File size: 7,671 Bytes
8efb28e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 | #include "ggml.h"
#include "ggml-cpu.h"
#include <chrono>
#include <iostream>
#include <cstdio>
#include <cstdlib>
#include <cassert>
#include <vector>
#include <thread>
#define MAX_NARGS 2
static void test_barrier(int n_threads, int n_rounds) {
struct ggml_init_params params = {
/* .mem_size = */ 1024*1024*1024,
/* .mem_buffer = */ NULL,
/* .no_alloc = */ false,
};
struct ggml_context * ctx = ggml_init(params);
// Create graph
struct ggml_cgraph * gf = ggml_new_graph(ctx);
// Lots of small, parallel ops where barriers in between will dominate
struct ggml_tensor * out = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 64);
for (int i = 0; i < 1000; i++) {
struct ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, 64, 128);
out = ggml_mul_mat(ctx, a, out);
struct ggml_tensor * d = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, 128, 64);
out = ggml_mul_mat(ctx, d, out);
}
ggml_build_forward_expand(gf, out);
int n_nodes = ggml_graph_n_nodes(gf);
// Create threadpool
struct ggml_threadpool_params tpp = ggml_threadpool_params_default(n_threads);
struct ggml_threadpool* threadpool = ggml_threadpool_new(&tpp);
if (!threadpool) {
fprintf(stderr, "threadpool create failed : n_threads %d\n", n_threads);
exit(1);
}
// The test runs with constant number of threads
struct ggml_cplan cplan = ggml_graph_plan(gf, n_threads, threadpool);
std::vector<uint8_t> work_data(cplan.work_size);
cplan.work_data = work_data.data();
std::cerr << "graph-compute with"
<< "\n n_threads: " << n_threads
<< "\n n_nodes: " << n_nodes
<< "\n n_rounds: " << n_rounds
<< "\n";
// ggml_graph_print(gf);
// Warmup
ggml_graph_compute(gf, &cplan);
auto t0 = std::chrono::high_resolution_clock::now();
for (int i=0; i < n_rounds; i++) {
ggml_graph_compute(gf, &cplan);
}
auto t1 = std::chrono::high_resolution_clock::now();
auto usec = std::chrono::duration_cast<std::chrono::microseconds>(t1-t0).count();
auto nsec = std::chrono::duration_cast<std::chrono::nanoseconds>(t1-t0).count();
std::cerr << "graph-compute took " << usec << " usec "
<< "\n " << (float) usec / n_rounds << " usec per-iter"
<< "\n " << (float) nsec / (n_rounds * n_nodes) << " nsec per-node"
<< "\n";
ggml_threadpool_free(threadpool);
ggml_free(ctx);
}
static void test_active(int n_threads, int n_rounds) {
struct ggml_init_params params = {
/* .mem_size = */ 1024*1024*1024,
/* .mem_buffer = */ NULL,
/* .no_alloc = */ false,
};
struct ggml_context * ctx = ggml_init(params);
// Create graph
struct ggml_cgraph * gf = ggml_new_graph(ctx);
// Small graph with, parallel ops with barriers
struct ggml_tensor * out = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 64);
for (int i = 0; i < 2; i++) {
struct ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, 64, 128);
out = ggml_mul_mat(ctx, a, out);
struct ggml_tensor * d = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, 128, 64);
out = ggml_mul_mat(ctx, d, out);
}
ggml_build_forward_expand(gf, out);
int n_nodes = ggml_graph_n_nodes(gf);
// Create threadpool
struct ggml_threadpool_params tpp = ggml_threadpool_params_default(n_threads);
struct ggml_threadpool* threadpool = ggml_threadpool_new(&tpp);
if (!threadpool) {
fprintf(stderr, "threadpool create failed : n_threads %d\n", n_threads);
exit(1);
}
std::cerr << "graph-compute with"
