diff --git a/.gitignore b/.gitignore
new file mode 100755
index 0000000..ce82017
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,3 @@
+/.idea/*
+/build/*
+/dist/*
\ No newline at end of file
diff --git a/Dockerfile b/Dockerfile
new file mode 100755
index 0000000..1726667
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,11 @@
+FROM nvcr.io/nvidia/pytorch:22.10-py3
+
+RUN apt-get update && apt-get install -y --no-install-recommends openssh-client openssh-server && \
+ mkdir -p /var/run/sshd
+
+ENV MPI_HOME=/opt/hpcx/ompi/
+ENV NCCL_INCLUDE=/usr/include
+ENV NCCL_LIB=/usr/lib/x86_64-linux-gnu/
+
+RUN git clone https://github.com/IST-DASLab/torch_cgx /torch_cgx &&\
+cd /torch_cgx && python setup.py install
\ No newline at end of file
diff --git a/LICENSE.txt b/LICENSE.txt
new file mode 100755
index 0000000..bbba84b
--- /dev/null
+++ b/LICENSE.txt
@@ -0,0 +1,617 @@
+ GNU AFFERO GENERAL PUBLIC LICENSE
+ Version 3, 19 November 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc.
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+ Preamble
+
+ The GNU Affero General Public License is a free, copyleft license for
+software and other kinds of works, specifically designed to ensure
+cooperation with the community in the case of network server software.
+
+ The licenses for most software and other practical works are designed
+to take away your freedom to share and change the works. By contrast,
+our General Public Licenses are intended to guarantee your freedom to
+share and change all versions of a program--to make sure it remains free
+software for all its users.
+
+ When we speak of free software, we are referring to freedom, not
+price. Our General Public Licenses are designed to make sure that you
+have the freedom to distribute copies of free software (and charge for
+them if you wish), that you receive source code or can get it if you
+want it, that you can change the software or use pieces of it in new
+free programs, and that you know you can do these things.
+
+ Developers that use our General Public Licenses protect your rights
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+you this License which gives you legal permission to copy, distribute
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+ A secondary benefit of defending all users' freedom is that
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+PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+ 16. Limitation of Liability.
+
+ IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+ 17. Interpretation of Sections 15 and 16.
+
+ If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
diff --git a/MANIFEST.in b/MANIFEST.in
new file mode 100755
index 0000000..150d74b
--- /dev/null
+++ b/MANIFEST.in
@@ -0,0 +1 @@
+graft src
\ No newline at end of file
diff --git a/README.md b/README.md
new file mode 100755
index 0000000..f525c81
--- /dev/null
+++ b/README.md
@@ -0,0 +1,90 @@
+# CGX
+
+CGX is a pytorch extension adding a backend for pytorch distributed supporting allreduce of quantized buffers.
+It supports quantizations of float16, float32 to 1-8 bits.
+
+CGX is based on MPI torch.distributed backend. The extension essentially only replaces allreduce primitive.
+
+## Quick Start
+
+### Prerequisites
+CGX, as a pytorch extension, requires `pytorch>=1.10.0`.
+
+For faster build we recommend to have `ninja` installed (`pip install ninja`).
+
+The compression is only supported for GPU-based buffers so either CUDA or ROCm is required.
+If CUDA or ROCm are installed not in the standard paths, set `[CUDA|ROCM]_HOME` or `[CUDA|ROCM]_PATH` accordingly.
+
+As long as it is based on MPI, it requires OpenMPI with GPU support installed (other MPI implementations were not tested).
+Also, the library supports NCCL based communications, so it requires NVIDIA NCCL library.
+
+### Build from source
+Set `MPI_HOME` environment variable to mpi home. In case of AMD GPU, set `CGX_CUDA` to 0.
+Set `NCCL_HOME` environment variable to NCCL home, or `NCCL_INCLUDE` and `NCCL_LIB`.
+Set `QSGD_DETERMENISTIC=0` if you want to have stochastic version QSGD.
