def benchmark_using_loadgen(scenario_str, mode_str, samples_in_mem, config_filepath): "Perform the benchmark using python API for the LoadGen librar" scenario = { 'SingleStream': lg.TestScenario.SingleStream, 'MultiStream': lg.TestScenario.MultiStream, 'Server': lg.TestScenario.Server, 'Offline': lg.TestScenario.Offline, }[scenario_str] mode = { 'AccuracyOnly': lg.TestMode.AccuracyOnly, 'PerformanceOnly': lg.TestMode.PerformanceOnly, 'SubmissionRun': lg.TestMode.SubmissionRun, }[mode_str] ts = lg.TestSettings() if (config_filepath): ts.FromConfig(config_filepath, 'random_model_name', scenario_str) ts.scenario = scenario ts.mode = mode sut = lg.ConstructSUT(issue_queries, flush_queries, process_latencies) qsl = lg.ConstructQSL(dataset_size, samples_in_mem, load_query_samples, unload_query_samples) log_settings = lg.LogSettings() log_settings.enable_trace = False lg.StartTestWithLogSettings(sut, qsl, ts, log_settings) lg.DestroyQSL(qsl) lg.DestroySUT(sut)
def benchmark_using_loadgen(): "Perform the benchmark using python API for the LoadGen library" scenario = { 'SingleStream': lg.TestScenario.SingleStream, 'MultiStream': lg.TestScenario.MultiStream, 'Server': lg.TestScenario.Server, 'Offline': lg.TestScenario.Offline, }[LOADGEN_SCENARIO] mode = { 'AccuracyOnly': lg.TestMode.AccuracyOnly, 'PerformanceOnly': lg.TestMode.PerformanceOnly, 'SubmissionRun': lg.TestMode.SubmissionRun, }[LOADGEN_MODE] ts = lg.TestSettings() ts.FromConfig(MLPERF_CONF_PATH, MODEL_NAME, LOADGEN_SCENARIO) ts.FromConfig(USER_CONF_PATH, MODEL_NAME, LOADGEN_SCENARIO) ts.scenario = scenario ts.mode = mode sut = lg.ConstructSUT(issue_queries, flush_queries, process_latencies) qsl = lg.ConstructQSL(LOADGEN_DATASET_SIZE, LOADGEN_BUFFER_SIZE, load_query_samples, unload_query_samples) log_settings = lg.LogSettings() log_settings.enable_trace = False lg.StartTestWithLogSettings(sut, qsl, ts, log_settings) lg.DestroyQSL(qsl) lg.DestroySUT(sut)
def benchmark_using_loadgen(): "Perform the benchmark using python API for the LoadGen library" global model # Load the [cached] Torch model torchvision_version = '' # master by default try: import torchvision torchvision_version = ':v' + torchvision.__version__ except Exception: pass model = torch.hub.load('pytorch/vision' + torchvision_version, MODEL_NAME, pretrained=True) model.eval() # move the model to GPU for speed if available if USE_CUDA: model.to('cuda') scenario = { 'SingleStream': lg.TestScenario.SingleStream, 'MultiStream': lg.TestScenario.MultiStream, 'Server': lg.TestScenario.Server, 'Offline': lg.TestScenario.Offline, }[LOADGEN_SCENARIO] mode = { 'AccuracyOnly': lg.TestMode.AccuracyOnly, 'PerformanceOnly': lg.TestMode.PerformanceOnly, 'SubmissionRun': lg.TestMode.SubmissionRun, }[LOADGEN_MODE] ts = lg.TestSettings() ts.FromConfig(MLPERF_CONF_PATH, MODEL_NAME, LOADGEN_SCENARIO) ts.FromConfig(USER_CONF_PATH, MODEL_NAME, LOADGEN_SCENARIO) ts.scenario = scenario ts.mode = mode if LOADGEN_MULTISTREAMNESS: ts.multi_stream_samples_per_query = int(LOADGEN_MULTISTREAMNESS) if LOADGEN_COUNT_OVERRIDE: ts.min_query_count = int(LOADGEN_COUNT_OVERRIDE) ts.max_query_count = int(LOADGEN_COUNT_OVERRIDE) sut = lg.ConstructSUT(issue_queries, flush_queries, process_latencies) qsl = lg.ConstructQSL(LOADGEN_DATASET_SIZE, LOADGEN_BUFFER_SIZE, load_query_samples, unload_query_samples) log_settings = lg.LogSettings() log_settings.enable_trace = False lg.StartTestWithLogSettings(sut, qsl, ts, log_settings) lg.DestroyQSL(qsl) lg.DestroySUT(sut)
def main(): args = get_args() if args.backend == "pytorch": assert not args.quantized, "Quantized model is only supported by onnxruntime backend!" assert not args.profile, "Profiling is only supported by onnxruntime backend!" from pytorch_SUT import get_pytorch_sut sut = get_pytorch_sut() elif args.backend == "tf": assert not args.quantized, "Quantized model is only supported by onnxruntime backend!" assert not args.profile, "Profiling is only supported by onnxruntime backend!" from tf_SUT import get_tf_sut sut = get_tf_sut() elif args.backend == "tf_estimator": assert not args.quantized, "Quantized model is only supported by onnxruntime backend!" assert not args.profile, "Profiling is only supported by onnxruntime backend!" from tf_estimator_SUT import get_tf_estimator_sut sut = get_tf_estimator_sut() elif args.backend == "onnxruntime": from onnxruntime_SUT import get_onnxruntime_sut sut = get_onnxruntime_sut(args) else: raise ValueError("Unknown backend: {:}".format(args.backend)) settings = lg.TestSettings() settings.scenario = scenario_map[args.scenario] settings.FromConfig(args.mlperf_conf, "bert", args.scenario) settings.FromConfig(args.user_conf, "bert", args.scenario) if args.accuracy: settings.mode = lg.TestMode.AccuracyOnly else: settings.mode = lg.TestMode.PerformanceOnly log_path = "build/logs" if not os.path.exists(log_path): os.makedirs(log_path) log_output_settings = lg.LogOutputSettings() log_output_settings.outdir = log_path log_output_settings.copy_summary_to_stdout = True log_settings = lg.LogSettings() log_settings.log_output = log_output_settings print("Running LoadGen test...") lg.StartTestWithLogSettings(sut.sut, sut.qsl.qsl, settings, log_settings) if args.accuracy: cmd = "python3 accuracy-squad.py" subprocess.check_call(cmd, shell=True) print("Done!") print("Destroying SUT...") lg.DestroySUT(sut.sut) print("Destroying QSL...") lg.DestroyQSL(sut.qsl.qsl)
def main(): args = get_args() batch_size = args.offline_batch_size if args.scenario == "Offline" else 1 settings = lg.TestSettings() settings.scenario = scenario_map[args.scenario] settings.FromConfig(args.mlperf_conf, "rnnt", args.scenario) settings.FromConfig(args.user_conf, "rnnt", args.scenario) issued_query_count = None if args.accuracy: settings.mode = lg.TestMode.AccuracyOnly issued_query_count = 2513 else: settings.mode = lg.TestMode.PerformanceOnly issued_query_count = settings.min_query_count log_path = args.log_dir os.makedirs(log_path, exist_ok=True) log_output_settings = lg.LogOutputSettings() log_output_settings.outdir = log_path log_output_settings.copy_summary_to_stdout = True log_settings = lg.LogSettings() log_settings.log_output = log_output_settings if args.backend == "pytorch": from pytorch_SUT import PytorchSUT sut = PytorchSUT(args.pytorch_config_toml, args.pytorch_checkpoint, args.dataset_dir, args.manifest, args.perf_count, issued_query_count, args.scenario, args.machine_conf, batch_size, args.cores_for_loadgen, args.cores_per_instance, args.debug, args.cosim, args.profile, args.ipex, args.bf16, args.warmup) else: raise ValueError("Unknown backend: {:}".format(args.backend)) print("Running Loadgen test...") lg.StartTestWithLogSettings(sut.sut, sut.qsl.qsl, settings, log_settings) if args.accuracy: cmd = f"python3 accuracy_eval.py --log_dir {log_path} --dataset_dir {args.dataset_dir} --manifest {args.manifest}" print(f"Running accuracy script: {cmd}") subprocess.check_call(cmd, shell=True) lg.DestroySUT(sut.sut) print("Done!")
