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develop/doc/_sources/design/prune.md.txt 0 → 100644
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# Prune
## Motivation
We want to support running inference, training and checkpointing in one `ProgramDesc`. We implement
`void Prune(const ProgramDesc* input, ProgramDesc* output)` function, which takes a `ProgramDesc`
and generate a pruned `ProgramDesc`.
## Challenge
Pruning need to support both variables and operators being evaluation targets. Consider the following
different situations.
```python
# Case 1: run foward pass.
cost_np = session.run(target=cost)
# Case 2: run backward passing.
opts_np, _ = session.run(target=[cost, opt])
# Case 3: run checkpointing
_ = session.run(target=checkpoint)
```
## Solution
To support evaluation of operators, we add `is_target` field in the `OpDesc`.
```c++
message OpDesc {
required string type = 3;
repeated Var inputs = 1;
repeated Var outputs = 2;
repeated Attr attrs = 4;
optional bool is_target = 5 [ default = false ];
};
```
To support evaluation of variables, we add [fetch_op](https://github.com/PaddlePaddle/Paddle/pull/4599).
For each variable in the `target`, we insert a `fetch_op` into the `ProgramDesc` with `variable` being
`fetch_op`'s input. Then we also set `fetch_op` is a target.
### Algorithm
If an operator needs to be run, it must fall into one of the following cases:
1. It is the target.
2. It is depended by some other ops, meaning its output is some other op's input.
The first case can be checked by `op_desc.is_traget()` . The second case can be implement as
```c++
bool HasDependentVar(const OpDesc& op_desc, const std::set<string>& dependent_vars) {
for (auto& var : op_desc.outputs()) {
for (auto& argu : var.arguments()) {
if (dependent_vars.count(argu) != 0) {
return true;
}
}
}
return false;
}
```
Then the whole algorithm can be implemented as the following [code](https://github.com/tonyyang-svail/Paddle/blob/prune_impl/paddle/framework/prune.cc).
develop/doc/_sources/design/refactorization.md.txt
浏览文件 @ f7e4b863
......@@ -177,9 +177,6 @@ REGISTER_OP(op_type, op_class, op_maker_class, grad_op_type, grad_op_class)
REGISTER_OP_WITHOUT_GRADIENT(op_type, op_class, op_maker_class)
```
### USE Macros
Make sure the registration process is executed and linked.
---
# Registration Process
1. Write an Op class and its gradient Op class, if required.
......@@ -188,8 +185,6 @@ Make sure the registration process is executed and linked.
1. Call maker class to complete `proto` and `checker`
2. Using the completed `proto` and `checker`, it will add a new key-value pair to the `OpInfoMap`
4. Invoke the `USE` macro in which the Op is used to make sure that it is linked.
---
# Backward Module (1/2)
### Create Backward Operator
......
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<div class="section" id="prune">
<span id="prune"></span><h1>Prune<a class="headerlink" href="#prune" title="Permalink to this headline"></a></h1>
<div class="section" id="motivation">
<span id="motivation"></span><h2>Motivation<a class="headerlink" href="#motivation" title="Permalink to this headline"></a></h2>
<p>We want to support running inference, training and checkpointing in one <code class="docutils literal"><span class="pre">ProgramDesc</span></code>. We implement
<code class="docutils literal"><span class="pre">void</span> <span class="pre">Prune(const</span> <span class="pre">ProgramDesc*</span> <span class="pre">input,</span> <span class="pre">ProgramDesc*</span> <span class="pre">output)</span></code> function, which takes a <code class="docutils literal"><span class="pre">ProgramDesc</span></code>
and generate a pruned <code class="docutils literal"><span class="pre">ProgramDesc</span></code>.</p>
</div>
<div class="section" id="challenge">
<span id="challenge"></span><h2>Challenge<a class="headerlink" href="#challenge" title="Permalink to this headline"></a></h2>
<p>Pruning need to support both variables and operators being evaluation targets. Consider the following
different situations.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1"># Case 1: run foward pass.</span>
<span class="n">cost_np</span> <span class="o">=</span> <span class="n">session</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">target</span><span class="o">=</span><span class="n">cost</span><span class="p">)</span>
