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develop/doc/_sources/design/refactorization.md.txt

@@ -17,7 +17,7 @@ The goals of refactoring include:
 
 1. A graph is composed of *variables* and *operators*.
 
-1. The description of graphs must be capable of being serialized/deserialized, so that
+1. The description of graphs must be capable of being serialized/deserialized, so that:
 
    1. It can to be sent to the cloud for distributed execution, and
    1. It can be sent to clients for mobile or enterprise deployment.
@@ -137,19 +137,18 @@ Compile Time -> IR -> Runtime
 * `Eigen::Tensor` contains basic math and element-wise functions.
     * Note that `Eigen::Tensor` has broadcast implementation.
     * Limit the number of `tensor.device(dev) = ` in your code.
-* `thrust::tranform` and `std::transform`.
-    * `thrust` has the same API as C++ standard library. Using `transform`, one can quickly implement customized elementwise kernels.
+* `thrust::transform` and `std::transform`.
+    * `thrust` has the same API as C++ standard library. Using `transform`, one can quickly implement customized element-wise kernels.
     * `thrust` also has more complex APIs, like `scan`, `reduce`, `reduce_by_key`.
 * Hand-writing `GPUKernel` and `CPU` code
     * Do not write in header (`.h`) files. CPU Kernel should be in cpp source (`.cc`) and GPU kernels should be in cuda (`.cu`) files. (GCC cannot compile GPU code.)
 ---
 # Operator Registration
 
-## Why registration is necessary?
+## Why is registration necessary?
 We need a method to build mappings between Op type names and Op classes.
 
 ## How is registration implemented?
-
 Maintaining a map, whose key is the type name and the value is the corresponding Op constructor.
 
 ---
@@ -170,7 +169,7 @@ Maintaining a map, whose key is the type name and the value is the corresponding
 # Related Concepts
 
 ### Op_Maker
-It's constructor takes `proto` and `checker`. They are compeleted during Op_Maker's construction. ([ScaleOpMaker](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37))
+It's constructor takes `proto` and `checker`. They are completed during Op_Maker's construction. ([ScaleOpMaker](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37))
 
