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# Design Doc: Operation Graph Based Parameter Server
## Abstract
We propose an approach to implement the parameter server. In this
approach, there is no fundamental difference between the trainer and
the parameter server: they both run subgraphs, but subgraphs of
different purposes.
## Background
The previous implementations of the parameter server does not run a
subgraph. parameter initialization, optimizer computation, network
communication and checkpointing are implemented twice on both the
trainer and the parameter server.
It would be great if we can write code once and use them on both the
trainer and the parameter server: reduces code duplication and
improves extensibility. Given that after the current refactor, we are
representing everything as a computing graph on the
trainer. Representing everything as a computing graph on the parameter
server becomes a natural extension.
## Design
### Graph Converter
The *graph converter* converts the user-defined operation (OP) graph
into subgraphs to be scheduled on different nodes with the following
steps:
1. OP placement: the OPs will be placed on different nodes according
to heuristic that minimizes estimated total computation
time. Currently we will use a simple heuristic that puts parameter
varable on parameter server workers and everything else on trainer
workers.
1. Add communication OPs to enable the communication between nodes.
We will need these OPs: *Send*, *Recv*, *Enqueue*, *Dequeue*.
Below is an example of converting the user defined graph to the
subgraphs for the trainer and the parameter server:
<img src="src/local-graph.png" width="300"/>
After converting:
<img src="src/dist-graph.png" width="700"/>
1. The parameter variable W and it's optimizer subgraph are placed on the parameter server.
1. Operators are added to the subgraphs.
- *Send* sends data to the connected *Recv* operator. The
scheduler on the receive node will only schedule *Recv* operator
to run when the *Send* operator has ran (the *Send* OP will mark
the *Recv* OP runnable automatically).
- *Enueue* enqueues the input variable, it can block until space
become available in the queue.
- *Dequeue* outputs configurable numbers of tensors from the
queue. It will block until the queue have the required number of
tensors.
### Benefits
- Model parallelism become easier to implement: it's an extension to
the trainer - parameter server approach. we already have the
communication OPs, but need to extend the graph converter's
placement functionality.
- User-defined optimizer is easier to add - user can now express it as
a subgraph.
- No more duplication logic inside the trainer and the parameter
server mentioned in the background section.
### Challenges
- It might be hard for the graph converter to cut a general graph
(without any hint for which subgraph is the optimizer). We may need
to label which subgraph inside the OP graph is the optimizer.
- It's important to balance the parameter shards of on multiple
parameter server. If a single parameter is very big (some
word-embedding, fully connected, softmax layer), we need to
automatically partition the single parameter onto different
parameter servers when possible (only element-wise optimizer depends
on the parameter variable).
### Discussion
- In the "Aync SGD" figure, the "W" variable on the parameter server
could be read and wrote concurrently, what is our locking strategy?
E.g., each variable have a lock cpp method to be invoked by every
OP, or, have a lock OP.
- Can the Enqueue OP be implemented under our current tensor design
(puts the input tensor into the queue tensor)?
- *Dequeue* OP will have variable numbers of output (depends on the
`min_count` attribute), does our current design support it? (similar
question for the *Add* OP)
### References:
[1] [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45166.pdf)
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<li>Design Doc: Operation Graph Based Parameter Server</li>
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<div class="section" id="design-doc-operation-graph-based-parameter-server">
<span id="design-doc-operation-graph-based-parameter-server"></span><h1>Design Doc: Operation Graph Based Parameter Server<a class="headerlink" href="#design-doc-operation-graph-based-parameter-server" title="Permalink to this headline"></a></h1>
<div class="section" id="abstract">
<span id="abstract"></span><h2>Abstract<a class="headerlink" href="#abstract" title="Permalink to this headline"></a></h2>
<p>We propose an approach to implement the parameter server. In this
approach, there is no fundamental difference between the trainer and
the parameter server: they both run subgraphs, but subgraphs of
different purposes.</p>
</div>
<div class="section" id="background">
<span id="background"></span><h2>Background<a class="headerlink" href="#background" title="Permalink to this headline"></a></h2>
<p>The previous implementations of the parameter server does not run a
subgraph. parameter initialization, optimizer computation, network
communication and checkpointing are implemented twice on both the
trainer and the parameter server.</p>
