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+#ifndef THIRD_PARTY_EIGEN3_TENSOR_BENCHMARKS_H_
+#define THIRD_PARTY_EIGEN3_TENSOR_BENCHMARKS_H_
+
+typedef int TensorIndex;
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
+
+#include "unsupported/Eigen/CXX11/Tensor"
+#include "benchmark.h"
+
+#define BENCHMARK_RANGE(bench, lo, hi) \
+ BENCHMARK(bench)->Range(lo, hi)
+
+using Eigen::Tensor;
+using Eigen::TensorMap;
+
+// TODO(bsteiner): also templatize on the input type since we have users
+// for int8 as well as floats.
+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) {
+ initialize();
+ }
+
+ BenchmarkSuite(const Device& device, size_t m)
+ : m_(m), k_(m), n_(m), device_(device) {
+ initialize();
+ }
+
+ ~BenchmarkSuite() {
+ device_.deallocate(a_);
+ device_.deallocate(b_);
+ device_.deallocate(c_);
+ }
+
+ void memcpy(int num_iters) {
+ eigen_assert(m_ == k_ && k_ == n_);
+ StartBenchmarkTiming();
+ for (int iter = 0; iter < num_iters; ++iter) {
+ 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);
+ }
+
+ void typeCasting(int num_iters) {
+ eigen_assert(m_ == n_);
+ Eigen::array<TensorIndex, 2> sizes;
+ if (sizeof(T) >= sizeof(int)) {
+ sizes[0] = m_;
+ sizes[1] = k_;
+ } else {
+ sizes[0] = m_ * sizeof(T) / sizeof(int);
+ sizes[1] = k_ * sizeof(T) / sizeof(int);
+ }
+ const TensorMap<Tensor<int, 2, 0, TensorIndex>, Eigen::Aligned> A((int*)a_, sizes);
+ TensorMap<Tensor<T, 2, 0, TensorIndex>, Eigen::Aligned> B(b_, sizes);
+
+ StartBenchmarkTiming();
+ for (int iter = 0; iter < num_iters; ++iter) {
+ B.device(device_) = A.template cast<T>();
+ }
+ // Record the number of values copied per second
+ finalizeBenchmark(static_cast<int64_t>(m_) * k_ * num_iters);
+ }
+
+ void random(int num_iters) {
+ eigen_assert(m_ == k_ && k_ == n_);
+ Eigen::array<TensorIndex, 2> sizes;
+ sizes[0] = m_;
+ sizes[1] = m_;
+ TensorMap<Tensor<T, 2>, Eigen::Aligned> C(c_, sizes);
+
+ StartBenchmarkTiming();
+ for (int iter = 0; iter < num_iters; ++iter) {
+ C.device(device_) = C.random();
+ }
+ // Record the number of random numbers generated per second
+ finalizeBenchmark(static_cast<int64_t>(m_) * m_ * num_iters);
+ }
+
+ void slicing(int num_iters) {
+ eigen_assert(m_ == k_ && k_ == n_);
+ Eigen::array<TensorIndex, 2> sizes;
+ sizes[0] = m_;
+ sizes[1] = m_;
+ 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);
+ 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) {
+ C.slice(first_quadrant, quarter_sizes).device(device_) =
+ A.slice(first_quadrant, quarter_sizes);
+ C.slice(second_quadrant, quarter_sizes).device(device_) =
+ B.slice(second_quadrant, quarter_sizes);
+ C.slice(third_quadrant, quarter_sizes).device(device_) =
+ A.slice(third_quadrant, quarter_sizes);
+ C.slice(fourth_quadrant, quarter_sizes).device(device_) =
+ B.slice(fourth_quadrant, quarter_sizes);
+ }
+ // Record the number of values copied from the rhs slice to the lhs slice
+ // each second
+ finalizeBenchmark(static_cast<int64_t>(m_) * m_ * num_iters);
+ }
+
+ void rowChip(int num_iters) {
+ Eigen::array<TensorIndex, 2> input_size;
+ input_size[0] = k_;
+ input_size[1] = n_;
+ 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<T, 1, 0, TensorIndex>, Eigen::Aligned> C(c_, output_size);
+
+ StartBenchmarkTiming();
+ for (int iter = 0; iter < num_iters; ++iter) {
+ C.device(device_) = B.chip(iter % k_, 0);
+ }
+ // Record the number of values copied from the rhs chip to the lhs.