 ### Register Macros
 ```cpp
@@ -200,7 +199,7 @@ Make sure the registration process is executed and linked.
 ---
 # Backward Module (2/2)
 ### Build Backward Network
-- **Input**: graph of forwarding operators
+- **Input**: graph of forward operators
 - **Output**: graph of backward operators
 - **Corner cases in construction**
 	- Shared Variables => insert an `Add` operator to combine gradients
@@ -224,7 +223,7 @@ Make sure the registration process is executed and linked.
 
 ---
 # Block (in design)
-## the difference with original RNNOp
+## the difference between original RNNOp and Block
 - As an operator is more intuitive than `RNNOp`,
 - Offers a new interface `Eval(targets)` to deduce the minimal block to `Run`,
 - Fits the compile-time/ runtime separation design paradigm.

develop/doc/design/refactorization.html

@@ -193,7 +193,7 @@
 <li>Please refer to <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/graph.md">computation graphs</a> for a concrete example.</li>
 <li>Users write Python programs to describe the graphs and run them (locally or remotely).</li>
 <li>A graph is composed of <em>variables</em> and <em>operators</em>.</li>
-<li>The description of graphs must be capable of being serialized/deserialized, so that<ol>
+<li>The description of graphs must be capable of being serialized/deserialized, so that:<ol>
 <li>It can to be sent to the cloud for distributed execution, and</li>
 <li>It can be sent to clients for mobile or enterprise deployment.</li>
 </ol>
@@ -341,8 +341,8 @@
 <li>Limit the number of <code class="docutils literal"><span class="pre">tensor.device(dev)</span> <span class="pre">=</span></code> in your code.</li>
 </ul>
 </li>
-<li><code class="docutils literal"><span class="pre">thrust::tranform</span></code> and <code class="docutils literal"><span class="pre">std::transform</span></code>.<ul>
-<li><code class="docutils literal"><span class="pre">thrust</span></code> has the same API as C++ standard library. Using <code class="docutils literal"><span class="pre">transform</span></code>, one can quickly implement customized elementwise kernels.</li>
+<li><code class="docutils literal"><span class="pre">thrust::transform</span></code> and <code class="docutils literal"><span class="pre">std::transform</span></code>.<ul>
+<li><code class="docutils literal"><span class="pre">thrust</span></code> has the same API as C++ standard library. Using <code class="docutils literal"><span class="pre">transform</span></code>, one can quickly implement customized element-wise kernels.</li>
 <li><code class="docutils literal"><span class="pre">thrust</span></code> also has more complex APIs, like <code class="docutils literal"><span class="pre">scan</span></code>, <code class="docutils literal"><span class="pre">reduce</span></code>, <code class="docutils literal"><span class="pre">reduce_by_key</span></code>.</li>
 </ul>
 </li>
@@ -355,8 +355,8 @@
 <hr class="docutils" />
 <div class="section" id="operator-registration">
 <span id="operator-registration"></span><h1>Operator Registration<a class="headerlink" href="#operator-registration" title="Permalink to this headline">¶</a></h1>
-<div class="section" id="why-registration-is-necessary">
-<span id="why-registration-is-necessary"></span><h2>Why registration is necessary?<a class="headerlink" href="#why-registration-is-necessary" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="why-is-registration-necessary">
+<span id="why-is-registration-necessary"></span><h2>Why is registration necessary?<a class="headerlink" href="#why-is-registration-necessary" title="Permalink to this headline">¶</a></h2>
 <p>We need a method to build mappings between Op type names and Op classes.</p>
 </div>
 <div class="section" id="how-is-registration-implemented">
@@ -384,7 +384,7 @@
 <span id="related-concepts"></span><h1>Related Concepts<a class="headerlink" href="#related-concepts" title="Permalink to this headline">¶</a></h1>
 <div class="section" id="op-maker">
 <span id="op-maker"></span><h2>Op_Maker<a class="headerlink" href="#op-maker" title="Permalink to this headline">¶</a></h2>
-<p>It&#8217;s constructor takes <code class="docutils literal"><span class="pre">proto</span></code> and <code class="docutils literal"><span class="pre">checker</span></code>. They are compeleted during Op_Maker&#8217;s construction. (<a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37">ScaleOpMaker</a>)</p>
+<p>It&#8217;s constructor takes <code class="docutils literal"><span class="pre">proto</span></code> and <code class="docutils literal"><span class="pre">checker</span></code>. They are completed during Op_Maker&#8217;s construction. (<a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37">ScaleOpMaker</a>)</p>
