Dynamic Tensor Rematerialization

Abstract

Checkpointing enables the training of deep learning models under restricted memory budgets by freeing intermediate activations from memory and recomputing them on demand. Current checkpointing techniques statically plan these recomputations offline and assume static computation graphs. We demonstrate that a simple online algorithm can achieve comparable performance by introducing Dynamic Tensor Rematerialization (DTR), a greedy online algorithm for checkpointing that is extensible and general, is parameterized by eviction policy, and supports dynamic models. We prove that DTR can train an N-layer linear feedforward network on an Ω(√ N) memory budget with only O(N) tensor operations. DTR closely matches the performance of optimal static checkpointing in simulated experiments. We incorporate a DTR prototype into PyTorch merely by interposing on tensor allocations and operator calls and collecting lightweight metadata on tensors.

Talk

ICLR 2021 talk by Steven Lyubomirsky.

Extra

Real Time^TM vaporwave DTR by Altan Haan.

Part of a DTR demo by the whole team for the Spring 2020 ADA Center Symposium.

BibTeX

@inproceedings{2021-iclr-dtr,
  title     = {Dynamic Tensor Rematerialization},
  author    = {Kirisame, Marisa and Lyubomirsky, Steven and Haan, Altan and Brennan, Jennifer and He, Mike and Roesch, Jared and Chen, Tianqi and Tatlock, Zachary},
  booktitle = {International Conference on Learning Representations},
  date      = {2021},
  url       = {https://openreview.net/forum?id=Vfs_2RnOD0H},
}

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