PyTorch 1.8.1 Release Notes

• New Features
• Improvements
• Bug Fixes
• Documentation

New Features

Revamp of profiling tools in torch.profiler

The torch.profiler submodule is now available. It leveraged the newly released kineto library for profiling.
You can find more details in this blogpost: https://pytorch.org/blog/introducing-pytorch-profiler-the-new-and-improved-performance-tool/

Enable use of autocast for pytorch xla (#48570)

The torch.cuda.autocast package can now be used in conjunction with torch xla to provide easy mixed-precision training.

Improvements

• Make torch. submodule import more autocomplete-friendly (#52339)
• Add support in ONNX for torch.{isinf,any,all} (#53529)
• Replace thrust with cub in GPU implementation of torch.randperm for performance (#54537)

Bug fixes

Misc

• Fixes for torch.distributions validation checks (#53763)
• Allow changing the padding vector for nn.Embedding (#53447)
• Fix TensorPipe for large copies and interoperability with CUDA (#53804)
• Properly de-sugar Ellipsis in TorchScript (#53766)
• Stop using OneDNN for group convolutions when groups size is a multiple of 24 (#54015)
• Use int8_t instead of char in {load,store}_scalar (#52616)
• Make ideep honor torch.set_num_thread (#53871)
• Fix dimension out of range in pixel_{un}shuffle (#54178)
• Update kineto to fix libtorch builds (#54205)
• Fix distributed autograd CUDA stream synchronization for send/recv operations (#54358)

ONNX

• Update error handling in ONNX to avoid ValueError (#53548)
• Update assign output shape for nested structure and dict output (#53311)
• Update embedding export wrt padding_idx (#53931)

Documentation

• Doc update for torch.fx (#53674)
• Fix distributed.rpc.options.TensorPipeRpcBackendOptions.set_device_map (#53508)
• Update example for nn.LSTMCell (#51983)
• Update doc for the padding_idx argument for nn.Embedding (#53809)
• Update general doc template (#54141)