def main(argv):
settings = mlperf_loadgen.TestSettings()
settings.scenario = mlperf_loadgen.TestScenario.SingleStream
settings.mode = mlperf_loadgen.TestMode.PerformanceOnly
sut = mlperf_loadgen.ConstructSUT(
issue_query, flush_queries, process_latencies)
qsl = mlperf_loadgen.ConstructQSL(
1024 * 1024, 1024, load_samples_to_ram, unload_samples_from_ram)
mlperf_loadgen.StartTest(sut, qsl, settings)
mlperf_loadgen.DestroyQSL(qsl)
mlperf_loadgen.DestroySUT(sut)
def main(argv):
settings = mlperf_loadgen.TestSettings()
settings.scenario = mlperf_loadgen.TestScenario.Offline
settings.mode = mlperf_loadgen.TestMode.PerformanceOnly
settings.offline_expected_qps = 1000
sut = mlperf_loadgen.ConstructSUT(
issue_query, flush_queries, process_latencies)
qsl = mlperf_loadgen.ConstructQSL(
1024, 128, load_samples_to_ram, unload_samples_from_ram)
mlperf_loadgen.StartTest(sut, qsl, settings)
mlperf_loadgen.DestroyQSL(qsl)
mlperf_loadgen.DestroySUT(sut)
def main(argv):
settings = mlperf_loadgen.TestSettings()
settings.scenario = mlperf_loadgen.TestScenario.MultiStream
settings.mode = mlperf_loadgen.TestMode.SubmissionRun
settings.samples_per_query = 4
settings.target_qps = 1000
settings.target_latency_ns = 1000000000
sut = mlperf_loadgen.ConstructSUT(issue_query)
qsl = mlperf_loadgen.ConstructQSL(1024, 128, load_samples_to_ram,
unload_samples_from_ram)
mlperf_loadgen.StartTest(sut, qsl, settings)
mlperf_loadgen.DestroyQSL(qsl)
mlperf_loadgen.DestroySUT(sut)
def main(argv):
settings = mlperf_loadgen.TestSettings()
settings.scenario = mlperf_loadgen.TestScenario.SingleStream
settings.mode = mlperf_loadgen.TestMode.AccuracyOnly
settings.single_stream_expected_latency_ns = 1000000
settings.min_query_count = 100
settings.min_duration_ms = 10000
sut = mlperf_loadgen.ConstructSUT(issue_query, flush_queries,
process_latencies)
qsl = mlperf_loadgen.ConstructQSL(1024, 128, load_samples_to_ram,
unload_samples_from_ram)
mlperf_loadgen.StartTest(sut, qsl, settings)
mlperf_loadgen.DestroyQSL(qsl)
mlperf_loadgen.DestroySUT(sut)
def main(argv):
settings = mlperf_loadgen.TestSettings()
settings.scenario = mlperf_loadgen.TestScenario.Server
settings.mode = mlperf_loadgen.TestMode.PerformanceOnly
settings.server_target_qps = 100
settings.server_target_latency_ns = 100000000
settings.min_query_count = 100
settings.min_duration_ms = 10000
sut = mlperf_loadgen.ConstructSUT(issue_query, flush_queries,
process_latencies)
qsl = mlperf_loadgen.ConstructQSL(1024, 128, load_samples_to_ram,
unload_samples_from_ram)
mlperf_loadgen.StartTest(sut, qsl, settings)
mlperf_loadgen.DestroyQSL(qsl)
mlperf_loadgen.DestroySUT(sut)
def main(argv):
settings = mlperf_loadgen.TestSettings()
settings.scenario = mlperf_loadgen.TestScenario.SingleStream
settings.mode = mlperf_loadgen.TestMode.PerformanceOnly
settings.single_stream_expected_latency_ns = 1000000
settings.enable_spec_overrides = True
settings.override_target_latency_ns = 100000000
settings.override_min_query_count = 100
settings.override_min_duration_ms = 10000
sut = mlperf_loadgen.ConstructSUT(issue_query, process_latencies)
qsl = mlperf_loadgen.ConstructQSL(1024, 128, load_samples_to_ram,
unload_samples_from_ram)
mlperf_loadgen.StartTest(sut, qsl, settings)
mlperf_loadgen.DestroyQSL(qsl)
mlperf_loadgen.DestroySUT(sut)
def main(argv):
del argv
settings = mlperf_loadgen.TestSettings()
settings.scenario = mlperf_loadgen.TestScenario.MultiStreamFree
settings.mode = mlperf_loadgen.TestMode.PerformanceOnly
settings.multi_stream_target_latency_ns = 100000000
settings.multi_stream_samples_per_query = 4
settings.multi_stream_max_async_queries = 2
settings.min_query_count = 100
settings.min_duration_ms = 10000
sut = mlperf_loadgen.ConstructSUT(issue_query, flush_queries,
process_latencies)
qsl = mlperf_loadgen.ConstructQSL(1024, 128, load_samples_to_ram,
unload_samples_from_ram)
mlperf_loadgen.StartTest(sut, qsl, settings)
mlperf_loadgen.DestroyQSL(qsl)
mlperf_loadgen.DestroySUT(sut)
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 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 = args.count
if not count:
if not args.accuracy:
count = 200
# 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),
}
#
# make one pass over the dataset to validate accuracy
#
count = ds.get_item_count()
# warmup
ds.load_query_samples([0])
for _ in range(5):
img, _ = ds.get_samples([0])
_ = backend.predict({backend.inputs[0]: img})
ds.unload_query_samples(None)
for scenario in 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.scenario = scenario
settings.mode = lg.TestMode.PerformanceOnly
if args.accuracy:
settings.mode = lg.TestMode.AccuracyOnly
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 scenario == lg.TestScenario.SingleStream:
settings.min_query_count = args.queries_single
settings.max_query_count = args.queries_single
elif scenario == lg.TestScenario.MultiStream:
settings.min_query_count = args.queries_multi
settings.max_query_count = args.queries_multi
settings.multi_stream_samples_per_query = 4
elif scenario == lg.TestScenario.Server:
max_latency = args.max_latency
elif scenario == lg.TestScenario.Offline:
settings.min_query_count = args.queries_offline
settings.max_query_count = args.queries_offline
