Making large AI models cheaper, faster and more accessible
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from functools import partial
import pytest
import torch
import torch.distributed as dist
from torch.distributed.distributed_c10d import _get_default_group
import colossalai
from colossalai.nn.parallel.reducer import Reducer
from colossalai.testing import rerun_if_address_is_in_use, spawn
from colossalai.utils.cuda import get_current_device
REDUCE_CNT = 0
def check_eq(grad, grad_clone):
global REDUCE_CNT
print(f'Rank{dist.get_rank()} check {REDUCE_CNT}')
REDUCE_CNT += 1
assert torch.allclose(grad, grad_clone)
def run_reducer():
grads = [torch.rand(64, i + 1, device=get_current_device()) for i in range(10)]
grads_clone = [g.clone().detach() for g in grads]
for g in grads:
dist.all_reduce(g)
reducer = Reducer(bucket_size_mb=1)
for g, g_clone in zip(grads, grads_clone):
reducer.all_reduce_async(g_clone, _get_default_group(), partial(check_eq, g))
reducer.flush()
def run_dist(rank, world_size, port):
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
run_reducer()
@pytest.mark.dist
@pytest.mark.parametrize('world_size', [1, 2])
@rerun_if_address_is_in_use()
def test_reducer(world_size):
spawn(run_dist, world_size)
if __name__ == '__main__':
test_reducer(2)