feat(ckpt): add async upload and ckpt snapshot (#161)

* use fp16 in instruction (#80)

* delete torch_dtype of README's example code (#100)

* feat(ckpt): support async ckpt upload and ckpt snapshot

---------

Co-authored-by: WRH <12756472+wangruohui@users.noreply.github.com>
Co-authored-by: x54-729 <45304952+x54-729@users.noreply.github.com>
Co-authored-by: wangguoteng.p <wangguoteng925@qq.com>

configs/7B_sft.py

@@ -7,22 +7,29 @@ MLP_RATIO = 8 / 3
 NUM_LAYER = 32
 VOCAB_SIZE = 103168
 
+MODEL_ONLY_FOLDER = "local:llm_ckpts/xxxx"
 # Ckpt folder format:
 # fs: 'local:/mnt/nfs/XXX'
-# oss: 'boto3:s3://model_weights/XXX'
-MODEL_ONLY_FOLDER = "local:llm_ckpts/xxxx"
 SAVE_CKPT_FOLDER = "local:llm_ckpts"
 LOAD_CKPT_FOLDER = "local:llm_ckpts/49"
+
+# boto3 Ckpt folder format:
+# import os
+# BOTO3_IP = os.environ["BOTO3_IP"] # boto3 bucket endpoint
+# SAVE_CKPT_FOLDER = f"boto3:s3://model_weights.{BOTO3_IP}/internlm"
+# LOAD_CKPT_FOLDER = f"boto3:s3://model_weights.{BOTO3_IP}/internlm/snapshot/1/"
+CHECKPOINT_EVERY = 50
 ckpt = dict(
-    # Path to save training ckpt.
+    enable_save_ckpt=False,  # enable ckpt save.
-    save_ckpt_folder=SAVE_CKPT_FOLDER,
+    save_ckpt_folder=SAVE_CKPT_FOLDER,  # Path to save training ckpt.
-    # Path to continue training ckpt (load model weights and scheduler/context states).
+    # load_ckpt_folder=LOAD_CKPT_FOLDER, # Ckpt path to resume training(load weights and scheduler/context states).
-    # load_ckpt_folder=LOAD_CKPT_FOLDER,
+    # load_model_only_folder=MODEL_ONLY_FOLDER, # Path to initialize with given model weights.
-    # Path to initialize with given model weights.
+    load_optimizer=True,  # Wheter to load optimizer states when continuing training.
-    # load_model_only_folder=MODEL_ONLY_FOLDER,
+    checkpoint_every=CHECKPOINT_EVERY,
-    checkpoint_every=50,
+    async_upload=True,  # async ckpt upload. (only work for boto3 ckpt)
-    # Wheter to load optimizer states when continuing training.
+    async_upload_tmp_folder="/dev/shm/internlm_tmp_ckpt/",  # path for temporarily files during asynchronous upload.
-    load_optimizer=True,
+    snapshot_ckpt_folder="/".join([SAVE_CKPT_FOLDER, "snapshot"]),  # directory for snapshot ckpt storage path.
+    oss_snapshot_freq=int(CHECKPOINT_EVERY / 2),  # snapshot ckpt save frequency.
 )
 
 TRAIN_FOLDER = "/path/to/dataset"

internlm/initialize/launch.py

@@ -11,6 +11,7 @@ import torch
 from internlm.core.context import Config
 from internlm.core.context import global_context as gpc
 from internlm.utils.logger import get_logger
+from internlm.utils.storage_manager import init_storage_manager
 
 logger = get_logger(__file__)
 
@@ -122,20 +123,44 @@ def args_sanity_check():
     if "load_model_only_folder" not in gpc.config.ckpt:
         gpc.config.ckpt._add_item("load_model_only_folder", None)
 
