Step-TP is a dataset providing grounded, atomic step-level IR transitions and CoT supervision to enable reliable multi-step LLM-guided tensor program optimization instead of end-to-end imitation.
Operator fusion in XLA: Analysis and evaluation
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Mambalaya fuses the entire Mamba layer into one on-chip computation group, achieving simulated 4.9x prefill and 1.9x generation speedups over a MARCA-like baseline.
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Step-TP: A Grounded, Step-Level Dataset with Chain-of-Thought Reasoning for LLM-Guided Tensor Program Optimization
Step-TP is a dataset providing grounded, atomic step-level IR transitions and CoT supervision to enable reliable multi-step LLM-guided tensor program optimization instead of end-to-end imitation.
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Mambalaya: Einsum-Based Fusion Optimizations on State-Space Models
Mambalaya fuses the entire Mamba layer into one on-chip computation group, achieving simulated 4.9x prefill and 1.9x generation speedups over a MARCA-like baseline.