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gerrit-public.fairphone.software / platform / external / XNNPACK / e13e63931357ad5f040c09a1d1f3d1a0dd5288ba
commite13e63931357ad5f040c09a1d1f3d1a0dd5288ba[log] [tgz]
authorMarat Dukhan <maratek@google.com>Mon Jul 26 22:22:35 2021 -0700
committerXNNPACK Team <xnnpack-github-robot@google.com>Mon Jul 26 22:23:18 2021 -0700
tree5a8b65cc9221d058dadf4317487df74a020690bd
parentb657605acc838d7cc707abd4a091ec5b61296bd4 [diff]
Align packed weights on 64 bytes in microkernel benchmarks

This match the behaviour of XNNPACK operators and ensures AVX512 loads are
aligned

PiperOrigin-RevId: 387030426
15 files changed
tree: 5a8b65cc9221d058dadf4317487df74a020690bd
  1. bench/
  2. cmake/
  3. eval/
  4. include/
  5. models/
  6. scripts/
  7. src/
  8. test/
  9. third_party/
  10. tools/
  11. .bazelrc
  12. .gitignore
  13. BUILD.bazel
  14. build_defs.bzl
  15. CMakeLists.txt
  16. CONTRIBUTING.md
  17. emscripten.bzl
  18. LICENSE
  19. preamble.js.lds
  20. README.md
  21. WORKSPACE
README.md

XNNPACK

XNNPACK is a highly optimized library of floating-point neural network inference operators for ARM, WebAssembly, and x86 platforms. XNNPACK is not intended for direct use by deep learning practitioners and researchers; instead it provides low-level performance primitives for accelerating high-level machine learning frameworks, such as TensorFlow Lite, TensorFlow.js, PyTorch, and MediaPipe.

Supported Architectures

  • ARM64 on Android, Linux, macOS, and iOS (including WatchOS and tvOS)
  • ARMv7 (with NEON) on Android, Linux, and iOS (including WatchOS)
  • x86 and x86-64 (up to AVX512) on Windows, Linux, macOS, Android, and iOS simulator
  • WebAssembly MVP
  • WebAssembly SIMD (experimental)

Operator Coverage

XNNPACK implements the following neural network operators:

  • 2D Convolution (including grouped and depthwise)
  • 2D Deconvolution (AKA Transposed Convolution)
  • 2D Average Pooling
  • 2D Max Pooling
  • 2D ArgMax Pooling (Max Pooling + indices)
  • 2D Unpooling
  • 2D Bilinear Resize
  • 2D Depth-to-Space (AKA Pixel Shuffle)
  • Add (including broadcasting, two inputs only)
  • Subtract (including broadcasting)
  • Divide (including broadcasting)
  • Maximum (including broadcasting)
  • Minimum (including broadcasting)
  • Multiply (including broadcasting)
  • Squared Difference (including broadcasting)
  • Global Average Pooling
  • Channel Shuffle
  • Fully Connected
  • Abs (absolute value)
  • Bankers' Rounding (rounding to nearest, ties to even)
  • Ceiling (rounding to integer above)
  • Clamp (includes ReLU and ReLU6)
  • Copy
  • ELU
  • Floor (rounding to integer below)
  • HardSwish
  • Leaky ReLU
  • Negate
  • Sigmoid
  • Softmax
  • Square
  • Truncation (rounding to integer towards zero)
  • PReLU

All operators in XNNPACK support NHWC layout, but additionally allow custom stride along the Channel dimension. Thus, operators can consume a subset of channels in the input tensor, and produce a subset of channels in the output tensor, providing a zero-cost Channel Split and Channel Concatenation operations.

Performance

Mobile phones

The table below presents single-threaded performance of XNNPACK library on three generations of MobileNet models and three generations of Pixel phones.

ModelPixel, msPixel 2, msPixel 3a, ms
MobileNet v1 1.0X828688
MobileNet v2 1.0X495355
MobileNet v3 Large394244
MobileNet v3 Small121414

The following table presents multi-threaded (using as many threads as there are big cores) performance of XNNPACK library on three generations of MobileNet models and three generations of Pixel phones.

ModelPixel, msPixel 2, msPixel 3a, ms
MobileNet v1 1.0X432746
MobileNet v2 1.0X261828
MobileNet v3 Large221624
MobileNet v3 Small768

Benchmarked on March 27, 2020 with end2end_bench --benchmark_min_time=5 on an Android/ARM64 build with Android NDK r21 (bazel build -c opt --config android_arm64 :end2end_bench) and neural network models with randomized weights and inputs.

Raspberry Pi

The table below presents multi-threaded performance of XNNPACK library on three generations of MobileNet models and three generations of Raspberry Pi boards.

ModelRPi Zero W (BCM2835), msRPi 2 (BCM2836), msRPi 3+ (BCM2837B0), msRPi 4 (BCM2711), ms
MobileNet v1 1.0X400433711672
MobileNet v2 1.0X20111958341
MobileNet v3 Large16941637038
MobileNet v3 Small482522313

Benchmarked on May 22, 2020 with end2end-bench --benchmark_min_time=5 on a Raspbian Buster build with CMake (./scripts/build-local.sh) and neural network models with randomized weights and inputs.

Publications

Ecosystem

Machine Learning Frameworks

Acknowledgements

XNNPACK is a based on QNNPACK library. Over time its codebase diverged a lot, and XNNPACK API is no longer compatible with QNNPACK.