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develop/doc/_sources/design/graph.md.txt
浏览文件 @ 36fd134d
# Design Doc: Computations as Graphs
# Design Doc: Computations as a Graph
A primary goal of the refactorization of PaddlePaddle is a more flexible representation of deep learning computation, in particular, a graph of operators and variables, instead of sequences of layers as before.
......@@ -8,6 +8,8 @@ This document explains that the construction of a graph as three steps:
- construct the backward part
- construct the optimization part
## The Construction of a Graph
Let us take the problem of image classification as a simple example. The application program that trains the model looks like:
```python
......@@ -25,7 +27,9 @@ The first four lines of above program build the forward part of the graph.
![](images/graph_construction_example_forward_only.png)
In particular, the first line `x = layer.data("images")` creates variable x and a Feed operator that copies a column from the minibatch to x. `y = layer.fc(x)` creates not only the FC operator and output variable y, but also two parameters, W and b.
In particular, the first line `x = layer.data("images")` creates variable x and a Feed operator that copies a column from the minibatch to x. `y = layer.fc(x)` creates not only the FC operator and output variable y, but also two parameters, W and b, and the initialization operators.
Initialization operators are kind of "run-once" operators -- the `Run` method increments a class data member counter so to run at most once. By doing so, a parameter wouldn't be initialized repeatedly, say, in every minibatch.
In this example, all operators are created as `OpDesc` protobuf messages, and all variables are `VarDesc`. These protobuf messages are saved in a `BlockDesc` protobuf message.
......@@ -49,3 +53,18 @@ According to the chain rule of gradient computation, `ConstructBackwardGraph` wo
For each parameter, like W and b created by `layer.fc`, marked as double circles in above graphs, `ConstructOptimizationGraph` creates an optimization operator to apply its gradient. Here results in the complete graph:
![](images/graph_construction_example_all.png)
## Block and Graph
The word block and graph are interchangable in the desgin of PaddlePaddle. A [Block[(https://github.com/PaddlePaddle/Paddle/pull/3708) is a metaphore of the code and local variables in a pair of curly braces in programming languages, where operators are like statements or instructions. A graph of operators and variables is a representation of the block.
A Block keeps operators in an array `BlockDesc::ops`
```protobuf
message BlockDesc {
repeated OpDesc ops = 1;
repeated VarDesc vars = 2;
}
```
in the order that there appear in user programs, like the Python program at the beginning of this article. We can imagine that in `ops`, we have some forward operators, followed by some gradient operators, and then some optimization operators.
develop/doc/design/graph.html
浏览文件 @ 36fd134d
......@@ -8,7 +8,7 @@
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Design Doc: Computations as Graphs &mdash; PaddlePaddle documentation</title>
<title>Design Doc: Computations as a Graph &mdash; PaddlePaddle documentation</title>
......@@ -168,7 +168,7 @@
<div role="navigation" aria-label="breadcrumbs navigation">
<ul class="wy-breadcrumbs">
<li>Design Doc: Computations as Graphs</li>
<li>Design Doc: Computations as a Graph</li>
</ul>
</div>
......@@ -177,8 +177,8 @@
<div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
<div itemprop="articleBody">
<div class="section" id="design-doc-computations-as-graphs">
<span id="design-doc-computations-as-graphs"></span><h1>Design Doc: Computations as Graphs<a class="headerlink" href="#design-doc-computations-as-graphs" title="Permalink to this headline"></a></h1>
<div class="section" id="design-doc-computations-as-a-graph">
<span id="design-doc-computations-as-a-graph"></span><h1>Design Doc: Computations as a Graph<a class="headerlink" href="#design-doc-computations-as-a-graph" title="Permalink to this headline"></a></h1>
<p>A primary goal of the refactorization of PaddlePaddle is a more flexible representation of deep learning computation, in particular, a graph of operators and variables, instead of sequences of layers as before.</p>
<p>This document explains that the construction of a graph as three steps:</p>
<ul class="simple">
......@@ -186,6 +186,8 @@
<li>construct the backward part</li>
