Build you own Task API

TensorFlow Lite Task Library provides prebuilt native/Android/iOS APIs on top of the same infrastructure that abstracts TensorFlow. You can extend the Task API infrastructure to build customized APIs if your model is not supported by existing Task libraries.

Overview

Task API infrastructure has a two-layer structure: the bottom C++ layer encapsulating the native TFLite runtime and the top Java/ObjC layer that communicates with the C++ layer through JNI or native wrapper.

Implementing all the TensorFlow logic in only C++ minimizes cost, maximizes inference performance and simplifies the overall workflow across platforms.

To create a Task class, extend the BaseTaskApi to provide conversion logic between TFLite model interface and Task API interface, then use the Java/ObjC utilities to create corresponding APIs. With all TensorFlow details hidden, you can deploy the TFLite model in your apps without any machine learning knowledge.

TensorFlow Lite provides some prebuilt APIs for most popular Vision and NLP tasks. You can build your own APIs for other tasks using the Task API infrastructure.

prebuilt_task_apis
Figure 1. prebuilt Task APIs

Build your own API with Task API infra

C++ API

All TFLite details are implemented in the native API. Create an API object by using one of the factory functions and get model results by calling functions defined in the interface.

Sample usage

Here is an example using the C++ BertQuestionAnswerer for MobileBert.

  char kBertModelPath[] = "path/to/model.tflite";
  // Create the API from a model file
  std::unique_ptr<BertQuestionAnswerer> question_answerer =
      BertQuestionAnswerer::CreateFromFile(kBertModelPath);

  char kContext[] = ...; // context of a question to be answered
  char kQuestion[] = ...; // question to be answered
  // ask a question
  std::vector<QaAnswer> answers = question_answerer.Answer(kContext, kQuestion);
  // answers[0].text is the best answer

Building the API

native_task_api
Figure 2. Native Task API

To build an API object,you must provide the following information by extending BaseTaskApi

  • Determine the API I/O - Your API should expose similar input/output across different platforms. e.g BertQuestionAnswerer takes two strings (std::string& context, std::string& question) as input and outputs a vector of possible answer and probabilities as std::vector<QaAnswer>. This is done by specifying the corresponding types in BaseTaskApi's template parameter. With the template parameters specified, the BaseTaskApi::Infer function will have the correct input/output types. This function can be directly called by API clients, but it is a good practice to wrap it inside a model-specific function, in this case, BertQuestionAnswerer::Answer.
class BertQuestionAnswerer : public BaseTaskApi<
                              std::vector<QaAnswer>, // OutputType
                              const std::string&, const std::string& // InputTypes
                              > {
  // Model specific function delegating calls to BaseTaskApi::Infer
  std::vector<QaAnswer> Answer(const std::string& context, const std::string& question) {
    return Infer(context, question).value();
  }
}
  • Provide conversion logic between API I/O and input/output tensor of the model - With input and output types specified, the subclasses also need to implement the typed functions BaseTaskApi::Preprocess and BaseTaskApi::Postprocess. The two functions provide inputs and outputs from the TFLite FlatBuffer. The subclass is responsible for assigning values from the API I/O to I/O tensors. See the complete implementation example in BertQuestionAnswerer.
class BertQuestionAnswerer : public BaseTaskApi<
                              std::vector<QaAnswer>, // OutputType
                              const std::string&, const std::string& // InputTypes
                              > {
  // Convert API input into into tensors
  absl::Status BertQuestionAnswerer::Preprocess(
    const std::vector<TfLiteTensor*>& input_tensors, // input tensors of the model
    const std::string& context, const std::string& query // InputType of the API
  ) {
    // Perform tokenization on input strings
    ...
    // Populate IDs, Masks and SegmentIDs to corresponding input tensors
    PopulateTensor(input_ids, input_tensors[0]);
    PopulateTensor(input_mask, input_tensors[1]);
    PopulateTensor(segment_ids, input_tensors[2]);
    return absl::OkStatus();
  }

  // Convert output tensors into API output
  StatusOr<std::vector<QaAnswer>> // OutputType
  BertQuestionAnswerer::Postprocess(
    const std::vector<const TfLiteTensor*>& output_tensors, // output tensors of the model
  ) {
    // Get start/end logits of prediction result from output tensors
    std::vector<float> end_logits;
    std::vector<float> start_logits;
    // output_tensors[0]: end_logits FLOAT[1, 384]
    PopulateVector(output_tensors[0], &end_logits);
    // output_tensors[1]: start_logits FLOAT[1, 384]
    PopulateVector(output_tensors[1], &start_logits);
    ...
    std::vector<QaAnswer::Pos> orig_results;
    // Look up the indices from vocabulary file and build results
    ...
    return orig_results;
  }
}
  • Create factory functions of the API - A model file and a OpResolver are needed to initialize the tflite::Interpreter. TaskAPIFactory provides utility functions to create BaseTaskApi instances.

    You must also provide any files associated with the model. e.g, BertQuestionAnswerer can also have an additional file for its tokenizer's vocabulary.

class BertQuestionAnswerer : public BaseTaskApi<
                              std::vector<QaAnswer>, // OutputType
                              const std::string&, const std::string& // InputTypes
                              > {
  // Factory function to create the API instance
  StatusOr<std::unique_ptr<QuestionAnswerer>>
  BertQuestionAnswerer::CreateBertQuestionAnswerer(
      const std::string& path_to_model, // model to passed to TaskApiFactory
      const std::string& path_to_vocab  // additional model specific files
  ) {
    // Creates an API object by calling one of the utils from TaskAPIFactory
    std::unique_ptr<BertQuestionAnswerer> api_to_init;
    ASSIGN_OR_RETURN(
        api_to_init,
        core::TaskAPIFactory::CreateFromFile<BertQuestionAnswerer>(
            path_to_model,
            absl::make_unique<tflite::ops::builtin::BuiltinOpResolver>(),
            kNumLiteThreads));

    // Perform additional model specific initializations
    // In this case building a vocabulary vector from the vocab file.
    api_to_init->InitializeVocab(path_to_vocab);
    return api_to_init;
  }
}

Android API

Create Android APIs by defining Java/Kotlin interface and delegating the logic to the C++ layer through JNI. Android API requires native API to be built first.

Sample usage

Here is an example using Java BertQuestionAnswerer for MobileBert.

  String BERT_MODEL_FILE = "path/to/model.tflite";
  String VOCAB_FILE = "path/to/vocab.txt";
  // Create the API from a model file and vocabulary file
    BertQuestionAnswerer bertQuestionAnswerer =
        BertQuestionAnswerer.createBertQuestionAnswerer(
            ApplicationProvider.getApplicationContext(), BERT_MODEL_FILE, VOCAB_FILE);

  String CONTEXT = ...; // context of a question to be answered
  String QUESTION = ...; // question to be answered
  // ask a question
  List<QaAnswer> answers = bertQuestionAnswerer.answer(CONTEXT, QUESTION);
  // answers.get(0).text is the best answer

Building the API

android_task_api
Figure 3. Android Task API

Similar to Native APIs, to build an API object, the client needs to provide the following information by extending BaseTaskApi, which provides JNI handlings for all Java Task APIs.

  • Determine the API I/O - This usually mirriors the native interfaces. e.g BertQuestionAnswerer takes (String context, String question) as input and outputs List<QaAnswer>. The implementation calls a private native function with similar signature, except it has an additional parameter long nativeHandle, which is the pointer returned from C++.
class BertQuestionAnswerer extends BaseTaskApi {
  public List<QaAnswer> answer(String context, String question) {
    return answerNative(getNativeHandle(), context, question);
  }

  private static native List<QaAnswer> answerNative(
                                        long nativeHandle, // C++ pointer
                                        String context, String question // API I/O
                                       );

