Making large AI models cheaper, faster and more accessible
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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", [3, 4])
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)