+    if "async_upload" not in gpc.config.ckpt:
+        gpc.config.ckpt._add_item("async_upload", False)
+    else:
+        if gpc.config.ckpt.async_upload:
+            assert "save_ckpt_folder" in gpc.config.ckpt
+            if "boto3:" not in gpc.config.ckpt.save_ckpt_folder:
+                if gpc.is_rank_for_log():
+                    logger.warning(
+                        "Storing ckpt on file system does not support asynchronous storage, will use sync save!"
+                    )
+                gpc.config.ckpt.async_upload = False
+            else:
+                if "async_upload_tmp_folder" not in gpc.config.ckpt:
+                    gpc.config.ckpt._add_item("async_upload_tmp_folder", "/dev/shm/internlm_tmp_ckpt/")
+
+    if "snapshot_ckpt_folder" not in gpc.config.ckpt:
+        gpc.config.ckpt._add_item("snapshot_ckpt_folder", os.path.join(gpc.config.ckpt.save_ckpt_folder), "snapshot")
+
+    if "oss_snapshot_freq" not in gpc.config.ckpt and gpc.config.ckpt.checkpoint_every != float("inf"):
+        gpc.config.ckpt._add_item("oss_snapshot_freq", gpc.config.ckpt.checkpoint_every / 2)
+        assert gpc.config.ckpt.oss_snapshot_freq > 0
+
     assert not (
         gpc.config.ckpt.load_ckpt_folder is not None and gpc.config.ckpt.load_model_only_folder is not None
     ), "'load_ckpt_folder' and 'load_model_only_folder' cannot be set at the same time."
 
-    gpc.config.ckpt._add_item(
+    if "enable_save_ckpt" not in gpc.config.ckpt:
-        "enable_ckpt", gpc.config.ckpt.save_ckpt_folder is not None and gpc.config.ckpt.checkpoint_every > 0
+        gpc.config.ckpt._add_item("enable_save_ckpt", False)
-    )
 
     if gpc.is_rank_for_log():
         logger.info("+" * 15 + " Ckpt Info " + "+" * 15)  # pylint: disable=W1201
-        logger.info(f"is enable save ckpt: {gpc.config.ckpt.enable_ckpt}")
+        logger.info(f"is enable save ckpt: {gpc.config.ckpt.enable_save_ckpt}")
         logger.info(f"save_ckpt_folder: {gpc.config.ckpt.save_ckpt_folder}")
         logger.info(f"checkpoint_every: {gpc.config.ckpt.checkpoint_every}")
 
+    # initialization storage manager
+    init_storage_manager(gpc.config.ckpt)
+
     # tensorboard writer config
     if "enable_tb" not in gpc.config:
         gpc.config._add_item("enable_tb", True)

internlm/utils/model_checkpoint.py

@@ -4,6 +4,7 @@
 import copy
 import os
 import time
+from enum import Enum
 from typing import Dict
 
 import torch
@@ -15,10 +16,22 @@ from internlm.solver.optimizer import HybridZeroOptimizer
 from internlm.utils.common import get_current_device
 from internlm.utils.logger import get_logger
 from internlm.utils.megatron_timers import megatron_timer as timer
-from internlm.utils.storage_manager import get_fns, llm_load, llm_save
+from internlm.utils.storage_manager import (
+    get_fns,
+    get_storage_manager,
+    llm_load,
+    llm_save,
+)
 
 logger = get_logger(__file__)
 
+quit_signal_handler = None
+
+
+class CheckpointType(Enum):
+    NORMAL_CHECKPOINT = 1
+    SNAPSHOT_CHECKPOINT = 2
+
 
 def get_model_topology(model):
     """
@@ -289,3 +302,77 @@ def load_scheduler(ckpt_path: str, lr_scheduler, optimizer, learning_rate, train
 
