From 878e6f7c54fd4448698bd38f7f658063189150b1 Mon Sep 17 00:00:00 2001 From: ying Date: Tue, 19 Dec 2017 13:32:27 +0800 Subject: [PATCH] update the data organization part. --- doc/howto/usage/capi/a_simple_example.md | 8 +- doc/howto/usage/capi/core_concepts.md | 0 doc/howto/usage/capi/images/csr.png | Bin 174346 -> 370051 bytes doc/howto/usage/capi/images/sequence_data.png | Bin 0 -> 481460 bytes .../usage/capi/organization_of_the_inputs.md | 174 +++++++++++++----- doc/howto/usage/capi/overview.md | 4 +- 6 files changed, 134 insertions(+), 52 deletions(-) delete mode 100644 doc/howto/usage/capi/core_concepts.md create mode 100644 doc/howto/usage/capi/images/sequence_data.png diff --git a/doc/howto/usage/capi/a_simple_example.md b/doc/howto/usage/capi/a_simple_example.md index efcae3518..ae2eaa3ce 100644 --- a/doc/howto/usage/capi/a_simple_example.md +++ b/doc/howto/usage/capi/a_simple_example.md @@ -115,15 +115,15 @@ - `Argument` 并不真正“存储”数据,而是将输入/输出数据有机地组织在一起。 - 在`Argument`内部由:1. `Matrix`(二维矩阵,存储浮点类型输入/输出);2. `IVector`(一维数组,**仅用于存储整型值**,多用于自然语言处理任务)来实际存储数据。 -*注:这篇文档使用的示例任务手写数字识别不涉及一维整型序列输入/输出,因此不讨论一维整型输入/输出数据相关的内容。更多信息请参考:[输入数据组织](organization_of_the_inputs.md)。* +*注:这篇文档使用的示例任务手写数字识别不涉及一维整型序列输入/输出,因此不讨论一维整型输入/输出数据相关的内容。更多信息请参考:[输入/输出数据组织](organization_of_the_inputs.md)。* -在这篇文档的后面部分,我们会使用`argument`来**特指** PaddlePaddle C-API中神经网的一个输入/输出,使用`matrix`**特指**`argument`中用于存储数据的`Matrix`类的对象,用`ivector`特指`argument`中用于存储数据的`IVector`类的对象。 +这篇文档的之后部分会使用`argument`来**特指** PaddlePaddle C-API中神经网的一个输入/输出,使用`paddle_matrix`**特指**`argument`中用于存储数据的`Matrix`类的对象。 于是,在组织神经网络输入,获取输出时,需要思考完成以下工作: 1. 为每一个输入/输出创建`argument`; -1. 为每一个`argument`创建`matrix`或者`ivector`来存储数据; +1. 为每一个`argument`创建`paddle_matrix`来存储数据; -与输入不同的是,输出`argument`的`matrix`变量并不需在使用C-API时为之要分配存储空间。PaddlePaddle内部,神经网络进行前向计算时会自己分配/管理每个计算层的存储空间;这些细节C-API会代为处理,只需在概念上理解,并按照约定调用相关的 C-API 接口即可。 +与输入不同的是,输出`argument`的`paddle_matrix`变量并不需在使用C-API时为之分配存储空间。PaddlePaddle内部,神经网络进行前向计算时会自己分配/管理每个计算层的存储空间;这些细节C-API会代为处理,只需在概念上理解,并按照约定调用相关的 C-API 接口即可。 下面是示例代码片段。在这段代码中,生成了一条随机输入数据作为测试样本。 ```c diff --git a/doc/howto/usage/capi/core_concepts.md b/doc/howto/usage/capi/core_concepts.md deleted file mode 100644 index e69de29bb..000000000 diff --git a/doc/howto/usage/capi/images/csr.png b/doc/howto/usage/capi/images/csr.png index 16454e9261872e9ab337f2d95d1143b77b554ecb..3dc10b8de4f6d3f517624956b1694b689405a031 100644 GIT binary patch literal 370051 zcmeEvWl$bVw>3e6Cb+u=2p-%C5G1&}ySuvwm*5@<7Tn$4-QC^g!TFw?