mirror of https://github.com/hpcaitech/ColossalAI
793 lines
35 KiB
Python
793 lines
35 KiB
Python
import copy
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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
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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 colossalai.checkpoint_io import CheckpointIndexFile, HybridParallelCheckpointIO
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from colossalai.checkpoint_io.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,
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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,
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save_state_dict_shards,
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sharded_optimizer_loading_epilogue,
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)
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from colossalai.interface import OptimizerWrapper
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from colossalai.moe.manager import MOE_MANAGER
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from colossalai.tensor.moe_tensor.api import (
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get_dp_group,
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get_dp_rank,
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get_dp_size,
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get_ep_group,
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get_ep_rank,
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get_ep_size,
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is_moe_tensor,
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)
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class MoECheckpointIO(HybridParallelCheckpointIO):
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def __init__(
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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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) -> None:
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assert zero_stage in [
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0,
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1,
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2,
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], f"zero_stage should be 0 or 1 or 2, got {zero_stage}"
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super().__init__(dp_group, pp_group, tp_group, zero_stage)
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self.parallel = MOE_MANAGER.parallel
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def pre_load_model(self, model: nn.Module, state_dict: dict) -> dict:
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"""
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Preprocess state_dict before loading and slice the state_dict of MOE tensors.
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"""
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for name, param in state_dict.items():
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if ".experts." in name:
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if name in dict(model.named_parameters()):
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model_param = dict(model.named_parameters())[name]
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if is_moe_tensor(model_param):
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ep_rank = get_ep_rank(model_param)
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ep_size = get_ep_size(model_param)
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expert_num = param.shape[0] // ep_size
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assert param.shape[0] % ep_size == 0
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param = param[ep_rank * expert_num : (ep_rank + 1) * expert_num]
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state_dict[name] = param
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dist.barrier()
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return state_dict
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def _model_sharder(
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self,
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state_dict: 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,
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) -> 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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for name, param in state_dict.items():
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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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# 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 load_unsharded_model(self, model: nn.Module, checkpoint: str, strict: bool) -> None:
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state_dict = torch.load(checkpoint)
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state_dict = self.pre_load_model(model, state_dict)
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model.load_state_dict(state_dict, strict=strict if self.pp_size == 1 else False)
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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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state_dict = self.pre_load_model(model, state_dict)
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missing_keys = []
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load_state_dict_into_model(
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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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)
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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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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 pre_save_model(self, model: nn.Module) -> dict:
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state_dict = model.state_dict()
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for name, param in model.named_parameters():
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if ".experts." in name and is_moe_tensor(param):
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ep_group = get_ep_group(param)
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ep_rank = get_ep_rank(param)
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ep_size = get_ep_size(param)
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dp_rank = get_dp_rank(param)
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if dp_rank == 0:
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param = param.data.cuda()
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all_param = [torch.zeros_like(param) for _ in range(ep_size)]
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# gather param from every ep rank
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dist.all_gather(all_param, param, group=ep_group)
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if ep_rank == 0:
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all_param = torch.cat(all_param, dim=0)
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state_dict[name] = all_param.cpu()
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if self.pp_size > 1:
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if self.dp_rank == 0:
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out = [None for _ in range(self.pp_size)]
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dist.all_gather_object(out, state_dict, group=self.pp_group)
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if self.pp_rank == 0:
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new_state_dict = {}
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for o in out:
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new_state_dict.update(o)
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state_dict = new_state_dict
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dist.barrier()
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return state_dict
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def save_unsharded_model(
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self,
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model: nn.Module,
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checkpoint: str,
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gather_dtensor: bool,
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use_safetensors: bool,
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):
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state_dict = self.pre_save_model(model)
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if dist.get_rank() == 0:
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torch.save(state_dict, checkpoint)
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dist.barrier()
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def save_sharded_model(
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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,
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) -> 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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The filenames are in the form of "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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torch.cuda.empty_cache()
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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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# 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 = self.pre_save_model(model)
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if dist.get_rank() == 0:
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state_dict_shard = self._model_sharder(state_dict, size_per_shard=size_per_shard)
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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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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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total_size = save_state_dict_shards(
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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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)
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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(
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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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)
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dist.barrier()
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torch.cuda.empty_cache()
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# ========================================================
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# Abstract methods for optimizer loading/saving implementation
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# ========================================================
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def pre_load_optim(
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self,
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state: OrderedDict,
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working_param,
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current_shape: torch.Size,
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original_shape: torch.Size,
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device: torch.device,
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inplace: bool,
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) -> OrderedDict:
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"""
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With complete optimizer states of a specific parameter loaded from checkpoint,
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slice out the sharded optimizer states kept by current device.
