FAQ | 中文

In recent years, the on-device deep learning applications are getting more and more popular. It's a challenging task for application developers to deploy their deep learning models in their applications. They need to choose a proper inference framework, optionally utilizing quantization or compression techniques regarding the precision-performance trade-off, and finally run the model on one or more of heterogeneous computing devices. How to make an appropriate decision among these choices is a tedious and time-consuming task.

Mobile AI Benchmark (i.e. MobileAIBench) is an end-to-end benchmark tool to test the models' runtime in the different neural network frameworks on mobile devices, which hopefully can provide insights for the developers.

Daily Benchmark Results

Please check benchmark step in daily CI pipeline page.

FAQ

Q: Why are benchmark results not stable on my device?

A: Due to power save considerations, some SoCs have aggressive and advanced power control scheduling to reduce power consumption which make performance quite unstable (especially CPU runtime). Benchmark results highly depend on states of devices, e.g., running processes, temperature, power control policy. It is recommended to disable power control policy (as shown in tools/power.sh) if possible (e.g., rooted phone). Otherwise, keep your device at idle state with low temperature, and benchmark one model on one framework each time.

Q: Why do some devices run faster (or slower) than expected in the CI benchmark result?

A: Some devices is rooted and has some specialized performance tuning while some others is not rooted and failed to make such tuning (see the code for more details).

Q: Why is ncnn initialization time much less than others?

A: ncnn benchmark uses fake model parameters and skips loading weights from filesystem.

Environment requirement

MobileAIBench supports several deep learning frameworks (MACE, SNPE, ncnn and TensorFlow Lite) currently, which may require the following dependencies:

Software	Installation command	Tested version
Python		2.7
ADB	apt-get install android-tools-adb	Required by Android run, >= 1.0.32
Android NDK	NDK installation guide	Required by Android build, r15c
Bazel	bazel installation guide	0.13.0
CMake	apt-get install cmake	>= 3.11.3
FileLock	pip install -I filelock==3.0.0	Required by Android run
PyYaml	pip install -I pyyaml==3.12	3.12.0
sh	pip install -I sh==1.12.14	1.12.14
SNPE (optional)	download and uncompress	1.15.0

Note: SNPE has strict license that disallows redistribution, so the default link in the Bazel WORKSPACE file is only accessible by the CI server. To benchmark SNPE in your local system (i.e. set --frameworks with all or SNPE explicitly), you need to download the SDK here, uncompress it and modify WORKSPACE as the following:

#new_http_archive(
#    name = "snpe",
#    build_file = "third_party/snpe/snpe.BUILD",
#    sha256 = "b11780e5e7f591e916c69bdface4a1ef75b0c19f7b43c868bd62c0f3747d3fbb",
#    strip_prefix = "snpe-1.15.0",
#    urls = [
#        "https://cnbj1-fds.api.xiaomi.net/aibench/third_party/snpe-1.15.0.zip",
#    ],
#)

new_local_repository(
    name = "snpe",
    build_file = "third_party/snpe/snpe.BUILD",
    path = "/path/to/snpe-1.15.0",
)

Architecture

+-----------------+         +------------------+      +---------------+
|   Benchmark     |         |   BaseExecutor   | <--- | MaceExecutor  |
+-----------------+         +------------------+      +---------------+
| - executor      |-------> | - framework      |
| - model_name    |         | - runtime        |      +---------------+
| - model_file    |         |                  | <--- | SnpeExecutor  |
| - input_names   |         +------------------+      +---------------+
| - input_files   |         | + Init()         |
| - input_shapes  |         | + Prepare()      |      +---------------+
| - output_names  |         | + Run()          | <--- | NcnnExecutor  |
| - output_shapes |         | + Finish()       |      +---------------+
+-----------------+         +------------------+               
| - Register()    |                                   +---------------+
| - Run()         |                              <--- | TfLiteExecutor|
+-----------------+                                   +---------------+

How To Use

Benchmark all models on all frameworks

python tools/benchmark.py --output_dir=output --frameworks=all \
                          --runtimes=all --model_names=all \
                          --target_abis=armeabi-v7a,arm64-v8a

