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update links r1.6

This commit is contained in:
yingchen 2022-01-11 14:14:55 +08:00
parent 5c643a207f
commit 516193c2ec
24 changed files with 79 additions and 79 deletions
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2
mindspore/python/mindspore/common/dump.py
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@ -26,7 +26,7 @@ def set_dump(target, enabled=True):
Target should be an instance of Cell or Primitive. The default enabled
status for a cell or primitive is False. Please note that this API takes
effect only when the dump_mode field in dump config file is 2. See the
`dump document <https://mindspore.cn/docs/programming_guide/zh-CN/master/dump_in_graph_mode.html>`_
`dump document <https://mindspore.cn/docs/programming_guide/zh-CN/r1.6/dump_in_graph_mode.html>`_
for details.
.. warning::

2
mindspore/python/mindspore/communication/__init__.py
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@ -15,7 +15,7 @@
"""
Collective communication interface. Note the API in the file needs to preset communication environment variables. For
the Ascend cards, users need to prepare the rank table, set rank_id and device_id. Please see the `Ascend tutorial \
<https://www.mindspore.cn/tutorials/zh-CN/master/intermediate/distributed_training/
<https://www.mindspore.cn/tutorials/zh-CN/r1.6/intermediate/distributed_training/
distributed_training_ascend.html>`_ for more details.
For the GPU device, users need to prepare the host file and mpi, please see the `GPU tutorial \
<https://www.mindspore.cn/tutorials/zh-CN/r1.5/intermediate/distributed_training/distributed_training_gpu.html>`_

2
mindspore/python/mindspore/context.py
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@ -798,7 +798,7 @@ def set_context(**kwargs):
set larger too, otherwise a `core dumped` exception may be raised because of system stack overflow.
enable_sparse (bool): Whether to enable sparsity feature. Default: False.
For details of sparsity and sparse tensor, please check
`sparse tensor <https://www.mindspore.cn/docs/programming_guide/en/master/tensor.html#sparse-tensor>`_.
`sparse tensor <https://www.mindspore.cn/docs/programming_guide/en/r1.6/tensor.html#sparse-tensor>`_.
grad_for_scalar (bool): Whether to get gradient for scalar. Default: False.
When grad_for_scalar is set to True, the function's scalar input can be derived.
The default value is False. Because the back-end does not support scaling operations currently,

2
mindspore/python/mindspore/dataset/__init__.py
View File

@ -21,7 +21,7 @@ Besides, this module provides APIs to sample data while loading.
We can enable cache in most of the dataset with its key arguments 'cache'. Please notice that cache is not supported
on Windows platform yet. Do not use it while loading and processing data on Windows. More introductions and limitations
can refer `Single-Node Tensor Cache <https://www.mindspore.cn/docs/programming_guide/en/master/cache.html>`_.
can refer `Single-Node Tensor Cache <https://www.mindspore.cn/docs/programming_guide/en/r1.6/cache.html>`_.
Common imported modules in corresponding API examples are as follows:

4
mindspore/python/mindspore/dataset/engine/cache_client.py
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@ -26,8 +26,8 @@ class DatasetCache:
"""
A client to interface with tensor caching service.
For details, please check `Tutorial <https://www.mindspore.cn/docs/programming_guide/en/master/enable_cache.html>`_,
`Programming guide <https://www.mindspore.cn/docs/programming_guide/en/master/cache.html>`_.
For details, please check `Tutorial <https://www.mindspore.cn/docs/programming_guide/en/r1.6/enable_cache.html>`_,
`Programming guide <https://www.mindspore.cn/docs/programming_guide/en/r1.6/cache.html>`_.
Args:
session_id (int): A user assigned session id for the current pipeline.

2
mindspore/python/mindspore/mindrecord/tools/cifar100_to_mr.py
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@ -40,7 +40,7 @@ class Cifar100ToMR:
Note:
For details about Examples, please refer to `Converting the CIFAR-10 Dataset <https://
www.mindspore.cn/docs/programming_guide/en/master/dataset_conversion.html#converting-the-cifar-10-dataset>`_.
www.mindspore.cn/docs/programming_guide/en/r1.6/dataset_conversion.html#converting-the-cifar-10-dataset>`_.
Args:
source (str): the cifar100 directory to be transformed.

2
mindspore/python/mindspore/mindrecord/tools/cifar10_to_mr.py
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@ -40,7 +40,7 @@ class Cifar10ToMR:
Note:
For details about Examples, please refer to `Converting the CIFAR-10 Dataset <https://
www.mindspore.cn/docs/programming_guide/en/master/dataset_conversion.html#converting-the-cifar-10-dataset>`_.
www.mindspore.cn/docs/programming_guide/en/r1.6/dataset_conversion.html#converting-the-cifar-10-dataset>`_.
Args:
source (str): the cifar10 directory to be transformed.

2
mindspore/python/mindspore/mindrecord/tools/csv_to_mr.py
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@ -36,7 +36,7 @@ class CsvToMR:
Note:
For details about Examples, please refer to `Converting CSV Dataset <https://
www.mindspore.cn/docs/programming_guide/en/master/dataset_conversion.html#converting-csv-dataset>`_.
www.mindspore.cn/docs/programming_guide/en/r1.6/dataset_conversion.html#converting-csv-dataset>`_.
Args:
source (str): the file path of csv.

2
mindspore/python/mindspore/mindrecord/tools/imagenet_to_mr.py
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@ -32,7 +32,7 @@ class ImageNetToMR:
Note:
For details about Examples, please refer to `Converting the ImageNet Dataset <https://
www.mindspore.cn/docs/programming_guide/en/master/dataset_conversion.html#converting-the-imagenet-dataset>`_.
www.mindspore.cn/docs/programming_guide/en/r1.6/dataset_conversion.html#converting-the-imagenet-dataset>`_.
Args:
map_file (str): the map file that indicates label. The map file content should be like this:

2
mindspore/python/mindspore/mindrecord/tools/tfrecord_to_mr.py
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@ -69,7 +69,7 @@ class TFRecordToMR:
Note:
For details about Examples, please refer to `Converting TFRecord Dataset <https://
www.mindspore.cn/docs/programming_guide/en/master/dataset_conversion.html#converting-tfrecord-dataset>`_.
www.mindspore.cn/docs/programming_guide/en/r1.6/dataset_conversion.html#converting-tfrecord-dataset>`_.
Args:
source (str): the TFRecord file to be transformed.

2
mindspore/python/mindspore/nn/cell.py
View File

@ -1364,7 +1364,7 @@ class Cell(Cell_):
accelerate the algorithm in the algorithm library.
If `boost_type` is not in the algorithm library. Please view the algorithm in the algorithm library through
`algorithm library <https://gitee.com/mindspore/mindspore/tree/master/mindspore/python/mindspore/boost>`_.
`algorithm library <https://gitee.com/mindspore/mindspore/tree/r1.6/mindspore/python/mindspore/boost>`_.
Note:
Some acceleration algorithms may affect the accuracy of the network, please choose carefully.

2
mindspore/python/mindspore/nn/optim/adam.py
View File

@ -408,7 +408,7 @@ class AdamWeightDecay(Optimizer):
There is usually no connection between a optimizer and mixed precision. But when `FixedLossScaleManager` is used
and `drop_overflow_update` in `FixedLossScaleManager` is set to False, optimizer needs to set the 'loss_scale'.
As this optimizer has no argument of `loss_scale`, so `loss_scale` needs to be processed by other means, refer
document `LossScale <https://www.mindspore.cn/docs/programming_guide/zh-CN/master/lossscale.html>`_ to process
document `LossScale <https://www.mindspore.cn/docs/programming_guide/zh-CN/r1.6/lossscale.html>`_ to process
`loss_scale` correctly.
If parameters are not grouped, the `weight_decay` in optimizer will be applied on the network parameters without

2
mindspore/python/mindspore/nn/optim/lamb.py
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@ -200,7 +200,7 @@ class Lamb(Optimizer):
There is usually no connection between a optimizer and mixed precision. But when `FixedLossScaleManager` is used
and `drop_overflow_update` in `FixedLossScaleManager` is set to False, optimizer needs to set the 'loss_scale'.
As this optimizer has no argument of `loss_scale`, so `loss_scale` needs to be processed by other means, refer
document `LossScale <https://www.mindspore.cn/docs/programming_guide/zh-CN/master/lossscale.html>`_ to process
document `LossScale <https://www.mindspore.cn/docs/programming_guide/zh-CN/r1.6/lossscale.html>`_ to process
`loss_scale` correctly.
If parameters are not grouped, the `weight_decay` in optimizer will be applied on the network parameters without

22
mindspore/python/mindspore/ops/operations/comm_ops.py
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@ -49,7 +49,7 @@ class ReduceOp:
The user needs to preset
communication environment variables before running the following example, please check the details on the
official website of `MindSpore \
<https://www.mindspore.cn/docs/api/zh-CN/master/api_python/mindspore.ops.html#communication-operators>`_.
<https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/mindspore.ops.html#communication-operators>`_.
Supported Platforms:
``Ascend`` ``GPU``
@ -104,7 +104,7 @@ class AllReduce(PrimitiveWithInfer):
The tensors must have the same shape and format in all processes of the collection. The user needs to preset
communication environment variables before running the following example, please check the details on the
official website of `MindSpore \
<https://www.mindspore.cn/docs/api/zh-CN/master/api_python/mindspore.ops.html#communication-operators>`_.
<https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/mindspore.ops.html#communication-operators>`_.
Args:
op (str): Specifies an operation used for element-wise reductions,
@ -182,7 +182,7 @@ class AllGather(PrimitiveWithInfer):
The tensors must have the same shape and format in all processes of the collection. The user needs to preset
communication environment variables before running the following example, please check the details on the
official website of `MindSpore \
<https://www.mindspore.cn/docs/api/zh-CN/master/api_python/mindspore.ops.html#communication-operators>`_.
<https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/mindspore.ops.html#communication-operators>`_.
Args:
group (str): The communication group to work on. Default: "GlobalComm.WORLD_COMM_GROUP".
@ -385,7 +385,7 @@ class ReduceScatter(PrimitiveWithInfer):
The tensors must have the same shape and format in all processes of the collection. The user needs to preset
communication environment variables before running the following example, please check the details on the
official website of `MindSpore \
<https://www.mindspore.cn/docs/api/zh-CN/master/api_python/mindspore.ops.html#communication-operators>`_.
<https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/mindspore.ops.html#communication-operators>`_.
Args:
op (str): Specifies an operation used for element-wise reductions,
@ -518,7 +518,7 @@ class Broadcast(PrimitiveWithInfer):
The tensors must have the same shape and format in all processes of the collection. The user needs to preset
communication environment variables before running the following example, please check the details on the
official website of `MindSpore \
<https://www.mindspore.cn/docs/api/zh-CN/master/api_python/mindspore.ops.html#communication-operators>`_.
<https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/mindspore.ops.html#communication-operators>`_.
Args:
root_rank (int): Source rank. Required in all processes except the one
@ -652,11 +652,11 @@ class NeighborExchange(Primitive):
The user needs to preset
communication environment variables before running the following example, please check the details on the
official website of `MindSpore \
<https://www.mindspore.cn/docs/api/zh-CN/master/api_python/mindspore.ops.html#communication-operators>`_.
<https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/mindspore.ops.html#communication-operators>`_.
This operator requires a full-mesh network topology, each device has the same vlan id, and the ip & mask are
in the same subnet, please check the details on the official website of `MindSpore \
<https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_ops.html#id2>`_.
<https://www.mindspore.cn/docs/programming_guide/zh-CN/r1.6/distributed_training_ops.html#id2>`_.
Args:
send_rank_ids (list(int)): Ranks which the data is sent to.
@ -728,11 +728,11 @@ class AlltoAll(PrimitiveWithInfer):
The tensors must have the same shape and format in all processes of the collection. The user needs to preset
communication environment variables before running the following example, please check the details on the
official website of `MindSpore \
<https://www.mindspore.cn/docs/api/zh-CN/master/api_python/mindspore.ops.html#communication-operators>`_.
<https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/mindspore.ops.html#communication-operators>`_.
This operator requires a full-mesh network topology, each device has the same vlan id, and the ip & mask are
in the same subnet, please check the details on the official website of `MindSpore \
<https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_ops.html#id2>`_.
<https://www.mindspore.cn/docs/programming_guide/zh-CN/r1.6/distributed_training_ops.html#id2>`_.
Args:
split_count (int): On each process, divide blocks into split_count number.
@ -818,11 +818,11 @@ class NeighborExchangeV2(Primitive):
The user needs to preset
communication environment variables before running the following example, please check the details on the
official website of `MindSpore \
<https://www.mindspore.cn/docs/api/zh-CN/master/api_python/mindspore.ops.html#communication-operators>`_.
<https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/mindspore.ops.html#communication-operators>`_.
This operator requires a full-mesh network topology, each device has the same vlan id, and the ip & mask are
in the same subnet, please check the details on the official website of `MindSpore \
<https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_ops.html#id2>`_.
<https://www.mindspore.cn/docs/programming_guide/zh-CN/r1.6/distributed_training_ops.html#id2>`_.
Args:
send_rank_ids (list(int)): Ranks which the data is sent to. 8 rank_ids represents 8 directions, if one

