mirror of https://github.com/hpcaitech/ColossalAI
293 lines
13 KiB
Python
293 lines
13 KiB
Python
import logging
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import os
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import warnings
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from functools import partial
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from pathlib import Path
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from typing import Callable, Iterator, List, Optional, Tuple, Union
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import torch
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import torch.nn as nn
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from torch import Tensor
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from torch.optim import Optimizer
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from torch.optim.lr_scheduler import _LRScheduler as LRScheduler
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from torch.utils._pytree import tree_map
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from torch.utils.data import DataLoader
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from colossalai.checkpoint_io import CheckpointIndexFile, CheckpointIO
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from colossalai.checkpoint_io.utils import (
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get_optimizer_base_filenames,
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get_shard_filename,
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save_param_groups,
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save_state_dict,
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)
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from colossalai.interface import ModelWrapper, OptimizerWrapper
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from colossalai.utils import get_current_device
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from colossalai.zero import LowLevelZeroOptimizer, zero_model_wrapper, zero_optim_wrapper
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from .dp_plugin_base import DPPluginBase
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from .torch_ddp_plugin import TorchDDPCheckpointIO
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__all__ = ['LowLevelZeroPlugin']
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def _convert_floating_point(x, dtype: torch.dtype = torch.float16):
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if isinstance(x, torch.Tensor) and torch.is_floating_point(x):
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return x.to(dtype)
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return x
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SUPPORTED_PRECISION = ['fp16', 'bf16', 'fp32']
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class LowLevelZeroCheckpointIO(TorchDDPCheckpointIO):
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def save_unsharded_optimizer(self, optimizer: OptimizerWrapper, checkpoint: str, gather_dtensor: bool = False):
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"""Save optimizer to checkpoint but only on master process.
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Args:
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optimizer (OptimizerWrapper): Optimizer to save state_dict
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checkpoint (str): Path to save checkpoint
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gather_dtensor (bool): Whether to gather_dtensor, not used
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"""
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# the `state_dict` in LowLevelZeroOptimizer has communication
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# if only the master rank collect state_dict and save,
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# the communication on each rank would not match
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state_dict = optimizer.state_dict()
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if self.coordinator.is_master():
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save_state_dict(state_dict, checkpoint, use_safetensors=False)
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def save_sharded_optimizer(self,
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optimizer: OptimizerWrapper,
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checkpoint: str,
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gather_dtensor: bool = False,
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prefix: str = None,
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size_per_shard: int = 1024):
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"""
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Save sharded Zero-optimizer checkpoint under the given checkpointing path.
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The following files will be created under the path:
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- An index file (pytorch_optim.bin.index.json) containing a map between optimizer states and file names
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- A group file (pytorch_optim_group.bin) recording information of param_groups
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- Multiple files (pytorch_optim-000XX.bin) that store state tensors of optimizer in a sharding way
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Args:
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optimizer (OptimizerWrapper): Optimizer to save sharded state_dict
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checkpoint (str): Path to save optimizer state_dict
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gather_dtensor (bool): Whether to gather_dtensor, not used
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prefix (str): Perfix of file to save
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size_per_shard (int): Max file size of each file that store state tensors
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"""
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if os.path.isfile(checkpoint):
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logging.error(f"Provided path ({checkpoint}) should be a directory, not a file")
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return
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Path(checkpoint).mkdir(parents=True, exist_ok=True)
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# state_dict only provide only 'param_groups'
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state_dict = optimizer.optim.state_dict()
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# state shard would be handled by the low-level zero optimizer
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sharded_state = optimizer.state_dict_shard(max_shard_size=size_per_shard)
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# Preparing file paths and index file.
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states_name, save_index_file, param_group_file = get_optimizer_base_filenames(prefix)
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index_file = CheckpointIndexFile(checkpoint)
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# Store the information of param groups to param_group_file.
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index_file.append_meta_data("param_groups", param_group_file)
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group_file_path = os.path.join(checkpoint, param_group_file)
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save_param_groups(state_dict, group_file_path)
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# Save shards of optimizer states.
