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 #include <cassert> #include <chrono> #include <functional> #include <iomanip> #include <iostream> #include <random> #include <utility> #include <vector>
#include <cuda_runtime.h> #include <mma.h>
#define CHECK_CUDA_ERROR(val) check((val), #val, __FILE__, __LINE__) template <typename T> void check(T err, const char* const func, const char* const file, int const line) { if (err != cudaSuccess) { std::cerr << "CUDA Runtime Error at: " << file << ":" << line << std::endl; std::cerr << cudaGetErrorString(err) << " " << func << std::endl; std::exit(EXIT_FAILURE); } }
#define CHECK_LAST_CUDA_ERROR() checkLast(__FILE__, __LINE__) void checkLast(const char* const file, int const line) { cudaError_t const err{cudaGetLastError()}; if (err != cudaSuccess) { std::cerr << "CUDA Runtime Error at: " << file << ":" << line << std::endl; std::cerr << cudaGetErrorString(err) << std::endl; std::exit(EXIT_FAILURE); } }
template <class T> float measure_performance(std::function<T(cudaStream_t)> bound_function, cudaStream_t stream, int num_repeats = 100, int num_warmups = 100) { cudaEvent_t start, stop; float time;
CHECK_CUDA_ERROR(cudaEventCreate(&start)); CHECK_CUDA_ERROR(cudaEventCreate(&stop));
for (int i{0}; i < num_warmups; ++i) { bound_function(stream); }
CHECK_CUDA_ERROR(cudaStreamSynchronize(stream));
CHECK_CUDA_ERROR(cudaEventRecord(start, stream)); for (int i{0}; i < num_repeats; ++i) { bound_function(stream); } CHECK_CUDA_ERROR(cudaEventRecord(stop, stream)); CHECK_CUDA_ERROR(cudaEventSynchronize(stop)); CHECK_LAST_CUDA_ERROR(); CHECK_CUDA_ERROR(cudaEventElapsedTime(&time, start, stop)); CHECK_CUDA_ERROR(cudaEventDestroy(start)); CHECK_CUDA_ERROR(cudaEventDestroy(stop));
float const latency{time / num_repeats};
return latency; }
template <typename T1, typename T2, int WMMA_M, int WMMA_N, int WMMA_K, typename WMMA_FRAG_LAYOUT_A, typename WMMA_FRAG_LAYOUT_B> __global__ void wmma_gemm_a_col_major_b_col_major( T1 const* A, T1 const* B, T2* C, uint32_t m, uint32_t n, uint32_t k, uint32_t lda, uint32_t ldb, uint32_t ldc, bool is_A_transpose, bool is_B_transpose, float alpha, float beta) { uint32_t const warpM{(blockIdx.x * blockDim.x + threadIdx.x) / warpSize}; uint32_t const warpN{blockIdx.y * blockDim.y + threadIdx.y};
nvcuda::wmma::fragment<nvcuda::wmma::matrix_a, WMMA_M, WMMA_N, WMMA_K, T1, WMMA_FRAG_LAYOUT_A> a_frag{}; nvcuda::wmma::fragment<nvcuda::wmma::matrix_b, WMMA_M, WMMA_N, WMMA_K, T1, WMMA_FRAG_LAYOUT_B> b_frag{}; nvcuda::wmma::fragment<nvcuda::wmma::accumulator, WMMA_M, WMMA_N, WMMA_K, T2> acc_frag{}; nvcuda::wmma::fragment<nvcuda::wmma::accumulator, WMMA_M, WMMA_N, WMMA_K, T2> c_frag{};
nvcuda::wmma::fill_fragment(acc_frag, static_cast<T2>(0));
for (uint32_t ki{0}; ki < k; ki += WMMA_K) { uint32_t const matrix_mma_a_row_idx{is_A_transpose ? ki : warpM * WMMA_M}; uint32_t const matrix_mma_a_col_idx{is_A_transpose ? warpM * WMMA_M : ki}; uint32_t const matrix_mma_b_row_idx{is_B_transpose ? warpN * WMMA_N : ki}; uint32_t const matrix_mma_b_col_idx{is_B_transpose ? ki : warpN * WMMA_N};
