PyTorch

This maintenance release addresses critical regressions in PyTorch 2.9.0, specifically fixing memory issues in 3D convolutions, Inductor compilation bugs for Gemma/vLLM, and various distributed and numeric stability fixes.

PyTorch 2.9.0 introduces Python 3.10 as the minimum requirement, defaults the ONNX exporter to the Dynamo-based pipeline, and adds support for symmetric memory and FlexAttention on new hardware.

PyTorch 2.8.0 introduces high-performance quantized LLM inference on Intel CPUs, SYCL support for CPP extensions, and stricter validation for autograd and torch.compile. It includes significant breaking changes regarding CUDA architecture support and internal configuration renames.

This maintenance release focuses on fixing regressions and silent correctness issues across torch.compile, Distributed, and Flex Attention, while also improving wheel sizes and platform-specific compatibility for MacOS, Windows, and XPU.

PyTorch 2.7.0 introduces Blackwell support and FlexAttention optimizations while enforcing stricter C++ API visibility and Python limited API compliance. It marks a significant shift in ONNX and Export workflows by deprecating legacy capture methods in favor of the unified torch.export API.

PyTorch 2.6 introduces Python 3.13 support for torch.compile, FP16 support for X86 CPUs, and new AOTInductor packaging APIs. It includes a significant security change making torch.load use weights_only=True by default and deprecates the official Anaconda channel.