# 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.
# ============================================================================
"""math Operations."""
import mindspore.ops as ops
from mindspore.ops import functional as F
from mindspore.ops.function.math_func import cummin
from mindspore.ops._primitive_cache import _get_cache_prim
def matmul(x1, x2, dtype=None):
"""
Returns the matrix product of two arrays.
Note:
Numpy arguments `out`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
On GPU, the supported dtypes are np.float16 and np.float32.
On CPU, the supported dtypes are np.float16 and np.float32.
Args:
x1 (Tensor): Input tensor, scalar not allowed.
The last dimension of `x1` must be the same size as the second last dimension of `x2`.
And the shape of x1 and x2 could be broadcast.
x2 (Tensor): Input tensor, scalar not allowed.
The last dimension of `x1` must be the same size as the second last dimension of `x2`.
And the shape of x1 and x2 could be broadcast.
dtype (:class:`mindspore.dtype`, optional): defaults to None. Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar, the matrix product of the inputs. This is a scalar only
when both `x1`, `x2` are 1-d vectors.
Raises:
ValueError: If the last dimension of `x1` is not the same size as the
second-to-last dimension of `x2`, or if a scalar value is passed in.
ValueError: If the shape of `x1` and `x2` could not broadcast together.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor, ops
>>> import mindspore
>>> # case 1 : Reasonable application of broadcast mechanism
>>> x1 = Tensor(np.arange(2*3*4).reshape(2, 3, 4), mindspore.float32)
>>> x2 = Tensor(np.arange(4*5).reshape(4, 5), mindspore.float32)
>>> output = ops.matmul(x1, x2)
>>> print(output)
[[[ 70. 76. 82. 88. 94.]
[ 190. 212. 234. 256. 278.]
[ 310. 348. 386. 424. 462.]]
[[ 430. 484. 538. 592. 646.]
[ 550. 620. 690. 760. 830.]
[ 670. 756. 842. 928. 1014.]]]
>>> print(output.shape)
(2, 3, 5)
>>> # case 2 : the rank of `x1` is 1
>>> x1 = Tensor(np.ones([1, 2]), mindspore.float32)
>>> x2 = Tensor(np.ones([2,]), mindspore.float32)
>>> output = ops.matmul(x1, x2)
>>> print(output)
[2.]
>>> print(output.shape)
(1,)
"""
res = F.matmul(x1, x2)
if dtype is not None:
res = res.astype(dtype)
return res
[文档]def mm(input, mat2):
r"""
Returns the matrix product of two arrays.
If `input` is a :math:`(n \times m)` Tensor, `mat2` is a
:math:`(m \times p)` Tensor, `out` will be a :math:`(n \times p)` Tensor.
Note:
- This function cannot support broadcasting.
Refer to :func:`mindspore.ops.matmul` instead if you need a broadcastable function.
- On Ascend, float64 doesn't be supported.
Args:
input (Tensor): The first matrix of matrix multiplication.
The last dimension of `input` must be the same size as the first dimension of `mat2`.
mat2 (Tensor): The second matrix of matrix multiplication.
The last dimension of `input` must be the same size as the first dimension of `mat2`.
Returns:
Tensor or scalar, the matrix product of the inputs.
Raises:
ValueError: If the last dimension of `input` is not the same size as the
second-to-last dimension of `mat2`.
ValueError: If `input` or `mat2` is not a Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore as ms
>>> from mindspore import ops
>>> import numpy as np
>>> x1 = ms.Tensor(np.random.rand(2, 3), ms.float32)
>>> x2 = ms.Tensor(np.random.rand(3, 4), ms.float32)
>>> out = ops.mm(x1, x2)
>>> print(out.shape)
(2, 4)
"""
_matmul = _get_cache_prim(ops.MatMul)()
out = _matmul(input, mat2)
return out