+
+```bash
+git clone https://github.com/IST-DASLab/torch_cgx
+export MPI_HOME=/path/to/mpi
+export NCCL_HOME=/path/to/nccl
+python setup.py install
+```
+
+### Usage
+The only changes to the training script using pytorch distributed required
+ are importing the built extension and specifying `cgx` as `torch.distributed.init_process_group` backend parameter.
+
+Example:
+``` python
+import torch
+import torch.distributed as dist
+import torch_cgx
+
+dist.init_process_group('cgx', init_method='env://', rank=args.local_rank)
+```
+Also, it order to perform layerwise compression and being able to filter small sensitive to gradient compression
+layers (typically these are batch norm layers and biases) the `cgx` needs to have information about the model.
+For that users need to register the communication hook. The minimal size of the layers can be
+controlled with `layer_min_size` parameter.
+
+``` python
+model = torch.
+from cgx_utils import cgx_hook, CGXState
+state = CGXState(torch.distributed.group.WORLD, layer_min_size=1024,
+ compression_params={"bits": args.quantization_bits,
+ "bucket_size": args.quantization_bucket_size})
+model.register_comm_hook(state, cgx_hook)
+```
+
+As long as the extension is based on MPI backend, it requires MPI-compliant launcher (`torch.distributed.launch` won't work):
+`$ mpirun -np 2 python train.py`
+
+Also, if your training script was run previously with `torch.distributed.launch` utility, due to MPI launcher you need to set an environment variables (see cifar_train.py in examples)
+```
+if "OMPI_COMM_WORLD_SIZE" in os.environ:
+ os.environ['MASTER_ADDR'] = '127.0.0.1'
+ os.environ['MASTER_PORT'] = '4040'
+ os.environ["WORLD_SIZE"] = os.environ["OMPI_COMM_WORLD_SIZE"]
+ os.environ["RANK"] = os.environ["OMPI_COMM_WORLD_RANK"]
+```
+
+## Tuning
+CGX can be tuned with the following environment variables:
+
+- `CGX_COMPRESSION_QUANTIZATION_BITS` - number of bits each value of buffer is quantized to (from 1 to 8). Default is 32 which means no quantization is applied. This variable must be used if the `cgx_hook` communication hook is not registered.
+- `CGX_COMPRESSION_BUCKET_SIZE` - size of subarray into which buffer is split before quantization. Default is 512.
+- `CGX_COMPRESSION_SKIP_INCOMPLETE_BUCKETS` - boolean variable (0 or 1). After the splitting buffer into buckets, some values of buffer may remain. The variable tells quantization algorithm to compress or not to compress the remaining values. Default 0.
+- `CGX_COMPRESSION_MINIMAL_SIZE` - minimal size of buffer (number of elements) to compress. Default is 0 but in fact minimal size is forced to be not less than 16.
+- `CGX_FUSION_BUFFER_SIZE_MB`. CGX is leveraging [Tensor Fusion](https://github.com/horovod/horovod#tensor-fusion), a performance feature introduced in Horovod. This feature batches small allreduce operations. This decreases a latency in Data Parallel training. The environment variable controls the size of maximal buffer (in MB) that is communicated within one iteration of allreduce algorithm. Default is 64. The variable must be set **before** loading the module.
+- `CGX_INNER_COMMUNICATOR_TYPE`. Specifies what library to use as communication backend for intra node communication (MPI, SHM, NCCL).
+- `CGX_CROSS_COMMUNICATOR_TYPE`. Specifies what library to use as communication backend for inter node communication (MPI, NCCL).
+- `CGX_INTRA_BROADCAST`. Parameter for multinode training. When enabled, inter-node communication is performed by only one gpu per node.
+
+## Examples
+
+Basic examples are provided under the [example](examples) folder.
+
+## Notes
+ - As Compression method, basic max-min uniform quantization function is used. In order to use max-min with random rounding like in QSGD, compile the library with QSGD_DETERMINISTIC=0
+ - Reduction algorithm: Scatter-Reduce-AllGather.
+ - Part of the source code is based on [Horovod](https://github.com/horovod/horovod) and [NCCL](https://github.com/NVIDIA/nccl) sources.
