# Copyright 2024 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.
# ============================================================================
"""Holding mint APIs"""
from mindspore._c_expression import _all_gather_matmul_instance
from mindspore._c_expression import _bitwise_not_instance
from mindspore._c_expression import _clamp_instance
from mindspore._c_expression import _div_instance
from mindspore._c_expression import _empty_instance
from mindspore._c_expression import _fmod_instance
from mindspore._c_expression import _lerp_instance
from mindspore._c_expression import _matmul_reduce_scatter_instance
from mindspore._c_expression import _max_instance
from mindspore._c_expression import _min_instance
from mindspore._c_expression import _nansum_instance
from mindspore._c_expression import _remainder_instance
from mindspore._c_expression import _repeat_interleave_instance
from mindspore._c_expression import _where_instance
[文档]def all_gather_matmul(*args, **kwargs):
r"""
all_gather_matmul(input, x2, group, world_size, *, bias=None, gather_index=0, gather_output=True, comm_turn=0, trans_input=False, trans_x2=False) -> Tensor
In the TP segmentation scenario, allgather and matmul are fused, and communication and computational pipelines
are parallelized within the fusion operator.
.. math::
output = allgather(input)@x2
gather\_out = allgather(input)
.. warning::
This is an experimental API that is subject to change or deletion.
Args:
input (Tensor): The left matrix of matmul, the dtype supports float16 and bfloat16, the shape supports 2
dimensions, and the data format supports ND.
x2 (Tensor): The right matrix of matmul, the dtype needs to be consistent with ``input`` , the shape
supports 2 dimensions, and the data format supports ND.
group (str): Communication group name, can be created by ``create_group`` method, or use the default group
``mindspore.communication.GlobalComm.WORLD_COMM_GROUP``.
world_size (int): The total number of ranks in the communication group, should be consistent with the number
of devices actually running, supporting ``2`` , ``4`` , and ``8`` .
Keyword Args:
bias (Tensor, optional): Currently only ``None`` is supported. Default: ``None`` .
gather_index (int, optional): Indicates the allgather operation object, ``0`` means gather ``input`` ,
``1`` means gather ``x2`` . Currently only ``0`` is supported. Default: ``0`` .
gather_output (bool, optional): Indicates whether gather output is required. Default: ``True`` .
comm_turn (int, optional): Indicates the granularity of communication between ranks. Currently only ``0``
is supported. Default: ``0`` .
trans_input (bool, optional): Indicates whether ``input`` is transposed. Currently only ``False`` is
supported. Default: ``False`` .
trans_x2 (bool, optional): Indicates whether ``x2`` is transposed. Default: ``False`` .
Returns:
- output (Tensor) - The result of allgather and matmul fusion calculations.
- gather_out (Tensor) - The result of allgather. If gather_output is ``False`` , ``gather_out`` returns a
tensor with shape 0.
Note:
- When using this interface, please ensure that the driver firmware package and CANN package are both the
matching 8.0.RC2 version or a higher version, otherwise an error will be reported, such as BUS ERROR.
- The shape of ``input`` is (m, k), the shape of ``x2`` is (k, n), k is required to be equal, and the value
range of k is [256, 65535). The shape of ``output`` is (m * world_size, n), and the shape of
``gather_out`` is (m * world_size, k).
- The common fusion operators in a model only support the same communication group.
Raises:
TypeError: Any arg is of wrong type.
RuntimeError: The dtype of ``input`` or ``x2`` is neither float16 nor bfloat16.
RuntimeError: The dtypes of ``input`` and ``x2`` are different.
RuntimeError: The shape of ``input`` or ``x2`` is not two-dimensional.
RuntimeError: The k axis of ``input`` shape and ``x2`` shape are not equal.
RuntimeError: k is less than ``256`` or greater than or equal to ``65535`` .
RuntimeError: ``bias`` is not None.
RuntimeError: ``group`` does not exist.
