path: root/tensorflow/contrib/lite/model.cc

/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#include <fcntl.h>
#include <stdint.h>
#include <stdio.h>
#include <stdlib.h>
#include <sys/mman.h>
#include <sys/stat.h>
#include <sys/types.h>
#include <unistd.h>

#include "tensorflow/contrib/lite/allocation.h"
#include "tensorflow/contrib/lite/builtin_op_data.h"
#include "tensorflow/contrib/lite/error_reporter.h"
#include "tensorflow/contrib/lite/model.h"
#include "tensorflow/contrib/lite/nnapi_delegate.h"
#include "tensorflow/contrib/lite/version.h"

namespace tflite {

namespace {
// Ensure that ErrorReporter is non-null.
ErrorReporter* ValidateErrorReporter(ErrorReporter* e) {
  return e ? e : DefaultErrorReporter();
}
}  // namespace

const char* kEmptyTensorName = "";

TfLiteStatus ConvertTensorType(TensorType tensor_type, TfLiteType* type,
                               ErrorReporter* error_reporter) {
  switch (tensor_type) {
    case TensorType_FLOAT32:
      *type = kTfLiteFloat32;
      break;
    case TensorType_INT16:
      *type = kTfLiteInt16;
      break;
    case TensorType_INT32:
      *type = kTfLiteInt32;
      break;
    case TensorType_UINT8:
      *type = kTfLiteUInt8;
      break;
    case TensorType_INT64:
      *type = kTfLiteInt64;
      break;
    case TensorType_STRING:
      *type = kTfLiteString;
      break;
    case TensorType_BOOL:
      *type = kTfLiteBool;
      break;
    case TensorType_COMPLEX64:
      *type = kTfLiteComplex64;
      break;
    default:
      error_reporter->Report("Unimplemented data type %s (%d) in tensor\n",
                             EnumNameTensorType(tensor_type), tensor_type);
      return kTfLiteError;
  }
  return kTfLiteOk;
}

// Loads a model from `filename`. If `mmap_file` is true then use mmap,
// otherwise make a copy of the model in a buffer.
std::unique_ptr<Allocation> GetAllocationFromFile(const char* filename,
                                                  bool mmap_file,
                                                  ErrorReporter* error_reporter,
                                                  bool use_nnapi) {
  std::unique_ptr<Allocation> allocation;
  if (mmap_file) {
    if (use_nnapi && NNAPIExists())
      allocation.reset(new NNAPIAllocation(filename, error_reporter));
    else
      allocation.reset(new MMAPAllocation(filename, error_reporter));
  } else {
    allocation.reset(new FileCopyAllocation(filename, error_reporter));
  }
  return allocation;
}

std::unique_ptr<FlatBufferModel> FlatBufferModel::BuildFromFile(
    const char* filename, ErrorReporter* error_reporter) {
  error_reporter = ValidateErrorReporter(error_reporter);

  std::unique_ptr<FlatBufferModel> model;
  auto allocation = GetAllocationFromFile(filename, /*mmap_file=*/true,
                                          error_reporter, /*use_nnapi=*/true);
  model.reset(new FlatBufferModel(allocation.release(), error_reporter));
  if (!model->initialized()) model.reset();
  return model;
}

std::unique_ptr<FlatBufferModel> FlatBufferModel::VerifyAndBuildFromFile(
    const char* filename, TfLiteVerifier* verifier,
    ErrorReporter* error_reporter) {
  error_reporter = ValidateErrorReporter(error_reporter);

  std::unique_ptr<FlatBufferModel> model;
  auto allocation = GetAllocationFromFile(filename, /*mmap_file=*/true,
                                          error_reporter, /*use_nnapi=*/true);
  if (verifier &&
      !verifier->Verify(static_cast<const char*>(allocation->base()),
                        allocation->bytes(), error_reporter)) {
    return model;
  }
  model.reset(new FlatBufferModel(allocation.release(), error_reporter));
  if (!model->initialized()) model.reset();
  return model;
}

std::unique_ptr<FlatBufferModel> FlatBufferModel::BuildFromBuffer(
    const char* buffer, size_t buffer_size, ErrorReporter* error_reporter) {
  error_reporter = ValidateErrorReporter(error_reporter);

  std::unique_ptr<FlatBufferModel> model;
  Allocation* allocation =
      new MemoryAllocation(buffer, buffer_size, error_reporter);
  model.reset(new FlatBufferModel(allocation, error_reporter));
  if (!model->initialized()) model.reset();
  return model;
}

std::unique_ptr<FlatBufferModel> FlatBufferModel::BuildFromModel(
    const tflite::Model* model_spec, ErrorReporter* error_reporter) {
  error_reporter = ValidateErrorReporter(error_reporter);

  std::unique_ptr<FlatBufferModel> model;
  model.reset(new FlatBufferModel(model_spec, error_reporter));
  if (!model->initialized()) model.reset();
  return model;
}

bool FlatBufferModel::CheckModelIdentifier() const {
  if (!tflite::ModelBufferHasIdentifier(allocation_->base())) {
    const char* ident = flatbuffers::GetBufferIdentifier(allocation_->base());
    error_reporter_->Report(
        "Model provided has model identifier '%c%c%c%c', should be '%s'\n",
        ident[0], ident[1], ident[2], ident[3], tflite::ModelIdentifier());
    return false;
  }
  return true;
}

