# 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.
# ============================================================================
"""basic"""
import numpy as np
import mindspore.common.dtype as mstype
from mindspore.common.seed import get_seed
from mindspore.common.tensor import Tensor
from mindspore.common.initializer import initializer
from mindspore._checkparam import check_int_positive, check_bool
from mindspore.ops import operations as P
from mindspore.ops import functional as F
from mindspore.ops.functional import identity
from mindspore.ops.operations import _inner_ops as inner
from mindspore.ops.primitive import constexpr
from mindspore.common.parameter import Parameter
from mindspore._extends import cell_attr_register
from mindspore.common.api import ms_function
from mindspore import context
from mindspore.ops import _selected_ops
from ..cell import Cell
from .activation import get_activation
from ..._checkparam import Validator as validator
from ..._checkparam import Rel
__all__ = ['Dropout', 'Flatten', 'Dense', 'ClipByNorm', 'Norm', 'OneHot', 'Pad', 'Unfold',
'MatrixDiag', 'MatrixDiagPart', 'MatrixSetDiag']
[docs]class Dropout(Cell):
r"""
Dropout layer for the input.
Randomly set some elements of the input tensor to zero with probability :math:`1 - keep\_prob` during training
using samples from a Bernoulli distribution.
Note:
Each channel will be zeroed out independently on every construct call.
The outputs are scaled by a factor of :math:`\frac{1}{keep\_prob}` during training so
that the output layer remains at a similar scale. During inference, this
layer returns the same tensor as the input.
This technique is proposed in paper `Dropout: A Simple Way to Prevent Neural Networks from Overfitting
<http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf>`_ and proved to be effective to reduce
over-fitting and prevents neurons from co-adaptation. See more details in `Improving neural networks by
preventing co-adaptation of feature detectors
<https://arxiv.org/pdf/1207.0580.pdf>`_.
Args:
keep_prob (float): The keep rate, greater than 0 and less equal than 1. E.g. rate=0.9,
dropping out 10% of input units. Default: 0.5.
dtype (:class:`mindspore.dtype`): Data type of input. Default: mindspore.float32.
Raises:
ValueError: If `keep_prob` is not in range (0, 1].
Inputs:
- **input** (Tensor) - The input tensor.
Outputs:
Tensor, output tensor with the same shape as the input.
Examples:
>>> x = Tensor(np.ones([2, 2, 3]), mindspore.float32)
>>> net = nn.Dropout(keep_prob=0.8)
>>> net(x)
[[[1.0, 1.0, 1.0],
[1.0, 1.0, 1.0]],
[[1.0, 1.0, 1.0],
[1.0, 1.0, 1.0]]]
"""
def __init__(self, keep_prob=0.5, dtype=mstype.float32):
super(Dropout, self).__init__()
if keep_prob <= 0 or keep_prob > 1:
raise ValueError("dropout probability should be a number in range (0, 1], but got {}".format(keep_prob))
validator.check_subclass("dtype", dtype, mstype.number_type, self.cls_name)
validator.check_value_type('keep_prob', keep_prob, [float], self.cls_name)
self.keep_prob = keep_prob
seed0 = get_seed()
self.seed0 = seed0 if seed0 is not None else 0
self.seed1 = 0
self.dtype = dtype
self.get_shape = P.Shape()
self.dropout_gen_mask = P.DropoutGenMask(Seed0=self.seed0, Seed1=self.seed1)
self.dropout_do_mask = P.DropoutDoMask()
self.cast = P.Cast()
self.is_gpu = context.get_context('device_target') in ["GPU"]
self.dropout = P.Dropout(keep_prob)
def construct(self, x):
if not self.training:
return x
if self.is_gpu:
out, _ = self.dropout(x)
return out
if self.keep_prob == 1:
return x
shape = self.get_shape(x)
dtype = P.DType()(x)
if _is_float_dtype(dtype):
keep_prob = self.cast(self.keep_prob, dtype)
else:
keep_prob = self.cast(self.keep_prob, mstype.float16)
output = self.dropout_gen_mask(shape, keep_prob)
return self.dropout_do_mask(x, output, keep_prob)
def extend_repr(self):
str_info = 'keep_prob={}, dtype={}'.format(self.keep_prob, self.dtype)
return str_info
[docs]class Flatten(Cell):
r"""
Flatten layer for the input.
