[Log] Optimize Startup Log (#26740)

Signed-off-by: yewentao256 <zhyanwentao@126.com>

vllm/compilation/backends.py

@@ -245,10 +245,14 @@ class CompilerManager:
             if graph_index == 0:
                 # adds some info logging for the first graph
                 if runtime_shape is None:
-                    logger.info("Cache the graph for dynamic shape for later use")
+                    logger.info_once(
+                        "Cache the graph for dynamic shape for later use", scope="local"
+                    )
                 else:
-                    logger.info(
-                        "Cache the graph of shape %s for later use", str(runtime_shape)
+                    logger.info_once(
+                        "Cache the graph of shape %s for later use",
+                        str(runtime_shape),
+                        scope="local",
                     )
             if runtime_shape is None:
                 logger.debug(
@@ -272,12 +276,17 @@ class CompilerManager:
             elapsed = now - compilation_start_time
             compilation_config.compilation_time += elapsed
             if runtime_shape is None:
-                logger.info("Compiling a graph for dynamic shape takes %.2f s", elapsed)
+                logger.info_once(
+                    "Compiling a graph for dynamic shape takes %.2f s",
+                    elapsed,
+                    scope="local",
+                )
             else:
-                logger.info(
+                logger.info_once(
                     "Compiling a graph for shape %s takes %.2f s",
                     runtime_shape,
                     elapsed,
+                    scope="local",
                 )
 
         return compiled_graph
@@ -604,10 +613,12 @@ class VllmBackend:
         disable_cache = envs.VLLM_DISABLE_COMPILE_CACHE
 
         if disable_cache:
-            logger.info("vLLM's torch.compile cache is disabled.")
+            logger.info_once("vLLM's torch.compile cache is disabled.", scope="local")
         else:
-            logger.info(
-                "Using cache directory: %s for vLLM's torch.compile", local_cache_dir
+            logger.info_once(
+                "Using cache directory: %s for vLLM's torch.compile",
+                local_cache_dir,
+                scope="local",
             )
 
         self.compiler_manager.initialize_cache(
@@ -620,7 +631,9 @@ class VllmBackend:
         from .monitor import torch_compile_start_time
 
         dynamo_time = time.time() - torch_compile_start_time
-        logger.info("Dynamo bytecode transform time: %.2f s", dynamo_time)
+        logger.info_once(
+            "Dynamo bytecode transform time: %.2f s", dynamo_time, scope="local"
+        )
         self.compilation_config.compilation_time += dynamo_time
 
         # we control the compilation process, each instance can only be
@@ -672,7 +685,9 @@ class VllmBackend:
             with open(graph_path, "w") as f:
                 f.write(src)
 
-            logger.debug("Computation graph saved to %s", graph_path)
+            logger.debug_once(
+                "Computation graph saved to %s", graph_path, scope="local"
+            )
 
         self._called = True
 

vllm/compilation/monitor.py

@@ -31,8 +31,10 @@ def start_monitoring_torch_compile(vllm_config: VllmConfig):
 def end_monitoring_torch_compile(vllm_config: VllmConfig):
     compilation_config: CompilationConfig = vllm_config.compilation_config
     if compilation_config.mode == CompilationMode.VLLM_COMPILE:
-        logger.info(
-            "torch.compile takes %.2f s in total", compilation_config.compilation_time
+        logger.info_once(
+            "torch.compile takes %.2f s in total",
+            compilation_config.compilation_time,
+            scope="local",
         )
         global context_manager
         if context_manager is not None:

vllm/distributed/device_communicators/cuda_communicator.py

@@ -13,7 +13,6 @@ from vllm.distributed.device_communicators.pynccl import register_nccl_symmetric
 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
 
@@ -118,11 +117,11 @@ class CudaCommunicator(DeviceCommunicatorBase):
             else:
                 raise ValueError(f"Unknown all2all backend: {self.all2all_backend}")
 
