fix: lint

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

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

@@ -23,6 +23,7 @@
 # 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,
@@ -32,7 +33,6 @@
 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
-from huggingface_hub import split_torch_state_dict_into_shards
 import torch
 import torch.distributed.checkpoint as dcp
 
@@ -424,30 +424,31 @@ def _infer_prefixes_and_module_names(
 
 
 def save_sharded_safetensors(
-    state_dict: Dict, 
-    save_directory: str, 
+    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")
+    filename_pattern = SAFE_WEIGHTS_NAME.replace(".bin", "{suffix}.bin").replace(
+        ".safetensors", "{suffix}.safetensors"
+    )
     state_dict_split = split_torch_state_dict_into_shards(
-        state_dict, filename_pattern=filename_pattern, max_shard_size=max_shard_size
+        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 
+    # Save the index
     with open(
-        os.path.join(save_directory, SAFE_WEIGHTS_INDEX_NAME), 
-        "w", encoding="utf-8"
+        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: state_dict[tensor].contiguous() for tensor in tensors}
+        shard = {tensor: input_state_dict[tensor].contiguous() for tensor in tensors}
         save_file(shard, os.path.join(save_directory, shard_file), metadata=metadata)