12
mindspore/python/mindspore/ops/operations/math_ops.py
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@ -2686,8 +2686,8 @@ class Div(_MathBinaryOp):
Inputs:
- **x** (Union[Tensor, number.Number, bool]) - The first input is a number.Number or
a bool or a tensor whose data type is
`number <https://www.mindspore.cn/docs/api/en/master/api_python/mindspore.html#mindspore.dtype>`_ or
`bool_ <https://www.mindspore.cn/docs/api/en/master/api_python/mindspore.html#mindspore.dtype>`_.
`number <https://www.mindspore.cn/docs/api/en/r1.6/api_python/mindspore.html#mindspore.dtype>`_ or
`bool_ <https://www.mindspore.cn/docs/api/en/r1.6/api_python/mindspore.html#mindspore.dtype>`_.
- **y** (Union[Tensor, number.Number, bool]) - The second input is a number.Number or
a bool when the first input is a tensor or a tensor whose data type is number or bool\_.
When the first input is Scalar, the second input must be a Tensor whose data type is number or bool\_.
@ -2749,8 +2749,8 @@ class DivNoNan(_MathBinaryOp):
Inputs:
- **x** (Union[Tensor, number.Number, bool]) - The first input is a number.Number or
a bool or a tensor whose data type is
`number <https://www.mindspore.cn/docs/api/en/master/api_python/mindspore.html#mindspore.dtype>`_ or
`bool_ <https://www.mindspore.cn/docs/api/en/master/api_python/mindspore.html#mindspore.dtype>`_.
`number <https://www.mindspore.cn/docs/api/en/r1.6/api_python/mindspore.html#mindspore.dtype>`_ or
`bool_ <https://www.mindspore.cn/docs/api/en/r1.6/api_python/mindspore.html#mindspore.dtype>`_.
- **y** (Union[Tensor, number.Number, bool]) - The second input is a number.Number or
a bool when the first input is a tensor or a tensor whose data type is number or bool\_.
When the first input is Scalar, the second input must be a Tensor whose data type is number or bool\_.
@ -3233,8 +3233,8 @@ class Xlogy(Primitive):
Inputs:
- **x** (Union[Tensor, number.Number, bool]) - The first input is a number.Number or
a bool or a tensor whose data type is
`number <https://www.mindspore.cn/docs/api/en/master/api_python/mindspore.html#mindspore.dtype>`_ or
`bool_ <https://www.mindspore.cn/docs/api/en/master/api_python/mindspore.html#mindspore.dtype>`_.
`number <https://www.mindspore.cn/docs/api/en/r1.6/api_python/mindspore.html#mindspore.dtype>`_ or
`bool_ <https://www.mindspore.cn/docs/api/en/r1.6/api_python/mindspore.html#mindspore.dtype>`_.
- **y** (Union[Tensor, number.Number, bool]) - The second input is a number.Number or
a bool when the first input is a tensor or a tensor whose data type is number or bool\_.
When the first input is Scalar, the second input must be a Tensor whose data type is number or bool\_.

