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# Design Doc: The C++ Class `Parameters`
`Parameters` is a concept we designed in Paddle V2 API. `Parameters` is a container of parameters, and make Paddle can shared parameter between topologies. We described usages of `Parameter` in [api.md](./api.md).
We used Python to implement Parameters when designing V2 API before. There are several defects for current implementation:
* We just use `memcpy` to share Parameters between topologies, but this is very inefficient.
* We did not implement share Parameters while training. We just trigger `memcpy` when start training.
It is necessary that we implement Parameters in CPP side. However, it could be a code refactoring for Paddle, because Paddle was designed for training only one topology before, i.e., each GradientMachine contains its Parameter as a data member. In current Paddle implementation, there are three concepts associated with `Parameters`:
1. `paddle::Parameter`. A `Parameters` is a container for `paddle::Parameter`.
It is evident that we should use `paddle::Parameter` when developing `Parameters`.
However, the `Parameter` class contains many functions and does not have a clear interface.
It contains `create/store Parameter`, `serialize/deserialize`, `optimize(i.e SGD)`, `randomize/zero`.
When we developing `Parameters`, we only use `create/store Parameter` functionality.
We should extract functionalities of Parameter into many classes to clean Paddle CPP implementation.
2. `paddle::GradientMachine` and its sub-classes, e.g., `paddle::MultiGradientMachine`, `paddle::NeuralNetwork`.
We should pass `Parameters` to `paddle::GradientMachine` when `forward/backward` to avoid `memcpy` between topologies.
Also, we should handle multi-GPU/CPU training, because `forward` and `backward` would perform on multi-GPUs and multi-CPUs.
`Parameters` should dispatch the parameter value to each device, and gather the parameter gradient from each device.
3. `paddle::ParameterUpdater`. The ParameterUpdater is used to update parameters in Paddle.
So `Parameters` should be used by `paddle::ParameterUpdater`, and `paddle::ParameterUpdater` should optimize `Parameters` (by SGD).
The step by step approach for implementation Parameters in Paddle C++ core is listed below. Each step should be a PR and could be merged into Paddle one by one.
1. Clean `paddle::Parameter` interface. Extract the functionalities of `paddle::Parameter` to prepare for the implementation of Parameters.
2. Implementation a `Parameters` class. It just stores the `paddle::Parameter` inside. Make `GradientMachine` uses `Parameters` as a class member.
3. Make `Parameters` support Multi-CPU and Multi-GPU training to prepare for sharing `Parameter` between topologies.
Because we need share `Parameters` between topologies, it is `Parameters`'s response to exchange Parameters between GPUs.
`GradientMachine` should not handle how to exchange Parameters because `GradientMachine` only used to train one topology and we need to support train many topologies in Paddle, i.e., there could be many GradientMachines use one `Parameters`.
* We should use a global function to exchange Parameters between GPUs, not a member function in `Parameters`. The `MultiGradientMachine` invoke this function, which uses `Parameters` as this function inputs.
* The MultiGradientMachine contains many functionalities. Extracting the Parameters exchanging logic could make MultiGradientMachine clearer and simpler.
4. Make `Parameters` as an argument for `forward/backward` function, not a data member for `GradientMachine`. For example, `forward` could be `forward(const Parameters& params, ...)` and `backward` could be `backward(Parameters* params, ...)`. After this step, Paddle could share `Parameters` between topologies.
5. `ParameterUpdater` is invoked by `GradientMachine` and `Trainer`, but it updates `Parameters`. In the end of this code refactoring, we could change `ParameterUpdater` directly uses `Parameters` to make `ParameterUpdater`'s implementation clear.
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<li>Design Doc: The C++ Class <code class="docutils literal"><span class="pre">Parameters</span></code></li>
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<div class="section" id="design-doc-the-c-class-parameters">
<span id="design-doc-the-c-class-parameters"></span><h1>Design Doc: The C++ Class <code class="docutils literal"><span class="pre">Parameters</span></code><a class="headerlink" href="#design-doc-the-c-class-parameters" title="Permalink to this headline"></a></h1>
<p><code class="docutils literal"><span class="pre">Parameters</span></code> is a concept we designed in Paddle V2 API. <code class="docutils literal"><span class="pre">Parameters</span></code> is a container of parameters, and make Paddle can shared parameter between topologies. We described usages of <code class="docutils literal"><span class="pre">Parameter</span></code> in <a class="reference internal" href="api.html"><span class="doc">api.md</span></a>.</p>
<p>We used Python to implement Parameters when designing V2 API before. There are several defects for current implementation:</p>
<ul class="simple">
<li>We just use <code class="docutils literal"><span class="pre">memcpy</span></code> to share Parameters between topologies, but this is very inefficient.</li>
<li>We did not implement share Parameters while training. We just trigger <code class="docutils literal"><span class="pre">memcpy</span></code> when start training.</li>
</ul>
<p>It is necessary that we implement Parameters in CPP side. However, it could be a code refactoring for Paddle, because Paddle was designed for training only one topology before, i.e., each GradientMachine contains its Parameter as a data member. In current Paddle implementation, there are three concepts associated with <code class="docutils literal"><span class="pre">Parameters</span></code>:</p>
<ol class="simple">
<li><code class="docutils literal"><span class="pre">paddle::Parameter</span></code>. A <code class="docutils literal"><span class="pre">Parameters</span></code> is a container for <code class="docutils literal"><span class="pre">paddle::Parameter</span></code>.
