add.cc

/* Copyright 2022 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/kernels/internal/reference/add.h"

#include "Include/arm_nnfunctions.h"
#include "tensorflow/lite/c/builtin_op_data.h"
#include "tensorflow/lite/kernels/internal/quantization_util.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/kernel_util.h"
#include "tensorflow/lite/micro/memory_helpers.h"
#include "tensorflow/lite/micro/micro_log.h"

namespace tflite {

namespace {
constexpr int kInputTensor1 = 0;
constexpr int kInputTensor2 = 1;
constexpr int kOutputTensor = 0;

struct OpData {
  bool requires_broadcast;

  // These fields are used in both the general 8-bit -> 8bit quantized path,
  // and the special 16-bit -> 16bit quantized path
  int input1_shift;
  int input2_shift;
  int32_t output_activation_min;
  int32_t output_activation_max;

  // These fields are used only in the general 8-bit -> 8bit quantized path
  int32_t input1_multiplier;
  int32_t input2_multiplier;
  int32_t output_multiplier;

  int output_shift;
  int left_shift;

  int32_t input1_offset;
  int32_t input2_offset;
  int32_t output_offset;

  // Used only for float evals:
  float output_activation_min_f32;
  float output_activation_max_f32;
};

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

  if (output->type == kTfLiteInt8 || output->type == kTfLiteInt16) {
    // 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;
}

void UpdateOpParams(tflite::ArithmeticParams* const op_params,
                    const OpData* data) {
  op_params->left_shift = data->left_shift;
  op_params->input1_offset = data->input1_offset;
  op_params->input1_multiplier = data->input1_multiplier;
  op_params->input1_shift = data->input1_shift;
  op_params->input2_offset = data->input2_offset;
  op_params->input2_multiplier = data->input2_multiplier;
  op_params->input2_shift = data->input2_shift;
  op_params->output_offset = data->output_offset;
  op_params->output_multiplier = data->output_multiplier;
  op_params->output_shift = data->output_shift;
  SetActivationParams(data->output_activation_min, data->output_activation_max,
                      op_params);
}

TfLiteStatus EvalAddQuantizedInt8(TfLiteContext* context, TfLiteNode* node,
                                  TfLiteAddParams* params, const OpData* data,
                                  const TfLiteEvalTensor* input1,
                                  const TfLiteEvalTensor* input2,
                                  TfLiteEvalTensor* output) {
  tflite::ArithmeticParams op_params;
  UpdateOpParams(&op_params, data);

  bool need_broadcast = reference_ops::ProcessBroadcastShapes(
      tflite::micro::GetTensorShape(input1),
      tflite::micro::GetTensorShape(input2), &op_params);

  if (need_broadcast) {
    reference_integer_ops::BroadcastAdd4DSlow(
        op_params, tflite::micro::GetTensorShape(input1),
        tflite::micro::GetTensorData<int8_t>(input1),
        tflite::micro::GetTensorShape(input2),
        tflite::micro::GetTensorData<int8_t>(input2),
        tflite::micro::GetTensorShape(output),
        tflite::micro::GetTensorData<int8_t>(output));
  } else {
    arm_elementwise_add_s8(
        tflite::micro::GetTensorData<int8_t>(input1),

        tflite::micro::GetTensorData<int8_t>(input2), op_params.input1_offset,
        op_params.input1_multiplier, op_params.input1_shift,
        op_params.input2_offset, op_params.input2_multiplier,
        op_params.input2_shift, op_params.left_shift,
        tflite::micro::GetTensorData<int8_t>(output), op_params.output_offset,
        op_params.output_multiplier, op_params.output_shift,
        op_params.quantized_activation_min, op_params.quantized_activation_max,
        MatchingElementsSize(tflite::micro::GetTensorShape(input1),
                             tflite::micro::GetTensorShape(input2),
                             tflite::micro::GetTensorShape(output)));
  }

  return kTfLiteOk;
}

TfLiteStatus EvalAddQuantizedInt16(TfLiteContext* context, TfLiteNode* node,
                                   TfLiteAddParams* params, const OpData* data,
                                   const TfLiteEvalTensor* input1,
                                   const TfLiteEvalTensor* input2,
                                   TfLiteEvalTensor* output) {
  tflite::ArithmeticParams op_params;
  UpdateOpParams(&op_params, data);

  bool need_broadcast = reference_ops::ProcessBroadcastShapes(
      tflite::micro::GetTensorShape(input1),
      tflite::micro::GetTensorShape(input2), &op_params);