 </div>
 <div class="section" id="register-macros">
 <span id="register-macros"></span><h2>Register Macros<a class="headerlink" href="#register-macros" title="Permalink to this headline">¶</a></h2>
@@ -429,7 +429,7 @@
 <div class="section" id="build-backward-network">
 <span id="build-backward-network"></span><h2>Build Backward Network<a class="headerlink" href="#build-backward-network" title="Permalink to this headline">¶</a></h2>
 <ul class="simple">
-<li><strong>Input</strong>: graph of forwarding operators</li>
+<li><strong>Input</strong>: graph of forward operators</li>
 <li><strong>Output</strong>: graph of backward operators</li>
 <li><strong>Corner cases in construction</strong><ul>
 <li>Shared Variables =&gt; insert an <code class="docutils literal"><span class="pre">Add</span></code> operator to combine gradients</li>
@@ -465,8 +465,8 @@
 <hr class="docutils" />
 <div class="section" id="block-in-design">
 <span id="block-in-design"></span><h1>Block (in design)<a class="headerlink" href="#block-in-design" title="Permalink to this headline">¶</a></h1>
-<div class="section" id="the-difference-with-original-rnnop">
-<span id="the-difference-with-original-rnnop"></span><h2>the difference with original RNNOp<a class="headerlink" href="#the-difference-with-original-rnnop" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="the-difference-between-original-rnnop-and-block">
+<span id="the-difference-between-original-rnnop-and-block"></span><h2>the difference between original RNNOp and Block<a class="headerlink" href="#the-difference-between-original-rnnop-and-block" title="Permalink to this headline">¶</a></h2>
 <ul class="simple">
 <li>As an operator is more intuitive than <code class="docutils literal"><span class="pre">RNNOp</span></code>,</li>
 <li>Offers a new interface <code class="docutils literal"><span class="pre">Eval(targets)</span></code> to deduce the minimal block to <code class="docutils literal"><span class="pre">Run</span></code>,</li>

develop/doc_cn/_sources/design/refactorization.md.txt

@@ -17,7 +17,7 @@ The goals of refactoring include:
 
 1. A graph is composed of *variables* and *operators*.
 
-1. The description of graphs must be capable of being serialized/deserialized, so that
+1. The description of graphs must be capable of being serialized/deserialized, so that:
 
    1. It can to be sent to the cloud for distributed execution, and
    1. It can be sent to clients for mobile or enterprise deployment.
@@ -137,19 +137,18 @@ Compile Time -> IR -> Runtime
 * `Eigen::Tensor` contains basic math and element-wise functions.
     * Note that `Eigen::Tensor` has broadcast implementation.
     * Limit the number of `tensor.device(dev) = ` in your code.
-* `thrust::tranform` and `std::transform`.
-    * `thrust` has the same API as C++ standard library. Using `transform`, one can quickly implement customized elementwise kernels.
+* `thrust::transform` and `std::transform`.
+    * `thrust` has the same API as C++ standard library. Using `transform`, one can quickly implement customized element-wise kernels.
     * `thrust` also has more complex APIs, like `scan`, `reduce`, `reduce_by_key`.
 * Hand-writing `GPUKernel` and `CPU` code
     * Do not write in header (`.h`) files. CPU Kernel should be in cpp source (`.cc`) and GPU kernels should be in cuda (`.cu`) files. (GCC cannot compile GPU code.)
 ---
 # Operator Registration
 
-## Why registration is necessary?
+## Why is registration necessary?
 We need a method to build mappings between Op type names and Op classes.
 
 ## How is registration implemented?
-
 Maintaining a map, whose key is the type name and the value is the corresponding Op constructor.
 
 ---
@@ -170,7 +169,7 @@ Maintaining a map, whose key is the type name and the value is the corresponding
 # Related Concepts
 
 ### Op_Maker
-It's constructor takes `proto` and `checker`. They are compeleted during Op_Maker's construction. ([ScaleOpMaker](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37))
+It's constructor takes `proto` and `checker`. They are completed during Op_Maker's construction. ([ScaleOpMaker](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37))
 
 ### Register Macros
 ```cpp
@@ -200,7 +199,7 @@ Make sure the registration process is executed and linked.
 ---
 # Backward Module (2/2)
 ### Build Backward Network
-- **Input**: graph of forwarding operators
+- **Input**: graph of forward operators
 - **Output**: graph of backward operators
 - **Corner cases in construction**
 	- Shared Variables => insert an `Add` operator to combine gradients
@@ -224,7 +223,7 @@ Make sure the registration process is executed and linked.
 