     if gpc.is_rank_for_log():
         logger.info(f"reload load_scheduler:{lr_scheduler}")
+
+
+class CheckpointSaveManager:
+    """StorageManagerContext"""
+
+    def __init__(
+        self,
+        ckpt_config,
+        model,
+        optimizer,
+        lr_scheduler,
+        model_config,
+    ) -> None:
+        """
+        CheckpointSaveManager is used to decide when to store ckpt. If it is an asynchronous
+        upload mode, you must call wait_async_upload_finish at the end of the program to wait
+        for the asynchronous ckpt upload to complete.
+
+        Args:
+            ckpt_config (dict): model checkpoint config.
+            model (nn.module): model obj
+            optimizer (object): optimzier obj.
+            lr_scheduler (object): lr_scheduler obj.
+            model_config (dict): model config.
+        """
+        self.enable_save_ckpt = ckpt_config.enable_save_ckpt
+        self.checkpoint_every = ckpt_config.checkpoint_every
+        self.save_ckpt_folder = ckpt_config.save_ckpt_folder
+        self.snapshot_ckpt_folder = ckpt_config.snapshot_ckpt_folder
+        self.oss_snapshot_freq: int = ckpt_config.oss_snapshot_freq
+        self.storage_manager = get_storage_manager()
+        self.snapshot_counter = 0
+
+        self.model = model
+        self.optimizer = optimizer
+        self.lr_scheduler = lr_scheduler
+        self.model_config = model_config
+
+    def try_save_checkpoint(self, train_state):
+        if not self.enable_save_ckpt:
+            return
+
+        save_ckpts, save_type = False, CheckpointType.NORMAL_CHECKPOINT
+        if self.oss_snapshot_freq > 1 and train_state.step_count % self.oss_snapshot_freq == 0:
+            save_ckpts, save_type = True, CheckpointType.SNAPSHOT_CHECKPOINT
+        if train_state.step_count % self.checkpoint_every == 0:
+            save_ckpts, save_type = True, CheckpointType.NORMAL_CHECKPOINT
+        if save_ckpts is False:
+            if quit_signal_handler is not None:
+                save_ckpts, save_type = quit_signal_handler(train_state)
+
+        if save_ckpts:
+            # Wait for the previous round of asynchronous upload storage to complete.
+            self.storage_manager.wait()
+            if save_type == CheckpointType.SNAPSHOT_CHECKPOINT:
+                # Snapshot number, with only two snapshots written alternately.
+                self.snapshot_counter = (self.snapshot_counter + 1) % 2
+                save_ckpt_folder = os.path.join(self.snapshot_ckpt_folder, f"{self.snapshot_counter}")
+            else:
+                save_ckpt_folder = self.save_ckpt_folder
+
+            save_checkpoint(
+                folder=save_ckpt_folder,
+                model=self.model,
+                optimizer=self.optimizer,
+                scheduler=self.lr_scheduler,
+                train_state=train_state,
+                model_config=self.model_config,
+            )
+
+    def wait_async_upload_finish(self):
+        """wait for all checkpoint uploads to be completed"""
+        self.storage_manager.wait()
+        torch.distributed.barrier()

internlm/utils/storage_manager.py

@@ -1,18 +1,26 @@
 #!/usr/bin/env python
 # -*- encoding: utf-8 -*-
 
+import asyncio
+import concurrent.futures
 import hashlib
 import io
 import os
+import pickle
 import re
 import socket
-from enum import Enum
+import stat
-from typing import Any, Dict, List, Union
+from asyncio import InvalidStateError
+from asyncio.tasks import ALL_COMPLETED
+from datetime import datetime
+from typing import Any, Awaitable, Callable, Dict, List, Union
 
 import boto3
 import botocore
 import torch
+import torch.distributed as dist
 
+from internlm.core.context import global_context as gpc
 from internlm.utils.common import SingletonMeta
 from internlm.utils.logger import get_logger
 
@@ -41,10 +49,6 @@ def llm_save(save_path: str, saved_obj: Any, *args, **kwargs):
     storage_manager.save(save_path, *args, saved_obj=saved_obj, **kwargs)
 
 
-class CheckpointType(Enum):
-    NORMAL_CHECKPOINT = 1
-
-
 class StorageClient:
     """
     StorageClient as a client for s3 storage access.
@@ -54,7 +58,7 @@ class StorageClient:
         self.handler = handler
 
     @staticmethod
-    def load(client, load_path: str, map_location):
+    def load(client, load_path: str, *args, **kwargs):
         raise NotImplementedError
 
