mirror of https://github.com/hpcaitech/ColossalAI
[hotfix] fix zero optim save/load state dict (#1381)
ver217
GitHub
parent
b6fd165f66
commit
828b9e5e0d
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@ -104,8 +104,8 @@ class ProcessGroup:
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def set_cpu_groups(self):
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if self.has_cpu_groups:
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return
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self.logger.info(
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f'{self._rank} Gloo initialize TP group on {self._tp_rank_list}, DP group on {self._dp_rank_list}')
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# self.logger.info(
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# f'{self._rank} Gloo initialize TP group on {self._tp_rank_list}, DP group on {self._dp_rank_list}')
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PYTORCHPGDICT_.get(self._tp_rank_list, 'gloo')
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PYTORCHPGDICT_.get(self._dp_rank_list, 'gloo')
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self._has_cpu_groups = True
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@ -8,6 +8,9 @@ from colossalai.amp.naive_amp.grad_scaler import DynamicGradScaler
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from colossalai.logging import get_dist_logger
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from colossalai.nn.optimizer import ColossalaiOptimizer
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from colossalai.utils import get_current_device, disposable
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from collections import defaultdict, abc as container_abcs
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from copy import deepcopy
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from itertools import chain
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class OptimState(Enum):
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@ -191,22 +194,105 @@ class ZeroOptimizer(ColossalaiOptimizer):
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self.chunk_manager.add_extern_static_tensor(val)
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def state_dict(self):
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r"""Returns the state of the optimizer as a :class:`dict`. For DP rank != 0, this function returns None.
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It contains two entries:
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* state - a dict holding current optimization state. Its content
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differs between optimizer classes.
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* param_groups - a list containing all parameter groups where each
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parameter group is a dict
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"""
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is_rank_0 = self.chunk_manager.process_group.dp_local_rank() == 0
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if not self.chunk_manager.enable_distributed_storage and not is_rank_0:
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return
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optim_state_dict = super().state_dict()
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scaler_state_dict = self.grad_scaler.state_dict()
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optim_state_dict['scaler'] = scaler_state_dict
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if not self.chunk_manager.enable_distributed_storage:
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return optim_state_dict
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local_state = {k: convert_state_dict_to_cpu(v) for k, v in optim_state_dict['state'].items() if len(v) > 0}
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if not self.chunk_manager.process_group.has_cpu_groups:
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self.chunk_manager.process_group.set_cpu_groups()
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dst_rank = self.chunk_manager.process_group.dp_rank_list()[0]
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output = [None for _ in range(self.chunk_manager.process_group.dp_world_size())]
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dist.gather_object(local_state,
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output if self.chunk_manager.process_group.dp_local_rank() == 0 else None,
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dst=dst_rank,
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group=self.chunk_manager.process_group.cpu_dp_process_group())
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if not is_rank_0:
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return
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for state in output:
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optim_state_dict['state'].update(state)
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return optim_state_dict
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def load_state_dict(self, *args, **kwargs):
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if 'scaler' not in args[0]:
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def load_state_dict(self, state_dict):
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r"""Loads the optimizer state.
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Args:
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state_dict (dict): optimizer state. Should be an object returned
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from a call to :meth:`state_dict`.
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"""
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if 'scaler' not in state_dict:
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self._logger.warning('Missing scaler when loading optimizer state dict', ranks=[0])
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else:
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scaler_state_dict = args[0].pop('scaler')
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self.grad_scaler.load_state_dict(scaler_state_dict)
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super().load_state_dict(*args, **kwargs)
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for group in self.optim.param_groups:
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for p in group['params']:
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state = self.optim.state[p]
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for k, v in state.items():
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if isinstance(v, torch.Tensor):
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state[k] = v.to(dtype=self.fp16_param_to_fp32_param[p].dtype,
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device=self.fp16_param_to_fp32_param[p].device)
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self.grad_scaler.load_state_dict(deepcopy(state_dict['scaler']))
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# Validate the state_dict
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groups = self.param_groups
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saved_groups = deepcopy(state_dict['param_groups'])
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if len(groups) != len(saved_groups):
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raise ValueError("loaded state dict has a different number of "
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"parameter groups")
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param_lens = (len(g['params']) for g in groups)
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saved_lens = (len(g['params']) for g in saved_groups)
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if any(p_len != s_len for p_len, s_len in zip(param_lens, saved_lens)):
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raise ValueError("loaded state dict contains a parameter group "
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"that doesn't match the size of optimizer's group")
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# Update the state
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id_map = {
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old_id: p for old_id, p in zip(chain.from_iterable((g['params'] for g in saved_groups
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)), chain.from_iterable((g['params'] for g in groups)))
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}
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def cast(param, value):
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r"""Make a deep copy of value, casting all tensors to device of param."""
