diff options
Diffstat (limited to 'platform/linux-generic/test/validation/api/ml/ml_linux.c')
| -rw-r--r-- | platform/linux-generic/test/validation/api/ml/ml_linux.c | 1183 |
1 files changed, 1183 insertions, 0 deletions
diff --git a/platform/linux-generic/test/validation/api/ml/ml_linux.c b/platform/linux-generic/test/validation/api/ml/ml_linux.c new file mode 100644 index 000000000..30d4ec6ba --- /dev/null +++ b/platform/linux-generic/test/validation/api/ml/ml_linux.c @@ -0,0 +1,1183 @@ +/* SPDX-License-Identifier: BSD-3-Clause + * Copyright (c) 2023 Nokia + */ + +#ifndef _GNU_SOURCE +#define _GNU_SOURCE +#endif + +#include <unistd.h> +#include <string.h> +#include <libgen.h> +#include <odp_api.h> +#include <odp/helper/odph_api.h> +#include "odp_cunit_common.h" + +#define TIMEOUT 5 +#define MODEL_NAME "Test" +#define NUM_INPUTS 1 +#define NUM_OUTPUTS 1 +#define RUN_NUM 2 +#define BUF_LEN 256 +#define CONFIG_MAX_MODEL_SIZE 500 + +#define COMPL_POOL_NAME "ML compl pool" +#define NUM_COMPL 10 + +/** + * About model simple_linear.onnx being tested in this suite + * + * Model info: + * Version: 1 + * Inputs: name: x, type: int32, shape: [1] + * Outputs: name: y, type: int32, shape: [1] + * + * The model is of form y = 3 * x + 4 + * Thus when x = 5, the output y should be 19. + */ +typedef struct global_t { + int disabled; + odp_ml_capability_t ml_capa; + odp_ml_config_t ml_config; + odp_ml_model_param_t model_param; + odp_ml_model_t ml_model; + odp_pool_t compl_pool; + odp_queue_t queue; + odp_ml_data_t data; + odp_ml_data_seg_t input_seg; + odp_ml_data_seg_t output_seg; + odp_ml_run_param_t run_param; + uint64_t wait_ns; + int32_t x; + int32_t y; + int32_t y_expected; + +} global_t; + +static global_t global; + +static int fill_model_param(const char *model_name, odp_ml_model_param_t *model_param) +{ + size_t size; + char *pos; + char *exe_dir; + size_t exe_dir_len; + FILE *model_file; + char exe_path[BUF_LEN]; + ssize_t exe_path_len; + char model_path[BUF_LEN]; + + /* Model file is placed in the same directory as the executable ml_linux */ + exe_path_len = readlink("/proc/self/exe", exe_path, BUF_LEN - 1); + if (exe_path_len != -1) { + exe_path[exe_path_len] = '\0'; + + pos = strstr(exe_path, ".libs"); + if (pos) + *(pos + 5) = '\0'; + + exe_dir = dirname(exe_path); + exe_dir_len = strlen(exe_dir); + + memcpy(model_path, exe_dir, exe_dir_len); + model_path[exe_dir_len] = '/'; + model_path[exe_dir_len + 1] = '\0'; + + strncat(model_path, model_name, BUF_LEN - strlen(model_path) - 1); + ODPH_DBG("model_path: %s\n", model_path); + model_file = fopen(model_path, "rb"); + } else { /* Can't get executable path, try to find model file at current dir*/ + model_file = fopen(model_name, "rb"); + } + + if (model_file == NULL) { + perror("Failed to open model file"); + return -1; + } + + /* Get the model file size in bytes */ + fseek(model_file, 0, SEEK_END); + model_param->size = ftell(model_file); + rewind(model_file); + + model_param->model = malloc(model_param->size); + if (!model_param->model) { + ODPH_ERR("\n\nMemory allocation failed\n"); + fclose(model_file); + return -1; + } + size = fread(model_param->model, model_param->size, 1, model_file); + + fclose(model_file); + if (size != 1) { + ODPH_ERR("\n\nRead model file failed\n"); + return -1; + } + + model_param->max_compl_id = 0; + + return 0; +} + +static int ml_suite_init(void) +{ + odp_ml_capability_t *ml_capa = &global.ml_capa; + odp_queue_param_t queue_param; + odp_ml_compl_pool_param_t ml_pool_param; + + memset(&global, 0, sizeof(global_t)); + global.queue = ODP_QUEUE_INVALID; + global.compl_pool = ODP_POOL_INVALID; + + if (odp_ml_capability(ml_capa)) { + ODPH_ERR("ML capability failed\n"); + return -1; + } + + if (ml_capa->max_models == 0) { + global.disabled = 1; + ODPH_DBG("ML test disabled\n"); + return 0; + } + + /* Configure ML */ + odp_ml_config_init(&global.ml_config); + global.ml_config.max_models_created = ml_capa->max_models; + global.ml_config.max_models_loaded = ml_capa->max_models_loaded; + global.ml_config.max_model_size = CONFIG_MAX_MODEL_SIZE; + + if (ml_capa->load.compl_mode_mask & ODP_ML_COMPL_MODE_SYNC) + global.ml_config.load_mode_mask |= ODP_ML_COMPL_MODE_SYNC; + + if (ml_capa->load.compl_mode_mask & ODP_ML_COMPL_MODE_POLL) + global.ml_config.load_mode_mask |= ODP_ML_COMPL_MODE_POLL; + + if (ml_capa->load.compl_mode_mask & ODP_ML_COMPL_MODE_EVENT) + global.ml_config.load_mode_mask |= ODP_ML_COMPL_MODE_EVENT; + + if (ml_capa->run.compl_mode_mask & ODP_ML_COMPL_MODE_SYNC) + global.ml_config.run_mode_mask |= ODP_ML_COMPL_MODE_SYNC; + + if (ml_capa->run.compl_mode_mask & ODP_ML_COMPL_MODE_POLL) + global.ml_config.run_mode_mask |= ODP_ML_COMPL_MODE_POLL; + + if (ml_capa->run.compl_mode_mask & ODP_ML_COMPL_MODE_EVENT) + global.ml_config.run_mode_mask |= ODP_ML_COMPL_MODE_EVENT; + + if (odp_ml_config(&global.ml_config)) { + ODPH_ERR("\n\nConfiguring ML failed\n"); + return -1; + } + + global.x = 5; + global.wait_ns = 500 * ODP_TIME_MSEC_IN_NS; + global.y_expected = 19; /* y = 3 * x + 4 = 3 * 5 + 4 = 19 */ + + /* Prepare data for running model inference */ + odp_ml_run_param_init(&global.run_param); + + global.data.num_input_seg = NUM_INPUTS; + global.data.input_seg = &global.input_seg; + global.input_seg.size = sizeof(int32_t); + global.input_seg.addr = &global.x; + + global.data.num_output_seg = NUM_OUTPUTS; + global.data.output_seg = &global.output_seg; + global.output_seg.size = sizeof(int32_t); + global.output_seg.addr = &global.y; + + if (fill_model_param("simple_linear.onnx", &global.model_param)) + return -1; + + /* Create ML model */ + global.ml_model = odp_ml_model_create(MODEL_NAME, &global.model_param); + if (global.ml_model == ODP_ML_MODEL_INVALID) { + ODPH_ERR("Create ML model failed\n"); + goto error; + } + + /* Asynchronous mode with event completion is not supported */ + if (!((ml_capa->load.compl_mode_mask & ODP_ML_COMPL_MODE_EVENT) || + (ml_capa->run.compl_mode_mask & ODP_ML_COMPL_MODE_EVENT))) + return 0; + + /* Create a queue for sending ML completion event to */ + odp_queue_param_init(&queue_param); + queue_param.type = ODP_QUEUE_TYPE_SCHED; + queue_param.sched.sync = ODP_SCHED_SYNC_PARALLEL; + queue_param.sched.prio = odp_schedule_default_prio(); + queue_param.sched.group = ODP_SCHED_GROUP_ALL; + + global.queue = odp_queue_create("ML compl queue", &queue_param); + if (global.queue == ODP_QUEUE_INVALID) { + ODPH_ERR("Queue create failed\n"); + goto error; + } + + /* Create an ML job completion pool */ + if (ml_capa->pool.max_num < NUM_COMPL) { + ODPH_ERR("Too small ML compl pool %u\n", ml_capa->pool.max_num); + goto error; + } + + odp_ml_compl_pool_param_init(&ml_pool_param); + ml_pool_param.num = NUM_COMPL; + + global.compl_pool = odp_ml_compl_pool_create(COMPL_POOL_NAME, &ml_pool_param); + if (global.compl_pool == ODP_POOL_INVALID) { + ODPH_ERR("Create ML completion pool failed\n"); + goto error; + } + + return 0; + +error: + free(global.model_param.model); + return -1; +} + +static int ml_suite_term(void) +{ + if (global.compl_pool != ODP_POOL_INVALID && + odp_pool_destroy(global.compl_pool)) { + ODPH_ERR("Completion pool destroy failed\n"); + return -1; + } + + if (global.ml_model && odp_ml_model_destroy(global.ml_model)) { + ODPH_ERR("Destroy ML model failed\n"); + return -1; + } + + if (global.queue != ODP_QUEUE_INVALID && + odp_queue_destroy(global.queue)) { + ODPH_ERR("Destroy ML queue failed\n"); + return -1; + } + + free(global.model_param.model); + + return 0; +} + +static int check_ml_support(void) +{ + if (global.disabled) + return ODP_TEST_INACTIVE; + + return ODP_TEST_ACTIVE; +} + +static int check_load_sync(void) +{ + if (global.disabled) + return ODP_TEST_INACTIVE; + + if (global.ml_config.load_mode_mask & ODP_ML_COMPL_MODE_SYNC) + return ODP_TEST_ACTIVE; + + return ODP_TEST_INACTIVE; +} + +static int check_load_poll(void) +{ + if (global.disabled) + return ODP_TEST_INACTIVE; + + if (global.ml_config.load_mode_mask & ODP_ML_COMPL_MODE_POLL) + return ODP_TEST_ACTIVE; + + return ODP_TEST_INACTIVE; +} + +static int check_load_event(void) +{ + if (global.disabled) + return ODP_TEST_INACTIVE; + + if (global.ml_config.load_mode_mask & ODP_ML_COMPL_MODE_EVENT) + return ODP_TEST_ACTIVE; + + return ODP_TEST_INACTIVE; +} + +static int check_run_sync(void) +{ + if (global.disabled) + return ODP_TEST_INACTIVE; + + /* Model run test uses synchronous load */ + if ((global.ml_config.run_mode_mask & ODP_ML_COMPL_MODE_SYNC) && + (global.ml_config.load_mode_mask & ODP_ML_COMPL_MODE_SYNC)) + return ODP_TEST_ACTIVE; + + return ODP_TEST_INACTIVE; +} + +static int check_run_poll(void) +{ + if (global.disabled) + return ODP_TEST_INACTIVE; + + /* Poll mode model run test uses synchronous