Add update for Tau NN with pT calibration

L1Trigger/Phase2L1ParticleFlow/interface/taus/TauNNIdHW.h

@@ -1,32 +1,24 @@
 #ifndef L1Trigger_Phase2L1ParticleFlow_TAUNNIDHW_H_
 #define L1Trigger_Phase2L1ParticleFlow_TAUNNIDHW_H_
 
-#include "DataFormats/L1TParticleFlow/interface/layer1_emulator.h"
-
 #include <cstdio>
 #include <complex>
-#include "ap_int.h"
-#include "ap_fixed.h"
+
 #include "L1Trigger/Phase2L1ParticleFlow/interface/taus/tau_parameters.h"
+#include "L1Trigger/Phase2L1ParticleFlow/interface/taus/defines.h"
 
+#include "DataFormats/L1TParticleFlow/interface/layer1_emulator.h"
 #include "DataFormats/L1TParticleFlow/interface/PFCandidate.h"
 
-#include "L1Trigger/Phase2L1ParticleFlow/interface/common/nnet_layer.h"
-#include "L1Trigger/Phase2L1ParticleFlow/interface/common/nnet_activation.h"
-
-//hls-fpga-machine-learning insert weights
-#include "L1Trigger/Phase2L1ParticleFlow/interface/taus/weights/w1.h"
-#include "L1Trigger/Phase2L1ParticleFlow/interface/taus/weights/b1.h"
-#include "L1Trigger/Phase2L1ParticleFlow/interface/taus/weights/w2.h"
-#include "L1Trigger/Phase2L1ParticleFlow/interface/taus/weights/b2.h"
-#include "L1Trigger/Phase2L1ParticleFlow/interface/taus/weights/w3.h"
-#include "L1Trigger/Phase2L1ParticleFlow/interface/taus/weights/b3.h"
-#include "L1Trigger/Phase2L1ParticleFlow/interface/taus/weights/w4.h"
-#include "L1Trigger/Phase2L1ParticleFlow/interface/taus/weights/b4.h"
-
 typedef ap_ufixed<16, 14> pt_t;
 typedef ap_fixed<10, 4> etaphi_t;
 
+// Tau NN returns two values
+struct Tau_NN_Result {
+  result_t nn_pt_correction;
+  result_t nn_id;
+};
+
 namespace L1TauEmu {
   // Data types and constants used in the FPGA and FPGA-optimized functions
   //etaphi_base maps physical eta phi units onto bits
@@ -148,8 +140,8 @@ class TauNNIdHW {
   void initialize(const std::string &iName, int iNParticles);
   void SetNNVectorVar();
   input_t *NNVectorVar() { return NNvectorVar_.data(); }
-  result_t EvaluateNN();
-  result_t compute(const l1t::PFCandidate &iSeed, std::vector<l1t::PFCandidate> &iParts);
+  Tau_NN_Result EvaluateNN();
+  Tau_NN_Result compute(const l1t::PFCandidate &iSeed, std::vector<l1t::PFCandidate> &iParts);
   //void print();
 
   std::string fInput_;

L1Trigger/Phase2L1ParticleFlow/interface/taus/defines.h

@@ -0,0 +1,70 @@
+#ifndef DEFINES_H_
+#define DEFINES_H_
+
+#include "ap_fixed.h"
+#include "ap_int.h"
+
+#include <cstddef>
+#include <cstdio>
+
+// hls-fpga-machine-learning insert numbers
+#define N_INPUT_1_1 80
+#define N_LAYER_2 25
+#define N_LAYER_2 25
+#define N_LAYER_5 25
+#define N_LAYER_5 25
+#define N_LAYER_8 15
+#define N_LAYER_8 15
+#define N_LAYER_11 15
+#define N_LAYER_11 15
+#define N_LAYER_14 10
+#define N_LAYER_14 10
+#define N_LAYER_17 1
+#define N_LAYER_17 1
+#define N_LAYER_20 1
+
+// hls-fpga-machine-learning insert layer-precision
+typedef ap_fixed<16, 6> input_t;
+typedef ap_fixed<24, 12> input2_t;
+typedef ap_fixed<16, 6> model_default_t;
+typedef ap_fixed<16, 6> layer2_t;
+typedef ap_fixed<9, 3> weight2_t;
+typedef ap_fixed<9, 3> bias2_t;
+typedef ap_uint<1> layer2_index;
+typedef ap_ufixed<9, 0, AP_RND_CONV, AP_SAT> layer4_t;
+typedef ap_fixed<18, 8> relu_1_table_t;
+typedef ap_fixed<16, 6> layer5_t;
+typedef ap_fixed<9, 3> weight5_t;
+typedef ap_fixed<9, 3> bias5_t;
+typedef ap_uint<1> layer5_index;
+typedef ap_ufixed<9, 0, AP_RND_CONV, AP_SAT> layer7_t;
+typedef ap_fixed<18, 8> relu_2_table_t;
+typedef ap_fixed<16, 6> layer8_t;
+typedef ap_fixed<9, 3> weight8_t;
+typedef ap_fixed<9, 3> bias8_t;
+typedef ap_uint<1> layer8_index;
+typedef ap_ufixed<9, 0, AP_RND_CONV, AP_SAT> layer10_t;
+typedef ap_fixed<18, 8> relu_3_table_t;
+typedef ap_fixed<16, 6> layer11_t;
+typedef ap_fixed<9, 3> weight11_t;
+typedef ap_fixed<9, 3> bias11_t;
+typedef ap_uint<1> layer11_index;
+typedef ap_ufixed<9, 0, AP_RND_CONV, AP_SAT> layer13_t;
+typedef ap_fixed<18, 8> relu_4_table_t;
+typedef ap_fixed<16, 6> layer14_t;
+typedef ap_fixed<9, 3> weight14_t;
+typedef ap_fixed<9, 3> bias14_t;
+typedef ap_uint<1> layer14_index;
+typedef ap_ufixed<9, 0, AP_RND_CONV, AP_SAT> layer16_t;
+typedef ap_fixed<18, 8> relu_5_table_t;
+typedef ap_fixed<16, 6> layer17_t;
+typedef ap_fixed<16, 7> weight17_t;
+typedef ap_fixed<16, 7> bias17_t;
+typedef ap_uint<1> layer17_index;
+typedef ap_fixed<16, 6> result_t;
+typedef ap_fixed<18, 8> jetID_output_table_t;
+typedef ap_fixed<16, 7> weight20_t;
+typedef ap_fixed<16, 7> bias20_t;
+typedef ap_uint<1> layer20_index;
+
+#endif

