+++ /dev/null
-#include "common.h"
-#include "ggml.h"
-
-#include <locale.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>
-
-#if defined(_MSC_VER)
-#pragma warning(disable: 4244 4267) // possible loss of data
-#endif
-
-static void ggml_graph_compute_helper(std::vector<uint8_t> & buf, ggml_cgraph * graph, int n_threads) {
- struct ggml_cplan plan = ggml_graph_plan(graph, n_threads, nullptr);
-
- if (plan.work_size > 0) {
- buf.resize(plan.work_size);
- plan.work_data = buf.data();
- }
-
- ggml_graph_compute(graph, &plan);
-}
-
-static float tensor_sum_elements(const ggml_tensor * tensor) {
- double 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;
-}
-
-static void tensor_dump(const ggml_tensor * tensor, const char * name) {
- printf("%15s: type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi) - ", name,
- tensor->type, ggml_type_name(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", name, sum);
-}
-
-#define TENSOR_DUMP(tensor) tensor_dump(tensor, #tensor)
-
-struct benchmark_params_struct {
- int n_threads = 1;
- int32_t n_iterations = 10;
-};
-
-static 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);
- }
-
- print_build_info();
- printf("Starting Test\n");
-
- // create the ggml context
- 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);
-
- // TODO: perform the bench for all types or for a user specified type
- const ggml_type qtype = GGML_TYPE_Q4_1;
-
- size_t ctx_size = 0;
- ctx_size += ggml_row_size(GGML_TYPE_F32, sizex*sizey);
- ctx_size += ggml_row_size(GGML_TYPE_F32, sizex*sizey);
- ctx_size += ggml_row_size(GGML_TYPE_F32, sizex*sizez);
- ctx_size += ggml_row_size(qtype, sizex*sizey);
- ctx_size += ggml_row_size(qtype, sizex*sizey);
- ctx_size += ggml_row_size(GGML_TYPE_F32, sizex*sizey); // BLAS
- ctx_size += ggml_row_size(GGML_TYPE_F32, sizex*sizey); // BLAS
- ctx_size += 1024*1024*16;
-
- printf("Allocating Memory of size %zi bytes, %zi 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 1;
- }
-
-
- 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_new_graph(ctx);
- ggml_build_forward_expand(gf, m11xm2);
-
- printf("n_threads=%i\n", benchmark_params.n_threads);
-
- TENSOR_DUMP(m11);
- TENSOR_DUMP(m2);
-
- std::vector<uint8_t> work_buffer;
-
- ggml_graph_compute_helper(work_buffer, gf, benchmark_params.n_threads);
-
- TENSOR_DUMP(ggml_graph_node(gf, 0));
-
- printf("\n------ Test 2 - Matrix Mult via %s code\n", ggml_type_name(qtype));
-
- int32_t nelements = sizex*sizey;
-
- // Set up a the benchmark matrices
- // printf("Creating new tensor q11 & Running quantize\n");
- struct ggml_tensor * q11 = ggml_new_tensor_2d(ctx, qtype, sizex, sizey);
- ggml_quantize_chunk(qtype, (const float *) m11->data, q11->data, 0, nelements/m11->ne[0], m11->ne[0], nullptr);
-
- // 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_new_graph(ctx);
- ggml_build_forward_expand(gf31, q31);
-
- // 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, qtype, sizex, sizey);
- ggml_quantize_chunk(qtype, (const float *) m12->data, q12->data, 0, nelements/m12->ne[0], m12->ne[0], nullptr);
-
- // 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_new_graph(ctx);
- ggml_build_forward_expand(gf32, q32);
- printf("n_threads=%i\n", benchmark_params.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 quantized multiplication
- float sum_of_F32_reference = tensor_sum_elements(ggml_graph_node(gf, 0));
-
- printf("Iteration;NThreads; SizeX; SizeY; SizeZ; Required_FLOPS; Elapsed_u_Seconds; gigaFLOPS\n");
- printf("=====================================================================================\n");
-
- double gflops_sum = 0;
- 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_helper(work_buffer, gf31, benchmark_params.n_threads);
-
- long long int stop = ggml_time_us();
- long long int usec = stop-start;
- double gflops = (double)(flops_per_matrix)/usec/1000.0;
- gflops_sum += gflops;
- printf("%9i;%8i;%6i;%6i;%6i;%15lli;%18lli;%10.2f\n",
- i,
- benchmark_params.n_threads,
- sizex, sizey, sizez, flops_per_matrix,
- usec,gflops);
-
-#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(ggml_graph_node(gf31, 0));
- float delta = std::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_helper(work_buffer, gf32, benchmark_params.n_threads);
- }
- printf("\n");
- printf("Average%78.2f\n",gflops_sum/((double)benchmark_params.n_iterations));
- printf("=====================================================================================\n");
-}
overview / pkg / ggml / sources / llama.cpp / commitdiff