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Args:
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state (OrderedDict): Complete optimizer states of a given parameter, loaded from checkpoint.
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current_shape (torch.Size): The size of parameter after sharding.
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original_shape (torch.Size): The size of parameter before sharding.
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device (torch.device): The destination device of loaded optimizer states.
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inplace (bool): If set to True, will update the values of argument 'state' in place. Else will make a copy of state.
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Returns:
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OrderedDict: The sharded optimizer state of the given parameter.
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"""
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state_ = state if inplace else copy.deepcopy(state)
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is_moe_tensor_flag = is_moe_tensor(working_param)
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if is_moe_tensor_flag:
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ep_rank = get_ep_rank(working_param)
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ep_size = get_ep_size(working_param)
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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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if is_moe_tensor_flag:
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with torch.no_grad():
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expert_num = v.shape[0] // ep_size
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assert v.shape[0] % ep_size == 0
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v = v[ep_rank * expert_num : (ep_rank + 1) * expert_num]
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else:
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# Shard state along data parallel group when using Zero.
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padding_size = (self.dp_size - v.numel() % self.dp_size) % self.dp_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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slice_size = v.numel() // self.dp_size
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v = v.split(slice_size, dim=0)[self.dp_rank]
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state_[k] = v.detach().clone().to(device)
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return state_
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def load_sharded_optimizer(self, optimizer: OptimizerWrapper, checkpoint_index_file: str, prefix: str = ""):
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"""
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Load sharded optimizer with the given path to index file of checkpoint folder.
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Args:
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optimizer (OptimizerWrapper): The optimizer to be loaded.
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checkpoint_index_file (str): Path to the index file of checkpointing folder.
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prefix (str): Not used.
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"""
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assert isinstance(optimizer, OptimizerWrapper), "Please boost the optimizer before loading!"
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def _get_param_id_from_optimizer_param(
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param: torch.Tensor, master_to_working_map: Optional[Dict[int, torch.Tensor]] = None, optimizer=None
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):
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if master_to_working_map is not None and id(param) in master_to_working_map:
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working_param = master_to_working_map[id(param)]
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elif hasattr(optimizer, "moe_master_to_working_map") and id(param) in optimizer.moe_master_to_working_map:
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working_param = optimizer.moe_master_to_working_map[id(param)]
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else:
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working_param = param
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return optimizer.param_info["param2id"][id(working_param)]
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# id_map is a mapping from param ids kept by current pipeline, to their corresponding parameter objects.
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# When Zero is used, the mapped parameter objects should be fp32 master parameters.
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# IDs should be obtained through saved param2id mapping earlier saved in optimizer.param_info.
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id_map = {}
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master_to_working_map = optimizer.get_master_to_working_map()
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for pg in optimizer.optim.param_groups:
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for param in pg["params"]:
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param_id = _get_param_id_from_optimizer_param(param, master_to_working_map, optimizer)
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id_map[param_id] = param
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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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weight_map = {int(k): v for k, v in weight_map.items()} # convert saved id from str to int
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# Load param_groups
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param_group_path = ckpt_index_file.get_param_group_filename()
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if param_group_path is None:
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raise RuntimeError(
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f"Invalid index file path {checkpoint_index_file} for an optimizer. \
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Lacking param group file under current directory."
