mirror of https://github.com/hpcaitech/ColossalAI
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
630 lines
29 KiB
630 lines
29 KiB
import copy
|
|
import logging
|
|
import os
|
|
from pathlib import Path
|
|
from shutil import rmtree
|
|
from typing import Dict, Iterator, Optional, OrderedDict, Tuple
|
|
|
|
import torch
|
|
import torch.distributed as dist
|
|
import torch.nn as nn
|
|
from torch.distributed import ProcessGroup
|
|
|
|
from colossalai.checkpoint_io import CheckpointIndexFile
|
|
from colossalai.checkpoint_io.hybrid_parallel_checkpoint_io import HybridParallelCheckpointIO
|
|
from colossalai.checkpoint_io.index_file import CheckpointIndexFile
|
|
from colossalai.checkpoint_io.utils import (
|
|
StateDictSharder,
|
|
gather_distributed_param,
|
|
get_model_base_filenames,
|
|
get_optimizer_base_filenames,
|
|
load_shard_state_dict,
|
|
load_states_into_optimizer,
|
|
save_config_file,
|
|
save_param_groups,
|
|
save_state_dict_shards,
|
|
search_tp_partition_dim,
|
|
sharded_optimizer_loading_epilogue,
|
|
)
|
|
from colossalai.interface import ModelWrapper, OptimizerWrapper
|
|
from colossalai.moe import MOE_MANAGER
|
|
from colossalai.tensor.moe_tensor.api import is_moe_tensor
|
|
|
|
try:
|
|
from torch.nn.modules.module import _EXTRA_STATE_KEY_SUFFIX
|
|
except ImportError:
|
|
_EXTRA_STATE_KEY_SUFFIX = "_extra_state"
|
|
|
|
|
|
class MixtralMoEHybridParallelCheckpointIO(HybridParallelCheckpointIO):
|
|
def __init__(
|
|
self,
|
|
dp_group: ProcessGroup,
|
|
pp_group: ProcessGroup,
|
|
tp_group: ProcessGroup,
|
|
zero_stage: int,
|
|
verbose: bool = True,
|
|
) -> None:
|
|
super().__init__(dp_group, pp_group, tp_group, zero_stage, verbose)
|
|
moe_info = MOE_MANAGER.parallel_info_dict[MOE_MANAGER.ep_size]
|
|
self.ep_group = moe_info.ep_group
|
|
self.ep_size = moe_info.ep_size
|
|
self.ep_rank = moe_info.ep_rank
|
|
self.real_dp_rank = moe_info.dp_rank
|
|
|
|
@staticmethod
|
|
def _model_sharder(
|
|
model: nn.Module,
|
|
prefix: str = "",
|
|
keep_vars: bool = False,
|
|
size_per_shard: int = 1024,
|
|
param_name_pattern: Optional[str] = None,
|
|
) -> Iterator[Tuple[OrderedDict, int]]:
|
|
# An internel method that breaks state_dict of model into shards within limited size.
|
|
|
|
state_dict_sharder = StateDictSharder(size_per_shard)
|
|
|
|
# Save parameters.
|
|
for name, param in model.named_parameters():
|
|
if param is None:
|
|
continue
|
|
if param_name_pattern is not None and param_name_pattern not in name:
|
|
continue
|
|
# Gather tensor pieces when using tensor parallel.
|
|
param_ = gather_distributed_param(param, keep_vars=False)
|
|
block, block_size = state_dict_sharder.append_param(prefix + name, param_)
|
|
if block is not None:
|
|
yield block, block_size
|
|
|
|
# Save buffers.
|
|
for name, buf in model.named_buffers():
|
|
if buf is not None and name not in model._non_persistent_buffers_set:
|
|
buffer = buf if keep_vars else buf.detach()
|
|
block, block_size = state_dict_sharder.append_param(prefix + name, buffer)
|
|
if block is not None:
|
|
yield block, block_size
|
|
|
|
# Save extra states.
|
|
extra_state_key = prefix + _EXTRA_STATE_KEY_SUFFIX
|
|
if (
|
|
getattr(model.__class__, "get_extra_state", torch.nn.Module.get_extra_state)
|
|
is not torch.nn.Module.get_extra_state
|
|
):
|
|
extra_state = model.get_extra_state()
|
|
block, block_size = state_dict_sharder.append_param(extra_state_key, extra_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_model(
|
|
self,
|
|
model: ModelWrapper,
|
|
checkpoint: str,
|
|
gather_dtensor: bool = True,
|
|
prefix: Optional[str] = None,
|
|
size_per_shard: int = 1024,
|
|
use_safetensors: bool = False,
|
|
) -> None:
|
|
"""
|
|
Save sharded model checkpoint under the given checkpointing path.
