mirror of https://github.com/hpcaitech/ColossalAI
38 lines
1.0 KiB
Python
38 lines
1.0 KiB
Python
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()
|