mirror of https://github.com/hpcaitech/ColossalAI
703 lines
35 KiB
Python
703 lines
35 KiB
Python
import copy
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import gc
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import logging
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import os
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from pathlib import Path
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from shutil import rmtree
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from typing import Dict, Iterator, Optional, OrderedDict, Tuple, Union
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import torch
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import torch.distributed as dist
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import torch.nn as nn
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from torch.distributed import ProcessGroup
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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 colossalai.interface import OptimizerWrapper
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from .general_checkpoint_io import GeneralCheckpointIO
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from .index_file import CheckpointIndexFile
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from .utils import (
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StateDictSharder,
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gather_distributed_param,
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get_model_base_filenames,
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get_optimizer_base_filenames,
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is_safetensors_available,
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load_shard_state_dict,
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load_state_dict_into_model,
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load_states_into_optimizer,
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save_config_file,
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save_param_groups,
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save_state_dict_shards,
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search_tp_partition_dim,
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sharded_optimizer_loading_epilogue,
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)
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try:
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from torch.nn.modules.module import _EXTRA_STATE_KEY_SUFFIX, _IncompatibleKeys
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except ImportError:
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_EXTRA_STATE_KEY_SUFFIX = '_extra_state'
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class HybridParallelCheckpointIO(GeneralCheckpointIO):
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"""
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CheckpointIO for Hybrid Parallel Training.
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Args:
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dp_group (ProcessGroup): Process group along data parallel dimension.
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pp_group (ProcessGroup): Process group along pipeline parallel dimension.
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tp_group (ProcessGroup): Process group along tensor parallel dimension.
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zero_stage (int): The zero stage of plugin. Should be in [0, 1, 2].
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verbose (bool, optional): Whether to print logging massage when saving/loading has been succesfully executed. Defaults to True.
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"""
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def __init__(self,
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dp_group: ProcessGroup,
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pp_group: ProcessGroup,
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tp_group: ProcessGroup,
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zero_stage: int,
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verbose: bool = True) -> None:
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super().__init__()
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self.dp_group = dp_group
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self.pp_group = pp_group
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self.tp_group = tp_group
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self.dp_rank = dist.get_rank(self.dp_group)
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self.tp_rank = dist.get_rank(self.tp_group)
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self.pp_rank = dist.get_rank(self.pp_group)
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self.dp_size = dist.get_world_size(dp_group)
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self.pp_size = dist.get_world_size(pp_group)
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self.tp_size = dist.get_world_size(tp_group)
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self.use_zero = (zero_stage > 0)
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self.verbose = verbose
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self.working_to_master_map = None
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self.master_to_working_map = None
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@staticmethod
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def _model_sharder(model: nn.Module,
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prefix: str = '',
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keep_vars: bool = False,
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size_per_shard: int = 1024) -> Iterator[Tuple[OrderedDict, int]]:
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# An internel method that breaks state_dict of model into shards within limited size.
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state_dict_sharder = StateDictSharder(size_per_shard)
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# Save parameters.
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for name, param in model.named_parameters():
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if param is None:
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continue
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# Gather tensor pieces when using tensor parallel.
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param_ = gather_distributed_param(param, keep_vars=False)
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block, block_size = state_dict_sharder.append_param(prefix + name, param_)
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if block is not None:
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yield block, block_size
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# Save buffers.
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for name, buf in model.named_buffers():
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if buf is not None and name not in model._non_persistent_buffers_set:
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buffer = buf if keep_vars else buf.detach()
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block, block_size = state_dict_sharder.append_param(prefix + name, buffer)
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if block is not None:
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yield block, block_size
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# Save extra states.
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extra_state_key = prefix + _EXTRA_STATE_KEY_SUFFIX
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if getattr(model.__class__, "get_extra_state",
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torch.nn.Module.get_extra_state) is not torch.nn.Module.get_extra_state:
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extra_state = model.get_extra_state()
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block, block_size = state_dict_sharder.append_param(extra_state_key, extra_state)
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if block is not None:
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yield block, block_size
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# Return the last block in sharder.
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yield state_dict_sharder.current_block, state_dict_sharder.current_block_size
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@staticmethod
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def _optimizer_sharder(optimizer: OptimizerWrapper,
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use_zero: bool,
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dp_group: ProcessGroup,
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tp_group: ProcessGroup,
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master_to_working_map: Optional[Dict[int, torch.Tensor]] = None,
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size_per_shard: int = 1024):
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# An internel method that breaks state_dict of optimizer into shards within limited size.
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state_dict_sharder = StateDictSharder(size_per_shard)
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param_info = optimizer.param_info
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for param, state in optimizer.optim.state.items():
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if param is None:
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continue
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if master_to_working_map is not None:
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working_param = master_to_working_map[id(param)]
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else:
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working_param = param
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param_id = param_info['param2id'][id(working_param)]
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original_shape = param_info['param2shape'][id(working_param)]
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state_ = HybridParallelCheckpointIO.gather_from_sharded_optimizer_state(state,
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working_param,
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original_shape=original_shape,
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dp_group=dp_group,
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tp_group=tp_group,
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use_zero=use_zero,
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inplace=False)
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block, block_size = state_dict_sharder.append_optim_state(param_id, state_)
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if block is not None:
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yield block, block_size
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# Return the last block in sharder.
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yield state_dict_sharder.current_block, state_dict_sharder.current_block_size
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def save_sharded_model(self,
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model: nn.Module,
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checkpoint: str,
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gather_dtensor: bool = True,
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prefix: Optional[str] = None,
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size_per_shard: int = 1024,
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use_safetensors: bool = False) -> None:
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"""
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Save sharded model 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_model.bin.index.json) containing a map between model params/buffers and file names.
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- Multiple files that store state tensors of models.
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If pipeline parallelism is used, the filenames are in the form of "pytorch_model.<prefix>-stage-000XX-shard-000XX.bin".
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If pipeline parallelism is not used, "pytorch_model.<prefix>-000XX.bin"
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Args:
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model (nn.Module): Model on local device to be saved.
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checkpoint (str): Checkpointing path which should be a directory path.
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gather_dtensor (bool, optional): Whether to gather_dtensor, currently not used. Defaults to True.
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prefix (str, optional): Perfix of file to save. Defaults to None.
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size_per_shard (int, optional): Size per shard in MB. Defaults to 1024.
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use_safetensors (bool, optional): Whether to use safe tensors. Defaults to False.
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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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# Devices along the same dp_group share the same copies of model.
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# So only let the device with dp_rank == 0 save the model.
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if self.dp_rank != 0:
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return
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# Then collect the sharded parameters & buffers along tp_group.
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# Only devices with tp_rank == 0 are responsible for model saving.
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state_dict_shard = HybridParallelCheckpointIO._model_sharder(model, size_per_shard=size_per_shard)
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weights_name, save_index_file = get_model_base_filenames(prefix, use_safetensors)
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index_file = CheckpointIndexFile(checkpoint)
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control_saving = (self.tp_rank == 0)
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if self.pp_size == 1:
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# When pipeline is not used, save the model shards as in general checkpointIO
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total_size = save_state_dict_shards(sharded_state_dict=state_dict_shard,
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checkpoint=checkpoint,
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index_file=index_file,
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base_filename=weights_name,
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is_master=control_saving,
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use_safetensors=use_safetensors)
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if control_saving:
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index_file.append_meta_data("total_size", total_size)
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index_file.write_index_file(save_index_file)
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save_config_file(model, checkpoint)
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if self.verbose:
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logging.info(f"The model is split into 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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else:
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# When pipeline is used, each stage produces its own shard files and index files.
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# Index files belonging to each stage are saved under a temporary folder ./tmp_index_files/
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# After all the state_dicts have been saved, the master rank integrates all the index files into one final index file and deletes the tmp folder.
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final_index_file_path = copy.deepcopy(save_index_file)
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tmp_index_file_folder = os.path.join(checkpoint, "tmp_index_files")
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Path(tmp_index_file_folder).mkdir(parents=True, exist_ok=True)
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# Manage filenames of sharded weights and index file for each pipeline stage.
