wehub-resource-sync
153 lines
4.4 KiB
C++
153 lines
4.4 KiB
C++
// basic tool to experiment with the Metal backend
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//
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// 1. Get GPU trace of a dummy graph:
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//
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// rm -rf /tmp/perf-metal.gputrace
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// make -j perf-metal && METAL_CAPTURE_ENABLED=1 ./bin/perf-metal
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// open /tmp/perf-metal.gputrace
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//
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// https://github.com/ggerganov/llama.cpp/issues/9507
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//
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#include "ggml.h"
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#include "ggml-alloc.h"
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#include "ggml-backend.h"
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#include "ggml-metal.h"
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#include <cstdio>
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#include <vector>
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#include <thread>
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int main(int argc, char ** argv) {
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int n_op = 1024;
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int n_iter = 128;
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if (argc > 1) {
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n_op = std::atoi(argv[1]);
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}
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if (argc > 2) {
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n_iter = std::atoi(argv[2]);
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}
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printf("%s: n_op = %d, n_iter = %d\n", __func__, n_op, n_iter);
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const int ne00 = 8;
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const int ne01 = 8;
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const int ne11 = 8;
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std::vector<float> data0(ne00*ne01, 1.0f);
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std::vector<float> data1(ne00*ne01, 1.0f/ne00);
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ggml_backend_t backend = ggml_backend_metal_init();
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if (!backend) {
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fprintf(stderr, "%s: ggml_backend_metal_init() failed\n", __func__);
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return 1;
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}
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const size_t ctx_size = 2 * ggml_tensor_overhead();
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struct ggml_init_params params = {
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/*.mem_size =*/ ctx_size,
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/*.mem_buffer =*/ NULL,
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/*.no_alloc =*/ true,
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};
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struct ggml_context * ctx = ggml_init(params);
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struct ggml_tensor * t0 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, ne00, ne01);
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struct ggml_tensor * t1 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, ne00, ne11);
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ggml_backend_buffer_t buffer = ggml_backend_alloc_ctx_tensors(ctx, backend);
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ggml_backend_tensor_set(t0, data0.data(), 0, ggml_nbytes(t0));
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ggml_backend_tensor_set(t1, data1.data(), 0, ggml_nbytes(t1));
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struct ggml_cgraph * gf = NULL;
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struct ggml_context * ctx_cgraph = NULL;
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// create a dummy compute graph:
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//
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// x = mul_mat(t0, t1)
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// x = x * 1.0f
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// x = mul_mat(x, t1)
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// x = x * 1.0f
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// ... repeat n_op times ...
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//
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{
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struct ggml_init_params params0 = {
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/*.mem_size =*/ 4*n_op*ggml_tensor_overhead() + ggml_graph_overhead(),
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/*.mem_buffer =*/ NULL,
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/*.no_alloc =*/ true,
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};
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ctx_cgraph = ggml_init(params0);
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gf = ggml_new_graph_custom(ctx_cgraph, 4*n_op, false);
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struct ggml_tensor * cur = ggml_mul_mat(ctx_cgraph, t0, t1);
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cur = ggml_scale(ctx_cgraph, cur, 1.0f);
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for (int i = 0; i < n_op - 1; i++) {
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cur = ggml_mul_mat(ctx_cgraph, cur, t1);
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cur = ggml_scale(ctx_cgraph, cur, 1.0f);
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}
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cur = ggml_scale(ctx_cgraph, cur, 42.0f);
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ggml_build_forward_expand(gf, cur);
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}
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printf("%s: graph nodes = %d\n", __func__, ggml_graph_n_nodes(gf));
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ggml_gallocr_t allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(backend));
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ggml_gallocr_alloc_graph(allocr, gf);
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{
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// warm-up
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ggml_backend_graph_compute(backend, gf);
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const int64_t t_start = ggml_time_us();
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for (int iter = 0; iter < n_iter; iter++) {
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ggml_backend_graph_compute(backend, gf);
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}
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const int64_t t_end = ggml_time_us();
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// actual trace
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ggml_backend_metal_capture_next_compute(backend);
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ggml_backend_graph_compute(backend, gf);
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//std::this_thread::sleep_for(std::chrono::milliseconds(1000)); // NOTE: these intervals do not appear in the XCode trace!
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ggml_backend_metal_capture_next_compute(backend);
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ggml_backend_graph_compute(backend, gf);
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//std::this_thread::sleep_for(std::chrono::milliseconds(1000)); // NOTE: these intervals do not appear in the XCode trace!
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ggml_backend_metal_capture_next_compute(backend);
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ggml_backend_graph_compute(backend, gf);
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printf("%s: time = %f ms\n", __func__, (t_end - t_start) / 1000.0 / n_iter);
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}
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{
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struct ggml_tensor * res = ggml_graph_node(gf, -1);
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std::vector<float> data(res->ne[0] * res->ne[1], 0.0f);
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ggml_backend_tensor_get(res, data.data(), 0, ggml_nbytes(res));
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for (int i1 = 0; i1 < res->ne[1]; i1++) {
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for (int i0 = 0; i0 < res->ne[0]; i0++) {
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printf("%f ", data[i1*res->ne[0] + i0]);
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}
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printf("\n");
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}
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}
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ggml_free(ctx_cgraph);
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ggml_gallocr_free(allocr);
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ggml_free(ctx);
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ggml_backend_buffer_free(buffer);
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ggml_backend_free(backend);
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return 0;
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}
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