Flash-Decoding for long-context inference
by Tri Dao, Daniel Haziza, Francisco Massa, Grigory Sizov Motivation Large language models (LLM) such as ChatGPT or Llama have received unprecedented attention lately. However, they remain massively expensive to run. Even though generating a single response can cost about $0.01 (a few seconds of an 8xA100 instance on AWS), the costs quickly add up when scaling to billions of users, who could have
Compromised PyTorch-nightly dependency chain between December 25th and December 30th, 2022.
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torchvision.models — PyTorch 1.7.0 documentation
torchvision.models¶ The models subpackage contains definitions of models for addressing different tasks, including: image classification, pixelwise semantic segmentation, object detection, instance segmentation, person keypoint detection and video classification. Classification¶ The models subpackage contains definitions for the following model architectures for image classification: AlexNet VGG R
torchvision.datasets — PyTorch 1.7.0 documentation
torchvision.datasets¶ All datasets are subclasses of torch.utils.data.Dataset i.e, they have __getitem__ and __len__ methods implemented. Hence, they can all be passed to a torch.utils.data.DataLoader which can load multiple samples parallelly using torch.multiprocessing workers. For example: imagenet_data = torchvision.datasets.ImageNet('path/to/imagenet_root/') data_loader = torch.utils.data.Dat
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Neural Transfer Using PyTorch — PyTorch Tutorials 2.4.0+cu121 documentation