if (matrix_mma_a_row_idx < (is_A_transpose ? k : m) && matrix_mma_a_col_idx < (is_A_transpose ? m : k) && matrix_mma_b_row_idx < (is_B_transpose ? n : k) && matrix_mma_b_col_idx < (is_B_transpose ? k : n)) { T1 const* matrix_mma_a_mptr{A + matrix_mma_a_row_idx + matrix_mma_a_col_idx * lda}; T1 const* matrix_mma_b_mptr{B + matrix_mma_b_row_idx + matrix_mma_b_col_idx * ldb}; nvcuda::wmma::load_matrix_sync(a_frag, matrix_mma_a_mptr, lda); nvcuda::wmma::load_matrix_sync(b_frag, matrix_mma_b_mptr, ldb);
nvcuda::wmma::mma_sync(acc_frag, a_frag, b_frag, acc_frag); } }
uint32_t const matrix_mma_c_row_idx{warpM * WMMA_M}; uint32_t const matrix_mma_c_col_idx{warpN * WMMA_N};
if (matrix_mma_c_row_idx < m && matrix_mma_c_col_idx < n) { T2* matrix_mma_c_mptr{C + matrix_mma_c_row_idx + matrix_mma_c_col_idx * ldc}; nvcuda::wmma::load_matrix_sync(c_frag, matrix_mma_c_mptr, ldc, nvcuda::wmma::mem_col_major); for (uint32_t i = 0; i < c_frag.num_elements; i++) { c_frag.x[i] = alpha * acc_frag.x[i] + beta * c_frag.x[i]; } nvcuda::wmma::store_matrix_sync(matrix_mma_c_mptr, c_frag, ldc, nvcuda::wmma::mem_col_major); } }
template <typename T1, typename T2> void launch_wmma_mm(T1 const* A, T1 const* B, T2* C, uint32_t m, uint32_t n, uint32_t k, bool is_A_transpose, bool is_B_transpose, cudaStream_t stream) { uint32_t const lda{is_A_transpose ? k : m}; uint32_t const ldb{is_B_transpose ? n : k}; uint32_t const ldc{m}; float const alpha{1.0f}; float const beta{0.0f};
constexpr int WMMA_M{16}; constexpr int WMMA_N{16}; constexpr int WMMA_K{16};
constexpr int WARP_SIZE{32};
dim3 gridDim; dim3 blockDim;
int const num_warps_x = 4; int const num_warps_y = 4; blockDim.x = num_warps_x * WARP_SIZE; blockDim.y = num_warps_y; gridDim.x = (m + (WMMA_M * num_warps_x  1)) / (WMMA_M * num_warps_x); gridDim.y = (n + WMMA_N * num_warps_y  1) / (WMMA_N * num_warps_y);
if ((!is_A_transpose) && (!is_B_transpose)) { wmma_gemm_a_col_major_b_col_major<T1, T2, WMMA_M, WMMA_N, WMMA_K, nvcuda::wmma::col_major, nvcuda::wmma::col_major> <<<gridDim, blockDim, 0, stream>>>(A, B, C, m, n, k, lda, ldb, ldc, is_A_transpose, is_B_transpose, alpha, beta); } else if ((is_A_transpose) && (!is_B_transpose)) { wmma_gemm_a_col_major_b_col_major<T1, T2, WMMA_M, WMMA_N, WMMA_K, nvcuda::wmma::row_major, nvcuda::wmma::col_major> <<<gridDim, blockDim, 0, stream>>>(A, B, C, m, n, k, lda, ldb, ldc, is_A_transpose, is_B_transpose, alpha, beta); } else if ((!is_A_transpose) && (is_B_transpose)) { wmma_gemm_a_col_major_b_col_major<T1, T2, WMMA_M, WMMA_N, WMMA_K, nvcuda::wmma::col_major, nvcuda::wmma::row_major> <<<gridDim, blockDim, 0, stream>>>(A, B, C, m, n, k, lda, ldb, ldc, is_A_transpose, is_B_transpose, alpha, beta); } else { wmma_gemm_a_col_major_b_col_major<T1, T2, WMMA_M, WMMA_N, WMMA_K, nvcuda::wmma::row_major, nvcuda::wmma::row_major> <<<gridDim, blockDim, 0, stream>>>(A, B, C, m, n, k, lda, ldb, ldc, is_A_transpose, is_B_transpose, alpha, beta); } CHECK_LAST_CUDA_ERROR(); }
