Consensus optimization enables autonomous agents to solve joint tasks through peer-to-peer exchanges alone. Classical decentralized gradient descent is appealing for its minimal state but fails to achieve exact consensus with fixed stepsizes unless additional trackers or dual variables are introduced. We revisit penalty methods and introduce a decentralized two-layer framework that couples an outer penalty-continuation loop with an inner plug-and-play saddle-point solver. Any primal-dual routine that satisfies simple stationarity and communication conditions can be used; when instantiated with a proximal-gradient solver, the framework yields the DP$^2$G algorithm, which reaches exact consensus with constant stepsizes, stores only one dual residual per agent, and requires exactly two short message exchanges per inner iteration. An explicit $\ell_1$ penalty enforces agreement and, once above a computable threshold, makes the penalized and constrained problems equivalent. Leveraging the Kurdyka-\L{}ojasiewicz property, we prove global convergence, vanishing disagreement, and linear rates for strongly convex objectives under
any admissible inner solver. Experiments on distributed least squares, logistic regression, and elastic-net tasks across various networks demonstrate that DP$^2$G outperforms DGD-type methods in both convergence speed and communication efficiency, is competitive with gradient-tracking approaches while using less memory, and naturally accommodates composite objectives.