}
  • Create factory functions of the API - This also mirrors native factory functions, except Android factory functions also need to take Context for file access. The implementation calls one of the utilities in TaskJniUtils to build the corresponding C++ API object and pass its pointer to the BaseTaskApi constructor.
  class BertQuestionAnswerer extends BaseTaskApi {
    private static final String BERT_QUESTION_ANSWERER_NATIVE_LIBNAME =
                                              "bert_question_answerer_jni";

    // Extending super constructor by providing the
    // native handle(pointer of corresponding C++ API object)
    private BertQuestionAnswerer(long nativeHandle) {
      super(nativeHandle);
    }

    public static BertQuestionAnswerer createBertQuestionAnswerer(
                                        Context context, // Accessing Android files
                                        String pathToModel, String pathToVocab) {
      return new BertQuestionAnswerer(
          // The util first try loads the JNI module with name
          // BERT_QUESTION_ANSWERER_NATIVE_LIBNAME, then opens two files,
          // converts them into ByteBuffer, finally ::initJniWithBertByteBuffers
          // is called with the buffer for a C++ API object pointer
          TaskJniUtils.createHandleWithMultipleAssetFilesFromLibrary(
              context,
              BertQuestionAnswerer::initJniWithBertByteBuffers,
              BERT_QUESTION_ANSWERER_NATIVE_LIBNAME,
              pathToModel,
              pathToVocab));
    }

    // modelBuffers[0] is tflite model file buffer, and modelBuffers[1] is vocab file buffer.
    // returns C++ API object pointer casted to long
    private static native long initJniWithBertByteBuffers(ByteBuffer... modelBuffers);

  }
  • Implement the JNI module for native functions - All Java native methods are implemented by calling a corresponding native function from the JNI module. The factory functions would create a native API object and return its pointer as a long type to Java. In later calls to Java API, the long type pointer is passed back to JNI and cast back to the native API object. The native API results are then converted back to Java results.

    For example, this is how bert_question_answerer_jni is implemented.

  // Implements BertQuestionAnswerer::initJniWithBertByteBuffers
  extern "C" JNIEXPORT jlong JNICALL
  Java_org_tensorflow_lite_task_text_qa_BertQuestionAnswerer_initJniWithBertByteBuffers(
      JNIEnv* env, jclass thiz, jobjectArray model_buffers) {
    // Convert Java ByteBuffer object into a buffer that can be read by native factory functions
    absl::string_view model =
        GetMappedFileBuffer(env, env->GetObjectArrayElement(model_buffers, 0));

    // Creates the native API object
    absl::StatusOr<std::unique_ptr<QuestionAnswerer>> status =
        BertQuestionAnswerer::CreateFromBuffer(
            model.data(), model.size());
    if (status.ok()) {
      // converts the object pointer to jlong and return to Java.
      return reinterpret_cast<jlong>(status->release());
    } else {
      return kInvalidPointer;
    }
  }

  // Implements BertQuestionAnswerer::answerNative
  extern "C" JNIEXPORT jobject JNICALL
  Java_org_tensorflow_lite_task_text_qa_BertQuestionAnswerer_answerNative(
  JNIEnv* env, jclass thiz, jlong native_handle, jstring context, jstring question) {
  // Convert long to native API object pointer
  QuestionAnswerer* question_answerer = reinterpret_cast<QuestionAnswerer*>(native_handle);

  // Calls the native API
  std::vector<QaAnswer> results = question_answerer->Answer(JStringToString(env, context),
                                         JStringToString(env, question));

  // Converts native result(std::vector<QaAnswer>) to Java result(List<QaAnswerer>)
  jclass qa_answer_class =
    env->FindClass("org/tensorflow/lite/task/text/qa/QaAnswer");
  jmethodID qa_answer_ctor =
    env->GetMethodID(qa_answer_class, "<init>", "(Ljava/lang/String;IIF)V");
  return ConvertVectorToArrayList<QaAnswer>(
    env, results,
    [env, qa_answer_class, qa_answer_ctor](const QaAnswer& ans) {
      jstring text = env->NewStringUTF(ans.text.data());
      jobject qa_answer =
          env->NewObject(qa_answer_class, qa_answer_ctor, text, ans.pos.start,
                         ans.pos.end, ans.pos.logit);
      env->DeleteLocalRef(text);
      return qa_answer;
    });
  }