FlatBufferModel::FlatBufferModel(const Model* model,
                                 ErrorReporter* error_reporter)
    : error_reporter_(ValidateErrorReporter(error_reporter)) {
  model_ = model;
}

FlatBufferModel::FlatBufferModel(Allocation* allocation,
                                 ErrorReporter* error_reporter)
    : error_reporter_(ValidateErrorReporter(error_reporter)) {
  allocation_ = allocation;
  if (!allocation_->valid() || !CheckModelIdentifier()) return;

  model_ = ::tflite::GetModel(allocation_->base());
}

FlatBufferModel::~FlatBufferModel() { delete allocation_; }

InterpreterBuilder::InterpreterBuilder(const FlatBufferModel& model,
                                       const OpResolver& op_resolver)
    : model_(model.GetModel()),
      op_resolver_(op_resolver),
      error_reporter_(ValidateErrorReporter(model.error_reporter())),
      allocation_(model.allocation()) {}

InterpreterBuilder::InterpreterBuilder(const ::tflite::Model* model,
                                       const OpResolver& op_resolver,
                                       ErrorReporter* error_reporter)
    : model_(model),
      op_resolver_(op_resolver),
      error_reporter_(ValidateErrorReporter(error_reporter)) {}

TfLiteStatus InterpreterBuilder::BuildLocalIndexToRegistrationMapping() {
  TfLiteStatus status = kTfLiteOk;
  auto opcodes = model_->operator_codes();
  for (const OperatorCode* opcode : *opcodes) {
    const TfLiteRegistration* registration = nullptr;
    auto builtin_code = opcode->builtin_code();
    int version = opcode->version();

    if (builtin_code > BuiltinOperator_MAX ||
        builtin_code < BuiltinOperator_MIN) {
      error_reporter_->Report(
          "Op builtin_code out or range: %d. Are you using old TFLite binary "
          "with newer model?",
          builtin_code);
      status = kTfLiteError;
    } else if (builtin_code != BuiltinOperator_CUSTOM) {
      registration = op_resolver_.FindOp(builtin_code, version);
      if (registration == nullptr) {
        error_reporter_->Report("Didn't find op for builtin opcode '%s'\n",
                                EnumNameBuiltinOperator(builtin_code));
        status = kTfLiteError;
      }
    } else if (!opcode->custom_code()) {
      error_reporter_->Report(
          "Operator with CUSTOM builtin_code has no custom_code.\n");
      status = kTfLiteError;
    } else {
      const char* name = opcode->custom_code()->c_str();
      registration = op_resolver_.FindOp(name, version);
      flatbuffer_op_index_to_registration_types_.push_back(
          BuiltinOperator_CUSTOM);
      if (registration == nullptr) {
        error_reporter_->Report(
            "Didn't find custom op for name '%s' with version %d\n", name,
            version);
        status = kTfLiteError;
      }
    }
    flatbuffer_op_index_to_registration_.push_back(registration);
  }
  return status;
}

namespace {
template <class T>
std::vector<int> FlatBufferIntArrayToVector(T* flat_array) {
  std::vector<int> ret(flat_array->Length());
  for (int i = 0; i < flat_array->Length(); i++) {
    ret[i] = flat_array->Get(i);
  }
  return ret;
}

// Copies the contents from the flatbuffer int vector `flatbuffer` into the
// int array `buffer`. `flat_vector` and `buffer` represent the same
// configuration operation for a given operation.
void FlatBufferIntVectorToArray(int max_size_of_buffer,
                                const flatbuffers::Vector<int32_t>* flat_vector,
                                int* buffer, ErrorReporter* error_reporter) {
  if (!flat_vector) {
    error_reporter->Report("Input array not provided for operation.\n");
  } else {
    int num_dimensions = flat_vector->Length();
    if (num_dimensions > max_size_of_buffer / sizeof(int)) {
      error_reporter->Report(
          "Found too many dimensions in the operation's input array.\n");
    } else {
      for (int i = 0; i < num_dimensions; ++i) {
        buffer[i] = flat_vector->Get(i);
      }
    }
  }
}

// Allocate a structure using C malloc, but make sure the structure is a
// POD structure that doesn't require constructors to run. The reason we do
// this, is that Interpreter's C extension part will take ownership and wants
// to use malloc() and free().
template <class T>
T* MallocPOD() {
  static_assert(std::is_pod<T>::value, "Builtin data structure must be POD.");
  return static_cast<T*>(malloc(sizeof(T)));
}

