mirror of https://github.com/hpcaitech/ColossalAI
aibig-modeldata-parallelismdeep-learningdistributed-computingfoundation-modelsheterogeneous-traininghpcinferencelarge-scalemodel-parallelismpipeline-parallelism
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
33 lines
1.0 KiB
33 lines
1.0 KiB
3 years ago
|
import pytest
|
||
|
|
|
||
|
|
import colossalai
|
||
|
|
from colossalai.utils.cuda import get_current_device
|
||
|
|
from colossalai.utils.memory import colo_set_process_memory_fraction, colo_device_memory_capacity
|
||
|
|
from colossalai.utils import free_port
|
||
|
|
|
||
|
|
from functools import partial
|
||
|
|
import torch.multiprocessing as mp
|
||
|
|
|
||
|
|
|
||
|
|
def _run_colo_set_process_memory_fraction_and_colo_device_memory_capacity():
|
||
|
|
frac1 = colo_device_memory_capacity(get_current_device())
|
||
|
|
colo_set_process_memory_fraction(0.5)
|
||
|
|
frac2 = colo_device_memory_capacity(get_current_device())
|
||
|
|
assert frac2 * 2 == frac1
|
||
|
|
|
||
|
|
|
||
|
|
def run_dist(rank, world_size, port):
|
||
|
|
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
|
||
|
|
_run_colo_set_process_memory_fraction_and_colo_device_memory_capacity()
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.dist
|
||
|
|
@pytest.mark.parametrize("world_size", [4, 5])
|
||
|
|
def test_memory_utils(world_size):
|
||
|
|
run_func = partial(run_dist, world_size=world_size, port=free_port())
|
||
|
|
mp.spawn(run_func, nprocs=world_size)
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == '__main__':
|
||
|
|
test_memory_utils(world_size=2)
|