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Add support for Intel GPU to ImageNet example #1322

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4 changes: 3 additions & 1 deletion imagenet/README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -33,7 +33,9 @@ python main.py -a resnet18 --dummy

## Multi-processing Distributed Data Parallel Training

You should always use the NCCL backend for multi-processing distributed training since it currently provides the best distributed training performance.
If running on CUDA, you should always use the NCCL backend for multi-processing distributed training since it currently provides the best distributed training performance.

For XPU multiprocessing is not supported as of PyTorch 2.6.

### Single node, multiple GPUs:

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27 changes: 21 additions & 6 deletions imagenet/main.py
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Expand Up @@ -147,7 +147,7 @@ def main_worker(gpu, ngpus_per_node, args):
print("=> creating model '{}'".format(args.arch))
model = models.__dict__[args.arch]()

if not torch.cuda.is_available() and not torch.backends.mps.is_available():
if not torch.cuda.is_available() and not torch.backends.mps.is_available() and not torch.xpu.is_available():
print('using CPU, this will be slow')
elif args.distributed:
# For multiprocessing distributed, DistributedDataParallel constructor
Expand All @@ -171,6 +171,9 @@ def main_worker(gpu, ngpus_per_node, args):
elif args.gpu is not None and torch.cuda.is_available():
torch.cuda.set_device(args.gpu)
model = model.cuda(args.gpu)
elif torch.xpu.is_available():
device = torch.device("xpu")
model = model.to(device)
elif torch.backends.mps.is_available():
device = torch.device("mps")
model = model.to(device)
Expand All @@ -187,10 +190,15 @@ def main_worker(gpu, ngpus_per_node, args):
device = torch.device('cuda:{}'.format(args.gpu))
else:
device = torch.device("cuda")
elif torch.xpu.is_available():
device = torch.device("xpu")
elif torch.backends.mps.is_available():
device = torch.device("mps")
else:
device = torch.device("cpu")

print (f"Device to use: ", {device.type})

# define loss function (criterion), optimizer, and learning rate scheduler
criterion = nn.CrossEntropyLoss().to(device)

Expand Down Expand Up @@ -354,14 +362,19 @@ def run_validate(loader, base_progress=0):
end = time.time()
for i, (images, target) in enumerate(loader):
i = base_progress + i
if args.gpu is not None and torch.cuda.is_available():
images = images.cuda(args.gpu, non_blocking=True)
if torch.backends.mps.is_available():
images = images.to('mps')
target = target.to('mps')

if torch.cuda.is_available():
if args.gpu is not None:
images = images.cuda(args.gpu, non_blocking=True)
target = target.cuda(args.gpu, non_blocking=True)

elif torch.xpu.is_available():
images = images.to("xpu")
target = target.to("xpu")
elif torch.backends.mps.is_available():
images = images.to('mps')
target = target.to('mps')

# compute output
output = model(images)
loss = criterion(output, target)
Expand Down Expand Up @@ -443,6 +456,8 @@ def update(self, val, n=1):
def all_reduce(self):
if torch.cuda.is_available():
device = torch.device("cuda")
elif torch.xpu.is_available():
device = torch.device("xpu")
elif torch.backends.mps.is_available():
device = torch.device("mps")
else:
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