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import argparse
import sys
import timeit
import torch
from torch.utils.benchmark import Timer
PARALLEL_TASKS_NUM = 4
INTERNAL_ITER = None
def loop_workload(x):
for i in range(INTERNAL_ITER):
x = torch.mm(x, x)
return x
def parallel_workload(x):
def parallel_task(x):
for i in range(int(INTERNAL_ITER / PARALLEL_TASKS_NUM)):
x = torch.mm(x, x)
return x
futs = []
for i in range(PARALLEL_TASKS_NUM):
futs.append(torch.jit._fork(parallel_task, x))
for i in range(PARALLEL_TASKS_NUM):
torch.jit._wait(futs[i])
return x
if __name__ == '__main__':
torch._C._set_graph_executor_optimize(False)
parser = argparse.ArgumentParser(
description='Profiler benchmark')
parser.add_argument('--with_cuda', action='store_true')
parser.add_argument('--with_stack', action='store_true')
parser.add_argument('--use_script', action='store_true')
parser.add_argument('--use_kineto', action='store_true')
parser.add_argument('--profiling_tensor_size', default=1, type=int)
parser.add_argument('--workload', default='loop', type=str)
parser.add_argument('--internal_iter', default=256, type=int)
parser.add_argument('--timer_min_run_time', default=10, type=int)
parser.add_argument('--cuda_only', action='store_true')
args = parser.parse_args()
if args.with_cuda and not torch.cuda.is_available():
print("No CUDA available")
sys.exit()
print("Payload: {}, {} iterations; timer min. runtime = {}\n".format(
args.workload, args.internal_iter, args.timer_min_run_time))
INTERNAL_ITER = args.internal_iter
for profiling_enabled in [False, True]:
print("Profiling {}, tensor size {}x{}, use cuda: {}, use kineto: {}, with stacks: {}, use script: {}".format(
"enabled" if profiling_enabled else "disabled",
args.profiling_tensor_size,
args.profiling_tensor_size,
args.with_cuda,
args.use_kineto,
args.with_stack,
args.use_script))
input_x = torch.rand(
args.profiling_tensor_size,
args.profiling_tensor_size)
if args.with_cuda:
input_x = input_x.cuda()
workload = None
assert args.workload in ["loop", "parallel"]
if args.workload == "loop":
workload = loop_workload
else:
workload = parallel_workload
if args.use_script:
traced_workload = torch.jit.trace(workload, (input_x,))
workload = traced_workload
if profiling_enabled:
def payload():
x = None
with torch.autograd.profiler.profile(
use_cuda=args.with_cuda,
with_stack=args.with_stack,
use_kineto=args.use_kineto,
use_cpu=not args.cuda_only) as prof:
x = workload(input_x)
return x
else:
def payload():
return workload(input_x)
t = Timer(
"payload()",
globals={"payload": payload},
timer=timeit.default_timer,
).blocked_autorange(min_run_time=args.timer_min_run_time)
print(t)
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