// Parse the appropriate data out of the op.
//
// This handles builtin data explicitly as there are flatbuffer schemas.
// If it returns kTfLiteOk, it passes the data out with `builtin_data`, which
// need to be released by calling `free`.`
// If it returns kTfLiteError, `builtin_data` will be `nullptr`.
TfLiteStatus ParseOpData(const Operator* op, BuiltinOperator op_type,
                         ErrorReporter* error_reporter, void** builtin_data) {
  auto parse_padding = [](Padding padding) {
    switch (padding) {
      case Padding_SAME:
        return kTfLitePaddingSame;
      case Padding_VALID:
        return kTfLitePaddingValid;
    }
    return kTfLitePaddingUnknown;
  };
  auto parse_activation = [](ActivationFunctionType activation) {
    switch (activation) {
      case ActivationFunctionType_NONE:
        return kTfLiteActNone;
      case ActivationFunctionType_RELU:
        return kTfLiteActRelu;
      case ActivationFunctionType_RELU_N1_TO_1:
        return kTfLiteActRelu1;
      case ActivationFunctionType_RELU6:
        return kTfLiteActRelu6;
      case ActivationFunctionType_TANH:
        return kTfLiteActTanh;
      case ActivationFunctionType_SIGN_BIT:
        return kTfLiteActSignBit;
    }
    return kTfLiteActNone;
  };
  auto parseLSHProjectionType = [](LSHProjectionType type) {
    switch (type) {
      case LSHProjectionType_SPARSE:
        return kTfLiteLshProjectionSparse;
      case LSHProjectionType_DENSE:
        return kTfLiteLshProjectionDense;
      default:
        return kTfLiteLshProjectionUnknown;
    }
  };
  auto parseCombinerType = [](CombinerType type) {
    switch (type) {
      case CombinerType_MEAN:
        return kTfLiteCombinerTypeMean;
      case CombinerType_SQRTN:
        return kTfLiteCombinerTypeSqrtn;
      case CombinerType_SUM:
      default:
        return kTfLiteCombinerTypeSum;
    }
  };

  *builtin_data = nullptr;
  switch (op_type) {
    case BuiltinOperator_CONV_2D: {
      TfLiteConvParams* params = MallocPOD<TfLiteConvParams>();
      if (auto* conv_params = op->builtin_options_as_Conv2DOptions()) {
        params->padding = parse_padding(conv_params->padding());
        params->stride_width = conv_params->stride_w();
        params->stride_height = conv_params->stride_h();
        params->activation =
            parse_activation(conv_params->fused_activation_function());

