def cholesky(a):
'''Cholesky decomposition.
Decompose a given two-dimensional square matrix into ``L * L.T``,
where ``L`` is a lower-triangular matrix and ``.T`` is a conjugate
transpose operator. Note that in the current implementation ``a`` must be
a real matrix, and only float32 and float64 are supported.
Args:
a (cupy.ndarray): The input matrix with dimension ``(N, N)``
.. seealso:: :func:`numpy.linalg.cholesky`
'''
if not cuda.cusolver_enabled:
raise RuntimeError('Current cupy only supports cusolver in CUDA 8.0')
# TODO(Saito): Current implementation only accepts two-dimensional arrays
_assert_cupy_array(a)
_assert_rank2(a)
_assert_nd_squareness(a)
# Cast to float32 or float64
if a.dtype.char == 'f' or a.dtype.char == 'd':
dtype = a.dtype.char
else:
dtype = numpy.find_common_type((a.dtype.char, 'f'), ()).char
x = a.astype(dtype, copy=True)
n = len(a)
handle = device.get_cusolver_handle()
dev_info = cupy.empty(1, dtype=numpy.int32)
if dtype == 'f':
buffersize = cusolver.spotrf_bufferSize(
handle, cublas.CUBLAS_FILL_MODE_UPPER, n, x.data.ptr, n)
workspace = cupy.empty(buffersize, dtype=numpy.float32)
cusolver.spotrf(
handle, cublas.CUBLAS_FILL_MODE_UPPER, n, x.data.ptr, n,
workspace.data.ptr, buffersize, dev_info.data.ptr)
else: # dtype == 'd'
buffersize = cusolver.dpotrf_bufferSize(
handle, cublas.CUBLAS_FILL_MODE_UPPER, n, x.data.ptr, n)
workspace = cupy.empty(buffersize, dtype=numpy.float64)
cusolver.dpotrf(
handle, cublas.CUBLAS_FILL_MODE_UPPER, n, x.data.ptr, n,
workspace.data.ptr, buffersize, dev_info.data.ptr)
status = int(dev_info[0])
if status > 0:
raise linalg.LinAlgError(
'The leading minor of order {} '
'is not positive definite'.format(status))
elif status < 0:
raise linalg.LinAlgError(
'Parameter error (maybe caused by a bug in cupy.linalg?)')
_tril(x, k=0)
return x
def tile(A, reps):
"""Construct an array by repeating A the number of times given by reps.
Args:
A (cupy.ndarray): Array to transform.
reps (int or tuple): The number of repeats.
Returns:
cupy.ndarray: Transformed array with repeats.
.. seealso:: :func:`numpy.tile`
"""
try:
tup = tuple(reps)
except TypeError:
tup = (reps,)
d = len(tup)
if tup.count(1) == len(tup) and isinstance(A, cupy.ndarray):
# Fixes the problem that the function does not make a copy if A is a
# array and the repetitions are 1 in all dimensions
return cupy.array(A, copy=True, ndmin=d)
else:
# Note that no copy of zero-sized arrays is made. However since they
# have no data there is no risk of an inadvertent overwrite.
c = cupy.array(A, copy=False, ndmin=d)
if (d < c.ndim):
tup = (1,) * (c.ndim - d) + tup
shape_out = tuple(s * t for s, t in zip(c.shape, tup))
if c.size == 0:
return cupy.empty(shape_out, dtype=c.dtype)
c_shape = []
ret_shape = []
for dim_in, nrep in zip(c.shape, tup):
if nrep == 1:
c_shape.append(dim_in)
ret_shape.append(dim_in)
elif dim_in == 1:
c_shape.append(dim_in)
ret_shape.append(nrep)
else:
c_shape.append(1)
c_shape.append(dim_in)
ret_shape.append(nrep)
ret_shape.append(dim_in)
ret = cupy.empty(ret_shape, dtype=c.dtype)
if ret.size:
ret[...] = c.reshape(c_shape)
return ret.reshape(shape_out)
def _generate_normal(self, func, size, dtype, *args):
# curand functions below don't support odd size.
# * curand.generateNormal
# * curand.generateNormalDouble
# * curand.generateLogNormal
# * curand.generateLogNormalDouble
size = core.get_size(size)
element_size = six.moves.reduce(operator.mul, size, 1)
if element_size % 2 == 0:
out = cupy.empty(size, dtype=dtype)
func(self._generator, out.data.ptr, out.size, *args)
return out
else:
out = cupy.empty((element_size + 1,), dtype=dtype)
func(self._generator, out.data.ptr, out.size, *args)
return out[:element_size].reshape(size)
def _get_crossentropyloss_gpu(probs, t):
kernel = _crossentropyloss_kernel()
N, M = probs.shape
loss = cp.empty((1,), dtype=np.float32)
kernel(grid=(N, 1, 1), block=(32, 1, 1), args=(probs, t, loss, np.int32(N), np.int32(M)))
return loss
def check_copy(self, dtype, src_id, dst_id):
with cuda.Device(src_id):
src = testing.shaped_arange((2, 3, 4), dtype=dtype)
with cuda.Device(dst_id):
dst = cupy.empty((2, 3, 4), dtype=dtype)
core.elementwise_copy(src, dst)
testing.assert_allclose(src, dst)
def test_copy_orders(self, order):
a = cupy.empty((2, 3, 4))
b = cupy.copy(a, order)
a_cpu = numpy.empty((2, 3, 4))
b_cpu = numpy.copy(a_cpu, order)
self.assertEqual(b.strides, b_cpu.strides)
def _pyfftw_rfftn_empty_aligned(shape, axes, dtype, order='C', n=None):
"""Patched version of :func:`sporco.linalg.pyfftw_rfftn_empty_aligned`.
"""
ashp = list(shape)
raxis = axes[-1]
ashp[raxis] = ashp[raxis] // 2 + 1
cdtype = _complex_dtype(dtype)
return cp.empty(ashp, cdtype, order)
def take(a, indices, axis=None, out=None):
"""Takes elements of an array at specified indices along an axis.
This is an implementation of "fancy indexing" at single axis.
This function does not support ``mode`` option.
Args:
a (cupy.ndarray): Array to extract elements.
indices (int or array-like): Indices of elements that this function
takes.
axis (int): The axis along which to select indices. The flattened input
is used by default.
out (cupy.ndarray): Output array. If provided, it should be of
appropriate shape and dtype.
Returns:
cupy.ndarray: The result of fancy indexing.
.. seealso:: :func:`numpy.take`
"""
if axis is None:
a = a.ravel()
lshape = ()
rshape = ()
else:
if axis >= a.ndim:
raise ValueError('Axis overrun')
lshape = a.shape[:axis]
rshape = a.shape[axis + 1:]
if numpy.isscalar(indices):
a = cupy.rollaxis(a, axis)
if out is None:
return a[indices].copy()
else:
out[:] = a[indices]
return out
elif not isinstance(indices, cupy.ndarray):
indices = cupy.array(indices, dtype=int)
out_shape = lshape + indices.shape + rshape
if out is None:
out = cupy.empty(out_shape, dtype=a.dtype)
else:
if out.dtype != a.dtype:
raise TypeError('Output dtype mismatch')
if out.shape != out_shape:
raise ValueError('Output shape mismatch')
cdim = indices.size
rdim = internal.prod(rshape)
indices = cupy.reshape(
indices, (1,) * len(lshape) + indices.shape + (1,) * len(rshape))
return _take_kernel(a, indices, cdim, rdim, out)
def _get_out_args(out_args, out_types, out_shape):
if not out_args:
return [cupy.empty(out_shape, t) for t in out_types]
for a in out_args:
if not isinstance(a, cupy.ndarray):
raise TypeError(
'Output arguments type must be cupy.ndarray')
if a.shape != out_shape:
raise ValueError('Out shape is mismatched')
return out_args
def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None):
"""Returns an array with evenly-spaced values within a given interval.
Instead of specifying the step width like :func:`cupy.arange`, this
function requires the total number of elements specified.
Args:
start: Start of the interval.
stop: End of the interval.
num: Number of elements.
endpoint (bool): If True, the stop value is included as the last
element. Otherwise, the stop value is omitted.
retstep (bool): If True, this function returns (array, step).
Otherwise, it returns only the array.
dtype: Data type specifier. It is inferred from the start and stop
arguments by default.
Returns:
cupy.ndarray: The 1-D array of ranged values.
"""
if num < 0:
raise ValueError('linspace with num<0 is not supported')
if dtype is None:
# In actual implementation, only float is used
dtype = float
ret = cupy.empty((num,), dtype=dtype)
if num == 0:
step = float('nan')
elif num == 1:
ret.fill(start)
step = float('nan')
else:
div = (num - 1) if endpoint else num
step = float(stop - start) / div
stop = float(stop)
if step == 0.0:
# for underflow
_linspace_ufunc_underflow(start, stop - start, div, ret,
casting='unsafe')
else:
_linspace_ufunc(start, step, ret, casting='unsafe')
if endpoint:
ret[-1] = stop
if retstep:
return ret, step
else:
return ret
def concatenate(tup, axis=0):
"""Joins arrays along an axis.
Args:
tup (sequence of arrays): Arrays to be joined. All of these should have
same dimensionalities except the specified axis.
axis (int): The axis to join arrays along.
Returns:
cupy.ndarray: Joined array.
.. seealso:: :func:`numpy.concatenate`
"""
ndim = None
shape = None
for a in tup:
if not isinstance(a, cupy.ndarray):
raise TypeError('Only cupy arrays can be concatenated')
if a.ndim == 0:
raise TypeError('zero-dimensional arrays cannot be concatenated')
if ndim is None:
ndim = a.ndim
shape = list(a.shape)
axis = _get_positive_axis(a.ndim, axis)
continue
if a.ndim != ndim:
raise ValueError(
'All arrays to concatenate must have the same ndim')
if any(i != axis and shape[i] != a.shape[i]
for i in six.moves.range(ndim)):
raise ValueError(
'All arrays must have same shape except the axis to '
'concatenate')
shape[axis] += a.shape[axis]
if ndim is None:
raise ValueError('Cannot concatenate from empty tuple')
dtype = numpy.find_common_type([a.dtype for a in tup], [])
ret = cupy.empty(shape, dtype=dtype)
skip = (slice(None),) * axis
i = 0
for a in tup:
aw = a.shape[axis]
ret[skip + (slice(i, i + aw),)] = a
i += aw
return ret
def arange(start, stop=None, step=1, dtype=None):
"""Rerurns an array with evenly spaced values within a given interval.
Values are generated within the half-open interval [start, stop). The first
three arguments are mapped like the ``range`` built-in function, i.e. start
and step are optional.
Args:
start: Start of the interval.
stop: End of the interval.
step: Step width between each pair of consecutive values.
dtype: Data type specifier. It is inferred from other arguments by
default.
Returns:
cupy.ndarray: The 1-D array of range values.
.. seealso:: :func:`numpy.arange`
"""
if dtype is None:
if any(numpy.dtype(type(val)).kind == 'f'
for val in (start, stop, step)):
dtype = float
else:
dtype = int
if stop is None:
stop = start
start = 0
size = int(numpy.ceil((stop - start) / step))
if size <= 0:
return cupy.empty((0,), dtype=dtype)
ret = cupy.empty((size,), dtype=dtype)
typ = numpy.dtype(dtype).type
_arange_ufunc(typ(start), typ(step), ret, dtype=dtype)
return ret
def _get_out_args_with_params(out_args, out_types, out_shape, out_params):
if not out_args:
for p in out_params:
if p.raw:
raise ValueError('Output array size is Undecided')
return [cupy.empty(out_shape, t) for t in out_types]
for a, p in six_zip(out_args, out_params):
if not isinstance(a, cupy.ndarray):
raise TypeError(
'Output arguments type must be cupy.ndarray')
if a.shape != out_shape and not p.raw:
raise ValueError('Out shape is mismatched')
return out_args
def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None):
"""Returns an array with evenly-spaced values within a given interval.
Instead of specifying the step width like :func:`cupy.arange`, this
function requires the total number of elements specified.
Args:
start: Start of the interval.
stop: End of the interval.
num: Number of elements.
endpoint (bool): If True, the stop value is included as the last
element. Otherwise, the stop value is omitted.
retstep (bool): If True, this function returns (array, step).
Otherwise, it returns only the array.
dtype: Data type specifier. It is inferred from the start and stop
arguments by default.
Returns:
cupy.ndarray: The 1-D array of ranged values.
