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Update bert_perf_test to test inputs with different padding ratio (#16963)

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Add --average_sequence_length and --random_sequence_length so that we
can test the performance of model on different padding ratio.
This commit is contained in:
Tianlei Wu 2023-08-02 10:28:39 -07:00 committed by GitHub
parent c392fdeb1b
commit 76aff63f37
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GPG key ID: 4AEE18F83AFDEB23
3 changed files with 122 additions and 21 deletions
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30
onnxruntime/python/tools/transformers/bert_perf_test.py
View file

@ -44,6 +44,8 @@ class TestSetting:
seed: int
verbose: bool
log_severity: int
average_sequence_length: int
random_sequence_length: bool
@dataclass
@ -365,7 +367,8 @@ def run_performance(model_setting, test_setting, perf_results):
input_ids,
segment_ids,
input_mask,
random_mask_length=False,
test_setting.average_sequence_length,
test_setting.random_sequence_length,
)
run_perf_tests(model_setting, test_setting, perf_results, all_inputs)
@ -473,6 +476,7 @@ def parse_arguments():
default=None,
help="input name for input ids",
)
parser.add_argument(
"--segment_ids_name",
required=False,
@ -480,6 +484,7 @@ def parse_arguments():
default=None,
help="input name for segment ids",
)
parser.add_argument(
"--input_mask_name",
required=False,
@ -494,6 +499,7 @@ def parse_arguments():
type=str,
help="tuning results (json) to be loaded before benchmark",
)
parser.add_argument(
"--output_tuning_results",
default=None,
@ -501,6 +507,23 @@ def parse_arguments():
help="tuning results (json) to be saved after benchmark",
)
parser.add_argument(
"-a",
"--average_sequence_length",
default=-1,
type=int,
help="average sequence length excluding padding",
)
parser.add_argument(
"-r",
"--random_sequence_length",
required=False,
action="store_true",
help="use uniform random instead of fixed sequence length",
)
parser.set_defaults(random_sequence_length=False)
args = parser.parse_args()
return args
@ -511,6 +534,9 @@ def main():
if args.test_times == 0:
args.test_times = max(1, int(1000 / args.samples))
if args.average_sequence_length <= 0:
args.average_sequence_length = args.sequence_length
manager = multiprocessing.Manager()
perf_results = manager.dict()
@ -541,6 +567,8 @@ def main():
args.seed,
args.verbose,
args.log_severity,
args.average_sequence_length,
args.random_sequence_length,
)
print("test setting", test_setting)