load */ + if ((global.ml_config.run_mode_mask & ODP_ML_COMPL_MODE_POLL) && + (global.ml_config.load_mode_mask & ODP_ML_COMPL_MODE_SYNC)) + return ODP_TEST_ACTIVE; + + return ODP_TEST_INACTIVE; +} + +static int check_run_event(void) +{ + if (global.disabled) + return ODP_TEST_INACTIVE; + + /* Poll mode model run test uses synchronous load */ + if ((global.ml_config.run_mode_mask & ODP_ML_COMPL_MODE_EVENT) && + (global.ml_config.load_mode_mask & ODP_ML_COMPL_MODE_SYNC)) + return ODP_TEST_ACTIVE; + + return ODP_TEST_INACTIVE; +} + +static int check_run_poll_event(void) +{ + if (global.disabled) + return ODP_TEST_INACTIVE; + + /* test_ml_run_start_multi uses synchronous load, poll mode and event mode run */ + if ((global.ml_config.run_mode_mask & ODP_ML_COMPL_MODE_EVENT) && + (global.ml_config.run_mode_mask & ODP_ML_COMPL_MODE_POLL) && + (global.ml_config.load_mode_mask & ODP_ML_COMPL_MODE_SYNC)) + return ODP_TEST_ACTIVE; + + return ODP_TEST_INACTIVE; +} + +static void test_ml_debug(void) +{ + uint64_t u64; + + u64 = odp_ml_model_to_u64(global.ml_model); + CU_ASSERT(u64 != odp_ml_model_to_u64(ODP_ML_MODEL_INVALID)); + printf("\n ML model handle: 0x%" PRIx64 "\n", u64); + + odp_ml_model_print(global.ml_model); +} + +static void test_ml_model_create(void) +{ + uint32_t i; + /* One for global.ml_model */ + uint32_t max_models = global.ml_config.max_models_created - 1; + odp_ml_model_t models[max_models]; + + for (i = 0; i < max_models; i++) { + models[i] = odp_ml_model_create(NULL, &global.model_param); + + if (models[i] == ODP_ML_MODEL_INVALID) { + ODPH_ERR("ML model create failed: %u / %u\n", i, max_models); + break; + } + } + + CU_ASSERT(i == max_models); + max_models = i; + + /* Destroy valid models */ + for (i = 0; i < max_models; i++) + CU_ASSERT_FATAL(odp_ml_model_destroy(models[i]) == 0); +} + +static void test_ml_model_lookup(void) +{ + odp_ml_model_t model2; + odp_ml_model_t model_lookup; + + /* Look up model with the same name, should find one with equal handle */ + model_lookup = odp_ml_model_lookup(MODEL_NAME); + CU_ASSERT_FATAL(model_lookup != ODP_ML_MODEL_INVALID); + CU_ASSERT(odp_ml_model_to_u64(global.ml_model) == odp_ml_model_to_u64(model_lookup)); + + /* Look up model with a different name, should return invalid handle */ + model_lookup = odp_ml_model_lookup("diff"); + CU_ASSERT_FATAL(model_lookup == ODP_ML_MODEL_INVALID); + + model2 = odp_ml_model_create(MODEL_NAME, &global.model_param); + CU_ASSERT_FATAL(model2 != ODP_ML_MODEL_INVALID); + CU_ASSERT(odp_ml_model_to_u64(global.ml_model) != odp_ml_model_to_u64(model2)); + + model_lookup = odp_ml_model_lookup(MODEL_NAME); + CU_ASSERT(odp_ml_model_to_u64(model_lookup) == odp_ml_model_to_u64(global.ml_model) || + odp_ml_model_to_u64(model_lookup) == odp_ml_model_to_u64(model2)); + + CU_ASSERT(odp_ml_model_destroy(model2) == 0); +} + +static void test_ml_model_long_name(void) +{ + odp_ml_model_t model; + char name[ODP_ML_MODEL_NAME_LEN]; + + memset(name, 'a', sizeof(name)); + name[sizeof(name) - 1] = 0; + + model = odp_ml_model_create(name, &global.model_param); + CU_ASSERT_FATAL(model != ODP_ML_MODEL_INVALID); + + CU_ASSERT(odp_ml_model_to_u64(model) == odp_ml_model_to_u64(odp_ml_model_lookup(name))); + CU_ASSERT(odp_ml_model_destroy(model) == 0); +} + +static void test_ml_model_info(void) +{ + int ret; + uint32_t num_ret; + odp_ml_model_info_t ml_info; + odp_ml_input_info_t input_info[2]; + odp_ml_output_info_t output_info[2]; + + /* Verify model info about global.ml_model, namely, simple_linear.onnx */ + memset(&ml_info, 0x88, sizeof(odp_ml_model_info_t)); + ret = odp_ml_model_info(global.ml_model, &ml_info); + CU_ASSERT(ret == 0); + CU_ASSERT(!strcmp(ml_info.name, MODEL_NAME)); + CU_ASSERT(ml_info.model_version == 1); + CU_ASSERT(ml_info.num_inputs == NUM_INPUTS); + CU_ASSERT(ml_info.num_outputs == NUM_OUTPUTS); + + num_ret = odp_ml_model_input_info(global.ml_model, input_info, NUM_INPUTS); + CU_ASSERT(num_ret == NUM_INPUTS); + CU_ASSERT(!strcmp(input_info[0].name, "x")); + CU_ASSERT(input_info[0].shape.num_dim == 1); + CU_ASSERT(input_info[0].shape.dim[0] == 1); + CU_ASSERT((int)input_info[0].data_type == ODP_ML_DATA_TYPE_INT32); + + /* When num is 0, return normally, and input_info is ignored */ + num_ret = odp_ml_model_input_info(global.ml_model, input_info, 0); + CU_ASSERT(num_ret == NUM_INPUTS); + + /* When num is