\ No newline at end of file
diff --git a/cgx_utils/__init__.py b/cgx_utils/__init__.py
new file mode 100755
index 0000000..f1f218a
--- /dev/null
+++ b/cgx_utils/__init__.py
@@ -0,0 +1 @@
+from .allreduce_hooks import CGXState, cgx_hook
\ No newline at end of file
diff --git a/cgx_utils/allreduce_hooks.py b/cgx_utils/allreduce_hooks.py
new file mode 100755
index 0000000..50e27e0
--- /dev/null
+++ b/cgx_utils/allreduce_hooks.py
@@ -0,0 +1,73 @@
+# pytorch-cgx
+#
+# Copyright (C) 2022 IST Austria
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Affero General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU Affero General Public License for more details.
+#
+# You should have received a copy of the GNU Affero General Public License
+# along with this program. If not, see .
+
+from typing import Dict
+import torch
+import torch.distributed as dist
+import torch_cgx
+import os
+
+COMPRESSION_QUANTIZATION_BITS = "CGX_COMPRESSION_QUANTIZATION_BITS"
+COMPRESSION_BUCKET_SIZE = "CGX_COMPRESSION_BUCKET_SIZE"
+COMPRESSION_MINIMAL_SIZE = "CGX_COMPRESSION_MINIMAL_SIZE"
+VALUE_NO_COMPRESS=32
+
+
+class CGXState(object):
+ def __init__(self, process_group: dist.ProcessGroup, layer_min_size: int = 1024,
+ compression_params: Dict[str, int] = None):
+ self.process_group = process_group if process_group is not None else dist.group.WORLD
+ min_size_to_compress = int(os.getenv(COMPRESSION_MINIMAL_SIZE, "16"))
+ self.layer_min_size = max(layer_min_size, min_size_to_compress)
+ self.quantization_bits = int(os.getenv(COMPRESSION_QUANTIZATION_BITS, str(VALUE_NO_COMPRESS)))
+ self.quantization_bucket_size = int(os.getenv(COMPRESSION_BUCKET_SIZE, "1024"))
+ self.step = 0
+ if compression_params is not None:
+ self.quantization_bits = compression_params.get("bits", self.quantization_bits)
+ self.quantization_bucket_size = compression_params.get("bucket_size", self.quantization_bucket_size)
+
+ def should_compress_(self, tensor: torch.Tensor):
+ if tensor.dim() <= 1 or tensor.numel() < self.layer_min_size:
+ return False
+ return True
+
+
+def _allreduce_fut(
+ process_group: dist.ProcessGroup, tensor: torch.Tensor
+) -> torch.futures.Future[torch.Tensor]:
+ "Averages the input gradient tensor by allreduce and returns a future."
+ group_to_use = process_group if process_group is not None else dist.group.WORLD
+ # Apply the division first to avoid overflow, especially for FP16.