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)
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saved_groups = torch.load(param_group_path)
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updated_groups = []
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for old_pg, saved_pg in zip(optimizer.optim.param_groups, saved_groups):
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# obtain updated param group
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new_pg = copy.deepcopy(saved_pg)
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new_pg["params"] = old_pg["params"] # The parameters in the same group shouldn't change.
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updated_groups.append(new_pg)
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# ep param group
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if len(optimizer.optim.param_groups) > len(saved_groups):
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new_pg = copy.deepcopy(saved_pg)
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new_pg["params"] = optimizer.optim.param_groups[-1]["params"]
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updated_groups.append(new_pg)
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optimizer.optim.__dict__.update({"param_groups": updated_groups})
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# Load saved states to optimizer.
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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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for pg in optimizer.optim.param_groups:
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for param in pg["params"]:
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if param is None:
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continue
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param_id = _get_param_id_from_optimizer_param(param, master_to_working_map, optimizer)
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if param_id not in weight_map:
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continue
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filename = weight_map[param_id]
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# If this param's states has been loaded before, directly return.
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if filename in loaded_file:
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continue
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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=False)
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# Then shard the loaded optimizer states if using tp/zero.
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for pid, state in list(state_dict.items()):
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if pid in id_map:
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param = id_map[pid]
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if master_to_working_map is not None and id(param) in master_to_working_map:
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working_param = master_to_working_map[id(param)]
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elif (
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hasattr(optimizer, "moe_master_to_working_map")
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and id(param) in optimizer.moe_master_to_working_map
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):
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working_param = optimizer.moe_master_to_working_map[id(param)]
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else:
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working_param = param
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original_shape = optimizer.param_info["param2shape"][id(working_param)]
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sharded_state = self.pre_load_optim(
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state,
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working_param,
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current_shape=working_param.shape,
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original_shape=original_shape,
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device="cpu",
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inplace=True,
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)
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state_dict[pid] = sharded_state
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load_states_into_optimizer(optimizer.optim, state_dict, id_map, strict=True)
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loaded_file.add(filename)
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sharded_optimizer_loading_epilogue(optimizer.optim)
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if self.verbose and self.coordinator.is_master():
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logging.info(f"The optimizer has been successfully loaded from sharded checkpoint: {ckpt_root_path}.")
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dist.barrier()
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def load_unsharded_optimizer(self, optimizer: OptimizerWrapper, checkpoint: str):
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"""
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Load optimizer from a file with given path.
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Args:
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optimizer (OptimizerWrapper): The optimizer to be loaded.
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checkpoint_index_file (str): Path to the checkpoint file.
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"""
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def _get_param_id_from_optimizer_param(
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param: torch.Tensor, master_to_working_map: Optional[Dict[int, torch.Tensor]] = None
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):
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if master_to_working_map is not None and id(param) in master_to_working_map:
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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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if id(working_param) in optimizer.param_info["param2id"]:
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return optimizer.param_info["param2id"][id(working_param)]
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else:
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None
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if self.coordinator.is_master():
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logging.warning("Please avoid using unsharded checkpointing methods when dealing with large models!")
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|
assert isinstance(optimizer, OptimizerWrapper), "Please boost the optimizer before loading!"
|
|
|
|
# Complete optimizer state_dict loaded from checkpoint, need to be processed later.
|
|
state_dict = load_state_dict(checkpoint)
|
|
|
|
# Load param_groups.
|
|
updated_groups = []
|
|
saved_groups = state_dict["param_groups"]
|
|
for old_pg, saved_pg in zip(optimizer.optim.param_groups, saved_groups):
|
|
new_pg = copy.deepcopy(saved_pg)
|
|
new_pg["params"] = old_pg["params"] # Only keep the parameters kept by current pipeline stage.
|
|
updated_groups.append(new_pg)
|
|
# ep extra group
|
|
if MOE_MANAGER.parallel == "EP":
|
|
new_pg = copy.deepcopy(saved_pg)
|
|
new_pg["params"] = optimizer.optim.param_groups[-1][
|
|
"params"
|
|
] # Only keep the parameters kept by current pipeline stage.