75
onnxruntime/python/tools/transformers/bert_test_data.py
View file

@ -78,7 +78,8 @@ def fake_input_mask_data(
input_mask: TensorProto,
batch_size: int,
sequence_length: int,
random_mask_length: bool,
average_sequence_length: int,
random_sequence_length: bool,
) -> np.ndarray:
"""Create input tensor based on the graph input of segment_ids.
@ -86,7 +87,8 @@ def fake_input_mask_data(
input_mask (TensorProto): graph input of the attention mask input tensor
batch_size (int): batch size
sequence_length (int): sequence length
random_mask_length (bool): whether mask according to random padding length
average_sequence_length (int): average sequence length excluding paddings
random_sequence_length (bool): whether use uniform random number for sequence length
Returns:
np.ndarray: the input tensor created
@ -98,13 +100,20 @@ def fake_input_mask_data(
TensorProto.INT64,
]
if random_mask_length:
actual_seq_len = random.randint(int(sequence_length * 2 / 3), sequence_length)
data = np.zeros((batch_size, sequence_length), dtype=np.int32)
temp = np.ones((batch_size, actual_seq_len), dtype=np.int32)
data[: temp.shape[0], : temp.shape[1]] = temp
data = np.zeros((batch_size, sequence_length), dtype=np.int32)
if random_sequence_length:
for i in range(batch_size):
# We use uniform distribution, so we find proper minimal and maximal so that the average is in the middle.
if 2 * average_sequence_length > sequence_length:
actual_seq_len = random.randint(2 * average_sequence_length - sequence_length, sequence_length)
else:
actual_seq_len = random.randint(1, 2 * average_sequence_length - 1)
for j in range(actual_seq_len):
data[i, j] = 1
else:
data = np.ones((batch_size, sequence_length), dtype=np.int32)
temp = np.ones((batch_size, average_sequence_length), dtype=np.int32)
data[: temp.shape[0], : temp.shape[1]] = temp
if input_mask.type.tensor_type.elem_type == TensorProto.FLOAT:
data = np.float32(data)
@ -149,7 +158,8 @@ def fake_test_data(
input_ids: TensorProto,
segment_ids: TensorProto,
input_mask: TensorProto,
random_mask_length: bool,
average_sequence_length: int,
random_sequence_length: bool,
):
"""Create given number of input data for testing
@ -163,7 +173,8 @@ def fake_test_data(
input_ids (TensorProto): graph input of input IDs
segment_ids (TensorProto): graph input of token type IDs
input_mask (TensorProto): graph input of attention mask
random_mask_length (bool): whether mask random number of words at the end
average_sequence_length (int): average sequence length excluding paddings
random_sequence_length (bool): whether use uniform random number for sequence length
Returns:
List[Dict[str,numpy.ndarray]]: list of test cases, where each test case is a dictionary
@ -183,7 +194,9 @@ def fake_test_data(
inputs[segment_ids.name] = fake_segment_ids_data(segment_ids, batch_size, sequence_length)
if input_mask:
inputs[input_mask.name] = fake_input_mask_data(input_mask, batch_size, sequence_length, random_mask_length)
inputs[input_mask.name] = fake_input_mask_data(
input_mask, batch_size, sequence_length, average_sequence_length, random_sequence_length
)
if verbose and len(all_inputs) == 0:
print("Example inputs", inputs)
@ -200,7 +213,8 @@ def generate_test_data(
input_ids: TensorProto,
segment_ids: TensorProto,
input_mask: TensorProto,
random_mask_length: bool,
average_sequence_length: int,
random_sequence_length: bool,
):
"""Create given number of input data for testing
@ -213,7 +227,8 @@ def generate_test_data(
input_ids (TensorProto): graph input of input IDs
segment_ids (TensorProto): graph input of token type IDs
input_mask (TensorProto): graph input of attention mask
random_mask_length (bool): whether mask random number of words at the end
average_sequence_length (int): average sequence length excluding paddings
random_sequence_length (bool): whether use uniform random number for sequence length
Returns:
List[Dict[str,numpy.ndarray]]: list of test cases, where each test case is a dictionary
@ -230,7 +245,8 @@ def generate_test_data(
input_ids,
segment_ids,
input_mask,
random_mask_length,
average_sequence_length,
random_sequence_length,
)
if len(all_inputs) != test_cases:
print("Failed to create test data for test.")
@ -441,6 +457,23 @@ def parse_arguments():
)
parser.set_defaults(only_input_tensors=False)
parser.add_argument(
"-a",
"--average_sequence_length",
default=-1,
type=int,
help="average sequence length excluding padding",
)
parser.add_argument(
"-r",
"--random_sequence_length",
required=False,
action="store_true",
help="use uniform random instead of fixed sequence length",
)
parser.set_defaults(random_sequence_length=False)
args = parser.parse_args()
return args
@ -457,6 +490,8 @@ def create_and_save_test_data(
segment_ids_name: Optional[str],
input_mask_name: Optional[str],
only_input_tensors: bool,
average_sequence_length: int,
random_sequence_length: bool,
):
"""Create test data for a model, and save test data to a directory.
@ -471,7 +506,9 @@ def create_and_save_test_data(
input_ids_name (str): graph input name of input_ids
segment_ids_name (str): graph input name of segment_ids
input_mask_name (str): graph input name of input_mask
only_input_tensors (bool): only save input tensors
only_input_tensors (bool): only save input tensors,
average_sequence_length (int): average sequence length excluding paddings
random_sequence_length (bool): whether use uniform random number for sequence length
"""
input_ids, segment_ids, input_mask = get_bert_inputs(model, input_ids_name, segment_ids_name, input_mask_name)
@ -484,7 +521,8 @@ def create_and_save_test_data(
input_ids,
segment_ids,
input_mask,
random_mask_length=False,
average_sequence_length,
random_sequence_length,
)
for i, inputs in enumerate(all_inputs):
@ -511,6 +549,9 @@ def create_and_save_test_data(
def main():
args = parse_arguments()
if args.average_sequence_length <= 0:
args.average_sequence_length = args.sequence_length
output_dir = args.output_dir
if output_dir is None:
# Default output directory is a sub-directory under the directory of model.
@ -536,6 +577,8 @@ def main():
args.segment_ids_name,
args.input_mask_name,
args.only_input_tensors,
args.average_sequence_length,
args.random_sequence_length,
)
print("Test data is saved to directory:", output_dir)

