Fix more logging lint errors

docs/source/conf.py

@@ -98,9 +98,10 @@ def setup(app):
 for mock_target in autodoc_mock_imports:
     if mock_target in sys.modules:
         logger.info(
-            f"Potentially problematic mock target ({mock_target}) found; "
+            "Potentially problematic mock target (%s) found; "
             "autodoc_mock_imports cannot mock modules that have already "
-            "been loaded into sys.modules when the sphinx build starts.")
+            "been loaded into sys.modules when the sphinx build starts.",
+            mock_target)
 
 
 class MockedClassDocumenter(autodoc.ClassDocumenter):

setup.py

@@ -63,7 +63,7 @@ def compute_num_jobs(self):
         num_jobs = os.environ.get("MAX_JOBS", None)
         if num_jobs is not None:
             num_jobs = int(num_jobs)
-            logger.info(f"Using MAX_JOBS={num_jobs} as the number of jobs.")
+            logger.info("Using MAX_JOBS=%d as the number of jobs.", num_jobs)
         else:
             try:
                 # os.sched_getaffinity() isn't universally available, so fall
@@ -81,8 +81,9 @@ def compute_num_jobs(self):
             nvcc_threads = os.getenv("NVCC_THREADS", None)
             if nvcc_threads is not None:
                 nvcc_threads = int(nvcc_threads)
-                logger.info(f"Using NVCC_THREADS={nvcc_threads} as the number"
-                            " of nvcc threads.")
+                logger.info(
+                    "Using NVCC_THREADS=%d as the number of nvcc threads.",
+                    nvcc_threads)
             else:
                 nvcc_threads = 1
             num_jobs = max(1, num_jobs // nvcc_threads)

vllm/distributed/utils.py

@@ -112,7 +112,7 @@ def gpu_p2p_access_check(i: int, j: int) -> bool:
         and (not os.path.exists(path)):
         # only the local master process (with local_rank == 0) can
         #  enter this block to calculate the cache
-        logger.info(f"generating GPU P2P access cache for in {path}")
+        logger.info("generating GPU P2P access cache for in %s", path)
         cache = {}
         for _i in range(num_dev):
             for _j in range(num_dev):
@@ -126,7 +126,7 @@ def gpu_p2p_access_check(i: int, j: int) -> bool:
     if is_distributed:
         cpu_world_group = get_cpu_world_group()
         dist.barrier(cpu_world_group)
-    logger.info(f"reading GPU P2P access cache from {path}")
+    logger.info("reading GPU P2P access cache from %s", path)
     with open(path, "r") as f:
         cache = json.load(f)
     _gpu_p2p_access_cache = cache

vllm/engine/async_llm_engine.py

@@ -117,7 +117,7 @@ def process_request_output(self,
         self._request_streams[request_id].put(request_output)
         if request_output.finished:
             if verbose:
-                logger.info(f"Finished request {request_id}.")
+                logger.info("Finished request %s.", request_id)
             self.abort_request(request_id)
 
     def process_exception(self,
@@ -128,7 +128,7 @@ def process_exception(self,
         """Propagate an exception from the engine."""
         self._request_streams[request_id].put(exception)
         if verbose:
-            logger.info(f"Finished request {request_id}.")
+            logger.info("Finished request %s.", request_id)
         self.abort_request(request_id)
 
     def add_request(self, request_id: str,
@@ -151,7 +151,7 @@ def add_request(self, request_id: str,
     def abort_request(self, request_id: str, *, verbose: bool = False) -> None:
         """Abort a request during next background loop iteration."""
         if verbose:
-            logger.info(f"Aborted request {request_id}.")
+            logger.info("Aborted request %s.", request_id)
 
         self._finished_requests.put_nowait(request_id)
 