-            if is_global_first_rank():
-                logger.info(
-                    "Using %s all2all manager.",
-                    self.all2all_manager.__class__.__name__,
-                )
+            logger.info_once(
+                "Using %s all2all manager.",
+                self.all2all_manager.__class__.__name__,
+                scope="global",
+            )
 
     def all_reduce(self, input_):
         # since currently we perform copy input -> symm_input -> out-of-place AR

vllm/distributed/device_communicators/custom_all_reduce.py

@@ -34,7 +34,7 @@ def _can_p2p(rank: int, world_size: int) -> bool:
         if i == rank:
             continue
         if envs.VLLM_SKIP_P2P_CHECK:
-            logger.info("Skipping P2P check and trusting the driver's P2P report.")
+            logger.debug("Skipping P2P check and trusting the driver's P2P report.")
             return torch.cuda.can_device_access_peer(rank, i)
         if not gpu_p2p_access_check(rank, i):
             return False

vllm/distributed/device_communicators/pynccl.py

@@ -108,7 +108,9 @@ class PyNcclCommunicator:
         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())
+            logger.info_once(
+                "vLLM is using nccl==%s", self.nccl.ncclGetVersion(), scope="local"
+            )
         else:
             # construct an empty unique id
             self.unique_id = ncclUniqueId()

vllm/distributed/device_communicators/shm_broadcast.py

@@ -312,7 +312,7 @@ class MessageQueue:
             remote_addr_ipv6=remote_addr_ipv6,
         )
 
-        logger.info("vLLM message queue communication handle: %s", self.handle)
+        logger.debug("vLLM message queue communication handle: %s", self.handle)
 
     def export_handle(self) -> Handle:
         return self.handle

vllm/distributed/parallel_state.py

@@ -1157,7 +1157,7 @@ def init_distributed_environment(
         ip = parallel_config.data_parallel_master_ip
         port = parallel_config.get_next_dp_init_port()
         distributed_init_method = get_distributed_init_method(ip, port)
-        logger.info(
+        logger.debug(
             "Adjusting world_size=%d rank=%d distributed_init_method=%s for DP",
             world_size,
             rank,
@@ -1322,7 +1322,7 @@ def initialize_model_parallel(
         group_ranks, get_world_group().local_rank, backend, group_name="ep"
     )
 
-    logger.info(
+    logger.info_once(
         "rank %s in world size %s is assigned as "
         "DP rank %s, PP rank %s, TP rank %s, EP rank %s",
         rank,
@@ -1625,6 +1625,29 @@ def is_global_first_rank() -> bool:
         return True
 
 
+def is_local_first_rank() -> bool:
+    """
+    Check if the current process is the first local rank (rank 0 on its node).
+    """
+    try:
+        # prefer the initialized world group if available
+        global _WORLD
+        if _WORLD is not None:
+            return _WORLD.local_rank == 0
+
+        if not torch.distributed.is_initialized():
+            return True
+
+        # fallback to environment-provided local rank if available
+        # note: envs.LOCAL_RANK is set when using env:// launchers (e.g., torchrun)
+        try:
+            return int(envs.LOCAL_RANK) == 0  # type: ignore[arg-type]
+        except Exception:
+            return torch.distributed.get_rank() == 0
+    except Exception:
+        return True
+
+
 def _node_count(pg: ProcessGroup | StatelessProcessGroup) -> int:
     """
     Returns the total number of nodes in the process group.

vllm/logger.py

@@ -13,7 +13,7 @@ from logging import Logger
 from logging.config import dictConfig
 from os import path
 from types import MethodType
-from typing import Any, cast
+from typing import Any, Literal, cast
 
 import vllm.envs as envs
 
@@ -59,20 +59,37 @@ DEFAULT_LOGGING_CONFIG = {
 
 @lru_cache
 def _print_debug_once(logger: Logger, msg: str, *args: Hashable) -> None:
-    # Set the stacklevel to 2 to print the original caller's line info
-    logger.debug(msg, *args, stacklevel=2)
+    # Set the stacklevel to 3 to print the original caller's line info
+    logger.debug(msg, *args, stacklevel=3)
 
