lint check

tools/convert2llama.py

@@ -1,16 +1,25 @@
 # Copyright (c) InternLM. All rights reserved.
 import argparse
-import os
 import json
+import os
 
 import torch
 from einops import rearrange
 from tqdm import tqdm
-from transformers import AutoConfig, LlamaTokenizer, LlamaConfig
+from transformers import AutoConfig, LlamaConfig, LlamaTokenizer
+
 
 def save_conifg(config, tgt):
     config_dict = config.to_dict()
-    unnecessary_keys = ["_name_or_path", "auto_map", "transformers_version", "model_type", "architectures",  "tokenizer_class", "attn_implementation"]
+    unnecessary_keys = [
+        "_name_or_path",
+        "auto_map",
+        "transformers_version",
+        "model_type",
+        "architectures",
+        "tokenizer_class",
+        "attn_implementation",
+    ]
     for k in unnecessary_keys:
         config_dict.pop(k, None)
     config_dict["attention_bias"] = config_dict.pop("bias")
@@ -30,7 +39,6 @@ def convert(src, tgt):
     head_dim = config.hidden_size // config.num_attention_heads
     num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
 
-
     # load index json file
     index_file = os.path.join(src, "pytorch_model.bin.index.json")
     if os.path.exists(index_file):
@@ -54,9 +62,7 @@ def convert(src, tgt):
                     gs=2 + num_key_value_groups,
                     d=head_dim,
                 )
-                wq, wk, wv = torch.split(
-                    v, [num_key_value_groups, 1, 1], dim=1
-                )
+                wq, wk, wv = torch.split(v, [num_key_value_groups, 1, 1], dim=1)
                 wq = rearrange(wq, "h gs d dim -> (h gs d) dim")
                 wk = rearrange(wk, "h gs d dim -> (h gs d) dim")
                 wv = rearrange(wv, "h gs d dim -> (h gs d) dim")
@@ -94,7 +100,7 @@ def convert(src, tgt):
                 llama_states[k] = v
 
         if index_dict is not None:
-            for k in llama_states.keys():
+            for k in llama_states:
                 index_dict["weight_map"][k] = filename
         print(f"Saving to {os.path.join(tgt, filename)}...", flush=True)
         torch.save(llama_states, os.path.join(tgt, filename))