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total_size = 0
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for idx, shard_pair in enumerate(sharded_state):
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shard, current_size = shard_pair
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shard_file = get_shard_filename(states_name, idx)
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total_size = total_size + current_size
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for param_id in shard.keys():
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index_file.append_weight_map(str(param_id), shard_file)
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checkpoint_file_path = os.path.join(checkpoint, shard_file)
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if self.coordinator.is_master():
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save_state_dict(shard, checkpoint_file_path, use_safetensors=False)
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# Wrap up index file.
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index_file.append_meta_data("total_size", total_size)
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if self.coordinator.is_master():
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index_file.write_index_file(save_index_file)
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logging.info(f"The optimizer is going to be split to checkpoint shards. "
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f"You can find where each parameters has been saved in the "
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f"index located at {save_index_file}.")
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def load_sharded_optimizer(self, optimizer: OptimizerWrapper, index_file_path: str, prefix: str):
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"""Load sharded optimizer with the given path to index file.
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Args:
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optimizer (OptimizerWrapper): Optimizer to load state_dict
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index_file_path (str): Path to the index file
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prefix (str): Not used.
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"""
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super().load_sharded_optimizer(optimizer, index_file_path, prefix)
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current_rank_state_dict = optimizer.optim.state_dict()['state']
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for param_idx, state in current_rank_state_dict.items():
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for k, v in state.items():
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if isinstance(v, torch.Tensor) and k != 'step':
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padding_size = (self.coordinator.world_size -
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v.numel() % self.coordinator.world_size) % self.coordinator.world_size
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with torch.no_grad():
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v = v.flatten()
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if padding_size > 0:
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v = torch.nn.functional.pad(v, [0, padding_size])
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v_list = v.split(v.numel() // self.coordinator.world_size)
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current_rank_state_dict[param_idx][k] = v_list[self.coordinator.rank].detach()
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class LowLevelZeroModel(ModelWrapper):
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def __init__(self, module: nn.Module, stage: int, precision: str) -> None:
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super().__init__(module)
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self.dtype = None
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if precision == 'fp16':
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self.dtype = torch.float16
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elif precision == 'bf16':
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self.dtype = torch.bfloat16
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module = zero_model_wrapper(module, zero_stage=stage)
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if self.dtype is not None:
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module = module.to(self.dtype)
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module = module.to(get_current_device())
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self.module = module
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self.convert_fn = None
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if self.dtype is not None:
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self.convert_fn = partial(_convert_floating_point, dtype=self.dtype)
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def forward(self, *args, **kwargs):
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if self.convert_fn is not None:
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args = tree_map(self.convert_fn, args)
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kwargs = tree_map(self.convert_fn, kwargs)
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return super().forward(*args, **kwargs)
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class LowLevelZeroPlugin(DPPluginBase):
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"""
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Plugin for low level zero.
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Example:
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>>> from colossalai.booster import Booster
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>>> from colossalai.booster.plugin import LowLevelZeroPlugin
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>>>
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>>> model, train_dataset, optimizer, criterion = ...
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>>> plugin = LowLevelZeroPlugin()
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>>> train_dataloader = plugin.prepare_dataloader(train_dataset, batch_size=8)
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>>> booster = Booster(plugin=plugin)
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>>> model, optimizer, train_dataloader, criterion = booster.boost(model, optimizer, train_dataloader, criterion)
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Args:
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strage (int, optional): ZeRO stage. Defaults to 1.
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precision (str, optional): precision. Support 'fp16', 'bf16' and 'fp32'. Defaults to 'fp16'.
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initial_scale (float, optional): Initial scale used by DynamicGradScaler. Defaults to 2**32.
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min_scale (float, optional): Min scale used by DynamicGradScaler. Defaults to 1.
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growth_factor (float, optional): growth_factor used by DynamicGradScaler. Defaults to 2.
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backoff_factor (float, optional): backoff_factor used by DynamicGradScaler. Defaults to 0.5.
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growth_interval (float, optional): growth_interval used by DynamicGradScaler. Defaults to 1000.
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hysteresis (float, optional): hysteresis used by DynamicGradScaler. Defaults to 2.