It is evident that we should use <code class="docutils literal"><span class="pre">paddle::Parameter</span></code> when developing <code class="docutils literal"><span class="pre">Parameters</span></code>.
However, the <code class="docutils literal"><span class="pre">Parameter</span></code> class contains many functions and does not have a clear interface.
It contains <code class="docutils literal"><span class="pre">create/store</span> <span class="pre">Parameter</span></code>, <code class="docutils literal"><span class="pre">serialize/deserialize</span></code>, <code class="docutils literal"><span class="pre">optimize(i.e</span> <span class="pre">SGD)</span></code>, <code class="docutils literal"><span class="pre">randomize/zero</span></code>.
When we developing <code class="docutils literal"><span class="pre">Parameters</span></code>, we only use <code class="docutils literal"><span class="pre">create/store</span> <span class="pre">Parameter</span></code> functionality.
We should extract functionalities of Parameter into many classes to clean Paddle CPP implementation.</li>
<li><code class="docutils literal"><span class="pre">paddle::GradientMachine</span></code> and its sub-classes, e.g., <code class="docutils literal"><span class="pre">paddle::MultiGradientMachine</span></code>, <code class="docutils literal"><span class="pre">paddle::NeuralNetwork</span></code>.
We should pass <code class="docutils literal"><span class="pre">Parameters</span></code> to <code class="docutils literal"><span class="pre">paddle::GradientMachine</span></code> when <code class="docutils literal"><span class="pre">forward/backward</span></code> to avoid <code class="docutils literal"><span class="pre">memcpy</span></code> between topologies.
Also, we should handle multi-GPU/CPU training, because <code class="docutils literal"><span class="pre">forward</span></code> and <code class="docutils literal"><span class="pre">backward</span></code> would perform on multi-GPUs and multi-CPUs.
<code class="docutils literal"><span class="pre">Parameters</span></code> should dispatch the parameter value to each device, and gather the parameter gradient from each device.</li>
<li><code class="docutils literal"><span class="pre">paddle::ParameterUpdater</span></code>. The ParameterUpdater is used to update parameters in Paddle.
So <code class="docutils literal"><span class="pre">Parameters</span></code> should be used by <code class="docutils literal"><span class="pre">paddle::ParameterUpdater</span></code>, and <code class="docutils literal"><span class="pre">paddle::ParameterUpdater</span></code> should optimize <code class="docutils literal"><span class="pre">Parameters</span></code> (by SGD).</li>
</ol>
<p>The step by step approach for implementation Parameters in Paddle C++ core is listed below. Each step should be a PR and could be merged into Paddle one by one.</p>
<ol class="simple">
<li>Clean <code class="docutils literal"><span class="pre">paddle::Parameter</span></code> interface. Extract the functionalities of <code class="docutils literal"><span class="pre">paddle::Parameter</span></code> to prepare for the implementation of Parameters.</li>
<li>Implementation a <code class="docutils literal"><span class="pre">Parameters</span></code> class. It just stores the <code class="docutils literal"><span class="pre">paddle::Parameter</span></code> inside. Make <code class="docutils literal"><span class="pre">GradientMachine</span></code> uses <code class="docutils literal"><span class="pre">Parameters</span></code> as a class member.</li>
<li>Make <code class="docutils literal"><span class="pre">Parameters</span></code> support Multi-CPU and Multi-GPU training to prepare for sharing <code class="docutils literal"><span class="pre">Parameter</span></code> between topologies.
Because we need share <code class="docutils literal"><span class="pre">Parameters</span></code> between topologies, it is <code class="docutils literal"><span class="pre">Parameters</span></code>&#8216;s response to exchange Parameters between GPUs.
