Extended the tensor benchmark suite to support types other than floats

1 files changed, 50 insertions, 50 deletions

diff --git a/bench/tensors/tensor_benchmarks.h b/bench/tensors/tensor_benchmarks.h
index 688f558d0..b208a401a 100644
--- a/bench/tensors/tensor_benchmarks.h
+++ b/bench/tensors/tensor_benchmarks.h
@@ -15,7 +15,7 @@ using Eigen::TensorMap;
 
 // TODO(bsteiner): also templatize on the input type since we have users
 // for int8 as well as floats.
-template <typename Device> class BenchmarkSuite {
+template <typename Device, typename T> class BenchmarkSuite {
  public:
   BenchmarkSuite(const Device& device, size_t m, size_t k, size_t n)
       : m_(m), k_(k), n_(n), device_(device) {
@@ -37,7 +37,7 @@ template <typename Device> class BenchmarkSuite {
     eigen_assert(m_ == k_ && k_ == n_);
     StartBenchmarkTiming();
     for (int iter = 0; iter < num_iters; ++iter) {
-      device_.memcpy(c_, a_, m_ * m_ * sizeof(float));
+      device_.memcpy(c_, a_, m_ * m_ * sizeof(T));
     }
     // Record the number of values copied per second
     finalizeBenchmark(static_cast<int64_t>(m_) * m_ * num_iters);
@@ -48,12 +48,12 @@ template <typename Device> class BenchmarkSuite {
     Eigen::array<TensorIndex, 2> sizes;
     sizes[0] = m_;
     sizes[1] = k_;
-    const TensorMap<Tensor<float, 2, 0, TensorIndex>, Eigen::Aligned> A(a_, sizes);
+    const TensorMap<Tensor<T, 2, 0, TensorIndex>, Eigen::Aligned> A(a_, sizes);
     TensorMap<Tensor<int, 2, 0, TensorIndex>, Eigen::Aligned> B((int*)b_, sizes);
 
     StartBenchmarkTiming();
     for (int iter = 0; iter < num_iters; ++iter) {
-      B.device(device_) = A.cast<int>();
+      B.device(device_) = A.template cast<int>();
     }
     // Record the number of values copied per second
     finalizeBenchmark(static_cast<int64_t>(m_) * k_ * num_iters);
@@ -64,7 +64,7 @@ template <typename Device> class BenchmarkSuite {
     Eigen::array<TensorIndex, 2> sizes;
     sizes[0] = m_;
     sizes[1] = m_;
-    TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizes);
+    TensorMap<Tensor<T, 2>, Eigen::Aligned> C(c_, sizes);
 
     StartBenchmarkTiming();
     for (int iter = 0; iter < num_iters; ++iter) {
@@ -79,9 +79,9 @@ template <typename Device> class BenchmarkSuite {
     Eigen::array<TensorIndex, 2> sizes;
     sizes[0] = m_;
     sizes[1] = 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 TensorMap<Tensor<T, 2>, Eigen::Aligned> A(a_, sizes);
+    const TensorMap<Tensor<T, 2>, Eigen::Aligned> B(b_, sizes);
+    TensorMap<Tensor<T, 2>, Eigen::Aligned> C(c_, sizes);
 
     const Eigen::DSizes<TensorIndex, 2> quarter_sizes(m_/2, m_/2);
     const Eigen::DSizes<TensorIndex, 2> first_quadrant(0, 0);
@@ -109,10 +109,10 @@ template <typename Device> class BenchmarkSuite {
     Eigen::array<TensorIndex, 2> input_size;
     input_size[0] = k_;
     input_size[1] = n_;
-    const TensorMap<Tensor<float, 2, 0, TensorIndex>, Eigen::Aligned> B(b_, input_size);
+    const TensorMap<Tensor<T, 2, 0, TensorIndex>, Eigen::Aligned> B(b_, input_size);
     Eigen::array<TensorIndex, 1> output_size;
     output_size[0] = n_;
-    TensorMap<Tensor<float, 1, 0, TensorIndex>, Eigen::Aligned> C(c_, output_size);
+    TensorMap<Tensor<T, 1, 0, TensorIndex>, Eigen::Aligned> C(c_, output_size);
 
     StartBenchmarkTiming();
     for (int iter = 0; iter < num_iters; ++iter) {
@@ -126,10 +126,10 @@ template <typename Device> class BenchmarkSuite {
     Eigen::array<TensorIndex, 2> input_size;
     input_size[0] = k_;
     input_size[1] = n_;
-    const TensorMap<Tensor<float, 2, 0, TensorIndex>, Eigen::Aligned> B(b_, input_size);
+    const TensorMap<Tensor<T, 2, 0, TensorIndex>, Eigen::Aligned> B(b_, input_size);
     Eigen::array<TensorIndex, 1> output_size;
     output_size[0] = n_;
-    TensorMap<Tensor<float, 1, 0, TensorIndex>, Eigen::Aligned> C(c_, output_size);
+    TensorMap<Tensor<T, 1, 0, TensorIndex>, Eigen::Aligned> C(c_, output_size);
 