L1Trigger/Phase2L1ParticleFlow/interface/taus/nnet_utils/nnet_activation.h

@@ -0,0 +1,94 @@
+#ifndef NNET_ACTIVATION_H_
+#define NNET_ACTIVATION_H_
+
+#include <cmath>
+#include "ap_fixed.h"
+#include "nnet_common.h"
+
+namespace nnet {
+
+  struct activ_config {
+    // IO size
+    static const unsigned n_in = 10;
+
+    // Internal info
+    static const unsigned table_size = 1024;
+
+    // Resource reuse info
+    static const unsigned io_type = io_parallel;
+    static const unsigned reuse_factor = 1;
+
+    // Internal data type definitions
+    typedef ap_fixed<18, 8> table_t;
+  };
+
+  // *************************************************
+  //       LINEAR Activation -- See Issue 53
+  // *************************************************
+  template <class data_T, class res_T, typename CONFIG_T>
+  void linear(data_T data[CONFIG_T::n_in], res_T res[CONFIG_T::n_in]) {
+    for (unsigned ii = 0; ii < CONFIG_T::n_in; ii++) {
+      res[ii] = data[ii];
+    }
+  }
+
+  // *************************************************
+  //       RELU Activation
+  // *************************************************
+  template <class data_T, class res_T, typename CONFIG_T>
+  void relu(data_T data[CONFIG_T::n_in], res_T res[CONFIG_T::n_in]) {
+    data_T datareg;
+    for (unsigned ii = 0; ii < CONFIG_T::n_in; ii++) {
+      datareg = data[ii];
+      if (datareg > 0)
+        res[ii] = datareg;
+      else
+        res[ii] = 0;
+    }
+  }
+
+  // *************************************************
+  //       Sigmoid Activation
+  // *************************************************
+  template <class out_T>
+  inline out_T sigmoid_fcn_float(float input) {
+    return 1.0 / (1 + exp(-input));
+  }
+
+  template <class res_T, typename CONFIG_T, int N_TABLE>
+  void init_sigmoid_table(res_T table_out[N_TABLE]) {
+    // Default logistic sigmoid function:
+    //   result = 1/(1+e^(-x))
+    for (unsigned ii = 0; ii < N_TABLE; ii++) {
+      // First, convert from table index to X-value (signed 8-bit, range -8 to +8)
+      float in_val = 2 * 8.0 * (ii - float(N_TABLE) / 2.0) / float(N_TABLE);
+      // Next, compute lookup table function
+      res_T real_val = sigmoid_fcn_float<res_T>(in_val);
+      //std::cout << "Lookup table In Value: " << in_val << " Result: " << real_val << std::endl;
+      table_out[ii] = (res_T)real_val;
+    }
+  }
+
+  template <class data_T, class res_T, typename CONFIG_T>
+  void sigmoid(data_T data[CONFIG_T::n_in], res_T res[CONFIG_T::n_in]) {
+    // Initialize the lookup table
+    res_T sigmoid_table[CONFIG_T::table_size];
+    init_sigmoid_table<res_T, CONFIG_T, CONFIG_T::table_size>(sigmoid_table);
+
+    // Index into the lookup table based on data
+    int data_round;
+    unsigned index;
+    for (unsigned ii = 0; ii < CONFIG_T::n_in; ii++) {
+      data_round = data[ii] * CONFIG_T::table_size / 16;
+      index = data_round + 8 * CONFIG_T::table_size / 16;
+      /*if (index < 0)
+        index = 0;*/
+      if (index > CONFIG_T::table_size - 1)
+        index = CONFIG_T::table_size - 1;
+      res[ii] = (res_T)sigmoid_table[index];
+    }
+  }
+
+}  // namespace nnet
+
+#endif

