From 216213e46e094de9d72614c09b058dceb1b35020 Mon Sep 17 00:00:00 2001
From: Marc Sun <marc.sun@hotmail.fr>
Date: Wed, 27 Sep 2023 16:18:06 +0000
Subject: [PATCH] style

---
 tests/benchmark/benchmark_gptq.py | 23 ++++++++++++++++-------
 1 file changed, 16 insertions(+), 7 deletions(-)

diff --git a/tests/benchmark/benchmark_gptq.py b/tests/benchmark/benchmark_gptq.py
index 369e6922e8e..45fdc262cee 100644
--- a/tests/benchmark/benchmark_gptq.py
+++ b/tests/benchmark/benchmark_gptq.py
@@ -5,6 +5,7 @@
 
 import numpy as np
 import torch
+from auto_gptq.utils import Perplexity
 from memory_tracker import MemoryTracker
 from tqdm import tqdm
 from transformers import (
@@ -14,11 +15,11 @@
     AutoTokenizer,
     BitsAndBytesConfig,
     GenerationConfig,
-    GPTQConfig
+    GPTQConfig,
 )
 
 from optimum.exporters import TasksManager
-from auto_gptq.utils import Perplexity
+
 
 def get_parser():
     parser = argparse.ArgumentParser()
@@ -102,7 +103,7 @@ def get_parser():
         default=None,
         help="Revision of the model to benchmark",
     )
-    
+
     return parser
 
 
@@ -279,7 +280,7 @@ def benchmark_memory(
 device = torch.device("cuda:0")
 memory_tracker = MemoryTracker()
 
-tokenizer = AutoTokenizer.from_pretrained(args.model,revision=args.revision, use_fast=False)
+tokenizer = AutoTokenizer.from_pretrained(args.model, revision=args.revision, use_fast=False)
 
 if not hasattr(tokenizer, "pad_token") or tokenizer.pad_token is None:
     tokenizer.pad_token = tokenizer.eos_token
@@ -303,8 +304,16 @@ def benchmark_memory(
 
 load_start = time.time_ns()
 if args.gptq:
-    quantization_config = GPTQConfig(bits=4, disable_exllama=args.disable_exllama, disable_exllamav2=args.disable_exllamav2)
-    model = autoclass.from_pretrained(args.model,revision=args.revision, quantization_config=quantization_config, torch_dtype=torch.float16, device_map="auto")
+    quantization_config = GPTQConfig(
+        bits=4, disable_exllama=args.disable_exllama, disable_exllamav2=args.disable_exllamav2
+    )
+    model = autoclass.from_pretrained(
+        args.model,
+        revision=args.revision,
+        quantization_config=quantization_config,
+        torch_dtype=torch.float16,
+        device_map="auto",
+    )
 elif args.bitsandbytes:
     quantization_config = BitsAndBytesConfig(
         load_in_4bit=True, bnb_4bit_quant_type="fp4", bnb_4bit_compute_dtype=torch.float16
@@ -338,7 +347,7 @@ def benchmark_memory(
         kernel = "autotogptq-cuda"
     else:
         kernel = "autogptq-cuda-old"
-        
+
 load_time = (load_end - load_start) * 1e-9
 print(f"Model load time: {load_time:.1f} s")