+ tensor.div_(group_to_use.size())
+ return (
+ dist.all_reduce(tensor, group=group_to_use, async_op=True)
+ .get_future()
+ .then(lambda fut: fut.value()[0])
+ )
+
+
+def cgx_hook(
+ state: CGXState, bucket: dist.GradBucket
+) -> torch.futures.Future[torch.Tensor]:
+ if state.step == 2:
+ for layer_idx, tensor in enumerate(bucket.gradients()):
+ bits = state.quantization_bits if state.should_compress_(tensor) else VALUE_NO_COMPRESS
+ torch_cgx.register_layer(bucket.index(), layer_idx, tensor.numel(),
+ bits, state.quantization_bucket_size)
+ if bucket.is_last():
+ state.step += 1
+ state.layer_idx = 0
+ return _allreduce_fut(state.process_group, bucket.buffer())
diff --git a/examples/cifar_train.py b/examples/cifar_train.py
new file mode 100755
index 0000000..81910b1
--- /dev/null
+++ b/examples/cifar_train.py
@@ -0,0 +1,239 @@
+import torch
+import argparse
+import torch.backends.cudnn as cudnn
+import torch.optim as optim
+import torch.utils.data.distributed
+from torchvision import datasets, transforms, models
+import os
+
+from tqdm import tqdm
+
+import torch.distributed as dist
+
+from torch.nn.parallel import DistributedDataParallel as DDP
+
+CIFAR100_TRAIN_MEAN = (0.5070751592371323, 0.48654887331495095, 0.4409178433670343)
+CIFAR100_TRAIN_STD = (0.2673342858792401, 0.2564384629170883, 0.27615047132568404)
+
+CIFAR10_TRAIN_MEAN = (0.4914, 0.4822, 0.4465)
+CIFAR10_TRAIN_STD = (0.2023, 0.1994, 0.2010)
+
+NCOLS_SCREEN = 85
+
+# Training settings
+parser = argparse.ArgumentParser(description='PyTorch CIFAR Example',
+ formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+parser.add_argument('--dataset-dir', default=os.path.expanduser('./cifar10'),
+ help='path to training data')
+parser.add_argument('--log-dir', default='./logs',
+ help='tensorboard log directory')
+
+parser.add_argument('--batch-size', type=int, default=256,
+ help='input batch size for training')
+parser.add_argument('--val-batch-size', type=int, default=256,
+ help='input batch size for validation')
+parser.add_argument('--epochs', type=int, default=200,
+ help='number of epochs to train')
+parser.add_argument('--base-lr', type=float, default=0.1,
+ help='learning rate for a single GPU')
+parser.add_argument('--momentum', type=float, default=0.9,
+ help='SGD momentum')
+parser.add_argument('--wd', type=float, default=1e-4,
+ help='weight decay')
+
+parser.add_argument('--no-cuda', action='store_true', default=False,
+ help='disables CUDA training')
+parser.add_argument('--seed', type=int, default=42,
+ help='random seed')
+parser.add_argument('--dist-backend', choices=['cgx', 'nccl', 'gloo'], default='nccl',
+ help='Backend for torch distributed')
+parser.add_argument('--quantization-bits', type=int, default=32,
+ help='Quantization bits for maxmin quantization')
+parser.add_argument('--quantization-bucket-size', type=int, default=1024,
+ help='Bucket size for quantization in maxmin quantization')
+parser.add_argument('--local_rank', type=int, default=-1,
+ help='Local rank in distributed launch')
+
+args = parser.parse_args()
+args.cuda = not args.no_cuda and torch.cuda.is_available()
+
+
+if "OMPI_COMM_WORLD_SIZE" in os.environ:
+ args.local_rank = int(os.environ["OMPI_COMM_WORLD_RANK"])
+ os.environ['MASTER_ADDR'] = '127.0.0.1'
+ os.environ['MASTER_PORT'] = '4040'
+ os.environ["WORLD_SIZE"] = os.environ["OMPI_COMM_WORLD_SIZE"]
+ os.environ["RANK"] = os.environ["OMPI_COMM_WORLD_RANK"]
+
+if "WORLD_SIZE" in os.environ:
+ import torch_cgx
+ args.distributed = int(os.environ["WORLD_SIZE"]) > 1
+ local_rank = args.local_rank % torch.cuda.device_count()
+ dist.init_process_group(backend=args.dist_backend, init_method="env://")
+ args.world_size = torch.distributed.get_world_size()
+ rank = torch.distributed.get_rank()
+else:
+ args.distributed = False
+ local_rank = 0
+ args.world_size = 1
+ rank = 0
+print(args)
+
+if args.cuda:
+ torch.cuda.set_device(local_rank)
+ torch.cuda.manual_seed(args.seed)
+
+cudnn.benchmark = True
+
+verbose = 1 if rank == 0 else 0
+
+torch.set_num_threads(4)
+
+is_cifar100 = "cifar100" in args.dataset_dir