 
 @lru_cache
 def _print_info_once(logger: Logger, msg: str, *args: Hashable) -> None:
-    # Set the stacklevel to 2 to print the original caller's line info
-    logger.info(msg, *args, stacklevel=2)
+    # Set the stacklevel to 3 to print the original caller's line info
+    logger.info(msg, *args, stacklevel=3)
 
 
 @lru_cache
 def _print_warning_once(logger: Logger, msg: str, *args: Hashable) -> None:
-    # Set the stacklevel to 2 to print the original caller's line info
-    logger.warning(msg, *args, stacklevel=2)
+    # Set the stacklevel to 3 to print the original caller's line info
+    logger.warning(msg, *args, stacklevel=3)
+
+
+LogScope = Literal["process", "global", "local"]
+
+
+def _should_log_with_scope(scope: LogScope) -> bool:
+    """Decide whether to log based on scope"""
+    if scope == "global":
+        from vllm.distributed.parallel_state import is_global_first_rank
+
+        return is_global_first_rank()
+    if scope == "local":
+        from vllm.distributed.parallel_state import is_local_first_rank
+
+        return is_local_first_rank()
+    # default "process" scope: always log
+    return True
 
 
 class _VllmLogger(Logger):
@@ -84,33 +101,43 @@ class _VllmLogger(Logger):
         `intel_extension_for_pytorch.utils._logger`.
     """
 
-    def debug_once(self, msg: str, *args: Hashable) -> None:
+    def debug_once(
+        self, msg: str, *args: Hashable, scope: LogScope = "process"
+    ) -> None:
         """
         As [`debug`][logging.Logger.debug], but subsequent calls with
         the same message are silently dropped.
         """
+        if not _should_log_with_scope(scope):
+            return
         _print_debug_once(self, msg, *args)
 
-    def info_once(self, msg: str, *args: Hashable) -> None:
+    def info_once(self, msg: str, *args: Hashable, scope: LogScope = "process") -> None:
         """
         As [`info`][logging.Logger.info], but subsequent calls with
         the same message are silently dropped.
         """
+        if not _should_log_with_scope(scope):
+            return
         _print_info_once(self, msg, *args)
 
-    def warning_once(self, msg: str, *args: Hashable) -> None:
+    def warning_once(
+        self, msg: str, *args: Hashable, scope: LogScope = "process"
+    ) -> None:
         """
         As [`warning`][logging.Logger.warning], but subsequent calls with
         the same message are silently dropped.
         """
+        if not _should_log_with_scope(scope):
+            return
         _print_warning_once(self, msg, *args)
 
 
 # Pre-defined methods mapping to avoid repeated dictionary creation
 _METHODS_TO_PATCH = {
-    "debug_once": _print_debug_once,
-    "info_once": _print_info_once,
-    "warning_once": _print_warning_once,
+    "debug_once": _VllmLogger.debug_once,
+    "info_once": _VllmLogger.info_once,
+    "warning_once": _VllmLogger.warning_once,
 }
 
 

vllm/model_executor/layers/fused_moe/layer.py

@@ -368,11 +368,13 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
                 logger.info_once(
                     "FlashInfer CUTLASS MoE is available for EP"
                     " but not enabled, consider setting"
-                    " VLLM_USE_FLASHINFER_MOE_FP16=1 to enable it."
+                    " VLLM_USE_FLASHINFER_MOE_FP16=1 to enable it.",
+                    scope="local",
                 )
             elif self.moe.moe_parallel_config.dp_size > 1:
                 logger.info_once(
-                    "FlashInfer CUTLASS MoE is currently not available for DP."
+                    "FlashInfer CUTLASS MoE is currently not available for DP.",
+                    scope="local",
                 )
             self.flashinfer_cutlass_moe = None  # type: ignore
 

vllm/model_executor/model_loader/default_loader.py

@@ -311,9 +311,10 @@ class DefaultModelLoader(BaseModelLoader):
             loaded_weights = load_weights_and_online_quantize(self, model, model_config)
 
         self.counter_after_loading_weights = time.perf_counter()
-        logger.info(
+        logger.info_once(
             "Loading weights took %.2f seconds",
             self.counter_after_loading_weights - self.counter_before_loading_weights,
+            scope="local",
         )
         # We only enable strict check for non-quantized models
         # that have loaded weights tracking currently.

