/* Copyright 2019 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_OPTIMIZED_INTEGER_OPS_ADD_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_OPTIMIZED_INTEGER_OPS_ADD_H_

#include "profiling/instrumentation.h"
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/optimized/cpu_check.h"
#include "tensorflow/lite/kernels/internal/types.h"

namespace tflite {
namespace optimized_integer_ops {

// Element-wise add that can often be used for inner loop of broadcast add as
// well as the non-broadcast add.
inline void AddElementwise(int size, const ArithmeticParams& params,
                           const int8* input1_data, const int8* input2_data,
                           int8* output_data) {
  gemmlowp::ScopedProfilingLabel label("AddElementwiseInt8/8bit");
  int i = 0;
  TFLITE_DCHECK_GT(params.input1_offset, -256);
  TFLITE_DCHECK_GT(params.input2_offset, -256);
  TFLITE_DCHECK_LT(params.input1_offset, 256);
  TFLITE_DCHECK_LT(params.input2_offset, 256);
#ifdef USE_NEON
  const int8x8_t output_activation_min_vector =
      vdup_n_s8(params.quantized_activation_min);
  const int8x8_t output_activation_max_vector =
      vdup_n_s8(params.quantized_activation_max);
  for (; i <= size - 8; i += 8) {
    const int8x8_t input1_val_original = vld1_s8(input1_data + i);
    const int8x8_t input2_val_original = vld1_s8(input2_data + i);
    const int16x8_t input1_val_s16 = vmovl_s8(input1_val_original);
    const int16x8_t input2_val_s16 = vmovl_s8(input2_val_original);
    const int16x8_t input1_val =
        vaddq_s16(input1_val_s16, vdupq_n_s16(params.input1_offset));
    const int16x8_t input2_val =
        vaddq_s16(input2_val_s16, vdupq_n_s16(params.input2_offset));
    const int16x4_t input1_val_high = vget_high_s16(input1_val);
    const int16x4_t input1_val_low = vget_low_s16(input1_val);
    const int16x4_t input2_val_high = vget_high_s16(input2_val);
    const int16x4_t input2_val_low = vget_low_s16(input2_val);
    int32x4_t x11 = vmovl_s16(input1_val_low);
    int32x4_t x12 = vmovl_s16(input1_val_high);
    int32x4_t x21 = vmovl_s16(input2_val_low);
    int32x4_t x22 = vmovl_s16(input2_val_high);
    const int32x4_t left_shift_dup = vdupq_n_s32(params.left_shift);
    x11 = vshlq_s32(x11, left_shift_dup);
    x12 = vshlq_s32(x12, left_shift_dup);
    x21 = vshlq_s32(x21, left_shift_dup);
    x22 = vshlq_s32(x22, left_shift_dup);
    x11 = vqrdmulhq_n_s32(x11, params.input1_multiplier);
    x12 = vqrdmulhq_n_s32(x12, params.input1_multiplier);
    x21 = vqrdmulhq_n_s32(x21, params.input2_multiplier);
    x22 = vqrdmulhq_n_s32(x22, params.input2_multiplier);
    const int32x4_t input1_shift_dup = vdupq_n_s32(params.input1_shift);
    const int32x4_t input2_shift_dup = vdupq_n_s32(params.input2_shift);
    x11 = vshlq_s32(x11, input1_shift_dup);
    x12 = vshlq_s32(x12, input1_shift_dup);
    x21 = vshlq_s32(x21, input2_shift_dup);
    x22 = vshlq_s32(x22, input2_shift_dup);
    int32x4_t s1 = vaddq_s32(x11, x21);
    int32x4_t s2 = vaddq_s32(x12, x22);
    s1 = vqrdmulhq_n_s32(s1, params.output_multiplier);
    s2 = vqrdmulhq_n_s32(s2, params.output_multiplier);
    using gemmlowp::RoundingDivideByPOT;
    s1 = RoundingDivideByPOT(s1, -params.output_shift);
    s2 = RoundingDivideByPOT(s2, -params.output_shift);
    const int16x4_t s1_narrowed = vmovn_s32(s1);
    const int16x4_t s2_narrowed = vmovn_s32(s2);
    const int16x8_t s = vaddq_s16(vcombine_s16(s1_narrowed, s2_narrowed),
                                  vdupq_n_s16(params.output_offset));
    const int8x8_t clamped =
        vmax_s8(output_activation_min_vector,
                vmin_s8(output_activation_max_vector, vqmovn_s16(s)));
    vst1_s8(output_data + i, clamped);
  }
#endif  // NEON

