vendor: OpenCV 5.0.0 snapshot at 40738fb16ceddb5fb3fea747585f7ce6abb0605b
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# MLAS (Microsoft Linear Algebra Subprograms)
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MLAS is a compute library containing processor-optimized GEMM kernels and
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platform-specific threading code. It is the default math kernel library used
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internally by ONNX Runtime.
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## Provenance
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- **Upstream**: https://github.com/microsoft/onnxruntime
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- **Source path**: `onnxruntime/core/mlas/`
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- **Imported**: 2026-05-04
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- **Upstream commit**: [`62f742f1aa0c3102745ed35e3d869eaee845b9ac`](https://github.com/microsoft/onnxruntime/tree/62f742f1aa0c3102745ed35e3d869eaee845b9ac/onnxruntime/core/mlas)
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(2026-04-30, last MLAS-touching commit on `main` at import time;
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released as part of ORT [v1.26.0](https://github.com/microsoft/onnxruntime/releases/tag/v1.26.0))
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- **License**: MIT (see [LICENSE](LICENSE))
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## What is vendored
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The SGEMM (single-precision GEMM) subset of MLAS plus `MlasFlashAttention`
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(fused multi-head attention). The rest of MLAS (quantized GEMM, conv,
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FP16-dispatch SoftMax, etc.) is excluded so the OpenCV DNN module gets the
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fast SGEMM and FlashAttention paths without dragging in the full library.
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Source files imported verbatim from upstream:
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- `lib/sgemm.cpp` — SGEMM dispatch and host-side glue.
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- `lib/compute.cpp` — softmax / exp / row-max / sum-exp kernels. Only the
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portable C++ fallbacks for `MlasReduceMaximumF32Kernel` and
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`MlasComputeSumExpF32Kernel` are exercised; no per-arch `.S` softmax
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kernels are vendored. The file is imported whole (FP16 / GQA template
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specializations compile but never run — `SoftmaxDispatch` stays nullptr).
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- `lib/flashattn.cpp` — the `MlasFlashAttention` / `MlasFlashAttentionThreaded`
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entry points. Depends on `MlasSgemmOperation` (in `sgemm.cpp`) and the two
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portable kernels above.
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- `lib/softmax.h` — header included by `compute.cpp`; pure FP16-dispatch
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typedefs, harmless under FP32-only builds.
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- Per-arch SGEMM kernels under `lib/<arch>/`.
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Top-level layout:
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- `inc/` — public MLAS headers (kept verbatim from upstream).
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- `lib/` — implementation, kept verbatim from upstream except for the local
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patches listed below. Per-architecture kernels live in subdirectories
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(`x86_64/`, `aarch64/`, `arm/`, `power/`, `riscv64/`, `loongarch64/`,
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`s390x/`, `sve/`, `kleidiai/`).
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- `CMakeLists.txt` — OpenCV-side build glue. Builds an OBJECT library
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(`opencv_dnn_mlas`) whose objects are linked directly into `opencv_dnn`.
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- `threading_opencv.cpp` — OpenCV-side replacement for `lib/threading.cpp`
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(see "Local patches" below). Carries the OpenCV license header.
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## Copyright
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Most files are © Microsoft Corporation and licensed MIT. Some upstream
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contributions in `lib/` carry additional MIT-licensed copyrights — they are
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preserved verbatim in the file headers:
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- `lib/kleidiai/mlasi_kleidiai.h` — © Arm Limited 2025.
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- `lib/erf_neon_fp16.{h,cpp}`, `lib/gelu_neon_fp16.{h,cpp}` — © FUJITSU
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LIMITED 2025 (jointly with Microsoft).
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The OpenCV-authored files in this directory (`CMakeLists.txt`,
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`threading_opencv.cpp`, this `README.md`) are licensed under OpenCV's
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top-level license (Apache 2.0).
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## Local patches against upstream
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These deviate from a clean upstream import and must be re-applied on every
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re-vendor. The unified-diff form of each in-tree edit lives under
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[`patches/`](patches/) (same convention as
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[`3rdparty/zlib/patches/`](../zlib/patches)); re-apply with
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`git apply --directory=3rdparty/mlas patches/*.diff` after re-importing.
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1. `lib/threading.cpp` is dropped (not vendored). Its three threading entry
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points (`MlasExecuteThreaded`, `MlasTrySimpleParallel`,
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`MlasTryBatchParallel`) are reimplemented in
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`modules/dnn/src/layers/cpu_kernels/mlas_threading.cpp` on top of
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`cv::parallel_for_`. No `.diff` — the file is simply absent.
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2. `lib/mlasi.h` — `MlasGetMaximumThreadCount()` returns `cv::getNumThreads()`
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when `MLAS_OPENCV_THREADING` is defined; `#include "core/mlas/inc/mlas.h"`
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is rewritten to `#include "../inc/mlas.h"` because the ORT in-tree path
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does not exist here. See `patches/0001-mlasi-opencv-threading.diff`.
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3. `lib/platform.cpp` — non-SGEMM dispatch is wrapped under `MLAS_GEMM_ONLY`
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so the SGEMM-only subset builds without the rest of the MLAS sources. The
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top-of-file `erf_neon_fp16.h` / `gelu_neon_fp16.h` includes are also gated
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on `!defined(MLAS_GEMM_ONLY)` because those headers transitively pull in
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non-vendored FP16 sources (`fp16_common.h`, `softmax_kernel_neon.h`).
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The `MLAS_GEMM_ONLY` ctor also assigns `ReduceMaximumF32Kernel` and
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`ComputeSumExpF32Kernel` to the portable `compute.cpp` fallbacks so
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`MlasFlashAttention` works without per-arch softmax kernels. See
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`patches/0002-platform-gemm-only.diff`.
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4. `inc/mlas.h` — guard `_MSC_VER` with `defined()` so `-Wundef` builds
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under GCC/Clang don't warn. See `patches/0003-mlas-h-msc-ver-guard.diff`.
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5. `lib/core/common/{narrow,common}.h` — minimal shims for ORT internals
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that MLAS calls (not present upstream as MLAS sources, only as ORT
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includes). These are new files, not edits — no `.diff` needed.
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## Build flags
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- `HAVE_MLAS` is set by this directory's `CMakeLists.txt` when the host
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arch/OS is wired up.
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- `BUILD_MLAS_NO_ONNXRUNTIME=1`, `MLAS_OPENCV_THREADING=1`,
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`MLAS_GEMM_ONLY=1` are set as private compile definitions on the OBJECT
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library.
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## Caller in OpenCV
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The thin wrapper that dispatches OpenCV GEMMs to MLAS lives at
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[modules/dnn/src/layers/cpu_kernels/mlas_gemm.{hpp,cpp}](../../modules/dnn/src/layers/cpu_kernels/).
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It only includes `mlas.h` (the public header) and falls back to the existing
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fast_gemm path when MLAS is unavailable or the requested shape is unsupported.
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## Upstream unit tests
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Unit tests for the SGEMM kernels live in upstream ONNX Runtime under
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`onnxruntime/test/mlas`. They are not vendored here; OpenCV's own DNN tests
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exercise the integration.
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