vllm/model_executor/model_loader/weight_utils.py

@@ -416,7 +416,7 @@ def download_weights_from_hf(
                 e,
             )
 
-    logger.info("Using model weights format %s", allow_patterns)
+    logger.debug("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):

vllm/platforms/__init__.py

@@ -222,10 +222,12 @@ def resolve_current_platform_cls_qualname() -> str:
         )
     elif len(activated_builtin_plugins) == 1:
         platform_cls_qualname = builtin_platform_plugins[activated_builtin_plugins[0]]()
-        logger.info("Automatically detected platform %s.", activated_builtin_plugins[0])
+        logger.debug(
+            "Automatically detected platform %s.", activated_builtin_plugins[0]
+        )
     else:
         platform_cls_qualname = "vllm.platforms.interface.UnspecifiedPlatform"
-        logger.info("No platform detected, vLLM is running on UnspecifiedPlatform")
+        logger.debug("No platform detected, vLLM is running on UnspecifiedPlatform")
     return platform_cls_qualname
 
 

vllm/platforms/cuda.py

@@ -298,7 +298,9 @@ class CudaPlatformBase(Platform):
             )
 
             if use_cutlassmla:
-                logger.info_once("Using Cutlass MLA backend on V1 engine.")
+                logger.info_once(
+                    "Using Cutlass MLA backend on V1 engine.", scope="local"
+                )
                 return "vllm.v1.attention.backends.mla.cutlass_mla.CutlassMLABackend"
             if use_flashinfermla:
                 from vllm.v1.attention.backends.utils import set_kv_cache_layout

vllm/utils/gc_utils.py

@@ -37,7 +37,7 @@ class GCDebugConfig:
             except Exception:
                 self.enabled = False
                 logger.error("Failed to parse VLLM_GC_DEBUG(%s)", envs.VLLM_GC_DEBUG)
-        logger.info("GC Debug Config. %s", str(self))
+        logger.debug("GC Debug Config. %s", str(self))
 
     def __repr__(self) -> str:
         return f"enabled:{self.enabled},top_objects:{self.top_objects}"

vllm/v1/core/kv_cache_utils.py

@@ -1226,7 +1226,7 @@ def _report_kv_cache_config(
             vllm_config.parallel_config.decode_context_parallel_size,
         )
     num_tokens_str = f"{num_tokens:,}"
-    logger.info("GPU KV cache size: %s tokens", num_tokens_str)
+    logger.info_once("GPU KV cache size: %s tokens", num_tokens_str, scope="local")
     max_model_len_str = f"{vllm_config.model_config.max_model_len:,}"
     max_concurrency = get_max_concurrency_for_kv_cache_config(
         vllm_config, kv_cache_config

vllm/v1/engine/core.py

@@ -19,7 +19,6 @@ import zmq
 
 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.envs import enable_envs_cache
 from vllm.logger import init_logger
 from vllm.logging_utils.dump_input import dump_engine_exception
@@ -90,7 +89,7 @@ class EngineCore:
         load_general_plugins()
 
         self.vllm_config = vllm_config
-        if is_global_first_rank():
+        if vllm_config.parallel_config.data_parallel_rank == 0:
             logger.info(
                 "Initializing a V1 LLM engine (v%s) with config: %s",
                 VLLM_VERSION,
@@ -235,9 +234,10 @@ class EngineCore:
         self.model_executor.initialize_from_config(kv_cache_configs)
 
         elapsed = time.time() - start
-        logger.info(
+        logger.info_once(
             ("init engine (profile, create kv cache, warmup model) took %.2f seconds"),
             elapsed,
+            scope="local",
         )
         return num_gpu_blocks, num_cpu_blocks, scheduler_kv_cache_config
 