<li>construct the optimization part</li>
</ul>
<div class="section" id="the-construction-of-a-graph">
<span id="the-construction-of-a-graph"></span><h2>The Construction of a Graph<a class="headerlink" href="#the-construction-of-a-graph" title="Permalink to this headline"></a></h2>
<p>Let us take the problem of image classification as a simple example. The application program that trains the model looks like:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">layer</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="s2">&quot;images&quot;</span><span class="p">)</span>
<span class="n">l</span> <span class="o">=</span> <span class="n">layer</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="s2">&quot;label&quot;</span><span class="p">)</span>
......@@ -196,14 +198,15 @@
</pre></div>
</div>
<div class="section" id="forward-part">
<span id="forward-part"></span><h2>Forward Part<a class="headerlink" href="#forward-part" title="Permalink to this headline"></a></h2>
<span id="forward-part"></span><h3>Forward Part<a class="headerlink" href="#forward-part" title="Permalink to this headline"></a></h3>
<p>The first four lines of above program build the forward part of the graph.</p>
<p><img alt="" src="../_images/graph_construction_example_forward_only.png" /></p>
<p>In particular, the first line <code class="docutils literal"><span class="pre">x</span> <span class="pre">=</span> <span class="pre">layer.data(&quot;images&quot;)</span></code> creates variable x and a Feed operator that copies a column from the minibatch to x. <code class="docutils literal"><span class="pre">y</span> <span class="pre">=</span> <span class="pre">layer.fc(x)</span></code> creates not only the FC operator and output variable y, but also two parameters, W and b.</p>
<p>In particular, the first line <code class="docutils literal"><span class="pre">x</span> <span class="pre">=</span> <span class="pre">layer.data(&quot;images&quot;)</span></code> creates variable x and a Feed operator that copies a column from the minibatch to x. <code class="docutils literal"><span class="pre">y</span> <span class="pre">=</span> <span class="pre">layer.fc(x)</span></code> creates not only the FC operator and output variable y, but also two parameters, W and b, and the initialization operators.</p>
<p>Initialization operators are kind of &#8220;run-once&#8221; operators &#8211; the <code class="docutils literal"><span class="pre">Run</span></code> method increments a class data member counter so to run at most once. By doing so, a parameter wouldn&#8217;t be initialized repeatedly, say, in every minibatch.</p>
<p>In this example, all operators are created as <code class="docutils literal"><span class="pre">OpDesc</span></code> protobuf messages, and all variables are <code class="docutils literal"><span class="pre">VarDesc</span></code>. These protobuf messages are saved in a <code class="docutils literal"><span class="pre">BlockDesc</span></code> protobuf message.</p>
</div>
<div class="section" id="backward-part">
<span id="backward-part"></span><h2>Backward Part<a class="headerlink" href="#backward-part" title="Permalink to this headline"></a></h2>
<span id="backward-part"></span><h3>Backward Part<a class="headerlink" href="#backward-part" title="Permalink to this headline"></a></h3>
<p>The fifth line <code class="docutils literal"><span class="pre">optimize(cost)</span></code> calls two functions, <code class="docutils literal"><span class="pre">ConstructBackwardGraph</span></code> and <code class="docutils literal"><span class="pre">ConstructOptimizationGraph</span></code>.</p>
<p><code class="docutils literal"><span class="pre">ConstructBackwardGraph</span></code> traverses the forward graph in the <code class="docutils literal"><span class="pre">BlockDesc</span></code> protobuf message and builds the backward part.</p>
<p><img alt="" src="../_images/graph_construction_example_forward_backward.png" /></p>
......@@ -216,10 +219,23 @@
</ol>
</div>
<div class="section" id="optimization-part">
<span id="optimization-part"></span><h2>Optimization Part<a class="headerlink" href="#optimization-part" title="Permalink to this headline"></a></h2>
<span id="optimization-part"></span><h3>Optimization Part<a class="headerlink" href="#optimization-part" title="Permalink to this headline"></a></h3>
