benchmark modifications to make it compilable in a standalone fashion.

1 files changed, 49 insertions, 38 deletions

diff --git a/bench/tensors/tensor_benchmarks.h b/bench/tensors/tensor_benchmarks.h
index 525b9acda..a1696afda 100644
--- a/bench/tensors/tensor_benchmarks.h
+++ b/bench/tensors/tensor_benchmarks.h
@@ -4,12 +4,23 @@
 typedef int TensorIndex;
 #define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
 
-#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
-#include "testing/base/public/benchmark.h"
+#include "unsupported/Eigen/CXX11/Tensor"
+#include "benchmark.h"
+
+#define BENCHMARK_RANGE(bench, lo, hi) \
+  BENCHMARK(bench)->Range(lo, hi)
+
+template <typename... Args>
+std::string StrCat(const Args... args) {
+  std::stringstream ss;
+  StrCatRecursive(ss, args...);
+  return ss.str();
+}
 
 using Eigen::Tensor;
 using Eigen::TensorMap;
 
+typedef int64_t int64;
 
 // TODO(bsteiner): also templatize on the input type since we have users
 // for int8 as well as floats.
@@ -43,7 +54,7 @@ template <typename Device> class BenchmarkSuite {
 
   void random(int num_iters) {
     eigen_assert(m_ == k_ && k_ == n_);
-    const Eigen::array<TensorIndex, 2> sizes(m_, m_);
+    const Eigen::array<TensorIndex, 2> sizes = {{m_, m_}};
     TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizes);
 
     StartBenchmarkTiming();
@@ -56,16 +67,16 @@ template <typename Device> class BenchmarkSuite {
 
   void slicing(int num_iters) {
     eigen_assert(m_ == k_ && k_ == n_);
-    const Eigen::array<TensorIndex, 2> sizes(m_, m_);
+    const Eigen::array<TensorIndex, 2> sizes = {{m_, m_}};
     const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, sizes);
     const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, sizes);
     TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizes);
 
-    const Eigen::DSizes<TensorIndex, 2> quarter_sizes(Eigen::array<TensorIndex, 2>(m_/2, m_/2));
-    const Eigen::DSizes<TensorIndex, 2> first_quadrant(Eigen::array<TensorIndex, 2>(0, 0));
-    const Eigen::DSizes<TensorIndex, 2> second_quadrant(Eigen::array<TensorIndex, 2>(0, m_/2));
-    const Eigen::DSizes<TensorIndex, 2> third_quadrant(Eigen::array<TensorIndex, 2>(m_/2, 0));
-    const Eigen::DSizes<TensorIndex, 2> fourth_quadrant(Eigen::array<TensorIndex, 2>(m_/2, m_/2));
+    const Eigen::DSizes<TensorIndex, 2> quarter_sizes(m_/2, m_/2);
+    const Eigen::DSizes<TensorIndex, 2> first_quadrant(0, 0);
+    const Eigen::DSizes<TensorIndex, 2> second_quadrant(0, m_/2);
+    const Eigen::DSizes<TensorIndex, 2> third_quadrant(m_/2, 0);
+    const Eigen::DSizes<TensorIndex, 2> fourth_quadrant(m_/2, m_/2);
 
     StartBenchmarkTiming();
     for (int iter = 0; iter < num_iters; ++iter) {
@@ -85,12 +96,12 @@ template <typename Device> class BenchmarkSuite {
 
   void shuffling(int num_iters) {
     eigen_assert(m_ == n_);
-    const Eigen::array<TensorIndex, 2> size_a(m_, k_);
+    const Eigen::array<TensorIndex, 2> size_a = {{m_, k_}};
     const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, size_a);
-    const Eigen::array<TensorIndex, 2> size_b(k_, m_);
+    const Eigen::array<TensorIndex, 2> size_b = {{k_, m_}};
     TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, size_b);
 
-    const Eigen::array<int, 2> shuffle(1, 0);
+    const Eigen::array<int, 2> shuffle = {{1, 0}};
 
     StartBenchmarkTiming();
     for (int iter = 0; iter < num_iters; ++iter) {
@@ -102,9 +113,9 @@ template <typename Device> class BenchmarkSuite {
 
  void padding(int num_iters) {
     eigen_assert(m_ == k_);
-    const Eigen::array<TensorIndex, 2> size_a(m_, k_-3);
+    const Eigen::array<TensorIndex, 2> size_a = {{m_, k_-3}};
     const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, size_a);
-    const Eigen::array<TensorIndex, 2> size_b(k_, m_);
+    const Eigen::array<TensorIndex, 2> size_b = {{k_, m_}};
     TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, size_b);
 
     Eigen::array<Eigen::IndexPair<TensorIndex>, 2> paddings;
@@ -121,12 +132,12 @@ template <typename Device> class BenchmarkSuite {
 
  void striding(int num_iters) {
     eigen_assert(m_ == k_);
-    const Eigen::array<TensorIndex, 2> size_a(m_, k_);
+    const Eigen::array<TensorIndex, 2> size_a = {{m_, k_}};
     const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, size_a);
-    const Eigen::array<TensorIndex, 2> size_b(m_, k_ / 2);
+    const Eigen::array<TensorIndex, 2> size_b = {{m_, k_ / 2}};
     TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, size_b);
 
-    const Eigen::array<TensorIndex, 2> strides(1, 2);
+    const Eigen::array<TensorIndex, 2> strides = {{1, 2}};
 
     StartBenchmarkTiming();
     for (int iter = 0; iter < num_iters; ++iter) {
@@ -137,14 +148,14 @@ template <typename Device> class BenchmarkSuite {
   }
 
   void broadcasting(int num_iters) {
-    const Eigen::array<TensorIndex, 2> size_a(m_, 1);
+    const Eigen::array<TensorIndex, 2> size_a = {{m_, 1}};
     const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, size_a);
-    const Eigen::array<TensorIndex, 2> size_c(m_, n_);
+    const Eigen::array<TensorIndex, 2> size_c = {{m_, n_}};
     TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, size_c);
 