sut = lg.ConstructSUT(issue_queries, flush_queries, process_latencies)
qsl = lg.ConstructQSL(count, min(count, 1000), ds.load_query_samples,
ds.unload_query_samples)
if scenario == lg.TestScenario.Server:
for target_latency in max_latency:
log.info("starting {}, latency={}".format(
scenario, target_latency))
settings.server_target_latency_ns = int(target_latency *
NANO_SEC)
result_dict = {
"good": 0,
"total": 0,
"scenario": str(scenario)
}
runner.start_run(result_dict, args.accuracy)
lg.StartTest(sut, qsl, settings)
if not last_timeing:
last_timeing = runner.result_timing
if args.accuracy:
post_proc.finalize(result_dict,
ds,
output_dir=os.path.dirname(args.output))
add_results(final_results,
"{}-{}".format(scenario, target_latency),
result_dict, last_timeing,
time.time() - ds.last_loaded, args.accuracy)
else:
log.info("starting {}".format(scenario))
result_dict = {"good": 0, "total": 0, "scenario": str(scenario)}
runner.start_run(result_dict, args.accuracy)
lg.StartTest(sut, qsl, settings)
if not last_timeing:
last_timeing = runner.result_timing
if args.accuracy:
post_proc.finalize(result_dict,
ds,
output_dir=os.path.dirname(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(args.output, "w") as f:
json.dump(final_results, f, sort_keys=True, indent=4)
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
ds.load_query_samples([0])
for _ in range(5):
img, _ = ds.get_samples([0])
_ = 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 count_override:
settings.min_query_count = count
# settings.max_query_count = count
if args.min_query_count:
settings.min_query_count = args.min_query_count
if args.samples_per_query:
settings.multi_stream_samples_per_query = args.samples_per_query
if args.max_latency:
settings.single_stream_expected_latency_ns = int(args.max_latency *
NANO_SEC)
settings.server_target_latency_ns = int(args.max_latency * NANO_SEC)
settings.multi_stream_target_latency_ns = int(args.max_latency *
NANO_SEC)
def set_qps(current_qps):
settings.server_target_qps = current_qps
settings.offline_expected_qps = current_qps
settings.multi_stream_target_qps = current_qps
return current_qps
if args.qps:
qps = set_qps(args.qps)
lower_qps = -1
upper_qps = -1
qps_passed = {}
while True:
print("schedual qps:", qps)
sut = lg.ConstructSUT(issue_queries, flush_queries, process_latencies)
qsl = lg.ConstructQSL(count, min(count, 500), 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)
print("max query count:", settings.max_query_count)
lg.StartTest(sut, qsl, settings)
if not last_timeing:
last_timeing = runner.result_timing
if args.accuracy:
post_proc.finalize(result_dict, ds, output_dir=args.output)
took = time.time() - ds.last_loaded
add_results(final_results, "{}".format(scenario), result_dict,
last_timeing, took, args.accuracy)
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)
if args.scenario != 'Server':
break
if args.auto_qps is False:
break
if lower_qps == -1 or upper_qps == -1:
base_qps = len(last_timeing) / took
upper_qps = base_qps * 1.5
lower_qps = base_qps * 0.5
qps = set_qps(lower_qps)
continue
latency_percentile_ns = np.percentile(
last_timeing,
settings.server_target_latency_percentile * 100) * NANO_SEC
if latency_percentile_ns < settings.server_target_latency_ns and qps > upper_qps * 0.98:
print("target qps:", qps)
break
if upper_qps - lower_qps < 1 and lower_qps in qps_passed:
print("target qps:", lower_qps)
break
if latency_percentile_ns > settings.server_target_latency_ns:
#reduce qps
print("reduce qps, bound:[%d, %d]" % (lower_qps, upper_qps))
upper_qps = qps
if qps == lower_qps:
lower_qps = lower_qps * 0.5
qps = set_qps(lower_qps)
continue
if qps > lower_qps:
qps = set_qps((lower_qps + upper_qps) / 2)
continue
if latency_percentile_ns < settings.server_target_latency_ns:
#increase qps
qps_passed[qps] = None
print("increase qps, bound:[%d, %d]" % (lower_qps, upper_qps))
lower_qps = qps
if qps == upper_qps:
upper_qps = upper_qps * 1.5
qps = set_qps(upper_qps)
continue
if qps < upper_qps:
qps = set_qps((lower_qps + upper_qps) / 2)
continue
runner.finish()
def main():
global last_timeing
args = get_args()
log.info(args)
# find backend
backend = get_backend(args.backend)
if getattr(backend, "max_batchsize", -1) != -1:
backend.max_batchsize = args.max_batchsize
# 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),
}
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)
audit_config_cp_loc = None
if args.output:
output_dir = os.path.abspath(args.output)
os.makedirs(output_dir, exist_ok=True)
# Check if audit.config file is used, copy to output directory before
# we chdir to that location so loadgen can find it
audit_files = glob.glob(
"ncoresw/mlperf/vision/classification_and_detection/*audit.config")
if len(audit_files):
log.info("Found audit.config (" + audit_files[0] + ")")
audit_config_cp_loc = os.path.join(output_dir, "audit.config")
# If user already put audit.config at `output` directory, then use
# that one. Otherwise, copy the one we found in the current
# directory (before chdir to new output directory).
if os.path.exists(audit_config_cp_loc):
log.info(
"WARNING: audit.config already exists, so cannot copy over new audit file!"
