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zhangshun
478
Eigen3/bench/tensors/tensor_benchmarks.h
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478
Eigen3/bench/tensors/tensor_benchmarks.h
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#ifndef THIRD_PARTY_EIGEN3_TENSOR_BENCHMARKS_H_
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#define THIRD_PARTY_EIGEN3_TENSOR_BENCHMARKS_H_
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typedef int TensorIndex;
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#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
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#include "unsupported/Eigen/CXX11/Tensor"
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#include "benchmark.h"
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#define BENCHMARK_RANGE(bench, lo, hi) \
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BENCHMARK(bench)->Range(lo, hi)
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using Eigen::Tensor;
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using Eigen::TensorMap;
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// TODO(bsteiner): also templatize on the input type since we have users
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// for int8 as well as floats.
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template <typename Device, typename T> class BenchmarkSuite {
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public:
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BenchmarkSuite(const Device& device, size_t m, size_t k, size_t n)
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: m_(m), k_(k), n_(n), device_(device) {
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initialize();
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}
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BenchmarkSuite(const Device& device, size_t m)
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: m_(m), k_(m), n_(m), device_(device) {
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initialize();
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}
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~BenchmarkSuite() {
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device_.deallocate(a_);
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device_.deallocate(b_);
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device_.deallocate(c_);
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}
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void memcpy(int num_iters) {
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eigen_assert(m_ == k_ && k_ == n_);
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StartBenchmarkTiming();
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for (int iter = 0; iter < num_iters; ++iter) {
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device_.memcpy(c_, a_, m_ * m_ * sizeof(T));
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}
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// Record the number of values copied per second
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finalizeBenchmark(static_cast<int64_t>(m_) * m_ * num_iters);
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}
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void typeCasting(int num_iters) {
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eigen_assert(m_ == n_);
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Eigen::array<TensorIndex, 2> sizes;
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if (sizeof(T) >= sizeof(int)) {
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sizes[0] = m_;
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sizes[1] = k_;
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} else {
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sizes[0] = m_ * sizeof(T) / sizeof(int);
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sizes[1] = k_ * sizeof(T) / sizeof(int);
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}
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const TensorMap<Tensor<int, 2, 0, TensorIndex>, Eigen::Aligned> A((int*)a_, sizes);
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TensorMap<Tensor<T, 2, 0, TensorIndex>, Eigen::Aligned> B(b_, sizes);
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StartBenchmarkTiming();
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for (int iter = 0; iter < num_iters; ++iter) {
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B.device(device_) = A.template cast<T>();
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}
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// Record the number of values copied per second
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finalizeBenchmark(static_cast<int64_t>(m_) * k_ * num_iters);
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}
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void random(int num_iters) {
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eigen_assert(m_ == k_ && k_ == n_);
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Eigen::array<TensorIndex, 2> sizes;
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sizes[0] = m_;
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sizes[1] = m_;
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TensorMap<Tensor<T, 2>, Eigen::Aligned> C(c_, sizes);
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StartBenchmarkTiming();
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for (int iter = 0; iter < num_iters; ++iter) {
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C.device(device_) = C.random();
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}
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// Record the number of random numbers generated per second
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finalizeBenchmark(static_cast<int64_t>(m_) * m_ * num_iters);
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}
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void slicing(int num_iters) {
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eigen_assert(m_ == k_ && k_ == n_);
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Eigen::array<TensorIndex, 2> sizes;
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sizes[0] = m_;
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sizes[1] = m_;
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const TensorMap<Tensor<T, 2>, Eigen::Aligned> A(a_, sizes);
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const TensorMap<Tensor<T, 2>, Eigen::Aligned> B(b_, sizes);
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TensorMap<Tensor<T, 2>, Eigen::Aligned> C(c_, sizes);
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const Eigen::DSizes<TensorIndex, 2> quarter_sizes(m_/2, m_/2);
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const Eigen::DSizes<TensorIndex, 2> first_quadrant(0, 0);
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const Eigen::DSizes<TensorIndex, 2> second_quadrant(0, m_/2);
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const Eigen::DSizes<TensorIndex, 2> third_quadrant(m_/2, 0);
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const Eigen::DSizes<TensorIndex, 2> fourth_quadrant(m_/2, m_/2);
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StartBenchmarkTiming();
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for (int iter = 0; iter < num_iters; ++iter) {
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C.slice(first_quadrant, quarter_sizes).device(device_) =
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A.slice(first_quadrant, quarter_sizes);
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C.slice(second_quadrant, quarter_sizes).device(device_) =
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B.slice(second_quadrant, quarter_sizes);
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C.slice(third_quadrant, quarter_sizes).device(device_) =
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A.slice(third_quadrant, quarter_sizes);
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C.slice(fourth_quadrant, quarter_sizes).device(device_) =
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B.slice(fourth_quadrant, quarter_sizes);
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}
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// Record the number of values copied from the rhs slice to the lhs slice
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// each second
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finalizeBenchmark(static_cast<int64_t>(m_) * m_ * num_iters);
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}
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void rowChip(int num_iters) {
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Eigen::array<TensorIndex, 2> input_size;
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input_size[0] = k_;
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input_size[1] = n_;
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const TensorMap<Tensor<T, 2, 0, TensorIndex>, Eigen::Aligned> B(b_, input_size);
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Eigen::array<TensorIndex, 1> output_size;
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output_size[0] = n_;
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TensorMap<Tensor<T, 1, 0, TensorIndex>, Eigen::Aligned> C(c_, output_size);
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StartBenchmarkTiming();
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for (int iter = 0; iter < num_iters; ++iter) {
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C.device(device_) = B.chip(iter % k_, 0);
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}
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// Record the number of values copied from the rhs chip to the lhs.
