/* 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 <string.h>

#include "tensorflow/contrib/lite/builtin_op_data.h"
#include "tensorflow/contrib/lite/kernels/internal/common.h"
#include "tensorflow/contrib/lite/kernels/activation_functor.h"
#include "tensorflow/contrib/lite/kernels/internal/optimized/tensor_utils_impl.h"

#ifdef USE_NEON

#define kFloatWeightsPerNeonLane 4

namespace tflite {
namespace tensor_utils {

void NeonMatrixBatchVectorMultiplyAccumulate(const float* matrix, int m_rows,
                                             int m_cols, const float* vector,
                                             int n_batch, float* result,
                                             int result_stride) {
  // If v_size is not divisible by kWeightsPerNeonLane, we cannot use the main
  // vectorized loop, and we need to process sequentially. postamble_start shows
  // the start index where this should happen.
  const int postamble_start =
      m_cols - (m_cols & (kFloatWeightsPerNeonLane - 1));

  // The arrays used to cache the vector.
  float32x4_t* vector_cache_float32x4 =
      new float32x4_t[(m_cols / kFloatWeightsPerNeonLane) *
                      sizeof(float32x4_t)];
  const int kUnrollSize = 2;
  for (int b = 0; b < n_batch; b++) {
    float* result_in_batch = result + b * m_rows * result_stride;
    const float* vector_in_batch = vector + b * m_cols;

    const float* matrix_ptr0 = matrix;
    // If there is only 1 row, we don't want to assign an illegal pointer.
    const float* matrix_ptr1 = nullptr;
    if (m_rows > 1) {
      matrix_ptr1 = matrix + m_cols;
    }

    // Cahce the vector.
    for (int c = 0; c < postamble_start; c += kFloatWeightsPerNeonLane) {
      vector_cache_float32x4[c >> 2] = vld1q_f32(vector_in_batch + c);
    }

    // Main matrix by vector multiplication loop, which handles two rows of
    // matrix by vector multiplication.
    for (int r = 0; r < (m_rows & ~(kUnrollSize - 1)); r += kUnrollSize) {
      float32x4_t acc0_32x4 = vmovq_n_f32(0.0);
      float32x4_t acc1_32x4 = vmovq_n_f32(0.0);
      for (int c = 0; c < postamble_start; c += kFloatWeightsPerNeonLane) {
        float32x4_t temp = vector_cache_float32x4[c >> 2];
        // Load 4 float values from vector1 and vector2 and accumulator.
        float32x4_t v0_f32x4 = vld1q_f32(matrix_ptr0 + c);
        float32x4_t v1_f32x4 = vld1q_f32(matrix_ptr1 + c);
        // Vector multiply-accumulate 4 float
        acc0_32x4 = vmlaq_f32(acc0_32x4, v0_f32x4, temp);
        acc1_32x4 = vmlaq_f32(acc1_32x4, v1_f32x4, temp);
      }
      // Add the 4 intermediate sum values to get the final dot-prod value for
      // this column.
      *result_in_batch +=
          (vgetq_lane_f32(acc0_32x4, 0) + vgetq_lane_f32(acc0_32x4, 1) +
           vgetq_lane_f32(acc0_32x4, 2) + vgetq_lane_f32(acc0_32x4, 3));
      *(result_in_batch + result_stride) +=
          (vgetq_lane_f32(acc1_32x4, 0) + vgetq_lane_f32(acc1_32x4, 1) +
           vgetq_lane_f32(acc1_32x4, 2) + vgetq_lane_f32(acc1_32x4, 3));
      for (int c = postamble_start; c < m_cols; c++) {
        *result_in_batch += matrix_ptr0[c] * vector_in_batch[c];
        *(result_in_batch + result_stride) +=
            matrix_ptr1[c] * vector_in_batch[c];
      }
      matrix_ptr0 += kUnrollSize * m_cols;
      matrix_ptr1 += kUnrollSize * m_cols;
      result_in_batch += kUnrollSize * result_stride;
    }
    for (int r = (m_rows & ~(kUnrollSize - 1)); r < m_rows; r++) {
      float32x4_t acc0_32x4 = vmovq_n_f32(0.0);
      for (int c = 0; c < postamble_start; c += kFloatWeightsPerNeonLane) {
        float32x4_t temp = vector_cache_float32x4[c >> 2];
        // Load 4 float values from vector1 and vector2 and accumulator.
        float32x4_t v0_f32x4 = vld1q_f32(matrix_ptr0 + c);
        // Vector multiply-accumulate 4 float
        acc0_32x4 = vmlaq_f32(acc0_32x4, v0_f32x4, temp);
      }
      // Add the 4 intermediate sum values to get the final dot-prod value for
      // this column.
      *result_in_batch +=
          (vgetq_lane_f32(acc0_32x4, 0) + vgetq_lane_f32(acc0_32x4, 1) +
           vgetq_lane_f32(acc0_32x4, 2) + vgetq_lane_f32(acc0_32x4, 3));
      for (int c = postamble_start; c < m_cols; c++) {
        *result_in_batch += matrix_ptr0[c] * vector_in_batch[c];
      }
      matrix_ptr0 += m_cols;
      result_in_batch += result_stride;
    }
  }
  delete[] vector_cache_float32x4;
}

