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# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import shutil
from pathlib import Path
import torch
from ..logging import get_logger
from .constants import FSDP_MODEL_NAME, OPTIMIZER_NAME, SAFE_WEIGHTS_NAME, WEIGHTS_NAME
from .modeling import is_peft_model
from .other import save
from .versions import is_torch_version
logger = get_logger(__name__)
def _get_model_state_dict(model, adapter_only=False):
if adapter_only and is_peft_model(model):
from peft import get_peft_model_state_dict
return get_peft_model_state_dict(model, adapter_name=model.active_adapter)
else:
return model.state_dict()
def _set_model_state_dict(model, state_dict, adapter_only=False):
if adapter_only and is_peft_model(model):
from peft import set_peft_model_state_dict
return set_peft_model_state_dict(model, state_dict, adapter_name=model.active_adapter)
else:
return model.load_state_dict(state_dict)
def save_fsdp_model(fsdp_plugin, accelerator, model, output_dir, model_index=0, adapter_only=False):
# Note: We import here to reduce import time from general modules, and isolate outside dependencies
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpoint.default_planner import DefaultSavePlanner
from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType
os.makedirs(output_dir, exist_ok=True)
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
# FSDP raises error when single GPU is used with `offload_to_cpu=True` for FULL_STATE_DICT
# so, only enable it when num_processes>1
is_multi_process = accelerator.num_processes > 1
fsdp_plugin.state_dict_config.offload_to_cpu = is_multi_process
fsdp_plugin.state_dict_config.rank0_only = is_multi_process
with FSDP.state_dict_type(
model, fsdp_plugin.state_dict_type, fsdp_plugin.state_dict_config, fsdp_plugin.optim_state_dict_config
):
state_dict = _get_model_state_dict(model, adapter_only=adapter_only)
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
weights_name = f"{FSDP_MODEL_NAME}.bin" if model_index == 0 else f"{FSDP_MODEL_NAME}_{model_index}.bin"
output_model_file = os.path.join(output_dir, weights_name)
if accelerator.process_index == 0:
logger.info(f"Saving model to {output_model_file}")
torch.save(state_dict, output_model_file)
logger.info(f"Model saved to {output_model_file}")
elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
weights_name = (
f"{FSDP_MODEL_NAME}_rank{accelerator.process_index}.bin"
if model_index == 0
else f"{FSDP_MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"
)
output_model_file = os.path.join(output_dir, weights_name)
logger.info(f"Saving model to {output_model_file}")
torch.save(state_dict, output_model_file)
logger.info(f"Model saved to {output_model_file}")
elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
ckpt_dir = os.path.join(output_dir, f"{FSDP_MODEL_NAME}_{model_index}")
os.makedirs(ckpt_dir, exist_ok=True)
logger.info(f"Saving model to {ckpt_dir}")
state_dict = {"model": state_dict}
dist_cp.save_state_dict(
state_dict=state_dict,
storage_writer=dist_cp.FileSystemWriter(ckpt_dir),
planner=DefaultSavePlanner(),
)
logger.info(f"Model saved to {ckpt_dir}")
def load_fsdp_model(fsdp_plugin, accelerator, model, input_dir, model_index=0, adapter_only=False):
# Note: We import here to reduce import time from general modules, and isolate outside dependencies
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner
from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType
accelerator.wait_for_everyone()
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
# FSDP raises error when single GPU is used with `offload_to_cpu=True` for FULL_STATE_DICT
# so, only enable it when num_processes>1
is_multi_process = accelerator.num_processes > 1