  if (need_broadcast) {
    reference_ops::BroadcastAdd4DSlow(
        op_params, tflite::micro::GetTensorShape(input1),
        tflite::micro::GetTensorData<int16_t>(input1),
        tflite::micro::GetTensorShape(input2),
        tflite::micro::GetTensorData<int16_t>(input2),
        tflite::micro::GetTensorShape(output),
        tflite::micro::GetTensorData<int16_t>(output));
  } else {
    arm_elementwise_add_s16(
        tflite::micro::GetTensorData<int16_t>(input1),
        tflite::micro::GetTensorData<int16_t>(input2), op_params.input1_offset,
        op_params.input1_multiplier, op_params.input1_shift,
        op_params.input2_offset, op_params.input2_multiplier,
        op_params.input2_shift, op_params.left_shift,
        tflite::micro::GetTensorData<int16_t>(output), op_params.output_offset,
        op_params.output_multiplier, op_params.output_shift,
        op_params.quantized_activation_min, op_params.quantized_activation_max,
        MatchingElementsSize(tflite::micro::GetTensorShape(input1),
                             tflite::micro::GetTensorShape(input2),
                             tflite::micro::GetTensorShape(output)));
  }

  return kTfLiteOk;
}

TfLiteStatus EvalAdd(TfLiteContext* context, TfLiteNode* node,
                     TfLiteAddParams* params, const OpData* data,
                     const TfLiteEvalTensor* input1,
                     const TfLiteEvalTensor* input2, TfLiteEvalTensor* output) {
  switch (output->type) {
    case kTfLiteFloat32: {
      tflite::ArithmeticParams op_params;
      SetActivationParams(data->output_activation_min_f32,
                          data->output_activation_max_f32, &op_params);
      if (data->requires_broadcast) {
        reference_ops::BroadcastAdd4DSlow(
            op_params, tflite::micro::GetTensorShape(input1),
            tflite::micro::GetTensorData<float>(input1),
            tflite::micro::GetTensorShape(input2),
            tflite::micro::GetTensorData<float>(input2),
            tflite::micro::GetTensorShape(output),
            tflite::micro::GetTensorData<float>(output));
      } else {
        reference_ops::Add(op_params, tflite::micro::GetTensorShape(input1),
                           tflite::micro::GetTensorData<float>(input1),
                           tflite::micro::GetTensorShape(input2),
                           tflite::micro::GetTensorData<float>(input2),
                           tflite::micro::GetTensorShape(output),
                           tflite::micro::GetTensorData<float>(output));
      }
    } break;
    case kTfLiteInt32: {
      tflite::ArithmeticParams op_params;
      SetActivationParams(std::numeric_limits<int32_t>::lowest(),
                          std::numeric_limits<int32_t>::max(), &op_params);
      if (data->requires_broadcast) {
        reference_ops::BroadcastAdd4DSlow(
            op_params, tflite::micro::GetTensorShape(input1),
            tflite::micro::GetTensorData<int32_t>(input1),
            tflite::micro::GetTensorShape(input2),
            tflite::micro::GetTensorData<int32_t>(input2),
            tflite::micro::GetTensorShape(output),
            tflite::micro::GetTensorData<int32_t>(output));
      } else {
        reference_ops::Add(op_params, tflite::micro::GetTensorShape(input1),
                           tflite::micro::GetTensorData<int32_t>(input1),
                           tflite::micro::GetTensorShape(input2),
                           tflite::micro::GetTensorData<int32_t>(input2),
                           tflite::micro::GetTensorShape(output),
                           tflite::micro::GetTensorData<int32_t>(output));
      }
    } break;
    default:
      MicroPrintf("Type %s (%d) not supported.",
                  TfLiteTypeGetName(output->type), output->type);
      return kTfLiteError;
  }

  return kTfLiteOk;
}

TfLiteStatus EvalAddQuantized(TfLiteContext* context, TfLiteNode* node,
                              TfLiteAddParams* params, const OpData* data,
                              const TfLiteEvalTensor* input1,
                              const TfLiteEvalTensor* input2,
                              TfLiteEvalTensor* output) {
  switch (output->type) {
    case kTfLiteInt8: {
      EvalAddQuantizedInt8(context, node, params, data, input1, input2, output);
      break;
    }
    case kTfLiteInt16: {
      EvalAddQuantizedInt16(context, node, params, data, input1, input2,
                            output);
      break;
    }
    default:
      MicroPrintf("Type %s (%d) not supported.",
                  TfLiteTypeGetName(output->type), output->type);
      return kTfLiteError;
  }

  return kTfLiteOk;
}

}  // namespace

void* InitAdd(TfLiteContext* context, const char* buffer, size_t length) {
  TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
  return context->AllocatePersistentBuffer(context, sizeof(OpData));
}

