mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-05-25 22:26:24 +00:00
Update profiler tool to support gpt2 and longformer models (#6011)
* support gpt2 and longformer in profiler tool * rename bert_profiler to profiler * Add --basic_optimization to allow user to use basic level of graph optimization * Add --kernel_time_only to filter kernel time and exclude fence time * Add --threshold to filter nodes that with low run time percentage.
This commit is contained in:
Tianlei Wu
GitHub
parent
925879a8b0
commit
51fbe87b9b
2 changed files with 370 additions and 188 deletions
|
|
@ -1,188 +0,0 @@
|
|||
import os
|
||||
import argparse
|
||||
import json
|
||||
import onnx
|
||||
import psutil
|
||||
import numpy
|
||||
|
||||
|
||||
def parse_arguments(argv=None):
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--model', required=True, type=str, help="onnx model path")
|
||||
|
||||
parser.add_argument('--batch_size', required=False, type=int, default=1, help="batch size of input")
|
||||
|
||||
parser.add_argument('--sequence_length', required=False, type=int, default=32, help="sequence length of input")
|
||||
|
||||
parser.add_argument('--samples',
|
||||
required=False,
|
||||
type=int,
|
||||
default=1000,
|
||||
help="number of test cases to be generated")
|
||||
|
||||
parser.add_argument("--thread_num", required=False, type=int, default=-1, help="number of threads to use")
|
||||
|
||||
parser.add_argument('--input_ids_name', required=False, type=str, default=None, help="input name for input ids")
|
||||
parser.add_argument('--segment_ids_name', required=False, type=str, default=None, help="input name for segment ids")
|
||||
parser.add_argument('--input_mask_name',
|
||||
required=False,
|
||||
type=str,
|
||||
default=None,
|
||||
help="input name for attention mask")
|
||||
|
||||
parser.add_argument('--use_dummy_inputs', required=False, action='store_true', help="use dummy inputs")
|
||||
parser.set_defaults(use_dummy_inputs=False)
|
||||
|
||||
parser.add_argument('--use_gpu', required=False, action='store_true', help="use GPU")
|
||||
parser.set_defaults(use_gpu=False)
|
||||
|
||||
parser.add_argument('--verbose', required=False, action='store_true')
|
||||
parser.set_defaults(verbose=False)
|
||||
|
||||
args = parser.parse_args(argv)
|
||||
return args
|
||||
|
||||
|
||||
def create_inputs(model, batch_size, sequence_length, samples, input_ids_name, segment_ids_name, input_mask_name):
|
||||
from bert_test_data import get_bert_inputs, generate_test_data
|
||||
input_ids, segment_ids, input_mask = get_bert_inputs(model, input_ids_name, segment_ids_name, input_mask_name)
|
||||
all_inputs = generate_test_data(batch_size,
|
||||
sequence_length,
|
||||
test_cases=samples,
|
||||
seed=123,
|
||||
verbose=False,
|
||||
input_ids=input_ids,
|
||||
segment_ids=segment_ids,
|
||||
input_mask=input_mask,
|
||||
random_mask_length=False)
|
||||
|
||||
return all_inputs
|
||||
|
||||
|
||||
def run_profile(onnx_model_path,
|
||||
use_gpu,
|
||||
thread_num,
|
||||
batch_size,
|
||||
sequence_length,
|
||||
samples=1,
|
||||
input_ids_name=None,
|
||||
segment_ids_name=None,
|
||||
input_mask_name=None,
|
||||
dummy_inputs=None):
|
||||
from benchmark_helper import create_onnxruntime_session
|
||||
|
||||
session = create_onnxruntime_session(onnx_model_path, use_gpu, num_threads=thread_num, enable_profiling=True)
|
||||
|
||||
if dummy_inputs is None:
|
||||
all_inputs = create_inputs(onnx_model_path, batch_size, sequence_length, samples, input_ids_name,
|
||||
segment_ids_name, input_mask_name)
|
||||
for inputs in all_inputs:
|
||||
_ = session.run(None, inputs)
|
||||
else:
|
||||
for i in range(samples):
|
||||
_ = session.run(None, dummy_inputs)
|
||||
|
||||
profile_file = session.end_profiling()
|
||||
return profile_file
|
||||
|
||||
|
||||
def parse_profile_results(profile_file):
|
||||
print(f"loading profile output {profile_file} ...")
