[Chore] Separate out profiling utilities from vllm.utils (#27150)

Signed-off-by: dongbo910220 <1275604947@qq.com>

docs/contributing/profiling.md

@@ -180,9 +180,13 @@ The profiling traces generated by the continuous profiling workflow are publicly
 The Python standard library includes
 [cProfile](https://docs.python.org/3/library/profile.html) for profiling Python
 code. vLLM includes a couple of helpers that make it easy to apply it to a section of vLLM.
-Both the `vllm.utils.cprofile` and `vllm.utils.cprofile_context` functions can be
+Both the `vllm.utils.profiling.cprofile` and `vllm.utils.profiling.cprofile_context` functions can be
 used to profile a section of code.
 
+!!! note
+    The legacy import paths `vllm.utils.cprofile` and `vllm.utils.cprofile_context` are deprecated.
+    Please use `vllm.utils.profiling.cprofile` and `vllm.utils.profiling.cprofile_context` instead.
+
 ### Example usage - decorator
 
 The first helper is a Python decorator that can be used to profile a function.
@@ -190,9 +194,9 @@ If a filename is specified, the profile will be saved to that file. If no filena
 specified, profile data will be printed to stdout.
 
 ```python
-import vllm.utils
+from vllm.utils.profiling import cprofile
 
-@vllm.utils.cprofile("expensive_function.prof")
+@cprofile("expensive_function.prof")
 def expensive_function():
     # some expensive code
     pass
@@ -204,13 +208,13 @@ The second helper is a context manager that can be used to profile a block of
 code. Similar to the decorator, the filename is optional.
 
 ```python
-import vllm.utils
+from vllm.utils.profiling import cprofile_context
 
 def another_function():
     # more expensive code
     pass
 
-with vllm.utils.cprofile_context("another_function.prof"):
+with cprofile_context("another_function.prof"):
     another_function()
 ```
 

vllm/utils/__init__.py

@@ -59,6 +59,29 @@ import vllm.envs as envs
 from vllm.logger import enable_trace_function_call, init_logger
 from vllm.ray.lazy_utils import is_in_ray_actor
 
+_DEPRECATED_PROFILING = {"cprofile", "cprofile_context"}
+
+
+def __getattr__(name: str) -> Any:  # noqa: D401 - short deprecation docstring
+    """Module-level getattr to handle deprecated profiling utilities."""
+    if name in _DEPRECATED_PROFILING:
+        warnings.warn(
+            f"vllm.utils.{name} is deprecated and will be removed in a future version. "
+            f"Use vllm.utils.profiling.{name} instead.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
+        import vllm.utils.profiling as _prof
+
+        return getattr(_prof, name)
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+
+
+def __dir__() -> list[str]:
+    # expose deprecated names in dir() for better UX/tab-completion
+    return sorted(list(globals().keys()) + list(_DEPRECATED_PROFILING))
+
+
 if TYPE_CHECKING:
     from argparse import Namespace
 
@@ -1412,51 +1435,7 @@ def warn_for_unimplemented_methods(cls: type[T]) -> type[T]:
     return cls
 
 
-@contextlib.contextmanager
-def cprofile_context(save_file: str | None = None):
-    """Run a cprofile
-
-    Args:
-        save_file: path to save the profile result. "1" or
-            None will result in printing to stdout.
-    """
-    import cProfile
-
-    prof = cProfile.Profile()
-    prof.enable()
-
-    try:
-        yield
-    finally:
-        prof.disable()
-        if save_file and save_file != "1":
-            prof.dump_stats(save_file)
-        else:
-            prof.print_stats(sort="cumtime")
-
-
-def cprofile(save_file: str | None = None, enabled: bool = True):
-    """Decorator to profile a Python method using cProfile.
-
-    Args:
-        save_file: Path to save the profile result.
-            If "1", None, or "", results will be printed to stdout.
-        enabled: Set to false to turn this into a no-op
-    """
-
-    def decorator(func: Callable):
-        @wraps(func)
-        def wrapper(*args, **kwargs):
-            if not enabled:
-                # If profiling is disabled, just call the function directly.
-                return func(*args, **kwargs)
-
-            with cprofile_context(save_file):
-                return func(*args, **kwargs)
-
-        return wrapper
-
-    return decorator
+## moved to vllm.utils.profiling (imported at module top)
 
 
 # Only relevant for models using ALiBi (e.g, MPT)

vllm/utils/profiling.py

@@ -0,0 +1,56 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from __future__ import annotations
+
+import contextlib
+from collections.abc import Callable
+from functools import wraps
+from typing import Any
+
+
+@contextlib.contextmanager
+def cprofile_context(save_file: str | None = None):
+    """Run a cprofile
+
+    Args:
+        save_file: path to save the profile result. "1" or
+            None will result in printing to stdout.
+    """
+    import cProfile
+
+    prof = cProfile.Profile()
+    prof.enable()
+
+    try:
+        yield
+    finally:
+        prof.disable()
+        if save_file and save_file != "1":
+            prof.dump_stats(save_file)
+        else:
+            prof.print_stats(sort="cumtime")
+
+
+def cprofile(save_file: str | None = None, enabled: bool = True):
+    """Decorator to profile a Python method using cProfile.
+
+    Args:
+        save_file: Path to save the profile result.
+            If "1", None, or "", results will be printed to stdout.
+        enabled: Set to false to turn this into a no-op
+    """
+
+    def decorator(func: Callable):
+        @wraps(func)
+        def wrapper(*args: Any, **kwargs: Any):
+            if not enabled:
+                # If profiling is disabled, just call the function directly.
+                return func(*args, **kwargs)
+
+            with cprofile_context(save_file):
+                return func(*args, **kwargs)
+
+        return wrapper
+
+    return decorator