  // Implements BaseTaskApi::deinitJni by delete the native object
  extern "C" JNIEXPORT void JNICALL Java_task_core_BaseTaskApi_deinitJni(
      JNIEnv* env, jobject thiz, jlong native_handle) {
    delete reinterpret_cast<QuestionAnswerer*>(native_handle);
  }

iOS API

Create iOS APIs by wrapping a native API object into a ObjC API object. The created API object can be used in either ObjC or Swift. iOS API requires the native API to be built first.

Sample usage

Here is an example using ObjC TFLBertQuestionAnswerer for MobileBert in Swfit.

  static let mobileBertModelPath = "path/to/model.tflite";
  // Create the API from a model file and vocabulary file
  let mobileBertAnswerer = TFLBertQuestionAnswerer.mobilebertQuestionAnswerer(
      modelPath: mobileBertModelPath)

  static let context = ...; // context of a question to be answered
  static let question = ...; // question to be answered
  // ask a question
  let answers = mobileBertAnswerer.answer(
      context: TFLBertQuestionAnswererTest.context, question: TFLBertQuestionAnswererTest.question)
  // answers.[0].text is the best answer

Building the API

ios_task_api
Figure 4. iOS Task API

iOS API is a simple ObjC wrapper on top of native API. Build the API by following the steps below:

  • Define the ObjC wrapper - Define an ObjC class and delegate the implementations to the corresponding native API object. Note the native dependencies can only appear in a .mm file due to Swift's inability to interop with C++.

    • .h file
  @interface TFLBertQuestionAnswerer : NSObject

  // Delegate calls to the native BertQuestionAnswerer::CreateBertQuestionAnswerer
  + (instancetype)mobilebertQuestionAnswererWithModelPath:(NSString*)modelPath
                                                vocabPath:(NSString*)vocabPath
      NS_SWIFT_NAME(mobilebertQuestionAnswerer(modelPath:vocabPath:));

  // Delegate calls to the native BertQuestionAnswerer::Answer
  - (NSArray<TFLQAAnswer*>*)answerWithContext:(NSString*)context
                                     question:(NSString*)question
      NS_SWIFT_NAME(answer(context:question:));
}
*   .mm file
  using BertQuestionAnswererCPP = ::tflite::task::text::qa::BertQuestionAnswerer;

  @implementation TFLBertQuestionAnswerer {
    // define an iVar for the native API object
    std::unique_ptr<QuestionAnswererCPP> _bertQuestionAnswerwer;
  }

  // Initilalize the native API object
  + (instancetype)mobilebertQuestionAnswererWithModelPath:(NSString *)modelPath
                                          vocabPath:(NSString *)vocabPath {
    absl::StatusOr<std::unique_ptr<QuestionAnswererCPP>> cQuestionAnswerer =
        BertQuestionAnswererCPP::CreateBertQuestionAnswerer(MakeString(modelPath),
                                                            MakeString(vocabPath));
    _GTMDevAssert(cQuestionAnswerer.ok(), @"Failed to create BertQuestionAnswerer");
    return [[TFLBertQuestionAnswerer alloc]
        initWithQuestionAnswerer:std::move(cQuestionAnswerer.value())];
  }

  // Calls the native API and converts C++ results into ObjC results
  - (NSArray<TFLQAAnswer *> *)answerWithContext:(NSString *)context question:(NSString *)question {
    std::vector<QaAnswerCPP> results =
      _bertQuestionAnswerwer->Answer(MakeString(context), MakeString(question));
    return [self arrayFromVector:results];
  }
}