Breaking the Memory Wall: Exact Analytical Differentiation via Tiled Operator-Space Evolution

TL;DR

This paper introduces Phase Gradient Flow (PGF), a novel method for exact analytical differentiation in State Space Models that drastically reduces memory usage, enabling large-scale genomic modeling on consumer hardware.

Contribution

We propose PGF, a framework that computes derivatives directly in the state-space manifold, achieving O(1) memory complexity and enabling chromosome-scale sensitivity analysis.

Findings

94% reduction in peak VRAM usage

23x increase in throughput over Autograd

Able to process 128,000-step sequences on a single GPU

Abstract

Selective State Space Models (SSMs) achieve linear-time inference, yet their gradient-based sensitivity analysis remains bottlenecked by O(L) memory scaling during backpropagation. This memory constraint precludes genomic-scale modeling (L > 10^5) on consumer-grade hardware. We introduce Phase Gradient Flow (PGF), a framework that computes exact analytical derivatives by operating directly in the state-space manifold, bypassing the need to materialize the intermediate computational graph. By reframing SSM dynamics as Tiled Operator-Space Evolution (TOSE), our method delivers O(1) memory complexity relative to sequence length, yielding a 94% reduction in peak VRAM and a 23x increase in throughput compared to standard Autograd. Unlike parallel prefix scans that exhibit numerical divergence in stiff ODE regimes, PGF ensures stability through invariant error scaling, maintaining near-machine precision across extreme sequences. We demonstrate the utility of PGF on an impulse-response benchmark with 128,000-step sequences - a scale where conventional Autograd encounters prohibitive memory overhead, often leading to out-of-memory (OOM) failures in multi-layered models. Our work enables chromosome-scale sensitivity analysis on a single GPU, bridging the gap between theoretical infinite-context models and practical hardware limitations.