[Misc] set single whitespace between log sentences

vllm/attention/backends/flashinfer.py

@@ -438,7 +438,7 @@ def __post_init__(self):
                 not in supported_head_sizes:
             raise ValueError(
                 f"Only {supported_head_sizes} are supported for head_dim,",
-                f"received {self.head_dim}.")
+                f" received {self.head_dim}.")
 
     def begin_forward(self):
         if self.num_prefill_tokens > 0:

vllm/attention/backends/mla/common.py

@@ -533,7 +533,7 @@ def __post_init__(self):
                 not in supported_head_sizes:
             raise ValueError(
                 f"Only {supported_head_sizes} are supported for head_dim,",
-                f"received {self.head_dim}.")
+                f" received {self.head_dim}.")
 
     @property
     def prefill_metadata(self) -> Optional["MLACommonMetadata"]:

vllm/attention/backends/rocm_flash_attn.py

@@ -497,7 +497,7 @@ def __init__(
             if logits_soft_cap is not None:
                 raise ValueError(
                     "ROCm Triton FlashAttention does not support attention"
-                    "logits soft capping."
+                    " logits soft capping."
                     " please try using the ROCm CK "
                     "FA backend instead by setting the env var "
                     "`VLLM_USE_TRITON_FLASH_ATTN=0`")
@@ -528,7 +528,7 @@ def __init__(
             if self.use_naive_attn:
                 if logits_soft_cap is not None:
                     raise ValueError(
-                        "ROCm Naive FlashAttention does not support"
+                        "ROCm Naive FlashAttention does not support "
                         "attention logits soft capping.")
 
                 self.attn_func = _sdpa_attention

vllm/config.py

@@ -906,8 +906,8 @@ def get_num_layers_by_block_type(
             layers_block_type_value = getattr(self.hf_config,
                                               "layers_block_type", None)
             if layers_block_type_value is None:
-                raise ValueError("The model is an hybrid without a"
-                                 "layers_block_type in the hf_config,"
+                raise ValueError("The model is an hybrid without a "
+                                 "layers_block_type in the hf_config, "
                                  "cannot determine the num of "
                                  f"{block_type.value} layers")
 
@@ -2494,7 +2494,7 @@ def _get_and_verify_dtype(
 
             if current_platform.is_hpu() and config_dtype == torch.float16:
                 logger.info(
-                    "For HPU, we cast models to bfloat16 instead of"
+                    "For HPU, we cast models to bfloat16 instead of "
                     "using float16 by default. Please specify `dtype` if you "
                     "want to use float16.")
                 torch_dtype = torch.bfloat16
@@ -2710,7 +2710,7 @@ def __post_init__(self):
             backend=self.guided_decoding_backend).backend_name
         if backend not in valid_guided_backends:
             raise ValueError(f"Invalid guided_decoding_backend '{backend},"
-                             f"must be one of {valid_guided_backends}")
+                             f" must be one of {valid_guided_backends}")
 
 
 @dataclass
@@ -2986,7 +2986,7 @@ def uuid(self):
         def model_post_init(self, __context: Any) -> None:
             if not self.enable_reshape and self.enable_fusion:
                 logger.warning_once(
-                    "Fusion enabled but reshape elimination disabled."
+                    "Fusion enabled but reshape elimination disabled. "
                     "RMSNorm + quant (fp8) fusion might not work")
 
     pass_config: PassConfig = Field(default_factory=PassConfig)
@@ -3541,7 +3541,7 @@ def set_current_vllm_config(vllm_config: VllmConfig, check_compile=False):
             logger.warning(
                 "`torch.compile` is turned on, but the model %s"
                 " does not support it. Please open an issue on GitHub"
-                "if you want it to be supported.",
+                " if you want it to be supported.",
                 vllm_config.model_config.model)
         _current_vllm_config = old_vllm_config
 