"""
if num <= 0:
# TODO(beam2d): Return zero-sized array
raise ValueError('linspace with num<=0 is not supported')
if dtype is None:
if any(numpy.dtype(type(val)).kind == 'f' for val in (start, stop)):
dtype = float
else:
dtype = int
ret = cupy.empty((num,), dtype=dtype)
if num == 0:
return ret
elif num == 1:
ret.fill(start)
return ret
if endpoint:
step = (stop - start) / (num - 1)
else:
step = (stop - start) / num
stop = start + step * (num - 1)
typ = numpy.dtype(dtype).type
_linspace_ufunc(typ(start), stop - start, num - 1, ret)
if retstep:
return ret, step
else:
return ret
def empty(shape, dtype=numpy.float32):
"""Creates an uninitialized cupy.ndarray object.
Args:
shape (tuple of ints): The shape of array.
dtype (numpy.dtype): Element type.
Returns:
cupy.ndarray: Uninitialized GPU array allocated by the memory pool.
"""
warnings.warn("chainer.cuda.empty is deprecated. Use cupy.empty instead.", DeprecationWarning)
check_cuda_available()
return cupy.empty(shape, dtype)
def roll(a, shift, axis=None):
"""Roll array elements along a given axis.
Args:
a (~cupy.ndarray): Array to be rolled.
shift (int): The number of places by which elements are shifted.
axis (int or None): The axis along which elements are shifted.
If ``axis`` is ``None``, the array is flattened before shifting,
and after that it is reshaped to the original shape.
Returns:
~cupy.ndarray: Output array.
.. seealso:: :func:`numpy.roll`
"""
if axis is None:
if a.size == 0:
return a
size = a.size
ra = a.ravel()
shift %= size
res = cupy.empty((size,), a.dtype)
res[:shift] = ra[size - shift:]
res[shift:] = ra[:size - shift]
return res.reshape(a.shape)
else:
axis = int(axis)
if axis < 0:
axis += a.ndim
if not 0 <= axis < a.ndim:
raise ValueError('axis must be >= %d and < %d' % (-a.ndim, a.ndim))
size = a.shape[axis]
if size == 0:
return a
shift %= size
prev = (slice(None),) * axis
rest = (slice(None),) * (a.ndim - axis - 1)
# Roll only the dimensiont at the given axis
# ind1 is [:, ..., size-shift:, ..., :]
# ind2 is [:, ..., :size-shift, ..., :]
ind1 = prev + (slice(size - shift, None, None),) + rest
ind2 = prev + (slice(None, size - shift, None),) + rest
r_ind1 = prev + (slice(None, shift, None),) + rest
r_ind2 = prev + (slice(shift, None, None),) + rest
res = cupy.empty_like(a)
res[r_ind1] = a[ind1]
res[r_ind2] = a[ind2]
return res
def diagonal(a, offset=0, axis1=0, axis2=1):
"""Returns specified diagonals.
This function extracts the diagonals along two specified axes. The other
axes are not changed. This function returns a writable view of this array
as NumPy 1.10 will do.
Args:
a (cupy.ndarray): Array from which the diagonals are taken.
offset (int): Index of the diagonals. Zero indicates the main
diagonals, a positive value upper diagonals, and a negative value
lower diagonals.
axis1 (int): The first axis to take diagonals from.
axis2 (int): The second axis to take diagonals from.
Returns:
cupy.ndarray: A view of the diagonals of ``a``.
.. seealso:: :func:`numpy.diagonal`
"""
if axis1 < axis2:
min_axis, max_axis = axis1, axis2
else:
min_axis, max_axis = axis2, axis1
tr = list(six.moves.range(a.ndim))
del tr[max_axis]
del tr[min_axis]
if offset >= 0:
a = cupy.transpose(a, tr + [axis1, axis2])
else:
a = cupy.transpose(a, tr + [axis2, axis1])
offset = -offset
diag_size = max(0, min(a.shape[-2], a.shape[-1] - offset))
ret_shape = a.shape[:-2] + (diag_size,)
if diag_size == 0:
return cupy.empty(ret_shape, dtype=a.dtype)
a = a[..., :diag_size, offset:offset + diag_size]
ret = a.view()
ret._shape = a.shape[:-2] + (diag_size,)
ret._strides = a.strides[:-2] + (a.strides[-1] + a.strides[-2],)
ret._size = internal.prod(ret._shape)
ret._c_contiguous = -1
ret._f_contiguous = -1
return ret
def empty_like(array):
"""Creates an uninitialized GPU array like the given one.
Args:
array (cupy.ndarray or numpy.ndarray): Base array.
Returns:
cupy.ndarray: GPU array of the same shape and dtype as `array`.
"""
warnings.warn("chainer.cuda.empty_like is deprecated. Use cupy.empty_like instead.", DeprecationWarning)
check_cuda_available()
if isinstance(array, cupy.ndarray):
return cupy.empty_like(array)
return cupy.empty(array.shape, dtype=array.dtype)
def _get_out_args(in_args, out_args, out_types, out_shape, out_params=None):
if len(out_args) == 0:
if out_params is not None and any(p.raw for p in out_params):
raise ValueError('Output array size is Undecided')
out_args = [cupy.empty(shape=out_shape, dtype=t)
for t in out_types]
else:
assert len(out_args) == len(out_types)
for i, a in enumerate(out_args):
if not isinstance(a, cupy.ndarray):
raise TypeError(
'Output arguments type must be cupy.ndarray')
if a.shape != out_shape:
if out_params is None or not out_params[i].raw:
raise ValueError('Out shape is mismatched')
return out_args
def lognormal(self, mean=0.0, sigma=1.0, size=None, dtype=float):
"""Returns an array of samples drawn from a log normal distribution.
.. seealso::
:func:`cupy.random.lognormal` for full documentation,
:meth:`numpy.random.RandomState.lognormal`
"""
dtype = _check_and_get_dtype(dtype)
out = cupy.empty(size, dtype=dtype)
if dtype.char == 'f':
func = curand.generateLogNormal
else:
func = curand.generateLogNormalDouble
func(self._generator, out.data.ptr, out.size, mean, sigma)
return out
def normal(self, loc=0.0, scale=1.0, size=None, dtype=float):
"""Returns an array of normally distributed samples.
.. seealso::
:func:`cupy.random.normal` for full documentation,
:meth:`numpy.random.RandomState.normal`
"""
dtype = _check_and_get_dtype(dtype)
out = cupy.empty(size, dtype=dtype)
if dtype.char == 'f':
func = curand.generateNormal
else:
func = curand.generateNormalDouble
func(self._generator, out.data.ptr, out.size, loc, scale)
return out
def asfortranarray(a, dtype=None):
"""Return an array laid out in Fortran order in memory.
Args:
a (~cupy.ndarray): The input array.
dtype (str or dtype object, optional): By default, the data-type is
inferred from the input data.
Returns:
~cupy.ndarray: The input `a` in Fortran, or column-major, order.
.. seealso:: :func:`numpy.asfortranarray`
"""
ret = cupy.empty(a.shape[::-1], a.dtype if dtype is None else dtype).T
ret[...] = a
return ret
def random_sample(self, size=None, dtype=float):
"""Returns an array of random values over the interval ``[0, 1)``.
.. seealso::
:func:`cupy.random.random_sample` for full documentation,
:meth:`numpy.random.RandomState.random_sample`
"""
dtype = _check_and_get_dtype(dtype)
out = cupy.empty(size, dtype=dtype)
if dtype.char == 'f':
func = curand.generateUniform
else:
func = curand.generateUniformDouble
func(self._generator, out.data.ptr, out.size)
RandomState._1m_kernel(out)
return out
def forward_gpu(self, inputs):
x = inputs[0]
W = inputs[1]
# Prepare BLAS call
handle = cuda.Device().cublas_handle
k, m = W.shape
n, l = x.shape[0] * x.shape[1], x.shape[2]
lda = max(1, x.shape[-1])
ldb = max(1, W.strides[0] // W.dtype.itemsize)
ldc = max(1, m)
Wx = cupy.empty((x.shape[0], x.shape[1], W.shape[1]),
dtype=numpy.float32)
sgemm(handle, False, False, m, n, k, 1, W.data.ptr, ldb,
x.data.ptr, lda, 0, Wx.data.ptr, ldc)
if len(inputs) > 2:
b = inputs[2]
Wx += b
return Wx,
def _forward_gpu(x):
T = x.shape[0]
N = x.shape[1]
M = x.shape[2]
y = cp.empty((N, T, M), dtype=np.float32)
if N == 1:
bdim, gdim = gpu.utils.Get_bdim_and_gdimRowVec(M)
elif M >= (N*4):
bdim, gdim = gpu.utils.Get_bdim_and_gdimSmallNBigM(N,M)
else:
bdim, gdim = gpu.utils.Get_bdim_and_gdim2D(N,M)
forward_kernel = _GetForward_kernel()
forward_kernel(grid=gdim, block=bdim,
args=(x, y,
T, N, M
)
)
return y
def hotdot(a, indices, out=None, dont_add=False):
"""
In:
a: a pycuda gpuarray
indices: hot indices a K-hot encoded matrix
out:
out: x.dot(a.T), where x is a K-hot encoded matrix
"""
HotDot1, HotDot2 = _get_HotDot_kernels()
H, D = a.shape
N, K = indices.shape
if N == 1:
bdim, gdim = Get_bdim_and_gdimRowVec(H)
elif H >= (N*4):
bdim, gdim = Get_bdim_and_gdimSmallNBigM(N,H)
else:
bdim, gdim = Get_bdim_and_gdim2D(N,H)
if dont_add:
B = np.int32(1)
else:
B = np.int32(0)
if out is None:
out = cp.empty((N,H), dtype=np.float32)
B = np.int32(1)
if K > 1:
HotDot1(grid=gdim, block=bdim,
args=(a, out, indices,
np.int32(K), np.int32(N), np.int32(H), np.int32(D), np.int32(B))
)
else:
HotDot2(grid=gdim, block=bdim,
args=(a, out, indices,
np.int32(N), np.int32(H), np.int32(D), np.int32(B))
)
return out
def asfortranarray(a, dtype=None):
"""Return an array laid out in Fortran order in memory.
Args:
a (~cupy.ndarray): The input array.
dtype (str or dtype object, optional): By default, the data-type is
inferred from the input data.
Returns:
~cupy.ndarray: The input `a` in Fortran, or column-major, order.
.. seealso:: :func:`numpy.asfortranarray`
"""
ret = cupy.empty(a.shape[::-1], a.dtype if dtype is None else dtype).T
if (a.flags.c_contiguous and
(a.dtype == numpy.float32 or a.dtype == numpy.float64) and
a.ndim == 2 and
dtype is None):
m, n = a.shape
if a.dtype == numpy.float32:
cupy.cuda.cublas.sgeam(
cupy.cuda.Device().cublas_handle,
1, # transpose a
1, # transpose ret
m, n, 1., a.data.ptr, n, 0., a.data.ptr, n,
ret.data.ptr, m)
elif a.dtype == numpy.float64:
cupy.cuda.cublas.dgeam(
cupy.cuda.Device().cublas_handle,
1, # transpose a
1, # transpose ret
m, n, 1., a.data.ptr, n, 0., a.data.ptr, n,
ret.data.ptr, m)
return ret
else:
ret[...] = a
return ret
def _backward_gpu(gy):
N = gy.shape[0]
T = gy.shape[1]
M = gy.shape[2]
gx = cp.empty((T, N, M), dtype=np.float32)
if N == 1:
bdim, gdim = gpu.utils.Get_bdim_and_gdimRowVec(M)
elif M >= (N*4):
bdim, gdim = gpu.utils.Get_bdim_and_gdimSmallNBigM(N,M)
else:
bdim, gdim = gpu.utils.Get_bdim_and_gdim2D(N,M)
Backward_kernel = _GetBackward_kernel()
Backward_kernel(grid=gdim, block=bdim,
args=(gy, gx,
T, N, M)
)
return gx
def _empty_aligned(shape, dtype, order='C', n=None):
"""Patched version of :func:`sporco.fft.empty_aligned`."""
return cp.empty(shape, dtype, order)
def add_buffers_gpu(species, float_recv_left, float_recv_right, uint_recv_left,
uint_recv_right):
"""
Add the particles stored in recv_left and recv_right
to the existing particle in species.
