Source code for mars.tensor.datasource.zeros

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# 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
# 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 ...serialization.serializables import (
from ...lib import sparse
from ...lib.sparse.core import get_sparse_module, get_array_module, naked
from ..array_utils import create_array
from ..utils import get_order
from .core import TensorNoInput, TensorLike
from .array import tensor

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

    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)
            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(, 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)