Flattens a tensor without changing dimension of batch size on the 0-th axis.
Inputs:
- **input** (Tensor) - Tensor of shape :math:`(N, \ldots)` to be flattened.
Outputs:
Tensor, the shape of the output tensor is :math:`(N, X)`, where :math:`X` is
the product of the remaining dimensions.
Examples:
>>> net = nn.Flatten()
>>> input = Tensor(np.array([[[1.2, 1.2], [2.1, 2.1]], [[2.2, 2.2], [3.2, 3.2]]]), mindspore.float32)
>>> input.shape
(2, 2, 2)
>>> net(input)
[[1.2 1.2 2.1 2.1]
[2.2 2.2 3.2 3.2]]
"""
def __init__(self):
super(Flatten, self).__init__()
def construct(self, x):
return F.reshape(x, (F.shape(x)[0], -1))
[docs]class Dense(Cell):
r"""
The fully connected layer.
Applies dense-connected layer for the input. This layer implements the operation as:
.. math::
\text{outputs} = \text{activation}(\text{inputs} * \text{kernel} + \text{bias}),
where :math:`\text{activation}` is the activation function passed as the activation
argument (if passed in), :math:`\text{kernel}` is a weight matrix with the same
data type as the inputs created by the layer, and :math:`\text{bias}` is a bias vector
with the same data type as the inputs created by the layer (only if has_bias is True).
Args:
in_channels (int): The number of channels in the input space.
out_channels (int): The number of channels in the output space.
weight_init (Union[Tensor, str, Initializer, numbers.Number]): The trainable weight_init parameter. The dtype
is same as input x. The values of str refer to the function `initializer`. Default: 'normal'.
bias_init (Union[Tensor, str, Initializer, numbers.Number]): The trainable bias_init parameter. The dtype is
same as input x. The values of str refer to the function `initializer`. Default: 'zeros'.
has_bias (bool): Specifies whether the layer uses a bias vector. Default: True.
activation (str): activate function applied to the output of the fully connected layer, eg. 'ReLU'.
Default: None.
Raises:
ValueError: If weight_init or bias_init shape is incorrect.
Inputs:
- **input** (Tensor) - Tensor of shape :math:`(N, in\_channels)`.
Outputs:
Tensor of shape :math:`(N, out\_channels)`.
Examples:
>>> net = nn.Dense(3, 4)
>>> input = Tensor(np.random.randint(0, 255, [2, 3]), mindspore.float32)
>>> net(input)
[[ 2.5246444 2.2738023 0.5711005 -3.9399147 ]
[ 1.0739875 4.0155234 0.94188046 -5.459526 ]]
"""
@cell_attr_register(attrs=['has_bias', 'activation'])
def __init__(self,
in_channels,
out_channels,
weight_init='normal',
bias_init='zeros',
has_bias=True,
activation=None):
super(Dense, self).__init__()
self.in_channels = check_int_positive(in_channels)
self.out_channels = check_int_positive(out_channels)
self.has_bias = check_bool(has_bias)
if isinstance(weight_init, Tensor):
if weight_init.dim() != 2 or weight_init.shape[0] != out_channels or \
weight_init.shape[1] != in_channels:
raise ValueError("weight_init shape error")
self.weight = Parameter(initializer(weight_init, [out_channels, in_channels]), name="weight")
if self.has_bias:
if isinstance(bias_init, Tensor):
if bias_init.dim() != 1 or bias_init.shape[0] != out_channels:
raise ValueError("bias_init shape error")
self.bias = Parameter(initializer(bias_init, [out_channels]), name="bias")
self.matmul = P.MatMul(transpose_b=True)
self.bias_add = _selected_ops.BiasAdd()
self.activation = get_activation(activation)
self.activation_flag = self.activation is not None
def construct(self, x):
output = self.matmul(x, self.weight)
if self.has_bias:
output = self.bias_add(output, self.bias)
if self.activation_flag:
return self.activation(output)
return output
def extend_repr(self):
str_info = 'in_channels={}, out_channels={}, weight={}, has_bias={}' \
.format(self.in_channels, self.out_channels, self.weight, self.has_bias)
if self.has_bias:
str_info = str_info + ', bias={}'.format(self.bias)
if self.activation_flag:
str_info = str_info + ', activation={}'.format(self.activation)
return str_info
@constexpr
def _is_equal_one(x):
if x is None:
return False
return bool(x.asnumpy().mean() == 1.0)
@constexpr
def _dtype_check(x_dtype):
if x_dtype not in [mstype.float32, mstype.float16]:
raise TypeError("The input type must be float32 or float16.")