vllm/distributed/device_communicators/pynccl_wrapper.py

@@ -227,10 +227,10 @@ def __init__(self, so_file: Optional[str] = None):
             self.lib = NCCLLibrary.path_to_library_cache[so_file]
         except Exception as e:
             logger.error(
-                "Failed to load NCCL library from %s ."
+                "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 "
-                "or it does not support the current platform %s."
+                "or it does not support the current platform %s. "
                 "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,

vllm/distributed/kv_transfer/kv_pipe/mooncake_pipe.py

@@ -137,7 +137,7 @@ def initialize(self, local_hostname: str, metadata_server: str,
             if metadata_backend not in supported_backend:
                 raise ValueError(
                     "Mooncake Configuration error. `metadata_backend`"
-                    f"should be one of {supported_backend}.")
+                    f" should be one of {supported_backend}.")
 
             self.engine.initializeExt(local_hostname, metadata_server,
                                       protocol, device_name, metadata_backend)

vllm/entrypoints/chat_utils.py

@@ -823,7 +823,7 @@ def _parse_chat_message_content_part(
     # content is empty, log a warning and skip
     if part_type in VALID_MESSAGE_CONTENT_MM_PART_TYPES and not content:
         logger.warning(
-            "Skipping multimodal part (type: '%s')"
+            "Skipping multimodal part (type: '%s') "
             "with empty / unparsable content.", part_type)
         return None
 

vllm/entrypoints/llm.py

@@ -1342,7 +1342,7 @@ def _add_guided_params(
             return params
 
         if params.guided_decoding is not None:
-            raise ValueError("Cannot set both guided_options_request and"
+            raise ValueError("Cannot set both guided_options_request and "
                              "params.guided_decoding.")
 
         params.guided_decoding = GuidedDecodingParams(

vllm/entrypoints/openai/api_server.py

@@ -575,7 +575,7 @@ async def do_rerank(request: RerankRequest, raw_request: Request):
 async def do_rerank_v1(request: RerankRequest, raw_request: Request):
     logger.warning_once(
         "To indicate that the rerank API is not part of the standard OpenAI"
-        " API, we have located it at `/rerank`. Please update your client"
+        " API, we have located it at `/rerank`. Please update your client "
         "accordingly. (Note: Conforms to JinaAI rerank API)")
 
     return await do_rerank(request, raw_request)

vllm/executor/ray_distributed_executor.py

@@ -513,7 +513,7 @@ def _check_ray_adag_installation(self):
         if cupy_spec is None and envs.VLLM_USE_RAY_COMPILED_DAG_NCCL_CHANNEL:
             raise ValueError(
                 "cupy is not installed but required since "
-                "VLLM_USE_RAY_COMPILED_DAG_NCCL_CHANNEL is set."
+                "VLLM_USE_RAY_COMPILED_DAG_NCCL_CHANNEL is set. "
                 "Run `pip install ray[adag]` and check cupy installation.")
 
     def _compiled_ray_dag(self, enable_asyncio: bool):

vllm/executor/ray_utils.py

@@ -317,7 +317,7 @@ def initialize_ray_cluster(
         if parallel_config.world_size > device_bundles:
             raise ValueError(
                 f"The number of required {device_str}s exceeds the total "
-                f"number of available {device_str}s in the placement group."
+                f"number of available {device_str}s in the placement group. "
                 f"Required number of devices: {parallel_config.world_size}. "
                 f"Total number of devices: {device_bundles}.")
     else:

vllm/lora/models.py

@@ -437,7 +437,7 @@ def _add_adapter(self, lora: LoRAModel):
     def pin_adapter(self, lora_id: int) -> bool:
         """Pin a LoRAModel in the manager cache."""
         raise NotImplementedError(
-            "Pinning is not supported in LoRAModelManager."
+            "Pinning is not supported in LoRAModelManager. "
             "Use LRUCacheLoRAModelManager for pinning")  # type: ignore
 
     def _set_adapter_mapping(self, mapping: LoRAMapping) -> None:

