Use tempfile for convert2hf.py

tools/transformers/convert2hf.py

@@ -4,6 +4,7 @@ import os
 import random
 import re
 import shutil
+import tempfile
 
 import torch
 from modeling_internlm import InternLMConfig, InternLMForCausalLM
@@ -15,10 +16,8 @@ NUM_SHARDS = {
 
 
 def convert2hf(model_config, states_tp_pps):
-    folder = f"/dev/shm/wait_to_upload_weight_tmp_{random.random()}/"
-    os.makedirs(folder, exist_ok=True)
 
-    try:
+    with tempfile.TemporaryDirectory() as folder:
         states = merge_pp(states_tp_pps)[0]
 
         if "embedding.word_embeddings.weight" in states:
@@ -91,9 +90,6 @@ def convert2hf(model_config, states_tp_pps):
         model = InternLMForCausalLM.from_pretrained(folder, torch_dtype=torch.float16, low_cpu_mem_usage=True)
         del model.config._name_or_path
 
-    finally:
-        shutil.rmtree(folder)
-
     return config, model