38
onnxruntime/python/tools/transformers/models/longformer/generate_test_data.py
View file

@ -42,6 +42,23 @@ def parse_arguments():
help="maximum sequence length of input",
)
parser.add_argument(
"-a",
"--average_sequence_length",
default=-1,
type=int,
help="average sequence length excluding padding",
)
parser.add_argument(
"-r",
"--random_sequence_length",
required=False,
action="store_true",
help="use uniform random instead of fixed sequence length",
)
parser.set_defaults(random_sequence_length=False)
parser.add_argument(
"--global_tokens",
required=False,
@ -190,7 +207,8 @@ def fake_test_data(
input_mask,
global_mask,
num_global_tokens,
random_mask_length=False,
average_sequence_length,
random_sequence_length,
):
"""
Generate fake input data for test.
@ -206,7 +224,9 @@ def fake_test_data(
inputs = {input_ids.name: input_1}
if input_mask:
inputs[input_mask.name] = fake_input_mask_data(input_mask, batch_size, sequence_length, random_mask_length)
inputs[input_mask.name] = fake_input_mask_data(
input_mask, batch_size, sequence_length, average_sequence_length, random_sequence_length
)
if global_mask:
inputs[global_mask.name] = fake_global_mask_data(
@ -230,7 +250,8 @@ def generate_test_data(
input_mask,
global_mask,
num_global_tokens,
random_mask_length=False,
average_sequence_length,
random_sequence_length,
):
dictionary_size = 10000
all_inputs = fake_test_data(
@ -244,7 +265,8 @@ def generate_test_data(
input_mask,
global_mask,
num_global_tokens,
random_mask_length,
average_sequence_length,
random_sequence_length,
)
if len(all_inputs) != test_cases:
print("Failed to create test data for test.")
@ -263,6 +285,8 @@ def create_longformer_test_data(
input_mask_name,
global_mask_name,
num_global_tokens,
average_sequence_length,
random_sequence_length,
):
input_ids, input_mask, global_mask = get_longformer_inputs(model, input_ids_name, input_mask_name, global_mask_name)
all_inputs = generate_test_data(
@ -275,6 +299,8 @@ def create_longformer_test_data(
input_mask,
global_mask,
num_global_tokens,
average_sequence_length,
random_sequence_length,
)
for i, inputs in enumerate(all_inputs):
@ -299,6 +325,9 @@ def main():
else:
print("Directory existed. test data files will be overwritten.")
if args.average_sequence_length <= 0:
args.average_sequence_length = args.sequence_length
create_longformer_test_data(
args.model,
output_dir,
@ -311,6 +340,7 @@ def main():
args.input_mask_name,
args.global_mask_name,
args.global_tokens,
args.average_sequence_length,
)
print("Test data is saved to directory:", output_dir)