[Misc] Move `print_*_once` from utils to logger (#11298)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
Signed-off-by: Maxime Fournioux <55544262+mfournioux@users.noreply.github.com>
Co-authored-by: Maxime Fournioux <55544262+mfournioux@users.noreply.github.com>

.github/workflows/lint-and-deploy.yaml

@@ -64,6 +64,7 @@ jobs:
         run: |
           export AWS_ACCESS_KEY_ID=minioadmin
           export AWS_SECRET_ACCESS_KEY=minioadmin
+          sleep 30 && kubectl -n ns-vllm logs -f "$(kubectl -n ns-vllm get pods | awk '/deployment/ {print $1;exit}')" &
           helm install --wait --wait-for-jobs --timeout 5m0s --debug --create-namespace --namespace=ns-vllm test-vllm examples/online_serving/chart-helm -f examples/online_serving/chart-helm/values.yaml --set secrets.s3endpoint=http://minio:9000 --set secrets.s3bucketname=testbucket --set secrets.s3accesskeyid=$AWS_ACCESS_KEY_ID --set secrets.s3accesskey=$AWS_SECRET_ACCESS_KEY --set resources.requests.cpu=1 --set resources.requests.memory=4Gi --set resources.limits.cpu=2 --set resources.limits.memory=5Gi --set image.env[0].name=VLLM_CPU_KVCACHE_SPACE --set image.env[1].name=VLLM_LOGGING_LEVEL --set-string image.env[0].value="1" --set-string image.env[1].value="DEBUG" --set-string extraInit.s3modelpath="opt-125m/" --set-string 'resources.limits.nvidia\.com/gpu=0' --set-string 'resources.requests.nvidia\.com/gpu=0' --set-string image.repository="vllm-cpu-env"
     
       - name: curl test

vllm/attention/backends/torch_sdpa.py

@@ -13,9 +13,12 @@ from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
 from vllm.attention.backends.utils import CommonAttentionState
 from vllm.attention.ops.ipex_attn import PagedAttention
 from vllm.attention.ops.paged_attn import PagedAttentionMetadata
-from vllm.utils import make_tensor_with_pad, print_warning_once
+from vllm.logger import init_logger
+from vllm.utils import make_tensor_with_pad
 from vllm.worker.cpu_model_runner import ModelInputForCPUBuilder
 
+logger = init_logger(__name__)
+
 
 class TorchSDPABackend(AttentionBackend):
 
@@ -396,7 +399,7 @@ class TorchSDPABackendImpl(AttentionImpl[TorchSDPAMetadata]):
             raise ValueError(
                 "Torch SPDA does not support block-sparse attention.")
         if logits_soft_cap is not None:
-            print_warning_once("Torch SPDA does not support logits soft cap. "
+            logger.warning_once("Torch SPDA does not support logits soft cap. "
                                 "Outputs may be slightly off.")
         self.num_heads = num_heads
         self.head_size = head_size

vllm/attention/backends/xformers.py

@@ -17,7 +17,9 @@ from vllm.attention.backends.utils import (
     is_all_cross_attn_metadata_set, is_all_encoder_attn_metadata_set)
 from vllm.attention.ops.paged_attn import (PagedAttention,
                                            PagedAttentionMetadata)
-from vllm.utils import print_warning_once
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
 
 
 class XFormersBackend(AttentionBackend):
@@ -385,7 +387,7 @@ class XFormersImpl(AttentionImpl[XFormersMetadata]):
             raise ValueError(
                 "XFormers does not support block-sparse attention.")
         if logits_soft_cap is not None:
-            print_warning_once("XFormers does not support logits soft cap. "
+            logger.warning_once("XFormers does not support logits soft cap. "
                                 "Outputs may be slightly off.")
         self.num_heads = num_heads
         self.head_size = head_size

vllm/config.py

@@ -32,8 +32,7 @@ from vllm.transformers_utils.config import (
 from vllm.transformers_utils.s3_utils import S3Model
 from vllm.transformers_utils.utils import is_s3
 from vllm.utils import (GiB_bytes, LayerBlockType, cuda_device_count_stateless,
-                        get_cpu_memory, print_warning_once, random_uuid,
-                        resolve_obj_by_qualname)
+                        get_cpu_memory, random_uuid, resolve_obj_by_qualname)
 
 if TYPE_CHECKING:
     from ray.util.placement_group import PlacementGroup
@@ -314,7 +313,7 @@ class ModelConfig:
                 sliding_window_len_min = get_min_sliding_window(
                     self.hf_text_config.sliding_window)
 
