PyTorch 2.5 Release Blog
by Team PyTorch
We are excited to announce the release of PyTorch® 2.5 (release note)! 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.
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.
