add converter script

inference/checkpoint_converter_fsdp_hf.py

@@ -0,0 +1,44 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
+
+# from accelerate import init_empty_weights, load_checkpoint_and_dispatch
+
+import fire
+import torch
+import os
+import sys
+from typing import List
+
+from transformers import LlamaTokenizer
+from safety_utils import get_safety_checker
+from model_utils import load_model, load_peft_model, load_llama_from_config
+from accelerate import init_empty_weights
+# Get the current file's directory
+current_directory = os.path.dirname(os.path.abspath(__file__))
+
+# Get the parent directory
+parent_directory = os.path.dirname(current_directory)
+
+# Append the parent directory to sys.path
+sys.path.append(parent_directory)
+from model_checkpointing import load_sharded_model_single_gpu
+
+def main(
+    model_name,
+    save_dir="", # Path to save the HF converted model checkpoints
+    model_path="" # Path/ name of the HF model that include config.json and tokenizer_config.json
+    ):
+    #load the HF model definition from config
+    model_def = load_llama_from_config(model_path)
+    print("model is loaded from config")
+    #load the FSDP sharded checkpoints into the model
+    model = load_sharded_model_single_gpu(model_def, model_name)
+    print("model is loaded from FSDP checkpoints")
+    #loading the tokenizer form the  model_path
+    tokenizer = LlamaTokenizer.from_pretrained(model_path)
+    tokenizer.save_pretrained(save_dir)
+    #save the FSDP sharded checkpoints in HF format
+    model.save_pretrained(save_dir)
+    print(f"HuggingFace model checkpoints has been saved in {save_dir}")
+if __name__ == "__main__":
+    fire.Fire(main)