def main(): args = get_args() if args.backend == "pytorch": from pytorch_SUT import get_pytorch_sut sut = get_pytorch_sut(args.model_dir, args.preprocessed_data_dir, args.performance_count) elif args.backend == "onnxruntime": from onnxruntime_SUT import get_onnxruntime_sut sut = get_onnxruntime_sut(args.onnx_model, args.preprocessed_data_dir, args.performance_count) else: raise ValueError("Unknown backend: {:}".format(args.backend)) settings = lg.TestSettings() settings.scenario = scenario_map[args.scenario] settings.FromConfig(args.mlperf_conf, "3d-unet", args.scenario) settings.FromConfig(args.user_conf, "3d-unet", args.scenario) if args.accuracy: settings.mode = lg.TestMode.AccuracyOnly else: settings.mode = lg.TestMode.PerformanceOnly log_path = "build/logs" if not os.path.exists(log_path): os.makedirs(log_path) log_output_settings = lg.LogOutputSettings() log_output_settings.outdir = log_path log_output_settings.copy_summary_to_stdout = True log_settings = lg.LogSettings() log_settings.log_output = log_output_settings print("Running Loadgen test...") lg.StartTestWithLogSettings(sut.sut, sut.qsl.qsl, settings, log_settings) if args.accuracy: print("Running accuracy script...") cmd = "python3 brats_eval.py" subprocess.check_call(cmd, shell=True) print("Done!") print("Destroying SUT...") lg.DestroySUT(sut.sut) print("Destroying QSL...") lg.DestroyQSL(sut.qsl.qsl)
def benchmark_using_loadgen(): "Perform the benchmark using python API for the LoadGen library" global num_classes global model_output_volume pycuda_context, max_batch_size, input_volume, model_output_volume, num_layers = initialize_predictor( ) num_classes = len(class_labels) scenario = { 'SingleStream': lg.TestScenario.SingleStream, 'MultiStream': lg.TestScenario.MultiStream, 'Server': lg.TestScenario.Server, 'Offline': lg.TestScenario.Offline, }[LOADGEN_SCENARIO] mode = { 'AccuracyOnly': lg.TestMode.AccuracyOnly, 'PerformanceOnly': lg.TestMode.PerformanceOnly, 'SubmissionRun': lg.TestMode.SubmissionRun, }[LOADGEN_MODE] ts = lg.TestSettings() ts.FromConfig(MLPERF_CONF_PATH, MODEL_NAME, LOADGEN_SCENARIO) ts.FromConfig(USER_CONF_PATH, MODEL_NAME, LOADGEN_SCENARIO) ts.scenario = scenario ts.mode = mode if LOADGEN_MULTISTREAMNESS: ts.multi_stream_samples_per_query = int(LOADGEN_MULTISTREAMNESS) if LOADGEN_COUNT_OVERRIDE: ts.min_query_count = int(LOADGEN_COUNT_OVERRIDE) ts.max_query_count = int(LOADGEN_COUNT_OVERRIDE) sut = lg.ConstructSUT(issue_queries, flush_queries, process_latencies) qsl = lg.ConstructQSL(LOADGEN_DATASET_SIZE, LOADGEN_BUFFER_SIZE, load_query_samples, unload_query_samples) log_settings = lg.LogSettings() log_settings.enable_trace = False lg.StartTestWithLogSettings(sut, qsl, ts, log_settings) lg.DestroyQSL(qsl) lg.DestroySUT(sut) pycuda_context.pop()
def __init__(self, session, ds, optimization_config, onnx_output_names): self.session = session self.threads = optimization_config.threads_num self.max_batchsize = optimization_config.dynamic_batching_size self.ds = ds self.onnx_output_names = onnx_output_names self.guess = None self.cv = threading.Condition() self.done = False self.q_idx = [] self.q_query_id = [] self.workers = [] self.settings = lg.TestSettings() self.settings.scenario = lg.TestScenario.Server self.settings.mode = lg.TestMode.FindPeakPerformance log_output_settings = lg.LogOutputSettings() log_output_settings.outdir = optimization_config.result_path log_output_settings.copy_summary_to_stdout = False self.log_settings = lg.LogSettings() self.log_settings.enable_trace = False self.log_settings.log_output = log_output_settings self.sut = lg.ConstructSUT(self.issue_queries, self.flush_queries, self.process_latencies) self.qsl = lg.ConstructQSL(QUERY_COUNT, QUERY_COUNT, ds.load_query_samples, ds.unload_query_samples) self.settings.server_coalesce_queries = True self.settings.server_target_latency_ns = int(optimization_config.max_latency_ms * NANO_SEC / MILLI_SEC) self.settings.server_target_latency_percentile = optimization_config.max_latency_percentile self.settings.min_duration_ms = optimization_config.min_duration_sec * MILLI_SEC # start all threads for _ in range(self.threads): worker = threading.Thread(target=self.handle_tasks, args=(self.cv,)) worker.daemon = True self.workers.append(worker) worker.start() time.sleep(1)
def start(self): """Starts the load test.""" settings = self.get_test_settings() log_settings = lg.LogSettings() log_settings.log_output.outdir = tempfile.mkdtemp() log_settings.log_output.copy_detail_to_stdout = True log_settings.log_output.copy_summary_to_stdout = True log_settings.enable_trace = False logging.info("Constructing SUT.") sut = lg.ConstructSUT(self.issue_queries, self.flush_queries, self.process_metrics) logging.info("Constructing QSL.") qsl = lg.ConstructQSL(self.total_sample_count, self.performance_sample_count, self.load_samples, self.unload_samples) logging.info("Starting test.") lg.StartTestWithLogSettings(sut, qsl, settings, log_settings) lg.DestroyQSL(qsl) lg.DestroySUT(sut)
def benchmark_using_loadgen(): "Perform the benchmark using python API for the LoadGen library" global pycuda_context initialize_predictor() scenario = { 'SingleStream': lg.TestScenario.SingleStream, 'MultiStream': lg.TestScenario.MultiStream, 'Server': lg.TestScenario.Server, 'Offline': lg.TestScenario.Offline, }[LOADGEN_SCENARIO] mode = { 'AccuracyOnly': lg.TestMode.AccuracyOnly, 'PerformanceOnly': lg.TestMode.PerformanceOnly, 'SubmissionRun': lg.TestMode.SubmissionRun, }[LOADGEN_MODE] ts = lg.TestSettings() if LOADGEN_CONF_FILE: ts.FromConfig(LOADGEN_CONF_FILE, 'random_model_name', LOADGEN_SCENARIO) ts.scenario = scenario ts.mode = mode if LOADGEN_MULTISTREAMNESS: ts.multi_stream_samples_per_query = int(LOADGEN_MULTISTREAMNESS) sut = lg.ConstructSUT(issue_queries, flush_queries, process_latencies) qsl = lg.ConstructQSL(LOADGEN_DATASET_SIZE, LOADGEN_BUFFER_SIZE, load_query_samples, unload_query_samples) log_settings = lg.LogSettings() log_settings.enable_trace = False lg.StartTestWithLogSettings(sut, qsl, ts, log_settings) lg.DestroyQSL(qsl) lg.DestroySUT(sut) pycuda_context.pop()
def main(): args = get_args() if args.backend == "pytorch": from pytorch_SUT import PytorchSUT sut = PytorchSUT(args.pytorch_config_toml, args.pytorch_checkpoint, args.dataset_dir, args.manifest, args.perf_count) else: raise ValueError("Unknown backend: {:}".format(args.backend)) settings = lg.TestSettings() settings.scenario = scenario_map[args.scenario] settings.FromConfig(args.mlperf_conf, "rnnt", args.scenario) settings.FromConfig(args.user_conf, "rnnt", args.scenario) if args.accuracy: settings.mode = lg.TestMode.AccuracyOnly else: settings.mode = lg.TestMode.PerformanceOnly log_path = args.log_dir os.makedirs(log_path, exist_ok=True) log_output_settings = lg.LogOutputSettings() log_output_settings.outdir = log_path log_output_settings.copy_summary_to_stdout = True log_settings = lg.LogSettings() log_settings.log_output = log_output_settings print("Running Loadgen test...") lg.StartTestWithLogSettings(sut.sut, sut.qsl.qsl, settings, log_settings) if args.accuracy: cmd = f"python3 accuracy_eval.py --log_dir {log_path} --dataset_dir {args.dataset_dir} --manifest {args.manifest}" print(f"Running accuracy script: {cmd}") subprocess.check_call(cmd, shell=True) print("Done!")