+ finalizeBenchmark(static_cast<int64_t>(n_) * num_iters);
+ }
+
+ void colChip(int num_iters) {
+ Eigen::array<TensorIndex, 2> input_size;
+ input_size[0] = k_;
+ input_size[1] = n_;
+ 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<T, 1, 0, TensorIndex>, Eigen::Aligned> C(c_, output_size);
+
+ StartBenchmarkTiming();
+ for (int iter = 0; iter < num_iters; ++iter) {
+ C.device(device_) = B.chip(iter % n_, 1);
+ }
+ // Record the number of values copied from the rhs chip to the lhs.
+ finalizeBenchmark(static_cast<int64_t>(n_) * num_iters);
+ }
+
+ void shuffling(int num_iters) {
+ eigen_assert(m_ == n_);
+ Eigen::array<TensorIndex, 2> size_a;
+ size_a[0] = m_;
+ size_a[1] = k_;
+ 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<T, 2>, Eigen::Aligned> B(b_, size_b);
+
+ Eigen::array<int, 2> shuffle;
+ shuffle[0] = 1;
+ shuffle[1] = 0;
+
+ StartBenchmarkTiming();
+ for (int iter = 0; iter < num_iters; ++iter) {
+ B.device(device_) = A.shuffle(shuffle);
+ }
+ // Record the number of values shuffled from A and copied to B each second
+ finalizeBenchmark(static_cast<int64_t>(m_) * k_ * num_iters);
+ }
+
+ void padding(int num_iters) {
+ eigen_assert(m_ == k_);
+ Eigen::array<TensorIndex, 2> size_a;
+ size_a[0] = m_;
+ size_a[1] = k_-3;
+ 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<T, 2>, Eigen::Aligned> B(b_, size_b);
+
+#if defined(EIGEN_HAS_INDEX_LIST)
+ Eigen::IndexPairList<Eigen::type2indexpair<0, 0>,
+ Eigen::type2indexpair<2, 1> > paddings;
+#else
+ Eigen::array<Eigen::IndexPair<TensorIndex>, 2> paddings;
+ paddings[0] = Eigen::IndexPair<TensorIndex>(0, 0);
+ paddings[1] = Eigen::IndexPair<TensorIndex>(2, 1);
+#endif
+
+ StartBenchmarkTiming();
+ for (int iter = 0; iter < num_iters; ++iter) {
+ B.device(device_) = A.pad(paddings);
+ }
+ // Record the number of values copied from the padded tensor A each second
+ finalizeBenchmark(static_cast<int64_t>(m_) * k_ * num_iters);
+ }
+
+ void striding(int num_iters) {
+ eigen_assert(m_ == k_);
+ Eigen::array<TensorIndex, 2> size_a;
+ size_a[0] = m_;
+ size_a[1] = k_;
+ 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<T, 2>, Eigen::Aligned> B(b_, size_b);
+
+#ifndef EIGEN_HAS_INDEX_LIST
+ Eigen::array<TensorIndex, 2> strides;
+ strides[0] = 1;
+ strides[1] = 2;
+#else
+ // Take advantage of cxx11 to give the compiler information it can use to
+ // optimize the code.
+ Eigen::IndexList<Eigen::type2index<1>, Eigen::type2index<2> > strides;
+#endif
+
+ StartBenchmarkTiming();
+ for (int iter = 0; iter < num_iters; ++iter) {
+ B.device(device_) = A.stride(strides);
+ }
+ // Record the number of values copied from the padded tensor A each second
+ finalizeBenchmark(static_cast<int64_t>(m_) * k_ * num_iters);
+ }
+
+ void broadcasting(int num_iters) {
+ Eigen::array<TensorIndex, 2> size_a;
+ size_a[0] = m_;
+ size_a[1] = 1;
+ 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<T, 2>, Eigen::Aligned> C(c_, size_c);
+
+#ifndef EIGEN_HAS_INDEX_LIST
+ Eigen::array<int, 2> broadcast;
+ broadcast[0] = 1;
+ broadcast[1] = n_;
+#else
+ // Take advantage of cxx11 to give the compiler information it can use to
+ // optimize the code.
