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unflatten_bench.py
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unflatten_bench.py
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#!/usr/bin/env python
# run the benchmark under timeit (-t), cProfile (-c), line_profiler (-l)
#
# usage:
# ./unflatten_bench.py -t
# ./unflatten_bench.py -c
# kernprof -l unflatten_bench.py -l; python -m line_profiler unflatten_bench.py.lprof
import argparse
import gc
import torch
from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
from deepspeed.ops.op_builder import UtilsBuilder
from apex_C import flatten as flatten_apex
from apex_C import unflatten as unflatten_apex
util_ops = UtilsBuilder().load()
flatten = util_ops.flatten
unflatten = util_ops.unflatten
torch.manual_seed(0)
# emulate a small typical model weights
x = [
torch.rand((512,
512)).cuda(),
torch.rand((512,
1024)).cuda(),
torch.rand((512,
30000)).cuda()
]
unflat_t = x * 30
# warm up and check that the same output is produced
flat_py = _flatten_dense_tensors(unflat_t)
flat_cpp = flatten(unflat_t)
flat_apex = flatten_apex(unflat_t)
#numel = flat_cpp.numel()
assert torch.eq(flat_py, flat_cpp).all(), "both produce the same tensor"
assert torch.eq(flat_py, flat_apex).all(), "both produce the same tensor"
flat_t = flat_py
unflat_py = _unflatten_dense_tensors(flat_py, unflat_t)
for i in range(len(unflat_t)):
assert torch.eq(unflat_t[i], unflat_py[i]).all()
unflat_cpp = _unflatten_dense_tensors(flat_cpp, unflat_t)
for i in range(len(unflat_t)):
assert torch.eq(unflat_t[i], unflat_cpp[i]).all()
unflat_apex = _unflatten_dense_tensors(flat_apex, unflat_t)
for i in range(len(unflat_t)):
assert torch.eq(unflat_t[i], unflat_apex[i]).all()
# the programs being tested
def py():
for i in range(1000):
unflat = _unflatten_dense_tensors(flat_t, unflat_t)
def cpp():
for i in range(1000):
unflat = unflatten(flat_t, unflat_t)
def apex():
for i in range(1000):
unflat = unflatten_apex(flat_t, unflat_t)
#### cProfile ####
import cProfile
def cprofileme():
print("--------------- cProfile -----------------")
print("py")
cProfile.run("py()", sort=-1)
gc.collect()
torch.cuda.empty_cache()
print("cpp")
cProfile.run("cpp()", sort=-1)
gc.collect()
torch.cuda.empty_cache()
print("apex")
cProfile.run("apex()", sort=-1)
gc.collect()
torch.cuda.empty_cache()
#### timeit ####
import timeit
def timeme():
print("--------------- timeit -----------------")
print(f'py ={timeit.Timer("py()", globals=globals()).timeit(number=1)}')
gc.collect()
torch.cuda.empty_cache()
print(f'cpp ={timeit.Timer("cpp()", globals=globals()).timeit(number=1)}')
gc.collect()
torch.cuda.empty_cache()
print(f'apex={timeit.Timer("apex()", globals=globals()).timeit(number=1)}')
gc.collect()
torch.cuda.empty_cache()
#### line_profiler ####
# this one requires a special way to be called
# pip install line_profiler
# kernprof -l unflatten_bench.py -l; python -m line_profiler unflatten_bench.py.lprof
def line_profileme():
print("--------------- line_profier -----------------")
print("py")
profile(py)()
gc.collect()
torch.cuda.empty_cache()
print("cpp")
profile(cpp)()
gc.collect()
torch.cuda.empty_cache()
print("apex")
profile(apex)()
gc.collect()
torch.cuda.empty_cache()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("-l", action='store_true')
parser.add_argument("-c", action='store_true')
parser.add_argument("-t", action='store_true')
args = parser.parse_args()
if args.l:
line_profileme()
elif args.c:
cprofileme()
elif args.t:
timeme()