Memory
2 long-form posts on Memory: machine-learning research by Taha Bouhsine, each built around live, in-browser interactive visualizations.
-
Reversible Backprop as a custom_vjp in JAX
A runnable companion to the memory post: the momentum block and its exact inverse, the custom_vjp whose backward pass reconstructs the trajectory instead of storing it, XLA's memory_analysis as the measuring instrument, and the (1/mu)^L float cliff reproduced in numpy float32. Every number is from the real Kaggle run.
-
Backprop Without the Memory
Training memory is a tax nobody chose: backprop must hold every activation of the forward pass hostage until the backward pass consumes it, so depth costs memory even when it costs little compute. This post spends the invertibility this series proved two posts ago: a momentum residual block can be run backward, so the backward pass can recompute the past instead of storing it. Measured on the same network, standard backprop's activation memory grows from 13 MB to 674 MB as depth goes 8 to 512; the reversible pass holds flat at 3.2 MB, pays 24% in step time, and returns the same gradient, until a one-line arithmetic of friction and float noise says it cannot.