Update torch requirement from <1.10,>=1.7 to >=1.7,<1.11

[dependabot]

Updates the requirements on torch to permit the latest version.

Release notes

Sourced from torch's releases.

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)

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

... (truncated)

Commits

• 36449ea (torch/elastic) add fqdn hostname to error printout (#66182) (#66662)
• b544cbd Handle shared memory cases in MathBitFallback (#66667)
• ddf3092 Disable .numpy() and .tolist() for tensor subclasses subclasses and f… (#66642)
• cc360fa Delete extraneous whitespaces
• 3c134b8 Disable .numpy() and .tolist() for tensor subclasses subclasses and fix .toli...
• 4a514dd Call PyArray_Check only if NumPy is available (#66433) (#66629)
• c3ea586 fix normal with empty std (#66524)
• 9509e8a Fix cosine similarity dim checks (#66214)
• 1774a6a [ONNX] Deprecate various args (#65962)
• a27906c Convert Sampler back to lazily construction (#63646) (#65926)
• Additional commits viewable in compare view

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