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import contextlib
import os
import platform
import shutil
import sys
from import (
from numba.cuda.cuda_paths import get_conda_ctk
from numba.cuda.cudadrv import driver, devices, libs
from numba.core import config
from import TestCase
import unittest
class CUDATestCase(SerialMixin, TestCase):
For tests that use a CUDA device. Test methods in a CUDATestCase must not
be run out of module order, because the ContextResettingTestCase may reset
the context and destroy resources used by a normal CUDATestCase if any of
its tests are run between tests from a CUDATestCase.
def setUp(self):
self._low_occupancy_warnings = config.CUDA_LOW_OCCUPANCY_WARNINGS
self._warn_on_implicit_copy = config.CUDA_WARN_ON_IMPLICIT_COPY
# Disable warnings about low gpu utilization in the test suite
# Disable warnings about host arrays in the test suite
def tearDown(self):
config.CUDA_LOW_OCCUPANCY_WARNINGS = self._low_occupancy_warnings
config.CUDA_WARN_ON_IMPLICIT_COPY = self._warn_on_implicit_copy
class ContextResettingTestCase(CUDATestCase):
For tests where the context needs to be reset after each test. Typically
these inspect or modify parts of the context that would usually be expected
to be internal implementation details (such as the state of allocations and
deallocations, etc.).
def tearDown(self):
from numba.cuda.cudadrv.devices import reset
def ensure_supported_ccs_initialized():
from numba.cuda import is_available as cuda_is_available
from numba.cuda.cudadrv import nvvm
if cuda_is_available():
# Ensure that is loaded and the list of supported compute
# capabilities in the nvvm module is populated before a fork. This is
# needed because some compilation tests don't require a CUDA context,
# but do use NVVM, and it is required that should be
# loaded before a fork (note that the requirement is not explicitly
# documented).
def skip_on_cudasim(reason):
"""Skip this test if running on the CUDA simulator"""
return unittest.skipIf(config.ENABLE_CUDASIM, reason)
def skip_unless_cudasim(reason):
"""Skip this test if running on CUDA hardware"""
return unittest.skipUnless(config.ENABLE_CUDASIM, reason)
def skip_unless_conda_cudatoolkit(reason):
"""Skip test if the CUDA toolkit was not installed by Conda"""
return unittest.skipUnless(get_conda_ctk() is not None, reason)
def skip_if_external_memmgr(reason):
"""Skip test if an EMM Plugin is in use"""
return unittest.skipIf(config.CUDA_MEMORY_MANAGER != 'default', reason)
def skip_under_cuda_memcheck(reason):
return unittest.skipIf(os.environ.get('CUDA_MEMCHECK') is not None, reason)
def skip_without_nvdisasm(reason):
nvdisasm_path = shutil.which('nvdisasm')
return unittest.skipIf(nvdisasm_path is None, reason)
def skip_with_nvdisasm(reason):
nvdisasm_path = shutil.which('nvdisasm')
return unittest.skipIf(nvdisasm_path is not None, reason)
def skip_on_arm(reason):
cpu = platform.processor()
is_arm = cpu.startswith('arm') or cpu.startswith('aarch')
return unittest.skipIf(is_arm, reason)
def cc_X_or_above(major, minor):
if not config.ENABLE_CUDASIM:
cc = devices.get_context().device.compute_capability
return cc >= (major, minor)
return True
def skip_unless_cc_32(fn):
return unittest.skipUnless(cc_X_or_above(3, 2), "requires cc >= 3.2")(fn)
def skip_unless_cc_50(fn):
return unittest.skipUnless(cc_X_or_above(5, 0), "requires cc >= 5.0")(fn)
def skip_unless_cc_53(fn):
return unittest.skipUnless(cc_X_or_above(5, 3), "requires cc >= 5.3")(fn)
def skip_unless_cc_60(fn):
return unittest.skipUnless(cc_X_or_above(6, 0), "requires cc >= 6.0")(fn)
def xfail_with_cuda_python(fn):
if driver.USE_NV_BINDING:
return unittest.expectedFailure(fn)
return fn
def cudadevrt_missing():
return False
except FileNotFoundError:
return True
return False
def skip_if_cudadevrt_missing(fn):
return unittest.skipIf(cudadevrt_missing(), 'cudadevrt missing')(fn)
class CUDATextCapture(object):
def __init__(self, stream):
self._stream = stream
def getvalue(self):
class PythonTextCapture(object):
def __init__(self, stream):
self._stream = stream
def getvalue(self):
return self._stream.getvalue()
def captured_cuda_stdout():
Return a minimal stream-like object capturing the text output of
either CUDA or the simulator.
# Prevent accidentally capturing previously output text
# The simulator calls print() on Python stdout
with captured_stdout() as stream:
yield PythonTextCapture(stream)
# The CUDA runtime writes onto the system stdout
from numba import cuda
with redirect_c_stdout() as stream:
yield CUDATextCapture(stream)
class ForeignArray(object):
Class for emulating an array coming from another library through the CUDA
Array interface. This just hides a DeviceNDArray so that it doesn't look
like a DeviceNDArray.
def __init__(self, arr):
self._arr = arr
self.__cuda_array_interface__ = arr.__cuda_array_interface__