RuntimeError: ``world_size`` is inconsistent with the actual number of running cards.
RuntimeError: ``world_size`` is not equal to ``2`` , ``4`` , or ``8`` .
RuntimeError: ``gather_index`` is not ``0`` .
RuntimeError: ``trans_input`` is ``True`` .
Supported Platforms:
``Ascend``
Examples:
.. note::
Before running the following examples, you need to configure the communication environment variables.
For Ascend/GPU/CPU devices, it is recommended to use the msrun startup method without any third-party or
configuration file dependencies. Please see the `msrun start up <https://www.mindspore.cn/docs/en/master/model_train/parallel/msrun_launcher.html>`_
for more details.
This example should be run with 2 devices.
>>> import mindspore as ms
>>> import numpy as np
>>> from mindspore import ops
>>> ms.communication.init()
>>> ms.set_context(mode=ms.PYNATIVE_MODE)
>>> ms.set_device(device_target="Ascend")
>>> rank = ms.communication.get_rank()
>>> np.random.seed(rank)
>>> input = ms.Tensor(np.random.randn(128, 256).astype(np.float32), dtype=ms.float16)
>>> x2 = ms.Tensor(np.random.randn(256, 512).astype(np.float32), dtype=ms.float16)
>>> group = ms.communication.GlobalComm.WORLD_COMM_GROUP
>>> world_size = ms.communication.get_group_size()
>>> output, gather_out = ops.all_gather_matmul(
... input,
... x2,
... group,
... world_size,
... bias=None,
... gather_index=0,
... gather_output=True,
... comm_turn=0,
... trans_input=False,
... trans_x2=False,
... )
>>> print(output.shape)
(256, 512)
>>> print(gather_out.shape)
(256, 256)
"""
return _all_gather_matmul_instance(*args, **kwargs)
def bitwise_not(*args, **kwargs):
r"""
bitwise_not(input) -> Tensor
Returns bitwise `not` of the input tensor.
.. warning::
This is an experimental API that is subject to change or deletion.
Args:
input (Tensor): The input tensor must be of integral or Boolean types.
Returns:
Tensor, has the same shape and type as `input`.
Raises:
TypeError: If `input` is not a Tensor.
RuntimeError: If dtype of `input` is not int or bool.
Supported Platforms:
``Ascend``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> x = Tensor(np.array([True, False, True, False]))
>>> y = mint.bitwise_not(x)
>>> print(y)
[False True False True]
"""
return _bitwise_not_instance(*args, **kwargs)
[文档]def clamp(*args, **kwargs):
r"""
clamp(input, min=None, max=None) -> Tensor
Clamps tensor values between the specified minimum value and maximum value.
Limits the value of :math:`input` to a range, whose lower limit is `min` and upper limit is `max` .
.. math::
out_i= \left\{
\begin{array}{align}
max & \text{ if } input_i\ge max \\
input_i & \text{ if } min \lt input_i \lt max \\
min & \text{ if } input_i \le min \\
\end{array}\right.
Note:
- `min` and `max` cannot be None at the same time;
- When `min` is None and `max` is not None, the elements in Tensor larger than `max` will become `max`;
- When `min` is not None and `max` is None, the elements in Tensor smaller than `min` will become `min`;
- If `min` is greater than `max`, the value of all elements in Tensor will be set to `max`;
- The data type of `input`, `min` and `max` should support implicit type conversion and cannot be bool type.
Args:
input (Tensor): Input data, which type is Tensor. Tensors of arbitrary dimensions are supported.
min (Union(Tensor, float, int), optional): The minimum value. Default: ``None`` .
max (Union(Tensor, float, int), optional): The maximum value. Default: ``None`` .
Returns:
Tensor, a clipped Tensor.
The data type and shape are the same as input.
Raises:
ValueError: If both `min` and `max` are None.
TypeError: If the type of `input` is not Tensor.
TypeError: If the type of `min` is not in None, Tensor, float or int.
