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Accelerating Generative AI with PyTorch II: GPT, Fast

This post is the second part of a multi-series blog focused on how to accelerate generative AI models with pure, native PyTorch. We are excited to share a breadth of newly released PyTorch performance features alongside practical examples to see how far we can push PyTorch native performance. In part one, we showed how to accelerate Segment Anything over 8x using only pure, native PyTorch. In this blog we’ll focus on LLM optimization.

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Andrej Karpathy , Team Pytorch , Segment Anything , Pytorch Developer Conference , Model Bandwidth Utilization , Using Tensor ,

Inside the Matrix: Visualizing Matrix Multiplication, Attention and Beyond

An open source machine learning framework that accelerates the path from research prototyping to production deployment. ....

Team Pytorch , Blockwise Parallel Transformer , Block Parallel Transformer , Low Rank Adaptation , Large Language Models ,

PyTorch 2.0: Our next generation release that is faster, more Pythonic and Dynamic as ever

An open source machine learning framework that accelerates the path from research prototyping to production deployment. ....

Pytorch Dtensor , Mkldnn One , Pytorch Distributedtensor Dtensor , Single Program Multiple Devices , Arm Compute Library , Team Pytorch , Pytorch Conference , Dynamic Shapes , Accelerated Transformers , Better Transformers , Performance Shaders , Pytorch Transformer , Transformer Decoders , Transformer Encoder , Self Attention , Accelerated Pytorch , Python Frame Evaluation Hooks , Stable Diffusion , With Pytorch , Memory Efficient Attention , Geomean Speedup , Message Passing , Compressed Sparse Row , Automatic Mixed Precision , Torch Distributedtensor , Pytorch Tensor Parallelism ,

PyTorch 1.13 release, including beta versions of functorch and improved support for Apple's new M1 chips.

We are excited to announce the release of PyTorch® 1.13 (release note)! This includes Stable versions of BetterTransformer. We deprecated CUDA 10.2 and 11.3 and completed migration of CUDA 11.6 and 11.7. Beta includes improved support for Apple M1 chips and functorch, a library that offers composable vmap (vectorization) and autodiff transforms, being included in-tree with the PyTorch release. This release is composed of over 3,749 commits and 467 contributors since 1.12.1. We want to sincerely thank our dedicated community for your contributions.

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Pytorch Apis , Pytorch Torchtext , Mlperf Bert , Pytorch Bettertransformer , Autocast Apis , Prototype Limited Python , Arm Compute Library , Technology Apis , Team Pytorch , Nested Tensors , Torch Bettertransformer , Better Transformer , Transformer Encoder , Nested Tensor , Tracing Technology , Intel Cooper Lake Processor , Sapphire Rapids , Intel Cooper Lake , Compute Library , Thread Sanitizer , Limited Python ,

PyTorch 1.10 Release, including CUDA Graphs APIs, Frontend and Compiler Improvements

PyTorch 1.10 Release, including CUDA Graphs APIs, Frontend and Compiler Improvements
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Android Neural Networks , Team Pytorch , Module Parametrization , Training Releases Now , Remote Module , Pytorch Profiler , Memory View , Automated Recommendations , Kernel View , Backward Pass , Tensor Core , Neural Networks , Graphics Processing Units , Neural Processing Units , Transfer Learning , Object Detection ,