From b968b1bce2649da359c6f8ef779d4467ab13c795 Mon Sep 17 00:00:00 2001 From: moguguo Date: Mon, 15 Nov 2021 16:15:03 +0800 Subject: [PATCH 1/7] =?UTF-8?q?=E5=8A=A8=E8=BD=AC=E9=9D=99=E6=A6=82?= =?UTF-8?q?=E8=BF=B0=E6=9B=B4=E6=96=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 更新动转静概述章节，补充两个Block，什么是动态图和静态图，什么场景下需要动态图转成静态图 --- docs/guides/04_dygraph_to_static/index_cn.rst | 32 +++++++++++++++++-- 1 file changed, 30 insertions(+), 2 deletions(-) diff --git a/docs/guides/04_dygraph_to_static/index_cn.rst b/docs/guides/04_dygraph_to_static/index_cn.rst index b54141fa883..1a1604b52a9 100644 --- a/docs/guides/04_dygraph_to_static/index_cn.rst +++ b/docs/guides/04_dygraph_to_static/index_cn.rst @@ -2,9 +2,37 @@ 动态图转静态图 ############### -动态图在接口易用性，交互式调试等方面具有诸多优势，但在工业界的许多部署场景中（如大型推荐系统、移动端）Python执行开销较大，与C++有一定的差距，静态图部署更具优势。 +========================= +什么是动态图和静态图？ +========================= + +从深度学习模型构建方式上看，飞桨框架支持动态图编程（Imperative programming）和静态图编程（Declarative programming）两种方式，其代码编写方式和执行方式均存在差异。 + +* **动态图编程：**采用Python风格的编程方式，解析式的执行方式。用户每写一行网络代码，即可同时获得计算结果。在 +`模型开发 <../02_paddle2.0_develop/index_cn.rst>`_章节中，介绍的都是动态图编程方式。 +* **静态图编程：**采用先编译后执行的方式。用户先预定义完整的神经网络结构，飞桨框架将神经网络描述为Program的数据结构，对Program进行编译优化，再调用执行器获得计算结果。 + +动态图编程体验更佳、更易调试，但是因为采用Python实时执行的方式，而Python执行开销较大，与C++有一定差距，性能方面不占优。静态图调试难度大，但是将前端Python编写的神经网络预定义为Program描述，转到C++端重新解析执行，脱离了Python依赖，往往执行性能更佳，并且预先拥有完整网络结构也更利于全局优化。 + +========================= +什么场景下需要动态图转静态图？ +========================= + +飞桨框架在设计时，考虑同时兼顾动态图的高易用性和静态图的高性能优势，采用『动静统一』的方案： +* **在模型开发时，推荐采用动态图编程：**用户可获得更好的编程体验、更易用的接口、更友好的调试交互机制。 +* **在模型训练或者推理部署时，只需添加一行装饰器 @to_static，即可将动态图代码转写为静态图代码，并在底层自动使用静态图执行器运行：**获得更好的模型运行性能。 + +方案如下图所示： +.. image:: images/dygraph_to_static.png +图1 飞桨框架动静统一方案示意图 + +> **说明：** +> 飞桨框架2.0默认的编程模式是动态图模式，包括使用高层API编程和基础的API编程。如果想切换到静态图模式编程，可以在程序的开始执行enable_static()函数。如果程序已经使用动态图的模式编写了，想转成静态图模式训练或者保存模型用于部署，可以使用装饰器@to_static。 + +想了解动态图和静态图的详细对比介绍，可参见 +`动态图和静态图的差异 `_。 + -PaddlePaddle 在2.0版本之后，正式支持动态图转静态图（@to_static）的功能，对动态图代码进行智能化分析，自动转换为静态图网络结构，兼顾了动态图易用性和静态图部署性能两方面的优势。 如下将详细地介绍动静转换的各个模块内容： From 81646f45059a2307e06e02ff5712310b655ca789 Mon Sep 17 00:00:00 2001 From: moguguo Date: Mon, 15 Nov 2021 16:16:41 +0800 Subject: [PATCH 2/7] =?UTF-8?q?=E5=8A=A8=E8=BD=AC=E9=9D=99=E6=A6=82?= =?UTF-8?q?=E8=BF=B0=E6=9B=B4=E6=96=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 增加动转静方案示意图 --- .../images/dygraph_to_static.png | Bin 0 -> 75142 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 docs/guides/04_dygraph_to_static/images/dygraph_to_static.png diff --git a/docs/guides/04_dygraph_to_static/images/dygraph_to_static.png b/docs/guides/04_dygraph_to_static/images/dygraph_to_static.png new file mode 100644 index 0000000000000000000000000000000000000000..9db5392c5b2823d30e76f28478bd5658c81e35fa GIT binary patch literal 75142 zcmeFZby!s07d9+i3ewWuAP7iz&d`lCN|#7TcSr~W4Ba(!NeW1dq||^C5(-0iH++No 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zeaapt+QK>Yzq@sKoy^62<>=p&{M{TMSH$(?oj74(a!K1>ynCf($1AS8uZ%px*Uh#0 zW)&LqdAIp&zYQgSQ=R*rRi#BTF$Oq_+8ShdroV3g$-(=gy=dBTmn5XxpA&`F_=T9c$*7)+ZJwJzgysqJKei+xovO z@qap{JpU(}tp8}UxQ4gA>&x=f7e9Be$aTK_s(8h#TY1xVIbB^o^|evYwAWHM^JXQR zJ}aH1y*PE}oMQ_ooepo`h(3tqu$R+khCyOqS^1phUG)<-KRNjDaQpi-_G`6|~IeWjXUJ}2u>}}|j9vQ85d8;D)rd1Xxm3W4RP0s^%p$ zNuAf~eJy!ZKPzUw%Js{y&DXE2Syi^ACM|W-@0g42MVpp9op7h^m45KvqeTpF7aN}U z)$>2M`g~p1{e2=^Ytp%&0g{;?A3doxAT5#%*|c*GW%WVsjY%Wau}mZ4$K_D zW_fqrlG{7wwx53aVLF?M<5V>@1@$v0ciE}GwA!nfe#(E=4(nN1#s&vwaDrBY9%P%l cMWgXQ`?L7!8{Z;0-ZKD!r>mdKI;Vst00qddqW}N^ literal 0 HcmV?d00001 From 1705fb30bb08e89a64be396b7c4961c560b232e1 Mon Sep 17 00:00:00 2001 From: Chen Long <1300851984@qq.com> Date: Tue, 16 Nov 2021 10:35:47 +0800 Subject: [PATCH 3/7] update style update style --- docs/guides/04_dygraph_to_static/index_cn.rst | 25 +++++++++++-------- 1 file changed, 14 insertions(+), 11 deletions(-) diff --git a/docs/guides/04_dygraph_to_static/index_cn.rst b/docs/guides/04_dygraph_to_static/index_cn.rst index 1a1604b52a9..4656fe5f01e 100644 --- a/docs/guides/04_dygraph_to_static/index_cn.rst +++ b/docs/guides/04_dygraph_to_static/index_cn.rst @@ -6,35 +6,38 @@ 什么是动态图和静态图？ ========================= -从深度学习模型构建方式上看，飞桨框架支持动态图编程（Imperative programming）和静态图编程（Declarative programming）两种方式，其代码编写方式和执行方式均存在差异。 +从深度学习模型构建方式上看，飞桨框架支持动态图编程和静态图编程两种方式，其代码编写方式和执行方式均存在差异。 -* **动态图编程：**采用Python风格的编程方式，解析式的执行方式。用户每写一行网络代码，即可同时获得计算结果。在 -`模型开发 <../02_paddle2.0_develop/index_cn.rst>`_章节中，介绍的都是动态图编程方式。 -* **静态图编程：**采用先编译后执行的方式。用户先预定义完整的神经网络结构，飞桨框架将神经网络描述为Program的数据结构，对Program进行编译优化，再调用执行器获得计算结果。 +* **动态图编程：** 采用 Python 风格的编程方式，解析式的执行方式。每写一行网络代码，即可同时获得计算结果。在 +`模型开发 <../02_paddle2.0_develop/index_cn.html>`_ 章节中，介绍的都是动态图编程方式。 -动态图编程体验更佳、更易调试，但是因为采用Python实时执行的方式，而Python执行开销较大，与C++有一定差距，性能方面不占优。静态图调试难度大，但是将前端Python编写的神经网络预定义为Program描述，转到C++端重新解析执行，脱离了Python依赖，往往执行性能更佳，并且预先拥有完整网络结构也更利于全局优化。 +* **静态图编程：** 采用先编译后执行的方式。先预定义完整的神经网络结构，飞桨框架将神经网络描述为 `Program` 的数据结构，对 `Program` 进行编译优化，再调用执行器获得计算结果。 + +动态图编程体验更佳、更易调试，但是因为采用 Python 实时执行的方式，而 Python 执行开销较大，与 C++ 有一定差距，性能方面不占优。静态图调试难度大，但是将前端 Python 编写的神经网络预定义为 Program描述，转到 C++ 端重新解析执行，脱离了 Python 依赖，往往执行性能更佳，并且预先拥有完整网络结构也更利于全局优化。 ========================= 什么场景下需要动态图转静态图？ ========================= 飞桨框架在设计时，考虑同时兼顾动态图的高易用性和静态图的高性能优势，采用『动静统一』的方案： -* **在模型开发时，推荐采用动态图编程：**用户可获得更好的编程体验、更易用的接口、更友好的调试交互机制。 -* **在模型训练或者推理部署时，只需添加一行装饰器 @to_static，即可将动态图代码转写为静态图代码，并在底层自动使用静态图执行器运行：**获得更好的模型运行性能。 + +* **在模型开发时，推荐采用动态图编程：** 可获得更好的编程体验、更易用的接口、更友好的调试交互机制。 + +* **在模型训练或者推理部署时，只需添加一行装饰器 @to_static，即可将动态图代码转写为静态图代码，并在底层自动使用静态图执行器运行：** 获得更好的模型运行性能。 方案如下图所示： + .. image:: images/dygraph_to_static.png 图1 飞桨框架动静统一方案示意图 -> **说明：** -> 飞桨框架2.0默认的编程模式是动态图模式，包括使用高层API编程和基础的API编程。如果想切换到静态图模式编程，可以在程序的开始执行enable_static()函数。如果程序已经使用动态图的模式编写了，想转成静态图模式训练或者保存模型用于部署，可以使用装饰器@to_static。 +.. note:: +飞桨框架2.0默认的编程模式是动态图模式，包括使用高层API编程和基础的API编程。如果想切换到静态图模式编程，可以在程序的开始执行enable_static()函数。如果程序已经使用动态图的模式编写了，想转成静态图模式训练或者保存模型用于部署，可以使用装饰器@to_static。 想了解动态图和静态图的详细对比介绍，可参见 `动态图和静态图的差异 `_。 - -如下将详细地介绍动静转换的各个模块内容： +以下将详细地介绍动静转换的各个模块内容： - `基础接口用法 `_ : 介绍了动静转换 @to_static 的基本用法 From dc8d12431c5a2aba0456c33074cb2f56c544992d Mon Sep 17 00:00:00 2001 From: moguguo Date: Tue, 16 Nov 2021 16:57:16 +0800 Subject: [PATCH 4/7] update style --- docs/guides/04_dygraph_to_static/index_cn.rst | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/docs/guides/04_dygraph_to_static/index_cn.rst b/docs/guides/04_dygraph_to_static/index_cn.rst index 4656fe5f01e..65626534596 100644 --- a/docs/guides/04_dygraph_to_static/index_cn.rst +++ b/docs/guides/04_dygraph_to_static/index_cn.rst @@ -23,15 +23,18 @@ * **在模型开发时，推荐采用动态图编程：** 可获得更好的编程体验、更易用的接口、更友好的调试交互机制。 -* **在模型训练或者推理部署时，只需添加一行装饰器 @to_static，即可将动态图代码转写为静态图代码，并在底层自动使用静态图执行器运行：** 获得更好的模型运行性能。 +* **在模型训练或者推理部署时，只需添加一行装饰器 @to_static，即可将动态图代码转写为静态图代码，并在底层自动使用静态图执行器运行：** 可获得更好的模型运行性能。 方案如下图所示： .. image:: images/dygraph_to_static.png -图1 飞桨框架动静统一方案示意图 + :width: 500px + :align: center + +.. centered:: 图1 飞桨框架动静统一方案示意图 .. note:: -飞桨框架2.0默认的编程模式是动态图模式，包括使用高层API编程和基础的API编程。如果想切换到静态图模式编程，可以在程序的开始执行enable_static()函数。如果程序已经使用动态图的模式编写了，想转成静态图模式训练或者保存模型用于部署，可以使用装饰器@to_static。 + 飞桨框架 2.0 及以上版本默认的编程模式是动态图模式，包括使用高层 API 编程和基础的 API 编程。如果想切换到静态图模式编程，可以在程序的开始执行 `enable_static()` 函数。如果程序已经使用动态图的模式编写了，想转成静态图模式训练或者保存模型用于部署，可以使用装饰器 @to_static。 想了解动态图和静态图的详细对比介绍，可参见 `动态图和静态图的差异 `_。 From 3e5bc03b65b8781966714e2a5e67fa089917e47d Mon Sep 17 00:00:00 2001 From: moguguo Date: Wed, 17 Nov 2021 11:17:29 +0800 Subject: [PATCH 5/7] Update index_cn.rst --- docs/guides/04_dygraph_to_static/index_cn.rst | 17 ++++++++--------- 1 file changed, 8 insertions(+), 9 deletions(-) diff --git a/docs/guides/04_dygraph_to_static/index_cn.rst b/docs/guides/04_dygraph_to_static/index_cn.rst index 65626534596..ce77e3bc6a0 100644 --- a/docs/guides/04_dygraph_to_static/index_cn.rst +++ b/docs/guides/04_dygraph_to_static/index_cn.rst @@ -6,14 +6,13 @@ 什么是动态图和静态图？ ========================= -从深度学习模型构建方式上看，飞桨框架支持动态图编程和静态图编程两种方式，其代码编写方式和执行方式均存在差异。 +在深度学习模型构建上，飞桨框架支持动态图编程和静态图编程两种方式，其代码编写和执行方式均存在差异。 -* **动态图编程：** 采用 Python 风格的编程方式，解析式的执行方式。每写一行网络代码，即可同时获得计算结果。在 -`模型开发 <../02_paddle2.0_develop/index_cn.html>`_ 章节中，介绍的都是动态图编程方式。 +* **动态图编程：** 采用 Python 的编程风格，解析式地执行每一行网络代码，并同时返回计算结果。在`模型开发 <../02_paddle2.0_develop/index_cn.html>`_ 章节中，介绍的都是动态图编程方式。 -* **静态图编程：** 采用先编译后执行的方式。先预定义完整的神经网络结构，飞桨框架将神经网络描述为 `Program` 的数据结构，对 `Program` 进行编译优化，再调用执行器获得计算结果。 +* **静态图编程：** 采用先编译后执行的方式。需先在代码中预定义完整的神经网络结构，飞桨框架会将神经网络描述为 `Program` 的数据结构，并对 `Program` 进行编译优化，再调用执行器获得计算结果。 -动态图编程体验更佳、更易调试，但是因为采用 Python 实时执行的方式，而 Python 执行开销较大，与 C++ 有一定差距，性能方面不占优。静态图调试难度大，但是将前端 Python 编写的神经网络预定义为 Program描述，转到 C++ 端重新解析执行，脱离了 Python 依赖，往往执行性能更佳，并且预先拥有完整网络结构也更利于全局优化。 +动态图编程体验更佳、更易调试，但是因为采用 Python 实时执行的方式，开销较大，在性能方面与 C++ 有一定差距；静态图调试难度大，但是将前端 Python 编写的神经网络预定义为 Program描述，转到 C++ 端重新解析执行，脱离了 Python 依赖，往往执行性能更佳，并且预先拥有完整网络结构也更利于全局优化。 ========================= 什么场景下需要动态图转静态图？ @@ -21,14 +20,14 @@ 飞桨框架在设计时，考虑同时兼顾动态图的高易用性和静态图的高性能优势，采用『动静统一』的方案： -* **在模型开发时，推荐采用动态图编程：** 可获得更好的编程体验、更易用的接口、更友好的调试交互机制。 +* **在模型开发时，推荐采用动态图编程。** 可获得更好的编程体验、更易用的接口、更友好的调试交互机制。 -* **在模型训练或者推理部署时，只需添加一行装饰器 @to_static，即可将动态图代码转写为静态图代码，并在底层自动使用静态图执行器运行：** 可获得更好的模型运行性能。 +* **在模型训练或者推理部署时，只需添加一行装饰器 @to_static，即可将动态图代码转写为静态图代码，并在底层自动使用静态图执行器运行。** 可获得更好的模型运行性能。 方案如下图所示： .. image:: images/dygraph_to_static.png - :width: 500px + :width: 800px :align: center .. centered:: 图1 飞桨框架动静统一方案示意图 @@ -40,7 +39,7 @@ `动态图和静态图的差异 `_。 -以下将详细地介绍动静转换的各个模块内容： +**以下将详细地介绍动静转换的各个模块内容：** - `基础接口用法 `_ : 介绍了动静转换 @to_static 的基本用法 From 6daef9871cd7893b9c88bad2faf575dd8e672709 Mon Sep 17 00:00:00 2001 From: moguguo Date: Wed, 17 Nov 2021 16:21:54 +0800 Subject: [PATCH 6/7] =?UTF-8?q?=E4=BF=AE=E5=A4=8D=E6=A0=BC=E5=BC=8F?= =?UTF-8?q?=E9=97=AE=E9=A2=98?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/guides/04_dygraph_to_static/index_cn.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/guides/04_dygraph_to_static/index_cn.rst b/docs/guides/04_dygraph_to_static/index_cn.rst index ce77e3bc6a0..2429f0f2938 100644 --- a/docs/guides/04_dygraph_to_static/index_cn.rst +++ b/docs/guides/04_dygraph_to_static/index_cn.rst @@ -8,7 +8,7 @@ 在深度学习模型构建上，飞桨框架支持动态图编程和静态图编程两种方式，其代码编写和执行方式均存在差异。 -* **动态图编程：** 采用 Python 的编程风格，解析式地执行每一行网络代码，并同时返回计算结果。在`模型开发 <../02_paddle2.0_develop/index_cn.html>`_ 章节中，介绍的都是动态图编程方式。 +* **动态图编程：** 采用 Python 的编程风格，解析式地执行每一行网络代码，并同时返回计算结果。在 `模型开发 <../02_paddle2.0_develop/index_cn.html>`_ 章节中，介绍的都是动态图编程方式。 * **静态图编程：** 采用先编译后执行的方式。需先在代码中预定义完整的神经网络结构，飞桨框架会将神经网络描述为 `Program` 的数据结构，并对 `Program` 进行编译优化，再调用执行器获得计算结果。 From 9c5b702b4df7043c1a18aa374336d9dc2380e209 Mon Sep 17 00:00:00 2001 From: Chen Long <1300851984@qq.com> Date: Fri, 19 Nov 2021 17:25:48 +0800 Subject: [PATCH 7/7] update codes --- docs/guides/04_dygraph_to_static/index_cn.rst | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/docs/guides/04_dygraph_to_static/index_cn.rst b/docs/guides/04_dygraph_to_static/index_cn.rst index 2429f0f2938..fe6a61bd9a5 100644 --- a/docs/guides/04_dygraph_to_static/index_cn.rst +++ b/docs/guides/04_dygraph_to_static/index_cn.rst @@ -26,12 +26,14 @@ 方案如下图所示： -.. image:: images/dygraph_to_static.png +.. figure:: images/dygraph_to_static.png :width: 800px :align: center + .. centered:: 图1 飞桨框架动静统一方案示意图 + .. note:: 飞桨框架 2.0 及以上版本默认的编程模式是动态图模式，包括使用高层 API 编程和基础的 API 编程。如果想切换到静态图模式编程，可以在程序的开始执行 `enable_static()` 函数。如果程序已经使用动态图的模式编写了，想转成静态图模式训练或者保存模型用于部署，可以使用装饰器 @to_static。