TypeError: If the type of `max` is not in None, Tensor, float or int.
Supported Platforms:
``Ascend``
Examples:
>>> # case 1: the data type of input is Tensor
>>> import mindspore
>>> from mindspore import Tensor, mint
>>> import numpy as np
>>> min_value = Tensor(5, mindspore.float32)
>>> max_value = Tensor(20, mindspore.float32)
>>> input = Tensor(np.array([[1., 25., 5., 7.], [4., 11., 6., 21.]]), mindspore.float32)
>>> output = mint.clamp(input, min_value, max_value)
>>> print(output)
[[ 5. 20. 5. 7.]
[ 5. 11. 6. 20.]]
>>> # case 2: the data type of input is number
>>> import mindspore
>>> from mindspore import Tensor, mint
>>> import numpy as np
>>> min_value = 5
>>> max_value = 20
>>> input = Tensor(np.array([[1., 25., 5., 7.], [4., 11., 6., 21.]]), mindspore.float32)
>>> output = mint.clamp(input, min_value, max_value)
>>> print(output)
[[ 5. 20. 5. 7.]
[ 5. 11. 6. 20.]]
"""
return _clamp_instance(*args, **kwargs)
def clip(*args, **kwargs):
r"""
clip(input, min=None, max=None) -> Tensor
Alias for :func:`mindspore.mint.clamp`.
"""
return _clamp_instance(*args, **kwargs)
[文档]def div(*args, **kwargs):
r"""
div(input, other, *, rounding_mode=None) -> Tensor
Divides each element of the `input` by the corresponding element of the `other` .
.. math::
out_{i} = input_{i} / other_{i}
.. note::
- When the two inputs have different shapes, they must be able to broadcast to a common shape.
- The two inputs can not be bool type at the same time,
[True, Tensor(True, bool\_), Tensor(np.array([True]), bool\_)] are all considered bool type.
- The two inputs comply with the implicit type conversion rules to make the data types
consistent.
Args:
input (Union[Tensor, Number, bool]): The dividend.
other (Union[Tensor, Number, bool]): The divisor.
Keyword Args:
rounding_mode (str, optional): Type of rounding applied to the result. Default: ``None`` .
Three types are defined as,
- None: Default behavior, which is the same as true division in Python or `true_divide` in NumPy.
- "floor": Rounds the division of the inputs down, which is the same as floor division in Python
or `floor_divide` in NumPy.
- "trunc": Rounds the division of the inputs towards zero, which is the same as C-style integer division.
Returns:
Tensor, the shape is the same as the one after broadcasting,
and the data type is the one with higher precision or higher digits among the two inputs.
Raises:
TypeError: If `input` and `other` is not one of the following: Tensor, Number, bool.
ValueError: If `rounding_mode` value is not None, "floor" or "trunc".
Supported Platforms:
``Ascend``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> x = Tensor(np.array([1.0, 2.0, 3.0]), mindspore.float32)
>>> y = Tensor(np.array([4.0, 5.0, 6.0]), mindspore.float32)
>>> output = mint.div(x, y)
>>> print(output)
[0.25 0.4 0.5]
"""
return _div_instance(*args, **kwargs)
def divide(*args, **kwargs):
r"""
divide(input, other, *, rounding_mode=None) -> Tensor
Alias for :func:`mindspore.mint.div`.
"""
return _div_instance(*args, **kwargs)
[文档]def empty(*args, **kwargs):
r"""
empty(*size, dtype=None, device=None) -> Tensor
Creates a tensor with uninitialized data, whose shape, dtype and device are described by the argument `size`,
`dtype` and `device` respectively.
.. warning::
This is an experimental API that is subject to change or deletion.
Args:
size (Union[tuple[int], list[int], int]): The specified shape of output tensor. Can be variable numbers of
positive integers or tupled or list containing positive integers.