def benchmark_using_loadgen(): "Perform the benchmark using python API for the LoadGen library" global funnel_should_be_running, warmup_mode, openme_data scenario = { 'SingleStream': lg.TestScenario.SingleStream, 'MultiStream': lg.TestScenario.MultiStream, 'Server': lg.TestScenario.Server, 'Offline': lg.TestScenario.Offline, }[LOADGEN_SCENARIO] mode = { 'AccuracyOnly': lg.TestMode.AccuracyOnly, 'PerformanceOnly': lg.TestMode.PerformanceOnly, 'SubmissionRun': lg.TestMode.SubmissionRun, }[LOADGEN_MODE] ts = lg.TestSettings() if LOADGEN_CONFIG_FILE: ts.FromConfig(LOADGEN_CONFIG_FILE, 'random_model_name', LOADGEN_SCENARIO) ts.scenario = scenario ts.mode = mode if LOADGEN_MULTISTREAMNESS: ts.multi_stream_samples_per_query = int(LOADGEN_MULTISTREAMNESS) if LOADGEN_MAX_DURATION_S: ts.max_duration_ms = int(LOADGEN_MAX_DURATION_S)*1000 if LOADGEN_COUNT_OVERRIDE: ts.min_query_count = int(LOADGEN_COUNT_OVERRIDE) ts.max_query_count = int(LOADGEN_COUNT_OVERRIDE) if LOADGEN_TARGET_QPS: target_qps = float(LOADGEN_TARGET_QPS) ts.multi_stream_target_qps = target_qps ts.server_target_qps = target_qps ts.offline_expected_qps = target_qps sut = lg.ConstructSUT(issue_queries, flush_queries, process_latencies) qsl = lg.ConstructQSL(LOADGEN_DATASET_SIZE, LOADGEN_BUFFER_SIZE, load_query_samples, unload_query_samples) log_settings = lg.LogSettings() log_settings.enable_trace = False funnel_thread = threading.Thread(target=send_responses, args=()) funnel_should_be_running = True funnel_thread.start() if LOADGEN_WARMUP_SAMPLES: warmup_id_range = list(range(LOADGEN_WARMUP_SAMPLES)) load_query_samples(warmup_id_range) warmup_mode = True print("Sending out the warm-up samples, waiting for responses...") issue_queries([lg.QuerySample(id,id) for id in warmup_id_range]) while len(in_progress)>0: # waiting for the in_progress queue to clear up time.sleep(1) print(" Done!") warmup_mode = False lg.StartTestWithLogSettings(sut, qsl, ts, log_settings) funnel_should_be_running = False # politely ask the funnel_thread to end funnel_thread.join() # wait for it to actually end from_workers.close() to_workers.close() lg.DestroyQSL(qsl) lg.DestroySUT(sut) if SIDELOAD_JSON: with open(SIDELOAD_JSON, 'w') as sideload_fd: json.dump(openme_data, sideload_fd, indent=4, sort_keys=True)
def eval_func(model): args = get_args() if args.backend == "pytorch": from pytorch_SUT import get_pytorch_sut sut = get_pytorch_sut(model, args.preprocessed_data_dir, args.performance_count) elif args.backend == "onnxruntime": from onnxruntime_SUT import get_onnxruntime_sut sut = get_onnxruntime_sut(args.model, args.preprocessed_data_dir, args.performance_count) elif args.backend == "tf": from tf_SUT import get_tf_sut sut = get_tf_sut(args.model, args.preprocessed_data_dir, args.performance_count) elif args.backend == "ov": from ov_SUT import get_ov_sut sut = get_ov_sut(args.model, args.preprocessed_data_dir, args.performance_count) else: raise ValueError("Unknown backend: {:}".format(args.backend)) settings = lg.TestSettings() settings.scenario = scenario_map[args.scenario] settings.FromConfig(args.mlperf_conf, "3d-unet", args.scenario) settings.FromConfig(args.user_conf, "3d-unet", args.scenario) if args.accuracy: settings.mode = lg.TestMode.AccuracyOnly else: settings.mode = lg.TestMode.PerformanceOnly log_path = "build/logs" if not os.path.exists(log_path): os.makedirs(log_path) log_output_settings = lg.LogOutputSettings() log_output_settings.outdir = log_path log_output_settings.copy_summary_to_stdout = True log_settings = lg.LogSettings() log_settings.log_output = log_output_settings print("Running Loadgen test...") if args.benchmark: start = time.time() lg.StartTestWithLogSettings(sut.sut, sut.qsl.qsl, settings, log_settings) if args.benchmark: end = time.time() if args.accuracy: print("Running accuracy script...") process = subprocess.Popen(['python3', 'accuracy-brats.py'], stdout=subprocess.PIPE, stderr=subprocess.PIPE) out, err = process.communicate() print(out) print("Done!", float(err)) if args.benchmark: print('Batch size = 1') print('Latency: %.3f ms' % ((end - start) * 1000 / sut.qsl.count)) print('Throughput: %.3f images/sec' % (sut.qsl.count / (end - start))) print('Accuracy: {mean:.5f}'.format(mean=float(err))) print("Destroying SUT...") lg.DestroySUT(sut.sut) print("Destroying QSL...") lg.DestroyQSL(sut.qsl.qsl) return float(err)
def main(): global num_ins global num_cpus global in_queue_cnt global out_queue_cnt global batching global queries_so_far global Latencies queries_so_far = 0 args = get_args() log.info(args) scenario = args.scenario accuracy_mode = args.accuracy perf_count = args.perf_count batch_size = args.batch_size num_ins = args.num_instance num_cpus = args.num_phy_cpus batching = args.batching # Read Loadgen and workload config parameters settings = lg.TestSettings() settings.scenario = scenario_map[scenario] settings.FromConfig(args.mlperf_conf, "bert", scenario) settings.FromConfig(args.user_conf, "bert", scenario) settings.mode = lg.TestMode.AccuracyOnly if accuracy_mode else lg.TestMode.PerformanceOnly # Establish communication queues lock = multiprocessing.Lock() init_counter = multiprocessing.Value("i", 0) calibrate_counter = multiprocessing.Value("i", 0) out_queue = multiprocessing.Queue() # Create consumers consumers = [] if scenario == "Server": from parse_server_config import configParser buckets = configParser("machine_conf.json") cutoffs = list(buckets.keys()) batch_sizes = {} in_queue = {j: multiprocessing.JoinableQueue() for j in buckets} proc_idx = 0 num_cpus = 0 total_ins = 0 for cutoff in list(buckets.keys()): batch_sizes[cutoff] = buckets[cutoff]["batch_size"] num_ins = buckets[cutoff]["instances"] cpus_per_instance = buckets[cutoff]["cpus_per_instance"] num_cpus = num_ins * cpus_per_instance total_ins += num_ins for j in range(num_ins): consumer = Consumer(in_queue[cutoff], out_queue, lock, init_counter, calibrate_counter, proc_idx, num_ins, args, cutoff) consumer.start_core_idx = proc_idx consumer.end_core_idx = proc_idx + cpus_per_instance - 1 consumers.append(consumer) proc_idx = consumer.end_core_idx + 1 num_ins = total_ins else: total_ins = num_ins in_queue = MultiprocessShapeBasedQueue() consumers = [ Consumer(in_queue, out_queue, lock, init_counter, calibrate_counter, i, num_ins, args) for i in range(num_ins) ] for c in consumers: c.start() # Dataset object used by constructQSL data_set = BERTDataSet(args.vocab, args.perf_count) if scenario == "Server": issue_queue = InQueueServer(in_queue, batch_sizes, data_set, settings.min_query_count) else: issue_queue = InQueue(in_queue, batch_size, data_set) # Wait until all sub-processors are ready block_until(init_counter, total_ins, 