237 lines
7.5 KiB
C++
237 lines
7.5 KiB
C++
#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;
|
|
}
|