<span class="c1"># Case 2: run backward passing.</span>
<span class="n">opts_np</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">session</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">target</span><span class="o">=</span><span class="p">[</span><span class="n">cost</span><span class="p">,</span> <span class="n">opt</span><span class="p">])</span>
<span class="c1"># Case 3: run checkpointing</span>
<span class="n">_</span> <span class="o">=</span> <span class="n">session</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">target</span><span class="o">=</span><span class="n">checkpoint</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="solution">
<span id="solution"></span><h2>Solution<a class="headerlink" href="#solution" title="Permalink to this headline"></a></h2>
<p>To support evaluation of operators, we add <code class="docutils literal"><span class="pre">is_target</span></code> field in the <code class="docutils literal"><span class="pre">OpDesc</span></code>.</p>
<div class="highlight-c++"><div class="highlight"><pre><span></span><span class="n">message</span> <span class="n">OpDesc</span> <span class="p">{</span>
<span class="n">required</span> <span class="n">string</span> <span class="n">type</span> <span class="o">=</span> <span class="mi">3</span><span class="p">;</span>
<span class="n">repeated</span> <span class="n">Var</span> <span class="n">inputs</span> <span class="o">=</span> <span class="mi">1</span><span class="p">;</span>
<span class="n">repeated</span> <span class="n">Var</span> <span class="n">outputs</span> <span class="o">=</span> <span class="mi">2</span><span class="p">;</span>
<span class="n">repeated</span> <span class="n">Attr</span> <span class="n">attrs</span> <span class="o">=</span> <span class="mi">4</span><span class="p">;</span>
<span class="n">optional</span> <span class="kt">bool</span> <span class="n">is_target</span> <span class="o">=</span> <span class="mi">5</span> <span class="p">[</span> <span class="k">default</span> <span class="o">=</span> <span class="nb">false</span> <span class="p">];</span>
<span class="p">};</span>
</pre></div>
</div>
<p>To support evaluation of variables, we add <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/pull/4599">fetch_op</a>.
For each variable in the <code class="docutils literal"><span class="pre">target</span></code>, we insert a <code class="docutils literal"><span class="pre">fetch_op</span></code> into the <code class="docutils literal"><span class="pre">ProgramDesc</span></code> with <code class="docutils literal"><span class="pre">variable</span></code> being
<code class="docutils literal"><span class="pre">fetch_op</span></code>&#8216;s input. Then we also set <code class="docutils literal"><span class="pre">fetch_op</span></code> is a target.</p>
<div class="section" id="algorithm">
<span id="algorithm"></span><h3>Algorithm<a class="headerlink" href="#algorithm" title="Permalink to this headline"></a></h3>
<p>If an operator needs to be run, it must fall into one of the following cases:</p>
<ol class="simple">
<li>It is the target.</li>
<li>It is depended by some other ops, meaning its output is some other op&#8217;s input.</li>
</ol>
<p>The first case can be checked by <code class="docutils literal"><span class="pre">op_desc.is_traget()</span></code> . The second case can be implement as</p>
<div class="highlight-c++"><div class="highlight"><pre><span></span><span class="kt">bool</span> <span class="nf">HasDependentVar</span><span class="p">(</span><span class="k">const</span> <span class="n">OpDesc</span><span class="o">&amp;</span> <span class="n">op_desc</span><span class="p">,</span> <span class="k">const</span> <span class="n">std</span><span class="o">::</span><span class="n">set</span><span class="o">&lt;</span><span class="n">string</span><span class="o">&gt;&amp;</span> <span class="n">dependent_vars</span><span class="p">)</span> <span class="p">{</span>
<span class="k">for</span> <span class="p">(</span><span class="k">auto</span><span class="o">&amp;</span> <span class="nl">var</span> <span class="p">:</span> <span class="n">op_desc</span><span class="p">.</span><span class="n">outputs</span><span class="p">())</span> <span class="p">{</span>
<span class="k">for</span> <span class="p">(</span><span class="k">auto</span><span class="o">&amp;</span> <span class="nl">argu</span> <span class="p">:</span> <span class="n">var</span><span class="p">.</span><span class="n">arguments</span><span class="p">())</span> <span class="p">{</span>
<span class="k">if</span> <span class="p">(</span><span class="n">dependent_vars</span><span class="p">.</span><span class="n">count</span><span class="p">(</span><span class="n">argu</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">)</span> <span class="p">{</span>
<span class="k">return</span> <span class="nb">true</span><span class="p">;</span>
<span class="p">}</span>
<span class="p">}</span>
<span class="p">}</span>
<span class="k">return</span> <span class="nb">false</span><span class="p">;</span>
<span class="p">}</span>
</pre></div>
</div>
<p>Then the whole algorithm can be implemented as the following <a class="reference external" href="https://github.com/tonyyang-svail/Paddle/blob/prune_impl/paddle/framework/prune.cc">code</a>.</p>
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develop/doc/design/refactorization.html
浏览文件 @ f7e4b863
......@@ -393,10 +393,6 @@
</pre></div>
</div>
</div>
<div class="section" id="use-macros">
<span id="use-macros"></span><h2>USE Macros<a class="headerlink" href="#use-macros" title="Permalink to this headline"></a></h2>
<p>Make sure the registration process is executed and linked.</p>
</div>
</div>
<hr class="docutils" />
<div class="section" id="registration-process">
......@@ -409,7 +405,6 @@
<li>Using the completed <code class="docutils literal"><span class="pre">proto</span></code> and <code class="docutils literal"><span class="pre">checker</span></code>, it will add a new key-value pair to the <code class="docutils literal"><span class="pre">OpInfoMap</span></code></li>
</ol>
</li>
<li>Invoke the <code class="docutils literal"><span class="pre">USE</span></code> macro in which the Op is used to make sure that it is linked.</li>
</ol>
</div>
<hr class="docutils" />
......
develop/doc/searchindex.js
浏览文件 @ f7e4b863
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/prune.md.txt 0 → 100644
浏览文件 @ f7e4b863
# Prune
## Motivation
We want to support running inference, training and checkpointing in one `ProgramDesc`. We implement
`void Prune(const ProgramDesc* input, ProgramDesc* output)` function, which takes a `ProgramDesc`
and generate a pruned `ProgramDesc`.
## Challenge
Pruning need to support both variables and operators being evaluation targets. Consider the following
different situations.
```python
# Case 1: run foward pass.
cost_np = session.run(target=cost)
# Case 2: run backward passing.
opts_np, _ = session.run(target=[cost, opt])
# Case 3: run checkpointing
_ = session.run(target=checkpoint)
```
## Solution
To support evaluation of operators, we add `is_target` field in the `OpDesc`.
```c++
message OpDesc {
required string type = 3;
repeated Var inputs = 1;
repeated Var outputs = 2;
repeated Attr attrs = 4;
optional bool is_target = 5 [ default = false ];
};
```
To support evaluation of variables, we add [fetch_op](https://github.com/PaddlePaddle/Paddle/pull/4599).
For each variable in the `target`, we insert a `fetch_op` into the `ProgramDesc` with `variable` being
`fetch_op`'s input. Then we also set `fetch_op` is a target.
### Algorithm
If an operator needs to be run, it must fall into one of the following cases:
1. It is the target.
2. It is depended by some other ops, meaning its output is some other op's input.
The first case can be checked by `op_desc.is_traget()` . The second case can be implement as
```c++
bool HasDependentVar(const OpDesc& op_desc, const std::set<string>& dependent_vars) {
for (auto& var : op_desc.outputs()) {
for (auto& argu : var.arguments()) {
if (dependent_vars.count(argu) != 0) {
return true;
}
}
}
return false;
}
```
Then the whole algorithm can be implemented as the following [code](https://github.com/tonyyang-svail/Paddle/blob/prune_impl/paddle/framework/prune.cc).
develop/doc_cn/_sources/design/refactorization.md.txt
浏览文件 @ f7e4b863
......@@ -177,9 +177,6 @@ REGISTER_OP(op_type, op_class, op_maker_class, grad_op_type, grad_op_class)
REGISTER_OP_WITHOUT_GRADIENT(op_type, op_class, op_maker_class)
```
### USE Macros
Make sure the registration process is executed and linked.
