NeurIPS 2020

AdaTune: Adaptive Tensor Program Compilation Made Efficient

Review 1

Summary and Contributions: The authors present a new autotuning algorithm with the following improvements. * early termination of evaluation runs if the CV of measurements is less than a threshold * adaptive surrogate modeling and using it to adaptively control exploitation vs exploration factor - This biases the search algorithm (in this case simulated annealing) to explore more if the model prediction is uncertain and to exploit more when the performance prediction is more certain.

Strengths: * The authors correctly identify the need for adaptive techniques in program auto-tuning. They demonstrate this in 3 contexts, adaptive early termination of evaluation runs, adaptive selection of candidates using a surrogate model, adaptive exploration vs exploitation threshold (this uses the surrogate model). * I really liked section 3 with observations. It gives insights as to why adaptive techniques for program auto-tuning is needed. * Their evaluation on the selected benchmarks compared to AutoTVM.

Weaknesses: OpenTuner * OpenTuner ( is a general-purpose auto-tuning framework that is widely used in compiler / automatic program optimization. The authors do not mention or compare against them. OpenTuner is built for tuning various knobs in a compiler / automatic program optimization setting. * In OpenTuner, the authors use a list of search techniques and then use an AUC meta technique to select the best search technique to use at a given time. The authors only use simulated annealing. I would like to see how the optimization times compare with OpenTuner which can adaptively use different search techniques. * I feel an evaluation with OpenTuner is essential to understand the state-of-the-art in program auto-tuning. Evaluation on larger NNs * Results from optimizing ResNet-50 would be more compelling. * End-to-end evaluation on a transformer model would also give evidence that this technique scales pretty well (may be BERT). Expected Improvement * It is not clear from the text how EI is used to select a promising plan. It would be better to mention how this is used in the fitness function in simulated annealing (line 5). Is it the entire fitness function or part of it? ****** post rebuttal comments ************ Thank you for the rebuttal response. Overall, the adaptive techniques introduced in this paper are valuable to the community. However, I would suggest the following changes to make the paper complete and correct w.r.t. to other works. * Since, the publication of the original AutoTVM paper, a few works that improve on this baseline have been published (including Adams et. al.). Therefore, I would urge the authors to change their state-of-the-art performance claims to "state-of-the-art with respect to a simulated annealing baseline". * Please add a citation to OpenTuner as well as discuss it in the paper (summary of the rebuttal response would be good due to page constraints).

Correctness: I believe that the empirical results shown in this paper are correct.

Clarity: Overall, the paper is well-written and easy to follow. Section 4.2.1 can be written better with more intuition.

Relation to Prior Work: * OpenTuner is not discussed nor compared against. It is a widely used compiler auto-tuning framework and is a key related prior work. I find this omission significant.

Reproducibility: Yes

Additional Feedback: * Please address my major concerns mentioned under weaknesses. The following are few minor details. * Figure 11 and 12, shouldn't the orange line climb up to the peak GFLOP plan faster than the rest to support your conclusions in section 5.3? Please explain if I am reading the graph wrong. * line 5 in algorithm 1 needs more explanation.

Review 2

Summary and Contributions: This paper proposes several methods to make adaptive tensor program compilation efficient. The contribution includes: - An analysis that reveals the inefficiency and challenges of the existing approaches - Several methods to make adaptive optimization more efficient

Strengths: - Insightful analysis of the inefficiency in existing approaches - Good and consistent results across different hardware platforms - Detail evaluation and ablation study

Weaknesses: - The evaluation section is confusing and not well-organized

Correctness: The claim and methods in the paper are correct and follow the widely accepted practice.

Clarity: This paper provides enough background information, but the evaluation section is not well organized and makes me confused. - Sec 5.2 Fig.7 looks good to me, but I think it is just a microbenchmark because it only tests four small cases. Then Fig.8, Fig.9, and Fig.10 confuse me a lot. Fig.9 is on GPU but Fig.10 is on CPU. Whether Fig.8 is on GPU or CPU is not documented. I would like to see all these comparisons on both CPU and GPU. - Sec 5.3 The main algorithm (RFEI + CSA + AE) in Fig. 12 performs worse than (RFEI + CSA + DE) on all cases in FIg.12. Why does that happen? Does it mean AE is useless at all?

Relation to Prior Work: This paper clearly discusses the its difference from previous works.

Reproducibility: Yes

Additional Feedback: The overall idea of this paper sounds good. But the evaluation section needs more work to make it clear. Typo: L233: Delete the redundant "2600 MHz" ========== Post Rebuttal Comments ========== Thanks for the response. It clarifies my concerns about the evaluation section, which are accidentally caused by the typo in Fig. 11 and Fig. 12. I would like to raise my score to 7.

Review 3

Summary and Contributions: The authors present an adaptive tensor compilation framework that vastly reduces the time needed to optimize tensor programs (e.g. ML pipelines) while matching or exceeding the performance of state of the art approaches.

Strengths: The performance of neuronal networks is crucial, especially since inference and training can consume vast amounts of compute. Optimizing these programs to run faster and be more efficient is a crucial aspect of ML research and highly relevant to the NeuroIPS audience. The authors demonstrate a significant reduction of time to optimize these models, while maintaining or exceeding the performance of the current state of the art.

Weaknesses: No major weaknesses

Correctness: Yes

Clarity: Very well written

Relation to Prior Work: Yes it relation to prior work is clearly established

Reproducibility: Yes

Additional Feedback: Thank you for writing such a clear and lucid paper, I haven't had the opportunity before to look at learn to compile, but your paper spawned an interest to learn more about the topic.