!49788 modify add function
Merge pull request !49788 from changzherui/mod_add
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@ -1,6 +1,6 @@
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mindspore.Tensor.add
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====================
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.. py:method:: mindspore.Tensor.add(y)
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.. py:method:: mindspore.Tensor.add(other)
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详情请参考 :func:`mindspore.ops.add`。
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@ -1,26 +1,26 @@
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mindspore.ops.add
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=================
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.. py:function:: mindspore.ops.add(x, y)
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.. py:function:: mindspore.ops.add(input, other)
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两个输入Tensor逐元素相加。
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把other的数值与input进行逐元素相加。
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.. math::
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out_{i} = x_{i} + y_{i}
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out_{i} = input_{i} + other_{i}
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.. note::
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- 输入 `x` 和 `y` 遵循 `隐式类型转换规则 <https://www.mindspore.cn/docs/zh-CN/master/note/operator_list_implicit.html>`_ ，使数据类型保持一致。
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- 输入 `input` 和 `other` 遵循 `隐式类型转换规则 <https://www.mindspore.cn/docs/zh-CN/master/note/operator_list_implicit.html>`_ ，使数据类型保持一致。
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- 输入必须是两个Tensor，或一个Tensor和一个Scalar。
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- 当输入是两个Tensor时，它们的数据类型不能同时是bool，并保证其shape可以广播。
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- 当输入是一个Tensor和一个Scalar时，Scalar只能是一个常数。
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参数：
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- **x** (Union[Tensor, number.Number, bool]) - 第一个输入，是一个number.Number、bool值或数据类型为 `number <https://www.mindspore.cn/docs/zh-CN/master/api_python/mindspore/mindspore.dtype.html#mindspore.dtype>`_ 或 `bool_ <https://www.mindspore.cn/docs/zh-CN/master/api_python/mindspore/mindspore.dtype.html#mindspore.dtype>`_ 的Tensor。
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- **y** (Union[Tensor, number.Number, bool]) - 第二个输入，当第一个输入是Tensor时，第二个输入应该是一个number.Number或bool值，或数据类型为number或bool_的Tensor。当第一个输入是Scalar时，第二个输入必须是数据类型为number或bool_的Tensor。
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- **input** (Union[Tensor, number.Number, bool]) - 第一个输入，是一个number.Number、bool值或数据类型为 `number <https://www.mindspore.cn/docs/zh-CN/master/api_python/mindspore/mindspore.dtype.html#mindspore.dtype>`_ 或 `bool_ <https://www.mindspore.cn/docs/zh-CN/master/api_python/mindspore/mindspore.dtype.html#mindspore.dtype>`_ 的Tensor。
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- **other** (Union[Tensor, number.Number, bool]) - 第二个输入，当第一个输入是Tensor时，第二个输入应该是一个number.Number或bool值，或数据类型为number或bool_的Tensor。当第一个输入是Scalar时，第二个输入必须是数据类型为number或bool_的Tensor。
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返回：
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Tensor，shape与输入 `x`，`y` 广播后的shape相同，数据类型为两个输入中精度较高的类型。
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Tensor，shape与输入 `input`，`other` 广播后的shape相同，数据类型为两个输入中精度较高的类型。
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异常：
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- **TypeError** - `x` 和 `y` 不是Tensor、number.Number或bool。
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- **TypeError** - `input` 和 `other` 不是Tensor、number.Number或bool。
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@ -3736,11 +3736,11 @@ def acosh(x):
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return F.acosh(x)
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def add(x, y):
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def add(input, other):
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r"""
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Computes the element-wise addition of input tensors.
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"""
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return F.add(x, y)
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return F.add(input, other)
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def addr(x, vec1, vec2, beta=1, alpha=1):
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@ -810,13 +810,12 @@ class Tensor(Tensor_, metaclass=_TensorMeta):
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self._init_check()
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return tensor_operator_registry.get('addcmul')()(self, x1, x2, value)
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def add(self, y):
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def add(self, other):
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r"""
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For details, please refer to :func:`mindspore.ops.add`.
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"""
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self._init_check()
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return tensor_operator_registry.get('add')()(self, y)
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return tensor_operator_registry.get('add')()(self, other)
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def subtract(self, other, *, alpha=1):
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r"""
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@ -252,36 +252,37 @@ def absolute(input):
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return abs(input)
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def add(x, y):
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def add(input, other):
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r"""
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Adds two input tensors element-wise.
