PyTorch 2.10 is now available with a series of optimizations aimed at improving performance and simplifying numerical debugging. This release includes the work of 536 contributors, with over 4160 commits since version 2.9. ## Key Features * **Python 3.14 Support:** `torch.compile()` now supports Python 3.14, including the *freethreaded* build (experimental). * **Combo-kernels:** Reduced latency thanks to horizontal kernel fusion in TorchInductor. * **varlen_attn():** New operation for handling *ragged* and *packed* sequences. * **DnXgeev:** Efficient eigenvalue decomposition execution on NVIDIA GPUs. * **use_deterministic_mode:** `torch.compile()` now respects deterministic mode. * **DebugMode:** Tool for tracking calls and facilitating the debugging of numerical divergences. ## Numerical Debugging Determining behavior across multiple runs is crucial for debugging. PyTorch 2.10 enables this functionality via `torch.use_deterministic_algorithms(True)`, ensuring consistency of operations even with `torch.compile()`. DebugMode offers advanced features such as *runtime logging*, tensor *hashing*, and *dispatch hooks* to isolate and analyze numerical divergences. ## Other News * **Torchscript Deprecated:** Torchscript has been deprecated and replaced by `torch.export`. * **tlparse & TORCH_TRACE:** Tools to simplify reporting compiler-related bugs. * **Release Cadence:** Starting in 2026, the release cadence will increase from quarterly to bi-monthly.