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.
42 lines
1.5 KiB
42 lines
1.5 KiB
import pytest
|
|
import torch
|
|
import torch.distributed as dist
|
|
|
|
import colossalai
|
|
from colossalai.tensor import ColoTensor, ColoTensorSpec, ComputePattern, ComputeSpec, ProcessGroup, ShardSpec
|
|
from colossalai.testing import rerun_if_address_is_in_use, spawn
|
|
from colossalai.utils.checkpoint.utils import gather_tensor, scatter_tensor
|
|
from tests.test_tensor.common_utils import tensor_shard_equal
|
|
|
|
|
|
def run_dist(rank, world_size, port, dp_degree, tp_degree):
|
|
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
|
|
pg = ProcessGroup(dp_degree=dp_degree, tp_degree=tp_degree)
|
|
x = torch.randn(4, 4)
|
|
param = ColoTensor(torch.nn.Parameter(x), spec=ColoTensorSpec(pg))
|
|
spec = ShardSpec([-1], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D)
|
|
param.set_tensor_spec(*spec)
|
|
|
|
gather_tensor(param)
|
|
if dist.get_rank() == 0:
|
|
assert torch.all(x == param)
|
|
else:
|
|
assert tensor_shard_equal(x, param.data, pg.tp_local_rank(), pg.tp_world_size())
|
|
dist.barrier()
|
|
|
|
scatter_tensor(param, spec[0])
|
|
assert tensor_shard_equal(x, param.data, pg.tp_local_rank(), pg.tp_world_size())
|
|
assert param.requires_grad is True
|
|
dist.barrier()
|
|
|
|
|
|
@pytest.mark.dist
|
|
@pytest.mark.parametrize('world_size', [4])
|
|
@rerun_if_address_is_in_use()
|
|
def test_checkpoint(world_size):
|
|
spawn(run_dist, world_size, dp_degree=2, tp_degree=world_size // 2)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test_checkpoint(world_size=4)
|