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PyTorch 2.4: Python 3.12, AOTInductor freezing, libuv backend for TCPStore

PyTorch 2.4 Release Notes

• Highlights
• Tracked Regressions
• Backward incompatible changes
• Deprecations
• New features
• Improvements
• Bug Fixes
• Performance
• Documentation
• Developers
• Security

Highlights

We are excited to announce the release of PyTorch® 2.4! PyTorch 2.4 adds support for the latest version of Python (3.12) for torch.compile. AOTInductor freezing gives developers running AOTInductor more performance based optimizations by allowing the serialization of MKLDNN weights. As well, a new default TCPStore server backend utilizing libuv has been introduced which should significantly reduce initialization times for users running large-scale jobs. Finally, a new Python Custom Operator API makes it easier than before to integrate custom kernels into PyTorch, especially for torch.compile.

This release is composed of 3661 commits and 475 contributors since PyTorch 2.3. We want to sincerely thank our dedicated community for your contributions. As always, we encourage you to try these out and report any issues as we improve 2.4. More information about how to get started with the PyTorch 2-series can be found at our Getting Started page.

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• d990dad [CMAKE] Look for Development.Module instead of Development (#129729)
• e4ee3be [Release only] use triton 3.0.x from pypi (#130336)
• 9afe4ec Update torchbench model expected accuracy values after pinning numpy (#129986)
• 499621e [CherryPick][FSDP2+TP] Disable 2D state_dict (#129519) (#129923)
• e5bda62 [CherryPick][DCP] Fix Optimizer Learning Rate not being loaded correctly (#12...
• 705e3ae Improve error message for weights_only load (#129783)
• b26cde4 [Windows] remove mkl shared library dependency. (#129740)
• 12ad767 [distributed] NCCL result code update (#129704)
• 1164d3c Add threadfence to 2-stage reduction for correct writes visibility (#129701)
• 9533637 Inductor to fail gracefully on Voltas for bf16 tensors (#129699)
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