I would recommend to stick to the nightly for now.
The error message indicates, that the sparse_optimizer doesn’t contain any parameters which have scaled gradients. The same error would be raised in this small code example:
model = nn.Linear(8, 8).cuda()
x = torch.randn(1, 8).cuda()
optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)
optimizer_unused = torch.optim.SGD([nn.Parameter(torch.randn(1))], lr=1.)
scaler = torch.cuda.amp.GradScaler()

with torch.cuda.amp.autocast():
    out = model(x)
    loss = out.mean()
    scaler.scale(loss).backward()
    scaler.step(optimizer) # works
    scaler.step(optimizer_unused) # your error