|
|
for param in new_pg["params"]:
|
|
param.data = param.data.to(torch.float32)
|
|
updated_groups.append(new_pg)
|
|
optimizer.optim.__dict__.update({"param_groups": updated_groups})
|
|
|
|
# Load saved states to optimizer. First discard those states not belonging to current pipeline stage.
|
|
master_to_working_map = optimizer.get_master_to_working_map()
|
|
id_map = {}
|
|
for pg in optimizer.optim.param_groups:
|
|
for param in pg["params"]:
|
|
param_id = _get_param_id_from_optimizer_param(param, master_to_working_map)
|
|
if param_id is not None:
|
|
id_map[param_id] = param
|
|
load_states_into_optimizer(optimizer.optim, state_dict["state"], id_map, strict=True)
|
|
|
|
# Then shard the loaded optimizer states if using tp/zero.
|
|
for param, state in optimizer.optim.state.items():
|
|
if param is None:
|
|
continue
|
|
device = param.device
|
|
if master_to_working_map is not None and id(param) in master_to_working_map:
|
|
working_param = master_to_working_map[id(param)]
|
|
else:
|
|
working_param = param
|
|
original_shape = optimizer.param_info["param2shape"][id(working_param)]
|
|
sharded_state = self.pre_load_optim(
|
|
state,
|
|
param,
|
|
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)
|
|
dist.barrier()
|
|
|
|
def pre_save_optim(
|
|
self,
|
|
state: OrderedDict,
|
|
param: torch.Tensor,
|
|
inplace: bool,
|
|
device: torch.device = torch.device("cpu"),
|
|
) -> 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.
|
|
device (torch.device): The destination device of loaded optimizer states. Defaults to torch.device('cpu').
|
|
|
|
Returns:
|
|
OrderedDict: The complete optimizer state of given parameter.
|
|
"""
|
|
if is_moe_tensor(param):
|
|
moe_dp_group = get_dp_group(param)
|
|
moe_dp_size = get_dp_size(param)
|
|
moe_ep_group = get_ep_group(param)
|
|
moe_ep_size = get_ep_size(param)
|
|
state_ = state if inplace else copy.deepcopy(state)
|
|
|
|
for k, v in state_.items():
|
|
if isinstance(v, torch.Tensor) and k != "step":
|
|
# moe param
|
|
if is_moe_tensor(param):
|
|
# dp gather
|
|
v = v.cuda()
|
|
gather_tensor = [torch.zeros_like(v) for _ in range(moe_dp_size)]
|
|
dist.all_gather(gather_tensor, v, group=moe_dp_group)
|
|
v = torch.stack(gather_tensor).view(-1)[: param.numel()].reshape_as(param)
|
|
# ep gather
|
|
gather_tensor = [torch.zeros_like(v) for _ in range(moe_ep_size)]
|
|
dist.all_gather(gather_tensor, v, group=moe_ep_group)
|
|
v = torch.cat(gather_tensor, dim=0)
|
|
else:
|
|
# global dp
|
|
v = v.cuda()
|
|
gather_tensor = [torch.zeros_like(v) for _ in range(dist.get_world_size(self.dp_group))]
|
|
dist.all_gather(gather_tensor, v, group=self.dp_group)
|
|
v = torch.stack(gather_tensor).view(-1)[: param.numel()].reshape_as(param)
|
|
|
|
state_[k] = v.detach().clone().to(device)
|
|
|
|
return state_
|
|
|
|
def _optimizer_sharder(
|
|
self,
|
|
optimizer: OptimizerWrapper,
|
|
size_per_shard: int = 1024,
|
|
):
