Published 17 Jun 2020 in cs.LG, cs.PL, and stat.ML
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 $\Omega(\sqrt{N})$ memory budget with only $\mathcal{O}(N)$ 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.
The paper "Dynamic Tensor Rematerialization" addresses the crucial challenge of training increasingly large deep learning (DL) models under constrained memory environments. This investigation proposes a novel method, Dynamic Tensor Rematerialization (DTR), which eschews the prevalent static planning approach for checkpointing in favor of an online, dynamic mechanism. This paradigm shift allows for more adaptability and immediate applicability in dynamic models, circumventing the need for exhaustive upfront model analysis.
Key Insights and Contributions
Algorithmic Innovation: The essence of DTR lies in its greedy online algorithm that functions analogously to a cache eviction policy. It dynamically decides which tensors to store or evict based on metadata such as staleness, size, and rematerialization cost. The algorithm supports dynamic models, such as those with data-dependent control flow, by allowing runtime decisions on tensor eviction and recomputation.
Formal Guarantees: It is theoretically proven that DTR, even with its online nature, can train an N-layer linear feedforward network on an Ω(N) memory budget with only O(N) tensor operations. This performance is within a constant factor of the optimal offline static checkpointing method described by Chen et al., demonstrating that dynamic rematerialization does not significantly compromise efficiency.
Heuristic Evaluation: DTR’s heuristic is outlined as being parameterized over caching principles, combining tensor staleness, operation cost, and memory usage. Various heuristic functions are explored, and empirical results suggest that capturing the full complexity of tensor-level dependencies can significantly optimize memory usage compared to simpler heuristics.
Prototype Implementation: The practicality of DTR is showcased by integrating a prototype into PyTorch, with minimal changes required to keyword the platform's existing mechanisms. The implementation was efficient and enabled experiments with real-world models underscoring the utility and simplicity of the approach.
Empirical and Practical Implications
The empirical trials indicate that the dynamic approach of DTR facilitates the training of both static and dynamic models with substantial reductions in memory usage while maintaining competitive computational overhead against static techniques such as those used in Checkmate. Notably, DTR can dynamically adapt to varying model architectures without requiring time-intensive ILP solutions, contrasting sharply with static methods that demand comprehensive upfront model knowledge.
Future Directions and Implications
The current work lays the groundwork for further exploration into hybrid models, which combine dynamic rematerialization with other memory management strategies such as tensor swapping. Additionally, there is potential for extending DTR to leverage machine learning techniques that could learn rematerialization patterns over time, potentially improving performance based on batch history. Implementing more sophisticated heuristics for more complex architectures and exploring cross-device memory management constitute intriguing avenues for advancement.
In summary, the proposal of Dynamic Tensor Rematerialization marks a significant progression in efficiently training DL models within restricted memory contexts. By enabling dynamic adaptability and reducing reliance on static analysis, this approach opens up new possibilities for AI research and deployment, especially on constrained hardware platforms.