     @staticmethod
@@ -71,25 +75,51 @@ class StorageClient:
 
 
 class Boto3MetaInfo:
-    def __init__(self, client: StorageClient, bucket_name: str, endpoint: str, file_path: str) -> None:
+    """Boto3 meta info for save/load etc."""
-        self.client = client
+
+    def __init__(
+        self,
+        is_async,
+        handler: StorageClient,
+        bucket_name: str,
+        endpoint: str,
+        file_path: str,
+        async_upload_fn: callable,
+        local_nvme_path=None,
+    ) -> None:
+        self.is_async = is_async
+        self.client = handler
         self.bucket_name = bucket_name
         self.endpoint = endpoint
         self.file_path = file_path
+        self.async_upload_fn = async_upload_fn
+        self.local_nvme_path = local_nvme_path
+
+    def __str__(self) -> str:
+        return f"is_async: {self.is_async}, bucket_name:{self.bucket_name}, endpoint:{self.endpoint}, \
+local_nvme_path: {self.local_nvme_path}"
 
 
 class LocalMetaInfo:
-    def __init__(self, client: StorageClient, dest_path: str) -> None:
+    """Local meta info for save/load etc."""
-        self.client = client
+
+    def __init__(self, handler: StorageClient, dest_path: str) -> None:
+        self.is_async = False
+        self.client = handler
         self.dest_path = dest_path
+        self.async_upload_fn = None
 
 
 def unpack_meta(meta):
     args = []
+    is_async = meta.is_async
     for k, v in meta.__dict__.items():
-        if k == "endpoint":
+        if k in ("endpoint", "async_upload_fn", "is_async"):
+            continue
+        if not is_async and k in ("local_nvme_path",):
             continue
         args.append(v)
+
     return args
 
 
@@ -101,21 +131,6 @@ def compute_file_md5_by_chunk(file_name: str):
     return hash_md5.hexdigest()
 
 
-def get_boto3_meta(fp: str) -> Boto3MetaInfo:
-    assert fp.startswith("s3://"), f"Path '{fp}' is not a boto3 url"
-    parts = fp.lstrip("s3://").split(os.path.sep)
-    match = boto3_url_re.match(parts[0])
-    assert match is not None, f"url '{fp}' is not a valid boto3 url"
-    bucket_name, endpoint = match.group(1), match.group(2)
-    endpoint = "http://" + endpoint + ":80"
-    return Boto3MetaInfo(None, bucket_name, endpoint, os.path.sep.join(parts[1:]))
-
-
-def get_local_meta(fp: str) -> LocalMetaInfo:
-    assert not fp.startswith("s3://"), f"Path '{fp}' is not a local path"
-    return LocalMetaInfo(None, fp)
-
-
 class Boto3Client(StorageClient):
     """
     Boto3Client
@@ -169,7 +184,9 @@ class Boto3Client(StorageClient):
         )
 
     @staticmethod
-    def sync_upload_fileobj(handler, bucket_name: str, fp: str, *args, saved_obj=None, **kwargs):
+    def sync_upload_fileobj(
+        handler, bucket_name: str, fp: str, local_nvme_path: str, *args, saved_obj=None, **kwargs
+    ):  # pylint: disable=W0613
         assert saved_obj is not None, "saved_obj is None!"
         try:
             with io.BytesIO() as f:
@@ -182,7 +199,14 @@ class Boto3Client(StorageClient):
             ) from exc
 
     @staticmethod
-    def load(handler, bucket_name: str, fp: str, *args, map_location="cpu", **kwargs) -> Dict:
+    def load(
+        handler,
+        bucket_name: str,
+        fp: str,
+        local_nvme_path: str,  # pylint: disable=W0613
+        *args,
+        **kwargs,
+    ) -> Dict:
         """
         Args:
             fp (str): Path to save, eg. s3://opennlplab/model_weights/xxx/ddd.pt
@@ -191,7 +215,7 @@ class Boto3Client(StorageClient):
             with io.BytesIO() as f:
                 handler.client.download_fileobj(bucket_name, fp, f, Config=handler.config)
                 f.seek(0)
-                states = torch.load(f, *args, map_location=map_location, **kwargs)
+                states = torch.load(f, *args, **kwargs)
         except handler.botocore.exceptions.EndpointConnectionError as exc:
             raise RuntimeError(
                 f"Boto3 Network Error: Please Check your Internet Connection in {socket.gethostname()}"
@@ -199,15 +223,11 @@ class Boto3Client(StorageClient):
         return states
 
     @staticmethod
-    def assert_fp_exists(
+    def assert_fp_exists(handler, bucket_name: str, fp: str, local_nvme_path: str):  # pylint: disable=W0613
-        handler,
-        bucket_name: str,
-        fp: str,
-    ):
         assert len(list(handler.client.list_objects(Bucket=bucket_name, Prefix=fp)["Contents"])) > 0, fp
 
     @staticmethod
-    def get_fns(handler, bucket_name: str, fp: str):
+    def get_fns(handler, bucket_name: str, fp: str, local_nvme_path: str, *args, **kwargs):  # pylint: disable=W0613
         """
         Ref: https://stackoverflow.com/questions/54314563/
         how-to-get-more-than-1000-objects-from-s3-by-using-list-objects-v2
@@ -222,6 +242,22 @@ class Boto3Client(StorageClient):
                 folder_name_list.append(fp.rsplit("/", maxsplit=1)[1])
         return folder_name_list
 
+    @staticmethod
+    def async_upload_fileobj(handler, bucket_name: str, fp: str, local_nvme_path: str):
+        try:
+            with open(local_nvme_path, "rb") as f:
+                handler.client.upload_fileobj(f, bucket_name, fp, Config=handler.config)
+        except handler.botocore.exceptions.EndpointConnectionError as exc:
+            raise RuntimeError(
+                f"Boto3 Network Error: Please Check your Internet Connection in {socket.gethostname()}"
+            ) from exc
+        except Exception as e:
+            raise e
+
+    @staticmethod
+    def delete_obj(handler, fp: str):
+        raise NotImplementedError("boto3 not support delete_obj")
+
 
 class LocalClient(StorageClient):
     """
@@ -241,11 +277,11 @@ class LocalClient(StorageClient):
         torch.save(saved_obj, fp, *args, **kwargs)
 
     @staticmethod
-    def load(handler, fp: str, *args, map_location="cpu", **kwargs):
+    def load(handler, fp: str, *args, **kwargs):  # pylint: disable=W0613
         assert isinstance(handler, LocalClient)
         assert os.path.exists(fp), f"{fp} is not found!"
         with open(fp, "rb") as f:
-            states = torch.load(f, map_location=map_location, *args, **kwargs)
+            states = torch.load(f, *args, **kwargs)
         return states
 
     @staticmethod
@@ -267,9 +303,77 @@ class LocalClient(StorageClient):
             os.remove(fp)
 
 
+def get_tmp_file_name(tmp_local_folder: str, fp: str):
+    """
+    It should be noted that all our temporary files will be stored in the same folder,
+    so the file name passed upstream must be unique.
+    """
+    base_path = os.path.join(tmp_local_folder, fp.split("/")[-1])
+    current_time = datetime.now().strftime("%b%d_%H-%M-%S")
+    pid = os.getpid()
+    # step = self.step_counter
+    return "-".join([base_path, current_time, str(pid)]) + ".tmpfile"  # , str(step)
+
+
+def get_boto3_meta(fp: str, tmp_local_folder: str, is_async: bool) -> Boto3MetaInfo:
+    assert fp.startswith("s3://"), f"Path '{fp}' is not a boto3 url"
+    parts = fp.lstrip("s3://").split(os.path.sep)
+    match = boto3_url_re.match(parts[0])
+    assert match is not None, f"url '{fp}' is not a valid boto3 url"
+    bucket_name, endpoint = match.group(1), match.group(2)
+    endpoint = "http://" + endpoint + ":80"
+    tmp_step_file = get_tmp_file_name(tmp_local_folder, fp)
+    return Boto3MetaInfo(
+        is_async=is_async,
+        handler=None,
+        bucket_name=bucket_name,
+        endpoint=endpoint,
+        file_path=os.path.sep.join(parts[1:]),
+        async_upload_fn=Boto3Client.async_upload_fileobj,
+        local_nvme_path=tmp_step_file,
+    )
+
+
+def get_local_meta(fp: str) -> LocalMetaInfo:
+    assert not fp.startswith("s3://"), f"Path '{fp}' is not a local path"
+    return LocalMetaInfo(None, fp)
+
+
+def get_mount_point_free_size(path: str):
+    """
+        Returns the remaining space of the temporary storage mount point as a percentage.
+    Args:
+        path (str): temporary storage folder path.
+
+    Raises:
+        FileNotFoundError: If the temporary storage folder does not exist,
+        an error will be reported。
+    """
+    if os.path.exists(path):
+        st = os.statvfs(path)
+        # f_bavail: Number of free blocks for unprivileged users.
+        # f_bsize: Filesystem block size.
+        # return unit is TB.
+        return st.f_bavail * st.f_bsize / (1024**3)
+
+
+def check_tmp_folder_accessibility(tmp_local_folder: str):
+    """
+    Check access permissions for temporary storage.
+    """
+    ret = True
+    if os.path.exists(tmp_local_folder):
+        ret &= os.access(tmp_local_folder, os.W_OK)
+        ret &= os.access(tmp_local_folder, os.R_OK)
+        if ret is False:
+            error_str = f'{socket.gethostname()} dose not have read and write permissions on {tmp_local_folder}"'
+            raise RuntimeError(error_str)
+
+
 class StorageManager(metaclass=SingletonMeta):
     """
     Storage Manager for saving or loading checkpoint.
+    TODO: add a thread to poll the asynchronous storage state.
     """
 
