mirror of https://github.com/hpcaitech/ColossalAI
49 lines
1.4 KiB
Python
49 lines
1.4 KiB
Python
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import pytest
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import colossalai
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import torch
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import torch.multiprocessing as mp
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from colossalai.testing import rerun_if_address_is_in_use
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from colossalai.utils.cuda import get_current_device
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from colossalai.utils import free_port
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from functools import partial
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from colossalai.nn.parallel.reducer import Reducer
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import torch.distributed as dist
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from torch.distributed.distributed_c10d import _get_default_group
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REDUCE_CNT = 0
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def check_eq(grad, grad_clone):
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global REDUCE_CNT
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print(f'Rank{dist.get_rank()} check {REDUCE_CNT}')
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REDUCE_CNT += 1
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assert torch.allclose(grad, grad_clone)
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def run_reducer():
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grads = [torch.rand(64, i + 1, device=get_current_device()) for i in range(10)]
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grads_clone = [g.clone().detach() for g in grads]
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for g in grads:
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dist.all_reduce(g)
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reducer = Reducer(bucket_size_mb=1)
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for g, g_clone in zip(grads, grads_clone):
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reducer.all_reduce_async(g_clone, _get_default_group(), partial(check_eq, g))
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reducer.flush()
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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_reducer()
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@pytest.mark.dist
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@pytest.mark.parametrize('world_size', [1, 2])
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@rerun_if_address_is_in_use()
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def test_reducer(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_reducer(2)
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