Gitiles
Code Review Sign In
gerrit-public.fairphone.software / platform / external / XNNPACK / a8b39941af353f57558e0426e54b8518a7c5e177
commita8b39941af353f57558e0426e54b8518a7c5e177[log] [tgz]
authorMarat Dukhan <maratek@google.com>Fri Dec 31 10:18:48 2021 -0800
committerXNNPACK Team <xnnpack-github-robot@google.com>Fri Dec 31 10:21:45 2021 -0800
tree5a28b9a45217d703e1bd1db6ef77ca61c00d8db7
parent9084fc80c1303d25a507ffa03aa24c6862d82ad9 [diff]
Remove mask_table literal from F32 DWCONV AVX microkernels

- Unblock amalgamation for F32 DWCONV AVX microkernels

PiperOrigin-RevId: 419114940
33 files changed
tree: 5a28b9a45217d703e1bd1db6ef77ca61c00d8db7
  1. bench/
  2. cmake/
  3. eval/
  4. include/
  5. models/
  6. scripts/
  7. src/
  8. test/
  9. third_party/
  10. tools/
  11. .bazelrc
  12. .clang-format
  13. .gitignore
  14. BUILD.bazel
  15. build_defs.bzl
  16. CMakeLists.txt
  17. CONTRIBUTING.md
  18. emscripten.bzl
  19. LICENSE
  20. preamble.js.lds
  21. README.md
  22. 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

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)
  • Convert (includes fixed-point and half-precision quantization and dequantization)
  • 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
FP32 MobileNet v1 1.0X828688
FP32 MobileNet v2 1.0X495355
FP32 MobileNet v3 Large394244
FP32 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
FP32 MobileNet v1 1.0X432746
FP32 MobileNet v2 1.0X261828
FP32 MobileNet v3 Large221624
FP32 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), msRPi 4 (BCM2711, ARM64), ms
FP32 MobileNet v1 1.0X39372991147276
FP32 MobileNet v2 1.0X1987187794144
FP32 MobileNet v3 Large1658158673841
FP32 MobileNet v3 Small48750231314
INT8 MobileNet v1 1.0X2598169612924
INT8 MobileNet v2 1.0X1487109402017

Benchmarked on Oct 15, 2021 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. INT8 inference was evaluated on per-channel quantization schema.

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.