fix exclusive cumsum calculation

[bkal01]

as @AKatydid pointed out on #72, the current exclusive cumsum computation is incorrect.

now we apply torch.cumsum on x (without the last elements along the dimension) and shifts them right by one by prepending 0s.

[simonguozirui]

Double-checked the math and wrote a short script to verify it against the PyTorch reference implementation. using roll (not efficient, but easy to check). Equivalent on both cpu and gpu.

def exclusive_cumsum_ref(x: torch.Tensor, dim: int) -> torch.Tensor:
    c = torch.cumsum(x, dim=dim)
    y = torch.roll(c, shifts=1, dims=dim)

    idx = [slice(None)] * x.ndim
    idx[dim] = 0
    y[tuple(idx)] = 0
    return y

Thanks for the fix @bkal01 and @AKatydid for pointing out on #72. Also started change log to document ongoing problem updates!