Keyword Args:
dtype (:class:`mindspore.dtype`, optional): The specified type of output tensor. If `dtype` is ``None`` ,
`mindspore.float32` will be used. Default: ``None`` .
device (string, optional): The specified device of the output tensor. Support ``CPU`` and ``Ascend``. If
`device = None`, `mindspore.context.device_target` will be used. Default ``None``.
Returns:
Tensor, whose dtype and size are defined by input.
Raises:
TypeError: If `size` is neither an int nor a tuple or list of int.
Supported Platforms:
``Ascend``
Examples:
>>> import mindspore
>>> from mindspore import ops
>>> output = ops.empty((2, 3), dtype=mindspore.float32)
>>> print(output)
[[0. 0. 0.]
[0. 0. 0.]]
"""
return _empty_instance(*args, **kwargs)
[文档]def fmod(*args, **kwargs):
r"""
fmod(input, other) -> Tensor
Computes the floating-point remainder of the division operation input/other.
.. math::
out = input - n * other
Where :math:`n` is :math:`input/other` with its fractional part truncated.
The returned value has the same sign as `input` and is less than `other` in magnitude.
.. warning::
This is an experimental API that is subject to change or deletion.
Args:
input (Tensor): the dividend.
other (Union[Tensor, Number]): the divisor.
Returns:
Tensor, the shape is the same as the one after broadcasting,
and the data type is the one with higher precision or higher digits among the two inputs.
Raises:
TypeError: If `input` is not a Tensor.
Supported Platforms:
``Ascend``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> input = Tensor(np.array([-4., -3.5, 0, 3.5, 4]), mindspore.float32)
>>> output = mint.fmod(input, 2.5)
>>> print(output)
[-1.5 -1. 0. 1. 1.5]
"""
return _fmod_instance(*args, **kwargs)
[文档]def lerp(*args, **kwargs):
r"""
lerp(input, end, weight) -> Tensor
Perform a linear interpolation of two tensors input and end based on a float or tensor weight.
If `weight` is a tensor, the shapes of three inputs need to be broadcast;
If `weight` is a float, the shapes of `input` and `end` need to be broadcast.
If `weight` is a float and platform is Ascend, the types of `input` and `end` need to be float32.
.. warning::
This is an experimental API that is subject to change or deletion.
.. math::
output_{i} = input_{i} + weight_{i} * (end_{i} - input_{i})
Args:
input (Tensor): The tensor with the starting points. Data type must be float16 or float32.
end (Tensor): The tensor with the ending points. Data type must be the same as `input`.
weight (Union[float, Tensor]): The weight for the interpolation formula. Must be a float scalar
or a tensor with float16 or float32 data type.
Returns:
Tensor, has the same type and shape as input `input`.
Raises:
TypeError: If `input` or `end` is not a tensor.
TypeError: If `weight` is neither scalar(float) nor tensor.
TypeError: If dtype of `input` or `end` is neither float16 nor float32.
TypeError: If dtype of `weight` is neither float16 nor float32 when it is a tensor.
TypeError: If `input` and `end` have different data types.
TypeError: If `input`, `end` and `weight` have different data types when `weight` is a tensor.
ValueError: If `end` could not be broadcast to a tensor with shape of `input`.
ValueError: If `weight` could not be broadcast to tensors with shapes of `input` and `end` when it is a tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> start = Tensor(np.array([1., 2., 3., 4.]), mindspore.float32)
>>> end = Tensor(np.array([10., 10., 10., 10.]), mindspore.float32)
>>> output = mint.lerp(start, end, 0.5)
>>> print(output)
[5.5 6. 6.5 7. ]
"""
return _lerp_instance(*args, **kwargs)
[文档]def matmul_reduce_scatter(*args, **kwargs):
r"""
matmul_reduce_scatter(input, x2, group, world_size, *, reduce_op='sum', bias=None, comm_turn=0, trans_input=False, trans_x2=False) -> Tensor
In the TP segmentation scenario, matmul and reducescatter are fused, and communication and computational
pipelines are parallelized within the fusion operator.
.. math::
output = reducescatter(input@x2)
.. warning::
This is an experimental API that is subject to change or deletion.
Args:
input (Tensor): The left matrix of matmul, the dtype supports float16 and bfloat16, the shape supports 2
dimensions, and the data format supports ND.
x2 (Tensor): The right matrix of matmul, the dtype needs to be consistent with ``input`` , the shape
supports 2 dimensions, and the data format supports ND.
group (str): Communication group name, can be created by ``create_group`` method, or use the default group
``mindspore.communication.GlobalComm.WORLD_COMM_GROUP``.
world_size (int): The total number of ranks in the communication group, should be consistent with the number
of devices actually running, supporting ``2`` , ``4`` , and ``8`` .
Keyword Args:
reduce_op (str, optional) The reduce operation type. Currently only ``'sum'`` is supported. Default:
``'sum'`` .
bias (Tensor, optional): Currently only ``None`` is supported. Default: ``None`` .
comm_turn (int, optional): Indicates the granularity of communication between ranks. Currently only ``0``
is supported. Default: ``0`` .
trans_input (bool, optional): Indicates whether ``input`` is transposed. Currently only ``False`` is
supported. Default: ``False`` .
trans_x2 (bool, optional): Indicates whether ``x2`` is transposed. Default: ``False`` .
Returns:
- output (Tensor) - The result of allgather and matmul fusion calculations.
Note:
- When using this interface, please ensure that the driver firmware package and CANN package are both the
matching 8.0.RC2 version or a higher version, otherwise an error will be reported, such as BUS ERROR.
- The shape of ``input`` is (m, k), the shape of ``x2`` is (k, n), k is required to be equal, and the value
range of k is [256, 65535), and m is required to be an integer multiple of ``world_size`` . The shape of
``output`` is (m * world_size, n).
- The common fusion operators in a model only support the same communication group.
Raises:
TypeError: Any arg is of wrong type.
RuntimeError: The dtype of ``input`` or ``x2`` is neither float16 nor bfloat16.
RuntimeError: The dtypes of ``input`` and ``x2`` are different.
RuntimeError: The shape of ``input`` or ``x2`` is not two-dimensional.
RuntimeError: The k axis of ``input`` shape and ``x2`` shape are not equal.
RuntimeError: k is less than ``256`` or greater than or equal to ``65535`` .
RuntimeError: ``bias`` is not None.
RuntimeError: ``group`` does not exist.
RuntimeError: ``world_size`` is inconsistent with the actual number of running cards.
RuntimeError: ``world_size`` is not equal to ``2`` , ``4`` , or ``8`` .
RuntimeError: ``reduce_op`` is not ``'sum'`` .
RuntimeError: ``trans_input`` is ``True`` .
Supported Platforms:
``Ascend``
Examples:
.. note::
Before running the following examples, you need to configure the communication environment variables.
For Ascend/GPU/CPU devices, it is recommended to use the msrun startup method without any third-party or
configuration file dependencies. Please see the `msrun start up <https://www.mindspore.cn/docs/en/master/model_train/parallel/msrun_launcher.html>`_
for more details.
This example should be run with 2 devices.
>>> import mindspore as ms
>>> from mindspore import ops
>>> import numpy as np
>>> ms.communication.init()
>>> ms.set_context(mode=ms.PYNATIVE_MODE)
>>> ms.set_device(device_target="Ascend")
>>> rank = ms.communication.get_rank()
>>> np.random.seed(rank)
>>> input = ms.Tensor(np.random.randn(1024, 256).astype(np.float32), dtype=ms.float16)
>>> x2 = ms.Tensor(np.random.randn(256, 512).astype(np.float32), dtype=ms.float16)
>>> group = ms.communication.GlobalComm.WORLD_COMM_GROUP
>>> world_size = ms.communication.get_group_size()
>>> reduce_op = ops.ReduceOp.SUM
>>> output = ops.matmul_reduce_scatter(
... input,
... x2,
... group,
... world_size,
... reduce_op=reduce_op,
... bias=None,
... comm_turn=0,
... trans_input=False,
... trans_x2=False,
... )
>>> print(output.shape)
(512, 512)
"""
return _matmul_reduce_scatter_instance(*args, **kwargs)
[文档]def max(*args, **kwargs):
r"""
max(input) -> Tensor
Returns the maximum value of the input tensor.
Args:
input (Tensor): The input tensor.
Returns:
Scalar Tensor with the same dtype as `input`, the maximum value of the input.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> x = Tensor(np.array([0.0, 0.4, 0.6, 0.7, 0.1]), mindspore.float32)
>>> output = mint.max(x)
>>> print(output)
0.7
.. function:: max(input, dim, keepdim=False) -> tuple(Tensor)
:noindex:
Calculates the maximum value along with the given dim for the input tensor, and returns the maximum values and
indices.
Args:
input (Tensor): The input tensor, can be any dimension. Set the shape of input tensor as
:math:`(input_1, input_2, ..., input_N)` , Complex tensor is not supported.
dim (int): The dimension to reduce.
keepdim (bool, optional): Whether to reduce dimension, if ``True`` the output will keep the same dimension as the
`input` , the output will reduce dimension if ``false``. Default: ``False``.
Returns:
tuple (Tensor), tuple of 2 tensors, containing the maximum value of the self tensor along the given
dimension `dim` and the corresponding index.
- **values** (Tensor) - The maximum value of input tensor, with the same shape as `index`, and same dtype as `input`.
- **index** (Tensor) - The index for the maximum value of the input tensor, with dtype int64. If `keepdim`
is ``True`` , the shape of output tensors is :math:`(input_1, input_2, ..., input_{dim-1}, 1, input_{dim+1}, ..., input_N)`.
Otherwise, the shape is :math:`(input_1, input_2, ..., input_{dim-1}, input_{dim+1}, ..., input_N)` .
Raises:
TypeError: If `input` is not Tensor.
TypeError: If `keepdim` is not a bool.
TypeError: If `dim` is not an int.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> x = Tensor(np.array([0.0, 0.4, 0.6, 0.7, 0.1]), mindspore.float32)
>>> output, index = mint.max(x, 0, keepdim=True)
>>> print(output, index)
[0.7] [3]
.. function:: max(input, other) -> Tensor
:noindex:
For details, please refer to :func:`mindspore.mint.maximum`.
"""
return _max_instance(*args, **kwargs)
[文档]def min(*args, **kwargs):
r"""
min(input) -> Tensor
Returns the minimum value of the input tensor.
Args:
input (Tensor): The input tensor.
Returns:
Scalar Tensor with the same dtype as `input`, the minimum value of the input.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> x = Tensor(np.array([0.0, 0.4, 0.6, 0.7, 0.1]), mindspore.float32)
>>> output = mint.min(x)
>>> print(output)
0.0
.. function:: min(input, dim, keepdim=False) -> Tensor
:noindex:
Calculates the minimum value along with the given dim for the input tensor, and returns the minimum values and
indices.
Args:
input (Tensor) - The input tensor, can be any dimension. Set the shape of input tensor as
:math:`(input_1, input_2, ..., input_N)` , Complex tensor is not supported.
dim (int): The dimension to reduce.
keepdim (bool, optional): Whether to reduce dimension, if ``True`` the output will keep the same dimension as the
input, the output will reduce dimension if ``false``. Default: ``False``.
Returns:
tuple (Tensor), tuple of 2 tensors, containing the minimum value of the self tensor along the given
dimension `dim` and the corresponding index.
- **values** (Tensor) - The minimum value of input tensor, with the same shape as `index`, and same dtype as `input`.
