# Copyright 2022-2023 XProbe Inc.
# derived from copyright 1999-2021 Alibaba Group Holding Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
from ... import opcodes as OperandDef
from ...lib import sparse
from ...lib.sparse.core import get_array_module, get_sparse_module, naked
from ...serialization.serializables import (
AnyField,
FieldTypes,
KeyField,
StringField,
TupleField,
)
from ..array_utils import create_array
from ..utils import get_order
from .array import tensor
from .core import TensorLike, TensorNoInput
class TensorZeros(TensorNoInput):
_op_type_ = OperandDef.TENSOR_ZEROS
order = StringField("order")
shape = TupleField("shape", FieldTypes.int64)
chunk_size = AnyField("chunk_size")
def __init__(self, shape=None, **kwargs):
if type(shape) is int:
shape = (shape,)
super().__init__(shape=shape, **kwargs)
def to_chunk_op(self, *args):
chunk_op = super().to_chunk_op(*args)
chunk_op.shape = args[0]
chunk_op.chunk_size = None
return chunk_op
@classmethod
def execute(cls, ctx, op):
chunk = op.outputs[0]
if op.sparse:
ctx[chunk.key] = sparse.zeros(op.shape, dtype=op.dtype, gpu=op.gpu)
else:
ctx[chunk.key] = create_array(op)(
"zeros", op.shape, dtype=op.dtype, order=op.order
)
[docs]def zeros(shape, dtype=None, chunk_size=None, gpu=None, sparse=False, order="C"):
"""
Return a new tensor of given shape and type, filled with zeros.
Parameters
----------
shape : int or sequence of ints
Shape of the new tensor, e.g., ``(2, 3)`` or ``2``.
dtype : data-type, optional
The desired data-type for the array, e.g., `mt.int8`. Default is
`mt.float64`.
chunk_size : int or tuple of int or tuple of ints, optional
Desired chunk size on each dimension
gpu : bool, optional
Allocate the tensor on GPU if True, False as default
sparse: bool, optional
Create sparse tensor if True, False as default
order : {'C', 'F'}, optional, default: 'C'
Whether to store multi-dimensional data in row-major
(C-style) or column-major (Fortran-style) order in
memory.
Returns
-------
out : Tensor
Tensor of zeros with the given shape, dtype, and order.
See Also
--------
zeros_like : Return a tensor of zeros with shape and type of input.
ones_like : Return a tensor of ones with shape and type of input.
empty_like : Return a empty tensor with shape and type of input.
ones : Return a new tensor setting values to one.
empty : Return a new uninitialized tensor.
Examples
--------
>>> import mars.tensor as mt
>>> mt.zeros(5).execute()
array([ 0., 0., 0., 0., 0.])
>>> mt.zeros((5,), dtype=int).execute()
array([0, 0, 0, 0, 0])
>>> mt.zeros((2, 1)).execute()
array([[ 0.],
[ 0.]])
>>> s = (2,2)
>>> mt.zeros(s).execute()
array([[ 0., 0.],
[ 0., 0.]])
>>> mt.zeros((2,), dtype=[('x', 'i4'), ('y', 'i4')]).execute() # custom dtype
array([(0, 0), (0, 0)],
dtype=[('x', '<i4'), ('y', '<i4')])
"""
tensor_order = get_order(
order,
None,
available_options="CF",
err_msg="only 'C' or 'F' order is permitted",
)
dtype = np.dtype(dtype or "f8")
op = TensorZeros(
dtype=dtype,
shape=shape,
chunk_size=chunk_size,
gpu=gpu,
sparse=sparse,
order=order,
)
return op(shape, chunk_size=chunk_size, order=tensor_order)
class TensorZerosLike(TensorLike):
_op_type_ = OperandDef.TENSOR_ZEROS_LIKE
_input = KeyField("input")
_order = StringField("order")
def __init__(self, dtype=None, gpu=None, sparse=False, order=None, **kw):
dtype = np.dtype(dtype) if dtype is not None else None
super().__init__(dtype=dtype, gpu=gpu, sparse=sparse, _order=order, **kw)
@property
def order(self):
return self._order
@classmethod
def execute(cls, ctx, op):
chunk = op.outputs[0]
if op.sparse:
in_data = naked(ctx[op.inputs[0].key])
xps = get_sparse_module(in_data)
xp = get_array_module(in_data)
ctx[chunk.key] = sparse.SparseNDArray(
xps.csr_matrix(
(
xp.zeros_like(in_data.data, dtype=op.dtype),
in_data.indices,
in_data.indptr,
),
shape=in_data.shape,
)
)
else:
ctx[chunk.key] = create_array(op)(
"zeros_like", ctx[op.inputs[0].key], dtype=op.dtype, order=op.order
)
[docs]def zeros_like(a, dtype=None, gpu=None, order="K"):
"""
Return a tensor of zeros with the same shape and type as a given tensor.
Parameters
----------
a : array_like
The shape and data-type of `a` define these same attributes of
the returned array.
dtype : data-type, optional
Overrides the data type of the result.
gpu : bool, optional
Allocate the tensor on GPU if True, None as default
order : {'C', 'F', 'A', or 'K'}, optional
Overrides the memory layout of the result. 'C' means C-order,
'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,
'C' otherwise. 'K' means match the layout of `a` as closely
as possible.
Returns
-------
out : Tensor
tensor of zeros with the same shape and type as `a`.
See Also
--------
ones_like : Return an array of ones with shape and type of input.
empty_like : Return an empty array with shape and type of input.
zeros : Return a new array setting values to zero.
ones : Return a new array setting values to one.
empty : Return a new uninitialized array.
Examples
--------
>>> import mars.tensr as mt
>>> x = mt.arange(6)
>>> x = x.reshape((2, 3))
>>> x.execute()
array([[0, 1, 2],
[3, 4, 5]])
>>> mt.zeros_like(x).execute()
array([[0, 0, 0],
[0, 0, 0]])
>>> y = mt.arange(3, dtype=float)
>>> y.execute()
array([ 0., 1., 2.])
>>> mt.zeros_like(y).execute()
array([ 0., 0., 0.])
"""
a = tensor(a)
tensor_order = get_order(order, a.order)
gpu = a.op.gpu if gpu is None else gpu
op = TensorZerosLike(dtype=dtype, gpu=gpu, sparse=a.issparse(), order=order)
return op(a, order=tensor_order)