Use Accelerate for quantized SDPA prefill on Apple - #22760
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Summary: Replace the iterator-returning `std::min_element` / `std::max_element` implementations of `torch::executor::vec_minf` and `vec_maxf` with four independent value-reduction lanes. A shared compile-time implementation computes only the requested extrema, with a narrowly scoped Clang vectorization hint for NEON/SSE2 targets and no fast-math requirement. Add `torch::executor::vec_minmaxf(const float* x, size_t size, float* min_out, float* max_out)` to compute both extrema together. Wire the per-tensor and both serial/parallel per-token `choose_qparams` paths to the fused helper, eliminating their separate minimum and maximum scans without changing scale/zero-point calculations. Differential Revision: D119391738
Summary: Dequantize multi-row int8 Q and K into per-thread scratch on Apple and use the platform BLAS for QK as the Apple library is very performant, more so than ET's INT8 path. Decode and non-Apple platforms keep the existing int8 dot-product kernel as it beats the Eigen path. Differential Revision: D119391820
JakeStevens
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larryliu0820,
manuelcandales and
mergennachin
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September 11, 2026 21:08
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/22760
Note: Links to docs will display an error until the docs builds have been completed. ❌ 5 New Failures, 2 Unrelated FailuresAs of commit 7551800 with merge base f03eeef ( NEW FAILURES - The following jobs have failed:
BROKEN TRUNK - The following jobs failed but were present on the merge base:👉 Rebase onto the `viable/strict` branch to avoid these failures
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Summary: Dequantize multi-row int8 Q and K into per-thread scratch on Apple and use the platform BLAS for QK as the Apple library is very performant, more so than ET's INT8 path. Decode and non-Apple platforms keep the existing int8 dot-product kernel as it beats the Eigen path.
Differential Revision: D119391820