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0185029 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 | # Copyright 2024 Bytedance Ltd. and/or its affiliates
#
# 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 typing import Any, Optional, Union
import torch
import torch.distributed as dist
from torch.distributed.checkpoint.state_dict import (
StateDictOptions,
get_model_state_dict,
get_state_dict,
set_state_dict,
)
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from transformers import PreTrainedModel, PreTrainedTokenizer, ProcessorMixin
from .checkpoint_manager import BaseCheckpointManager
def _payload_nbytes(payload: Any) -> int:
to_local = getattr(payload, "to_local", None)
tensor = to_local() if callable(to_local) else payload
if isinstance(tensor, torch.Tensor):
return tensor.numel() * tensor.element_size()
if isinstance(payload, dict):
return sum(_payload_nbytes(value) for value in payload.values())
if isinstance(payload, (list, tuple, set)):
return sum(_payload_nbytes(value) for value in payload)
return 0
class FSDPCheckpointManager(BaseCheckpointManager):
"""
A checkpoint manager that saves and loads
- model
- optimizer
- lr_scheduler
- extra_states
in a SPMD way.
We save
- sharded model states and optimizer states
- full lr_scheduler states
- huggingface tokenizer and config for ckpt merge
"""
def __init__(
self,
model: FSDP,
optimizer: torch.optim.Optimizer,
lr_scheduler: torch.optim.lr_scheduler.LRScheduler,
processing_class: Union[PreTrainedTokenizer, ProcessorMixin],
):
super().__init__(model, optimizer, lr_scheduler, processing_class)
def load_checkpoint(self, path: Optional[str] = None):
if path is None:
return
# every rank download its own checkpoint
model_path = os.path.join(path, f"model_world_size_{self.world_size}_rank_{self.rank}.pt")
optim_path = os.path.join(path, f"optim_world_size_{self.world_size}_rank_{self.rank}.pt")
extra_path = os.path.join(path, f"extra_state_world_size_{self.world_size}_rank_{self.rank}.pt")
print(f"[rank-{self.rank}]: Loading model from {os.path.abspath(model_path)}.")
print(f"[rank-{self.rank}]: Loading optimizer from {os.path.abspath(optim_path)}.")
print(f"[rank-{self.rank}]: Loading extra_state from {os.path.abspath(extra_path)}.")
model_state_dict = torch.load(model_path, weights_only=False)
optim_state_dict = torch.load(optim_path, weights_only=False)
extra_state_dict = torch.load(extra_path, weights_only=False)
state_dict_options = StateDictOptions(cpu_offload=True)
set_state_dict(
model=self.model,
optimizers=self.optimizer,
model_state_dict=model_state_dict,
optim_state_dict=optim_state_dict,
options=state_dict_options,
)
self.lr_scheduler.load_state_dict(extra_state_dict["lr_scheduler"])
# recover random state
if "rng" in extra_state_dict:
self.load_rng_state(extra_state_dict["rng"])
def _save_shard(self, payload: Any, destination: str, label: str) -> None:
"""Write one shard atomically so a truncated file never survives.
A full or over-quota checkpoint volume surfaces inside ``torch.save`` as
an opaque zip error, and the partial ``.pt`` left behind then fails again
at load time. Check capacity up front and publish via rename instead.
"""
directory = os.path.dirname(destination) or "."
required = _payload_nbytes(payload)
if required > 0:
free = shutil.disk_usage(directory).free
if free < required:
raise RuntimeError(
f"[rank-{self.rank}]: Refusing to save {label}: {directory} has "
f"{free / 2**30:.2f} GiB free but this shard alone needs about "
f"{required / 2**30:.2f} GiB, and all {self.world_size} ranks write here. "
f"Free space or raise the quota on the checkpoint volume."
)
temporary = f"{destination}.tmp.rank{self.rank}"
try:
torch.save(payload, temporary)
os.replace(temporary, destination)
except Exception as error:
if os.path.exists(temporary):
os.remove(temporary)
raise RuntimeError(
f"[rank-{self.rank}]: Failed to write {label} to {destination} "
f"({required / 2**30:.2f} GiB). The checkpoint volume is most likely "
f"full or over quota."
) from error
def save_checkpoint(self, path: str, save_model_only: bool = False):
path = self.local_mkdir(path)
dist.barrier()
# every rank will save its own model and optim shard
model_path = os.path.join(path, f"model_world_size_{self.world_size}_rank_{self.rank}.pt")
optim_path = os.path.join(path, f"optim_world_size_{self.world_size}_rank_{self.rank}.pt")
extra_path = os.path.join(path, f"extra_state_world_size_{self.world_size}_rank_{self.rank}.pt")
state_dict_options = StateDictOptions(cpu_offload=True)
if save_model_only:
model_state_dict = get_model_state_dict(self.model, options=state_dict_options)
print(f"[rank-{self.rank}]: Saving model to {os.path.abspath(model_path)}.")
self._save_shard(model_state_dict, model_path, "model")
else:
model_state_dict, optim_state_dict = get_state_dict(self.model, self.optimizer, options=state_dict_options)
extra_state_dict = {
"lr_scheduler": self.lr_scheduler.state_dict(),
"rng": self.get_rng_state(),
}
print(f"[rank-{self.rank}]: Saving model to {os.path.abspath(model_path)}.")
print(f"[rank-{self.rank}]: Saving optimizer to {os.path.abspath(optim_path)}.")
print(f"[rank-{self.rank}]: Saving extra_state to {os.path.abspath(extra_path)}.")
self._save_shard(model_state_dict, model_path, "model")
self._save_shard(optim_state_dict, optim_path, "optimizer")
self._save_shard(extra_state_dict, extra_path, "extra_state")
# wait for everyone to dump to local
dist.barrier()
if self.rank == 0:
hf_path = os.path.join(path, "huggingface")
os.makedirs(hf_path, exist_ok=True)
assert isinstance(self.model._fsdp_wrapped_module, PreTrainedModel)
self.model._fsdp_wrapped_module.config.save_pretrained(hf_path)
self.model._fsdp_wrapped_module.generation_config.save_pretrained(hf_path)
self.processing_class.save_pretrained(hf_path)
dist.barrier()
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