@constexpr
def _is_float_dtype(dtype):
if dtype in [mstype.float32, mstype.float16]:
return True
return False
[docs]class ClipByNorm(Cell):
r"""
Clips tensor values to a maximum :math:`L_2`-norm.
The output of this layer remains the same if the :math:`L_2`-norm of the input tensor
is not greater than the argument clip_norm. Otherwise the tensor will be normalized as:
.. math::
\text{output}(X) = \frac{\text{clip_norm} * X}{L_2(X)},
where :math:`L_2(X)` is the :math:`L_2`-norm of :math:`X`.
Inputs:
- **input** (Tensor) - Tensor of shape N-D. The type must be float32 or float16.
- **clip_norm** (Tensor) - A scalar Tensor of shape :math:`()` or :math:`(1)`.
Outputs:
Tensor, clipped tensor with the same shape as the input, whose type is float32.
Examples:
>>> net = nn.ClipByNorm()
>>> input = Tensor(np.random.randint(0, 10, [4, 16]), mindspore.float32)
>>> clip_norm = Tensor(np.array([100]).astype(np.float32))
>>> net(input, clip_norm)
"""
def __init__(self):
super(ClipByNorm, self).__init__()
self.reduce_sum = P.ReduceSum(keep_dims=True)
self.select_ = P.Select()
self.greater_ = P.Greater()
self.cast = P.Cast()
self.sqrt = P.Sqrt()
self.max_op = P.Maximum()
self.shape = P.Shape()
self.reshape = P.Reshape()
self.fill = P.Fill()
self.expand_dims = P.ExpandDims()
self.dtype = P.DType()
[docs] @ms_function
def construct(self, x, clip_norm):
"""add ms_function decorator for pynative mode"""
mul_x = F.square(x)
l2sum = self.cast(self.reduce_sum(mul_x), mstype.float32)
cond = self.greater_(l2sum, 0)
ones_ = self.fill(self.dtype(cond), self.shape(cond), 1.0)
l2sum_safe = self.select_(cond, l2sum, self.cast(ones_, self.dtype(l2sum)))
l2norm = self.select_(cond, self.sqrt(l2sum_safe), l2sum)
_dtype_check(self.dtype(x))
if _is_equal_one(clip_norm):
intermediate = x
else:
intermediate = x * clip_norm
max_norm = self.max_op(l2norm, clip_norm)
values_clip = self.cast(intermediate, mstype.float32) / self.expand_dims(max_norm, -1)
values_clip = self.reshape(values_clip, self.shape(x))
values_clip = identity(values_clip)
return values_clip
[docs]class Norm(Cell):
"""
Computes the norm of vectors, currently including Euclidean norm, i.e., :math:`L_2`-norm.
Args:
axis (Union[tuple, int]): The axis over which to compute vector norms. Default: ().
keep_dims (bool): If true, the axis indicated in `axis` are kept with size 1. Otherwise,
the dimensions in `axis` are removed from the output shape. Default: False.
Inputs:
- **input** (Tensor) - Tensor which is not empty.
Outputs:
Tensor, output tensor with dimensions in 'axis' reduced to 1 will be returned if 'keep_dims' is True;
otherwise a Tensor with dimensions in 'axis' removed is returned.
Examples:
>>> net = nn.Norm(axis=0)
>>> input = Tensor(np.random.randint(0, 10, [2, 4]), mindspore.float32)
>>> net(input)
[2.236068 9.848858 4. 5.656854]
"""
def __init__(self, axis=(), keep_dims=False):
super(Norm, self).__init__()
validator.check_value_type("keep_dims", keep_dims, [bool], self.cls_name)
self.axis = axis
self.keep_dims = keep_dims
self.reduce_sum = P.ReduceSum(True)
self.sqrt = P.Sqrt()
self.squeeze = P.Squeeze(self.axis)
def construct(self, x):
x = self.sqrt(self.reduce_sum(F.square(x), self.axis))
if not self.keep_dims:
x = self.squeeze(x)
return x
def extend_repr(self):
str_info = 'axis={}, keep_dims={}'.format(self.axis, self.keep_dims)
return str_info
[docs]class OneHot(Cell):
"""
Returns a one-hot tensor.