@@ -521,11 +521,11 @@ async def add_request(
                 if shortened_token_ids is not None:
                     shortened_token_ids = shortened_token_ids[:self.
                                                               max_log_len]
-            logger.info(f"Received request {request_id}: "
-                        f"prompt: {shortened_prompt!r}, "
-                        f"sampling_params: {sampling_params}, "
-                        f"prompt_token_ids: {shortened_token_ids}, "
-                        f"lora_request: {lora_request}.")
+            logger.info(
+                "Received request %s: prompt: %r, "
+                "sampling_params: %s, prompt_token_ids: %s, "
+                "lora_request: %s.", request_id, shortened_prompt,
+                sampling_params, shortened_token_ids, lora_request)
 
         if not self.is_running:
             if self.start_engine_loop:
@@ -717,4 +717,4 @@ async def check_health(self) -> None:
                 raise RuntimeError("Engine is dead.") from e
         else:
             await self.engine.check_health_async()
-        logger.debug(f"Health check took {time.perf_counter()-t}s")
+        logger.debug("Health check took %fs", time.perf_counter() - t)

vllm/engine/ray_utils.py

@@ -43,9 +43,9 @@ def execute_model_compiled_dag_remote(self, ignored):
             return output
 
 except ImportError as e:
-    logger.warning(f"Failed to import Ray with {e!r}. "
-                   "For distributed inference, please install Ray with "
-                   "`pip install ray`.")
+    logger.warning(
+        "Failed to import Ray with %r. For distributed inference, "
+        "please install Ray with `pip install ray`.", e)
     ray = None  # type: ignore
     RayWorkerWrapper = None  # type: ignore
 

vllm/executor/cpu_executor.py

@@ -69,7 +69,7 @@ def initialize_cache(self, num_gpu_blocks: int,
         # NOTE: `cpu block` for CPU backend is located on CPU memory but is
         # referred as `gpu block`. Because we want to reuse the existing block
         # management procedure.
-        logger.info(f"# CPU blocks: {num_gpu_blocks}")
+        logger.info("# CPU blocks: %d", num_gpu_blocks)
         self.driver_worker.initialize_cache(num_gpu_blocks, num_cpu_blocks)
 
     def execute_model(self,

vllm/lora/models.py

@@ -342,8 +342,8 @@ def activate_lora(
         index, _ = first_free_slot
         self._active_loras[lora_id] = None
         lora_model = self._registered_loras[lora_id]
-        logger.debug(
-            f"Activating LoRA. int id: {lora_model.id}, slot index: {index}")
+        logger.debug("Activating LoRA. int id: %d, slot index: %d",
+                     lora_model.id, index)
         self.lora_index_to_id[index] = lora_model.id
         for module_name, module in self.modules.items():
             module_lora = lora_model.get_lora(module_name)
@@ -563,7 +563,7 @@ def __init__(self, capacity: int, deactivate_lora_fn: Callable[[Hashable],
         self.deactivate_lora_fn = deactivate_lora_fn
 
     def _on_remove(self, key: Hashable, value: LoRAModel):
-        logger.debug(f"Removing LoRA. int id: {key}")
+        logger.debug("Removing LoRA. int id: %d", key)
         self.deactivate_lora_fn(key)
         return super()._on_remove(key, value)
 

vllm/model_executor/model_loader/tensorizer.py

@@ -334,10 +334,10 @@ def deserialize(self):
         per_second = convert_bytes(deserializer.total_tensor_bytes / duration)
         after_mem = get_mem_usage()
         deserializer.close()
-        logger.info(f"Deserialized {total_bytes_str} in "
-                    f"{end - start:0.2f}s, {per_second}/s")
-        logger.info(f"Memory usage before: {before_mem}")
-        logger.info(f"Memory usage after: {after_mem}")
+        logger.info("Deserialized %s in %0.2fs, %f/s", total_bytes_str,
+                    end - start, per_second)
+        logger.info("Memory usage before: %s", before_mem)
+        logger.info("Memory usage after: %s", after_mem)
 
         self._check_tensors_on_meta_device()
         self._resize_lora_embeddings()

vllm/model_executor/model_loader/weight_utils.py

@@ -190,7 +190,7 @@ def download_weights_from_hf(model_name_or_path: str,
             allow_patterns = [pattern]
             break
 
