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Auto-Vectorizing TensorFlow Graphs: Jacobians, Auto-Batching And Beyond (1903.04243v1)
Published 8 Mar 2019 in cs.DC, cs.LG, and cs.MS
Abstract: 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.