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

@@ -71,7 +71,7 @@ def __init__(
         if not (self.weight_quant.strategy == QuantizationStrategy.TENSOR
                 and self.input_quant.strategy == QuantizationStrategy.TENSOR):
             raise ValueError(
-                "For FP8 Fused MoE layers, only per-tensor scales"
+                "For FP8 Fused MoE layers, only per-tensor scales "
                 "for weights and activations are supported. Found "
                 f"{self.weight_quant}, {self.input_quant}")
 

vllm/model_executor/layers/quantization/gptq.py

@@ -74,7 +74,7 @@ def __init__(
     def __repr__(self) -> str:
         return (f"GPTQConfig(weight_bits={self.weight_bits}, "
                 f"group_size={self.group_size}, "
-                f"desc_act={self.desc_act}),"
+                f"desc_act={self.desc_act}), "
                 f"lm_head_quantized={self.lm_head_quantized}), "
                 f"dynamic={self.dynamic}")
 

vllm/model_executor/layers/quantization/modelopt.py

@@ -56,7 +56,7 @@ def from_config(cls, config: Dict[str, Any]) -> "ModelOptFp8Config":
         quant_method = quant_config["quant_algo"]
         is_checkpoint_fp8_serialized = ("FP8" in quant_method)
         if not is_checkpoint_fp8_serialized:
-            raise ValueError("ModelOpt currently only supports static FP8"
+            raise ValueError("ModelOpt currently only supports static FP8 "
                              "quantization in vLLM. Please check the "
                              "`hf_quant_config.json` file for your model's "
                              "quant configuration.")

vllm/model_executor/layers/quantization/neuron_quant.py

@@ -25,8 +25,8 @@ def __init__(
         if self.quant_dtype not in SUPPORTED_QUANT_DTYPE_LIST:
             raise ValueError(
                 f"Neuron quantization datatype {self.quant_dtype} is not valid,"
-                f"the quantization datatype should match one of the below types"
-                f"{SUPPORTED_QUANT_DTYPE_LIST}")
+                f" the quantization datatype should match one of the below "
+                f"types {SUPPORTED_QUANT_DTYPE_LIST}")
         self.dequant_dtype = dequant_dtype
         self.quantize_method = quantize_method
 

vllm/model_executor/layers/quantization/quark/quark_moe.py

@@ -55,7 +55,7 @@ def __init__(self, weight_config: Dict[str, Any], input_config: Dict[str,
         if not (weight_qscheme == "per_tensor"
                 and input_qscheme == "per_tensor"):
             raise ValueError(
-                "For FP8 Fused MoE layers, only per-tensor scales"
+                "For FP8 Fused MoE layers, only per-tensor scales "
                 "for weights and activations are supported. Found "
                 f"{weight_qscheme}, {input_qscheme}")  # noqa E501
 

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

@@ -118,7 +118,7 @@ def verify_marlin_supports_shape(output_size_per_partition: int,
             and input_size_per_partition % group_size != 0):
         raise ValueError(
             f"Weight input_size_per_partition = {input_size_per_partition}"
-            f" is not divisible by group_size = {group_size}."
+            f" is not divisible by group_size = {group_size}. "
             "Consider reducing tensor_parallel_size or running "
             "with --quantization gptq.")
 

vllm/model_executor/model_loader/loader.py

@@ -1087,7 +1087,7 @@ def _load_weights(self, model_config: ModelConfig,
         self.model_type = type(model).__name__
 
         logger.info("Loading weights with BitsAndBytes quantization. "
-                    " May take a while ...")
+                    "May take a while ...")
 
         quant_config = getattr(model_config.hf_config, "quantization_config",
                                None)

vllm/model_executor/models/deepseek_vl2.py

@@ -563,7 +563,7 @@ def _process_image_input(
                 # 3D tensor
                 return list(torch.unbind(image_data, dim=0))
             raise ValueError(
-                "We expect batched 2D tensors;"
+                "We expect batched 2D tensors; "
                 "this can be either a list of 2D tensors or a single 3D tensor."
             )
 

vllm/model_executor/models/fuyu.py

@@ -291,7 +291,7 @@ def _validate_shape(d: torch.Tensor):
                 expected_expr = str(expected_dims)
                 raise ValueError(
                     "The expected shape of pixel values per image per batch "
-                    f" per patch is {expected_expr}. "
+                    f"per patch is {expected_expr}. "
                     f"You supplied {tuple(d.shape)}.")
 
