mirror of https://github.com/hpcaitech/ColossalAI
42 lines
1.5 KiB
Python
42 lines
1.5 KiB
Python
import pytest
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import torch
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import torch.distributed as dist
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import colossalai
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from colossalai.tensor import ColoTensor, ColoTensorSpec, ComputePattern, ComputeSpec, ProcessGroup, ShardSpec
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from colossalai.testing import rerun_if_address_is_in_use, spawn
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from colossalai.utils.checkpoint.utils import gather_tensor, scatter_tensor
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from tests.test_tensor.common_utils import tensor_shard_equal
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def run_dist(rank, world_size, port, dp_degree, tp_degree):
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colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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pg = ProcessGroup(dp_degree=dp_degree, tp_degree=tp_degree)
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x = torch.randn(4, 4)
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param = ColoTensor(torch.nn.Parameter(x), spec=ColoTensorSpec(pg))
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spec = ShardSpec([-1], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D)
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param.set_tensor_spec(*spec)
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gather_tensor(param)
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if dist.get_rank() == 0:
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assert torch.all(x == param)
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else:
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assert tensor_shard_equal(x, param.data, pg.tp_local_rank(), pg.tp_world_size())
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dist.barrier()
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scatter_tensor(param, spec[0])
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assert tensor_shard_equal(x, param.data, pg.tp_local_rank(), pg.tp_world_size())
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assert param.requires_grad is True
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dist.barrier()
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@pytest.mark.dist
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@pytest.mark.parametrize('world_size', [4])
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@rerun_if_address_is_in_use()
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def test_checkpoint(world_size):
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spawn(run_dist, world_size, dp_degree=2, tp_degree=world_size // 2)
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if __name__ == '__main__':
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test_checkpoint(world_size=4)
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