bigger than actual number of inputs, extra input_info is left untouched */ + input_info[1].data_type = (odp_ml_data_type_t)-1; + num_ret = odp_ml_model_input_info(global.ml_model, input_info, NUM_INPUTS + 1); + CU_ASSERT(num_ret == NUM_INPUTS); + CU_ASSERT(!strcmp(input_info[0].name, "x")); + CU_ASSERT(input_info[0].shape.num_dim == 1); + CU_ASSERT(input_info[0].shape.dim[0] == 1); + CU_ASSERT((int)input_info[0].data_type == ODP_ML_DATA_TYPE_INT32); + /* input_info[1] is left untouched */ + CU_ASSERT(input_info[1].data_type == (odp_ml_data_type_t)-1); + + num_ret = odp_ml_model_output_info(global.ml_model, output_info, NUM_OUTPUTS); + CU_ASSERT(num_ret == NUM_OUTPUTS); + CU_ASSERT(!strcmp(output_info[0].name, "y")); + CU_ASSERT(output_info[0].shape.num_dim == 1); + CU_ASSERT(output_info[0].shape.dim[0] == 1); + CU_ASSERT((int)output_info[0].data_type == ODP_ML_DATA_TYPE_INT32); + + /* When num is 0, return normally, and input_info is ignored */ + num_ret = odp_ml_model_output_info(global.ml_model, output_info, 0); + CU_ASSERT(num_ret == NUM_OUTPUTS); + + /* When num is bigger than actual number of inputs, extra output_info is left untouched */ + num_ret = odp_ml_model_output_info(global.ml_model, output_info, NUM_OUTPUTS + 1); + output_info[1].shape.num_dim = 98876; + CU_ASSERT(num_ret == NUM_OUTPUTS); + CU_ASSERT(!strcmp(output_info[0].name, "y")); + CU_ASSERT(output_info[0].shape.num_dim == 1); + CU_ASSERT(output_info[0].shape.dim[0] == 1); + CU_ASSERT((int)output_info[0].data_type == ODP_ML_DATA_TYPE_INT32); + /* output_info[1] is left untouched */ + CU_ASSERT(output_info[1].shape.num_dim == 98876); +} + +static void test_ml_model_load(void) +{ + int ret; + odp_ml_model_t test_model; + odp_ml_load_result_t result; + + test_model = odp_ml_model_create(NULL, &global.model_param); + CU_ASSERT_FATAL(test_model != ODP_ML_MODEL_INVALID); + + ret = odp_ml_model_load(test_model, &result); + CU_ASSERT(ret == 0); + CU_ASSERT(result.error_code == 0); + + ret = odp_ml_model_unload(test_model, NULL); + CU_ASSERT(ret == 0); + + CU_ASSERT(odp_ml_model_destroy(test_model) == 0); +} + +/* Test asynchronous model loading in ODP_ML_COMPL_MODE_POLL mode */ +static void test_ml_model_load_async_poll(void) +{ + int ret; + odp_ml_load_result_t result; + odp_ml_compl_param_t compl_param; + int dummy = 6; + void *user_ptr = &dummy; + uint64_t wait_ns = 500 * ODP_TIME_MSEC_IN_NS; + + memset(&result, 0, sizeof(result)); + odp_ml_compl_param_init(&compl_param); + compl_param.mode = ODP_ML_COMPL_MODE_POLL; + compl_param.compl_id = 0; + compl_param.user_ptr = user_ptr; + + ret = odp_ml_model_load_start(global.ml_model, &compl_param); + CU_ASSERT_FATAL(ret == 0); + + /* When odp_ml_model_load_start() succeeded, continue to check completion status */ + for (int i = 0; i < TIMEOUT; i++) { + ret = odp_ml_model_load_status(global.ml_model, 0, &result); + if (ret) + break; + + /* ret = 0 meaning run has not finished, continue to check status */ + odp_time_wait_ns(wait_ns); + } + + CU_ASSERT(ret > 0); + CU_ASSERT(result.error_code == 0); + CU_ASSERT(result.user_ptr == user_ptr); + /* odp_ml_model_load does not modify data in user_ptr */ + if (result.user_ptr) + CU_ASSERT(*(int *)result.user_ptr == dummy); + + ret = odp_ml_model_unload_start(global.ml_model, &compl_param); + CU_ASSERT_FATAL(ret == 0); + + /* When odp_ml_model_unload_start() succeeded, continue to check completion + * status */ + for (int i = 0; i < TIMEOUT; i++) { + ret = odp_ml_model_unload_status(global.ml_model, 0, &result); + if (ret) + break; + + /* ret = 0 meaning run has not finished, continue to check status */ + odp_time_wait_ns(wait_ns); + } + + CU_ASSERT_FATAL(ret > 0); + CU_ASSERT(result.error_code == 0); + CU_ASSERT(result.user_ptr == user_ptr); + + /* odp_ml_model_unload does not modify data in user_ptr */ + if (result.user_ptr) + CU_ASSERT(*(int *)result.user_ptr == dummy); +} + +static int +get_result_from_ml_compl_event(odp_ml_load_result_t *load_result, odp_ml_run_result_t *run_result) +{ + int ret; + odp_event_t ev; + odp_ml_compl_t compl; + odp_event_type_t ev_type; + odp_queue_t from_queue = ODP_QUEUE_INVALID; + uint64_t sched_wait = odp_schedule_wait_time(global.wait_ns); + + /* Run event scheduler to find the ml completion event */ + for (int i = 0; i < TIMEOUT; i++) { + ev = odp_schedule(&from_queue, sched_wait); + if (ev != ODP_EVENT_INVALID) + break; + } + + CU_ASSERT(ev != ODP_EVENT_INVALID); + if (ev == ODP_EVENT_INVALID) { + ODPH_ERR("Timeout while waiting for completion event\n"); + return -1; + } + + ev_type = odp_event_type(ev); + CU_ASSERT(from_queue == global.queue); + CU_ASSERT(ev_type == ODP_EVENT_ML_COMPL); + if (from_queue != global.queue || ev_type != ODP_EVENT_ML_COMPL) { + odp_event_free(ev); + ODPH_ERR("Received unexpected event while waiting for completion\n"); + return -1; + } + + compl = odp_ml_compl_from_event(ev); + CU_ASSERT(compl != ODP_ML_COMPL_INVALID); + + if (load_result) { + CU_ASSERT(odp_ml_compl_load_result(compl, NULL) == 0); + ret = odp_ml_compl_load_result(compl, load_result); + } else { + CU_ASSERT(odp_ml_compl_run_result(compl, NULL) == 0); + ret = odp_ml_compl_run_result(compl, run_result); + } + + CU_ASSERT(ret == 0); + odp_ml_compl_free(compl); + + return ret; +} + +/* Test asynchronous model loading in ODP_ML_COMPL_MODE_EVENT mode */ +static void test_ml_model_load_async_event(void) +{ + int ret; + odp_ml_compl_t compl; + odp_ml_load_result_t result; + odp_ml_compl_param_t compl_param; + int dummy = 6; + void *user_ptr = &dummy; + + compl = odp_ml_compl_alloc(global.compl_pool); + CU_ASSERT_FATAL(compl != ODP_ML_COMPL_INVALID); + + odp_ml_compl_param_init(&compl_param); + compl_param.mode = ODP_ML_COMPL_MODE_EVENT; + compl_param.event = odp_ml_compl_to_event(compl); + compl_param.queue = global.queue; + compl_param.user_ptr = user_ptr; + + ret = odp_ml_model_load_start(global.ml_model, &compl_param); + CU_ASSERT(ret == 0); + + /* Return when odp_ml_model_load_start() failed */ + if (ret) { + odp_ml_compl_free(compl); + ODPH_ERR("ML model odp_ml_model_load_start() failed\n"); + return; + } + + /* Run event scheduler to find the ml completion event and verify it */ + if (get_result_from_ml_compl_event(&result, NULL)) + return; + + CU_ASSERT(result.error_code == 0); + CU_ASSERT(result.user_ptr == user_ptr); + + /* Model load does not modify data in user_ptr */ + if (result.user_ptr) + CU_ASSERT(*(int *)result.user_ptr == dummy); + + compl = odp_ml_compl_alloc(global.compl_pool); + CU_ASSERT(compl != ODP_ML_COMPL_INVALID); + + if (compl == ODP_ML_COMPL_INVALID) + return; + + compl_param.event = odp_ml_compl_to_event(compl); + ret = odp_ml_model_unload_start(global.ml_model, &compl_param); + CU_ASSERT_FATAL(ret == 0); + + /* Run event scheduler to find the ml completion event and verify it */ + if (get_result_from_ml_compl_event(&result, NULL)) + return; + + CU_ASSERT(result.error_code == 0); + CU_ASSERT(result.user_ptr == user_ptr); + + /* odp_ml_model_unload does not modify data in user_ptr */ + if (result.user_ptr) + CU_ASSERT(*(int *)result.user_ptr == dummy); +} + +/* About model batch_add.onnx being tested in this function + * + * Model info: + * Version: 1 + * Inputs: + * inputs[0]: name: x1, type: double, shape: [c, 3] + * inputs[1]: name: x2, type: double, shape: [c, 3] + * Outputs: + * Outputs[0]: name: y, type: double, shape: [c, 3] + * + * The model computes element-wise sum of input tensors x1 and x2 and stores them + * in y. The first dimension of input and output tensors represent batch size, + * thus it must be the same for all tensors here. The dynamic dimension size + * in the output tensor here can be deduced from the given batch size, thus no + * need for the implementation to fill it. + */ +#define NUM_COLUMN 3 +#define MAX_BATCH_SIZE 4 +#define SIZE (NUM_COLUMN * MAX_BATCH_SIZE * sizeof(double)) +static void run_model_batch_add(void) +{ + int ret; + odp_ml_data_t data; + odp_ml_model_t model; + odp_ml_data_seg_t input_segs[SIZE * 2]; + odp_ml_data_seg_t output_segs[SIZE]; + odp_ml_run_result_t result; + odp_ml_run_param_t run_param; + odp_ml_model_param_t model_param; + + double y[12]; + double y_expected[12]; + uint32_t batch_size = MAX_BATCH_SIZE; + double x1[12] = {97, 47, 62, 19, 93, 59, 67, 42, 28, 55, 46, 31}; + double x2[12] = {81, 56, 27, 4, 69, 12, 91, 98, 23, 90, 52, 64}; + + for (int i = 0; i < 12; i++) + y_expected[i] = x1[i] + x2[i]; + + odp_ml_model_param_init(&model_param); + + odp_ml_data_format_t input_format[2] = { + { + .data_type = ODP_ML_DATA_TYPE_FP64, + .data_type_size = 8, + .shape.type = ODP_ML_SHAPE_BATCH, + .shape.num_dim = 2, + .shape.dim = {ODP_ML_DIM_DYNAMIC, NUM_COLUMN}, + .shape.dim_max = {MAX_BATCH_SIZE, NUM_COLUMN} + }, + { + .data_type = ODP_ML_DATA_TYPE_FP64, + .data_type_size = 8, + .shape.type = ODP_ML_SHAPE_BATCH, + .shape.num_dim = 2, + .shape.dim = {ODP_ML_DIM_DYNAMIC, NUM_COLUMN}, + .shape.dim_max = {MAX_BATCH_SIZE, NUM_COLUMN} + } + }; + + model_param.extra_info.num_inputs = 2; + model_param.extra_info.input_format = input_format; + + /* Verify model info about matrix_mul.onnx */ + if (fill_model_param("batch_add.onnx", &model_param)) + return; + + model = odp_ml_model_create("batch_add", &model_param); + free(model_param.model); + CU_ASSERT(model != ODP_ML_MODEL_INVALID); + if (!model) + return; + + if (odp_ml_model_load(model, NULL)) { + CU_ASSERT(odp_ml_model_destroy(model) == 0); + return; + } + + odp_ml_model_print(model); + + /* Prepare parameters for running inference */ + odp_ml_run_param_init(&run_param); + run_param.result = &result; + + data.num_input_seg = 2; + data.input_seg = input_segs; + input_segs[0].addr = x1; + input_segs[1].addr = x2; + + data.num_output_seg = 1; + data.output_seg = output_segs; + output_segs[0].size = sizeof(y); + output_segs[0].addr = y; + + /* Test different batch sizes */ + for (int i = 0; i < MAX_BATCH_SIZE; i++) { + run_param.batch_size = batch_size; + input_segs[0].size = sizeof(double) * NUM_COLUMN * batch_size; + input_segs[1].size = sizeof(double) * NUM_COLUMN * batch_size; + ret = odp_ml_run(model, &data, &run_param); + CU_ASSERT(ret == 1); + if (ret != 1) + goto fail; + + for (uint32_t j = 0; j < batch_size * NUM_COLUMN; j++) + CU_ASSERT(y[j] == y_expected[j]); + + batch_size--; + } + + /* Test also without run results */ + run_param.result = NULL; + ret = odp_ml_run(model, &data, &run_param); + CU_ASSERT(ret == 1); + + /* Test different segment sizes */ + batch_size = MAX_BATCH_SIZE; + odp_ml_run_param_init(&run_param); + run_param.result = &result; + run_param.batch_size = batch_size; + data.input_seg = input_segs; + data.output_seg = output_segs; + + for (int seg_size = SIZE; seg_size > 0; seg_size--) { + int num_seg = (SIZE + seg_size - 1) / seg_size; + + if ((uint32_t)num_seg > global.ml_capa.max_segs_per_input || + (uint32_t)num_seg > global.ml_capa.max_segs_per_output) + break; + + data.num_input_seg = num_seg * 2; + data.num_output_seg = num_seg; + + for (int seg = 0; seg < num_seg; seg++) { + int size = seg_size; + + if (seg == num_seg - 1) + size = SIZE - seg * seg_size; + + input_segs[seg].addr = (char *)x1 + seg * seg_size; + input_segs[seg].size = size; + input_segs[seg + num_seg].addr = (char *)x2 + seg * seg_size; + input_segs[seg + num_seg].size = size; + output_segs[seg].addr = (char *)y + seg * seg_size; + output_segs[seg].size = size; + } + + memset(y, 0, sizeof(y)); + ret = odp_ml_run(model, &data, &run_param); + CU_ASSERT(ret == 1); + if (ret != 1) + goto fail; + + for (uint32_t j = 0; j < batch_size * NUM_COLUMN; j++) + CU_ASSERT(y[j] == y_expected[j]); + } + +fail: + CU_ASSERT_FATAL(odp_ml_model_unload(model, NULL) == 0); + CU_ASSERT(odp_ml_model_destroy(model) == 0); +} + +static void run_global_ml_model(void) +{ + int ret = 0; + odp_ml_run_result_t result; + + ret = odp_ml_model_load(global.ml_model, NULL); + CU_ASSERT_FATAL(ret == 0); + + global.run_param.result = &result; + + ret = odp_ml_run(global.ml_model, &global.data, &global.run_param); + CU_ASSERT(ret == 1); + CU_ASSERT(!result.error_code); + CU_ASSERT(*(int32_t *)global.output_seg.addr == global.y_expected); + + ret = odp_ml_model_unload(global.ml_model, NULL); + CU_ASSERT_FATAL(ret == 0); + global.run_param.result = NULL; +} + +static void test_ml_run(void) +{ + run_global_ml_model(); + run_model_batch_add(); +} + +static void test_ml_run_multi(void) +{ + int ret; + int32_t y; + int32_t x = 8; + int32_t y_expected = 28; + odp_ml_data_t data[RUN_NUM]; + odp_ml_data_seg_t input_seg; + odp_ml_data_seg_t output_seg; + odp_ml_run_param_t param[RUN_NUM]; + odp_ml_run_result_t result[RUN_NUM]; + uint64_t wait_ns = 500 * ODP_TIME_MSEC_IN_NS; + + ret = odp_ml_model_load(global.ml_model, NULL); + CU_ASSERT_FATAL(ret == 0); + + param[0] = global.run_param; + param[0].result = &result[0]; + odp_ml_run_param_init(¶m[1]); + param[1].result = &result[1]; + + /* Prepare data for running model inference */ + data[0] = global.data; + data[1].num_input_seg = NUM_INPUTS; + data[1].input_seg = &input_seg; + input_seg.size = sizeof(int32_t); + input_seg.addr = &x; + + data[1].num_output_seg = NUM_OUTPUTS; + data[1].output_seg = &output_seg; + output_seg.size = sizeof(int32_t); + output_seg.addr = &y; + + int num_completed = 0; + + for (int i = 0; i < TIMEOUT; i++) { + ret = odp_ml_run_multi(global.ml_model, data + num_completed, param + num_completed, + RUN_NUM - num_completed); + CU_ASSERT(ret >= 0); + if (ret < 0) + break; + + num_completed += ret; + + if (num_completed >= RUN_NUM) + break; + + odp_time_wait_ns(wait_ns); + } + + CU_ASSERT(num_completed == RUN_NUM); + CU_ASSERT(!result[0].error_code); + CU_ASSERT(!result[1].error_code); + CU_ASSERT(*(int32_t *)global.output_seg.addr == global.y_expected); + CU_ASSERT(*(int32_t *)output_seg.addr == y_expected); + + ret = odp_ml_model_unload(global.ml_model, NULL); + CU_ASSERT_FATAL(ret == 0); +} + +/* Test asynchronous inference running in ODP_ML_COMPL_MODE_EVENT mode */ +static void test_ml_model_run_async_event(void) +{ + int ret; + void *user_ptr; + odp_ml_compl_t compl; + odp_ml_run_result_t result; + odp_ml_data_seg_t *outputs; + odp_ml_compl_param_t compl_param; + + /* Load model in order to run inference */ + ret = odp_ml_model_load(global.ml_model, NULL); + CU_ASSERT_FATAL(ret == 0); + + compl = odp_ml_compl_alloc(global.compl_pool); + CU_ASSERT_FATAL(compl != ODP_ML_COMPL_INVALID); + + odp_ml_compl_param_init(&compl_param); + compl_param.mode = ODP_ML_COMPL_MODE_EVENT; + compl_param.event = odp_ml_compl_to_event(compl); + compl_param.queue = global.queue; + + /* user_ptr structure maintains the output data pointer for output retrieval */ + user_ptr = &global.output_seg; + compl_param.user_ptr = user_ptr; + + memset(global.output_seg.addr, 0, global.output_seg.size); + ret = odp_ml_run_start(global.ml_model, &global.data, &compl_param, NULL); + CU_ASSERT_FATAL(ret == 1); + + /* Run event scheduler to find the ml completion event and verify it */ + if (get_result_from_ml_compl_event(NULL, &result)) + return; + + CU_ASSERT(!result.error_code); + CU_ASSERT(result.user_ptr == user_ptr); + + outputs = (odp_ml_data_seg_t *)result.user_ptr; + CU_ASSERT(*(int32_t *)outputs[0].addr == global.y_expected); + + /* Unload model */ + ret = odp_ml_model_unload(global.ml_model, NULL); + CU_ASSERT_FATAL(ret == 0); +} + +/* Test asynchronous inference running in ODP_ML_COMPL_MODE_POLL mode */ +static void test_ml_model_run_async_poll(void) +{ + int ret; + void *user_ptr; + odp_ml_run_result_t result; + odp_ml_data_seg_t *outputs; + odp_ml_compl_param_t compl_param; + uint64_t wait_ns = 500 * ODP_TIME_MSEC_IN_NS; + + memset(&result, 0, sizeof(result)); + /* Load model in order to run inference */ + ret = odp_ml_model_load(global.ml_model, NULL); + CU_ASSERT_FATAL(ret == 0); + + odp_ml_compl_param_init(&compl_param); + compl_param.mode = ODP_ML_COMPL_MODE_POLL; + compl_param.compl_id = 0; + + /* user_ptr structure maintains the output data pointer for output retrieval */ + user_ptr = &global.output_seg; + compl_param.user_ptr = user_ptr; + + memset(global.output_seg.addr, 0, global.output_seg.size); + ret = odp_ml_run_start(global.ml_model, &global.data, &compl_param, NULL); + CU_ASSERT_FATAL(ret == 1); + + /* When odp_ml_run_start() succeeded, continue to check completion status */ + for (int i = 0; i < TIMEOUT; i++) { + ret = odp_ml_run_status(global.ml_model, 0, &result); + if (ret) + break; + + /* ret = 0 meaning run has not finished, continue to check status */ + odp_time_wait_ns(wait_ns); + } + + outputs = (odp_ml_data_seg_t *)result.user_ptr; + + CU_ASSERT(ret > 0); + CU_ASSERT(!result.error_code); + CU_ASSERT(result.user_ptr == user_ptr); + CU_ASSERT(*(int32_t *)outputs[0].addr == global.y_expected); + + /* Unload model */ + ret = odp_ml_model_unload(global.ml_model, NULL); + CU_ASSERT_FATAL(ret == 0); +} + +static void test_ml_run_start_multi(void) +{ + int ret; + int32_t y; + odp_ml_compl_t compl; + odp_ml_data_t data[RUN_NUM]; + odp_ml_data_seg_t input_seg; + odp_ml_data_seg_t output_seg; + odp_ml_data_seg_t *outputs[RUN_NUM]; + odp_ml_compl_param_t compl_param[RUN_NUM]; + odp_ml_run_result_t run_result[RUN_NUM]; + int32_t x = 5; + int32_t y_expected = 19; + uint64_t wait_ns = 500 * ODP_TIME_MSEC_IN_NS; + + /* Load model in order to run inference */ + ret = odp_ml_model_load(global.ml_model, NULL); + CU_ASSERT_FATAL(ret == 0); + + compl = odp_ml_compl_alloc(global.compl_pool); + CU_ASSERT_FATAL(compl != ODP_ML_COMPL_INVALID); + + /* Prepare data for running model inference */ + data[0] = global.data; + + data[1].num_input_seg = NUM_INPUTS; + data[1].input_seg = &input_seg; + input_seg.size = sizeof(int32_t); + input_seg.addr = &x; + + data[1].num_output_seg = NUM_OUTPUTS; + data[1].output_seg = &output_seg; + output_seg.size = sizeof(int32_t); + output_seg.addr = &y; + + /* Two completion parameters: one use event mode, another poll mode */ + odp_ml_compl_param_init(&compl_param[0]); + compl_param[0].mode = ODP_ML_COMPL_MODE_EVENT; + compl_param[0].event = odp_ml_compl_to_event(compl); + compl_param[0].queue = global.queue; + /* user_ptr structure maintains the output data pointer for output retrieval */ + compl_param[0].user_ptr = &global.output_seg; + + odp_ml_compl_param_init(&compl_param[1]); + compl_param[1].mode = ODP_ML_COMPL_MODE_POLL; + compl_param[1].compl_id = 0; + /* user_ptr structure maintains the output data pointer for output retrieval */ + compl_param[1].user_ptr = &output_seg; + + memset(global.output_seg.addr, 0, sizeof(int32_t)); + + int num_completed = 0; + + for (int i = 0; i < TIMEOUT; i++) { + ret = odp_ml_run_start_multi(global.ml_model, data + num_completed, + compl_param + num_completed, NULL, + RUN_NUM - num_completed); + CU_ASSERT(ret >= 0); + if (ret < 0) + break; + + num_completed += ret; + + if (num_completed >= RUN_NUM) + break; + + odp_time_wait_ns(wait_ns); + } + + CU_ASSERT(num_completed == RUN_NUM); + + /* Run event scheduler to find the ml completion event and verify it */ + if (get_result_from_ml_compl_event(NULL, &run_result[0])) { + ret = odp_ml_model_unload(global.ml_model, NULL); + return; + } + + CU_ASSERT(!run_result[0].error_code); + CU_ASSERT(run_result[0].user_ptr == &global.output_seg); + outputs[0] = (odp_ml_data_seg_t *)run_result[0].user_ptr; + CU_ASSERT(*(int32_t *)outputs[0][0].addr == global.y_expected); + + /* Check completion status for the poll mode */ + for (int i = 0; i < TIMEOUT; i++) { + ret = odp_ml_run_status(global.ml_model, 0, &run_result[1]); + if (ret) + break; + + /* ret = 0 meaning run has not finished, continue to check status */ + odp_time_wait_ns(wait_ns); + } + + outputs[1] = (odp_ml_data_seg_t *)run_result[1].user_ptr; + CU_ASSERT(ret > 0); + CU_ASSERT(!run_result[1].error_code); + CU_ASSERT(run_result[1].user_ptr == &output_seg); + CU_ASSERT(*(int32_t *)outputs[1][0].addr == y_expected); + + /* Unload model */ + ret = odp_ml_model_unload(global.ml_model, NULL); + CU_ASSERT_FATAL(ret == 0); +} + +static void test_ml_model_extra_stat_info(void) +{ + int ret; + + ret = odp_ml_model_extra_stat_info(global.ml_model, NULL, 0); + CU_ASSERT(ret >= 0); +} + +static void test_ml_model_extra_stats(void) +{ + int ret; + + ret = odp_ml_model_extra_stats(global.ml_model, NULL, 0); + CU_ASSERT(ret >= 0); +} + +odp_testinfo_t ml_suite[] = { + ODP_TEST_INFO_CONDITIONAL(test_ml_debug, check_ml_support), + ODP_TEST_INFO_CONDITIONAL(test_ml_model_create, check_ml_support), + ODP_TEST_INFO_CONDITIONAL(test_ml_model_lookup, check_ml_support), + ODP_TEST_INFO_CONDITIONAL(test_ml_model_long_name, check_ml_support), + ODP_TEST_INFO_CONDITIONAL(test_ml_model_info, check_ml_support), + ODP_TEST_INFO_CONDITIONAL(test_ml_model_load, check_load_sync), + ODP_TEST_INFO_CONDITIONAL(test_ml_model_load_async_poll, check_load_poll), + ODP_TEST_INFO_CONDITIONAL(test_ml_model_load_async_event, check_load_event), + /* Synchronous load/unload is used load/unload model before/after model run */ + ODP_TEST_INFO_CONDITIONAL(test_ml_run, check_run_sync), + ODP_TEST_INFO_CONDITIONAL(test_ml_run_multi, check_run_sync), + ODP_TEST_INFO_CONDITIONAL(test_ml_model_run_async_event, check_run_event), + ODP_TEST_INFO_CONDITIONAL(test_ml_model_run_async_poll, check_run_poll), + ODP_TEST_INFO_CONDITIONAL(test_ml_run_start_multi, check_run_poll_event), + ODP_TEST_INFO_CONDITIONAL(test_ml_model_extra_stat_info, check_ml_support), + ODP_TEST_INFO_CONDITIONAL(test_ml_model_extra_stats, check_ml_support), + ODP_TEST_INFO_NULL +}; + +odp_suiteinfo_t ml_suites[] = { + {"ML", ml_suite_init, ml_suite_term, ml_suite}, + ODP_SUITE_INFO_NULL +}; + +int main(int argc, char *argv[]) +{ + int ret; + + /* parse common options: */ + if (odp_cunit_parse_options(&argc, argv)) + return -1; + + ret = odp_cunit_register(ml_suites); + + if (ret == 0) + ret = odp_cunit_run(); + + return ret; +} |