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weights_name = weights_name.replace(".bin", f"-stage-{self.pp_rank+1:05d}-shard.bin")
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weights_name = weights_name.replace(".safetensors", f"-stage-{self.pp_rank+1:05d}-shard.safetensors")
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save_index_file = save_index_file.replace(".json", f"-stage-{self.pp_rank+1:05d}.json")
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save_index_file = os.path.join("tmp_index_files", save_index_file)
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total_size = save_state_dict_shards(sharded_state_dict=state_dict_shard,
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checkpoint=checkpoint,
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index_file=index_file,
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base_filename=weights_name,
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is_master=control_saving,
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use_safetensors=use_safetensors,
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use_pp_format=True)
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if control_saving:
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assert self.dp_rank == 0 and self.tp_rank == 0, "The saving process should have both dp_rank and tp_rank as 0."
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index_file.append_meta_data("total_size", total_size)
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index_file.write_index_file(save_index_file)
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else:
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return
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dist.barrier(self.pp_group)
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# The global master rank integrates the index files and clean the folder.
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if self.pp_rank == 0:
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final_index_file = CheckpointIndexFile(checkpoint)
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final_index_file.append_meta_data("total_size", 0)
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for filename in os.listdir(tmp_index_file_folder):
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stage_index_file = CheckpointIndexFile.from_file(os.path.join(tmp_index_file_folder, filename))
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final_index_file.metadata["total_size"] += stage_index_file.metadata["total_size"]
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for weight, weight_filename in stage_index_file.weight_map.items():
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final_index_file.append_weight_map(weight, weight_filename)
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final_index_file.write_index_file(final_index_file_path)
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save_config_file(model, checkpoint)
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rmtree(tmp_index_file_folder)
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if self.verbose:
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logging.info(f"The model is split into 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 {final_index_file_path}.")
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def load_sharded_model(self, model: nn.Module, checkpoint_index_file: Path, strict: bool = False):
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"""
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Load sharded model with the given path to index file of checkpoint folder.
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Args:
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model (nn.Module): The model to be loaded.
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checkpoint_index_file (str): Path to the index file of checkpointing folder.
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strict (bool, optional): For name matching during loading state_dict. Defaults to False.
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This argument should be manually set to False since params on same device might be stored in different files.
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"""
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# Check whether the checkpoint uses safetensors.
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use_safetensors = False
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if "safetensors" in checkpoint_index_file.name:
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use_safetensors = True
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if use_safetensors and not is_safetensors_available():
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raise ImportError("`safe_serialization` requires the `safetensors` library: `pip install safetensors`.")
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# Read checkpoint index file.
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ckpt_index_file = CheckpointIndexFile.from_file(checkpoint_index_file)
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ckpt_root_path = ckpt_index_file.root_path
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weight_map = ckpt_index_file.weight_map
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strict = False
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# Load params & buffers to model.
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# Keep a record of loaded files so that file will not be repeatedly loaded.
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loaded_file = set()
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def _load(name: str):
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if name not in weight_map:
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raise ValueError(f"{name} is not stored in checkpoint, please check your checkpointing configuration!")
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filename = weight_map[name]
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# If this param/buffer has been loaded before, directly return.
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if filename in loaded_file:
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return
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file_path = os.path.join(ckpt_root_path, filename)
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state_dict = load_shard_state_dict(Path(file_path), use_safetensors)
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missing_keys = []
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load_state_dict_into_model(model,
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state_dict,
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missing_keys=missing_keys,
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strict=strict,
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load_sub_module=True)
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loaded_file.add(filename)
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# Load parameters.
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for name, _ in model.named_parameters():
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_load(name)
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# Load buffers.
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non_persistent_buffers = set()
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for n, m in model.named_modules():
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non_persistent_buffers |= set('.'.join((n, b)) for b in m._non_persistent_buffers_set)
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for name, buf in model.named_buffers():
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if buf is not None and name not in non_persistent_buffers:
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_load(name)
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# Load extra states.
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extra_state_key = _EXTRA_STATE_KEY_SUFFIX
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if getattr(model.__class__, "get_extra_state",
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torch.nn.Module.get_extra_state) is not torch.nn.Module.get_extra_state:
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_load(extra_state_key)
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# Update master params if mixed-precision training is enabled.