The locations represented by indices in argument 'indices' take value on_value,
while all other locations take value off_value.
Note:
If the input indices is rank :math:`N`, the output will have rank :math:`N+1`. The new
axis is created at dimension `axis`.
Args:
axis (int): Features x depth if axis is -1, depth x features
if axis is 0. Default: -1.
depth (int): A scalar defining the depth of the one hot dimension. Default: 1.
on_value (float): A scalar defining the value to fill in output[i][j]
when indices[j] = i. Default: 1.0.
off_value (float): A scalar defining the value to fill in output[i][j]
when indices[j] != i. Default: 0.0.
dtype (:class:`mindspore.dtype`): Data type of 'on_value' and 'off_value', not the
data type of indices. Default: mindspore.float32.
Inputs:
- **indices** (Tensor) - A tensor of indices of data type mindspore.int32 and arbitrary shape.
Outputs:
Tensor, the one-hot tensor of data type 'dtype' with dimension at 'axis' expanded to 'depth' and filled with
on_value and off_value.
Examples:
>>> net = nn.OneHot(depth=4, axis=1)
>>> indices = Tensor([[1, 3], [0, 2]], dtype=mindspore.int32)
>>> net(indices)
[[[0. 0.]
[1. 0.]
[0. 0.]
[0. 1.]]
[[1. 0.]
[0. 0.]
[0. 1.]
[0. 0.]]]
"""
def __init__(self, axis=-1, depth=1, on_value=1.0, off_value=0.0, dtype=mstype.float32):
super(OneHot, self).__init__()
self.onehot = P.OneHot(axis)
self.depth = depth
self.dtype = dtype
self.on_value = on_value
self.off_value = off_value
def construct(self, indices):
return self.onehot(indices, self.depth, F.cast(self.on_value, self.dtype), F.cast(self.off_value, self.dtype))
[docs]class Pad(Cell):
"""
Pads the input tensor according to the paddings and mode.
Args:
paddings (tuple): The shape of parameter `paddings` is (N, 2). N is the rank of input data. All elements of
paddings are int type. For `D` th dimension of input, paddings[D, 0] indicates how many sizes to be
extended ahead of the `D` th dimension of the input tensor, and paddings[D, 1] indicates how many sizes to
be extended behind of the `D` th dimension of the input tensor.
mode (str): Specifies padding mode. The optional values are "CONSTANT", "REFLECT", "SYMMETRIC".
Default: "CONSTANT".
Inputs:
- **input_x** (Tensor) - The input tensor.
Outputs:
Tensor, the tensor after padding.
- If `mode` is "CONSTANT", it fills the edge with 0, regardless of the values of the `input_x`.
If the `input_x` is [[1,2,3],[4,5,6],[7,8,9]] and `paddings` is [[1,1],[2,2]], then the
Outputs is [[0,0,0,0,0,0,0],[0,0,1,2,3,0,0],[0,0,4,5,6,0,0],[0,0,7,8,9,0,0],[0,0,0,0,0,0,0]].
- If `mode` is "REFLECT", it uses a way of symmetrical copying throught the axis of symmetry to fill in.
If the `input_x` is [[1,2,3],[4,5,6],[7,8,9]] and `paddings` is [[1,1],[2,2]], then the
Outputs is [[6,5,4,5,6,5,4],[3,2,1,2,3,2,1],[6,5,4,5,6,5,4],[9,8,7,8,9,8,7],[6,5,4,5,6,5,4]].
- If `mode` is "SYMMETRIC", the filling method is similar to the "REFLECT". It is also copied
according to the symmetry axis, except that it includes the symmetry axis. If the `input_x`
is [[1,2,3],[4,5,6],[7,8,9]] and `paddings` is [[1,1],[2,2]], then the Outputs is
[[2,1,1,2,3,3,2],[2,1,1,2,3,3,2],[5,4,4,5,6,6,5],[8,7,7,8,9,9,8],[8,7,7,8,9,9,8]].