---
# Registration Process
1. Write an Op class and its gradient Op class, if required.
......@@ -188,8 +185,6 @@ Make sure the registration process is executed and linked.
1. Call maker class to complete `proto` and `checker`
2. Using the completed `proto` and `checker`, it will add a new key-value pair to the `OpInfoMap`
4. Invoke the `USE` macro in which the Op is used to make sure that it is linked.
---
# Backward Module (1/2)
### Create Backward Operator
......
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<li>Prune</li>
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<div class="section" id="prune">
<span id="prune"></span><h1>Prune<a class="headerlink" href="#prune" title="永久链接至标题"></a></h1>
<div class="section" id="motivation">
<span id="motivation"></span><h2>Motivation<a class="headerlink" href="#motivation" title="永久链接至标题"></a></h2>
<p>We want to support running inference, training and checkpointing in one <code class="docutils literal"><span class="pre">ProgramDesc</span></code>. We implement
<code class="docutils literal"><span class="pre">void</span> <span class="pre">Prune(const</span> <span class="pre">ProgramDesc*</span> <span class="pre">input,</span> <span class="pre">ProgramDesc*</span> <span class="pre">output)</span></code> function, which takes a <code class="docutils literal"><span class="pre">ProgramDesc</span></code>
and generate a pruned <code class="docutils literal"><span class="pre">ProgramDesc</span></code>.</p>
</div>
<div class="section" id="challenge">
<span id="challenge"></span><h2>Challenge<a class="headerlink" href="#challenge" title="永久链接至标题"></a></h2>
<p>Pruning need to support both variables and operators being evaluation targets. Consider the following
different situations.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1"># Case 1: run foward pass.</span>
<span class="n">cost_np</span> <span class="o">=</span> <span class="n">session</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">target</span><span class="o">=</span><span class="n">cost</span><span class="p">)</span>
<span class="c1"># Case 2: run backward passing.</span>
<span class="n">opts_np</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">session</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">target</span><span class="o">=</span><span class="p">[</span><span class="n">cost</span><span class="p">,</span> <span class="n">opt</span><span class="p">])</span>
<span class="c1"># Case 3: run checkpointing</span>
<span class="n">_</span> <span class="o">=</span> <span class="n">session</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">target</span><span class="o">=</span><span class="n">checkpoint</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="solution">
<span id="solution"></span><h2>Solution<a class="headerlink" href="#solution" title="永久链接至标题"></a></h2>
<p>To support evaluation of operators, we add <code class="docutils literal"><span class="pre">is_target</span></code> field in the <code class="docutils literal"><span class="pre">OpDesc</span></code>.</p>
<div class="highlight-c++"><div class="highlight"><pre><span></span><span class="n">message</span> <span class="n">OpDesc</span> <span class="p">{</span>
<span class="n">required</span> <span class="n">string</span> <span class="n">type</span> <span class="o">=</span> <span class="mi">3</span><span class="p">;</span>
<span class="n">repeated</span> <span class="n">Var</span> <span class="n">inputs</span> <span class="o">=</span> <span class="mi">1</span><span class="p">;</span>
<span class="n">repeated</span> <span class="n">Var</span> <span class="n">outputs</span> <span class="o">=</span> <span class="mi">2</span><span class="p">;</span>
<span class="n">repeated</span> <span class="n">Attr</span> <span class="n">attrs</span> <span class="o">=</span> <span class="mi">4</span><span class="p">;</span>
<span class="n">optional</span> <span class="kt">bool</span> <span class="n">is_target</span> <span class="o">=</span> <span class="mi">5</span> <span class="p">[</span> <span class="k">default</span> <span class="o">=</span> <span class="nb">false</span> <span class="p">];</span>
<span class="p">};</span>
</pre></div>
</div>
<p>To support evaluation of variables, we add <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/pull/4599">fetch_op</a>.