        params->dilation_width_factor = conv_params->dilation_w_factor();
        params->dilation_height_factor = conv_params->dilation_h_factor();
      }
      *builtin_data = reinterpret_cast<void*>(params);
      break;
    }
    case BuiltinOperator_CAST: {
      TfLiteCastParams* params = MallocPOD<TfLiteCastParams>();
      if (auto* schema_params = op->builtin_options_as_CastOptions()) {
        auto in_status =
            ConvertTensorType(schema_params->in_data_type(),
                              &params->in_data_type, error_reporter);
        auto out_status =
            ConvertTensorType(schema_params->out_data_type(),
                              &params->out_data_type, error_reporter);
        if (in_status != kTfLiteOk || out_status != kTfLiteOk) {
          free(params);
          return kTfLiteError;
        }
      }
      *builtin_data = reinterpret_cast<void*>(params);
      break;
    }
    case BuiltinOperator_LSH_PROJECTION: {
      TfLiteLSHProjectionParams* params =
          MallocPOD<TfLiteLSHProjectionParams>();
      if (auto* lshParams = op->builtin_options_as_LSHProjectionOptions()) {
        params->type = parseLSHProjectionType(lshParams->type());
      }
      *builtin_data = reinterpret_cast<void*>(params);
      break;
    }
    case BuiltinOperator_AVERAGE_POOL_2D:
    case BuiltinOperator_MAX_POOL_2D:
    case BuiltinOperator_L2_POOL_2D: {
      TfLitePoolParams* params = MallocPOD<TfLitePoolParams>();
      if (auto* pool_params = op->builtin_options_as_Pool2DOptions()) {
        params->padding = parse_padding(pool_params->padding());
        params->stride_width = pool_params->stride_w();
        params->stride_height = pool_params->stride_h();
        params->filter_width = pool_params->filter_width();
        params->filter_height = pool_params->filter_height();
        params->activation =
            parse_activation(pool_params->fused_activation_function());
      }
      *builtin_data = reinterpret_cast<void*>(params);
      break;
    }
    case BuiltinOperator_DEPTHWISE_CONV_2D: {
      TfLiteDepthwiseConvParams* params =
          MallocPOD<TfLiteDepthwiseConvParams>();
      if (auto* conv_params = op->builtin_options_as_DepthwiseConv2DOptions()) {
        params->padding = parse_padding(conv_params->padding());
        params->stride_width = conv_params->stride_w();
        params->stride_height = conv_params->stride_h();
        params->depth_multiplier = conv_params->depth_multiplier();
        params->activation =
            parse_activation(conv_params->fused_activation_function());
      }
      *builtin_data = reinterpret_cast<void*>(params);
      break;
    }
    case BuiltinOperator_SVDF: {
      TfLiteSVDFParams* params = MallocPOD<TfLiteSVDFParams>();
      if (auto* svdf_params = op->builtin_options_as_SVDFOptions()) {
        params->rank = svdf_params->rank();
        params->activation =
            parse_activation(svdf_params->fused_activation_function());
      }
      *builtin_data = reinterpret_cast<void*>(params);
      break;
    }
    case BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN:
    case BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN: {
      TfLiteSequenceRNNParams* params = MallocPOD<TfLiteSequenceRNNParams>();
      if (auto* sequence_rnn_params =
              op->builtin_options_as_SequenceRNNOptions()) {
        params->activation =
            parse_activation(sequence_rnn_params->fused_activation_function());
        params->time_major = sequence_rnn_params->time_major();
      }
      *builtin_data = reinterpret_cast<void*>(params);
      break;
    }
    case BuiltinOperator_RNN: {
      TfLiteRNNParams* params = MallocPOD<TfLiteRNNParams>();
      if (auto* rnn_params = op->builtin_options_as_RNNOptions()) {
        params->activation =
            parse_activation(rnn_params->fused_activation_function());
      }
      *builtin_data = reinterpret_cast<void*>(params);
      break;
    }
    case BuiltinOperator_EMBEDDING_LOOKUP_SPARSE: {
      TfLiteEmbeddingLookupSparseParams* params =
          MallocPOD<TfLiteEmbeddingLookupSparseParams>();
      if (auto* embedding_params =
              op->builtin_options_as_EmbeddingLookupSparseOptions()) {
        params->combiner = parseCombinerType(embedding_params->combiner());
      }
      *builtin_data = reinterpret_cast<void*>(params);
      break;
    }
    case BuiltinOperator_FULLY_CONNECTED: {
      TfLiteFullyConnectedParams* params =
          MallocPOD<TfLiteFullyConnectedParams>();
      if (auto* fully_connected_params =
              op->builtin_options_as_FullyConnectedOptions()) {
        params->activation = parse_activation(
            fully_connected_params->fused_activation_function());
        switch (fully_connected_params->weights_format()) {
          case FullyConnectedOptionsWeightsFormat_DEFAULT:
            params->weights_format = kTfLiteFullyConnectedWeightsFormatDefault;
            break;
          case FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8:
            params->weights_format =
                kTfLiteFullyConnectedWeightsFormatShuffled4x16Int8;
            break;
          default:
            error_reporter->Report("Unhandled fully-connected weights format.");
            return kTfLiteError;
        }
      }
      *builtin_data = reinterpret_cast<void*>(params);
      break;
    }
    case BuiltinOperator_HASHTABLE_LOOKUP:
      // no-op.
      break;
    case BuiltinOperator_SOFTMAX: {
      TfLiteSoftmaxParams* params = MallocPOD<TfLiteSoftmaxParams>();
      if (auto* softmax_params = op->builtin_options_as_SoftmaxOptions()) {
        params->beta = softmax_params->beta();
      }
      *builtin_data = reinterpret_cast<void*>(params);
      break;
    }
    case BuiltinOperator_CONCATENATION: {
      TfLiteConcatenationParams* params =
          MallocPOD<TfLiteConcatenationParams>();
      if (auto* concatenation_params =
              op->builtin_options_as_ConcatenationOptions()) {
        params->activation =
            parse_activation(concatenation_params->fused_activation_function());
        params->axis = concatenation_params->axis();
      }
      *builtin_data = reinterpret_cast<void*>(params);
      break;
    }
    case BuiltinOperator_MUL: {
      auto* params = MallocPOD<TfLiteMulParams>();
      if (auto* schema_params = op->builtin_options_as_MulOptions()) {
        params->activation =
            parse_activation(schema_params->fused_activation_function());
      }
      *builtin_data = reinterpret_cast<void*>(params);
      break;
    }
    case BuiltinOperator_ADD: {
      auto* params = MallocPOD<TfLiteAddParams>();
      if (auto* schema_params = op->builtin_options_as_AddOptions()) {
        params->activation =
            parse_activation(schema_params->fused_activation_function());
      }
      *builtin_data = reinterpret_cast<void*>(params);
      break;
    }
    case BuiltinOperator_DIV: {
      auto* params = MallocPOD<TfLiteDivParams>();
      if (auto* schema_params = op->builtin_options_as_DivOptions()) {
        params->activation =
            parse_activation(schema_params->fused_activation_function());
      }
      *builtin_data = reinterpret_cast<void*>(params);
      break;
    }
    case BuiltinOperator_SUB: {
      auto* params = MallocPOD<TfLiteSubParams>();
      if (auto* schema_params = op->builtin_options_as_SubOptions()) {
        params->activation =
            parse_activation(schema_params->fused_activation_function());
      }
      *builtin_data = reinterpret_cast<void*>(params);
      break;
    }
    case BuiltinOperator_L2_NORMALIZATION: {
      auto* params = MallocPOD<TfLiteL2NormParams>();
      if (auto* schema_params = op->builtin_options_as_L2NormOptions()) {
        params->activation =
            parse_activation(schema_params->fused_activation_function());
      }
      *builtin_data = reinterpret_cast<void*>(params);
      break;
    }
    case BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION: {
      auto* params = MallocPOD<TfLiteLocalResponseNormParams>();
      if (auto* schema_params =
              op->builtin_options_as_LocalResponseNormalizationOptions()) {
        params->radius = schema_params->radius();
        params->bias = schema_params->bias();
        params->alpha = schema_params->alpha();
        params->beta = schema_params->beta();
      }
      *builtin_data = reinterpret_cast<void*>(params);
      break;
    }
    case BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM:
    case BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM:
    case BuiltinOperator_LSTM: {
      TfLiteLSTMParams* params = MallocPOD<TfLiteLSTMParams>();
      if (auto* lstm_params = op->builtin_options_as_LSTMOptions()) {
        params->activation =
            parse_activation(lstm_params->fused_activation_function());
        params->cell_clip = lstm_params->cell_clip();
        params->proj_clip = lstm_params->proj_clip();
        switch (lstm_params->kernel_type()) {
          case LSTMKernelType_FULL:
            params->kernel_type = kTfLiteLSTMFullKernel;
            break;
          case LSTMKernelType_BASIC:
            params->kernel_type = kTfLiteLSTMBasicKernel;
            break;
        }
      }
      *builtin_data = reinterpret_cast<void*>(params);
      break;
    }
    case BuiltinOperator_RESIZE_BILINEAR: {
      auto* params = MallocPOD<TfLiteResizeBilinearParams>();
      if (auto* schema_params =
              op->builtin_options_as_ResizeBilinearOptions()) {
        params->align_corners = schema_params->align_corners();
      }
      *builtin_data = reinterpret_cast<void*>(params);
      break;
    }
    case BuiltinOperator_RESHAPE: {
      auto* params = MallocPOD<TfLiteReshapeParams>();
      if (auto* schema_params = op->builtin_options_as_ReshapeOptions()) {
        auto* new_shape = schema_params->new_shape();
        FlatBufferIntVectorToArray(sizeof(params->shape), new_shape,
                                   params->shape, error_reporter);
        params->num_dimensions = new_shape->Length();
      }
      *builtin_data = reinterpret_cast<void*>(params);
      break;
    }
    case BuiltinOperator_SKIP_GRAM: {
      TfLiteSkipGramParams* params = MallocPOD<TfLiteSkipGramParams>();
      if (auto* skip_gram_params = op->builtin_options_as_SkipGramOptions()) {
        params->ngram_size = skip_gram_params->ngram_size();
        params->max_skip_size = skip_gram_params->max_skip_size();
        params->include_all_ngrams = skip_gram_params->include_all_ngrams();
      }
      *builtin_data = reinterpret_cast<void*>(params);
      break;
    }
    case BuiltinOperator_SPACE_TO_DEPTH: {
      auto* params = MallocPOD<TfLiteSpaceToDepthParams>();
      if (auto* schema_params = op->builtin_options_as_SpaceToDepthOptions()) {
        params->block_size = schema_params->block_size();
      }
      *builtin_data = reinterpret_cast<void*>(params);
      break;
    }
    case BuiltinOperator_GATHER: {
      TfLiteGatherParams* params = MallocPOD<TfLiteGatherParams>();
      params->axis = 0;
      if (auto* gather_params = op->builtin_options_as_GatherOptions()) {
        params->axis = gather_params->axis();
      }

