from functools import partial import colossalai import pytest import torch.multiprocessing as mp from colossalai.amp import AMP_TYPE from colossalai.core import global_context as gpc from colossalai.utils import free_port from tests.components_to_test.registry import non_distributed_component_funcs from colossalai.testing import parameterize, rerun_if_address_is_in_use CONFIG = dict(parallel=dict(pipeline=dict(size=1), tensor=dict(size=1, mode=None)), fp16=dict(mode=None), clip_grad_norm=1.0) @parameterize('model_name', ['repeated_computed_layers', 'resnet18', 'repeated_computed_layers']) @parameterize('amp_mode', [AMP_TYPE.APEX, AMP_TYPE.TORCH, AMP_TYPE.NAIVE, None]) def run_train(model_name, amp_mode): # FIXME: test bert get_components_func = non_distributed_component_funcs.get_callable(model_name) gpc.config.fp16['mode'] = amp_mode model_builder, train_dataloader, _, optimizer_class, criterion = get_components_func() model = model_builder(checkpoint=False) engine, train_dataloader, *args = colossalai.initialize(model=model, optimizer=optimizer_class(model.parameters(), lr=1e-3), criterion=criterion, train_dataloader=train_dataloader) try: engine.train() for data, label in train_dataloader: engine.zero_grad() data = data.cuda() label = label.cuda() if criterion: output = engine(data) loss = engine.criterion(output, label) else: loss = engine(data, label) engine.backward(loss) engine.step() break except IndexError: # if using apex amp, NetWithRepeatedlyComputedLayers will raise an index out of range issue # the following check fails in apex # if cached_x.grad_fn.next_functions[1][0].variable is not x: pass def run_engine(rank, world_size, port): # init dist env colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') run_train() @pytest.mark.dist @rerun_if_address_is_in_use() def test_engine(): world_size = 2 run_func = partial(run_engine, world_size=world_size, port=free_port()) mp.spawn(run_func, nprocs=world_size) if __name__ == '__main__': test_engine()