ColossalAI/tests/test_moe/test_moe_group.py

83 lines
2.6 KiB
Python

import pytest
import torch.distributed as dist
import torch.nn as nn
import colossalai
from colossalai.accelerator import get_accelerator
from colossalai.moe.experts import MLPExperts
from colossalai.moe.manager import MOE_MANAGER
from colossalai.moe.utils import sync_moe_model_param
from colossalai.testing import assert_equal_in_group, rerun_if_address_is_in_use, spawn
HIDDEN_SIZE = 4
INTERMEDIATE_SIZE = 8
def run_moe_init(expert_parallel):
MOE_MANAGER.__init__()
MOE_MANAGER.setup(parallel=expert_parallel)
expert_args = dict(
hidden_size=HIDDEN_SIZE,
intermediate_size=INTERMEDIATE_SIZE,
expert_parallel=expert_parallel,
)
exp0 = MLPExperts(1, **expert_args)
exp1 = MLPExperts(2, **expert_args)
exp2 = MLPExperts(4, **expert_args)
if expert_parallel == "EP":
assert exp0.num_local_experts == 1
assert exp1.num_local_experts == 1
assert exp2.num_local_experts == 2
else:
assert exp0.num_local_experts == 1
assert exp1.num_local_experts == 2
assert exp2.num_local_experts == 4
parallel_info_dict = MOE_MANAGER.parallel_info_dict
rank = dist.get_rank()
# group creation assert
assert len(parallel_info_dict) == 2
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[2].dp_group) == rank // 2
assert dist.get_rank(parallel_info_dict[1].dp_group) == rank
model = nn.ModuleList([exp0, exp1, exp2])
model = model.to(get_accelerator().get_current_device())
sync_moe_model_param(model)
# MOE experts layout success when ep_size = 1
assert_equal_in_group(exp0.wi.data, parallel_info_dict[1].dp_group)
assert_equal_in_group(exp0.wo.data, parallel_info_dict[1].dp_group)
# MOE experts layout success when ep_size = 2
assert_equal_in_group(exp1.wi.data, parallel_info_dict[2].dp_group)
assert_equal_in_group(exp1.wo.data, parallel_info_dict[2].dp_group)
def _run_test(rank, world_size, port, expert_parallel):
colossalai.launch(
config=dict(),
rank=rank,
world_size=world_size,
host="localhost",
port=port,
backend="nccl",
)
run_moe_init(expert_parallel)
@pytest.mark.dist
@pytest.mark.parametrize("expert_parallel", ["EP", "TP"])
@rerun_if_address_is_in_use()
def test_moe_initialization(expert_parallel):
spawn(_run_test, 2, expert_parallel=expert_parallel)
if __name__ == "__main__":
test_moe_initialization("EP")
test_moe_initialization("TP")