add hf support

Author: ankitageorge

tests/integration_tests.py

@@ -118,6 +118,21 @@ def build_test_list():
             "Checkpoint Integration Test - Save Load Full Checkpoint",
             "full_checkpoint",
         ),
+        OverrideDefinitions(
+            [
+                [
+                    "--checkpoint.enable_checkpoint",
+                    "--checkpoint.enable_hf_safetensors_format",
+                ],
+                [
+                    "--checkpoint.enable_checkpoint",
+                    "--checkpoint.enable_hf_safetensors_format",
+                    "--training.steps 20",
+                ],
+            ],
+            "Checkpoint Integration Test - Save Load Full Checkpoint",
+            "full_checkpoint_hf_safetensors",
+        ),
         OverrideDefinitions(
             [
                 [

torchtitan/components/checkpoint.py

@@ -12,14 +12,19 @@
 import shutil
 import threading
 import time
-from typing import Any
+from concurrent.futures import Future
+from typing import Any, Optional
 
 import torch
 import torch.distributed as dist
 import torch.distributed.checkpoint as dcp
 import torch.multiprocessing as mp
 import torch.nn as nn
 from torch.distributed._state_dict_utils import _copy_state_dict, _create_cpu_state_dict
+from torch.distributed.checkpoint import (
+    HuggingFaceStorageReader,
+    HuggingFaceStorageWriter,
+)
 from torch.distributed.checkpoint.state_dict import (
     get_model_state_dict,
     set_model_state_dict,
@@ -93,8 +98,64 @@ class SaveDone:
 
 
 @torch.no_grad()
-def save_with_gc(state, checkpoint_id):
-    dcp.save(state, checkpoint_id=checkpoint_id)
+def dcp_save(
+    state_dict: dict[str, Any],
+    checkpoint_id: str,
+    is_async: bool,
+    hf_safetensors_format: bool,
+    pg: Optional[dist.ProcessGroup] = None,
+) -> Optional[Future]:
+    """Save the checkpoint with dcp.
+    Args:
+        state_dict (dict): The state dict to save.
+        checkpoint_id (str): The checkpoint id to save.
+        is_async (bool): Whether the checkpoint is async.
+        hf_safetensors_format (bool): Whether to use the HuggingFace safetensors format.
+        pg (Optional[dist.ProcessGroup]): The process group to use.
+    """
+    if hf_safetensors_format:
+        storage_writer = HuggingFaceStorageWriter(path=checkpoint_id, save_sharded=True)
+        if is_async:
+            return dcp.async_save(
+                state_dict, storage_writer=storage_writer, process_group=pg
+            )
+        else:
+            return dcp.save(state_dict, storage_writer=storage_writer)
+    else:
+        if is_async:
+            return dcp.async_save(
+                state_dict, checkpoint_id=checkpoint_id, process_group=pg
+            )
+        else:
+            return dcp.save(state_dict, checkpoint_id=checkpoint_id)
+
+
+def dcp_load(
+    state_dict: dict[str, Any], checkpoint_id: str, hf_safetensors_format: bool
+) -> None:
+    """Load the checkpoint with dcp.
+    Args:
+        state_dict (dict): The state dict to load.
+        checkpoint_id (str): The checkpoint id to load.
+        hf_safetensors_format (bool): Whether to use the HuggingFace safetensors format.
+    """
+    if hf_safetensors_format:
+        storage_reader = HuggingFaceStorageReader(path=checkpoint_id)
+        dcp.load(state_dict, storage_writer=storage_reader)
+    else:
+        dcp.load(state_dict, checkpoint_id=checkpoint_id)
+
+
+@torch.no_grad()
+def save_with_gc(
+    state: dict[str, Any], checkpoint_id: str, hf_safetensors_format: bool
+) -> None:
+    dcp_save(
+        state,
+        checkpoint_id=checkpoint_id,
+        is_async=False,
+        hf_safetensors_format=hf_safetensors_format,
+    )
     GarbageCollection.collect("GC collection invoked by checkpointer.")
 
