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We are excited to announce the release of PyTorch® 2.0 which we highlighted during the PyTorch Conference on 12/2/22! PyTorch 2.0 offers the same eager-mode development and user experience, while fundamentally changing and supercharging...
In collaboration with the Metal engineering team at Apple, we are excited to announce support for GPU-accelerated PyTorch training on Mac. Until now, PyTorch training on Mac only leveraged the...
We now live in a world where ML workflows (pre-training, post training, etc) are heterogeneous, must contend with hardware failures, are increasingly asynchronous and highly dynamic. Traditionally, PyTorch has relied...
If you installed PyTorch-nightly on Linux via pip between December 25, 2022 and December 30, 2022, please uninstall it and torchtriton immediately, and use the latest nightly binaries (newer than...
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...
Use 3D to visualize matrix multiplication expressions, attention heads with real weights, and more. Matrix multiplications (matmuls) are the building blocks of today’s ML models. This note presents mm, a visualization...
In theory, Attention is All You Need. In practice, however, we also need optimized attention implementations like FlashAttention. Although these fused attention implementations have substantially improved performance and enabled long...
We are excited to announce the release of PyTorch 1.10. This release is composed of over 3,400 commits since 1.9, made by 426 contributors. We want to sincerely thank our...
We’re happy to officially launch torchao, a PyTorch native library that makes models faster and smaller by leveraging low bit dtypes, quantization and sparsity. torchao is an accessible toolkit of techniques written...
Introduction to Helion In modern machine learning, the demand for high-performance computation has led to a proliferation of custom kernels. While these kernels can deliver impressive performance, they are often...
Today, we’re announcing the availability of PyTorch 1.5, along with new and updated libraries. This release includes several major new API additions and improvements. PyTorch now includes a significant update...
Hidet is a powerful deep learning compiler that simplifies the process of implementing high-performing deep learning operators on modern accelerators (e.g., NVIDIA GPUs). With the new feature of torch.compile(...) in PyTorch 2.0, integrating...
Reproducibility is an essential requirement for many fields of research including those based on machine learning techniques. However, many machine learning publications are either not reproducible or are difficult to...