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The original was posted on /r/machinelearning by /u/parlancex on 2024-10-17 22:11:36+00:00.

Highlights: 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.

Some of my favorite improvements:

• Faster torch.compile compilation by re-using repeated modules
• torch.compile support for torch.istft
• FlexAttention: A flexible API that enables implementing various attention mechanisms such as Sliding Window, Causal Mask, and PrefixLM with just a few lines of idiomatic PyTorch code. This API leverages torch.compile to generate a fused FlashAttention kernel, which eliminates extra memory allocation and achieves performance comparable to handwritten implementations. Additionally, we automatically generate the backwards pass using PyTorch’s autograd machinery. Furthermore, our API can take advantage of sparsity in the attention mask, resulting in significant improvements over standard attention implementations.