void NeonVectorVectorCwiseProduct(const float* vector1, const float* vector2,
                                  int v_size, float* result) {
  // If v_size is not divisible by kWeightsPerNeonLane, we cannot use the main
  // vectorized loop, and we need to process sequentially. postamble_start shows
  // the start index where this should happen.
  const int postamble_start =
      v_size - (v_size & (kFloatWeightsPerNeonLane - 1));
  for (int v = 0; v < postamble_start; v += kFloatWeightsPerNeonLane) {
    // Load 4 float values from vector1 and vector2.
    float32x4_t v1_f32x4 = vld1q_f32(vector1 + v);
    float32x4_t v2_f32x4 = vld1q_f32(vector2 + v);
    // Vector multiply 4 float
    float32x4_t mul_32x4 = vmulq_f32(v1_f32x4, v2_f32x4);
    // Save to result array.
    vst1q_f32(&result[v], mul_32x4);
  }
  for (int v = postamble_start; v < v_size; v++) {
    result[v] = vector1[v] * vector2[v];
  }
}

void NeonVectorVectorCwiseProductAccumulate(const float* vector1,
                                            const float* vector2, int v_size,
                                            float* result) {
  // If v_size is not divisible by kWeightsPerNeonLane, we cannot use the main
  // vectorized loop, and we need to process sequentially. postamble_start shows
  // the start index where this should happen.
  const int postamble_start =
      v_size - (v_size & (kFloatWeightsPerNeonLane - 1));
  for (int v = 0; v < postamble_start; v += kFloatWeightsPerNeonLane) {
    // Load 4 float values from vector1 and vector2 and accumulator.
    float32x4_t v1_f32x4 = vld1q_f32(vector1 + v);
    float32x4_t v2_f32x4 = vld1q_f32(vector2 + v);
    float32x4_t acc_32x4 = vld1q_f32(result + v);
    // Vector multiply-accumulate 4 float
    acc_32x4 = vmlaq_f32(acc_32x4, v1_f32x4, v2_f32x4);
    // Save to result array.
    vst1q_f32(&result[v], acc_32x4);
  }
  for (int v = postamble_start; v < v_size; v++) {
    result[v] += vector1[v] * vector2[v];
  }
}

void NeonVectorBatchVectorCwiseProductAccumulate(const float* vector,
                                                 int v_size,
                                                 const float* batch_vector,
                                                 int n_batch, float* result) {
  // If v_size is not divisible by kWeightsPerNeonLane, we cannot use the main
  // vectorized loop, and we need to process sequentially. postamble_start shows
  // the start index where this should happen.
  const int postamble_start =
      v_size - (v_size & (kFloatWeightsPerNeonLane - 1));

  // The arrays used to cache the vector.
  float32x4_t* vector_cache_float32x4 =
      new float32x4_t[(v_size / kFloatWeightsPerNeonLane) *
                      sizeof(float32x4_t)];
  for (int v = 0; v < postamble_start; v += kFloatWeightsPerNeonLane) {
    vector_cache_float32x4[v >> 2] = vld1q_f32(vector + v);
  }

  float* result_ptr = result;
  const float* batch_vector_ptr = batch_vector;
  for (int b = 0; b < n_batch; b++) {
    for (int v = 0; v < postamble_start; v += kFloatWeightsPerNeonLane) {
      // Load from memory to vectors.
      float32x4_t result_f32x4 = vld1q_f32(result_ptr + v);
      float32x4_t batch_vector_f32x4 = vld1q_f32(batch_vector_ptr + v);
      // Multiply-accumulate.
      result_f32x4 = vmlaq_f32(result_f32x4, batch_vector_f32x4,
                               vector_cache_float32x4[v >> 2]);
      // Store.
      vst1q_f32(result_ptr + v, result_f32x4);
    }
    // Postamble loop
    for (int v = postamble_start; v < v_size; v++) {
      result_ptr[v] += vector[v] * batch_vector_ptr[v];
    }
    // Update the pointers.
    result_ptr += v_size;
    batch_vector_ptr += v_size;
  }
  delete[] vector_cache_float32x4;
}

void NeonSub1Vector(const float* vector, int v_size, float* result) {
  // If v_size is not divisible by kWeightsPerNeonLane, we cannot use the main
  // vectorized loop, and we need to process sequentially. postamble_start shows
  // the start index where this should happen.
  const int postamble_start =
      v_size - (v_size & (kFloatWeightsPerNeonLane - 1));

  float32x4_t one_f32x4 = vmovq_n_f32(1.0);
  for (int v = 0; v < postamble_start; v += kFloatWeightsPerNeonLane) {
    // Load 4 float values from the current pointers of the input column and
    // subtract from 1.
    float32x4_t v_f32x4 = vld1q_f32(vector + v);
    float32x4_t result_f32x4 = vsubq_f32(one_f32x4, v_f32x4);
    // Save to output.
    vst1q_f32(result + v, result_f32x4);
  }
  for (int v = postamble_start; v < v_size; v++) {
    result[v] = 1.0f - vector[v];
  }
}

void NeonClipVector(const float* vector, int v_size, float abs_limit,
                    float* result) {
  // If v_size is not divisible by kWeightsPerNeonLane, we cannot use the main
  // vectorized loop, and we need to process sequentially. postamble_start shows
  // the start index where this should happen.
  const int postamble_start =
      v_size - (v_size & (kFloatWeightsPerNeonLane - 1));

  // Replicate abs_limit and -abs_limit in two vectors.
  const float32x4_t abs_limit_f32x4 = vmovq_n_f32(abs_limit);
  const float32x4_t neg_abs_limit_f32x4 = vmovq_n_f32(-abs_limit);

  for (int v = 0; v < postamble_start; v += kFloatWeightsPerNeonLane) {
    // Load from memory to vector.
    float32x4_t v_f32x4 = vld1q_f32(vector + v);
    // Clip between abs_limit and -abs_limit.
    float32x4_t result_f32x4 = vminq_f32(abs_limit_f32x4, v_f32x4);
    result_f32x4 = vmaxq_f32(neg_abs_limit_f32x4, result_f32x4);
    // Save to output.
    vst1q_f32(result + v, result_f32x4);
  }
  // Postamble loop.
  for (int v = postamble_start; v < v_size; v++) {
    result[v] = (abs_limit < vector[v]) ? abs_limit : vector[v];
    result[v] = (-abs_limit > result[v]) ? -abs_limit : result[v];
  }
}

