Fused DeepLIFT: Rewriting Attribution as a Computation Graph for 7x Speedup

jmschreiber91 explains fused DeepLIFT, which replaces hook-based gradient overriding by materializing DeepLIFT as a standalone computation graph on a specific model, letting PyTorch optimize it for roughly 7x faster attribution.

2026-10-26 ~ 2026-10-26 · 4 related posts

• What a 'Fused DeepLIFT' Actually Means: Gradient Overrides Without Hooks — jmschreiber91 · 2026-10-26
• Fused DeepLIFT explained: why attribution hooks are cheap for Borzoi/AlphaGenome but hurt small models — jmschreiber91 · 2026-10-26
• Laying out DeepLIFT as a standalone computation graph: from sequence to attributions directly — jmschreiber91 · 2026-10-26
• Why fused DeepLIFT is fast: PyTorch does the op fusion when attribution is a computation graph — jmschreiber91 · 2026-10-26