|
||||
|
||||
with open(profile_file, "r") as f:
|
||||
sess_time = json.load(f)
|
||||
|
||||
assert isinstance(sess_time, list)
|
||||
|
||||
node_time = {}
|
||||
node_provider = {}
|
||||
total = 0
|
||||
for item in sess_time:
|
||||
if item["cat"] == "Node" and "args" in item and "provider" in item["args"]:
|
||||
device = "CPU" if item["args"]["provider"] == "CPUExecutionProvider" else "CUDA"
|
||||
if item["name"] not in node_provider:
|
||||
node_provider[item["name"]] = device
|
||||
else:
|
||||
assert node_provider[item["name"]] == device
|
||||
|
||||
if item["name"] in node_time:
|
||||
node_time[item["name"]] += item["dur"]
|
||||
else:
|
||||
node_time[item["name"]] = item["dur"]
|
||||
total += item["dur"]
|
||||
|
||||
results = [f"Duration\tPercentage\tProvider\tName"]
|
||||
for k, v in sorted(node_time.items(), key=lambda x: x[1], reverse=True):
|
||||
results.append(f"{v}\t{v * 100.0 / total:5.2f}\t{node_provider[k]}\t{k}")
|
||||
|
||||
return results
|
||||
|
||||
|
||||
def get_dim_from_type_proto(dim):
|
||||
return getattr(dim, dim.WhichOneof('value')) if type(dim.WhichOneof('value')) == str else None
|
||||
|
||||
|
||||
def get_shape_from_type_proto(type_proto):
|
||||
return [get_dim_from_type_proto(d) for d in type_proto.tensor_type.shape.dim]
|
||||
|
||||
|
||||
def create_dummy_inputs(onnx_model_path, batch_size, sequence_length):
|
||||
from onnx import TensorProto
|
||||
from onnx_model import OnnxModel
|
||||
|
||||
onnx_model = OnnxModel(onnx.load(onnx_model_path))
|
||||
dummy_inputs = {}
|
||||
for input in onnx_model.get_graph_inputs_excluding_initializers():
|
||||
shape = get_shape_from_type_proto(input.type)
|
||||
symbol_dims = []
|
||||
for i, dim in enumerate(shape):
|
||||
if type(dim) == str:
|
||||
symbol_dims.append(i)
|
||||
|
||||
# allowed symbolic dimensions: batch_size and sequence_length
|
||||
if len(symbol_dims) > 2:
|
||||
return None
|
||||
if len(symbol_dims) > 0:
|
||||
shape[symbol_dims[0]] = batch_size
|
||||
if len(symbol_dims) > 1:
|
||||
shape[symbol_dims[1]] = sequence_length
|
||||
|
||||
elem_type = input.type.tensor_type.elem_type
|
||||
assert elem_type in [TensorProto.FLOAT, TensorProto.INT32, TensorProto.INT64]
|
||||
data_type = numpy.float32 if elem_type == TensorProto.FLOAT else (
|
||||
numpy.int64 if elem_type == TensorProto.INT64 else numpy.int32)
|
||||
data = numpy.ones(shape, dtype=data_type)
|
||||
dummy_inputs[input.name] = data
|
||||
|
||||
return dummy_inputs
|
||||
|
||||
|
||||
def run(args):
|
||||
num_threads = args.thread_num if args.thread_num > 0 else psutil.cpu_count(logical=False)
|
||||
|
||||
# Set OMP environment variable before importing onnxruntime. Needed for cpu only, and no impact for onnxruntime-gpu package.
|
||||
if "OMP_NUM_THREADS" not in os.environ:
|
||||
os.environ["OMP_NUM_THREADS"] = str(num_threads)
|
||||
|
||||
dummy_inputs = create_dummy_inputs(args.model, args.batch_size,
|
||||
args.sequence_length) if args.use_dummy_inputs else None
|
||||
profile_file = run_profile(args.model, args.use_gpu, args.thread_num, args.batch_size, args.sequence_length,
|
||||
args.samples, args.input_ids_name, args.segment_ids_name, args.input_mask_name,
|
||||
dummy_inputs)
|
||||
|
||||
return parse_profile_results(profile_file)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
args = parse_arguments()
|
||||
print("Arguments", args)
|
||||
|
||||
from benchmark_helper import setup_logger
|
||||
setup_logger(args.verbose)
|
||||
|
||||
results = run(args)
|
||||
|
||||
print("Results:")
|
||||
print("-" * 64)
|
||||
for line in results:
|
||||
print(line)
|
||||
370
onnxruntime/python/tools/transformers/profiler.py
Normal file
370
onnxruntime/python/tools/transformers/profiler.py
Normal file
|
|
@ -0,0 +1,370 @@
|
|||
import os
|
||||
import argparse
|
||||
import json
|
||||
import onnx
|
||||
import psutil
|
||||
import numpy
|
||||
"""
|
||||
This profiler tool could run a transformer model and print out the kernel time spent on each Node of the model.
