mirror of https://github.com/hpcaitech/ColossalAI
aibig-modeldata-parallelismdeep-learningdistributed-computingfoundation-modelsheterogeneous-traininghpcinferencelarge-scalemodel-parallelismpipeline-parallelism
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71 lines
2.5 KiB
71 lines
2.5 KiB
from functools import partial |
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import pytest |
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import torch.nn as nn |
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import torch.multiprocessing as mp |
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import torch.distributed as dist |
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import colossalai |
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from colossalai.utils import free_port, get_current_device |
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from colossalai.nn.layer.moe import Experts |
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from colossalai.context.moe_context import MOE_CONTEXT |
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from colossalai.utils.moe import sync_moe_model_param |
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from colossalai.testing import assert_equal_in_group, rerun_if_address_is_in_use |
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D_MODEL = 4 |
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D_FF = 8 |
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CONFIG = dict() |
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def run_test(rank, port): |
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world_size = 4 |
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colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') |
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expert_module = nn.Linear |
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expert_factor = dict(in_features=D_MODEL, out_features=D_FF, device=get_current_device()) |
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MOE_CONTEXT.setup(42) # MOE environment initialization |
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exp0 = Experts(expert_module, 1, **expert_factor) |
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exp1 = Experts(expert_module, 2, **expert_factor) |
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exp2 = Experts(expert_module, 4, **expert_factor) |
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exp3 = Experts(expert_module, 8, **expert_factor) |
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assert exp0.num_local_experts == 1 |
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assert exp1.num_local_experts == 1 |
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assert exp2.num_local_experts == 1 |
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assert exp3.num_local_experts == 2 |
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# experts deployment passed |
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parallel_info_dict = MOE_CONTEXT.parallel_info_dict |
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rank = dist.get_rank() |
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assert len(parallel_info_dict) == 3 |
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assert dist.get_rank(parallel_info_dict[4].ep_group) == rank |
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assert dist.get_rank(parallel_info_dict[2].ep_group) == rank % 2 |
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assert dist.get_rank(parallel_info_dict[1].ep_group) == 0 |
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assert dist.get_rank(parallel_info_dict[4].dp_group) == 0 |
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assert dist.get_rank(parallel_info_dict[2].dp_group) == rank // 2 |
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assert dist.get_rank(parallel_info_dict[1].dp_group) == rank |
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# group creation passed |
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model = nn.ModuleList([exp0, exp1, exp2, exp3]) |
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model = model.to(get_current_device()) |
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sync_moe_model_param(model) |
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assert_equal_in_group(exp0.experts[0].weight.data, parallel_info_dict[1].dp_group) |
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assert_equal_in_group(exp0.experts[0].bias.data, parallel_info_dict[1].dp_group) |
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# MOE experts layout success when ep_size = 1 |
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assert_equal_in_group(exp1.experts[0].weight.data, parallel_info_dict[2].dp_group) |
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assert_equal_in_group(exp1.experts[0].bias.data, parallel_info_dict[2].dp_group) |
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# MOE experts layout success when ep_size = 2 |
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@pytest.mark.dist |
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@rerun_if_address_is_in_use() |
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def test_moe_initialization(): |
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world_size = 4 |
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run_func = partial(run_test, port=free_port()) |
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mp.spawn(run_func, nprocs=world_size) |
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if __name__ == '__main__': |
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test_moe_initialization()
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