Parameters
----------
species: a Particles object
Contain the particles that stayed on the present processors
float_recv_left, float_recv_right, uint_recv_left, uint_recv_right:
arrays of shape (n_float,Nptcl) and (n_int,Nptcl) where Nptcl
is the number of particles that are received to the left
proc and right proc respectively, and where n_float and n_int
are the number of float and integer quantities respectively
These arrays are always on the CPU (since they were used for MPI)
"""
# Get the new number of particles
old_Ntot = species.Ntot
n_left = float_recv_left.shape[1]
n_right = float_recv_right.shape[1]
new_Ntot = old_Ntot + n_left + n_right
# Get the threads per block and the blocks per grid
n_left_grid, n_left_block = cuda_tpb_bpg_1d(n_left)
n_right_grid, n_right_block = cuda_tpb_bpg_1d(n_right)
n_old_grid, n_old_block = cuda_tpb_bpg_1d(old_Ntot)
# Iterate over particle attributes
# Build list of float attributes to copy
attr_list = [ (species,'x'), (species,'y'), (species,'z'), \
(species,'ux'), (species,'uy'), (species,'uz'), \
(species,'inv_gamma'), (species,'w') ]
if species.ionizer is not None:
attr_list += [(species.ionizer, 'w_times_level')]
# Loop through the float quantities
for i_attr in range(len(attr_list)):
# Copy the proper buffers to the GPU
left_buffer = cupy.asarray(float_recv_left[i_attr])
right_buffer = cupy.asarray(float_recv_right[i_attr])
# Initialize the new particle array
particle_array = cupy.empty((new_Ntot, ), dtype=np.float64)
# Merge the arrays on the GPU
stay_buffer = getattr(attr_list[i_attr][0], attr_list[i_attr][1])
if n_left != 0:
copy_particles[n_left_grid, n_left_block](n_left, left_buffer, 0,
particle_array, 0)
if old_Ntot != 0:
copy_particles[n_old_grid, n_old_block](old_Ntot, stay_buffer, 0,
particle_array, n_left)
if n_right != 0:
copy_particles[n_right_grid,
n_right_block](n_right, right_buffer, 0,
particle_array, n_left + old_Ntot)
# Assign the stay_buffer to the initial particle data array
# and fill the sending buffers (if needed for MPI)
setattr(attr_list[i_attr][0], attr_list[i_attr][1], particle_array)
# Build list of integer quantities to copy
attr_list = []
if species.tracker is not None:
attr_list.append((species.tracker, 'id'))
if species.ionizer is not None:
attr_list.append((species.ionizer, 'ionization_level'))
# Loop through the integer quantities
for i_attr in range(len(attr_list)):
# Copy the proper buffers to the GPU
left_buffer = cupy.asarray(uint_recv_left[i_attr])
right_buffer = cupy.asarray(uint_recv_right[i_attr])
# Initialize the new particle array
particle_array = cupy.empty((new_Ntot, ), dtype=np.uint64)
# Merge the arrays on the GPU
stay_buffer = getattr(attr_list[i_attr][0], attr_list[i_attr][1])
if n_left != 0:
copy_particles[n_left_grid, n_left_block](n_left, left_buffer, 0,
particle_array, 0)
if old_Ntot != 0:
copy_particles[n_old_grid, n_old_block](old_Ntot, stay_buffer, 0,
particle_array, n_left)
if n_right != 0:
copy_particles[n_right_grid,
n_right_block](n_right, right_buffer, 0,
particle_array, n_left + old_Ntot)
# Assign the stay_buffer to the initial particle data array
# and fill the sending buffers (if needed for MPI)
setattr(attr_list[i_attr][0], attr_list[i_attr][1], particle_array)
# Adapt the total number of particles
species.Ntot = new_Ntot
def eigsh(a,
k=6,
*,
which='LM',
ncv=None,
maxiter=None,
tol=0,
return_eigenvectors=True):
"""Finds ``k`` eigenvalues and eigenvectors of the real symmetric matrix.
Solves ``Ax = wx``, the standard eigenvalue problem for ``w`` eigenvalues
with corresponding eigenvectors ``x``.
Args:
a (ndarray, spmatrix or LinearOperator): A symmetric square matrix with
dimension ``(n, n)``. ``a`` must :class:`cupy.ndarray`,
:class:`cupyx.scipy.sparse.spmatrix` or
:class:`cupyx.scipy.sparse.linalg.LinearOperator`.
k (int): The number of eigenvalues and eigenvectors to compute. Must be
``1 <= k < n``.
which (str): 'LM' or 'LA'. 'LM': finds ``k`` largest (in magnitude)
eigenvalues. 'LA': finds ``k`` largest (algebraic) eigenvalues.
ncv (int): The number of Lanczos vectors generated. Must be
``k + 1 < ncv < n``. If ``None``, default value is used.
maxiter (int): Maximum number of Lanczos update iterations.
If ``None``, default value is used.
tol (float): Tolerance for residuals ``||Ax - wx||``. If ``0``, machine
precision is used.
return_eigenvectors (bool): If ``True``, returns eigenvectors in
addition to eigenvalues.
Returns:
tuple:
If ``return_eigenvectors is True``, it returns ``w`` and ``x``
where ``w`` is eigenvalues and ``x`` is eigenvectors. Otherwise,
it returns only ``w``.
.. seealso:: :func:`scipy.sparse.linalg.eigsh`
.. note::
This function uses the thick-restart Lanczos methods
(https://sdm.lbl.gov/~kewu/ps/trlan.html).
"""
n = a.shape[0]
if a.ndim != 2 or a.shape[0] != a.shape[1]:
raise ValueError('expected square matrix (shape: {})'.format(a.shape))
if a.dtype.char not in 'fdFD':
raise TypeError('unsupprted dtype (actual: {})'.format(a.dtype))
if k <= 0:
raise ValueError('k must be greater than 0 (actual: {})'.format(k))
if k >= n:
raise ValueError('k must be smaller than n (actual: {})'.format(k))
if which not in ('LM', 'LA'):
raise ValueError('which must be \'LM\' or \'LA\' (actual: {})'
''.format(which))
if ncv is None:
ncv = min(max(2 * k, k + 32), n - 1)
else:
ncv = min(max(ncv, k + 2), n - 1)
if maxiter is None:
maxiter = 10 * n
if tol == 0:
tol = numpy.finfo(a.dtype).eps
alpha = cupy.zeros((ncv, ), dtype=a.dtype)
beta = cupy.zeros((ncv, ), dtype=a.dtype.char.lower())
V = cupy.empty((ncv, n), dtype=a.dtype)
# Set initial vector
u = cupy.random.random((n, )).astype(a.dtype)
V[0] = u / cublas.nrm2(u)
# Choose Lanczos implementation, unconditionally use 'fast' for now
upadte_impl = 'fast'
if upadte_impl == 'fast':
lanczos = _lanczos_fast(a, n, ncv)
else:
lanczos = _lanczos_asis
# Lanczos iteration
lanczos(a, V, u, alpha, beta, 0, ncv)
iter = ncv
w, s = _eigsh_solve_ritz(alpha, beta, None, k, which)
x = V.T @ s
# Compute residual
beta_k = beta[-1] * s[-1, :]
res = cublas.nrm2(beta_k)
while res > tol and iter < maxiter:
# Setup for thick-restart
beta[:k] = 0
alpha[:k] = w
V[:k] = x.T
u -= u.T @ V[:k].conj().T @ V[:k]
V[k] = u / cublas.nrm2(u)
u[...] = a @ V[k]
cublas.dotc(V[k], u, out=alpha[k])
u -= alpha[k] * V[k]
u -= V[:k].T @ beta_k
cublas.nrm2(u, out=beta[k])
V[k + 1] = u / beta[k]
# Lanczos iteration
lanczos(a, V, u, alpha, beta, k + 1, ncv)
iter += ncv - k
w, s = _eigsh_solve_ritz(alpha, beta, beta_k, k, which)
x = V.T @ s
# Compute residual
beta_k = beta[-1] * s[-1, :]
res = cublas.nrm2(beta_k)
if return_eigenvectors:
idx = cupy.argsort(w)
return w[idx], x[:, idx]
else:
return cupy.sort(w)
def aux(A, V, u, alpha, beta, i_start, i_end):
assert A is outer_A
# Get ready for spmv if enabled
if cusparse_handle is not None:
# Note: I would like to reuse descriptors and working buffer
# on the next update, but I gave it up because it sometimes
# caused illegal memory access error.
spmv_desc_A = cusparse.SpMatDescriptor.create(A)
spmv_desc_v = cusparse.DnVecDescriptor.create(v)
spmv_desc_u = cusparse.DnVecDescriptor.create(u)
buff_size = _cusparse.spMV_bufferSize(
cusparse_handle, spmv_op_a, spmv_alpha.ctypes.data,
spmv_desc_A.desc, spmv_desc_v.desc, spmv_beta.ctypes.data,
spmv_desc_u.desc, spmv_cuda_dtype, spmv_alg)
spmv_buff = cupy.empty(buff_size, cupy.int8)
v[...] = V[i_start]
for i in range(i_start, i_end):
# Matrix-vector multiplication
if cusparse_handle is None:
u[...] = A @ v
else:
_cusparse.spMV(cusparse_handle, spmv_op_a,
spmv_alpha.ctypes.data, spmv_desc_A.desc,
spmv_desc_v.desc, spmv_beta.ctypes.data,
spmv_desc_u.desc, spmv_cuda_dtype, spmv_alg,
spmv_buff.data.ptr)
# Call dotc
_cublas.setPointerMode(cublas_handle,
_cublas.CUBLAS_POINTER_MODE_DEVICE)
try:
dotc(cublas_handle, n, v.data.ptr, 1, u.data.ptr, 1,
alpha.data.ptr + i * alpha.itemsize)
finally:
_cublas.setPointerMode(cublas_handle, cublas_pointer_mode)
# Orthogonalize
gemm(cublas_handle, _cublas.CUBLAS_OP_C, _cublas.CUBLAS_OP_N, 1,
i + 1, n, one.ctypes.data, u.data.ptr, n, V.data.ptr, n,
zero.ctypes.data, uu.data.ptr, 1)
gemm(cublas_handle, _cublas.CUBLAS_OP_N, _cublas.CUBLAS_OP_C, n, 1,
i + 1, mone.ctypes.data, V.data.ptr, n, uu.data.ptr, 1,
one.ctypes.data, u.data.ptr, n)
# Call nrm2
_cublas.setPointerMode(cublas_handle,
_cublas.CUBLAS_POINTER_MODE_DEVICE)
try:
nrm2(cublas_handle, n, u.data.ptr, 1,
beta.data.ptr + i * beta.itemsize)
finally:
_cublas.setPointerMode(cublas_handle, cublas_pointer_mode)
# Break here as the normalization below touches V[i+1]
if i >= i_end - 1:
break
# Normalize
_kernel_normalize(u, beta, i, n, v, V)
def _lanczos_fast(A, n, ncv):
cublas_handle = device.get_cublas_handle()
cublas_pointer_mode = _cublas.getPointerMode(cublas_handle)
if A.dtype.char == 'f':
dotc = _cublas.sdot
nrm2 = _cublas.snrm2
gemm = _cublas.sgemm
elif A.dtype.char == 'd':
dotc = _cublas.ddot
nrm2 = _cublas.dnrm2
gemm = _cublas.dgemm
elif A.dtype.char == 'F':
dotc = _cublas.cdotc
nrm2 = _cublas.scnrm2
gemm = _cublas.cgemm
elif A.dtype.char == 'D':
dotc = _cublas.zdotc
nrm2 = _cublas.dznrm2
gemm = _cublas.zgemm
else:
raise TypeError('invalid dtype ({})'.format(A.dtype))
cusparse_handle = None
if csr.isspmatrix_csr(A) and cusparse.check_availability('spmv'):
cusparse_handle = device.get_cusparse_handle()
spmv_op_a = _cusparse.CUSPARSE_OPERATION_NON_TRANSPOSE
spmv_alpha = numpy.array(1.0, A.dtype)
spmv_beta = numpy.array(0.0, A.dtype)
spmv_cuda_dtype = cusparse._dtype_to_DataType(A.dtype)
spmv_alg = _cusparse.CUSPARSE_MV_ALG_DEFAULT
v = cupy.empty((n, ), dtype=A.dtype)
uu = cupy.empty((ncv, ), dtype=A.dtype)
one = numpy.array(1.0, dtype=A.dtype)
zero = numpy.array(0.0, dtype=A.dtype)
mone = numpy.array(-1.0, dtype=A.dtype)
outer_A = A
def aux(A, V, u, alpha, beta, i_start, i_end):
assert A is outer_A
# Get ready for spmv if enabled
if cusparse_handle is not None:
# Note: I would like to reuse descriptors and working buffer
# on the next update, but I gave it up because it sometimes
# caused illegal memory access error.
spmv_desc_A = cusparse.SpMatDescriptor.create(A)
spmv_desc_v = cusparse.DnVecDescriptor.create(v)
spmv_desc_u = cusparse.DnVecDescriptor.create(u)
buff_size = _cusparse.spMV_bufferSize(
cusparse_handle, spmv_op_a, spmv_alpha.ctypes.data,
spmv_desc_A.desc, spmv_desc_v.desc, spmv_beta.ctypes.data,
spmv_desc_u.desc, spmv_cuda_dtype, spmv_alg)
spmv_buff = cupy.empty(buff_size, cupy.int8)
v[...] = V[i_start]
for i in range(i_start, i_end):
# Matrix-vector multiplication
if cusparse_handle is None:
u[...] = A @ v
else:
_cusparse.spMV(cusparse_handle, spmv_op_a,
spmv_alpha.ctypes.data, spmv_desc_A.desc,
spmv_desc_v.desc, spmv_beta.ctypes.data,
spmv_desc_u.desc, spmv_cuda_dtype, spmv_alg,
spmv_buff.data.ptr)
# Call dotc
_cublas.setPointerMode(cublas_handle,
_cublas.CUBLAS_POINTER_MODE_DEVICE)
try:
dotc(cublas_handle, n, v.data.ptr, 1, u.data.ptr, 1,
alpha.data.ptr + i * alpha.itemsize)
finally:
_cublas.setPointerMode(cublas_handle, cublas_pointer_mode)
# Orthogonalize
gemm(cublas_handle, _cublas.CUBLAS_OP_C, _cublas.CUBLAS_OP_N, 1,
i + 1, n, one.ctypes.data, u.data.ptr, n, V.data.ptr, n,
zero.ctypes.data, uu.data.ptr, 1)
gemm(cublas_handle, _cublas.CUBLAS_OP_N, _cublas.CUBLAS_OP_C, n, 1,
i + 1, mone.ctypes.data, V.data.ptr, n, uu.data.ptr, 1,
one.ctypes.data, u.data.ptr, n)
# Call nrm2
_cublas.setPointerMode(cublas_handle,
_cublas.CUBLAS_POINTER_MODE_DEVICE)
try:
nrm2(cublas_handle, n, u.data.ptr, 1,
beta.data.ptr + i * beta.itemsize)
finally:
_cublas.setPointerMode(cublas_handle, cublas_pointer_mode)
# Break here as the normalization below touches V[i+1]
if i >= i_end - 1:
break
# Normalize
_kernel_normalize(u, beta, i, n, v, V)
return aux
def remove_particles_gpu(species, fld, n_guard, left_proc, right_proc):
"""
Remove the particles that are outside of the physical domain (i.e.
in the guard cells). Store them in sending buffers, which are returned.
Parameters
----------
species: a Particles object
Contains the data of this species
fld: a Fields object
Contains information about the dimension of the grid,
and the prefix sum (when using the GPU)
n_guard: int
Number of guard cells
left_proc, right_proc: int or None
Indicate whether there is a left or right processor or if the
boundary is open (None).
Returns
-------
float_send_left, float_send_right, uint_send_left, uint_send_right:
arrays of shape (n_float,Nptcl) and (n_int,Nptcl) where Nptcl
is the number of particles that are sent to the left
proc and right proc respectively, and where n_float and n_int
are the number of float and integer quantities respectively
"""
# Check if particles are sorted
# (The particles are usually expected to be sorted from the previous
# iteration at this point - except at the first iteration of `step`.)
if species.sorted == False:
species.sort_particles(fld=fld)
species.sorted = True
# Get the particle indices between which to remove the particles
# (Take into account the fact that the moving window may have
# shifted the grid since the particles were last sorted: prefix_sum_shift)
prefix_sum = species.prefix_sum
Nz = fld.Nz
Nr = fld.Nr
# Find the z index of the first cell for which particles are kept
iz_min = max(n_guard + species.prefix_sum_shift, 0)
# Find the z index of the first cell for which particles are removed again
iz_max = min(Nz - n_guard + species.prefix_sum_shift + 1, Nz)
# Find the corresponding indices in the particle array
# Reminder: prefix_sum[i] is the cumulative sum of the number of particles
# in cells 0 to i (where cell i is included)
if iz_min * (Nr + 1) - 1 >= 0:
i_min = int(prefix_sum[iz_min * (Nr + 1) - 1])
else:
i_min = 0
i_max = int(prefix_sum[iz_max * (Nr + 1) - 1])
# Total number of particles in each particle group
N_send_l = i_min
new_Ntot = i_max - i_min
N_send_r = species.Ntot - i_max
# Allocate the sending buffers on the CPU
n_float = species.n_float_quantities
n_int = species.n_integer_quantities
if left_proc is not None:
float_send_left = np.empty((n_float, N_send_l), dtype=np.float64)
uint_send_left = np.empty((n_int, N_send_l), dtype=np.uint64)
else:
float_send_left = np.empty((n_float, 0), dtype=np.float64)
uint_send_left = np.empty((n_int, 0), dtype=np.uint64)
if right_proc is not None:
float_send_right = np.empty((n_float, N_send_r), dtype=np.float64)
uint_send_right = np.empty((n_int, N_send_r), dtype=np.uint64)
else:
float_send_right = np.empty((n_float, 0), dtype=np.float64)
uint_send_right = np.empty((n_int, 0), dtype=np.uint64)
# Get the threads per block and the blocks per grid
dim_grid_1d, dim_block_1d = cuda_tpb_bpg_1d(species.Ntot)
# Float quantities:
# Build list of float attributes to copy
attr_list = [(species, 'x'), (species, 'y'), (species, 'z'),
(species, 'ux'), (species, 'uy'), (species, 'uz'),
(species, 'inv_gamma'), (species, 'w')]
if species.ionizer is not None:
attr_list.append((species.ionizer, 'w_times_level'))
# Loop through the float attributes
for i_attr in range(n_float):
# Initialize 3 buffer arrays on the GPU (need to be initialized
# inside the loop, as `copy_to_host` invalidates these arrays)
left_buffer = cupy.empty((N_send_l, ), dtype=np.float64)
right_buffer = cupy.empty((N_send_r, ), dtype=np.float64)
stay_buffer = cupy.empty((new_Ntot, ), dtype=np.float64)
# Check that the buffers are still on GPU
# (safeguard against automatic memory management)
assert type(left_buffer) != np.ndarray
assert type(right_buffer) != np.ndarray
assert type(left_buffer) != np.ndarray
# Split the particle array into the 3 buffers on the GPU
particle_array = getattr(attr_list[i_attr][0], attr_list[i_attr][1])
split_particles_to_buffers[dim_grid_1d,
dim_block_1d](particle_array, left_buffer,
stay_buffer, right_buffer,
i_min, i_max)
# Assign the stay_buffer to the initial particle data array
# and fill the sending buffers (if needed for MPI)
setattr(attr_list[i_attr][0], attr_list[i_attr][1], stay_buffer)
if left_proc is not None:
left_buffer.get(out=float_send_left[i_attr])
if right_proc is not None:
right_buffer.get(out=float_send_right[i_attr])
# Integer quantities:
if n_int > 0:
attr_list = []
if species.tracker is not None:
attr_list.append((species.tracker, 'id'))
if species.ionizer is not None:
attr_list.append((species.ionizer, 'ionization_level'))
for i_attr in range(n_int):
# Initialize 3 buffer arrays on the GPU (need to be initialized
# inside the loop, as `copy_to_host` invalidates these arrays)
left_buffer = cupy.empty((N_send_l, ), dtype=np.uint64)
right_buffer = cupy.empty((N_send_r, ), dtype=np.uint64)
stay_buffer = cupy.empty((new_Ntot, ), dtype=np.uint64)
# Split the particle array into the 3 buffers on the GPU
particle_array = getattr(attr_list[i_attr][0], attr_list[i_attr][1])
split_particles_to_buffers[dim_grid_1d,
dim_block_1d](particle_array, left_buffer,
stay_buffer, right_buffer,
i_min, i_max)
# Assign the stay_buffer to the initial particle data array
# and fill the sending buffers (if needed for MPI)
setattr(attr_list[i_attr][0], attr_list[i_attr][1], stay_buffer)
if left_proc is not None:
left_buffer.get(out=uint_send_left[i_attr])
if right_proc is not None:
right_buffer.get(out=uint_send_right[i_attr])
# Change the new total number of particles
species.Ntot = new_Ntot
# Return the sending buffers
return (float_send_left, float_send_right, uint_send_left, uint_send_right)
def geam(transa, transb, alpha, a, beta, b, out=None):
"""Computes alpha * op(a) + beta * op(b)
op(a) = a if transa is 'N', op(a) = a.T if transa is 'T',
op(a) = a.T.conj() if transa is 'H'.
op(b) = b if transb is 'N', op(b) = b.T if transb is 'T',
op(b) = b.T.conj() if transb is 'H'.
"""
assert a.ndim == b.ndim == 2
assert a.dtype == b.dtype
dtype = a.dtype.char
if dtype == 'f':
func = cublas.sgeam
elif dtype == 'd':
func = cublas.dgeam
elif dtype == 'F':
func = cublas.cgeam
elif dtype == 'D':
func = cublas.zgeam
else:
raise TypeError('invalid dtype')
transa = _trans_to_cublas_op(transa)
transb = _trans_to_cublas_op(transb)
if transa == cublas.CUBLAS_OP_N:
m, n = a.shape
else:
n, m = a.shape
if transb == cublas.CUBLAS_OP_N:
assert b.shape == (m, n)
else:
assert b.shape == (n, m)
if out is None:
out = cupy.empty((m, n), dtype=dtype, order='F')
else:
assert out.ndim == 2
assert out.shape == (m, n)
assert out.dtype == dtype
alpha, alpha_ptr = _get_scalar_ptr(alpha, a.dtype)
beta, beta_ptr = _get_scalar_ptr(beta, a.dtype)
handle = device.get_cublas_handle()
orig_mode = cublas.getPointerMode(handle)
if isinstance(alpha, cupy.ndarray) or isinstance(beta, cupy.ndarray):
if not isinstance(alpha, cupy.ndarray):
alpha = cupy.array(alpha)
alpha_ptr = alpha.data.ptr
if not isinstance(beta, cupy.ndarray):
beta = cupy.array(beta)
beta_ptr = beta.data.ptr
cublas.setPointerMode(handle, cublas.CUBLAS_POINTER_MODE_DEVICE)
else:
cublas.setPointerMode(handle, cublas.CUBLAS_POINTER_MODE_HOST)
lda, transa = _decide_ld_and_trans(a, transa)
ldb, transb = _decide_ld_and_trans(b, transb)
if not (lda is None or ldb is None):
if out._f_contiguous:
try:
func(handle, transa, transb, m, n, alpha_ptr, a.data.ptr, lda,
beta_ptr, b.data.ptr, ldb, out.data.ptr, m)
finally:
cublas.setPointerMode(handle, orig_mode)
return out
elif out._c_contiguous:
# Computes alpha * a.T + beta * b.T
try:
func(handle, 1 - transa, 1 - transb, n, m, alpha_ptr,
a.data.ptr, lda, beta_ptr, b.data.ptr, ldb, out.data.ptr,
n)
finally:
cublas.setPointerMode(handle, orig_mode)
return out
a, lda = _change_order_if_necessary(a, lda)
b, ldb = _change_order_if_necessary(b, ldb)
c = out
if not out._f_contiguous:
c = out.copy(order='F')
try:
func(handle, transa, transb, m, n, alpha_ptr, a.data.ptr, lda,
beta_ptr, b.data.ptr, ldb, c.data.ptr, m)
finally:
cublas.setPointerMode(handle, orig_mode)
if not out._f_contiguous:
out[...] = c
return out
def create_dropout_states(handle):
state_size = cudnn.dropoutGetStatesSize(handle)
return cupy.empty((state_size, ), dtype='b')
def test_empty_zero_sized_array_strides(self, order):
a = numpy.empty((1, 0, 2), dtype='d', order=order)
b = cupy.empty((1, 0, 2), dtype='d', order=order)
self.assertEqual(b.strides, a.strides)
def solve(a, b):
'''Solves a linear matrix equation.
It computes the exact solution of ``x`` in ``ax = b``,
where ``a`` is a square and full rank matrix.
Args:
a (cupy.ndarray): The matrix with dimension ``(M, M)``
b (cupy.ndarray): The vector with ``M`` elements, or
the matrix with dimension ``(M, K)``
Returns:
cupy.ndarray:
The vector with ``M`` elements, or the matrix with dimension
``(M, K)``.