<p>It would be great if we can write code once and use them on both the
trainer and the parameter server: reduces code duplication and
improves extensibility. Given that after the current refactor, we are
representing everything as a computing graph on the
trainer. Representing everything as a computing graph on the parameter
server becomes a natural extension.</p>
</div>
<div class="section" id="design">
<span id="design"></span><h2>Design<a class="headerlink" href="#design" title="Permalink to this headline"></a></h2>
<div class="section" id="graph-converter">
<span id="graph-converter"></span><h3>Graph Converter<a class="headerlink" href="#graph-converter" title="Permalink to this headline"></a></h3>
<p>The <em>graph converter</em> converts the user-defined operation (OP) graph
into subgraphs to be scheduled on different nodes with the following
steps:</p>
<ol class="simple">
<li>OP placement: the OPs will be placed on different nodes according
to heuristic that minimizes estimated total computation
time. Currently we will use a simple heuristic that puts parameter
varable on parameter server workers and everything else on trainer
workers.</li>
<li>Add communication OPs to enable the communication between nodes.</li>
</ol>
<p>We will need these OPs: <em>Send</em>, <em>Recv</em>, <em>Enqueue</em>, <em>Dequeue</em>.</p>
<p>Below is an example of converting the user defined graph to the
subgraphs for the trainer and the parameter server:</p>
<p><img src="src/local-graph.png" width="300"/></p>
<p>After converting:</p>
<p><img src="src/dist-graph.png" width="700"/></p>
<ol class="simple">
<li>The parameter variable W and it&#8217;s optimizer subgraph are placed on the parameter server.</li>
<li>Operators are added to the subgraphs.<ul>
<li><em>Send</em> sends data to the connected <em>Recv</em> operator. The
scheduler on the receive node will only schedule <em>Recv</em> operator
to run when the <em>Send</em> operator has ran (the <em>Send</em> OP will mark
the <em>Recv</em> OP runnable automatically).</li>
<li><em>Enueue</em> enqueues the input variable, it can block until space
become available in the queue.</li>
<li><em>Dequeue</em> outputs configurable numbers of tensors from the
queue. It will block until the queue have the required number of
tensors.</li>
</ul>
</li>
</ol>
</div>
<div class="section" id="benefits">
<span id="benefits"></span><h3>Benefits<a class="headerlink" href="#benefits" title="Permalink to this headline"></a></h3>
<ul class="simple">
<li>Model parallelism become easier to implement: it&#8217;s an extension to
the trainer - parameter server approach. we already have the
communication OPs, but need to extend the graph converter&#8217;s
placement functionality.</li>
<li>User-defined optimizer is easier to add - user can now express it as
a subgraph.</li>
<li>No more duplication logic inside the trainer and the parameter
server mentioned in the background section.</li>
</ul>
</div>
<div class="section" id="challenges">
<span id="challenges"></span><h3>Challenges<a class="headerlink" href="#challenges" title="Permalink to this headline"></a></h3>
<ul class="simple">
<li>It might be hard for the graph converter to cut a general graph
(without any hint for which subgraph is the optimizer). We may need
to label which subgraph inside the OP graph is the optimizer.</li>
<li>It&#8217;s important to balance the parameter shards of on multiple
parameter server. If a single parameter is very big (some
word-embedding, fully connected, softmax layer), we need to
automatically partition the single parameter onto different
parameter servers when possible (only element-wise optimizer depends
on the parameter variable).</li>
</ul>
</div>
<div class="section" id="discussion">
<span id="discussion"></span><h3>Discussion<a class="headerlink" href="#discussion" title="Permalink to this headline"></a></h3>
<ul class="simple">
<li>In the &#8220;Aync SGD&#8221; figure, the &#8220;W&#8221; variable on the parameter server
could be read and wrote concurrently, what is our locking strategy?
E.g., each variable have a lock cpp method to be invoked by every
OP, or, have a lock OP.</li>
<li>Can the Enqueue OP be implemented under our current tensor design
(puts the input tensor into the queue tensor)?</li>
<li><em>Dequeue</em> OP will have variable numbers of output (depends on the
<code class="docutils literal"><span class="pre">min_count</span></code> attribute), does our current design support it? (similar
question for the <em>Add</em> OP)</li>
</ul>
</div>
<div class="section" id="references">
<span id="references"></span><h3>References:<a class="headerlink" href="#references" title="Permalink to this headline"></a></h3>
<p>[1] <a class="reference external" href="https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45166.pdf">TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems</a></p>
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浏览文件 @ 730d2b54
# Design Doc: Operation Graph Based Parameter Server
## Abstract
We propose an approach to implement the parameter server. In this
approach, there is no fundamental difference between the trainer and
the parameter server: they both run subgraphs, but subgraphs of
different purposes.