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max_scale (int, optional): max_scale used by DynamicGradScaler. Defaults to 2**32.
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max_norm (float, optional): max_norm used for `clip_grad_norm`. You should notice that you shall not do
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clip_grad_norm by yourself when using ZeRO DDP. The ZeRO optimizer will take care of clip_grad_norm.
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norm_type (float, optional): norm_type used for `clip_grad_norm`.
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reduce_bucket_size_in_m (int, optional): grad reduce bucket size in M. Defaults to 12.
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communication_dtype (torch.dtype, optional): communication dtype. If not specified, the dtype of param will be used. Defaults to None.
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overlap_communication (bool, optional): whether to overlap communication and computation. Defaults to True.
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cpu_offload (bool, optional): whether to offload grad, master weight and optimizer state to cpu. Defaults to False.
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verbose (bool, optional): verbose mode. Debug info including grad overflow will be printed. Defaults to False.
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"""
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def __init__(
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self,
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stage: int = 1,
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precision: str = 'fp16',
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initial_scale: float = 2**32,
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min_scale: float = 1,
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growth_factor: float = 2,
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backoff_factor: float = 0.5,
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growth_interval: int = 1000,
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hysteresis: int = 2,
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max_scale: float = 2**32,
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max_norm: float = 0.0,
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norm_type: float = 2.0,
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reduce_bucket_size_in_m: int = 12,
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communication_dtype: Optional[torch.dtype] = None,
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overlap_communication: bool = True,
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cpu_offload: bool = False,
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verbose: bool = False,
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) -> None:
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super().__init__()
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assert stage in (1, 2), f'LowLevelZeroPlugin only supports stage 1/2 training'
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assert precision in SUPPORTED_PRECISION, f'LowLevelZeroPlugin only supports {SUPPORTED_PRECISION} training'
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self.stage = stage
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self.precision = precision
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self.zero_optim_config = dict(reduce_bucket_size=reduce_bucket_size_in_m * 1024 * 1024,
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communication_dtype=communication_dtype,
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overlap_communication=overlap_communication,
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cpu_offload=cpu_offload)
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self.optim_kwargs = dict(initial_scale=initial_scale,
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growth_factor=growth_factor,
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backoff_factor=backoff_factor,
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growth_interval=growth_interval,
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hysteresis=hysteresis,
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min_scale=min_scale,
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max_scale=max_scale,
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max_norm=max_norm,
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norm_type=norm_type)
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self.verbose = verbose
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# set class name with stage, for better error message
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setattr(self.__class__, "__name__", f"LowLevelZeroPlugin_ZeRO-{stage}")
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def support_no_sync(self) -> bool:
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return self.stage == 1
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def control_precision(self) -> bool:
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return True
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def supported_precisions(self) -> List[str]:
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return SUPPORTED_PRECISION
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def control_device(self) -> bool:
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return True
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def supported_devices(self) -> List[str]:
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return ['cuda']
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def configure(
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self,
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model: nn.Module,
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optimizer: Optional[Optimizer] = None,
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criterion: Optional[Callable] = None,
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dataloader: Optional[DataLoader] = None,
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lr_scheduler: Optional[LRScheduler] = None,
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) -> Tuple[nn.Module, OptimizerWrapper, Callable, DataLoader, LRScheduler]:
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if not isinstance(model, ModelWrapper):
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model = LowLevelZeroModel(model, self.stage, self.precision)
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if optimizer is not None and \
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not isinstance(optimizer, OptimizerWrapper):
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optimizer = zero_optim_wrapper(model.unwrap(),
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optimizer,
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optim_config=self.zero_optim_config,
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**self.optim_kwargs,
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verbose=self.verbose)
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return model, optimizer, criterion, dataloader, lr_scheduler
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def control_checkpoint_io(self) -> bool:
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return True
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def get_checkpoint_io(self) -> CheckpointIO:
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return LowLevelZeroCheckpointIO()
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def no_sync(self, model: nn.Module, optimizer: OptimizerWrapper) -> Iterator[None]:
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assert isinstance(optimizer, LowLevelZeroOptimizer)
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return optimizer.optim.no_sync()
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