tensorflow/lite/micro/kernels/add_common.cc

/* Copyright 2021 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 "tensorflow/lite/c/builtin_op_data.h"
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/kernels/internal/quantization_util.h"
#include "tensorflow/lite/kernels/internal/reference/add.h"
#include "tensorflow/lite/kernels/internal/reference/integer_ops/add.h"
#include "tensorflow/lite/kernels/internal/reference/process_broadcast_shapes.h"
#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
#include "tensorflow/lite/kernels/kernel_util.h"
#include "tensorflow/lite/kernels/op_macros.h"
#include "tensorflow/lite/micro/kernels/add.h"
#include "tensorflow/lite/micro/kernels/kernel_util.h"
#include "tensorflow/lite/micro/memory_helpers.h"

namespace tflite {

const int kAddInputTensor1 = 0;
const int kAddInputTensor2 = 1;
const int kAddOutputTensor = 0;

TfLiteStatus CalculateOpDataAdd(TfLiteContext* context, TfLiteAddParams* params,
                                const TfLiteTensor* input1,
                                const TfLiteTensor* input2,
                                TfLiteTensor* output, OpDataAdd* data) {
  data->requires_broadcast = !HaveSameShapes(input1, input2);

  if (output->type == kTfLiteInt8 || output->type == kTfLiteInt16) {
    TFLITE_CHECK_NE(output->quantization.type, kTfLiteNoQuantization);

    // 8bit -> 8bit general quantized path, with general rescalings
    data->input1_offset = -input1->params.zero_point;
    data->input2_offset = -input2->params.zero_point;
    data->output_offset = output->params.zero_point;
    data->left_shift = (output->type == kTfLiteInt16) ? 15 : 20;
    const double twice_max_input_scale =
        2 * static_cast<double>(
                std::max(input1->params.scale, input2->params.scale));
    const double real_input1_multiplier =
        static_cast<double>(input1->params.scale) / twice_max_input_scale;
    const double real_input2_multiplier =
        static_cast<double>(input2->params.scale) / twice_max_input_scale;
    const double real_output_multiplier =
        twice_max_input_scale /
        ((1 << data->left_shift) * static_cast<double>(output->params.scale));

    QuantizeMultiplierSmallerThanOneExp(
        real_input1_multiplier, &data->input1_multiplier, &data->input1_shift);

    QuantizeMultiplierSmallerThanOneExp(
        real_input2_multiplier, &data->input2_multiplier, &data->input2_shift);

    QuantizeMultiplierSmallerThanOneExp(
        real_output_multiplier, &data->output_multiplier, &data->output_shift);

    TF_LITE_ENSURE_STATUS(CalculateActivationRangeQuantized(
        context, params->activation, output, &data->output_activation_min,
        &data->output_activation_max));
  } else if (output->type == kTfLiteFloat32) {
    CalculateActivationRange(params->activation,
                             &data->output_activation_min_f32,
                             &data->output_activation_max_f32);
  }

  return kTfLiteOk;
}

TfLiteStatus AddPrepare(TfLiteContext* context, TfLiteNode* node) {
  TFLITE_DCHECK(node->user_data != nullptr);
  TFLITE_DCHECK(node->builtin_data != nullptr);

  MicroContext* micro_context = GetMicroContext(context);
  TfLiteTensor* input1 =
      micro_context->AllocateTempInputTensor(node, kAddInputTensor1);
  TF_LITE_ENSURE(context, input1 != nullptr);
  TfLiteTensor* input2 =
      micro_context->AllocateTempInputTensor(node, kAddInputTensor2);
  TF_LITE_ENSURE(context, input2 != nullptr);
  TfLiteTensor* output =
      micro_context->AllocateTempOutputTensor(node, kAddOutputTensor);
  TF_LITE_ENSURE(context, output != nullptr);

  OpDataAdd* data = static_cast<OpDataAdd*>(node->user_data);
  auto* params = reinterpret_cast<TfLiteAddParams*>(node->builtin_data);

  TF_LITE_ENSURE_STATUS(
      CalculateOpDataAdd(context, params, input1, input2, output, data));

  if (output->type == kTfLiteInt32) {
    // Only support int32 unquantized add for now.
    TF_LITE_ENSURE_EQ(context, input1->quantization.type,
                      kTfLiteNoQuantization);
    TF_LITE_ENSURE_EQ(context, input2->quantization.type,
                      kTfLiteNoQuantization);
  }

  micro_context->DeallocateTempTfLiteTensor(input1);
  micro_context->DeallocateTempTfLiteTensor(input2);
  micro_context->DeallocateTempTfLiteTensor(output);
  return kTfLiteOk;
}

}  // namespace tflite