+if is_cifar100:
+ transform_mean, transform_std = CIFAR100_TRAIN_MEAN, CIFAR100_TRAIN_STD
+else:
+ transform_mean, transform_std = CIFAR10_TRAIN_MEAN, CIFAR10_TRAIN_STD
+
+kwargs = {'num_workers': 0, 'pin_memory': True} if args.cuda else {}
+transform_train = transforms.Compose([
+ transforms.RandomCrop(32, padding=4),
+ transforms.RandomHorizontalFlip(),
+ transforms.ToTensor(),
+ transforms.Normalize(transform_mean, transform_std),
+])
+
+if is_cifar100:
+ train_dataset = datasets.CIFAR100(root=args.dataset_dir, train=True, download=True, transform=transform_train)
+else:
+ train_dataset = datasets.CIFAR10(root=args.dataset_dir, train=True, download=True, transform=transform_train)
+
+train_sampler = torch.utils.data.distributed.DistributedSampler(
+ train_dataset, num_replicas=args.world_size, rank=rank)
+train_loader = torch.utils.data.DataLoader(
+ train_dataset, batch_size=args.batch_size,
+ sampler=train_sampler, **kwargs)
+
+transform_test = transforms.Compose([
+ transforms.ToTensor(),
+ transforms.Normalize(transform_mean, transform_std),
+])
+
+if is_cifar100:
+ val_dataset = datasets.CIFAR100(root=args.dataset_dir, train=False, download=True, transform=transform_test)
+else:
+ val_dataset = datasets.CIFAR10(root=args.dataset_dir, train=False, download=True, transform=transform_test)
+val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=args.val_batch_size,
+ **kwargs)
+
+if is_cifar100:
+ num_classes = 100
+else:
+ num_classes = 10
+model = models.resnet18(num_classes=num_classes)
+
+if args.cuda:
+ # Move model to GPU.
+ model.cuda()
+
+optimizer = optim.SGD(model.parameters(),
+ lr=args.base_lr,
+ momentum=args.momentum, weight_decay=args.wd)
+if args.distributed:
+ model = DDP(model, device_ids=[local_rank])
+ if args.dist_backend == 'cgx':
+ assert "OMPI_COMM_WORLD_SIZE" in os.environ, "CGX only works with with mpirun launch"
+ from cgx_utils import cgx_hook, CGXState
+ state = CGXState(torch.distributed.group.WORLD,
+ compression_params={"bits": args.quantization_bits,
+ "bucket_size": args.quantization_bucket_size})
+ model.register_comm_hook(state, cgx_hook)
+
+
+def adjust_learning_rate(epoch, batch_idx):
+ if epoch < 60:
+ lr_adj = 1.
+ elif epoch < 120:
+ lr_adj = 2e-1
+ elif epoch < 160:
+ lr_adj = 4e-2
+ else:
+ lr_adj = 8e-3
+ for param_group in optimizer.param_groups:
+ param_group['lr'] = args.base_lr * lr_adj
+
+
+def train(epoch):
+ model.train()
+ criterion = torch.nn.CrossEntropyLoss()
+ train_loss = Metric('train_loss')
+ train_accuracy = Metric('train_accuracy')
+
+ with tqdm(total=len(train_loader),
+ desc='TEpoch #{}'.format(epoch + 1), disable=not verbose, ncols=NCOLS_SCREEN) as t:
+ for batch_idx, (data, target) in enumerate(train_loader):
+ adjust_learning_rate(epoch, batch_idx)
+ if args.cuda:
+ data, target = data.cuda(), target.cuda()
+ optimizer.zero_grad()
+ output = model(data)
+ train_accuracy.update(accuracy(output, target))
+ loss = criterion(output, target)
+ train_loss.update(loss)
+ t.set_postfix({'loss': train_loss.avg.item(),
+ 'accuracy': 100. * train_accuracy.avg.item()})
+ loss.backward()
+ optimizer.step()
+ t.update(1)
+
+
+def validate(epoch):
+ model.eval()
+ criterion = torch.nn.CrossEntropyLoss()
+ val_loss = Metric('val_loss')
+ val_accuracy = Metric('val_accuracy')
+ with tqdm(total=len(val_loader),
+ desc='Validate Epoch #{}'.format(epoch + 1),
+ disable=not verbose, ncols=NCOLS_SCREEN) as t:
+ with torch.no_grad():
+ for data, target in val_loader:
+ if args.cuda:
+ data, target = data.cuda(), target.cuda()
+ output = model(data)
+ val_loss.update(criterion(output, target))
+
+ val_accuracy.update(accuracy(output, target))
+ t.set_postfix({'loss': val_loss.avg.item(),
+ 'accuracy': 100. * val_accuracy.avg.item()})
+ t.update(1)
+
+
+def accuracy(output, target):
+ # get the index of the max log-probability
+ pred = output.max(1, keepdim=True)[1]
+ return pred.eq(target.view_as(pred)).cpu().float().mean()
+
+
+class Metric(object):
+ def __init__(self, name):
+ self.name = name
+ self.sum = torch.tensor(0.)