  for (; i < size; ++i) {
    const int32 input1_val = params.input1_offset + input1_data[i];
    const int32 input2_val = params.input2_offset + input2_data[i];
    const int32 shifted_input1_val = input1_val * (1 << params.left_shift);
    const int32 shifted_input2_val = input2_val * (1 << params.left_shift);
    const int32 scaled_input1_val =
        MultiplyByQuantizedMultiplierSmallerThanOneExp(
            shifted_input1_val, params.input1_multiplier, params.input1_shift);
    const int32 scaled_input2_val =
        MultiplyByQuantizedMultiplierSmallerThanOneExp(
            shifted_input2_val, params.input2_multiplier, params.input2_shift);
    const int32 raw_sum = scaled_input1_val + scaled_input2_val;
    const int32 raw_output =
        MultiplyByQuantizedMultiplierSmallerThanOneExp(
            raw_sum, params.output_multiplier, params.output_shift) +
        params.output_offset;
    const int32 clamped_output =
        std::min(params.quantized_activation_max,
                 std::max(params.quantized_activation_min, raw_output));
    output_data[i] = static_cast<int8>(clamped_output);
  }
}

// Scalar-broadcast add that can be used for inner loop of more general
// broadcast add, so that, for example, scalar-broadcast with batch will still
// be fast.
inline void AddScalarBroadcast(int size, const ArithmeticParams& params,
                               int8 input1_data, const int8* input2_data,
                               int8* output_data) {
  using gemmlowp::RoundingDivideByPOT;

  gemmlowp::ScopedProfilingLabel label("AddScalarBroadcastInt8/8bit");
  TFLITE_DCHECK_GT(params.input1_offset, -256);
  TFLITE_DCHECK_GT(params.input2_offset, -256);
  TFLITE_DCHECK_LT(params.input1_offset, 256);
  TFLITE_DCHECK_LT(params.input2_offset, 256);

  int i = 0;

#ifdef USE_NEON
  const int32x4_t left_shift_dup = vdupq_n_s32(params.left_shift);
  const int8x8_t output_activation_min_vector =
      vdup_n_s8(params.quantized_activation_min);
  const int8x8_t output_activation_max_vector =
      vdup_n_s8(params.quantized_activation_max);

  // Process broadcast scalar.
  const int8x8_t input1_val_original = vdup_n_s8(input1_data);
  const int16x8_t input1_val_s16 = vmovl_s8(input1_val_original);
  const int16x8_t input1_val =
      vaddq_s16(input1_val_s16, vdupq_n_s16(params.input1_offset));
  const int16x4_t input1_val_high = vget_high_s16(input1_val);
  const int16x4_t input1_val_low = vget_low_s16(input1_val);
  int32x4_t x11 = vmovl_s16(input1_val_low);
  int32x4_t x12 = vmovl_s16(input1_val_high);
  x11 = vshlq_s32(x11, left_shift_dup);
  x12 = vshlq_s32(x12, left_shift_dup);
  x11 = vqrdmulhq_n_s32(x11, params.input1_multiplier);
  x12 = vqrdmulhq_n_s32(x12, params.input1_multiplier);
  const int32x4_t input1_shift_dup = vdupq_n_s32(params.input1_shift);
  x11 = vshlq_s32(x11, input1_shift_dup);
  x12 = vshlq_s32(x12, input1_shift_dup);

