Handling visc.node.id and selecting NodeConfigs using IDs

llvm/projects/hpvm-tensor-rt/tensor_runtime/src/hpvm-rt-controller.cpp

@@ -416,9 +416,18 @@ std::vector<float> &RuntimeController::getQuantizationRanges(const char *data) {
 }
 
 NodeConfiguration *RuntimeController::getNodeConfiguration(const char *data) {
-  std::string s(data);
-  // All nodes are expected to have a configuration
-  return (*Configurations)[configurationIdx]->setup.at(s);
+
+  // if visc.node.id Not specified for this HPVM Node
+  if (currentTensorID == -1){
+    std::string s(data);
+    // All nodes are expected to have a configuration
+    return (*Configurations)[configurationIdx]->setup.at(s);
+  }
+  else{
+    DEBUG("-- currentTensorID = \%u \n", currentTensorID); 
+    return (*Configurations)[configurationIdx]->idConfigMap.at(currentTensorID);
+  }
+  
 }
 
 void RuntimeController::init(const char *Cstr, const char *Qstr) {
@@ -810,7 +819,7 @@ void RuntimeController::readConfigurationFile(const char *str) {
       // FIXME: Do same for CPU and PROMISE configs
       InitialConfigurations.back().idConfigMap.insert(
           std::make_pair(firstTensorID, NodeConf));
-      printf ("*** firstTensorID = %d \n\n", firstTensorID);
+      DEBUG("*** firstTensorID = %d \n\n", firstTensorID);
       
       unsigned idx = 2;
       while (idx < tokens.size()) {

llvm/projects/hpvm-tensor-rt/tensor_runtime/src/legacy/wrapper_runtime_back.cu

+
+#include <stdio.h>
+#include <cstdio>
+#include <cstdlib>
+#include <iostream>
+#include <string>
+#include <vector>
+
+#include <cuda_runtime.h>
+#include <device_launch_parameters.h>
+
+#include <cublas_v2.h>
+#include <cudnn.h>
+#include <cublas_api.h>
+#include <cuda_fp16.h>
+#include <driver_types.h>
+
+
+// Tensor runtime header files
+#include "../include/tensor_runtime.h"
+#include "../include/tensor_utils.h"
+#include "../include/debug.h"
+#include "../include/profiling.h"
+#include "../include/fp16_conversion.h"
+#include "../include/global_data.h"
+#include "../include/error.h"
+#include "../include/tensor.h"
+#include "../include/op_overheads.h"
+#include "../include/half_precision_api.h"
+
+
+
+/*********** Generic Layer API **************/
+
+void* ConvLayerWrapper(void* input, 
+		void* filter, 
+		void* bias, 
+		int conv_pad_h, int conv_pad_w,
+		int conv_stride_h, int conv_stride_w,
+		int pool_id, int pool_size,
+		int activation_id,
+		// NOTE: out_min, out_max are only relevant for ClippedRelu
+		float out_min, float out_max){
+ 			
+  
+  void* conv_out = tensorConvolution(input, filter,
+				     conv_pad_h, conv_pad_w,
+				     conv_stride_h, conv_stride_w,
+				     1, 0);
+  
+  void* conv_add = tensorAdd(conv_out, bias);
+
+  void* pool_out;
+  // NOTE: Skip pooling when pool size is not greater than 0
+  if(pool_size > 0){
+    //FIXME: Currently only using MaxPooling
+    pool_out = tensorPooling(conv_add, 0, pool_size, pool_size, 0, 0, pool_size, pool_size);
+  }
+  else{
+    pool_out = conv_add;
+  }
+  
+  void* activation_out;  
+  switch(activation_id){
+  case -1:
+    activation_out = pool_out;
+    INFO("NO Activation Function \n");
+    break;
+  case 0:
+    activation_out = tensorTanh(pool_out);
+    break;
+  case 1:
+    activation_out = tensorRelu(pool_out);
+    break;
+  case 2:
+    activation_out = tensorRelu2(pool_out, out_min, out_max);
+    break;
+  default:
+    ERROR("Activation id %d NOT supported \n", activation_out);
+    break;
+  }
+
+  return activation_out;
+}
+
+
+void* FCLayerWrapper(void* input, 
+	      void* weights, 
+	      void* bias, 
+	      int activation_id,
+	      // NOTE: out_min and out_max are only relevant for ClippedRelu
+	      float out_min, float out_max){ 
+
+  
+  void* gemm_out = tensorGemmGPU(input, weights);
+
+  void* gemmbias_out = tensorAdd(gemm_out, bias);
+  
+  void* activation_out;
+  switch(activation_id){
+
+  case -1:
+    activation_out = gemmbias_out;
+    INFO("No Activation Function \n");
+    break;
+  case 0:
+    activation_out = tensorTanh(gemmbias_out);
+    break;
+  case 1:
+    activation_out = tensorRelu(gemmbias_out);
+    break;
+  case 2:
+    activation_out = tensorRelu2(gemmbias_out, out_min, out_max);
+    break;
+  default:
+    ERROR("Activation id %d NOT supported \n", activation_out);
+    break;
+  }
+  
+  return activation_out;
+}
+
+