<p>For each parameter, like W and b created by <code class="docutils literal"><span class="pre">layer.fc</span></code>, marked as double circles in above graphs, <code class="docutils literal"><span class="pre">ConstructOptimizationGraph</span></code> creates an optimization operator to apply its gradient. Here results in the complete graph:</p>
<p><img alt="" src="../_images/graph_construction_example_all.png" /></p>
</div>
</div>
<div class="section" id="block-and-graph">
<span id="block-and-graph"></span><h2>Block and Graph<a class="headerlink" href="#block-and-graph" title="Permalink to this headline"></a></h2>
<p>The word block and graph are interchangable in the desgin of PaddlePaddle. A [Block[(https://github.com/PaddlePaddle/Paddle/pull/3708) is a metaphore of the code and local variables in a pair of curly braces in programming languages, where operators are like statements or instructions. A graph of operators and variables is a representation of the block.</p>
<p>A Block keeps operators in an array <code class="docutils literal"><span class="pre">BlockDesc::ops</span></code></p>
<div class="highlight-protobuf"><div class="highlight"><pre><span></span><span class="kd">message</span> <span class="nc">BlockDesc</span> <span class="p">{</span>
<span class="k">repeated</span> <span class="n">OpDesc</span> <span class="na">ops</span> <span class="o">=</span> <span class="mi">1</span><span class="p">;</span>
<span class="k">repeated</span> <span class="n">VarDesc</span> <span class="na">vars</span> <span class="o">=</span> <span class="mi">2</span><span class="p">;</span>
<span class="p">}</span>
</pre></div>
</div>
<p>in the order that there appear in user programs, like the Python program at the beginning of this article. We can imagine that in <code class="docutils literal"><span class="pre">ops</span></code>, we have some forward operators, followed by some gradient operators, and then some optimization operators.</p>
</div>
</div>
......
develop/doc/searchindex.js
浏览文件 @ 36fd134d
因为 它太大了无法显示 source diff 。你可以改为 查看blob。
develop/doc_cn/_sources/design/graph.md.txt
浏览文件 @ 36fd134d
# Design Doc: Computations as Graphs
# Design Doc: Computations as a Graph
A primary goal of the refactorization of PaddlePaddle is a more flexible representation of deep learning computation, in particular, a graph of operators and variables, instead of sequences of layers as before.
......@@ -8,6 +8,8 @@ This document explains that the construction of a graph as three steps:
- construct the backward part
- construct the optimization part
## The Construction of a Graph
Let us take the problem of image classification as a simple example. The application program that trains the model looks like:
```python
......@@ -25,7 +27,9 @@ The first four lines of above program build the forward part of the graph.
![](images/graph_construction_example_forward_only.png)
In particular, the first line `x = layer.data("images")` creates variable x and a Feed operator that copies a column from the minibatch to x. `y = layer.fc(x)` creates not only the FC operator and output variable y, but also two parameters, W and b.
In particular, the first line `x = layer.data("images")` creates variable x and a Feed operator that copies a column from the minibatch to x. `y = layer.fc(x)` creates not only the FC operator and output variable y, but also two parameters, W and b, and the initialization operators.
Initialization operators are kind of "run-once" operators -- the `Run` method increments a class data member counter so to run at most once. By doing so, a parameter wouldn't be initialized repeatedly, say, in every minibatch.
In this example, all operators are created as `OpDesc` protobuf messages, and all variables are `VarDesc`. These protobuf messages are saved in a `BlockDesc` protobuf message.
......@@ -49,3 +53,18 @@ According to the chain rule of gradient computation, `ConstructBackwardGraph` wo
For each parameter, like W and b created by `layer.fc`, marked as double circles in above graphs, `ConstructOptimizationGraph` creates an optimization operator to apply its gradient. Here results in the complete graph:
![](images/graph_construction_example_all.png)
## Block and Graph
The word block and graph are interchangable in the desgin of PaddlePaddle. A [Block[(https://github.com/PaddlePaddle/Paddle/pull/3708) is a metaphore of the code and local variables in a pair of curly braces in programming languages, where operators are like statements or instructions. A graph of operators and variables is a representation of the block.
