# Copyright 2020 Huawei Technologies Co., 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.
# ============================================================================
"""Tensor implementation."""
import numpy as np
from .._c_expression import Tensor as Tensor_
from .._c_expression import MetaTensor
from .._checkparam import check_type, check_typename
from . import dtype as mstype
from ._register_for_tensor import tensor_operator_registry
__all__ = ['Tensor', 'MetaTensor', 'RowTensor', 'SparseTensor']
np_types = (np.int8, np.int16, np.int32, np.int64,
np.uint8, np.uint16, np.uint32, np.uint64, np.float16,
np.float32, np.float64, np.bool_)
[docs]class Tensor(Tensor_):
"""
Tensor is used for data storage.
Tensor inherits tensor object in C++.
Some functions are implemented in C++ and some functions are implemented in Python.
Args:
input_data (Tensor, float, int, bool, tuple, list, numpy.ndarray): Input data of the tensor.
dtype (:class:`mindspore.dtype`): Input data should be None, bool or numeric type defined in `mindspore.dtype`.
The argument is used to define the data type of the output tensor. If it is None, the data type of the
output tensor will be as same as the `input_data`. Default: None.
Outputs:
Tensor, with the same shape as `input_data`.
Examples:
>>> # initialize a tensor with input data
>>> t1 = Tensor(np.zeros([1, 2, 3]), mindspore.float32)
>>> assert isinstance(t1, Tensor)
>>> assert t1.shape == (1, 2, 3)
>>> assert t1.dtype == mindspore.float32
>>>
>>> # initialize a tensor with a float scalar
>>> t2 = Tensor(0.1)
>>> assert isinstance(t2, Tensor)
>>> assert t2.dtype == mindspore.float64
"""
def __init__(self, input_data, dtype=None):
# If input data is numpy number, convert it to np array
if isinstance(input_data, np_types):
input_data = np.array(input_data)
# If input_data is tuple/list/numpy.ndarray, it's support in check_type method.
check_type('tensor input_data', input_data, (Tensor_, float, int))
if dtype is not None:
check_typename('dtype', dtype, mstype.number_type + (mstype.bool_,))
if isinstance(input_data, np.ndarray) and (not input_data.flags['FORC']):
input_data = np.ascontiguousarray(input_data)
if dtype is None:
Tensor_.__init__(self, input_data)
else:
Tensor_.__init__(self, input_data, dtype)
self._virtual_flag = False
def __repr__(self):
return Tensor_.__repr__(self)
def __add__(self, other):
out = tensor_operator_registry.get('__add__')(self, other)
return out
def __eq__(self, other):
if not isinstance(other, (int, float, Tensor)):
return False
# bool type is not supported for `Equal` operator in backend.
if self.dtype == mstype.bool_ or (isinstance(other, Tensor) and other.dtype == mstype.bool_):
if isinstance(other, Tensor):
return Tensor(np.array(self.asnumpy() == other.asnumpy()))
return Tensor(np.array(self.asnumpy() == other))
return tensor_operator_registry.get('__eq__')(self, other)
def __ne__(self, other):
if not isinstance(other, (int, float, Tensor)):
return True
# bool type is not supported for `NotEqual` operator in backend.
if self.dtype == mstype.bool_ or (isinstance(other, Tensor) and other.dtype == mstype.bool_):
return Tensor(np.array(self.asnumpy() != other.asnumpy()))
return tensor_operator_registry.get('__ne__')(self, other)
def __hash__(self):
return hash(id(self))
def __mul__(self, other):
out = tensor_operator_registry.get('__mul__')(self, other)
return out
def __neg__(self):
out = tensor_operator_registry.get('__neg__')(self)
return out
def __bool__(self):
data = self.asnumpy()
if data.shape == ():
return bool(data)
if data.shape == (1,):
return bool(data[0])
raise ValueError("The truth value of an array with several elements is ambiguous.")
def __pos__(self):
return self
def __iadd__(self, other):
return self.__add__(other)
def __radd__(self, other):
out = tensor_operator_registry.get('__add__')(self, other)
return out
def __imul__(self, other):
return self.__mul__(other)
def __rmul__(self, other):
out = tensor_operator_registry.get('__mul__')(self, other)
return out
def __truediv__(self, other):
out = tensor_operator_registry.get('__truediv__')(self, other)
return out
def __rtruediv__(self, other):
out = tensor_operator_registry.get('__truediv__')(other, self)
return out
def __sub__(self, other):
out = tensor_operator_registry.get('__sub__')(self, other)
return out
def __isub__(self, other):
return self.__sub__(other)
def __rsub__(self, other):
out = tensor_operator_registry.get('__sub__')(other, self)
return out
def __lt__(self, other):
out = tensor_operator_registry.get('__lt__')(self, other)
return out
def __le__(self, other):
out = tensor_operator_registry.get('__le__')(self, other)
return out
def __getitem__(self, index):
out = tensor_operator_registry.get('__getitem__')(self, index)
return out
def __setitem__(self, index, value):
out = tensor_operator_registry.get('__setitem__')(self, index, value)
self.assign_value(out)
return self
def __gt__(self, other):
out = tensor_operator_registry.get('__gt__')(self, other)
return out
def __ge__(self, other):
out = tensor_operator_registry.get('__ge__')(self, other)
return out
def __len__(self):
out = tensor_operator_registry.get('shape')(self)
if not out:
return 1
return out[0]
def __mod__(self, other):
return tensor_operator_registry.get('__mod__')(self, other)
def __imod__(self, other):
return self.__mod__(other)
def __rmod__(self, other):
return tensor_operator_registry.get('__mod__')(other, self)
def __pow__(self, other):
return tensor_operator_registry.get('__pow__')(self, other)
def __floordiv__(self, other):
return tensor_operator_registry.get('__floordiv__')(self, other)
def __ifloordiv__(self, other):
return self.__floordiv__(other)
def __rfloordiv__(self, other):
return tensor_operator_registry.get('__floordiv__')(other, self)
def __str__(self):
if self.dtype == mstype.type_none:
return "Unknown Tensor type!"