^Pbm|-0xJ~ z`!m!~HT2AMPp@8k?Y(=Zhaf2lp;s_iFd!fxuSA3eWI#X=2|+-h^Pye>OEh<2KtVuW zkQwvyOPTTu@mrc%+Q?dI>*~MPv((X-5#pm~V`BvYA&=74(vlITBJb5k($eZ3qNISa zag_P=DO5(wv$wT_V6e3(V<00fK}~HH17WoT1e^{F)vcks6)KbVz-O>MrU$lGMkeI5 z_a#2TumlbHTWu|^L6Gv;0Z|yVRB8TF^4H&!+*F^(MA$ZO5ng|GX6Qa{&zJZ)K1$@cQ_J0%PK>+nphVmnCrLD(% zeQ5(1!5|-Ey00>*oEOnob||56a6_0FylbzeAK4Ia(n56%5p&yP2FQto-7>bDjb@oE%_@hh5xBgfla?n=7 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-
图1. CSR存储示意图. +
图1. CSR存储示意图.

- 在PaddlePaddle C-API中通过以下接口创建稀疏矩阵: + CSR存储格式通过:(1)非零元素的值(上图中的`values`);(2)行偏移(上图中的`row offsets`):每一行元素在`values`中的起始偏移,`row offsets`中元素个数总是等于行数 + 1;(3)非零元素的列号(上图中的`column indices`)来确定稀疏矩阵的内容。 + + 在PaddlePaddle C-API中,通过调用以下接口创建稀疏矩阵: ```cpp PD_API paddle_matrix paddle_matrix_create_sparse( uint64_t height, uint64_t width, uint64_t nnz, bool isBinary, bool useGpu); ``` + 1. 创建稀疏矩阵时需要显示地指定矩阵的(1)高度(`height`,在神经网络中等于一次预测处理的样本数)(2)宽度(`width`,`paddle.layer.data`的`size`)以及(3)非零元个数(`nnz`)。 1. 当上述接口第4个参数`isBinary`指定为`true`时,**只需要设置行偏移(`row_offset`)和列号(`colum indices`),不需要提供元素值(`values`)**,这时行偏移和列号指定的元素默认其值为1。 @@ -129,26 +115,124 @@ PaddlePaddle 支持两种序列类型: sizeof(values) / sizeof(float))); ``` -### 组织序列数据 +- 注意事项: + 1. 移动端预测**不支持**稀疏矩阵及相关的接口。 +### 组织序列信息 +多个排成一列的元素(可以是整型、浮点数、浮点数向量等)构成一个序列,元素之间的顺序是序列所携带的重要信息。不同序列可能会含有不同数目个元素。在 PaddlePaddle 中,序列输入/输出数据是在上文介绍的**数据输入(一维整型数组,二维浮点数矩阵)基础上,附加上序列信息**。下面详细解释什么是“序列信息”。 -### Python 端数据类型说明 +我们将神经网络一次计算接受的所有输入样本称之为一个`batch`(可以含有一条或多条样本),每一个序列在整个`batch`中的偏移,就是PaddlePaddle中所指的**序列信息**,称之为“sequence start positions”。PaddlePaddle 支持两种序列类型: -下表列出了Python端训练接口暴露的数据类型(`paddle.layer.data`函数`type`字段的取值)对应于调用C-API时需要创建的数据类型: +1. 单层序列 + - 序列中的每一个元素是非序列,是进行计算的基本单位,不可再进行拆分。 + - 例如:自然语言中的句子是一个序列,序列中的元素是词语; +1. 双层序列 + - 序列中的每一个元素又是一个序列。 + - 例如:自然语言中的段落是一个双层序列;段落是由句子构成的序列;句子是由词语构成的序列。 + - 双层序列在处理长序列的任务或是构建层级模型时会发挥作用。 +这篇文档之后部分会统一使用`sequence_start_positions`来特指:PaddlePaddle中神经网络计算层输入/输出所携带的序列信息。 + +对双层序列来讲,不仅要提供每一个外层序列在整个`batch`中的偏移,每一个外层序列又含有若干个内层序列,需要同时提供每一个内层序列在整个`batch`中的偏移。也就是说:**双层序列需要设置分别为外层序列和内层序列分别设置`sequence_start_positions`信息**。 + +**注:** +1. 不论序列中的元素在内存中占用多少实际存储空间,`sequence_start_positions`表示的偏移是以“序列中的一个元素”作为统计的基本单位,而不是相对`batch`起始存储地址以数据的存储大小为单位的偏移。 +2. 非序列输入不携带`sequence_start_positions`,非序列输入无需构造`sequence_start_positions`。 +3. **不论是单层序列还是双层序列的序列信息,都使用`paddle_ivector`(也就是PaddlePaddle中的一维整型数组)来存储。** + +图2 是PaddlePaddle中单层序列和双层序列存储示意图。 +

+
图2. 序列输入示意图. +

+ +- 单层序列 + + 图2 (a) 展示了一个含有4个序列的`batch`输入: + 1. 4个序列的长度分别为:5、3、2、4; + 2. 这时的`sequence_start_positions`为:`[0, 5, 8, 10, 14]`; + 3. 不论数据域是`paddle_ivector`类型还是`paddle_matrix`类型,都可以通过调用下面的接口为原有的数据输入附加上序列信息,使之变为一个单层序列输入,代码片段如下: + + ```cpp + int seq_pos_array[] = {0, 5, 8, 10, 14}; + paddle_ivector seq_pos = paddle_ivector_create( + seq_pos_array, sizeof(seq_pos_array) / sizeof(int), false, false); + // Suppose the network only has one input data layer. + CHECK(paddle_arguments_set_sequence_start_pos(in_args, 0, 0, seq_pos)); + ``` + +- 双层序列 + + 图2 (b) 展示了一个含有4个序列的`batch`输入; + 1. 4个序列的长度分别为:5、3、2、4;这四个序列又分别含有3、2、1、2个子序列; + 1. 这时的需要同时提供: + - 1. 外层序列在`batch`中的起始偏移`:[0, 5, 8, 10, 14]`; + - 2. 内层序列在`batch`中的起始偏移:`[0, 2, 3, 5, 7, 8, 10, 13, 14]`; + + 1. 不论数据域是`paddle_ivector`类型还是`paddle_matrix`类型,这时需要调用创建序列信息和为`argument`设置序列信息的接口**两次**,分别为数据输入添加外层序列和内层序列的序列信息,使之变为一个双层序列输入,代码片段如下: + ```cpp + // set the sequence start positions for the outter sequences. + int outter_seq_pos_array[] = {0, 5, 8, 10, 14}; + paddle_ivector seq_pos = + paddle_ivector_create(outter_seq_pos_array, + sizeof(outter_pos_array) / sizeof(int), + false, + false); + // The third parameter of this API indicates the sequence level. + // 0 for the outter sequence. 1 for the inner sequence. + // If the input is a sequence not the nested sequence, the third parameter is + // fixed to be 0. + CHECK(paddle_arguments_set_sequence_start_pos(in_args, 0, 0, seq_pos)); + + // set the sequence start positions for the outter sequences. + int inner_seq_pos_array[] = {0, 2, 3, 5, 7, 8, 10, 13, 14}; + paddle_ivector seq_pos = paddle_ivector_create( + inner_pos_array, sizeof(inner_pos_array) / sizeof(int), false, false); + // The third parameter of this API indicates the sequence level. + // 0 for the outter sequence. 1 for the inner sequence. + CHECK(paddle_arguments_set_sequence_start_pos(in_args, 0, 1, seq_pos)); + ``` + +- 注意事项: + 1. 当一个`batch`中含有多个序列,**不支持序列长度为`0`的序列(也就是空输入)** 作为输入。