A Block keeps operators in an array `BlockDesc::ops`
```protobuf
message BlockDesc {
repeated OpDesc ops = 1;
repeated VarDesc vars = 2;
}
```
in the order that there appear in user programs, like the Python program at the beginning of this article. We can imagine that in `ops`, we have some forward operators, followed by some gradient operators, and then some optimization operators.
develop/doc_cn/design/graph.html
浏览文件 @ 36fd134d
......@@ -8,7 +8,7 @@
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Design Doc: Computations as Graphs &mdash; PaddlePaddle 文档</title>
<title>Design Doc: Computations as a Graph &mdash; PaddlePaddle 文档</title>
......@@ -175,7 +175,7 @@
<div role="navigation" aria-label="breadcrumbs navigation">
<ul class="wy-breadcrumbs">
<li>Design Doc: Computations as Graphs</li>
<li>Design Doc: Computations as a Graph</li>
</ul>
</div>
......@@ -184,8 +184,8 @@
<div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
<div itemprop="articleBody">
<div class="section" id="design-doc-computations-as-graphs">
<span id="design-doc-computations-as-graphs"></span><h1>Design Doc: Computations as Graphs<a class="headerlink" href="#design-doc-computations-as-graphs" title="永久链接至标题"></a></h1>
<div class="section" id="design-doc-computations-as-a-graph">
<span id="design-doc-computations-as-a-graph"></span><h1>Design Doc: Computations as a Graph<a class="headerlink" href="#design-doc-computations-as-a-graph" title="永久链接至标题"></a></h1>
<p>A primary goal of the refactorization of PaddlePaddle is a more flexible representation of deep learning computation, in particular, a graph of operators and variables, instead of sequences of layers as before.</p>
<p>This document explains that the construction of a graph as three steps:</p>
<ul class="simple">
......@@ -193,6 +193,8 @@
<li>construct the backward part</li>
<li>construct the optimization part</li>
</ul>
<div class="section" id="the-construction-of-a-graph">
<span id="the-construction-of-a-graph"></span><h2>The Construction of a Graph<a class="headerlink" href="#the-construction-of-a-graph" title="永久链接至标题"></a></h2>
<p>Let us take the problem of image classification as a simple example. The application program that trains the model looks like:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">layer</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="s2">&quot;images&quot;</span><span class="p">)</span>
<span class="n">l</span> <span class="o">=</span> <span class="n">layer</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="s2">&quot;label&quot;</span><span class="p">)</span>
......@@ -203,14 +205,15 @@
</pre></div>
</div>
<div class="section" id="forward-part">
<span id="forward-part"></span><h2>Forward Part<a class="headerlink" href="#forward-part" title="永久链接至标题"></a></h2>
<span id="forward-part"></span><h3>Forward Part<a class="headerlink" href="#forward-part" title="永久链接至标题"></a></h3>
<p>The first four lines of above program build the forward part of the graph.</p>
<p><img alt="" src="../_images/graph_construction_example_forward_only.png" /></p>
<p>In particular, the first line <code class="docutils literal"><span class="pre">x</span> <span class="pre">=</span> <span class="pre">layer.data(&quot;images&quot;)</span></code> creates variable x and a Feed operator that copies a column from the minibatch to x. <code class="docutils literal"><span class="pre">y</span> <span class="pre">=</span> <span class="pre">layer.fc(x)</span></code> creates not only the FC operator and output variable y, but also two parameters, W and b.</p>
<p>In particular, the first line <code class="docutils literal"><span class="pre">x</span> <span class="pre">=</span> <span class="pre">layer.data(&quot;images&quot;)</span></code> creates variable x and a Feed operator that copies a column from the minibatch to x. <code class="docutils literal"><span class="pre">y</span> <span class="pre">=</span> <span class="pre">layer.fc(x)</span></code> creates not only the FC operator and output variable y, but also two parameters, W and b, and the initialization operators.</p>
