# Auto-Vectorizing TensorFlow Graphs: Jacobians, Auto-Batching And Beyond

arXiv: https://arxiv.org/abs/1903.04243 · HTML: https://mlsanity.com/paper/1903.04243/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2019.

## Built on by (most cited first, 3 of 3)

- [Deep Learning with Label Differential Privacy](https://mlsanity.com/paper/2102.06062/) (2021)
- [ACRoBat: Optimizing Auto-batching of Dynamic Deep Learning at Compile Time](https://mlsanity.com/paper/2305.10611/) (2023)
- [Adaptive Optimization with Examplewise Gradients](https://mlsanity.com/paper/2112.00174/) (2021)

## Abstract (arXiv)

We propose a static loop vectorization optimization on top of high level dataflow IR used by frameworks like TensorFlow. A new statically vectorized parallel-for abstraction is provided on top of TensorFlow, and used for applications ranging from auto-batching and per-example gradients, to jacobian computation, optimized map functions and input pipeline optimization. We report huge speedups compared to both loop based implementations, as well as run-time batching adopted by the DyNet framework.