Adding CIFAR-10 Promise version

llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar10_promise_quant.cc

+
+#include <stdio.h> 
+#include <stdlib.h> 
+#include <unistd.h> 
+#include <fcntl.h> 
+#include <sys/types.h> 
+#include <sys/stat.h> 
+#include <string.h> 
+#include "../../../tensor_runtime/include/tensor_runtime.h" 
+#include "../../include/utils.h" 
+
+int main(){ 
+
+llvm_hpvm_initTensorRt(0); 
+
+ int total_runs = 1; 
+ for (int i = 0 ; i < total_runs; i++){ 
+
+
+   startMemTracking(); 
+
+   int test_input_size = 10000; 
+   int batch_size = 2500; 
+   int batch_count = test_input_size / batch_size; 
+   float final_accuracy = 0.0; 
+
+   for(int i = 0; i < batch_count; i++){ 
+
+     std::string dir_prefix = std::string("../model_params/vgg16_cifar10_2/");
+       
+     std::string input_path =  dir_prefix + std::string("input.bin"); 
+     std::string labels_path =  dir_prefix + std::string("labels.bin"); 
+     std::string conv2d_1_w_path =  dir_prefix + std::string("conv2d_1_w.bin"); 
+     void* conv2d_1_w =  readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,3,3); 
+     std::string conv2d_1_b_path =  dir_prefix + std::string("conv2d_1_b.bin"); 
+     void* conv2d_1_b =  readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1); 
+     std::string conv2d_2_w_path =  dir_prefix + std::string("conv2d_2_w.bin"); 
+     void* conv2d_2_w =  readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,64,3,3); 
+     std::string conv2d_2_b_path =  dir_prefix + std::string("conv2d_2_b.bin"); 
+     void* conv2d_2_b =  readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1); 
+     std::string conv2d_3_w_path =  dir_prefix + std::string("conv2d_3_w.bin"); 
+     void* conv2d_3_w =  readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,3,3); 
+     std::string conv2d_3_b_path =  dir_prefix + std::string("conv2d_3_b.bin"); 
+     void* conv2d_3_b =  readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,128,1,1); 
+     std::string conv2d_4_w_path =  dir_prefix + std::string("conv2d_4_w.bin"); 
+     void* conv2d_4_w =  readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,3,3); 
+     std::string conv2d_4_b_path =  dir_prefix + std::string("conv2d_4_b.bin"); 
+     void* conv2d_4_b =  readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,128,1,1); 
+     std::string conv2d_5_w_path =  dir_prefix + std::string("conv2d_5_w.bin"); 
+     void* conv2d_5_w =  readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,3,3); 
+     std::string conv2d_5_b_path =  dir_prefix + std::string("conv2d_5_b.bin"); 
+     void* conv2d_5_b =  readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1); 
+     std::string conv2d_6_w_path =  dir_prefix + std::string("conv2d_6_w.bin"); 
+     void* conv2d_6_w =  readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,3,3); 
+     std::string conv2d_6_b_path =  dir_prefix + std::string("conv2d_6_b.bin"); 
+     void* conv2d_6_b =  readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,256,1,1); 
+     std::string conv2d_7_w_path =  dir_prefix + std::string("conv2d_7_w.bin"); 
+     void* conv2d_7_w =  readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3); 
+     std::string conv2d_7_b_path =  dir_prefix + std::string("conv2d_7_b.bin"); 
+     void* conv2d_7_b =  readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1); 
+     std::string conv2d_8_w_path =  dir_prefix + std::string("conv2d_8_w.bin"); 
+     void* conv2d_8_w =  readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3); 
+     std::string conv2d_8_b_path =  dir_prefix + std::string("conv2d_8_b.bin"); 
+     void* conv2d_8_b =  readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1); 
+     std::string conv2d_9_w_path =  dir_prefix + std::string("conv2d_9_w.bin"); 
+     void* conv2d_9_w =  readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3); 
+     std::string conv2d_9_b_path =  dir_prefix + std::string("conv2d_9_b.bin"); 
+     void* conv2d_9_b =  readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1); 
+     std::string conv2d_10_w_path =  dir_prefix + std::string("conv2d_10_w.bin"); 
+     void* conv2d_10_w =  readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3); 
+     std::string conv2d_10_b_path =  dir_prefix + std::string("conv2d_10_b.bin"); 
+     void* conv2d_10_b =  readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1); 
+     std::string conv2d_11_w_path =  dir_prefix + std::string("conv2d_11_w.bin"); 
+     void* conv2d_11_w =  readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3); 
+     std::string conv2d_11_b_path =  dir_prefix + std::string("conv2d_11_b.bin"); 
+     void* conv2d_11_b =  readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1); 
+     std::string conv2d_12_w_path =  dir_prefix + std::string("conv2d_12_w.bin"); 
+     void* conv2d_12_w =  readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3); 
