1252 lines
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HTML
1252 lines
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<li><a href="#axolotl.utils.schedulers" id="toc-axolotl.utils.schedulers" class="nav-link active" data-scroll-target="#axolotl.utils.schedulers">utils.schedulers</a>
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<section id="axolotl.utils.schedulers" class="level1">
|
||
<h1>utils.schedulers</h1>
|
||
<p><code>utils.schedulers</code></p>
|
||
<p>Module for custom LRScheduler class</p>
|
||
<section id="classes" class="level2">
|
||
<h2 class="anchored" data-anchor-id="classes">Classes</h2>
|
||
<table class="caption-top table">
|
||
<thead>
|
||
<tr class="header">
|
||
<th>Name</th>
|
||
<th>Description</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr class="odd">
|
||
<td><a href="#axolotl.utils.schedulers.InterpolatingLogScheduler">InterpolatingLogScheduler</a></td>
|
||
<td>A scheduler that interpolates learning rates in a logarithmic fashion</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td><a href="#axolotl.utils.schedulers.JaggedLRRestartScheduler">JaggedLRRestartScheduler</a></td>
|
||
<td>Wraps another scheduler to apply per-lora-restart learning rate warmups.</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td><a href="#axolotl.utils.schedulers.RexLR">RexLR</a></td>
|
||
<td>Reflected Exponential (REX) learning rate scheduler.</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<section id="axolotl.utils.schedulers.InterpolatingLogScheduler" class="level3">
|
||
<h3 class="anchored" data-anchor-id="axolotl.utils.schedulers.InterpolatingLogScheduler">InterpolatingLogScheduler</h3>
|
||
<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a>utils.schedulers.InterpolatingLogScheduler(</span>
|
||
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a> optimizer,</span>
|
||
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a> num_steps,</span>
|
||
<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a> min_lr,</span>
|
||
<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a> max_lr,</span>
|
||
<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a> last_epoch<span class="op">=-</span><span class="dv">1</span>,</span>
|
||
<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
|
||
<p>A scheduler that interpolates learning rates in a logarithmic fashion</p>
|
||
</section>
|
||
<section id="axolotl.utils.schedulers.JaggedLRRestartScheduler" class="level3">
|
||
<h3 class="anchored" data-anchor-id="axolotl.utils.schedulers.JaggedLRRestartScheduler">JaggedLRRestartScheduler</h3>
|
||
<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>utils.schedulers.JaggedLRRestartScheduler(</span>
|
||
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a> optimizer,</span>
|
||
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a> inner_schedule,</span>
|
||
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a> jagged_restart_steps,</span>
|
||
<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a> jagged_restart_warmup_steps,</span>
|
||
<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a> jagged_restart_anneal_steps<span class="op">=</span><span class="dv">1</span>,</span>
|
||
<span id="cb2-7"><a href="#cb2-7" aria-hidden="true" tabindex="-1"></a> min_lr_scale<span class="op">=</span><span class="fl">0.001</span>,</span>
|
||
<span id="cb2-8"><a href="#cb2-8" aria-hidden="true" tabindex="-1"></a>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
|
||
<p>Wraps another scheduler to apply per-lora-restart learning rate warmups.</p>
|
||
</section>
|
||
<section id="axolotl.utils.schedulers.RexLR" class="level3">
|
||
<h3 class="anchored" data-anchor-id="axolotl.utils.schedulers.RexLR">RexLR</h3>
|
||
<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>utils.schedulers.RexLR(</span>
|
||
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a> optimizer,</span>
|
||
<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a> max_lr,</span>
|
||
<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a> min_lr,</span>
|
||
<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a> total_steps<span class="op">=</span><span class="dv">0</span>,</span>
|
||
<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a> num_warmup_steps<span class="op">=</span><span class="dv">0</span>,</span>
|
||
<span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a> last_step<span class="op">=</span><span class="dv">0</span>,</span>
|
||
<span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
|
||
<p>Reflected Exponential (REX) learning rate scheduler.</p>
|
||
<ul>
|
||
<li>Original implementation: https://github.com/IvanVassi/REX_LR</li>
|
||
<li>Original license: Apache 2.0</li>
|
||
<li>Based on: https://arxiv.org/abs/2107.04197</li>
|
||
</ul>
|
||
<section id="parameters" class="level4 doc-section doc-section-parameters">
|
||
<h4 class="doc-section doc-section-parameters anchored" data-anchor-id="parameters">Parameters</h4>
|
||
<table class="caption-top table">
|
||
<colgroup>
|
||
<col style="width: 17%">
|
||
<col style="width: 22%">
|
||
<col style="width: 48%">
|
||
<col style="width: 11%">
|
||
</colgroup>
|
||
<thead>
|
||
<tr class="header">
|
||
<th>Name</th>
|
||
<th>Type</th>
|
||
<th>Description</th>
|
||
<th>Default</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr class="odd">
|
||
<td>optimizer</td>
|
||
<td>torch.optim.Optimizer</td>
|
||
<td>The optimizer to schedule the learning rate for.</td>
|
||
<td><em>required</em></td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>max_lr</td>
|
||
<td>float</td>
|
||
<td>The maximum learning rate.</td>
|
||
<td><em>required</em></td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>min_lr</td>
|
||
<td>float</td>
|
||
<td>The minimum learning rate.</td>
|
||
<td><em>required</em></td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>total_steps</td>
|
||
<td>int</td>
|
||
<td>The total number of training steps.</td>
|
||
<td><code>0</code></td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>num_warmup_steps</td>
|
||
<td>int</td>
|
||
<td>The number of warmup steps.</td>
|
||
<td><code>0</code></td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>last_step</td>
|
||
<td>int</td>
|
||
<td>The index of last step.</td>
|
||
<td><code>0</code></td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</section>
|
||
</section>
|
||
</section>
|
||
<section id="functions" class="level2">
|
||
<h2 class="anchored" data-anchor-id="functions">Functions</h2>
|
||
<table class="caption-top table">
|
||
<thead>
|
||
<tr class="header">
|
||
<th>Name</th>
|
||
<th>Description</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr class="odd">
|
||
<td><a href="#axolotl.utils.schedulers.get_cosine_schedule_with_min_lr">get_cosine_schedule_with_min_lr</a></td>
|
||
<td></td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td><a href="#axolotl.utils.schedulers.get_cosine_schedule_with_quadratic_warmup">get_cosine_schedule_with_quadratic_warmup</a></td>
|
||
<td>Create a schedule with a learning rate that decreases following the values of the cosine function between the</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td><a href="#axolotl.utils.schedulers.get_cosine_schedule_with_warmup_decay_constant">get_cosine_schedule_with_warmup_decay_constant</a></td>
|
||
<td>Implementation of Continual Pre-Training of Large Language Models: How to (re)warm your model? (https://arxiv.org/pdf/2308.04014.pdf)</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<section id="axolotl.utils.schedulers.get_cosine_schedule_with_min_lr" class="level3">
|
||
<h3 class="anchored" data-anchor-id="axolotl.utils.schedulers.get_cosine_schedule_with_min_lr">get_cosine_schedule_with_min_lr</h3>
|
||
<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>utils.schedulers.get_cosine_schedule_with_min_lr(</span>
|
||
<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a> optimizer,</span>
|
||
<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a> num_warmup_steps,</span>
|
||
<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a> num_training_steps,</span>
|
||
<span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a> min_lr_ratio<span class="op">=</span><span class="fl">0.0</span>,</span>
|
||
<span id="cb4-6"><a href="#cb4-6" aria-hidden="true" tabindex="-1"></a>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
|
||
<section id="create-a-learning-rate-schedule-which-has" class="level4 doc-section doc-section-create-a-learning-rate-schedule-which-has">
|
||
<h4 class="doc-section doc-section-create-a-learning-rate-schedule-which-has anchored" data-anchor-id="create-a-learning-rate-schedule-which-has">Create a learning rate schedule which has</h4>
|
||
<ul>
|
||
<li>linear warmup from 0 -> <code>max_lr</code> over <code>num_warmup_steps</code></li>
|
||
<li>cosine learning rate annealing from <code>max_lr</code> -> <code>min_lr</code> over <code>num_training_steps</code></li>
|
||
</ul>
|
||
</section>
|
||
</section>
|
||
<section id="axolotl.utils.schedulers.get_cosine_schedule_with_quadratic_warmup" class="level3">
|
||
<h3 class="anchored" data-anchor-id="axolotl.utils.schedulers.get_cosine_schedule_with_quadratic_warmup">get_cosine_schedule_with_quadratic_warmup</h3>
|
||
<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>utils.schedulers.get_cosine_schedule_with_quadratic_warmup(</span>
|
||
<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a> optimizer,</span>
|
||
<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a> num_warmup_steps,</span>
|
||
<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a> num_training_steps,</span>
|
||
<span id="cb5-5"><a href="#cb5-5" aria-hidden="true" tabindex="-1"></a> num_cycles<span class="op">=</span><span class="fl">0.5</span>,</span>
|
||
<span id="cb5-6"><a href="#cb5-6" aria-hidden="true" tabindex="-1"></a> last_epoch<span class="op">=-</span><span class="dv">1</span>,</span>
|
||
<span id="cb5-7"><a href="#cb5-7" aria-hidden="true" tabindex="-1"></a>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
|
||
<p>Create a schedule with a learning rate that decreases following the values of the cosine function between the
|
||
initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the
|
||
initial lr set in the optimizer.</p>
|
||
<section id="parameters-1" class="level4 doc-section doc-section-parameters">
|
||
<h4 class="doc-section doc-section-parameters anchored" data-anchor-id="parameters-1">Parameters</h4>
|
||
<table class="caption-top table">
|
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<colgroup>
|
||
<col style="width: 10%">
|
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<col style="width: 19%">
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|
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|
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|
||
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|
||
</thead>
|
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<tbody>
|
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<tr class="odd">
|
||
<td>optimizer</td>
|
||
<td>[<code>~torch.optim.Optimizer</code>]</td>
|
||
<td>The optimizer for which to schedule the learning rate.</td>
|
||
<td><em>required</em></td>
|
||
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|
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<tr class="even">
|
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<td>num_warmup_steps</td>
|
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<td><code>int</code></td>
|
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|
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<td><em>required</em></td>
|
||
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|
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<tr class="odd">
|
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<td>num_training_steps</td>
|
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|
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|
||
<td><em>required</em></td>
|
||
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|
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<tr class="even">
|
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<td>num_cycles</td>
|
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<td><code>float</code>, <em>optional</em>, defaults to 0.5</td>
|
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<td>The number of waves in the cosine schedule (the defaults is to just decrease from the max value to 0 following a half-cosine).</td>
|
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<td><code>0.5</code></td>
|
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|
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<tr class="odd">
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<td>last_epoch</td>
|
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|
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<td>The index of the last epoch when resuming training.</td>
|
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<td><code>-1</code></td>
|
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</tr>
|
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</tbody>
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</table>
|
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</section>
|
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<section id="return" class="level4 doc-section doc-section-return">
|
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<h4 class="doc-section doc-section-return anchored" data-anchor-id="return">Return</h4>
|
||
<p><code>torch.optim.lr_scheduler.LambdaLR</code> with the appropriate schedule.</p>
|
||
</section>
|
||
</section>
|
||
<section id="axolotl.utils.schedulers.get_cosine_schedule_with_warmup_decay_constant" class="level3">
|
||
<h3 class="anchored" data-anchor-id="axolotl.utils.schedulers.get_cosine_schedule_with_warmup_decay_constant">get_cosine_schedule_with_warmup_decay_constant</h3>
|
||
<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>utils.schedulers.get_cosine_schedule_with_warmup_decay_constant(</span>
|
||
<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a> optimizer,</span>
|
||
<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a> num_warmup_steps,</span>
|
||
<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a> num_training_steps,</span>
|
||
<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a> constant_lr_ratio,</span>
|
||
<span id="cb6-6"><a href="#cb6-6" aria-hidden="true" tabindex="-1"></a> min_lr_ratio,</span>
|
||
<span id="cb6-7"><a href="#cb6-7" aria-hidden="true" tabindex="-1"></a> num_cycles<span class="op">=</span><span class="fl">0.5</span>,</span>
|
||
<span id="cb6-8"><a href="#cb6-8" aria-hidden="true" tabindex="-1"></a> last_epoch<span class="op">=-</span><span class="dv">1</span>,</span>
|
||
<span id="cb6-9"><a href="#cb6-9" aria-hidden="true" tabindex="-1"></a>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
|
||
<p>Implementation of Continual Pre-Training of Large Language Models: How to (re)warm your model? (https://arxiv.org/pdf/2308.04014.pdf)
|
||
Create a schedule with a learning rate that decreases following the values of the cosine function between the
|
||
initial lr set in the optimizer to min_lr_ratio until num_training_steps * constant_lr_ratio, after constant_rate returns constant value of min_rate
|
||
, after a warmup period during which it increases linearly between 0 and the initial lr set in the optimizer.</p>
|
||
<section id="parameters-2" class="level4 doc-section doc-section-parameters">
|
||
<h4 class="doc-section doc-section-parameters anchored" data-anchor-id="parameters-2">Parameters</h4>
|
||
<table class="caption-top table">
|
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<colgroup>
|
||
<col style="width: 10%">
|
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<col style="width: 19%">
|
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<col style="width: 64%">
|