+ Eigen::IndexList<Eigen::type2index<1>, int> broadcast;
+ broadcast.set(1, n_);
+#endif
+
+ StartBenchmarkTiming();
+ for (int iter = 0; iter < num_iters; ++iter) {
+ C.device(device_) = A.broadcast(broadcast);
+ }
+ // Record the number of values broadcasted from A and copied to C each second
+ finalizeBenchmark(static_cast<int64_t>(m_) * n_ * num_iters);
+ }
+
+ void coeffWiseOp(int num_iters) {
+ eigen_assert(m_ == k_ && k_ == n_);
+ Eigen::array<TensorIndex, 2> sizes;
+ sizes[0] = m_;
+ sizes[1] = m_;
+ 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) {
+ C.device(device_) = A * A.constant(static_cast<T>(3.14)) + B * B.constant(static_cast<T>(2.7));
+ }
+ // Record the number of FLOP executed per second (2 multiplications and
+ // 1 addition per value)
+ finalizeBenchmark(static_cast<int64_t>(3) * m_ * m_ * num_iters);
+ }
+
+ void algebraicFunc(int num_iters) {
+ eigen_assert(m_ == k_ && k_ == n_);
+ Eigen::array<TensorIndex, 2> sizes;
+ sizes[0] = m_;
+ sizes[1] = m_;
+ 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) {
+ C.device(device_) = A.rsqrt() + B.sqrt() * B.square();
+ }
+ // Record the number of FLOP executed per second (assuming one operation
+ // per value)
+ finalizeBenchmark(static_cast<int64_t>(m_) * m_ * num_iters);
+ }
+
+ void transcendentalFunc(int num_iters) {
+ eigen_assert(m_ == k_ && k_ == n_);
+ Eigen::array<TensorIndex, 2> sizes;
+ sizes[0] = m_;
+ sizes[1] = m_;
+ 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) {
+ C.device(device_) = A.exp() + B.log();
+ }
+ // Record the number of FLOP executed per second (assuming one operation
+ // per value)
+ finalizeBenchmark(static_cast<int64_t>(m_) * m_ * num_iters);
+ }
+
+ // Row reduction
+ void rowReduction(int num_iters) {
+ Eigen::array<TensorIndex, 2> input_size;
+ input_size[0] = k_;
+ input_size[1] = n_;
+ 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<T, 1, 0, TensorIndex>, Eigen::Aligned> C(c_, output_size);
+
+#ifndef EIGEN_HAS_INDEX_LIST
+ Eigen::array<TensorIndex, 1> sum_along_dim;
+ sum_along_dim[0] = 0;
+#else
+ // Take advantage of cxx11 to give the compiler information it can use to
+ // optimize the code.
+ Eigen::IndexList<Eigen::type2index<0>> sum_along_dim;
+#endif
+
+ StartBenchmarkTiming();
+ for (int iter = 0; iter < num_iters; ++iter) {
+ C.device(device_) = B.sum(sum_along_dim);
+ }
+ // Record the number of FLOP executed per second (assuming one operation
+ // per value)
+ finalizeBenchmark(static_cast<int64_t>(k_) * n_ * num_iters);
+ }
+
+ // Column reduction
+ void colReduction(int num_iters) {
+ Eigen::array<TensorIndex, 2> input_size;
+ input_size[0] = k_;
+ input_size[1] = n_;
+ const TensorMap<Tensor<T, 2, 0, TensorIndex>, Eigen::Aligned> B(
+ b_, input_size);
+ Eigen::array<TensorIndex, 1> output_size;
+ output_size[0] = k_;
+ TensorMap<Tensor<T, 1, 0, TensorIndex>, Eigen::Aligned> C(
+ c_, output_size);
+
+#ifndef EIGEN_HAS_INDEX_LIST
+ Eigen::array<TensorIndex, 1> sum_along_dim;
+ sum_along_dim[0] = 1;
+#else
+ // Take advantage of cxx11 to give the compiler information it can use to
+ // optimize the code.