)
log.info(audit_config_cp_loc)
audit_config_cp_loc = None
else:
shutil.copy(audit_files[0], audit_config_cp_loc)
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(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)
# 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 = 900000
sut = lg.ConstructSUT(issue_queries, flush_queries, process_latencies)
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)
lg.StartTest(sut, qsl, settings)
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)
# Dump the summary logs to stdout for convenience
log.info("Output dir: " + os.path.abspath(output_dir))
with open(os.path.join(output_dir, "mlperf_log_summary.txt"), 'r') as f:
log.info(f.read())
# Output accuracy txt file
if args.accuracy:
with open(os.path.join(output_dir, "accuracy.txt"), "w") as f_acc:
# SSD accuracy calculation
#----------------------------------------
# The mAP is already stored in result_dict["mAP"], but we'll call
# `accuracy_coco()` just to keep the submission process consistent.
if args.model_name == "ssd-mobilenet":
accuracy_str = accuracy.CocoAcc(
mlperf_accuracy_file=os.path.join(
output_dir, "mlperf_log_accuracy.json"),
coco_dir=args.dataset_path).get_accuracy() + "\n"
f_acc.write(accuracy_str)
log.info(accuracy_str)
if args.model_name == "ssd-resnet34":
accuracy_str = accuracy.CocoAcc(
mlperf_accuracy_file=os.path.join(
output_dir, "mlperf_log_accuracy.json"),
coco_dir=args.dataset_path,
use_inv_map=True,
remove_48_empty_images=False).get_accuracy() + "\n"
f_acc.write(accuracy_str)
log.info(accuracy_str)
# ImageNet accuracy calculation
#----------------------------------------
# The good / total values are already stored in result_dict["good"]
# and result_dict["total"], but we'll call `accuracy_imagenet()`
# just to keep the submission process consistent.
else:
accuracy_str = accuracy.ImagenetAcc(
mlperf_accuracy_file=os.path.join(
output_dir, "mlperf_log_accuracy.json"),
imagenet_val_file=os.path.join(
args.dataset_path,
"val_map.txt")).get_accuracy() + "\n"
f_acc.write(accuracy_str)
log.info(accuracy_str)
#
# write final results
#
if args.output:
with open("results.json", "w") as f:
json.dump(final_results, f, sort_keys=True, indent=4)
if audit_config_cp_loc != None:
os.remove(audit_config_cp_loc)
backend_destroy = getattr(backend, "destroy", None)
if callable(backend_destroy):
backend.destroy()
def main():
global last_timeing
args = get_args()
log.info(args)
backend = BackendTensorRT()
ds = Imagenet(
data_path=args.dataset_path,
use_cache=args.cache,
batch_size=args.batch_size,
image_size=args.image_size,
calib_file='cal_image_list_option_%d.txt' % args.calib_file)
model = backend.load(args, ds=ds)
final_results = {
"runtime": model.name(),
"version": model.version(),
"time": int(time.time()),
"cmdline": str(args),
}
config = os.path.abspath(args.config)
assert(os.path.exists(config)), "%s not existed!" % config
user_config = os.path.abspath(args.user_config)
assert(os.path.exists(user_config)), "%s not existed!" % user_config
base_path = os.path.dirname(os.path.realpath(__file__))
if args.output:
output_dir = os.path.abspath(args.output)
os.makedirs(output_dir, exist_ok=True)
os.chdir(output_dir)
post_proc = PostProcessCommon(offset=0)
runner = QueueRunner(
model, ds, args.threads, post_proc=post_proc, batch_size=args.batch_size)
def issue_queries(ids, indices):
runner.enqueue(ids, indices)
def flush_queries():
pass
def process_latencies(latencies_ns):
global last_timeing
last_timeing = [t / NANO_SEC for t in latencies_ns]
settings = lg.TestSettings()
model_name = 'OFAnet-AutoSinian'
settings.FromConfig(config, model_name, args.scenario)
settings.FromConfig(user_config, model_name, args.scenario)
if args.audit_test:
audit_config_path = base_path + '/audit%s.config' % args.audit_test
settings.FromConfig(audit_config_path, model_name, args.scenario)
scenario = SCENARIO_MAP[args.scenario]
settings.scenario = scenario
settings.mode = lg.TestMode.PerformanceOnly
if args.accuracy:
settings.mode = lg.TestMode.AccuracyOnly
sut = lg.ConstructFastSUT(issue_queries, flush_queries, process_latencies)
qsl = lg.ConstructQSL(ds.get_item_count(), args.batch_size,
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)
start = time.time()
lg.StartTest(sut, qsl, settings)
post_proc.finalize(result_dict)
add_results(final_results, "{}".format(scenario), result_dict, last_timeing,
runner.finishTime - ds.last_loaded, args)
runner.finish()
lg.DestroyQSL(qsl)
lg.DestroyFastSUT(sut)
if args.output:
with open("results.json", "w") as f:
json.dump(final_results, f, sort_keys=True, indent=2)
.format(self.count, qitem.sample_id[0]))
self.count += 1
return self.count
if __name__ == "__main__":
runner = DummyRunner()
runner.start_worker()
settings = mlperf_loadgen.TestSettings()
settings.scenario = mlperf_loadgen.TestScenario.SingleStream
settings.mode = mlperf_loadgen.TestMode.PerformanceOnly
# Specify exactly how many queries need to be made
settings.min_query_count = 3003
settings.max_query_count = 3003
total_queries = 256 # Maximum sample ID + 1
perf_queries = 8 # TBD: Doesn't seem to have an effect
sut = mlperf_loadgen.ConstructSUT(runner.enqueue, flush_queries,
process_latencies)
qsl = mlperf_loadgen.ConstructQSL(total_queries, perf_queries,
runner.load_samples_to_ram,
runner.unload_samples_from_ram)
mlperf_loadgen.StartTest(sut, qsl, settings)
mlperf_loadgen.DestroyQSL(qsl)
mlperf_loadgen.DestroySUT(sut)
def main():
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(
)
# 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=args.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),
}
#
# make one pass over the dataset to validate accuracy
#
count = args.count if args.count else ds.get_item_count()
runner = Runner(model, ds, args.threads, post_proc=post_proc)
runner.start_pool()
# warmup
log.info("warmup ...")