 ---
 # Block (in design)
-## the difference with original RNNOp
+## the difference between original RNNOp and Block
 - As an operator is more intuitive than `RNNOp`,
 - Offers a new interface `Eval(targets)` to deduce the minimal block to `Run`,
 - Fits the compile-time/ runtime separation design paradigm.

develop/doc_cn/design/refactorization.html

@@ -207,7 +207,7 @@
 <li>Please refer to <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/graph.md">computation graphs</a> for a concrete example.</li>
 <li>Users write Python programs to describe the graphs and run them (locally or remotely).</li>
 <li>A graph is composed of <em>variables</em> and <em>operators</em>.</li>
-<li>The description of graphs must be capable of being serialized/deserialized, so that<ol>
+<li>The description of graphs must be capable of being serialized/deserialized, so that:<ol>
 <li>It can to be sent to the cloud for distributed execution, and</li>
 <li>It can be sent to clients for mobile or enterprise deployment.</li>
 </ol>
@@ -355,8 +355,8 @@
 <li>Limit the number of <code class="docutils literal"><span class="pre">tensor.device(dev)</span> <span class="pre">=</span></code> in your code.</li>
 </ul>
 </li>
-<li><code class="docutils literal"><span class="pre">thrust::tranform</span></code> and <code class="docutils literal"><span class="pre">std::transform</span></code>.<ul>
-<li><code class="docutils literal"><span class="pre">thrust</span></code> has the same API as C++ standard library. Using <code class="docutils literal"><span class="pre">transform</span></code>, one can quickly implement customized elementwise kernels.</li>
+<li><code class="docutils literal"><span class="pre">thrust::transform</span></code> and <code class="docutils literal"><span class="pre">std::transform</span></code>.<ul>
+<li><code class="docutils literal"><span class="pre">thrust</span></code> has the same API as C++ standard library. Using <code class="docutils literal"><span class="pre">transform</span></code>, one can quickly implement customized element-wise kernels.</li>
 <li><code class="docutils literal"><span class="pre">thrust</span></code> also has more complex APIs, like <code class="docutils literal"><span class="pre">scan</span></code>, <code class="docutils literal"><span class="pre">reduce</span></code>, <code class="docutils literal"><span class="pre">reduce_by_key</span></code>.</li>
 </ul>
 </li>
@@ -369,8 +369,8 @@
 <hr class="docutils" />
 <div class="section" id="operator-registration">
 <span id="operator-registration"></span><h1>Operator Registration<a class="headerlink" href="#operator-registration" title="永久链接至标题">¶</a></h1>
-<div class="section" id="why-registration-is-necessary">
-<span id="why-registration-is-necessary"></span><h2>Why registration is necessary?<a class="headerlink" href="#why-registration-is-necessary" title="永久链接至标题">¶</a></h2>
+<div class="section" id="why-is-registration-necessary">
+<span id="why-is-registration-necessary"></span><h2>Why is registration necessary?<a class="headerlink" href="#why-is-registration-necessary" title="永久链接至标题">¶</a></h2>
 <p>We need a method to build mappings between Op type names and Op classes.</p>
 </div>
 <div class="section" id="how-is-registration-implemented">
@@ -398,7 +398,7 @@
 <span id="related-concepts"></span><h1>Related Concepts<a class="headerlink" href="#related-concepts" title="永久链接至标题">¶</a></h1>
 <div class="section" id="op-maker">
 <span id="op-maker"></span><h2>Op_Maker<a class="headerlink" href="#op-maker" title="永久链接至标题">¶</a></h2>
-<p>It&#8217;s constructor takes <code class="docutils literal"><span class="pre">proto</span></code> and <code class="docutils literal"><span class="pre">checker</span></code>. They are compeleted during Op_Maker&#8217;s construction. (<a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37">ScaleOpMaker</a>)</p>
+<p>It&#8217;s constructor takes <code class="docutils literal"><span class="pre">proto</span></code> and <code class="docutils literal"><span class="pre">checker</span></code>. They are completed during Op_Maker&#8217;s construction. (<a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37">ScaleOpMaker</a>)</p>
 </div>
 <div class="section" id="register-macros">
 <span id="register-macros"></span><h2>Register Macros<a class="headerlink" href="#register-macros" title="永久链接至标题">¶</a></h2>
@@ -443,7 +443,7 @@
 <div class="section" id="build-backward-network">
 <span id="build-backward-network"></span><h2>Build Backward Network<a class="headerlink" href="#build-backward-network" title="永久链接至标题">¶</a></h2>
 <ul class="simple">
-<li><strong>Input</strong>: graph of forwarding operators</li>
+<li><strong>Input</strong>: graph of forward operators</li>
 <li><strong>Output</strong>: graph of backward operators</li>
 <li><strong>Corner cases in construction</strong><ul>
 <li>Shared Variables =&gt; insert an <code class="docutils literal"><span class="pre">Add</span></code> operator to combine gradients</li>
@@ -479,8 +479,8 @@
 <hr class="docutils" />
 <div class="section" id="block-in-design">
 <span id="block-in-design"></span><h1>Block (in design)<a class="headerlink" href="#block-in-design" title="永久链接至标题">¶</a></h1>
-<div class="section" id="the-difference-with-original-rnnop">
-<span id="the-difference-with-original-rnnop"></span><h2>the difference with original RNNOp<a class="headerlink" href="#the-difference-with-original-rnnop" title="永久链接至标题">¶</a></h2>
+<div class="section" id="the-difference-between-original-rnnop-and-block">
+<span id="the-difference-between-original-rnnop-and-block"></span><h2>the difference between original RNNOp and Block<a class="headerlink" href="#the-difference-between-original-rnnop-and-block" title="永久链接至标题">¶</a></h2>
 <ul class="simple">
 <li>As an operator is more intuitive than <code class="docutils literal"><span class="pre">RNNOp</span></code>,</li>
 <li>Offers a new interface <code class="docutils literal"><span class="pre">Eval(targets)</span></code> to deduce the minimal block to <code class="docutils literal"><span class="pre">Run</span></code>,</li>