|
|
The following files will be created under the path:
|
|
- An index file (pytorch_model.bin.index.json) containing a map between model params/buffers and file names.
|
|
- Multiple files that store state tensors of models.
|
|
If pipeline parallelism is used, the filenames are in the form of "pytorch_model.<prefix>-stage-000XX-shard-000XX.bin".
|
|
If pipeline parallelism is not used, "pytorch_model.<prefix>-000XX.bin"
|
|
|
|
|
|
Args:
|
|
model (nn.Module): Model on local device to be saved.
|
|
checkpoint (str): Checkpointing path which should be a directory path.
|
|
gather_dtensor (bool, optional): Whether to gather_dtensor, currently not used. Defaults to True.
|
|
prefix (str, optional): Perfix of file to save. Defaults to None.
|
|
size_per_shard (int, optional): Size per shard in MB. Defaults to 1024.
|
|
use_safetensors (bool, optional): Whether to use safe tensors. Defaults to False.
|
|
"""
|
|
|
|
assert isinstance(model, ModelWrapper), "Please boost the model before saving!"
|
|
model = model.unwrap()
|
|
|
|
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)
|
|
|
|
if self.real_dp_rank != 0:
|
|
dist.barrier()
|
|
return
|
|
|
|
# ep_rank 0 saves all the parameters and buffers.
|
|
# other ep_ranks save only experts
|
|
ep_param_pattern = "experts." if self.ep_rank != 0 else None
|
|
|
|
# Then collect the sharded parameters & buffers along tp_group.
|
|
# Only devices with tp_rank == 0 are responsible for model saving.
|
|
state_dict_shard = MixtralMoEHybridParallelCheckpointIO._model_sharder(
|
|
model, size_per_shard=size_per_shard, param_name_pattern=ep_param_pattern
|
|
)
|
|
weights_name, save_index_file = get_model_base_filenames(prefix, use_safetensors)
|
|
index_file = CheckpointIndexFile(checkpoint)
|
|
control_saving = self.tp_rank == 0
|
|
|
|
if self.pp_size == 1 and self.ep_size == 1:
|
|
# When pipeline is not used, save the model 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=weights_name,
|
|
is_master=control_saving,
|
|
use_safetensors=use_safetensors,
|
|
)
|
|
if control_saving:
|
|
index_file.append_meta_data("total_size", total_size)
|
|
index_file.write_index_file(save_index_file)
|
|
save_config_file(model, checkpoint)
|
|
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 {save_index_file}."
|
|
)
|
|
|
|
dist.barrier()
|
|
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.
|
|
weights_name = weights_name.replace(".bin", f"-stage-{self.pp_rank+1:05d}-{self.ep_rank+1:05d}-shard.bin")
|
|
weights_name = weights_name.replace(
|
|
".safetensors", f"-stage-{self.pp_rank+1:05d}-{self.ep_rank+1:05d}-shard.safetensors"
|
|
)
|
|
save_index_file = save_index_file.replace(".json", f"-stage-{self.pp_rank+1:05d}-{self.ep_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=weights_name,
|
|
is_master=control_saving,
|
|
use_safetensors=use_safetensors,
|
|
use_pp_format=True,
|
|
)
|
|
if control_saving:
|
|
index_file.append_meta_data("total_size", total_size)
|
|
index_file.write_index_file(save_index_file)
|
|
else:
|
|
dist.barrier()
|
|
return
|
|
|
|
dist.barrier()
|
|
|
|
# The global master rank integrates the index files and clean the folder.
|
|
if self.coordinator.is_master():
|
|
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 weight, weight_filename in stage_index_file.weight_map.items():
|
|
final_index_file.append_weight_map(weight, weight_filename)
|
|
|
|
final_index_file.write_index_file(final_index_file_path)
|
|
save_config_file(model, checkpoint)
|
|
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}."
|
|
)
|
|
|
|
@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,
|
|
is_moe_param: 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.