ds.load_query_samples([0])
for _ in range(50):
img, _ = ds.get_samples([0])
_ = backend.predict({backend.inputs[0]: img})
ds.unload_query_samples(None)
def issue_query(query_samples):
idx = [q.index for q in query_samples]
query_id = [q.id for q in query_samples]
data, label = ds.get_samples(idx)
runner.enqueue(query_id, idx, data, label)
def process_latencies(latencies_ns):
global last_timeing
last_timeing = [t / 10000000. for t in latencies_ns]
sut = lg.ConstructSUT(issue_query, process_latencies)
qsl = lg.ConstructQSL(count, count, ds.load_query_samples,
ds.unload_query_samples)
scenarios = [
lg.TestScenario.SingleStream,
lg.TestScenario.MultiStream,
lg.TestScenario.Server,
# lg.TestScenario.Offline,
]
for scenario in scenarios:
for target_latency in args.max_latency:
log.info("starting {}, latency={}".format(scenario,
target_latency))
settings = lg.TestSettings()
settings.scenario = scenario
if args.qps:
settings.enable_spec_overrides = True
qps = float(args.qps)
settings.server_target_qps = qps
settings.offline_expected_qps = qps
if args.time:
settings.enable_spec_overrides = True
settings.override_min_duration_ms = args.time * MILLI_SEC
settings.override_max_duration_ms = args.time * MILLI_SEC
qps = args.qps or 100
settings.override_min_query_count = qps * args.time
settings.override_max_query_count = qps * args.time
if args.time or args.qps:
settings.mode = lg.TestMode.PerformanceOnly
# FIXME: add SubmissionRun once available
settings.enable_spec_overrides = True # FIXME: needed because of override_target_latency_ns
settings.single_stream_expected_latency_ns = int(target_latency *
NANO_SEC)
settings.override_target_latency_ns = int(target_latency *
NANO_SEC)
# reset result capture
result_dict = {"good": 0, "total": 0}
runner.start_run(result_dict, True)
start = time.time()
lg.StartTest(sut, qsl, settings)
# aggregate results
post_proc.finalize(result_dict, ds)
add_results(final_results, "{}-{}".format(scenario,
target_latency),
result_dict, last_timeing,
time.time() - start)
#
# write final results
#
if args.output:
with open(args.output, "w") as f:
json.dump(final_results, f, sort_keys=True, indent=4)
runner.finish()
lg.DestroyQSL(qsl)
lg.DestroySUT(sut)
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(
)
# 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=args.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),
}
#
# make one pass over the dataset to validate accuracy
#
count = args.count if args.count else ds.get_item_count()
runner = Runner(model, ds, args.threads, post_proc=post_proc)
#
# warmup
#
log.info("warmup ...")
