GRLNeuro.cc

/**************************************************************************
 * basf2 (Belle II Analysis Software Framework)                           *
 * Author: The Belle II Collaboration                                     *
 *                                                                        *
 * See git log for contributors and copyright holders.                    *
 * This file is licensed under LGPL-3.0, see LICENSE.md.                  *
 **************************************************************************/
 
#include <framework/logging/Logger.h>
#include <trg/grl/GRLNeuro.h>
#include <trg/grl/dataobjects/GRLMLP.h>
#include <cdc/geometry/CDCGeometryPar.h>
#include <framework/gearbox/Const.h>
#include <framework/gearbox/Unit.h>
#include <framework/datastore/StoreObjPtr.h>
#include <framework/datastore/StoreArray.h>
#include <trg/cdc/dbobjects/CDCTriggerNeuroConfig.h>
#include <trg/cdc/dataobjects/CDCTriggerTrack.h>
#include <string>
#include <cmath>
#include <TFile.h>
#include <algorithm>
#include <iomanip>
#include <iostream>
#include <vector>
#include <array>
#include <cstdint>
#include <utility>
using namespace Belle2;
using namespace CDC;
using namespace std;
 
 
 
// ========== ap_(u)fixed  ========

inline float sim_ap_fixed(float val, int total_bits, int int_bits,
                          bool is_signed = true,
                          int quant_mode = 0,
                          int overflow_mode = 0,
                          int saturation_bits = 0)
{
  //for normal ap_types
  if (total_bits >= int_bits) {
 
    // Effective bits (handle saturation_bits and total_bits < int_bits cases)
    int effective_total = (saturation_bits > 0) ?
                          std::min(saturation_bits, total_bits) : total_bits;
    int effective_I = std::min(int_bits, effective_total);
    int F = effective_total - effective_I;  // Fractional bits ≥ 0
    if (int_bits == 0 && is_signed == true) {
      F = total_bits ;
      effective_I = total_bits + 1;
    }
 
    // --- 2. Fast Scale Calculation (2^F) ---
    const float scale = [F]() {
      if (F <= 0) {return 1.0f;}  // No fractional bits
      else {
        // Bit-shift is faster than std::pow(2, F)
        const uint32_t scale_int = 1U << F;
        return 1.0f / static_cast<float>(scale_int);
      }
    }();
 
    // --- 3. Quantization (Mode-Specific Rounding) ---
    const auto quantize = [](float scaled_val, int mode) -> int64_t {
      using QuantizerFuncPtr = int64_t(*)(float);
      static constexpr QuantizerFuncPtr quantizers[] = {
        [](float x) -> int64_t { return static_cast<int64_t>(std::trunc(x)); },
        [](float x) -> int64_t { return static_cast<int64_t>(std::round(x)); },
        [](float x) -> int64_t {
          return (x >= 0) ? static_cast<int64_t>(std::floor(x))
          : static_cast<int64_t>(std::ceil(x));
        },
        [](float x) -> int64_t { return static_cast<int64_t>(std::floor(x)); },
        [](float x) -> int64_t { return static_cast<int64_t>(std::ceil(x)); },
        [](float x) -> int64_t { return llrint(x); }
      };
      const int clamped_mode = std::clamp(mode, 0, static_cast<int>(sizeof(quantizers) / sizeof(quantizers[0]) - 1));
      return quantizers[clamped_mode](scaled_val);
    };
 
    int64_t fixed_val = quantize(val / scale, quant_mode);
 
    // --- 4. Dynamic Range Calculation ---
    const auto [min_val, max_val] = [ = ]() -> std::pair<int64_t, int64_t> {
      if (is_signed)
      {
        const int64_t int_max = (1LL << (effective_I - 1)) - 1;
        const int64_t int_min = -(1LL << (effective_I - 1));
        const int64_t frac_mask = (F > 0) ? ((1LL << F) - 1) : 0;
        return {int_min << F, (int_max << F) | frac_mask};
      } else
      {
        const int64_t int_max = (1LL << effective_I) - 1;
        const int64_t frac_mask = (F > 0) ? ((1LL << F) - 1) : 0;
        return {0, (int_max << F) | frac_mask};
      }
    }();
 
 
    // --- 5. Overflow Handling ---
    const auto handle_overflow = [&]() {
      const int64_t bit_mask = (1LL << effective_total) - 1;
      const int64_t sign_bit = 1LL << (effective_total - 1);
 
      switch (overflow_mode) {
        case 0:  // AP_SAT: Clamp to [min_val, max_val]
          fixed_val = std::clamp(fixed_val, min_val, max_val);
          break;
 
        case 1:  // AP_SAT_ZERO: Set to 0 on overflow
          if (fixed_val > max_val || fixed_val < min_val) fixed_val = 0;
          break;
 
        case 2:  // AP_WRAP: Modular wrap-around
          if (is_signed) {
            fixed_val = (fixed_val & bit_mask);
            if (fixed_val & sign_bit) {
              fixed_val -= (1LL << effective_total);
            }
          } else {
            fixed_val &= bit_mask;
          }
          break;
 
        case 3:  // AP_WRAP_SM: Sign-magnitude wrap
          if (is_signed) {
            const int64_t magnitude_mask = (1LL << (effective_total - 1)) - 1;
            const bool is_negative = (fixed_val < 0);
            fixed_val = std::abs(fixed_val) & magnitude_mask;
            if (is_negative) fixed_val = -fixed_val;
          } else {
            fixed_val &= bit_mask;
          }
          break;
 
        case 4:  // AP_SAT_SYM: Symmetric saturation
          if (is_signed) {
            const int64_t sym_limit = std::min(max_val, -min_val);
            fixed_val = std::clamp(fixed_val, -sym_limit, sym_limit);
          } else {
            fixed_val = std::min(fixed_val, max_val);
          }
          break;
 
        default:  // Default to AP_WRAP
          fixed_val &= bit_mask;
          if (is_signed && (fixed_val & sign_bit)) {
            fixed_val -= (1LL << effective_total);
          }
      }
    };
    handle_overflow();
 
    // --- 6. Final Conversion ---
    return static_cast<float>(fixed_val) * scale;
  }
  //for sperical case with total_bits < int-bits (only used to this code, need modify for generic using)
  else {
    // 1. calculate the bits
    const int zero_bits = std::max(int_bits - total_bits, 0);//So called negative fraction bits
    const int useful_bits = total_bits - 1;
 
    // 2. value collections
    std::vector<float> legal_values;
    for (int64_t i = 0; i < (1LL << useful_bits); ++i) {
      int64_t value = i << zero_bits;
      legal_values.push_back(static_cast<float>(value));
      legal_values.push_back(static_cast<float>(-1 * value)); //need confirm
    }
    std::sort(legal_values.begin(), legal_values.end());
 
    // 3. find the closest values in legal_values
    auto closest = std::min_element(legal_values.begin(), legal_values.end(),
    [val](float a, float b) {
      float dist_a = std::abs(a - val);
      float dist_b = std::abs(b - val);
 
      if (std::abs(dist_a - dist_b) < 1e-6f) {
        return a > b;
      }
      return dist_a < dist_b;
    });
 
    return *closest;
  }
 
}
 
// ---  Wrappers for this code---
 
//=== for input layer ===//
inline float sim_input_layer_t(float val)
{
  return sim_ap_fixed(val, 13, 12, false, 0, 4, 0); // AP_TRN,AP_SAT
}
 
inline float sim_dense_0_iq_t(float val)
{
  return sim_ap_fixed(val, 12, 12, false, 1, 2, 0); // AP_RND,AP_WAP
}
 
inline float sim_ap_dense_0_iq(float val, int w, int i)
{
  return sim_ap_fixed(val, w, i, false, 1, 4, 0);  // AP_RND, AP_SAT_SYM
}
 
// === for weights, bias and hiden layers ===
inline float sim_fixed(float val, int total_bits, int int_bits,  bool is_signed = true,
                       int rounding = 0,     // 0: trunc, 1: round
                       int saturation = 2)   // 2: wrap
{
  return sim_ap_fixed(val, total_bits, int_bits, is_signed, rounding, saturation, 0);  // AP_TRN, AP_SAT_SYM
}
 
// === unsign for result ===
inline float sim_result_t(float val)
{
  return sim_ap_fixed(val, 25, 9, true, 0, 2, 0);  // AP_RND, AP_SAT_SYM
}
 
 
void
GRLNeuro::initialize(const Parameters& p)
{
  using std::vector;
 
  B2DEBUG(10, "GRLNeuro::initialize: nMLP=" << p.nMLP);
 
  // ------------------------------------------------------------------
  // Basic parameter validation (fatal in initialize)
  // ------------------------------------------------------------------
 
  if (p.nHidden.size() != 1 && p.nHidden.size() != p.nMLP) {
    B2FATAL("Number of nHidden lists should be 1 or " << p.nMLP);
  }
 
  // ------------------------------------------------------------------
  // Comprehensive parameter-size validation
  // ------------------------------------------------------------------
 
  auto check_size = [&](const auto & v, const char* name) {
    const size_t s = v.size();
    if (s != 1 && s != static_cast<size_t>(p.nMLP)) {
      B2FATAL(std::string(name) + " size (" + std::to_string(s)
              + ") != 1 and != nMLP (" + std::to_string(p.nMLP) + ")");
    }
  };
 
  // sector / structure parameters
  check_size(p.i_cdc_sector, "i_cdc_sector");
  check_size(p.i_ecl_sector, "i_ecl_sector");
  check_size(p.nHidden, "nHidden");
  check_size(p.nn_thres, "nn_thres");
 
  // bias
  check_size(p.total_bit_bias, "total_bit_bias");
  check_size(p.int_bit_bias, "int_bit_bias");
  check_size(p.is_signed_bias, "is_signed_bias");
  check_size(p.rounding_bias, "rounding_bias");
  check_size(p.saturation_bias, "saturation_bias");
 
  // accumulator
  check_size(p.total_bit_accum, "total_bit_accum");
  check_size(p.int_bit_accum, "int_bit_accum");
  check_size(p.is_signed_accum, "is_signed_accum");
  check_size(p.rounding_accum, "rounding_accum");
  check_size(p.saturation_accum, "saturation_accum");
 
  // weights
  check_size(p.total_bit_weight, "total_bit_weight");
  check_size(p.int_bit_weight, "int_bit_weight");
  check_size(p.is_signed_weight, "is_signed_weight");
  check_size(p.rounding_weight, "rounding_weight");
  check_size(p.saturation_weight, "saturation_weight");
 
  // relu
  check_size(p.total_bit_relu, "total_bit_relu");
  check_size(p.int_bit_relu, "int_bit_relu");
  check_size(p.is_signed_relu, "is_signed_relu");
  check_size(p.rounding_relu, "rounding_relu");
  check_size(p.saturation_relu, "saturation_relu");
 
  // generic
  check_size(p.total_bit, "total_bit");
  check_size(p.int_bit, "int_bit");
  check_size(p.is_signed, "is_signed");
  check_size(p.rounding, "rounding");
  check_size(p.saturation, "saturation");
 
  // inputs
  check_size(p.W_input, "W_input");
  check_size(p.I_input, "I_input");
 
  // ------------------------------------------------------------------
  // Initialize MLPs
  // ------------------------------------------------------------------
 
  m_MLPs.clear();
  m_MLPs.reserve(p.nMLP);
 
  for (unsigned iMLP = 0; iMLP < p.nMLP; ++iMLP) {
 
    // helper: broadcast if size == 1
    auto pick = [&](const auto & container) -> const auto& {
      return (container.size() == 1) ? container[0] : container[iMLP];
    };
 
    unsigned short i_cdc = static_cast<unsigned short>(pick(p.i_cdc_sector));
    unsigned short i_ecl = static_cast<unsigned short>(pick(p.i_ecl_sector));
    unsigned short nInput = static_cast<unsigned short>(i_cdc + i_ecl);
 
