mirror of https://github.com/hpcaitech/ColossalAI
aibig-modeldata-parallelismdeep-learningdistributed-computingfoundation-modelsheterogeneous-traininghpcinferencelarge-scalemodel-parallelismpipeline-parallelism
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37 lines
1.0 KiB
37 lines
1.0 KiB
import pytest |
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import torch |
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import torch.distributed as dist |
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from torch.distributed import ReduceOp |
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from colossalai.device.device_mesh import DeviceMesh |
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from colossalai.initialize import launch |
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from colossalai.testing import rerun_if_address_is_in_use, spawn |
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def check_layer(rank, world_size, port): |
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launch(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 == dist.get_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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for axis in range(len(mesh_shape)): |
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tensor = torch.ones(4).cuda() |
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pg = device_mesh.get_process_group(axis=axis) |
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dist.all_reduce(tensor, op=ReduceOp.SUM, group=pg) |
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assert tensor.equal(tensor_to_check) |
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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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spawn(check_layer, 4) |
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if __name__ == "__main__": |
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test_logical_pg()
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