Sourced from torch's\r\nreleases.
\r\n\r\n\r\nPyTorch 2.2: FlashAttention-v2, AOTInductor
\r\nPyTorch 2.2 Release Notes
\r\n\r\n
\r\n- Highlights
\r\n- Backwards Incompatible Changes
\r\n- Deprecations
\r\n- New Features
\r\n- Improvements
\r\n- Bug fixes
\r\n- Performance
\r\n- Documentation
\r\nHighlights
\r\nWe are excited to announce the release of PyTorch® 2.2! PyTorch 2.2\r\noffers ~2x performance improvements to\r\n
\r\nscaled_dot_product_attention
via FlashAttention-v2\r\nintegration, as well as AOTInductor, a new ahead-of-time compilation and\r\ndeployment tool built for non-python server-side deployments.This release also includes improved torch.compile support for\r\nOptimizers, a number of new inductor optimizations, and a new logging\r\nmechanism called TORCH_LOGS.
\r\nPlease note that we are deprecating\r\nmacOS x86 support, and PyTorch 2.2.x will be the last version that\r\nsupports macOS x64.
\r\nAlong with 2.2, we are also releasing a series of updates to the\r\nPyTorch domain libraries. More details can be found in the library\r\nupdates blog.
\r\nThis release is composed of 3,628 commits and 521 contributors since\r\nPyTorch 2.1. We want to sincerely thank our dedicated community for your\r\ncontributions. As always, we encourage you to try these out and report\r\nany issues as we improve 2.2. More information about how to get started\r\nwith the PyTorch 2-series can be found at our Getting Started\r\npage.
\r\nSummary:
\r\n\r\n
\r\n\r\n- \r\n
scaled_dot_product_attention
(SDPA) now supports\r\nFlashAttention-2, yielding around 2x speedups compared to previous\r\nversions.- PyTorch 2.2 introduces a new ahead-of-time extension of\r\nTorchInductor called AOTInductor, designed to compile and deploy PyTorch\r\nprograms for non-python server-side.
\r\n- \r\n
torch.distributed
supports a new abstraction for\r\ninitializing and representing ProcessGroups called device_mesh.- PyTorch 2.2 ships a standardized, configurable logging mechanism\r\ncalled TORCH_LOGS.
\r\n- A number of torch.compile improvements are included in PyTorch 2.2,\r\nincluding improved support for compiling Optimizers and improved\r\nTorchInductor fusion and layout optimizations.
\r\n- Please note that we are deprecating macOS x86 support, and PyTorch\r\n2.2.x will be the last version that supports macOS x64.
\r\n- \r\n
torch.ao.quantization
now offers a prototype\r\ntorch.export
based flow
... (truncated)
\r\n8ac9b20
\r\nRun docker release build on final tag (#117131)\r\n(#117182)2490352
\r\nFix cuInit test on Windows (#117095)3a44bb7
\r\n[CI] Test that cuInit is not called during import (#117043)1c8ba38
\r\n[CI] Use jemalloc for CUDA builds (#116900)\r\n(#116988)96d2ddb
\r\nStore user model to simplify\r\nONNXProgram.{adapt_torch_*,call} APIs (#1152...738b4a5
\r\nUpdate ONNX's IO Adapter to support FakeTensor with ExportedProgram (#114407)...4cf10bf
\r\n[Cherry-pick] [Quant] [PT2] Enable batchnorm in\r\n_move_exported_model_to_eval ...7e97e4b
\r\n[AARCH64] Fall back to GEMM if mkldnn_matmul fails (#115936)\r\n(#116666)1a3e3c7
\r\n[CUDA] baddmm should fall back to addmm for batch=1 (#114992)\r\n(#116518)ab7505f
\r\nFix broken PyYAML 6.0 on MacOS x86 (#115956)\r\n(#116551)fourier.arima.xreg
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