      *builtin_data = reinterpret_cast<void*>(params);
      break;
    }
    case BuiltinOperator_MEAN:
    case BuiltinOperator_SUM: {
      auto* params = MallocPOD<TfLiteReducerParams>();
      if (auto* schema_params = op->builtin_options_as_ReducerOptions()) {
        params->keep_dims = schema_params->keep_dims();
      }
      *builtin_data = reinterpret_cast<void*>(params);
      break;
    }
    case BuiltinOperator_SPLIT: {
      auto* params = MallocPOD<TfLiteSplitParams>();
      if (auto* schema_params = op->builtin_options_as_SplitOptions()) {
        params->num_splits = schema_params->num_splits();
      }
      *builtin_data = reinterpret_cast<void*>(params);
      break;
    }
    case BuiltinOperator_SQUEEZE: {
      auto* params = MallocPOD<TfLiteSqueezeParams>();
      if (auto* schema_params = op->builtin_options_as_SqueezeOptions()) {
        const auto& squeeze_dims = schema_params->squeeze_dims();
        FlatBufferIntVectorToArray(sizeof(params->squeeze_dims), squeeze_dims,
                                   params->squeeze_dims, error_reporter);
        params->num_squeeze_dims = squeeze_dims->Length();
      }
      *builtin_data = reinterpret_cast<void*>(params);
      break;
    }
    case BuiltinOperator_STRIDED_SLICE: {
      auto* params = MallocPOD<TfLiteStridedSliceParams>();
      if (auto* schema_params = op->builtin_options_as_StridedSliceOptions()) {
        params->begin_mask = schema_params->begin_mask();
        params->end_mask = schema_params->end_mask();
        params->ellipsis_mask = schema_params->ellipsis_mask();
        params->new_axis_mask = schema_params->new_axis_mask();
        params->shrink_axis_mask = schema_params->shrink_axis_mask();
      }
      *builtin_data = reinterpret_cast<void*>(params);
      break;
    }
    case BuiltinOperator_ARG_MAX: {
      auto* params = MallocPOD<TfLiteArgMaxParams>();
      if (auto* schema_params = op->builtin_options_as_ArgMaxOptions()) {
        ConvertTensorType(schema_params->output_type(), &params->output_type,
                          error_reporter);
      }
      *builtin_data = reinterpret_cast<void*>(params);
      break;
    }
    case BuiltinOperator_TRANSPOSE_CONV: {
      TfLiteTransposeConvParams* params =
          MallocPOD<TfLiteTransposeConvParams>();
      if (auto* transpose_conv_params =
              op->builtin_options_as_TransposeConvOptions()) {
        params->padding = parse_padding(transpose_conv_params->padding());
        params->stride_width = transpose_conv_params->stride_w();
        params->stride_height = transpose_conv_params->stride_h();
      }
      *builtin_data = reinterpret_cast<void*>(params);
      break;
    }
    case BuiltinOperator_SPARSE_TO_DENSE: {
      TfLiteSparseToDenseParams* params =
          MallocPOD<TfLiteSparseToDenseParams>();
      if (auto* sparse_to_dense_params =
              op->builtin_options_as_SparseToDenseOptions()) {
        params->validate_indices = sparse_to_dense_params->validate_indices();
      }
      *builtin_data = reinterpret_cast<void*>(params);
      break;
    }
    case BuiltinOperator_SHAPE: {
      auto* params = MallocPOD<TfLiteShapeParams>();
      if (auto* schema_params = op->builtin_options_as_ShapeOptions()) {
        ConvertTensorType(schema_params->out_type(), &params->out_type,
                          error_reporter);
      }
      *builtin_data = static_cast<void*>(params);
      break;
    }
    case BuiltinOperator_DELEGATE: {
      // TODO(ycling): Revisit when supporting saving delegated models.
      error_reporter->Report("DELEGATE op shouldn't exist in model.");
      return kTfLiteError;
    }