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finalizeBenchmark(static_cast<int64_t>(n_) * num_iters);
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}
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void colChip(int num_iters) {
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Eigen::array<TensorIndex, 2> input_size;
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input_size[0] = k_;
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input_size[1] = n_;
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const TensorMap<Tensor<T, 2, 0, TensorIndex>, Eigen::Aligned> B(b_, input_size);
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Eigen::array<TensorIndex, 1> output_size;
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output_size[0] = n_;
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TensorMap<Tensor<T, 1, 0, TensorIndex>, Eigen::Aligned> C(c_, output_size);
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StartBenchmarkTiming();
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for (int iter = 0; iter < num_iters; ++iter) {
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C.device(device_) = B.chip(iter % n_, 1);
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}
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// Record the number of values copied from the rhs chip to the lhs.
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finalizeBenchmark(static_cast<int64_t>(n_) * num_iters);
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}
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void shuffling(int num_iters) {
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eigen_assert(m_ == n_);
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Eigen::array<TensorIndex, 2> size_a;
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size_a[0] = m_;
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size_a[1] = k_;
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const TensorMap<Tensor<T, 2>, Eigen::Aligned> A(a_, size_a);
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Eigen::array<TensorIndex, 2> size_b;
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size_b[0] = k_;
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size_b[1] = m_;
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TensorMap<Tensor<T, 2>, Eigen::Aligned> B(b_, size_b);
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Eigen::array<int, 2> shuffle;
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shuffle[0] = 1;
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shuffle[1] = 0;
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StartBenchmarkTiming();
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for (int iter = 0; iter < num_iters; ++iter) {
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B.device(device_) = A.shuffle(shuffle);
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}
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// Record the number of values shuffled from A and copied to B each second
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finalizeBenchmark(static_cast<int64_t>(m_) * k_ * num_iters);
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}
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void padding(int num_iters) {
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eigen_assert(m_ == k_);
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Eigen::array<TensorIndex, 2> size_a;
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size_a[0] = m_;
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size_a[1] = k_-3;
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const TensorMap<Tensor<T, 2>, Eigen::Aligned> A(a_, size_a);
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Eigen::array<TensorIndex, 2> size_b;
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size_b[0] = k_;
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size_b[1] = m_;
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TensorMap<Tensor<T, 2>, Eigen::Aligned> B(b_, size_b);
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#if defined(EIGEN_HAS_INDEX_LIST)
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Eigen::IndexPairList<Eigen::type2indexpair<0, 0>,
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Eigen::type2indexpair<2, 1> > paddings;
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#else
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Eigen::array<Eigen::IndexPair<TensorIndex>, 2> paddings;
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paddings[0] = Eigen::IndexPair<TensorIndex>(0, 0);
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paddings[1] = Eigen::IndexPair<TensorIndex>(2, 1);
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#endif
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StartBenchmarkTiming();
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for (int iter = 0; iter < num_iters; ++iter) {
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B.device(device_) = A.pad(paddings);
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}
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// Record the number of values copied from the padded tensor A each second
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finalizeBenchmark(static_cast<int64_t>(m_) * k_ * num_iters);
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}
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void striding(int num_iters) {
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eigen_assert(m_ == k_);
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Eigen::array<TensorIndex, 2> size_a;
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size_a[0] = m_;
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size_a[1] = k_;
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const TensorMap<Tensor<T, 2>, Eigen::Aligned> A(a_, size_a);
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Eigen::array<TensorIndex, 2> size_b;
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size_b[0] = m_;
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size_b[1] = k_/2;
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TensorMap<Tensor<T, 2>, Eigen::Aligned> B(b_, size_b);
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#ifndef EIGEN_HAS_INDEX_LIST
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Eigen::array<TensorIndex, 2> strides;
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strides[0] = 1;
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strides[1] = 2;
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#else
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// Take advantage of cxx11 to give the compiler information it can use to
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// optimize the code.