template <typename T1, typename T2> void mm_a_col_major_b_col_major(T1 const* A, T1 const* B, T2* C, uint32_t m, uint32_t n, uint32_t k, uint32_t lda, uint32_t ldb, uint32_t ldc, bool is_A_transpose, bool is_B_transpose) { for (uint32_t ni{0}; ni < n; ++ni) { for (uint32_t mi{0}; mi < m; ++mi) { T2 accum{0}; if ((!is_A_transpose) && (!is_B_transpose)) { for (uint32_t ki{0}; ki < k; ++ki) { accum += A[ki * lda + mi] * B[ni * ldb + ki]; } } else if ((is_A_transpose) && (!is_B_transpose)) { for (uint32_t ki{0}; ki < k; ++ki) { accum += A[mi * lda + ki] * B[ni * ldb + ki]; } } else if ((!is_A_transpose) && (is_B_transpose)) { for (uint32_t ki{0}; ki < k; ++ki) { accum += A[ki * lda + mi] * B[ki * ldb + ni]; } } else { for (uint32_t ki{0}; ki < k; ++ki) { accum += A[mi * lda + ki] * B[ki * ldb + ni]; } } C[ni * ldc + mi] = accum; } } }
template <typename T1, typename T2> void launch_mm(T1 const* A, T1 const* B, T2* C, uint32_t m, uint32_t n, uint32_t k, bool is_A_transpose, bool is_B_transpose) { uint32_t const lda{is_A_transpose ? k : m}; uint32_t const ldb{is_B_transpose ? n : k}; uint32_t const ldc{m}; mm_a_col_major_b_col_major(A, B, C, m, n, k, lda, ldb, ldc, is_A_transpose, is_B_transpose); }
void fill_random_float_values(float* arr, size_t n, std::default_random_engine& e) { std::uniform_real_distribution<float> uniform_dist(256, 256); for (size_t i{0}; i < n; ++i) { arr[i] = uniform_dist(e); } }
void fill_random_int8_values(int8_t* arr, size_t n, std::default_random_engine& e) { std::uniform_int_distribution<int8_t> uniform_dist(128, 127); for (size_t i{0}; i < n; ++i) { arr[i] = uniform_dist(e); } }
void fill_random_int32_values(int32_t* arr, size_t n, std::default_random_engine& e) { std::uniform_int_distribution<int32_t> uniform_dist(128, 127); for (size_t i{0}; i < n; ++i) { arr[i] = uniform_dist(e); } }
void float2half(__half* half_arr, float const* float_arr, size_t n) { for (size_t i{0}; i < n; ++i) { half_arr[i] = __float2half(float_arr[i]); } }
template <typename T> float get_avg_abs_diff_ratio(T const* arr_1, T const* arr_2, size_t n) { float sum_abs_diff_ratio{0}; for (size_t i{0}; i < n; ++i) { sum_abs_diff_ratio += std::abs(static_cast<float>(arr_1[i])  static_cast<float>(arr_2[i])) / std::abs(static_cast<float>(arr_1[i]) + static_cast<float>(arr_2[i])); } return sum_abs_diff_ratio / n; }
template <typename T> bool array_equal(T const* arr_1, T const* arr_2, size_t n) { for (size_t i{0}; i < n; ++i) { if (arr_1[i] != arr_2[i]) { return false; } } return true; }
void print_test_header(bool is_A_transpose, bool is_B_transpose) { if ((!is_A_transpose) && (!is_B_transpose)) { std::cout << "C = A * B" << std::endl; } else if ((is_A_transpose) && (!is_B_transpose)) { std::cout << "C = A^T * B" << std::endl; } else if ((!is_A_transpose) && (is_B_transpose)) { std::cout << "C = A * B^T" << std::endl; } else { std::cout << "C = A^T * B^T" << std::endl; } }
int main() { constexpr int num_repeats{10}; constexpr int num_warmups{10};
uint32_t const matrix_size_m{1024}; uint32_t const matrix_size_n{1024}; uint32_t const matrix_size_k{1024}; std::cout << "Matrix Sizes" << std::endl; std::cout << "M: " << matrix_size_m << std::endl; std::cout << "N: " << matrix_size_n << std::endl; std::cout << "K: " << matrix_size_k << std::endl;
