![]() ![]() We then measure speedups and validate accuracy across these models. We don’t modify these open-source models except to add a pile call wrapping them. 56 models from TorchBench: a curated set of popular code-bases from across github.61 models from TIMM: a collection of state-of-the-art PyTorch image models by Ross Wightman.46 models from HuggingFace Transformers.We separate the benchmarks into three categories: We built this benchmark carefully to include tasks such as Image Classification, Object Detection, Image Generation, various NLP tasks such as Language Modeling, Q&A, Sequence Classification, Recommender Systems and Reinforcement Learning. ![]() To validate these technologies, we used a diverse set of 163 open-source models across various machine learning domains. the ability to send in Tensors of different sizes without inducing a recompilation), making them flexible, easily hackable and lowering the barrier of entry for developers and vendors. TorchDynamo, AOTAutograd, PrimTorch and TorchInductor are written in Python and support dynamic shapes (i.e. For NVIDIA and AMD GPUs, it uses OpenAI Triton as a key building block.
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