fsdp_plugin.state_dict_config.offload_to_cpu = is_multi_process
fsdp_plugin.state_dict_config.rank0_only = is_multi_process
with FSDP.state_dict_type(
model, fsdp_plugin.state_dict_type, fsdp_plugin.state_dict_config, fsdp_plugin.optim_state_dict_config
):
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
if type(model) != FSDP and accelerator.process_index != 0:
if not fsdp_plugin.sync_module_states:
raise ValueError(
"Set the `sync_module_states` flag to `True` so that model states are synced across processes when "
"initializing FSDP object"
)
return
weights_name = f"{FSDP_MODEL_NAME}.bin" if model_index == 0 else f"{FSDP_MODEL_NAME}_{model_index}.bin"
input_model_file = os.path.join(input_dir, weights_name)
logger.info(f"Loading model from {input_model_file}")
state_dict = torch.load(input_model_file)
logger.info(f"Model loaded from {input_model_file}")
elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
weights_name = (
f"{FSDP_MODEL_NAME}_rank{accelerator.process_index}.bin"
if model_index == 0
else f"{FSDP_MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"
)
input_model_file = os.path.join(input_dir, weights_name)
logger.info(f"Loading model from {input_model_file}")
state_dict = torch.load(input_model_file)
logger.info(f"Model loaded from {input_model_file}")
elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
ckpt_dir = (
os.path.join(input_dir, f"{FSDP_MODEL_NAME}_{model_index}")
if f"{FSDP_MODEL_NAME}" not in input_dir
else input_dir
)
logger.info(f"Loading model from {ckpt_dir}")
state_dict = {"model": _get_model_state_dict(model, adapter_only=adapter_only)}
dist_cp.load_state_dict(
state_dict=state_dict,
storage_reader=dist_cp.FileSystemReader(ckpt_dir),
planner=DefaultLoadPlanner(),
)
state_dict = state_dict["model"]
logger.info(f"Model loaded from {ckpt_dir}")
load_result = _set_model_state_dict(model, state_dict, adapter_only=adapter_only)
return load_result
def save_fsdp_optimizer(fsdp_plugin, accelerator, optimizer, model, output_dir, optimizer_index=0):
# Note: We import here to reduce import time from general modules, and isolate outside dependencies
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpoint.default_planner import DefaultSavePlanner
from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType
os.makedirs(output_dir, exist_ok=True)
with FSDP.state_dict_type(
model, fsdp_plugin.state_dict_type, fsdp_plugin.state_dict_config, fsdp_plugin.optim_state_dict_config
):
optim_state = FSDP.optim_state_dict(model, optimizer)
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
if accelerator.process_index == 0:
optim_state_name = (
f"{OPTIMIZER_NAME}.bin" if optimizer_index == 0 else f"{OPTIMIZER_NAME}_{optimizer_index}.bin"
)
output_optimizer_file = os.path.join(output_dir, optim_state_name)
logger.info(f"Saving Optimizer state to {output_optimizer_file}")
torch.save(optim_state, output_optimizer_file)
logger.info(f"Optimizer state saved in {output_optimizer_file}")
else:
ckpt_dir = os.path.join(output_dir, f"{OPTIMIZER_NAME}_{optimizer_index}")
os.makedirs(ckpt_dir, exist_ok=True)
logger.info(f"Saving Optimizer state to {ckpt_dir}")
dist_cp.save_state_dict(
state_dict={"optimizer": optim_state},
storage_writer=dist_cp.FileSystemWriter(ckpt_dir),
planner=DefaultSavePlanner(),
)
logger.info(f"Optimizer state saved in {ckpt_dir}")
def load_fsdp_optimizer(fsdp_plugin, accelerator, optimizer, model, input_dir, optimizer_index=0, adapter_only=False):
# Note: We import here to reduce import time from general modules, and isolate outside dependencies
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType
accelerator.wait_for_everyone()
with FSDP.state_dict_type(
model, fsdp_plugin.state_dict_type, fsdp_plugin.state_dict_config, fsdp_plugin.optim_state_dict_config
