format style

tests/llamafy_internlm2.py

@@ -3,32 +3,28 @@
 # Usage: python llamafy_internlm2.py --input_dir input --output_dir output --shard_size 10GB
 # Warning: We have found that the converted model cannot infer correctly. It will be fixed later.
 
-import os
-import fire
 import json
-import torch
-from tqdm import tqdm
+import os
 from collections import OrderedDict
-from safetensors.torch import save_file
-from transformers.modeling_utils import (
-    shard_checkpoint,
-    SAFE_WEIGHTS_NAME,
-    SAFE_WEIGHTS_INDEX_NAME,
-    WEIGHTS_NAME,
-    WEIGHTS_INDEX_NAME
-)
 from typing import Any, Dict, Optional
 
+import fire
+import torch
+from safetensors.torch import save_file
+from tqdm import tqdm
+from transformers.modeling_utils import (
+    SAFE_WEIGHTS_INDEX_NAME,
+    SAFE_WEIGHTS_NAME,
+    WEIGHTS_INDEX_NAME,
+    WEIGHTS_NAME,
+    shard_checkpoint,
+)
+
 
 CONFIG_NAME = "config.json"
 
 
-def save_weight(
-    input_dir: str,
-    output_dir: str,
-    shard_size: str,
-    save_safetensors: bool
-):
+def save_weight(input_dir: str, output_dir: str, shard_size: str, save_safetensors: bool):
     with open(os.path.join(input_dir, CONFIG_NAME), "r", encoding="utf-8") as f:
         internlm2_config_dict: Dict[str, Any] = json.load(f)
 
@@ -50,8 +46,10 @@ def save_weight(
             q_size = value.size(0) // (num_q_heads + 2 * num_kv_heads) * num_q_heads
             kv_size = value.size(0) // (num_q_heads + 2 * num_kv_heads) * num_kv_heads
             llama2_state_dict[key.replace("attention.wqkv", "self_attn.q_proj")] = value[:q_size, ...]
-            llama2_state_dict[key.replace("attention.wqkv", "self_attn.k_proj")] = value[q_size:q_size+kv_size, ...]
-            llama2_state_dict[key.replace("attention.wqkv", "self_attn.v_proj")] = value[q_size+kv_size:, ...]
+            llama2_state_dict[key.replace("attention.wqkv", "self_attn.k_proj")] = value[
+                q_size : q_size + kv_size, ...
+            ]
+            llama2_state_dict[key.replace("attention.wqkv", "self_attn.v_proj")] = value[q_size + kv_size :, ...]
         elif "wo" in key:
             llama2_state_dict[key.replace("attention.wo", "self_attn.o_proj")] = value
         elif "attention_norm" in key:
@@ -85,10 +83,7 @@ def save_weight(
         print("Model weights saved in {}".format(output_dir))
 
 
-def save_config(
-    input_dir: str,
-    output_dir: str
-):
+def save_config(input_dir: str, output_dir: str):
     with open(os.path.join(input_dir, CONFIG_NAME), "r", encoding="utf-8") as f:
         llama2_config_dict: Dict[str, Any] = json.load(f)
 
@@ -103,12 +98,7 @@ def save_config(
     print("Model config saved in {}".format(os.path.join(output_dir, CONFIG_NAME)))
 
 
-def llamafy_internlm2(
-    input_dir: str,
-    output_dir: str,
-    shard_size: str,
-    save_safetensors: Optional[bool] = False
-):
+def llamafy_internlm2(input_dir: str, output_dir: str, shard_size: str, save_safetensors: Optional[bool] = False):
     try:
         os.makedirs(output_dir, exist_ok=False)
     except Exception as e: