ColossalAI/tests/test_gemini/update/test_zeroddp_state_dict.py

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import pytest
import colossalai
import torch
import torch.multiprocessing as mp
import torch.distributed as dist
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 functools import partial
from tests.test_tensor.common_utils import set_seed
from tests.components_to_test.registry import non_distributed_component_funcs
from colossalai.nn.parallel import ZeroDDP
from colossalai.zero import ZeroOptimizer
from colossalai.testing import parameterize
from colossalai.gemini.gemini_mgr import GeminiManager
from tests.test_tensor.common_utils import debug_print
from colossalai.gemini.chunk import search_chunk_configuration, ChunkManager
@parameterize('placement_policy', ['cuda', 'cpu', 'auto'])
@parameterize('keep_gathered', [True, False])
def exam_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()
torch_model = model_builder()
for torch_p, p in zip(torch_model.parameters(), model.parameters()):
torch_p.data.copy_(p.data)
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
chunk_manager = ChunkManager(config_dict)
gemini_manager = GeminiManager(placement_policy, chunk_manager)
model = ZeroDDP(model, gemini_manager, pin_memory=True)
model.train()
zero_dict = model.state_dict(only_rank_0=False)
torch_dict = torch_model.state_dict()
for key, value in torch_dict.items():
if key == 'model.lm_head.weight':
continue
assert key in zero_dict, "{} not in ZeRO dictionary.".format(key)
temp_zero_value = zero_dict[key].to(device=value.device, dtype=value.dtype)
assert torch.equal(value, temp_zero_value), "parameter '{}' has problem.".format(key)
@parameterize('placement_policy', ['cuda', 'cpu', 'auto'])
@parameterize('keep_gathered', [True, False])
def exam_load_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)
torch_dict = torch_model.state_dict()
model.load_state_dict(torch_dict, strict=False)
zero_dict = model.state_dict(only_rank_0=False)
for key, value in torch_dict.items():
if key == 'model.lm_head.weight':
continue
assert key in zero_dict, "{} not in ZeRO dictionary.".format(key)
temp_zero_value = zero_dict[key].to(device=value.device, dtype=value.dtype)
assert torch.equal(value, temp_zero_value), "parameter '{}' has problem.".format(key)
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_state_dict()
exam_load_state_dict()
@pytest.mark.dist
@pytest.mark.parametrize('world_size', [1, 4])
@rerun_if_address_is_in_use()
def test_zero_ddp(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_ddp(1)