-                print_warning_once(
+                logger.warning_once(
                     f"{self.hf_text_config.model_type} has interleaved "
                     "attention, which is currently not supported by the "
                     "XFORMERS backend. Disabling sliding window and capping "
@@ -2758,7 +2757,7 @@ class CompilationConfig(BaseModel):
 
         def model_post_init(self, __context: Any) -> None:
             if not self.enable_reshape and self.enable_fusion:
-                print_warning_once(
+                logger.warning_once(
                     "Fusion enabled but reshape elimination disabled."
                     "RMSNorm + quant (fp8) fusion might not work")
 
@@ -3151,7 +3150,7 @@ class VllmConfig:
             self.scheduler_config.chunked_prefill_enabled and \
             self.model_config.dtype == torch.float32 and \
             current_platform.get_device_capability() == (7, 5):
-            print_warning_once(
+            logger.warning_once(
                 "Turing devices tensor cores do not support float32 matmul. "
                 "To workaround this limitation, vLLM will set 'ieee' input "
                 "precision for chunked prefill triton kernels.")

vllm/entrypoints/chat_utils.py

@@ -35,7 +35,6 @@ from vllm.logger import init_logger
 from vllm.multimodal import MultiModalDataDict
 from vllm.multimodal.utils import MediaConnector
 from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer
-from vllm.utils import print_warning_once
 
 logger = init_logger(__name__)
 
@@ -985,14 +984,14 @@ def apply_mistral_chat_template(
     **kwargs: Any,
 ) -> List[int]:
     if chat_template is not None:
-        print_warning_once(
+        logger.warning_once(
             "'chat_template' cannot be overridden for mistral tokenizer.")
     if "add_generation_prompt" in kwargs:
-        print_warning_once(
+        logger.warning_once(
             "'add_generation_prompt' is not supported for mistral tokenizer, "
             "so it will be ignored.")
     if "continue_final_message" in kwargs:
-        print_warning_once(
+        logger.warning_once(
             "'continue_final_message' is not supported for mistral tokenizer, "
             "so it will be ignored.")
 

vllm/inputs/preprocess.py

@@ -10,7 +10,6 @@ from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
 from vllm.multimodal.inputs import MultiModalDataDict, MultiModalInputsV2
 from vllm.prompt_adapter.request import PromptAdapterRequest
 from vllm.transformers_utils.tokenizer_group import BaseTokenizerGroup
-from vllm.utils import print_info_once, print_warning_once
 
 from .data import (DecoderOnlyInputs, EncoderDecoderInputs, ProcessorInputs,
                    PromptType, SingletonInputs, SingletonPrompt, token_inputs)
@@ -68,19 +67,22 @@ class InputPreprocessor:
         '''
 
         if not self.model_config.is_encoder_decoder:
-            print_warning_once("Using None for decoder start token id because "
+            logger.warning_once(
+                "Using None for decoder start token id because "
                 "this is not an encoder/decoder model.")
             return None
 
         if (self.model_config is None or self.model_config.hf_config is None):
-            print_warning_once("Using None for decoder start token id because "
+            logger.warning_once(
+                "Using None for decoder start token id because "
                 "model config is not available.")
             return None
 
         dec_start_token_id = getattr(self.model_config.hf_config,
                                      'decoder_start_token_id', None)
         if dec_start_token_id is None:
-            print_warning_once("Falling back on <BOS> for decoder start token "
+            logger.warning_once(
+                "Falling back on <BOS> for decoder start token "
                 "id because decoder start token id is not "
                 "available.")
             dec_start_token_id = self.get_bos_token_id()
@@ -231,7 +233,7 @@ class InputPreprocessor:
         # updated to use the new multi-modal processor
         can_process_multimodal = self.mm_registry.has_processor(model_config)
         if not can_process_multimodal:
-            print_info_once(
+            logger.info_once(
                 "Your model uses the legacy input pipeline instead of the new "
                 "multi-modal processor. Please note that the legacy pipeline "
                 "will be removed in a future release. For more details, see: "

