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wire 2026-07-09
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PYTORCH 2.13 SHIPS WITH FLEXATTENTION ON APPLE SILICON, NCCL2 BACKEND LANDS IN-TREE

By RepoJournal · Filed · About PyTorch · Composed from the cited sources · methodology

GregoryComer JakeStevens huydhn
3 people shipped this
GregoryComer @GregoryComer 2 cited JakeStevens @JakeStevens 1 cited huydhn @huydhn 1 cited

PyTorch's biggest release in months brings native Apple Silicon acceleration, refactors distributed training infrastructure, and ships quantized embedding support across CPU and accelerators.

PyTorch 2.13.0 is live [1], and the marquee feature is FlexAttention landing on Apple Silicon (MPS) with up to 12x speedup over SDPA on sparse patterns, making M-series deployments finally competitive for attention-heavy workloads. Simultaneously, the NCCL2 backend port [2] begins shipping in-tree as a c10d-native backend selected via init_process_group(backend='nccl2'), replacing the external torchcomms dependency and centralizing distributed training under one roof. ExecutorCh rounds out the release with bf16 quantized embedding support on CPU [3], bf16/fp16 activations in SDPA [4], and XNNPACK delegation fixes for even-kernel same-padding convolutions [5], unblocking deployment patterns that previously fell back to slow paths. The profiler lands a critical CUPTI timestamp callback fix [6] that eliminates per-record clock conversions, and the test suite gains hardware-independent SAC-ILP tests [7] to stop flakiness across GPU models. Test-infra ships release 2.13 go-live [8], advancing stable and candidate to 2.13.0 together, while also introducing a new 'infra_issue' AI verdict [9] to separate real CI failures from code problems.

Action items

References

  1. [1] PyTorch 2.13.0 Release ↗ pytorch/pytorch
  2. [2] [c10d][nccl2] Port torchcomms NCCL backend foundation (utils, CUDA API, batch) (#188582) pytorch/pytorch ↗
  3. [3] Support bf16-out quantized embeddings on CPU ↗ pytorch/executorch
  4. [4] Support bf16/fp16 activations in CPU SDPA (#20611) ↗ pytorch/executorch
  5. [5] Delegate even-kernel 'same'-padding convs via a quantized static pad (#20553) ↗ pytorch/executorch
  6. [6] [profiler][cupti] Engage the approx-clock timestamp callback via the per-subscriber attribute (#189168) pytorch/pytorch ↗
  7. [7] Make test_sac_ilp hardware-independent by pinning device datasheet (#189278) pytorch/pytorch ↗
  8. [8] Release 2.13 go live. Update release matrix (#8261) ↗ pytorch/test-infra
  9. [9] [autorevert] Add infra_issue AI advisor verdict (treated like not_related) ↗ pytorch/test-infra

$ ls pytorch/reviews/ # the step-back reads

the week in review Helion autotunes Metal, ending default 16x16x16 tiles the month in review ExecuTorch MoE quantization, Helion runtime splits, and a broader cleanup wave
Topics: Python AI / ML Full archive → Weekly reviews →

Quick answers

What shipped in PyTorch on July 9, 2026?
PyTorch's biggest release in months brings native Apple Silicon acceleration, refactors distributed training infrastructure, and ships quantized embedding support across CPU and accelerators. In total, 84 commits, 26 pull requests, and 2 releases landed.
Who contributed to PyTorch on July 9, 2026?
3 developers shipped this update, including GregoryComer, JakeStevens, and huydhn.
What were the notable PyTorch updates?
PyTorch 2.13.0 Release, [c10d][nccl2] Port torchcomms NCCL backend foundation (utils, CUDA API, batch) (#188582), and Support bf16-out quantized embeddings on CPU.
♥

$ log @pytorch --recent

torchtitan fixes multi-node HF export, torch._native opens to non-aten namespaces 57 shipped Fake-tensor hardening, ROCm graph fixes, and an XNNPACK use-after-free 180 shipped CPU caching allocator teardown race fixed, torchtitan shares one training engine 67 shipped ROCm test timeouts traced to duplicated runs, and distributed/test_store drops 15 minutes 100 shipped

$ log --all # elsewhere on the wire

@openclaw Cold-start Gateway diagnostics stop crying wolf, doctor repairs plugin drift Agentic Coding Cline desktop bundles shrink, goose unblocks its own clicks

$ ls showcases/

Rails Django Laravel Node.js Go Vercel Vue.js Kubernetes HashiCorp Arch Linux Linux Anthropic

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