from functools import partial import pytest import torch import torch.multiprocessing as mp import colossalai from colossalai.gemini.chunk import ChunkManager, search_chunk_configuration from colossalai.gemini.gemini_mgr import GeminiManager from colossalai.gemini.memory_tracer.runtime_mem_tracer import RuntimeMemTracer from colossalai.nn.optimizer.gemini_optimizer import GeminiAdamOptimizer from colossalai.nn.parallel import GeminiDDP, 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.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 # run gemini use the runtime memory tracer @parameterize('placement_policy', ['auto']) @parameterize('keep_gather', [False]) @parameterize('model_name', ['repeated_computed_layers', 'bert', 'albert', 'gpt2']) @parameterize('use_grad_checkpoint', [False, True]) def run_gemini_use_rmt(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='cpu'): model = model_builder(use_grad_checkpoint) print(f'model_name {model_name}') runtime_mem_tracer = RuntimeMemTracer(model) for i, (input_ids, label) in enumerate(train_dataloader): if i > 0: break input_ids, label = input_ids.cuda(), label.cuda() # mem tracing if i == 0: run_fwd_bwd(runtime_mem_tracer, input_ids, label, criterion, runtime_mem_tracer) memstats = runtime_mem_tracer.memstats() runtime_tracer_non_model_data = runtime_mem_tracer._memstats._non_model_data_cuda_list print('runtime tracer non model data points: ', len(runtime_tracer_non_model_data)) print('runtime tracer: ', runtime_tracer_non_model_data) print([memstats.param_used_timestep(p) for p in model.parameters()]) if model_name == 'repeated_computed_layers': for idx, p in enumerate(model.parameters()): step_list = memstats.param_used_timestep(p) if idx < 4: assert len(step_list) == 4 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, memstats) model = ZeroDDP(model, gemini_manager, pin_memory=True) pg = ProcessGroup() 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. # print(f'iteration {i}') if i > 4: break input_ids, label = input_ids.cuda(), label.cuda() set_seed(42) loss = run_fwd_bwd(model, input_ids, label, criterion, model) gemini_non_model_data = gemini_manager._mem_stats_collector._memstats.non_model_data_list('cuda') # print('gemini non model data:', gemini_non_model_data) assert len(gemini_non_model_data) == len(runtime_tracer_non_model_data), \ f'model_name {model_name} {len(gemini_non_model_data)} vs {len(runtime_tracer_non_model_data)}' def run_dist(rank, world_size, port): config = {} colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') run_gemini_use_rmt() @pytest.mark.dist @pytest.mark.parametrize('world_size', [1, 4]) @rerun_if_address_is_in_use() def test_gemini_use_rmt(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_gemini_use_rmt(1)