Making large AI models cheaper, faster and more accessible
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import pytest
import torch
import torch.distributed as dist
from torch.distributed import ReduceOp
from colossalai.device.device_mesh import DeviceMesh
from colossalai.initialize import launch
from colossalai.testing import rerun_if_address_is_in_use, spawn
def check_layer(rank, world_size, port):
launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
physical_mesh_id = torch.arange(0, 4)
assert rank == dist.get_rank()
tensor_to_check = torch.tensor([2, 2, 2, 2]).cuda()
mesh_shape = (2, 2)
# [[0, 1,
# [2, 3]]
device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True)
for axis in range(len(mesh_shape)):
tensor = torch.ones(4).cuda()
pg = device_mesh.get_process_group(axis=axis)
dist.all_reduce(tensor, op=ReduceOp.SUM, group=pg)
assert tensor.equal(tensor_to_check)
@pytest.mark.dist
@rerun_if_address_is_in_use()
def test_logical_pg():
spawn(check_layer, 4)
if __name__ == "__main__":
test_logical_pg()