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cuda_samples_utils.py
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144 lines (122 loc) · 4.48 KB
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# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# distribution and/or other materials provided with the distribution.
# * Neither the name of NVIDIA CORPORATION nor the names of its
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""
Common CUDA utilities for Python samples.
This module provides common utility functions for CUDA samples including:
- Package requirements checking
- Result verification
- GPU device information
Requirements:
- Python 3.10+
- CUDA Toolkit 13.0+ (recommended; matches cuda-python 13.x)
- cuda-python >= 13.0.0
- cuda-core >= 0.6.0
- cupy-cuda13x >= 13.0.0
- numpy >= 2.3.2 (when used with samples that install it)
"""
def check_cuda_requirements() -> bool:
"""
Check if required CUDA packages are available.
Returns
-------
bool
True if requirements are met, False otherwise
"""
try:
import cupy as cp # noqa: F401
from cuda.core import Device # noqa: F401
return True
except ImportError as e:
print(f"Error: Required package not found: {e}")
print("Please install from requirements.txt:")
print(" pip install -r requirements.txt")
return False
def verify_array_result(
result, expected, rtol: float = 1e-5, atol: float = 1e-8, verbose: bool = True
) -> bool:
"""
Verify that computed result matches expected result.
Automatically detects whether arrays are NumPy or CuPy and uses the
appropriate library without unnecessary data transfers.
Parameters
----------
result : numpy.ndarray or cupy.ndarray
Computed result array.
expected : numpy.ndarray or cupy.ndarray
Expected result array.
rtol : float
Relative tolerance (default: 1e-5)
atol : float
Absolute tolerance (default: 1e-8)
verbose : bool
Whether to print verification result (default: True).
Returns
-------
bool
True if results match, False otherwise.
Raises
------
TypeError
If arrays are not both NumPy or both CuPy, or if CuPy is needed
but not available.
"""
import numpy as np
is_np = isinstance(result, np.ndarray) and isinstance(expected, np.ndarray)
if is_np:
allclose = np.allclose
abs_ = np.abs
max_ = np.max
else:
import cupy as cp
is_cp = isinstance(result, cp.ndarray) and isinstance(expected, cp.ndarray)
if not is_cp:
raise TypeError(
"verify_array_result expects both arrays to be either "
"numpy.ndarray or cupy.ndarray"
)
allclose = cp.allclose
abs_ = cp.abs
max_ = cp.max
if allclose(result, expected, rtol=rtol, atol=atol):
if verbose:
print("Test PASSED")
return True
else:
max_error = max_(abs_(result - expected))
if verbose:
print(f"Test FAILED - Max error: {max_error}")
return False
def print_gpu_info(device) -> None:
"""
Print GPU device information.
Parameters
----------
device : cuda.core.Device
CUDA device object
"""
print(f"Device: {device.name}")
cc = device.compute_capability
print(f"Compute Capability: {cc.major}.{cc.minor}")