     StartBenchmarkTiming();
     for (int iter = 0; iter < num_iters; ++iter) {
@@ -144,11 +144,11 @@ template <typename Device> class BenchmarkSuite {
     Eigen::array<TensorIndex, 2> size_a;
     size_a[0] = m_;
     size_a[1] = k_;
-    const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, size_a);
+    const TensorMap<Tensor<T, 2>, Eigen::Aligned> A(a_, size_a);
     Eigen::array<TensorIndex, 2> size_b;
     size_b[0] = k_;
     size_b[1] = m_;
-    TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, size_b);
+    TensorMap<Tensor<T, 2>, Eigen::Aligned> B(b_, size_b);
 
     Eigen::array<int, 2> shuffle;
     shuffle[0] = 1;
@@ -167,11 +167,11 @@ template <typename Device> class BenchmarkSuite {
     Eigen::array<TensorIndex, 2> size_a;
     size_a[0] = m_;
     size_a[1] = k_-3;
-    const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, size_a);
+    const TensorMap<Tensor<T, 2>, Eigen::Aligned> A(a_, size_a);
     Eigen::array<TensorIndex, 2> size_b;
     size_b[0] = k_;
     size_b[1] = m_;
-    TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, size_b);
+    TensorMap<Tensor<T, 2>, Eigen::Aligned> B(b_, size_b);
 
     Eigen::array<Eigen::IndexPair<TensorIndex>, 2> paddings;
     paddings[0] = Eigen::IndexPair<TensorIndex>(0, 0);
@@ -190,11 +190,11 @@ template <typename Device> class BenchmarkSuite {
     Eigen::array<TensorIndex, 2> size_a;
     size_a[0] = m_;
     size_a[1] = k_;
-    const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, size_a);
+    const TensorMap<Tensor<T, 2>, Eigen::Aligned> A(a_, size_a);
     Eigen::array<TensorIndex, 2> size_b;
     size_b[0] = m_;
     size_b[1] = k_/2;
-    TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, size_b);
+    TensorMap<Tensor<T, 2>, Eigen::Aligned> B(b_, size_b);
 
     Eigen::array<TensorIndex, 2> strides;
     strides[0] = 1;
@@ -212,11 +212,11 @@ template <typename Device> class BenchmarkSuite {
     Eigen::array<TensorIndex, 2> size_a;
     size_a[0] = m_;
     size_a[1] = 1;
-    const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, size_a);
+    const TensorMap<Tensor<T, 2>, Eigen::Aligned> A(a_, size_a);
     Eigen::array<TensorIndex, 2> size_c;
     size_c[0] = m_;
     size_c[1] = n_;
-    TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, size_c);
+    TensorMap<Tensor<T, 2>, Eigen::Aligned> C(c_, size_c);
 
 #ifndef EIGEN_HAS_INDEX_LIST
     Eigen::array<int, 2> broadcast;
@@ -242,9 +242,9 @@ template <typename Device> class BenchmarkSuite {
     Eigen::array<TensorIndex, 2> sizes;
     sizes[0] = m_;
     sizes[1] = 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 TensorMap<Tensor<T, 2>, Eigen::Aligned> A(a_, sizes);
+    const TensorMap<Tensor<T, 2>, Eigen::Aligned> B(b_, sizes);
+    TensorMap<Tensor<T, 2>, Eigen::Aligned> C(c_, sizes);
 
     StartBenchmarkTiming();
     for (int iter = 0; iter < num_iters; ++iter) {
@@ -260,9 +260,9 @@ template <typename Device> class BenchmarkSuite {
     Eigen::array<TensorIndex, 2> sizes;
     sizes[0] = m_;
     sizes[1] = 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 TensorMap<Tensor<T, 2>, Eigen::Aligned> A(a_, sizes);
+    const TensorMap<Tensor<T, 2>, Eigen::Aligned> B(b_, sizes);
+    TensorMap<Tensor<T, 2>, Eigen::Aligned> C(c_, sizes);
 
     StartBenchmarkTiming();
     for (int iter = 0; iter < num_iters; ++iter) {
@@ -278,9 +278,9 @@ template <typename Device> class BenchmarkSuite {
     Eigen::array<TensorIndex, 2> sizes;
     sizes[0] = m_;
     sizes[1] = 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 TensorMap<Tensor<T, 2>, Eigen::Aligned> A(a_, sizes);
+    const TensorMap<Tensor<T, 2>, Eigen::Aligned> B(b_, sizes);
+    TensorMap<Tensor<T, 2>, Eigen::Aligned> C(c_, sizes);
 