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Adds other value to input Tensor.
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.. math::
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out_{i} = x_{i} + y_{i}
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out_{i} = input_{i} + other_{i}
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.. note::
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- Inputs of `x` and `y` comply with the implicit type conversion rules to make the data types consistent.
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- Inputs of `input` and `other` comply with the implicit type conversion rules to make
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the data types consistent.
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- The inputs must be two tensors or one tensor and one scalar.
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- When the inputs are two tensors,
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dtypes of them cannot be bool at the same time, and the shapes of them can be broadcast.
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- When the inputs are one tensor and one scalar, the scalar could only be a constant.
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Args:
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x (Union[Tensor, number.Number, bool]): The first input is a number.Number or
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input (Union[Tensor, number.Number, bool]): The first input is a number.Number or
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a bool or a tensor whose data type is
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`number <https://www.mindspore.cn/docs/en/master/api_python/mindspore.html#mindspore.dtype>`_ or
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`bool_ <https://www.mindspore.cn/docs/en/master/api_python/mindspore.html#mindspore.dtype>`_.
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y (Union[Tensor, number.Number, bool]): The second input, when the first input is a Tensor,
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other (Union[Tensor, number.Number, bool]): The second input, when the first input is a Tensor,
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the second input should be a number.Number or bool value, or a Tensor whose data type is number or bool\_.
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When the first input is Scalar, the second input must be a Tensor whose data type is number or bool\_.
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Returns:
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Tensor, the shape is the same as the one of the input `x` , `y` after broadcasting,
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Tensor, the shape is the same as the one of the input `input` , `other` after broadcasting,
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and the data type is the one with higher precision or higher digits among the two inputs.
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Raises:
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TypeError: If `x` and `y` is not one of the following: Tensor, number.Number, bool.
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TypeError: If `input` and `other` is not one of the following: Tensor, number.Number, bool.
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Supported Platforms:
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``Ascend`` ``GPU`` ``CPU``
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@ -304,7 +305,7 @@ def add(x, y):
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>>> print(output.dtype)
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Float32
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"""
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return tensor_add(x, y)
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return _get_cache_prim(P.Add)()(input, other)
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def addcdiv(input_data, x1, x2, value):
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@ -0,0 +1,100 @@
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# Copyright 2023 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import numpy as np
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import pytest
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import mindspore.common.dtype as mstype
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore import context
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from mindspore import ops
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class Net(nn.Cell):
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def construct(self, x, other):
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return x.add(other)
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class NetAdd(nn.Cell):
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def construct(self, x, other):
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return ops.add(x, other)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_arm_cpu
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
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def test_add(mode):
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"""
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Feature: tensor.subtract()
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Description: Verify the result of tensor.subtract
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Expectation: success
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"""
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context.set_context(mode=mode)
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net = Net()
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x = Tensor([1, 2, 3], dtype=mstype.float32)
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y = Tensor([4, 5, 6], dtype=mstype.float32)
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output = net(x, y)
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expected = np.array([5, 7, 9], dtype=np.float32)
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assert np.allclose(output.asnumpy(), expected)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_arm_cpu
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
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def test_add_alpha(mode):
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"""
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Feature: tensor.subtract()
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Description: Verify the result of tensor.subtract
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Expectation: success
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"""
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context.set_context(mode=mode)
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net = Net()
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x = Tensor([1, 2, 3], dtype=mstype.float32)
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y = Tensor([2, 2, 2], dtype=mstype.float32)
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output = net(x, y)
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expected = np.array([3, 4, 5], dtype=np.float32)
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assert np.allclose(output.asnumpy(), expected)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_arm_cpu
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
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def test_add_ops(mode):
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"""
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Feature: tensor.subtract()
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Description: Verify the result of tensor.subtract
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Expectation: success
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"""
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context.set_context(mode=mode)
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net = NetAdd()
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x = Tensor([3, 4, 5], dtype=mstype.float32)
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y = Tensor([1, 2, 3], dtype=mstype.float32)
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output = net(x, y)
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expected = np.array([4, 6, 8], dtype=np.float32)
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assert np.allclose(output.asnumpy(), expected)
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