     BACKEND_TYPE = {"boto3", "local"}
@@ -279,8 +383,39 @@ class StorageManager(metaclass=SingletonMeta):
     }
     CLI_DICT = {}
 
-    def __init__(self) -> None:
+    def __init__(self, enable_save, tmp_local_folde="/dev/shm/test/", async_mode=True, n_async_workers=8) -> None:
-        pass
+        self._exception_list = []
+        self._to_be_del_files = []
+        self._async_stack = []
+        self.upload_count = 0
+        self.tmp_local_folder = tmp_local_folde
+        self.async_mode = async_mode
+        self.has_warning = False
+
+        if enable_save and self.async_mode:
+            self._async_loop = asyncio.new_event_loop()
+            self._thread_pool = concurrent.futures.ThreadPoolExecutor(max_workers=n_async_workers)
+
+            check_tmp_folder_accessibility(os.path.dirname(self.tmp_local_folder))
+
+            # Try to create tmp folder
+            try:
+                os.makedirs(self.tmp_local_folder, exist_ok=True)
+                os.chmod(self.tmp_local_folder, stat.S_IRWXU | stat.S_IRWXG | stat.S_IRWXO)
+            except FileExistsError:
+                pass
+
+            # In case it is a directory created by other users, we check the permissions again.
+            check_tmp_folder_accessibility(self.tmp_local_folder)
+
+            # Try to clean tmp folder's empty folder.
+            self.try_delete_tmpfile(self.tmp_local_folder)
+
+            # Avaliable storeage space check.
+            free_size = get_mount_point_free_size(self.tmp_local_folder)
+            if free_size < 0.1:
+                logger.error(f'tmp_local_folder only have "{free_size}" GB free space, less then 100 GB!')
+                raise RuntimeError(f"Insufficient temporary storage space on {socket.gethostname()}")
 
     def _get_client(self, path=str) -> Union[Boto3MetaInfo, LocalMetaInfo]:
         """
@@ -301,7 +436,7 @@ class StorageManager(metaclass=SingletonMeta):
             meta_info = get_local_meta(path)
             backend_key = backend
         elif backend == "boto3":
-            meta_info = get_boto3_meta(path)
+            meta_info = get_boto3_meta(path, self.tmp_local_folder, self.async_mode)
             backend_key = backend + ":" + meta_info.endpoint
             init_args = (meta_info.endpoint,)
             if (
@@ -310,10 +445,12 @@ class StorageManager(metaclass=SingletonMeta):
                 or "HTTP_PROXY" in os.environ
                 or "HTTPS_PROXY" in os.environ
             ):
-                raise RuntimeWarning(
+                if not self.has_warning:
-                    "HTTP/HTTPS proxy is detected when using boto3, incorrectly setting \
+                    logger.warning(
-the proxy may make boto3 unavailable or affect performance."
+                        "HTTP/HTTPS proxy is detected when using boto3, incorrectly setting \
-                )
+    the proxy may make boto3 unavailable or affect performance."
+                    )
+                    self.has_warning = True
 
         assert backend in StorageManager.BACKEND_TYPE, f"Unkown backend: {backend}"
 
