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Pytorch 2: Faster machine learning through dynamic python bytecode transformation and graph compilation
This paper introduces two extensions to the popular PyTorch machine learning framework,
TorchDynamo and TorchInductor, which implement the torch. compile feature released in …
TorchDynamo and TorchInductor, which implement the torch. compile feature released in …
Specinfer: Accelerating large language model serving with tree-based speculative inference and verification
This paper introduces SpecInfer, a system that accelerates generative large language model
(LLM) serving with tree-based speculative inference and verification. The key idea behind …
(LLM) serving with tree-based speculative inference and verification. The key idea behind …
Olive: Accelerating large language models via hardware-friendly outlier-victim pair quantization
Transformer-based large language models (LLMs) have achieved great success with the
growing model size. LLMs' size grows by 240× every two years, which outpaces the …
growing model size. LLMs' size grows by 240× every two years, which outpaces the …
Welder: Scheduling deep learning memory access via tile-graph
With the growing demand for processing higher fidelity data and the use of faster computing
cores in newer hardware accelerators, modern deep neural networks (DNNs) are becoming …
cores in newer hardware accelerators, modern deep neural networks (DNNs) are becoming …