pytorch-sbs/network/CPP_NoCuda/MultiApp.cpp

#include "MultiApp.h"

#include <omp.h>
#include <stdio.h>
#include <string.h>

#include <algorithm>
#include <cassert>
#include <cmath>
#include <iostream>
#include <vector>

#include "approximation_multiplication_function.cpp"

MultiApp::MultiApp()
{

};

MultiApp::~MultiApp()
{

};

bool MultiApp::update(float* np_input_pointer,
  float* np_weight_pointer,
  float* np_output_pointer, int64_t pattern_dim,
  int64_t feature_dim, int64_t x_dim, int64_t y_dim,
  int64_t input_channel_dim, int64_t id_pattern,
  bool approximation_enable, int64_t number_of_trunc_bits,
  int64_t number_of_frac_bits)
{

  assert((id_pattern >= 0));
  assert((id_pattern < pattern_dim));

  float* np_input_pointer_pattern;
  float* np_output_pointer_pattern;

  float* input_ptr;
  float* output_ptr;
  float* w_ptr;

  uint64_t pattern_size = input_channel_dim;

  std::vector<float> ap_h_vector;
  ap_h_vector.resize(pattern_size);
  float* ap_h_ptr = ap_h_vector.data();

  std::vector<uint32_t> ap_x_vector;
  ap_x_vector.resize(pattern_size);
  uint32_t* ap_x_ptr = ap_x_vector.data();

  std::vector<uint32_t> ap_y_vector;
  ap_y_vector.resize(pattern_size);
  uint32_t* ap_y_ptr = ap_y_vector.data();

  std::vector<uint32_t> ap_x_exponent_vector;
  ap_x_exponent_vector.resize(pattern_size);
  uint32_t* ap_x_exponent_ptr = ap_x_exponent_vector.data();

  std::vector<uint32_t> ap_y_exponent_vector;
  ap_y_exponent_vector.resize(pattern_size);
  uint32_t* ap_y_exponent_ptr = ap_y_exponent_vector.data();

  std::vector<uint32_t> ap_h_exponent_vector;
  ap_h_exponent_vector.resize(pattern_size);
  uint32_t* ap_h_exponent_ptr = ap_h_exponent_vector.data();

  std::vector<uint64_t> ap_res_vector;
  ap_res_vector.resize(pattern_size);
  uint64_t* ap_res_ptr = ap_res_vector.data();

  uint32_t ap_mask = static_cast<uint64_t>(pow(2, number_of_trunc_bits)) - 1;

  std::vector<uint32_t> sign_temp_vector;
  sign_temp_vector.resize(pattern_size);
  uint32_t* sign_temp_ptr = sign_temp_vector.data();

  uint64_t input_pattern_size = input_channel_dim * x_dim * y_dim;
  uint64_t output_pattern_size = feature_dim * x_dim * y_dim;

  np_input_pointer_pattern = np_input_pointer + id_pattern * input_pattern_size;
  np_output_pointer_pattern =
    np_output_pointer + id_pattern * output_pattern_size;

  uint64_t counter;

  uint64_t counter_x;
  uint64_t counter_y;
  uint64_t counter_feature;
  uint64_t pos_xy;
  uint64_t pos_xy_if;

  float temp_sum;

  uint64_t pattern_c_2 = x_dim * y_dim;

  for (counter_x = 0; counter_x < x_dim; counter_x++)
  {
    for (counter_y = 0; counter_y < y_dim; counter_y++)
    {
      pos_xy = counter_y + counter_x * y_dim;
      for (counter_feature = 0; counter_feature < feature_dim;
        counter_feature++)
      {
        pos_xy_if = counter_feature * pattern_c_2 + pos_xy;

        input_ptr = np_input_pointer_pattern + pos_xy;
        output_ptr = np_output_pointer_pattern + pos_xy_if;
        w_ptr = np_weight_pointer + counter_feature * input_channel_dim;

#pragma omp simd
        for (counter = 0; counter < pattern_size; counter++)
        {
          ap_h_ptr[counter] = input_ptr[counter * pattern_c_2];
        }

        approximation_multiplication_function(
          ap_h_ptr, w_ptr, pattern_size, number_of_trunc_bits,
          number_of_frac_bits, ap_x_ptr, ap_y_ptr, ap_x_exponent_ptr,
          ap_y_exponent_ptr, ap_h_exponent_ptr, ap_mask, ap_res_ptr,
          sign_temp_ptr, approximation_enable);

        temp_sum = 0.0;
#pragma omp simd reduction(+ \
                           : temp_sum)
        for (counter = 0; counter < pattern_size; counter++)
        {
          temp_sum += ap_h_ptr[counter];
        }

        output_ptr[0] = temp_sum;
      }
    }
  }

  return true;
};

bool MultiApp::update_with_init_vector_multi_pattern(
  int64_t np_input_pointer_addr, int64_t np_weight_pointer_addr,
  int64_t np_output_pointer_addr, int64_t pattern_dim, int64_t feature_dim,
  int64_t x_dim, int64_t y_dim, int64_t input_channel_dim,
  int64_t number_of_processes, bool approximation_enable,
  int64_t number_of_trunc_bits, int64_t number_of_frac)
{
  int64_t number_of_pattern = pattern_dim;
  int64_t pattern_id;

  float* np_input_pointer = (float*)np_input_pointer_addr;
  float* np_weight_pointer = (float*)np_weight_pointer_addr;
  float* np_output_pointer = (float*)np_output_pointer_addr;

  assert((np_input_pointer != nullptr));
  assert((np_output_pointer != nullptr));
  assert((np_weight_pointer != nullptr));

  assert((pattern_dim > 0));
  assert((feature_dim > 0));
  assert((x_dim > 0));
  assert((y_dim > 0));
  assert((input_channel_dim > 0));

  if (number_of_processes > 0)
  {
    omp_set_num_threads(number_of_processes);
    // For debugging: Only one thread
    // omp_set_num_threads(1);

#pragma omp parallel for
    for (pattern_id = 0; pattern_id < number_of_pattern; pattern_id++)
    {
      update(np_input_pointer, np_weight_pointer,
        np_output_pointer, pattern_dim, feature_dim, x_dim, y_dim,
        input_channel_dim, pattern_id, approximation_enable,
        number_of_trunc_bits, number_of_frac);
    }
  }
  else
  {
    std::cout << "Error: number_of_processes <= 0" << std::endl;
    return false;
  }
  return true;
};