mirror of https://github.com/hpcaitech/ColossalAI
aibig-modeldata-parallelismdeep-learningdistributed-computingfoundation-modelsheterogeneous-traininghpcinferencelarge-scalemodel-parallelismpipeline-parallelism
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44 lines
1.3 KiB
44 lines
1.3 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.core import global_context as gpc |
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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(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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spawn(check_layer, 4) |
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if __name__ == '__main__': |
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test_logical_pg()
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