mirror of https://github.com/hpcaitech/ColossalAI
101 lines
3.7 KiB
Python
101 lines
3.7 KiB
Python
import pytest
|
|
import colossalai
|
|
import torch
|
|
import torch.multiprocessing as mp
|
|
from colossalai.testing import rerun_if_address_is_in_use
|
|
from colossalai.utils.cuda import get_current_device
|
|
from colossalai.utils import free_port
|
|
from colossalai.utils.model.colo_init_context import ColoInitContext
|
|
from colossalai.gemini import ChunkManager
|
|
from functools import partial
|
|
from tests.components_to_test.registry import non_distributed_component_funcs
|
|
from colossalai.nn.parallel import ZeroDDP
|
|
from colossalai.nn.optimizer import HybridAdam
|
|
from colossalai.zero import ZeroOptimizer
|
|
from colossalai.testing import parameterize
|
|
from colossalai.gemini.gemini_mgr import GeminiManager
|
|
from colossalai.tensor import ProcessGroup
|
|
|
|
|
|
def check_state(s1, s2):
|
|
for v1, v2 in zip(s1.values(), s2.values()):
|
|
if isinstance(v1, torch.Tensor):
|
|
v1 = v1.to(v2.device)
|
|
assert torch.equal(v1, v2), f'{torch.sum((v1-v2).abs())}'
|
|
else:
|
|
assert v1 == v2
|
|
|
|
|
|
def check_load_state_dict(optim, torch_optim):
|
|
for group, torch_group in zip(optim.optim.param_groups, torch_optim.param_groups):
|
|
for p, torch_p in zip(group['params'], torch_group['params']):
|
|
state = optim.optim.state[p]
|
|
torch_state = torch_optim.state[torch_p]
|
|
if p.storage().size() == 0:
|
|
assert len(state) == 0
|
|
check_state(state, torch_state)
|
|
|
|
|
|
def check_state_dict(state_dict, torch_state_dict):
|
|
for (k1, s1), (k2, s2) in zip(state_dict['state'].items(), torch_state_dict['state'].items()):
|
|
assert k1 == k2
|
|
check_state(s1, s2)
|
|
|
|
|
|
@parameterize('use_chunk', [False, True])
|
|
@parameterize('use_zero', [False, True])
|
|
@parameterize('placement_policy', ['cuda', 'cpu', 'auto'])
|
|
@parameterize('only_rank_0', [False, True])
|
|
def run_zero_optim_state_dict(use_chunk, use_zero, placement_policy, only_rank_0):
|
|
get_components_func = non_distributed_component_funcs.get_callable('gpt2')
|
|
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
|
|
|
|
with ColoInitContext(device=get_current_device()):
|
|
model = model_builder()
|
|
model = model.cuda()
|
|
torch_model = model_builder().cuda()
|
|
|
|
pg = ProcessGroup()
|
|
|
|
chunk_size = ChunkManager.search_chunk_size(model, 8192, 8) if use_chunk else None
|
|
chunk_manager = ChunkManager(chunk_size,
|
|
pg,
|
|
enable_distributed_storage=use_zero,
|
|
init_device=GeminiManager.get_default_device(placement_policy))
|
|
gemini_manager = GeminiManager(placement_policy, chunk_manager)
|
|
model = ZeroDDP(model, gemini_manager)
|
|
optim = HybridAdam(model.parameters(), lr=1e-3)
|
|
optim = ZeroOptimizer(optim, model, initial_scale=1)
|
|
|
|
torch_optim = torch.optim.Adam(torch_model.parameters(), lr=1e-3)
|
|
|
|
for p in torch_model.parameters():
|
|
p.grad = torch.rand_like(p)
|
|
|
|
torch_optim.step()
|
|
torch_state_dict = torch_optim.state_dict()
|
|
optim.load_state_dict(torch_state_dict)
|
|
check_load_state_dict(optim, torch_optim)
|
|
|
|
state_dict = optim.state_dict(only_rank_0)
|
|
if not only_rank_0 or pg.rank() == 0:
|
|
check_state_dict(state_dict, torch_state_dict)
|
|
|
|
|
|
def run_dist(rank, world_size, port):
|
|
config = {}
|
|
colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
|
|
run_zero_optim_state_dict()
|
|
|
|
|
|
@pytest.mark.dist
|
|
@pytest.mark.parametrize('world_size', [1, 2])
|
|
@rerun_if_address_is_in_use()
|
|
def test_zero_optim_state_dict(world_size):
|
|
run_func = partial(run_dist, world_size=world_size, port=free_port())
|
|
mp.spawn(run_func, nprocs=world_size)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test_zero_optim_state_dict(2)
|