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[Log] Optimize startup log (#28948)

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Signed-off-by: yewentao256 <zhyanwentao@126.com>
Signed-off-by: Wentao Ye <44945378+yewentao256@users.noreply.github.com>
Co-authored-by: Nick Hill <nhill@redhat.com>
This commit is contained in:
Wentao Ye 2025-11-21 11:46:20 -05:00 committed by GitHub
parent b7f1f490a6
commit a42ab317ac
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GPG Key ID: B5690EEEBB952194
4 changed files with 26 additions and 18 deletions
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6
vllm/model_executor/layers/fused_moe/fused_moe.py
View File

@ -872,8 +872,10 @@ def get_moe_configs(
for config_file_path in config_file_paths:
if os.path.exists(config_file_path):
with open(config_file_path) as f:
logger.info(
"Using configuration from %s for MoE layer.", config_file_path
logger.info_once(
"Using configuration from %s for MoE layer.",
config_file_path,
scope="global",
)
# If a configuration has been found, return it
tuned_config = json.load(f)

10
vllm/model_executor/layers/quantization/fp8.py
View File

@ -162,9 +162,11 @@ def get_fp8_moe_backend(block_quant: bool) -> Fp8MoeBackend:
# deepGEMM on supported platforms with block-quantized weights
if envs.VLLM_USE_DEEP_GEMM and envs.VLLM_MOE_USE_DEEP_GEMM and block_quant:
if not has_deep_gemm():
logger.warning_once("DeepGEMM backend requested but not available.")
logger.warning_once(
"DeepGEMM backend requested but not available.", scope="local"
)
elif is_deep_gemm_supported():
logger.info_once("Using DeepGEMM backend for FP8 MoE")
logger.info_once("Using DeepGEMM backend for FP8 MoE", scope="local")
return Fp8MoeBackend.DEEPGEMM
# CUTLASS BlockScaled GroupedGemm on SM100 with block-quantized weights
@ -173,7 +175,9 @@ def get_fp8_moe_backend(block_quant: bool) -> Fp8MoeBackend:
and current_platform.is_device_capability(100)
and block_quant
):
logger.info_once("Using Cutlass BlockScaled GroupedGemm backend for FP8 MoE")
logger.info_once(
"Using Cutlass BlockScaled GroupedGemm backend for FP8 MoE", scope="local"
)
return Fp8MoeBackend.CUTLASS_BLOCK_SCALED_GROUPED_GEMM
# default to Triton

25
vllm/profiler/gpu_profiler.py
View File

@ -139,18 +139,19 @@ class TorchProfilerWrapper(WorkerProfiler):
self.local_rank = local_rank
torch_profiler_trace_dir = envs.VLLM_TORCH_PROFILER_DIR
logger.info(
"Torch profiling enabled. Traces will be saved to: %s",
torch_profiler_trace_dir,
)
logger.debug(
"Profiler config: record_shapes=%s,"
"profile_memory=%s,with_stack=%s,with_flops=%s",
envs.VLLM_TORCH_PROFILER_RECORD_SHAPES,
envs.VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY,
envs.VLLM_TORCH_PROFILER_WITH_STACK,
envs.VLLM_TORCH_PROFILER_WITH_FLOPS,
)
if local_rank in (None, 0):
logger.info(
"Torch profiling enabled. Traces will be saved to: %s",
torch_profiler_trace_dir,
)
logger.debug(
"Profiler config: record_shapes=%s,"
"profile_memory=%s,with_stack=%s,with_flops=%s",
envs.VLLM_TORCH_PROFILER_RECORD_SHAPES,
envs.VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY,
envs.VLLM_TORCH_PROFILER_WITH_STACK,
envs.VLLM_TORCH_PROFILER_WITH_FLOPS,
)
self.profiler = torch.profiler.profile(
activities=[
torch.profiler.ProfilerActivity.CPU,

3
vllm/v1/core/kv_cache_utils.py
View File

@ -1236,10 +1236,11 @@ def _report_kv_cache_config(
max_concurrency = get_max_concurrency_for_kv_cache_config(
vllm_config, kv_cache_config
)
logger.info(
logger.info_once(
"Maximum concurrency for %s tokens per request: %.2fx",
max_model_len_str,
max_concurrency,
scope="local",
)