Files
nvbench/python/cuda/bench/__init__.py
Oleksandr Pavlyk d63a2761eb Implement Timer, and support State.exec(fn, timer=True) (#364)
* Add type annotations for future functionality

```python
class Timer:
    def start(self) -> None: ...
    def stop(self) -> None: ...
```

and overloaded `State.exec` so:

  - normal mode accepts `Callable[[Launch], None]`
  - `timer=True` accepts `Callable[[Launch, Timer], None]`

No implementation yet. Type annotation checked with

```
(py313) :~/repos/nvbench/python$ python -m mypy --ignore-missing-imports /tmp/check_timer.py
/tmp/check_timer.py:24: error: No overload variant of "exec" of "State" matches argument types "Callable[[Launch], None]", "bool"  [call-overload]
/tmp/check_timer.py:24: note: Possible overload variants:
/tmp/check_timer.py:24: note:     def exec(self, Callable[[Launch], None], /, *, batched: bool | None = ..., sync: bool | None = ..., timer: Literal[False] = ...) -> None
/tmp/check_timer.py:24: note:     def exec(self, Callable[[Launch, Timer], None], /, *, timer: Literal[True], sync: bool | None = ...) -> None
/tmp/check_timer.py:25: error: Argument 1 to "exec" of "State" has incompatible type "Callable[[Launch, Timer], None]"; expected "Callable[[Launch], None]"  [arg-type]
/tmp/check_timer.py:26: error: No overload variant of "exec" of "State" matches argument types "Callable[[Launch, int], None]", "bool"  [call-overload]
/tmp/check_timer.py:26: note: Possible overload variants:
/tmp/check_timer.py:26: note:     def exec(self, Callable[[Launch], None], /, *, batched: bool | None = ..., sync: bool | None = ..., timer: Literal[False] = ...) -> None
/tmp/check_timer.py:26: note:     def exec(self, Callable[[Launch, Timer], None], /, *, timer: Literal[True], sync: bool | None = ...) -> None
Found 3 errors in 1 file (checked 1 source file)

(py313) :~/repos/nvbench/python$ nl -ba /tmp/check_timer.py
     1  # /tmp/check_nvbench_timer.py
     2  import cuda.bench as bench
     3
     4  def normal_ok(launch: bench.Launch) -> None:
     5      pass
     6
     7  def timer_ok(launch: bench.Launch, timer: bench.Timer) -> None:
     8      timer.start()
     9      timer.stop()
    10
    11  def missing_timer(launch: bench.Launch) -> None:
    12      pass
    13
    14  def extra_timer(launch: bench.Launch, timer: bench.Timer) -> None:
    15      pass
    16
    17  def wrong_timer_type(launch: bench.Launch, timer: int) -> None:
    18      pass
    19
    20  def state_bench(state: bench.State) -> None:
    21      state.exec(normal_ok)
    22      state.exec(normal_ok, timer=False)
    23      state.exec(timer_ok, timer=True)
    24      state.exec(missing_timer, timer=True)       # should fail
    25      state.exec(extra_timer)                     # should fail
    26      state.exec(wrong_timer_type, timer=True)    # should fail
```

* Implement cuda.bench.Timer object

The Timer class is not user-constructible. It exposes two nullary
methods timer.start() and timer.stop().

The instance of Timer class would be provided to launchable object
passed to State.exec with timer=True.

* Implement support for State.exec( launch_fn, timer=True)

* Change type annotation for batch to default to None

None is interpreted as `not timer`, i.e., it effectively
defaults to True (as before) for usage without timer set,
but starts defaulting to `False` is `timer=True` is set.

The batched keyword type is `bool | None`.

* Implement default batched=None behavior

API allows one to specify all 3 keywords, sync, batched,
and timer. batched is None by default, run-time interpreted
as `(not timer)`.

* Update tests for new behavior of batched/time combination

* Add python/examples/exec_tag_timer.py

* Expand Timer class and methods docstrings

* Reworked python/example/exec_tag_timer.py to align with C++ example.

* Replace ::cuda::std::name with cuda::std::name

* Resolve review feedback
2026-05-15 10:19:40 -05:00

151 lines
4.0 KiB
Python

# Copyright 2025-2026 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 with the LLVM exception
# (the "License"); you may not use this file except in compliance with
# the License.
#
# You may obtain a copy of the License at
#
# http://llvm.org/foundation/relicensing/LICENSE.txt
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""CUDA Kernel Benchmarking Library Python API."""
import functools
import importlib
import importlib.metadata
import warnings
from ._decorators import axis as axis
from ._decorators import make_register as _make_register
from ._decorators import option as option
try:
__version__ = importlib.metadata.version("cuda-bench")
except Exception as e:
__version__ = "0.0.0dev"
warnings.warn(
"Could not retrieve version of cuda-bench package dynamically from its metadata. "
f"Exception {e} was raised. "
f"Version is set to fall-back value '{__version__}' instead."
)
_NVBENCH_EXPORTS = (
"Benchmark",
"CudaStream",
"Launch",
"NVBenchRuntimeError",
"State",
"Timer",
"run_all_benchmarks",
)
_PUBLIC_EXPORTS = (
*_NVBENCH_EXPORTS,
"axis",
"option",
"register",
)
_NVBENCH_TEST_EXPORTS = (
"_test_cpp_exception",
"_test_py_exception",
)
__all__ = list(_PUBLIC_EXPORTS)
# Optional test override used by decorator tests.
_register = None
# Detect CUDA runtime version and load appropriate extension
def _get_cuda_major_version():
"""Detect the CUDA runtime major version."""
try:
import cuda.bindings
# Get CUDA version from cuda-bindings package version
# cuda-bindings version is in format like "12.9.1" or "13.0.0"
version_str = cuda.bindings.__version__
major = int(version_str.split(".")[0])
return major
except ImportError:
raise ImportError(
"cuda-bindings is required for runtime CUDA version detection. "
"Install with: pip install cuda-bench[cu12] or pip install cuda-bench[cu13]"
)
def _bind_nvbench_module(module):
for name in _NVBENCH_EXPORTS:
globals()[name] = getattr(module, name)
# Set module of exposed objects
globals()[name].__module__ = __name__
for name in _NVBENCH_TEST_EXPORTS:
globals()[name] = getattr(module, name)
# Expose the module as _nvbench for backward compatibility (e.g., for tests)
globals()["_nvbench"] = module
@functools.lru_cache(maxsize=1)
def _load_nvbench_module():
cuda_major = _get_cuda_major_version()
extra_name = f"cu{cuda_major}"
module_fullname = f"cuda.bench.{extra_name}._nvbench"
try:
module = importlib.import_module(module_fullname)
except ImportError as e:
raise ImportError(
f"No cuda-bench extension found for CUDA {cuda_major}.x. "
f"This wheel may not include support for your CUDA version. "
f"Supported CUDA versions: 12, 13. "
f"Original error: {e}"
) from e
_bind_nvbench_module(module)
return module
def __getattr__(name):
if name == "_nvbench":
return _load_nvbench_module()
if name in _NVBENCH_EXPORTS + _NVBENCH_TEST_EXPORTS:
_load_nvbench_module()
return globals()[name]
raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
def __dir__():
return sorted(
set(globals())
| set(_PUBLIC_EXPORTS)
| set(_NVBENCH_TEST_EXPORTS)
| {"_nvbench"}
)
__doc__ = """
CUDA Kernel Benchmarking Library Python API
"""
def _get_register():
if _register is not None:
return _register
return _load_nvbench_module().register
register = _make_register(_get_register)
register.__module__ = __name__