[CI] Disable non-lazy string operation on logging (#4326)

Co-authored-by: Danny Guinther <dguinther@neuralmagic.com>

vllm/distributed/device_communicators/custom_all_reduce.py

@@ -37,7 +37,7 @@ def init_custom_ar() -> None:
         return
 
     if world_size not in _SUPPORTED_WORLD_SIZES:
-        logger.warn(
+        logger.warning(
             "Custom allreduce is disabled due to an unsupported world size: "
             "%d. Supported world sizes: %s. To silence this warning, specify"
             " disable_custom_all_reduce=True explicitly.", world_size,
@@ -47,7 +47,7 @@ def init_custom_ar() -> None:
     # note: num dev can be larger than world_size if we're only using
     # first few GPUs
     if num_dev < world_size:
-        logger.warn(
+        logger.warning(
             "Cannot test GPU P2P because not all GPUs are visible to the "
             "current process. This might be the case if 'CUDA_VISIBLE_DEVICES'"
             " is set.")
@@ -62,7 +62,7 @@ def init_custom_ar() -> None:
     # this checks hardware and driver support for NVLink
     full_nvlink = _is_full_nvlink(device_ids)
     if world_size > 2 and not full_nvlink:
-        logger.warn(
+        logger.warning(
             "Custom allreduce is disabled because it's not supported on more"
             " than two PCIe-only GPUs. To silence this warning, specify"
             " disable_custom_all_reduce=True explicitly.")
@@ -71,7 +71,7 @@ def init_custom_ar() -> None:
     # this is expensive to compute at the first time
     # then we cache the result
     if not _can_p2p(rank, world_size):
-        logger.warn(
+        logger.warning(
             "Custom allreduce is disabled because your platform lacks GPU P2P"
             " capability or P2P test failed. To silence this warning, specify"
             " disable_custom_all_reduce=True explicitly.")

vllm/distributed/device_communicators/pynccl.py

@@ -43,15 +43,16 @@ try:
     nccl = ctypes.CDLL(so_file)
 except Exception as e:
     logger.error(
-        f"Failed to load NCCL library from {so_file} ."
+        "Failed to load NCCL library from %s ."
         "It is expected if you are not running on NVIDIA/AMD GPUs."
         "Otherwise, the nccl library might not exist, be corrupted "
-        f"or it does not support the current platform {platform.platform()}."
-        f"One solution is to download libnccl2 version 2.18 from "
-        f"https://developer.download.nvidia.com/compute/cuda/repos/ "
-        f"and extract the libnccl.so.2 file. If you already have the "
-        f"library, please set the environment variable VLLM_NCCL_SO_PATH"
-        " to point to the correct nccl library path.")
+        "or it does not support the current platform %s."
+        "One solution is to download libnccl2 version 2.18 from "
+        "https://developer.download.nvidia.com/compute/cuda/repos/ "
+        "and extract the libnccl.so.2 file. If you already have the "
+        "library, please set the environment variable VLLM_NCCL_SO_PATH"
+        " to point to the correct nccl library path.", so_file,
+        platform.platform())
     raise e
 
 # === export types and functions from nccl to Python ===

vllm/distributed/device_communicators/pynccl_utils.py

@@ -14,7 +14,7 @@ try:
 except Exception as e:
     # in non-NVIDIA environments, we can't import the nccl module
     # e.g. when running on machines with AMD GPUs
-    logger.info(f"Failed to import NCCL library: {e}")
+    logger.info("Failed to import NCCL library: %s", e)
     logger.info("It is expected if you are not running on NVIDIA GPUs.")
     pass
 
@@ -40,7 +40,7 @@ def set_pynccl_stream(stream: torch.cuda.Stream):
 def init_process_group(group: Optional[ProcessGroup] = None) -> None:
     assert not is_initialized()
     global comm
-    logger.info(f"vLLM is using nccl=={ncclGetVersion()}")
+    logger.info("vLLM is using nccl==%s", ncclGetVersion())
     comm = NCCLCommunicator(group=group)