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Tensor Comprehensions: Framework-Agnostic High …
Tensor Comprehensions: Framework-AgnosticHigh-Performance Machine Learning AbstractionsNicolas VasilacheFacebook AI ZinenkoInria & ENS, TheodoridisETH Z GoyalFacebook AI DeVitoFacebook AI S. MosesMIT VerdoolaegePolly Labs & Facebook AI AdamsFacebook AI CohenInria & ENS, DI & Facebook AI learning models with convolutional and recurrent networks are now ubiq-uitous and analyze massive amounts of audio, image, video, text and graph data,with applications in automatic translation, speech-to-text, scene understanding,ranking user preferences, ad placement, etc. Competing frameworks for buildingthese networks such as TensorFlow, Chainer, CNTK, Torch/PyTorch, Caffe1/2,MXNet and Theano, explore different tradeoffs between usability and expressive-ness, research or production orientation and supported hardware. They operateon a DAG of computational operators, wrapping high-performance libraries suchas CUDNN (for NVIDIA GPUs) or NNPACK (for various CPUs), and automatememory allocation, synchronization, distribution.
1 Introduction Deep neural networks trained with back-propagation learning [52] are a method of choice to solve complex problems with sufficient data.
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