verbose level in oneccl_bindings_for_pytorch |
| ONECCL_BINDINGS_FOR_PYTORCH_ENV_WAIT_GDB | 0 | Set 1 to force the oneccl_bindings_for_pytorch wait for GDB attaching |
| TORCH_LLM_ALLREDUCE | 0 | Set 1 to enable this prototype feature for better scale-up performance. This is a prototype feature to provide better scale-up performance by enabling optimized collective algorithms in oneCCL and asynchronous execution in torch-ccl. This feature requires XeLink enabled for cross-cards communication. |
| CCL_BLOCKING_WAIT | 0 | Set 1 to enable this prototype feature, which is to control whether collectives execution on XPU is host blocking or non-blocking. |
| CCL_SAME_STREAM | 0 | Set 1 to enable this prototype feature, which is to allow using a computation stream as communication stream to minimize overhead for streams synchronization. |
-
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_pytorch# for CPU Backend Only python setup.py install # for XPU Backend: use DPC++ Compiler to enable support for Intel XPU # build with oneCCL from third party COMPUTE_BACKEND=dpcpp python setup.py install # build with oneCCL from basekit export INTELONEAPIROOT=${HOME}/intel/oneapi USE_SYSTEM_ONECCL=ON COMPUTE_BACKEND=dpcpp python setup.py install
Wheel files are available for the following Python versions. Please always use the latest release to get started.
| Extension Version | Python 3.6 | Python 3.7 | Python 3.8 | Python 3.9 | Python 3.10 | Python 3.11 |
|---|---|---|---|---|---|---|
| 2.3.100 | √ | √ | √ | √ | ||
| 2.1.400 | √ | √ | √ | √ | ||
| 2.1.300 | √ | √ | √ | √ | ||
| 2.1.200 | √ | √ | √ | √ | ||
| 2.1.100 | √ | √ | √ | √ | ||
| 2.0.100 | √ | √ | √ | √ | ||
| 1.13 | √ | √ | √ | √ | ||
| 1.12.100 | √ | √ | √ | √ | ||
| 1.12.0 | √ | √ | √ | √ | ||
| 1.11.0 | √ | √ | √ | √ | ||
| 1.10.0 | √ | √ | √ | √ |
python -m pip install oneccl_bind_pt==2.3.100 --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/Note: Please set proxy or update URL address to https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/ if you meet connection issue.
- If oneccl_bindings_for_pytorch is built without oneCCL and use oneCCL in system, dynamic link oneCCl from oneAPI basekit (recommended usage):
source $basekit_root/ccl/latest/env/vars.shNote: Make sure you have installed basekit when using Intel® oneCCL Bindings for Pytorch* on Intel® GPUs.
- If oneccl_bindings_for_pytorch is built with oneCCL from third party or installed from prebuilt wheel: Dynamic link oneCCL and Intel MPI libraries:
source $(python -c "import oneccl_bindings_for_pytorch as torch_ccl;print(torch_ccl.cwd)")/env/setvars.shDynamic link oneCCL only (not including Intel MPI):
source $(python -c "import oneccl_bindings_for_pytorch as torch_ccl;print(torch_ccl.cwd)")/env/vars.shNote: Please import torch and import intel_extension_for_pytorch, prior to import oneccl_bindings_for_pytorch.
example.py
import torch
import intel_extension_for_pytorch
import oneccl_bindings_for_pytorch
import torch.nn.parallel
import torch.distributed as dist
...
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 built without oneCCL, use oneCCL and MPI(if needed) in system)
source $basekit_root/ccl/latest/env/vars.sh
source $basekit_root/mpi/latest/env/vars.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]
For Point-to-point communication, directly call dist.send/recv after initializing the process group in launch script will trigger runtime error. Because all ranks of the group are expected to participate in this call to create communicators in our current implementation, while dist.send/recv only has a pair of ranks' participation. As a result, dist.send/recv should be used after collective call, which ensures all ranks' participation. The further solution for supporting directly call dist.send/recv after initializing the process group is still under investigation.