    // Below are the ops with no builtin_data strcture.
    case BuiltinOperator_BATCH_TO_SPACE_ND:
    // TODO(aselle): Implement call in BuiltinOptions, but nullptrs are
    // ok for now, since there is no call implementation either.
    case BuiltinOperator_CALL:
    case BuiltinOperator_CONCAT_EMBEDDINGS:
    case BuiltinOperator_CUSTOM:
    case BuiltinOperator_DEQUANTIZE:
    case BuiltinOperator_EMBEDDING_LOOKUP:
    case BuiltinOperator_EQUAL:
    case BuiltinOperator_EXP:
    case BuiltinOperator_EXPAND_DIMS:
    case BuiltinOperator_FLOOR:
    case BuiltinOperator_GREATER:
    case BuiltinOperator_GREATER_EQUAL:
    case BuiltinOperator_LESS:
    case BuiltinOperator_LESS_EQUAL:
    case BuiltinOperator_LOG:
    case BuiltinOperator_LOGISTIC:
    case BuiltinOperator_LOG_SOFTMAX:
    case BuiltinOperator_MAXIMUM:
    case BuiltinOperator_MINIMUM:
    case BuiltinOperator_NEG:
    case BuiltinOperator_NOT_EQUAL:
    case BuiltinOperator_PAD:
    case BuiltinOperator_PADV2:
    case BuiltinOperator_PRELU:
    case BuiltinOperator_RELU:
    case BuiltinOperator_RELU6:
    case BuiltinOperator_RELU_N1_TO_1:
    case BuiltinOperator_RSQRT:
    case BuiltinOperator_SELECT:
    case BuiltinOperator_SIN:
    case BuiltinOperator_SLICE:
    case BuiltinOperator_SPACE_TO_BATCH_ND:
    case BuiltinOperator_SQRT:
    case BuiltinOperator_TANH:
    case BuiltinOperator_TILE:
    case BuiltinOperator_TOPK_V2:
    case BuiltinOperator_TRANSPOSE:
    case BuiltinOperator_POW:
      break;
  }
  return kTfLiteOk;
}