-    logger.info(f"Using model weights format {allow_patterns}")
+    logger.info("Using model weights format %s", allow_patterns)
     # Use file lock to prevent multiple processes from
     # downloading the same model weights at the same time.
     with get_lock(model_name_or_path, cache_dir):
@@ -310,17 +310,17 @@ def kv_cache_scales_loader(
             return layer_scales_map.items()
 
     except FileNotFoundError:
-        logger.error(f"File or directory '{filename}' not found.")
+        logger.error("File or directory '%s' not found.", filename)
     except json.JSONDecodeError:
-        logger.error(f"Error decoding JSON in file '{filename}'.")
+        logger.error("Error decoding JSON in file '%s'.", filename)
     except Exception as e:
-        logger.error(f"An error occurred while reading '{filename}': {e}")
+        logger.error("An error occurred while reading '%s': %s", filename, e)
     # This section is reached if and only if any of the excepts are hit
     # Return an empty iterable (list) => no KV cache scales are loaded
     # which ultimately defaults to 1.0 scales
-    logger.warning("Defaulting to KV cache scaling factors = 1.0 "
-                   f"for all layers in TP rank {tp_rank} "
-                   "as an error occurred during loading.")
+    logger.warning(
+        "Defaulting to KV cache scaling factors = 1.0 for all "
+        "layers in TP rank %d as an error occurred during loading.", tp_rank)
     return []
 
 

vllm/model_executor/models/__init__.py

@@ -90,8 +90,8 @@ def load_model_cls(model_arch: str) -> Optional[Type[nn.Module]]:
                     "ROCm for now.")
             if model_arch in _ROCM_PARTIALLY_SUPPORTED_MODELS:
                 logger.warning(
-                    f"Model architecture {model_arch} is partially supported "
-                    "by ROCm: " + _ROCM_PARTIALLY_SUPPORTED_MODELS[model_arch])
+                    "Model architecture %s is partially supported by ROCm: %s",
+                    model_arch, _ROCM_PARTIALLY_SUPPORTED_MODELS[model_arch])
 
         module_name, model_cls_name = _MODELS[model_arch]
         module = importlib.import_module(
@@ -106,9 +106,9 @@ def get_supported_archs() -> List[str]:
     def register_model(model_arch: str, model_cls: Type[nn.Module]):
         if model_arch in _MODELS:
             logger.warning(
-                f"Model architecture {model_arch} is already registered, "
-                "and will be overwritten by the new model "
-                f"class {model_cls.__name__}.")
+                "Model architecture %s is already registered, and will be "
+                "overwritten by the new model class %s.", model_arch,
+                model_cls.__name__)
         global _OOT_MODELS
         _OOT_MODELS[model_arch] = model_cls
 

vllm/model_executor/models/gemma.py

@@ -55,10 +55,10 @@ def _get_gemma_act_fn(
                 "in the config JSON file when it was initially released. "
                 "Changing the activation function to approximate GeLU "
                 "(`gelu_pytorch_tanh`). If you want to use the legacy "
-                f"`{hidden_act}`, edit the config JSON to set "
-                f"`hidden_activation={hidden_act}` instead of `hidden_act`. "
+                "`%s`, edit the config JSON to set "
+                "`hidden_activation=%s` instead of `hidden_act`. "
                 "See https://github.com/huggingface/transformers/pull/29402 "
-                "for more details.")
+                "for more details.", hidden_act, hidden_act)
         return GeluAndMul(approximate="tanh")
     elif hidden_activation == "gelu_pytorch_tanh":
         return GeluAndMul(approximate="tanh")

vllm/transformers_utils/configs/dbrx.py

@@ -72,9 +72,10 @@ def from_pretrained(
             and config_dict["model_type"] != cls.model_type
         ):
             logger.warning(
-                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
-                + f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
-            )
+                "You are using a model of type %s to instantiate a model of "
+                "type %s. This is not supported for all configurations of "
+                "models and can yield errors.",
+                config_dict["model_type"], cls.model_type)
 
         return cls.from_dict(config_dict, **kwargs)
 