2) # Start response thread response_worker = threading.Thread(target=response_loadgen, args=(out_queue, )) response_worker.daemon = True response_worker.start() def issue_queries(query_samples): # It's called by loadgen to send query to SUT issue_queue.put(query_samples) sut = lg.ConstructSUT(issue_queries, flush_queries, process_latencies) qsl = lg.ConstructQSL(data_set.count, data_set.perf_count, load_query_samples, unload_query_samples) log_path = "build/logs" if not os.path.exists(log_path): os.makedirs(log_path) log_output_settings = lg.LogOutputSettings() log_output_settings.outdir = log_path log_output_settings.copy_summary_to_stdout = True log_settings = lg.LogSettings() log_settings.log_output = log_output_settings lg.StartTestWithLogSettings(sut, qsl, settings, log_settings) # Wait until outQueue done while out_queue_cnt < in_queue_cnt: time.sleep(0.2) if scenario == "Server": for i in in_queue: in_queue[i].join() for j in range(buckets[i]["cpus_per_instance"]): in_queue[i].put(None) else: for i in range(num_ins): in_queue.put(None) for c in consumers: c.join() out_queue.put(None) if accuracy_mode: cmd = "python accuracy-squad.py --log_file={}/mlperf_log_accuracy.json".format( log_path) subprocess.check_call(cmd, shell=True) lg.DestroyQSL(qsl) lg.DestroySUT(sut)
def main(argv): del argv global last_timeing if FLAGS.scenario == "Server": # Disable garbage collection for realtime performance. gc.disable() # define backend backend = BackendTensorflow() # override image format if given image_format = FLAGS.data_format if FLAGS.data_format else backend.image_format( ) # dataset to use wanted_dataset, pre_proc, post_proc, kwargs = SUPPORTED_DATASETS[ FLAGS.dataset] ds = wanted_dataset(data_path=FLAGS.dataset_path, image_list=FLAGS.dataset_list, name=FLAGS.dataset, image_format=image_format, use_cache=FLAGS.cache, count=FLAGS.count, cache_dir=FLAGS.cache_dir, annotation_file=FLAGS.annotation_file, use_space_to_depth=FLAGS.use_space_to_depth) # load model to backend # TODO(wangtao): parse flags to params. params = dict(ssd_model.default_hparams().values()) params["conv0_space_to_depth"] = FLAGS.use_space_to_depth params["use_bfloat16"] = FLAGS.use_bfloat16 params["use_fused_bn"] = FLAGS.use_fused_bn masters = [] tpu_names = FLAGS.tpu_name tpu_names = tpu_names.split(",") for tpu_name in tpu_names: tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver( tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project) masters.append(tpu_cluster_resolver.get_master()) # # make one pass over the dataset to validate accuracy # count = FLAGS.count if FLAGS.count else ds.get_item_count() # # warmup # log.info("warmup ...") batch_size = FLAGS.batch_size[0] if FLAGS.scenario == "Offline" else 1 backend_lists = [] for _ in range(len(tpu_names)): backend = BackendTensorflow() backend_lists.append(backend) runner = QueueRunner(backend_lists, ds, FLAGS.threads, post_proc=post_proc, max_batchsize=batch_size) runner.start_run({}, FLAGS.accuracy) def issue_queries(query_samples): for i in [1]: runner.enqueue(query_samples) def flush_queries(): pass def process_latencies(latencies_ns): # called by loadgen to show us the recorded latencies global last_timeing last_timeing = [t / NANO_SEC for t in latencies_ns] tf.logging.info("starting {}, latency={}".format(FLAGS.scenario, FLAGS.max_latency)) settings = lg.TestSettings() tf.logging.info(FLAGS.scenario) settings.scenario = SCENARIO_MAP[FLAGS.scenario] settings.qsl_rng_seed = FLAGS.qsl_rng_seed settings.sample_index_rng_seed = FLAGS.sample_index_rng_seed settings.schedule_rng_seed = FLAGS.schedule_rng_seed if FLAGS.accuracy: settings.mode = lg.TestMode.AccuracyOnly else: settings.mode = lg.TestMode.PerformanceOnly if FLAGS.qps: qps = float(FLAGS.qps) settings.server_target_qps = qps settings.offline_expected_qps = qps if FLAGS.time: settings.min_duration_ms = FLAGS.time * MILLI_SEC settings.max_duration_ms = 0 qps = FLAGS.qps or 100 settings.min_query_count = qps * FLAGS.time settings.max_query_count = 0 else: settings.min_query_count = 270336 settings.max_query_count = 0 target_latency_ns = int(float(FLAGS.max_latency) * NANO_SEC) settings.single_stream_expected_latency_ns = target_latency_ns settings.multi_stream_target_latency_ns = target_latency_ns settings.server_target_latency_ns = target_latency_ns log_settings = lg.LogSettings() log_settings.log_output.outdir = tempfile.mkdtemp() log_settings.log_output.copy_detail_to_stdout = True log_settings.log_output.copy_summary_to_stdout = True log_settings.enable_trace = False def load_query_samples(sample_list): """Load query samples and warmup the model.""" ds.load_query_samples(sample_list) data = ds.get_image_list_inmemory() def init_fn(cloud_tpu_id): tf.logging.info("Load model for %dth cloud tpu", cloud_tpu_id) runner.models[cloud_tpu_id].load( FLAGS.model, FLAGS.output_model_dir, data, params, batch_size=FLAGS.batch_size, master=masters[cloud_tpu_id], scenario=FLAGS.scenario, batch_timeout_micros=FLAGS.batch_timeout_micros) # Init TPU. for it in range(FLAGS.init_iterations): tf.logging.info("Initialize cloud tpu at iteration %d", it) for batch_size in FLAGS.batch_size: example, _ = ds.get_indices([sample_list[0]] * batch_size) _ = runner.models[cloud_tpu_id].predict(example) threads = [] for i in range(len(tpu_names)): thread = threading.Thread(target=init_fn, args=(i, )) threads.append(thread) thread.start() for thread in threads: thread.join() sut = lg.ConstructSUT(issue_queries, flush_queries, process_latencies) qsl = lg.ConstructQSL(count, min(count, 350), load_query_samples, ds.unload_query_samples) lg.StartTestWithLogSettings(sut, qsl, settings, log_settings) runner.finish() lg.DestroyQSL(qsl) lg.DestroySUT(sut) tf.io.gfile.mkdir(FLAGS.outdir) for oldfile in tf.gfile.Glob( os.path.join(log_settings.log_output.outdir, "*")): basename = os.path.basename(oldfile) newfile = os.path.join(FLAGS.outdir, basename) tf.gfile.Copy(oldfile, newfile, overwrite=True) if FLAGS.accuracy: with tf.gfile.Open(os.path.join(FLAGS.outdir, "results.txt"), "w") as f: results = {"mAP": accuracy_coco.main()} json.dump(results, f, sort_keys=True, indent=4)
