October 25, 2024

Intel GPU Support Now Available in PyTorch 2.5

Support for Intel GPUs is now available in PyTorch® 2.5, providing improved functionality and performance for Intel GPUs which including Intel® Arc™ discrete graphics, Intel® Core™ Ultra processors with built-in Intel® Arc™ graphics and Intel® Data Center GPU Max Series. This integration brings Intel GPUs and the SYCL* software stack into the official PyTorch stack, ensuring a consistent user experience and enabling more extensive AI application scenarios, particularly in the AI PC domain.

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October 24, 2024

ExecuTorch Beta: On-Device AI and LLMs, Stability, and Acceleration with Partners

ExecuTorch has achieved Beta status with the release of v0.4, providing stable APIs and runtime, as well as extensive kernel coverage. ExecuTorch is the recommended on-device inference engine for Llama 3.2 1B/3B models, offering enhanced performance and memory efficiency for both original and quantized models. There has been a significant increase in adoption and ecosystem growth for ExecuTorch, and the focus is now on improving reliability, performance, and coverage for non-CPU backen...

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October 23, 2024

TorchRec and FBGEMM 1.0 Stable Release

We are happy to announce the stable release, 1.0, for TorchRec and FBGEMM. TorchRec is the PyTorch native recommendation systems library, powered by FBGEMM’s (Facebook GEneral Matrix Multiplication) efficient, low-level kernels.

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October 17, 2024

PyTorch 2.5 Release Blog

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 en...

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October 15, 2024

The Path to Achieve PyTorch Performance Boost on Windows CPU

The challenge of PyTorch’s lower CPU performance on Windows compared to Linux has been a significant issue. There are multiple factors leading to this performance disparity. Through our investigation, we’ve identified several reasons for poor CPU performance on Windows, two primary issues have been pinpointed: the inefficiency of the Windows default malloc memory allocator and the absence of SIMD for vectorization optimizations on the Windows platform. In this article, we show how PyTorch CPU...

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October 08, 2024

PyTorch Foundation Technical Advisory Council Elects New Leadership

We are pleased to announce the first-ever Chair and Vice Chair of the PyTorch Foundation’s Technical Advisory Council (TAC): Luca Antiga as the Chair and Jiong Gong as Vice Chair. Both leaders bring extensive experience and deep commitment to the PyTorch community, and they are set to guide the TAC in its mission to foster an open, diverse, and innovative PyTorch technical community. Meet the New Leadership Luca Antiga is the CTO at Lightning AI since 2022. He is an early contributor to P...

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