GoKawiilPyTorch gains knapsack solver option cutting memory-planning RAM use 20x
PyTorch's activation memory budget solver, which decides which operations in a computation graph to save versus recompute to fit a memory budget, has a new configurable algorithm called dp_knapsack_sliding_hirschberg. Switching the setting from the default dp to this new option reportedly reduces peak RAM usage during memory planning by roughly a factor of 20. The feature is described as a drop-in configuration change via torch._functorch.config.activation_memory_budget_solver.
GoKawiil's interpretation of the reporting above, not reported fact.
Lower memory overhead during graph compilation could let developers train or compile larger models on the same hardware without hitting out-of-memory errors during the planning phase itself, separate from runtime memory use. Because this is framed as a configuration toggle rather than a default change, broader adoption likely depends on PyTorch maintainers validating it across more workloads before making it standard.
- PyTorch's activation memory planning uses a 0/1 knapsack algorithm to choose which operations to save versus recompute.
- A new solver option, dp_knapsack_sliding_hirschberg, can replace the default dp solver via a config flag.
- The new option is reported to cut peak RAM usage during memory planning by about 20x.
Source: jedrzej.maczan.pl, 2026-10-14
Published there as: “0/1 Knapsack Problem in PyTorch”
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