|
||||
Example of profiling of longformer model:
|
||||
python profiler.py --model longformer-base-4096_fp32.onnx --batch_size 1 --sequence_length 4096 --global_length 8 --samples 1000 --thread_num 8 --dummy_inputs longformer --use_gpu
|
||||
"""
|
||||
|
||||
|
||||
def parse_arguments(argv=None):
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('-m', '--model', required=True, type=str, help="onnx model path")
|
||||
|
||||
parser.add_argument('-b', '--batch_size', required=False, type=int, default=1, help="batch size of input")
|
||||
|
||||
parser.add_argument('-s',
|
||||
'--sequence_length',
|
||||
required=False,
|
||||
type=int,
|
||||
default=32,
|
||||
help="sequence length of input")
|
||||
|
||||
parser.add_argument('--past_sequence_length',
|
||||
required=False,
|
||||
type=int,
|
||||
default=1,
|
||||
help="past sequence length for gpt2")
|
||||
|
||||
parser.add_argument('--global_length',
|
||||
required=False,
|
||||
type=int,
|
||||
default=1,
|
||||
help="number of global tokens for longformer")
|
||||
|
||||
parser.add_argument(
|
||||
'--samples',
|
||||
required=False,
|
||||
type=int,
|
||||
default=1000,
|
||||
help="number of samples to test. Set it large enough to reduce the variance of performance result.")
|
||||
|
||||
parser.add_argument(
|
||||
'--threshold',
|
||||
required=False,
|
||||
type=float,
|
||||
default=0,
|
||||
help=
|
||||
"Threshold of ratio of run time of a node among all nodes. Nodes that nodes with lower ratio will not be in detail results."
|
||||
)
|
||||
|
||||
parser.add_argument("--thread_num", required=False, type=int, default=-1, help="number of threads to use")
|
||||
|
||||
parser.add_argument('--input_ids_name',
|
||||
required=False,
|
||||
type=str,
|
||||
default=None,
|
||||
help="input name for input ids, for bert")
|
||||
parser.add_argument('--segment_ids_name',
|
||||
required=False,
|
||||
type=str,
|
||||
default=None,
|
||||
help="input name for segment ids, for bert")
|
||||
parser.add_argument('--input_mask_name',
|
||||
required=False,
|
||||
type=str,
|
||||
default=None,
|
||||
help="input name for attention mask, for bert")
|
||||
|
||||
parser.add_argument('--dummy_inputs',
|
||||
required=False,
|
||||
default='default',
|
||||
choices=['bert', 'gpt2', 'longformer', 'default'],
|
||||
help="Way to create dummy inputs. If your model is not aa")
|
||||
|
||||
parser.add_argument('-g', '--use_gpu', required=False, action='store_true', help="use GPU")
|
||||
parser.set_defaults(use_gpu=False)
|
||||
|
||||
parser.add_argument(
|
||||
'--basic_optimization',
|
||||
required=False,
|
||||
action='store_true',
|
||||
help="Enable only basic graph optimizations. By default, all optimizations are enabled in OnnxRuntime")
|
||||
parser.set_defaults(basic_optimization=False)
|
||||
|
||||
parser.add_argument('--kernel_time_only',
|
||||
required=False,
|
||||
action='store_true',
|
||||
help="Only include the kernel time and no fence time")
|
||||
parser.set_defaults(kernel_time_only=False)
|
||||
|
||||
parser.add_argument('-v', '--verbose', required=False, action='store_true')