ds.load_query_samples([0])
for _ in range(5):
img, _ = ds.get_samples([0])
_ = backend.predict({backend.inputs[0]: img})
ds.unload_query_samples(None)
if args.accuracy:
#
# accuracy pass
#
log.info("starting accuracy pass on {} items".format(count))
runner.start_pool(nolg=True)
result_dict = {
"good": 0,
"total": 0,
"scenario": "Accuracy",
"timing": []
}
runner.start_run(result_dict, True)
start = time.time()
for idx in range(0, count):
ds.load_query_samples([idx])
data, label = ds.get_samples([idx])
runner.enqueue([idx], [idx], data, label)
runner.finish()
# aggregate results
post_proc.finalize(result_dict,
ds,
output_dir=os.path.dirname(args.output))
last_timeing = result_dict["timing"]
del result_dict["timing"]
add_results(final_results, "Accuracy", result_dict, last_timeing,
time.time() - start)
#
# run the benchmark with timing
#
runner.start_pool()
def issue_query(query_samples):
idx = [q.index for q in query_samples]
query_id = [q.id for q in query_samples]
data, label = ds.get_samples(idx)
runner.enqueue(query_id, idx, data, label)
def process_latencies(latencies_ns):
global last_timeing
last_timeing = [t / 1e9 for t in latencies_ns]
sut = lg.ConstructSUT(issue_query, process_latencies)
qsl = lg.ConstructQSL(count, min(count, 1000), ds.load_query_samples,
ds.unload_query_samples)
for scenario in args.scenario:
for target_latency in args.max_latency:
log.info("starting {}, latency={}".format(scenario,
target_latency))
settings = lg.TestSettings()
log.info(scenario)
if str(scenario) == 'TestMode.AccuracyOnly':
settings.mode = scenario
else:
settings.scenario = scenario
if args.qps:
settings.enable_spec_overrides = True
qps = float(args.qps)
settings.server_target_qps = qps
settings.offline_expected_qps = qps
if args.time:
settings.enable_spec_overrides = True
settings.override_min_duration_ms = args.time * MILLI_SEC
settings.override_max_duration_ms = args.time * MILLI_SEC
qps = args.qps or 100
settings.override_min_query_count = qps * args.time
settings.override_max_query_count = qps * args.time
if args.time or args.qps and str(
scenario) != 'TestMode.AccuracyOnly':
settings.mode = lg.TestMode.PerformanceOnly
# FIXME: add SubmissionRun once available
settings.enable_spec_overrides = True
settings.single_stream_expected_latency_ns = int(target_latency *
NANO_SEC)
settings.override_target_latency_ns = int(target_latency *
NANO_SEC)
result_dict = {"good": 0, "total": 0, "scenario": str(scenario)}
runner.start_run(result_dict, False)
lg.StartTest(sut, qsl, settings)
add_results(final_results, "{}-{}".format(scenario,
target_latency),
result_dict, last_timeing,
time.time() - ds.last_loaded)
#
# write final results
#
if args.output:
with open(args.output, "w") as f:
json.dump(final_results, f, sort_keys=True, indent=4)
runner.finish()
lg.DestroyQSL(qsl)
lg.DestroySUT(sut)
def main():
global qcount
global num_sockets
global cpus_per_socket
global cpus_per_process
global cpus_per_instance
global total_instances
global start_time
global item_total
global last_timeing
args = get_args()
log.info(args)
config = os.path.abspath(args.config)
user_config = os.path.abspath(args.user_config)
if not os.path.exists(config):
log.error("{} not found".format(config))
sys.exit(1)
if not os.path.exists(user_config):
log.error("{} not found".format(user_config))
sys.exit(1)
if args.output:
output_dir = os.path.abspath(args.output)
os.makedirs(output_dir, exist_ok=True)
if os.path.exists("./audit.config"):
copyfile("./audit.config", output_dir + "/audit.config")
os.chdir(output_dir)
settings = lg.TestSettings()
settings.FromConfig(config, args.model, args.scenario)
settings.FromConfig(user_config, args.model, args.scenario)
settings.mode = lg.TestMode.PerformanceOnly
cpus_for_loadgen = 1
left_cores = cpus_per_socket * num_sockets - total_procs * cpus_per_process
first_instance_start_core = cpus_for_loadgen
if left_cores > cpus_for_loadgen:
first_instance_start_core = 0
cpus_for_loadgen = left_cores
total_instances = 0
instances_per_proc = (cpus_per_process // cpus_per_instance)
for i in range(total_procs):
if i == 0 and first_instance_start_core > 0:
total_instances = total_instances + ((cpus_per_process - first_instance_start_core) // cpus_per_instance)
if (cpus_per_instance - first_instance_start_core) >= (cpus_per_instance // 2):
total_instances = total_instances + 1
else:
total_instances = total_instances + instances_per_proc
#print("Setup {} Instances !!".format(total_instances))
lock = multiprocessing.Lock()
barrier = multiprocessing.Barrier(total_instances)
init_counter = multiprocessing.Value("i", 0)
total_samples = multiprocessing.Value("i", 0)
finished_samples = multiprocessing.Value("i", 0)
dsQueue = multiprocessing.Queue()
numOutQ = num_sockets
outQueues = [multiprocessing.Queue() for i in range(numOutQ)]
#inQueue = multiprocessing.JoinableQueue()
inQueue = multiprocessing.Queue()
consumers = [Consumer(inQueue, outQueues[i%numOutQ], dsQueue, lock, init_counter, finished_samples, barrier, i, args, settings.min_query_count, first_instance_start_core)
for i in range(total_procs)]
for c in consumers:
c.start()
# Wait until subprocess ready
while init_counter.value < total_procs: time.sleep(2)
import torch
import criteo
torch.set_num_threads(cpus_per_socket * num_sockets)
dlrm_dataset = get_dataset(args)
total_samples.value = dlrm_dataset.get_item_count()
scenario = SCENARIO_MAP[args.scenario]
runner_map = {
lg.TestScenario.Server: QueueRunner,
lg.TestScenario.Offline: QueueRunner
}
settings.scenario = scenario
runner = runner_map[scenario](inQueue, dlrm_dataset, total_samples.value, args.max_sample_size, args.max_batchsize)
# Start response thread
response_workers = [threading.Thread(
target=response_loadgen, args=(outQueues[i], args.accuracy)) for i in range(numOutQ)]
for response_worker in response_workers:
response_worker.daemon = True
response_worker.start()
def issue_queries(response_ids, query_sample_indexes):
runner.enqueue(response_ids, query_sample_indexes)
def flush_queries():
runner.unload_query_samples()
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]
if args.accuracy:
settings.mode = lg.TestMode.AccuracyOnly
settings.performance_sample_count_override = total_samples.value
if args.find_peak_performance:
settings.mode = lg.TestMode.FindPeakPerformance
if args.duration:
settings.min_duration_ms = args.duration
settings.max_duration_ms = args.duration
if args.target_qps:
settings.server_target_qps = float(args.target_qps)
settings.offline_expected_qps = float(args.target_qps)
if args.count_queries:
settings.min_query_count = args.count_queries
settings.max_query_count = args.count_queries
if args.samples_per_query_multistream:
settings.multi_stream_samples_per_query = args.samples_per_query_multistream
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)
if args.accuracy:
qcount = total_samples.value
else:
qcount = settings.min_query_count
def load_query_samples(sample_list):
# Wait until subprocess ready
global start_time
global total_instances
for _ in range(total_instances):
dsQueue.put(sample_list)
while init_counter.value < total_procs + total_instances: time.sleep(2)
start_time = time.time()
def unload_query_samples(sample_list):
pass
sut = lg.ConstructFastSUT(issue_queries, flush_queries, process_latencies)
qsl = lg.ConstructQSL(total_samples.value, min(total_samples.value, args.samples_per_query_offline), load_query_samples, unload_query_samples)
log.info("starting {}".format(scenario))
result_dict = {"good": 0, "total": 0, "roc_auc": 0, "scenario": str(scenario)}
torch.set_num_threads(cpus_for_loadgen)
lg.StartTest(sut, qsl, settings)
if not last_timeing:
last_timeing = item_timing
if args.accuracy:
result_dict["good"] = item_good
result_dict["total"] = item_total
result_dict["roc_auc"] = criteo.auc_score(item_results)
final_results = {
"runtime": "pytorch-native-dlrm",
"version": torch.__version__,
"time": int(time.time()),
"cmdline": str(args),
}
add_results(final_results, "{}".format(scenario),
result_dict, last_timeing, time.time() - start_time, args.accuracy)
#inQueue.join()
for _ in range(total_instances):
inQueue.put(None)
for c in consumers:
c.join()
for i in range(numOutQ):
outQueues[i].put(None)
lg.DestroyQSL(qsl)
lg.DestroyFastSUT(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 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()
# 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=args.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),
}
#
# make one pass over the dataset to validate accuracy
#
count = args.count if args.count else ds.get_item_count()
if args.accuracy:
#
# accuracy pass
#
log.info("starting accuracy pass on {} items".format(count))
last_timeing = []
runner = RunnerBase(model, ds, args.threads, post_proc=post_proc)
result_dict = {"good": 0, "total": 0, "scenario": "Accuracy"}
runner.start_run(result_dict, True)
start = time.time()
for idx in range(0, count):
ds.load_query_samples([idx])
data, label = ds.get_samples([idx])
start_one = time.time()
runner.enqueue([idx], [idx], data, label)
last_timeing.append(time.time() - start_one)
runner.finish()
# aggregate results
post_proc.finalize(result_dict, ds, output_dir=os.path.dirname(args.output))
add_results(final_results, "Accuracy", result_dict, last_timeing, time.time() - start)
# warmup
ds.load_query_samples([0])
for _ in range(5):
img, _ = ds.get_samples([0])
_ = backend.predict({backend.inputs[0]: img})
ds.unload_query_samples(None)
for scenario in 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)
def issue_query(query_samples):
# called by loadgen to issue queries
idx = [q.index for q in query_samples]
query_id = [q.id for q in query_samples]
data, label = ds.get_samples(idx)
runner.enqueue(query_id, idx, data, label)
def process_latencies(latencies_ns):
# called by loadgen to show us the recorded latencies
global last_timeing
last_timeing = [t / 1e9 for t in latencies_ns]
settings = lg.TestSettings()
settings.enable_spec_overrides = True
settings.scenario = scenario
settings.mode = lg.TestMode.PerformanceOnly
settings.multi_stream_samples_per_query = 8
if args.time:
# override the time we want to run
settings.enable_spec_overrides = True
settings.override_min_duration_ms = args.time * MILLI_SEC
settings.override_max_duration_ms = args.time * MILLI_SEC
if args.qps:
qps = float(args.qps)
settings.server_target_qps = qps
settings.offline_expected_qps = qps
# mlperf rules - min queries
if scenario == lg.TestScenario.SingleStream:
settings.override_min_query_count = args.queries_single
settings.override_max_query_count = args.queries_single
else:
settings.override_min_query_count = args.queries_multi
settings.override_max_query_count = args.queries_multi
sut = lg.ConstructSUT(issue_query, process_latencies)
qsl = lg.ConstructQSL(count, min(count, 1000), ds.load_query_samples, ds.unload_query_samples)
for target_latency in args.max_latency:
log.info("starting {}, latency={}".format(scenario, target_latency))
settings.single_stream_expected_latency_ns = int(target_latency * NANO_SEC)
settings.override_target_latency_ns = int(target_latency * NANO_SEC)
result_dict = {"good": 0, "total": 0, "scenario": str(scenario)}
runner.start_run(result_dict, False)
lg.StartTest(sut, qsl, settings)
add_results(final_results, "{}-{}".format(scenario, target_latency),
result_dict, last_timeing, time.time() - ds.last_loaded)
runner.finish()
lg.DestroyQSL(qsl)
lg.DestroySUT(sut)
#
# write final results
#
if args.output:
with open(args.output, "w") as f:
json.dump(final_results, f, sort_keys=True, indent=4)
def main():
args = get_args()
print(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(
)
# dataset to use
wanted_dataset, preprocessor, postprocessor, 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=preprocessor,
use_cache=args.cache,
count=args.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),
}
#
# make one pass over the dataset to validate accuracy
#
count = args.count if args.count else ds.get_item_count()
runner = Runner(model, ds, args.threads, post_process=postprocessor)
runner.start_pool()
# warmup
log.info("warmup ...")
