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Made sure the number of floating point operations done by a benchmark is computed using 64 bit integers to avoid overflows.

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This commit is contained in:
Benoit Steiner 2016-01-28 17:10:40 -08:00
parent 120e13b1b6
commit bd2e5a788a
1 changed files with 19 additions and 21 deletions
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40
bench/tensors/tensor_benchmarks.h
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@ -13,8 +13,6 @@ typedef int TensorIndex;
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.
template <typename Device> class BenchmarkSuite {
@ -42,7 +40,7 @@ template <typename Device> class BenchmarkSuite {
device_.memcpy(c_, a_, m_ * m_ * sizeof(float));
}
// Record the number of values copied per second
finalizeBenchmark(m_ * m_ * num_iters);
finalizeBenchmark(static_cast<int64_t>(m_) * m_ * num_iters);
}
void typeCasting(int num_iters) {
@ -56,7 +54,7 @@ template <typename Device> class BenchmarkSuite {
B.device(device_) = A.cast<int>();
}
// Record the number of values copied per second
finalizeBenchmark(m_ * k_ * num_iters);
finalizeBenchmark(static_cast<int64_t>(m_) * k_ * num_iters);
}
void random(int num_iters) {
@ -69,7 +67,7 @@ template <typename Device> class BenchmarkSuite {
C.device(device_) = C.random();
}
// Record the number of random numbers generated per second
finalizeBenchmark(m_ * m_ * num_iters);
finalizeBenchmark(static_cast<int64_t>(m_) * m_ * num_iters);
}
void slicing(int num_iters) {
@ -98,7 +96,7 @@ template <typename Device> class BenchmarkSuite {
}
// Record the number of values copied from the rhs slice to the lhs slice
// each second
finalizeBenchmark(m_ * m_ * num_iters);
finalizeBenchmark(static_cast<int64_t>(m_) * m_ * num_iters);
}
void rowChip(int num_iters) {
@ -112,7 +110,7 @@ template <typename Device> class BenchmarkSuite {
C.device(device_) = B.chip(iter % k_, 0);
}
// Record the number of values copied from the rhs chip to the lhs.
finalizeBenchmark(n_ * num_iters);
finalizeBenchmark(static_cast<int64_t>(n_) * num_iters);
}
void colChip(int num_iters) {
@ -126,7 +124,7 @@ template <typename Device> class BenchmarkSuite {
C.device(device_) = B.chip(iter % n_, 1);
}
// Record the number of values copied from the rhs chip to the lhs.
finalizeBenchmark(n_ * num_iters);
finalizeBenchmark(static_cast<int64_t>(n_) * num_iters);
}
void shuffling(int num_iters) {
@ -143,7 +141,7 @@ template <typename Device> class BenchmarkSuite {
B.device(device_) = A.shuffle(shuffle);
}
// Record the number of values shuffled from A and copied to B each second
finalizeBenchmark(m_ * k_ * num_iters);
finalizeBenchmark(static_cast<int64_t>(m_) * k_ * num_iters);
}
void padding(int num_iters) {
@ -162,7 +160,7 @@ template <typename Device> class BenchmarkSuite {
B.device(device_) = A.pad(paddings);
}
// Record the number of values copied from the padded tensor A each second
finalizeBenchmark(m_ * k_ * num_iters);
finalizeBenchmark(static_cast<int64_t>(m_) * k_ * num_iters);
}
void striding(int num_iters) {
@ -179,7 +177,7 @@ template <typename Device> class BenchmarkSuite {
B.device(device_) = A.stride(strides);
}
// Record the number of values copied from the padded tensor A each second
finalizeBenchmark(m_ * k_ * num_iters);
finalizeBenchmark(static_cast<int64_t>(m_) * k_ * num_iters);
}
void broadcasting(int num_iters) {
@ -202,7 +200,7 @@ template <typename Device> class BenchmarkSuite {
C.device(device_) = A.broadcast(broadcast);
}
// Record the number of values broadcasted from A and copied to C each second
finalizeBenchmark(m_ * n_ * num_iters);
finalizeBenchmark(static_cast<int64_t>(m_) * n_ * num_iters);
}
void coeffWiseOp(int num_iters) {
@ -218,7 +216,7 @@ template <typename Device> class BenchmarkSuite {
}
// Record the number of FLOP executed per second (2 multiplications and
// 1 addition per value)
finalizeBenchmark(3 * m_ * m_ * num_iters);
finalizeBenchmark(static_cast<int64_t>(3) * m_ * m_ * num_iters);
}
void algebraicFunc(int num_iters) {
@ -234,7 +232,7 @@ template <typename Device> class BenchmarkSuite {
}
// Record the number of FLOP executed per second (assuming one operation
// per value)
finalizeBenchmark(m_ * m_ * num_iters);
finalizeBenchmark(static_cast<int64_t>(m_) * m_ * num_iters);
}
void transcendentalFunc(int num_iters) {
@ -250,7 +248,7 @@ template <typename Device> class BenchmarkSuite {
}
// Record the number of FLOP executed per second (assuming one operation
// per value)
finalizeBenchmark(m_ * m_ * num_iters);
finalizeBenchmark(static_cast<int64_t>(m_) * m_ * num_iters);
}
// Row reduction
@ -274,7 +272,7 @@ template <typename Device> class BenchmarkSuite {
}
// Record the number of FLOP executed per second (assuming one operation
// per value)
finalizeBenchmark(k_ * n_ * num_iters);
finalizeBenchmark(static_cast<int64_t>(k_) * n_ * num_iters);
}
// Column reduction
@ -300,7 +298,7 @@ template <typename Device> class BenchmarkSuite {
}
// Record the number of FLOP executed per second (assuming one operation
// per value)
finalizeBenchmark(k_ * n_ * num_iters);
finalizeBenchmark(static_cast<int64_t>(k_) * n_ * num_iters);
}
// do a contraction which is equivalent to a matrix multiplication
@ -322,7 +320,7 @@ template <typename Device> class BenchmarkSuite {
}
// Record the number of FLOP executed per second (size_ multiplications and
// additions for each value in the resulting tensor)
finalizeBenchmark(static_cast<int64>(2) * m_ * n_ * k_ * num_iters);
finalizeBenchmark(static_cast<int64_t>(2) * m_ * n_ * k_ * num_iters);
}
void convolution(int num_iters, int kernel_x, int kernel_y) {
@ -341,8 +339,8 @@ template <typename Device> class BenchmarkSuite {
}
// Record the number of FLOP executed per second (kernel_size
// multiplications and additions for each value in the resulting tensor)
finalizeBenchmark(
(m_ - kernel_x + 1) * (n_ - kernel_y + 1) * kernel_x * kernel_y * 2 * num_iters);
finalizeBenchmark(static_cast<int64_t>(2) *
(m_ - kernel_x + 1) * (n_ - kernel_y + 1) * kernel_x * kernel_y * num_iters);
}
private:
@ -360,7 +358,7 @@ template <typename Device> class BenchmarkSuite {
//BenchmarkUseRealTime();
}
inline void finalizeBenchmark(int64 num_items) {
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();