float NeonVectorVectorDotProduct(const float* vector1, const float* vector2,
                                 int v_size) {
  // If v_size is not divisible by kWeightsPerNeonLane, we cannot use the main
  // vectorized loop, and we need to process sequentially. postamble_start shows
  // the start index where this should happen.
  const int postamble_start =
      v_size - (v_size & (kFloatWeightsPerNeonLane - 1));
  float32x4_t acc_32x4 = vmovq_n_f32(0.0);
  for (int v = 0; v < postamble_start; v += kFloatWeightsPerNeonLane) {
    // Load 4 float values from vector1 and vector2 and accumulator.
    float32x4_t v1_f32x4 = vld1q_f32(vector1 + v);
    float32x4_t v2_f32x4 = vld1q_f32(vector2 + v);
    // Vector multiply-accumulate 4 float
    acc_32x4 = vmlaq_f32(acc_32x4, v1_f32x4, v2_f32x4);
  }

  float result = (vgetq_lane_f32(acc_32x4, 0) + vgetq_lane_f32(acc_32x4, 1) +
                  vgetq_lane_f32(acc_32x4, 2) + vgetq_lane_f32(acc_32x4, 3));
  // Postamble loop.
  for (int v = postamble_start; v < v_size; v++) {
    result += vector1[v] * vector2[v];
  }
  return result;
}

void NeonBatchVectorBatchVectorDotProduct(const float* vector1,
                                          const float* vector2, int v_size,
                                          int n_batch, float* result,
                                          int result_stride) {
  float* result_ptr = result;
  const float* vector1_ptr = vector1;
  const float* vector2_ptr = vector2;
  for (int b = 0; b < n_batch; b++) {
    *result_ptr = NeonVectorVectorDotProduct(vector1_ptr, vector2_ptr, v_size);
    vector1_ptr += v_size;
    vector2_ptr += v_size;
    result_ptr += result_stride;
  }
}

void NeonReductionSumVector(const float* input_vector, float* output_vector,
                            int output_size, int reduction_size) {
  const float* input_vector_ptr = input_vector;
  for (int o = 0; o < output_size; o++) {
    // If reduction_size is not divisible by kWeightsPerNeonLane, we cannot use
    // the main vectorized loop, and we need to process sequentially.
    // postamble_start shows the start index where this should happen.
    const int postamble_start =
        reduction_size - (reduction_size & (kFloatWeightsPerNeonLane - 1));
    float32x4_t sum_f32x4 = vmovq_n_f32(0.0);
    for (int r = 0; r < postamble_start; r += kFloatWeightsPerNeonLane) {
      float32x4_t v1_f32x4 = vld1q_f32(input_vector_ptr + r);
      sum_f32x4 = vaddq_f32(sum_f32x4, v1_f32x4);
    }
    output_vector[o] +=
        (vgetq_lane_f32(sum_f32x4, 0) + vgetq_lane_f32(sum_f32x4, 1) +
         vgetq_lane_f32(sum_f32x4, 2) + vgetq_lane_f32(sum_f32x4, 3));
    input_vector_ptr += postamble_start;

    // Postamble loop.
    for (int r = postamble_start; r < reduction_size; r++) {
      output_vector[o] += *input_vector_ptr++;
    }
  }
}

void NeonVectorShiftLeft(float* vector, int v_size, float shift_value) {
  // This variable keeps track of the next to the last index which is being
  // copied to make sure we are not out of the vector boundary.
  int last_index_copy = kFloatWeightsPerNeonLane;
  int current_index_copy = 0;
  while (last_index_copy < v_size) {
    float32x4_t v_f32x4 = vld1q_f32(vector + current_index_copy + 1);
    vst1q_f32(vector + current_index_copy, v_f32x4);
    current_index_copy += kFloatWeightsPerNeonLane;
    last_index_copy += kFloatWeightsPerNeonLane;
  }
  // Postamble loop.
  for (int i = current_index_copy; i < v_size - 1; i++) {
    vector[i] = vector[i + 1];
  }
  vector[v_size - 1] = shift_value;
}

}  // namespace tensor_utils
}  // namespace tflite

#endif  // USE_NEON