vllm/inputs/registry.py

@@ -12,7 +12,7 @@ from vllm.logger import init_logger
 from vllm.transformers_utils.processor import cached_get_processor
 from vllm.transformers_utils.tokenizer import AnyTokenizer
 from vllm.utils import (ClassRegistry, get_allowed_kwarg_only_overrides,
-                        print_warning_once, resolve_mm_processor_kwargs)
+                        resolve_mm_processor_kwargs)
 
 from .data import ProcessorInputs, SingletonInputs
 from .parse import is_encoder_decoder_inputs
@@ -352,7 +352,7 @@ class InputRegistry:
         num_tokens = dummy_data.seq_data.prompt_token_ids
         if len(num_tokens) < seq_len:
             if is_encoder_data:
-                print_warning_once(
+                logger.warning_once(
                     f"Expected at least {seq_len} dummy encoder tokens for "
                     f"profiling, but found {len(num_tokens)} tokens instead.")
             else:

vllm/logger.py

@@ -4,11 +4,12 @@ import json
 import logging
 import os
 import sys
-from functools import partial
+from functools import lru_cache, partial
 from logging import Logger
 from logging.config import dictConfig
 from os import path
-from typing import Dict, Optional
+from types import MethodType
+from typing import Any, Optional, cast
 
 import vllm.envs as envs
 
@@ -49,8 +50,44 @@ DEFAULT_LOGGING_CONFIG = {
 }
 
 
+@lru_cache
+def _print_info_once(logger: Logger, msg: str) -> None:
+    # Set the stacklevel to 2 to print the original caller's line info
+    logger.info(msg, stacklevel=2)
+
+
+@lru_cache
+def _print_warning_once(logger: Logger, msg: str) -> None:
+    # Set the stacklevel to 2 to print the original caller's line info
+    logger.warning(msg, stacklevel=2)
+
+
+class _VllmLogger(Logger):
+    """
+    Note:
+        This class is just to provide type information.
+        We actually patch the methods directly on the :class:`logging.Logger`
+        instance to avoid conflicting with other libraries such as
+        `intel_extension_for_pytorch.utils._logger`.
+    """
+
+    def info_once(self, msg: str) -> None:
+        """
+        As :meth:`info`, but subsequent calls with the same message
+        are silently dropped.
+        """
+        _print_info_once(self, msg)
+
+    def warning_once(self, msg: str) -> None:
+        """
+        As :meth:`warning`, but subsequent calls with the same message
+        are silently dropped.
+        """
+        _print_warning_once(self, msg)
+
+
 def _configure_vllm_root_logger() -> None:
-    logging_config: Dict = {}
+    logging_config = dict[str, Any]()
 
     if not VLLM_CONFIGURE_LOGGING and VLLM_LOGGING_CONFIG_PATH:
         raise RuntimeError(
@@ -84,12 +121,22 @@ def _configure_vllm_root_logger() -> None:
         dictConfig(logging_config)
 
 
-def init_logger(name: str) -> Logger:
+def init_logger(name: str) -> _VllmLogger:
     """The main purpose of this function is to ensure that loggers are
     retrieved in such a way that we can be sure the root vllm logger has
     already been configured."""
 
-    return logging.getLogger(name)
+    logger = logging.getLogger(name)
+
+    methods_to_patch = {
+        "info_once": _print_info_once,
+        "warning_once": _print_warning_once,
+    }
+
+    for method_name, method in methods_to_patch.items():
+        setattr(logger, method_name, MethodType(method, logger))
+
+    return cast(_VllmLogger, logger)
 
 
 # The root logger is initialized when the module is imported.

vllm/lora/peft_helper.py

@@ -4,7 +4,9 @@ import math
 from dataclasses import MISSING, dataclass, field, fields
 from typing import Literal, Optional, Union
 
-from vllm.utils import print_info_once
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
 
 
 @dataclass
@@ -42,7 +44,7 @@ class PEFTHelper:
     def __post_init__(self):
         self._validate_features()
         if self.use_rslora:
-            print_info_once("Loading LoRA weights trained with rsLoRA.")
+            logger.info_once("Loading LoRA weights trained with rsLoRA.")
             self.vllm_lora_scaling_factor = self.lora_alpha / math.sqrt(self.r)
         else:
             self.vllm_lora_scaling_factor = self.lora_alpha / self.r

vllm/lora/punica_wrapper/punica_selector.py

+from vllm.logger import init_logger
 from vllm.platforms import current_platform
-from vllm.utils import print_info_once
 
