ColossalAI/tests/test_gemini/update/test_zerooptim_state_dict.py

96 lines
3.4 KiB
Python

from functools import partial
import pytest
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
import colossalai
from colossalai.gemini.chunk import ChunkManager, search_chunk_configuration
from colossalai.gemini.gemini_mgr import GeminiManager
from colossalai.nn.optimizer import HybridAdam
from colossalai.nn.optimizer.zero_optimizer import ZeroOptimizer
from colossalai.nn.parallel import ZeroDDP
from colossalai.testing import parameterize, rerun_if_address_is_in_use
from colossalai.utils import free_port
from colossalai.utils.cuda import get_current_device
from colossalai.utils.model.colo_init_context import ColoInitContext
from tests.components_to_test.registry import non_distributed_component_funcs
from tests.test_tensor.common_utils import debug_print, set_seed
@parameterize('placement_policy', ['cuda', 'cpu', 'auto'])
@parameterize('keep_gathered', [True, False])
def exam_zero_optim_state_dict(placement_policy, keep_gathered):
set_seed(431)
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()
set_seed(451)
torch_model = model_builder() # get a different model
world_size = torch.distributed.get_world_size()
config_dict, _ = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100)
config_dict[world_size]['chunk_size'] = 5000
config_dict[world_size]['keep_gathered'] = keep_gathered
if placement_policy != 'cuda':
init_device = torch.device('cpu')
else:
init_device = None
chunk_manager = ChunkManager(config_dict, init_device=init_device)
gemini_manager = GeminiManager(placement_policy, chunk_manager)
model = ZeroDDP(model, gemini_manager, pin_memory=True)
optimizer = HybridAdam(model.parameters())
optim = ZeroOptimizer(optimizer, model, initial_scale=32) # initialize the link between chunk16 and chunk32
set_seed(dist.get_rank() * 3 + 128)
model.train()
for i, (input_ids, attn_mask) in enumerate(train_dataloader):
if i > 0:
break
optim.zero_grad()
logits = model(input_ids, attn_mask)
logits = logits.float()
loss = criterion(logits, input_ids)
optim.backward(loss)
optim.step()
optim_state_dict = optim.state_dict()
optim.load_state_dict(optim_state_dict)
new_state = optim.state_dict()['state']
org_state = optim_state_dict['state']
for k, v in org_state.items():
w = new_state[k]
for n, m in v.items():
if isinstance(m, torch.Tensor):
o = w[n]
if m.device != o.device:
o = o.to(m.device)
assert torch.equal(m, o)
else:
assert m == w[n]
def run_dist(rank, world_size, port):
config = {}
colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
exam_zero_optim_state_dict()
@pytest.mark.dist
@pytest.mark.parametrize('world_size', [1, 4])
@rerun_if_address_is_in_use()
def test_zero_optim(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(1)