from functools import partial import pytest import torch import torch.multiprocessing as mp from torch.nn.parallel import DistributedDataParallel as DDP import colossalai from colossalai.amp import convert_to_apex_amp from colossalai.gemini.chunk import search_chunk_configuration from colossalai.nn.optimizer.gemini_optimizer import GeminiAdamOptimizer from colossalai.nn.parallel import GeminiDDP, ZeroDDP from colossalai.tensor import ColoTensor, ColoTensorSpec, ComputePattern, ComputeSpec, ProcessGroup, ShardSpec 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 set_seed, tensor_shard_equal from tests.test_tensor.model.test_gpt2 import init_megatron_spec def check_param(model: ZeroDDP, torch_model: torch.nn.Module, pg: ProcessGroup): zero_dict = model.state_dict(only_rank_0=False) torch_dict = torch_model.state_dict() for key, value in torch_dict.items(): # key is 'module.model.PARAMETER', so we truncate it key = key[7:] assert key in zero_dict, "{} not in ZeRO dictionary.".format(key) temp_zero_value = zero_dict[key].to(device=value.device, dtype=value.dtype) # debug_print([0], "max range: ", key, torch.max(torch.abs(value - temp_zero_value))) assert tensor_shard_equal(value, temp_zero_value, pg.tp_local_rank(), pg.tp_world_size()), \ "parameter '{}' has problem.".format(key) def run_fwd_bwd(model, criterion, optimizer, input_ids): optimizer.zero_grad() logits = model(input_ids) logits = logits.float() loss = criterion(logits, input_ids) optimizer.backward(loss) return logits def init_1d_row_spec(model, pg: ProcessGroup): spec = (ShardSpec([0], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D)) for n, p in model.named_parameters(): p.set_process_group(pg) if 'weight' in n and 'ln' not in n: p.set_tensor_spec(*spec) def init_1d_col_spec(model, pg: ProcessGroup): spec = (ShardSpec([-1], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D)) for n, p in model.named_parameters(): p.set_process_group(pg) if 'ln' not in n and ('weight' in n or 'bias' in n): p.set_tensor_spec(*spec) @parameterize('placement_policy', ['cuda', 'cpu']) def run_gpt(placement_policy, tp_init_spec_func=None): set_seed(42) 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() for torch_p, p in zip(torch_model.parameters(), model.parameters()): torch_p.data.copy_(p.data) world_size = torch.distributed.get_world_size() # world size, dp = 2, tp =2, construct a hybrid parallelism. if world_size == 4: pg = ProcessGroup(tp_degree=2) else: pg = ProcessGroup(tp_degree=world_size) if tp_init_spec_func: tp_init_spec_func(model, pg) dp_world_size = pg.dp_world_size() config_dict, _ = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100) config_dict[dp_world_size]['chunk_size'] = 5000 config_dict[dp_world_size]['keep_gathered'] = False if placement_policy != 'cuda': init_device = torch.device('cpu') else: init_device = None model = GeminiDDP(model, init_device, placement_policy, True, False, 32) # The same as the following 3 lines # chunk_manager = ChunkManager(config_dict, init_device=init_device) # gemini_manager = GeminiManager(placement_policy, chunk_manager) # model = ZeroDDP(model, gemini_manager, pin_memory=True) zero_optim = GeminiAdamOptimizer(model, lr=1e-3, initial_scale=1) # The same as the following 2 lines # optimizer = HybridAdam(model.parameters(), lr=1e-3) # zero_optim = ZeroOptimizer(optimizer, model, initial_scale=1) 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()) check_param(model, torch_model, pg) model.eval() torch_model.eval() set_seed(pg.dp_local_rank()) for i, (input_ids, label) in enumerate(train_dataloader): if i > 2: break input_ids_colo = ColoTensor.from_torch_tensor(input_ids, ColoTensorSpec(pg)) zero_logits = run_fwd_bwd(model, criterion, zero_optim, input_ids_colo) torch_logits = run_fwd_bwd(torch_model, criterion, torch_optim, input_ids) assert torch.allclose(zero_logits, torch_logits, rtol=1e-3, atol=1e-2) zero_optim.step() torch_optim.step() check_param(model, torch_model, pg) def run_dist(rank, world_size, port): config = {} colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') if world_size == 4: run_gpt(tp_init_spec_func=init_megatron_spec) else: run_gpt(tp_init_spec_func=init_1d_col_spec) run_gpt(tp_init_spec_func=init_1d_row_spec) @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)