## Background
The previous implementations of the parameter server does not run a
subgraph. parameter initialization, optimizer computation, network
communication and checkpointing are implemented twice on both the
trainer and the parameter server.
It would be great if we can write code once and use them on both the
trainer and the parameter server: reduces code duplication and
improves extensibility. Given that after the current refactor, we are
representing everything as a computing graph on the
trainer. Representing everything as a computing graph on the parameter
server becomes a natural extension.
## Design
### Graph Converter
The *graph converter* converts the user-defined operation (OP) graph
into subgraphs to be scheduled on different nodes with the following
steps:
1. OP placement: the OPs will be placed on different nodes according
to heuristic that minimizes estimated total computation
time. Currently we will use a simple heuristic that puts parameter
varable on parameter server workers and everything else on trainer
workers.
1. Add communication OPs to enable the communication between nodes.
We will need these OPs: *Send*, *Recv*, *Enqueue*, *Dequeue*.
Below is an example of converting the user defined graph to the
subgraphs for the trainer and the parameter server:
<img src="src/local-graph.png" width="300"/>
After converting:
<img src="src/dist-graph.png" width="700"/>
1. The parameter variable W and it's optimizer subgraph are placed on the parameter server.
1. Operators are added to the subgraphs.
- *Send* sends data to the connected *Recv* operator. The
scheduler on the receive node will only schedule *Recv* operator
to run when the *Send* operator has ran (the *Send* OP will mark
the *Recv* OP runnable automatically).
- *Enueue* enqueues the input variable, it can block until space
become available in the queue.
- *Dequeue* outputs configurable numbers of tensors from the
queue. It will block until the queue have the required number of
tensors.
### Benefits
- Model parallelism become easier to implement: it's an extension to
the trainer - parameter server approach. we already have the
communication OPs, but need to extend the graph converter's
placement functionality.
- User-defined optimizer is easier to add - user can now express it as
a subgraph.
- No more duplication logic inside the trainer and the parameter
server mentioned in the background section.
### Challenges
- It might be hard for the graph converter to cut a general graph
(without any hint for which subgraph is the optimizer). We may need
to label which subgraph inside the OP graph is the optimizer.
- It's important to balance the parameter shards of on multiple
parameter server. If a single parameter is very big (some
word-embedding, fully connected, softmax layer), we need to
automatically partition the single parameter onto different
parameter servers when possible (only element-wise optimizer depends
on the parameter variable).
### Discussion
- In the "Aync SGD" figure, the "W" variable on the parameter server
could be read and wrote concurrently, what is our locking strategy?
E.g., each variable have a lock cpp method to be invoked by every
OP, or, have a lock OP.
- Can the Enqueue OP be implemented under our current tensor design
(puts the input tensor into the queue tensor)?