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with torch.no_grad():
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if self.working_to_master_map is not None:
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for param in model.parameters():
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if (param is None) or (id(param) not in self.working_to_master_map):
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continue
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master_param = self.working_to_master_map[id(param)]
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if self.use_zero:
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# master_param is sharded under Zero setting
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padding_size = (self.dp_size - param.numel() % self.dp_size) % self.dp_size
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if padding_size > 0:
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padded_param = torch.nn.functional.pad(param.data.view(-1), [0, padding_size])
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else:
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padded_param = param.data.view(-1)
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sharded_param = padded_param.split(padded_param.numel() // self.dp_size)[self.dp_rank]
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master_param.data.copy_(sharded_param.data)
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else:
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master_param.data.copy_(param.data)
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if self.verbose:
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logging.info(f"The model has been successfully loaded from sharded checkpoint: {ckpt_root_path}.")
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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 = True,
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prefix: Optional[str] = None,
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size_per_shard: int = 1024):
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"""
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Save sharded 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 that store state tensors of optimizers.
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If pipeline parallelism is used, the filenames are in the form of "pytorch_optim.<prefix>-stage-000XX-shard-000XX.bin".
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If pipeline parallelism is not used, "pytorch_optim.<prefix>-000XX.bin"
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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 shard 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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# Devices along the same dp_group share the same copies of states when zero is not used.
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# In this case only let the device with dp_rank == 0 save the model.
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if not self.use_zero and self.dp_rank != 0:
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return
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# Then collect the sharded states along dp_group(if using zero)/tp_group.
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# Only devices with (dp_rank == 0 and tp_rank == 0) are responsible for states saving.
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state_dict_shard = HybridParallelCheckpointIO._optimizer_sharder(
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optimizer,
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use_zero=self.use_zero,
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dp_group=self.dp_group,
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tp_group=self.tp_group,
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master_to_working_map=self.master_to_working_map,
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size_per_shard=size_per_shard)
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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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control_saving = (self.dp_rank == 0 and self.tp_rank == 0)
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if self.pp_size == 1:
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# When pipeline is not used, save the optimizer shards as in general checkpointIO
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total_size = save_state_dict_shards(sharded_state_dict=state_dict_shard,
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checkpoint=checkpoint,
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index_file=index_file,
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base_filename=states_name,
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is_master=control_saving)
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if control_saving:
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# Store param groups.
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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(optimizer.param_info, group_file_path)
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# Store index file.
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index_file.append_meta_data("total_size", total_size)
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index_file.write_index_file(save_index_file)
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if self.verbose:
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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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else:
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# When pipeline is used, each stage produces its own shard files and index files.
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# Index files belonging to each stage are saved under a temporary folder ./tmp_index_files/
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# After all the state_dicts have been saved, the master rank integrates all the index files into one final index file and deletes the tmp folder.
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final_index_file_path = copy.deepcopy(save_index_file)
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tmp_index_file_folder = os.path.join(checkpoint, "tmp_index_files")
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Path(tmp_index_file_folder).mkdir(parents=True, exist_ok=True)
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# Manage filenames of sharded weights and index file for each pipeline stage.
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states_name = states_name.replace(".bin", f"-stage-{self.pp_rank+1:05d}-shard.bin")
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save_index_file = save_index_file.replace(".json", f"-stage-{self.pp_rank+1:05d}.json")
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save_index_file = os.path.join("tmp_index_files", save_index_file)
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|
|
total_size = save_state_dict_shards(sharded_state_dict=state_dict_shard,
|
|
checkpoint=checkpoint,
|
|
index_file=index_file,
|
|
base_filename=states_name,
|
|
is_master=control_saving,
|
|
use_pp_format=True)
|
|
|
|
if control_saving:
|
|
assert self.dp_rank == 0 and self.tp_rank == 0, "The saving process should have both dp_rank and tp_rank as 0."
