import pytest import torch import torch.distributed as dist from torch.distributed.distributed_c10d import _get_default_group import colossalai from colossalai.accelerator import get_accelerator from colossalai.tensor import ColoParameter from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn from colossalai.zero.gemini import TensorState from colossalai.zero.gemini.chunk import Chunk def dist_sum(x): temp = torch.tensor([x], device=get_accelerator().get_current_device()) dist.all_reduce(temp) return temp.item() def add_param(param_list, param_cp_list, *args, **kwargs): param = ColoParameter(torch.randn(*args, **kwargs)) param_list.append(param) param_cp_list.append(param.clone()) def check_equal(param, param_cp): if param.device != param_cp.device: temp = param.data.to(param_cp.device) else: temp = param.data return torch.equal(temp, param_cp.data) @parameterize("init_device", [None, torch.device("cpu")]) @parameterize("keep_gathered", [True, False]) @parameterize("pin_memory", [True, False]) def exam_chunk_basic(init_device, keep_gathered, pin_memory): world_size = torch.distributed.get_world_size() pg = _get_default_group() my_chunk = Chunk( chunk_size=1024, zero_group=pg, dtype=torch.float32, init_device=init_device, cpu_shard_init=True, keep_gathered=keep_gathered, pin_memory=pin_memory, ) param_list = [] param_cp_list = [] add_param(param_list, param_cp_list, 8, 8, 8, device="cuda") add_param(param_list, param_cp_list, 4, 4) add_param(param_list, param_cp_list, 4, 8, 2, device="cuda") add_param(param_list, param_cp_list, 1, 1, 5) for param in param_list: my_chunk.append_tensor(param) assert my_chunk.utilized_size == 597 for param, param_cp in zip(param_list, param_cp_list): check_equal(param, param_cp) my_chunk.close_chunk() if keep_gathered is False: assert my_chunk.cpu_shard.size(0) == 1024 // world_size assert my_chunk.device_type == "cpu" assert my_chunk.can_move my_chunk.shard_move(get_accelerator().get_current_device()) else: assert my_chunk.cuda_global_chunk.size(0) == 1024 assert my_chunk.device_type == "cuda" assert not my_chunk.can_move assert dist_sum(my_chunk.valid_end) == my_chunk.utilized_size flag = my_chunk.has_inf_or_nan assert not flag, "has_inf_or_nan is {}".format(flag) my_chunk.access_chunk() assert my_chunk.device_type == "cuda" for param, param_cp in zip(param_list, param_cp_list): check_equal(param, param_cp) assert my_chunk.tensor_state_cnter[TensorState.HOLD] == 4 my_chunk.tensor_trans_state(param_list[0], TensorState.COMPUTE) assert my_chunk.tensor_state_cnter[TensorState.HOLD] == 3 assert my_chunk.tensor_state_cnter[TensorState.COMPUTE] == 1 assert not my_chunk.can_release for param in param_list: my_chunk.tensor_trans_state(param, TensorState.COMPUTE) my_chunk.tensor_trans_state(param, TensorState.HOLD_AFTER_BWD) my_chunk.tensor_trans_state(param, TensorState.READY_FOR_REDUCE) assert my_chunk.tensor_state_cnter[TensorState.READY_FOR_REDUCE] == 4 assert my_chunk.can_reduce my_chunk.reduce() assert my_chunk.tensor_state_cnter[TensorState.HOLD] == 4 if keep_gathered is False: assert my_chunk.cuda_shard.size(0) == 1024 // world_size assert my_chunk.device_type == "cuda" assert my_chunk.can_move else: assert my_chunk.cuda_global_chunk.size(0) == 1024 assert my_chunk.device_type == "cuda" assert not my_chunk.can_move def run_dist(rank, world_size, port): colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl") exam_chunk_basic() @pytest.mark.dist @pytest.mark.parametrize("world_size", [1, 2, 4]) @rerun_if_address_is_in_use() def test_chunk_function(world_size): spawn(run_dist, world_size) if __name__ == "__main__": test_chunk_function(4)