 from .punica_base import PunicaWrapperBase
 
+logger = init_logger(__name__)
+
 
 def get_punica_wrapper(*args, **kwargs) -> PunicaWrapperBase:
     if current_platform.is_cuda_alike():
         # Lazy import to avoid ImportError
         from vllm.lora.punica_wrapper.punica_gpu import PunicaWrapperGPU
-        print_info_once("Using PunicaWrapperGPU.")
+        logger.info_once("Using PunicaWrapperGPU.")
         return PunicaWrapperGPU(*args, **kwargs)
     elif current_platform.is_hpu():
         # Lazy import to avoid ImportError
         from vllm.lora.punica_wrapper.punica_hpu import PunicaWrapperHPU
-        print_info_once("Using PunicaWrapperHPU.")
+        logger.info_once("Using PunicaWrapperHPU.")
         return PunicaWrapperHPU(*args, **kwargs)
     else:
         raise NotImplementedError

vllm/model_executor/custom_op.py

@@ -5,7 +5,6 @@ import torch.nn as nn
 from vllm.config import get_current_vllm_config
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
-from vllm.utils import print_warning_once
 
 logger = init_logger(__name__)
 
@@ -91,7 +90,7 @@ class CustomOp(nn.Module):
         compilation_config = get_current_vllm_config().compilation_config
         custom_ops = compilation_config.custom_ops
         if not hasattr(cls, "name"):
-            print_warning_once(
+            logger.warning_once(
                 f"Custom op {cls.__name__} was not registered, "
                 f"which means it won't appear in the op registry. "
                 f"It will be enabled/disabled based on the global settings.")

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py

@@ -8,6 +8,7 @@ from compressed_tensors.quantization import QuantizationStrategy
 
 import vllm.model_executor.layers.fused_moe  # noqa
 from vllm import _custom_ops as ops
+from vllm.logger import init_logger
 from vllm.model_executor.layers.fused_moe import (FusedMoE, FusedMoEMethodBase,
                                                   FusedMoeWeightScaleSupported)
 from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
@@ -16,7 +17,8 @@ from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
     all_close_1d, normalize_e4m3fn_to_e4m3fnuz, per_tensor_dequantize)
 from vllm.model_executor.utils import set_weight_attrs
 from vllm.platforms import current_platform
-from vllm.utils import print_warning_once
+
+logger = init_logger(__name__)
 
 
 class GPTQMarlinState(Enum):
@@ -142,10 +144,10 @@ class CompressedTensorsW8A8Fp8MoEMethod(CompressedTensorsMoEMethod):
                     "activation scales are None.")
             if (not all_close_1d(layer.w13_input_scale)
                     or not all_close_1d(layer.w2_input_scale)):
-                print_warning_once(
+                logger.warning_once(
                     "Found input_scales that are not equal for "
                     "fp8 MoE layer. Using the maximum across experts "
-                    "for each layer. ")
+                    "for each layer.")
             layer.w13_input_scale = torch.nn.Parameter(
                 layer.w13_input_scale.max(), requires_grad=False)
             layer.w2_input_scale = torch.nn.Parameter(

vllm/model_executor/layers/quantization/fp8.py

@@ -28,7 +28,6 @@ from vllm.model_executor.parameter import (BlockQuantScaleParameter,
                                            PerTensorScaleParameter)
 from vllm.model_executor.utils import set_weight_attrs
 from vllm.platforms import current_platform
-from vllm.utils import print_warning_once
 
 ACTIVATION_SCHEMES = ["static", "dynamic"]
 
@@ -539,10 +538,10 @@ class Fp8MoEMethod(FusedMoEMethodBase):
                         "activation scales are None.")
                 if (not all_close_1d(layer.w13_input_scale)
                         or not all_close_1d(layer.w2_input_scale)):
-                    print_warning_once(
+                    logger.warning_once(
                         "Found input_scales that are not equal for "
                         "fp8 MoE layer. Using the maximum across experts "
-                        "for each layer. ")
+                        "for each layer.")
                 layer.w13_input_scale = torch.nn.Parameter(
                     layer.w13_input_scale.max(), requires_grad=False)
                 layer.w2_input_scale = torch.nn.Parameter(

vllm/model_executor/layers/quantization/kv_cache.py

 import torch
 
+from vllm.logger import init_logger
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig, QuantizeMethodBase)
-from vllm.utils import print_warning_once
+
+logger = init_logger(__name__)
 