|
|
index_file.append_meta_data("total_size", total_size)
|
|
index_file.write_index_file(save_index_file)
|
|
else:
|
|
return
|
|
|
|
dist.barrier(self.pp_group)
|
|
|
|
# The global master rank integrates the index files and clean the folder.
|
|
if self.pp_rank == 0:
|
|
|
|
final_index_file = CheckpointIndexFile(checkpoint)
|
|
final_index_file.append_meta_data("total_size", 0)
|
|
|
|
for filename in os.listdir(tmp_index_file_folder):
|
|
stage_index_file = CheckpointIndexFile.from_file(os.path.join(tmp_index_file_folder, filename))
|
|
final_index_file.metadata["total_size"] += stage_index_file.metadata["total_size"]
|
|
for param_id, state_filename in stage_index_file.weight_map.items():
|
|
final_index_file.append_weight_map(param_id, state_filename)
|
|
|
|
# Store param groups.
|
|
final_index_file.append_meta_data("param_groups", param_group_file)
|
|
group_file_path = os.path.join(checkpoint, param_group_file)
|
|
save_param_groups(optimizer.param_info, group_file_path)
|
|
|
|
final_index_file.write_index_file(final_index_file_path)
|
|
rmtree(tmp_index_file_folder)
|
|
|
|
if self.verbose:
|
|
logging.info(f"The model is split into checkpoint shards. "
|
|
f"You can find where each parameters has been saved in the "
|
|
f"index located at {final_index_file_path}.")
|
|
|
|
def load_sharded_optimizer(self, optimizer: OptimizerWrapper, checkpoint_index_file: str, prefix: str = ""):
|
|
"""
|
|
Load sharded optimizer with the given path to index file of checkpoint folder.
|
|
|
|
Args:
|
|
optimizer (OptimizerWrapper): The optimizer to be loaded.
|
|
checkpoint_index_file (str): Path to the index file of checkpointing folder.
|
|
prefix (str): Not used.
|
|
"""
|
|
|
|
def _get_param_id_from_optimizer_param(param: torch.Tensor,
|
|
master_to_working_map: Optional[Dict[int, torch.Tensor]] = None):
|
|
if master_to_working_map is not None:
|
|
working_param = master_to_working_map[id(param)]
|
|
else:
|
|
working_param = param
|
|
return optimizer.param_info['param2id'][id(working_param)]
|
|
|
|
# id_map is a mapping from param ids kept by current pipeline, to their corresponding parameter objects.
|
|
# When Zero is used, the mapped parameter objects should be fp32 master parameters.
|
|
# IDs should be obtained through saved param2id mapping earlier saved in optimizer.param_info.
|
|
id_map = {}
|
|
for pg in optimizer.optim.param_groups:
|
|
for param in pg['params']:
|
|
param_id = _get_param_id_from_optimizer_param(param, self.master_to_working_map)
|
|
id_map[param_id] = param
|
|
|
|
# Read checkpoint index file.
|
|
ckpt_index_file = CheckpointIndexFile.from_file(checkpoint_index_file)
|
|
ckpt_root_path = ckpt_index_file.root_path
|
|
weight_map = ckpt_index_file.weight_map
|
|
weight_map = {int(k): v for k, v in weight_map.items()} # convert saved id from str to int
|
|
|
|
# Load param_groups
|
|
param_group_path = ckpt_index_file.get_param_group_filename()
|
|
if param_group_path is None:
|
|
raise RuntimeError(f'Invalid index file path {checkpoint_index_file} for an optimizer. \
|
|
Lacking param group file under current directory.')
|
|
saved_groups = torch.load(param_group_path)
|
|
|
|
updated_groups = []
|
|
for old_pg, saved_pg in zip(optimizer.optim.param_groups, saved_groups):
|
|
# obtain updated param group
|
|
new_pg = copy.deepcopy(saved_pg)
|
|
new_pg['params'] = old_pg['params'] # The parameters in the same group shouln't change.
|
|
updated_groups.append(new_pg)
|
|
optimizer.optim.__dict__.update({'param_groups': updated_groups})
|
|
|
|
# Load saved states to optimizer.
|
|
# Keep a record of loaded files so that file will not be repeatedly loaded.
|
|
loaded_file = set()
|
|
for pg in optimizer.optim.param_groups:
|
|
for param in pg['params']:
|
|
if param is None:
|
|
continue
|
|
param_id = _get_param_id_from_optimizer_param(param, self.master_to_working_map)
|
|
if param_id not in weight_map:
|
|
continue
|
|
filename = weight_map[param_id]
|
|
|
|
# If this param's states has been loaded before, directly return.
|
|
if filename in loaded_file:
|
|
continue
|
|
|
|
file_path = os.path.join(ckpt_root_path, filename)
|
|
state_dict = load_shard_state_dict(Path(file_path), use_safetensors=False)
|
|
load_states_into_optimizer(optimizer.optim, state_dict, id_map, strict=True)
|
|
loaded_file.add(filename)
|
|
|
|
# Then shard the loaded optimizer states if using tp/zero.
|
|
for param, state in optimizer.optim.state.items():
|
|
device = param.device
|
|
if self.master_to_working_map is not None:
|
|
working_param = self.master_to_working_map[id(param)]
|
|
else:
|
|
working_param = param
|
|
original_shape = optimizer.param_info['param2shape'][id(working_param)]
|
|
sharded_state = self.shard_from_complete_optimizer_state(state,
|
|
current_shape=working_param.shape,
|
|
original_shape=original_shape,
|
|
device=device,
|
|
inplace=True)
|
|
optimizer.optim.state[param] = sharded_state
|
|
|
|
sharded_optimizer_loading_epilogue(optimizer.optim)
|
|
if self.verbose:
|
|
logging.info(f"The optimizer has been successfully loaded from sharded checkpoint: {ckpt_root_path}.")
|
|
|
|
def load_unsharded_model(self, model: nn.Module, checkpoint: str, strict: bool = True):
|
|
# TODO(Baizhou): support this feature after implementing complete state_dict collection
|
|
raise NotImplementedError
|
|
|
|
def save_unsharded_model(self, model: nn.Module, checkpoint: str, gather_dtensor: bool, use_safetensors: bool):
|
|
# TODO(Baizhou): support this feature after implementing complete state_dict collection
|
|
raise NotImplementedError
|
|
|
|
def save_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: str, gather_dtensor: bool):
|
|
# TODO(Baizhou): support this feature after implementing complete state_dict collection
|
|
raise NotImplementedError
|
|
|
|
def load_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: str, gather_dtensor: bool):
|
|
# TODO(Baizhou): support this feature after implementing complete state_dict collection
|
|
raise NotImplementedError
|
|
|
|
def save_lr_scheduler(self, lr_scheduler: LRScheduler, checkpoint: str):
|
|
"""
|
|
Save lr scheduler to checkpoint but only on master process.
|
|
"""
|
|
if self.coordinator.is_master():
|
|
super().save_lr_scheduler(lr_scheduler, checkpoint)
|
|
|
|
def link_master_and_working_param(self, working_to_master_map: Dict[Union[int, torch.Tensor], torch.Tensor],
|
|
master_to_working_map: Dict[Union[int, torch.Tensor], torch.Tensor]):
|
|
"""
|
|
Create mappings between working params (for forward/backward) and master params (for optimizer update) with passed in mappings.
|
|
This mapping can only be created when mixied precision is used.
|
|
The created mappings should be mappings from integer parameter addresses to parameter objects.
|
|
|
|
Args:
|
|
working_to_master_map (Dict[Union[int, torch.Tensor], torch.Tensor]): A mapping from working parameters objects/addresses to master parameter objects.
|
|
master_to_working_map (Dict[Union[int, torch.Tensor], torch.Tensor]): A mapping from master parameters objects/addresses to working parameter objects.
|
|
"""
|
|
self.working_to_master_map = dict()
|
|
for k, v in working_to_master_map.items():
|
|
if isinstance(k, torch.Tensor):
|
|
self.working_to_master_map[id(k)] = v
|
|
elif isinstance(k, int):
|
|
self.working_to_master_map[k] = v
|
|
else:
|
|
raise ValueError(
|
|
f"The passed in mapping should have keys of type 'int' or 'torch.Tensor', but got {type(k)}!")
|
|
|
|
self.master_to_working_map = dict()
|
|
for k, v in master_to_working_map.items():
|
|
if isinstance(k, torch.Tensor):
|
|
self.master_to_working_map[id(k)] = v
|
|
elif isinstance(k, int):
|
|
self.master_to_working_map[k] = v
|
|
else:
|
|
raise ValueError(
|
|
f"The passed in mapping should have keys of type 'int' or 'torch.Tensor', but got {type(k)}!")
