def __call__(self, a, axis=None, dtype=None, out=None, keepdims=False):
if not isinstance(a, cupy.ndarray):
raise TypeError('Input type must be cupy.ndarray')
if self.identity is None:
assert a.size != 0
in_args = [a]
if out is None:
out_args = []
else:
out_args = [out]
internal.check_args_device(in_args + out_args)
in_types, out_types, routine = self._guess_routine(in_args, dtype)
axis = _get_axis(axis, a.ndim)
out_shape = _get_out_shape(a.shape, axis, keepdims)
out_args = elementwise._get_out_args(in_args, out_args, out_types,
out_shape)
in_args, in_shape = _get_trans_args(in_args, axis, in_args[0].shape)
in_indexer = cindexer.Indexer(in_shape)
out_indexer = cindexer.Indexer(out_shape)
out_clp2_size = 2**int.bit_length(int(out_indexer.size - 1))
inout_args, is_ndarray = _get_inout_args(in_args, out_args, in_indexer,
out_indexer, out_clp2_size,
self._params, True)
param_types = elementwise._get_kernel_param_types(inout_args)
params = elementwise._get_kernel_params(self._params, is_ndarray,
param_types)
block_size = 512
reduce_type = routine[3]
if reduce_type is None:
reduce_type = elementwise._get_typename(out_types[0])
type_preamble = (
'typedef {} type_in0_raw; typedef {} type_out0_raw;'.format(
elementwise._get_typename(in_args[0].dtype),
elementwise._get_typename(out_args[0].dtype)))
kern = _make_reduction_function_kernel(
self.name, block_size, reduce_type, params, self.identity,
routine[0], routine[1], routine[2], type_preamble,
self._input_expr, self._output_expr, self._preamble)
shared_mem = 32 * block_size
if out_clp2_size > 256:
shared_mem = 0
# TODO(okuta) set actual size
kern.linear_launch(max(out_indexer.size, block_size),
inout_args,
shared_mem=shared_mem,
block_max_size=block_size)
if len(out_args) == 1:
return out_args[0]
return tuple(out_args)
def __call__(self, *args, **kwargs):
"""Applies the universal function to arguments elementwise.
Args:
args: Input arguments. Each of them can be a cupy.ndarray object or
a scalar. The output arguments can be omitted or be specified
by the ``out`` argument.
out (cupy.ndarray): Output array. It outputs to new arrays
default.
dtype: Data type specifier.
Returns:
Output array or a tuple of output arrays.
"""
out = kwargs.get('out', None)
dtype = kwargs.get('dtype', None)
if not (len(args) == self.nin or len(args) == self.nargs):
raise TypeError('Wrong number of arguments for %s' % self.name)
assert all(i is not None for i in args)
brod = cupy.broadcast(*args)
in_args = brod.values[:self.nin]
out_args = list(args[self.nin:])
if out is not None:
assert len(out_args) == 0
internal.check_args_device((out, ))
out_args = [out]
internal.check_args_device(in_args + out_args)
in_types, out_types, routine = self._guess_routine(in_args, dtype)
in_args = [
x if isinstance(x, cupy.ndarray) else t.type(x)
for x, t in six.moves.zip(in_args, in_types)
]
out_args = _get_out_args(in_args, out_args, out_types, brod.shape)
if len(out_args) == 1:
ret = out_args[0]
else:
ret = tuple(out_args)
if 0 in brod.shape:
return ret
indexer = cindexer.Indexer(brod.shape)
inout_args, is_ndarray = _get_inout_args(in_args + out_args, indexer,
self._params, True)
param_types = _get_kernel_param_types(inout_args)
out_raw_types = tuple(x.dtype for x in out_args)
kern = _get_ufunc_kernel(in_types, out_types, out_raw_types,
is_ndarray, param_types, self._params,
routine, self.name, self._preamble)
kern.linear_launch(indexer.size, inout_args)
return ret
def __call__(self, *args, **kwargs):
"""Applies the universal function to arguments elementwise.
Args:
args: Input arguments. Each of them can be a cupy.ndarray object or
a scalar. The output arguments can be omitted or be specified
by the ``out`` argument.
out (cupy.ndarray): Output array. It outputs to new arrays
default.
dtype: Data type specifier.
Returns:
Output array or a tuple of output arrays.
