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b5f9e37c70
* [legacy] remove outdated codes of pipeline (#4692) * [legacy] remove cli of benchmark and update optim (#4690) * [legacy] remove cli of benchmark and update optim * [doc] fix cli doc test * [legacy] fix engine clip grad norm * [legacy] remove outdated colo tensor (#4694) * [legacy] remove outdated colo tensor * [test] fix test import * [legacy] move outdated zero to legacy (#4696) * [legacy] clean up utils (#4700) * [legacy] clean up utils * [example] update examples * [legacy] clean up amp * [legacy] fix amp module * [legacy] clean up gpc (#4742) * [legacy] clean up context * [legacy] clean core, constants and global vars * [legacy] refactor initialize * [example] fix examples ci * [example] fix examples ci * [legacy] fix tests * [example] fix gpt example * [example] fix examples ci * [devops] fix ci installation * [example] fix examples ci
38 lines
1.0 KiB
Python
38 lines
1.0 KiB
Python
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(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 == 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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