mirror of https://github.com/hpcaitech/ColossalAI
51 lines
1.9 KiB
Python
51 lines
1.9 KiB
Python
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import math
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import torch
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import torch.distributed as dist
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import pytest
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import colossalai
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import torch.multiprocessing as mp
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from torch.distributed.distributed_c10d import _get_default_group
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from colossalai.testing import rerun_if_address_is_in_use
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from colossalai.utils import free_port
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from colossalai.tensor import dist_spec, DistSpecManager
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from functools import partial
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def run():
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group = _get_default_group()
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rank = dist.get_rank()
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size = dist.get_world_size()
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depth = int(math.sqrt(size))
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assert depth == math.sqrt(size)
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x = torch.rand(8, 8).cuda()
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old_dist_spec = dist_spec.replicate()
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row_spec = dist_spec.shard(group, [0], [size])
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col_spec = dist_spec.shard(group, [-1], [size])
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mat_spec = dist_spec.shard(group, [0, 1], [depth, depth])
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row_shard = DistSpecManager._shard_as(x, old_dist_spec, row_spec)
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assert torch.equal(x.chunk(size, 0)[rank], row_shard)
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assert torch.equal(x, DistSpecManager._gather(row_shard, row_spec))
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col_shard = DistSpecManager._shard_as(x, old_dist_spec, col_spec)
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assert torch.equal(x.chunk(size, -1)[rank], col_shard)
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assert torch.equal(x, DistSpecManager._gather(col_shard, col_spec))
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mat_shard = DistSpecManager._shard_as(x, old_dist_spec, mat_spec)
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assert torch.equal(x.chunk(depth, 0)[rank // depth].chunk(depth, 1)[rank % depth], mat_shard)
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assert torch.equal(x, DistSpecManager._gather(mat_shard, mat_spec))
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def run_dist(rank, world_size, port):
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colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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run()
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@pytest.mark.dist
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@pytest.mark.parametrize('world_size', [1, 4])
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@rerun_if_address_is_in_use()
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def test_dist_spec_mgr(world_size):
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run_func = partial(run_dist, 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_dist_spec_mgr(4)
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