Examples:
>>> from mindspore import Tensor
>>> from mindspore.ops import operations as P
>>> import mindspore.nn as nn
>>> import numpy as np
>>> class Net(nn.Cell):
>>> def __init__(self):
>>> super(Net, self).__init__()
>>> self.pad = nn.Pad(paddings=((1,1),(2,2)), mode="CONSTANT")
>>> def construct(self, x):
>>> return self.pad(x)
>>> x = np.random.random(size=(2, 3)).astype(np.float32)
>>> pad = Net()
>>> ms_output = pad(Tensor(x))
"""
def __init__(self, paddings, mode="CONSTANT"):
super(Pad, self).__init__()
self.mode = mode
self.paddings = paddings
validator.check_string('mode', self.mode, ["CONSTANT", "REFLECT", "SYMMETRIC"], self.cls_name)
if not isinstance(paddings, tuple):
raise TypeError('Paddings must be tuple type.')
for item in paddings:
if len(item) != 2:
raise ValueError('The shape of paddings must be (n, 2).')
if len(paddings) > 4:
raise ValueError('Only padding up to 4 dims is supported')
if mode == "CONSTANT":
self.pad = P.Pad(self.paddings)
else:
self.paddings = Tensor(np.array(self.paddings))
self.pad = P.MirrorPad(mode=mode)
def construct(self, x):
if self.mode == "CONSTANT":
x = self.pad(x)
else:
x = self.pad(x, self.paddings)
return x
[docs]class Unfold(Cell):
"""
Extract patches from images.
The input tensor must be a 4-D tensor and the data format is NCHW.
Args:
ksizes (Union[tuple[int], list[int]]): The size of sliding window, must be a tuple or a list of integers,
and the format is [1, ksize_row, ksize_col, 1].
strides (Union[tuple[int], list[int]]): Distance between the centers of the two consecutive patches,
must be a tuple or list of int, and the format is [1, stride_row, stride_col, 1].
rates (Union[tuple[int], list[int]]): In each extracted patch, the gap between the corresponding dimension
pixel positions, must be a tuple or a list of integers, and the format is [1, rate_row, rate_col, 1].
padding (str): The type of padding algorithm, is a string whose value is "same" or "valid",
not case sensitive. Default: "valid".
- same: Means that the patch can take the part beyond the original image, and this part is filled with 0.
- valid: Means that the taken patch area must be completely covered in the original image.
Inputs:
- **input_x** (Tensor) - A 4-D tensor whose shape is [in_batch, in_depth, in_row, in_col] and
data type is number.
Outputs:
Tensor, a 4-D tensor whose data type is same as 'input_x',
and the shape is [out_batch, out_depth, out_row, out_col], the out_batch is the same as the in_batch.
Examples:
>>> net = Unfold(ksizes=[1, 2, 2, 1], strides=[1, 1, 1, 1], rates=[1, 1, 1, 1])
>>> image = Tensor(np.ones([1, 1, 3, 3]), dtype=mstype.float16)
>>> net(image)
Tensor ([[[[1, 1] [1, 1]] [[1, 1], [1, 1]] [[1, 1] [1, 1]], [[1, 1], [1, 1]]]],
shape=(1, 4, 2, 2), dtype=mstype.float16)
"""
def __init__(self, ksizes, strides, rates, padding="valid"):
super(Unfold, self).__init__()
self.extract_image_patches = inner.ExtractImagePatches(ksizes, strides, rates, padding)
self.transpose = P.Transpose()
self.format_NHWC = (0, 2, 3, 1)
self.format_NCHW = (0, 3, 1, 2)
self.is_ge = context.get_context("enable_ge")
def construct(self, input_x):
if self.is_ge:
x_transpose = self.transpose(input_x, self.format_NHWC)
ret = self.extract_image_patches(x_transpose)
result = self.transpose(ret, self.format_NCHW)
else:
result = self.extract_image_patches(input_x)
return result
@constexpr
def _get_matrix_diag_assist(x_shape, x_dtype):
validator.check_integer("x rank", len(x_shape), 1, Rel.GE, "_get_matrix_diag_assist")
base_eye = np.eye(x_shape[-1], x_shape[-1]).reshape(-1)
assist = np.tile(base_eye, x_shape[:-1]).reshape(x_shape + (x_shape[-1],))
return Tensor(assist, x_dtype)
@constexpr
def _get_matrix_diag_part_assist(x_shape, x_dtype):
validator.check_integer("x rank", len(x_shape), 2, Rel.GE, "_get_matrix_diag_part_assist")
base_eye = np.eye(x_shape[-2], x_shape[-1]).reshape(-1)
assist = np.tile(base_eye, x_shape[:-2]).reshape(x_shape)
return Tensor(assist, x_dtype)
[docs]class MatrixDiag(Cell):
"""
Returns a batched diagonal tensor with a given batched diagonal values.