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Eigen::IndexList<Eigen::type2index<1>, Eigen::type2index<2> > strides;
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#endif
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StartBenchmarkTiming();
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for (int iter = 0; iter < num_iters; ++iter) {
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B.device(device_) = A.stride(strides);
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}
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// Record the number of values copied from the padded tensor A each second
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finalizeBenchmark(static_cast<int64_t>(m_) * k_ * num_iters);
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}
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void broadcasting(int num_iters) {
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Eigen::array<TensorIndex, 2> size_a;
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size_a[0] = m_;
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size_a[1] = 1;
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const TensorMap<Tensor<T, 2>, Eigen::Aligned> A(a_, size_a);
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Eigen::array<TensorIndex, 2> size_c;
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size_c[0] = m_;
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size_c[1] = n_;
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TensorMap<Tensor<T, 2>, Eigen::Aligned> C(c_, size_c);
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#ifndef EIGEN_HAS_INDEX_LIST
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Eigen::array<int, 2> broadcast;
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broadcast[0] = 1;
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broadcast[1] = n_;
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#else
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// Take advantage of cxx11 to give the compiler information it can use to
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// optimize the code.
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Eigen::IndexList<Eigen::type2index<1>, int> broadcast;
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broadcast.set(1, n_);
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#endif
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StartBenchmarkTiming();
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for (int iter = 0; iter < num_iters; ++iter) {
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C.device(device_) = A.broadcast(broadcast);
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}
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// Record the number of values broadcasted from A and copied to C each second
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finalizeBenchmark(static_cast<int64_t>(m_) * n_ * num_iters);
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}
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void coeffWiseOp(int num_iters) {
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eigen_assert(m_ == k_ && k_ == n_);
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Eigen::array<TensorIndex, 2> sizes;
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sizes[0] = m_;
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sizes[1] = m_;
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const TensorMap<Tensor<T, 2>, Eigen::Aligned> A(a_, sizes);
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const TensorMap<Tensor<T, 2>, Eigen::Aligned> B(b_, sizes);
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TensorMap<Tensor<T, 2>, Eigen::Aligned> C(c_, sizes);
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StartBenchmarkTiming();
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for (int iter = 0; iter < num_iters; ++iter) {
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C.device(device_) = A * A.constant(static_cast<T>(3.14)) + B * B.constant(static_cast<T>(2.7));
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}
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// Record the number of FLOP executed per second (2 multiplications and
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// 1 addition per value)
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finalizeBenchmark(static_cast<int64_t>(3) * m_ * m_ * num_iters);
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}
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void algebraicFunc(int num_iters) {
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eigen_assert(m_ == k_ && k_ == n_);
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Eigen::array<TensorIndex, 2> sizes;
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sizes[0] = m_;
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sizes[1] = m_;
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const TensorMap<Tensor<T, 2>, Eigen::Aligned> A(a_, sizes);
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const TensorMap<Tensor<T, 2>, Eigen::Aligned> B(b_, sizes);
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TensorMap<Tensor<T, 2>, Eigen::Aligned> C(c_, sizes);
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StartBenchmarkTiming();
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for (int iter = 0; iter < num_iters; ++iter) {
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C.device(device_) = A.rsqrt() + B.sqrt() * B.square();
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}
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// Record the number of FLOP executed per second (assuming one operation
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// per value)
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finalizeBenchmark(static_cast<int64_t>(m_) * m_ * num_iters);
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}
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void transcendentalFunc(int num_iters) {
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eigen_assert(m_ == k_ && k_ == n_);
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Eigen::array<TensorIndex, 2> sizes;
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sizes[0] = m_;
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sizes[1] = m_;
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const TensorMap<Tensor<T, 2>, Eigen::Aligned> A(a_, sizes);
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const TensorMap<Tensor<T, 2>, Eigen::Aligned> B(b_, sizes);
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TensorMap<Tensor<T, 2>, Eigen::Aligned> C(c_, sizes);
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StartBenchmarkTiming();
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for (int iter = 0; iter < num_iters; ++iter) {
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C.device(device_) = A.exp() + B.log();
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}
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// Record the number of FLOP executed per second (assuming one operation
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// per value)
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finalizeBenchmark(static_cast<int64_t>(m_) * m_ * num_iters);
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}
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// Row reduction
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void rowReduction(int num_iters) {
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Eigen::array<TensorIndex, 2> input_size;
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input_size[0] = k_;
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input_size[1] = n_;
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const TensorMap<Tensor<T, 2, 0, TensorIndex>, Eigen::Aligned> B(b_, input_size);
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Eigen::array<TensorIndex, 1> output_size;
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output_size[0] = n_;
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TensorMap<Tensor<T, 1, 0, TensorIndex>, Eigen::Aligned> C(c_, output_size);
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#ifndef EIGEN_HAS_INDEX_LIST
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Eigen::array<TensorIndex, 1> sum_along_dim;
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sum_along_dim[0] = 0;
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#else
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// Take advantage of cxx11 to give the compiler information it can use to
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// optimize the code.