def main(): global num_ins global num_cpus global in_queue_cnt global out_queue_cnt global batching global bs_step args = get_args() log.info(args) scenario = args.scenario accuracy_mode = args.accuracy perf_count = args.perf_count batch_size = args.batch_size num_ins = args.num_instance num_cpus = args.num_phy_cpus batching = args.batching ## TODO, remove log.info('Run with {} instance on {} cpus: '.format(num_ins, num_cpus)) # Establish communication queues lock = multiprocessing.Lock() init_counter = multiprocessing.Value("i", 0) calibrate_counter = multiprocessing.Value("i", 0) out_queue = multiprocessing.Queue() in_queue = MultiprocessShapeBasedQueue() if args.perf_calibrate: with open('prof_new.py', 'w') as f: print('prof_bs_step = {}'.format(bs_step), file=f) print('prof_map = {', file=f) # Start consumers consumers = [ Consumer(in_queue, out_queue, lock, init_counter, calibrate_counter, i, num_ins, args) for i in range(num_ins) ] for c in consumers: c.start() # used by constructQSL data_set = BERTDataSet(args.vocab, args.perf_count) issue_queue = InQueue(in_queue, batch_size, data_set) # Wait until all sub-processors ready to do calibration block_until(calibrate_counter, num_ins) # Wait until all sub-processors done calibration block_until(calibrate_counter, 2 * num_ins) if args.perf_calibrate: with open('prof_new.py', 'a') as f: print('}', file=f) sys.exit(0) # Wait until all sub-processors are ready block_until(init_counter, num_ins) # Start response thread response_worker = threading.Thread(target=response_loadgen, args=(out_queue, )) response_worker.daemon = True response_worker.start() # Start loadgen settings = lg.TestSettings() settings.scenario = scenario_map[scenario] settings.FromConfig(args.mlperf_conf, "bert", scenario) settings.FromConfig(args.user_conf, "bert", scenario) settings.mode = lg.TestMode.AccuracyOnly if accuracy_mode else lg.TestMode.PerformanceOnly # TODO, for debug, remove #settings.server_target_qps = 40 #settings.server_target_latency_ns = 100000000 #settings.min_query_count = 100 #settings.min_duration_ms = 10000 def issue_queries(query_samples): # It's called by loadgen to send query to SUT issue_queue.put(query_samples) sut = lg.ConstructSUT(issue_queries, flush_queries, process_latencies) qsl = lg.ConstructQSL(data_set.count, data_set.perf_count, load_query_samples, unload_query_samples) log_path = "build/logs" if not os.path.exists(log_path): os.makedirs(log_path) log_output_settings = lg.LogOutputSettings() log_output_settings.outdir = log_path log_output_settings.copy_summary_to_stdout = True log_settings = lg.LogSettings() log_settings.log_output = log_output_settings #lg.StartTest(sut, qsl, settings) lg.StartTestWithLogSettings(sut, qsl, settings, log_settings) # Wait until outQueue done while out_queue_cnt < in_queue_cnt: time.sleep(0.2) in_queue.join() for i in range(num_ins): in_queue.put(None) for c in consumers: c.join() out_queue.put(None) if accuracy_mode: cmd = "python accuracy-squad.py --log_file={}/mlperf_log_accuracy.json".format( log_path) subprocess.check_call(cmd, shell=True) lg.DestroyQSL(qsl) lg.DestroySUT(sut)
def main(): args = get_args() if args.backend == "pytorch": from pytorch_SUT import PytorchSUT sut = PytorchSUT(args.pytorch_config_toml, args.pytorch_checkpoint, args.dataset_dir, args.manifest, args.perf_count) model = sut.greedy_decoder._model else: raise ValueError("Unknown backend: {:}".format(args.backend)) settings = lg.TestSettings() settings.scenario = scenario_map[args.scenario] settings.FromConfig(args.mlperf_conf, "rnnt", args.scenario) settings.FromConfig(args.user_conf, "rnnt", args.scenario) if args.accuracy: settings.mode = lg.TestMode.AccuracyOnly else: settings.mode = lg.TestMode.PerformanceOnly log_path = args.log_dir os.makedirs(log_path, exist_ok=True) log_output_settings = lg.LogOutputSettings() log_output_settings.outdir = log_path log_output_settings.copy_summary_to_stdout = True log_settings = lg.LogSettings() log_settings.log_output = log_output_settings pattern = [ 'accuracy=\d+.\d+', 'samples_per_query : \d+', 'Samples per second: \d+.\d+' ] def eval_func(model): print("Running Loadgen test...") sut.greedy_decoder._model = model lg.StartTestWithLogSettings(sut.sut, sut.qsl.qsl, settings, log_settings) if args.accuracy: cmd = f"python3 accuracy_eval.py --log_dir {log_path} \ --dataset_dir {args.dataset_dir} --manifest {args.manifest}" out = subprocess.check_output(cmd, shell=True) out = out.decode() regex_accu = re.compile(pattern[0]) accu = float(regex_accu.findall(out)[0].split('=')[1]) return accu return 0 def perf_func(model): print("Running Loadgen test...") sut.greedy_decoder._model = model lg.StartTestWithLogSettings(sut.sut, sut.qsl.qsl, settings, log_settings) if not args.accuracy: file_path = os.path.join(log_path, 'mlperf_log_summary.txt') f = open(file_path, 'r', encoding='UTF-8') file_content = f.read() f.close() regex_batch = re.compile(pattern[1]) regex_thro = re.compile(pattern[2]) samples_per_query = int( regex_batch.findall(file_content)[0].split(': ')[1]) samples_per_second = float( regex_thro.findall(file_content)[0].split(': ')[1]) print('Batch size = %d' % samples_per_query) print('Latency: %.3f ms' % ((1 / samples_per_second) * 1000)) print('Throughput: %.3f samples/sec' % samples_per_second) if args.tune: # Dynamic Quantization with LPOT from lpot.experimental import Quantization, common quantizer = Quantization("./conf.yaml") quantizer.model = common.Model(model) quantizer.eval_func = eval_func q_model = quantizer() q_model.save(args.tuned_checkpoint) if args.benchmark: if args.int8: from lpot.utils.pytorch import load new_model = load( os.path.abspath(os.path.expanduser(args.tuned_checkpoint)), model) else: new_model = model perf_func(new_model) print("Done!", flush=True)
def main(): """ Runs 3D UNet performing KiTS19 Kidney Tumore Segmentation task as below: 1. instantiate SUT and QSL for the chosen backend 2. configure LoadGen for the chosen scenario 3. configure MLPerf logger 4. start LoadGen 5. collect logs and if needed evaluate inference results 6. clean up """ # scenarios in LoadGen scenario_map = { "SingleStream": lg.TestScenario.SingleStream, "Offline": lg.TestScenario.Offline, "Server": lg.TestScenario.Server, "MultiStream": lg.TestScenario.MultiStream } args = get_args() # instantiate SUT as per requested backend; QSL is also instantiated if args.backend == "pytorch": from pytorch_SUT import get_sut elif args.backend == "pytorch_checkpoint": from pytorch_checkpoint_SUT import get_sut elif args.backend == "onnxruntime": from onnxruntime_SUT import get_sut elif args.backend == "tensorflow": from tensorflow_SUT import get_sut else: raise ValueError("Unknown backend: {:}".format(args.backend)) sut = get_sut(args.model, args.preprocessed_data_dir, args.performance_count) # setup LoadGen settings = lg.TestSettings() settings.scenario = scenario_map[args.scenario] settings.FromConfig(args.mlperf_conf, "3d-unet", args.scenario) settings.FromConfig(args.user_conf, "3d-unet", args.scenario) if args.accuracy: settings.mode = lg.TestMode.AccuracyOnly else: settings.mode = lg.TestMode.PerformanceOnly # set up mlperf logger log_path = Path("build", "logs").absolute() log_path.mkdir(parents=True, exist_ok=True) log_output_settings = lg.LogOutputSettings() log_output_settings.outdir = str(log_path) log_output_settings.copy_summary_to_stdout = True log_settings = lg.LogSettings() log_settings.log_output = log_output_settings # start running test, from LoadGen print("Running Loadgen test...") lg.StartTestWithLogSettings(sut.sut, sut.qsl.qsl, settings, log_settings) # if needed check accuracy if args.accuracy: print("Checking accuracy...") cmd = "python3 accuracy_kits.py" subprocess.check_call(cmd, shell=True) # all done print("Done!") # cleanup print("Destroying SUT...") lg.DestroySUT(sut.sut) print("Destroying QSL...") lg.DestroyQSL(sut.qsl.qsl)
