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<h1>Model Worker</h1>
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<h2> Contents </h2>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#master-model-worker-interaction">Master-Model Worker Interaction</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#communication-protocol">Communication Protocol</a><ul class="nav section-nav flex-column">
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#request-reply-pattern">Request-Reply Pattern</a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#request-hooks">Request Hooks</a></li>
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#data-transfer-process">Data Transfer Process</a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#mfc-output-handling">MFC Output Handling</a></li>
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<section class="tex2jax_ignore mathjax_ignore" id="model-worker">
<h1>Model Worker<a class="headerlink" href="#model-worker" title="Link to this heading">#</a></h1>
<section id="master-model-worker-interaction">
<h2>Master-Model Worker Interaction<a class="headerlink" href="#master-model-worker-interaction" title="Link to this heading">#</a></h2>
<p>The master worker sends remote procedure calls (RPCs) to model workers to execute actual computations like <code class="docutils literal notranslate"><span class="pre">actor_gen</span></code> and <code class="docutils literal notranslate"><span class="pre">actor_train</span></code>. The figure below illustrates their interaction throughout an experiment:</p>
<p><img alt="" src="../_images/master-model-interaction.png" /></p>
<p>Model worker “compute” involves running a model interface with a specific backend (covered in detail later). For PPO algorithms, model workers sequentially execute:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">actor_gen</span></code>: <code class="docutils literal notranslate"><span class="pre">actor</span></code> model with SGlang backend + <code class="docutils literal notranslate"><span class="pre">PPOActorInterface.generate</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">rew_inf</span></code>: <code class="docutils literal notranslate"><span class="pre">reward</span></code> model (can be null for RLVR) + <code class="docutils literal notranslate"><span class="pre">MultiTaskRewardInterface.inference</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">actor_train</span></code>: <code class="docutils literal notranslate"><span class="pre">actor</span></code> model with Megatron backend + <code class="docutils literal notranslate"><span class="pre">PPOActorInterface.train_step</span></code></p></li>
</ul>
</section>
<section id="communication-protocol">
<h2>Communication Protocol<a class="headerlink" href="#communication-protocol" title="Link to this heading">#</a></h2>
<section id="request-reply-pattern">
<h3>Request-Reply Pattern<a class="headerlink" href="#request-reply-pattern" title="Link to this heading">#</a></h3>
<p>The master worker and model workers communicate through a <code class="docutils literal notranslate"><span class="pre">request_reply_stream</span></code> channel that handles requests and metadata responses (actual data like <code class="docutils literal notranslate"><span class="pre">input_ids</span></code> transfers through other channels).</p>
<p>Master (client) can send these requests to model workers (servers):</p>
<ul class="simple">
<li><p><strong>fetch</strong>: Worker loads local dataset data and sends metadata (e.g., sequence length) to master for buffer storage</p></li>
<li><p><strong>spec</strong>: Worker returns dataset specifications for master to calculate experiment steps</p></li>
<li><p><strong>model_config</strong>: Worker provides transformer model configuration</p></li>
<li><p><strong>clear_data_cache</strong>: Worker clears data transfer and GPU caches</p></li>
<li><p><strong>initialize</strong>: Worker initializes parameters, gradient buffers, and optimizer states</p></li>
<li><p><strong>generate/inference/train_step</strong>: Worker executes corresponding computation (note: “inference” refers to single forward pass)</p></li>
</ul>
</section>
<section id="request-hooks">
<h3>Request Hooks<a class="headerlink" href="#request-hooks" title="Link to this heading">#</a></h3>
<p>Computation requests (“generate”/”inference”/”train_step”) support pre- and post-hooks for:</p>
<ul class="simple">
<li><p>Data transfer (pre-hook)</p></li>
<li><p>Evaluation</p></li>
<li><p>Offloading</p></li>
<li><p>Parameter reallocation</p></li>
<li><p>Checkpointing (post-hooks)</p></li>
</ul>
<p>These hooks often require NCCL communication/synchronization between workers. Implementing them as dedicated hooks prevents deadlocks that could occur if these operations interleaved with other NCCL communications.</p>
</section>
<section id="request-types">
<h3>Request Types<a class="headerlink" href="#request-types" title="Link to this heading">#</a></h3>
<ul class="simple">
<li><p><strong>Blocking requests</strong>: Long-running operations requiring NCCL synchronization. Workers cant execute immediately since concurrent blocking requests may need coordinated data transfers. Master sends a “flush” request to indicate all concurrent requests have been sent.</p></li>
<li><p><strong>Non-blocking requests</strong>: Shorter operations without NCCL requirements that can execute immediately.</p></li>
</ul>
</section>
</section>
<section id="data-management">
<h2>Data Management<a class="headerlink" href="#data-management" title="Link to this heading">#</a></h2>
<section id="distributed-dataset-storage">
<h3>Distributed Dataset Storage<a class="headerlink" href="#distributed-dataset-storage" title="Link to this heading">#</a></h3>
<p>Datasets distribute across model workers without overlap. For each model:</p>
<ul class="simple">
<li><p>Processes with PP rank = -1 and TP rank = 0 serve as DP heads</p></li>
<li><p>Data stores on DP heads of the model used in the first MFC (e.g., actor model DP heads for PPO)</p></li>
</ul>
<p>During “fetch” requests:</p>
<ol class="arabic simple">
<li><p>DP head worker loads data into local buffer</p></li>
<li><p>Sends metadata to master</p></li>
<li><p>Master tracks metadata and later instructs workers which data to use for each MFC via computation request hooks</p></li>
</ol>
</section>
<section id="data-transfer-process">
<h3>Data Transfer Process<a class="headerlink" href="#data-transfer-process" title="Link to this heading">#</a></h3>
<p>For each MFC, the master:</p>
<ol class="arabic simple">
<li><p>Specifies which data to use</p></li>
<li><p>Provides data locations across workers</p></li>
<li><p>Workers redistribute data using:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">Redistributor</span></code>: Generates NCCL broadcast/gather/scatter communication plan</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">DataManager</span></code>: Executes the plan</p></li>
</ul>
</li>
</ol>
<p>After redistribution, workers with same DP rank receive identical input data.</p>
</section>
<section id="mfc-output-handling">
<h3>MFC Output Handling<a class="headerlink" href="#mfc-output-handling" title="Link to this heading">#</a></h3>
<p>Only workers with PP rank=-1 and TP rank=0 produce output data. These workers:</p>
<ol class="arabic simple">
<li><p>Store data locally</p></li>
<li><p>Notify master of data locations</p></li>
<li><p>Master generates new redistribution plans for subsequent MFCs based on this layout information</p></li>
</ol>
</section>
</section>
</section>
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