.. seealso:: :func:`numpy.linalg.solve`
'''
# NOTE: Since cusolver in CUDA 8.0 does not support gesv,
# we manually solve a linear system with QR decomposition.
# For details, please see the following:
# http://docs.nvidia.com/cuda/cusolver/index.html#qr_examples
if not cuda.cusolver_enabled:
raise RuntimeError('Current cupy only supports cusolver in CUDA 8.0')
# TODO(Saito): Current implementation only accepts two-dimensional arrays
util._assert_cupy_array(a, b)
util._assert_rank2(a)
util._assert_nd_squareness(a)
if 2 < b.ndim:
raise linalg.LinAlgError('{}-dimensional array given. Array must be '
'one or two-dimensional'.format(b.ndim))
if len(a) != len(b):
raise linalg.LinAlgError('The number of rows of array a must be '
'the same as that of array b')
# Cast to float32 or float64
if a.dtype.char == 'f' or a.dtype.char == 'd':
dtype = a.dtype.char
else:
dtype = numpy.find_common_type((a.dtype.char, 'f'), ()).char
m, k = (b.size, 1) if b.ndim == 1 else b.shape
a = a.transpose().astype(dtype, order='C', copy=True)
b = b.transpose().astype(dtype, order='C', copy=True)
cusolver_handle = device.get_cusolver_handle()
cublas_handle = device.get_cublas_handle()
dev_info = cupy.empty(1, dtype=numpy.int32)
if dtype == 'f':
geqrf = cusolver.sgeqrf
geqrf_bufferSize = cusolver.sgeqrf_bufferSize
ormqr = cusolver.sormqr
trsm = cublas.strsm
else: # dtype == 'd'
geqrf = cusolver.dgeqrf
geqrf_bufferSize = cusolver.dgeqrf_bufferSize
ormqr = cusolver.dormqr
trsm = cublas.dtrsm
# 1. QR decomposition (A = Q * R)
buffersize = geqrf_bufferSize(cusolver_handle, m, m, a.data.ptr, m)
workspace = cupy.empty(buffersize, dtype=dtype)
tau = cupy.empty(m, dtype=dtype)
geqrf(cusolver_handle, m, m, a.data.ptr, m, tau.data.ptr,
workspace.data.ptr, buffersize, dev_info.data.ptr)
_check_status(dev_info)
# 2. ormqr (Q^T * B)
ormqr(cusolver_handle, cublas.CUBLAS_SIDE_LEFT, cublas.CUBLAS_OP_T, m, k,
m, a.data.ptr, m, tau.data.ptr, b.data.ptr, m, workspace.data.ptr,
buffersize, dev_info.data.ptr)
_check_status(dev_info)
# 3. trsm (X = R^{-1} * (Q^T * B))
trsm(cublas_handle, cublas.CUBLAS_SIDE_LEFT, cublas.CUBLAS_FILL_MODE_UPPER,
cublas.CUBLAS_OP_N, cublas.CUBLAS_DIAG_NON_UNIT, m, k, 1, a.data.ptr,
m, b.data.ptr, m)
return b.transpose()
def warp_coords(coord_map, shape, dtype=np.float64):
"""Build the source coordinates for the output of a 2-D image warp.
Parameters
----------
coord_map : callable like GeometricTransform.inverse
Return input coordinates for given output coordinates.
Coordinates are in the shape (P, 2), where P is the number
of coordinates and each element is a ``(row, col)`` pair.
shape : tuple
Shape of output image ``(rows, cols[, bands])``.
dtype : np.dtype or string
dtype for return value (sane choices: float32 or float64).
Returns
-------
coords : (ndim, rows, cols[, bands]) array of dtype `dtype`
Coordinates for `scipy.ndimage.map_coordinates`, that will yield
an image of shape (orows, ocols, bands) by drawing from source
points according to the `coord_transform_fn`.
Notes
-----
This is a lower-level routine that produces the source coordinates for 2-D
images used by `warp()`.
It is provided separately from `warp` to give additional flexibility to
users who would like, for example, to re-use a particular coordinate
mapping, to use specific dtypes at various points along the the
image-warping process, or to implement different post-processing logic
than `warp` performs after the call to `ndi.map_coordinates`.
Examples
--------
Produce a coordinate map that shifts an image up and to the right:
>>> import cupy as cp
>>> from skimage import data
>>> from scipy.ndimage import map_coordinates
>>>
>>> def shift_up10_left20(xy):
... return xy - cp.array([-20, 10])[None, :]
>>>
>>> image = data.astronaut().astype(np.float32)
>>> coords = warp_coords(shift_up10_left20, image.shape)
>>> warped_image = map_coordinates(image, coords)
"""
shape = safe_as_int(shape)
rows, cols = shape[0], shape[1]
coords_shape = [len(shape), rows, cols]
if len(shape) == 3:
coords_shape.append(shape[2])
coords = cp.empty(coords_shape, dtype=dtype)
# Reshape grid coordinates into a (P, 2) array of (row, col) pairs
tf_coords = cp.indices((cols, rows), dtype=dtype).reshape(2, -1).T
# Map each (row, col) pair to the source image according to
# the user-provided mapping
tf_coords = coord_map(tf_coords)
# Reshape back to a (2, M, N) coordinate grid
tf_coords = tf_coords.T.reshape((-1, cols, rows)).swapaxes(1, 2)
# Place the y-coordinate mapping
_stackcopy(coords[1, ...], tf_coords[0, ...])
# Place the x-coordinate mapping
_stackcopy(coords[0, ...], tf_coords[1, ...])
if len(shape) == 3:
coords[2, ...] = cp.arange(shape[2], dtype=coords.dtype)
return coords
def binopt_csr(a, b, op_name):
check_shape_for_pointwise_op(a.shape, b.shape)
a_m, a_n = a.shape
b_m, b_n = b.shape
m, n = max(a_m, b_m), max(a_n, b_n)
a_nnz = a.nnz * (m // a_m) * (n // a_n)
b_nnz = b.nnz * (m // b_m) * (n // b_n)
a_info = cupy.zeros(a_nnz + 1, dtype=a.indices.dtype)
b_info = cupy.zeros(b_nnz + 1, dtype=b.indices.dtype)
a_valid = cupy.zeros(a_nnz, dtype=numpy.int8)
b_valid = cupy.zeros(b_nnz, dtype=numpy.int8)
c_indptr = cupy.zeros(m + 1, dtype=a.indptr.dtype)
in_dtype = numpy.promote_types(a.dtype, b.dtype)
a_data = a.data.astype(in_dtype, copy=False)
b_data = b.data.astype(in_dtype, copy=False)
funcs = _GET_ROW_ID_
if op_name == '_maximum_':
funcs += _BINOPT_MAX_
out_dtype = in_dtype
elif op_name == '_minimum_':
funcs += _BINOPT_MIN_
out_dtype = in_dtype
elif op_name == '_eq_':
funcs += _BINOPT_EQ_
out_dtype = numpy.bool
elif op_name == '_ne_':
funcs += _BINOPT_NE_
out_dtype = numpy.bool
elif op_name == '_lt_':
funcs += _BINOPT_LT_
out_dtype = numpy.bool
elif op_name == '_gt_':
funcs += _BINOPT_GT_
out_dtype = numpy.bool
elif op_name == '_le_':
funcs += _BINOPT_LE_
out_dtype = numpy.bool
elif op_name == '_ge_':
funcs += _BINOPT_GE_
out_dtype = numpy.bool
else:
raise ValueError('invalid op_name: {}'.format(op_name))
a_tmp_data = cupy.empty(a_nnz, dtype=out_dtype)
b_tmp_data = cupy.empty(b_nnz, dtype=out_dtype)
a_tmp_indices = cupy.empty(a_nnz, dtype=a.indices.dtype)
b_tmp_indices = cupy.empty(b_nnz, dtype=b.indices.dtype)
_size = a_nnz + b_nnz
cupy_binopt_csr_step1(op_name, preamble=funcs)(m,
n,
a.indptr,
a.indices,
a_data,
a_m,
a_n,
a.nnz,
a_nnz,
b.indptr,
b.indices,
b_data,
b_m,
b_n,
b.nnz,
b_nnz,
a_info,
a_valid,
a_tmp_indices,
a_tmp_data,
b_info,
b_valid,
b_tmp_indices,
b_tmp_data,
c_indptr,
size=_size)
a_info = cupy.cumsum(a_info, dtype=a_info.dtype)
b_info = cupy.cumsum(b_info, dtype=b_info.dtype)
c_indptr = cupy.cumsum(c_indptr, dtype=c_indptr.dtype)
c_nnz = int(c_indptr[-1])
c_indices = cupy.empty(c_nnz, dtype=a.indices.dtype)
c_data = cupy.empty(c_nnz, dtype=out_dtype)
cupy_binopt_csr_step2(op_name)(a_info,
a_valid,
a_tmp_indices,
a_tmp_data,
a_nnz,
b_info,
b_valid,
b_tmp_indices,
b_tmp_data,
b_nnz,
c_indices,
c_data,
size=_size)
return csr_matrix((c_data, c_indices, c_indptr), shape=(m, n))
def cross(a, b, axisa=-1, axisb=-1, axisc=-1, axis=None):
"""Returns the cross product of two vectors.
The cross product of ``a`` and ``b`` in :math:`R^3` is a vector
perpendicular to both ``a`` and ``b``. If ``a`` and ``b`` are arrays
of vectors, the vectors are defined by the last axis of ``a`` and ``b``
by default, and these axes can have dimensions 2 or 3. Where the
dimension of either ``a`` or ``b`` is 2, the third component of the input
vector is assumed to be zero and the cross product calculated accordingly.
In cases where both input vectors have dimension 2, the z-component of
the cross product is returned.
Args:
a (cupy.ndarray): Components of the first vector(s).
b (cupy.ndarray): Components of the second vector(s).
axisa (int, optional):
Axis of ``a`` that defines the vector(s).
By default, the last axis.
axisb (int, optional):
Axis of ``b`` that defines the vector(s).
By default, the last axis.
axisc (int, optional):
Axis of ``c`` containing the cross product vector(s). Ignored if
both input vectors have dimension 2, as the return is scalar.
By default, the last axis.
axis (int, optional):
If defined, the axis of ``a``, ``b`` and ``c``
that defines the vector(s) and cross product(s).
Overrides ``axisa``, ``axisb`` and ``axisc``.
Returns:
cupy.ndarray :
Vector cross product(s).
.. seealso:: :func:`numpy.cross`
"""
if axis is not None:
axisa, axisb, axisc = (axis, ) * 3
a = cupy.asarray(a)
b = cupy.asarray(b)
# Check axisa and axisb are within bounds
axisa = internal._normalize_axis_index(axisa, a.ndim)
axisb = internal._normalize_axis_index(axisb, b.ndim)
# Move working axis to the end of the shape
a = cupy.moveaxis(a, axisa, -1)
b = cupy.moveaxis(b, axisb, -1)
if a.shape[-1] not in (2, 3) or b.shape[-1] not in (2, 3):
msg = ('incompatible dimensions for cross product\n'
'(dimension must be 2 or 3)')
raise ValueError(msg)
# Create the output array
shape = cupy.broadcast(a[..., 0], b[..., 0]).shape
if a.shape[-1] == 3 or b.shape[-1] == 3:
shape += (3, )
# Check axisc is within bounds
axisc = internal._normalize_axis_index(axisc, len(shape))
dtype = cupy.promote_types(a.dtype, b.dtype)
cp = cupy.empty(shape, dtype)
# create local aliases for readability
a0 = a[..., 0]
a1 = a[..., 1]
if a.shape[-1] == 3:
a2 = a[..., 2]
b0 = b[..., 0]
b1 = b[..., 1]
if b.shape[-1] == 3:
b2 = b[..., 2]
if cp.ndim != 0 and cp.shape[-1] == 3:
cp0 = cp[..., 0]
cp1 = cp[..., 1]
cp2 = cp[..., 2]
if a.shape[-1] == 2:
if b.shape[-1] == 2:
# a0 * b1 - a1 * b0
cupy.multiply(a0, b1, out=cp)
cp -= a1 * b0
return cp
else:
assert b.shape[-1] == 3
# cp0 = a1 * b2 - 0 (a2 = 0)
# cp1 = 0 - a0 * b2 (a2 = 0)
# cp2 = a0 * b1 - a1 * b0
cupy.multiply(a1, b2, out=cp0)
cupy.multiply(a0, b2, out=cp1)
cupy.negative(cp1, out=cp1)
cupy.multiply(a0, b1, out=cp2)
cp2 -= a1 * b0
else:
assert a.shape[-1] == 3
if b.shape[-1] == 3:
# cp0 = a1 * b2 - a2 * b1
# cp1 = a2 * b0 - a0 * b2
# cp2 = a0 * b1 - a1 * b0
cupy.multiply(a1, b2, out=cp0)
tmp = a2 * b1
cp0 -= tmp
cupy.multiply(a2, b0, out=cp1)
cupy.multiply(a0, b2, out=tmp)
cp1 -= tmp
cupy.multiply(a0, b1, out=cp2)
cupy.multiply(a1, b0, out=tmp)
cp2 -= tmp
else:
assert b.shape[-1] == 2
# cp0 = 0 - a2 * b1 (b2 = 0)
# cp1 = a2 * b0 - 0 (b2 = 0)
# cp2 = a0 * b1 - a1 * b0
cupy.multiply(a2, b1, out=cp0)
cupy.negative(cp0, out=cp0)
cupy.multiply(a2, b0, out=cp1)
cupy.multiply(a0, b1, out=cp2)
cp2 -= a1 * b0
return cupy.moveaxis(cp, -1, axisc)
def sum_duplicates(self):
"""Eliminate duplicate matrix entries by adding them together.
