@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.
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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 2.14 brings CuTeDSL-generated CUTLASS kernels to Inductor through NVGEMM, a new nccl2 backend for PyTorch Distr…
PyTorch 2.14 introduces significant updates including NVGEMM for CUTLASS kernels in Inductor, a new nccl2 backend for distributed computing, first-class fault tolerance, and native linear algebra on Apple Silicon.