tracing_based_selective_build.rst

(prototype) Tracing-based Selective Build Mobile Interpreter in Android and iOS
===============================================================================


*Author*: Chen Lai <https://github.com/cccclai>, Dhruv Matani <https://github.com/dhruvbird>

.. warning::
    Tracing-based selective build a prototype feature to minimize library size. Since the traced result relies on the model input and traced environment, if the tracer runs in a different environment than mobile interpreter, the operator list might be different from the actual used operator list and missing operators error might raise.

Introduction
------------


This tutorial introduces a new way to custom build mobile interpreter to further optimize mobile interpreter size. It restricts the set of operators included in the compiled binary to only the set of operators actually needed by target models. It is a technique to reduce the binary size of PyTorch for mobile deployments. Tracing Based Selective Build runs a model with specific representative inputs, and records which operators were called. The build then includes just those operators.


Following are the processes to use tracing-based selective approach to build a custom mobile interpreter.

1. *Prepare model with bundled input*

.. code:: python

    import numpy as np
    import torch
    import torch.jit
    import torch.utils
    import torch.utils.bundled_inputs
    from PIL import Image
    from torchvision import transforms

    # Step 1. Get the model
    model = torch.hub.load('pytorch/vision:v0.7.0', 'deeplabv3_resnet50', pretrained=True)
    model.eval()

    scripted_module = torch.jit.script(model)
    # Export full jit version model (not compatible lite interpreter), leave it here for comparison
    scripted_module.save("deeplabv3_scripted.pt")
    # Export lite interpreter version model (compatible with lite interpreter)
    # path = "<base directory where models are stored>"

    scripted_module._save_for_lite_interpreter(f"${path}/deeplabv3_scripted.ptl")

    model_file = f"${path}/deeplabv3_scripted.ptl"

    # Step 2. Prepare inputs for the model
    input_image_1 = Image.open(f"${path}/dog.jpg")
    preprocess = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    ])

    input_tensor_1 = preprocess(input_image_1)
    input_batch_1 = input_tensor_1.unsqueeze(0) # create a mini-batch as expected by the model

    scripted_module = torch.jit.load(model_file)
    scripted_module.forward(input_batch_1) # optional, to validate the model can run with the input_batch_1

    input_image_2 = Image.open(f"${path}/deeplab.jpg")
    input_tensor_2 = preprocess(input_image_2)
    input_batch_2 = input_tensor_2.unsqueeze(0) # create a mini-batch as expected by the model

    scripted_module = torch.jit.load(model_file)
    scripted_module.forward(input_batch_2) # optional, to validate the model can run with the input_batch_2

    # Step 3. Bundle the model with the prepared input from step2. Can bundle as many input as possible.
    bundled_model_input = [
        (torch.utils.bundled_inputs.bundle_large_tensor(input_batch_1), ),
        (torch.utils.bundled_inputs.bundle_large_tensor(input_batch_2), )]
    bundled_model = torch.utils.bundled_inputs.bundle_inputs(scripted_module, bundled_model_input)
    bundled_model._save_for_lite_interpreter(f"${path}/deeplabv3_scripted_with_bundled_input.ptl")

2. Build tracer

.. code:: shell

 MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ MAX_JOBS=16 TRACING_BASED=1 python setup.py develop

3. Run tracer with the model with bundled input

.. code:: shell

 ./build/bin/model_tracer --model_input_path ${path}/deeplabv3_scripted_with_bundled_input.ptl --build_yaml_path ${path}/deeplabv3_scripted.yaml



Android
-------

Get the Image Segmentation demo app in Android: https://github.com/pytorch/android-demo-app/tree/master/ImageSegmentation

1. **Tracing-based build libtorch lite for android**: Build libtorch for android for all 4 android abis (``armeabi-v7a``, ``arm64-v8a``, ``x86``, ``x86_64``) by running

.. code-block:: bash

   SELECTED_OP_LIST=${path}/deeplabv3_scripted.yaml TRACING_BASED=1  ./scripts/build_pytorch_android.sh

if it will be tested on Pixel 4 emulator with ``x86``, use cmd ``BUILD_LITE_INTERPRETER=1 ./scripts/build_pytorch_android.sh x86`` to specify abi to save build time.