ds.load_query_samples([0])
for _ in range(100):
img, _ = ds.get_samples([0])
_ = backend.predict({backend.inputs[0]: img})
def issue_query(query_samples):
idx = [q.index for q in query_samples]
query_id = [q.id for q in query_samples]
data, label = ds.get_samples(idx)
runner.enqueue(query_id, data, label)
sut = lg.ConstructSUT(issue_query)
qsl = lg.ConstructQSL(count, args.time, ds.load_query_samples,
ds.unload_query_samples)
scenarios = [
# lg.TestScenario.SingleStream,
lg.TestScenario.MultiStream,
# lg.TestScenario.Cloud,
# lg.TestScenario.Offline,
]
for scenario in scenarios:
for target_latency in args.max_latency:
log.info("starting {}, latency={}".format(scenario,
target_latency))
settings = lg.TestSettings()
settings.scenario = scenario
settings.mode = lg.TestMode.SubmissionRun
settings.samples_per_query = 4 # FIXME: we don't want to know about this
settings.target_qps = 1000 # FIXME: we don't want to know about this
settings.target_latency_ns = int(target_latency * 1000000000)
result_list = []
result_dict = {"good": 0, "total": 0}
runner.start_run(result_list, result_dict)
start = time.time()
lg.StartTest(sut, qsl, settings)
add_results(final_results, "{}-{}".format(scenario,
target_latency),
result_dict, result_list,
time.time() - start)
runner.finish()
lg.DestroyQSL(qsl)
lg.DestroySUT(sut)
#
# write final results
#
if args.output:
with open(args.output, "w") as f:
json.dump(final_results, f, sort_keys=True, indent=4)
def main():
global last_timeing
args = get_args()
log.info(args)
# find backend
backend = get_backend(args.backend, args.dataset, args.max_ind_range, args.data_sub_sample_rate, args.use_gpu)
# dataset to use
wanted_dataset, pre_proc, post_proc, kwargs = SUPPORTED_DATASETS[args.dataset]
# --count-samples can be used to limit the number of samples used for testing
ds = wanted_dataset(data_path=args.dataset_path,
name=args.dataset,
pre_process=pre_proc, # currently an identity function
use_cache=args.cache, # currently not used
count=args.count_samples,
samples_to_aggregate_fix=args.samples_to_aggregate_fix,
samples_to_aggregate_min=args.samples_to_aggregate_min,
samples_to_aggregate_max=args.samples_to_aggregate_max,
samples_to_aggregate_quantile_file=args.samples_to_aggregate_quantile_file,
samples_to_aggregate_trace_file=args.samples_to_aggregate_trace_file,
test_num_workers=args.test_num_workers,
max_ind_range=args.max_ind_range,
sub_sample_rate=args.data_sub_sample_rate,
mlperf_bin_loader=args.mlperf_bin_loader,
**kwargs)
# load model to backend
model = backend.load(args.model_path, inputs=args.inputs, outputs=args.outputs)
final_results = {
"runtime": model.name(),
"version": model.version(),
"time": int(time.time()),
"cmdline": str(args),
}
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)
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
ds.load_query_samples([0])
for _ in range(5):
batch_dense_X, batch_lS_o, batch_lS_i, _, _ = ds.get_samples([0])
_ = backend.predict(batch_dense_X, batch_lS_o, batch_lS_i)
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(mlperf_conf, args.model_path, args.scenario)
settings.FromConfig(user_conf, args.model_path, 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.duration:
settings.min_duration_ms = args.duration
settings.max_duration_ms = args.duration
if args.target_qps:
settings.server_target_qps = float(args.target_qps)
settings.offline_expected_qps = float(args.target_qps)
if args.count_queries:
settings.min_query_count = args.count_queries
settings.max_query_count = args.count_queries
if args.samples_per_query_multistream:
settings.multi_stream_samples_per_query = args.samples_per_query_multistream
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, args.samples_per_query_offline), ds.load_query_samples, ds.unload_query_samples)
log.info("starting {}".format(scenario))
result_dict = {"good": 0, "total": 0, "roc_auc": 0, "scenario": str(scenario)}
runner.start_run(result_dict, args.accuracy)
lg.StartTest(sut, qsl, settings)
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 num_sockets
global start_time
global item_total
global last_timeing
args = get_args()
log.info(args)
config = os.path.abspath(args.config)
user_config = os.path.abspath(args.user_config)
if not os.path.exists(config):
log.error("{} not found".format(config))
sys.exit(1)
if not os.path.exists(user_config):
log.error("{} not found".format(user_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)
lock = multiprocessing.Lock()
init_counter = multiprocessing.Value("i", 0)
total_samples = multiprocessing.Value("i", 0)
dsQueue = multiprocessing.Queue()
outQueue = multiprocessing.Queue()
inQueue = multiprocessing.JoinableQueue(num_sockets * 4)
consumers = [
Consumer(inQueue, outQueue, dsQueue, lock, init_counter, total_samples,
i, args) for i in range(num_sockets)
]
for c in consumers:
c.start()
# Wait until subprocess ready
while init_counter.value < num_sockets:
time.sleep(2)
# Start response thread
response_worker = threading.Thread(target=response_loadgen,
args=(outQueue, args.accuracy))
response_worker.daemon = True
response_worker.start()
scenario = SCENARIO_MAP[args.scenario]
runner_map = {
lg.TestScenario.Server: QueueRunner,
lg.TestScenario.Offline: QueueRunner
}
runner = runner_map[scenario](inQueue, max_batchsize=args.max_batchsize)
def issue_queries(response_ids, query_sample_indexes):
runner.enqueue(response_ids, query_sample_indexes)
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, args.scenario)
settings.FromConfig(user_config, args.model, args.scenario)
settings.scenario = scenario
settings.mode = lg.TestMode.PerformanceOnly
if args.accuracy:
settings.mode = lg.TestMode.AccuracyOnly
settings.performance_sample_count_override = total_samples.value
if args.find_peak_performance:
settings.mode = lg.TestMode.FindPeakPerformance
if args.duration:
settings.min_duration_ms = args.duration
settings.max_duration_ms = args.duration
if args.target_qps:
settings.server_target_qps = float(args.target_qps)
settings.offline_expected_qps = float(args.target_qps)
if args.count_queries:
settings.min_query_count = args.count_queries
settings.max_query_count = args.count_queries
if args.samples_per_query_multistream:
settings.multi_stream_samples_per_query = args.samples_per_query_multistream
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)
def load_query_samples(sample_list):
# Wait until subprocess ready
global start_time
for _ in range(num_sockets):
dsQueue.put(sample_list)
while init_counter.value < 2 * num_sockets:
time.sleep(2)
start_time = time.time()
def unload_query_samples(sample_list):
pass
import torch
import criteo
sut = lg.ConstructFastSUT(issue_queries, flush_queries, process_latencies)
qsl = lg.ConstructQSL(
total_samples.value,
min(total_samples.value, args.samples_per_query_offline),
load_query_samples, unload_query_samples)
log.info("starting {}".format(scenario))
result_dict = {
"good": 0,
"total": 0,
"roc_auc": 0,
"scenario": str(scenario)
}
lg.StartTest(sut, qsl, settings)
if not last_timeing:
last_timeing = item_timing
if args.accuracy:
result_dict["good"] = item_good
result_dict["total"] = item_total
result_dict["roc_auc"] = criteo.auc_score(item_results)
final_results = {
"runtime": "pytorch-native-dlrm",
"version": torch.__version__,
"time": int(time.time()),
"cmdline": str(args),
}
add_results(final_results, "{}".format(scenario), result_dict,
last_timeing,
time.time() - start_time, args.accuracy)
inQueue.join()
for _ in consumers:
inQueue.put(None)
for c in consumers:
c.join()
outQueue.put(None)
lg.DestroyQSL(qsl)
lg.DestroyFastSUT(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 num_ins
global num_phy_cpus
global in_queue_cnt
global out_queue_cnt
args = get_args()
log.info(args)
num_ins = args.num_instance
num_phy_cpus = args.num_phy_cpus
log.info('Run with {} instance on {} cpus'.format(num_ins, num_phy_cpus))
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)
image_format = 'NCHW'
dataset = "imagenet"
wanted_dataset, pre_proc, post_proc, kwargs = SUPPORTED_DATASETS[dataset]
ds = wanted_dataset(data_path=args.dataset_path,
image_list=args.dataset_list,
name=dataset,
image_format=image_format,
pre_process=pre_proc,
use_cache=args.cache,
cache_dir=args.cache_dir,
count=args.count,
use_int8=args.use_int8_dataset,
num_workers=num_phy_cpus,
**kwargs)
# Establish communication queues
log.info('Start comsumer queue and response thread')
lock = multiprocessing.Lock()
init_counter = multiprocessing.Value("i", 0)
in_queue = multiprocessing.JoinableQueue()
out_queue = multiprocessing.Queue()
ds_queue = multiprocessing.Queue()
# Start consumers
consumers = [Consumer(in_queue, out_queue, ds_queue, lock, init_counter, i, args)
for i in range(num_ins)]
for c in consumers:
c.start()
# Wait until all sub-processors are ready
block_until(init_counter, num_ins, 2)
# Start response thread
response_worker = threading.Thread(
target=response_loadgen, args=(out_queue,))
response_worker.daemon = True
response_worker.start()
scenario = SCENARIO_MAP[args.scenario]
runner = QueueRunner(in_queue, args.batch_size)
def issue_queries(response_ids, query_sample_indexes):
runner.put(response_ids, query_sample_indexes)
def flush_queries():
pass
def process_latencies(latencies_ns):
log.info("Average latency: {}".format(np.mean(latencies_ns)))
log.info("Median latency: {}".format(np.percentile(latencies_ns, 50)))
log.info("90 percentile latency: {}".format(np.percentile(latencies_ns, 90)))
def load_query_samples(sample_list):
for _ in range(num_ins):
ds_queue.put(sample_list)
block_until(init_counter, 2 * num_ins, 2)
def unload_query_samples(sample_list):
pass
settings = lg.TestSettings()
settings.FromConfig(mlperf_conf, "resnet50", args.scenario)
settings.FromConfig(user_conf, "resnet50", 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.qps:
qps = float(args.qps)
settings.server_target_qps = qps
settings.offline_expected_qps = qps
count = ds.get_item_count()
perf_count = 1024
if args.accuracy:
perf_count = count
sut = lg.ConstructFastSUT(issue_queries, flush_queries, process_latencies)
qsl = lg.ConstructQSL(count, perf_count, load_query_samples, unload_query_samples)
log.info("starting {}".format(scenario))
lg.StartTest(sut, qsl, 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('DONE')
for c in consumers:
c.join()
out_queue.put('DONE')
if args.accuracy:
output_file = 'accuracy.txt'
if args.output_file:
output_file = args.output_file
cmd = "python tools/accuracy-imagenet.py " \
"--mlperf-accuracy-file=mlperf_log_accuracy.json " \
"--imagenet-val-file=val_map.txt --output-file={}".format(output_file)
cmd = cmd.split(' ')
subprocess.check_call(cmd)
lg.DestroyQSL(qsl)
lg.DestroyFastSUT(sut)
log.info('Test done.')