- *Dequeue* OP will have variable numbers of output (depends on the
`min_count` attribute), does our current design support it? (similar
question for the *Add* OP)
### References:
[1] [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45166.pdf)
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<div class="section" id="design-doc-operation-graph-based-parameter-server">
<span id="design-doc-operation-graph-based-parameter-server"></span><h1>Design Doc: Operation Graph Based Parameter Server<a class="headerlink" href="#design-doc-operation-graph-based-parameter-server" title="永久链接至标题"></a></h1>
<div class="section" id="abstract">
<span id="abstract"></span><h2>Abstract<a class="headerlink" href="#abstract" title="永久链接至标题"></a></h2>
<p>We propose an approach to implement the parameter server. In this
approach, there is no fundamental difference between the trainer and
the parameter server: they both run subgraphs, but subgraphs of
different purposes.</p>
</div>
<div class="section" id="background">
<span id="background"></span><h2>Background<a class="headerlink" href="#background" title="永久链接至标题"></a></h2>
<p>The previous implementations of the parameter server does not run a
subgraph. parameter initialization, optimizer computation, network
communication and checkpointing are implemented twice on both the
trainer and the parameter server.</p>
<p>It would be great if we can write code once and use them on both the
trainer and the parameter server: reduces code duplication and
improves extensibility. Given that after the current refactor, we are
representing everything as a computing graph on the
trainer. Representing everything as a computing graph on the parameter
server becomes a natural extension.</p>
</div>
<div class="section" id="design">
<span id="design"></span><h2>Design<a class="headerlink" href="#design" title="永久链接至标题"></a></h2>
<div class="section" id="graph-converter">
<span id="graph-converter"></span><h3>Graph Converter<a class="headerlink" href="#graph-converter" title="永久链接至标题"></a></h3>
<p>The <em>graph converter</em> converts the user-defined operation (OP) graph
into subgraphs to be scheduled on different nodes with the following
steps:</p>
<ol class="simple">
<li>OP placement: the OPs will be placed on different nodes according
to heuristic that minimizes estimated total computation
time. Currently we will use a simple heuristic that puts parameter
varable on parameter server workers and everything else on trainer
workers.</li>
<li>Add communication OPs to enable the communication between nodes.</li>
</ol>
<p>We will need these OPs: <em>Send</em>, <em>Recv</em>, <em>Enqueue</em>, <em>Dequeue</em>.</p>
<p>Below is an example of converting the user defined graph to the
subgraphs for the trainer and the parameter server:</p>
<p><img src="src/local-graph.png" width="300"/></p>
<p>After converting:</p>
<p><img src="src/dist-graph.png" width="700"/></p>
<ol class="simple">
<li>The parameter variable W and it&#8217;s optimizer subgraph are placed on the parameter server.</li>
<li>Operators are added to the subgraphs.<ul>
<li><em>Send</em> sends data to the connected <em>Recv</em> operator. The
scheduler on the receive node will only schedule <em>Recv</em> operator
to run when the <em>Send</em> operator has ran (the <em>Send</em> OP will mark
the <em>Recv</em> OP runnable automatically).</li>
<li><em>Enueue</em> enqueues the input variable, it can block until space
become available in the queue.</li>
<li><em>Dequeue</em> outputs configurable numbers of tensors from the
queue. It will block until the queue have the required number of
tensors.</li>
</ul>
</li>
</ol>
</div>
<div class="section" id="benefits">
<span id="benefits"></span><h3>Benefits<a class="headerlink" href="#benefits" title="永久链接至标题"></a></h3>
<ul class="simple">
<li>Model parallelism become easier to implement: it&#8217;s an extension to
the trainer - parameter server approach. we already have the
communication OPs, but need to extend the graph converter&#8217;s
placement functionality.</li>
<li>User-defined optimizer is easier to add - user can now express it as
a subgraph.</li>
<li>No more duplication logic inside the trainer and the parameter
server mentioned in the background section.</li>
</ul>
</div>
<div class="section" id="challenges">
<span id="challenges"></span><h3>Challenges<a class="headerlink" href="#challenges" title="永久链接至标题"></a></h3>
<ul class="simple">
<li>It might be hard for the graph converter to cut a general graph
(without any hint for which subgraph is the optimizer). We may need
to label which subgraph inside the OP graph is the optimizer.</li>
<li>It&#8217;s important to balance the parameter shards of on multiple
parameter server. If a single parameter is very big (some
word-embedding, fully connected, softmax layer), we need to
automatically partition the single parameter onto different
parameter servers when possible (only element-wise optimizer depends
on the parameter variable).</li>
</ul>
</div>
<div class="section" id="discussion">
<span id="discussion"></span><h3>Discussion<a class="headerlink" href="#discussion" title="永久链接至标题"></a></h3>
<ul class="simple">
<li>In the &#8220;Aync SGD&#8221; figure, the &#8220;W&#8221; variable on the parameter server
could be read and wrote concurrently, what is our locking strategy?
E.g., each variable have a lock cpp method to be invoked by every
OP, or, have a lock OP.</li>
<li>Can the Enqueue OP be implemented under our current tensor design
(puts the input tensor into the queue tensor)?</li>
<li><em>Dequeue</em> OP will have variable numbers of output (depends on the
<code class="docutils literal"><span class="pre">min_count</span></code> attribute), does our current design support it? (similar
question for the <em>Add</em> OP)</li>
</ul>
</div>
<div class="section" id="references">
<span id="references"></span><h3>References:<a class="headerlink" href="#references" title="永久链接至标题"></a></h3>
<p>[1] <a class="reference external" href="https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45166.pdf">TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems</a></p>
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