Transcription of Tensor Comprehensions: Framework-Agnostic High …
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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.
semantics allows for efficient memory management and mapping to complex parallel platforms. We address the second challenge by specializing a polyhedral intermediate representation and its
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