-#if defined(__CUDACC__)
+#ifndef EIGEN_HAS_INDEX_LIST
     // nvcc doesn't support cxx11
-    const Eigen::array<int, 2> broadcast(1, n_);
+    const Eigen::array<int, 2> broadcast = {{1, n_}};
 #else
     // Take advantage of cxx11 to give the compiler information it can use to
     // optimize the code.
@@ -162,7 +173,7 @@ template <typename Device> class BenchmarkSuite {
 
   void coeffWiseOp(int num_iters) {
     eigen_assert(m_ == k_ && k_ == n_);
-    const Eigen::array<TensorIndex, 2> sizes(m_, m_);
+    const Eigen::array<TensorIndex, 2> sizes = {{m_, m_}};
     const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, sizes);
     const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, sizes);
     TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizes);
@@ -178,7 +189,7 @@ template <typename Device> class BenchmarkSuite {
 
   void algebraicFunc(int num_iters) {
     eigen_assert(m_ == k_ && k_ == n_);
-    const Eigen::array<TensorIndex, 2> sizes(m_, m_);
+    const Eigen::array<TensorIndex, 2> sizes = {{m_, m_}};
     const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, sizes);
     const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, sizes);
     TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizes);
@@ -194,7 +205,7 @@ template <typename Device> class BenchmarkSuite {
 
   void transcendentalFunc(int num_iters) {
     eigen_assert(m_ == k_ && k_ == n_);
-    const Eigen::array<TensorIndex, 2> sizes(m_, m_);
+    const Eigen::array<TensorIndex, 2> sizes = {{m_, m_}};
     const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, sizes);
     const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, sizes);
     TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizes);
@@ -210,12 +221,12 @@ template <typename Device> class BenchmarkSuite {
 
   // Simple reduction
   void reduction(int num_iters) {
-    const Eigen::array<TensorIndex, 2> input_size(k_, n_);
+    const Eigen::array<TensorIndex, 2> input_size = {{k_, n_}};
     const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, input_size);
-    const Eigen::array<TensorIndex, 1> output_size(n_);
+    const Eigen::array<TensorIndex, 1> output_size = {{n_}};
     TensorMap<Tensor<float, 1>, Eigen::Aligned> C(c_, output_size);
 
-    const Eigen::array<TensorIndex, 1> sum_along_dim(0);
+    const Eigen::array<TensorIndex, 1> sum_along_dim = {{0}};
 
     StartBenchmarkTiming();
     for (int iter = 0; iter < num_iters; ++iter) {
@@ -228,16 +239,16 @@ template <typename Device> class BenchmarkSuite {
 
   // do a contraction which is equivalent to a matrix multiplication
   void contraction(int num_iters) {
-    const Eigen::array<TensorIndex, 2> sizeA(m_, k_);
-    const Eigen::array<TensorIndex, 2> sizeB(k_, n_);
-    const Eigen::array<TensorIndex, 2> sizeC(m_, n_);
+    const Eigen::array<TensorIndex, 2> sizeA = {{m_, k_}};
+    const Eigen::array<TensorIndex, 2> sizeB = {{k_, n_}};
+    const Eigen::array<TensorIndex, 2> sizeC = {{m_, n_}};
 
     const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, sizeA);
     const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, sizeB);
     TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizeC);
 
     typedef typename Tensor<float, 2>::DimensionPair DimPair;
-    const Eigen::array<DimPair, 1> dims(DimPair(1, 0));
+    const Eigen::array<DimPair, 1> dims = {{DimPair(1, 0)}};
 
     StartBenchmarkTiming();
     for (int iter = 0; iter < num_iters; ++iter) {
@@ -249,14 +260,14 @@ template <typename Device> class BenchmarkSuite {
   }
 
   void convolution(int num_iters, int kernel_x, int kernel_y) {
-    const Eigen::array<TensorIndex, 2> input_sizes(m_, n_);
+    const Eigen::array<TensorIndex, 2> input_sizes = {{m_, n_}};
     TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, input_sizes);
-    const Eigen::array<TensorIndex, 2> kernel_sizes(kernel_x, kernel_y);
+    const Eigen::array<TensorIndex, 2> kernel_sizes = {{kernel_x, kernel_y}};
     TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, kernel_sizes);
-    const Eigen::array<TensorIndex, 2> result_sizes(
-        m_ - kernel_x + 1, n_ - kernel_y + 1);
+    const Eigen::array<TensorIndex, 2> result_sizes =
+        {{m_ - kernel_x + 1, n_ - kernel_y + 1}};
     TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, result_sizes);
-    Eigen::array<Tensor<float, 2>::Index, 2> dims(0, 1);
+    Eigen::array<Tensor<float, 2>::Index, 2> dims = {{0, 1}};
 
     StartBenchmarkTiming();
     for (int iter = 0; iter < num_iters; ++iter) {
@@ -280,7 +291,7 @@ template <typename Device> class BenchmarkSuite {
     device_.memset(b_, 23, k_ * n_ * sizeof(float));
     device_.memset(c_, 31, m_ * n_ * sizeof(float));
 
-    BenchmarkUseRealTime();
+    //BenchmarkUseRealTime();
   }
 
   inline void finalizeBenchmark(int64 num_items) {
@@ -290,7 +301,7 @@ template <typename Device> class BenchmarkSuite {
     }
 #endif
     StopBenchmarkTiming();
-    SetBenchmarkItemsProcessed(num_items);
+    SetBenchmarkBytesProcessed(num_items);
   }