We are excited to announce the release of PyTorch® 2.5! This release features a new CuDNN backend for SDPA, enabling speedups by default for users of SDPA on H100s or newer GPUs. As well, regional compilation of torch.compile offers a way to reduce the cold start up time for torch.compile by allowing users to compile a repeated nn.Module (e.g. a transformer layer in LLM) without recompilations. Finally, TorchInductor CPP backend offers solid performance speedup with numerous enhancements like FP16 support, CPP wrapper, AOT-Inductor mode, and max-autotune mode.
This release is composed of 4095 commits from 504 contributors since PyTorch 2.4. 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 2.5. More information about how to get started with the PyTorch 2-series can be found at our Getting Started page.
As well, please check out our new ecosystem projects releases with TorchRec and TorchFix.
Beta
Prototype
CuDNN backend for SDPA
FlexAttention
torch.compile regional compilation without recompilations
Compiled Autograd
TorchDynamo added support for exception handling & MutableMapping types
Flight Recorder
TorchInductor CPU backend optimization
Max-autotune Support on CPU with GEMM Template
TorchInductor on Windows
FP16 support on CPU path for both eager mode and TorchInductor CPP backend
Autoload Device Extension
Enhanced Intel GPU support
*To see a full list of public feature submissions click here.
BETA FEATURES
[Beta] CuDNN backend for SDPA
The cuDNN "Fused Flash Attention" backend was landed for torch.nn.functional.scaled_dot_product_attention. On NVIDIA H100 GPUs this can provide up to 75% speed-up over FlashAttentionV2. This speedup is enabled by default for all users of SDPA on H100 or newer GPUs.
[Beta] torch.compile regional compilation without recompilations
Regional compilation without recompilations, via torch._dynamo.config.inline_inbuilt_nn_modules which default to True in 2.5+. This option allows users to compile a repeated nn.Module (e.g. a transformer layer in LLM) without recompilations. Compared to compiling the full model, this option can result in smaller compilation latencies with 1%-5% performance degradation compared to full model compilation.
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Commits
a8d6afb Disabling amp context when invoking compiler (#138659)
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Bumps torch from 2.4.1 to 2.5.1.
Release notes
Sourced from torch's releases.
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Commits
a8d6afbDisabling amp context when invoking compiler (#138659)f31b8bb[MPS] Fix sliced cast (#138535)848e7ac[SDPA-CUDNN] Make CuDNN Attention Opt in (#138587)885c823Update doc copyrights to 2024 (#138650)8c3ed97Update cpuinfo submodule (#138600)70cf2bbAdd link to torch.compile the missing manual in troubleshooting (#137369)cde6b38Don't try to load cufile (#138539)4076a73[Cherry-Pick] Use cuda 12.4 pytorch_extra_install_requirements as default (#1...a97c151update getting started xpu (#138090)32f585d[Release only] use triton 3.1.x from pypi (#137895)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
@dependabot rebase.Dependabot commands and options
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