|
|
# An internel method that breaks state_dict of optimizer into shards within limited size.
|
|
|
|
state_dict_sharder = StateDictSharder(size_per_shard)
|
|
param_info = optimizer.param_info
|
|
master_to_working_map = optimizer.get_master_to_working_map()
|
|
|
|
for param, state in optimizer.optim.state.items():
|
|
if param is None:
|
|
continue
|
|
|
|
if master_to_working_map is not None and id(param) in master_to_working_map:
|
|
working_param = master_to_working_map[id(param)]
|
|
elif hasattr(optimizer, "moe_master_to_working_map") and id(param) in optimizer.moe_master_to_working_map:
|
|
working_param = optimizer.moe_master_to_working_map[id(param)]
|
|
else:
|
|
working_param = param
|
|
|
|
param_id = param_info["param2id"][id(working_param)]
|
|
state_ = self.pre_save_optim(
|
|
state,
|
|
working_param,
|
|
inplace=False,
|
|
device=torch.device("cuda"),
|
|
)
|
|
|
|
block, block_size = state_dict_sharder.append_optim_state(param_id, state_)
|
|
if block is not None:
|
|
yield block, block_size
|
|
|
|
# Return the last block in sharder.
|
|
yield state_dict_sharder.current_block, state_dict_sharder.current_block_size
|
|
|
|
def save_sharded_optimizer(
|
|
self,
|
|
optimizer: OptimizerWrapper,
|
|
checkpoint: str,
|
|
gather_dtensor: bool = True,
|
|
prefix: Optional[str] = None,
|
|
size_per_shard: int = 1024,
|
|
):
|
|
"""
|
|
Save sharded optimizer checkpoint under the given checkpointing path.
|
|
The following files will be created under the path:
|
|
- An index file (pytorch_optim.bin.index.json) containing a map between optimizer states and file names
|
|
- A group file (pytorch_optim_group.bin) recording information of param_groups
|
|
- Multiple files that store state tensors of optimizers.
|
|
If pipeline parallelism is used, the filenames are in the form of "pytorch_optim.<prefix>-stage-000XX-shard-000XX.bin".
|
|
If pipeline parallelism is not used, "pytorch_optim.<prefix>-000XX.bin"
|
|
|
|
Args:
|
|
optimizer (OptimizerWrapper): Optimizer to save sharded state_dict
|
|
checkpoint (str): Path to save optimizer state_dict
|
|
gather_dtensor (bool): Whether to gather_dtensor, not used
|
|
prefix (str): Perfix of file to save
|
|
size_per_shard (int): Max file size of each file shard that store state tensors
|
|
"""
|
|
torch.cuda.empty_cache()
|
|
assert isinstance(optimizer, OptimizerWrapper), "Please boost the optimizer before saving!"
|
|
if os.path.isfile(checkpoint):
|
|
logging.error(f"Provided path ({checkpoint}) should be a directory, not a file")
|
|
return
|
|
|
|
Path(checkpoint).mkdir(parents=True, exist_ok=True)
|
|
|
|
# Devices along the same dp_group share the same copies of states when zero is not used.
|
|
# In this case only let the device with dp_rank == 0 save the model.
|
|
if not self.use_zero and self.dp_rank != 0:
|
|
return
|
|
|
|
# Then collect the sharded states along dp_group(if using zero)/tp_group.
|
|
# Only devices with (dp_rank == 0 and tp_rank == 0) are responsible for states saving.
|
|
state_dict_shard = self._optimizer_sharder(
|
|
optimizer,
|
|
size_per_shard=size_per_shard,
|
|
)
|
|
states_name, save_index_file, param_group_file = get_optimizer_base_filenames(prefix)
|
|
index_file = CheckpointIndexFile(checkpoint)
|
|
control_saving = self.dp_rank == 0 and self.tp_rank == 0
|
|
if self.pp_size == 1:
|
|
# When pipeline is not used, save the optimizer shards as in general checkpointIO
|
|
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,
|
|
)
|
|
|
|
if control_saving:
|
|
# Store param groups.
|
|
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)
|
|
# Store index file.
|
|
index_file.append_meta_data("total_size", total_size)
|
|
index_file.write_index_file(save_index_file)
|
|
if self.verbose and self.coordinator.is_master():
|
|
logging.info(
|
|
f"The optimizer is going to be split to checkpoint shards. "
|
|
f"You can find where each parameters has been saved in the "
|
|
f"index located at {save_index_file}."
