mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-10-30 08:42:00 +00:00
261 lines
9.0 KiB
C++
261 lines
9.0 KiB
C++
#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);
|
|
|
|
}
|
|
|
|
}
|