Updated test case

llvm/test/VISC/DNN_Benchmarks/benchmarks/canny_test/src/canny_direct_call.cpp

+#include "tensor_runtime.h"
+#include "visc.h"
+
+Tensor *gaussianFilter(float sigma, size_t w, size_t h, size_t n_chan) {
+  int64_t m = (w - 1) / 2, n = (h - 1) / 2;
+  auto *data = new float[w * h];
+  float sum = 0.0f;
+  for (int64_t i = -m; i <= m; i++)
+    for (int64_t j = -n; j <= n; j++) {
+      size_t idx = (i + m) * h + (j + n);
+      float exponent = -(i * i + j * j) / (2.0 * sigma * sigma);
+      data[idx] = exp(exponent);
+      sum += data[idx];
+    }
+  if (sum != 0.0f)
+    for (size_t i = 0; i < w * h; i++)
+      data[i] /= sum;
+  return (Tensor *)createFilterFromData(CUDNN_DATA_FLOAT, data, w, h, n_chan);
+}
+
+std::pair<Tensor *, Tensor *> getSobelKernels() {
+  std::vector<float> k1({-1, 0, 1, -2, 0, 2, -1, 0, 1});
+  std::vector<float> k2({1, 2, 1, 0, 0, 0, -1, -2, -1});
+  auto *t1 =
+      (Tensor *)createFilterFromData(CUDNN_DATA_FLOAT, k1.data(), 3, 3, 1);
+  auto *t2 =
+      (Tensor *)createFilterFromData(CUDNN_DATA_FLOAT, k2.data(), 3, 3, 1);
+  return std::make_pair(t1, t2);
+}
+
+void *main_fp32(void *dataset) {
+  Tensor *gaussian = gaussianFilter(1.4, 5, 5, 1);
+  Tensor *kernel_x, *kernel_y;
+  std::tie(kernel_x, kernel_y) = getSobelKernels();
+
+  // 0. Grayscale
+  auto *summed_image =
+      wrapper_tensorReduce("1", dataset, 1, device::fadd_ptrptr);
+  auto *grayscale_image =
+      wrapper_tensorMap1("2", device::favg3_ptrptr, summed_image);
+  // 1. Denoise
+  auto *image2 = wrapper_ConvLayer(
+      "3", grayscale_image, gaussian, nullptr, 2, 2, 1, 1, 0, 0, -1, 0.0, 0.0);
+  // 2. Get edge gradient / direction
+  auto *grad_x = wrapper_ConvLayer(
+      "4", image2, kernel_x, nullptr, 1, 1, 1, 1, 0, 0, -1, 0.0, 0.0);
+  auto *grad_y = wrapper_ConvLayer(
+      "5", image2, kernel_y, nullptr, 1, 1, 1, 1, 0, 0, -1, 0.0, 0.0);
+  auto *grad_mag =
+      wrapper_tensorMap2("6", device::fhypot_ptrptr, grad_x, grad_y);
+  // 2.5. Normalize grad magnitude
+  auto *grad_max_1D =
+      wrapper_tensorReduce("7", grad_mag, 2, device::fmax_ptrptr);
+  auto *grad_max =
+      wrapper_tensorReduce("8", grad_max_1D, 3, device::fmax_ptrptr);
+  auto *grad_mag_norm =
+      wrapper_tensorMap2("9", device::fdiv_ptrptr, grad_mag, grad_max);
+  return grad_mag_norm;
+}
+
+extern void llvm_hpvm_initializeRuntimeController(
+    const char *ConfigFile, const char *QRangeFile);
+
+extern void llvm_hpvm_clearRuntimeController();
+
+extern void llvm_hpvm_initTensorRt(int gpuid);
+
+extern void llvm_hpvm_nextIter();
+
+const size_t batch_size = 1000;
+
+int main(int argc, char *argv[]) {
+  if (argc < 3)
+    return 0;
+  llvm_hpvm_initTensorRt(0);
+  llvm_hpvm_initializeRuntimeController("data/tuner_confs.txt", "");
+
+  size_t bstart = 0;
+  startMemTracking();
+  startProfiling();
+  while (true) {
+    Tensor *batch = readDataSet(argv[1], bstart, batch_size);
+    if (batch == nullptr)
+      break;
+
+    auto *result_fp32 = main_fp32(batch);
+    saveDataSet(argv[2], (Tensor *)result_fp32, bstart);
+
+    bstart += batch_size;
+    llvm_hpvm_nextIter();
+    freeBatchMemory();
+    clearTensorMap();
+  }
+  stopProfiling();
+  llvm_hpvm_clearRuntimeController();
+
+  // std::vector<float> psnrs = PSNR(result_fp32, result_fp16);
+}

llvm/test/VISC/DNN_Benchmarks/benchmarks/canny_test/src/canny_test.cpp

@@ -43,7 +43,7 @@ void var_3_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
   __visc__hint(visc::GPU_TARGET);
   __visc__attributes(2, t1, t2, 0);
 
-  void *r = __visc__tensor_convolution(t1, t2, 2, 2, 1, 1);
+  void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
   __visc__return(2, r, (size_t)0);
 }
 
@@ -51,7 +51,7 @@ void var_4_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
   __visc__hint(visc::GPU_TARGET);
   __visc__attributes(2, t1, t2, 0);
 
-  void *r = __visc__tensor_convolution(t1, t2, 2, 2, 1, 1);
+  void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
   __visc__return(2, r, (size_t)0);
 }