 
 class BaseKVCacheMethod(QuantizeMethodBase):
@@ -67,7 +69,7 @@ class BaseKVCacheMethod(QuantizeMethodBase):
             layer._v_scale = v_scale
             if (layer._k_scale == 1.0 and layer._v_scale == 1.0
                     and "e5m2" not in layer.kv_cache_dtype):
-                print_warning_once(
+                logger.warning_once(
                     "Using KV cache scaling factor 1.0 for fp8_e4m3. This "
                     "may cause accuracy issues. Please make sure k/v_scale "
                     "scaling factors are available in the fp8 checkpoint.")

vllm/model_executor/layers/quantization/utils/marlin_utils_fp8.py

@@ -3,11 +3,13 @@ from typing import Optional
 import torch
 
 import vllm._custom_ops as ops
+from vllm.logger import init_logger
 from vllm.platforms import current_platform
-from vllm.utils import print_warning_once
 
 from .marlin_utils import marlin_make_workspace, marlin_permute_scales
 
+logger = init_logger(__name__)
+
 
 def is_fp8_marlin_supported():
     return current_platform.has_device_capability(80)
@@ -47,7 +49,7 @@ def apply_fp8_marlin_linear(
 
 def prepare_fp8_layer_for_marlin(layer: torch.nn.Module,
                                  strategy: str = "tensor") -> None:
-    print_warning_once(
+    logger.warning_once(
         "Your GPU does not have native support for FP8 computation but "
         "FP8 quantization is being used. Weight-only FP8 compression will "
         "be used leveraging the Marlin kernel. This may degrade "

vllm/model_executor/model_loader/weight_utils.py

@@ -25,7 +25,7 @@ from vllm.model_executor.layers.quantization import (QuantizationConfig,
                                                      get_quantization_config)
 from vllm.model_executor.layers.quantization.schema import QuantParamSchema
 from vllm.platforms import current_platform
-from vllm.utils import PlaceholderModule, print_warning_once
+from vllm.utils import PlaceholderModule
 
 try:
     from runai_model_streamer import SafetensorsStreamer
@@ -673,7 +673,7 @@ def maybe_remap_kv_scale_name(name: str, params_dict: dict) -> Optional[str]:
         None: If the remapped name is not found in params_dict.
     """
     if name.endswith(".kv_scale"):
-        print_warning_once(
+        logger.warning_once(
             "DEPRECATED. Found kv_scale in the checkpoint. "
             "This format is deprecated in favor of separate k_scale and "
             "v_scale tensors and will be removed in a future release. "
@@ -682,7 +682,7 @@ def maybe_remap_kv_scale_name(name: str, params_dict: dict) -> Optional[str]:
         # NOTE: we remap the deprecated kv_scale to k_scale
         remapped_name = name.replace(".kv_scale", ".attn.k_scale")
         if remapped_name not in params_dict:
-            print_warning_once(
+            logger.warning_once(
                 f"Found kv_scale in the checkpoint (e.g. {name}), "
                 "but not found the expected name in the model "
                 f"(e.g. {remapped_name}). kv_scale is "
@@ -695,7 +695,7 @@ def maybe_remap_kv_scale_name(name: str, params_dict: dict) -> Optional[str]:
         if name.endswith(scale_name):
             remapped_name = name.replace(scale_name, f".attn{scale_name}")
             if remapped_name not in params_dict:
-                print_warning_once(
+                logger.warning_once(
                     f"Found {scale_name} in the checkpoint (e.g. {name}), "
                     "but not found the expected name in the model "
                     f"(e.g. {remapped_name}). {scale_name} is "

vllm/model_executor/models/chameleon.py

@@ -11,6 +11,7 @@ from transformers import (BatchFeature, ChameleonConfig, ChameleonProcessor,
 from vllm.attention import Attention, AttentionMetadata
 from vllm.config import CacheConfig, VllmConfig
 from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
+from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
@@ -35,13 +36,14 @@ from vllm.multimodal.processing import (BaseMultiModalProcessor,
                                         BaseProcessingInfo, PromptReplacement)
 from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
 from vllm.sequence import IntermediateTensors
-from vllm.utils import print_warning_once
 
 from .interfaces import SupportsMultiModal, SupportsPP
 from .utils import (is_pp_missing_parameter,
                     make_empty_intermediate_tensors_factory, make_layers,
                     maybe_prefix, merge_multimodal_embeddings)
 
