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.
72 lines
2.6 KiB
72 lines
2.6 KiB
from functools import partial
|
|
import pytest
|
|
import torch.nn as nn
|
|
import torch.multiprocessing as mp
|
|
import torch.distributed as dist
|
|
import colossalai
|
|
from colossalai.utils import free_port, get_current_device
|
|
from colossalai.nn.layer.moe import Experts
|
|
from colossalai.context.moe_context import MOE_CONTEXT
|
|
from colossalai.utils.moe import sync_moe_model_param
|
|
from colossalai.testing import assert_equal_in_group, rerun_on_exception
|
|
|
|
D_MODEL = 4
|
|
D_FF = 8
|
|
CONFIG = dict()
|
|
|
|
|
|
def run_test(rank, port):
|
|
world_size = 4
|
|
colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
|
|
expert_module = nn.Linear
|
|
expert_factor = dict(in_features=D_MODEL, out_features=D_FF, device=get_current_device())
|
|
|
|
MOE_CONTEXT.setup(42) # MOE environment initialization
|
|
exp0 = Experts(expert_module, 1, **expert_factor)
|
|
exp1 = Experts(expert_module, 2, **expert_factor)
|
|
exp2 = Experts(expert_module, 4, **expert_factor)
|
|
exp3 = Experts(expert_module, 8, **expert_factor)
|
|
|
|
assert exp0.num_local_experts == 1
|
|
assert exp1.num_local_experts == 1
|
|
assert exp2.num_local_experts == 1
|
|
assert exp3.num_local_experts == 2
|
|
# experts deployment passed
|
|
|
|
parallel_info_dict = MOE_CONTEXT.parallel_info_dict
|
|
rank = dist.get_rank()
|
|
|
|
assert len(parallel_info_dict) == 3
|
|
assert dist.get_rank(parallel_info_dict[4].ep_group) == rank
|
|
assert dist.get_rank(parallel_info_dict[2].ep_group) == rank % 2
|
|
assert dist.get_rank(parallel_info_dict[1].ep_group) == 0
|
|
|
|
assert dist.get_rank(parallel_info_dict[4].dp_group) == 0
|
|
assert dist.get_rank(parallel_info_dict[2].dp_group) == rank // 2
|
|
assert dist.get_rank(parallel_info_dict[1].dp_group) == rank
|
|
# group creation passed
|
|
|
|
model = nn.ModuleList([exp0, exp1, exp2, exp3])
|
|
model = model.to(get_current_device())
|
|
sync_moe_model_param(model)
|
|
|
|
assert_equal_in_group(exp0.experts[0].weight.data, parallel_info_dict[1].dp_group)
|
|
assert_equal_in_group(exp0.experts[0].bias.data, parallel_info_dict[1].dp_group)
|
|
# MOE experts layout success when ep_size = 1
|
|
|
|
assert_equal_in_group(exp1.experts[0].weight.data, parallel_info_dict[2].dp_group)
|
|
assert_equal_in_group(exp1.experts[0].bias.data, parallel_info_dict[2].dp_group)
|
|
# MOE experts layout success when ep_size = 2
|
|
|
|
|
|
@pytest.mark.dist
|
|
@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*")
|
|
def test_moe_initialization():
|
|
world_size = 4
|
|
run_func = partial(run_test, port=free_port())
|
|
mp.spawn(run_func, nprocs=world_size)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test_moe_initialization()
|