2
mindspore/python/mindspore/ops/operations/nn_ops.py
View File

@ -490,7 +490,7 @@ class ReLU(Primitive):
Inputs:
- **input_x** (Tensor) - Tensor of shape :math:`(N, *)`, where :math:`*` means, any number of
additional dimensions, data type is
`number <https://www.mindspore.cn/docs/api/en/master/api_python/mindspore.html#mindspore.dtype>`_.
`number <https://www.mindspore.cn/docs/api/en/r1.6/api_python/mindspore.html#mindspore.dtype>`_.
Outputs:
Tensor of shape :math:`(N, *)`, with the same type and shape as the `input_x`.

64
mindspore/python/mindspore/ops/operations/sponge_ops.py
View File

@ -33,7 +33,7 @@ class BondForce(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
.. math::
@ -118,7 +118,7 @@ class BondEnergy(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
.. math::
@ -206,7 +206,7 @@ class BondAtomEnergy(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
Args:
atom_numbers(int32): the number of atoms n.
@ -284,7 +284,7 @@ class BondForceWithAtomEnergy(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
Args:
atom_numbers(int32): the number of atoms n.
@ -368,7 +368,7 @@ class BondForceWithAtomVirial(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
The Virial part is as follows:
@ -461,7 +461,7 @@ class DihedralForce(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
Args:
dihedral_numbers(int32): the number of dihedral terms m.
@ -563,7 +563,7 @@ class DihedralEnergy(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
Args:
dihedral_numbers(int32): the number of dihedral terms m.
@ -666,7 +666,7 @@ class DihedralAtomEnergy(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
The calculation formula is the same as operator DihedralEnergy().
@ -771,7 +771,7 @@ class DihedralForceWithAtomEnergy(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
Args:
dihedral_numbers(int32): the number of dihedral terms m.
@ -876,7 +876,7 @@ class AngleForce(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
.. math::
dr_{ab} = (x_b-x_a, y_b-y_a, z_b-z_a)
@ -971,7 +971,7 @@ class AngleEnergy(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
.. math::
dr_{ab} = (x_b-x_a, y_b-y_a, z_b-z_a)
@ -1063,7 +1063,7 @@ class AngleAtomEnergy(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
Args:
angle_numbers(int32): the number of angles m.
@ -1146,7 +1146,7 @@ class AngleForceWithAtomEnergy(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
Args:
angle_numbers(int32): the number of angles m.
@ -1234,7 +1234,7 @@ class Dihedral14LJForce(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
.. math::
dr = (x_a-x_b, y_a-y_b, z_a-z_b)
@ -1337,7 +1337,7 @@ class Dihedral14LJEnergy(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
.. math::
dr = (x_a-x_b, y_a-y_b, z_a-z-b)
@ -1440,7 +1440,7 @@ class Dihedral14LJForceWithDirectCF(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
The calculation formula of the Lennard-Jones part is the same as operator
Dihedral14LJForce(), and the Coulomb part is as follows:
@ -1558,7 +1558,7 @@ class Dihedral14LJCFForceWithAtomEnergy(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
Args:
nb14_numbers (int32): the number of necessary dihedral 1,4 terms m.
@ -1667,7 +1667,7 @@ class Dihedral14LJAtomEnergy(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
Args:
nb14_numbers (int32): the number of necessary dihedral 1,4 terms m.
@ -1769,7 +1769,7 @@ class Dihedral14CFEnergy(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
.. math::
@ -1867,7 +1867,7 @@ class Dihedral14CFAtomEnergy(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
Args:
nb14_numbers (int32): the number of necessary dihedral 1,4 terms m.
@ -1961,7 +1961,7 @@ class PMEReciprocalForce(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
Args:
atom_numbers(int32): the number of atoms, n.
@ -2044,7 +2044,7 @@ class PMEExcludedForce(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
Args:
atom_numbers(int32): the number of atoms, n.
@ -2128,7 +2128,7 @@ class PMEEnergy(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
.. math::
@ -2264,7 +2264,7 @@ class LJEnergy(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
.. math::
@ -2362,7 +2362,7 @@ class LJForce(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
.. math::
@ -2461,7 +2461,7 @@ class LJForceWithPMEDirectForce(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
Args:
atom_numbers(int32): the number of atoms, n.
@ -2554,7 +2554,7 @@ class MDTemperature(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
Args:
residue_numbers (int32): the number of residues m.
@ -2626,7 +2626,7 @@ class MDIterationLeapFrogWithRF(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
Inputs:
- **float4_numbers** (Scalar) - total length to store random numbers.
@ -2741,7 +2741,7 @@ class MDIterationLeapFrogLiujian(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
Args:
atom_numbers(int32): the number of atoms n.
@ -2836,7 +2836,7 @@ class CrdToUintCrd(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
Args:
atom_numbers(int32): the number of atoms n.
@ -2882,7 +2882,7 @@ class MDIterationSetupRandState(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
Args:
atom_numbers(int32): the number of atoms n.
@ -2922,7 +2922,7 @@ class TransferCrd(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
Args:
start_serial(int32): the index start position.

