Releases · pytorch/pytorch · GitHub

PyTorch 2.8.0 Release Notes Highlights Backwards Incompatible Changes Deprecations New Features Improvements Bug fixes Performance Documentation Developers Highlights Unstable torch::stable::Tensor High-performance quantized LLM inference on Intel CPUs with native PyTorch Experimental Wheel Variant Support Inductor CUTLASS backend support Inductor Graph Partition for CUDAGraph Control Flow Operator Library HuggingFace SafeTensors support in PyTorch Distributed Checkpointing SYCL support in PyTorch CPP Extension API A16W4 on XPU Device Hierarchical compilation with torch.compile Intel GPU distributed backend (XCCL) support For more details about these highlighted features, you can look at the release blogpost. Below are the full release notes for this release. Tracked Regressions Windows wheel builds with CUDA 12.9.1 stack overflow during build (#156181) Due to a bug introduced in CUDA 12.9.1, we are unable to complete full Windows wheel builds with this version, as compilation of torch.segment_reduce() crashes the build. Thus, we provide a wheel without torch.segment_reduce() included in order to sidestep the issue. If you need support for torch.segment_reduce(), please utilize a different version. Backwards Incompatible Changes CUDA Support Removed support for Maxwell and Pascal architectures with CUDA 12.8 and 12.9 builds (#157517, #158478, #158744) Due to binary size limitations, support for sm50 - sm60 architectures with CUDA 12.8 and 12.9 has been dropped for the 2.8.0 release. If you need support for these architectures, please utilize CUDA 12.6 instead. Python Frontend Calling an op with an input dtype that is unsupported now raises NotImplementedError instead of RuntimeError (#155470) Please update exception handling logic to reflect this. In 2.7.0 try: torch.nn.Hardshrink()(torch.randint(0, 5, (10,))) except RuntimeError: ... In 2.8.0 try: torch.nn.Hardshrink()(torch.randint(0, 5, (10,))) except NotImplementedError: ... Added missing in-place on view check