From 828b9e5e0d0e7b5c074e54ad1a75dd1fdaaaab2f Mon Sep 17 00:00:00 2001 From: ver217 Date: Thu, 28 Jul 2022 17:19:39 +0800 Subject: [PATCH] [hotfix] fix zero optim save/load state dict (#1381) --- colossalai/tensor/process_group.py | 4 +- colossalai/zero/zero_optimizer.py | 110 ++++++++++++++-- tests/test_zero/test_zero_optim_state_dict.py | 121 +++++++++--------- 3 files changed, 160 insertions(+), 75 deletions(-) diff --git a/colossalai/tensor/process_group.py b/colossalai/tensor/process_group.py index a9c04244a..454998c04 100644 --- a/colossalai/tensor/process_group.py +++ b/colossalai/tensor/process_group.py @@ -104,8 +104,8 @@ class ProcessGroup: def set_cpu_groups(self): if self.has_cpu_groups: return - self.logger.info( - f'{self._rank} Gloo initialize TP group on {self._tp_rank_list}, DP group on {self._dp_rank_list}') + # self.logger.info( + # f'{self._rank} Gloo initialize TP group on {self._tp_rank_list}, DP group on {self._dp_rank_list}') PYTORCHPGDICT_.get(self._tp_rank_list, 'gloo') PYTORCHPGDICT_.get(self._dp_rank_list, 'gloo') self._has_cpu_groups = True diff --git a/colossalai/zero/zero_optimizer.py b/colossalai/zero/zero_optimizer.py index 245f69008..c78d517e5 100644 --- a/colossalai/zero/zero_optimizer.py +++ b/colossalai/zero/zero_optimizer.py @@ -8,6 +8,9 @@ from colossalai.amp.naive_amp.grad_scaler import DynamicGradScaler from colossalai.logging import get_dist_logger from colossalai.nn.optimizer import ColossalaiOptimizer from colossalai.utils import get_current_device, disposable +from collections import defaultdict, abc as container_abcs +from copy import deepcopy +from itertools import chain class OptimState(Enum): @@ -191,22 +194,105 @@ class ZeroOptimizer(ColossalaiOptimizer): self.chunk_manager.add_extern_static_tensor(val) def state_dict(self): + r"""Returns the state of the optimizer as a :class:`dict`. For DP rank != 0, this function returns None. + + It contains two entries: + + * state - a dict holding current optimization state. Its content + differs between optimizer classes. + * param_groups - a list containing all parameter groups where each + parameter group is a dict + """ + is_rank_0 = self.chunk_manager.process_group.dp_local_rank() == 0 + if not self.chunk_manager.enable_distributed_storage and not is_rank_0: + return optim_state_dict = super().state_dict() scaler_state_dict = self.grad_scaler.state_dict() optim_state_dict['scaler'] = scaler_state_dict + if not self.chunk_manager.enable_distributed_storage: + return optim_state_dict + local_state = {k: convert_state_dict_to_cpu(v) for k, v in optim_state_dict['state'].items() if len(v) > 0} + if not self.chunk_manager.process_group.has_cpu_groups: + self.chunk_manager.process_group.set_cpu_groups() + dst_rank = self.chunk_manager.process_group.dp_rank_list()[0] + output = [None for _ in range(self.chunk_manager.process_group.dp_world_size())] + dist.gather_object(local_state, + output if self.chunk_manager.process_group.dp_local_rank() == 0 else None, + dst=dst_rank, + group=self.chunk_manager.process_group.cpu_dp_process_group()) + if not is_rank_0: + return + for state in output: + optim_state_dict['state'].update(state) return optim_state_dict - def load_state_dict(self, *args, **kwargs): - if 'scaler' not in args[0]: + def load_state_dict(self, state_dict): + r"""Loads the optimizer state. + + Args: + state_dict (dict): optimizer state. Should be an object returned + from a call to :meth:`state_dict`. + """ + if 'scaler' not in state_dict: self._logger.warning('Missing scaler when loading optimizer state dict', ranks=[0]) else: - scaler_state_dict = args[0].pop('scaler') - self.grad_scaler.load_state_dict(scaler_state_dict) - super().load_state_dict(*args, **kwargs) - for group in self.optim.param_groups: - for p in group['params']: - state = self.optim.state[p] - for k, v in state.items(): - if isinstance(v, torch.Tensor): - state[k] = v.to(dtype=self.fp16_param_to_fp32_param[p].dtype, - device=self.fp16_param_to_fp32_param[p].device) + self.grad_scaler.load_state_dict(deepcopy(state_dict['scaler'])) + + # Validate the state_dict + groups = self.param_groups + saved_groups = deepcopy(state_dict['param_groups']) + + if len(groups) != len(saved_groups): + raise ValueError("loaded state dict has a different number of " + "parameter groups") + param_lens = (len(g['params']) for g in groups) + saved_lens = (len(g['params']) for g in saved_groups) + if any(p_len != s_len for p_len, s_len in zip(param_lens, saved_lens)): + raise ValueError("loaded state dict contains a parameter group " + "that doesn't match the size of optimizer's group") + + # Update the state + id_map = { + old_id: p for old_id, p in zip(chain.from_iterable((g['params'] for g in saved_groups + )), chain.from_iterable((g['params'] for g in groups))) + } + + def cast(param, value): + r"""Make a deep copy of value, casting all tensors to device of param.""" + if isinstance(value, torch.Tensor): + # Floating-point types are a bit special here. They are the only ones + # that are assumed to always match the type of params. + if param.is_floating_point(): + value = value.to(param.dtype) + value = value.to(param.device) + return value + elif isinstance(value, dict): + return {k: cast(param, v) for k, v in value.items()} + elif isinstance(value, container_abcs.Iterable): + return type(value)(cast(param, v) for v in value) + else: + return value + + # Copy state assigned to params (and cast tensors to appropriate types). + # State that is not assigned to params is copied as is (needed for + # backward compatibility). + state = defaultdict(dict) + for k, v in state_dict['state'].items(): + if k in id_map: + param = self.fp16_param_to_fp32_param[id_map[k]] + if param.storage().size() > 0: + state[param] = cast(param, deepcopy(v)) + else: + state[k] = deepcopy(v) + + # Update parameter groups, setting their 'params' value + def update_group(group, new_group): + new_group['params'] = group['params'] + return new_group + + param_groups = [update_group(g, ng) for g, ng in zip(groups, saved_groups)] + self.__setstate__({'state': state, 'param_groups': param_groups}) + + +def convert_state_dict_to_cpu(state: Dict[str, torch.Tensor]): + return {k: v.cpu() if isinstance(v, torch.Tensor) else v for k, v in state.items()} diff --git a/tests/test_zero/test_zero_optim_state_dict.py b/tests/test_zero/test_zero_optim_state_dict.py index 5ab6ee0be..104ca21bc 100644 --- a/tests/test_zero/test_zero_optim_state_dict.py +++ b/tests/test_zero/test_zero_optim_state_dict.py @@ -1,100 +1,99 @@ import pytest import colossalai import torch -from colossalai.context.parallel_mode import ParallelMode import torch.multiprocessing as mp 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.core import global_context as gpc +from colossalai.gemini import ChunkManager from functools import partial -from tests.test_tensor.common_utils import set_seed from tests.components_to_test.registry import non_distributed_component_funcs -from colossalai.nn.parallel.data_parallel import ZeroDDP -from colossalai.gemini import ChunkManager, GeminiManager -from colossalai.testing import parameterize +from colossalai.nn.parallel import ZeroDDP from colossalai.nn.optimizer import HybridAdam from colossalai.zero import ZeroOptimizer +from colossalai.testing import parameterize +from colossalai.gemini.gemini_mgr import GeminiManager from colossalai.tensor import ProcessGroup -def init_zero(model, use_chunk, use_zero, placement_policy): +def check_state(s1, s2): + for v1, v2 in zip(s1.values(), s2.values()): + if isinstance(v1, torch.Tensor): + v1 = v1.to(v2.device) + assert torch.equal(v1, v2), f'{torch.sum((v1-v2).abs())}' + else: + assert v1 == v2 + + +def check_load_state_dict(optim, torch_optim): + for group, torch_group in zip(optim.optim.param_groups, torch_optim.param_groups): + for