Making large AI models cheaper, faster and more accessible
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from collections import OrderedDict
import pytest
import torch
import colossalai
from colossalai.nn.parallel import ColoDDP
from colossalai.tensor import ColoParameter, ProcessGroup
from colossalai.testing import rerun_if_address_is_in_use, spawn
from colossalai.utils.cuda import get_current_device
from colossalai.zero import ColoInitContext
from tests.components_to_test.registry import non_distributed_component_funcs
def check_state_dict_equal(state_dict: OrderedDict, other_state_dict: OrderedDict):
for (k1, t1), (k2, t2) in zip(state_dict.items(), other_state_dict.items()):
assert k1 == k2
if t1.device != t2.device:
temp_t2 = t2.to(t1.device)
else:
temp_t2 = t2
assert torch.equal(t1, temp_t2), "\t{}\n\t{}".format(t1, temp_t2)
def init_ddp(module: torch.nn.Module) -> ColoDDP:
pg = ProcessGroup()
return ColoDDP(module, process_group=pg)
def run_ddp_state_dict():
get_components_func = non_distributed_component_funcs.get_callable('gpt2')
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
torch_model = model_builder().cuda()
with ColoInitContext(device=get_current_device()):
model = model_builder()
model = init_ddp(model)
torch_state_dict = torch_model.state_dict()
for param in model.parameters():
if isinstance(param, ColoParameter):
assert param.get_process_group() is not None
model.load_state_dict(torch_state_dict)
for param in model.parameters():
if isinstance(param, ColoParameter):
assert param.get_process_group() is not None
state_dict = model.state_dict()
check_state_dict_equal(torch_state_dict, state_dict)
def run_dist(rank, world_size, port):
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
run_ddp_state_dict()
@pytest.mark.dist
@pytest.mark.parametrize('world_size', [1, 2])
@rerun_if_address_is_in_use()
def test_state_dict(world_size):
spawn(run_dist, world_size)
if __name__ == '__main__':
test_state_dict(2)