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if isinstance(value, torch.Tensor):
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# Floating-point types are a bit special here. They are the only ones
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# that are assumed to always match the type of params.
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if param.is_floating_point():
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value = value.to(param.dtype)
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value = value.to(param.device)
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return value
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elif isinstance(value, dict):
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return {k: cast(param, v) for k, v in value.items()}
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elif isinstance(value, container_abcs.Iterable):
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return type(value)(cast(param, v) for v in value)
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else:
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return value
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# Copy state assigned to params (and cast tensors to appropriate types).
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# State that is not assigned to params is copied as is (needed for
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# backward compatibility).
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state = defaultdict(dict)
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for k, v in state_dict['state'].items():
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if k in id_map:
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param = self.fp16_param_to_fp32_param[id_map[k]]
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if param.storage().size() > 0:
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state[param] = cast(param, deepcopy(v))
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else:
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state[k] = deepcopy(v)
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# Update parameter groups, setting their 'params' value
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def update_group(group, new_group):
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new_group['params'] = group['params']
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return new_group
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param_groups = [update_group(g, ng) for g, ng in zip(groups, saved_groups)]
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self.__setstate__({'state': state, 'param_groups': param_groups})
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def convert_state_dict_to_cpu(state: Dict[str, torch.Tensor]):
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return {k: v.cpu() if isinstance(v, torch.Tensor) else v for k, v in state.items()}
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@ -1,100 +1,99 @@
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import pytest
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import colossalai
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import torch
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from colossalai.context.parallel_mode import ParallelMode
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import torch.multiprocessing as mp
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from colossalai.testing import rerun_if_address_is_in_use
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from colossalai.utils.cuda import get_current_device
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from colossalai.utils import free_port
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from colossalai.utils.model.colo_init_context import ColoInitContext
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from colossalai.core import global_context as gpc
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from colossalai.gemini import ChunkManager
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from functools import partial
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from tests.test_tensor.common_utils import set_seed
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from tests.components_to_test.registry import non_distributed_component_funcs
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from colossalai.nn.parallel.data_parallel import ZeroDDP
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from colossalai.gemini import ChunkManager, GeminiManager
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from colossalai.testing import parameterize
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from colossalai.nn.parallel import ZeroDDP
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from colossalai.nn.optimizer import HybridAdam
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from colossalai.zero import ZeroOptimizer
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from colossalai.testing import parameterize
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from colossalai.gemini.gemini_mgr import GeminiManager
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from colossalai.tensor import ProcessGroup
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def init_zero(model, use_chunk, use_zero, placement_policy):
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def check_state(s1, s2):
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for v1, v2 in zip(s1.values(), s2.values()):
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if isinstance(v1, torch.Tensor):
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v1 = v1.to(v2.device)
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assert torch.equal(v1, v2), f'{torch.sum((v1-v2).abs())}'
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else:
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assert v1 == v2
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def check_load_state_dict(optim, torch_optim):
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for group, torch_group in zip(optim.optim.param_groups, torch_optim.param_groups):
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for p, torch_p in zip(group['params'], torch_group['params']):
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state = optim.optim.state[p]
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torch_state = torch_optim.state[torch_p]
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if p.storage().size() == 0:
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assert len(state) == 0
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check_state(state, torch_state)
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def check_state_dict(state_dict, torch_state_dict):
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for (k1, s1), (k2, s2) in zip(state_dict['state'].items(), torch_state_dict['state'].items()):
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assert k1 == k2
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check_state(s1, s2)
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@parameterize('use_chunk', [False, True])
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@parameterize('use_zero', [False, True])
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@parameterize('placement_policy', ['cuda', 'cpu', 'auto'])
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def run_zero_optim_state_dict(use_chunk, use_zero, placement_policy):
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get_components_func = non_distributed_component_funcs.get_callable('gpt2')
