Merge pull request #1 from cms-hls4ml/cleanup

start cleanup

AXOL1TL_v1/GTADModel_emulator_v1.cpp

@@ -2,78 +2,50 @@
 #include "emulator.h"
 #include "NN/nnet_utils/nnet_common.h"
 #include <any>
+#include <array>
 #include "ap_fixed.h"
 #include "ap_int.h"
 #include "scales.h"
 
-//type notes
-//AD_NN_OUT_SQ_T = resultsq_t = typedef ap_ufixed<18,14> resultsq_t;
-//AD_NN_IN_T = input_t = typedef ap_fixed<8, 6, AP_RND_CONV, AP_SAT> input_t;
-//AD_NN_OUT_T = result_t = typedef ap_fixed<10,7> result_t;
-//unscaled_t = typedef ap_fixed<18,13> unscaled_t;
-//resultsq_t = typedef ap_ufixed<18,14> resultsq_t;
-
-//AD_NNNOUTPUTS = N_LAYER_6 = 13
-//AD_NNNINPUTS = N_INPUT_1_1 = 57
-
 class GTADModel_emulator_v1 : public hls4mlEmulator::Model {
 
 private:
-    input_t _input[N_INPUT_1_1];
+    unscaled_t _unscaled_input[N_INPUT_1_1];
+    input_t _scaled_input[N_INPUT_1_1];
     result_t _result[N_LAYER_6];
+    resultsq_t _loss;
 
 public: 
   virtual void prepare_input(std::any input) {
-    // input_t *input_p = std::any_cast<input_t*>(input);
     unscaled_t *unscaled_input_p = std::any_cast<unscaled_t*>(input);
-    unscaled_t unscaled_input[N_INPUT_1_1];
-    input_t scaled_input[N_INPUT_1_1];
 
-    //first get unscaled inputs
+    // first get unscaled inputs
     for (int i = 0; i < N_INPUT_1_1; i++) {
-      // _input[i] = std::any_cast<input_t>(input_p[i]);
-      unscaled_input[i] = std::any_cast<unscaled_t>(unscaled_input_p[i]);
+      _unscaled_input[i] = std::any_cast<unscaled_t>(unscaled_input_p[i]);
     }
 
-    //scale inputs
-    scaleNNInputs(unscaled_input, scaled_input);
-
-    //then fill scaled inputs
-    for (int i = 0; i < N_INPUT_1_1; i++) { 
-      // _input[i] = std::any_cast<input_t>(scaled_input[i]) 
-      _input[i] = scaled_input[i]; //dont think any_cast needed here but correct me if I'm wrong
-    }
-
+    // scale inputs
+    scaleNNInputs(_unscaled_input, _scaled_input);
   }
 
-
-
   virtual void predict() {
-    GTADModel_v1(_input, _result);    
+    GTADModel_v1(_scaled_input, _result);
+    _loss = computeLoss(_result);
   }
-
-
 
   virtual void read_result(std::any result) {
-    //original just read result
-    // result_t *result_p = std::any_cast<result_t*>(result);
-    // for (int i = 0; i < N_LAYER_6; i++) {
-    //   result_p[i] = _result[i];
-    // }
-
-    //new: compute loss to read result
-    result_t result_p[N_LAYER_6]; //store preloss _result in result_p, maybe not needed
+    // return results as an std::pair
+    // first = reconstructed output
+    // second = loss
+    std::pair<std::array<result_t, N_LAYER_6>, resultsq_t> result_p = std::any_cast<std::pair<std::array<result_t, N_LAYER_6>, resultsq_t>>(result);
     for (int i = 0; i < N_LAYER_6; i++) {
-      result_p[i] = _result[i];
-    }    
-    // resultsq_t *loss = std::any_cast<resultsq_t*>(result);  //causes ambiguous conversion error
-    resultsq_t loss = computeLoss(result_p);
-    result = loss; //so output loss as "result"
+      result_p.first[i] = _result[i];
+    }
+    result_p.second = _loss;
   }
-
-
 
-  //scaleNNInputs function from https://github.com/cms-l1-globaltrigger/mp7_ugt_legacy/blob/anomaly_detection_trigger/firmware/hls/anomaly_detection/anomaly_detection.cpp#L28
+  // scaleNNInputs function from
+  // https://github.com/cms-l1-globaltrigger/mp7_ugt_legacy/blob/anomaly_detection_trigger/firmware/hls/anomaly_detection/anomaly_detection.cpp#L28
   virtual void scaleNNInputs(unscaled_t unscaled[N_INPUT_1_1], input_t scaled[N_INPUT_1_1])
   {
     for (int i = 0; i < N_INPUT_1_1; i++)
@@ -84,17 +56,14 @@ class GTADModel_emulator_v1 : public hls4mlEmulator::Model {
       }
   }
 
-
-  // computeLoss function from https://github.com/cms-l1-globaltrigger/mp7_ugt_legacy/blob/anomaly_detection_trigger/firmware/hls/anomaly_detection/anomaly_detection.cpp#L7
+  // computeLoss function from
+  // https://github.com/cms-l1-globaltrigger/mp7_ugt_legacy/blob/anomaly_detection_trigger/firmware/hls/anomaly_detection/anomaly_detection.cpp#L7
   virtual resultsq_t computeLoss(result_t result_p[N_LAYER_6]) { 
-    //      result_t *result_p = std::any_cast<result_t*>(result); 
       resultsq_t squares[N_LAYER_6];
       resultsq_t square_sum;
 
       for (int i = 0; i < N_LAYER_6; i++) { 
-	// result_p[i] = _result[i]; //not needed, should already be the case
-	resultsq_t sq  = result_p[i] * result_p[i];
-	squares[i] = sq;
+	squares[i]  = result_p[i] * result_p[i];
       }
       nnet::Op_add<resultsq_t> op;
       square_sum = nnet::reduce<resultsq_t, N_LAYER_6, nnet::Op_add<resultsq_t>>(squares, op);

AXOL1TL_v1/Makefile

@@ -10,4 +10,4 @@ GTADModel_v1.so: GTADModel_emulator_v1.o GTADModel_v1.o
 	$(CXX) $(CXXFLAGS) $(INCLUDES) -c $< -o $@
 
 clean:
-	rm GTADModel_v1.so
+	rm GTADModel_emulator_v1.o GTADModel_v1.o GTADModel_v1.so