+     std::string conv2d_12_b_path =  dir_prefix + std::string("conv2d_12_b.bin"); 
+     void* conv2d_12_b =  readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1); 
+     std::string conv2d_13_w_path =  dir_prefix + std::string("conv2d_13_w.bin"); 
+     void* conv2d_13_w =  readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3); 
+     std::string conv2d_13_b_path =  dir_prefix + std::string("conv2d_13_b.bin"); 
+     void* conv2d_13_b =  readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1); 
+     std::string dense_1_w_path =  dir_prefix + std::string("dense_1_w.bin"); 
+     void* dense_1_w =  readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,512,512); 
+     std::string dense_1_b_path =  dir_prefix + std::string("dense_1_b.bin"); 
+     void* dense_1_b =  readTrainedWeights(dense_1_b_path.c_str(), 0,1,512,1,1); 
+     std::string dense_2_w_path =  dir_prefix + std::string("dense_2_w.bin"); 
+     void* dense_2_w =  readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,512,10); 
+     std::string dense_2_b_path =  dir_prefix + std::string("dense_2_b.bin"); 
+     void* dense_2_b =  readTrainedWeights(dense_2_b_path.c_str(), 0,1,10,1,1); 
+
+
+     int start = i * batch_size; 
+     int end = (i + 1) * batch_size; 
+
+     void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32); 
+
+     void* var_0 = ConvLayer_PROMISE(input, -1.8816367, 2.0934217, conv2d_1_w, -0.53275156, 0.49437004, conv2d_1_b, -0.6403629, 0.2490165, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.3590874671936035, 9); 
+     void* var_1 = ConvLayer_PROMISE(var_0, 0.0, 1.3590874671936035, conv2d_2_w, -0.2688396, 0.20639156, conv2d_2_b, -0.7745511, 0.82006615, 1, 1, 1, 1, 0, 2, 1, 0.0, 2.521231179237361, 9); 
+     void* var_2 = ConvLayer_PROMISE(var_1, 0.0, 2.521231179237361, conv2d_3_w, -0.16776876, 0.14878987, conv2d_3_b, -0.35283303, 0.5154362, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.2011985784769053, 9); 
+     void* var_3 = ConvLayer_PROMISE(var_2, 0.0, 1.2011985784769053, conv2d_4_w, -0.088948585, 0.114222586, conv2d_4_b, -0.30250227, 0.36856708, 1, 1, 1, 1, 0, 2, 1, 0.0, 1.0359880930185312, 9); 
+     void* var_4 = ConvLayer_PROMISE(var_3, 0.0, 1.0359880930185312, conv2d_5_w, -0.07739562, 0.10973293, conv2d_5_b, -0.15568458, 0.17634983, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.3004955950379369, 9); 
+     void* var_5 = ConvLayer_PROMISE(var_4, 0.0, 0.3004955950379369, conv2d_6_w, -0.051649556, 0.05435231, conv2d_6_b, -0.07395447, 0.07996062, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.11490475405007583, 9); 
+     void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 0.11490475405007583, conv2d_7_w, -0.043513633, 0.07577866, conv2d_7_b, -0.06921874, 0.02660573, 1, 1, 1, 1, 0, 2, 1, 0.0, 0.16232508487999475, 9); 
+     void* var_7 = ConvLayer_PROMISE(var_6, 0.0, 0.16232508487999475, conv2d_8_w, -0.033842053, 0.045218028, conv2d_8_b, -0.022827804, 0.023845317, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.12424996573477909, 9); 
+     void* var_8 = ConvLayer_PROMISE(var_7, 0.0, 0.12424996573477909, conv2d_9_w, -0.02211613, 0.032084666, conv2d_9_b, -0.02699063, 0.03773564, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.1746344865113496, 9); 
+     void* var_9 = ConvLayer_PROMISE(var_8, 0.0, 0.1746344865113496, conv2d_10_w, -0.01979376, 0.034854397, conv2d_10_b, -0.036107242, 0.07056531, 1, 1, 1, 1, 0, 2, 1, 0.0, 0.5751757621765137, 9); 
+     void* var_10 = ConvLayer_PROMISE(var_9, 0.0, 0.5751757621765137, conv2d_11_w, -0.03452098, 0.046055835, conv2d_11_b, -0.051925894, 0.07039055, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.7718751144409115, 9); 
+     void* var_11 = ConvLayer_PROMISE(var_10, 0.0, 0.7718751144409115, conv2d_12_w, -0.025946895, 0.040090334, conv2d_12_b, -0.06049362, 0.12658806, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.1728516906499844, 9); 
+     void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 1.1728516906499844, conv2d_13_w, -0.021766115, 0.03315237, conv2d_13_b, -0.20705001, 0.117947325, 1, 1, 1, 1, 0, 2, 1, 0.0, 2.0015769386291495, 9); 
+     void* var_13 = FCLayer_PROMISE(var_12, 0.0, 2.0015769386291495, dense_1_w, -0.042597745, 0.046707444, dense_1_b, -0.21937433, 0.2545502, 1, 0.0, 2.002361118793486, 9); 
+     void* var_14 = FCLayer_PROMISE(var_13, 0.0, 2.002361118793486, dense_2_w, -0.32550547, 0.30829763, dense_2_b, -1.1787822, 1.2378151, -1, -18.251470546722413, 24.17363445281988, 9); 
+     void* var_15 = tensorSoftmax(var_14); 
+
+     uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end); 
+
+     float accuracy = computeAccuracy2(labels, batch_size, var_15); 
+     final_accuracy += accuracy; 
+     freeBatchMemory(); 
+ 
+   }
+
+   final_accuracy = final_accuracy / batch_count; 
+   dumpFinalAccuracy(final_accuracy); 
+
+
+ }
+
+ dumpExecutionAccuracies(); 
+
+ llvm_hpvm_cleanupTensorRt(); 
+
+ return 0; 
+
+}