         for d in data:

vllm/model_executor/models/gritlm.py

@@ -90,8 +90,8 @@ def _get_instruction_len(self, prompt_token_ids: array) -> int:
 
         # Return no instruction in case of missing BOS token.
         if prompt_token_ids[0] != self.token_ids["<s>"]:
-            logger.warning("BOS token not found in prompt,"
-                           "thus using empty string for instruction."
+            logger.warning("BOS token not found in prompt, "
+                           "thus using empty string for instruction. "
                            "GritLM requires BOS token in prompt.")
             return instruction_len
 
@@ -111,8 +111,8 @@ def _get_instruction_len(self, prompt_token_ids: array) -> int:
         if found_embed_pattern_idx != -1:
             instruction_len = found_embed_pattern_idx + len(embed_pattern_ids)
         else:
-            logger.warning("Query instruction not found in prompt,"
-                           "thus using BOS token as instruction instead."
+            logger.warning("Query instruction not found in prompt, "
+                           "thus using BOS token as instruction instead. "
                            "GritLM requires query instruction in prompt.")
             instruction_len = 1
 

vllm/model_executor/models/minicpmv.py

@@ -673,7 +673,7 @@ def check_mm_inputs(self, inputs: Dict[str, object],
         for modality, count in counts.items():
             if modality not in inputs or not inputs[modality]:
                 raise ValueError(f"None input data of {modality}."
-                                 "But prompt requires.")
+                                 " But prompt requires.")
             counter_key = self.get_modality_num_counter(modality)
             if len(inputs[modality][counter_key]) != count:
                 raise ValueError(f"The prompt requires {count} "

vllm/model_executor/models/phi3v.py

@@ -640,7 +640,7 @@ def _process_image_input(
                 # 3D tensor
                 return list(torch.unbind(image_data, dim=0))
             raise ValueError(
-                "We expect batched 2D tensors;"
+                "We expect batched 2D tensors; "
                 "this can be either a list of 2D tensors or a single 3D tensor."
             )
 

vllm/model_executor/models/prithvi_geospatial_mae.py

@@ -154,8 +154,8 @@ def __init__(self, vllm_config: VllmConfig, prefix: str = ""):
             vllm_config.model_config.hf_config.to_dict()["pretrained_cfg"])
         if self.model is None:
             raise ValueError(
-                "Unsupported task."
-                "Only SemanticSegmentationTask is supported for now"
+                "Unsupported task. "
+                "Only SemanticSegmentationTask is supported for now "
                 "by PrithviGeospatialMAE.")
 
     def _parse_and_validate_multimodal_data(

vllm/multimodal/profiling.py

@@ -160,7 +160,7 @@ def get_dummy_data(
 
         if mm_counts.keys() != mm_max_tokens_per_item.keys():
             raise AssertionError(
-                "The keys returned by `get_supported_mm_limits`"
+                "The keys returned by `get_supported_mm_limits` "
                 f"({set(mm_counts.keys())}) should be the same as those "
                 "returned by `get_mm_max_tokens_per_item` "
                 f"({set(mm_max_tokens_per_item.keys())})")

vllm/platforms/cuda.py

@@ -190,7 +190,7 @@ def get_attn_backend_cls(cls, selected_backend, head_size, dtype,
                 "Cannot use FlashAttention-2 backend for FP8 KV cache.")
             logger.warning(
                 "Please use FlashInfer backend with FP8 KV Cache for "
-                "better performance by setting environment variable  "
+                "better performance by setting environment variable "
                 "VLLM_ATTENTION_BACKEND=FLASHINFER")
             target_backend = _Backend.XFORMERS
         elif block_size % 16 != 0:

vllm/platforms/openvino.py

@@ -97,7 +97,7 @@ def check_and_update_config(cls, vllm_config: VllmConfig) -> None:
 
         if envs.VLLM_OPENVINO_CPU_KV_CACHE_PRECISION == "u8":
             if not OpenVinoPlatform.is_openvino_cpu():
-                logger.info("VLLM_OPENVINO_CPU_KV_CACHE_PRECISION is"
+                logger.info("VLLM_OPENVINO_CPU_KV_CACHE_PRECISION is "
                             "ignored for GPU, f16 data type will be used.")
                 cache_config.cache_dtype = ov.Type.f16
             else:

vllm/platforms/xpu.py

@@ -73,7 +73,7 @@ def check_and_update_config(cls, vllm_config: VllmConfig) -> None:
                 logger.warning(
                     "bfloat16 is only supported on Intel Data Center GPU, "
                     "Intel Arc GPU is not supported yet. Your device is %s,"
-                    "which is not supported. will fallback to float16",
+                    " which is not supported. will fallback to float16",
                     cls.get_device_name())
                 model_config.dtype = torch.float16
         if not model_config.enforce_eager:

vllm/prompt_adapter/models.py

@@ -226,7 +226,7 @@ def register_module(self, module_name: str, module: nn.Module):
     def pin_adapter(self, prompt_adapter_id: int) -> bool:
         """Pin a PromptAdapterModel in the manager cache."""
         raise NotImplementedError(
-            "Pinning is not supported in PromptAdapterModelManager."
+            "Pinning is not supported in PromptAdapterModelManager. "
             "Use LRUCachePromptAdapterModelManager for pinning"
         )  # type: ignore
 

vllm/spec_decode/draft_model_runner.py

@@ -16,7 +16,7 @@
             ROCmFlashAttentionMetadata as FlashAttentionMetadata)
 except (ModuleNotFoundError, ImportError) as err:
     raise RuntimeError(
-        "Draft model speculative decoding currently only supports"
+        "Draft model speculative decoding currently only supports "
         "CUDA and ROCm flash attention backend.") from err
 
 from vllm.logger import init_logger

vllm/transformers_utils/configs/jais.py

@@ -212,26 +212,26 @@ def _alibi_scaling_validation(self):
         if (not isinstance(self.alibi_scaling, dict)
                 or len(self.alibi_scaling) != 2):
             raise ValueError(
-                "`alibi_scaling` must be a dictionary with two fields,"
+                "`alibi_scaling` must be a dictionary with two fields, "
                 "`type` and `factor` or `type` and `train_seq_len`, "
                 f"got {self.alibi_scaling}")
         alibi_scaling_type = self.alibi_scaling.get("type", None)
         alibi_scaling_factor = self.alibi_scaling.get("factor", None)
         alibi_dynamic_scaling = self.alibi_scaling.get("train_seq_len", None)
         if alibi_scaling_type is None or alibi_scaling_type != "linear":
-            raise ValueError(f"`alibi_scaling`'s type field must be 'linear',"
+            raise ValueError(f"`alibi_scaling`'s type field must be 'linear', "
                              f"got {alibi_scaling_type}")
         if (alibi_scaling_factor is not None
                 and not isinstance(alibi_scaling_factor, float)
                 or (alibi_scaling_factor is not None
                     and alibi_scaling_factor <= 1.0)):
             raise ValueError(
-                f"`alibi_scaling`'s factor field must be a float > 1.0,"
+                f"`alibi_scaling`'s factor field must be a float > 1.0, "
                 f"got {alibi_scaling_factor}")
         if (alibi_dynamic_scaling is not None
                 and not isinstance(alibi_dynamic_scaling, int)
                 or (alibi_dynamic_scaling is not None
                     and alibi_dynamic_scaling <= 1)):
             raise ValueError(
-                f"`alibi_scaling`'s `train_seq_len` field must be an"
+                f"`alibi_scaling`'s `train_seq_len` field must be an "
                 f"integer > 1, got {alibi_dynamic_scaling}")