November 29, 2023
PyTorch 2.1 Contains New Performance Features for AI Developers
We are excited to see the release of PyTorch 2.1. In this blog, we discuss the five features for which Intel made significant contributions to PyTorch 2.1:
November 16, 2023
🎉 PyTorch Docathon H2 2023 Wrap-up 🎉
We are thrilled to announce the successful completion of the Fall 2023 PyTorch Docathon! The event was a resounding success, and we want to extend our heartfelt gratitude to all the participants who made it possible. Dedication, expertise, and tireless efforts of our open-source contributors have once again helped us to improve PyTorch documentation.
November 16, 2023
Accelerating Generative AI with PyTorch: Segment Anything, Fast
This post is the first 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 of how these features can be combined to see how far we can push PyTorch native performance.
November 07, 2023
PyTorch compile to speed up inference on Llama 2
In this blog, we discuss how to improve the inference latencies of the Llama 2 family of models using PyTorch native optimizations such as native fast kernels, compile transformations from torch compile, and tensor parallel for distributed inference. Our approach results in 29ms/token latency for single user requests on the 70B LLaMa model (as measured on 8 A100 GPUs). We are excited to share our findings with the community and make our code available here.
November 06, 2023
High-Performance Llama 2 Training and Inference with PyTorch/XLA on Cloud TPUs
In a landscape where AI innovation is accelerating at an unprecedented pace, Meta’s Llama family of open sourced large language models (LLMs) stands out as a notable breakthrough. Llama marked a significant step forward for LLMs, demonstrating the power of pre-trained architectures for a wide range of applications. Llama 2 further pushed the boundaries of scale and capabilities, inspiring advancements in language understanding, generation, and beyond.
November 02, 2023
Accelerating Inference on x86-64 Machines with oneDNN Graph
Supported in PyTorch 2.0 as a beta feature, oneDNN Graph leverages aggressive fusion patterns to accelerate inference on x86-64 machines, especially Intel® Xeon® Scalable processors.
October 31, 2023
AMD Extends Support for PyTorch Machine Learning Development on Select RDNA™ 3 GPUs with ROCm™ 5.7
Researchers and developers working with Machine Learning (ML) models and algorithms using PyTorch can now use AMD ROCm 5.7 on Ubuntu® Linux® to tap into the parallel computing power of the Radeon™ RX 7900 XTX and the Radeon™ PRO W7900 graphics cards which are based on the AMD RDNA™ 3 GPU architecture.