14
mindspore/python/mindspore/ops/operations/sponge_update_ops.py
View File

@ -1258,7 +1258,7 @@ class BondForceWithAtomEnergyAndVirial(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
.. warning::
This is an experimental prototype that is subject to change and/or deletion.
@ -1346,7 +1346,7 @@ class LJForceWithVirialEnergy(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
.. warning::
This is an experimental prototype that is subject to change and/or deletion.
@ -1454,7 +1454,7 @@ class LJForceWithPMEDirectForceUpdate(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
.. warning::
This is an experimental prototype that is subject to change and/or deletion.
@ -1564,7 +1564,7 @@ class PMEReciprocalForceUpdate(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
.. warning::
This is an experimental prototype that is subject to change and/or deletion.
@ -1659,7 +1659,7 @@ class PMEExcludedForceUpdate(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
.. warning::
This is an experimental prototype that is subject to change and/or deletion.
@ -1761,7 +1761,7 @@ class LJForceWithVirialEnergyUpdate(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
.. warning::
This is an experimental prototype that is subject to change and/or deletion.
@ -1985,7 +1985,7 @@ class PMEEnergyUpdate(PrimitiveWithInfer):
Because there is a large amount of inputs and each of them are related,
there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore
<https://gitee.com/mindspore/mindscience/blob/master/MindSPONGE/docs/simple_formula.md>`_.
<https://gitee.com/mindspore/mindscience/blob/r0.2/MindSPONGE/docs/simple_formula.md>`_.
.. warning::
This is an experimental prototype that is subject to change and/or deletion.

2
mindspore/python/mindspore/parallel/algo_parameter_config.py
View File

@ -224,7 +224,7 @@ def set_algo_parameters(**kwargs):
"""
Set parameters in the algorithm for parallel strategy searching. See a typical use in
`test_auto_parallel_resnet.py
<https://gitee.com/mindspore/mindspore/blob/master/tests/ut/python/parallel/test_auto_parallel_resnet.py>`_.
<https://gitee.com/mindspore/mindspore/blob/r1.6/tests/ut/python/parallel/test_auto_parallel_resnet.py>`_.
Note:
The attribute name is required. This interface works ONLY in AUTO_PARALLEL mode.

