mirror of https://github.com/hpcaitech/ColossalAI
50 lines
1.6 KiB
Python
50 lines
1.6 KiB
Python
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import torch
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from functools import partial
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import pytest
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import torch.distributed as dist
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import torch.multiprocessing as mp
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from torch.distributed import ReduceOp
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from colossalai.core import global_context as gpc
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from colossalai.initialize import launch
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from colossalai.utils import free_port
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from colossalai.testing import rerun_if_address_is_in_use
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from colossalai.device.device_mesh import DeviceMesh
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def check_layer(rank, world_size, port):
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launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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physical_mesh_id = torch.arange(0, 4)
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assert rank == gpc.get_global_rank()
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tensor_to_check = torch.tensor([2, 2, 2, 2]).cuda()
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mesh_shape = (2, 2)
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# [[0, 1,
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# [2, 3]]
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device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True)
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logical_pg_dict = {0: [[0, 2], [1, 3]], 1: [[0, 1], [2, 3]]}
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logical_process_groups = device_mesh.process_groups_dict
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for mesh_dim, pgs in logical_pg_dict.items():
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for index, pg in enumerate(pgs):
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if rank in pg:
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tensor = torch.ones(4).cuda()
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group = logical_process_groups[mesh_dim][index][1]
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dist.all_reduce(tensor, op=ReduceOp.SUM, group=group)
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assert tensor.equal(tensor_to_check)
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gpc.destroy()
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@pytest.mark.dist
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@rerun_if_address_is_in_use()
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def test_logical_pg():
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world_size = 4
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run_func = partial(check_layer, world_size=world_size, 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_logical_pg()
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