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main.cpp
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189 lines (147 loc) · 6.85 KB
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#include <benchmark/benchmark.h>
#include <cstdlib>
#include <iostream>
#include <unsupported/Eigen/CXX11/Tensor>
#ifndef THREADS
#define THREADS 1
#endif
struct MatmulInputs {
size_t out_size;
size_t inp_size;
size_t weight_size;
size_t B;
size_t T;
size_t C;
size_t OC;
};
// Tensor types like in TensorFlow
template <typename T>
using ConstMatrix = Eigen::TensorMap<Eigen::Tensor<const T, 2, Eigen::RowMajor>, Eigen::Aligned>;
template <typename T>
using Matrix = Eigen::TensorMap<Eigen::Tensor<T, 2, Eigen::RowMajor>, Eigen::Aligned>;
extern "C" {
void matmul_matrixmultiply(double* out, const double* inp, const double* weight, size_t B, size_t T, size_t C, size_t OC);
void matmul_cblas(double* out, const double* inp, const double* weight, size_t B, size_t T, size_t C, size_t OC);
}
void matmul_eigen(Matrix<double> y, Eigen::ThreadPoolDevice device, ConstMatrix<double> x1, ConstMatrix<double> x2, Eigen::array<Eigen::IndexPair<int>, 1> dims) {
y.device(device) = x1.contract(x2, dims);
}
std::vector<MatmulInputs> configs = {
{196608, 786432, 2359296, 4, 64, 3072, 768},
{196608, 196608, 589824, 4, 64, 768, 768},
{786432, 196608, 2359296, 4, 64, 768, 3072},
{12877824, 196608, 38633472, 4, 64, 768, 50304},
};
constexpr double tol = 1e-8;
void check_correctness(const std::vector<double>& c_eigen,
const std::vector<double>& c_matrixmultiply,
const std::vector<double>& c_cblas,
size_t total_elements) {
for (size_t i = 0; i < total_elements; ++i) {
if (std::abs(c_eigen[i] - c_matrixmultiply[i]) > tol ||
std::abs(c_eigen[i] - c_cblas[i]) > tol) {
std::cerr << "MISMATCH at index " << i
<< ": Eigen=" << c_eigen[i]
<< ", matrixmultiply=" << c_matrixmultiply[i]
<< ", CBLAS=" << c_cblas[i] << "\n";
std::exit(EXIT_FAILURE);
}
}
}
static void prepare_and_check(const MatmulInputs& cfg) {
int rep = std::max<int>(1, 10000000 / cfg.B / cfg.T / cfg.C);
std::vector<double> a(cfg.inp_size);
std::vector<double> b(cfg.weight_size);
std::vector<double> c_eigen(cfg.out_size);
std::vector<double> c_matrixmultiply(cfg.out_size);
std::vector<double> c_cblas(cfg.out_size);
for (auto& v : a) v = static_cast<double>(rand()) / RAND_MAX;
for (auto& v : b) v = static_cast<double>(rand()) / RAND_MAX;
ConstMatrix<double> x1(a.data(), cfg.B * cfg.T, cfg.C);
ConstMatrix<double> x2(b.data(), cfg.C, cfg.OC);
Matrix<double> y(c_eigen.data(), cfg.B * cfg.T, cfg.OC);
Eigen::ThreadPool g_thread_pool(THREADS);
Eigen::ThreadPoolDevice g_device(&g_thread_pool, THREADS);
Eigen::array<Eigen::IndexPair<int>, 1> dims = {Eigen::IndexPair<int>(1, 0)};
// Run once each to fill output buffers
matmul_eigen(y, g_device, x1, x2, dims);
matmul_matrixmultiply(c_matrixmultiply.data(), a.data(), b.data(), cfg.B, cfg.T, cfg.C, cfg.OC);
matmul_cblas(c_cblas.data(), a.data(), b.data(), cfg.B, cfg.T, cfg.C, cfg.OC);
check_correctness(c_eigen, c_matrixmultiply, c_cblas, cfg.B * cfg.T * cfg.OC);
}
static void BM_Eigen(benchmark::State& state) {
const MatmulInputs& cfg = configs[state.range(0)];
int rep = std::max<int>(1, 10000000 / cfg.B / cfg.T / cfg.C);