}  // namespace

TfLiteStatus InterpreterBuilder::ParseNodes(
    const flatbuffers::Vector<flatbuffers::Offset<Operator>>* operators,
    Interpreter* interpreter) {
  TfLiteStatus status = kTfLiteOk;
  for (int i = 0; i < operators->Length(); ++i) {
    const auto* op = operators->Get(i);
    int index = op->opcode_index();
    if (index < 0 || index >= flatbuffer_op_index_to_registration_.size()) {
      error_reporter_->Report("Missing registration for opcode_index %d\n",
                              index);
      status = kTfLiteError;
      continue;
    }

    const TfLiteRegistration* registration =
        flatbuffer_op_index_to_registration_[index];
    if (registration == nullptr) {
      error_reporter_->Report("Skipping op for opcode_index %d\n", index);
      status = kTfLiteError;
      continue;
    }

    BuiltinOperator op_type =
        static_cast<BuiltinOperator>(registration->builtin_code);

    if (op_type != BuiltinOperator_CUSTOM && op->custom_options()) {
      error_reporter_->Report(
          "Found builtin operator %s with custom options.\n",
          EnumNameBuiltinOperator(op_type));
    }

    if (op->custom_options()) {
      interpreter->AddNodeWithParameters(
          FlatBufferIntArrayToVector(op->inputs()),
          FlatBufferIntArrayToVector(op->outputs()),
          reinterpret_cast<const char*>(op->custom_options()->data()),
          op->custom_options()->size(), nullptr, registration);
    } else {
      void* builtin_data = nullptr;
      TF_LITE_ENSURE_STATUS(
          ParseOpData(op, op_type, error_reporter_, &builtin_data));
      interpreter->AddNodeWithParameters(
          FlatBufferIntArrayToVector(op->inputs()),
          FlatBufferIntArrayToVector(op->outputs()), nullptr, 0, builtin_data,
          registration);
    }
  }

  return status;
}

TfLiteStatus InterpreterBuilder::ParseTensors(
    const flatbuffers::Vector<flatbuffers::Offset<Buffer>>* buffers,
    const flatbuffers::Vector<flatbuffers::Offset<Tensor>>* tensors,
    Interpreter* interpreter) {
  TfLiteStatus status = kTfLiteOk;

  // A little helper to get the names of inputs and outputs. Note that they
  // must outlive the interpreter.
  auto get_name = [](const tflite::Tensor* t) -> const char* {
    auto name = t->name();
    if (name) return name->c_str();
    return kEmptyTensorName;
  };

  for (int i = 0; i < tensors->Length(); ++i) {
    const auto* tensor = tensors->Get(i);
    std::vector<int> dims = FlatBufferIntArrayToVector(tensor->shape());

    TfLiteQuantizationParams quantization;
    quantization.scale = 0;
    quantization.zero_point = 0;
    auto* q_params = tensor->quantization();
    if (q_params) {
      // Note that the schema could hold per-channel quantization parameters
      // but we really only support one value for the whole tensor.
      // TODO(aselle): This breaks as well if these are nullptr's.
      // TODO(aselle): This assumes non per-channel quantization.

      if (q_params->scale()) {
        if (q_params->scale()->size() != 1) {
          error_reporter_->Report(
              "QuantizationParam has %d scale values (only 1 is supported).",
              q_params->scale()->size());
          return kTfLiteError;
        }
        quantization.scale = q_params->scale()->Get(0);
      }

      if (q_params->zero_point()) {
        if (q_params->zero_point()->size() != 1) {
          error_reporter_->Report(
              "QuantizationParam has %d zero_point values"
              " (only 1 is supported).",
              q_params->zero_point()->size());
          return kTfLiteError;
        }
        quantization.zero_point = q_params->zero_point()->Get(0);
      }
    }