     StartBenchmarkTiming();
     for (int iter = 0; iter < num_iters; ++iter) {
@@ -296,9 +296,9 @@ template <typename Device> class BenchmarkSuite {
     Eigen::array<TensorIndex, 2> input_size;
     input_size[0] = k_;
     input_size[1] = n_;
-    const TensorMap<Tensor<float, 2, 0, TensorIndex>, Eigen::Aligned> B(b_, input_size);
+    const TensorMap<Tensor<T, 2, 0, TensorIndex>, Eigen::Aligned> B(b_, input_size);
     const Eigen::array<TensorIndex, 1> output_size = {{n_}};
-    TensorMap<Tensor<float, 1, 0, TensorIndex>, Eigen::Aligned> C(c_, output_size);
+    TensorMap<Tensor<T, 1, 0, TensorIndex>, Eigen::Aligned> C(c_, output_size);
 
 #ifndef EIGEN_HAS_INDEX_LIST
     Eigen::array<TensorIndex, 1> sum_along_dim;
@@ -323,10 +323,10 @@ template <typename Device> class BenchmarkSuite {
     Eigen::array<TensorIndex, 2> input_size;
     input_size[0] = k_;
     input_size[1] = n_;
-    const TensorMap<Tensor<float, 2, 0, TensorIndex>, Eigen::Aligned> B(
+    const TensorMap<Tensor<T, 2, 0, TensorIndex>, Eigen::Aligned> B(
         b_, input_size);
     const Eigen::array<TensorIndex, 1> output_size = {{k_}};
-    TensorMap<Tensor<float, 1, 0, TensorIndex>, Eigen::Aligned> C(
+    TensorMap<Tensor<T, 1, 0, TensorIndex>, Eigen::Aligned> C(
         c_, output_size);
 
 #ifndef EIGEN_HAS_INDEX_LIST
@@ -359,11 +359,11 @@ template <typename Device> class BenchmarkSuite {
     sizeC[0] = m_;
     sizeC[1] = 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);
+    const TensorMap<Tensor<T, 2>, Eigen::Aligned> A(a_, sizeA);
+    const TensorMap<Tensor<T, 2>, Eigen::Aligned> B(b_, sizeB);
+    TensorMap<Tensor<T, 2>, Eigen::Aligned> C(c_, sizeC);
 
-    typedef typename Tensor<float, 2>::DimensionPair DimPair;
+    typedef typename Tensor<T, 2>::DimensionPair DimPair;
     Eigen::array<DimPair, 1> dims;
     dims[0] = DimPair(1, 0);
 
@@ -380,16 +380,16 @@ template <typename Device> class BenchmarkSuite {
     Eigen::array<TensorIndex, 2> input_sizes;
     input_sizes[0] = m_;
     input_sizes[1] = n_;
-    TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, input_sizes);
+    TensorMap<Tensor<T, 2>, Eigen::Aligned> A(a_, input_sizes);
     Eigen::array<TensorIndex, 2> kernel_sizes;
     kernel_sizes[0] = kernel_x;
     kernel_sizes[1] = kernel_y;
-    TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, kernel_sizes);
+    TensorMap<Tensor<T, 2>, Eigen::Aligned> B(b_, kernel_sizes);
     Eigen::array<TensorIndex, 2> result_sizes;
     result_sizes[0] = m_ - kernel_x + 1;
     result_sizes[1] = n_ - kernel_y + 1;
-    TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, result_sizes);
-    Eigen::array<Tensor<float, 2>::Index, 2> dims;
+    TensorMap<Tensor<T, 2>, Eigen::Aligned> C(c_, result_sizes);
+    Eigen::array<TensorIndex, 2> dims;
     dims[0] = 0;
     dims[1] = 1;
 
@@ -405,15 +405,15 @@ template <typename Device> class BenchmarkSuite {
 
  private:
   void initialize() {
-    a_ = (float *) device_.allocate(m_ * k_ * sizeof(float));
-    b_ = (float *) device_.allocate(k_ * n_ * sizeof(float));
-    c_ = (float *) device_.allocate(m_ * n_ * sizeof(float));
+    a_ = (T *) device_.allocate(m_ * k_ * sizeof(T));
+    b_ = (T *) device_.allocate(k_ * n_ * sizeof(T));
+    c_ = (T *) device_.allocate(m_ * n_ * sizeof(T));
 
     // Initialize the content of the memory pools to prevent asan from
     // complaining.
-    device_.memset(a_, 12, m_ * k_ * sizeof(float));
-    device_.memset(b_, 23, k_ * n_ * sizeof(float));
-    device_.memset(c_, 31, m_ * n_ * sizeof(float));
+    device_.memset(a_, 12, m_ * k_ * sizeof(T));
+    device_.memset(b_, 23, k_ * n_ * sizeof(T));
+    device_.memset(c_, 31, m_ * n_ * sizeof(T));
 
     //BenchmarkUseRealTime();
   }
@@ -432,9 +432,9 @@ template <typename Device> class BenchmarkSuite {
   TensorIndex m_;
   TensorIndex k_;
   TensorIndex n_;
-  float* a_;
-  float* b_;
-  float* c_;
+  T* a_;
+  T* b_;
+  T* c_;
   Device device_;
 };
 #endif  // THIRD_PARTY_EIGEN3_TENSOR_BENCHMARKS_H_