.. seealso::
:meth:`scipy.sparse.coo_matrix.sum_duplicates`
"""
if self._has_canonical_format:
return
if self.data.size == 0:
self._has_canonical_format = True
return
keys = cupy.stack([self.row, self.col])
order = cupy.lexsort(keys)
src_data = self.data[order]
src_row = self.row[order]
src_col = self.col[order]
diff = cupy.ElementwiseKernel(
'raw int32 row, raw int32 col', 'int32 diff', '''
int index;
if (i == 0 || row[i - 1] == row[i] && col[i - 1] == col[i]) {
diff = 0;
} else {
diff = 1;
}
''', 'sum_duplicates_diff')(src_row, src_col, size=self.row.size)
if diff[1:].all():
# All elements have different indices.
data = src_data
row = src_row
col = src_col
else:
index = cupy.cumsum(diff, dtype='i')
size = int(index[-1]) + 1
data = cupy.zeros(size, dtype=self.data.dtype)
row = cupy.empty(size, dtype='i')
col = cupy.empty(size, dtype='i')
if self.data.dtype.kind == 'f':
cupy.ElementwiseKernel(
'T src_data, int32 src_row, int32 src_col, int32 index',
'raw T data, raw int32 row, raw int32 col', '''
atomicAdd(&data[index], src_data);
row[index] = src_row;
col[index] = src_col;
''', 'sum_duplicates_assign')(src_data, src_row, src_col,
index, data, row, col)
elif self.data.dtype.kind == 'c':
cupy.ElementwiseKernel(
'T src_real, T src_imag, int32 src_row, int32 src_col, '
'int32 index',
'raw T real, raw T imag, raw int32 row, raw int32 col', '''
atomicAdd(&real[index], src_real);
atomicAdd(&imag[index], src_imag);
row[index] = src_row;
col[index] = src_col;
''',
'sum_duplicates_assign_complex')(src_data.real,
src_data.imag, src_row,
src_col, index, data.real,
data.imag, row, col)
self.data = data
self.row = row
self.col = col
self._has_canonical_format = True
def inv(a):
"""Computes the inverse of a matrix.
This function computes matrix ``a_inv`` from n-dimensional regular matrix
``a`` such that ``dot(a, a_inv) == eye(n)``.
Args:
a (cupy.ndarray): The regular matrix
Returns:
cupy.ndarray: The inverse of a matrix.
.. seealso:: :func:`numpy.linalg.inv`
"""
if a.ndim >= 3:
return _batched_inv(a)
if not cuda.cusolver_enabled:
raise RuntimeError('Current cupy only supports cusolver in CUDA 8.0')
# to prevent `a` to be overwritten
a = a.copy()
util._assert_cupy_array(a)
util._assert_rank2(a)
util._assert_nd_squareness(a)
# support float32, float64, complex64, and complex128
if a.dtype.char in 'fdFD':
dtype = a.dtype.char
else:
dtype = numpy.find_common_type((a.dtype.char, 'f'), ()).char
cusolver_handle = device.get_cusolver_handle()
dev_info = cupy.empty(1, dtype=numpy.int32)
ipiv = cupy.empty((a.shape[0], 1), dtype=numpy.intc)
if dtype == 'f':
getrf = cusolver.sgetrf
getrf_bufferSize = cusolver.sgetrf_bufferSize
getrs = cusolver.sgetrs
elif dtype == 'd':
getrf = cusolver.dgetrf
getrf_bufferSize = cusolver.dgetrf_bufferSize
getrs = cusolver.dgetrs
elif dtype == 'F':
getrf = cusolver.cgetrf
getrf_bufferSize = cusolver.cgetrf_bufferSize
getrs = cusolver.cgetrs
elif dtype == 'D':
getrf = cusolver.zgetrf
getrf_bufferSize = cusolver.zgetrf_bufferSize
getrs = cusolver.zgetrs
else:
raise ValueError('unsupported dtype')
m = a.shape[0]
buffersize = getrf_bufferSize(cusolver_handle, m, m, a.data.ptr, m)
workspace = cupy.empty(buffersize, dtype=dtype)
# LU factorization
getrf(cusolver_handle, m, m, a.data.ptr, m, workspace.data.ptr,
ipiv.data.ptr, dev_info.data.ptr)
b = cupy.eye(m, dtype=dtype)
# solve for the inverse
getrs(cusolver_handle, 0, m, m, a.data.ptr, m, ipiv.data.ptr, b.data.ptr,
m, dev_info.data.ptr)
return b
def sinkhorn_knopp(a, b, M, reg, numItermax=1000, stopThr=1e-9,
verbose=False, log=False, to_numpy=True, **kwargs):
"""
Solve the entropic regularization optimal transport on GPU
If the input matrix are in numpy format, they will be uploaded to the
GPU first which can incur significant time overhead.
The function solves the following optimization problem:
.. math::
\gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma)
s.t. \gamma 1 = a
\gamma^T 1= b
\gamma\geq 0
where :
- M is the (ns,nt) metric cost matrix
- :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})`
- a and b are source and target weights (sum to 1)
The algorithm used for solving the problem is the Sinkhorn-Knopp matrix scaling algorithm as proposed in [2]_
Parameters
----------
a : np.ndarray (ns,)
samples weights in the source domain
b : np.ndarray (nt,) or np.ndarray (nt,nbb)
samples in the target domain, compute sinkhorn with multiple targets
and fixed M if b is a matrix (return OT loss + dual variables in log)
M : np.ndarray (ns,nt)
loss matrix
reg : float
Regularization term >0
numItermax : int, optional
Max number of iterations
stopThr : float, optional
Stop threshol on error (>0)
verbose : bool, optional
Print information along iterations
log : bool, optional
record log if True
to_numpy : boolean, optional (default True)
If true convert back the GPU array result to numpy format.
Returns
-------
gamma : (ns x nt) ndarray
Optimal transportation matrix for the given parameters
log : dict
log dictionary return only if log==True in parameters
Examples
--------
>>> import ot
>>> a=[.5,.5]
>>> b=[.5,.5]
>>> M=[[0.,1.],[1.,0.]]
>>> ot.sinkhorn(a,b,M,1)
array([[ 0.36552929, 0.13447071],
[ 0.13447071, 0.36552929]])
References
----------
.. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal Transport, Advances in Neural Information Processing Systems (NIPS) 26, 2013
See Also
--------
ot.lp.emd : Unregularized OT
ot.optim.cg : General regularized OT
"""
a = cp.asarray(a)
b = cp.asarray(b)
M = cp.asarray(M)
if len(a) == 0:
a = np.ones((M.shape[0],)) / M.shape[0]
if len(b) == 0:
b = np.ones((M.shape[1],)) / M.shape[1]
# init data
Nini = len(a)
Nfin = len(b)
if len(b.shape) > 1:
nbb = b.shape[1]
else:
nbb = 0
if log:
log = {'err': []}
# we assume that no distances are null except those of the diagonal of
# distances
if nbb:
u = np.ones((Nini, nbb)) / Nini
v = np.ones((Nfin, nbb)) / Nfin
else:
u = np.ones(Nini) / Nini
v = np.ones(Nfin) / Nfin
# print(reg)
# Next 3 lines equivalent to K= np.exp(-M/reg), but faster to compute
K = np.empty(M.shape, dtype=M.dtype)
np.divide(M, -reg, out=K)
np.exp(K, out=K)
# print(np.min(K))
tmp2 = np.empty(b.shape, dtype=M.dtype)
Kp = (1 / a).reshape(-1, 1) * K
cpt = 0
err = 1
while (err > stopThr and cpt < numItermax):
uprev = u
vprev = v
KtransposeU = np.dot(K.T, u)
v = np.divide(b, KtransposeU)
u = 1. / np.dot(Kp, v)
if (np.any(KtransposeU == 0) or
np.any(np.isnan(u)) or np.any(np.isnan(v)) or
np.any(np.isinf(u)) or np.any(np.isinf(v))):
# we have reached the machine precision
# come back to previous solution and quit loop
print('Warning: numerical errors at iteration', cpt)
u = uprev
v = vprev
break
if cpt % 10 == 0:
# we can speed up the process by checking for the error only all
# the 10th iterations
if nbb:
err = np.sum((u - uprev)**2) / np.sum((u)**2) + \
np.sum((v - vprev)**2) / np.sum((v)**2)
else:
# compute right marginal tmp2= (diag(u)Kdiag(v))^T1
tmp2 = np.sum(u[:, None] * K * v[None, :], 0)
#tmp2=np.einsum('i,ij,j->j', u, K, v)
err = np.linalg.norm(tmp2 - b)**2 # violation of marginal
if log:
log['err'].append(err)
if verbose:
if cpt % 200 == 0:
print(
'{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19)
print('{:5d}|{:8e}|'.format(cpt, err))
cpt = cpt + 1
if log:
log['u'] = u
log['v'] = v
if nbb: # return only loss
#res = np.einsum('ik,ij,jk,ij->k', u, K, v, M) (explodes cupy memory)
res = np.empty(nbb)
for i in range(nbb):
res[i] = np.sum(u[:, None, i] * (K * M) * v[None, :, i])
if to_numpy:
res = utils.to_np(res)
if log:
return res, log
else:
return res
else: # return OT matrix
res = u.reshape((-1, 1)) * K * v.reshape((1, -1))
if to_numpy:
res = utils.to_np(res)
if log:
return res, log
else:
return res
def f3():
[cupy.empty((s,), dtype='b') for s in sizes]
def test_sum_out_wrong_shape(self):
a = testing.shaped_arange((2, 3, 4))
b = cupy.empty((2, 3))
with self.assertRaises(ValueError):
a.sum(axis=1, out=b)
def make_data(self, parse=False):
if self.mask_sum == 0.:
if parse:
self.parse_mask()
else:
self.make_mask()
if self.bg_count is not None:
if parse:
self.parse_mask(bg=True)
else:
self.make_mask(bg=True)
with h5py.File(self.out_file, 'a') as fptr:
if 'ones' in fptr:
del fptr['ones']
if 'multi' in fptr:
del fptr['multi']
if 'place_ones' in fptr:
del fptr['place_ones']
if 'place_multi' in fptr:
del fptr['place_multi']
if 'count_multi' in fptr:
del fptr['count_multi']
if 'num_pix' in fptr:
del fptr['num_pix']
if 'true_angles' in fptr:
del fptr['true_angles']
if 'bg' in fptr:
del fptr['bg']
if self.bgmask_sum > 0:
fptr['bg'] = self.bgmask.get()
fptr['num_pix'] = np.array([self.size**2])
dtype = h5py.special_dtype(vlen=np.dtype('i4'))
place_ones = fptr.create_dataset('place_ones', (self.num_data, ),
dtype=dtype)
place_multi = fptr.create_dataset('place_multi', (self.num_data, ),
dtype=dtype)
count_multi = fptr.create_dataset('count_multi', (self.num_data, ),
dtype=dtype)
ones = fptr.create_dataset('ones', (self.num_data, ), dtype='i4')
multi = fptr.create_dataset('multi', (self.num_data, ), dtype='i4')
ang = np.random.rand(self.num_data).astype('f8') * 2. * cp.pi
fptr['true_angles'] = ang
if self.fluence == 'gamma':
if 'scale' in fptr:
del fptr['scale']
scale = np.random.gamma(2., 0.5, self.num_data)
else:
scale = np.ones(self.num_data, dtype='f8')
rot_mask = cp.empty(self.size**2, dtype='f8')
bsize_model = int(np.ceil(self.size / 32.))
stime = time.time()
for i in range(self.num_data):
kernels.slice_gen((bsize_model, ) * 2, (32, ) * 2,
(self.mask, ang[i], scale[i], self.size,
self.bgmask, 0, rot_mask))
frame = cp.random.poisson(rot_mask, dtype='i4').ravel()
place_ones[i] = cp.where(frame == 1)[0].get()
place_multi[i] = cp.where(frame > 1)[0].get()
count_multi[i] = frame[frame > 1].get()
ones[i] = place_ones[i].shape[0]
multi[i] = place_multi[i].shape[0]
sys.stderr.write('\rWritten %d/%d frames (%d) ' %
(i + 1, self.num_data, int(frame.sum())))
etime = time.time()
sys.stderr.write('\nTime taken (make_data): %f s\n' %
(etime - stime))
def interp(x, xp, fp, left=None, right=None, period=None):
""" One-dimensional linear interpolation.