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Eigen::IndexList<Eigen::type2index<0>> sum_along_dim;
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#endif
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StartBenchmarkTiming();
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for (int iter = 0; iter < num_iters; ++iter) {
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C.device(device_) = B.sum(sum_along_dim);
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}
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// Record the number of FLOP executed per second (assuming one operation
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// per value)
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finalizeBenchmark(static_cast<int64_t>(k_) * n_ * num_iters);
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}
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// Column reduction
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void colReduction(int num_iters) {
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Eigen::array<TensorIndex, 2> input_size;
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input_size[0] = k_;
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input_size[1] = n_;
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const TensorMap<Tensor<T, 2, 0, TensorIndex>, Eigen::Aligned> B(
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b_, input_size);
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Eigen::array<TensorIndex, 1> output_size;
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output_size[0] = k_;
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TensorMap<Tensor<T, 1, 0, TensorIndex>, Eigen::Aligned> C(
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c_, output_size);
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#ifndef EIGEN_HAS_INDEX_LIST
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Eigen::array<TensorIndex, 1> sum_along_dim;
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sum_along_dim[0] = 1;
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#else
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// Take advantage of cxx11 to give the compiler information it can use to
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// optimize the code.
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Eigen::IndexList<Eigen::type2index<1>> sum_along_dim;
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#endif
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StartBenchmarkTiming();
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for (int iter = 0; iter < num_iters; ++iter) {
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C.device(device_) = B.sum(sum_along_dim);
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}
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// Record the number of FLOP executed per second (assuming one operation
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// per value)
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finalizeBenchmark(static_cast<int64_t>(k_) * n_ * num_iters);
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}
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// Full reduction
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void fullReduction(int num_iters) {
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Eigen::array<TensorIndex, 2> input_size;
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input_size[0] = k_;
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input_size[1] = n_;
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const TensorMap<Tensor<T, 2, 0, TensorIndex>, Eigen::Aligned> B(
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b_, input_size);
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Eigen::array<TensorIndex, 0> output_size;
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TensorMap<Tensor<T, 0, 0, TensorIndex>, Eigen::Aligned> C(
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c_, output_size);
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StartBenchmarkTiming();
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for (int iter = 0; iter < num_iters; ++iter) {
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C.device(device_) = B.sum();
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}
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// Record the number of FLOP executed per second (assuming one operation
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// per value)
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finalizeBenchmark(static_cast<int64_t>(k_) * n_ * num_iters);
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}
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// do a contraction which is equivalent to a matrix multiplication
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void contraction(int num_iters) {
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Eigen::array<TensorIndex, 2> sizeA;
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sizeA[0] = m_;
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sizeA[1] = k_;
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Eigen::array<TensorIndex, 2> sizeB;
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sizeB[0] = k_;
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sizeB[1] = n_;
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Eigen::array<TensorIndex, 2> sizeC;
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sizeC[0] = m_;
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sizeC[1] = n_;
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const TensorMap<Tensor<T, 2>, Eigen::Aligned> A(a_, sizeA);
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const TensorMap<Tensor<T, 2>, Eigen::Aligned> B(b_, sizeB);
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TensorMap<Tensor<T, 2>, Eigen::Aligned> C(c_, sizeC);
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typedef typename Tensor<T, 2>::DimensionPair DimPair;
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Eigen::array<DimPair, 1> dims;
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dims[0] = DimPair(1, 0);
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StartBenchmarkTiming();
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for (int iter = 0; iter < num_iters; ++iter) {
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C.device(device_) = A.contract(B, dims);
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}
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// 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_
|
||||
Reference in New Issue
Block a user