.. code-block:: bash

   SELECTED_OP_LIST=${path}/deeplabv3_scripted.yaml TRACING_BASED=1  ./scripts/build_pytorch_android.sh x86


After the build finish, it will show the library path:

.. code-block:: bash

   BUILD SUCCESSFUL in 55s
   134 actionable tasks: 22 executed, 112 up-to-date
   + find /Users/chenlai/pytorch/android -type f -name '*aar'
   + xargs ls -lah
   -rw-r--r--  1 chenlai  staff    13M Feb 11 11:48 /Users/chenlai/pytorch/android/pytorch_android/build/outputs/aar/pytorch_android-release.aar
   -rw-r--r--  1 chenlai  staff    36K Feb  9 16:45 /Users/chenlai/pytorch/android/pytorch_android_torchvision/build/outputs/aar/pytorch_android_torchvision-release.aar

2. **Use the PyTorch Android libraries built from source in the ImageSegmentation app**: Create a folder `libs` in the path, the path from repository root will be `ImageSegmentation/app/libs`. Copy `pytorch_android-release` to the path ``ImageSegmentation/app/libs/pytorch_android-release.aar``. Copy `pytorch_android_torchvision` (downloaded from `Pytorch Android Torchvision Nightly <https://oss.sonatype.org/#nexus-search;quick~torchvision_android/>`_) to the path ``ImageSegmentation/app/libs/pytorch_android_torchvision.aar``. Update the `dependencies` part of ``ImageSegmentation/app/build.gradle`` to

.. code:: gradle

   dependencies {
       implementation 'androidx.appcompat:appcompat:1.2.0'
       implementation 'androidx.constraintlayout:constraintlayout:2.0.2'
       testImplementation 'junit:junit:4.12'
       androidTestImplementation 'androidx.test.ext:junit:1.1.2'
       androidTestImplementation 'androidx.test.espresso:espresso-core:3.3.0'


       implementation(name:'pytorch_android-release', ext:'aar')
       implementation(name:'pytorch_android_torchvision', ext:'aar')

       implementation 'com.android.support:appcompat-v7:28.0.0'
       implementation 'com.facebook.fbjni:fbjni-java-only:0.0.3'
   }

Update `all projects` part in ``ImageSegmentation/build.gradle`` to


.. code:: gradle

    allprojects {
        repositories {
            google()
            jcenter()
            flatDir {
                dirs 'libs'
            }
        }
    }


3. **Test app**: Build and run the `ImageSegmentation` app in Android Studio


iOS
---

Get ImageSegmentation demo app in iOS: https://github.com/pytorch/ios-demo-app/tree/master/ImageSegmentation


1. **Build libtorch lite for iOS**:

.. code-block:: bash

   SELECTED_OP_LIST=${path}/deeplabv3_scripted.yaml TRACING_BASED=1 IOS_PLATFORM=SIMULATOR ./scripts/build_ios.sh


2. **Remove Cocoapods from the project** (this step is only needed if you ran `pod install`):


.. code-block:: bash

   pod deintegrate


3.  **Link ImageSegmentation demo app with the custom built library**:

Open your project in XCode, go to your project Target’s **Build Phases - Link Binaries With Libraries**, click the **+** sign and add all the library files located in `build_ios/install/lib`. Navigate to the project **Build Settings**, set the value **Header Search Paths** to `build_ios/install/include` and **Library Search Paths** to `build_ios/install/lib`.
In the build settings, search for **other linker flags**. Add a custom linker flag below `-all_load`.
Finally, disable bitcode for your target by selecting the Build Settings, searching for Enable Bitcode, and set the value to **No**.


4. **Build and test the app in Xcode.**



Conclusion
----------

In this tutorial, we demonstrated a new way to custom build PyTorch's efficient mobile interpreter - tracing-based selective build, in an Android and iOS app.

We walked through an Image Segmentation example to show how to bundle inputs to a model, generated operator list by tracing the model with bundled input, and build a custom torch library from source with the operator list from tracing result.

The custom build is still under development, and we will continue improving its size in the future. Note, however, that the APIs are subject to change in future versions.

Thanks for reading! As always, we welcome any feedback, so please create an issue here <https://github.com/pytorch/pytorch/issues>`.

Learn More


- To learn more about PyTorch Mobile, please refer to PyTorch Mobile Home Page <https://pytorch.org/mobile/home/>

* To learn more about Image Segmentation, please refer to the Image Segmentation DeepLabV3 on Android Recipe <https://pytorch.org/tutorials/beginner/deeplabv3_on_android.html>_