Title: TFLMS: Large Model Support in TensorFlow by Graph Rewriting,
Abstract: While accelerators such as GPUs have limited memory, deep neural networks are
becoming larger and will not fit with the memory limitation of accelerators for
training. We propose an approach to tackle this problem by rewriting the
computational graph of a neural network, in which swap-out and swap-in
operations are inserted to temporarily store intermediate results on CPU
memory. In particular, we first revise the concept of a computational graph by
defining a concrete semantics for variables in a graph. We then formally show
how to derive swap-out and swap-in operations from an existing graph and
present rules to optimize the graph. To realize our approach, we developed a
module in TensorFlow, named TFLMS. TFLMS is published as a pull request in the
TensorFlow repository for contributing to the TensorFlow community. With TFLMS,
we were able to train ResNet-50 and 3DUnet with 4.7x and 2x larger batch size,
respectively. In particular, we were able to train 3DUNet using images of size
of $192^3$ for image segmentation, which, without TFLMS, had been done only by
dividing the images to smaller images, which affects the accuracy.