==============
Advanced usage
==============

This part contains the full usage of MACE.

=========
Overview
=========

As mentioned in the previous part, a model deployment file defines a case of model deployment.
The whole building process is loading a deployment file, converting models, building MACE and packing generated files.

================
Deployment file
================


One deployment file will generate one library normally, but if more than one ABIs are specified,
one library will be generated for each ABI.
A deployment file can also contain multiple models. For example, an AI camera application may
contain face recognition, object recognition, and voice recognition models, all of which can be defined
in one deployment file.

* **Example**

    Here is an example deployment file used by an Android demo application.

    .. literalinclude:: models/demo_app_models.yml
        :language: yaml


* **Configurations**


.. list-table::
    :header-rows: 1

    * - Options
      - Usage
    * - library_name
      - Library name.
    * - target_abis
      - The target ABI(s) to build, could be 'host', 'armeabi-v7a' or 'arm64-v8a'.
        If more than one ABIs will be used, separate them by commas.
    * - target_socs
      - [optional] Build for specific SoCs.
    * - model_graph_format
      - model graph format, could be 'file' or 'code'. 'file' for converting model graph to ProtoBuf file(.pb) and 'code' for converting model graph to c++ code.
    * - model_data_format
      - model data format, could be 'file' or 'code'. 'file' for converting model weight to data file(.data) and 'code' for converting model weight to c++ code.
    * - model_name
      - model name should be unique if there are more than one models.
        **LIMIT: if build_type is code, model_name will be used in c++ code so that model_name must comply with c++ name specification.**
    * - platform
      - The source framework, tensorflow or caffe.
    * - model_file_path
      - The path of your model file which can be local path or remote URL.
    * - model_sha256_checksum
      - The SHA256 checksum of the model file.
    * - weight_file_path
      - [optional] The path of Caffe model weights file.
    * - weight_sha256_checksum
      - [optional] The SHA256 checksum of Caffe model weights file.
    * - subgraphs
      - subgraphs key. **DO NOT EDIT**
    * - input_tensors
      - The input tensor name(s) (tensorflow) or top name(s) of inputs' layer (caffe).
        If there are more than one tensors, use one line for a tensor.
    * - output_tensors
      - The output tensor name(s) (tensorflow) or top name(s) of outputs' layer (caffe).
        If there are more than one tensors, use one line for a tensor.
    * - input_shapes
      - The shapes of the input tensors, in NHWC order.
    * - output_shapes
      - The shapes of the output tensors, in NHWC order.
    * - input_ranges
      - The numerical range of the input tensors' data, default [-1, 1]. It is only for test.
    * - validation_inputs_data
      - [optional] Specify Numpy validation inputs. When not provided, [-1, 1] random values will be used.
    * - runtime
      - The running device, one of [cpu, gpu, dsp, cpu_gpu]. cpu_gpu contains CPU and GPU model definition so you can run the model on both CPU and GPU.
    * - data_type
      - [optional] The data type used for specified runtime. [fp16_fp32, fp32_fp32] for GPU, default is fp16_fp32, [fp32] for CPU and [uint8] for DSP.
    * - limit_opencl_kernel_time
      - [optional] Whether splitting the OpenCL kernel within 1 ms to keep UI responsiveness, default is 0.
    * - nnlib_graph_mode
      - [optional] Control the DSP precision and performance, default to 0 usually works for most cases.
    * - obfuscate
      - [optional] Whether to obfuscate the model operator name, default to 0.
    * - winograd
      - [optional] Which type winograd to use, could be [0, 2, 4]. 0 for disable winograd, 2 and 4 for enable winograd, 4 may be faster than 2 but may take more memory.


.. note::

    Some command tools:

    .. code:: bash

        # Get device's soc info.
        adb shell getprop | grep platform

        # command for generating sha256_sum
        sha256sum /path/to/your/file


==============
Advanced Usage
==============

There are two common advanced use cases: 1. convert a model to CPP code. 2. tuning for specific SOC if use GPU.

* **Convert model(s) to CPP code**

    .. warning::

         If you want to use this case, you can just use static mace library.

    * **1. Change the model deployment file(.yml)**

        If you want to protect your model, you can convert model to CPP code. there are also two cases:

        * convert model graph to code and model weight to file with below model configuration.

        .. code:: sh

            model_graph_format: code
            model_data_format: file

        * convert both model graph and model weight to code with below model configuration.

        .. code:: sh

            model_graph_format: code
            model_data_format: code

        .. note::

             Another model protection method is using ``obfuscate`` to obfuscate names of model's operators.

    * **2. Convert model(s) to code**

        .. code:: sh

            python tools/converter.py convert --config=/path/to/model_deployment_file.yml

        The command will generate **${library_name}.a** in **builds/${library_name}/model** directory and
        ** *.h ** in **builds/${library_name}/include** like the following dir-tree.

        .. code::

             # model_graph_format: code
             # model_data_format: file

             builds
               ├── include
               │   └── mace
               │       └── public
               │           ├── mace_engine_factory.h
               │           └── mobilenet_v1.h
               └── model
                   ├── mobilenet-v1.a
                   └── mobilenet_v1.data


    * **3. Deployment**
        * Link `libmace.a` and `${library_name}.a` to your target.
        * Refer to \ ``mace/examples/example.cc``\ for full usage. The following list the key steps.

