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# Design Doc: ProgramDesc
The basic structure of a PaddlePaddle program is some nested blocks, as a C++ or Java program.
As described in [graph.md](./graph.md), the first five lines of the following PaddlePaddle program
```python
x = layer.data("images")
l = layer.data("label")
y = layer.fc(x)
cost = layer.mse(y, l)
optimize(cost)
train(cost, reader=mnist.train())
```
generates, or compiles, a PaddelPaddle program, which is represented by the following protobuf message:
```protobuf
message ProgramDesc {
repeated BlockDesc blocks = 1;
}
message BlockDesc {
required int32 parent = 1;
repeated VarDesc vars = 2;
repeated OpDesc ops = 3;
}
message OpDesc {
AttrDesc attrs = 1;
...
}
message AttrDesc {
required AttrType type = 1;
// index into ProgramDesc::blocks when type==BLOCK
optional int32 block = 2;
...
}
```
When each of the first five lines runs, related Python function, e.g., `layer.fc`, calls C++ InferShape functions. This InferShape function needs to access the properties of VarDesc's accessed by the current OpDesc. These VarDesc's might not be defined in the current block, but in some ancestor blocks. This requires that we can trace the parent of a block.
A nested block is often an attribute of an operator, most likely, an IfElseOp or a WhileOp. In above solution, all blocks are in `ProgramDesc::blocks`, this implicitly assigns a zero-based ID to each block -- the index of the block in `ProgramDesc::blocks`. So that `AttrDesc::block` could be an integer block ID.
With this design, the InferShape function should take the following parameters:
```c++
void InferShape(int current_block,
int current_operator,
ProgramDesc* program // might change VarDesc values.
) {
...
}
```
where
- `current_block` indices into `ProgramDesc::blocks`,
- `current_operator` indices into `BlockDesc::ops`.
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<li>Design Doc: ProgramDesc</li>
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<div class="section" id="design-doc-programdesc">
<span id="design-doc-programdesc"></span><h1>Design Doc: ProgramDesc<a class="headerlink" href="#design-doc-programdesc" title="Permalink to this headline"></a></h1>
<p>The basic structure of a PaddlePaddle program is some nested blocks, as a C++ or Java program.</p>
<p>As described in <a class="reference internal" href="graph.html"><span class="doc">graph.md</span></a>, the first five lines of the following PaddlePaddle program</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>
<span class="n">y</span> <span class="o">=</span> <span class="n">layer</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">cost</span> <span class="o">=</span> <span class="n">layer</span><span class="o">.</span><span class="n">mse</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">l</span><span class="p">)</span>
<span class="n">optimize</span><span class="p">(</span><span class="n">cost</span><span class="p">)</span>
<span class="n">train</span><span class="p">(</span><span class="n">cost</span><span class="p">,</span> <span class="n">reader</span><span class="o">=</span><span class="n">mnist</span><span class="o">.</span><span class="n">train</span><span class="p">())</span>
</pre></div>
</div>
<p>generates, or compiles, a PaddelPaddle program, which is represented by the following protobuf message:</p>
<div class="highlight-protobuf"><div class="highlight"><pre><span></span><span class="kd">message</span> <span class="nc">ProgramDesc</span> <span class="p">{</span>
<span class="k">repeated</span> <span class="n">BlockDesc</span> <span class="na">blocks</span> <span class="o">=</span> <span class="mi">1</span><span class="p">;</span>
<span class="p">}</span>
<span class="kd">message</span> <span class="nc">BlockDesc</span> <span class="p">{</span>
<span class="k">required</span> <span class="kt">int32</span> <span class="na">parent</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="k">repeated</span> <span class="n">OpDesc</span> <span class="na">ops</span> <span class="o">=</span> <span class="mi">3</span><span class="p">;</span>
<span class="p">}</span>
<span class="kd">message</span> <span class="nc">OpDesc</span> <span class="p">{</span>
<span class="n">AttrDesc</span> <span class="na">attrs</span> <span class="o">=</span> <span class="mi">1</span><span class="p">;</span>
<span class="o">...</span>
<span class="p">}</span>
