mirror of https://github.com/hpcaitech/ColossalAI
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
76 lines
2.6 KiB
76 lines
2.6 KiB
from functools import partial
|
|
|
|
import pytest
|
|
import torch
|
|
import torch.distributed as dist
|
|
import torch.multiprocessing as mp
|
|
from colossalai.communication import all_gather, all_reduce, reduce_scatter
|
|
from colossalai.context import ParallelMode
|
|
from colossalai.core import global_context as gpc
|
|
from colossalai.initialize import launch
|
|
from colossalai.utils import free_port, get_current_device
|
|
from colossalai.testing import rerun_on_exception
|
|
|
|
CONFIG = dict(parallel=dict(data=8, pipeline=1, tensor=dict(mode=None, size=1)))
|
|
|
|
SIZE = 8
|
|
|
|
|
|
def check_all_gather():
|
|
tensor = torch.tensor([dist.get_rank() * SIZE + j for j in range(SIZE)])
|
|
tensor = tensor.to(get_current_device())
|
|
print('Before: Rank {0} - {1}'.format(dist.get_rank(), tensor))
|
|
tensor, op = all_gather(tensor, 0, ParallelMode.GLOBAL, async_op=True)
|
|
print('After: Rank {0} - {1}'.format(dist.get_rank(), tensor))
|
|
op.wait()
|
|
print('Complete: Rank {0} - {1}'.format(dist.get_rank(), tensor))
|
|
torch.cuda.synchronize()
|
|
|
|
|
|
def check_reduce_scatter():
|
|
tensor = torch.tensor([dist.get_rank() * SIZE + j for j in range(SIZE)])
|
|
tensor = tensor.to(get_current_device())
|
|
print('Before: Rank {0} - {1}'.format(dist.get_rank(), tensor))
|
|
tensor, op = reduce_scatter(tensor, 0, ParallelMode.GLOBAL, async_op=True)
|
|
print('After: Rank {0} - {1}'.format(dist.get_rank(), tensor))
|
|
op.wait()
|
|
print('Complete: Rank {0} - {1}'.format(dist.get_rank(), tensor))
|
|
torch.cuda.synchronize()
|
|
|
|
|
|
def check_all_reduce():
|
|
tensor = torch.tensor([dist.get_rank() * SIZE + j for j in range(SIZE)])
|
|
tensor = tensor.to(get_current_device())
|
|
print('Before: Rank {0} - {1}'.format(dist.get_rank(), tensor))
|
|
tensor, op = all_reduce(tensor, ParallelMode.GLOBAL, async_op=True)
|
|
print('After: Rank {0} - {1}'.format(dist.get_rank(), tensor))
|
|
op.wait()
|
|
print('Complete: Rank {0} - {1}'.format(dist.get_rank(), tensor))
|
|
torch.cuda.synchronize()
|
|
|
|
|
|
def check_layer(rank, world_size, port):
|
|
launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
|
|
|
|
assert dist.get_rank() == gpc.get_global_rank()
|
|
print('Rank {} / {}'.format(dist.get_rank(), dist.get_world_size()))
|
|
|
|
check_all_gather()
|
|
check_reduce_scatter()
|
|
check_all_reduce()
|
|
|
|
gpc.destroy()
|
|
torch.cuda.empty_cache()
|
|
|
|
|
|
@pytest.mark.dist
|
|
@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*")
|
|
def test_comm():
|
|
world_size = 4
|
|
run_func = partial(check_layer, world_size=world_size, port=free_port())
|
|
mp.spawn(run_func, nprocs=world_size)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test_comm()
|