不同计算层对空输入的处理策略有可能不同,潜在会引起未定义行为,或者引起行时错误,请在输入时进行合法性检查。 + +### Python 端数据类型说明 + +下表列出了Python端训练接口暴露的数据类型(`paddle.layer.data`函数`type`字段的取值)对应于调用C-API需要创建的数据类型: Python 端数据类型 | C-API 输入数据类型| :-------------: | :-------------: -`paddle.data_type.integer_value` |一维整型数组,无需附加序列信息| -`paddle.data_type.dense_vector` |二维浮点型稠密矩阵,无需附加序列信息| -`paddle.data_type.sparse_binary_vector` |二维浮点型稀疏矩阵,无需提供非零元的值,默认为1,无需附加序列信息| -`paddle.data_type.sparse_vector` |二维浮点型稀疏矩阵,需提供非零元的值,无需附加序列信息| -`paddle.data_type.integer_value_sequence` |一维整型数组,需附加序列信息| -`paddle.data_type.dense_vector_sequence` |二维浮点型稠密矩阵,需附加序列信息| -`paddle.data_type.sparse_binary_vector_sequence` |二维浮点型稀疏矩阵,无需提供非零元的值,默认为1,需附加序列信息| -`paddle.data_type.sparse_vector_sequence` |二维浮点型稀疏矩阵,需提供非零元的值,需附加序列信息| -`paddle.data_type.integer_value_sub_sequence` |一维整型数组,需附加双层序列信息| -`paddle.data_type.dense_vector_sub_sequence` |二维浮点型稠密矩阵,需附加双层序列信息| -`paddle.data_type.sparse_binary_vector_sub_sequence` |二维浮点型稀疏矩阵,无需提供非零元的值,默认为1,需附加双层序列信息| -`paddle.data_type.sparse_vector_sub_sequence` |二维浮点型稀疏矩阵,需提供非零元的值,需附加双层序列信息| +`paddle.data_type.integer_value` |整型数组,无需附加序列信息| +`paddle.data_type.dense_vector` |浮点型稠密矩阵,无需附加序列信息| +`paddle.data_type.sparse_binary_vector` |浮点型稀疏矩阵,无需提供非零元的值,默认为1,无需附加序列信息| +`paddle.data_type.sparse_vector` |浮点型稀疏矩阵,需提供非零元的值,无需附加序列信息| +`paddle.data_type.integer_value_sequence` |整型数组,需附加序列信息| +`paddle.data_type.dense_vector_sequence` |浮点型稠密矩阵,需附加序列信息| +`paddle.data_type.sparse_binary_vector_sequence` |浮点型稀疏矩阵,无需提供非零元的值,默认为1,需附加序列信息| +`paddle.data_type.sparse_vector_sequence` |浮点型稀疏矩阵,需提供非零元的值,需附加序列信息| +`paddle.data_type.integer_value_sub_sequence` |整型数组,需附加双层序列信息| +`paddle.data_type.dense_vector_sub_sequence` |浮点型稠密矩阵,需附加双层序列信息| +`paddle.data_type.sparse_binary_vector_sub_sequence` |浮点型稀疏矩阵,无需提供非零元的值,默认为1,需附加双层序列信息| +`paddle.data_type.sparse_vector_sub_sequence` |浮点型稀疏矩阵,需提供非零元的值,需附加双层序列信息| + +### 输出数据 + +PaddlePaddle中一个计算层的输出数据组织方式和输入数据组织方式完全相同。一个输出数据同样被组织为一个`argument`,`argument`通过`paddle_matrix`或`paddle_ivector`存数数据,如果输出是一个序列,那么会携带有`sequence_start_positions`信息。调用C-API相关接口,读取需要的结果即可。 + +### 总结 + +- 在PaddlePaddle内部,神经网络中一个计算层的输入/输出被组织为`argument`。 +- `argument`并不真正“存储”数据,而是将输入/输出信息有机地组织在一起。 +- 在`argument`内部由`paddle_ivector`(一维整型数组)和`paddle_matrix`(二维浮点型矩阵)来实际存储数据。 +如果是一个序列输入/输出由 `sequence start positions` 来记录输入/输出的序列信息。 + +于是,在组织神经网络输入时,需要思考完成以下工作: +1. 为每一个输入/输出创建`argument`。 + - C-API 中操作`argument`的接口请查看[argument.h](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/arguments.h)。 +1. 为每一个`argument`创建`paddle_matrix`或者`paddle_ivector`来存储数据。 + - C-API 中操作`paddle_ivector`的接口请查看 [vector.h](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/vector.h)。 + - C-API 中操作`paddle_matrix`的接口请查看[matrix.h](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/matrix.h)。 +1. 如果输入是序列数据,需要创建并填写`sequence_start_positions`信息。 + - 通过调用 [`paddle_arguments_set_sequence_start_pos`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/arguments.h#L137) 来为一个`argument`添加序列信息。 + - 通过调用 [`paddle_arguments_get_sequence_start_pos`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/arguments.h#L150) 来读取一个`argument`添加序列信息。 + - 接口说明请查看 [argument.h](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/arguments.h) 文件。 diff --git a/doc/howto/usage/capi/overview.md b/doc/howto/usage/capi/overview.md index 5ec3bd028..c55d39bac 100644 --- a/doc/howto/usage/capi/overview.md +++ b/doc/howto/usage/capi/overview.md @@ -1,5 +1,3 @@ - [编译 PaddlePaddle 链接库](compile_paddle_lib.md) +- [输入/输出数据组织](organization_of_the_inputs.md) - [C-API 使用示例](a_simple_example.md) -- [输入数据组织](organize_input_data.md) -- [核心概念介绍](core_concepts.md) -- [F&Q]() -- GitLab

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会通过判数据是否附带有序列信息来判断一个向量/矩阵是否是一个序列**。关于什么是“序列信息”,下文会进行详细地介绍。 - -PaddlePaddle 支持两种序列类型: -1. 单层序列 - - 序列中的每一个元素是非序列,是进行计算的基本单位,不可再进行拆分。 - - 例如:自然语言中的句子是一个序列,序列中的元素是词语; -1. 双层序列 - - 序列中的每一个元素又是一个序列。 - - 例如:自然语言中的段落是一个双层序列;段落是有句子构成的序列;句子是由词语构成的序列。 - - 双层序列在处理长序列的任务或是构建层级模型时会发挥作用。 + 1. 逻辑上高于二维的数据(例如含有多个通道的图片,视频等)在程序实现中都会转化为二维矩阵,转化方法在相应的领域都有通用解决方案,需要使用者自己了解并完成转化; + 1. 二维矩阵可以表示行向量和列向量,任何时候如果需要浮点型数组(向量),都应使用C-API中的矩阵来表示,而不是C-API中的一维数组。 + 1. 不论是一维整型数组还是二维浮点数矩阵,**为它们附加上序列信息将变成序列输入。PaddlePaddle 会通过判数据是否附带有序列信息来判断一个向量/矩阵是否是一个序列**。当非序列输入时,无需关心和处理序列信息。关于什么是“序列信息”,下文会详细进行介绍。 ### 基本使用概念 - 在PaddlePaddle内部,神经网络中一个计算层的输入/输出被组织为一个 `Argument` 结构体,如果神经网络有多个输入或者多个输入,每一个输入/输入都会对应有自己的`Argument`。 - `Argument` 并不真正“存储”数据,而是将输入/输出信息有机地组织在一起。 -- 在`Argument`内部由`IVector`(对应着上文提到的一维整型数组)和`Matrix`(对应着上文提到的二维浮点型矩阵)来实际存储数据;由 `sequence start position` (下文详细解释) 来记录输入/输出的序列信息。 +- 在`Argument`内部由`IVector`(对应着上文提到的一维整型数组)和`Matrix`(对应着上文提到的二维浮点型矩阵)来实际存储数据;由 `Sequence Start Positions` (下文详细解释) 来描述输入/输出的序列信息。 -**注意:这篇文档之后部分将会统一使用`argument`来特指PaddlePaddle中神经网络计算层一个输入/输出数据;使用`ivector`来特指PaddlePaddle中的一维整型数组;使用`matrix`来特指PaddlePaddle中的二维浮点型矩阵;使用`sequence_start_position`来特指PaddlePaddle中的序列信息。** +- **注**: + 1. 这篇文档之后部分将会统一使用`argument`来特指PaddlePaddle中神经网络计算层一个输入/输出数据。 + 2. 使用`paddle_ivector`来特指PaddlePaddle中的一维整型数组。 + 3. 使用`paddle_matrix`来特指PaddlePaddle中的二维浮点型矩阵。 -于是,在组织神经网络输入时,需要思考完成以下工作: -1. 为每一个输入/输出创建`argument`。 - - C-API 中操作`argument`的接口请查看[argument.h](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/arguments.h)。 -1. 为每一个`argument`创建`matrix`或者`ivector`来存储数据。 - - C-API 中操作`ivector`的接口请查看 [vector.h](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/vector.h)。 - - C-API 中操作`matrix`的接口请查看[matrix.h](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/matrix.h)。 -1. 如果输入是序列数据,需要创建并填写`sequence_start_position`信息。 - - 通过调用 [`paddle_arguments_set_sequence_start_pos`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/arguments.h#L137) 来为一个`argument`添加序列信息; - - 通过调用 [`paddle_arguments_get_sequence_start_pos`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/arguments.h#L150) 来读取一个`argument`添加序列信息; - - 接口说明请查看 [argument.h](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/arguments.h) 文件。 - -### 组织非序列数据 +### 组织输入数据 - 一维整型数组 概念上可以将`paddle_ivector`理解为一个一维的整型数组,通常用于表示离散的类别标签,或是在自然语言处理任务中表示词语在字典中的序号。下面的代码片段创建了含有三个元素`1`、`2`、`3`的`paddle_ivector`。 @@ -56,6 +38,7 @@ PaddlePaddle 支持两种序列类型: paddle_ivector_create(ids, sizeof(ids) / sizeof(int), false, false); CHECK(paddle_arguments_set_ids(in_args, 0, ids_array)); ``` + - **稠密矩阵** - 一个$m×n$的稠密矩阵是一个由$m$行$n$列元素排列成的矩形阵列,矩阵里的元素是浮点数。对神经网络来说,矩阵的高度$m$是一次预测接受的样本数目,宽度$n$是神经网络定义时,`paddle.layer.data`的`size`。 - 下面的代码片段创建了一个高度为1,宽度为`layer_size`的稠密矩阵,矩阵中每个元素的值随机生成。 @@ -83,17 +66,20 @@ PaddlePaddle 支持两种序列类型: - **稀疏矩阵** - PaddlePaddle C-API 中 稀疏矩阵使用[CSR(Compressed Sparse Row Format)](https://en.wikipedia.org/wiki/Sparse_matrix#Compressed_sparse_row_(CSR,_CRS_or_Yale_format))格式存储。下图是CSR存储稀疏矩阵的示意图,在CSR表示方式中,通过(1)行偏移;(2)列号;(3)值;来决定矩阵的内容。 + PaddlePaddle C-API 中 稀疏矩阵使用[CSR(Compressed Sparse Row Format)](https://en.wikipedia.org/wiki/Sparse_matrix#Compressed_sparse_row_(CSR,_CRS_or_Yale_format))格式存储。下图是CSR存储稀疏矩阵的示意图。