The whole benchmark may take a few time, and continuous benchmarking may heat the device very quickly, so you may set the following arguments according to your interests.

option	type	default	explanation
--output_dir	str	output	Benchmark output directory.
--frameworks	str	all	Frameworks(MACE/SNPE/NCNN/TFLITE), comma separated list or all.
--runtimes	str	all	Runtimes(CPU/GPU/DSP), comma separated list or all.
--target_abis	str	armeabi-v7a	Target ABIs(armeabi-v7a,arm64-v8a), comma separated list.
--model_names	str	all	Model names(InceptionV3,MobileNetV1...), comma separated list or all.
--run_interval	int	10	Run interval between benchmarks, seconds.
--num_threads	int	4	The number of threads.

Adding a model to run on existing framework

• Register model benchmark

  Register benchmark in aibench/benchmark/benchmark_main.cc:

       #ifdef AIBENCH_ENABLE_YOUR_FRAMEWORK
       std::unique_ptr<aibench::YourFrameworkExecutor>
           your_framework_executor(new aibench::YourFrameworkExecutor());
       AIBENCH_BENCHMARK(your_framework_executor.get(), MODEL_NAME, FRAMEWORK_NAME, RUNTIME,
                         MODEL_FILE, (std::vector<std::string>{INPUT_NAME}),
                         (std::vector<std::string>{INPUT_FILE}),
                         (std::vector<std::vector<int64_t>>{INPUT_SHAPE}),
                         (std::vector<std::string>{OUTPUT_NAME}),
                         (std::vector<std::vector<int64_t>>{OUTPUT_SHAPE}));
       #endif

  e.g.

     AIBENCH_BENCHMARK(mobilenetv1_mace_cpu_executor.get(), MobileNetV1, MACE,
                       CPU, mobilenet_v1, (std::vector<std::string>{"input"}),
                       (std::vector<std::string>{"dog.npy"}),
                       (std::vector<std::vector<int64_t>>{{1, 224, 224, 3}}),
                       (std::vector<std::string>{
                           "MobilenetV1/Predictions/Reshape_1"}),
                       (std::vector<std::vector<int64_t>>{{1, 1001}}));

• Register model in tools/model_list.py.

• Configure model file and input file

  Configure MODEL_FILE and INPUT_FILE in tools/model_and_input.yml.

• Run benchmark

   python tools/benchmark.py --output_dir=output --frameworks=MACE \
                             --runtimes=CPU --model_names=MobileNetV1 \
                             --target_abis=armeabi-v7a,arm64-v8a
  

• Check benchmark result

   cat output/report.csv

Adding your new AI framework

• Define executor and implement the interfaces:

  class YourFrameworkExecutor : public BaseExecutor {
   public:
    YourFrameworkExecutor() : BaseExecutor(FRAMEWORK_NAME, RUNTIME) {}
    
    // Init method should invoke the initializing process for your framework 
    // (e.g.  Mace needs to compile OpenCL kernel once per target). It will be
    // called only once when creating framework engine.
    virtual Status Init(const char *model_name, int num_threads);
  
    // Load model and prepare to run. It will be called only once before 
    // benchmarking the model.
    virtual Status Prepare(const char *model_name);
    
    // Run the model. It will be called more than once.
    virtual Status Run(const std::map<std::string, BaseTensor> &inputs,
                       std::map<std::string, BaseTensor> *outputs);
    
    // Unload model and free the memory after benchmarking. It will be called
    // only once.
    virtual void Finish();
  };

• Include your framework header in aibench/benchmark/benchmark_main.cc:

  #ifdef AIBENCH_ENABLE_YOUR_FRAMEWORK
  #include "aibench/executors/your_framework/your_framework_executor.h"
  #endif

• Add dependencies to third_party/your_framework, aibench/benchmark/BUILD and WORKSPACE. Put macro AIBENCH_ENABLE_YOUR_FRAMEWORK into aibench/benchmark/BUILD at model_benchmark target.

• Benchmark a model on existing framework

  Refer to [Adding a model to run on existing framework](#Adding a model to run on existing framework).

License

Apache License 2.0.

Notice

For third party dependencies, please refer to their licenses.