  for (; i <= size - 8; i += 8) {
    const int8x8_t input2_val_original = vld1_s8(input2_data + i);
    const int16x8_t input2_val_s16 = vmovl_s8(input2_val_original);
    const int16x8_t input2_val =
        vaddq_s16(input2_val_s16, vdupq_n_s16(params.input2_offset));
    const int16x4_t input2_val_high = vget_high_s16(input2_val);
    const int16x4_t input2_val_low = vget_low_s16(input2_val);
    int32x4_t x21 = vmovl_s16(input2_val_low);
    int32x4_t x22 = vmovl_s16(input2_val_high);
    x21 = vshlq_s32(x21, left_shift_dup);
    x22 = vshlq_s32(x22, left_shift_dup);
    x21 = vqrdmulhq_n_s32(x21, params.input2_multiplier);
    x22 = vqrdmulhq_n_s32(x22, params.input2_multiplier);
    const int32x4_t input2_shift_dup = vdupq_n_s32(params.input2_shift);
    x21 = vshlq_s32(x21, input2_shift_dup);
    x22 = vshlq_s32(x22, input2_shift_dup);
    int32x4_t s1 = vaddq_s32(x11, x21);
    int32x4_t s2 = vaddq_s32(x12, x22);
    s1 = vqrdmulhq_n_s32(s1, params.output_multiplier);
    s2 = vqrdmulhq_n_s32(s2, params.output_multiplier);
    s1 = RoundingDivideByPOT(s1, -params.output_shift);
    s2 = RoundingDivideByPOT(s2, -params.output_shift);
    const int16x4_t s1_narrowed = vmovn_s32(s1);
    const int16x4_t s2_narrowed = vmovn_s32(s2);
    const int16x8_t s = vaddq_s16(vcombine_s16(s1_narrowed, s2_narrowed),
                                  vdupq_n_s16(params.output_offset));
    const int8x8_t clamped =
        vmax_s8(output_activation_min_vector,
                vmin_s8(output_activation_max_vector, vqmovn_s16(s)));
    vst1_s8(output_data + i, clamped);
  }
#endif  // NEON

  if (i < size) {
    // Process broadcast scalar.
    const int32 input1_val = params.input1_offset + input1_data;
    const int32 shifted_input1_val = input1_val * (1 << params.left_shift);
    const int32 scaled_input1_val =
        MultiplyByQuantizedMultiplierSmallerThanOneExp(
            shifted_input1_val, params.input1_multiplier, params.input1_shift);

    for (; i < size; ++i) {
      const int32 input2_val = params.input2_offset + input2_data[i];
      const int32 shifted_input2_val = input2_val * (1 << params.left_shift);
      const int32 scaled_input2_val =
          MultiplyByQuantizedMultiplierSmallerThanOneExp(
              shifted_input2_val, params.input2_multiplier,
              params.input2_shift);
      const int32 raw_sum = scaled_input1_val + scaled_input2_val;
      const int32 raw_output =
          MultiplyByQuantizedMultiplierSmallerThanOneExp(
              raw_sum, params.output_multiplier, params.output_shift) +
          params.output_offset;
      const int32 clamped_output =
          std::min(params.quantized_activation_max,
                   std::max(params.quantized_activation_min, raw_output));
      output_data[i] = static_cast<int8>(clamped_output);
    }
  }
}

inline void Add(const ArithmeticParams& params,
                const RuntimeShape& input1_shape, const int8* input1_data,
                const RuntimeShape& input2_shape, const int8* input2_data,
                const RuntimeShape& output_shape, int8* output_data) {
  TFLITE_DCHECK_LE(params.quantized_activation_min,
                   params.quantized_activation_max);
  gemmlowp::ScopedProfilingLabel label("AddInt8/8bit");
  const int flat_size =
      MatchingFlatSize(input1_shape, input2_shape, output_shape);

  TFLITE_DCHECK_GT(params.input1_offset, -256);
  TFLITE_DCHECK_GT(params.input2_offset, -256);
  TFLITE_DCHECK_LT(params.input1_offset, 256);
  TFLITE_DCHECK_LT(params.input2_offset, 256);
  AddElementwise(flat_size, params, input1_data, input2_data, output_data);
}

inline void BroadcastAddFivefold(const ArithmeticParams& unswitched_params,
                                 const RuntimeShape& unswitched_input1_shape,
                                 const int8* unswitched_input1_data,
                                 const RuntimeShape& unswitched_input2_shape,
                                 const int8* unswitched_input2_data,
                                 const RuntimeShape& output_shape,
                                 int8* output_data) {
  gemmlowp::ScopedProfilingLabel label("BroadcastAddFivefoldInt8/8bit");

