general family · filed under optimization
Selective tensor recomputation for activation-memory reduction
Selectively checkpointing intermediate tensors so activations can be recomputed to reduce memory use.
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Evidence
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we introduce a recomputation strategy that selectively checkpoints intermediate tensors.
usedoptimizationin DeepSeek-V4DeepSeek
Filed alongside
Other methods under optimization :: training runtime.
Offline checkpoint mergingAsynchronous checkpointingAsymmetric local-read and single-writer cache pathsAutotune configuration generationBatch-level embedding prefetchCaching the distributed checkpoint save planComposable activation storage policiesCPU-resident optimizer statesCross-rank remote activation offloadingDouble-buffered chunked streaming of reference-model weightsElement-wise activation recomputationGPU-to-GPU weight synchronization over GPUDirect RDMAHeartbeat-driven fault toleranceIn-flight recovery systemIn-flight weight updatesJob-level eviction and reclaimMinimal recomputation-graph extraction by backward traversalOverlapping NCCL transfers with device-to-host copiesPersistent checkpoint worker processesPersistent shared-storage cache for compiled artifactsPolicy-model gradient-buffer reuse for reference-model weightsPost-iteration NVMe offloading of training statesPre-admission hardware stress testingSame-node sticky pod respawn