nn.Dropout uses it’s probability p to drop activations randomly, so it’s expected behavior to get different results using this layer.
Have a look at this small example resulting in a new drop mask for every run:
drop = nn.Dropout(p=0.5)
x = torch.randn(1, 10)

for _ in range(10):
    output = drop(x)
    print(output)