    // nHidden: vector<vector<float>>
    const vector<float>& nhidden = pick(p.nHidden);
    vector<unsigned short> nNodes = { nInput };
 
    for (unsigned iHid = 0; iHid < nhidden.size(); ++iHid) {
      if (p.multiplyHidden) {
        nNodes.push_back(static_cast<unsigned short>(nhidden[iHid] * nNodes[0]));
      } else {
        nNodes.push_back(static_cast<unsigned short>(nhidden[iHid]));
      }
    }
    nNodes.push_back(static_cast<float>(pick(p.nOutput)));
 
    GRLMLP grlmlp_temp(nNodes);
 
    // bias
    grlmlp_temp.set_total_bit_bias(pick(p.total_bit_bias));
    grlmlp_temp.set_int_bit_bias(pick(p.int_bit_bias));
    grlmlp_temp.set_is_signed_bias(pick(p.is_signed_bias));
    grlmlp_temp.set_rounding_bias(pick(p.rounding_bias));
    grlmlp_temp.set_saturation_bias(pick(p.saturation_bias));
 
    // accumulator
    grlmlp_temp.set_total_bit_accum(pick(p.total_bit_accum));
    grlmlp_temp.set_int_bit_accum(pick(p.int_bit_accum));
    grlmlp_temp.set_is_signed_accum(pick(p.is_signed_accum));
    grlmlp_temp.set_rounding_accum(pick(p.rounding_accum));
    grlmlp_temp.set_saturation_accum(pick(p.saturation_accum));
 
    // weights
    grlmlp_temp.set_total_bit_weight(pick(p.total_bit_weight));
    grlmlp_temp.set_int_bit_weight(pick(p.int_bit_weight));
    grlmlp_temp.set_is_signed_weight(pick(p.is_signed_weight));
    grlmlp_temp.set_rounding_weight(pick(p.rounding_weight));
    grlmlp_temp.set_saturation_weight(pick(p.saturation_weight));
 
    // relu
    grlmlp_temp.set_total_bit_relu(pick(p.total_bit_relu));
    grlmlp_temp.set_int_bit_relu(pick(p.int_bit_relu));
    grlmlp_temp.set_is_signed_relu(pick(p.is_signed_relu));
    grlmlp_temp.set_rounding_relu(pick(p.rounding_relu));
    grlmlp_temp.set_saturation_relu(pick(p.saturation_relu));
 
    // generic
    grlmlp_temp.set_total_bit(pick(p.total_bit));
    grlmlp_temp.set_int_bit(pick(p.int_bit));
    grlmlp_temp.set_is_signed(pick(p.is_signed));
    grlmlp_temp.set_rounding(pick(p.rounding));
    grlmlp_temp.set_saturation(pick(p.saturation));
 
    // inputs
    grlmlp_temp.set_W_input(pick(p.W_input));
    grlmlp_temp.set_I_input(pick(p.I_input));
 
    //threshold
    grlmlp_temp.set_nn_thres(pick(p.nn_thres));
 
    m_MLPs.push_back(std::move(grlmlp_temp));
  }
 
  B2DEBUG(10, "GRLNeuro::initialize finished. created "
          << m_MLPs.size() << " MLP(s).");
}
 
