[Log] Optimize Startup Log

vllm/distributed/device_communicators/cuda_communicator.py

@@ -14,6 +14,7 @@
 from vllm.distributed.device_communicators.pynccl_allocator import (
     is_symmetric_memory_enabled,
 )
+from vllm.distributed.parallel_state import is_global_first_rank
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
 
@@ -96,35 +97,35 @@ def __init__(
                 from .all2all import NaiveAll2AllManager
 
                 self.all2all_manager = NaiveAll2AllManager(self.cpu_group)
-                logger.info("Using naive all2all manager.")
             elif all2all_backend == "allgather_reducescatter":
                 from .all2all import AgRsAll2AllManager
 
                 self.all2all_manager = AgRsAll2AllManager(self.cpu_group)
-                logger.info("Using AllGather-ReduceScatter all2all manager.")
             elif all2all_backend == "pplx":
                 from .all2all import PPLXAll2AllManager
 
                 self.all2all_manager = PPLXAll2AllManager(self.cpu_group)
-                logger.info("Using PPLX all2all manager.")
             elif all2all_backend == "deepep_high_throughput":
                 from .all2all import DeepEPHTAll2AllManager
 
                 self.all2all_manager = DeepEPHTAll2AllManager(self.cpu_group)
-                logger.info("Using DeepEP High-Throughput all2all manager.")
             elif all2all_backend == "deepep_low_latency":
                 from .all2all import DeepEPLLAll2AllManager
 
                 self.all2all_manager = DeepEPLLAll2AllManager(self.cpu_group)
-                logger.info("Using DeepEP Low-Latency all2all manager.")
             elif all2all_backend == "flashinfer_all2allv":
                 from .all2all import FlashInferAllToAllManager
 
                 self.all2all_manager = FlashInferAllToAllManager(self.cpu_group)
-                logger.info("Using Flashinfer all2allv manager.")
             else:
                 raise ValueError(f"Unknown all2all backend: {all2all_backend}")
 
+            if is_global_first_rank():
+                logger.info(
+                    "Using %s all2all manager.",
+                    self.all2all_manager.__class__.__name__,
+                )
+
     def all_reduce(self, input_):
         # since currently we perform copy input -> symm_input -> out-of-place AR
         # return symm_output, we don't need to check if input is symmetric

vllm/distributed/device_communicators/pynccl.py

@@ -106,11 +106,10 @@ def __init__(
         self.disabled = False
 
         self.nccl_version = self.nccl.ncclGetRawVersion()
-        logger.info("vLLM is using nccl==%s", self.nccl.ncclGetVersion())
-
         if self.rank == 0:
             # get the unique id from NCCL
             self.unique_id = self.nccl.ncclGetUniqueId()
+            logger.info("vLLM is using nccl==%s", self.nccl.ncclGetVersion())
         else:
             # construct an empty unique id
             self.unique_id = ncclUniqueId()

vllm/utils/__init__.py

@@ -1143,7 +1143,7 @@ def find_nccl_library() -> str:
             so_file = "librccl.so.1"
         else:
             raise ValueError("NCCL only supports CUDA and ROCm backends.")
-        logger.info("Found nccl from library %s", so_file)
+        logger.debug_once("Found nccl from library %s", so_file)
     return so_file
 
 

vllm/v1/attention/backends/mla/cutlass_mla.py

@@ -139,7 +139,7 @@ def __init__(
         #       FORCE_NUM_KV_SPLITS=1
         force_num_kv_splits = os.environ.get("FORCE_NUM_KV_SPLITS", None)
         if force_num_kv_splits:
-            logger.warning_once("Forcing num_kv_splits to %d", int(force_num_kv_splits))
+            logger.debug_once("Forcing num_kv_splits to %d", int(force_num_kv_splits))
             self._num_kv_splits = int(force_num_kv_splits)
         else:
             self._num_kv_splits = -1  # => Auto-detect

vllm/v1/engine/core.py

@@ -19,6 +19,7 @@
 
 from vllm.config import ParallelConfig, VllmConfig
 from vllm.distributed import stateless_destroy_torch_distributed_process_group
+from vllm.distributed.parallel_state import is_global_first_rank
 from vllm.logger import init_logger
 from vllm.logging_utils.dump_input import dump_engine_exception
 from vllm.lora.request import LoRARequest
@@ -91,11 +92,12 @@ def __init__(
         load_general_plugins()
 
         self.vllm_config = vllm_config
-        logger.info(
-            "Initializing a V1 LLM engine (v%s) with config: %s",
-            VLLM_VERSION,
-            vllm_config,
-        )
+        if is_global_first_rank():
+            logger.info(
+                "Initializing a V1 LLM engine (v%s) with config: %s",
+                VLLM_VERSION,
+                vllm_config,
+            )
 
         self.log_stats = log_stats
 

vllm/v1/worker/gpu_model_runner.py

@@ -2888,7 +2888,6 @@ def load_model(self, eep_scale_up: bool = False) -> None:
         with DeviceMemoryProfiler() as m:
             time_before_load = time.perf_counter()
             model_loader = get_model_loader(self.load_config)
-            logger.info("Loading model from scratch...")
             self.model = model_loader.load_model(
                 vllm_config=self.vllm_config, model_config=self.model_config
             )