def main(): global last_timeing args = get_args() log.info(args) # find backend backend = get_backend(args.backend) # override image format if given image_format = args.data_format if args.data_format else backend.image_format() # --count applies to accuracy mode only and can be used to limit the number of images # for testing. For perf model we always limit count to 200. count_override = False count = args.count if count: count_override = True # dataset to use wanted_dataset, pre_proc, post_proc, kwargs = SUPPORTED_DATASETS[args.dataset] ds = wanted_dataset(data_path=args.dataset_path, image_list=args.dataset_list, name=args.dataset, image_format=image_format, pre_process=pre_proc, use_cache=args.cache, count=count, **kwargs) # load model to backend model = backend.load(args.model, inputs=args.inputs, outputs=args.outputs) final_results = { "runtime": model.name(), "version": model.version(), "time": int(time.time()), "cmdline": str(args), } config = os.path.abspath(args.config) if not os.path.exists(config): log.error("{} not found".format(config)) sys.exit(1) if args.output: output_dir = os.path.abspath(args.output) os.makedirs(output_dir, exist_ok=True) os.chdir(output_dir) # # make one pass over the dataset to validate accuracy # count = ds.get_item_count() # warmup warmup_queries = range(args.max_batchsize) ds.load_query_samples(warmup_queries) for _ in range(2): img, _ = ds.get_samples(warmup_queries) _ = backend.predict({backend.inputs[0]: img}) ds.unload_query_samples(None) scenario = SCENARIO_MAP[args.scenario] runner_map = { lg.TestScenario.SingleStream: RunnerBase, lg.TestScenario.MultiStream: QueueRunner, lg.TestScenario.Server: QueueRunner, lg.TestScenario.Offline: QueueRunner } runner = runner_map[scenario](model, ds, args.threads, post_proc=post_proc, max_batchsize=args.max_batchsize) def issue_queries(query_samples): runner.enqueue(query_samples) def flush_queries(): pass def process_latencies(latencies_ns): # called by loadgen to show us the recorded latencies global last_timeing last_timeing = [t / NANO_SEC for t in latencies_ns] settings = lg.TestSettings() settings.FromConfig(config, args.model_name, args.scenario) settings.scenario = scenario settings.mode = lg.TestMode.PerformanceOnly if args.accuracy: settings.mode = lg.TestMode.AccuracyOnly if args.find_peak_performance: settings.mode = lg.TestMode.FindPeakPerformance if args.time: # override the time we want to run settings.min_duration_ms = args.time * MILLI_SEC settings.max_duration_ms = args.time * MILLI_SEC if args.qps: qps = float(args.qps) settings.server_target_qps = qps settings.offline_expected_qps = qps if count_override: settings.min_query_count = count settings.max_query_count = count if args.samples_per_query: settings.multi_stream_samples_per_query = args.samples_per_query if args.max_latency: settings.server_target_latency_ns = int(args.max_latency * NANO_SEC) settings.multi_stream_target_latency_ns = int(args.max_latency * NANO_SEC) # override target latency when it needs to be less than 1ms if args.model_name == "mobilenet": settings.single_stream_expected_latency_ns = 200000 elif args.model_name == "resnet50": settings.single_stream_expected_latency_ns = 900000 elif args.model_name == "ssd-mobilenet": settings.single_stream_expected_latency_ns = 1000000 sut = lg.ConstructSUT(issue_queries, flush_queries, process_latencies) #qsl = lg.ConstructQSL(count, min(count, 500), ds.load_query_samples, ds.unload_query_samples) qsl = lg.ConstructQSL(count, min(count, 1024), ds.load_query_samples, ds.unload_query_samples) log.info("starting {}".format(scenario)) result_dict = {"good": 0, "total": 0, "scenario": str(scenario)} runner.start_run(result_dict, args.accuracy) if args.enable_trace: lg.StartTest(sut, qsl, settings) else: logsettings = lg.LogSettings() logsettings.enable_trace = False lg.StartTestWithLogSettings(sut, qsl, settings, logsettings) if not last_timeing: last_timeing = runner.result_timing if args.accuracy: post_proc.finalize(result_dict, ds, output_dir=args.output) add_results(final_results, "{}".format(scenario), result_dict, last_timeing, time.time() - ds.last_loaded, args.accuracy) runner.finish() lg.DestroyQSL(qsl) lg.DestroySUT(sut) # # write final results # if args.output: with open("results.json", "w") as f: json.dump(final_results, f, sort_keys=True, indent=4)
def main(): global so global last_timeing global last_loaded global result_timeing args = get_args() log.info(args) # find backend backend = get_backend(args.backend) # --count applies to accuracy mode only and can be used to limit the number of images # for testing. For perf model we always limit count to 200. count_override = False count = args.count if count: count_override = True """ Python signature go_initialize(backend, model_path, dataset_path, count, use_gpu, gpu_id, trace_level, max_batchsize) """ count, err = go_initialize(backend, args.model_path, args.dataset_path, count, args.use_gpu, args.gpu_id, args.trace_level, args.max_batchsize) if (err != 'nil'): print(err) raise RuntimeError('initialization in go failed') mlperf_conf = os.path.abspath(args.mlperf_conf) if not os.path.exists(mlperf_conf): log.error("{} not found".format(mlperf_conf)) sys.exit(1) user_conf = os.path.abspath(args.user_conf) if not os.path.exists(user_conf): log.error("{} not found".format(user_conf)) sys.exit(1) log_dir = None if args.log_dir: log_dir = os.path.abspath(args.log_dir) os.makedirs(log_dir, exist_ok=True) scenario = SCENARIO_MAP[args.scenario] def issue_queries(query_samples): global so global last_timeing global result_timeing idx = np.array([q.index for q in query_samples]).astype(np.int32) query_id = [q.id for q in query_samples] if args.dataset == 'brats2019': start = time.time() response_array_refs = [] response = [] for i, qid in enumerate(query_id): processed_results = so.IssueQuery(1, idx[i][np.newaxis]) processed_results = json.loads( processed_results.decode('utf-8')) response_array = array.array( "B", np.array(processed_results[0], np.float16).tobytes()) response_array_refs.append(response_array) bi = response_array.buffer_info() response.append(lg.QuerySampleResponse(qid, bi[0], bi[1])) result_timeing.append(time.time() - start) lg.QuerySamplesComplete(response) else: start = time.time() processed_results = so.IssueQuery(len(idx), idx) result_timeing.append(time.time() - start) processed_results = json.loads(processed_results.decode('utf-8')) response_array_refs = [] response = [] for idx, qid in enumerate(query_id): response_array = array.array( "B", np.array(processed_results[idx], np.float32).tobytes()) response_array_refs.append(response_array) bi = response_array.buffer_info() response.append(lg.QuerySampleResponse(qid, bi[0], bi[1])) lg.QuerySamplesComplete(response) def flush_queries(): pass def process_latencies(latencies_ns): # called by loadgen to show us the recorded latencies global last_timeing last_timeing = [t / NANO_SEC for t in latencies_ns] def load_query_samples(sample_list): global so global last_loaded err = go_load_query_samples(sample_list, so) last_loaded = time.time() if (err != ''): print(err) raise RuntimeError('load query samples failed') def unload_query_samples(sample_list): global so err = go_unload_query_samples(sample_list, so) if (err != ''): print(err) raise RuntimeError('unload query samples failed') settings = lg.TestSettings() if args.model_name != "": settings.FromConfig(mlperf_conf, args.model_name, args.scenario) settings.FromConfig(user_conf, args.model_name, args.scenario) settings.scenario = scenario settings.mode = lg.TestMode.PerformanceOnly if args.accuracy: settings.mode = lg.TestMode.AccuracyOnly if args.find_peak_performance: settings.mode = lg.TestMode.FindPeakPerformance if args.time: # override the time we want to run settings.min_duration_ms = args.time * MILLI_SEC settings.max_duration_ms = args.time * MILLI_SEC if args.qps: qps = float(args.qps) settings.server_target_qps = qps settings.offline_expected_qps = qps if count_override: settings.min_query_count = count settings.max_query_count = count if args.samples_per_query: settings.multi_stream_samples_per_query = args.samples_per_query if args.max_latency: settings.server_target_latency_ns = int(args.max_latency * NANO_SEC) settings.multi_stream_target_latency_ns = int(args.max_latency * NANO_SEC) sut = lg.ConstructSUT(issue_queries, flush_queries, process_latencies) qsl = lg.ConstructQSL(count, min(count, 500), load_query_samples, unload_query_samples) log.info("starting {}".format(scenario)) log_path = os.path.realpath(args.log_dir) log_output_settings = lg.LogOutputSettings() log_output_settings.outdir = log_path log_output_settings.copy_summary_to_stdout = True log_settings = lg.LogSettings() log_settings.log_output = log_output_settings # log_settings.enable_trace = True # lg.StartTest(sut, qsl, settings) lg.StartTestWithLogSettings(sut, qsl, settings, log_settings) if not last_timeing: last_timeing = result_timeing if args.accuracy: accuracy_script_paths = { 'coco': os.path.realpath( '../inference/vision/classification_and_detection/tools/accuracy-coco.py' ), 'imagenet': os.path.realpath( '../inference/vision/classification_and_detection/tools/accuracy-imagenet.py' ), 'squad': os.path.realpath('../inference/language/bert/accuracy-squad.py'), 'brats2019': os.path.realpath( '../inference/vision/medical_imaging/3d-unet/accuracy-brats.py' ), } accuracy_script_path = accuracy_script_paths[args.dataset] accuracy_file_path = os.path.join(log_dir, 'mlperf_log_accuracy.json') data_dir = os.environ['DATA_DIR'] if args.dataset == 'coco': if args.use_inv_map: subprocess.check_call( 'python3 {} --mlperf-accuracy-file {} --coco-dir {} --use-inv-map' .format(accuracy_script_path, accuracy_file_path, data_dir), shell=True) else: subprocess.check_call( 'python3 {} --mlperf-accuracy-file {} --coco-dir {}'. format(accuracy_script_path, accuracy_file_path, data_dir), shell=True) elif args.dataset == 'imagenet': # imagenet subprocess.check_call( 'python3 {} --mlperf-accuracy-file {} --imagenet-val-file {}'. format(accuracy_script_path, accuracy_file_path, os.path.join(data_dir, 'val_map.txt')), shell=True) elif args.dataset == 'squad': # squad vocab_path = os.path.join(data_dir, 'vocab.txt') val_path = os.path.join(data_dir, 'dev-v1.1.json') out_path = os.path.join(log_dir, 'predictions.json') cache_path = os.path.join(data_dir, 'eval_features.pickle') subprocess.check_call( 'python3 {} --vocab_file {} --val_data {} --log_file {} --out_file {} --features_cache_file {} --max_examples {}' .format(accuracy_script_path, vocab_path, val_path, accuracy_file_path, out_path, cache_path, count), shell=True) elif args.dataset == 'brats2019': # brats2019 base_dir = os.path.realpath( '../inference/vision/medical_imaging/3d-unet/build') post_dir = os.path.join(base_dir, 'postprocessed_data') label_dir = os.path.join( base_dir, 'raw_data/nnUNet_raw_data/Task043_BraTS2019/labelsTr') os.makedirs(post_dir, exist_ok=True) subprocess.check_call( 'python3 {} --log_file {} --preprocessed_data_dir {} --postprocessed_data_dir {} --label_data_dir {}' .format(accuracy_script_path, accuracy_file_path, data_dir, post_dir, label_dir), shell=True) else: raise RuntimeError('Dataset not Implemented.') lg.DestroyQSL(qsl) lg.DestroySUT(sut) """ Python signature go_finalize(so) """ err = go_finalize(so) if (err != ''): print(err) raise RuntimeError('finialize in go failed')
def run(): """Runs the offline mode.""" global last_timing # Initiazation final_results, count, runner = setup() # # run the benchmark with timing # runner.start_pool() def issue_query_offline(query_samples): """Adds query to the queue.""" for i in [1]: idx = np.array([q.index for q in query_samples]) query_id = np.array([q.id for q in query_samples]) batch_size = FLAGS.batch_size[0] for i in range(0, len(query_samples), batch_size): runner.enqueue(query_id[i:i + batch_size], idx[i:i + batch_size]) def flush_queries(): pass def process_latencies(latencies_ns): global last_timing last_timing = [t / 1e9 for t in latencies_ns] sut = lg.ConstructSUT(issue_query_offline, flush_queries, process_latencies) masters = [] outdir = FLAGS.outdir if FLAGS.outdir else tempfile.mkdtemp() export_outdir = FLAGS.export_outdir if FLAGS.export_outdir else outdir export_outdir = os.path.join(export_outdir, "export_model") def load_query_samples(sample_list): """Load query samples.""" runner.ds.load_query_samples(sample_list) # Find tpu master. if FLAGS.num_tpus == 1: runner.model.update_qsl(runner.ds.get_image_list_inmemory()) else: for i in range(FLAGS.num_tpus): runner.models[i].update_qsl(runner.ds.get_image_list_inmemory()) def warmup(): """Warmup the TPUs.""" load_query_samples([0]) if