 
 
std::vector<float>
GRLNeuro::runMLP(unsigned isector, const std::vector<float>& input)
{
  const GRLMLP& expert = m_MLPs[isector];
  vector<float> weights = expert.get_weights();
  vector<float> bias = expert.get_bias();
  vector<int> total_bit_bias = expert.get_total_bit_bias();
  vector<int> int_bit_bias = expert.get_int_bit_bias();
  vector<bool> is_signed_bias = expert.get_is_signed_bias();
  vector<int> rounding_bias = expert.get_rounding_bias();
  vector<int> saturation_bias = expert.get_saturation_bias();
  vector<int> total_bit_accum = expert.get_total_bit_accum();
  vector<int> int_bit_accum = expert.get_int_bit_accum();
  vector<bool> is_signed_accum = expert.get_is_signed_accum();
  vector<int> rounding_accum = expert.get_rounding_accum();
  vector<int> saturation_accum = expert.get_saturation_accum();
  vector<int> total_bit_weight = expert.get_total_bit_weight();
  vector<int> int_bit_weight = expert.get_int_bit_weight();
  vector<bool> is_signed_weight = expert.get_is_signed_weight();
  vector<int> rounding_weight = expert.get_rounding_weight();
  vector<int> saturation_weight = expert.get_saturation_weight();
  vector<int> total_bit_relu = expert.get_total_bit_relu();
  vector<int> int_bit_relu = expert.get_int_bit_relu();
  vector<bool> is_signed_relu = expert.get_is_signed_relu();
  vector<int> rounding_relu = expert.get_rounding_relu();
  vector<int> saturation_relu = expert.get_saturation_relu();
  vector<int> total_bit = expert.get_total_bit();
  vector<int> int_bit = expert.get_int_bit();
  vector<bool> is_signed = expert.get_is_signed();
  vector<int> rounding = expert.get_rounding();
  vector<int> saturation = expert.get_saturation();
  vector<vector<int>> W_input = expert.get_W_input();
  vector<vector<int>> I_input = expert.get_I_input();
 
 
  //input layer
  vector<float> layerinput = input;
 
  // quantizer the inputs
  for (size_t i = 0; i < layerinput.size(); ++i) {
 
    int W_arr[24] = { 12, 12, 11, 11, 8, 8, 7, 7, 5, 6, 6, 6, 8, 8, 6, 5, 4, 5, 7, 7, 6, 4, 5, 5 };
    int I_arr[24] = { 12, 12, 11, 11, 10, 9, 7, 7, 7, 7, 7, 7, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9 };
    if (i != 23) {
      layerinput[i] = sim_input_layer_t(layerinput[i]);
      layerinput[i] = sim_ap_dense_0_iq(layerinput[i], W_arr[i], I_arr[i]);
 
    } else layerinput[i] = 0;
  }
 
  //hidden layer and output layer
  vector<float> layeroutput = {};
  unsigned num_layers = expert.get_number_of_layers();
 
  //output layer
  vector<float> final_fixed = {};
 
  unsigned num_total_neurons = 0;
  unsigned iw = 0;
  for (unsigned i_layer = 0; i_layer < num_layers - 1; i_layer++) {
    //read bias
    unsigned num_neurons = expert.get_number_of_nodes_layer(i_layer + 1);
    layeroutput.clear();
    layeroutput.assign(num_neurons, 0.);
    layeroutput.shrink_to_fit();
 
    for (unsigned io = 0; io < num_neurons; ++io) {
      float bias_raw = bias[io + num_total_neurons];
      float bias_fixed   = sim_fixed(bias_raw, total_bit_bias[i_layer], int_bit_bias[i_layer], is_signed_bias[i_layer],
                                     rounding_bias[i_layer], saturation_bias[i_layer]);
      float bias_contrib = sim_fixed(bias_fixed, total_bit_accum[i_layer], int_bit_accum[i_layer], is_signed_accum[i_layer],
                                     rounding_accum[i_layer], saturation_accum[i_layer]);
      layeroutput[io] = bias_contrib;
 
    }
    num_total_neurons += num_neurons;
 
