[checkpointio] General Checkpointing of Sharded Optimizers (#3984)

colossalai/checkpoint_io/general_checkpoint_io.py

@@ -11,15 +11,21 @@ from torch.optim import Optimizer
 from .checkpoint_io_base import CheckpointIO
 from .index_file import CheckpointIndexFile
 from .utils import (
-    get_base_filenames,
+    get_model_base_filenames,
+    get_optimizer_base_filenames,
     get_shard_filename,
     has_index_file,
     is_safetensors_available,
+    load_param_groups_into_optimizer,
     load_shard_state_dict,
     load_state_dict,
     load_state_dict_into_model,
+    load_states_into_optimizer,
+    save_param_groups,
     save_state_dict,
-    shard_checkpoint,
+    shard_model_checkpoint,
+    shard_optimizer_checkpoint,
+    sharded_optimizer_loading_epilogue,
 )
 
 __all__ = ['GeneralCheckpointIO']
@@ -44,12 +50,30 @@ class GeneralCheckpointIO(CheckpointIO):
         # save the checkpoint
         save_state_dict(state_dict, checkpoint, use_safetensors)
 
-    def load_sharded_optimizer(self, optimizer: Optimizer, checkpoint: Path, prefix: str, size_per_shard: int):
-        raise NotImplementedError("Sharded optimizer checkpoint is not supported yet.")
+    def load_sharded_optimizer(self, optimizer: Optimizer, index_file_path: str, prefix: str, size_per_shard: int):
+        """
+        Load sharded optimizer with the given path to index file.
+        """
+        optimizer.load_state_dict
+        # Read checkpoint index file.
+        ckpt_index_file = CheckpointIndexFile.from_file(index_file_path)
 
-    def load_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: Path):
-        checkpoint = load_state_dict(checkpoint)
-        optimizer.load_state_dict(checkpoint)
+        # Load param_groups
+        param_group_path = ckpt_index_file.get_param_group_filename()
+        if param_group_path is None:
+            raise RuntimeError(f'Invalid index file path {index_file_path} for an optimizer. \
+                               Lacking param group file under current directory.')
+        id_map = load_param_groups_into_optimizer(optimizer, param_group_path)
+
+        checkpoint_files, _ = ckpt_index_file.get_checkpoint_filenames()
+
+        for shard_file in checkpoint_files:
+            state_dict = load_shard_state_dict(Path(shard_file), use_safetensors=False)
+            load_states_into_optimizer(optimizer, state_dict, id_map)
+            del state_dict
+            gc.collect()
+
+        sharded_optimizer_loading_epilogue(optimizer)
 
     def save_sharded_optimizer(
         self,
@@ -59,7 +83,54 @@ class GeneralCheckpointIO(CheckpointIO):
         prefix: str,
         size_per_shard: int,
     ):
-        raise NotImplementedError("Sharded optimizer checkpoint is not supported yet.")
+        """
+        Save sharded optimizer checkpoint under the given checkpointing path.
+        The following files will be created under the path:
+        - An index file (pytorch_optim.bin.index.json) containing a map between optimizer states and file names
+        - A group file (pytorch_optim_group.bin) recording information of param_groups
+        - Multiple files (pytorch_optim-000XX.bin) that store state tensors of optimizer in a sharding way
+        """
+        if os.path.isfile(checkpoint):
+            logging.error(f"Provided path ({checkpoint}) should be a directory, not a file")
+            return
+
+        Path(checkpoint).mkdir(parents=True, exist_ok=True)
+
+        # Offload optimizer states. States are broken into shards within max_shard_size.
+        state_dict = optimizer.state_dict()
+        sharded_state = shard_optimizer_checkpoint(state_dict, max_shard_size=size_per_shard)
+
+        # Preparing file paths and index file.
+        states_name, save_index_file, param_group_file = get_optimizer_base_filenames(prefix)
+        index_file = CheckpointIndexFile(checkpoint)
+
+        # Store the information of param groups to param_group_file.
+        index_file.append_meta_data("param_groups", param_group_file)
+        group_file_path = os.path.join(checkpoint, param_group_file)
+        save_param_groups(state_dict, group_file_path)
+
+        # Save shards of optimizer states.
+        total_size = 0
+        for idx, shard_pair in enumerate(sharded_state):
+            shard, current_size = shard_pair
+            shard_file = get_shard_filename(states_name, idx)
+            total_size = total_size + current_size
+            for param_id in shard.keys():
+                index_file.append_weight_map(str(param_id), shard_file)
+
+            checkpoint_file_path = os.path.join(checkpoint, shard_file)
+            save_state_dict(shard, checkpoint_file_path, use_safetensors=False)
+
+        # Wrap up index file.
+        index_file.append_meta_data("total_size", total_size)
+        index_file.write_index_file(save_index_file)
+        logging.info(f"The optimizer is going to be split to checkpoint shards. "
+                     f"You can find where each parameters has been saved in the "
+                     f"index located at {save_index_file}.")
+
+    def load_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: Path):
+        checkpoint = load_state_dict(checkpoint)
+        optimizer.load_state_dict(checkpoint)
 
     def save_unsharded_optimizer(
         self,
@@ -74,7 +145,7 @@ class GeneralCheckpointIO(CheckpointIO):
                            model: nn.Module,
                            checkpoint_path: str,
                            gather_dtensor: bool = False,
-                           variant: Optional[str] = None,
+                           prefix: Optional[str] = None,
                            max_shard_size: int = 1024,
                            use_safetensors: bool = False):
         """
@@ -89,9 +160,9 @@ class GeneralCheckpointIO(CheckpointIO):
 
         # shard checkpoint
         state_dict = model.state_dict()
-        state_dict_shard = shard_checkpoint(state_dict, max_shard_size=max_shard_size)
+        state_dict_shard = shard_model_checkpoint(state_dict, max_shard_size=max_shard_size)
 
-        weights_name, save_index_file = get_base_filenames(variant, use_safetensors)
+        weights_name, save_index_file = get_model_base_filenames(prefix, use_safetensors)
         total_size = 0
         index_file = CheckpointIndexFile(checkpoint_path)
         for idx, shard_pair in enumerate(state_dict_shard):
@@ -128,7 +199,7 @@ class GeneralCheckpointIO(CheckpointIO):
 
         # read checkpoint index file
         ckpt_index_file = CheckpointIndexFile.from_file(checkpoint_index_file)
-        checkpoint_files, _ = ckpt_index_file.get_checkpoint_fileanames()
+        checkpoint_files, _ = ckpt_index_file.get_checkpoint_filenames()
         missing_keys = []
 
         for shard_file in checkpoint_files: