Update torch requirement from <1.9.0,>=1.7.0 to >=1.7.0,<1.10.0

[dependabot]

Updates the requirements on torch to permit the latest version.

Release notes

Sourced from torch's releases.

PyTorch 1.9 Release, including Torch.Linalg and Mobile Interpreter

PyTorch 1.9 Release Notes

• Highlights
• Backwards Incompatible Change
• Deprecations
• New Features
• Improvements
• Bug Fixes
• Performance
• Documentation

Highlights

We are excited to announce the release of PyTorch 1.9. The release is composed of more than 3,400 commits since 1.8, made by 398 contributors. Highlights include:

• Major improvements to support scientific computing, including torch.linalg, torch.special, and Complex Autograd
• Major improvements in on-device binary size with Mobile Interpreter
• Native support for elastic-fault tolerance training through the upstreaming of TorchElastic into PyTorch Core
• Major updates to the PyTorch RPC framework to support large scale distributed training with GPU support
• New APIs to optimize performance and packaging for model inference deployment
• Support for Distributed training, GPU utilization and SM efficiency in the PyTorch Profiler

We’d like to thank the community for their support and work on this latest release. We’d especially like to thank Quansight and Microsoft for their contributions.

You can find more details on all the highlighted features in the PyTorch 1.9 Release blogpost.

Backwards Incompatible changes

Python API

• torch.divide with rounding_mode='floor' now returns infinity when a non-zero number is divided by zero ([#56893](pytorch/pytorch#56893)). This fixes the rounding_mode='floor' behavior to return the same non-finite values as other rounding modes when there is a division by zero. Previously it would always result in a NaN value, but a non-zero number divided by zero should return +/- infinity in IEEE floating point arithmetic. Note this does not effect torch.floor_divide or the floor division operator, which currently use rounding_mode='trunc' (and are also deprecated for that reason).

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Changelog

Sourced from torch's changelog.

Releasing PyTorch

• General Overview
• Cutting release branches
  • Making release branch specific changes
  • Getting CI signal on release branches:
• Drafting RCs (https://github.com/pytorch/pytorch/blob/master/Release Candidates)
  • Release Candidate Storage
  • Cherry Picking Fixes
• Promoting RCs to Stable
• Special Topics
  • Updating submodules for a release

General Overview

Releasing a new version of PyTorch generally entails 3 major steps:

1. Cutting a release branch and making release branch specific changes
2. Drafting RCs (Release Candidates), and merging cherry picks
3. Promoting RCs to stable

Cutting release branches

Release branches are typically cut from the branch viable/strict as to ensure that tests are passing on the release branch.

Release branches should be prefixed like so:

release/{MAJOR}.{MINOR}

An example of this would look like:

release/1.8

Please make sure to create branch that pins divergent point of release branch from the main branch, i.e. orig/release/{MAJOR}.{MINOR}

Making release branch specific changes

These are examples of changes that should be made to release branches so that CI / tooling can function normally on them:

• Update target determinator to use release branch:
  • Example: pytorch/pytorch#40712
• Cutting a release branch on pytorch/xla
  • Example: pytorch/pytorch#40721
• Update backwards compatibility tests to use RC binaries instead of nightlies

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Commits

• d69c22d [docs] Add torch.package documentation for beta release (#59886)
• 4ad4f6d hold references to storages during TorchScript serializaiton (#59672)
• 90e6773 [Release/1.9] Link whole CuDNN for CUDA-11.1 (#59873)
• 43c581a Make detach return an alias even under inference mode (#59633) (#59757)
• bc446f6 Fix test_randperm_device_compatibility for 1 GPU (#59484) (#59502)
• abe996a Move CUDA async warning to suffix (#59467) (#59501)
• 795df76 Do not use gold linker for CUDA builds (#59490) (#59500)
• 3b9cd08 Prefer accurate reciprocal on ARMv8 (#59361) (#59470)
• 226c274 Search for static OpenBLAS compiled with OpenMP (#59428) (#59463)
• ce24cab Fix torch.randperm for CUDA (#59352) (#59452)
• Additional commits viewable in compare view

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