mirror of https://github.com/hpcaitech/ColossalAI
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.
57 lines
1.7 KiB
57 lines
1.7 KiB
import pytest
|
|
import torch
|
|
import torch.distributed as dist
|
|
import torch.nn as nn
|
|
|
|
import colossalai
|
|
from colossalai.tensor import ProcessGroup
|
|
from colossalai.testing import spawn
|
|
from colossalai.utils import get_current_device
|
|
from colossalai.zero import ColoInitContext, LowLevelZeroOptimizer
|
|
|
|
|
|
class MlpModel(nn.Module):
|
|
|
|
def __init__(self):
|
|
super(MlpModel, self).__init__()
|
|
self.linear1 = nn.Linear(128, 256)
|
|
self.linear2 = nn.Linear(256, 512)
|
|
|
|
def forward(self, x):
|
|
x = self.linear1(x)
|
|
x = self.linear2(x)
|
|
return x
|
|
|
|
|
|
def exam_zero_init():
|
|
dp_2_tp_2_pg = ProcessGroup(dp_degree=2, tp_degree=2)
|
|
model1 = MlpModel().cuda()
|
|
with ColoInitContext(device=get_current_device(), default_pg=dp_2_tp_2_pg):
|
|
model2 = MlpModel()
|
|
optimizer1 = LowLevelZeroOptimizer(torch.optim.Adam(model1.parameters(), lr=1))
|
|
optimizer2 = LowLevelZeroOptimizer(torch.optim.Adam(model2.parameters(), lr=1))
|
|
|
|
assert optimizer1._local_rank == optimizer2._local_rank
|
|
assert optimizer1._world_size == optimizer2._world_size
|
|
assert optimizer1._dp_global_ranks == optimizer2._dp_global_ranks
|
|
|
|
mp_group1 = optimizer1._mp_torch_group
|
|
mp_group2 = optimizer2._mp_torch_group
|
|
assert dist.get_world_size(mp_group1) == dist.get_world_size(mp_group2)
|
|
assert dist.get_rank(mp_group1) == dist.get_rank(mp_group2)
|
|
|
|
|
|
def run_dist(rank, world_size, port):
|
|
config_dict = dict(parallel=dict(data=2, tensor=dict(size=2, mode='1d')))
|
|
colossalai.launch(config=config_dict, rank=rank, world_size=world_size, port=port, host='localhost')
|
|
exam_zero_init()
|
|
|
|
|
|
@pytest.mark.dist
|
|
def test_zero_init():
|
|
spawn(run_dist, 4)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test_zero_init()
|