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

Ashish Agarwal; Igor Ganichev

arxiv: 1903.04243 · v1 · pith:66EJKULMnew · submitted 2019-03-08 · 💻 cs.DC · cs.LG· cs.MS

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

Ashish Agarwal , Igor Ganichev This is my paper

classification 💻 cs.DC cs.LGcs.MS

keywords tensorflowauto-batchingloopoptimizationusedabstractionadoptedapplications

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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.

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