        .. code:: cpp

            // Include the headers
            #include "mace/public/mace.h"
            #include "mace/public/mace_runtime.h"
            // If the model_graph_format is code
            #include "mace/public/${model_name}.h"
            #include "mace/public/mace_engine_factory.h"

            // ... Same with the code in basic usage

            // 4. Create MaceEngine instance
            std::shared_ptr<mace::MaceEngine> engine;
            MaceStatus create_engine_status;
            // Create Engine from compiled code
            create_engine_status =
                CreateMaceEngineFromCode(model_name.c_str(),
                                         nullptr,
                                         input_names,
                                         output_names,
                                         device_type,
                                         &engine);
            if (create_engine_status != MaceStatus::MACE_SUCCESS) {
              // Report error
            }

            // ... Same with the code in basic usage


* **Tuning for specific SOC's GPU**

    If you want to use the GPU of a specific device, you can just specify the ``target_socs`` in your YAML file and
    then tune the MACE lib for it, which may get 1~10% performance improvement.

    * **1. Change the model deployment file(.yml)**

        Specify ``target_socs`` in your model deployment file(.yml):

        .. code:: sh

            target_socs: [sdm845]

        .. note::

            Get device's soc info: `adb shell getprop | grep platform`

    * **2. Convert model(s)**

        .. code:: sh

            python tools/converter.py convert --config=/path/to/model_deployment_file.yml

    * **3. Tuning**

        The tools/converter.py will enable automatic tuning for GPU kernels. This usually takes some
        time to finish depending on the complexity of your model.

        .. note::

             You should plug in device(s) with the specific SoC(s).


        .. code:: sh

            python tools/converter.py run --config=/path/to/model_deployment_file.yml --validate

        The command will generate two files in `builds/${library_name}/opencl`, like the following dir-tree.

        .. code::

              builds
              └── mobilenet-v2
                  ├── model
                  │   ├── mobilenet_v2.data
                  │   └── mobilenet_v2.pb
                  └── opencl
                      └── arm64-v8a
                         ├── moblinet-v2_compiled_opencl_kernel.MiNote3.sdm660.bin
                         └── moblinet-v2_tuned_opencl_parameter.MiNote3.sdm660.bin


        * **mobilenet-v2-gpu_compiled_opencl_kernel.MI6.msm8998.bin** stands for the OpenCL binaries
          used for your models, which could accelerate the initialization stage.
          Details please refer to `OpenCL Specification <https://www.khronos.org/registry/OpenCL/sdk/1.0/docs/man/xhtml/clCreateProgramWithBinary.html>`__.
        * **mobilenet-v2-tuned_opencl_parameter.MI6.msm8998.bin** stands for the tuned OpenCL parameters
          for the SOC.

    * **4. Deployment**
        * Change the names of files generated above for not collision and push them to **your own device's directory**.
        * Use like the previous procedure, below lists the key steps differently.

        .. code:: cpp

            // Include the headers
            #include "mace/public/mace.h"
            #include "mace/public/mace_runtime.h"

            // 0. Set pre-compiled OpenCL binary program file paths and OpenCL parameters file path when available
            if (device_type == DeviceType::GPU) {
              mace::SetOpenCLBinaryPaths(path/to/opencl_binary_paths);
              mace::SetOpenCLParameterPath(path/to/opencl_parameter_file);
            }

            // ... Same with the code in basic usage.


===============
Useful Commands
===============
* **run the model**

.. code:: sh

    # Test model run time
    python tools/converter.py run --config=/path/to/model_deployment_file.yml --round=100

    # Validate the correctness by comparing the results against the
    # original model and framework, measured with cosine distance for similarity.
    python tools/converter.py run --config=/path/to/model_deployment_file.yml --validate

    # Check the memory usage of the model(**Just keep only one model in deployment file**)
    python tools/converter.py run --config=/path/to/model_deployment_file.yml --round=10000 &
    sleep 5
    adb shell dumpsys meminfo | grep mace_run
    kill %1


.. warning::

    ``run`` rely on ``convert`` command, you should ``convert`` before ``run``.

* **benchmark and profile model**

.. code:: sh

    # Benchmark model, get detailed statistics of each Op.
    python tools/converter.py benchmark --config=/path/to/model_deployment_file.yml


.. warning::

    ``benchmark`` rely on ``convert`` command, you should ``benchmark`` after ``convert``.

**Common arguments**

    .. list-table::
        :header-rows: 1

        * - option
          - type
          - default
          - commands
          - explanation
        * - --omp_num_threads
          - int
          - -1
          - ``run``/``benchmark``
          - number of threads
        * - --cpu_affinity_policy
          - int
          - 1
          - ``run``/``benchmark``
          - 0:AFFINITY_NONE/1:AFFINITY_BIG_ONLY/2:AFFINITY_LITTLE_ONLY
        * - --gpu_perf_hint
          - int
          - 3
          - ``run``/``benchmark``
          - 0:DEFAULT/1:LOW/2:NORMAL/3:HIGH
        * - --gpu_perf_hint
          - int
          - 3
          - ``run``/``benchmark``
          - 0:DEFAULT/1:LOW/2:NORMAL/3:HIGH
        * - --gpu_priority_hint
          - int
          - 3
          - ``run``/``benchmark``
          - 0:DEFAULT/1:LOW/2:NORMAL/3:HIGH

Use ``-h`` to get detailed help.

.. code:: sh

    python tools/converter.py -h
    python tools/converter.py build -h
    python tools/converter.py run -h
    python tools/converter.py benchmark -h