"""
out = kwargs.get('out', None)
dtype = kwargs.get('dtype', None)
if not (len(args) == self.nin or len(args) == self.nargs):
raise TypeError('Wrong number of arguments for %s' % self.name)
assert all(i is not None for i in args)
brod = cupy.broadcast(*args)
in_args = brod.values[:self.nin]
out_args = list(args[self.nin:])
if out is not None:
assert len(out_args) == 0
internal.check_args_device((out,))
out_args = [out]
internal.check_args_device(in_args + out_args)
in_types, out_types, routine = self._guess_routine(in_args, dtype)
in_args = [x if isinstance(x, cupy.ndarray) else t.type(x)
for x, t in six.moves.zip(in_args, in_types)]
out_args = _get_out_args(in_args, out_args, out_types, brod.shape)
if len(out_args) == 1:
ret = out_args[0]
else:
ret = tuple(out_args)
if 0 in brod.shape:
return ret
indexer = cindexer.Indexer(brod.shape)
inout_args, is_ndarray = _get_inout_args(
in_args + out_args, indexer, self._params, True)
param_types = _get_kernel_param_types(inout_args)
out_raw_types = tuple(x.dtype for x in out_args)
kern = _get_ufunc_kernel(
in_types, out_types, out_raw_types,
is_ndarray, param_types, self._params,
routine, self.name, self._preamble)
kern.linear_launch(indexer.size, inout_args)
return ret
def __call__(self, *args, **kwargs):
"""Compiles and invokes the reduction kernel.
The compilation runs only if the kernel is not cached. Note that the
kernels with different argument dtypes, ndims, or axis are not
compatible. It means that single ReductionKernel object may be compiled
into multiple kernel binaries.
Args:
args: Arguments of the kernel.
Returns:
Arrays are returned according to the ``out_params`` argument of the
``__init__`` method.
"""
out = kwargs.pop('out', None)
axis = kwargs.get('axis', None)
keepdims = kwargs.get('keepdims', False)
if not (len(args) == self.nin or
len(args) == self.nin + self.nout):
raise TypeError('Wrong number of arguments for %s' % self.name)
assert all(i is not None for i in args)
out_args = list(args[self.nin:])
if out is not None:
if self.nout != 1:
raise NotImplementedError('')
if len(out_args) != 0:
raise ValueError("cannot specify 'out' as both "
"a positional and keyword argument")
out_args = [out]
brod, in_args = elementwise._broadcast(args, self.in_params)
internal.check_args_device(in_args + out_args)
if self.identity is None:
assert brod.size != 0
in_types, out_types, types = elementwise._decide_params_type(
self.in_params, self.out_params,
elementwise._get_ndarray_dtype(in_args),
elementwise._get_ndarray_dtype(out_args))
axis = _get_axis(axis, brod.nd)
out_shape = _get_out_shape(brod.shape, axis, keepdims)
in_args = [x if isinstance(x, cupy.ndarray) else t.type(x)
for x, t in six.moves.zip(in_args, in_types)]
in_args, in_shape = _get_trans_args(
in_args, axis, brod.shape, self.in_params)
out_args = elementwise._get_out_args(
in_args, out_args, out_types, out_shape, self.out_params)
in_indexer = cindexer.Indexer(in_shape)
out_indexer = cindexer.Indexer(out_shape)
out_clp2_size = 2 ** int.bit_length(int(out_indexer.size - 1))
inout_args, is_ndarray = _get_inout_args(
in_args, out_args, in_indexer, out_indexer, out_clp2_size,
self.params, self.reduce_dims)
param_types = elementwise._get_kernel_param_types(inout_args)
exprs = _get_reduction_kernel(
self.params, is_ndarray, param_types, types)
block_size = 512
kern = _make_reduction_function_kernel(
self.name, block_size, self.reduce_type, exprs[0], self.identity,
self.map_expr, self.reduce_expr, self.post_map_expr,
exprs[1], exprs[2], exprs[3], self.preamble)
shared_mem = 32 * block_size
if out_clp2_size > 256:
shared_mem = 0
# TODO(okuta) set actual size
kern.linear_launch(max(out_indexer.size, block_size), inout_args,
shared_mem=shared_mem,
block_max_size=block_size)
return out_args[0]
def __call__(self, a, axis=None, dtype=None, out=None, keepdims=False):
if not isinstance(a, cupy.ndarray):
raise TypeError('Input type must be cupy.ndarray')
if self.identity is None:
assert a.size != 0
in_args = [a]
if out is None:
out_args = []
else:
out_args = [out]
internal.check_args_device(in_args + out_args)
in_types, out_types, routine = self._guess_routine(in_args, dtype)
axis = _get_axis(axis, a.ndim)
out_shape = _get_out_shape(a.shape, axis, keepdims)
out_args = elementwise._get_out_args(
in_args, out_args, out_types, out_shape)
in_args, in_shape = _get_trans_args(
in_args, axis, in_args[0].shape)
in_indexer = cindexer.Indexer(in_shape)
out_indexer = cindexer.Indexer(out_shape)
out_clp2_size = 2 ** int.bit_length(int(out_indexer.size - 1))
inout_args, is_ndarray = _get_inout_args(
in_args, out_args, in_indexer, out_indexer, out_clp2_size,
self._params, True)
param_types = elementwise._get_kernel_param_types(inout_args)
params = elementwise._get_kernel_params(
self._params, is_ndarray, param_types)
block_size = 512
reduce_type = routine[3]
if reduce_type is None:
reduce_type = elementwise._get_typename(out_types[0])
type_preamble = (
'typedef {} type_in0_raw; typedef {} type_out0_raw;'.format(
elementwise._get_typename(in_args[0].dtype),
elementwise._get_typename(out_args[0].dtype)))
kern = _make_reduction_function_kernel(
self.name,
block_size,
reduce_type,
params,
self.identity,
routine[0], routine[1], routine[2],
type_preamble, self._input_expr, self._output_expr,
self._preamble)
shared_mem = 32 * block_size
if out_clp2_size > 256:
shared_mem = 0
# TODO(okuta) set actual size
kern.linear_launch(max(out_indexer.size, block_size), inout_args,
shared_mem=shared_mem,
block_max_size=block_size)
if len(out_args) == 1:
return out_args[0]
return tuple(out_args)
def __call__(self, *args, **kwargs):
"""Compiles and invokes the elementwise kernel.