@@ -333,19 +470,137 @@ the proxy may make boto3 unavailable or affect performance."
         meta = self._get_client(path=folder)
         return meta.client.get_fns(*unpack_meta(meta))
 
-    def save(self, save_path: str, saved_obj: Any, *args, **kwargs):
+    def save(self, save_path: str, saved_obj: Any, *args, async_upload=None, **kwargs):
         meta = self._get_client(path=save_path)
 
-        meta.client.sync_upload_fileobj(*unpack_meta(meta), *args, saved_obj=saved_obj, **kwargs)
+        if async_upload is None:
-
+            async_upload = self.async_mode
-    def load(self, load_path: str, *args, map_location="cpu", **kwargs) -> Any:
+        if async_upload:
+            assert (
+                self.tmp_local_folder
+            ), "StorageManager is not setted tmp_local_folder, so async save cannot be performed."
+            tmp_step_file = meta.local_nvme_path
+            self._to_be_del_files.append(tmp_step_file)
+            with open(tmp_step_file, "wb") as f:
+                torch.save(saved_obj, f, pickle_protocol=pickle.HIGHEST_PROTOCOL)
+            self.async_executor(meta.async_upload_fn, *unpack_meta(meta))
+            os.chmod(tmp_step_file, stat.S_IRWXU | stat.S_IRWXG | stat.S_IRWXO)
+        else:
+            meta.client.sync_upload_fileobj(*unpack_meta(meta), *args, saved_obj=saved_obj, **kwargs)
+            self.upload_count += 1
 
+    def load(self, load_path: str, *args, **kwargs) -> Any:
+        self.wait()
         meta = self._get_client(path=load_path)
-        return meta.client.load(*unpack_meta(meta), map_location=map_location, *args, **kwargs)
+        return meta.client.load(*unpack_meta(meta), *args, **kwargs)
 
     def delete_obj(self, fp: str):
         meta = self._get_client(path=fp)
         meta.client.delete_obj(*unpack_meta(meta))
 
+    def _del_tmp_folder(self):
+        for fp in self._to_be_del_files:
+            try:
+                os.remove(fp)
+            except FileNotFoundError:
+                pass
+            except SystemError as e:
+                logger.error(f'delete file: {fp}, failed for reason:"{e}"')
+            else:
+                pass
 
-storage_manager = StorageManager()
+    def try_delete_tmpfile(self, tmp_dir: str):
+        """Delete temporary files in tmp_dir."""
+
+        for filename in os.listdir(tmp_dir):
+            if filename.endswith(".tmpfile"):
+                file_path = os.path.join(tmp_dir, filename)
+                try:
+                    os.remove(file_path)
+                    logger.info(f"Delete tmpfile: {file_path}")
+                except OSError:
+                    # Ignore deletion errors
+                    pass
+
+    async def _sync_tasks(self) -> Awaitable[None]:
+
+        if not self._async_stack:
+            return
+
+        await asyncio.wait(self._async_stack, return_when=ALL_COMPLETED)
+
+        for task in self._async_stack:
+            try:
+                task.exception()
+            except InvalidStateError:
+                continue
+            except Exception as e:
+                file_id = len(self._exception_list)
+                self._exception_list.append((e, file_id))
+
+                logger.error(f"File: {self._to_be_del_files[file_id]}, " f"upload failed with {e}")
+
+        self._async_stack.clear()
+
+    def async_executor(self, fn: Callable, *args, **kwargs) -> None:
+        """
+        Overview:
+            Execute task in background, then apppend the future instance in _async_stack.
+        Arguments:
+            - fn (:obj:`Callable`): Synchronization fuction.
+        """
+        if not self._async_loop:
+            raise RuntimeError("Event loop was not initialized, please call this function in async or parallel mode")
+        t = self._async_loop.run_in_executor(self._thread_pool, fn, *args, **kwargs)
+        self._async_stack.append(t)
+
+    def wait(self) -> bool:
+        """Wait for async operations to complete."""
+
+        if not self.async_mode:
+            return
+
+        if self._async_loop:
+            self._async_loop.run_until_complete(self._sync_tasks())
+
+        if self._exception_list:
+            for file_id, error_msg in self._exception_list:
+                logger.error(
+                    f"Node:{socket.gethostname()}, Error: Checkpoint {self._to_be_del_files[file_id]} "
+                    f"failed on step {self.upload_count}: {error_msg}"
+                )
+
+                # TODO: Re-upload in sync mode
+                raise RuntimeError(
+                    f"Failed to upload {self._to_be_del_files[file_id]} " f"on step {self.upload_count}: {error_msg}"
+                )
+
+        self._del_tmp_folder()
+        self._exception_list.clear()
+        self._to_be_del_files.clear()
+
+        if gpc.is_rank_for_log():
+            logger.info("all async uploads succeeded!")
+            self.upload_count += 1
+
+
+storage_manager: StorageManager = None
+
+
+def init_storage_manager(ckpt_config):
+    global storage_manager
+    storage_manager = StorageManager(
+        ckpt_config.enable_save_ckpt,
+        tmp_local_folde=ckpt_config.async_upload_tmp_folder,
+        async_mode=ckpt_config.async_upload,
+    )
+
+
+def get_storage_manager():
+    assert storage_manager is not None, "storage_manager has not been init!"
+    return storage_manager
+
+
+def wait_async_upload_finish():
+    dist.barrier()
+    storage_manager.wait()

