feat: Checkpoint utils safetensors (#116)

* checkpoint conversion handle non-dcp case

Signed-off-by: Yu Chin Fabian Lim <[email protected]>

* improvements

Signed-off-by: Yu Chin Fabian Lim <[email protected]>

* fix: sharded safetensors save

Signed-off-by: Will Johnson <[email protected]>

* fix: lint

Signed-off-by: Will Johnson <[email protected]>

* fmt

Signed-off-by: Will Johnson <[email protected]>

---------

Signed-off-by: Yu Chin Fabian Lim <[email protected]>
Signed-off-by: Will Johnson <[email protected]>
Co-authored-by: Yu Chin Fabian Lim <[email protected]>

Author: willmj

plugins/accelerated-moe/src/fms_acceleration_moe/utils/checkpoint_utils.py

@@ -14,21 +14,25 @@
 
 # Standard
 from collections import defaultdict
-from typing import List
+from typing import Dict, List, Union
 import json
 import os
 import re
+import shutil
 
 # Third Party
 from accelerate.logging import get_logger
 from accelerate.utils.constants import FSDP_MODEL_NAME, OPTIMIZER_NAME
+from huggingface_hub import split_torch_state_dict_into_shards
+from safetensors.torch import load_file, save_file
 from torch.distributed.checkpoint.default_planner import (
     DefaultLoadPlanner,
     DefaultSavePlanner,
 )
 from torch.distributed.checkpoint.state_dict import get_state_dict, set_state_dict
 from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType
 from transformers import PretrainedConfig
+from transformers.utils import CONFIG_NAME, SAFE_WEIGHTS_INDEX_NAME, SAFE_WEIGHTS_NAME
 import torch
 import torch.distributed.checkpoint as dcp
 
@@ -213,24 +217,10 @@ def _dict_from_json_file(resolved_config_file):
     return os.path.dirname(result)
 
 
-# function to get the ScatterMoE state dict from its DCP checkpoint
-# - if the original pretrained_model_name_or_path is specified, will use the checkpoint as hints
-#   to map the ScatterMoE checkpoint to that of the original model. This is useful so that we
-#   can restore the checkpoint to be loaded by the original architecture.
-def recover_original_state_dict_from_dcp_checkpoint(
+# function to get the state dict from dcp_checkpoint
+def get_state_dict_from_dcp_checkpoint(
     dcp_checkpoint_dir: str,
-    pretrained_model_name_or_path: str = None,
 ):
-    """
-    Parameters:
-        dcp_checkpoint_dir (str): the DCP to be converted.
-        pretrained_model_name_or_path (str): Optional, if provided we will
-            use the hints to remap the
-    """
-
-    # reference dcp_to_torch_save from torch.distributed.checkpoint.format_utils.py
-    # - strategy is to use _EmptyStateDictLoadPlanner to populate the state dict, then we remap
-
     # guarded, load some internal functions
     # pylint: disable=import-outside-toplevel
     # Third Party
@@ -245,11 +235,46 @@ def recover_original_state_dict_from_dcp_checkpoint(
         planner=_EmptyStateDictLoadPlanner(),
         no_dist=True,
     )
-    sd = sd[KEY_MODEL]
+    return [KEY_MODEL]
+
+
+# function to get state dict from regular checkoint
+# - note this assumes sharded safetensors, we do not support
+#  the non-sharded case for now
+def get_state_dict_from_safe_checkpoint(
+    safe_checkpoint_dir: str,
+):
+    # Load the index
+    safe_index_file = os.path.join(safe_checkpoint_dir, SAFE_WEIGHTS_INDEX_NAME)
+    with open(safe_index_file, "r", encoding="utf-8") as f:
+        index = json.load(f)
+
+    sd = {}
+    shard_files = list(set(index["weight_map"].values()))
+    for shard_file in shard_files:
+        for key, v in load_file(os.path.join(safe_checkpoint_dir, shard_file)).items():
+            sd[key] = v
+
+    return sd
 
-    # if not provided
-    if pretrained_model_name_or_path is None:
-        return sd
+
+# function to get the ScatterMoE state dict from its DCP checkpoint
+# - if the original pretrained_model_name_or_path is specified, will use the checkpoint as hints
+#   to map the ScatterMoE checkpoint to that of the original model. This is useful so that we
+#   can restore the checkpoint to be loaded by the original architecture.
+def recover_original_state_dict_from_checkpoint(
+    sd: Dict,
+    pretrained_model_name_or_path: str = None,
+):
+    """
+    Parameters:
+        dcp_checkpoint_dir (str): the DCP to be converted.
+        pretrained_model_name_or_path (str): Optional, if provided we will
+            use the hints to remap the
+    """
+
+    # reference dcp_to_torch_save from torch.distributed.checkpoint.format_utils.py
+    # - strategy is to use _EmptyStateDictLoadPlanner to populate the state dict, then we remap
 
     # now do the remap
     loc = get_resolved_checkpoint_location(pretrained_model_name_or_path)
@@ -398,6 +423,37 @@ def _infer_prefixes_and_module_names(
     return sd
 
 
+def save_sharded_safetensors(
+    input_state_dict: Dict,
+    save_directory: str,
+    metadata: Dict,
+    max_shard_size: Union[int, str] = "5GB",
+):
+    filename_pattern = SAFE_WEIGHTS_NAME.replace(".bin", "{suffix}.bin").replace(
+        ".safetensors", "{suffix}.safetensors"
+    )
+    state_dict_split = split_torch_state_dict_into_shards(
+        input_state_dict,
+        filename_pattern=filename_pattern,
+        max_shard_size=max_shard_size,
+    )
+    index = {
+        "metadata": state_dict_split.metadata,
+        "weight_map": state_dict_split.tensor_to_filename,
+    }
+    # Save the index
+    with open(
+        os.path.join(save_directory, SAFE_WEIGHTS_INDEX_NAME), "w", encoding="utf-8"
+    ) as f:
+        content = json.dumps(index, indent=2, sort_keys=True) + "\n"
+        f.write(content)
+
+    filename_to_tensors = state_dict_split.filename_to_tensors.items()
+    for shard_file, tensors in filename_to_tensors:
+        shard = {tensor: input_state_dict[tensor].contiguous() for tensor in tensors}
+        save_file(shard, os.path.join(save_directory, shard_file), metadata=metadata)
+
+
 # --------------------------- SCRIPT -------------------------
 
 
@@ -417,8 +473,8 @@ def _infer_prefixes_and_module_names(
     )
 
     parser.add_argument(
-        "dcp_checkpoint_dir",
-        help="Path to the distributed checkpoint.",
+        "checkpoint_dir",
+        help="Path to the checkpoint.",
     )
 
     parser.add_argument(
@@ -432,37 +488,62 @@ def _infer_prefixes_and_module_names(
             "the original pretrained model checkpoint (from which this "
             "checkpoint is obtained)."
         ),
+        default=None,
     )
 
     args = parser.parse_args()
 
-    # search for the checkpint. By the code above, it must
+    # search for an FSDP checkpoint. If it is an FSDP checkpoint, it must
     # start with FSDP_MODEL_NAME
-    if args.dcp_checkpoint_dir.startswith(FSDP_MODEL_NAME):
-        checkpoint_dir = args.dcp_checkpoint_dir
+    if args.checkpoint_dir.startswith(FSDP_MODEL_NAME):
+        checkpoint_dir = args.checkpoint_dir
+        loader = get_state_dict_from_dcp_checkpoint
     else:
         checkpoint_dir = [
             x
-            for x in os.listdir(args.dcp_checkpoint_dir)
-            if os.path.isdir(os.path.join(args.dcp_checkpoint_dir, x))
+            for x in os.listdir(args.checkpoint_dir)
+            if os.path.isdir(os.path.join(args.checkpoint_dir, x))
             and x.startswith(FSDP_MODEL_NAME)
         ]
-        if len(checkpoint_dir) > 1:
+        if len(checkpoint_dir) == 1:
+            checkpoint_dir = os.path.join(args.checkpoint_dir, checkpoint_dir[0])
+            loader = get_state_dict_from_dcp_checkpoint
+        elif len(checkpoint_dir) > 1:
             raise ValueError(
-                f"Found > 1 dirs in dcp checkpoint dir {args.dcp_checkpoint_dir} "
+                f"Found > 1 dirs in dcp checkpoint dir {args.checkpoint_dir} "
                 f"that starts with {FSDP_MODEL_NAME}. Please spectify the exact dir."
             )
-        if len(checkpoint_dir) == 0:
-            raise ValueError(
-                f"Found no dirs in dcp checkpoint dir {args.dcp_checkpoint_dir} "
-                f"that starts with {FSDP_MODEL_NAME}. Nothing to convert"
-            )
-        checkpoint_dir = os.path.join(args.dcp_checkpoint_dir, checkpoint_dir[0])
-
-    # get the converted statedict
-    state_dict = recover_original_state_dict_from_dcp_checkpoint(
-        checkpoint_dir, args.pretrained_model_name_or_path
+        else:
+            # then take it as a safetensors checkpoint
+            # - do not support .bin checkpoints
+            checkpoint_dir = args.checkpoint_dir
+            loader = get_state_dict_from_safe_checkpoint
+
+    # - pretrained model name
+    _name_or_path = args.pretrained_model_name_or_path
+
+    # assume output directory exists, we do not create it
+    # - copy the config file if exists
+    config_file = os.path.join(checkpoint_dir, CONFIG_NAME)
+    target_config_file = os.path.join(args.output_dir, CONFIG_NAME)
+    if os.path.exists(config_file):
+        shutil.copyfile(config_file, target_config_file)
+
+        # try to populate pretrained_model_name_or_path from the config path
+        # if it was None
+        if not _name_or_path:
+            with open(target_config_file, "r", encoding="utf-8") as file:
+                _name_or_path = json.load(file).get("_name_or_path")
+
+    # get the state_dict
+    state_dict = loader(checkpoint_dir)
+
+    # recover the original state dict
+    state_dict = recover_original_state_dict_from_checkpoint(state_dict, _name_or_path)
+
+    # save it as a safetensors file
+    save_sharded_safetensors(
+        {k: v.contiguous() for k, v in state_dict.items()},
+        args.output_dir,
+        metadata={"format": "pt"},
     )
-
-    # save it
-    torch.save(state_dict, args.output_dir)