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<h1>Master 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="#overview">Overview</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#buffer-and-mfc-execution-order">Buffer and MFC Execution Order</a></li>
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<article class="bd-article">
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<section class="tex2jax_ignore mathjax_ignore" id="master-worker">
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<h1>Master Worker<a class="headerlink" href="#master-worker" title="Link to this heading">#</a></h1>
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<section id="overview">
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<h2>Overview<a class="headerlink" href="#overview" title="Link to this heading">#</a></h2>
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<p><img alt="" src="../_images/master_arch.png" /></p>
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<p>The worker architecture of AReaL consists of a single master worker coordinating multiple model workers.</p>
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<p>An RL algorithm typically contains several model function calls (MFCs) that need to be executed in a certain order. For example in PPO,</p>
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<ol class="arabic simple">
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<li><p><code class="docutils literal notranslate"><span class="pre">actor_gen</span></code> generates responses given a batch of user prompts;</p></li>
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<li><p><code class="docutils literal notranslate"><span class="pre">ref_inf</span></code> computes the log-probabilities of the tokens under the reference policy;</p></li>
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<li><p><code class="docutils literal notranslate"><span class="pre">rew_inf</span></code> computes the rewards of the responses;</p></li>
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<li><p><code class="docutils literal notranslate"><span class="pre">actor_train</span></code> updates the policy with the PPO learning objective.</p></li>
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</ol>
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<p>Here model function calls 2 and 3 depends on the output of 1. Model function call 4 depends on the outputs of 1, 2, and 3.</p>
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<p>The MFCs are coordinated by a <code class="docutils literal notranslate"><span class="pre">FunctionExecutor</span></code> instance. It creates a <code class="docutils literal notranslate"><span class="pre">ModelFunctionCall</span></code> instance for each MFC. The actual computation is performed on model workers via remote procedure call.</p>
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</section>
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<section id="buffer-and-mfc-execution-order">
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<h2>Buffer and MFC Execution Order<a class="headerlink" href="#buffer-and-mfc-execution-order" title="Link to this heading">#</a></h2>
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<p><img alt="" src="../_images/buffer_arch.png" /></p>
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<p>The master worker creates a <code class="docutils literal notranslate"><span class="pre">AsyncIOSequenceBuffer</span></code>, which is referenced by the <code class="docutils literal notranslate"><span class="pre">FunctionExecutor</span></code> and the <code class="docutils literal notranslate"><span class="pre">ModelFunctionCall</span></code>’s. The buffer is responsible for managing (meta)data and deciding the execution order of the MFCs.</p>
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<p>Each datapoint can be seen as a <code class="docutils literal notranslate"><span class="pre">dict</span></code> of tensors. For example, the keys may include <code class="docutils literal notranslate"><span class="pre">packed_prompts</span></code> and <code class="docutils literal notranslate"><span class="pre">task_ids</span></code>. Recall that some MFC may rely on the output of another. For example in PPO, the MFC <code class="docutils literal notranslate"><span class="pre">ref_inf</span></code> requires <code class="docutils literal notranslate"><span class="pre">packed_input_ids</span></code>, which is not presented initially. Instead, <code class="docutils literal notranslate"><span class="pre">packed_input_ids</span></code> appears as one of the results of the MFC <code class="docutils literal notranslate"><span class="pre">actor_gen</span></code>.</p>
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<p>The buffer keeps track of the available keys of each datapoint. Each <code class="docutils literal notranslate"><span class="pre">ModelFunctionCall</span></code>instance obtains its next batch via <code class="docutils literal notranslate"><span class="pre">self.get_batch_for_rpc</span></code>, which waits for enough datapoints with all the required keys. This means that it would not start execution until all required keys are ready. After a model function call execution, it calls <code class="docutils literal notranslate"><span class="pre">self.amend_batch</span></code> and updates the corresponding datapoints with new keys.</p>
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<p>While some keys are the results of MFCs, some are loaded from the dataset via <code class="docutils literal notranslate"><span class="pre">FunctionExecutor.load_data</span></code>. Also note that instead of the actual data, the buffer stores only metadata (data indices, keys, etc.) to reduce the cost of data transfer.</p>
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