|
|
)
|
|
|
|
else:
|
|
# When pipeline is used, each stage produces its own shard files and index files.
|
|
# Index files belonging to each stage are saved under a temporary folder ./tmp_index_files/
|
|
# 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.
|
|
|
|
final_index_file_path = copy.deepcopy(save_index_file)
|
|
tmp_index_file_folder = os.path.join(checkpoint, "tmp_index_files")
|
|
Path(tmp_index_file_folder).mkdir(parents=True, exist_ok=True)
|
|
|
|
# Manage filenames of sharded weights and index file for each pipeline stage.
|
|
states_name = states_name.replace(".bin", f"-stage-{self.pp_rank+1:05d}-shard.bin")
|
|
save_index_file = save_index_file.replace(".json", f"-stage-{self.pp_rank+1:05d}.json")
|
|
save_index_file = os.path.join("tmp_index_files", save_index_file)
|
|
|
|
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 and self.coordinator.is_master():
|
|
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}."
|
|
)
|
|
torch.cuda.empty_cache()
|
|
|
|
def save_unsharded_optimizer(self, optimizer: OptimizerWrapper, checkpoint: str, gather_dtensor: bool):
|
|
"""
|
|
Save optimizer state dict to a file with given path.
|
|
|
|
Args:
|
|
optimizer (OptimizerWrapper): Optimizer to save sharded state_dict.
|
|
checkpoint (str): Path to save optimizer state_dict.
|
|
gather_dtensor (bool): Whether to gather_dtensor, not used.
|
|
"""
|
|
if self.coordinator.is_master():
|
|
logging.warning("Please avoid using unsharded checkpointing methods when dealing with large models!")
|
|
|
|
assert isinstance(optimizer, OptimizerWrapper), "Please boost the optimizer before saving!"
|
|
|
|
# optimizer states of parameters kept by local device('s pipeline stage)
|
|
local_states = dict()
|
|
|
|
for param, state in optimizer.optim.state.items():
|
|
if param is None:
|
|
continue
|
|
|
|
# working param is needed for obtaining correct param_id
|
|
master_to_working_map = optimizer.get_master_to_working_map()
|
|
if master_to_working_map is not None and id(param) in master_to_working_map:
|
|
working_param = master_to_working_map[id(param)]
|
|
else:
|
|
working_param = param
|
|
|
|
# gather complete state from tp shards & dp shards
|
|
param_id = optimizer.param_info["param2id"][id(working_param)]
|
|
local_states[param_id] = self.pre_save_optim(
|
|
state,
|
|
working_param,
|
|
inplace=False,
|
|
device=torch.device("cuda"),
|
|
)
|
|
|
|
if self.pp_size == 1:
|
|
# When pipeline is not used, let master rank directly save the collected state_dict.
|
|
state_dict = {"param_groups": optimizer.optim.param_groups, "state": local_states}
|
|
if self.coordinator.is_master():
|
|
save_state_dict(state_dict, checkpoint, use_safetensors=False)
|
|
else:
|
|
# When pipeline is used, first collect state_dict from every pipeline stage, then save the complete state_dict.
|
|
states_list = [None for _ in range(self.pp_size)]
|
|
dist.barrier(self.pp_group)
|
|
dist.all_gather_object(states_list, local_states, self.pp_group)
|
|
|
|
# Only the master rank do the saving.
|
|
if self.coordinator.is_master():
|
|
state_dict = {"param_groups": optimizer.optim.param_groups, "state": dict()}
|
|
for _states in states_list:
|
|
state_dict["state"].update(_states)
|
|
save_state_dict(state_dict, checkpoint, use_safetensors=False)
|
|
dist.barrier()
|