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jonatasgrosman / asrecognition

ASRecognition: just an easy-to-use library for Automatic Speech Recognition.
MIT License
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build(deps): bump torch from 1.9.0 to 1.10.1 #26

Closed dependabot[bot] closed 2 years ago

dependabot[bot] commented 2 years ago

Bumps torch from 1.9.0 to 1.10.1.

Release notes

Sourced from torch's releases.

PyTorch 1.10.1 Release, small bug fix release

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 1.10 Release, including CUDA Graphs APIs, Frontend and compiler improvements

1.10.0 Release Notes

  • Highlights
  • Backwards Incompatible Change
  • New Features
  • Improvements
  • Performance
  • Documentation

Highlights

We are excited to announce the release of PyTorch 1.10. This release is composed of over 3,400 commits since 1.9, made by 426 contributors. We want to sincerely thank our community for continuously improving PyTorch.

PyTorch 1.10 updates are focused on improving training and performance of PyTorch, and developer usability. Highlights include:

  • CUDA Graphs APIs are integrated to reduce CPU overheads for CUDA workloads.
  • Several frontend APIs such as FX, torch.special, and nn.Module Parametrization, have moved from beta to stable.
  • Support for automatic fusion in JIT Compiler expands to CPUs in addition to GPUs.
  • Android NNAPI support is now available in beta.

You can check the blogpost that shows the new features here.

Backwards Incompatible changes

Python API

torch.any/torch.all behavior changed slightly to be more consistent for zero-dimension, uint8 tensors. (#64642)

These two functions match the behavior of NumPy, returning an output dtype of bool for all support dtypes, except for uint8 (in which case they return a 1 or a 0, but with uint8 dtype). In some cases with 0-dim tensor inputs, the returned uint8 value could mistakenly take on a value > 1. This has now been fixed.

... (truncated)

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dependabot[bot] commented 2 years ago

Superseded by #29.