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")