| 281 | } |
| 282 | |
| 283 | static std::pair<int, int> test_grad( |
| 284 | enum ggml_opt_optimizer_type optim, |
| 285 | ggml_backend_sched_t backend_sched, ggml_backend_t backend) { |
| 286 | int ntest = 0; |
| 287 | int npass = 0; |
| 288 | |
| 289 | struct helper_ctx_data cd = helper_get_ctx_data(optim, backend_sched, backend, /*init_opt_ctx =*/ true, /*optimizer_defaults =*/ false, |
| 290 | /*nbatch_logical =*/ 999999, /*nbatch_physical =*/ 1); |
| 291 | |
| 292 | std::vector<float> grad_history(ndata); |
| 293 | for (int64_t idata = 0; idata < ndata; ++idata) { |
| 294 | grad_history[idata] = NAN; |
| 295 | } |
| 296 | |
| 297 | for (int idata = 0; idata < ndata; ++idata) { |
| 298 | const float idataf = idata; |
| 299 | ggml_opt_alloc(cd.opt_ctx, /*backward =*/ true); |
| 300 | // leaked |
| 301 | ggml_backend_tensor_set(cd.inputs, &idataf, 0, ggml_nbytes(cd.inputs)); |
| 302 | ggml_opt_eval(cd.opt_ctx, cd.result); |
| 303 | ggml_backend_tensor_get(ggml_opt_grad_acc(cd.opt_ctx, cd.weights), grad_history.data() + idata, 0, sizeof(float)); |
| 304 | } |
| 305 | |
| 306 | { |
| 307 | bool subtest_ok = true; |
| 308 | for (int idata = 0; idata < ndata; ++idata) { |
| 309 | if (grad_history[idata] != idata + 1) { |
| 310 | subtest_ok = false; |
| 311 | } |
| 312 | } |
| 313 | printf(" %s(): ", __func__); |
| 314 | if (subtest_ok) { |
| 315 | printf("\033[1;32mOK\033[0m\n"); |
| 316 | npass++; |
| 317 | } else { |
| 318 | printf("\033[1;31mFAIL\033[0m\n"); |
| 319 | } |
| 320 | ntest++; |
| 321 | } |
| 322 | |
| 323 | helper_free_ctx_data(cd); |
| 324 | |
| 325 | return std::make_pair(npass, ntest); |
| 326 | } |
| 327 | |
| 328 | static void helper_after_test_forward_backward( |
| 329 | enum ggml_opt_optimizer_type optim, |
no test coverage detected