):
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
optim_state = None
if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
optimizer_name = (
f"{OPTIMIZER_NAME}.bin" if optimizer_index == 0 else f"{OPTIMIZER_NAME}_{optimizer_index}.bin"
)
input_optimizer_file = os.path.join(input_dir, optimizer_name)
logger.info(f"Loading Optimizer state from {input_optimizer_file}")
optim_state = torch.load(input_optimizer_file)
logger.info(f"Optimizer state loaded from {input_optimizer_file}")
else:
ckpt_dir = (
os.path.join(input_dir, f"{OPTIMIZER_NAME}_{optimizer_index}")
if f"{OPTIMIZER_NAME}" not in input_dir
else input_dir
)
logger.info(f"Loading Optimizer from {ckpt_dir}")
optim_state = load_sharded_optimizer_state_dict(
model_state_dict=_get_model_state_dict(model, adapter_only=adapter_only),
optimizer_key="optimizer",
storage_reader=dist_cp.FileSystemReader(ckpt_dir),
)
optim_state = optim_state["optimizer"]
logger.info(f"Optimizer loaded from {ckpt_dir}")
flattened_osd = FSDP.optim_state_dict_to_load(model=model, optim=optimizer, optim_state_dict=optim_state)
optimizer.load_state_dict(flattened_osd)
def _distributed_checkpoint_to_merged_weights(checkpoint_dir: str, save_path: str, safe_serialization: bool = True):
"""
Passthrough to `torch.distributed.checkpoint.format_utils.dcp_to_torch_save`
Will save under `save_path` as either `model.safetensors` or `pytorch_model.bin`.
"""
# Note: We import here to reduce import time from general modules, and isolate outside dependencies
import torch.distributed.checkpoint as dist_cp
import torch.distributed.checkpoint.format_utils as dist_cp_format_utils
state_dict = {}
save_path = Path(save_path)
save_path.mkdir(exist_ok=True)
dist_cp_format_utils._load_state_dict(
state_dict,
storage_reader=dist_cp.FileSystemReader(checkpoint_dir),
planner=dist_cp_format_utils._EmptyStateDictLoadPlanner(),
no_dist=True,
)
save_path = save_path / SAFE_WEIGHTS_NAME if safe_serialization else save_path / WEIGHTS_NAME
# To handle if state is a dict like {model: {...}}
if len(state_dict.keys()) == 1:
state_dict = state_dict[list(state_dict)[0]]
save(state_dict, save_path, safe_serialization=safe_serialization)
return save_path
def merge_fsdp_weights(
checkpoint_dir: str, output_path: str, safe_serialization: bool = True, remove_checkpoint_dir: bool = False
):
"""
Merge the weights from sharded FSDP model checkpoints into a single combined checkpoint. Should be used if
`SHARDED_STATE_DICT` was used for the model. Weights will be saved to `{output_path}/model.safetensors` if
`safe_serialization` else `pytorch_model.bin`.
Note: this is a CPU-bound process.
Args:
checkpoint_dir (`str`):
The directory containing the FSDP checkpoints (can be either the model or optimizer).
output_path (`str`):
The path to save the merged checkpoint.
safe_serialization (`bool`, *optional*, defaults to `True`):
Whether to save the merged weights with safetensors (recommended).
remove_checkpoint_dir (`bool`, *optional*, defaults to `False`):
Whether to remove the checkpoint directory after merging.
"""
from accelerate.state import PartialState
if not is_torch_version(">=", "2.3.0"):
raise ValueError("`merge_fsdp_weights` requires PyTorch >= 2.3.0`")
# To setup `save` to work
state = PartialState()
if state.is_main_process:
logger.info(f"Merging FSDP weights from {checkpoint_dir}")
save_path = _distributed_checkpoint_to_merged_weights(checkpoint_dir, output_path, safe_serialization)
logger.info(f"Successfully merged FSDP weights and saved to {save_path}")
if remove_checkpoint_dir:
logger.info(f"Removing old checkpoint directory {checkpoint_dir}")
shutil.rmtree(checkpoint_dir)
state.wait_for_everyone()
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