This release is meant to fix the following issues (regressions / silent correctness):

Optim

• Remove overly restrictive assert in adam https://github.com/pytorch/pytorch/pull/80222

Autograd

• Convolution forward over reverse internal asserts in specific case https://github.com/pytorch/pytorch/issues/81111
• 25% Performance regression from v0.1.1 to 0.2.0 when calculating hessian https://github.com/pytorch/pytorch/pull/82504

Distributed

• Fix distributed store to use add for the counter of DL shared seed https://github.com/pytorch/pytorch/pull/80348
• Raise proper timeout when sharing the distributed shared seed https://github.com/pytorch/pytorch/pull/81666

NN

• Allow register float16 weight_norm on cpu and speed up test https://github.com/pytorch/pytorch/pull/80600
• Fix weight norm backward bug on CPU when OMP_NUM_THREADS <= 2 https://github.com/pytorch/pytorch/pull/80930
• Weight_norm is not working with float16 https://github.com/pytorch/pytorch/issues/80599
• New release breaks torch.nn.weight_norm backwards pass and breaks all Wav2Vec2 implementations https://github.com/pytorch/pytorch/issues/80569
• Disable src mask for transformer and multiheadattention fastpath https://github.com/pytorch/pytorch/pull/81277
• Make nn.stateless correctly reset parameters if the forward pass fails https://github.com/pytorch/pytorch/pull/81262
• torchvision.transforms.functional.rgb_to_grayscale() + torch.nn.Conv2d() don`t work on 1080 GPU https://github.com/pytorch/pytorch/issues/81106
• Transformer and CPU path with src_mask raises error with torch 1.12 https://github.com/pytorch/pytorch/issues/81129

Data Loader

• [Locking lower ranks seed recepients https://github.com/pytorch/pytorch/pull/81071

CUDA

• os.environ["CUDA_VISIBLE_DEVICES"] has no effect https://github.com/pytorch/pytorch/issues/80876
• share_memory() on CUDA tensors no longer no-ops and instead crashes https://github.com/pytorch/pytorch/issues/80733
• [Prims] Unbreak CUDA lazy init https://github.com/pytorch/pytorch/pull/80899
• PyTorch 1.12 cu113 wheels cudnn discoverability issue https://github.com/pytorch/pytorch/issues/80637
• Remove overly restrictive checks for cudagraph https://github.com/pytorch/pytorch/pull/80881

ONNX

• ONNX cherry picks https://github.com/pytorch/pytorch/pull/82435

MPS

• MPS cherry picks https://github.com/pytorch/pytorch/issues/80898

Other

• Don't error if _warned_capturable_if_run_uncaptured not set https://github.com/pytorch/pytorch/pull/80345
• Initializing libiomp5.dylib, but found libomp.dylib already initialized. https://github.com/pytorch/pytorch/issues/78490
• Assertion error - _dl_shared_seed_recv_cnt - pt 1.12 - multi node https://github.com/pytorch/pytorch/issues/80845
• Add 3.10 stdlib to torch.package https://github.com/pytorch/pytorch/pull/81261
• CPU-only c++ extension libraries (functorch, torchtext) built against PyTorch wheels are not fully compatible with PyTorch wheels https://github.com/pytorch/pytorch/issues/80489

This release is meant to deploy additional fixes not included in 1.10.1 release:

• fix pybind issue for get_autocast_cpu_dtype and get_autocast_gpu_dtype #66396
• Remove fgrad_input from slow_conv2d #64280
• fix formatting CIRCLE_TAG when building docs #67026

This release is meant to fix the following issues (regressions / silent correctness):

• torch.nn.cross_entropy silently incorrect in PyTorch 1.10 on CUDA on non-contiguous inputs #67167
• channels_last significantly degrades accuracy #67239
• Potential strict aliasing rule violation in bitwise_binary_op (on ARM/NEON) #66119
• torch.get_autocast_cpu_dtype() returns a new dtype #65786
• Conv2d grad bias gets wrong value for bfloat16 case #68048

The release tracker should contain all relevant pull requests related to this release as well as links to related issues

PyTorch 1.9.1 Release Notes

• Improvements
• Bug Fixes
• Documentation

Improvements

• Stop warning on .names() access in max_pool2d #60059
• Remove Caffe2 thread-pool leak warning #60318
• Add option to skip GitHub tag validation for torch.hub.load #62139
• Use log.warning in torch.distributed.run to print OMP_NUM_THREADS warning #63953
• TorchElastic: Pretty print the failure message captured by @record #64036
• torch.distribtued.run to set nproc_per_node to 1 by default #61552
• Remove experimental API warning from torch.distributed.elastic.utils.store #60807
• Deprecate use_env in torch.distributed.run #59409
• Better engineering changes for torch.distributed launcher #59152

Bug fixes

Distributed / TorchElastic

• Make init_method=tcp:// compatible with torch.distributed.run #63910
• Fix default parameters (number of restarts, log level, number of processes per node) that regressed with the transition from torch.distributed.launch and torch.distributed.run and clarify the documentation accordingly #61294

Hub

• Fix HTTP/403 error when calling torch.hub.load for TorchVision models #62072

Misc

• torch.mm to check input matrix sizes shapes #61394

Documentation

• Fix broken link in elastic launch doc #62378
• Fix typo in torch.distribtued.run warning message #61127

PyTorch 1.8.2 Release Notes

• Highlights
• Bug Fixes

Highlights

We are excited to announce the release of PyTorch 1.8.2. This is the first release we are making as part of the Pytorch Enterprise Support Program. This release includes a bug fix requested by a customer in an LTS branch. We'd like to thank Microsoft for their support and work on this release.

Bug Fixes

• Wrap cub in its own namespace (#55292) (#61605)