fix(ray): correct misleading warning message for multi-node clusters

Fixes #31005

The warning message incorrectly stated "Tensor parallel size" when it
was actually comparing the world_size (TP × PP) against locally visible
GPUs. This was confusing for multi-node Ray deployments where:

1. The world_size includes both tensor and pipeline parallelism
2. The "available GPUs" count only reflects the local node, not the
   full Ray cluster

Changes:
- Replaced "Tensor parallel size" with "World size"
- Clarified that the GPU count is for locally visible devices
- Added context about multi-node Ray clusters
- Included the actual TP and PP values in the message for clarity

Signed-off-by: yurekami <yurekami@users.noreply.github.com>

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

vllm/config/model.py

@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import warnings
-from collections.abc import Callable
+from collections.abc import Callable, Iterator
 from dataclasses import InitVar, field
 from functools import cached_property
 from typing import TYPE_CHECKING, Any, Literal, cast, get_args
@@ -1806,7 +1806,7 @@ class ModelConfig:
         return getattr(self.hf_config, "quantization_config", None) is not None
 
 
-def get_served_model_name(model: str, served_model_name: str | list[str] | None):
+def get_served_model_name(model: str, served_model_name: str | list[str] | None) -> str:
     """
     If the input is a non-empty list, the first model_name in
     `served_model_name` is taken.
@@ -1844,7 +1844,9 @@ _SUFFIX_TO_DEFAULTS: list[tuple[str, tuple[RunnerType, ConvertType]]] = [
 ]
 
 
-def iter_architecture_defaults():
+def iter_architecture_defaults() -> Iterator[
+    tuple[str, tuple[RunnerType, ConvertType]]
+]:
     yield from _SUFFIX_TO_DEFAULTS
 
 
@@ -1877,7 +1879,7 @@ _STR_DTYPE_TO_TORCH_DTYPE = {
 }
 
 
-def str_dtype_to_torch_dtype(type: str):
+def str_dtype_to_torch_dtype(type: str) -> torch.dtype | None:
     return _STR_DTYPE_TO_TORCH_DTYPE.get(type)
 
 
@@ -1891,14 +1893,14 @@ _FLOAT16_NOT_SUPPORTED_MODELS = {
 }
 
 
-def _is_valid_dtype(model_type: str, dtype: torch.dtype):
+def _is_valid_dtype(model_type: str, dtype: torch.dtype) -> bool:
     if model_type in _FLOAT16_NOT_SUPPORTED_MODELS and dtype == torch.float16:  # noqa: E501, SIM103
         return False
 
     return True
 
 
-def _check_valid_dtype(model_type: str, dtype: torch.dtype):
+def _check_valid_dtype(model_type: str, dtype: torch.dtype) -> bool:
     if model_type in _FLOAT16_NOT_SUPPORTED_MODELS and dtype == torch.float16:
         reason = _FLOAT16_NOT_SUPPORTED_MODELS[model_type]
         raise ValueError(
@@ -1913,7 +1915,7 @@ def _find_dtype(
     config: PretrainedConfig,
     *,
     revision: str | None,
-):
+) -> torch.dtype:
     # NOTE: getattr(config, "dtype", torch.float32) is not correct
     # because config.dtype can be None.
     config_dtype = getattr(config, "dtype", None)
@@ -1953,7 +1955,7 @@ def _resolve_auto_dtype(
     config_dtype: torch.dtype,
     *,
     is_pooling_model: bool,
-):
+) -> torch.dtype:
     from vllm.platforms import current_platform
 
     supported_dtypes = [