import pytest import torch import torch.distributed as dist 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.nn.optimizer import HybridAdam from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn from colossalai.utils.cuda import get_current_device from colossalai.zero import ColoInitContext, ZeroDDP, ZeroOptimizer from colossalai.zero.gemini.chunk import ChunkManager, search_chunk_configuration from colossalai.zero.gemini.gemini_mgr import GeminiManager 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_param(model: ZeroDDP, torch_model: torch.nn.Module): 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_close(value, temp_zero_value, rtol=1e-3, atol=4e-3) @parameterize('placement_policy', ['cuda', 'cpu', 'auto', 'const']) @parameterize('model_name', ['gpt2']) def exam_grad_clipping(placement_policy, model_name: str): set_seed(1912) get_components_func = non_distributed_component_funcs.get_callable(model_name) model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func() torch_model = model_builder().cuda() amp_config = dict(opt_level='O2', keep_batchnorm_fp32=False, loss_scale=32) 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=[dist.get_rank()]) init_dev = get_current_device() with ColoInitContext(device=init_dev): model = model_builder() for torch_p, p in zip(torch_model.parameters(), model.parameters()): p.data.copy_(torch_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'] = False 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(), lr=1e-3) zero_optim = ZeroOptimizer(optimizer, model, initial_scale=32, clipping_norm=1.0) model.train() torch_model.train() set_seed(dist.get_rank() * 3 + 128) for i, (data, label) in enumerate(train_dataloader): if i > 2: break data = data.cuda() label = label.cuda() zero_optim.zero_grad() torch_optim.zero_grad() torch_loss = run_fwd_bwd(torch_model, data, label, criterion, torch_optim) loss = run_fwd_bwd(model, data, label, criterion, zero_optim) assert_close(torch_loss, loss) import apex.amp as apex_amp torch.nn.utils.clip_grad_norm_(apex_amp.master_params(torch_optim), 1.0) torch_optim.step() zero_optim.step() check_param(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_grad_clipping() @pytest.mark.dist @pytest.mark.parametrize('world_size', [1, 2]) @rerun_if_address_is_in_use() def test_grad_clip(world_size): spawn(run_dist, world_size) if __name__ == '__main__': test_grad_clip(2)