Fixing tensorUtils definitions to avoid undefs in LLVM IR

hpvm/include/nvdla/tensorUtils.h

@@ -15,41 +15,111 @@ std::string model_params_path = "../../test/dnn_benchmarks/model_params/";
 
 
 
-struct Tensor *readTrainedWeights(const char *file_name, int data_type,
-                                  long int dim1_size, long int dim2_size,
-                                  long int dim3_size, long int dim4_size) {
+__attribute__((noinline)) struct Tensor *readTrainedWeights(const char *file_name, int data_type,
+							    long int dim1_size, long int dim2_size,
+							    long int dim3_size, long int dim4_size) {
+  
+  int type_size = 4; // NOTE: Assuming floating point tensors
+  long int num_elems = dim1_size * dim2_size * dim3_size * dim4_size;
+  long int size_in_bytes =
+      type_size * dim1_size * dim2_size * dim3_size * dim4_size;
+  float *tensor_data = (float *)malloc(sizeof(float) * num_elems);
+ 
+  int file_header_size = 0;
+
+  FILE *file = fopen(file_name, "rb");
+  if (file == NULL) {
+    printf("Data file %s is not found. Aborting... \n", file_name);
+    abort();
+  }
+
+  fseek(file, file_header_size, SEEK_CUR); // Skipping the file header
+  size_t bytes_read = fread(tensor_data, 1, size_in_bytes, file);
+
+  fclose(file);
 
+
+  // Returning a dummy tensor 
   struct Tensor* weightsTensor = new struct Tensor;
   return weightsTensor;
 }
 
 
-struct Tensor *readInputBatch(const char *file_name, long data_type,
-			      long start, long end,
-			      long dim2_size, long dim3_size, long dim4_size) {
+__attribute__((noinline)) struct Tensor *readInputBatch(const char *file_name, long data_type,
+							long start, long end,
+							long dim2_size, long dim3_size, long dim4_size) {
+  long int dim1_size = end - start;
+  // FIXIT: Don't assume floating point types
+  long int type_size = 4; // NOTE: Assuming floating point tensors
+  long int num_elems = dim1_size * dim2_size * dim3_size * dim4_size;
+  long int size_in_bytes =
+      type_size * dim1_size * dim2_size * dim3_size * dim4_size;
+  float *tensor_data = (float *)malloc(sizeof(float) * num_elems);
+  long int file_header_size =
+      type_size * start * dim2_size * dim3_size * dim4_size;
+
+  FILE *file = fopen(file_name, "rb");
+  if (file == NULL) {
+    printf("Data file %s is not found. Aborting... \n", file_name);
+    abort();
+  }
+
+  fseek(file, file_header_size, SEEK_SET); // Skipping the file header
+  size_t bytes_read = fread(tensor_data, 1, size_in_bytes, file);
+
+  fclose(file);
 
+  // Creating and returning empty tensor
   struct Tensor* inputTensor = new struct Tensor;
   return inputTensor;
 }
 
-uint8_t *readLabels(const char *labels_file, int num_labels) {
+__attribute__((noinline)) uint8_t *readLabels(const char *labels_file, int num_labels) {
 
-  uint8_t* dummyLabels = (uint8_t*) malloc(sizeof(uint8_t) * num_labels);
-  return dummyLabels;		 
+  uint8_t *labels = (uint8_t *)malloc(sizeof(uint8_t) * num_labels);
+  FILE *file = fopen(labels_file, "rb");
+  if (file == NULL) {
+    printf("Data file %s is not found. Aborting...\n", labels_file);
+    abort();
+  }
+
+  size_t bytes_read = fread(labels, 1, sizeof(uint8_t) * num_labels, file);
+
+  fclose(file);
+  
+  return labels;	 
 }
 
-uint32_t *readLabels3(const char *labels_file, int num_labels) {
+__attribute__((noinline)) uint32_t *readLabels3(const char *labels_file, int num_labels) {
+
+  uint32_t *labels = (uint32_t *) malloc(sizeof(uint32_t) * num_labels);
+  FILE *file = fopen(labels_file, "rb");
+  if (file == NULL) {
+    printf("Data file %s is not found. Aborting...\n", labels_file);
+    abort();
+  }
 
-  uint32_t* dummyLabels = (uint32_t*) malloc(sizeof(uint32_t) * num_labels);
-  return dummyLabels;		 
+  size_t bytes_read = fread(labels, 1, sizeof(uint32_t) * num_labels, file);
+  fclose(file);
+
+  return labels; 
 }
 
 
-uint32_t *readLabelsBatch3(const char *labels_file, int start, int end) {
+__attribute__((noinline)) uint32_t *readLabelsBatch3(const char *labels_file, int start, int end) {
 
   long int num_labels = end - start;
-  uint32_t* dummyLabels = (uint32_t*) malloc(sizeof(uint32_t) * num_labels);
-  return dummyLabels;		 
+  uint32_t *labels = (uint32_t *) malloc(sizeof(uint32_t) * num_labels);
+  FILE *file = fopen(labels_file, "rb");
+  if (file == NULL) {
+    printf("Data file %s is not found. Aborting...\n", labels_file);
+    abort();
+  }
+
+  size_t bytes_read = fread(labels, 1, sizeof(uint32_t) * num_labels, file);
+  fclose(file);
+
+  return labels; 
 }
 
 
@@ -63,9 +133,14 @@ float computeAccuracy3(uint32_t *labels, void *result_ptr) {
 // tensor_runtime.h empty definitions - for NVDLA-based compilation to work (functions not actually used)
 
 
-void *create4DTensor(int data_type, int data_format, size_t dim1_size,
-                     size_t dim2_size, size_t dim3_size, size_t dim4_size){
+__attribute__((noinline)) void *create4DTensor(int data_type, int data_format, size_t dim1_size,
+					       size_t dim2_size, size_t dim3_size, size_t dim4_size){
 
+  long int type_size = 4; // NOTE: Assuming floating point tensors
+  long int num_elems = dim1_size * dim2_size * dim3_size * dim4_size;
+  long int size_in_bytes =
+      type_size * dim1_size * dim2_size * dim3_size * dim4_size;
+  
   struct Tensor* weightsTensor = new struct Tensor;
   return weightsTensor;
 }