def loop_fn(model, loader, device, context):
loss_fn = nn.NLLLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
for data, target in loader:
with xu.TimedScope(msg='Training loop: ', printfn=None):
optimizer.zero_grad()
output = xu.timed(lambda: model(data), msg='Model: ', printfn=None)
loss = xu.timed(
lambda: loss_fn(output, target), msg='Loss: ', printfn=None)
xu.timed(loss.backward, msg='LossBkw: ', printfn=None)
xu.timed(
lambda: xm.optimizer_step(optimizer), msg='Step: ', printfn=None)
self.assertLess(loss.cpu().item(), 3.0)
def threadfn(i):
device = devices[i]
xdevices = [device] * len(send_list[i])
for n in range(0, args.test_count):
with xu.TimedScope(msg='Send[{}][{}]: '.format(i, n), printfn=print):
_ = torch_xla._XLAC._xla_tensors_from_aten(send_list[i], xdevices)