<code class="docutils literal"><span class="pre">GradientMachine</span></code> should not handle how to exchange Parameters because <code class="docutils literal"><span class="pre">GradientMachine</span></code> only used to train one topology and we need to support train many topologies in Paddle, i.e., there could be many GradientMachines use one <code class="docutils literal"><span class="pre">Parameters</span></code>.<ul>
<li>We should use a global function to exchange Parameters between GPUs, not a member function in <code class="docutils literal"><span class="pre">Parameters</span></code>. The <code class="docutils literal"><span class="pre">MultiGradientMachine</span></code> invoke this function, which uses <code class="docutils literal"><span class="pre">Parameters</span></code> as this function inputs.</li>
<li>The MultiGradientMachine contains many functionalities. Extracting the Parameters exchanging logic could make MultiGradientMachine clearer and simpler.</li>
</ul>
</li>
<li>Make <code class="docutils literal"><span class="pre">Parameters</span></code> as an argument for <code class="docutils literal"><span class="pre">forward/backward</span></code> function, not a data member for <code class="docutils literal"><span class="pre">GradientMachine</span></code>. For example, <code class="docutils literal"><span class="pre">forward</span></code> could be <code class="docutils literal"><span class="pre">forward(const</span> <span class="pre">Parameters&amp;</span> <span class="pre">params,</span> <span class="pre">...)</span></code> and <code class="docutils literal"><span class="pre">backward</span></code> could be <code class="docutils literal"><span class="pre">backward(Parameters*</span> <span class="pre">params,</span> <span class="pre">...)</span></code>. After this step, Paddle could share <code class="docutils literal"><span class="pre">Parameters</span></code> between topologies.</li>
<li><code class="docutils literal"><span class="pre">ParameterUpdater</span></code> is invoked by <code class="docutils literal"><span class="pre">GradientMachine</span></code> and <code class="docutils literal"><span class="pre">Trainer</span></code>, but it updates <code class="docutils literal"><span class="pre">Parameters</span></code>. In the end of this code refactoring, we could change <code class="docutils literal"><span class="pre">ParameterUpdater</span></code> directly uses <code class="docutils literal"><span class="pre">Parameters</span></code> to make <code class="docutils literal"><span class="pre">ParameterUpdater</span></code>&#8216;s implementation clear.</li>
</ol>
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# Design Doc: The C++ Class `Parameters`
`Parameters` is a concept we designed in Paddle V2 API. `Parameters` is a container of parameters, and make Paddle can shared parameter between topologies. We described usages of `Parameter` in [api.md](./api.md).
We used Python to implement Parameters when designing V2 API before. There are several defects for current implementation:
* We just use `memcpy` to share Parameters between topologies, but this is very inefficient.
* We did not implement share Parameters while training. We just trigger `memcpy` when start training.
It is necessary that we implement Parameters in CPP side. However, it could be a code refactoring for Paddle, because Paddle was designed for training only one topology before, i.e., each GradientMachine contains its Parameter as a data member. In current Paddle implementation, there are three concepts associated with `Parameters`:
1. `paddle::Parameter`. A `Parameters` is a container for `paddle::Parameter`.
It is evident that we should use `paddle::Parameter` when developing `Parameters`.
However, the `Parameter` class contains many functions and does not have a clear interface.
It contains `create/store Parameter`, `serialize/deserialize`, `optimize(i.e SGD)`, `randomize/zero`.
When we developing `Parameters`, we only use `create/store Parameter` functionality.
We should extract functionalities of Parameter into many classes to clean Paddle CPP implementation.
2. `paddle::GradientMachine` and its sub-classes, e.g., `paddle::MultiGradientMachine`, `paddle::NeuralNetwork`.
We should pass `Parameters` to `paddle::GradientMachine` when `forward/backward` to avoid `memcpy` between topologies.
Also, we should handle multi-GPU/CPU training, because `forward` and `backward` would perform on multi-GPUs and multi-CPUs.
`Parameters` should dispatch the parameter value to each device, and gather the parameter gradient from each device.
3. `paddle::ParameterUpdater`. The ParameterUpdater is used to update parameters in Paddle.
So `Parameters` should be used by `paddle::ParameterUpdater`, and `paddle::ParameterUpdater` should optimize `Parameters` (by SGD).
The step by step approach for implementation Parameters in Paddle C++ core is listed below. Each step should be a PR and could be merged into Paddle one by one.
1. Clean `paddle::Parameter` interface. Extract the functionalities of `paddle::Parameter` to prepare for the implementation of Parameters.