The compilation runs only if the kernel is not cached. Note that the
kernels with different argument dtypes or ndims are not compatible. It
means that single ElementwiseKernel object may be compiled into
multiple kernel binaries.
Args:
args: Argumens of the kernel.
size (int): Range size of the indices. If specified, the variable
``n`` is set to this value. Otherwise, the result of
broadcasting is used to determine the value of ``n``.
Returns:
Arrays are returned according to the ``out_params`` argument of the
``__init__`` method.
"""
n = kwargs.pop('size', None)
if not (len(args) == self.nin or
len(args) == self.nin + self.nout):
raise TypeError('Wrong number of arguments for %s' % self.name)
for i in args:
if isinstance(i, numpy.ndarray):
raise TypeError('Unsupported type %s' % type(i))
assert not any(i is None for i in args)
internal.check_args_device(args)
brod, value = _broadcast(args, self.params, n is None)
in_args = value[:self.nin]
out_args = value[self.nin:]
in_types, out_types, types = _decide_params_type(
self.in_params, self.out_params,
_get_ndarray_dtype(in_args), _get_ndarray_dtype(out_args))
in_args = [x if isinstance(x, cupy.ndarray) else t.type(x)
for x, t in six.moves.zip(in_args, in_types)]
out_args = _get_out_args(
in_args, out_args, out_types, brod.shape, self.out_params)
if len(out_args) == 1:
ret = out_args[0]
else:
ret = tuple(out_args)
if n is None:
indexer = cindexer.Indexer(brod.shape)
else:
indexer = cindexer.Indexer((n,))
if brod.size == 0:
return ret
inout_args, is_ndarray = _get_inout_args(
in_args + out_args, indexer, self.params, self.reduce_dims)
param_types = _get_kernel_param_types(inout_args)
kern = _get_elementwise_kernel(
self.params, is_ndarray, param_types, types, self.operation,
self.name, self.options, **self.kwargs)
kern.linear_launch(indexer.size, inout_args)
return ret
def __call__(self, *args, **kwargs):
"""Compiles and invokes the elementwise kernel.
The compilation runs only if the kernel is not cached. Note that the
kernels with different argument dtypes or ndims are not compatible. It
means that single ElementwiseKernel object may be compiled into
multiple kernel binaries.
Args:
args: Argumens of the kernel.
size (int): Range size of the indices. If specified, the variable
``n`` is set to this value. Otherwise, the result of
broadcasting is used to determine the value of ``n``.
Returns:
Arrays are returned according to the ``out_params`` argument of the
``__init__`` method.
"""
n = kwargs.pop('size', None)
if not (len(args) == self.nin or len(args) == self.nin + self.nout):
raise TypeError('Wrong number of arguments for %s' % self.name)
for i in args:
if isinstance(i, numpy.ndarray):
raise TypeError('Unsupported type %s' % type(i))
assert not any(i is None for i in args)
internal.check_args_device(args)
brod, value = _broadcast(args, self.params, n is None)
in_args = value[:self.nin]
out_args = value[self.nin:]
in_types, out_types, types = _decide_params_type(
self.in_params, self.out_params, _get_ndarray_dtype(in_args),
_get_ndarray_dtype(out_args))
in_args = [
x if isinstance(x, cupy.ndarray) else t.type(x)
for x, t in six.moves.zip(in_args, in_types)
]
out_args = _get_out_args(in_args, out_args, out_types, brod.shape,
self.out_params)
if len(out_args) == 1:
ret = out_args[0]
else:
ret = tuple(out_args)
if n is None:
indexer = cindexer.Indexer(brod.shape)
else:
indexer = cindexer.Indexer((n, ))
if brod.size == 0:
return ret
inout_args, is_ndarray = _get_inout_args(in_args + out_args, indexer,
self.params, self.reduce_dims)
param_types = _get_kernel_param_types(inout_args)
kern = _get_elementwise_kernel(self.params, is_ndarray, param_types,
types, self.operation, self.name,
self.options, **self.kwargs)
kern.linear_launch(indexer.size, inout_args)
return ret