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model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
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with ColoInitContext(device=get_current_device()):
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model = model_builder()
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model = model.cuda()
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torch_model = model_builder().cuda()
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pg = ProcessGroup()
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chunk_size = ChunkManager.search_chunk_size(model, 8192, 8) if use_chunk else None
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chunk_manager = ChunkManager(chunk_size,
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pg,
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enable_distributed_storage=use_zero,
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init_device=GeminiManager.get_default_device(placement_policy))
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gemini_manager = GeminiManager(placement_policy, chunk_manager)
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return ZeroDDP(model, gemini_manager)
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def run_step(model, optim, criterion, data, label):
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optim.zero_grad()
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logits = model(data)
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loss = criterion(logits, label)
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optim.backward(loss)
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optim.step()
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def check_state_dict_eq(state_dict, other):
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for p, state in state_dict['state'].items():
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other_state = other['state'][p]
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for k, v in state.items():
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if isinstance(v, torch.Tensor):
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assert torch.allclose(v, other_state[k], atol=1e-3), f'{v} vs {other_state[k]}'
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else:
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assert v == other_state[k]
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@parameterize('use_chunk', [False, True])
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@parameterize('use_zero', [False, True])
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@parameterize('placement_policy', ['cuda', 'cpu'])
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def run_nested_model(use_chunk, use_zero, placement_policy):
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get_components_func = non_distributed_component_funcs.get_callable('nested_model')
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model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
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set_seed(42)
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with ColoInitContext(device=get_current_device()):
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model = model_builder()
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set_seed(42)
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with ColoInitContext(device=get_current_device()):
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model_copy = model_builder()
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model = init_zero(model, use_chunk, use_zero, placement_policy)
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model_copy = init_zero(model_copy, use_chunk, use_zero, placement_policy)
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model = ZeroDDP(model, gemini_manager)
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optim = HybridAdam(model.parameters(), lr=1e-3)
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optim = ZeroOptimizer(optim, model, initial_scale=32)
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optim_copy = HybridAdam(model_copy.parameters(), lr=1e-3)
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optim_copy = ZeroOptimizer(optim_copy, model_copy, initial_scale=32)
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optim = ZeroOptimizer(optim, model, initial_scale=1)
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model.train()
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model_copy.train()
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set_seed(gpc.get_local_rank(ParallelMode.DATA))
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data_iter = iter(train_dataloader)
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torch_optim = torch.optim.Adam(torch_model.parameters(), lr=1e-3)
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data, label = map(lambda x: x.cuda(), next(data_iter))
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run_step(model, optim, criterion, data, label)
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optim_copy.load_state_dict(optim.state_dict())
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check_state_dict_eq(optim.state_dict(), optim_copy.state_dict())
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for p in torch_model.parameters():
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p.grad = torch.rand_like(p)
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data, label = map(lambda x: x.cuda(), next(data_iter))
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run_step(model_copy, optim_copy, criterion, data, label)
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torch_optim.step()
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torch_state_dict = torch_optim.state_dict()
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optim.load_state_dict(torch_state_dict)
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check_load_state_dict(optim, torch_optim)
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state_dict = optim.state_dict()
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if pg.rank() == 0:
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check_state_dict(state_dict, torch_state_dict)
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def run_dist(rank, world_size, port):
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colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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run_nested_model()
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config = {}
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colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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run_zero_optim_state_dict()
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@pytest.mark.dist
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@pytest.mark.parametrize('world_size', [1, 2])
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@rerun_if_address_is_in_use()
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def test_zero_optim_state_dist(world_size):
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def test_zero_optim_state_dict(world_size):
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run_func = partial(run_dist, world_size=world_size, port=free_port())
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mp.spawn(run_func, nprocs=world_size)
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if __name__ == '__main__':
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test_zero_optim_state_dist(2)
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test_zero_optim_state_dict(2)
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