|
|
|
|
@staticmethod
|
|
def gather_from_sharded_optimizer_state(state: OrderedDict, param: torch.Tensor, original_shape: torch.Size,
|
|
dp_group: ProcessGroup, tp_group: ProcessGroup, use_zero: bool,
|
|
inplace: bool) -> OrderedDict:
|
|
"""
|
|
With given parameter and its optimizer states, gather the complete optimizer state for saving.
|
|
|
|
Args:
|
|
state (OrderedDict): Optimizer states of given parameter, might be distributed among tp/dp group if using TP/Zero.
|
|
param (torch.Tensor): The given parameter. It should be working_param when using Zero.
|
|
original_shape (torch.Size): The size of parameter before sharding.
|
|
dp_group (ProcessGroup): The process group of data parallel.
|
|
tp_group (ProcessGroup): The process group of tensor parallel.
|
|
use_zero (bool): Whether Zero is used.
|
|
inplace (bool): If set to True, will update the values of argument 'state' in place. Else will make a copy of state.
|
|
|
|
Returns:
|
|
OrderedDict: The complete optimizer state of given parameter.
|
|
"""
|
|
dp_size = dist.get_world_size(dp_group)
|
|
tp_size = dist.get_world_size(tp_group)
|
|
current_shape = param.shape
|
|
state_ = state if inplace else copy.deepcopy(state)
|
|
|
|
for k, v in state_.items():
|
|
if isinstance(v, torch.Tensor) and k != 'step':
|
|
|
|
# First gather Zero shards.
|
|
if use_zero:
|
|
v = v.cuda()
|
|
gather_tensor = [torch.zeros_like(v) for _ in range(dp_size)]
|
|
dist.all_gather(gather_tensor, v, group=dp_group)
|
|
v = torch.stack(gather_tensor).view(-1)[:param.numel()].reshape_as(param)
|
|
|
|
# Then gather TP shards.
|
|
partition_dim = search_tp_partition_dim(current_shape, original_shape, tp_size)
|
|
if partition_dim is not None:
|
|
gather_tensor = [torch.zeros_like(v) for _ in range(tp_size)]
|
|
dist.all_gather(gather_tensor, v, group=tp_group)
|
|
v = torch.cat(gather_tensor, dim=partition_dim)
|
|
|
|
state_[k] = v.detach().clone().cpu()
|
|
|
|
return state_
|
|
|
|
def shard_from_complete_optimizer_state(self, state: OrderedDict, current_shape: torch.Size,
|
|
original_shape: torch.Size, device: torch.device,
|
|
inplace: bool) -> OrderedDict:
|
|
"""
|
|
With complete optimizer states of a specific parameter loaded from checkpoint,
|
|
slice out the sharded optimizer states kept by current device.
|
|
|
|
Args:
|
|
state (OrderedDict): Complete optimizer states of a given parameter, loaded from checkpoint.
|
|
current_shape (torch.Size): The size of parameter after sharding.
|
|
original_shape (torch.Size): The size of parameter before sharding.
|
|
device (torch.device): The destination device of loaded optimizer states.
|
|
inplace (bool): If set to True, will update the values of argument 'state' in place. Else will make a copy of state.
|
|
|
|
Returns:
|
|
OrderedDict: The sharded optimizer state of the given parameter.
|
|
"""
|
|
state_ = state if inplace else copy.deepcopy(state)
|
|
|
|
for k, v in state_.items():
|
|
if isinstance(v, torch.Tensor) and k != 'step':
|
|
|
|
# Shard state along tensor parallel group.
|
|
partition_dim = search_tp_partition_dim(current_shape, original_shape, self.tp_size)
|
|
if partition_dim is not None:
|
|
slice_size = current_shape[partition_dim]
|
|
v = v.split(slice_size, dim=partition_dim)[self.tp_rank]
|
|
|
|
# Shard state along data parallel group when using Zero.
|
|
if self.use_zero:
|
|
padding_size = (self.dp_size - v.numel() % self.dp_size) % self.dp_size
|
|
with torch.no_grad():
|
|
v = v.flatten()
|
|
if padding_size > 0:
|
|
v = torch.nn.functional.pad(v, [0, padding_size])
|
|
slice_size = v.numel() // self.dp_size
|
|
v = v.split(slice_size, dim=0)[self.dp_rank]
|
|
|
|
state_[k] = v.detach().clone().to(device)
|
|
|
|
return state_
|