| commit | 7d2c1f24707f237b64574d10eded0539b0cba619 | [log] [tgz] |
|---|---|---|
| author | Frank Barchard <fbarchard@google.com> | Mon Sep 14 16:43:53 2020 -0700 |
| committer | XNNPACK Team <xnnpack-github-robot@google.com> | Mon Sep 14 16:44:32 2020 -0700 |
| tree | 1cfdbfe963623785c175e340f8c726d947d7f0c7 | |
| parent | 66f3ccddcc6ef647542b696810ec8f7f39b23f43 [diff] |
FP16 tests allow for results that are near zero. When a float reference test produces a result that is not representable as a halffloat, the halffloat result will be zero. The tester tests for 1% error but will fail on these near zero reference values. So a minimum error of 0.001 is allowed. This change increases the range of tests values to include near zero and in some cases increases the accuracy requirement, but allows a minimum error. PiperOrigin-RevId: 331656463
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.
XNNPACK implements the following neural network operators:
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.
The table below presents single-threaded performance of XNNPACK library on three generations of MobileNet models and three generations of Pixel phones.
| Model | Pixel, ms | Pixel 2, ms | Pixel 3a, ms |
|---|---|---|---|
| MobileNet v1 1.0X | 82 | 86 | 88 |
| MobileNet v2 1.0X | 49 | 53 | 55 |
| MobileNet v3 Large | 39 | 42 | 44 |
| MobileNet v3 Small | 12 | 14 | 14 |
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.
| Model | Pixel, ms | Pixel 2, ms | Pixel 3a, ms |
|---|---|---|---|
| MobileNet v1 1.0X | 43 | 27 | 46 |
| MobileNet v2 1.0X | 26 | 18 | 28 |
| MobileNet v3 Large | 22 | 16 | 24 |
| MobileNet v3 Small | 7 | 6 | 8 |
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.
The table below presents multi-threaded performance of XNNPACK library on three generations of MobileNet models and three generations of Raspberry Pi boards.
| Model | RPi Zero W (BCM2835), ms | RPi 2 (BCM2836), ms | RPi 3+ (BCM2837B0), ms | RPi 4 (BCM2711), ms |
|---|---|---|---|---|
| MobileNet v1 1.0X | 4004 | 337 | 116 | 72 |
| MobileNet v2 1.0X | 2011 | 195 | 83 | 41 |
| MobileNet v3 Large | 1694 | 163 | 70 | 38 |
| MobileNet v3 Small | 482 | 52 | 23 | 13 |
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.
XNNPACK is a based on QNNPACK library. Over time its codebase diverged a lot, and XNNPACK API is no longer compatible with QNNPACK.