def setUp(self):
ndim = self.ndim
dtype = self.dtype
batches = 2
in_channels_a_group = 3
out_channels_a_group = 2
in_channels = in_channels_a_group * self.groups
out_channels = out_channels_a_group * self.groups
ksize = 3
stride = 2
pad = ksize // stride * self.dilate
self.strides = (stride,) * ndim
self.pads = (pad,) * ndim
self.dilations = (self.dilate,) * ndim
self.x = cupy.zeros(
(batches, in_channels) + (ksize,) * ndim, dtype)
self.gy = cupy.zeros((batches, out_channels) + (2,) * ndim, dtype)
self.gW = cupy.ones(
(out_channels, in_channels_a_group) + (ksize,) * ndim, dtype)
version = libcudnn.getVersion()
deterministic = self.deterministic
self.err = None
if ((self.dilate > 1 and version < 6000) or
(self.groups > 1 and version < 7000)):
self.err = ValueError
elif ((self.dilate > 1 and deterministic and version < 7000) or
(ndim > 2 and deterministic and version < 6000) or
(ndim > 2 and deterministic and self.dtype == numpy.float64)):
self.err = libcudnn.CuDNNError
self._workspace_size = cudnn.get_max_workspace_size()
cudnn.set_max_workspace_size(self.max_workspace_size)
def test_cudnn(self):
preload_version = self._get_config()['cudnn']['version']
major, minor, patchlevel = (int(x) for x in preload_version.split('.'))
expected_version = major * 1000 + minor * 100 + patchlevel
assert libcudnn.available
assert libcudnn.get_build_version() == expected_version
assert libcudnn.getVersion() == expected_version
def test_activation_backward_version(self):
if libcudnn.getVersion() >= 4000:
patch = 'cupy.cuda.cudnn.activationBackward_v4'
else:
patch = 'cupy.cuda.cudnn.activationBackward_v3'
with mock.patch(patch) as func:
cupy.cudnn.activation_backward(self.x, self.y, self.g, self.mode)
self.assertEqual(func.called, True)
def test_activation_backward_version(self):
if libcudnn.getVersion() >= 4000:
patch = 'cupy.cuda.cudnn.activationBackward_v4'
else:
patch = 'cupy.cuda.cudnn.activationBackward_v3'
with mock.patch(patch) as func:
cupy.cudnn.activation_backward(self.x, self.y, self.g, self.mode)
self.assertEqual(func.called, True)
def test_cudnn(self):
config = cupy._environment.get_preload_config()
assert config is not None
preload_version = config['cudnn']['version']
major, minor, patchlevel = (int(x) for x in preload_version.split('.'))
expected_version = major * 1000 + minor * 100 + patchlevel
assert libcudnn.available
assert libcudnn.get_build_version() == expected_version
assert libcudnn.getVersion() == expected_version
def setUp(self):
ndim = self.ndim
dtype = self.dtype
batches = 2
in_channels_a_group = 3
out_channels_a_group = 2
in_channels = in_channels_a_group * self.groups
out_channels = out_channels_a_group * self.groups
# TODO(anaruse): increase test cases.
ksize = 3
stride = 2
pad = ksize // stride * self.dilate
self.strides = (stride, ) * ndim
self.pads = (pad, ) * ndim
self.dilations = (self.dilate, ) * ndim
self.W = cupy.zeros(
(out_channels, in_channels_a_group) + (ksize, ) * ndim, dtype)
self.gy = cupy.zeros((batches, out_channels) + (2, ) * ndim, dtype)
self.b = None
if self.bias:
self.b = cupy.zeros((in_channels, ), dtype)
self.gx = cupy.ones((batches, in_channels) + (ksize, ) * ndim, dtype)
version = libcudnn.getVersion()
deterministic = self.deterministic
self.err = None
if ((self.dilate > 1 and version < 6000)
or (self.groups > 1 and version < 7000)):
self.err = ValueError
elif (sys.platform.startswith('win32') and version == 7605
and deterministic and self.dtype == numpy.float16
and self.ndim == 3 and self.dilate == 2 and self.groups == 2):
# see https://github.com/cupy/cupy/pull/4893
self.err = RuntimeError
elif deterministic and (
(self.dilate > 1 and
(ndim != 2 and version < 8100 or version < 7300)) or
(ndim > 2 and version < 6000) or
(ndim > 2 and self.dtype == numpy.float64 and version < 8100)):
self.err = libcudnn.CuDNNError
elif (8000 <= version < 8100
and int(cupy.cuda.device.get_compute_capability()) < 70
and self.dilate > 1 and self.groups > 1 and ndim > 2
and self.dtype == numpy.float16):
self.err = RuntimeError
_workspace_size = cudnn.get_max_workspace_size()
cudnn.set_max_workspace_size(self.max_workspace_size)
yield
cudnn.set_max_workspace_size(_workspace_size)
def __init__(self):
self.cupy_version = cupy.__version__
self.cuda_path = cupy.cuda.get_cuda_path()
self.cuda_build_version = cupy.cuda.driver.get_build_version()
self.cuda_driver_version = cupy.cuda.runtime.driverGetVersion()
self.cuda_runtime_version = cupy.cuda.runtime.runtimeGetVersion()
if cudnn is not None:
self.cudnn_build_version = cudnn.get_build_version()
self.cudnn_version = cudnn.getVersion()
if nccl is not None:
self.nccl_build_version = nccl.get_version()
def setUp(self):
ndim = self.ndim
dtype = self.dtype
batches = 2
if self.layout == libcudnn.CUDNN_TENSOR_NHWC:
# channel size must be multiple of 4
in_channels_a_group = 4
out_channels_a_group = 4
else:
in_channels_a_group = 3
out_channels_a_group = 2
in_channels = in_channels_a_group * self.groups
out_channels = out_channels_a_group * self.groups
# TODO(anaruse): increase test cases.
ksize = 3
stride = 2
pad = ksize // stride * self.dilate
self.strides = (stride, ) * ndim
self.pads = (pad, ) * ndim
self.dilations = (self.dilate, ) * ndim
if self.layout == libcudnn.CUDNN_TENSOR_NHWC:
self.x = cupy.zeros(
(batches, ) + (ksize, ) * ndim + (in_channels, ), dtype)
self.W = cupy.zeros(
(out_channels, ) + (ksize, ) * ndim + (in_channels_a_group, ),
dtype)
self.y = cupy.ones((batches, ) + (2, ) * ndim + (out_channels, ),
dtype)
else:
self.x = cupy.zeros((batches, in_channels) + (ksize, ) * ndim,
dtype)
self.W = cupy.zeros(
(out_channels, in_channels_a_group) + (ksize, ) * ndim, dtype)
self.y = cupy.ones((batches, out_channels) + (2, ) * ndim, dtype)
self.b = None
if self.bias:
self.b = cupy.zeros((out_channels, ), dtype)
version = libcudnn.getVersion()
self.err = None
if ((self.dilate > 1 and version < 6000)
or (self.groups > 1 and version < 7000)):
self.err = ValueError
elif ndim > 2 and self.dilate > 1:
self.err = libcudnn.CuDNNError
_workspace_size = cudnn.get_max_workspace_size()
cudnn.set_max_workspace_size(self.max_workspace_size)
yield
cudnn.set_max_workspace_size(_workspace_size)
def test_call(self):
if self.layout == libcudnn.CUDNN_TENSOR_NHWC:
version = libcudnn.getVersion()
if self.groups > 1:
return unittest.SkipTest()
if self.dilate > 1 and version < 7300:
return unittest.SkipTest()
if self.dtype is numpy.float64 and version < 7100:
return unittest.SkipTest()
if self.err is None:
self.call()
self.assertTrue((self.y == 0).all())
else:
with self.assertRaises(self.err):
self.call()
def test_call(self):
if self.layout == libcudnn.CUDNN_TENSOR_NHWC:
version = libcudnn.getVersion()
if self.groups > 1:
pytest.skip()
if self.dilate > 1 and version < 7300:
pytest.skip()
if self.dtype is numpy.float64 and version < 7100:
pytest.skip()
if self.err is None:
self.call()
assert (self.y == 0).all()
else:
with pytest.raises(self.err):
self.call()
def setUp(self):
ndim = self.ndim
dtype = self.dtype
batches = 2
in_channels_a_group = 3
out_channels_a_group = 2
in_channels = in_channels_a_group * self.groups
out_channels = out_channels_a_group * self.groups
# TODO(anaruse): increase test cases.
ksize = 3
stride = 2
pad = ksize // stride * self.dilate
self.strides = (stride,) * ndim
self.pads = (pad,) * ndim
self.dilations = (self.dilate,) * ndim
self.W = cupy.zeros(
(out_channels, in_channels_a_group) + (ksize,) * ndim, dtype)
self.gy = cupy.zeros((batches, out_channels) + (2,) * ndim, dtype)
self.b = None
if self.bias:
self.b = cupy.zeros((in_channels,), dtype)
self.gx = cupy.ones(
(batches, in_channels) + (ksize,) * ndim, dtype)
version = libcudnn.getVersion()
deterministic = self.deterministic
self.err = None
if ((self.dilate > 1 and version < 6000) or
(self.groups > 1 and version < 7000)):
self.err = ValueError
elif deterministic and (
(self.dilate > 1 and (ndim != 2 or version < 7300)) or
(ndim > 2 and version < 6000) or
(ndim > 2 and self.dtype == numpy.float64)):
self.err = libcudnn.CuDNNError
elif (8000 <= version and
int(cupy.cuda.device.get_compute_capability()) < 70 and
self.dilate > 1 and self.groups > 1 and ndim > 2 and
self.dtype == numpy.float16):
self.err = RuntimeError
self._workspace_size = cudnn.get_max_workspace_size()
cudnn.set_max_workspace_size(self.max_workspace_size)
libcudnn.CUDNN_ACTIVATION_SIGMOID,
libcudnn.CUDNN_ACTIVATION_RELU,
libcudnn.CUDNN_ACTIVATION_TANH,
]
import cupy.cudnn
except ImportError:
cudnn_enabled = False
modes = []
from cupy import testing
@testing.parameterize(*testing.product({
'dtype': [numpy.float32, numpy.float64],
'mode': modes,
}))
@unittest.skipUnless(cudnn_enabled and libcudnn.getVersion() >= 3000,
'cuDNN >= 3.0 is supported')
class TestCudnnActivation(unittest.TestCase):
def setUp(self):
self.x = testing.shaped_arange((3, 4), cupy, self.dtype)
self.y = testing.shaped_arange((3, 4), cupy, self.dtype)
self.g = testing.shaped_arange((3, 4), cupy, self.dtype)
def test_activation_forward_version(self):
if libcudnn.getVersion() >= 4000:
patch = 'cupy.cuda.cudnn.activationForward_v4'
else:
patch = 'cupy.cuda.cudnn.activationForward_v3'
with mock.patch(patch) as func:
cupy.cudnn.activation_forward(self.x, self.mode)
self.assertEqual(func.called, True)
import atexit
import functools
import numpy
import operator
import six
import cupy
from cupy import cuda
from cupy.cuda import cudnn
_cudnn_version = cudnn.getVersion()
_handles = {}
def get_handle():
global _handles
device = cuda.Device()
handle = _handles.get(device.id, None)
if handle is None:
handle = cudnn.create()
_handles[device.id] = handle
return handle
@atexit.register
def reset_handles():
global _handles
handles = _handles
_handles = {}
try:
import cupy.cuda.cudnn as libcudnn
cudnn_enabled = True
modes = [
libcudnn.CUDNN_ACTIVATION_SIGMOID,
libcudnn.CUDNN_ACTIVATION_RELU,
libcudnn.CUDNN_ACTIVATION_TANH,
]
coef_modes = [
libcudnn.CUDNN_ACTIVATION_CLIPPED_RELU,
]
layouts = [
libcudnn.CUDNN_TENSOR_NCHW,
libcudnn.CUDNN_TENSOR_NHWC,
]
if libcudnn.getVersion() >= 6000:
coef_modes.append(libcudnn.CUDNN_ACTIVATION_ELU)
from cupy import cudnn
except ImportError:
cudnn_enabled = False
modes = []
coef_modes = []
layouts = []
from cupy import testing
@testing.parameterize(*testing.product({
'dtype': [numpy.float32, numpy.float64],
'mode': modes,
try:
import cupy.cuda.cudnn as libcudnn
cudnn_enabled = True
modes = [
libcudnn.CUDNN_ACTIVATION_SIGMOID,
libcudnn.CUDNN_ACTIVATION_RELU,
libcudnn.CUDNN_ACTIVATION_TANH,
]
coef_modes = [
libcudnn.CUDNN_ACTIVATION_CLIPPED_RELU,
]
layouts = [
libcudnn.CUDNN_TENSOR_NCHW,
libcudnn.CUDNN_TENSOR_NHWC,
]
cudnn_version = libcudnn.getVersion()
if cudnn_version >= 6000:
coef_modes.append(libcudnn.CUDNN_ACTIVATION_ELU)
from cupy import cudnn
except ImportError:
cudnn_enabled = False
cudnn_version = -1
modes = []
coef_modes = []
layouts = []
from cupy import testing
@testing.parameterize(*testing.product({
self.g = testing.shaped_arange((3, 4), cupy, self.dtype)
def test_activation_forward(self):
cupy.cudnn.activation_forward(self.x, self.mode)
def test_activation_backward(self):
cupy.cudnn.activation_backward(self.x, self.y, self.g, self.mode)
@testing.parameterize(*testing.product({
'dtype': [numpy.float32, numpy.float64],
'ratio': [0.0, 0.1, 0.2, 0.5],
'seed': [0, 100]
}))
@unittest.skipUnless(
cudnn_enabled and libcudnn.getVersion() >= 5000,
'cuDNN >= 5.0 is supported')
class TestCudnnDropout(unittest.TestCase):
def setUp(self):
self.x = testing.shaped_arange((3, 4), cupy, self.dtype)
self.gy = testing.shaped_arange((3, 4), cupy, self.dtype)
self.states = cupy.cudnn.DropoutStates(
cupy.cudnn.get_handle(), self.seed)
def test_dropout_forward(self):
_, y = self.states.forward(
cupy.cudnn.get_handle(), self.x, self.ratio)
if self.ratio == 0:
self.assertTrue(cupy.all(self.x == y))
else:
import numpy
import cupy
try:
import cupy.cuda.cudnn as libcudnn
cudnn_enabled = True
modes = [
libcudnn.CUDNN_ACTIVATION_SIGMOID,
libcudnn.CUDNN_ACTIVATION_RELU,
libcudnn.CUDNN_ACTIVATION_TANH,
]
coef_modes = [
libcudnn.CUDNN_ACTIVATION_CLIPPED_RELU,
]
if libcudnn.getVersion() >= 6000:
coef_modes.append(libcudnn.CUDNN_ACTIVATION_ELU)
import cupy.cudnn
except ImportError:
cudnn_enabled = False
modes = []
coef_modes = []
from cupy import testing
@testing.parameterize(*testing.product({
'dtype': [numpy.float32, numpy.float64],
'mode': modes,
}))
@unittest.skipUnless(cudnn_enabled, 'cuDNN is not available')
libcudnn.CUDNN_ACTIVATION_RELU,
libcudnn.CUDNN_ACTIVATION_TANH,
]
import cupy.cudnn
except ImportError:
cudnn_enabled = False
modes = []
from cupy import testing
@testing.parameterize(*testing.product({
'dtype': [numpy.float32, numpy.float64],
'mode': modes,
}))
@unittest.skipUnless(
cudnn_enabled and libcudnn.getVersion() >= 3000,
'cuDNN >= 3.0 is supported')
class TestCudnnActivation(unittest.TestCase):
def setUp(self):
self.x = testing.shaped_arange((3, 4), cupy, self.dtype)
self.y = testing.shaped_arange((3, 4), cupy, self.dtype)
self.g = testing.shaped_arange((3, 4), cupy, self.dtype)
def test_activation_forward_version(self):
if libcudnn.getVersion() >= 4000:
patch = 'cupy.cuda.cudnn.activationForward_v4'
else:
patch = 'cupy.cuda.cudnn.activationForward_v3'
with mock.patch(patch) as func:
cupy.cudnn.activation_forward(self.x, self.mode)
import atexit
import functools
import numpy
import operator
import six
import cupy
from cupy import cuda
from cupy.cuda import cudnn
_cudnn_version = cudnn.getVersion()
_handles = {}
def get_handle():
dev = cuda.get_device_id()
if dev in _handles:
return _handles[dev]
handle = cudnn.create()
_handles[dev] = handle
return handle
@atexit.register
def reset_handles():
global _handles
handles = _handles
_handles = {}
try:
import cupy.cuda.cudnn as libcudnn
cudnn_enabled = True
modes = [
libcudnn.CUDNN_ACTIVATION_SIGMOID,
libcudnn.CUDNN_ACTIVATION_RELU,
libcudnn.CUDNN_ACTIVATION_TANH,
]
coef_modes = [
libcudnn.CUDNN_ACTIVATION_CLIPPED_RELU,
]
layouts = [
libcudnn.CUDNN_TENSOR_NCHW,
libcudnn.CUDNN_TENSOR_NHWC,
]
if libcudnn.getVersion() >= 6000:
coef_modes.append(libcudnn.CUDNN_ACTIVATION_ELU)
from cupy import cudnn
except ImportError:
cudnn_enabled = False
modes = []
coef_modes = []
layouts = []
from cupy import testing
@testing.parameterize(*testing.product({
'dtype': [numpy.float32, numpy.float64],
'mode': modes,
