STT-tensorflow/tensorflow/lite/tools/benchmark/android

TFLite Model Benchmark Tool with Android Apk

Description

This Android benchmark app is a simple wrapper around the TensorFlow Lite command-line benchmark utility.

Pushing and executing binaries directly on an Android device is a valid approach to benchmarking, but it can result in subtle (but observable) differences in performance relative to execution within an actual Android app. In particular, Android's scheduler tailors behavior based on thread and process priorities, which differ between a foreground Activity/Application and a regular background binary executed via adb shell .... This tailored behavior is most evident when enabling multi-threaded CPU execution with TensorFlow Lite.

To that end, this app offers perhaps a more faithful view of runtime performance that developers can expect when deploying TensorFlow Lite with their application.

To build/install/run

(0) Refer to https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/android to edit the WORKSPACE to configure the android NDK/SDK.

(1) Build for your specific platform, e.g.:

bazel build -c opt \
  --config=android_arm64 \
  tensorflow/lite/tools/benchmark/android:benchmark_model

(Optional) To enable Hexagon delegate with --use_hexagon=true option, you can download and install the libraries as the guided in [hexagon delegate] (https://www.tensorflow.org/lite/performance/hexagon_delegate#step_2_add_hexagon_libraries_to_your_android_app) page. For example, if you installed the libraries at third_party/hexagon_nn_skel and created third_party/hexagon_nn_skel/BUILD with a build target,

filegroup(
    name = "libhexagon_nn_skel",
    srcs = glob(["*.so"]),
)

you need to modify tflite_hexagon_nn_skel_libraries macro in tensorflow/lite/special_rules.bzl to specify the build target.

return ["//third_party/hexagon_nn_skel:libhexagon_nn_skel"]

(2) Connect your phone. Install the benchmark APK to your phone with adb:

adb install -r -d -g bazel-bin/tensorflow/lite/tools/benchmark/android/benchmark_model.apk

Note: Make sure to install with "-g" option to grant the permission for reading external storage.

(3) Push the compute graph that you need to test.

adb push mobilenet_quant_v1_224.tflite /data/local/tmp

(4) Run the benchmark. Additional command-line flags are documented here and can be appended to the args string alongside the required --graph flag (note that all args must be nested in the single quoted string that follows the args key).

adb shell am start -S \
  -n org.tensorflow.lite.benchmark/.BenchmarkModelActivity \
  --es args '"--graph=/data/local/tmp/mobilenet_quant_v1_224.tflite \
  --num_threads=4"'

(5) The results will be available in Android's logcat, e.g.:

adb logcat | grep "Average inference"

... tflite  : Average inference timings in us: Warmup: 91471, Init: 4108, Inference: 80660.1

To trace Tensorflow Lite internals including operator invocation

The steps described here follows the method of https://developer.android.com/topic/performance/tracing/on-device. Refer to the page for more detailed information.

(0)-(3) Follow the steps (0)-(3) of build/install/run section.

(4) Enable platform tracing.

adb shell setprop debug.tflite.trace 1

(5) Set up Quick Settings tile for System Tracing app on your device. Follow the instruction. The System Tracing tile will be added to the Quick Settings panel.

Optionally, you can set up other configurations for tracing from the app menu. Refer to the guide for more information.

(6) Tap the System Tracing tile, which has the label "Record trace". The tile becomes enabled, and a persistent notification appears to notify you that the system is now recording a trace.

(7) Run the benchmark with platform tracing enabled.

adb shell am start -S \
  -n org.tensorflow.lite.benchmark/.BenchmarkModelActivity \
  --es args '"--graph=/data/local/tmp/mobilenet_quant_v1_224.tflite \
  --num_threads=4"'

(8) Wait until the benchmark finishes. It can be checked from Android log messages, e.g.,

adb logcat | grep "Average inference"

... tflite  : Average inference timings in us: Warmup: 91471, Init: 4108, Inference: 80660.1

(9) Stop tracing by tapping either the System Tracing tile in the Quick Settings panel or on the System Tracing notification. The system displays a new notification that contains the message "Saving trace". When saving is complete, the system dismisses the notification and displays a third notification "Trace saved", confirming that your trace has been saved and that you're ready to share the system trace.

(10) Share a trace file, convert between tracing formats and create an HTML report. Note that, the captured tracing file format is either in Perfetto format or in Systrace format depending on the Android version of your device. Select the appropriate method to handle the generated file.

(11) Disable platform tracing.

adb shell setprop debug.tflite.trace 0