October 28, 2024
Unleashing the Power of AI on Mobile: LLM Inference for Llama 3.2 Quantized Models with ExecuTorch and KleidiAI
At the recent PyTorch Conference, Arm highlighted the widespread impact of its technology, spanning from cloud to edge, emphasizing its commitment to delivering its advanced AI computing capabilities seamlessly to millions of developers worldwide.
October 28, 2024
Getting started with PyTorch, ExecuTorch, and Ethos-U85 in three easy steps
ExecuTorch support for Ethos-U85
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.
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...
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.
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...
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...
