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