<< "\n n_threads: " << n_threads
<< "\n n_nodes: " << n_nodes
<< "\n n_rounds: " << n_rounds
<< "\n";
// ggml_graph_print(gf);
// In this test we keep changing the number of threads every 4th iteration
// to test for race conditions in that path
for (int i=0; i < n_rounds; i++) {
struct ggml_cplan cplan = ggml_graph_plan(gf, (i % 4) == 0 ? 1 : n_threads, threadpool);
std::vector<uint8_t> work_data(cplan.work_size);
cplan.work_data = work_data.data();
ggml_graph_compute(gf, &cplan);
}
ggml_threadpool_free(threadpool);
ggml_free(ctx);
}
static void test_multi_graph(int n_threads, int n_rounds) {
struct ggml_init_params params = {
/* .mem_size = */ 1024*1024*1024,
/* .mem_buffer = */ NULL,
/* .no_alloc = */ false,
};
struct ggml_context * ctx = ggml_init(params);
// Create graphs
struct ggml_cgraph * gf0 = ggml_new_graph(ctx);
{
// Small graph with parallel ops with barriers
struct ggml_tensor * out = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 64);
for (int i = 0; i < 2; i++) {
struct ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, 64, 128);
out = ggml_mul_mat(ctx, a, out);
struct ggml_tensor * d = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, 128, 64);
out = ggml_mul_mat(ctx, d, out);
}
ggml_build_forward_expand(gf0, out);
}
struct ggml_cgraph * gf1 = ggml_new_graph(ctx);
{
// Small graph with parallel ops with barriers
// Use larger tensors to make sure work_data size is larger than gf0
struct ggml_tensor * out = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 256);
for (int i = 0; i < 4; i++) {
struct ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, 256, 128);
out = ggml_mul_mat(ctx, a, out);
struct ggml_tensor * d = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, 128, 256);
out = ggml_mul_mat(ctx, d, out);
}
ggml_build_forward_expand(gf1, out);
}
// Create threadpool
struct ggml_threadpool_params tpp = ggml_threadpool_params_default(n_threads);
struct ggml_threadpool* threadpool = ggml_threadpool_new(&tpp);
if (!threadpool) {
fprintf(stderr, "threadpool create failed : n_threads %d\n", n_threads);
exit(1);
}
std::cerr << "graph-compute with"
<< "\n gf0 n_nodes: " << ggml_graph_n_nodes(gf0)
<< "\n gf1 n_nodes: " << ggml_graph_n_nodes(gf1)
<< "\n n_threads: " << n_threads
<< "\n n_rounds: " << n_rounds
<< "\n";
// In this test we keep changing the number of threads every 4th iteration
// and we compute two graphs back to back to test graph frequent graph switching
for (int i=0; i < n_rounds; i++) {
struct ggml_cplan cplan0 = ggml_graph_plan(gf0, (i % 4) == 0 ? 1 : n_threads, threadpool);
std::vector<uint8_t> work_data0(cplan0.work_size);
cplan0.work_data = work_data0.data();
struct ggml_cplan cplan1 = ggml_graph_plan(gf1, (i % 4) == 0 ? 1 : n_threads, threadpool);
std::vector<uint8_t> work_data1(cplan1.work_size);
cplan1.work_data = work_data1.data();
ggml_graph_compute(gf0, &cplan0);
ggml_graph_compute(gf1, &cplan1);
}
ggml_threadpool_free(threadpool);
ggml_free(ctx);
}
int main(int argc, char *argv[]) {
int n_threads = std::max(1, std::min(4, (int) std::thread::hardware_concurrency()));
int n_rounds = 100;
if (argc > 1) {
n_threads = std::atoi(argv[1]);
}
if (argc > 2) {
n_rounds = std::atoi(argv[2]);
}
test_barrier(n_threads, n_rounds);
test_active(n_threads, n_rounds * 100);
test_multi_graph(n_threads, n_rounds * 10);
return 0;
}
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