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.
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.
torch |
oneccl_bindings_for_pytorch |
|---|---|
master |
master |
| v2.2.0 | v2.2.0+cpu |
| v2.1.0 | ccl_torch2.1.0+cpu |
| v2.0.1 | ccl_torch2.0.100 |
| v1.13 | ccl_torch1.13 |
| v1.12.1 | ccl_torch1.12.100 |
| 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 v2.2.0 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 3.8 or later and a C++17 compiler
-
PyTorch v2.2.0
The following build options are supported in Intel® oneCCL Bindings for PyTorch*.
| Build Option | Default Value | Description |
|---|---|---|
| COMPUTE_BACKEND | Set oneCCL COMPUTE_BACKEDN,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 to Wheel name |
The following lunch options are supported in Intel® oneCCL Bindings for PyTorch*.
| Lunch 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 |
-
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 without oneCCL export INTELONEAPIROOT=${HOME}/intel/oneapi USE_SYSTEM_ONECCL=ON COMPUTE_BACKEND=dpcpp python 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 | Python 3.11 |
|---|---|---|---|---|---|---|
| 2.2.0 | √ | √ | √ | √ | ||
| 2.1.0 | √ | √ | √ | √ | ||
| 2.0.100 | √ | √ | √ | √ | ||
| 1.13 | √ | √ | √ | √ | ||
| 1.12.100 | √ | √ | √ | √ | ||
| 1.12.0 | √ | √ | √ | √ | ||
| 1.11.0 | √ | √ | √ | √ | ||
| 1.10.0 | √ | √ | √ | √ |
Install CPU version
python -m pip install oneccl_bind_pt --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/cpu/us/Install XPU version
python -m pip install oneccl_bind_pt -f https://developer.intel.com/ipex-whl-stable-xpu- 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.shexample.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 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.shmpirun -n -ppn -f python example.py
## Performance Debugging
For debugging performance of communication primitives PyTorch's [Autograd profiler](https://pytorch.org/docs/stable/autograd.html#profiler)
can be used to inspect time spent inside oneCCL calls.
Example:
profiling.py
```python
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.
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