Merge b6e53f1d4c into sapling-pr-archive-EntilZha

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,

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: