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Diffstat (limited to 'examples/benchmark/benchmark-matmult.cpp')
| -rw-r--r-- | examples/benchmark/benchmark-matmult.cpp | 260 |
1 files changed, 260 insertions, 0 deletions
diff --git a/examples/benchmark/benchmark-matmult.cpp b/examples/benchmark/benchmark-matmult.cpp new file mode 100644 index 0000000..19cbab1 --- /dev/null +++ b/examples/benchmark/benchmark-matmult.cpp @@ -0,0 +1,260 @@ +#include <locale.h> +#include "ggml.h" +#include <assert.h> +#include <math.h> +#include <cstring> +#include <cstdio> +#include <cinttypes> +#include <unordered_map> +#include <queue> +#include <string.h> +#include <cassert> +#include <fstream> +#include <string> +#include <iterator> +#include <algorithm> + +float tensor_sum_elements(struct ggml_tensor * tensor) { + float sum = 0; + if (tensor->type==GGML_TYPE_F32) { + for (int j = 0; j < tensor->ne[1]; j++) { + for (int k = 0; k < tensor->ne[0]; k++) { + sum += ((float *) tensor->data)[j*tensor->ne[0]+k]; + } + } + } + return sum; +} + + +/* + These are mapping to unknown + GGML_TYPE_I8, + GGML_TYPE_I16, + GGML_TYPE_I32, + GGML_TYPE_COUNT, +*/ + +#define TENSOR_TYPE_AS_STR(TYPE) TYPE == GGML_TYPE_F32 ? "FP32" : TYPE == GGML_TYPE_F16 ? "FP16" : TYPE == GGML_TYPE_Q4_0 ? "Q4_0" : TYPE == GGML_TYPE_Q4_1 ? "Q4_1" : "UNKNOWN" + +#define TENSOR_DUMP(TENSOR) printf("%15s: type = %i (%5s) ne = %5ld x %5ld x %5ld, nb = (%5li, %5li, %5li) - ", #TENSOR, \ + TENSOR->type,TENSOR_TYPE_AS_STR(TENSOR->type),\ + TENSOR->ne[0], TENSOR->ne[1], TENSOR->ne[2], TENSOR->nb[0], TENSOR->nb[1], TENSOR->nb[2]); \ + { float sum = tensor_sum_elements(TENSOR); printf("Sum of tensor %s is %6.2f\n",#TENSOR, sum); } + +struct benchmark_params_struct { + int32_t n_threads = 1; + int32_t n_iterations = 10; +}; + +void print_usage(int /*argc*/, char ** argv, struct benchmark_params_struct params) { + fprintf(stderr, "usage: %s [options]\n", argv[0]); + fprintf(stderr, "\n"); + fprintf(stderr, "options:\n"); + fprintf(stderr, " -h, --help show this help message and exit\n"); + fprintf(stderr, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads); + fprintf(stderr, " -i N, --iter N number of iterations to use during computation (default: %d)\n", params.n_iterations); + fprintf(stderr, "\n"); +} + +int main(int argc, char ** argv) { + + + struct benchmark_params_struct benchmark_params; + + bool invalid_param = false; + std::string arg; + for (int i = 1; i < argc; i++) { + arg = argv[i]; + + if (arg == "-t" || arg == "--threads") { + if (++i >= argc) { + invalid_param = true; + break; + } + benchmark_params.n_threads = std::stoi(argv[i]); + } else if (arg == "-i" || arg == "--iter") { + if (++i >= argc) { + invalid_param = true; + break; + } + benchmark_params.n_iterations = std::stoi(argv[i]); + } else if (arg == "-h" || arg == "--help") { + print_usage(argc, argv, benchmark_params); + exit(0); + } + if (invalid_param) { + fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str()); + print_usage(argc, argv, benchmark_params); + exit(1); + } + } + + // create the ggml context + printf("Starting Test\n"); + + struct ggml_context * ctx; + //const int sizex = 4096; + //const int sizey = 11008; + +#undef VERBOSE_DEBUGGING +#ifndef VERBOSE_DEBUGGING + const int sizey = 4096; + const int sizex = 11008; + const int sizez = 128; +#else + /* Working - let's increase size */ + const int sizey = 1; + const int sizex = (8*32); + const int sizez = 1; + + /*const int sizey = 1; + const int sizex = 3*(8*32); + const int sizez = 1;*/ +#endif + + //printf("Memsize required = %i\n", sizex*sizex); + + size_t ctx_size = 0; + ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_F32); + ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_F32); + ctx_size += sizex*sizez*ggml_type_sizef(GGML_TYPE_F32); + ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_Q4_0); + ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_Q4_0); + ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_F32); // BLAS + ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_F32); // BLAS + ctx_size += 1024*1024*16; + + printf("Allocating Memory of size %li bytes, %li MB\n",ctx_size, (ctx_size/1024/1024)); + + struct ggml_init_params params = { + /*.mem_size =*/ ctx_size, + /*.mem_buffer =*/ NULL, + /* no_alloc =*/ 0 + }; + + ctx = ggml_init(params); + if (!ctx) { + fprintf(stderr, "%s: ggml_init() failed\n", __func__); + return false; + } + + + printf("Creating new tensors\n"); + // printf("Creating new tensor m1\n"); + struct ggml_tensor * m11 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, sizex, sizey); + ggml_set_f32(m11, 1.0f); + + // printf("Creating new tensor m1\n"); + struct ggml_tensor * m12 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, sizex, sizey); + ggml_set_f32(m12, 1.5f); + + // printf("Creating new tensor m2\n"); + struct ggml_tensor * m2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, sizex, sizez); + ggml_set_f32(m2, 2.0f); + + printf("\n------ Test 1 - Matrix Mult via F32 code ------------------------------------------------------------------------------\n"); + // printf("Creating new tensor m11xm2\n"); + struct ggml_tensor * m11xm2 = ggml_mul_mat(ctx, m11, m2); + + // printf("Creating compute graph\n"); + struct ggml_cgraph gf = ggml_build_forward(m11xm2); + + gf.n_threads=benchmark_params.n_threads; + printf("cgraph->n_threads=%i\n",gf.n_threads); + + TENSOR_DUMP(m11); + TENSOR_DUMP(m2); + + ggml_graph_compute(ctx, &gf); + + TENSOR_DUMP(gf.nodes[0]); + + printf("\n------ Test 2 - Matrix Mult via Q4_0 code ------------------------------------------------------------------------------\n"); + + int32_t nelements = sizex*sizey; + int32_t ne[2] = { sizex, sizey }; + + std::vector<int64_t> hist_cur(1 << 4, 0); + + // Set up a the benchmark matrices + // printf("Creating new tensor q11 & Running quantize\n"); + struct ggml_tensor * q11 = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, sizex, sizey); + ggml_quantize_q4_0((const float *) m11->data, q11->data, nelements, ne[0], hist_cur.data()); + + // Set up a the compute graph + // printf("Creating new tensor q31\n"); + struct ggml_tensor * q31 = ggml_mul_mat(ctx, q11, m2); + + // printf("Creating compute graph\n"); + struct ggml_cgraph gf31 = ggml_build_forward(q31); + gf31.n_threads=benchmark_params.n_threads; + + // Set up a second graph computation to make sure we override the CPU cache lines + // printf("Creating new tensor q12 & Running quantize\n"); + struct ggml_tensor * q12 = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, sizex, sizey); + ggml_quantize_q4_0((const float *) m12->data, q12->data, nelements, ne[0], hist_cur.data()); + + // printf("Creating new tensor q32\n"); + struct ggml_tensor * q32 = ggml_mul_mat(ctx, q12, m2); + + //printf("Creating compute graph\n"); + struct ggml_cgraph gf32 = ggml_build_forward(q32); + gf32.n_threads=benchmark_params.n_threads; + printf("cgraph->n_threads=%i\n",gf31.n_threads); + + const int dimx = sizex; + const int dimy = sizey; + const int dimz = sizez; + long long int flops_per_dot_product = dimy + dimy; + long long int flops_per_matrix = flops_per_dot_product * dimx * dimz; ; + printf("Matrix Multiplication of (%i,%i,%i) x (%i,%i,%i) - about %6.2f gFLOPS\n\n", sizex, sizey, 1, sizex, sizez, 1, 1.0f*flops_per_matrix / 1000 / 1000 / 1000); + + + // Let's use the F32 result from above as a reference for the q4_0 multiplication + float sum_of_F32_reference = tensor_sum_elements(gf.nodes[0]); + + + printf("Iteration;NThreads; SizeX; SizeY; SizeZ; Required_FLOPS; Elapsed_u_Seconds; FLOPS_per_u_Second\n"); + printf("==============================================================================================\n"); + + for (int i=0;i<benchmark_params.n_iterations ;i++) { + + long long int start = ggml_time_us(); + //printf("Running ggml_graph_compute\n"); + ggml_graph_compute(ctx, &gf31); + long long int stop = ggml_time_us(); + long long int usec = stop-start; + float flops_per_usec = (1.0f*flops_per_matrix)/usec; + printf("%9i;%8i;%6i;%6i;%6i;%15lli;%18lli;%19.2f\n", + i, + gf31.n_threads, + sizex, sizey, sizez, flops_per_matrix, + usec,flops_per_usec); + +#ifdef VERBOSE_DEBUGGING + TENSOR_DUMP("res",gf31.nodes[0]) +#endif + + // Check that the matrix multiplication result is in the right ballpark + // We cannot use the exact value from the F32 multiplication because the quantizuation will be slightly different + float sum_of_Q4_result = tensor_sum_elements(gf31.nodes[0]); + float delta = abs(sum_of_Q4_result - sum_of_F32_reference); + float allowed_delta = (sum_of_F32_reference) / 1000 / 1000; // Let's accept an epsilon of 10^-6 + + if (delta > allowed_delta) { + printf("\nABORT - ERROR in Matrix Multiplication result - expected %6.2f, got %6.2f (delta %6.2f > allowed_delta %6.2f)\n", + sum_of_F32_reference, + sum_of_Q4_result, + delta, + allowed_delta + ); + exit(0); + } + + // Running a different graph computation to make sure we override the CPU cache lines + ggml_graph_compute(ctx, &gf32); + + } + +} |