2
mindspore/python/mindspore/run_check/_check_version.py
View File

@ -401,7 +401,7 @@ def check_version_and_env_config():
except OSError:
logger.warning(
"Pre-Load Lirary libgomp.so.1 failed, this might cause cannot allocate TLS memory problem, "
"if so find solution in FAQ in https://www.mindspore.cn/docs/faq/en/master/index.html.")
"if so find solution in FAQ in https://www.mindspore.cn/docs/faq/en/r1.6/index.html.")
elif __package_name__.lower() == "mindspore-gpu":
env_checker = GPUEnvChecker()
else:

2
mindspore/python/mindspore/train/callback/_callback.py
View File

@ -82,7 +82,7 @@ class Callback:
Callback function will execute some operations in the current step or epoch.
To create a custom callback, subclass Callback and override the method associated
with the stage of interest. For details of Callback fusion, please check
`Callback <https://www.mindspore.cn/docs/programming_guide/zh-CN/master/custom_debugging_info.html>`_.
`Callback <https://www.mindspore.cn/docs/programming_guide/zh-CN/r1.6/custom_debugging_info.html>`_.
It holds the information of the model. Such as `network`, `train_network`, `epoch_num`, `batch_num`,
`loss_fn`, `optimizer`, `parallel_mode`, `device_number`, `list_callback`, `cur_epoch_num`,

2
mindspore/python/mindspore/train/callback/_summary_collector.py
View File

@ -101,7 +101,7 @@ class SummaryCollector(Callback):
training computational graph is collected. Default: True.
- collect_train_lineage (bool): Whether to collect lineage data for the training phase,
this field will be displayed on the `lineage page \
<https://www.mindspore.cn/mindinsight/docs/en/master/lineage_and_scalars_comparison.html>`_
<https://www.mindspore.cn/mindinsight/docs/en/r1.6/lineage_and_scalars_comparison.html>`_
of MindInsight. Default: True.
- collect_eval_lineage (bool): Whether to collect lineage data for the evaluation phase,
this field will be displayed on the lineage page of MindInsight. Default: True.

6
mindspore/python/mindspore/train/serialization.py
View File

@ -1354,7 +1354,7 @@ def build_searched_strategy(strategy_filename):
"""
Build strategy of every parameter in network. Used in the case of distributed inference.
For details of it, please check:
`<https://www.mindspore.cn/docs/programming_guide/en/master/save_load_model_hybrid_parallel.html>`_.
`<https://www.mindspore.cn/docs/programming_guide/en/r1.6/save_load_model_hybrid_parallel.html>`_.
Args:
strategy_filename (str): Name of strategy file.
@ -1405,7 +1405,7 @@ def merge_sliced_parameter(sliced_parameters, strategy=None):
"""
Merge parameter slices into one parameter. Used in the case of distributed inference.
For details of it, please check:
`<https://www.mindspore.cn/docs/programming_guide/en/master/save_load_model_hybrid_parallel.html>`_.
`<https://www.mindspore.cn/docs/programming_guide/en/r1.6/save_load_model_hybrid_parallel.html>`_.
Args:
sliced_parameters (list[Parameter]): Parameter slices in order of rank id.
@ -1498,7 +1498,7 @@ def load_distributed_checkpoint(network, checkpoint_filenames, predict_strategy=
"""
Load checkpoint into net for distributed predication. Used in the case of distributed inference.
For details of distributed inference, please check:
`<https://www.mindspore.cn/docs/programming_guide/en/master/distributed_inference.html>`_.
`<https://www.mindspore.cn/docs/programming_guide/en/r1.6/distributed_inference.html>`_.
Args:
network (Cell): Network for distributed predication.

2
mindspore/python/mindspore/train/summary/summary_record.py
View File

@ -325,7 +325,7 @@ class SummaryRecord:
Raises:
TypeError: `step` is not int，or `train_network` is not `mindspore.nn.Cell \
<https://www.mindspore.cn/docs/api/en/master/api_python/nn/mindspore.nn.Cell.html#mindspore-nn-cell>`_ 。
<https://www.mindspore.cn/docs/api/en/r1.6/api_python/nn/mindspore.nn.Cell.html#mindspore-nn-cell>`_ 。
Examples:
>>> from mindspore.train.summary import SummaryRecord