L1Trigger/Phase2L1ParticleFlow/interface/taus/nnet_utils/nnet_common.h

@@ -0,0 +1,52 @@
+#ifndef NNET_COMMON_H_
+#define NNET_COMMON_H_
+
+#include "ap_fixed.h"
+
+// This is a substitute for "ceil(n/(float)d)".
+#define DIV_ROUNDUP(n, d) ((n + d - 1) / d)
+#define MIN(n, d) (n > d ? d : n)
+#define MAX(n, d) (n > d ? n : d)
+
+#define STRINGIFY(x) #x
+#define EXPAND_STRING(x) STRINGIFY(x)
+
+namespace nnet {
+
+  // Common type definitions
+  enum io_type { io_parallel = 0, io_stream };
+  enum strategy { latency, resource };
+
+  template <class T>
+  class Op_add {
+  public:
+    T operator()(T a, T b) { return a + b; }
+  };
+
+  template <class T>
+  class Op_and {
+  public:
+    T operator()(T a, T b) { return a && b; }
+  };
+
+  template <class T>
+  class Op_or {
+  public:
+    T operator()(T a, T b) { return a || b; }
+  };
+
+  template <class T>
+  class Op_max {
+  public:
+    T operator()(T a, T b) { return a >= b ? a : b; }
+  };
+
+  template <class T>
+  class Op_min {
+  public:
+    T operator()(T a, T b) { return a <= b ? a : b; }
+  };
+
+}  // namespace nnet
+
+#endif

L1Trigger/Phase2L1ParticleFlow/interface/taus/nnet_utils/nnet_dense.h

@@ -0,0 +1,72 @@
+#ifndef NNET_DENSE_H_
+#define NNET_DENSE_H_
+
+#include "nnet_common.h"
+#include "nnet_mult.h"
+#include <math.h>
+
+namespace nnet {
+
+  struct dense_config {
+    // Internal data type definitions
+    typedef float bias_t;
+    typedef float weight_t;
+    typedef float accum_t;
+
+    // Layer Sizes
+    static const unsigned n_in = 10;
+    static const unsigned n_out = 10;
+
+    // Resource reuse info
+    int io_type = io_parallel;
+    int strategy = latency;
+    int reuse_factor = 1;
+    static const bool store_weights_in_bram = false;
+    int n_zeros = 0;
+    // partitioning arrays cyclically to go with roll factors?
+    // Product function to use
+    template <class x_T, class y_T>
+    using product = nnet::product::mult<x_T, y_T>;
+  };
+
+  template <class data_T, class res_T, typename CONFIG_T>
+  void dense(data_T data[CONFIG_T::n_in],
+             res_T res[CONFIG_T::n_out],
+             typename CONFIG_T::weight_t weights[CONFIG_T::n_in * CONFIG_T::n_out],
+             typename CONFIG_T::bias_t biases[CONFIG_T::n_out]) {
+    data_T cache;
+    typename CONFIG_T::accum_t mult[CONFIG_T::n_in * CONFIG_T::n_out];
+    typename CONFIG_T::accum_t acc[CONFIG_T::n_out];
+
+    // Do the matrix-multiply
+    for (unsigned ii = 0; ii < CONFIG_T::n_in; ii++) {
+      cache = data[ii];
+      for (unsigned jj = 0; jj < CONFIG_T::n_out; jj++) {
+        unsigned index = ii * CONFIG_T::n_out + jj;
+        mult[index] = CONFIG_T::template product<data_T, typename CONFIG_T::weight_t>::product(cache, weights[index]);
+      }
+    }
+
+    // Initialize accumulator with input biases
+    for (unsigned iacc = 0; iacc < CONFIG_T::n_out; iacc++) {
+      acc[iacc] = (typename CONFIG_T::accum_t)biases[iacc];
+    }
+
+    // Accumulate multiplication result
+    for (unsigned ii = 0; ii < CONFIG_T::n_in; ii++) {
+      for (unsigned jj = 0; jj < CONFIG_T::n_out; jj++) {
+        unsigned index = ii * CONFIG_T::n_out + jj;
+        acc[jj] += mult[index];
+      }
+    }
+
+    // Cast to "res_t" type
+    for (unsigned ires = 0; ires < CONFIG_T::n_out; ires++) {
+      // res[ires] = (res_T) (acc[ires]);
+      res[ires] = cast<data_T, res_T, CONFIG_T>(acc[ires]);
+    }
+  }
+
+}  // namespace nnet
+
+#endif