|
||||
parser.set_defaults(verbose=False)
|
||||
|
||||
args = parser.parse_args(argv)
|
||||
return args
|
||||
|
||||
|
||||
def create_bert_inputs(model, batch_size, sequence_length, samples, input_ids_name, segment_ids_name, input_mask_name):
|
||||
from bert_test_data import get_bert_inputs, generate_test_data
|
||||
input_ids, segment_ids, input_mask = get_bert_inputs(model, input_ids_name, segment_ids_name, input_mask_name)
|
||||
all_inputs = generate_test_data(batch_size,
|
||||
sequence_length,
|
||||
test_cases=samples,
|
||||
seed=123,
|
||||
verbose=False,
|
||||
input_ids=input_ids,
|
||||
segment_ids=segment_ids,
|
||||
input_mask=input_mask,
|
||||
random_mask_length=False)
|
||||
|
||||
return all_inputs
|
||||
|
||||
|
||||
def run_profile(onnx_model_path, use_gpu, basic_optimization, thread_num, batch_size, sequence_length, all_inputs):
|
||||
from benchmark_helper import create_onnxruntime_session
|
||||
|
||||
session = create_onnxruntime_session(onnx_model_path,
|
||||
use_gpu,
|
||||
enable_all_optimization=not basic_optimization,
|
||||
num_threads=thread_num,
|
||||
enable_profiling=True)
|
||||
|
||||
for inputs in all_inputs:
|
||||
_ = session.run(None, inputs)
|
||||
|
||||
profile_file = session.end_profiling()
|
||||
return profile_file
|
||||
|
||||
|
||||
def load_profile_json(profile_file):
|
||||
print(f"loading profile output {profile_file} ...")
|
||||
|
||||
with open(profile_file, "r") as f:
|
||||
sess_time = json.load(f)
|
||||
|
||||
assert isinstance(sess_time, list)
|
||||
return sess_time
|
||||
|
||||
|
||||
def parse_profile_results(sess_time, kernel_time_only=False, threshold=0):
|
||||
node_time = {}
|
||||
node_provider = {}
|
||||
total = 0
|
||||
for item in sess_time:
|
||||
if item["cat"] == "Node" and "dur" in item and "args" in item and "op_name" in item["args"]:
|
||||
if "provider" in item["args"]:
|
||||
device = "CPU" if item["args"]["provider"] == "CPUExecutionProvider" else "CUDA"
|
||||
if item["name"] not in node_provider:
|
||||
node_provider[item["name"]] = device
|
||||
else:
|
||||
assert node_provider[item["name"]] == device
|
||||
elif kernel_time_only:
|
||||
continue
|
||||
|
||||
if item["name"] in node_time:
|
||||
node_time[item["name"]] += item["dur"]
|
||||
else:
|
||||
node_time[item["name"]] = item["dur"]
|
||||
total += item["dur"]
|
||||
|
||||
results = []
|
||||
if (threshold > 0):
|
||||
results.append(f"Threshold of Percentage > {threshold:.2f}%")
|
||||
|
||||
results.append(f"Duration\tPercentage\tProvider\tName")
|
||||
for k, v in sorted(node_time.items(), key=lambda x: x[1], reverse=True):
|
||||
provider = node_provider[k] if k in node_provider else ""
|
||||
ratio = v / total
|
||||
if ratio > threshold:
|
||||
results.append(f"{v}\t{ratio * 100.0:5.2f}\t{provider}\t{k}")
|
||||
|
||||
return results
|
||||
|
||||
|
||||
def group_profile_results(sess_time, kernel_time_only=False, threshold=0):
|
||||
op_time = {}
|
||||
op_records = {}
|
||||
op_cpu_time = {}
|
||||
op_cpu_records = {}
|
||||
total = 0
|
||||
for item in sess_time:
|
||||
if item["cat"] == "Node" and "dur" in item and "args" in item and "op_name" in item["args"]:
|
||||
if kernel_time_only and "provider" not in item["args"]:
|
||||
continue
|
||||
|
||||
op_name = item["args"]["op_name"]
|
||||
if op_name in op_time:
|
||||
op_time[op_name] += item["dur"]
|
||||
op_records[op_name] += 1
|
||||
else:
|
||||
op_time[op_name] = item["dur"]
|
||||
op_records[op_name] = 1
|
||||
|
||||
total += item["dur"]
|
||||
|
||||
is_cpu = "provider" in item["args"] and item["args"]["provider"] == "CPUExecutionProvider"
|
||||
if is_cpu:
|
||||
if op_name in op_cpu_time:
|
||||
op_cpu_time[op_name] += item["dur"]
|
||||
op_cpu_records[op_name] += 1
|
||||
else:
|
||||
op_cpu_time[op_name] = item["dur"]
|
||||
op_cpu_records[op_name] = 1
|
||||
|
||||
results = [f"Duration\tPercentage\tCalls\tCpu_Duration\tCpu_Calls\tName"]
|
||||
for k, v in sorted(op_time.items(), key=lambda x: x[1], reverse=True):
|
||||
calls = op_records[k]
|
||||
cpu_time = op_cpu_time[k] if k in op_cpu_time else 0
|
||||
cpu_calls = op_cpu_records[k] if k in op_cpu_records else 0
|
||||
ratio = v / total
|
||||
if ratio > threshold:
|
||||
results.append(f"{v}\t{ratio * 100.0:5.2f}\t{calls}\t{cpu_time}\t{cpu_calls}\t{k}")
|
||||
return results
|
||||
|
||||
|
||||
def get_dim_from_type_proto(dim):
|
||||
return getattr(dim, dim.WhichOneof('value')) if type(dim.WhichOneof('value')) == str else None
|
||||
|
||||
|
||||
def get_shape_from_type_proto(type_proto):
|
||||
return [get_dim_from_type_proto(d) for d in type_proto.tensor_type.shape.dim]
|
||||
|
||||
|
||||
def create_dummy_inputs(onnx_model_path, batch_size, sequence_length, samples):
|
||||
from onnx import TensorProto
|
||||
from onnx_model import OnnxModel
|
||||
|
||||
onnx_model = OnnxModel(onnx.load(onnx_model_path))
|
||||
dummy_inputs = {}
|
||||
for input in onnx_model.get_graph_inputs_excluding_initializers():
|
||||
shape = get_shape_from_type_proto(input.type)
|
||||
symbol_dims = []
|
||||
for i, dim in enumerate(shape):
|
||||
if type(dim) == str:
|
||||
symbol_dims.append(i)
|
||||
|
||||
# allowed symbolic dimensions: batch_size and sequence_length
|
||||
if len(symbol_dims) > 2:
|
||||
return None
|
||||
if len(symbol_dims) > 0:
|
||||
shape[symbol_dims[0]] = batch_size
|
||||
if len(symbol_dims) > 1:
|
||||
shape[symbol_dims[1]] = sequence_length
|
||||
|
||||
elem_type = input.type.tensor_type.elem_type
|
||||
assert elem_type in [TensorProto.FLOAT, TensorProto.INT32, TensorProto.INT64]
|
||||
data_type = numpy.float32 if elem_type == TensorProto.FLOAT else (
|
||||
numpy.int64 if elem_type == TensorProto.INT64 else numpy.int32)
|
||||
data = numpy.ones(shape, dtype=data_type)
|
||||
dummy_inputs[input.name] = data
|
||||
|
||||
all_inputs = [dummy_inputs for _ in range(samples)]
|
||||
return all_inputs
|
||||
|
||||
|
||||
def create_gpt2_inputs(onnx_model_path, batch_size, sequence_length, past_sequence_length, samples):
|
||||
from onnx import TensorProto
|
||||
from onnx_model import OnnxModel
|
||||
|
||||
onnx_model = OnnxModel(onnx.load(onnx_model_path))