return str(self.asnumpy())
@property
def shape(self):
"""The shape of tensor is a tuple."""
return self._shape
@property
def dtype(self):
"""The dtype of tensor is a mindspore type."""
return self._dtype
@property
def virtual_flag(self):
"""Mark tensor is virtual."""
return self._virtual_flag
@virtual_flag.setter
def virtual_flag(self, value):
"""The setter of virtual_flag."""
if not isinstance(value, bool):
raise TypeError("virtual_flag must be bool.")
self._virtual_flag = value
[docs] def asnumpy(self):
"""Convert tensor to numpy array."""
return Tensor_.asnumpy(self)
[docs] def all(self, axis=(), keep_dims=False):
"""
Check all array elements along a given axis evaluate to True.
Args:
axis (Union[None, int, tuple(int)): Dimensions of reduction,
when axis is None or empty tuple, reduce all dimensions.
Default: (), reduce all dimensions.
keep_dims (bool): Whether to keep the reduced dimensions.
Default : False, don't keep these reduced dimensions.
Returns:
Tensor, has the same data type as x.
"""
if axis is None:
axis = ()
return tensor_operator_registry.get('all')(keep_dims)(self, axis)
[docs] def any(self, axis=(), keep_dims=False):
"""
Check any array element along a given axis evaluate to True.
Args:
axis (Union[None, int, tuple(int)): Dimensions of reduction,
when axis is None or empty tuple, reduce all dimensions.
Default: (), reduce all dimensions.
keep_dims (bool): Whether to keep the reduced dimensions.
Default : False, don't keep these reduced dimensions.
Returns:
Tensor, has the same data type as x.
"""
if axis is None:
axis = ()
return tensor_operator_registry.get('any')(keep_dims)(self, axis)
[docs]class RowTensor:
"""
A sparse representation of a set of tensor slices at given indices.
An RowTensor is typically used to represent a subset of a larger
tensor dense of shape [L0, D1, .. , DN] where L0 >> D0.
The values in indices are the indices in the first dimension of the slices
that have been extracted from the larger tensor.
The dense tensor dense represented by an RowTensor slices has
`dense[slices.indices[i], :, :, :, ...] = slices.values[i, :, :, :, ...]`.
RowTensor can only be used in the `Cell`'s contruct method.
It is not supported in pynative mode at the moment.
Args:
indices (Tensor): A 1-D integer Tensor of shape [D0].
values (Tensor): A Tensor of any dtype of shape [D0, D1, ..., Dn].
dense_shape (tuple): An integer tuple which contains the shape
of the corresponding dense tensor.
Returns:
RowTensor, composed of `indices`, `values`, and `dense_shape`.
Examples:
>>> class Net(nn.Cell):
>>> def __init__(self, dense_shape):
>>> super(Net, self).__init__()
>>> self.dense_shape = dense_shape
>>> def construct(self, indices, values):
>>> x = RowTensor(indices, values, self.dense_shape)
>>> return x.values, x.indices, x.dense_shape
>>>
>>> indices = Tensor([0])
>>> values = Tensor([[1, 2]], dtype=ms.float32)
>>> Net((3, 2))(indices, values)
"""
def __init__(self, indices, values, dense_shape):
"Init RowTensor"
self.__indices = indices
self.__values = values
self.__dense_shape = dense_shape
@property
def indices(self):
return self.__indices
@property
def values(self):
return self.__values
@property
def dense_shape(self):
return self.__dense_shape
[docs]class SparseTensor:
"""
A sparse representation of a set of nonzero elememts from a tensor at given indices.
SparseTensor can only be used in the `Cell`'s construct method.
Pynative mode not supported at the moment.
For a tensor dense, its SparseTensor(indices, values, dense_shape) has
`dense[indices[i]] = values[i]`.
Args:
indices (Tensor): A 2-D integer Tensor of shape `[N, ndims]`,
where N and ndims are the number of values and number of dimensions in
the SparseTensor, respectively.
values (Tensor): A 1-D tensor of any type and shape `[N]`, which
supplies the values for each element in indices.
dense_shape (tuple): A integer tuple of size `ndims`,
which specifies the dense_shape of the sparse tensor.
Returns:
SparseTensor, composed of `indices`, `values`, and `dense_shape`.
Examples:
>>> class Net(nn.Cell):
>>> def __init__(self, dense_shape):
>>> super(Net, self).__init__()
>>> self.dense_shape = dense_shape
>>> def construct(self, indices, values):
>>> x = SparseTensor(indices, values, self.dense_shape)
>>> return x.values, x.indices, x.dense_shape
>>>
>>> indices = Tensor([[0, 1], [1, 2]])
>>> values = Tensor([1, 2], dtype=ms.float32)
>>> Net((3, 4))(indices, values)
"""
def __init__(self, indices, values, dense_shape):
"Init SparseTensor"
self.__indices = indices
self.__values = values
self.__dense_shape = dense_shape
@property
def indices(self):
return self.__indices
@property
def values(self):
return self.__values
@property
def dense_shape(self):
return self.__dense_shape