# Copyright 2022 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
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# ============================================================================
"""Defines parameter operators with functional form."""
from mindspore.ops import operations as P
from mindspore.ops._primitive_cache import _get_cache_prim
assign_ = P.Assign()
[文档]def assign(variable, value):
"""
Assigns `Parameter` with a value.
Args of `variable` and `value` comply with the implicit type conversion rules to make the data types consistent.
If they have different data types, the lower priority data type will be converted to
the relatively highest priority data type.
Args:
variable (Parameter): The `Parameter`. :math:`(N,*)` where :math:`*` means,
any number of additional dimensions.
value (Tensor): The value to be assigned, has the same shape with `variable`.
Returns:
Tensor, has the same data type and shape as original `variable`.
Raises:
TypeError: If `variable` is not a Parameter.
TypeError: If `value` is not a Tensor.
RuntimeError: If the data type of `variable` and `value` conversion of Parameter
is required when data type conversion of Parameter is not supported.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> value = Tensor([2.0], mindspore.float32)
>>> variable = mindspore.Parameter(Tensor([1.0], mindspore.float32), name="variable")
>>> ops.assign(variable, value)
>>> print(variable.asnumpy())
[2.]
"""
return assign_(variable, value)
assign_sub_ = P.AssignSub()
[文档]def assign_sub(variable, value):
"""
Updates a `Parameter` by subtracting a value from it.
Args of `variable` and `value` comply with the implicit type conversion rules to make the data types consistent.
If they have different data types, the lower priority data type will be converted to
the relatively highest priority data type.
If `value` is a number, the number is automatically converted to Tensor,
and the data type is consistent with the Tensor data type involved in the operation.
Note:
Since `variable` is a data type Parameter, the data type cannot be changed,
so only the type of `value` is allowed to be promoted to the type of `variable`.
And the conversion type supported by different devices will be different,
it is recommended to use the same data type when using this operator.
Args:
variable (Parameter): The `Parameter`.
:math:`(N,*)` where :math:`*` means, any number of additional dimensions.
value (Tensor): The value to be subtracted from the `variable`.
It must have the same shape as `variable`.
it is recommended to use the same data type when using this operator.
Returns:
Tensor, has the same data type and shape as original `variable`.
Raises:
TypeError: If `value` is neither Number nor Tensor.
RuntimeError: If the data type of `x`, `y` conversion of Parameter is required
when data type conversion of Parameter is not supported.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> variable = mindspore.Parameter(initializer(1, [1], mindspore.int32), name="global_step")
>>> value = Tensor(np.ones([1]).astype(np.int32) * 100)
>>> ops.assign_sub(variable, value)
>>> print(variable.asnumpy())
[-99]
"""
return assign_sub_(variable, value)
assign_add_ = P.AssignAdd()
[文档]def assign_add(variable, value):
"""
Updates a `Parameter` by adding a value to it.
Args of `variable` and `value` comply with the implicit type conversion rules to make the data types consistent.
If they have different data types, the lower priority data type will be converted to
the relatively highest priority data type.
If `value` is a number, the number is automatically converted to Tensor,
and the data type is consistent with the Tensor data type involved in the operation.
Note:
Since `variable` is a data type Parameter, the data type cannot be changed,
so only the type of `value` is allowed to be promoted to the type of `variable`.
And the conversion type supported by different devices will be different,
it is recommended to use the same data type when using this operator.
Args:
variable (Parameter): The `Parameter`.
:math:`(N,*)` where :math:`*` means, any number of additional dimensions.
value (Tensor): The value to be added to the `variable`.
It must have the same shape as `variable`.
it is recommended to use the same data type when using this operator.
Returns:
Tensor, has the same data type and shape as original `variable`.
Raises:
TypeError: If `value` is neither Number nor Tensor.
RuntimeError: If the data type of `variable` and `value` conversion of Parameter
is required when data type conversion of Parameter is not supported.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> variable = mindspore.Parameter(initializer(1, [1], mindspore.int32), name="global_step")
>>> value = Tensor(np.ones([1]).astype(np.int32) * 100)
>>> ops.assign_add(variable, value)
>>> print(variable.asnumpy())
[101]
"""
return assign_add_(variable, value)
[文档]def index_add(x, indices, y, axis, use_lock=True, check_index_bound=True):
"""
Adds tensor `y` to specified axis and indices of Parameter `x`. The axis should be in [0, len(x.dim) - 1],
and indices should be in [0, x.shape[axis] - 1] at the axis dimension.
Args:
x (Parameter): The input Parameter to add to.
indices (Tensor): Add the value of `x` and `y` along the dimension of the `axis` according to the
specified index value, with data type int32.
The `indices` must be 1D with the same size as the size of `y` in the `axis` dimension. The values
of `indices` should be in [0, b), where the b is the size of `x` in the `axis` dimension.
y (Tensor): The input tensor with the value to add. Must have same data type as `x`.
The shape must be the same as `x` except the `axis` th dimension.
axis (int): The dimension along which to index.
use_lock (bool): Whether to enable a lock to protect the updating process of variable tensors.
If true, when updating the value of `x`, this process will be protected by a lock by using atomic operation.
If false, the result may be unpredictable. Default: True.
check_index_bound (bool): If true, check index boundary. If false, don't check index boundary. Default: True.
Returns:
Tensor, has the same shape and dtype as `x`.
Raises:
TypeError: If `x` is not a Parameter.
TypeError: If neither `indices` nor `y` is a Tensor.
ValueError: If axis is out of `x` rank's range.
ValueError: If `x` rank is not the same as `y` rank.
ValueError: If shape of `indices` is not 1D or size of `indices` is not equal to dimension of y[axis].
ValueError: If `y`'s shape is not the same as `x` except the `axis` th dimension.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> import mindspore
>>> from mindspore import Tensor, Parameter
>>> from mindspore import ops
>>> x = Parameter(Tensor(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), mindspore.float32), name="name_x")
>>> indices = Tensor(np.array([0, 2]), mindspore.int32)
>>> y = Tensor(np.array([[0.5, 1.0], [1.0, 1.5], [2.0, 2.5]]), mindspore.float32)
>>> output = ops.index_add(x, indices, y, 1)
>>> print(output)
[[ 1.5 2. 4. ]
[ 5. 5. 7.5]
[ 9. 8. 11.5]]
"""
_index_add = _get_cache_prim(P.IndexAdd)(axis, use_lock, check_index_bound)
return _index_add(x, indices, y)
__all__ = [
'assign',
'assign_sub',
'assign_add',
'index_add'
]
__all__.sort()