mirror of https://github.com/hpcaitech/ColossalAI
69 lines
2.4 KiB
Python
69 lines
2.4 KiB
Python
import pytest
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import torch.distributed as dist
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import torch.nn as nn
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import colossalai
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from colossalai.context.moe_context import MOE_CONTEXT
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from colossalai.nn.layer.moe import Experts
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from colossalai.testing import assert_equal_in_group, rerun_if_address_is_in_use, spawn
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from colossalai.utils import get_current_device
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from colossalai.utils.moe import sync_moe_model_param
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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, world_size, 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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spawn(run_test, 4)
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if __name__ == "__main__":
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test_moe_initialization()
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