+ self.n = torch.tensor(0.)
+
+ def update(self, val):
+ self.sum += val.detach().cpu()
+ self.n += 1
+
+ @property
+ def avg(self):
+ return self.sum / self.n
+
+
+num_images = len(train_loader)
+for epoch in range(0, args.epochs):
+ if args.world_size > 0:
+ train_sampler.set_epoch(epoch)
+ train(epoch)
+ validate(epoch)
+ if args.distributed:
+ dist.barrier()
diff --git a/examples/requirements.txt b/examples/requirements.txt
new file mode 100755
index 0000000..fa9cf06
--- /dev/null
+++ b/examples/requirements.txt
@@ -0,0 +1 @@
+tqdm
\ No newline at end of file
diff --git a/examples/run_cifar.sh b/examples/run_cifar.sh
new file mode 100755
index 0000000..416bbab
--- /dev/null
+++ b/examples/run_cifar.sh
@@ -0,0 +1,6 @@
+NUM_NODES=${1:-2}
+batch_size=$(( 512 / $NUM_NODES ))
+
+mpirun -np $NUM_NODES --tag-output --allow-run-as-root -bind-to none -map-by slot -mca pml ob1 -mca btl ^openib -mca coll ^hcoll \
+--mca btl_tcp_if_exclude lo,docker0 python cifar_train.py --epochs 10 --dataset-dir ./cifar10 \
+--quantization-bits 8 --quantization-bucket-size 1024 --dist-backend cgx --batch-size $batch_size
\ No newline at end of file
diff --git a/setup.cfg b/setup.cfg
new file mode 100755
index 0000000..224a779
--- /dev/null
+++ b/setup.cfg
@@ -0,0 +1,2 @@
+[metadata]
+description-file = README.md
\ No newline at end of file
diff --git a/setup.py b/setup.py
new file mode 100755
index 0000000..3b36366
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,77 @@
+from setuptools import setup, Extension
+from torch.utils import cpp_extension
+import os
+import subprocess
+
+src = ['src/mpi_allreduce_operations.cc', 'src/ProcessGroupCGX.cc',
+ 'src/common/reducer.cc', 'src/common/buffer.cc', 'src/common/mpi_context.cc',
+ 'src/common/mpi_communicator.cc', 'src/common/shm_communicator.cc',
+ 'src/common/scatter_reduce_allgather.cc', 'src/common/ring.cc', 'src/common/utils.cc',
+ 'src/common/compressor.cc', 'src/common/layer.cc', 'src/common/shm_utils.cc',
+ 'src/common/compression/gpu_compression_operations.cc', 'src/common/nccl_reduce.cc']
+
+MPI_HOME=os.environ.get("MPI_HOME", "/usr/local/mpi")
+NCCL_HOME=os.environ.get("NCCL_HOME", "/usr/local/nccl")
+NCCL_INCLUDE=os.environ.get("NCCL_INCLUDE", "/usr/local/nccl/include")
+NCCL_LIB=os.environ.get("NCCL_LIB", "/usr/local/nccl/lib")
+IS_CUDA=int(os.environ.get("CGX_CUDA", "1")) != 0
+CUDA_VECTORIZED=int(os.environ.get("CUDA_VECTORIZED", "1")) != 0
+QSGD_DETERMENISTIC=int(os.environ.get("QSGD_DETERMENISTIC", "1")) != 0
+link_args = ['-L'+ os.path.join(MPI_HOME, 'lib'), '-lmpi']
+ompi_info_bin=os.path.join(MPI_HOME, 'bin', 'ompi_info')
+env = os.environ
+try:
+ ompi_info_out = subprocess.check_output([ompi_info_bin, '--parsable'], env=env)
+except OSError as e:
