نقطة تفتيش التدرج

Training normally retains intermediate activations needed for backpropagation. Activation checkpointing saves selected boundary tensors and recomputes internal activations during backward. This trades additional computation for a lower activation-memory footprint; the actual savings and runtime overhead depend on checkpoint placement, operations, model graph, and hardware. In a simplified chain of N similarly sized layers, a textbook schedule with checkpoint boundaries spaced about sqrt(N) layers apart can reduce activation storage to order sqrt(N) checkpoints, with recomputation cost governed by that schedule. This asymptotic example is not a universal prediction for framework implementations. PyTorch exposes activation-checkpointing utilities, and its current guidance also describes selective rematerialization and compiler-assisted choices. It can combine with FSDP and mixed precision, but benchmark the full training workload.