std::default_random_engine random_engine(0);
cudaStream_t stream; CHECK_CUDA_ERROR(cudaStreamCreate(&stream));
std::cout << "FP16 HMMA" << std::endl; std::vector<float> matrix_a_float(matrix_size_m * matrix_size_k); std::vector<float> matrix_b_float(matrix_size_k * matrix_size_n); std::vector<__half> matrix_a_half(matrix_size_m * matrix_size_k); std::vector<__half> matrix_b_half(matrix_size_k * matrix_size_n); std::vector<float> matrix_c_float(matrix_size_m * matrix_size_n); std::vector<float> matrix_c_float_reference(matrix_size_m * matrix_size_n);
float* h_matrix_a_float{matrix_a_float.data()}; float* h_matrix_b_float{matrix_b_float.data()}; __half* h_matrix_a_half{matrix_a_half.data()}; __half* h_matrix_b_half{matrix_b_half.data()}; float* h_matrix_c_float{matrix_c_float.data()}; float* h_matrix_c_float_reference{matrix_c_float_reference.data()};
fill_random_float_values(h_matrix_a_float, matrix_a_float.size(), random_engine); fill_random_float_values(h_matrix_b_float, matrix_b_float.size(), random_engine); fill_random_float_values(h_matrix_c_float, matrix_c_float.size(), random_engine); fill_random_float_values(h_matrix_c_float_reference, matrix_c_float_reference.size(), random_engine); float2half(h_matrix_a_half, h_matrix_a_float, matrix_a_float.size()); float2half(h_matrix_b_half, h_matrix_b_float, matrix_b_float.size());
half *d_matrix_a_half, *d_matrix_b_half; float* d_matrix_c_float;
CHECK_CUDA_ERROR(cudaMalloc(&d_matrix_a_half, matrix_size_m * matrix_size_k * sizeof(half))); CHECK_CUDA_ERROR(cudaMalloc(&d_matrix_b_half, matrix_size_k * matrix_size_n * sizeof(half))); CHECK_CUDA_ERROR(cudaMalloc(&d_matrix_c_float, matrix_size_m * matrix_size_n * sizeof(float)));
CHECK_CUDA_ERROR(cudaMemcpy(d_matrix_a_half, h_matrix_a_half, matrix_a_float.size() * sizeof(__half), cudaMemcpyHostToDevice)); CHECK_CUDA_ERROR(cudaMemcpy(d_matrix_b_half, h_matrix_b_half, matrix_b_float.size() * sizeof(__half), cudaMemcpyHostToDevice));
for (bool is_A_transpose : {true, false}) { for (bool is_B_transpose : {true, false}) { print_test_header(is_A_transpose, is_B_transpose); launch_mm(h_matrix_a_float, h_matrix_b_float, h_matrix_c_float_reference, matrix_size_m, matrix_size_n, matrix_size_k, is_A_transpose, is_B_transpose); launch_wmma_mm(d_matrix_a_half, d_matrix_b_half, d_matrix_c_float, matrix_size_m, matrix_size_n, matrix_size_k, is_A_transpose, is_B_transpose, stream); CHECK_CUDA_ERROR(cudaStreamSynchronize(stream));
CHECK_CUDA_ERROR(cudaMemcpy(h_matrix_c_float, d_matrix_c_float, matrix_c_float.size() * sizeof(float), cudaMemcpyDeviceToHost));
float const avg_abs_diff_ratio{get_avg_abs_diff_ratio( h_matrix_c_float, h_matrix_c_float_reference, matrix_c_float.size())}; if (avg_abs_diff_ratio > 0.01) { std::cout << "Got high average absolute diff ratio: " << avg_abs_diff_ratio << std::endl; }
std::function<void(cudaStream_t)> const function_hmma{std::bind( launch_wmma_mm<__half, float>, d_matrix_a_half, d_matrix_b_half, d_matrix_c_float, matrix_size_m, matrix_size_n, matrix_size_k, is_A_transpose, is_B_transpose, std::placeholders::_1)}; float const latency_hmma{measure_performance( function_hmma, stream, num_repeats, num_warmups)}; std::cout << std::fixed << std::setprecision(3) << "HMMA Latency: " << latency_hmma << " ms" << std::endl; } }
