This work aims to generalize demographic parity to continuous sensitive attributes while preserving tractable computation. Current fairness metrics for continuous sensitive attributes largely rely on intractable statistical independence between variables, such as Hirschfeld-Gebelein-Renyi (HGR) and mutual information. Statistical fairness metrics estimation relying on either tractable bounds or neural network approximation, however, are not sufficiently trustful to rank algorithms prediction bias due to lack of estimation accuracy guarantee. To make fairness metrics trustable, we propose \textit{\underline{G}eneralized \underline{D}emographic \underline{P}arity} (GDP), a group fairness metric for continuous and discrete attributes. We show the understanding of GDP from the probability perspective and theoretically reveal the connection between GDP regularizer and adversarial debiasing. To estimate GDP, we adopt hard and soft group strategies via the one-hot or the soft group indicator, representing the membership of each sample in different groups of the sensitive attribute. We provably and numerically show that the soft group strategy achieves a faster estimation error convergence rate. Experiments show the better bias mitigation performance of GDP regularizer, compared with adversarial debiasing, for regression and classification tasks in tabular and graph benchmarks.