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.
47 lines
1.3 KiB
47 lines
1.3 KiB
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)
|
|
|