p, torch_p in zip(group['params'], torch_group['params']): + state = optim.optim.state[p] + torch_state = torch_optim.state[torch_p] + if p.storage().size() == 0: + assert len(state) == 0 + check_state(state, torch_state) + + +def check_state_dict(state_dict, torch_state_dict): + for (k1, s1), (k2, s2) in zip(state_dict['state'].items(), torch_state_dict['state'].items()): + assert k1 == k2 + check_state(s1, s2) + + +@parameterize('use_chunk', [False, True]) +@parameterize('use_zero', [False, True]) +@parameterize('placement_policy', ['cuda', 'cpu', 'auto']) +def run_zero_optim_state_dict(use_chunk, use_zero, placement_policy): + 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() + pg = ProcessGroup() + chunk_size = ChunkManager.search_chunk_size(model, 8192, 8) if use_chunk else None chunk_manager = ChunkManager(chunk_size, pg, enable_distributed_storage=use_zero, init_device=GeminiManager.get_default_device(placement_policy)) gemini_manager = GeminiManager(placement_policy, chunk_manager) - return ZeroDDP(model, gemini_manager) - - -def run_step(model, optim, criterion, data, label): - optim.zero_grad() - logits = model(data) - loss = criterion(logits, label) - optim.backward(loss) - optim.step() - - -def check_state_dict_eq(state_dict, other): - for p, state in state_dict['state'].items(): - other_state = other['state'][p] - for k, v in state.items(): - if isinstance(v, torch.Tensor): - assert torch.allclose(v, other_state[k], atol=1e-3), f'{v} vs {other_state[k]}' - else: - assert v == other_state[k] - - -@parameterize('use_chunk', [False, True]) -@parameterize('use_zero', [False, True]) -@parameterize('placement_policy', ['cuda', 'cpu']) -def run_nested_model(use_chunk, use_zero, placement_policy): - get_components_func = non_distributed_component_funcs.get_callable('nested_model') - model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func() - - set_seed(42) - with ColoInitContext(device=get_current_device()): - model = model_builder() - set_seed(42) - with ColoInitContext(device=get_current_device()): - model_copy = model_builder() - model = init_zero(model, use_chunk, use_zero, placement_policy) - model_copy = init_zero(model_copy, use_chunk, use_zero, placement_policy) - + model = ZeroDDP(model, gemini_manager) optim = HybridAdam(model.parameters(), lr=1e-3) - optim = ZeroOptimizer(optim, model, initial_scale=32) - optim_copy = HybridAdam(model_copy.parameters(), lr=1e-3) - optim_copy = ZeroOptimizer(optim_copy, model_copy, initial_scale=32) + optim = ZeroOptimizer(optim, model, initial_scale=1) - model.train() - model_copy.train() - set_seed(gpc.get_local_rank(ParallelMode.DATA)) - data_iter = iter(train_dataloader) + torch_optim = torch.optim.Adam(torch_model.parameters(), lr=1e-3) - data, label = map(lambda x: x.cuda(), next(data_iter)) - run_step(model, optim, criterion, data, label) - optim_copy.load_state_dict(optim.state_dict()) - check_state_dict_eq(optim.state_dict(), optim_copy.state_dict()) + for p in torch_model.parameters(): + p.grad = torch.rand_like(p) - data, label = map(lambda x: x.cuda(), next(data_iter)) - run_step(model_copy, optim_copy, criterion, data, label) + torch_optim.step() + torch_state_dict = torch_optim.state_dict() + optim.load_state_dict(torch_state_dict) + check_load_state_dict(optim, torch_optim) + + state_dict = optim.state_dict() + if pg.rank() == 0: + check_state_dict(state_dict, torch_state_dict) def run_dist(rank, world_size, port): - colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - run_nested_model() + config = {} + colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + run_zero_optim_state_dict() @pytest.mark.dist @pytest.mark.parametrize('world_size', [1, 2]) @rerun_if_address_is_in_use() -def test_zero_optim_state_dist(world_size): +def test_zero_optim_state_dict(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_zero_optim_state_dist(2) + test_zero_optim_state_dict(2)