std::vector<double> a(cfg.inp_size);
std::vector<double> b(cfg.weight_size);
std::vector<double> c(cfg.out_size);
for (auto& v : a) v = static_cast<double>(rand()) / RAND_MAX;
for (auto& v : b) v = static_cast<double>(rand()) / RAND_MAX;
ConstMatrix<double> x1(a.data(), cfg.B * cfg.T, cfg.C);
ConstMatrix<double> x2(b.data(), cfg.C, cfg.OC);
Matrix<double> y(c.data(), cfg.B * cfg.T, cfg.OC);
Eigen::ThreadPool g_thread_pool(THREADS);
Eigen::ThreadPoolDevice g_device(&g_thread_pool, THREADS);
Eigen::array<Eigen::IndexPair<int>, 1> dims = {Eigen::IndexPair<int>(1, 0)};
for (auto _ : state) {
for (int i = 0; i < rep; ++i) {
matmul_eigen(y, g_device, x1, x2, dims);
}
}
}
static void BM_MatrixMultiply(benchmark::State& state) {
const MatmulInputs& cfg = configs[state.range(0)];
int rep = std::max<int>(1, 10000000 / cfg.B / cfg.T / cfg.C);
std::vector<double> a(cfg.inp_size);
std::vector<double> b(cfg.weight_size);
std::vector<double> c(cfg.out_size);
for (auto& v : a) v = static_cast<double>(rand()) / RAND_MAX;
for (auto& v : b) v = static_cast<double>(rand()) / RAND_MAX;
for (auto _ : state) {
for (int i = 0; i < rep; ++i) {
matmul_matrixmultiply(c.data(), a.data(), b.data(), cfg.B, cfg.T, cfg.C, cfg.OC);
}
}
}
static void BM_CBLAS(benchmark::State& state) {
const MatmulInputs& cfg = configs[state.range(0)];
int rep = std::max<int>(1, 10000000 / cfg.B / cfg.T / cfg.C);
std::vector<double> a(cfg.inp_size);
std::vector<double> b(cfg.weight_size);
std::vector<double> c(cfg.out_size);
for (auto& v : a) v = static_cast<double>(rand()) / RAND_MAX;
for (auto& v : b) v = static_cast<double>(rand()) / RAND_MAX;
for (auto _ : state) {
for (int i = 0; i < rep; ++i) {
matmul_cblas(c.data(), a.data(), b.data(), cfg.B, cfg.T, cfg.C, cfg.OC);
}
}
}
int main(int argc, char* argv[]) {
setenv("MKL_NUM_THREADS", std::to_string(THREADS).c_str(), 1);
setenv("OMP_NUM_THREADS", std::to_string(THREADS).c_str(), 1);
setenv("MATMUL_NUM_THREADS", std::to_string(THREADS).c_str(), 1);
if (THREADS > 4) {
std::cout << "***WARNING*** " << THREADS << " threads specified, but matrixmultiply supports a maximum of 4 threads. Using 4 threads instead!\n" << std::endl;
}
// Check correctness for each config once before benchmarking
for (size_t i = 0; i < configs.size(); ++i) {
std::cout << "Checking correctness for config " << i << "..." << std::endl;
prepare_and_check(configs[i]);
std::cout << "Results MATCH!\n" << std::endl;
}
BENCHMARK(BM_Eigen)->Args({0})->Unit(benchmark::kMillisecond);
BENCHMARK(BM_Eigen)->Args({1})->Unit(benchmark::kMillisecond);
BENCHMARK(BM_Eigen)->Args({2})->Unit(benchmark::kMillisecond);
BENCHMARK(BM_Eigen)->Args({3})->Unit(benchmark::kMillisecond);
BENCHMARK(BM_MatrixMultiply)->Args({0})->Unit(benchmark::kMillisecond);
BENCHMARK(BM_MatrixMultiply)->Args({1})->Unit(benchmark::kMillisecond);
BENCHMARK(BM_MatrixMultiply)->Args({2})->Unit(benchmark::kMillisecond);
BENCHMARK(BM_MatrixMultiply)->Args({3})->Unit(benchmark::kMillisecond);
BENCHMARK(BM_CBLAS)->Args({0})->Unit(benchmark::kMillisecond);
BENCHMARK(BM_CBLAS)->Args({1})->Unit(benchmark::kMillisecond);
BENCHMARK(BM_CBLAS)->Args({2})->Unit(benchmark::kMillisecond);
BENCHMARK(BM_CBLAS)->Args({3})->Unit(benchmark::kMillisecond);
benchmark::RunSpecifiedBenchmarks();
return 0;
}