    //input*weight
    unsigned num_inputs = layerinput.size();
    for (unsigned ii = 0; ii < num_inputs; ++ii) {
      float input_val = layerinput[ii];
      for (unsigned io = 0; io < num_neurons; ++io) {
        float weight_raw = weights[iw];
        float weight_fixed = sim_fixed(weight_raw, total_bit_weight[i_layer], int_bit_weight[i_layer], is_signed_weight[i_layer],
                                       rounding_weight[i_layer], saturation_weight[i_layer]);
        float product = input_val * weight_fixed;
        float contrib = sim_fixed(product, total_bit_accum[i_layer], int_bit_accum[i_layer], is_signed_accum[i_layer],
                                  rounding_accum[i_layer], saturation_accum[i_layer]);
        layeroutput[io] += contrib;
        ++iw;
      }
    }
    //     input*weight done
    if (i_layer < num_layers - 2) {
      //relu
      for (unsigned io = 0; io < num_neurons; ++io) {
        float fixed_val = sim_fixed(layeroutput[io], total_bit[i_layer], int_bit[i_layer], is_signed[i_layer], rounding[i_layer],
                                    saturation[i_layer]);
        float relu_val = (fixed_val > 0) ? fixed_val : 0;
        layeroutput[io] = sim_fixed(relu_val, total_bit_relu[i_layer], int_bit_relu[i_layer], is_signed_relu[i_layer],
                                    rounding_relu[i_layer], saturation_relu[i_layer]);
      }
 
 
      //input to next layer
      layerinput.clear();
      layerinput.assign(num_neurons, 0);
      layerinput.shrink_to_fit();
 
      for (unsigned i = 0; i < num_neurons; ++i) {
 
        layerinput[i] = sim_ap_fixed(layeroutput[i], W_input[i_layer][i], I_input[i_layer][i], false, 1, 4, 0);
 
      }
      if (i_layer == 0) {}
      else if (i_layer == 1) {
 
        layerinput[1] = 0;
        layerinput[10] = 0;
        layerinput[18] = 0;
 
      } else if (i_layer == 2) {
        layerinput[2] = 0;
        layerinput[16] = 0;
 
      }
 
 
    } else {
 
      // ===  HLS result_t: ap_fixed<19,5,AP_RND,AP_SAT_SYM,0> ===
      unsigned num_final_fixed = layeroutput.size();
      for (unsigned io = 0; io < num_final_fixed; ++io) {
        final_fixed.push_back(sim_result_t(layeroutput[io]));
      }
 
    }
  }
  return final_fixed;
}
 
 
bool GRLNeuro::load(unsigned isector, const string& weightfilename, const string& biasfilename)
{
  if (weightfilename.size() < 1) {
    B2ERROR("Could not load Neurotrigger weights from database!");
    return false;
  } else if (biasfilename.size() < 1) {
    B2ERROR("Could not load Neurotrigger bias from database!");
    return false;
  } else {
    std::ifstream wfile(weightfilename);
    if (!wfile.is_open()) {
      B2WARNING("Could not open file " << weightfilename);
      return false;
    } else {
      std::ifstream bfile(biasfilename);
      if (!bfile.is_open()) {
        B2WARNING("Could not open file " << biasfilename);
        return false;
      } else {
        GRLMLP& expert = m_MLPs[isector];
        std::vector<float> warray;
        std::vector<float> barray;
        warray.clear();
        barray.clear();
 
        float element;
        while (wfile >> element) {
          warray.push_back(element);
        }
        while (bfile >> element) {
          barray.push_back(element);
        }
 
        if (warray.size() != expert.n_weights_cal()) {
          B2ERROR("Number of weights is not equal to registered architecture!");
          return false;
        } else expert.set_weights(warray);
        if (barray.size() != expert.n_bias_cal()) {
          B2ERROR("Number of bias is not equal to registered architecture!");
          return false;
        }
 
        expert.set_weights(warray);
        expert.set_bias(barray);
        return true;
      }
    }
  }
}
 
bool GRLNeuro::load(unsigned isector, std::vector<float> warray, std::vector<float> barray)
{
  GRLMLP& expert = m_MLPs[isector];
  expert.set_weights(warray);
  expert.set_bias(barray);
  return true;
}