Moonwalk: Inverse-Forward Differentiation

Backpropagation’s main limitation is its need to store intermediate activations (residuals) during the forward pass, which restricts the depth of trainable networks. This raises a fundamental question: can we avoid storing these activations? We address this by revisiting the structure of gradient computation. Backpropagation computes gradients through a sequence of vector–Jacobian products, an operation that is generally irreversible. The lost information lies in the cokernel of each layer’s Jacobian. We define submersive networks—networks whose layer Jacobians have trivial cokernels—in which gradients can be reconstructed exactly in a forward sweep without storing activations. For non-submersive layers, we introduce fragmental gradient checkpointing, which records only the minimal subset of residuals necessary to restore the cotangents erased by the Jacobian. Central to our approach is a novel operator, the vector–inverse-Jacobian product (vijp), which inverts gradient flow outside the cokernel. Our mixed-mode algorithm first computes input gradients with a memory-efficient backward pass, then reconstructs parameter gradients in a forward sweep that does not need to store activations. We implement this method, called Moonwalk, and show that it matches backpropagation’s runtime while training networks more than twice as deep under the same memory budget.