- **index** (Tensor) - The index for the minimum value of the input tensor, with dtype int64. If `keepdim`
is ``True`` , the shape of output tensors is :math:`(input_1, input_2, ..., input_{dim-1}, 1, input_{dim+1}, ..., input_N)`.
Otherwise, the shape is :math:`(input_1, input_2, ..., input_{dim-1}, input_{dim+1}, ..., input_N)` .
Raises:
TypeError: If `input` is not Tensor.
TypeError: If `keepdim` is not a bool.
TypeError: If `dim` is not an int.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> x = Tensor(np.array([0.0, 0.4, 0.6, 0.7, 0.1]), mindspore.float32)
>>> output, index = mint.min(x, 0, keepdim=True)
>>> print(output, index)
[0.0] [0]
.. function:: min(input, other) -> Tensor
:noindex:
For details, please refer to :func:`mindspore.mint.minimum`.
"""
return _min_instance(*args, **kwargs)
[文档]def nansum(*args, **kwargs):
r"""
nansum(input, dim=None, keepdim=False, *, dtype=None) -> Tensor
Computes sum of `input` over a given dimension, treating NaNs as zero.
.. warning::
It is only supported on Atlas A2 Training Series Products.
This is an experimental API that is subject to change or deletion.
Args:
input (Tensor): The input Tensor.
dim (Union[int, tuple(int)], optional): The dimensions to sum.
Dim must be in the range [-rank(input), rank(input)). Default: ``None``, which indicates the sum of all
elements in a tensor.
keepdim (bool, optional): Whether the output Tensor keeps dimensions or not. Default: ``False``, indicating that no dimension is kept.
Keyword Args:
dtype (:class:`mindspore.dtype`, optional): The dtype of output Tensor. Default: ``None``.
Returns:
Tensor, the sum of input `input` in the given dimension dim, treating NaNs as zero.
- If dim is None, keepdim is False,
the output is a 0-D Tensor representing the sum of all elements in the input Tensor.
- If dim is int, set as 2, and keepdim is False,
the shape of output is :math:`(input_1, input_3, ..., input_R)`.
- If dim is tuple(int) or list(int), set as (2, 3), and keepdim is False,
the shape of output is :math:`(input_1, input_4, ..., input_R)`.
Raises:
TypeError: If `input` is not Tensor.
TypeError: If `keepdim` is not a bool.
TypeError: If the dtype of `input` or `dtype` is complex type.
ValueError: If `dim` is not in [-rank(input), rank(input)).
Supported Platforms:
``Ascend``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> x = Tensor(np.array([[float("nan"), 2, 3], [1, 2, float("nan")]]), mindspore.float32)
>>> output1 = mint.nansum(x, dim=0, keepdim=False, dtype=mindspore.float32)
>>> output2 = mint.nansum(x, dim=0, keepdim=True, dtype=mindspore.float32)
>>> print(output1)
[1. 4. 3.]
>>> print(output2)
[[1. 4. 3.]]
"""
return _nansum_instance(*args, **kwargs)
[文档]def remainder(*args, **kwargs):
r"""
remainder(input, other) -> Tensor
Computes the remainder of `input` divided by `other` element-wise. The result has the same sign as the divisor and
its absolute value is less than that of `other`.
Supports broadcasting to a common shape and implicit type promotion.
.. code:: python
remainder(input, other) == input - input.div(other, rounding_mode="floor") * other
Note:
Complex inputs are not supported. At least one input need to be tensor, but not both are bool tensors.
Args:
input (Union[Tensor, numbers.Number, bool]): The dividend is a numbers.Number or
a bool or a tensor whose data type is
`number <https://www.mindspore.cn/docs/en/master/api_python/mindspore/mindspore.dtype.html>`_ or
`bool_ <https://www.mindspore.cn/docs/en/master/api_python/mindspore/mindspore.dtype.html>`_.
other (Union[Tensor, numbers.Number, bool]): The divisor is a numbers.Number or
a bool or a tensor whose data type is number or bool\_ when the dividend is a tensor.