<span class="kd">message</span> <span class="nc">AttrDesc</span> <span class="p">{</span>
<span class="k">required</span> <span class="n">AttrType</span> <span class="na">type</span> <span class="o">=</span> <span class="mi">1</span><span class="p">;</span>
<span class="c1">// index into ProgramDesc::blocks when type==BLOCK</span>
<span class="k">optional</span> <span class="kt">int32</span> <span class="na">block</span> <span class="o">=</span> <span class="mi">2</span><span class="p">;</span>
<span class="o">...</span>
<span class="p">}</span>
</pre></div>
</div>
<p>When each of the first five lines runs, related Python function, e.g., <code class="docutils literal"><span class="pre">layer.fc</span></code>, calls C++ InferShape functions. This InferShape function needs to access the properties of VarDesc&#8217;s accessed by the current OpDesc. These VarDesc&#8217;s might not be defined in the current block, but in some ancestor blocks. This requires that we can trace the parent of a block.</p>
<p>A nested block is often an attribute of an operator, most likely, an IfElseOp or a WhileOp. In above solution, all blocks are in <code class="docutils literal"><span class="pre">ProgramDesc::blocks</span></code>, this implicitly assigns a zero-based ID to each block &#8211; the index of the block in <code class="docutils literal"><span class="pre">ProgramDesc::blocks</span></code>. So that <code class="docutils literal"><span class="pre">AttrDesc::block</span></code> could be an integer block ID.</p>
<p>With this design, the InferShape function should take the following parameters:</p>
<div class="highlight-c++"><div class="highlight"><pre><span></span><span class="kt">void</span> <span class="nf">InferShape</span><span class="p">(</span><span class="kt">int</span> <span class="n">current_block</span><span class="p">,</span>
<span class="kt">int</span> <span class="n">current_operator</span><span class="p">,</span>
<span class="n">ProgramDesc</span><span class="o">*</span> <span class="n">program</span> <span class="c1">// might change VarDesc values.</span>
<span class="p">)</span> <span class="p">{</span>
<span class="p">...</span>
<span class="p">}</span>
</pre></div>
</div>
<p>where</p>
<ul class="simple">
<li><code class="docutils literal"><span class="pre">current_block</span></code> indices into <code class="docutils literal"><span class="pre">ProgramDesc::blocks</span></code>,</li>
<li><code class="docutils literal"><span class="pre">current_operator</span></code> indices into <code class="docutils literal"><span class="pre">BlockDesc::ops</span></code>.</li>
</ul>
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# Design Doc: ProgramDesc
The basic structure of a PaddlePaddle program is some nested blocks, as a C++ or Java program.
As described in [graph.md](./graph.md), the first five lines of the following PaddlePaddle program
```python
x = layer.data("images")
l = layer.data("label")
y = layer.fc(x)
cost = layer.mse(y, l)
optimize(cost)
train(cost, reader=mnist.train())
```
generates, or compiles, a PaddelPaddle program, which is represented by the following protobuf message:
```protobuf
message ProgramDesc {
repeated BlockDesc blocks = 1;
}
message BlockDesc {
required int32 parent = 1;
repeated VarDesc vars = 2;
repeated OpDesc ops = 3;
}
message OpDesc {
AttrDesc attrs = 1;
...
}
message AttrDesc {
required AttrType type = 1;
// index into ProgramDesc::blocks when type==BLOCK
optional int32 block = 2;
...
}
```
When each of the first five lines runs, related Python function, e.g., `layer.fc`, calls C++ InferShape functions. This InferShape function needs to access the properties of VarDesc's accessed by the current OpDesc. These VarDesc's might not be defined in the current block, but in some ancestor blocks. This requires that we can trace the parent of a block.
A nested block is often an attribute of an operator, most likely, an IfElseOp or a WhileOp. In above solution, all blocks are in `ProgramDesc::blocks`, this implicitly assigns a zero-based ID to each block -- the index of the block in `ProgramDesc::blocks`. So that `AttrDesc::block` could be an integer block ID.
With this design, the InferShape function should take the following parameters:
```c++
void InferShape(int current_block,
int current_operator,
ProgramDesc* program // might change VarDesc values.
) {
...
}
```
where
- `current_block` indices into `ProgramDesc::blocks`,
- `current_operator` indices into `BlockDesc::ops`.