+ Eigen::IndexList<Eigen::type2index<1>> sum_along_dim;
+#endif
+
+ StartBenchmarkTiming();
+ for (int iter = 0; iter < num_iters; ++iter) {
+ C.device(device_) = B.sum(sum_along_dim);
+ }
+ // Record the number of FLOP executed per second (assuming one operation
+ // per value)
+ finalizeBenchmark(static_cast<int64_t>(k_) * n_ * num_iters);
+ }
+
+ // Full reduction
+ void fullReduction(int num_iters) {
+ Eigen::array<TensorIndex, 2> input_size;
+ input_size[0] = k_;
+ input_size[1] = n_;
+ const TensorMap<Tensor<T, 2, 0, TensorIndex>, Eigen::Aligned> B(
+ b_, input_size);
+ Eigen::array<TensorIndex, 0> output_size;
+ TensorMap<Tensor<T, 0, 0, TensorIndex>, Eigen::Aligned> C(
+ c_, output_size);
+
+ StartBenchmarkTiming();
+ for (int iter = 0; iter < num_iters; ++iter) {
+ C.device(device_) = B.sum();
+ }
+ // Record the number of FLOP executed per second (assuming one operation
+ // per value)
+ finalizeBenchmark(static_cast<int64_t>(k_) * n_ * num_iters);
+ }
+
+ // do a contraction which is equivalent to a matrix multiplication
+ void contraction(int num_iters) {
+ Eigen::array<TensorIndex, 2> sizeA;
+ sizeA[0] = m_;
+ sizeA[1] = k_;
+ Eigen::array<TensorIndex, 2> sizeB;
+ sizeB[0] = k_;
+ sizeB[1] = n_;
+ Eigen::array<TensorIndex, 2> sizeC;
+ sizeC[0] = m_;
+ sizeC[1] = n_;
+
+ 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<T, 2>::DimensionPair DimPair;
+ Eigen::array<DimPair, 1> dims;
+ dims[0] = DimPair(1, 0);
+
+ StartBenchmarkTiming();
+ for (int iter = 0; iter < num_iters; ++iter) {
+ C.device(device_) = A.contract(B, dims);
+ }
+ // Record the number of FLOP executed per second (size_ multiplications and
+ // additions for each value in the resulting tensor)
+ finalizeBenchmark(static_cast<int64_t>(2) * m_ * n_ * k_ * num_iters);
+ }
+
+ void convolution(int num_iters, int kernel_x, int kernel_y) {
+ Eigen::array<TensorIndex, 2> input_sizes;
+ input_sizes[0] = m_;
+ input_sizes[1] = n_;
+ 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<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<T, 2>, Eigen::Aligned> C(c_, result_sizes);
+ Eigen::array<TensorIndex, 2> dims;
+ dims[0] = 0;
+ dims[1] = 1;
+
+ StartBenchmarkTiming();
+ for (int iter = 0; iter < num_iters; ++iter) {
+ C.device(device_) = A.convolve(B, dims);
+ }
+ // Record the number of FLOP executed per second (kernel_size
+ // multiplications and additions for each value in the resulting tensor)
+ finalizeBenchmark(static_cast<int64_t>(2) *
+ (m_ - kernel_x + 1) * (n_ - kernel_y + 1) * kernel_x * kernel_y * num_iters);
+ }
+
+ private:
+ void initialize() {
+ 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(T));
+ device_.memset(b_, 23, k_ * n_ * sizeof(T));
+ device_.memset(c_, 31, m_ * n_ * sizeof(T));
+
+ //BenchmarkUseRealTime();
+ }
+
+ inline void finalizeBenchmark(int64_t num_items) {
+#if defined(EIGEN_USE_GPU) && defined(__CUDACC__)
+ if (Eigen::internal::is_same<Device, Eigen::GpuDevice>::value) {
+ device_.synchronize();
+ }
+#endif
+ StopBenchmarkTiming();
+ SetBenchmarkFlopsProcessed(num_items);
+ }
+
+
+ TensorIndex m_;
+ TensorIndex k_;
+ TensorIndex n_;
+ T* a_;
+ T* b_;
+ T* c_;
+ Device device_;
+};
+#endif // THIRD_PARTY_EIGEN3_TENSOR_BENCHMARKS_H_
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