Unified Convolution Framework: A compiler-based approach to support sparse convolutions

Jaeyeon Won · Changwan Hong · Charith Mendis · Joel Emer · Saman Amarasinghe

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This paper introduces a Unified Convolution Framework (UCF) that incorporates various existing sparse convolutions in a unified abstraction. This work is in contrast to the common library-based approach that requires much engineering effort because each different sparse convolution must be implemented separately. Instead, it employs a tensor compiler approach that can flexibly explore convolutions with various program transformations; however, no compiler can currently support various sparse convolutions flexibly to our knowledge. In particular, the Tensor Algebra Compiler (TACO) can support a variety of sparse formats but cannot declare convolutions because a tensor cannot be accessed by a linear combination of index variables. We extend TACO's Einsum language to support an affine index expression to declare a convolution. Our method is also compatible with TACO's format and scheduling language, enabling various sparse convolution implementations to be explored. Our experimental results demonstrate that TACO-UCF achieves 1.32× and 8.3× average speedups on a filter sparse convolution and a submanifold sparse convolution, respectively, over state-of-the-art libraries on CPU. TACO-UCF on GPU outperforms the state-of-the-art GPU library on filter sparse convolution of ResNet50 by an average of 1.47× at 80% sparsity. We also demonstrate TACO-UCF outperforms on a neighbor retrieval of a submanifold sparse convolution by an average of 2.55× and 3.34× over MinkowskiEngine and TorchSparse on GPU, respectively.

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