|
|
"""
|
|
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 and not is_moe_param:
|
|
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().to(device)
|
|
|
|
return state_
|
|
|
|
@staticmethod
|
|
def _optimizer_sharder(
|
|
optimizer: OptimizerWrapper,
|
|
use_zero: bool,
|
|
dp_group: ProcessGroup,
|
|
tp_group: ProcessGroup,
|
|
size_per_shard: int = 1024,
|
|
only_moe_param: bool = False,
|
|
):
|
|
# 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:
|
|
working_param = master_to_working_map[id(param)]
|
|
else:
|
|
working_param = param
|
|
|
|
param_id = param_info["param2id"][id(working_param)]
|
|
original_shape = param_info["param2shape"][id(working_param)]
|
|
state_ = MixtralMoEHybridParallelCheckpointIO.gather_from_sharded_optimizer_state(
|
|
state,
|
|
working_param,
|
|
original_shape=original_shape,
|
|
dp_group=dp_group,
|
|
tp_group=tp_group,
|
|
use_zero=use_zero,
|
|
inplace=False,
|
|
is_moe_param=is_moe_tensor(working_param),
|
|
)
|
|
|
|
if only_moe_param and not is_moe_tensor(working_param):
|
|
continue
|
|
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
|
|
"""
|
|
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.real_dp_rank != 0:
|
|
dist.barrier()
|
|
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 = MixtralMoEHybridParallelCheckpointIO._optimizer_sharder(
|
|
optimizer,
|
|
use_zero=self.use_zero,
|
|
dp_group=self.dp_group,
|
|
tp_group=self.tp_group,
|
|
size_per_shard=size_per_shard,
|
|
only_moe_param=self.ep_rank != 0,
|
|
)
|
|
states_name, save_index_file, param_group_file = get_optimizer_base_filenames(prefix)
|
|
index_file = CheckpointIndexFile(checkpoint)
|
|
control_saving = self.real_dp_rank == 0 and self.tp_rank == 0
|
|
|
|
if self.pp_size == 1 and self.ep_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)
|
|
param_groups = [
|
|
{**group, "params": group_info["params"]}
|
|
for group, group_info in zip(optimizer.param_groups, optimizer.param_info["param_groups"])
|
|
]
|
|
save_param_groups({"param_groups": param_groups}, 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}."
|
|
)
|
|
|
|
dist.barrier()
|
|
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}-{self.ep_rank+1:05d}-shard.bin")
|
|
save_index_file = save_index_file.replace(".json", f"-stage-{self.pp_rank+1:05d}-{self.ep_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:
|
|
index_file.append_meta_data("total_size", total_size)
|
|
index_file.write_index_file(save_index_file)
|
|
else:
|
|
dist.barrier()
|
|
return
|
|
|
|
dist.barrier()
|
|
|
|
# The global master rank integrates the index files and clean the folder.
|
|
if self.coordinator.is_master():
|
|
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)
|
|
param_groups = [
|
|
{**group, "params": group_info["params"]}
|
|
for group, group_info in zip(optimizer.param_groups, optimizer.param_info["param_groups"])
|
|
]
|
|
save_param_groups({"param_groups": param_groups}, 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}."
|
|
)
|
|
|
|
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.
|
|
"""
|
|
assert isinstance(optimizer, OptimizerWrapper), "Please boost the optimizer before loading!"
|
|
|
|
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 = {}
|
|
master_to_working_map = optimizer.get_master_to_working_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)
|
|
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)
|
|
# ep param groups
|
|
if len(optimizer.optim.param_groups) == len(saved_groups) + 1:
|
|
new_pg = copy.deepcopy(saved_pg)
|
|
new_pg["params"] = optimizer.optim.param_groups[-1]["params"]
|
|
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, 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 master_to_working_map is not None:
|
|
working_param = 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,
|
|
is_moe_param=is_moe_tensor(working_param),
|
|
)
|
|
optimizer.optim.state[param] = sharded_state
|
|
|
|
sharded_optimizer_loading_epilogue(optimizer.optim)
|
|
if self.verbose and self.coordinator.is_master():
|
|
logging.info(f"The optimizer has been successfully loaded from sharded checkpoint: {ckpt_root_path}.")
|
|
|
|
def shard_from_complete_optimizer_state(
|
|
self,
|
|
state: OrderedDict,
|
|
current_shape: torch.Size,
|
|
original_shape: torch.Size,
|
|
device: torch.device,
|
|
inplace: bool,
|
|
is_moe_param: 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 and not is_moe_param:
|
|
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_
|
|
|
|
def save_unsharded_model(self, model: ModelWrapper, checkpoint: str, gather_dtensor: bool, use_safetensors: bool):
|
|
raise NotImplementedError
|
|
|
|
def save_unsharded_optimizer(self, optimizer: OptimizerWrapper, checkpoint: str, gather_dtensor: bool):
|
|
raise NotImplementedError
|
|
|
|
def load_unsharded_optimizer(self, optimizer: OptimizerWrapper, checkpoint: str, strict: bool = False):
|
|
raise NotImplementedError
|