For each variable in the <code class="docutils literal"><span class="pre">target</span></code>, we insert a <code class="docutils literal"><span class="pre">fetch_op</span></code> into the <code class="docutils literal"><span class="pre">ProgramDesc</span></code> with <code class="docutils literal"><span class="pre">variable</span></code> being
<code class="docutils literal"><span class="pre">fetch_op</span></code>&#8216;s input. Then we also set <code class="docutils literal"><span class="pre">fetch_op</span></code> is a target.</p>
<div class="section" id="algorithm">
<span id="algorithm"></span><h3>Algorithm<a class="headerlink" href="#algorithm" title="永久链接至标题"></a></h3>
<p>If an operator needs to be run, it must fall into one of the following cases:</p>
<ol class="simple">
<li>It is the target.</li>
<li>It is depended by some other ops, meaning its output is some other op&#8217;s input.</li>
</ol>
<p>The first case can be checked by <code class="docutils literal"><span class="pre">op_desc.is_traget()</span></code> . The second case can be implement as</p>
<div class="highlight-c++"><div class="highlight"><pre><span></span><span class="kt">bool</span> <span class="nf">HasDependentVar</span><span class="p">(</span><span class="k">const</span> <span class="n">OpDesc</span><span class="o">&amp;</span> <span class="n">op_desc</span><span class="p">,</span> <span class="k">const</span> <span class="n">std</span><span class="o">::</span><span class="n">set</span><span class="o">&lt;</span><span class="n">string</span><span class="o">&gt;&amp;</span> <span class="n">dependent_vars</span><span class="p">)</span> <span class="p">{</span>
<span class="k">for</span> <span class="p">(</span><span class="k">auto</span><span class="o">&amp;</span> <span class="nl">var</span> <span class="p">:</span> <span class="n">op_desc</span><span class="p">.</span><span class="n">outputs</span><span class="p">())</span> <span class="p">{</span>
<span class="k">for</span> <span class="p">(</span><span class="k">auto</span><span class="o">&amp;</span> <span class="nl">argu</span> <span class="p">:</span> <span class="n">var</span><span class="p">.</span><span class="n">arguments</span><span class="p">())</span> <span class="p">{</span>
<span class="k">if</span> <span class="p">(</span><span class="n">dependent_vars</span><span class="p">.</span><span class="n">count</span><span class="p">(</span><span class="n">argu</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">)</span> <span class="p">{</span>
<span class="k">return</span> <span class="nb">true</span><span class="p">;</span>
<span class="p">}</span>
<span class="p">}</span>
<span class="p">}</span>
<span class="k">return</span> <span class="nb">false</span><span class="p">;</span>
<span class="p">}</span>
</pre></div>
</div>
<p>Then the whole algorithm can be implemented as the following <a class="reference external" href="https://github.com/tonyyang-svail/Paddle/blob/prune_impl/paddle/framework/prune.cc">code</a>.</p>
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......@@ -407,10 +407,6 @@
</pre></div>
</div>
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<div class="section" id="use-macros">
<span id="use-macros"></span><h2>USE Macros<a class="headerlink" href="#use-macros" title="永久链接至标题"></a></h2>
<p>Make sure the registration process is executed and linked.</p>
</div>
</div>
<hr class="docutils" />
<div class="section" id="registration-process">
......@@ -423,7 +419,6 @@
<li>Using the completed <code class="docutils literal"><span class="pre">proto</span></code> and <code class="docutils literal"><span class="pre">checker</span></code>, it will add a new key-value pair to the <code class="docutils literal"><span class="pre">OpInfoMap</span></code></li>
</ol>
</li>
<li>Invoke the <code class="docutils literal"><span class="pre">USE</span></code> macro in which the Op is used to make sure that it is linked.</li>
</ol>
</div>
<hr class="docutils" />
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