Inputs:
- **x** (Tensor) - The diagonal values. It can be one of the following data types:
float32, float16, int32, int8, and uint8.
Outputs:
Tensor, has the same type as input `x`. The shape must be x.shape + (x.shape[-1], ).
Examples:
>>> x = Tensor(np.array([1, -1]), mstype.float32)
>>> matrix_diag = nn.MatrixDiag()
>>> result = matrix_diag(x)
[[1. 0.]
[0. -1.]]
"""
def __init__(self):
super(MatrixDiag, self).__init__()
self.matrix_diag = inner.MatrixDiag()
self.dtype = P.DType()
def construct(self, input_x):
x_shape = F.shape(input_x)
x_dtype = self.dtype(input_x)
assist = _get_matrix_diag_assist(x_shape, x_dtype)
out_matrix_diag = self.matrix_diag(input_x, assist)
return out_matrix_diag
[docs]class MatrixDiagPart(Cell):
r"""
Returns the batched diagonal part of a batched tensor.
Inputs:
- **x** (Tensor) - The batched tensor. It can be one of the following data types:
float32, float16, int32, int8, and uint8.
Outputs:
Tensor, has the same type as input `x`. The shape must be x.shape[:-2] + [min(x.shape[-2:])].
Examples:
>>> x = Tensor([[[-1, 0], [0, 1]], [[-1, 0], [0, 1]], [[-1, 0], [0, 1]]], mindspore.float32)
>>> matrix_diag_part = nn.MatrixDiagPart()
>>> result = matrix_diag_part(x)
[[-1., 1.], [-1., 1.], [-1., 1.]]
"""
def __init__(self):
super(MatrixDiagPart, self).__init__()
self.matrix_diag_part = inner.MatrixDiagPart()
self.dtype = P.DType()
def construct(self, input_x):
x_shape = F.shape(input_x)
x_dtype = self.dtype(input_x)
assist = _get_matrix_diag_part_assist(x_shape, x_dtype)
out_matrix_diag_part = self.matrix_diag_part(input_x, assist)
return out_matrix_diag_part
[docs]class MatrixSetDiag(Cell):
r"""
Modify the batched diagonal part of a batched tensor.
Inputs:
- **x** (Tensor) - The batched tensor. It can be one of the following data types:
float32, float16, int32, int8, and uint8.
- **diagonal** (Tensor) - The diagonal values.
Outputs:
Tensor, has the same type and shape as input `x`.
Examples:
>>> x = Tensor([[[-1, 0], [0, 1]], [[-1, 0], [0, 1]], [[-1, 0], [0, 1]]], mindspore.float32)
>>> diagonal = Tensor([[-1., 2.], [-1., 1.], [-1., 1.]], mindspore.float32)
>>> matrix_set_diag = nn.MatrixSetDiag()
>>> result = matrix_set_diag(x, diagonal)
[[[-1, 0], [0, 2]], [[-1, 0], [0, 1]], [[-1, 0], [0, 1]]]
"""
def __init__(self):
super(MatrixSetDiag, self).__init__()
self.matrix_set_diag = inner.MatrixSetDiag()
self.dtype = P.DType()
def construct(self, input_x, diagonal):
x_shape = F.shape(input_x)
x_dtype = self.dtype(input_x)
assist = _get_matrix_diag_part_assist(x_shape, x_dtype)
out_matrix_set_diag = self.matrix_set_diag(input_x, diagonal, assist)
return out_matrix_set_diag