FLAGS.num_tpus == 1: log.info("warmup ...") runner.warmup(0) log.info("warmup done") else: for cloud_tpu_id in range(FLAGS.num_tpus): log.info("warmup %d...", cloud_tpu_id) runner.warmup(0, cloud_tpu_id) log.info("warmup %d done", cloud_tpu_id) # After warmup, give the system a moment to quiesce before putting it under # load. time.sleep(1) if FLAGS.num_tpus == 1: tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver( FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project) master = tpu_cluster_resolver.get_master() runner.model.build_and_export( FLAGS.model, export_model_path=export_outdir, batch_size=FLAGS.batch_size, master=master, scenario=FLAGS.scenario) runner.model.load(export_model_path=export_outdir, master=master) else: # Use the first TPU instance to build and export the graph. tpu_names = FLAGS.tpu_name tpu_names = tpu_names.split(",") for tpu_name in tpu_names: tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver( tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project) masters.append(tpu_cluster_resolver.get_master()) runner.models[0].build_and_export( FLAGS.model, export_model_path=export_outdir, batch_size=FLAGS.batch_size, master=masters[0], scenario=FLAGS.scenario) def init_fn(cloud_tpu_id): """Init and warmup each cloud tpu.""" runner.models[cloud_tpu_id].load( export_model_path=export_outdir, master=masters[cloud_tpu_id]) threads = [] for i in range(FLAGS.num_tpus): thread = threading.Thread(target=init_fn, args=(i,)) threads.append(thread) thread.start() for thread in threads: thread.join() warmup() qsl = lg.ConstructQSL(count, min(count, 1024), load_query_samples, runner.ds.unload_query_samples) test_scenarios = FLAGS.scenario if test_scenarios is None: test_scenarios_list = [] else: test_scenarios_list = test_scenarios.split(",") max_latency = FLAGS.max_latency max_latency_list = max_latency.split(",") for scenario in test_scenarios_list: for target_latency in max_latency_list: log.info("starting %s, latency=%s", scenario, target_latency) settings = lg.TestSettings() log.info(scenario) if FLAGS.accuracy: settings.mode = lg.TestMode.AccuracyOnly settings.scenario = utils.SCENARIO_MAP[scenario] if FLAGS.qps: qps = float(FLAGS.qps) settings.server_target_qps = qps settings.offline_expected_qps = qps if FLAGS.time: settings.min_duration_ms = 60 * MILLI_SEC settings.max_duration_ms = 0 qps = FLAGS.qps or 100 settings.min_query_count = qps * FLAGS.time settings.max_query_count = int(1.1 * qps * FLAGS.time) else: settings.min_query_count = (1 << 21) if FLAGS.time or FLAGS.qps and FLAGS.accuracy: settings.mode = lg.TestMode.PerformanceOnly # FIXME: add SubmissionRun once available target_latency_ns = int(float(target_latency) * (NANO_SEC / MILLI_SEC)) settings.single_stream_expected_latency_ns = target_latency_ns settings.multi_stream_target_latency_ns = target_latency_ns settings.server_target_latency_ns = target_latency_ns log_settings = lg.LogSettings() # TODO(brianderson): figure out how to use internal file path. log_settings.log_output.outdir = tempfile.mkdtemp() log_settings.log_output.copy_detail_to_stdout = True log_settings.log_output.copy_summary_to_stdout = True log_settings.enable_trace = False result_dict = {"good": 0, "total": 0, "scenario": str(scenario)} runner.start_run(result_dict, FLAGS.accuracy) lg.StartTestWithLogSettings(sut, qsl, settings, log_settings) if FLAGS.accuracy: runner.get_post_process().finalize(result_dict, runner.ds) utils.add_results( final_results, "{}-{}".format(scenario, target_latency), result_dict, last_timing, time.time() - runner.ds.last_loaded) # # write final results # if FLAGS.outdir: outfile = os.path.join(FLAGS.outdir, "results.txt") with tf.gfile.Open(outfile, "w") as f: json.dump(final_results, f, sort_keys=True, indent=4) else: json.dump(final_results, sys.stdout, sort_keys=True, indent=4) runner.finish() lg.DestroyQSL(qsl) lg.DestroySUT(sut)
def main(argv): del argv settings = mlperf_loadgen.TestSettings() settings.qsl_rng_seed = FLAGS.qsl_rng_seed settings.sample_index_rng_seed = FLAGS.sample_index_rng_seed settings.schedule_rng_seed = FLAGS.schedule_rng_seed if FLAGS.accuracy_mode: settings.mode = mlperf_loadgen.TestMode.AccuracyOnly else: settings.mode = mlperf_loadgen.TestMode.PerformanceOnly settings.scenario = SCENARIO_MAP[FLAGS.scenario] if FLAGS.qps: qps = float(FLAGS.qps) settings.server_target_qps = qps settings.offline_expected_qps = qps if FLAGS.scenario == "Offline" or FLAGS.scenario == "Server": masters = FLAGS.master masters = masters.split(",") if len(masters) < 1: masters = [FLAGS.master] runner = loadgen_gnmt.GNMTRunner(input_file=FLAGS.input_file, ckpt_path=FLAGS.ckpt_path, hparams_path=FLAGS.hparams_path, vocab_prefix=FLAGS.vocab_prefix, outdir=FLAGS.outdir, batch_size=FLAGS.batch_size, verbose=FLAGS.verbose, masters=masters, scenario=FLAGS.scenario) runner.load(FLAGS.batch_timeout_micros) # Specify exactly how many queries need to be made settings.min_query_count = FLAGS.qps * FLAGS.time settings.max_query_count = 0 settings.min_duration_ms = 60 * MILLI_SEC settings.max_duration_ms = 0 settings.server_target_latency_ns = int(0.25 * NANO_SEC) settings.server_target_latency_percentile = 0.97 else: print("Invalid scenario selected") assert False # Create a thread in the GNMTRunner to start accepting work runner.start_worker() # Maximum sample ID + 1 total_queries = FLAGS.query_count # Select the same subset of $perf_queries samples perf_queries = FLAGS.query_count sut = mlperf_loadgen.ConstructSUT(runner.enqueue, flush_queries, generic_loadgen.process_latencies) qsl = mlperf_loadgen.ConstructQSL(total_queries, perf_queries, runner.load_samples_to_ram, runner.unload_samples_from_ram) log_settings = mlperf_loadgen.LogSettings() log_settings.log_output.outdir = tempfile.mkdtemp() # Disable detail logs to prevent it from stepping on the summary # log in stdout on some systems. log_settings.log_output.copy_detail_to_stdout = False log_settings.log_output.copy_summary_to_stdout = True log_settings.enable_trace = False mlperf_loadgen.StartTestWithLogSettings(sut, qsl, settings, log_settings) runner.finish() mlperf_loadgen.DestroyQSL(qsl) mlperf_loadgen.DestroySUT(sut) for oldfile in tf.gfile.Glob( os.path.join(log_settings.log_output.outdir, "*")): basename = os.path.basename(oldfile) newfile = os.path.join(FLAGS.outdir, basename) tf.gfile.Copy(oldfile, newfile, overwrite=True) if FLAGS.accuracy_mode: log_accuracy = os.path.join(log_settings.log_output.outdir, "mlperf_log_accuracy.json") tf.gfile.Copy(FLAGS.reference, "/tmp/reference") bleu = process_accuracy.get_accuracy("/tmp/reference", log_accuracy) print("BLEU: %.2f" % (bleu * 100)) # pylint: disable=superfluous-parens