ExecuTorch is a unified PyTorch-native deployment framework that enables seamless on-device execution of AI models across heterogeneous hardware while preserving original PyTorch semantics.
Reed and Zachary DeVito and Horace He and Ansley Ussery and Jason Ansel , title =
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GraphMend uses two Jaseci-based code transformations to eliminate dynamic-control-flow and side-effect graph breaks in PyTorch 2, reducing breaks to zero in six of eight Hugging Face models and yielding up to 75% latency reduction on RTX 3090 and A40 GPUs.
Flint generates compiler-derived workload graphs that support cluster-free design space exploration for distributed machine learning systems.
citing papers explorer
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ExecuTorch -- A Unified PyTorch Solution to Run AI Models On-Device
ExecuTorch is a unified PyTorch-native deployment framework that enables seamless on-device execution of AI models across heterogeneous hardware while preserving original PyTorch semantics.
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GraphMend: Code Transformations for Fixing Graph Breaks in PyTorch 2
GraphMend uses two Jaseci-based code transformations to eliminate dynamic-control-flow and side-effect graph breaks in PyTorch 2, reducing breaks to zero in six of eight Hugging Face models and yielding up to 75% latency reduction on RTX 3090 and A40 GPUs.
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Flint: Compiler Enabled Cluster-Free Design Space Exploration for Distributed ML
Flint generates compiler-derived workload graphs that support cluster-free design space exploration for distributed machine learning systems.