  ArithmeticParams switched_params = unswitched_params;
  switched_params.input1_offset = unswitched_params.input2_offset;
  switched_params.input1_multiplier = unswitched_params.input2_multiplier;
  switched_params.input1_shift = unswitched_params.input2_shift;
  switched_params.input2_offset = unswitched_params.input1_offset;
  switched_params.input2_multiplier = unswitched_params.input1_multiplier;
  switched_params.input2_shift = unswitched_params.input1_shift;

  const bool use_unswitched =
      unswitched_params.broadcast_category ==
      tflite::BroadcastableOpCategory::kFirstInputBroadcastsFast;

  const ArithmeticParams& params =
      use_unswitched ? unswitched_params : switched_params;
  const int8* input1_data =
      use_unswitched ? unswitched_input1_data : unswitched_input2_data;
  const int8* input2_data =
      use_unswitched ? unswitched_input2_data : unswitched_input1_data;

  // Fivefold nested loops. The second input resets its position for each
  // iteration of the second loop. The first input resets its position at the
  // beginning of the fourth loop. The innermost loop is an elementwise add of
  // sections of the arrays.
  int8* output_data_ptr = output_data;
  const int8* input1_data_ptr = input1_data;
  const int8* input2_data_reset = input2_data;
  // In the fivefold pattern, y0, y2 and y4 are not broadcast, and so shared
  // between input shapes. y3 for input 1 is always broadcast, and so the
  // dimension there is 1, whereas optionally y1 might be broadcast for input 2.
  // Put another way,
  // input1.shape.FlatSize = y0 * y1 * y2 * y4,
  // input2.shape.FlatSize = y0 * y2 * y3 * y4.
  int y0 = params.broadcast_shape[0];
  int y1 = params.broadcast_shape[1];
  int y2 = params.broadcast_shape[2];
  int y3 = params.broadcast_shape[3];
  int y4 = params.broadcast_shape[4];
  if (y4 > 1) {
    // General fivefold pattern, with y4 > 1 so there is a non-broadcast inner
    // dimension.
    for (int i0 = 0; i0 < y0; ++i0) {
      const int8* input2_data_ptr = nullptr;
      for (int i1 = 0; i1 < y1; ++i1) {
        input2_data_ptr = input2_data_reset;
        for (int i2 = 0; i2 < y2; ++i2) {
          for (int i3 = 0; i3 < y3; ++i3) {
            AddElementwise(y4, params, input1_data_ptr, input2_data_ptr,
                           output_data_ptr);
            input2_data_ptr += y4;
            output_data_ptr += y4;
          }
          // We have broadcast y4 of input1 data y3 times, and now move on.
          input1_data_ptr += y4;
        }
      }
      // We have broadcast y2*y3*y4 of input2 data y1 times, and now move on.
      input2_data_reset = input2_data_ptr;
    }
  } else {
    // Special case of y4 == 1, in which the innermost loop is a single element
    // and can be combined with the next (y3) as an inner broadcast.
    //
    // Note that this handles the case of pure scalar broadcast when
    // y0 == y1 == y2 == 1. With low overhead it handles cases such as scalar
    // broadcast with batch (as y2 > 1).
    //
    // NOTE The process is the same as the above general case except simplified
    // for y4 == 1 and the loop over y3 is contained within the
    // AddScalarBroadcast function.
    for (int i0 = 0; i0 < y0; ++i0) {
      const int8* input2_data_ptr = nullptr;
      for (int i1 = 0; i1 < y1; ++i1) {
        input2_data_ptr = input2_data_reset;
        for (int i2 = 0; i2 < y2; ++i2) {
          AddScalarBroadcast(y3, params, *input1_data_ptr, input2_data_ptr,
                             output_data_ptr);
          input2_data_ptr += y3;
          output_data_ptr += y3;
          input1_data_ptr += 1;
        }
      }
      input2_data_reset = input2_data_ptr;
    }
  }
}

}  // namespace optimized_integer_ops
}  // namespace tflite

#endif  // TENSORFLOW_LITE_KERNELS_INTERNAL_OPTIMIZED_INTEGER_OPS_ADD_H_
