import pytest from functools import partial from _utils import tensor_equal, tensor_shard_equal, set_seed import torch from torch.nn.parallel import DistributedDataParallel as DDP import torch.multiprocessing as mp import colossalai 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.tensor import ColoTensorSpec, ComputePattern, ComputeSpec, DistSpecManager, distspec, ProcessGroup from colossalai.nn.parallel.data_parallel import ColoDDP from colossalai.core import global_context as gpc from colossalai.context.parallel_mode import ParallelMode from tests.components_to_test.registry import non_distributed_component_funcs def init_1d_row_spec(model, pg: ProcessGroup): tensor_spec = (distspec.shard([0], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D)) with DistSpecManager.no_grad(): for n, p in model.named_parameters(): if 'weight' in n and 'ln' not in n: p.set_tensor_spec(*tensor_spec) def init_1d_col_spec(model, pg: ProcessGroup): spec = (distspec.shard([-1], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D)) with DistSpecManager.no_grad(): for n, p in model.named_parameters(): if 'ln' not in n and ('weight' in n or 'bias' in n): p.set_tensor_spec(*spec) def check_param_equal(model, torch_model, pg: ProcessGroup): for p, torch_p in zip(model.parameters(), torch_model.parameters()): assert pg.tp_local_rank() is not None, f"{pg.rank()} {pg.tp_world_size()} {pg._tp_degree} {pg.tp_local_rank()}1" assert pg.tp_world_size() is not None assert tensor_shard_equal(torch_p, p, pg.tp_local_rank(), pg.tp_world_size()) def check_grad_equal(model, torch_model, pg: ProcessGroup): for p, torch_p in zip(model.parameters(), torch_model.parameters()): assert tensor_shard_equal(torch_p.grad, p.grad, pg.tp_local_rank(), pg.tp_world_size()) def run_gpt(init_spec_func, use_ddp): world_size = torch.distributed.get_world_size() pg = ProcessGroup(dp_degree=(2 if (use_ddp and world_size >= 2) else 1)) 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() if use_ddp: # torch_model = DDP(torch_model, device_ids=[pg.rank()], process_group=pg) # torch.distributed.barrier() torch_model = DDP(torch_model, device_ids=[gpc.get_global_rank()], process_group=gpc.get_group(ParallelMode.DATA)) model = ColoDDP(model, process_group=pg) for torch_p, p in zip(torch_model.parameters(), model.parameters()): torch_p.data.copy_(p) init_spec_func(model, pg) check_param_equal(model, torch_model, pg) model.train() torch_model.train() set_seed(pg.tp_local_rank()) for i, (input_ids, attn_mask) in enumerate(train_dataloader): logits = model(input_ids, attn_mask) torch_logits = torch_model(input_ids, attn_mask) assert tensor_equal(torch_logits, logits), f"{torch_logits - logits}" loss = criterion(logits, input_ids) torch_loss = criterion(torch_logits, input_ids) if use_ddp: model.backward(loss) else: loss.backward() torch_loss.backward() check_grad_equal(model, torch_model, pg) if i > 0: break def run_dist(rank, world_size, port, use_ddp): if use_ddp and world_size == 1: return tp_world_size = world_size // 2 if use_ddp else world_size config = dict(parallel=dict(tensor=dict(mode="1d", size=tp_world_size),)) colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') # run_gpt(init_1d_row_spec, use_ddp) run_gpt(init_1d_col_spec, use_ddp) @pytest.mark.dist @pytest.mark.skip("under development") @pytest.mark.parametrize('world_size', [1, 4]) @pytest.mark.parametrize('use_ddp', [False, True]) @rerun_if_address_is_in_use() def test_gpt(world_size, use_ddp): run_func = partial(run_dist, world_size=world_size, port=free_port(), use_ddp=use_ddp) mp.spawn(run_func, nprocs=world_size) if __name__ == '__main__': test_gpt(4, True)