trunk/364516d4725fd98c0f2fd2301366409bc54cbc5b: [dtensor][pointwise_ops] removing dead code (#178975)
Stack from ghstack (oldest at bottom): resubmission of #177208 -> #178975 Pull Request resolved: #178975 Approved by: https://github.com/Skylion007
Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
resubmission of https://github.com/pytorch/pytorch/pull/177208
- -> #178975
Pull Request resolved: https://github.com/pytorch/pytorch/pull/178975 Approved by: https://github.com/Skylion007`
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https://github.com/pytorch/pytorch/releases/tag/trunk%2F364516d4725fd98c0f2fd2301366409bc54cbc5bSign in to highlight and annotate this article
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go-typedpipe: A Typed, Context-Aware Pipe for Go
Background Go channels are one of the best things about the language. But the moment you need context cancellation, error propagation, and safe concurrent shutdown all at once, a simple chan T starts asking you to write a lot of code just to use it correctly. A common pattern looks something like this: out := make ( chan Result , len ( urls )) errc := make ( chan error , 1 ) go func () { defer close ( out ) for _ , url := range urls { select { case ctx . Done () : errc ctx . Err () return default : } resp , err := fetch ( ctx , url ) if err != nil { errc err return } select { case out Result { Data : resp } : case ctx . Done () : errc ctx . Err () return } } }() This works. But if you're not careful, it's easy to introduce bugs: Double-close panics — closing a channel twice crashes the pro
WSVD: Weighted Low-Rank Approximation for Fast and Efficient Execution of Low-Precision Vision-Language Models
arXiv:2604.02570v1 Announce Type: new Abstract: Singular Value Decomposition (SVD) has become an important technique for reducing the computational burden of Vision Language Models (VLMs), which play a central role in tasks such as image captioning and visual question answering. Although multiple prior works have proposed efficient SVD variants to enable low-rank operations, we find that in practice it remains difficult to achieve substantial latency reduction during model execution. To address this limitation, we introduce a new computational pattern and apply SVD at a finer granularity, enabling real and measurable improvements in execution latency. Furthermore, recognizing that weight elements differ in their relative importance, we adaptively allocate relative importance to each elemen
I technically got an LLM running locally on a 1998 iMac G3 with 32 MB of RAM
Hardware: • Stock iMac G3 Rev B (October 1998). 233 MHz PowerPC 750, 32 MB RAM, Mac OS 8.5. No upgrades. • Model: Andrej Karpathy’s 260K TinyStories (Llama 2 architecture). ~1 MB checkpoint. Toolchain: • Cross-compiled from a Mac mini using Retro68 (GCC for classic Mac OS → PEF binaries) • Endian-swapped model + tokenizer from little-endian to big-endian for PowerPC • Files transferred via FTP to the iMac over Ethernet Challenges: • Mac OS 8.5 gives apps a tiny memory partition by default. Had to use MaxApplZone() + NewPtr() from the Mac Memory Manager to get enough heap • RetroConsole crashes on this hardware, so all output writes to a text file you open in SimpleText • The original llama2.c weight layout assumes n_kv_heads == n_heads. The 260K model uses grouped-query attention (kv_heads
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