From 40e9051217f2479fc66753a3ac078d113f3faf30 Mon Sep 17 00:00:00 2001 From: zhangyi Date: Tue, 20 Apr 2021 19:31:00 +0800 Subject: [PATCH] add some code examples. --- security/comments_specification_en.md | 204 +++++++++++++++++- ...ion.md => comments_specification_zh_cn.md} | 204 +++++++++++++++++- 2 files changed, 398 insertions(+), 10 deletions(-) rename security/{comments_specification.md => comments_specification_zh_cn.md} (64%) diff --git a/security/comments_specification_en.md b/security/comments_specification_en.md index de63ec8..7b3b317 100644 --- a/security/comments_specification_en.md +++ b/security/comments_specification_en.md @@ -52,7 +52,7 @@ Examples: >>> Sample Code ``` -The format items are described as follows: +The format items are described as follows: - `Summary`: briefly describes the API function. - `More elaborate description`: describes the function and usage of an API in detail. @@ -86,21 +86,215 @@ Supported Platforms: - When an API is generated by directory, the comments in the \_\_init\_\_ file header are displayed at the beginning of the web page. When an API is generated by file, the comments at the beginning of the file are displayed at the beginning of the web page. The comments must contain the overall description of the corresponding modules. - If a comment contains a backslash (\\), change `"""` in the header to `r"""`. - Colon requirements: Keywords (such as `Args` and `Returns`) and parameter names (such as `Arg1` or `Arg2`) are followed by colons (:). A colon must be followed by a space. The content of `Summary` and `Returns` cannot contain colons. - - Blank line requirements: A blank line is required between contents of different types (such as `Args` and `Returns`). A blank line is not required between contents of the same type (for example, `Arg1` and `Arg2`). If the content is described in an unordered or ordered list, a blank line must be added between the content in the list and the content above the list. - - Space requirements: The newline characters of `Args` and `Raises` must be indented by four spaces. The sub-parameters or values of `Args` and line breaks for disordered or ordered content of `Inputs`, `Outputs` and `Returns` do not need indents and are aligned with the start position of the previous line. In `Args`, there must be a space between the parameter name and the `(` of the type. + - Blank line requirements: + + A blank line is required between contents of different types (such as `Args` and `Returns`). A blank line is not required between contents of the same type (for example, `Arg1` and `Arg2`). + + ```python + """ + High-Level API for Training or Testing. + + Args: + network (Cell): A training or testing network. + loss_fn (Cell): Objective function, if loss_fn is None, the + network should contain the logic of loss and grads calculation, and the logic + of parallel if needed. Default: None. + + Returns: + function, original function. + ``` + + If the content is described in an unordered or ordered list, a blank line must be added between the content in the list and the content above the list. + + ```python + """ + Args: + amp_level (str): Option for argument `level` in `mindspore.amp.build_train_network`, level for mixed + precision training. Supports ["O0", "O2", "O3", "auto"]. Default: "O0". + + - O0: Do not change. + - O2: Cast network to float16, keep batchnorm run in float32, using dynamic loss scale. + - O3: Cast network to float16, with additional property 'keep_batchnorm_fp32=False'. + - auto: Set to level to recommended level in different devices. Set level to O2 on GPU, Set + level to O3 Ascend. The recommended level is choose by the export experience, cannot + always generalize. User should specify the level for special network. + + O2 is recommended on GPU, O3 is recommended on Ascend. + """ + ``` + + - Space requirements: + + The newline characters of `Args` and `Raises` must be indented by four spaces. + + ```python + """ + Args: + lr_power (float): Learning rate power controls how the learning rate decreases during training, + must be less than or equal to zero. Use fixed learning rate if `lr_power` is zero. + use_locking (bool): If `True`, the var and accumulation tensors will be protected from being updated. + Default: False. + + Raises: + TypeError: If `lr`, `l1`, `l2`, `lr_power` or `use_locking` is not a float. + If `use_locking` is not a bool. + If dtype of `var`, `accum`, `linear` or `grad` is neither float16 nor float32. + If dtype of `indices` is not int32. + """ + ``` + + The sub-parameters or values of `Args` and line breaks for disordered or ordered content of `Inputs`, `Outputs` and `Returns` do not need indents and are aligned with the start position of the previous line. + + ```python + """ + Args + parallel_mode (str): There are five kinds of parallel modes, "stand_alone", "data_parallel", + "hybrid_parallel", "semi_auto_parallel" and "auto_parallel". Default: "stand_alone". + + - stand_alone: Only one processor is working. + - data_parallel: Distributes the data across different processors. + - hybrid_parallel: Achieves data parallelism and model parallelism + manually. + - semi_auto_parallel: Achieves data parallelism and model parallelism by + setting parallel strategies. + + Inputs: + - **var** (Parameter) - The variable to be updated. The data type must be float16 or float32. + - **accum** (Parameter) - The accumulation to be updated, must be same data type and shape as `var`. + - **linear** (Parameter) - the linear coefficient to be updated, must be same data type and shape + as `var`. + - **grad** (Tensor) - A tensor of the same type as `var`, for the gradient. + - **indices** (Tensor) - A vector of indices in the first dimension of `var` and `accum`. + The shape of `indices` must be the same as `grad` in the first dimension. The type must be int32. + + Outputs: + Tuple of 3 Tensor, the updated parameters. + + - **var** (Tensor) - Tensor, has the same shape and data type as `var`. + - **accum** (Tensor) - Tensor, has the same shape and data type as `accum`. + - **linear** (Tensor) - Tensor, has the same shape and data type as `linear`. + + Returns: + Function, if `fn` is not None, returns a callable function that will execute the compiled function; If `fn` is + None, returns a decorator and when this decorator invokes with a single `fn` argument, the callable function is + equal to the case when `fn` is not None. + """ + ``` + + In `Args`, there must be a space between the parameter name and the `(` of the type. + + ```python + """ + Args: + lr (float): The learning rate value, must be positive. + """ + ``` + - `Args` Comment - Common parameter types are as follows: - Basic data types: `int`, `float`, `bool`, `str`, `list`, `dict`, `set`, `tuple` and `numpy.ndarray`. + + ```python + """ + Args: + arg1 (int): Some description. + arg2 (float): Some description. + arg3 (bool): Some description. + arg4 (str): Some description. + arg5 (list): Some description. + arg6 (dict): Some description. + arg7 (set): Some description. + arg8 (tuple): Some description. + arg9 (numpy.ndarray): Some description. + """ + ``` + - dtype: For the value of mindspore.dtype, set this parameter to `mindspore.dtype`. For the value of the numpy type, set this parameter to `numpy.dtype`. Set other parameters based on the actual requirements. + + ```python + """ + Args: + arg1 (mindspore.dtype): Some description. + """ + ``` + - One parameter with multiple optional types: Union [type 1, type 2], for example, `Union[Tensor, Number]`. + + ```python + """ + Args: + arg1 (Union[Tensor, Number]): Some description. + """ + ``` + - List type: list[Specific type], for example, `list[str]`. + + ```python + """ + Args: + arg1 (list[str]): Some description. + """ + ``` + - The format of optional types is as follows: (Type, optional). + + ```python + """ + Args: + arg1 (bool, optional): Some description. + """ + ``` + - Other types: Tensor, other specific types, or method names. + + ```python + """ + Args: + arg1 (Tensor): Some description. + """ + ``` + - `Returns` Comment - If the return value type or dimension changes, describe the relationship between the return value and the input. - If there are multiple return values, write them in different lines. The line difference is not displayed on the web page. The unordered list supports return values in different lines. + + ```python + """ + Returns: + - DatasetNode, the root node of the IR tree. + - Dataset, the root dataset of the IR tree. + """ + ``` + - `Examples` Comment - For the content in `Examples`, ```>>>``` should be added at the beginning of each line of code. For multiple lines (including classes, function definitions or manual switch line) and blank lines, ```...