2. Implementation a `Parameters` class. It just stores the `paddle::Parameter` inside. Make `GradientMachine` uses `Parameters` as a class member.
3. Make `Parameters` support Multi-CPU and Multi-GPU training to prepare for sharing `Parameter` between topologies.
Because we need share `Parameters` between topologies, it is `Parameters`'s response to exchange Parameters between GPUs.
`GradientMachine` should not handle how to exchange Parameters because `GradientMachine` only used to train one topology and we need to support train many topologies in Paddle, i.e., there could be many GradientMachines use one `Parameters`.
* We should use a global function to exchange Parameters between GPUs, not a member function in `Parameters`. The `MultiGradientMachine` invoke this function, which uses `Parameters` as this function inputs.
* The MultiGradientMachine contains many functionalities. Extracting the Parameters exchanging logic could make MultiGradientMachine clearer and simpler.
4. Make `Parameters` as an argument for `forward/backward` function, not a data member for `GradientMachine`. For example, `forward` could be `forward(const Parameters& params, ...)` and `backward` could be `backward(Parameters* params, ...)`. After this step, Paddle could share `Parameters` between topologies.
5. `ParameterUpdater` is invoked by `GradientMachine` and `Trainer`, but it updates `Parameters`. In the end of this code refactoring, we could change `ParameterUpdater` directly uses `Parameters` to make `ParameterUpdater`'s implementation clear.
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<li>Design Doc: The C++ Class <code class="docutils literal"><span class="pre">Parameters</span></code></li>
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<div class="section" id="design-doc-the-c-class-parameters">
<span id="design-doc-the-c-class-parameters"></span><h1>Design Doc: The C++ Class <code class="docutils literal"><span class="pre">Parameters</span></code><a class="headerlink" href="#design-doc-the-c-class-parameters" title="永久链接至标题"></a></h1>
<p><code class="docutils literal"><span class="pre">Parameters</span></code> is a concept we designed in Paddle V2 API. <code class="docutils literal"><span class="pre">Parameters</span></code> is a container of parameters, and make Paddle can shared parameter between topologies. We described usages of <code class="docutils literal"><span class="pre">Parameter</span></code> in <a class="reference internal" href="api.html"><span class="doc">api.md</span></a>.</p>
<p>We used Python to implement Parameters when designing V2 API before. There are several defects for current implementation:</p>
<ul class="simple">
<li>We just use <code class="docutils literal"><span class="pre">memcpy</span></code> to share Parameters between topologies, but this is very inefficient.</li>
<li>We did not implement share Parameters while training. We just trigger <code class="docutils literal"><span class="pre">memcpy</span></code> when start training.</li>
</ul>
<p>It is necessary that we implement Parameters in CPP side. However, it could be a code refactoring for Paddle, because Paddle was designed for training only one topology before, i.e., each GradientMachine contains its Parameter as a data member. In current Paddle implementation, there are three concepts associated with <code class="docutils literal"><span class="pre">Parameters</span></code>:</p>
<ol class="simple">
<li><code class="docutils literal"><span class="pre">paddle::Parameter</span></code>. A <code class="docutils literal"><span class="pre">Parameters</span></code> is a container for <code class="docutils literal"><span class="pre">paddle::Parameter</span></code>.
It is evident that we should use <code class="docutils literal"><span class="pre">paddle::Parameter</span></code> when developing <code class="docutils literal"><span class="pre">Parameters</span></code>.
However, the <code class="docutils literal"><span class="pre">Parameter</span></code> class contains many functions and does not have a clear interface.
It contains <code class="docutils literal"><span class="pre">create/store</span> <span class="pre">Parameter</span></code>, <code class="docutils literal"><span class="pre">serialize/deserialize</span></code>, <code class="docutils literal"><span class="pre">optimize(i.e</span> <span class="pre">SGD)</span></code>, <code class="docutils literal"><span class="pre">randomize/zero</span></code>.
When we developing <code class="docutils literal"><span class="pre">Parameters</span></code>, we only use <code class="docutils literal"><span class="pre">create/store</span> <span class="pre">Parameter</span></code> functionality.
We should extract functionalities of Parameter into many classes to clean Paddle CPP implementation.</li>
<li><code class="docutils literal"><span class="pre">paddle::GradientMachine</span></code> and its sub-classes, e.g., <code class="docutils literal"><span class="pre">paddle::MultiGradientMachine</span></code>, <code class="docutils literal"><span class="pre">paddle::NeuralNetwork</span></code>.