train.py

@@ -46,12 +46,12 @@ from internlm.utils.evaluation import evaluate_on_val_dls, switch_sequence_paral
 from internlm.utils.logger import get_logger, initialize_uniscale_logger
 from internlm.utils.megatron_timers import megatron_timer as timer
 from internlm.utils.model_checkpoint import (
+    CheckpointSaveManager,
     load_context,
     load_model_checkpoint,
     load_optimizer_checkpoint,
     load_sampler,
     load_scheduler,
-    save_checkpoint,
 )
 from internlm.utils.parallel import (
     get_parallel_log_file_name,
@@ -432,11 +432,6 @@ def main(args):
     label_smoothing = gpc.config.loss.label_smoothing
     lr = gpc.config.adam.lr
 
-    # ckpt setting
-    save_ckpt_folder = gpc.config.ckpt.save_ckpt_folder
-    enable_save_ckpt = gpc.config.ckpt.enable_ckpt
-    checkpoint_every = gpc.config.ckpt.checkpoint_every
-
     load_model_only_folder = gpc.config.ckpt.get("load_model_only_folder", None)
     load_resume_ckpt_folder = gpc.config.ckpt.get("load_ckpt_folder", None)
 
@@ -527,6 +522,14 @@ def main(args):
         if load_optimizer:
             load_optimizer_checkpoint(load_resume_ckpt_folder, optimizer)
 
+    ckpt_save_manager = CheckpointSaveManager(
+        ckpt_config=gpc.config.ckpt,
+        model=model,
+        optimizer=optimizer,
+        lr_scheduler=lr_scheduler,
+        model_config=gpc.config.model,
+    )
+
     # initialize metric for calculating accuracy and perplexity
     metric = AccPerplex(
         device=torch.cuda.current_device(),
@@ -645,19 +648,10 @@ def main(args):
                 )
 
         # checkpoint the training states in specific steps, which is determined by the args "checkpoint_every"
-        # save batch sampler that tracks the true consumed samples
+        # # save batch sampler that tracks the true consumed samples
-        if enable_save_ckpt and train_state.step_count % checkpoint_every == 0:
+        ckpt_save_manager.try_save_checkpoint(train_state)
-            save_checkpoint(
-                folder=save_ckpt_folder,
-                model=model,
-                optimizer=optimizer,
-                scheduler=lr_scheduler,
-                train_state=train_state,
-                model_config=gpc.config.model,
-            )
 
-    # wait for all checkpoint uploads to be completed
+    ckpt_save_manager.wait_async_upload_finish()
-    dist.barrier()
 
 
 if __name__ == "__main__":