|
||||
# The symbolic name shall be same as those used in Gpt2Helper.export_onnx(...) function.
|
||||
symbols = {
|
||||
'batch_size': batch_size,
|
||||
'seq_len': sequence_length,
|
||||
'past_seq_len': past_sequence_length,
|
||||
'total_seq_len': sequence_length + past_sequence_length
|
||||
}
|
||||
|
||||
dummy_inputs = {}
|
||||
for input in onnx_model.get_graph_inputs_excluding_initializers():
|
||||
shape = get_shape_from_type_proto(input.type)
|
||||
for i, dim in enumerate(shape):
|
||||
if type(dim) == str and dim not in symbols.keys():
|
||||
raise RuntimeError(f"symbol is not supported: {dim}")
|
||||
else:
|
||||
shape[i] = symbols[dim]
|
||||
|
||||
elem_type = input.type.tensor_type.elem_type
|
||||
assert elem_type in [TensorProto.FLOAT, TensorProto.INT32, TensorProto.INT64]
|
||||
data_type = numpy.float32 if elem_type == TensorProto.FLOAT else (
|
||||
numpy.int64 if elem_type == TensorProto.INT64 else numpy.int32)
|
||||
data = numpy.ones(shape, dtype=data_type)
|
||||
dummy_inputs[input.name] = data
|
||||
|
||||
all_inputs = [dummy_inputs for _ in range(samples)]
|
||||
return all_inputs
|
||||
|
||||
|
||||
def create_longformer_inputs(onnx_model_path, batch_size, sequence_length, global_length, samples):
|
||||
from onnx import TensorProto
|
||||
from onnx_model import OnnxModel
|
||||
|
||||
onnx_model = OnnxModel(onnx.load(onnx_model_path))
|
||||
symbols = {'batch_size': batch_size, 'sequence_length': sequence_length}
|
||||
|
||||
dummy_inputs = {}
|
||||
for input in onnx_model.get_graph_inputs_excluding_initializers():
|
||||
shape = get_shape_from_type_proto(input.type)
|
||||
for i, dim in enumerate(shape):
|
||||
if type(dim) == str and dim not in symbols.keys():
|
||||
raise RuntimeError(f"symbol is not supported: {dim}")
|
||||
else:
|
||||
shape[i] = symbols[dim]
|
||||
|
||||
elem_type = input.type.tensor_type.elem_type
|
||||
assert elem_type in [TensorProto.FLOAT, TensorProto.INT32, TensorProto.INT64]
|
||||
data_type = numpy.float32 if elem_type == TensorProto.FLOAT else (
|
||||
numpy.int64 if elem_type == TensorProto.INT64 else numpy.int32)
|
||||
|
||||
if "global" in input.name:
|
||||
data = numpy.zeros(shape, dtype=data_type)
|
||||
data[:, :global_length] = 1
|
||||
else:
|
||||
data = numpy.ones(shape, dtype=data_type)
|
||||
dummy_inputs[input.name] = data
|
||||
|
||||
all_inputs = [dummy_inputs for _ in range(samples)]
|
||||
return all_inputs
|
||||
|
||||
|
||||
def run(args):
|
||||
num_threads = args.thread_num if args.thread_num > 0 else psutil.cpu_count(logical=False)
|
||||
|
||||
# Set OMP environment variable before importing onnxruntime. Needed for cpu only, and no impact for onnxruntime-gpu package.
|
||||
if "OMP_NUM_THREADS" not in os.environ:
|
||||
os.environ["OMP_NUM_THREADS"] = str(num_threads)
|
||||
|
||||
all_inputs = None
|
||||
if args.dummy_inputs == 'bert':
|
||||
all_inputs = create_bert_inputs(args.model, args.batch_size, args.sequence_length, args.samples,
|
||||
args.input_ids_name, args.segment_ids_name, args.input_mask_name)
|
||||
elif args.dummy_inputs == 'gpt2':
|
||||
all_inputs = create_gpt2_inputs(args.model, args.batch_size, args.sequence_length, args.past_sequence_length,
|
||||
args.samples)
|
||||
elif args.dummy_inputs == 'longformer':
|
||||
all_inputs = create_longformer_inputs(args.model, args.batch_size, args.sequence_length, args.global_length,
|
||||
args.samples)
|
||||
else: # default
|
||||
all_inputs = create_dummy_inputs(args.model, args.batch_size, args.sequence_length, args.samples)
|
||||
|
||||
profile_file = run_profile(args.model, args.use_gpu, args.basic_optimization, args.thread_num, args.batch_size,
|
||||
args.sequence_length, all_inputs)
|
||||
|
||||
profile_records = load_profile_json(profile_file)
|
||||
|
||||
lines = parse_profile_results(profile_records, args.kernel_time_only, args.threshold)
|
||||
|
||||
lines.append("-" * 64)
|
||||
lines += group_profile_results(profile_records, args.kernel_time_only, args.threshold)
|
||||
|
||||
return lines
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
args = parse_arguments()
|
||||
print("Arguments", args)
|
||||
|
||||
from benchmark_helper import setup_logger
|
||||
setup_logger(args.verbose)
|
||||
|
||||
results = run(args)
|
||||
|
||||
print("Results:")
|
||||
print("-" * 64)
|
||||
for line in results:
|
||||
print(line)
|
||||
Loading…
Reference in a new issue