Args:
x (cupy.ndarray): a 1D array of points on which the interpolation
is performed.
xp (cupy.ndarray): a 1D array of points on which the function values
(``fp``) are known.
fp (cupy.ndarray): a 1D array containing the function values at the
the points ``xp``.
left (float or complex): value to return if ``x < xp[0]``. Default is
``fp[0]``.
right (float or complex): value to return if ``x > xp[-1]``. Default is
``fp[-1]``.
period (None or float): a period for the x-coordinates. Parameters
``left`` and ``right`` are ignored if ``period`` is specified.
Default is ``None``.
Returns:
cupy.ndarray: The interpolated values, same shape as ``x``.
.. note::
This function may synchronize if ``left`` or ``right`` is not already
on the device.
.. seealso:: :func:`numpy.interp`
"""
if xp.ndim != 1 or fp.ndim != 1:
raise ValueError('xp and fp must be 1D arrays')
if xp.size != fp.size:
raise ValueError('fp and xp are not of the same length')
if xp.size == 0:
raise ValueError('array of sample points is empty')
if not x.flags.c_contiguous:
raise NotImplementedError('Non-C-contiguous x is currently not '
'supported')
x_dtype = cupy.common_type(x, xp)
if not cupy.can_cast(x_dtype, cupy.float64):
raise TypeError('Cannot cast array data from'
' {} to {} according to the rule \'safe\''.format(
x_dtype, cupy.float64))
if period is not None:
# The handling of "period" below is modified from NumPy's
if period == 0:
raise ValueError("period must be a non-zero value")
period = abs(period)
left = None
right = None
x = x.astype(cupy.float64)
xp = xp.astype(cupy.float64)
# normalizing periodic boundaries
x %= period
xp %= period
asort_xp = cupy.argsort(xp)
xp = xp[asort_xp]
fp = fp[asort_xp]
xp = cupy.concatenate((xp[-1:] - period, xp, xp[0:1] + period))
fp = cupy.concatenate((fp[-1:], fp, fp[0:1]))
assert xp.flags.c_contiguous
assert fp.flags.c_contiguous
# NumPy always returns float64 or complex128, so we upcast all values
# on the fly in the kernel
out_dtype = 'D' if fp.dtype.kind == 'c' else 'd'
output = cupy.empty(x.shape, dtype=out_dtype)
idx = cupy.searchsorted(xp, x, side='right')
left = fp[0] if left is None else cupy.array(left, fp.dtype)
right = fp[-1] if right is None else cupy.array(right, fp.dtype)
kern = _get_interp_kernel(out_dtype == 'D')
kern(x, idx, xp, fp, xp.size, left, right, output)
return output
def add_buffers_to_particles(species, float_recv_left, float_recv_right,
uint_recv_left, uint_recv_right):
"""
Add the particles stored in recv_left and recv_right
to the existing particle in species.
Resize the auxiliary arrays of the particles Ex, Ey, Ez, Bx, By, Bz,
as well as cell_idx, sorted_idx and sorting_buffer
Parameters
----------
species: a Particles object
Contain the particles that stayed on the present processors
float_recv_left, float_recv_right, uint_recv_left, uint_recv_right:
arrays of shape (n_float,Nptcl) and (n_int,Nptcl) where Nptcl
is the number of particles that are received to the left
proc and right proc respectively, and where n_float and n_int
are the number of float and integer quantities respectively
These arrays are always on the CPU (since they were used for MPI)
"""
# Copy the buffers to an enlarged array
if species.use_cuda:
add_buffers_gpu(species, float_recv_left, float_recv_right,
uint_recv_left, uint_recv_right)
else:
add_buffers_cpu(species, float_recv_left, float_recv_right,
uint_recv_left, uint_recv_right)
# Reallocate the particles auxiliary arrays. This needs to be done,
# as the total number of particles in this domain has changed.
if species.use_cuda:
shape = (species.Ntot, )
# Reallocate empty field-on-particle arrays on the GPU
species.Ex = cupy.empty(shape, dtype=np.float64)
species.Ex = cupy.empty(shape, dtype=np.float64)
species.Ey = cupy.empty(shape, dtype=np.float64)
species.Ez = cupy.empty(shape, dtype=np.float64)
species.Bx = cupy.empty(shape, dtype=np.float64)
species.By = cupy.empty(shape, dtype=np.float64)
species.Bz = cupy.empty(shape, dtype=np.float64)
# Reallocate empty auxiliary sorting arrays on the GPU
species.cell_idx = cupy.empty(shape, dtype=np.int32)
species.sorted_idx = cupy.empty(shape, dtype=np.intp)
species.sorting_buffer = cupy.empty(shape, dtype=np.float64)
if species.n_integer_quantities > 0:
species.int_sorting_buffer = \
cupy.empty( shape, dtype=np.uint64 )
else:
# Reallocate empty field-on-particle arrays on the CPU
species.Ex = np.empty(species.Ntot, dtype=np.float64)
species.Ey = np.empty(species.Ntot, dtype=np.float64)
species.Ez = np.empty(species.Ntot, dtype=np.float64)
species.Bx = np.empty(species.Ntot, dtype=np.float64)
species.By = np.empty(species.Ntot, dtype=np.float64)
species.Bz = np.empty(species.Ntot, dtype=np.float64)
# The particles are unsorted after adding new particles.
species.sorted = False
def sum_duplicates(self):
"""Eliminate duplicate matrix entries by adding them together.
.. warning::
When sorting the indices, CuPy follows the convention of cuSPARSE,
which is different from that of SciPy. Therefore, the order of the
output indices may differ:
.. code-block:: python
>>> # 1 0 0
>>> # A = 1 1 0
>>> # 1 1 1
>>> data = cupy.array([1, 1, 1, 1, 1, 1], 'f')
>>> row = cupy.array([0, 1, 1, 2, 2, 2], 'i')
>>> col = cupy.array([0, 0, 1, 0, 1, 2], 'i')
>>> A = cupyx.scipy.sparse.coo_matrix((data, (row, col)),
... shape=(3, 3))
>>> a = A.get()
>>> A.sum_duplicates()
>>> a.sum_duplicates() # a is scipy.sparse.coo_matrix
>>> A.row
array([0, 1, 1, 2, 2, 2], dtype=int32)
>>> a.row
array([0, 1, 2, 1, 2, 2], dtype=int32)
>>> A.col
array([0, 0, 1, 0, 1, 2], dtype=int32)
>>> a.col
array([0, 0, 0, 1, 1, 2], dtype=int32)
.. warning::
Calling this function might synchronize the device.
.. seealso::
:meth:`scipy.sparse.coo_matrix.sum_duplicates`
"""
if self.has_canonical_format:
return
# Note: The sorting order below follows the cuSPARSE convention (first
# row then col, so-called row-major) and differs from that of SciPy, as
# the cuSPARSE functions such as cusparseSpMV() assume this sorting
# order.
# See https://docs.nvidia.com/cuda/cusparse/index.html#coo-format
keys = cupy.stack([self.col, self.row])
order = cupy.lexsort(keys)
src_data = self.data[order]
src_row = self.row[order]
src_col = self.col[order]
diff = self._sum_duplicates_diff(src_row, src_col, size=self.row.size)
if diff[1:].all():
# All elements have different indices.
data = src_data
row = src_row
col = src_col
else:
# TODO(leofang): move the kernels outside this method
index = cupy.cumsum(diff, dtype='i')
size = int(index[-1]) + 1
data = cupy.zeros(size, dtype=self.data.dtype)
row = cupy.empty(size, dtype='i')
col = cupy.empty(size, dtype='i')
if self.data.dtype.kind == 'f':
cupy.ElementwiseKernel(
'T src_data, int32 src_row, int32 src_col, int32 index',
'raw T data, raw int32 row, raw int32 col',
'''
atomicAdd(&data[index], src_data);
row[index] = src_row;
col[index] = src_col;
''',
'sum_duplicates_assign'
)(src_data, src_row, src_col, index, data, row, col)
elif self.data.dtype.kind == 'c':
cupy.ElementwiseKernel(
'T src_real, T src_imag, int32 src_row, int32 src_col, '
'int32 index',
'raw T real, raw T imag, raw int32 row, raw int32 col',
'''
atomicAdd(&real[index], src_real);
atomicAdd(&imag[index], src_imag);
row[index] = src_row;
col[index] = src_col;
''',
'sum_duplicates_assign_complex'
)(src_data.real, src_data.imag, src_row, src_col, index,
data.real, data.imag, row, col)
self.data = data
self.row = row
self.col = col
self.has_canonical_format = True
def test_concatenate_wrong_shape(self):
a = cupy.empty((2, 3, 4))
b = cupy.empty((3, 3, 4))
c = cupy.empty((4, 4, 4))
with self.assertRaises(ValueError):
cupy.concatenate((a, b, c))
def batched_gesv(a, b):
"""Solves multiple linear matrix equations using cublas<t>getr[fs]Batched().
Computes the solution to system of linear equation ``ax = b``.
Args:
a (cupy.ndarray): The matrix with dimension ``(..., M, M)``.
b (cupy.ndarray): The matrix with dimension ``(..., M)`` or
``(..., M, K)``.
Returns:
cupy.ndarray:
The matrix with dimension ``(..., M)`` or ``(..., M, K)``.
"""
_util._assert_cupy_array(a, b)
_util._assert_nd_squareness(a)
if not ((a.ndim == b.ndim or a.ndim == b.ndim + 1)
and a.shape[:-1] == b.shape[:a.ndim - 1]):
raise ValueError(
'a must have (..., M, M) shape and b must have (..., M) '
'or (..., M, K)')
dtype, out_dtype = _util.linalg_common_type(a, b)
if dtype == 'f':
t = 's'
elif dtype == 'd':
t = 'd'
elif dtype == 'F':
t = 'c'
elif dtype == 'D':
t = 'z'
else:
raise TypeError('invalid dtype')
getrf = getattr(cublas, t + 'getrfBatched')
getrs = getattr(cublas, t + 'getrsBatched')
bs = numpy.prod(a.shape[:-2]) if a.ndim > 2 else 1
n = a.shape[-1]
nrhs = b.shape[-1] if a.ndim == b.ndim else 1
b_shape = b.shape
a_data_ptr = a.data.ptr
b_data_ptr = b.data.ptr
a = cupy.ascontiguousarray(a.reshape(bs, n, n).transpose(0, 2, 1),
dtype=dtype)
b = cupy.ascontiguousarray(b.reshape(bs, n, nrhs).transpose(0, 2, 1),
dtype=dtype)
if a.data.ptr == a_data_ptr:
a = a.copy()
if b.data.ptr == b_data_ptr:
b = b.copy()
if n > get_batched_gesv_limit():
warnings.warn('The matrix size ({}) exceeds the set limit ({})'.format(
n, get_batched_gesv_limit()))
handle = device.get_cublas_handle()
lda = n
a_step = lda * n * a.itemsize
a_array = cupy.arange(a.data.ptr,
a.data.ptr + a_step * bs,
a_step,
dtype=cupy.uintp)
ldb = n
b_step = ldb * nrhs * b.itemsize
b_array = cupy.arange(b.data.ptr,
b.data.ptr + b_step * bs,
b_step,
dtype=cupy.uintp)
pivot = cupy.empty((bs, n), dtype=numpy.int32)
dinfo = cupy.empty((bs, ), dtype=numpy.int32)
info = numpy.empty((1, ), dtype=numpy.int32)
# LU factorization (A = L * U)
getrf(handle, n, a_array.data.ptr, lda, pivot.data.ptr, dinfo.data.ptr, bs)
_util._check_cublas_info_array_if_synchronization_allowed(getrf, dinfo)
# Solves Ax = b
getrs(handle, cublas.CUBLAS_OP_N, n, nrhs, a_array.data.ptr, lda,
pivot.data.ptr, b_array.data.ptr, ldb, info.ctypes.data, bs)
if info[0] != 0:
msg = 'Error reported by {} in cuBLAS. '.format(getrs.__name__)
if info[0] < 0:
msg += 'The {}-th parameter had an illegal value.'.format(-info[0])
raise linalg.LinAlgError(msg)
return b.transpose(0, 2, 1).reshape(b_shape).astype(out_dtype, copy=False)
def interval(self, mx, size):
"""Generate multiple integers independently sampled uniformly from ``[0, mx]``.
