Source code for mars.tensor.datasource.empty

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 1999-2020 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

import numpy as np

from ... import opcodes as OperandDef
from ...serialize import KeyField, StringField
from ...lib.sparse import SparseNDArray
from ...lib.sparse.core import naked, get_array_module, get_sparse_module
from ..array_utils import create_array
from ..utils import get_order
from .core import TensorNoInput, TensorLike
from .array import tensor

class TensorEmptyBase(object):
    __slots__ = ()

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

    def _gen_rand(self):
        if getattr(self, '_rand', None) is None:
            self._obj_set('_rand', np.random.random())

    def to_chunk_op(self, *args):
        op = self.copy().reset_key()
        op._rand = None
        return op

class TensorEmpty(TensorEmptyBase, TensorNoInput):
    __slots__ = '_rand',
    _op_type_ = OperandDef.TENSOR_EMPTY

    _order = StringField('order')

    def __init__(self, dtype=None, order=None, **kw):
        dtype = np.dtype(dtype or 'f8')
        super().__init__(dtype=dtype, _order=order, **kw)

    def order(self):
        return self._order

    def execute(cls, ctx, op):
        chunk = op.outputs[0]
        ctx[chunk.key] = create_array(op)('empty', chunk.shape, dtype=op.dtype,

[docs]def empty(shape, dtype=None, chunk_size=None, gpu=False, order='C'): """ Return a new tensor of given shape and type, without initializing entries. Parameters ---------- shape : int or tuple of int Shape of the empty tensor dtype : data-type, optional Desired output data-type. 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 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 uninitialized (arbitrary) data of the given shape, dtype, and order. Object arrays will be initialized to None. See Also -------- empty_like, zeros, ones Notes ----- `empty`, unlike `zeros`, does not set the array values to zero, and may therefore be marginally faster. On the other hand, it requires the user to manually set all the values in the array, and should be used with caution. Examples -------- >>> import mars.tensor as mt >>> mt.empty([2, 2]).execute() array([[ -9.74499359e+001, 6.69583040e-309], [ 2.13182611e-314, 3.06959433e-309]]) #random >>> mt.empty([2, 2], dtype=int).execute() array([[-1073741821, -1067949133], [ 496041986, 19249760]]) #random """ tensor_order = get_order(order, None, available_options='CF', err_msg="only 'C' or 'F' order is permitted") op = TensorEmpty(dtype=dtype, gpu=gpu, order=order) return op(shape, chunk_size=chunk_size, order=tensor_order)
class TensorEmptyLike(TensorEmptyBase, TensorLike): __slots__ = '_rand', _op_type_ = OperandDef.TENSOR_EMPTY_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, _order=order, _sparse=sparse, **kw) @property def order(self): return self._order @classmethod def execute(cls, ctx, op): chunk = op.outputs[0] if op.issparse(): in_data = naked(ctx[op.inputs[0].key]) xps = get_sparse_module(in_data) xp = get_array_module(in_data) ctx[chunk.key] = SparseNDArray(xps.csr_matrix( (xp.empty_like(, dtype=op.dtype), in_data.indices, in_data.indptr), shape=in_data.shape )) else: ctx[chunk.key] = create_array(op)( 'empty_like', ctx[op.inputs[0].key], dtype=op.dtype, order=op.order)
[docs]def empty_like(a, dtype=None, gpu=None, order='K'): """ Return a new tensor 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 tensor. 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 ``prototype`` is Fortran contiguous, 'C' otherwise. 'K' means match the layout of ``prototype`` as closely as possible. Returns ------- out : Tensor Array of uninitialized (arbitrary) data with the same shape and type as `a`. See Also -------- ones_like : Return a tensor of ones with shape and type of input. zeros_like : Return a tensor of zeros with shape and type of input. empty : Return a new uninitialized tensor. ones : Return a new tensor setting values to one. zeros : Return a new tensor setting values to zero. Notes ----- This function does *not* initialize the returned tensor; to do that use `zeros_like` or `ones_like` instead. It may be marginally faster than the functions that do set the array values. Examples -------- >>> import mars.tensor as mt >>> a = ([1,2,3], [4,5,6]) # a is array-like >>> mt.empty_like(a).execute() array([[-1073741821, -1073741821, 3], #ranm [ 0, 0, -1073741821]]) >>> a = mt.array([[1., 2., 3.],[4.,5.,6.]]) >>> mt.empty_like(a).execute() array([[ -2.00000715e+000, 1.48219694e-323, -2.00000572e+000],#random [ 4.38791518e-305, -2.00000715e+000, 4.17269252e-309]]) """ a = tensor(a) tensor_order = get_order(order, a.order) gpu = a.op.gpu if gpu is None else gpu op = TensorEmptyLike(dtype=dtype, gpu=gpu, sparse=a.issparse(), order=order) return op(a, order=tensor_order)