CHECK_CUDA_ERROR(cudaFree(d_matrix_a_half)); CHECK_CUDA_ERROR(cudaFree(d_matrix_b_half)); CHECK_CUDA_ERROR(cudaFree(d_matrix_c_float));
std::cout << "INT8 IMMA" << std::endl; std::vector<int8_t> matrix_a_int8(matrix_size_m * matrix_size_k); std::vector<int8_t> matrix_b_int8(matrix_size_k * matrix_size_n); std::vector<int32_t> matrix_c_int32(matrix_size_m * matrix_size_n); std::vector<int32_t> matrix_c_int32_reference(matrix_size_m * matrix_size_n);
int8_t* h_matrix_a_int8{matrix_a_int8.data()}; int8_t* h_matrix_b_int8{matrix_b_int8.data()}; int32_t* h_matrix_c_int32{matrix_c_int32.data()}; int32_t* h_matrix_c_int32_reference{matrix_c_int32_reference.data()};
fill_random_int8_values(h_matrix_a_int8, matrix_a_int8.size(), random_engine); fill_random_int8_values(h_matrix_b_int8, matrix_b_int8.size(), random_engine); fill_random_int32_values(h_matrix_c_int32, matrix_c_int32.size(), random_engine); fill_random_int32_values(h_matrix_c_int32_reference, matrix_c_int32_reference.size(), random_engine);
int8_t *d_matrix_a_int8, *d_matrix_b_int8; int32_t* d_matrix_c_int32;
CHECK_CUDA_ERROR(cudaMalloc( &d_matrix_a_int8, matrix_size_m * matrix_size_k * sizeof(int8_t))); CHECK_CUDA_ERROR(cudaMalloc( &d_matrix_b_int8, matrix_size_k * matrix_size_n * sizeof(int8_t))); CHECK_CUDA_ERROR(cudaMalloc( &d_matrix_c_int32, matrix_size_m * matrix_size_n * sizeof(int32_t)));
CHECK_CUDA_ERROR(cudaMemcpy(d_matrix_a_int8, h_matrix_a_int8, matrix_a_int8.size() * sizeof(int8_t), cudaMemcpyHostToDevice)); CHECK_CUDA_ERROR(cudaMemcpy(d_matrix_b_int8, h_matrix_b_int8, matrix_b_int8.size() * sizeof(int8_t), cudaMemcpyHostToDevice));
for (bool is_A_transpose : {true, false}) { for (bool is_B_transpose : {true, false}) { print_test_header(is_A_transpose, is_B_transpose); launch_mm(h_matrix_a_int8, h_matrix_b_int8, h_matrix_c_int32_reference, matrix_size_m, matrix_size_n, matrix_size_k, is_A_transpose, is_B_transpose); launch_wmma_mm(d_matrix_a_int8, d_matrix_b_int8, d_matrix_c_int32, matrix_size_m, matrix_size_n, matrix_size_k, is_A_transpose, is_B_transpose, stream); CHECK_CUDA_ERROR(cudaStreamSynchronize(stream)); CHECK_CUDA_ERROR(cudaMemcpy(h_matrix_c_int32, d_matrix_c_int32, matrix_c_int32.size() * sizeof(int32_t), cudaMemcpyDeviceToHost)); assert(array_equal(h_matrix_c_int32, h_matrix_c_int32_reference, matrix_c_int32.size()));
std::function<void(cudaStream_t)> const function_imma{ std::bind(launch_wmma_mm<int8_t, int32_t>, d_matrix_a_int8, d_matrix_b_int8, d_matrix_c_int32, matrix_size_m, matrix_size_n, matrix_size_k, is_A_transpose, is_B_transpose, std::placeholders::_1)}; float const latency_imma{measure_performance( function_imma, stream, num_repeats, num_warmups)}; std::cout << std::fixed << std::setprecision(3) << "IMMA Latency: " << latency_imma << " ms" << std::endl; } }
CHECK_CUDA_ERROR(cudaFree(d_matrix_a_int8)); CHECK_CUDA_ERROR(cudaFree(d_matrix_b_int8)); CHECK_CUDA_ERROR(cudaFree(d_matrix_c_int32));
CHECK_CUDA_ERROR(cudaStreamDestroy(stream)); }