Args:
mx (int): Upper bound of the interval
size (None or int or tuple): Shape of the array or the scalar
returned.
Returns:
int or cupy.ndarray: If ``None``, an :class:`cupy.ndarray` with
shape ``()`` is returned.
If ``int``, 1-D array of length size is returned.
If ``tuple``, multi-dimensional array with shape
``size`` is returned.
Currently, only 32 bit integers can be sampled.
If 0 :math:`\\leq` ``mx`` :math:`\\leq` 0x7fffffff,
a ``numpy.int32`` array is returned.
If 0x80000000 :math:`\\leq` ``mx`` :math:`\\leq` 0xffffffff,
a ``numpy.uint32`` array is returned.
"""
if size is None:
return self.interval(mx, 1).reshape(())
elif isinstance(size, int):
size = (size, )
if mx == 0:
return cupy.zeros(size, dtype=numpy.int32)
if mx < 0:
raise ValueError('mx must be non-negative (actual: {})'.format(mx))
elif mx <= 0x7fffffff:
dtype = numpy.int32
elif mx <= 0xffffffff:
dtype = numpy.uint32
else:
raise ValueError(
'mx must be within uint32 range (actual: {})'.format(mx))
mask = (1 << mx.bit_length()) - 1
mask = cupy.array(mask, dtype=dtype)
n = functools.reduce(operator.mul, size, 1)
sample = cupy.empty((n, ), dtype=dtype)
n_rem = n # The number of remaining elements to sample
ret = None
while n_rem > 0:
curand.generate(self._generator, sample.data.ptr, sample.size)
# Drop the samples that exceed the upper limit
sample &= mask
success = sample <= mx
if ret is None:
# If the sampling has finished in the first iteration,
# just return the sample.
if success.all():
n_rem = 0
ret = sample
break
# Allocate the return array.
ret = cupy.empty((n, ), dtype=dtype)
n_succ = min(n_rem, int(success.sum()))
ret[n - n_rem:n - n_rem + n_succ] = sample[success][:n_succ]
n_rem -= n_succ
assert n_rem == 0
return ret.reshape(size)
def test_vstack_wrong_ndim(self):
a = cupy.empty((3, ))
b = cupy.empty((3, 1))
with self.assertRaises(ValueError):
cupy.vstack((a, b))
def create_dropout_states(handle):
state_size = cudnn.dropoutGetStatesSize(handle)
return cupy.empty((state_size,), dtype="b")
def f4():
buf = []
for i, s in enumerate(sizes):
buf.append(cupy.empty((s,), dtype='b'))
if i % 10 == 0:
buf[i // 10] = None
def _tensordot_core(a, b, out, n, m, k, ret_shape):
ret_dtype = a.dtype.char
if ret_dtype != b.dtype.char:
ret_dtype = numpy.find_common_type((ret_dtype, b.dtype), ()).char
# Cast to float32 or float64
if ret_dtype == 'f' or ret_dtype == 'd':
dtype = ret_dtype
else:
dtype = numpy.find_common_type((ret_dtype, 'f'), ()).char
a = a.astype(dtype, copy=False)
b = b.astype(dtype, copy=False)
if not a.size or not b.size:
if a.size or b.size:
raise ValueError('cannot dot zero-sized and non-zero-sized arrays')
if out is None:
return cupy.zeros(ret_shape, dtype=ret_dtype)
else:
out.fill(0)
return out
if out is None:
out = cupy.empty(ret_shape, dtype)
if dtype == ret_dtype:
ret = out
else:
ret = cupy.empty(ret_shape, ret_dtype)
else:
ret = out
if out.dtype != dtype:
out = cupy.empty(ret_shape, dtype)
# It copies the operands if needed
if a.shape != (k, n):
a = cupy.reshape(a, (k, n))
if b.shape != (k, m):
b = cupy.reshape(b, (k, m))
c = out
if c.shape != (n, m):
c = c.view()
c.shape = (n, m)
# Be careful that cuBLAS uses the FORTRAN-order matrix representation.
if k == 1:
if n == 1:
# Scalar-vector product
cupy.multiply(a, b, c)
elif m == 1:
# Scalar-vector product
cupy.multiply(a.T, b, c)
else:
# Outer product A^T * B
# c is C-contiguous while cuBLAS requires F-contiguous arrays, so
# we compute C^T = B^T * A here.
handle = cuda.Device().cublas_handle
c.fill(0)
a, inca = _to_cublas_vector(a, 1)
b, incb = _to_cublas_vector(b, 1)
if dtype == 'f':
ger = cublas.sger
elif dtype == 'd':
ger = cublas.dger
ger(handle, m, n, 1, b.data.ptr, incb, a.data.ptr, inca,
c.data.ptr, m)
if dtype != ret_dtype:
elementwise.copy(out, ret)
return ret
handle = cuda.Device().cublas_handle
if n == 1:
if m == 1:
# Inner product
a, inca = _to_cublas_vector(a, 0)
b, incb = _to_cublas_vector(b, 0)
mode = cublas.getPointerMode(handle)
cublas.setPointerMode(handle,
cublas.CUBLAS_POINTER_MODE_DEVICE)
if dtype == 'f':
dot = cublas.sdot
elif dtype == 'd':
dot = cublas.ddot
try:
dot(handle, k, a.data.ptr, inca, b.data.ptr, incb, c.data.ptr)
finally:
cublas.setPointerMode(handle, mode)
else:
# Matrix-vector product B^T * A
a, inca = _to_cublas_vector(a, 0)
b, transb, ldb = _mat_to_cublas_contiguous(b, 1)
if transb:
# gemv requires (m, k) as the original matrix dimensions
# rather than the transposed dimensions.
m, k = k, m
if dtype == 'f':
gemv = cublas.sgemv
elif dtype == 'd':
gemv = cublas.dgemv
gemv(handle, transb, m, k, 1, b.data.ptr, ldb, a.data.ptr, inca,
0, c.data.ptr, 1)
elif m == 1:
# Matrix-vector product A^T * B
a, transa, lda = _mat_to_cublas_contiguous(a, 1)
b, incb = _to_cublas_vector(b, 0)
if transa:
# gemv requires (n, k) as the original matrix dimensions rather
# than the transposed dimensions.
n, k = k, n
if dtype == 'f':
gemv = cublas.sgemv
elif dtype == 'd':
gemv = cublas.dgemv
gemv(handle, transa, n, k, 1, a.data.ptr, lda, b.data.ptr, incb, 0,
c.data.ptr, 1)
else:
# Matrix-Matrix product A^T * B
# c is C-contiguous while cuBLAS assumes F-contiguous inputs, so we
# compute C^T = B^T * A here.
a, transa, lda = _mat_to_cublas_contiguous(a, 0)
b, transb, ldb = _mat_to_cublas_contiguous(b, 1)
if dtype == 'f':
gemm = cublas.sgemm
elif dtype == 'd':
gemm = cublas.dgemm
gemm(handle, transb, transa, m, n, k, 1, b.data.ptr, ldb, a.data.ptr,
lda, 0, c.data.ptr, m)
if dtype != ret_dtype:
elementwise.copy(out, ret)
return ret
def test_concatenate_wrong_ndim(self):
a = cupy.empty((2, 3))
b = cupy.empty((2, ))
with self.assertRaises(ValueError):
cupy.concatenate((a, b))
def _batched_inv(a):
assert (a.ndim >= 3)
util._assert_cupy_array(a)
util._assert_nd_squareness(a)
if a.dtype == cupy.float32:
getrf = cupy.cuda.cublas.sgetrfBatched
getri = cupy.cuda.cublas.sgetriBatched
elif a.dtype == cupy.float64:
getrf = cupy.cuda.cublas.dgetrfBatched
getri = cupy.cuda.cublas.dgetriBatched
elif a.dtype == cupy.complex64:
getrf = cupy.cuda.cublas.cgetrfBatched
getri = cupy.cuda.cublas.cgetriBatched
elif a.dtype == cupy.complex128:
getrf = cupy.cuda.cublas.zgetrfBatched
getri = cupy.cuda.cublas.zgetriBatched
else:
msg = ('dtype must be float32, float64, complex64 or float128'
' (actual: {})'.format(a.dtype))
raise ValueError(msg)
if 0 in a.shape:
return cupy.empty_like(a)
a_shape = a.shape
# copy is necessary to present `a` to be overwritten.
a = a.copy().reshape(-1, a_shape[-2], a_shape[-1])
handle = device.get_cublas_handle()
batch_size = a.shape[0]
n = a.shape[1]
lda = n
step = n * lda * a.itemsize
start = a.data.ptr
stop = start + step * batch_size
a_array = cupy.arange(start, stop, step, dtype=cupy.uintp)
pivot_array = cupy.empty((batch_size, n), dtype=cupy.int32)
info_array = cupy.empty((batch_size, ), dtype=cupy.int32)
getrf(handle, n, a_array.data.ptr, lda, pivot_array.data.ptr,
info_array.data.ptr, batch_size)
err = False
err_detail = ''
for i in range(batch_size):
info = info_array[i]
if info < 0:
err = True
err_detail += ('\tmatrix[{}]: illegal value at {}-the parameter.'
'\n'.format(i, -info))
if info > 0:
err = True
err_detail += '\tmatrix[{}]: matrix is singular.\n'.format(i)
if err:
raise RuntimeError('matrix inversion failed at getrf.\n' + err_detail)
c = cupy.empty_like(a)
ldc = lda
step = n * ldc * c.itemsize
start = c.data.ptr
stop = start + step * batch_size
c_array = cupy.arange(start, stop, step, dtype=cupy.uintp)
getri(handle, n, a_array.data.ptr, lda, pivot_array.data.ptr,
c_array.data.ptr, ldc, info_array.data.ptr, batch_size)
for i in range(batch_size):
info = info_array[i]
if info > 0:
err = True
err_detail += '\tmatrix[{}]: matrix is singular.\n'.format(i)
if err:
raise RuntimeError('matrix inversion failed at getri.\n' + err_detail)
return c.reshape(a_shape)
def unique(ar, return_index=False, return_inverse=False,
return_counts=False, axis=None):
"""Find the unique elements of an array.
Returns the sorted unique elements of an array. There are three optional
outputs in addition to the unique elements:
* the indices of the input array that give the unique values
* the indices of the unique array that reconstruct the input array
* the number of times each unique value comes up in the input array
Args:
ar(array_like): Input array. This will be flattened if it is not
already 1-D.
return_index(bool, optional): If True, also return the indices of `ar`
(along the specified axis, if provided, or in the flattened array)
that result in the unique array.
return_inverse(bool, optional): If True, also return the indices of the
unique array (for the specified axis, if provided) that can be used
to reconstruct `ar`.
return_counts(bool, optional): If True, also return the number of times
each unique item appears in `ar`.
axis(int or None, optional): Not supported yet.
Returns:
cupy.ndarray or tuple:
If there are no optional outputs, it returns the
:class:`cupy.ndarray` of the sorted unique values. Otherwise, it
returns the tuple which contains the sorted unique values and
followings.
* The indices of the first occurrences of the unique values in the
original array. Only provided if `return_index` is True.
* The indices to reconstruct the original array from the
unique array. Only provided if `return_inverse` is True.
* The number of times each of the unique values comes up in the
original array. Only provided if `return_counts` is True.
.. warning::
This function may synchronize the device.
.. seealso:: :func:`numpy.unique`
"""
if axis is not None:
raise NotImplementedError('axis option is not supported yet.')
ar = cupy.asarray(ar).flatten()
if return_index or return_inverse:
perm = ar.argsort()
aux = ar[perm]
else:
ar.sort()
aux = ar
mask = cupy.empty(aux.shape, dtype=cupy.bool_)
mask[0] = True
mask[1:] = aux[1:] != aux[:-1]
ret = aux[mask]
if not return_index and not return_inverse and not return_counts:
return ret
ret = ret,
if return_index:
ret += perm[mask],
if return_inverse:
imask = cupy.cumsum(mask) - 1
inv_idx = cupy.empty(mask.shape, dtype=cupy.intp)
inv_idx[perm] = imask
ret += inv_idx,
if return_counts:
nonzero = cupy.nonzero(mask)[0] # may synchronize
idx = cupy.empty((nonzero.size + 1,), nonzero.dtype)
idx[:-1] = nonzero
idx[-1] = mask.size
ret += idx[1:] - idx[:-1],
return ret