Run yapf and ruff

Signed-off-by: wchen61 <[email protected]>

examples/offline_inference.py

@@ -5,6 +5,7 @@
 from vllm.utils import FlexibleArgumentParser
 from transformers import AutoTokenizer
 
+
 def get_prompts(args):
     # The default sample prompts.
     prompts = [
@@ -32,22 +33,22 @@ def get_prompts(args):
         prompts = [prompt for _ in range(args.batch_size)]
 
     if args.batch_size != len(prompts):
-        prompts = (prompts * ((args.batch_size // len(prompts)) + 1))[:args.batch_size]
+        prompts = (prompts *
+                   ((args.batch_size // len(prompts)) + 1))[:args.batch_size]
 
     return prompts
 
+
 def main(args):
     # Create prompts
     prompts = get_prompts(args)
 
     # Create a sampling params object.
-    sampling_params = SamplingParams(
-        n=args.n,
-        temperature=args.temperature,
-        top_p=args.top_p,
-        top_k=args.top_k,
-        max_tokens=args.output_len
-    )
+    sampling_params = SamplingParams(n=args.n,
+                                     temperature=args.temperature,
+                                     top_p=args.top_p,
+                                     top_k=args.top_k,
+                                     max_tokens=args.output_len)
 
     # Create an LLM.
     # The default model is 'facebook/opt-125m', ensured by the default parameters of EngineArgs
@@ -63,25 +64,41 @@ def main(args):
         generated_text = output.outputs[0].text
         print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
 
+
 if __name__ == '__main__':
     parser = FlexibleArgumentParser()
-    parser.add_argument("--batch-size", type=int, default=4,
-                    help="Batch size for inference, default is lenght of sample prompts")
-    parser.add_argument("--input-len", type=int, default=None,
-                    help="Use fake fixed-length prompt as input if set")
-    parser.add_argument("--output-len", type=int, default=16,
-                    help="Output length for sampling")
-    parser.add_argument('--n', type=int, default=1,
-                    help='Number of generated sequences per prompt')
-    parser.add_argument('--temperature', type=float, default=0.8,
-                    help='Temperature for text generation')
-    parser.add_argument('--top-p', type=float, default=0.95,
-                    help='top_p for text generation')
-    parser.add_argument('--top-k', type=int, default=-1,
-                    help='top_k for text generation')
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        default=4,
+        help="Batch size for inference, default is lenght of sample prompts")
+    parser.add_argument("--input-len",
+                        type=int,
+                        default=None,
+                        help="Use fake fixed-length prompt as input if set")
+    parser.add_argument("--output-len",
+                        type=int,
+                        default=16,
+                        help="Output length for sampling")
+    parser.add_argument('--n',
+                        type=int,
+                        default=1,
+                        help='Number of generated sequences per prompt')
+    parser.add_argument('--temperature',
+                        type=float,
+                        default=0.8,
+                        help='Temperature for text generation')
+    parser.add_argument('--top-p',
+                        type=float,
+                        default=0.95,
+                        help='top_p for text generation')
+    parser.add_argument('--top-k',
+                        type=int,
+                        default=-1,
+                        help='top_k for text generation')
 
     EngineArgs.add_cli_args(parser)
     args = parser.parse_args()
     if args.tokenizer is None:
         args.tokenizer = args.model
-    main(args)
+    main(args)