Upload convert_fsdp_to_hf.py

convert_fsdp_to_hf.py

@@ -0,0 +1,39 @@
+#!/usr/bin/env python
+# encoding: utf-8
+from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
+import torch
+import fire
+from glob import glob
+from collections import defaultdict
+
+
+def main(fsdp_checkpoint_path, huggingface_model_path, output_path):
+    state_dict = defaultdict(list)
+
+    world_size = 8
+    for rank in range(world_size):
+        filepath = f"{fsdp_checkpoint_path}/model_world_size_{world_size}_rank_{rank}.pt"
+        print('loading', filepath)
+        this_state_dict = torch.load(filepath)
+        for key, value in this_state_dict.items():
+            state_dict[key].append(value.to_local())
+
+    for key in state_dict:
+        state_dict[key] = torch.cat(state_dict[key], dim=0)
+
+    config = AutoConfig.from_pretrained(huggingface_model_path)
+    model = AutoModelForCausalLM.from_config(config)
+    model.load_state_dict(state_dict)
+
+    #for filepath in glob(f'{fsdp_checkpoint_path}/model_*.pt'):
+    #    part_state_dict = torch.load(filepath)
+    #    model.load_state_dict(part_state_dict)
+
+    model.save_pretrained(output_path)
+
+    tokenizer = AutoTokenizer.from_pretrained(huggingface_model_path)
+    tokenizer.save_pretrained(output_path)
+
+
+if __name__ == "__main__":
+    fire.Fire(main)