Researchers at Microsoft have proposed a deep learning compiler called Permutation Invariant Transformation (PIT) to optimize models for dynamic sparsity. PIT leverages a mathematically proven property to consolidate sparsely located micro-tiles into dense tiles without changing computation results. The solution accelerates dynamic sparsity computation by up to 5.9 times compared to state-of-the-art compilers and offers potential applications in sparse training scenarios. PIT represents a significant advancement in deep learning optimization.
Introducing PIT: A Deep Learning Compiler for Dynamic Sparsity
Researchers at Microsoft have developed a groundbreaking solution called Permutation Invariant Transformation (PIT) to optimize deep learning models for dynamic sparsity. Traditional solutions struggle with static sparsity patterns, but PIT can efficiently handle dynamic sparsity patterns that are only known at runtime.
How does PIT work?
PIT is a deep-learning compiler that leverages Permutation Invariant Transformation, a mathematically proven property. It consolidates sparsely located micro-tiles into a dense tile without changing computation results. This approach balances high GPU utilization and minimal coverage waste, revolutionizing dynamic sparsity handling.
PIT’s workflow involves identifying PIT rules for all operators in a model and generating efficient GPU kernels tailored to dynamic sparsity requirements. This process occurs at runtime, allowing PIT to adapt to unfolding sparsity patterns. Two critical primitives, SRead and SWrite, enable rapid execution of PIT rules for dynamic sparsity online.
Impressive results
PIT has been extensively evaluated across diverse models, demonstrating its ability to accelerate dynamic sparsity computation by up to 5.9 times compared to state-of-the-art compilers. This performance boost highlights the tangible impact of PIT in addressing computational challenges posed by dynamic sparsity.
Applications and versatility
PIT’s contribution extends to sparse training scenarios, making it a versatile and robust solution. The research provides a comprehensive toolkit for handling dynamic sparsity, paving the way for transformative advancements in deep learning optimization.
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- Microsoft Researchers Propose PIT (Permutation Invariant Transformation): A Deep Learning Compiler for Dynamic Sparsity
- MarkTechPost
- Twitter – @itinaicom
