PyTorch 1.7.1 Release Notes

• New Features
• Critical Fixes
• Other Fixes

New Features

Add Python 3.9 binaries for linux and macOS (#48133) and Windows (#48218)

NOTE: Conda installs for Python 3.9 will require the conda-forge channel, example:
conda install -y -c pytorch -c conda-forge pytorch.

Upgrade CUDA binaries to use cuDNN 8.0.5 (builder repo #571)

This upgrade fix regressions on Ampere cards introduced in cuDNN 8.0.4.
It will improve performance for 3090 RTX cards, and may improve performance in other RTX-30 series card.

Critical Fixes

Python 3.9

• Use custom version of pybind11 to work around Python 3.9 issues (#48312)
• Fix jit Python 3.9 parsing (#48744)
• Fix cpp_extension to work with Python 3.9 (#48768)

Build

• Fix cpp_extension to properly handle env variable on Windows (#48937)
• Properly package libomp.dylib for macOS binaries (#48337)
• Fix build for statically linked OpenBLAS on aarch64 (#48819)

Misc

• torch.sqrt: fix wrong output values for very large complex input (#48216)
• max_pool1d: fix for discontiguous inputs (#48219)
• collect_env: fix detection of DEBUG flag (#48319)
• collect_env: Fix to work when PyTorch is not installed (#48311)
• Fix amp memory usage when running in no_grad() mode (#48936)
• nn.ParameterList and nn.ParameterDict: Remove spurious warnings (#48215)
• Tensor Expression fuser bugfixes (#48137)

Other Fixes

• Tensor Expression fix for CUDA 11.0 (#48309)
• torch.overrides: doc fix (#47843)
• torch.max: Fix output type for Tensor subclasses (#47735)
• torch.mul: Add support for boolean Tensors (#48310)
• Add user friendly error when trying to compile from source with Python 2 (#48317)