When the dividend is Scalar, the divisor must be a Tensor whose data type is number or bool\_.
Returns:
Tensor, with dtype promoted and shape broadcasted.
Raises:
TypeError: If `input` and `other` are not of types: (tensor, tensor), (tensor, number), (tensor, bool),
(number, tensor) or (bool, tensor).
ValueError: If `input` and `other` are not broadcastable.
Supported Platforms:
``Ascend``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> x = Tensor(np.array([-4.0, 5.0, 6.0]).astype(np.float32))
>>> y = Tensor(np.array([3.0, 2.0, 3.0]).astype(np.float64))
>>> output = mint.remainder(x, y)
>>> print(output)
[2. 1. 0.]
"""
return _remainder_instance(*args, **kwargs)
[文档]def repeat_interleave(*args, **kwargs):
r"""
repeat_interleave(input, repeats, dim=None, *, output_size=None) -> Tensor
Repeat elements of a tensor along an axis, like :func:`mindspore.numpy.repeat`.
.. warning::
Only support on Atlas A2 training series.
Args:
input (Tensor): The tensor to repeat values for. Must be of types: float16,
float32, int8, uint8, int16, int32, or int64.
repeats (Union[int, tuple, list, Tensor]): The number of times to repeat, must be positive.
dim (int, optional): The dim along which to repeat, Default: ``None``. If dims is None,
the input Tensor will be flattened and the output will alse be flattened.
Keyword Args:
output_size (int, optional): Total output size for the given axis (e.g. sum of repeats),
Default: ``None``.
Returns:
One tensor with values repeated along the specified dim. If input has shape
:math:`(s1, s2, ..., sn)` and dim is i, the output will have shape :math:`(s1, s2, ...,
si * repeats, ..., sn)`. The output type will be the same as the type of `input`.
Supported Platforms:
``Ascend``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> input = Tensor(np.array([[0, 1, 2], [3, 4, 5]]), mindspore.int32)
>>> output = mint.repeat_interleave(input, repeats=2, dim=0)
>>> print(output)
[[0 1 2]
[0 1 2]
[3 4 5]
[3 4 5]]
"""
return _repeat_interleave_instance(*args, **kwargs)
[文档]def where(*args, **kwargs):
r"""
where(condition, input, other) -> Tensor
Selects elements from `input` or `other` based on `condition` and returns a tensor.
.. math::
output_i = \begin{cases} input_i,\quad &if\ condition_i \\ other_i,\quad &otherwise \end{cases}
Args:
condition (Tensor[bool]): If true, yield `input`, otherwise yield `other`.
input (Union[Tensor, Scalar]): When `condition` is true, values to select from.
other (Union[Tensor, Scalar]): When `condition` is false, values to select from.
Returns:
Tensor, elements are selected from `input` and `other`.
Raises:
TypeError: If `condition` is not a tensor.
TypeError: If both `input` and `other` are scalars.
ValueError: If `condition`, `input` and `other` can not broadcast to each other.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import tensor, ops
>>> from mindspore import dtype as mstype
>>> a = tensor(np.arange(4).reshape((2, 2)), mstype.float32)
>>> b = tensor(np.ones((2, 2)), mstype.float32)
>>> condition = a < 3
>>> output = ops.where(condition, a, b)
>>> print(output)
[[0. 1.]
[2. 1.]]
.. function:: where(condition) -> Tensor
:noindex:
Identical to :func:`mindspore.ops.nonzero` with input `condition` and `as_tuple` being True.
Supported Platforms:
``Ascend``
"""
return _where_instance(*args, **kwargs)
__all__ = [
"all_gather_matmul",
"bitwise_not",
"clamp",
"clip",
"div",
"divide",
"empty",
"fmod",
"lerp",
"matmul_reduce_scatter",
"max",
"min",
"nansum",
"remainder",
"repeat_interleave",
"where",
]