``` is needed at the beginning of these lines. There is no need to add any symbols at the beginning of the output result line. + + ```python + """ + Examples: + >>> import mindspore as ms + >>> import mindspore.nn as nn + >>> class Net(nn.Cell): + ... def __init__(self, dense_shape): + ... super(Net, self).__init__() + ... self.dense_shape = dense_shape + ... def construct(self, indices, values): + ... x = SparseTensor(indices, values, self.dense_shape) + ... return x.values, x.indices, x.dense_shape + ... + >>> indices = Tensor([[0, 1], [1, 2]]) + >>> values = Tensor([1, 2], dtype=ms.float32) + >>> out = Net((3, 4))(indices, values) + >>> print(out[0]) + [1. 2.] + >>> print(out[1]) + [[0 1] + [1 2]] + >>> print(out[2]) + (3, 4) + """ + ``` + - Actual code needs to be provided in `Examples`. If you need to refer to other Examples, use Note. - The comments of the ops operator are written in PyNative mode. If the operator can be executed, the execution result must be provided. - Import can be omitted in the case of industry consensus, such as np and nn. @@ -344,7 +538,7 @@ For details about the display effect, click [here](https://www.mindspore.cn/doc/ ```markdown # The name of namespace - + The link of header file. ## The name of class @@ -354,5 +548,5 @@ For details about the display effect, click [here](https://www.mindspore.cn/doc/ The name of attribute or function. The description of attribute or function. - + ``` diff --git a/security/comments_specification.md b/security/comments_specification_zh_cn.md similarity index 64% rename from security/comments_specification.md rename to security/comments_specification_zh_cn.md index 8bdebad..6078ad4 100644 --- a/security/comments_specification.md +++ b/security/comments_specification_zh_cn.md @@ -52,7 +52,7 @@ Examples: >>> Sample Code ``` -其中， +其中， - `Summary`：简单描述该接口的功能。 - `More elaborate description`：详细描述该接口的功能和如何使用等信息。 @@ -86,21 +86,215 @@ Supported Platforms: - 以目录为粒度生成API时，__init__文件头部的注释内容会呈现在网页开头；以文件为粒度生成API时，该文件头部的注释内容会呈现在网页开头。这些注释内容需包含相应模块的整体说明。 - 注释中包含反斜杠时，需要将头部的`"""`改成`r"""`。 - 冒号要求：关键字（如`Args`、`Returns`等）后面有冒号":"；参数名（如`Arg1`、`Arg2`等）后面有冒号":"，冒号后需有空格。`Summary`和`Returns`的内容中不能包含冒号。 - - 空行要求：不同类型（如`Args`、`Returns`等）的内容之间需有空行，同种类型（如`Arg1`、`Arg2`等）的内容之间不需要空行。采用无序或有序列表描述内容时，整个列表内容与上方内容之间需增加一个空行。 - - 空格要求：`Args`和`Raises`内容的换行需要缩进4个空格，`Args`的子参数或取值、`Inputs`、`Outputs`和`Returns`等无序或有序列表内容的换行不需要缩进，与上一行的正文起始位置对齐。`Args`中参数名和类型的`(`之间需要有空格。 + - 空行要求： + + 不同类型（如`Args`、`Returns`等）的内容之间需有空行，同种类型（如`Arg1`、`Arg2`等）的内容之间不需要空行。 + + ```python + """ + High-Level API for Training or Testing. + + Args: + network (Cell): A training or testing network. + loss_fn (Cell): Objective function, if loss_fn is None, the + network should contain the logic of loss and grads calculation, and the logic + of parallel if needed. Default: None. + + Returns: + function, original function. + ``` + + 采用无序或有序列表描述内容时，整个列表内容与上方内容之间需增加一个空行。 + + ```python + """ + Args: + amp_level (str): Option for argument `level` in `mindspore.amp.build_train_network`, level for mixed + precision training. Supports ["O0", "O2", "O3", "auto"]. Default: "O0". + + - O0: Do not change. + - O2: Cast network to float16, keep batchnorm run in float32, using dynamic loss scale. + - O3: Cast network to float16, with additional property 'keep_batchnorm_fp32=False'. + - auto: Set to level to recommended level in different devices. Set level to O2 on GPU, Set + level to O3 Ascend. The recommended level is choose by the export experience, cannot + always generalize. User should specify the level for special network. + + O2 is recommended on GPU, O3 is recommended on Ascend. + """ + ``` + + - 空格要求： + + `Args`和`Raises`内容的换行需要缩进4个空格。 + + ```python + """ + Args: + lr_power (float): Learning rate power controls how the learning rate decreases during training, + must be less than or equal to zero. Use fixed learning rate if `lr_power` is zero. + use_locking (bool): If `True`, the var and accumulation tensors will be protected from being updated. + Default: False. + + Raises: + TypeError: If `lr`, `l1`, `l2`, `lr_power` or `use_locking` is not a float. + If `use_locking` is not a bool. + If dtype of `var`, `accum`, `linear` or `grad` is neither float16 nor float32. + If dtype of `indices` is not int32. + """ + ``` + + `Args`的子参数或取值、`Inputs`、`Outputs`和`Returns`等无序或有序列表内容的换行不需要缩进，与上一行的正文起始位置对齐。 + + ```python + """ + Args + parallel_mode (str): There are five kinds of parallel modes, "stand_alone", "data_parallel", + "hybrid_parallel", "semi_auto_parallel" and "auto_parallel". Default: "stand_alone". + + - stand_alone: Only one processor is working. + - data_parallel: Distributes the data across different processors. + - hybrid_parallel: Achieves data parallelism and model parallelism + manually. + - semi_auto_parallel: Achieves data parallelism and model parallelism by + setting parallel strategies. + + Inputs: + - **var** (Parameter) - The variable to be updated. The data type must be float16 or float32. + - **accum** (Parameter) - The accumulation to be updated, must be same data type and shape as `var`. + - **linear** (Parameter) - the linear coefficient to be updated, must be same data type and shape + as `var`. + - **grad** (Tensor) - A tensor of the same type as `var`, for the gradient. + - **indices** (Tensor) - A vector of indices in the first dimension of `var` and `accum`. + The shape of `indices` must be the same as `grad` in the first dimension. The type must be int32. + + Outputs: + Tuple of 3 Tensor, the updated parameters. + + - **var** (Tensor) - Tensor, has the same shape and data type as `var`. + - **accum** (Tensor) - Tensor, has the same shape and data type as `accum`. + - **linear** (Tensor) - Tensor, has the same shape and data type as `linear`. + + Returns: + Function, if `fn` is not None, returns a callable function that will execute the compiled function; If `fn` is + None, returns a decorator and when this decorator invokes with a single `fn` argument, the callable function is + equal to the case when `fn` is not None. + """ + ``` + + `Args`中参数名和类型的`(`之间需要有空格。 + + ```python + """ + Args: + lr (float): The learning rate value, must be positive. + """ + ``` + - `Args`注释说明 - 常见参数类型有： - 基本数据类型：`int`、`float`、`bool`、`str`、`list`、`dict`、`set`、`tuple`、`numpy.ndarray`。 + + ```python + """ + Args: + arg1 (int): Some description. + arg2 (float): Some description. + arg3 (bool): Some description. + arg4 (str): Some description. + arg5 (list): Some description. + arg6 (dict): Some description. + arg7 (set): Some description. + arg8 (tuple): Some description. + arg9 (numpy.ndarray): Some description. + """ + ``` + - dtype：如果是mindspore.dtype里的值，写成`mindspore.dtype`，如果是numpy类型，写成`numpy.dtype`。其他按实际情况写。 + + ```python + """ + Args: + arg1 (mindspore.dtype): Some description. + """ + ``` + - 一个参数有多个可选类型：Union[类型1, 类型2]，如`Union[Tensor, Number]`。 + + ```python + """ + Args: + arg1 (Union[Tensor, Number]): Some description. + """ + ``` + - list类型：list[具体类型]，如`list[str]`。 + + ```python + """ + Args: + arg1 (list[str]): Some description. + """ + ``` + - 可选类型统一格式：(类型, optional)。 + + ```python + """ + Args: + arg1 (bool, optional): Some description. + """ + ``` + - 其他类型：Tensor或其他具体类型或方法名。 + + ```python + """ + Args: + arg1 (Tensor): Some description. + """ + ``` + - `Returns`注释说明 - 如果返回值类型或维度发生变化，需要说明返回值与输入的关系。 - 多个返回值时，分行写，网页显示时不分行，无序列表的方式可支持分行。 + + ```python + """ + Returns: + - DatasetNode, the root node of the IR tree. + - Dataset, the root dataset of the IR tree. + """ + ``` + - `Examples`注释说明 - `Examples`中的内容需在每行代码开头加上```>>>```，多行代码（含类或函数定义、人为换行等）或空行的开头需加上```...```，输出结果行开头不需要加任何符号。 + + ```python + """ + Examples: + >>> import mindspore as ms + >>> import mindspore.nn as nn + >>> class Net(nn.Cell): + ... def __init__(self, dense_shape): + ... super(Net, self).__init__() + ... self.dense_shape = dense_shape + ... def construct(self, indices, values): + ... x = SparseTensor(indices, values, self.dense_shape) + ... return x.values, x.indices, x.dense_shape + ... + >>> indices = Tensor([[0, 1], [1, 2]]) + >>> values = Tensor([1, 2], dtype=ms.float32) + >>> out = Net((3, 4))(indices, values) + >>> print(out[0]) + [1. 2.] + >>> print(out[1]) + [[0 1] + [1 2]] + >>> print(out[2]) + (3, 4) + """ + ``` + - `Examples`中需提供实际的代码，如果需提示参考其他Examples，请使用Note。 - ops算子注释采用PyNative模式写作，可运行的需给出运行结果。 - 业界共识的情况可省略import，如np、nn等。 @@ -344,7 +538,7 @@ class BatchNorm(PrimitiveWithInfer): ```markdown # The name of namespace - + The link of header file. ## The name of class @@ -354,5 +548,5 @@ class BatchNorm(PrimitiveWithInfer): The name of attribute or function. The description of attribute or function. - + ```