Lints

Signed-off-by: Rafael Vasquez <rafvasq21@gmail.com>

tests/utils.py

@@ -31,10 +31,7 @@ def __init__(self, cli_args: List[str], *, wait_url: str,
             env = os.environ.copy()
             env["PYTHONUNBUFFERED"] = "1"
             self.proc = subprocess.Popen(
-                [
-                    "vllm", "serve",
-                    *cli_args
-                ],
+                ["vllm", "serve", *cli_args],
                 env=env,
                 stdout=sys.stdout,
                 stderr=sys.stderr,

vllm/entrypoints/openai/api_server.py

@@ -156,7 +156,8 @@ def build_app(args):
     @app.exception_handler(RequestValidationError)
     async def validation_exception_handler(_, exc):
         err = openai_serving_chat.create_error_response(message=str(exc))
-        return JSONResponse(err.model_dump(), status_code=HTTPStatus.BAD_REQUEST)
+        return JSONResponse(err.model_dump(),
+                            status_code=HTTPStatus.BAD_REQUEST)
 
     if token := envs.VLLM_API_KEY or args.api_key:
 
@@ -182,10 +183,10 @@ async def authentication(request: Request, call_next):
         else:
             raise ValueError(f"Invalid middleware {middleware}. "
                              f"Must be a function or a class.")
-        
+
     return app
 
-        
+
 def run_server(args, llm_engine=None):
     app = build_app(args)
 
@@ -211,9 +212,8 @@ def run_server(args, llm_engine=None):
             "vision language models with the vLLM API server.")
 
     engine = (llm_engine
-                if llm_engine is not None
-                else AsyncLLMEngine.from_engine_args(
-                    engine_args, usage_context=UsageContext.OPENAI_API_SERVER))
+              if llm_engine is not None else AsyncLLMEngine.from_engine_args(
+                  engine_args, usage_context=UsageContext.OPENAI_API_SERVER))
 
     event_loop: Optional[asyncio.AbstractEventLoop]
     try:
@@ -228,7 +228,7 @@ def run_server(args, llm_engine=None):
     else:
         # When using single vLLM without engine_use_ray
         model_config = asyncio.run(engine.get_model_config())
-        
+
     global openai_serving_chat
     global openai_serving_completion
     global openai_serving_embedding
@@ -272,4 +272,3 @@ def run_server(args, llm_engine=None):
     args = postprocess_tgis_args(args)
 
     run_server(args)
-

vllm/entrypoints/openai/cli_args.py

@@ -10,7 +10,6 @@
 
 from vllm.engine.arg_utils import AsyncEngineArgs, nullable_str
 from vllm.entrypoints.openai.serving_engine import LoRAModulePath
-from vllm.tgis_utils.args import EnvVarArgumentParser
 
 
 class LoRAParserAction(argparse.Action):
@@ -114,6 +113,8 @@ def make_arg_parser(
     parser = AsyncEngineArgs.add_cli_args(parser)
     return parser
 
+
 def create_parser_for_docs() -> argparse.ArgumentParser:
-    parser_for_docs = argparse.ArgumentParser(prog="-m vllm.entrypoints.openai.api_server")
+    parser_for_docs = argparse.ArgumentParser(
+        prog="-m vllm.entrypoints.openai.api_server")
     return make_arg_parser(parser_for_docs)

vllm/scripts.py

@@ -57,11 +57,13 @@ def tgis_cli(args: argparse.Namespace) -> None:
     registrer_signal_handlers()
 
     if args.command == "download-weights":
-        download_weights(args.model_name, args.revision, args.token, args.extension, args.auto_convert)
+        download_weights(args.model_name, args.revision, args.token,
+                         args.extension, args.auto_convert)
     elif args.command == "convert-to-safetensors":
         convert_to_safetensors(args.model_name, args.revision)
     elif args.command == "convert-to-fast-tokenizer":
-        convert_to_fast_tokenizer(args.model_name, args.revision, args.output_path)
+        convert_to_fast_tokenizer(args.model_name, args.revision,
+                                  args.output_path)
 
 
 def complete(model_name: str, client: OpenAI) -> None:
@@ -310,28 +312,28 @@ def main():
     download_weights_parser.add_argument("--revision")
     download_weights_parser.add_argument("--token")
     download_weights_parser.add_argument("--extension", default=".safetensors")
-    download_weights_parser.add_argument("--auto_convert" , default=True)
+    download_weights_parser.add_argument("--auto_convert", default=True)
     download_weights_parser.set_defaults(dispatch_function=tgis_cli,
-                                 command="download-weights")
-    
+                                         command="download-weights")
+
     convert_to_safetensors_parser = subparsers.add_parser(
         "convert-to-safetensors",
         help=("Convert model weights to safetensors"),
         usage="vllm convert-to-safetensors <model_name> [options]")
     convert_to_safetensors_parser.add_argument("model_name")
     convert_to_safetensors_parser.add_argument("--revision")
-    convert_to_safetensors_parser.set_defaults(dispatch_function=tgis_cli,
-                                 command="convert-to-safetensors")
-    
+    convert_to_safetensors_parser.set_defaults(
+        dispatch_function=tgis_cli, command="convert-to-safetensors")
+
     convert_to_fast_tokenizer_parser = subparsers.add_parser(
         "convert-to-fast-tokenizer",
         help=("Convert to fast tokenizer"),
         usage="vllm convert-to-fast-tokenizer <model_name> [options]")
     convert_to_fast_tokenizer_parser.add_argument("model_name")
     convert_to_fast_tokenizer_parser.add_argument("--revision")
     convert_to_fast_tokenizer_parser.add_argument("--output_path")
-    convert_to_fast_tokenizer_parser.set_defaults(dispatch_function=tgis_cli,
-                                 command="convert-to-fast-tokenizer")
+    convert_to_fast_tokenizer_parser.set_defaults(
+        dispatch_function=tgis_cli, command="convert-to-fast-tokenizer")
 
     args = parser.parse_args()
     # One of the sub commands should be executed.

vllm/tgis_utils/hub.py

@@ -258,13 +258,13 @@ def convert_files(pt_files: List[Path],
         logger.warning("No pytorch .bin weight files found to convert")
         return
 
-    logger.info(f"Converting {N} pytorch .bin files to .safetensors...")
+    logger.info("Converting %d pytorch .bin files to .safetensors...", N)
 
     for i, (pt_file, sf_file) in enumerate(pairs):
-        logger.info(f'Converting: [{i + 1}/{N}] "{pt_file.name}"')
+        file_count = (i + 1) / N
+        logger.info('Converting: [%d] "$s"', file_count, pt_file.name)
         start = datetime.datetime.now()
         convert_file(pt_file, sf_file, discard_names)
         elapsed = datetime.datetime.now() - start
-        logger.info(
-            f'Converted: [{i + 1}/{N}] "{sf_file.name}" -- Took: {elapsed}')
-
+        logger.info('Converted: [%d] "%s" -- Took: %d', file_count,
+                    sf_file.name, elapsed)