def test_trace_memory(self):
MODEL_ID = "sshleifer/tiny-gpt2"
def _check_summary_is_not_empty(summary):
self.assertTrue(hasattr(summary, "sequential"))
self.assertTrue(hasattr(summary, "cumulative"))
self.assertTrue(hasattr(summary, "current"))
self.assertTrue(hasattr(summary, "total"))
with tempfile.TemporaryDirectory() as tmp_dir:
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=True,
no_inference=False,
sequence_lengths=[8],
batch_sizes=[1],
log_filename=os.path.join(tmp_dir, "log.txt"),
log_print=True,
trace_memory_line_by_line=True,
no_multi_process=True,
)
benchmark = PyTorchBenchmark(benchmark_args)
result = benchmark.run()
_check_summary_is_not_empty(result.inference_summary)
_check_summary_is_not_empty(result.train_summary)
self.assertTrue(Path(os.path.join(tmp_dir, "log.txt")).exists())
def test_save_csv_files(self):
MODEL_ID = "sshleifer/tiny-gpt2"
with tempfile.TemporaryDirectory() as tmp_dir:
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=True,
no_inference=False,
save_to_csv=True,
sequence_lengths=[8],
batch_sizes=[1],
inference_time_csv_file=os.path.join(tmp_dir, "inf_time.csv"),
train_memory_csv_file=os.path.join(tmp_dir, "train_mem.csv"),
inference_memory_csv_file=os.path.join(tmp_dir, "inf_mem.csv"),
train_time_csv_file=os.path.join(tmp_dir, "train_time.csv"),
env_info_csv_file=os.path.join(tmp_dir, "env.csv"),
no_multi_process=True,
)
benchmark = PyTorchBenchmark(benchmark_args)
benchmark.run()
self.assertTrue(
Path(os.path.join(tmp_dir, "inf_time.csv")).exists())
self.assertTrue(
Path(os.path.join(tmp_dir, "train_time.csv")).exists())
self.assertTrue(
Path(os.path.join(tmp_dir, "inf_mem.csv")).exists())
self.assertTrue(
Path(os.path.join(tmp_dir, "train_mem.csv")).exists())
self.assertTrue(Path(os.path.join(tmp_dir, "env.csv")).exists())
def test_train_no_configs(self):
MODEL_ID = "sshleifer/tiny-gpt2"
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID], training=True, no_inference=True, sequence_lengths=[8], batch_sizes=[1]
)
benchmark = PyTorchBenchmark(benchmark_args)
results = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result)
self.check_results_dict_not_empty(results.memory_train_result)
def test_train_encoder_decoder_with_configs(self):
MODEL_ID = "sshleifer/tinier_bart"
config = AutoConfig.from_pretrained(MODEL_ID)
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID], training=True, no_inference=True, sequence_lengths=[8], batch_sizes=[1]
)
benchmark = PyTorchBenchmark(benchmark_args, configs=[config])
results = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result)
self.check_results_dict_not_empty(results.memory_train_result)
def test_inference_with_configs(self):
MODEL_ID = "sshleifer/tiny-gpt2"
config = GPT2Config.from_pretrained(MODEL_ID)
benchmark_args = PyTorchBenchmarkArguments(models=[MODEL_ID],
training=False,
no_inference=False,
sequence_lengths=[8],
batch_sizes=[1])
benchmark = PyTorchBenchmark(benchmark_args, configs=[config])
results = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def test_inference_fp16(self):
MODEL_ID = "sshleifer/tiny-gpt2"
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=False,
no_inference=False,
fp16=True,
sequence_lengths=[8],
batch_sizes=[1],
no_multi_process=True,
)
benchmark = PyTorchBenchmark(benchmark_args)
results = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def test_inference_no_configs_only_pretrain(self):
MODEL_ID = "sshleifer/tiny-distilbert-base-uncased-finetuned-sst-2-english"
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=False,
no_inference=False,
sequence_lengths=[8],
batch_sizes=[1],
no_multi_process=True,
only_pretrain_model=True,
)
benchmark = PyTorchBenchmark(benchmark_args)
results = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def test_inference_no_configs_only_pretrain(self):
MODEL_ID = "sgugger/tiny-distilbert-classification"
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=False,
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
multi_process=False,
only_pretrain_model=True,
)
benchmark = PyTorchBenchmark(benchmark_args)
results = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def test_inference_no_model_no_architectures(self):
MODEL_ID = "sshleifer/tiny-gpt2"
config = AutoConfig.from_pretrained(MODEL_ID)
# set architectures equal to `None`
config.architectures = None
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=True,
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
multi_process=False,
)
benchmark = PyTorchBenchmark(benchmark_args, configs=[config])
results = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def main():
parser = HfArgumentParser(PyTorchBenchmarkArguments)
try:
benchmark_args = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
arg_error_msg = "Arg --no_{0} is no longer used, please use --no-{0} instead."
begin_error_msg = " ".join(str(e).split(" ")[:-1])
full_error_msg = ""
depreciated_args = eval(str(e).split(" ")[-1])
wrong_args = []
for arg in depreciated_args:
# arg[2:] removes '--'
if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
# arg[5:] removes '--no_'
full_error_msg += arg_error_msg.format(arg[5:])
else:
wrong_args.append(arg)
if len(wrong_args) > 0:
full_error_msg = full_error_msg + begin_error_msg + str(wrong_args)
raise ValueError(full_error_msg)
benchmark = PyTorchBenchmark(args=benchmark_args)
benchmark.run()
def main():
parser = HfArgumentParser(PyTorchBenchmarkArguments)
benchmark_args = parser.parse_args_into_dataclasses()[0]
benchmark = PyTorchBenchmark(args=benchmark_args)
benchmark.run()
args = [
'--models', model, '--batch_sizes',
'{}'.format(batch_size), '--sequence_lengths',
'{}'.format(seq_length), '--inference_time_csv_file',
'{}.inference_time.csv'.format(prefix),
'--inference_memory_csv_file',
'{}.inference_memory.csv'.format(prefix),
'--no_env_print', '--repeat', '3', '--save_to_csv'
]
if use_fp16:
args.append('--fp16')
if torch_script:
args.append('--torchscript')
benchmark_args = parser.parse_args_into_dataclasses(
args)[0]
benchmark = PyTorchBenchmark(args=benchmark_args)
p = Process(target=benchmark.run)
p.start()
p.join()
try:
inference_time_df = pd.read_csv(
'{}.inference_time.csv'.format(prefix))
inference_memory_df = pd.read_csv(
'{}.inference_memory.csv'.format(prefix))
latency = inference_time_df['result'][0]
memory = inference_memory_df['result'][0]
os.remove('{}.inference_time.csv'.format(prefix))
os.remove('{}.inference_memory.csv'.format(prefix))
except Exception:
latency = math.nan
memory = math.nan