TfLiteStatus PrepareAdd(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, kInputTensor1);
  TF_LITE_ENSURE(context, input1 != nullptr);
  TfLiteTensor* input2 =
      micro_context->AllocateTempInputTensor(node, kInputTensor2);
  TF_LITE_ENSURE(context, input2 != nullptr);
  TfLiteTensor* output =
      micro_context->AllocateTempOutputTensor(node, kOutputTensor);
  TF_LITE_ENSURE(context, output != nullptr);

  if (input1->type == kTfLiteInt16) {
    TF_LITE_ENSURE_EQ(context, input1->params.zero_point, 0);
    TF_LITE_ENSURE_EQ(context, input2->params.zero_point, 0);
    TF_LITE_ENSURE_EQ(context, output->params.zero_point, 0);
  }

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

  TF_LITE_ENSURE_STATUS(
      CalculateOpData(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;
}

TfLiteStatus EvalAdd(TfLiteContext* context, TfLiteNode* node) {
  auto* params = reinterpret_cast<TfLiteAddParams*>(node->builtin_data);

  const TfLiteEvalTensor* input1 =
      tflite::micro::GetEvalInput(context, node, kInputTensor1);
  const TfLiteEvalTensor* input2 =
      tflite::micro::GetEvalInput(context, node, kInputTensor2);
  TfLiteEvalTensor* output =
      tflite::micro::GetEvalOutput(context, node, kOutputTensor);

  TFLITE_DCHECK(node->user_data != nullptr);
  const OpData* data = static_cast<const OpData*>(node->user_data);

  if (output->type == kTfLiteFloat32 || output->type == kTfLiteInt32) {
    TF_LITE_ENSURE_OK(
        context, EvalAdd(context, node, params, data, input1, input2, output));
  } else if (output->type == kTfLiteInt8 || output->type == kTfLiteInt16) {
    TF_LITE_ENSURE_OK(context, EvalAddQuantized(context, node, params, data,
                                                input1, input2, output));
  } else {
    MicroPrintf("Type %s (%d) not supported.", TfLiteTypeGetName(output->type),
                output->type);
    return kTfLiteError;
  }

  return kTfLiteOk;
}

TfLiteStatus EvalAddInt8(TfLiteContext* context, TfLiteNode* node) {
  auto* params = reinterpret_cast<TfLiteAddParams*>(node->builtin_data);

  const TfLiteEvalTensor* input1 =
      tflite::micro::GetEvalInput(context, node, kInputTensor1);
  const TfLiteEvalTensor* input2 =
      tflite::micro::GetEvalInput(context, node, kInputTensor2);
  TfLiteEvalTensor* output =
      tflite::micro::GetEvalOutput(context, node, kOutputTensor);

  TFLITE_DCHECK(node->user_data != nullptr);
  TFLITE_DCHECK(output->type == kTfLiteInt8);
  const OpData* data = static_cast<const OpData*>(node->user_data);

  TF_LITE_ENSURE_OK(context, EvalAddQuantizedInt8(context, node, params, data,
                                                  input1, input2, output));

  return kTfLiteOk;
}

TfLiteStatus EvalAddInt16(TfLiteContext* context, TfLiteNode* node) {
  auto* params = reinterpret_cast<TfLiteAddParams*>(node->builtin_data);

  const TfLiteEvalTensor* input1 =
      tflite::micro::GetEvalInput(context, node, kInputTensor1);
  const TfLiteEvalTensor* input2 =
      tflite::micro::GetEvalInput(context, node, kInputTensor2);
  TfLiteEvalTensor* output =
      tflite::micro::GetEvalOutput(context, node, kOutputTensor);

  TFLITE_DCHECK(node->user_data != nullptr);
  TFLITE_DCHECK(output->type == kTfLiteInt16);
  const OpData* data = static_cast<const OpData*>(node->user_data);

  TF_LITE_ENSURE_OK(context, EvalAddQuantizedInt16(context, node, params, data,
                                                   input1, input2, output));

  return kTfLiteOk;
}

TFLMRegistration Register_ADD() {
  return tflite::micro::RegisterOp(InitAdd, PrepareAdd, EvalAdd);
}

TFLMRegistration Register_ADD_INT8() {
  return tflite::micro::RegisterOp(InitAdd, PrepareAdd, EvalAddInt8);
}

TFLMRegistration Register_ADD_INT16() {
  return tflite::micro::RegisterOp(InitAdd, PrepareAdd, EvalAddInt16);
}

}  // namespace tflite