We should pass <code class="docutils literal"><span class="pre">Parameters</span></code> to <code class="docutils literal"><span class="pre">paddle::GradientMachine</span></code> when <code class="docutils literal"><span class="pre">forward/backward</span></code> to avoid <code class="docutils literal"><span class="pre">memcpy</span></code> between topologies.
Also, we should handle multi-GPU/CPU training, because <code class="docutils literal"><span class="pre">forward</span></code> and <code class="docutils literal"><span class="pre">backward</span></code> would perform on multi-GPUs and multi-CPUs.
<code class="docutils literal"><span class="pre">Parameters</span></code> should dispatch the parameter value to each device, and gather the parameter gradient from each device.</li>
<li><code class="docutils literal"><span class="pre">paddle::ParameterUpdater</span></code>. The ParameterUpdater is used to update parameters in Paddle.
So <code class="docutils literal"><span class="pre">Parameters</span></code> should be used by <code class="docutils literal"><span class="pre">paddle::ParameterUpdater</span></code>, and <code class="docutils literal"><span class="pre">paddle::ParameterUpdater</span></code> should optimize <code class="docutils literal"><span class="pre">Parameters</span></code> (by SGD).</li>
</ol>
<p>The step by step approach for implementation Parameters in Paddle C++ core is listed below. Each step should be a PR and could be merged into Paddle one by one.</p>
<ol class="simple">
<li>Clean <code class="docutils literal"><span class="pre">paddle::Parameter</span></code> interface. Extract the functionalities of <code class="docutils literal"><span class="pre">paddle::Parameter</span></code> to prepare for the implementation of Parameters.</li>
<li>Implementation a <code class="docutils literal"><span class="pre">Parameters</span></code> class. It just stores the <code class="docutils literal"><span class="pre">paddle::Parameter</span></code> inside. Make <code class="docutils literal"><span class="pre">GradientMachine</span></code> uses <code class="docutils literal"><span class="pre">Parameters</span></code> as a class member.</li>
<li>Make <code class="docutils literal"><span class="pre">Parameters</span></code> support Multi-CPU and Multi-GPU training to prepare for sharing <code class="docutils literal"><span class="pre">Parameter</span></code> between topologies.
Because we need share <code class="docutils literal"><span class="pre">Parameters</span></code> between topologies, it is <code class="docutils literal"><span class="pre">Parameters</span></code>&#8216;s response to exchange Parameters between GPUs.
<code class="docutils literal"><span class="pre">GradientMachine</span></code> should not handle how to exchange Parameters because <code class="docutils literal"><span class="pre">GradientMachine</span></code> only used to train one topology and we need to support train many topologies in Paddle, i.e., there could be many GradientMachines use one <code class="docutils literal"><span class="pre">Parameters</span></code>.<ul>
<li>We should use a global function to exchange Parameters between GPUs, not a member function in <code class="docutils literal"><span class="pre">Parameters</span></code>. The <code class="docutils literal"><span class="pre">MultiGradientMachine</span></code> invoke this function, which uses <code class="docutils literal"><span class="pre">Parameters</span></code> as this function inputs.</li>
<li>The MultiGradientMachine contains many functionalities. Extracting the Parameters exchanging logic could make MultiGradientMachine clearer and simpler.</li>
</ul>
</li>
<li>Make <code class="docutils literal"><span class="pre">Parameters</span></code> as an argument for <code class="docutils literal"><span class="pre">forward/backward</span></code> function, not a data member for <code class="docutils literal"><span class="pre">GradientMachine</span></code>. For example, <code class="docutils literal"><span class="pre">forward</span></code> could be <code class="docutils literal"><span class="pre">forward(const</span> <span class="pre">Parameters&amp;</span> <span class="pre">params,</span> <span class="pre">...)</span></code> and <code class="docutils literal"><span class="pre">backward</span></code> could be <code class="docutils literal"><span class="pre">backward(Parameters*</span> <span class="pre">params,</span> <span class="pre">...)</span></code>. After this step, Paddle could share <code class="docutils literal"><span class="pre">Parameters</span></code> between topologies.</li>
<li><code class="docutils literal"><span class="pre">ParameterUpdater</span></code> is invoked by <code class="docutils literal"><span class="pre">GradientMachine</span></code> and <code class="docutils literal"><span class="pre">Trainer</span></code>, but it updates <code class="docutils literal"><span class="pre">Parameters</span></code>. In the end of this code refactoring, we could change <code class="docutils literal"><span class="pre">ParameterUpdater</span></code> directly uses <code class="docutils literal"><span class="pre">Parameters</span></code> to make <code class="docutils literal"><span class="pre">ParameterUpdater</span></code>&#8216;s implementation clear.</li>
</ol>
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