@PyTorch: PyTorch 2.12 introduces major updates across compilation, export, distributed training, and accelerator support. Highli…
Summary
PyTorch 2.12 release includes major updates to compilation, export, distributed training, and accelerator support, with up to 100x faster batched linalg.eigh on CUDA and new APIs like torch.accelerator.Graph.
Similar Articles
PyTorch 2.12 Release Highlights (7 minute read)
PyTorch 2.12 introduces significant performance improvements including up to 100x faster batched eigendecomposition on CUDA, a new device-agnostic torch.accelerator.Graph API, and support for Microscaling quantization in torch.export, continuing the framework's evolution into a unified production platform.
@PyTorch: PyTorch 2.14 introduces updates to Inductor, PyTorch Distributed, c10d, dynamic shapes, Apple Silicon, and accelerator …
PyTorch 2.14 introduces updates to Inductor, PyTorch Distributed, dynamic shapes, Apple Silicon support, and more, with a live Q&A and PyTorchCon announcement.
@PyTorch: PyTorch releases include thousands of changes, and our Release Live Q&A gives you direct access to the maintainers and …
PyTorch 2.12 release with major updates is highlighted, and a live Q&A event on May 20 with maintainers is announced to discuss compilation, distributed systems, quantization, and more.
@PyTorch: PyTorch 2.13 is here, with 3,328 commits from 526 contributors and updates across FlexAttention, CuTeDSL, nn.LinearCros…
PyTorch 2.13 is released with major updates including FlexAttention on Apple Silicon (up to 12x speedup), CuTeDSL backend, nn.LinearCrossEntropyLoss for 4x memory reduction, new torchcomms communications backend, FSDP2 communication overlap, Python 3.15 wheel support, and broader platform support. A live Q&A is scheduled for July 22.
@PyTorch: PyTorch 2.15 is here, with 3,117 commits from 583 contributors and updates across distributed training, NVGEMM, Apple S…
PyTorch 2.15 发布，包含 3,117 次提交，重写的 NCCL 后端成为默认、新增 NIXL 单向传输、FSDP2 与流水线并行的更细粒度控制，并在 NVIDIA Blackwell（NVFP4）和 Apple Silicon（FP8）上带来更快的 LLM 解码，同时扩展了 AMD、Intel、ROCm 等平台支持。