+logger = init_logger(__name__)
+
 
 class ChameleonImagePixelInputs(TypedDict):
     type: Literal["pixel_values"]
@@ -1111,7 +1113,7 @@ class ChameleonForConditionalGeneration(nn.Module, SupportsMultiModal,
                         remapped_kv_scale_name = name.replace(
                             ".kv_scale", ".attn.kv_scale")
                         if remapped_kv_scale_name not in params_dict:
-                            print_warning_once(
+                            logger.warning_once(
                                 "Found kv scale in the checkpoint (e.g. "
                                 f"{name}), but not found the expected name in "
                                 f"the model (e.g. {remapped_kv_scale_name}). "

vllm/model_executor/models/olmoe.py

@@ -20,6 +20,7 @@ from vllm.attention import Attention, AttentionMetadata
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, VllmConfig
 from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
+from vllm.logger import init_logger
 from vllm.model_executor.layers.fused_moe import FusedMoE
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (QKVParallelLinear,
@@ -34,13 +35,14 @@ from vllm.model_executor.layers.vocab_parallel_embedding import (
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
-from vllm.utils import print_warning_once
 
 from .interfaces import SupportsPP
 from .utils import (is_pp_missing_parameter,
                     make_empty_intermediate_tensors_factory, make_layers,
                     maybe_prefix)
 
+logger = init_logger(__name__)
+
 
 class OlmoeMoE(nn.Module):
     """A tensor-parallel MoE implementation for Olmoe that shards each expert
@@ -446,7 +448,7 @@ class OlmoeForCausalLM(nn.Module, SupportsPP):
                         remapped_kv_scale_name = name.replace(
                             ".kv_scale", ".attn.kv_scale")
                         if remapped_kv_scale_name not in params_dict:
-                            print_warning_once(
+                            logger.warning_once(
                                 "Found kv scale in the checkpoint "
                                 f"(e.g. {name}), but not found the expected "
                                 f"name in the model "

vllm/model_executor/models/qwen2_moe.py

@@ -34,6 +34,7 @@ from vllm.config import CacheConfig, VllmConfig
 from vllm.distributed import (get_pp_group,
                               get_tensor_model_parallel_world_size,
                               tensor_model_parallel_all_reduce)
+from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.fused_moe import FusedMoE
 from vllm.model_executor.layers.layernorm import RMSNorm
@@ -50,13 +51,14 @@ from vllm.model_executor.layers.vocab_parallel_embedding import (
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
-from vllm.utils import print_warning_once
 
 from .interfaces import SupportsPP
 from .utils import (extract_layer_index, is_pp_missing_parameter,
                     make_empty_intermediate_tensors_factory, make_layers,
                     maybe_prefix)
 
+logger = init_logger(__name__)
+
 
 class Qwen2MoeMLP(nn.Module):
 
@@ -524,7 +526,7 @@ class Qwen2MoeForCausalLM(nn.Module, SupportsPP):
                         remapped_kv_scale_name = name.replace(
                             ".kv_scale", ".attn.kv_scale")
                         if remapped_kv_scale_name not in params_dict:
-                            print_warning_once(
+                            logger.warning_once(
                                 "Found kv scale in the checkpoint "
                                 f"(e.g. {name}), but not found the expected "
                                 f"name in the model "

vllm/model_executor/models/vision.py

@@ -7,8 +7,10 @@ from transformers import PretrainedConfig
 import vllm.envs as envs
 from vllm.attention.selector import (backend_name_to_enum,
                                      get_global_forced_attn_backend)
+from vllm.logger import init_logger
 from vllm.platforms import _Backend, current_platform
-from vllm.utils import print_warning_once
+
+logger = init_logger(__name__)
 
 _C = TypeVar("_C", bound=PretrainedConfig)
 
@@ -87,7 +89,7 @@ def get_vit_attn_backend(support_fa: bool = False) -> _Backend:
             if is_flash_attn_2_available():
                 selected_backend = _Backend.FLASH_ATTN
             else:
-                print_warning_once(
+                logger.warning_once(
                     "Current `vllm-flash-attn` has a bug inside vision module, "
                     "so we use xformers backend instead. You can run "
                     "`pip install flash-attn` to use flash-attention backend.")