+ raise RuntimeError('CMake failed: {}'.format(str(e)))
+
+if "bindings:cxx:yes" in str(ompi_info_out):
+ link_args.append('-lmpi_cxx')
+if IS_CUDA:
+ src.extend(['src/common/compression/cuda_compression_operations.cu', 'src/common/cuda_operations.cc'])
+else:
+ src.extend(['src/common/compression/hip_compression_operations.cc', 'src/common/hip_operations.cc'])
+include_dirs = [os.path.join(MPI_HOME, "include")]
+cxx_compile_args = ["-D_GLIBCXX_USE_CXX11_ABI=0"]
+nvcc_compile_args = []
+if IS_CUDA:
+ cxx_compile_args.append("-DHAVE_CUDA=1")
+ nvcc_compile_args.append("-DHAVE_CUDA=1")
+ os.environ['TORCH_CUDA_ARCH_LIST'] = '7.0;7.5;8.6'
+else:
+ cxx_compile_args.append("-DHAVE_ROCM=1")
+ nvcc_compile_args.append("-DHAVE_ROCM=1")
+
+if os.path.isdir(NCCL_HOME):
+ include_dirs.append(os.path.join(NCCL_HOME, "include"))
+ link_args.append('-L' + os.path.join(NCCL_HOME, 'lib'))
+ link_args.append('-lnccl')
+elif os.path.isdir(NCCL_INCLUDE) and os.path.isdir(NCCL_LIB):
+ include_dirs.append(os.path.join(NCCL_INCLUDE))
+ link_args.append('-L' + os.path.join(NCCL_LIB))
+ link_args.append('-lnccl')
+else:
+ raise ValueError("NCCL is not available")
+
+if CUDA_VECTORIZED:
+ nvcc_compile_args.append("-DCUDA_VECTORIZED=1")
+ cxx_compile_args.append("-DCUDA_VECTORIZED=1")
+if QSGD_DETERMENISTIC:
+ nvcc_compile_args.append("-DQSGD_DETERMENISTIC=1")
+ cxx_compile_args.append("-DQSGD_DETERMENISTIC=1")
+
+setup(name='torch_cgx',
+ packages=['cgx_utils'],
+ version='0.1.0',
+ description='pytorch extension adding a backend '
+ 'supporting allreduce of quantized buffers.',
+ author='Ilia Markov',
+ author_email='ilia.markov@ist.ac.at',
+ url='https://github.com/IST-DASLab/torch_cgx/',
+ download_url="https://github.com/IST-DASLab/torch_cgx/archive/refs/tags/v0.1.0.tar.gz",
+ ext_modules=[cpp_extension.CUDAExtension('torch_cgx', sources=src,
+ include_dirs=include_dirs,
+ extra_compile_args={'cxx': cxx_compile_args, 'nvcc': nvcc_compile_args},
+ extra_link_args=link_args)],
+ cmdclass={'build_ext': cpp_extension.BuildExtension},
+ )
diff --git a/src/ProcessGroupCGX.cc b/src/ProcessGroupCGX.cc
new file mode 100755
index 0000000..8cffee1
--- /dev/null
+++ b/src/ProcessGroupCGX.cc
@@ -0,0 +1,859 @@
+/*
+ * pytorch-cgx
+ *
+ * Copyright (C) 2022 Institute of Science and Technology Austria (ISTA).
+ * All Rights Reserved.
+ * This program is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU Affero General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * This program is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ * GNU Affero General Public License for more details.
+ *
+ * You should have received a copy of the GNU Affero General Public License
+ * along with this program. If not, see .
+ */
+
+#include "ProcessGroupCGX.h"
+
+#include
+#include