 
@@ -125,7 +186,9 @@ def checkpoint_mp(recv: mp.Queue, send: mp.Queue):
             assert isinstance(obj, tuple)
             begin = time.monotonic()
             state, checkpoint_id = obj
-            save_with_gc(state, checkpoint_id=checkpoint_id)
+            save_with_gc(
+                state, checkpoint_id=checkpoint_id, hf_safetensors_format=False
+            )
             logger.info(
                 "Finish saving the checkpoint in the background process in %.2f seconds.",
                 time.monotonic() - begin,
@@ -227,6 +290,7 @@ def __init__(
     ) -> None:
         ckpt_config = job_config.checkpoint
         self.enable_checkpoint = ckpt_config.enable_checkpoint
+        self.enable_hf_safetensors_format = ckpt_config.enable_hf_safetensors_format
         self.ft_manager = ft_manager.manager if ft_manager.enabled else None
 
         if self.ft_manager:
@@ -391,12 +455,20 @@ def save(self, curr_step: int, last_step: bool = False) -> None:
                 self._async_with_pinned_memory(checkpoint_id)
             elif self.async_mode == AsyncMode.ASYNC:
                 GarbageCollection.collect("GC collection invoked by checkpointer.")
-                self.async_future = dcp.async_save(
-                    self.states, checkpoint_id=checkpoint_id, process_group=self.pg
+                self.async_future = dcp_save(
+                    self.states,
+                    checkpoint_id=checkpoint_id,
+                    is_async=True,
+                    hf_safetensors_format=self.enable_hf_safetensors_format,
+                    pg=self.pg,
                 )
                 GarbageCollection.collect("GC collection invoked by checkpointer.")
             else:
-                save_with_gc(self.states, checkpoint_id=checkpoint_id)
+                save_with_gc(
+                    self.states,
+                    checkpoint_id=checkpoint_id,
+                    hf_safetensors_format=self.enable_hf_safetensors_format,
+                )
             self._purge_stale_checkpoints()
 
             logger.info(
@@ -461,7 +533,11 @@ def load(self, step: int = -1) -> bool:
         logger.info(f"Loading the checkpoint from {checkpoint_id}.")
         begin = time.monotonic()
         states = self._states_to_load(model_only)
-        dcp.load(states, checkpoint_id=checkpoint_id)
+        dcp_load(
+            states,
+            checkpoint_id=checkpoint_id,
+            hf_safetensors_format=self.enable_hf_safetensors_format,
+        )
         GarbageCollection.collect("GC collection for checkpoint loading.")
         logger.info(
             f"Finished loading the checkpoint in {time.monotonic() - begin:.2f} seconds."
@@ -540,8 +616,12 @@ def _ft_save(self, step: int) -> None:
         begin = time.monotonic()
         self._async_wait()
         checkpoint_id = self._create_checkpoint_id(step, folder=self._ft_folder())
-        self.async_future = dcp.async_save(
-            self.ft_states, checkpoint_id=checkpoint_id, process_group=self.pg
+        self.async_future = dcp_save(
+            self.ft_states,
+            checkpoint_id=checkpoint_id,
+            is_async=True,
+            hf_safetensors_format=self.enable_hf_safetensors_format,
+            pg=self.pg,
         )
         logger.info(f"Staging ft checkpoint took {time.monotonic() - begin} secs.")
 
@@ -553,7 +633,11 @@ def _ft_load(self) -> None:
         begin = time.monotonic()
         logger.info(f"Loading the FT checkpoint at step {step}.")
         checkpoint_id = self._create_checkpoint_id(step, folder=self._ft_folder())
-        dcp.load(self.ft_states, checkpoint_id=checkpoint_id)
+        dcp_load(
+            self.ft_states,
+            checkpoint_id=checkpoint_id,
+            hf_safetensors_format=self.enable_hf_safetensors_format,
+        )
         GarbageCollection.collect("GC collection for checkpoint loading.")
         logger.info(
             f"Finished loading the ft checkpoint in {time.monotonic() - begin:.2f} seconds."
@@ -614,7 +698,11 @@ def _save_last_step(self, curr_step: int) -> None:
         else:
             logger.info(f"Saving a full checkpoint at last step, step {curr_step}.")
 
-        save_with_gc(self.states, checkpoint_id=self._create_checkpoint_id(curr_step))
+        save_with_gc(
+            self.states,
+            checkpoint_id=self._create_checkpoint_id(curr_step),
+            hf_safetensors_format=self.enable_hf_safetensors_format,
+        )
 
     def _should_save(self, curr_step: int, last_step: bool = False) -> bool:
         if not self.enable_checkpoint:

torchtitan/config_manager.py

@@ -467,6 +467,12 @@ class Checkpoint:
     for many steps or checkpointing too frequently. The default value is False.
     """
 
+    enable_hf_safetensors_format: bool = False
+    """
+    Enable the use of safetensors format for checkpointing. This will save checkpoints
+    in safetensors format instead of the default DCP format. The default value is False.
+    """
+
 
 @dataclass
 class ActivationCheckpoint: