from functools import partial import pytest import torch import torch.multiprocessing as mp from torch.nn.parallel import DistributedDataParallel as DDP from torch.testing import assert_close import colossalai from colossalai.amp import convert_to_apex_amp from colossalai.gemini.chunk import ChunkManager, search_chunk_configuration from colossalai.gemini.gemini_mgr import GeminiManager from colossalai.nn.parallel import ZeroDDP from colossalai.tensor import ProcessGroup 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 import run_fwd_bwd from tests.components_to_test.registry import non_distributed_component_funcs from tests.test_tensor.common_utils import set_seed def check_grad(model: ZeroDDP, torch_model: torch.nn.Module): chunk_manager = model.chunk_manager param_list = [p for p in model.parameters()] chunk_list = chunk_manager.get_chunks(param_list) for chunk in chunk_list: chunk_manager.access_chunk(chunk) for (p0, p1) in zip(model.parameters(), torch_model.parameters()): assert_close(p0, p1.grad, rtol=1e-3, atol=5e-5) @parameterize('placement_policy', ['cuda', 'cpu', 'auto', 'const']) @parameterize('keep_gather', [False, True]) @parameterize('model_name', ['gpt2', 'bert']) @parameterize('use_grad_checkpoint', [False, True]) def exam_gpt_fwd_bwd(placement_policy, keep_gather, model_name: str, use_grad_checkpoint: bool = False): set_seed(42) get_components_func = non_distributed_component_funcs.get_callable(model_name) model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func() with ColoInitContext(device=get_current_device()): model = model_builder(use_grad_checkpoint) torch_model = model_builder(use_grad_checkpoint).cuda() 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_gather chunk_manager = ChunkManager(config_dict) gemini_manager = GeminiManager(placement_policy, chunk_manager) model = ZeroDDP(model, gemini_manager, pin_memory=True) pg = ProcessGroup() amp_config = dict(opt_level='O2', keep_batchnorm_fp32=False, loss_scale=1) torch_optim = torch.optim.Adam(torch_model.parameters(), lr=1e-3) torch_model, torch_optim = convert_to_apex_amp(torch_model, torch_optim, amp_config) torch_model = DDP(torch_model, device_ids=[pg.rank()], process_group=pg.dp_process_group()) model.eval() torch_model.eval() set_seed(pg.dp_local_rank()) for i, (input_ids, label) in enumerate(train_dataloader): # you can only test a single fwd + bwd. # after bwd param is grad for Gemini, due to the chunk reuse optimization. if i > 0: break torch_loss = run_fwd_bwd(torch_model, input_ids.cuda(), label.cuda(), criterion, use_init_ctx=False) loss = run_fwd_bwd(model, input_ids.cuda(), label.cuda(), criterion, use_init_ctx=True) assert torch.equal(torch_loss, loss) check_grad(model, torch_model) 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_gpt_fwd_bwd() @pytest.mark.dist @pytest.mark.parametrize('world_size', [1, 4]) @rerun_if_address_is_in_use() def test_gpt(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_gpt(4)