Obtaining high-performance implementations of tensor programs such as deep neural networks on a wide range of hardware remains a challenging task. Search-based tensor program optimizers can automatically find high-performance programs on a given hardware platform, but the search process in existing tools suffer from low efficiency, requiring hours or days of time to discover good programs due to the size of the search space. We present Felix, a novel gradient-based compiler optimization framework for tensor-based programs. Felix creates a differentiable space of tensor programs that is amenable to search by gradient descent. Felix applies continuous relaxation on the space of programs and creates differentiable estimator of program latency, allowing efficient search of program candidates using gradient descent, in contrast to conventional approaches that search over a non-differentiable objective function over a discrete search space. We perform an extensive evaluation on six deep neural networks for vision and natural language processing tasks on three GPU-based platforms. Our experiments show that Felix surpasses the performance of off-the-shelf inference frameworks - PyTorch, Tensorflow, and TensorRT - within 7 minutes of search time on average. Felix also finds optimized programs significantly faster than TVM Ansor, a state-of-the-art search-based optimizer for tensor programs.

@inproceedings{ZhaoSAM24,
  title = {Felix: Optimizing Tensor Programs with Gradient Descent},
  author = {Yifan Zhao and Hashim Sharif and Vikram Adve and Sasa Misailovic},
  booktitle = {ASPLOS},
  year = {2024},
  doi = {10.1145/3620666.3651348}
}