linalg.py 文件源码

def backward(ctx, grad_output):
        L, = ctx.saved_variables

        if ctx.upper:
            L = L.t()
            grad_output = grad_output.t()

        # make sure not to double-count variation, since
        # only half of output matrix is unique
        Lbar = grad_output.tril()

        P = Potrf.phi(torch.mm(L.t(), Lbar))
        S = torch.gesv(P + P.t(), L.t())[0]
        S = torch.gesv(S.t(), L.t())[0]
        S = Potrf.phi(S)

        return S, None