@@ -713,7 +713,7 @@ class EngineCoreProc(EngineCore):
         )
 
         # Receive initialization message.
-        logger.info("Waiting for init message from front-end.")
+        logger.debug("Waiting for init message from front-end.")
         if not handshake_socket.poll(timeout=HANDSHAKE_TIMEOUT_MINS * 60_000):
             raise RuntimeError(
                 "Did not receive response from front-end "

vllm/v1/metrics/loggers.py

@@ -215,7 +215,7 @@ class LoggingStatLogger(StatLoggerBase):
 
     def log_engine_initialized(self):
         if self.vllm_config.cache_config.num_gpu_blocks:
-            logger.info(
+            logger.debug(
                 "Engine %03d: vllm cache_config_info with initialization "
                 "after num_gpu_blocks is: %d",
                 self.engine_index,

vllm/v1/sample/ops/topk_topp_sampler.py

@@ -33,7 +33,10 @@ class TopKTopPSampler(nn.Module):
         ):
             if envs.VLLM_USE_FLASHINFER_SAMPLER:
                 # Users must opt in explicitly via VLLM_USE_FLASHINFER_SAMPLER=1.
-                logger.info_once("Using FlashInfer for top-p & top-k sampling.")
+                logger.info_once(
+                    "Using FlashInfer for top-p & top-k sampling.",
+                    scope="global",
+                )
                 self.forward = self.forward_cuda
             else:
                 logger.debug_once(

vllm/v1/worker/dp_utils.py

@@ -6,7 +6,7 @@ import torch
 import torch.distributed as dist
 
 from vllm.config import ParallelConfig
-from vllm.distributed.parallel_state import get_dp_group, is_global_first_rank
+from vllm.distributed.parallel_state import get_dp_group
 from vllm.logger import init_logger
 from vllm.v1.worker.ubatch_utils import (
     UBatchSlices,
@@ -132,12 +132,12 @@ def _synchronize_dp_ranks(
     should_ubatch = _post_process_ubatch(tensor)
 
     if should_ubatch and not should_dp_pad:
-        if is_global_first_rank():
-            logger.debug(
-                "Microbatching has been triggered and requires DP padding. "
-                "Enabling DP padding even though it has been explicitly "
-                "disabled."
-            )
+        logger.debug_once(
+            "Microbatching has been triggered and requires DP padding. "
+            "Enabling DP padding even though it has been explicitly "
+            "disabled.",
+            scope="global",
+        )
         should_dp_pad = True
 
     # Pad all DP ranks up to the maximum token count across ranks if

vllm/v1/worker/gpu_model_runner.py

@@ -2850,7 +2850,11 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
         Args:
             eep_scale_up: the model loading is for elastic EP scale up.
         """
-        logger.info("Starting to load model %s...", self.model_config.model)
+        logger.info_once(
+            "Starting to load model %s...",
+            self.model_config.model,
+            scope="global",
+        )
         if eep_scale_up:
             from vllm.distributed.parallel_state import get_ep_group
 
@@ -2911,10 +2915,11 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
                 self.model.set_aux_hidden_state_layers(aux_layers)
             time_after_load = time.perf_counter()
         self.model_memory_usage = m.consumed_memory
-        logger.info(
+        logger.info_once(
             "Model loading took %.4f GiB and %.6f seconds",
             self.model_memory_usage / GiB_bytes,
             time_after_load - time_before_load,
+            scope="local",
         )
         prepare_communication_buffer_for_model(self.model)
 
@@ -3838,10 +3843,11 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
         elapsed_time = end_time - start_time
         cuda_graph_size = start_free_gpu_memory - end_free_gpu_memory
         # This usually takes 5~20 seconds.
-        logger.info(
+        logger.info_once(
             "Graph capturing finished in %.0f secs, took %.2f GiB",
             elapsed_time,
             cuda_graph_size / (1 << 30),
+            scope="local",
         )
         return cuda_graph_size