Update torch requirement from >=2.1.0 to >=2.12.1

[dependabot]

Updates the requirements on torch to permit the latest version.

Release notes

Sourced from torch's releases.

PyTorch 2.12.1 Release, bug fix release

This release is meant to fix the following regressions and silent correctness issues:

Regression fixes

• Fix nondeterministic outputs in test_batch_invariance with FLASH_ATTN on NVIDIA B200 GPUs (#181248), fixed by updating Triton to 3.7.1 (#186814)
• Fix illegal memory access in the Triton convolution2d_bwd_weight kernel on B100/B200 (sm100) GPUs (#187081), fixed by updating Triton to 3.7.1 (#186814)
• Fix fill_ on byte-dtype views with misaligned storage offset (#186821)

Releng / Build

• Drop CPython 3.13t from the binary build matrix (#182951)

Changelog

Sourced from torch's changelog.

Releasing PyTorch

• Release Compatibility Matrix
  • PyTorch CUDA Support Matrix
• Release Cadence
• General Overview
  • Frequently Asked Questions
• Cutting a release branch preparations
• Cutting release branches
  • pytorch/pytorch
  • PyTorch ecosystem libraries
  • Making release branch specific changes for PyTorch
  • Making release branch specific changes for ecosystem libraries
• Running Launch Execution team Core XFN sync
• Drafting RCs (https://github.com/pytorch/pytorch/blob/main/Release Candidates) for PyTorch and domain libraries
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  • Cherry Picking Fixes
    • How to do Cherry Picking
  • Cherry Picking Reverts
• Preparing and Creating Final Release Candidate
• Promoting RCs to Stable
• Additional Steps to prepare for release day
  • Modify release matrix
  • Open Google Colab issue
• Patch Releases
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  • Patch Release Process
    • Patch Release Process Description
    • Triage
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    • Building a release schedule / cherry picking
    • Building Binaries / Promotion to Stable
• Hardware / Software Support in Binary Build Matrix
  • Python
  • Accelerator Software
    • Special support cases
  • Operating Systems
• Submitting Tutorials
• Special Topics
  • Updating submodules for a release
  • Triton dependency for the release

Release Compatibility Matrix

Following is the Release Compatibility Matrix for PyTorch releases:

... (truncated)

Commits

• 7269437 Update triton to 3.7.1 release (#186814)
• 88f16c2 [MPS] Fix fill_ on byte-dtype views with misaligned storage offset (#186821)
• ccf6e67 [release-only] Update version to 2.12.1 (#186813)
• 88a6dc7 Revive CUDA 12.9 nightly binary builds (#186015)
• ded5505 [CD] Drop CPython 3.13t from binary build matrix (#182951) (#186654)
• 0d62256 [release] Dockerfile: skip torchaudio install when CUDA_PATH=cu132 (#183346)
• 7661cd9 [MPS] Fix SDPA wrong output for permuted q/k/v with B > 1 (#181886)
• 9da6087 Fix stale PYTORCH_RELEASES_CODE_CC dict (fixes #182250) (#182369)
• e4c37cc Avoid raw stream name collisions in Inductor (#182178)
• 822d047 [MPS] Fix bool mask handling in 1-pass SDPA decode kernel (#182285) (#182311)
• Additional commits viewable in compare view

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🛠️ PR Summary

_{Made with ❤️ by Ultralytics Actions}

🌟 Summary

Updates the optional “full” dependency set to require a newer PyTorch version for improved compatibility and stability 🚀

📊 Key Changes

• Raised the torch requirement in pyproject.toml from >=2.1.0 to >=2.12.1 🛠️
• The change applies only to the full optional dependencies group, which includes benchmarking and dataset tools 📦
• No source code or model logic was changed ✅

🎯 Purpose & Impact

• Helps ensure MobileCLIP users installing the full feature set get a more recent PyTorch version 🔥
• May improve compatibility with newer tooling, dependencies, and runtime environments ⚙️
• Users with older PyTorch installations may need to upgrade when installing the full extra ⬆️

[dependabot]

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