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We are excited to announce the release of PyTorch® 2.2! PyTorch 2.2 offers ~2x performance improvements to scaled_dot_product_attention via FlashAttention-v2 integration, as well as AOTInductor, a new ahead-of-time compilation and deployment tool built for non-python server-side deployments.
This release also includes improved torch.compile support for Optimizers, a number of new inductor optimizations, and a new logging mechanism called TORCH_LOGS.
Please note that we are deprecating macOS x86 support, and PyTorch 2.2.x will be the last version that supports macOS x64.
Along with 2.2, we are also releasing a series of updates to the PyTorch domain libraries. More details can be found in the library updates blog.
This release is composed of 3,628 commits and 521 contributors since PyTorch 2.1. 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 2.2. More information about how to get started with the PyTorch 2-series can be found at our Getting Started page.
Summary:
scaled_dot_product_attention (SDPA) now supports FlashAttention-2, yielding around 2x speedups compared to previous versions.
PyTorch 2.2 introduces a new ahead-of-time extension of TorchInductor called AOTInductor, designed to compile and deploy PyTorch programs for non-python server-side.
torch.distributed supports a new abstraction for initializing and representing ProcessGroups called device_mesh.
PyTorch 2.2 ships a standardized, configurable logging mechanism called TORCH_LOGS.
A number of torch.compile improvements are included in PyTorch 2.2, including improved support for compiling Optimizers and improved TorchInductor fusion and layout optimizations.
Please note that we are deprecating macOS x86 support, and PyTorch 2.2.x will be the last version that supports macOS x64.
torch.ao.quantization now offers a prototype torch.export based flow
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dependencies
Bumps torch from 1.11.0 to 2.2.0.
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Commits
8ac9b20Run docker release build on final tag (#117131) (#117182)2490352Fix cuInit test on Windows (#117095)3a44bb7[CI] Test that cuInit is not called during import (#117043)1c8ba38[CI] Use jemalloc for CUDA builds (#116900) (#116988)96d2ddbStore user model to simplify ONNXProgram.{adapt_torch_*,call} APIs (#1152...738b4a5Update ONNX's IO Adapter to support FakeTensor with ExportedProgram (#114407)...4cf10bf[Cherry-pick] [Quant] [PT2] Enable batchnorm in _move_exported_model_to_eval ...7e97e4b[AARCH64] Fall back to GEMM if mkldnn_matmul fails (#115936) (#116666)1a3e3c7[CUDA] baddmm should fall back to addmm for batch=1 (#114992) (#116518)ab7505fFix broken PyYAML 6.0 on MacOS x86 (#115956) (#116551)Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting
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