@@ -151,9 +152,9 @@ def from_pretrained(
             and config_dict["model_type"] != cls.model_type
         ):
             logger.warning(
-                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
-                + f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
-            )
+                "You are using a model of type %s to instantiate a model of "
+                "type {cls.model_type}. This is not supported for all "
+                "configurations of models and can yield errors.", config_dict["model_type"], cls.model_type)
 
         return cls.from_dict(config_dict, **kwargs)
 

vllm/transformers_utils/tokenizer.py

@@ -137,9 +137,8 @@ def get_lora_tokenizer(lora_request: LoRARequest, *args,
         # No tokenizer was found in the LoRA folder,
         # use base model tokenizer
         logger.warning(
-            f"No tokenizer found in {lora_request.lora_local_path}, "
-            "using base model tokenizer instead. "
-            f"(Exception: {str(e)})")
+            "No tokenizer found in %s, using base model tokenizer instead. "
+            "(Exception: %s)", lora_request.lora_local_path, e)
         tokenizer = None
     return tokenizer
 

vllm/worker/model_runner.py

@@ -170,8 +170,8 @@ def load_model(self) -> None:
             )
 
         self.model_memory_usage = m.consumed_memory
-        logger.info(f"Loading model weights took "
-                    f"{self.model_memory_usage / float(2**30):.4f} GB")
+        logger.info("Loading model weights took %.4f GB",
+                    self.model_memory_usage / float(2**30))
 
         if self.lora_config:
             assert hasattr(self.model, "supported_lora_modules"
@@ -196,18 +196,19 @@ def load_model(self) -> None:
                     self.model.load_kv_cache_scales(
                         self.model_config.quantization_param_path)
                 else:
-                    raise RuntimeError("Using FP8 KV cache and scaling "
-                                       "factors provided but model "
-                                       f"{self.model.__class__} does not "
-                                       "support loading scaling factors.")
+                    raise RuntimeError(
+                        "Using FP8 KV cache and scaling factors provided but "
+                        "model %s does not support loading scaling factors.",
+                        self.model.__class__)
             else:
-                logger.warn("Using FP8 KV cache but no scaling factors "
-                            "provided. Defaulting to scaling factors of 1.0. "
-                            "This may lead to less accurate results!")
+                logger.warning(
+                    "Using FP8 KV cache but no scaling factors "
+                    "provided. Defaulting to scaling factors of 1.0. "
+                    "This may lead to less accurate results!")
         elif self.model_config.quantization_param_path is not None:
-            logger.warn("KV cache scaling factors provided, "
-                        "but the KV cache data type is not FP8. "
-                        "KV cache scaling factors will not be used.")
+            logger.warning("KV cache scaling factors provided, "
+                           "but the KV cache data type is not FP8. "
+                           "KV cache scaling factors will not be used.")
 
     def set_block_size(self, block_size: int) -> None:
         self.block_size = block_size
@@ -1054,7 +1055,7 @@ def capture_model(self, kv_caches: List[torch.Tensor]) -> None:
         end_time = time.perf_counter()
         elapsed_time = end_time - start_time
         # This usually takes < 10 seconds.
-        logger.info(f"Graph capturing finished in {elapsed_time:.0f} secs.")
+        logger.info("Graph capturing finished in %.0f secs.", elapsed_time)
 
     def __del__(self) -> None:
         # Delete the CUDA graphs before deleting the pynccl communicator.