Session

Compilers

Ballroom C

Moderator: Hui Guan

Abstract:

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Ballroom B - Position 34
ALCOP: Automatic Load-Compute Pipelining in Deep Learning Compiler for AI-GPUs

Guyue Huang · Yang Bai · Liu Liu · Yuke Wang · Bei Yu · Yufei Ding · Yuan Xie

Pipelining between data loading and computation is a critical tensor program optimization for GPUs. In order to unleash the high performance of latest GPUs, we must perform a synergetic optimization of multi-stage pipelining across the multi-level buffer hierarchy of GPU. Existing frameworks rely on hand-written libraries such as cuBLAS to perform pipelining optimization, which is inextensible to new operators and un-composable with prior tensor compiler optimizations. This paper presents ALCOP, the first framework that is compiler-native and fully supports multi-stage multi-level pipelining. ALCOP overcomes three critical obstacles in generating code for pipelining: detection of pipelining-applicable buffers, program transformation for multi-level multi-stage pipelining, and efficient schedule parameter search by incorporating static analysis. Experiments show that ALCOP can generate programs with 1.23× speedup on average (up to 1.73×) over vanilla TVM. On end-to-end models, ALCOP can improve upon TVM by up to 1.18×, and XLA by up to 1.64×. Besides, our performance model significantly improves the efficiency of the schedule tuning process and can find schedules with 99% of the performance given by exhaustive search while costing 40× fewer trials.


Ballroom B - Position 35
SIRIUS: Harvesting Whole-Program Optimization Opportunities for DNNs

YIJIN LI · Jiacheng Zhao · Sun Qianqi · Haohui Mai · Lei Chen · Wanlu Cao · Yanfan Chen · Li zhicheng · YING LIU · Xinyuan Zhang · Xiyu Shi · Jie Zhao · Jingling Xue · HUIMIN CUI · XiaoBing Feng

As emerging applications are rapidly moving to accelerators, a greatdeal of research has been proposed to improve the performance of the accelerators. For the AI applications, fruitful software-driven research has been focused on proposing new programming languages, new kernel fusion heuristics,new optimization tuning approaches, and new software execution engines. However, how to leverage classical compiler optimizations to generate efficient code is an overlooked aspect of performance. In this paper, we propose a whole-program analysis and optimization compiler framework, SIRIUS, to uniformly model the host and kernel computations in a unified polyhedral representation and,further, seek maximal fusion opportunities from the global view so that the fused kernel can benefit from classical optimizations. Evaluations over representative DNN models demonstrate that SIRIUS can achieve up to 11.98x speedup over TensorRT, and 154.84x speedup over TensorFlow. In particular, for BERT, SIRIUS can achieve 1.46x speedup over TensorRT.


Ballroom B - Position 36
X-RLFLOW: GRAPH REINFORCEMENT LEARNING FOR NEURAL NETWORK SUBGRAPHS TRANSFORMATION

Guoliang HE · Sean Parker · Eiko Yoneki

Tensor graph superoptimisation systems perform a sequence of subgraph substitution to neural networks, to find the optimal computation graph structure. Such a graph transformation process naturally falls into the framework of sequential decision-making, and existing systems typically employ a greedy search approach, which cannot explore the whole search space as it cannot tolerate a temporary loss of performance. In this paper, we address the tensor graph superoptimisation problem by exploring an alternative search approach, reinforcement learning (RL). Our proposed approach, X-RLflow, can learn to perform neural network dataflow graph rewriting, which substitutes a subgraph one at a time. X-RLflow is based on a model-free RL agent that uses a graph neural network (GNN) to encode the target computation graph and outputs a transformed computation graph iteratively. We show that our approach can outperform state-of-the-art superoptimisation systems over a range of deep learning models and achieve by up to 40% on those that are based on transformer-style architectures. We show that our approach can outperform state-of-the-art superoptimisation systems over a range of deep learning models and achieve by up to 40% on those that are based on transformer-style architectures.