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<span id="design-doc-programdesc"></span><h1>Design Doc: ProgramDesc<a class="headerlink" href="#design-doc-programdesc" title="永久链接至标题"></a></h1>
<p>The basic structure of a PaddlePaddle program is some nested blocks, as a C++ or Java program.</p>
<p>As described in <a class="reference internal" href="graph.html"><span class="doc">graph.md</span></a>, the first five lines of the following PaddlePaddle program</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>
<span class="n">y</span> <span class="o">=</span> <span class="n">layer</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">cost</span> <span class="o">=</span> <span class="n">layer</span><span class="o">.</span><span class="n">mse</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">l</span><span class="p">)</span>
<span class="n">optimize</span><span class="p">(</span><span class="n">cost</span><span class="p">)</span>
<span class="n">train</span><span class="p">(</span><span class="n">cost</span><span class="p">,</span> <span class="n">reader</span><span class="o">=</span><span class="n">mnist</span><span class="o">.</span><span class="n">train</span><span class="p">())</span>
</pre></div>
</div>
<p>generates, or compiles, a PaddelPaddle program, which is represented by the following protobuf message:</p>
<div class="highlight-protobuf"><div class="highlight"><pre><span></span><span class="kd">message</span> <span class="nc">ProgramDesc</span> <span class="p">{</span>
<span class="k">repeated</span> <span class="n">BlockDesc</span> <span class="na">blocks</span> <span class="o">=</span> <span class="mi">1</span><span class="p">;</span>
<span class="p">}</span>
<span class="kd">message</span> <span class="nc">BlockDesc</span> <span class="p">{</span>
<span class="k">required</span> <span class="kt">int32</span> <span class="na">parent</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="k">repeated</span> <span class="n">OpDesc</span> <span class="na">ops</span> <span class="o">=</span> <span class="mi">3</span><span class="p">;</span>
<span class="p">}</span>
<span class="kd">message</span> <span class="nc">OpDesc</span> <span class="p">{</span>
<span class="n">AttrDesc</span> <span class="na">attrs</span> <span class="o">=</span> <span class="mi">1</span><span class="p">;</span>
<span class="o">...</span>
<span class="p">}</span>
<span class="kd">message</span> <span class="nc">AttrDesc</span> <span class="p">{</span>
<span class="k">required</span> <span class="n">AttrType</span> <span class="na">type</span> <span class="o">=</span> <span class="mi">1</span><span class="p">;</span>
<span class="c1">// index into ProgramDesc::blocks when type==BLOCK</span>
<span class="k">optional</span> <span class="kt">int32</span> <span class="na">block</span> <span class="o">=</span> <span class="mi">2</span><span class="p">;</span>
<span class="o">...</span>
<span class="p">}</span>
</pre></div>
</div>
<p>When each of the first five lines runs, related Python function, e.g., <code class="docutils literal"><span class="pre">layer.fc</span></code>, calls C++ InferShape functions. This InferShape function needs to access the properties of VarDesc&#8217;s accessed by the current OpDesc. These VarDesc&#8217;s might not be defined in the current block, but in some ancestor blocks. This requires that we can trace the parent of a block.</p>
<p>A nested block is often an attribute of an operator, most likely, an IfElseOp or a WhileOp. In above solution, all blocks are in <code class="docutils literal"><span class="pre">ProgramDesc::blocks</span></code>, this implicitly assigns a zero-based ID to each block &#8211; the index of the block in <code class="docutils literal"><span class="pre">ProgramDesc::blocks</span></code>. So that <code class="docutils literal"><span class="pre">AttrDesc::block</span></code> could be an integer block ID.</p>
<p>With this design, the InferShape function should take the following parameters:</p>
<div class="highlight-c++"><div class="highlight"><pre><span></span><span class="kt">void</span> <span class="nf">InferShape</span><span class="p">(</span><span class="kt">int</span> <span class="n">current_block</span><span class="p">,</span>
<span class="kt">int</span> <span class="n">current_operator</span><span class="p">,</span>
<span class="n">ProgramDesc</span><span class="o">*</span> <span class="n">program</span> <span class="c1">// might change VarDesc values.</span>
<span class="p">)</span> <span class="p">{</span>
<span class="p">...</span>
<span class="p">}</span>
</pre></div>
</div>
<p>where</p>
<ul class="simple">
<li><code class="docutils literal"><span class="pre">current_block</span></code> indices into <code class="docutils literal"><span class="pre">ProgramDesc::blocks</span></code>,</li>
<li><code class="docutils literal"><span class="pre">current_operator</span></code> indices into <code class="docutils literal"><span class="pre">BlockDesc::ops</span></code>.</li>
</ul>
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