diff --git a/bytelatent/checkpoint.py b/bytelatent/checkpoint.py index f213c84..6631673 100644 --- a/bytelatent/checkpoint.py +++ b/bytelatent/checkpoint.py @@ -4,8 +4,6 @@ import json import logging import os import re -from pathlib import Path -from typing import List, Optional, Tuple import fsspec import torch @@ -70,26 +68,29 @@ def consolidate_checkpoints(fs: fsspec.AbstractFileSystem, ckpt_dir: str): Returns the path to the consolidated checkpoint """ - consolidate_path = Path(ckpt_dir) / CONSOLIDATE_FOLDER - if not (consolidate_path / CONSOLIDATE_NAME).exists(): - consolidate_path.mkdir(exist_ok=True) - logger.info(f"Consolidating to: {str(consolidate_path)}") - dcp_to_torch_save(ckpt_dir, str(consolidate_path / CONSOLIDATE_NAME)) - (consolidate_path / CONFIG_NAME).write_text( - (Path(ckpt_dir) / CONFIG_NAME).read_text() + consolidate_path = os.path.join(ckpt_dir, CONSOLIDATE_FOLDER) + consolidate_name = os.path.join(consolidate_path, CONSOLIDATE_NAME) + if not fs.exists(consolidate_name): + fs.mkdirs(consolidate_path, exist_ok=True) + logger.info(f"Consolidating to: {consolidate_path}") + dcp_to_torch_save(ckpt_dir, consolidate_name) + fs.write_text( + os.path.join(consolidate_path, CONFIG_NAME), + fs.read_text(os.path.join(ckpt_dir, CONFIG_NAME)), ) logger.info("Consolidated !") return consolidate_path def load_from_checkpoint( + fs: fsspec.AbstractFileSystem, ckpt_dir: str, model: nn.Module, - optimizer: Optional[torch.optim.Optimizer] = None, + optimizer: torch.optim.Optimizer | None = None, model_key: str = "model", optim_key: str = "optim", ): - if not (Path(ckpt_dir) / ".metadata").exists(): + if not fs.exists(os.path.join(ckpt_dir, ".metadata")): raise ValueError( f"Please convert the checkpoint distcp format using `torch.distributed.checkpoint.format_utils.torch_save_to_dcp` before loading it" ) @@ -121,13 +122,13 @@ class CheckpointManager: self.existing_saves = self.get_existing_saves() - def get_existing_saves(self) -> List[Path]: + def get_existing_saves(self) -> list[str]: folders = [ p - for p in Path(self.path).iterdir() - if p.is_dir() and re.match(RE_FOLDER, p.name) + for p in self.fs.ls(self.path) + if self.fs.isdir(p) and re.match(RE_FOLDER, os.path.basename(p)) ] - folders.sort(key=lambda p: _get_key_step(p.name)) + folders.sort(key=lambda p: _get_key_step(os.path.basename(p))) return folders def clean_up(self): @@ -136,8 +137,9 @@ class CheckpointManager: eval_folders = [] other_folders = [] for p in self.existing_saves: - is_dump = _get_key_step(p.name) % self.dump_every.every == 0 - is_eval = _get_key_step(p.name) % self.eval_every.every == 0 + assert isinstance(p, str), f"Base path type: {p}" + is_dump = _get_key_step(os.path.basename(p)) % self.dump_every.every == 0 + is_eval = _get_key_step(os.path.basename(p)) % self.eval_every.every == 0 if is_dump: dump_folders.append(p) if is_eval: @@ -161,40 +163,39 @@ class CheckpointManager: if dist.get_rank() == 0: for folder in folder_to_remove: - for file in folder.iterdir(): - if file.is_file(): - file.unlink() - elif file.is_dir(): - assert file.name in [CONSOLIDATE_FOLDER] - for f in file.iterdir(): - f.unlink() - file.rmdir() - folder.rmdir() + for file in self.fs.ls(folder): + if self.fs.isfile(file): + self.fs.rm_file(file) + elif self.fs.isdir(file): + assert os.path.name(file) in [CONSOLIDATE_FOLDER] + for f in self.fs.ls(file): + self.fs.rm(f) + self.fs.rmdir(file) + self.fs.rmdir(folder) dist.barrier() self.existing_saves = list(folder_to_keep) - self.existing_saves.sort(key=lambda p: _get_key_step(p.name)) + self.existing_saves.sort(key=lambda p: _get_key_step(os.path.basename(p))) - def get_last_step_path(self, dp_rank: int = 0) -> Optional[Path]: + def get_last_step_path(self, dp_rank: int = 0) -> str | None: path = None for p in reversed(self.existing_saves): - if (p / TRAIN_STATE_NAME.format(dp_rank)).is_file(): + + if self.fs.isfile(os.path.join(p, TRAIN_STATE_NAME.format(dp_rank))): path = p break return path - def _create_folder(self, base_path: Path, folder_name: str) -> Path: - folder = base_path / folder_name + def _create_folder(self, base_path: str, folder_name: str) -> str: + folder = os.path.join(base_path, folder_name) if get_is_master(): - folder.mkdir(parents=False, exist_ok=True) + self.fs.mkdirs(folder, exist_ok=True) if dist.is_initialized(): dist.barrier() return folder - def _get_dp_tp_mesh( - self, device_mesh: Optional[DeviceMesh] = None - ) -> Tuple[int, int]: + def _get_dp_tp_mesh(self, device_mesh: DeviceMesh | None = None) -> tuple[int, int]: dp_rank = 0 tp_rank = 0 if device_mesh is not None: @@ -222,14 +223,14 @@ class CheckpointManager: model, optimizer, train_state, - config, - device_mesh: Optional[DeviceMesh] = None, + config: BaseModel, + device_mesh: DeviceMesh | None = None, ) -> bool: # When creating directory check if only rank0 or is there other solution - path = Path(self.path) + path = self.path curr_save_dir = self._create_folder(path, FOLDER_NAME.format(train_state.step)) - logger.info(f"Saving to: {str(curr_save_dir)}") + logger.info(f"Saving to: {curr_save_dir}") if dist.is_initialized(): dist.barrier() @@ -242,17 +243,19 @@ class CheckpointManager: if dist.is_initialized(): dist.barrier() + print("config type", type(config)) if get_is_master(): - config.dump_to_yaml_file(curr_save_dir / CONFIG_NAME) + self.fs.write_text( + os.path.join(curr_save_dir, CONFIG_NAME), config.model_dump_json() + ) # Add json dump here dp_rank, tp_rank = self._get_dp_tp_mesh(device_mesh) if tp_rank == 0: train_state_name = TRAIN_STATE_NAME.format(dp_rank) - logger.info( - f"Saving train state to: {str(curr_save_dir / train_state_name)}" - ) - with open(curr_save_dir / train_state_name, "w") as f: + train_state_full_path = os.path.join(curr_save_dir, train_state_name) + logger.info(f"Saving train state to: {train_state_full_path}") + with self.fs.open(train_state_full_path, "w") as f: json.dump(train_state.state_dict(), f) logger.info("Train state saved !") @@ -271,7 +274,7 @@ class CheckpointManager: optimizer, train_state, device_mesh: DeviceMesh, - path: Optional[Path] = None, + path: str | None = None, ): dp_rank, tp_rank = self._get_dp_tp_mesh(device_mesh) # Loading tries to load the provided path, if not available the last saved step and finally from the init path @@ -284,12 +287,12 @@ class CheckpointManager: # Only load train state if it's provided, the files exist and we're not loading from init path train_state_name = TRAIN_STATE_NAME.format(dp_rank) logger.info("Reloading train state") - with open(path / train_state_name, "r") as f: + with self.fs.open(os.path.join(path, train_state_name), "r") as f: train_state_dict = json.load(f) train_state.load_state_dict(train_state_dict) logger.info("Train state reloaded") - logger.info(f"Loading from: {str(path)}") + logger.info(f"Loading from: {path}") state_dict = self.get_state_dict( model=model, optimizer=optimizer, diff --git a/bytelatent/train.py b/bytelatent/train.py index 86d1c7a..c80a74c 100644 --- a/bytelatent/train.py +++ b/bytelatent/train.py @@ -25,6 +25,7 @@ from torch.optim import lr_scheduler from bytelatent.args import TrainArgs, parse_args from bytelatent.checkpoint import CheckpointManager, load_from_checkpoint +from bytelatent.data.file_util import get_fs from bytelatent.data.iterators.multiprocess_iterator import ( MultiprocessIterator, MultiprocessIteratorState, @@ -313,8 +314,11 @@ def train(args: TrainArgs): if args.checkpoint.init_ckpt_path: logger.info(f"Loading initial model from {args.checkpoint.init_ckpt_path}") + ckpt_fs = get_fs( + args.checkpoint.init_ckpt_path, s3_profile=args.checkpoint.s3_profile + ) load_from_checkpoint( - args.checkpoint.init_ckpt_path, model, model_key="model" + ckpt_fs, args.checkpoint.init_ckpt_path, model, model_key="model" ) # Put model_key="" if its directly the model checkpoint model.rope_embeddings.reset_parameters() # For RoPe initialization since it's a buffer it might not be loaded else: