[misc] update pre-commit and run all files (#4752)

* [misc] update pre-commit

* [misc] run pre-commit

* [misc] remove useless configuration files

* [misc] ignore cuda for clang-format

examples/tutorial/opt/inference/script/process-opt-175b/README.md

@@ -43,4 +43,3 @@ Finally, you will get 8 files in `<new_output_dir>` with following checksums:
 5d63b8750d827a1aa7c8ae5b02a3a2ca  reshard-model_part-6.pt
 f888bd41e009096804fe9a4b48c7ffe8  reshard-model_part-7.pt
 ```
-

examples/tutorial/opt/inference/script/process-opt-175b/convert_ckpt.py

@@ -14,42 +14,45 @@ def load_json(path: str):
 
 
 def parse_shape_info(flat_dir: str):
-    data = load_json(os.path.join(flat_dir, 'shape.json'))
+    data = load_json(os.path.join(flat_dir, "shape.json"))
     flat_info = defaultdict(lambda: defaultdict(list))
     for k, shape in data.items():
-        matched = re.match(r'decoder.layers.\d+', k)
+        matched = re.match(r"decoder.layers.\d+", k)
         if matched is None:
-            flat_key = 'flat_param_0'
+            flat_key = "flat_param_0"
         else:
-            flat_key = f'{matched[0]}.flat_param_0'
-        flat_info[flat_key]['names'].append(k)
-        flat_info[flat_key]['shapes'].append(shape)
-        flat_info[flat_key]['numels'].append(int(np.prod(shape)))
+            flat_key = f"{matched[0]}.flat_param_0"
+        flat_info[flat_key]["names"].append(k)
+        flat_info[flat_key]["shapes"].append(shape)
+        flat_info[flat_key]["numels"].append(int(np.prod(shape)))
     return flat_info
 
 
 def convert(flat_dir: str, output_dir: str, part: int):
-    flat_path = os.path.join(flat_dir, f'reshard-model_part-{part}-shard0.pt')
-    output_path = os.path.join(output_dir, f'reshard-model_part-{part}.pt')
-    flat_meta = load_json(os.path.join(flat_dir, 'flat-meta.json'))
+    flat_path = os.path.join(flat_dir, f"reshard-model_part-{part}-shard0.pt")
+    output_path = os.path.join(output_dir, f"reshard-model_part-{part}.pt")
+    flat_meta = load_json(os.path.join(flat_dir, "flat-meta.json"))
     flat_sd = torch.load(flat_path)
-    print(f'Loaded flat state dict from {flat_path}')
+    print(f"Loaded flat state dict from {flat_path}")
     output_sd = {}
     for flat_key, param_meta in flat_meta.items():
-        flat_param = flat_sd['model'][flat_key]
-        assert sum(param_meta['numels']) == flat_param.numel(
+        flat_param = flat_sd["model"][flat_key]
+        assert (
+            sum(param_meta["numels"]) == flat_param.numel()
         ), f'flat {flat_key} {flat_param.numel()} vs {sum(param_meta["numels"])}'
-        for name, shape, param in zip(param_meta['names'], param_meta['shapes'], flat_param.split(param_meta['numels'])):
+        for name, shape, param in zip(
+            param_meta["names"], param_meta["shapes"], flat_param.split(param_meta["numels"])
+        ):
             output_sd[name] = param.view(shape)
 
     torch.save(output_sd, output_path)
-    print(f'Saved unflat state dict to {output_path}')
+    print(f"Saved unflat state dict to {output_path}")
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    parser.add_argument('flat_dir')
-    parser.add_argument('output_dir')
-    parser.add_argument('part', type=int)
+    parser.add_argument("flat_dir")
+    parser.add_argument("output_dir")
+    parser.add_argument("part", type=int)
     args = parser.parse_args()
     convert(args.flat_dir, args.output_dir, args.part)

examples/tutorial/opt/inference/script/processing_ckpt_66b.py

@@ -1,7 +1,8 @@
 import os
-import torch
 from multiprocessing import Pool
 
+import torch
+
 # download pytorch model ckpt in https://huggingface.co/facebook/opt-66b/tree/main
 # you can use whether wget or git lfs
 
@@ -20,14 +21,14 @@ with Pool(14) as pool:
 
 restored = {}
 for ckpt in ckpts:
-    for k,v in ckpt.items():
-        if(k[0] == 'm'):
-            k = k[6:]      
-        if(k == "lm_head.weight"):
+    for k, v in ckpt.items():
+        if k[0] == "m":
+            k = k[6:]
+        if k == "lm_head.weight":
             k = "head.dense.weight"
-        if(k == "decoder.final_layer_norm.weight"):
+        if k == "decoder.final_layer_norm.weight":
             k = "decoder.layer_norm.weight"
-        if(k == "decoder.final_layer_norm.bias"):
+        if k == "decoder.final_layer_norm.bias":
             k = "decoder.layer_norm.bias"
         restored[k] = v
 restored["decoder.version"] = "0.0"
@@ -37,11 +38,11 @@ split_num = len(restored.keys()) // 60
 count = 0
 file_count = 1
 tmp = {}
-for k,v in restored.items():
+for k, v in restored.items():
     print(k)
     tmp[k] = v
-    count = count + 1    
-    if(count == split_num):
+    count = count + 1
+    if count == split_num:
         filename = str(file_count) + "-restored.pt"
         torch.save(tmp, os.path.join(new_path, filename))
         file_count = file_count + 1
@@ -50,6 +51,3 @@ for k,v in restored.items():
 
 filename = str(file_count) + "-restored.pt"
 torch.save(tmp, os.path.join(new_path, filename))
-
-
-