Adding Promise sources for Alexnet2-CIFAR10

llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet2_cifar10_promise.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"
+
+
+bool Opentuner_run = false;
+
+/* NOTE: Reference Architecture to use for profiling */
+void testLenetTanh(){
+
+  int total_runs = 1;
+  if(Opentuner_run){
+    total_runs = 100000;
+  }
+  
+  printf("********* Lenet-2 Architecture ********** \n");
+
+  int test_batch_size = 5000;
+
+  uint8_t* labels = readLabels("../model_params/alexnet2_cifar10/test_labels.bin", test_batch_size);
+
+  for(int i = 0; i < total_runs; i++){
+
+    void* input = readTrainedWeights("../model_params/alexnet2_cifar10/norm_cifar_input.bin",
+			  	   float_type,
+				   test_batch_size, 3, 32, 32);
+    
+    void* conv1_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv1.bin",
+					    float_type, 32, 3, 3, 3);  
+    void* conv1_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv1_bias.bin",
+					  float_type, 1, 32, 1, 1);  
+    void* conv2_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv2.bin",
+					    float_type, 32, 32, 3, 3);  
+    void* conv2_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv2_bias.bin",
+					  float_type, 1, 32, 1, 1);
+    void* conv3_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv3.bin",
+					    float_type, 64, 32, 3, 3);  
+    void* conv3_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv3_bias.bin",
+					  float_type, 1, 64, 1, 1);  
+    void* conv4_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv4.bin",
+					    float_type, 64, 64, 3, 3);  
+    void* conv4_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv4_bias.bin",
+					  float_type, 1, 64, 1, 1);
+    void* conv5_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv5.bin",
+					    float_type, 128, 64, 3, 3);  
+    void* conv5_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv5_bias.bin",
+					  float_type, 1, 128, 1, 1);
+    void* conv6_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv6.bin",
+					    float_type, 128, 128, 3, 3);  
+    void* conv6_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv6_bias.bin",
+					  float_type, 1, 128, 1, 1);
+  
+    void* fc1_weights = readTrainedWeights("../model_params/alexnet2_cifar10/fc1.bin",
+					   float_type, 1, 1, 2048, 10);  
+    void* fc1_bias = readTrainedWeights("../model_params/alexnet2_cifar10/fc1_bias.bin",
+					float_type, 1, 10, 1, 1);  
+ 
+  
+    clearTensorMap();  
+
+    if(Opentuner_run){
+
+      char* myfifo = "/tmp/myfifo";
+      int fd = open(myfifo, O_RDONLY);
+
+      int ret_val = fcntl(fd, F_GETFD);
+      if(ret_val == -1){
+	printf("Invalid descriptor \n");
+	abort();
+      }
+
+      char str[100];
+      read(fd, str, 80);
+      if(strcmp(str, "stop_run") == 0){
+	abort();
+      }
+
+      close(fd);
+    }
+
+    
+    readOpenTunerFlags("opentuner_flags"); // Resets the OpenTuner counters
+
+    // Start power and performance profiling 
+    startProfiling();
+
+    //-1.881, 2.09
+    //-0.18,0.174
+    void* conv1_out = ConvLayer_PROMISE(input, -1.881, 2.09, conv1_filter, -0.542,0.371, conv1_bias, -0.066,0.04,
+					1, 1, 1, 1, 0, 0, 0, -1,1, 9);
+
+    void* conv2_out = ConvLayer_PROMISE(conv1_out, -1,1, conv2_filter, -0.424,0.314, conv2_bias, -0.355,-0.172, 
+					1, 1, 1, 1, 0, 2, 0, -1,1, 9);
+    
+    void* conv3_out = ConvLayer_PROMISE(conv2_out, -1,1, conv3_filter, -0.441,0.795, conv3_bias, -0.804,0.753, 
+   				       1, 1, 1, 1, 0, 0, 0, -1,1, 9);
+
+    void* conv4_out = ConvLayer_PROMISE(conv3_out, -1,1, conv4_filter, -0.288,0.31, conv4_bias, -0.635,0.29, 
+				        1, 1, 1, 1, 0, 2, 0, -1,1, 9);
+
+    void* conv5_out = ConvLayer_PROMISE(conv4_out, -1,1, conv5_filter, -0.279,0.376, conv5_bias, -1.13, 1.239,
+					1, 1, 1, 1, 0, 0, 0, -1,1, 9);
+
+    void* conv6_out = ConvLayer_PROMISE(conv5_out, -1,1, conv6_filter, -0.27,0.279, conv6_bias, -0.503,0.127,
+					1, 1, 1, 1, 0, 2, 0, -1,1, 9);
+
+    // No Activation
+    void* fc1_out = FCLayer_PROMISE(conv6_out, -1,1, fc1_weights, -0.242,0.584, fc1_bias, -0.537,0.558, -1, -1,1, 9);
+    
+    void* result = tensorSoftmax(fc1_out);
+
+    // End profiling and dump output to profile.txt
+    stopProfiling();
+  
+    computeAccuracy2(labels, test_batch_size, result);
+    
+    dumpAccuracyNorms();
+    freeOutputTensors();  
+
+    if(Opentuner_run){
+
+      char* myfifo = "/tmp/myfifo";
+      int fd_out = open(myfifo, O_WRONLY);
+      int ret_val = fcntl(fd_out, F_GETFD);
+      if(ret_val == -1){
+	printf("Invalid descriptor \n");
+	abort();
+      }
+      
+      const char* str = "completed***!\n\0";
+      write(fd_out, str, 80);
+      close(fd_out);
+    }
+    
+  }
+
+
+  
+}
+
+
+int main(int argc, char* argv[]){
+
+  if(argc > 1)
+    Opentuner_run = true;
+
+  llvm_hpvm_initTensorRt(1);
+
+  testLenetTanh();
+
+  llvm_hpvm_cleanupTensorRt();
+
+  return 0;
+}
+