<p>Initialization operators are kind of &#8220;run-once&#8221; operators &#8211; the <code class="docutils literal"><span class="pre">Run</span></code> method increments a class data member counter so to run at most once. By doing so, a parameter wouldn&#8217;t be initialized repeatedly, say, in every minibatch.</p>
<p>In this example, all operators are created as <code class="docutils literal"><span class="pre">OpDesc</span></code> protobuf messages, and all variables are <code class="docutils literal"><span class="pre">VarDesc</span></code>. These protobuf messages are saved in a <code class="docutils literal"><span class="pre">BlockDesc</span></code> protobuf message.</p>
</div>
<div class="section" id="backward-part">
<span id="backward-part"></span><h2>Backward Part<a class="headerlink" href="#backward-part" title="永久链接至标题"></a></h2>
<span id="backward-part"></span><h3>Backward Part<a class="headerlink" href="#backward-part" title="永久链接至标题"></a></h3>
<p>The fifth line <code class="docutils literal"><span class="pre">optimize(cost)</span></code> calls two functions, <code class="docutils literal"><span class="pre">ConstructBackwardGraph</span></code> and <code class="docutils literal"><span class="pre">ConstructOptimizationGraph</span></code>.</p>
<p><code class="docutils literal"><span class="pre">ConstructBackwardGraph</span></code> traverses the forward graph in the <code class="docutils literal"><span class="pre">BlockDesc</span></code> protobuf message and builds the backward part.</p>
<p><img alt="" src="../_images/graph_construction_example_forward_backward.png" /></p>
......@@ -223,10 +226,23 @@
</ol>
</div>
<div class="section" id="optimization-part">
<span id="optimization-part"></span><h2>Optimization Part<a class="headerlink" href="#optimization-part" title="永久链接至标题"></a></h2>
<span id="optimization-part"></span><h3>Optimization Part<a class="headerlink" href="#optimization-part" title="永久链接至标题"></a></h3>
<p>For each parameter, like W and b created by <code class="docutils literal"><span class="pre">layer.fc</span></code>, marked as double circles in above graphs, <code class="docutils literal"><span class="pre">ConstructOptimizationGraph</span></code> creates an optimization operator to apply its gradient. Here results in the complete graph:</p>
<p><img alt="" src="../_images/graph_construction_example_all.png" /></p>
</div>
</div>
<div class="section" id="block-and-graph">
<span id="block-and-graph"></span><h2>Block and Graph<a class="headerlink" href="#block-and-graph" title="永久链接至标题"></a></h2>
<p>The word block and graph are interchangable in the desgin of PaddlePaddle. A [Block[(https://github.com/PaddlePaddle/Paddle/pull/3708) is a metaphore of the code and local variables in a pair of curly braces in programming languages, where operators are like statements or instructions. A graph of operators and variables is a representation of the block.</p>
<p>A Block keeps operators in an array <code class="docutils literal"><span class="pre">BlockDesc::ops</span></code></p>
<div class="highlight-protobuf"><div class="highlight"><pre><span></span><span class="kd">message</span> <span class="nc">BlockDesc</span> <span class="p">{</span>
<span class="k">repeated</span> <span class="n">OpDesc</span> <span class="na">ops</span> <span class="o">=</span> <span class="mi">1</span><span class="p">;</span>
<span class="k">repeated</span> <span class="n">VarDesc</span> <span class="na">vars</span> <span class="o">=</span> <span class="mi">2</span><span class="p">;</span>
<span class="p">}</span>
</pre></div>
</div>
<p>in the order that there appear in user programs, like the Python program at the beginning of this article. We can imagine that in <code class="docutils literal"><span class="pre">ops</span></code>, we have some forward operators, followed by some gradient operators, and then some optimization operators.</p>
</div>
</div>
......
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