    TfLiteType type;
    if (ConvertTensorType(tensor->type(), &type, error_reporter_) !=
        kTfLiteOk) {
      status = kTfLiteError;
      continue;
    }
    auto get_readonly_data = [&](const char** buffer_data,
                                 size_t* buffer_size) {
      // TODO(aselle): Check what happens if we have an unspecified size
      // constant.
      *buffer_data = nullptr;
      if (tensor->buffer() == 0) return kTfLiteOk;
      if (tensor->buffer() >= buffers->size()) {
        error_reporter_->Report(
            "Tensor %d specifies out of range buffer %d (only %d buffers).\n",
            i, tensor->buffer(), buffers->size());
        return kTfLiteError;
      }
      if (auto* buffer = (*buffers)[tensor->buffer()]) {
        if (auto* array = buffer->data()) {
          if (size_t size = array->size()) {
            *buffer_size = size;
            *buffer_data = reinterpret_cast<const char*>(array->data());
            return kTfLiteOk;
          }
        }
      }
      return kTfLiteOk;
    };
    size_t buffer_size = 0;
    const char* buffer_ptr;
    TF_LITE_ENSURE_STATUS(get_readonly_data(&buffer_ptr, &buffer_size));

    bool is_variable = tensor->is_variable();
    if (buffer_ptr) {
      if (is_variable) {
        error_reporter_->Report(
            "Tensor %d is a variable tensor with buffer. "
            "It's not supported now.\n",
            i);
        status = kTfLiteError;
      }

      if (interpreter->SetTensorParametersReadOnly(
              i, type, get_name(tensor), dims, quantization, buffer_ptr,
              buffer_size, allocation_) != kTfLiteOk) {
        error_reporter_->Report("Tensor %d is invalidly specified in schema.\n",
                                i);
        status = kTfLiteError;
      }
    } else {
      if (interpreter->SetTensorParametersReadWrite(i, type, get_name(tensor),
                                                    dims, quantization,
                                                    is_variable) != kTfLiteOk) {
        error_reporter_->Report("Tensor %d is invalidly specified in schema.\n",
                                i);
        status = kTfLiteError;
      }
    }
  }

  return status;
}

TfLiteStatus InterpreterBuilder::operator()(
    std::unique_ptr<Interpreter>* interpreter) {
  return operator()(interpreter, /*num_threads=*/-1);
}

TfLiteStatus InterpreterBuilder::operator()(
    std::unique_ptr<Interpreter>* interpreter, int num_threads) {
  if (!interpreter) {
    error_reporter_->Report(
        "Null output pointer passed to InterpreterBuilder.");
    return kTfLiteError;
  }

  // Safe exit by deleting partially created interpreter, to reduce verbosity
  // on error conditions. Use by return cleanup_on_error();
  auto cleanup_and_error = [&interpreter]() {
    interpreter->reset();
    return kTfLiteError;
  };

  if (!model_) {
    error_reporter_->Report("Null pointer passed in as model.");
    return cleanup_and_error();
  }

  if (model_->version() != TFLITE_SCHEMA_VERSION) {
    error_reporter_->Report(
        "Model provided is schema version %d not equal "
        "to supported version %d.\n",
        model_->version(), TFLITE_SCHEMA_VERSION);
    return cleanup_and_error();
  }

  if (BuildLocalIndexToRegistrationMapping() != kTfLiteOk) {
    error_reporter_->Report("Registration failed.\n");
    return cleanup_and_error();
  }

  // Flatbuffer model schemas define a list of opcodes independent of the graph.
  // We first map those to registrations. This reduces string lookups for custom
  // ops since we only do it once per custom op rather than once per custom op
  // invocation in the model graph.
  // Construct interpreter with correct number of tensors and operators.
  auto* subgraphs = model_->subgraphs();
  auto* buffers = model_->buffers();
  if (subgraphs->size() != 1) {
    error_reporter_->Report("Only 1 subgraph is currently supported.\n");
    return cleanup_and_error();
  }
  const tflite::SubGraph* subgraph = (*subgraphs)[0];
  auto operators = subgraph->operators();
  auto tensors = subgraph->tensors();
  if (!operators || !tensors || !buffers) {
    error_reporter_->Report(
        "Did not get operators, tensors, or buffers in input flat buffer.\n");
    return cleanup_and_error();
  }
  interpreter->reset(new Interpreter(error_reporter_));
  if ((**interpreter).AddTensors(tensors->Length()) != kTfLiteOk) {
    return cleanup_and_error();
  }
  // Set num threads
  (**interpreter).SetNumThreads(num_threads);
  // Parse inputs/outputs
  (**interpreter).SetInputs(FlatBufferIntArrayToVector(subgraph->inputs()));
  (**interpreter).SetOutputs(FlatBufferIntArrayToVector(subgraph->outputs()));

  // Finally setup nodes and tensors
  if (ParseNodes(operators, interpreter->get()) != kTfLiteOk)
    return cleanup_and_error();
  if (ParseTensors(buffers, tensors, interpreter->get()) != kTfLiteOk)
    return cleanup_and_error();

  std::vector<int> variables;
  for (int i = 0; i < (*interpreter)->tensors_size(); ++i) {
    auto* tensor = (*interpreter)->tensor(i);
    if (tensor->is_variable) {
      variables.push_back(i);
    }
  }
  (**interpreter).SetVariables(variables);

  return kTfLiteOk;
}

}  // namespace tflite