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<col style="width: 6%">
|
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</colgroup>
|
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<thead>
|
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<tr class="header">
|
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<th>Name</th>
|
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<th>Type</th>
|
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<th>Description</th>
|
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<th>Default</th>
|
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</tr>
|
||
</thead>
|
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<tbody>
|
||
<tr class="odd">
|
||
<td>optimizer</td>
|
||
<td>[<code>~torch.optim.Optimizer</code>]</td>
|
||
<td>The optimizer for which to schedule the learning rate.</td>
|
||
<td><em>required</em></td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>num_warmup_steps</td>
|
||
<td><code>int</code></td>
|
||
<td>The number of steps for the warmup phase.</td>
|
||
<td><em>required</em></td>
|
||
</tr>
|
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<tr class="odd">
|
||
<td>num_training_steps</td>
|
||
<td><code>int</code></td>
|
||
<td>The total number of training steps.</td>
|
||
<td><em>required</em></td>
|
||
</tr>
|
||
<tr class="even">
|
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<td>constant_lr_ratio</td>
|
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<td>float</td>
|
||
<td>(<code>float</code>): The ratio of num_training_steps to decrease by cosine function.</td>
|
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<td><em>required</em></td>
|
||
</tr>
|
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<tr class="odd">
|
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<td>min_lr_ratio</td>
|
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<td>float</td>
|
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<td>(<code>float): The ratio of maximum learning rate for cosine function to decay to minimum learning rate. | _required_ | | num_cycles |</code>float<code>, *optional*, defaults to 0.5 | The number of waves in the cosine schedule (the defaults is to just decrease from the max value to 0 following a half-cosine). |</code>0.5<code>| | last_epoch |</code>int<code>, *optional*, defaults to -1 | The index of the last epoch when resuming training. |</code>-1`</td>
|
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<td></td>
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</tr>
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</tbody>
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</table>
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</section>
|
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<section id="return-1" class="level4 doc-section doc-section-return">
|
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<h4 class="doc-section doc-section-return anchored" data-anchor-id="return-1">Return</h4>
|
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<p><code>torch.optim.lr_scheduler.LambdaLR</code> with the appropriate schedule.</p>
|
||
|
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|
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</section>
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</section>
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||
height = bottom - top;
|
||
}
|
||
if (top !== null && height !== null && parent !== null) {
|
||
// cook up a div (if necessary) and position it
|
||
let div = window.document.getElementById("code-annotation-line-highlight");
|
||
if (div === null) {
|
||
div = window.document.createElement("div");
|
||
div.setAttribute("id", "code-annotation-line-highlight");
|
||
div.style.position = 'absolute';
|
||
parent.appendChild(div);
|
||
}
|
||
div.style.top = top - 2 + "px";
|
||
div.style.height = height + 4 + "px";
|
||
div.style.left = 0;
|
||
let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter");
|
||
if (gutterDiv === null) {
|
||
gutterDiv = window.document.createElement("div");
|
||
gutterDiv.setAttribute("id", "code-annotation-line-highlight-gutter");
|
||
gutterDiv.style.position = 'absolute';
|
||
const codeCell = window.document.getElementById(targetCell);
|
||
const gutter = codeCell.querySelector('.code-annotation-gutter');
|
||
gutter.appendChild(gutterDiv);
|
||
}
|
||
gutterDiv.style.top = top - 2 + "px";
|
||
gutterDiv.style.height = height + 4 + "px";
|
||
}
|
||
selectedAnnoteEl = annoteEl;
|
||
}
|
||
};
|
||
const unselectCodeLines = () => {
|
||
const elementsIds = ["code-annotation-line-highlight", "code-annotation-line-highlight-gutter"];
|
||
elementsIds.forEach((elId) => {
|
||
const div = window.document.getElementById(elId);
|
||
if (div) {
|
||
div.remove();
|
||
}
|
||
});
|
||
selectedAnnoteEl = undefined;
|
||
};
|
||
// Handle positioning of the toggle
|
||
window.addEventListener(
|
||
"resize",
|
||
throttle(() => {
|
||
elRect = undefined;
|
||
if (selectedAnnoteEl) {
|
||
selectCodeLines(selectedAnnoteEl);
|
||
}
|
||
}, 10)
|
||
);
|
||
function throttle(fn, ms) {
|
||
let throttle = false;
|
||
let timer;
|
||
return (...args) => {
|
||
if(!throttle) { // first call gets through
|
||
fn.apply(this, args);
|
||
throttle = true;
|
||
} else { // all the others get throttled
|
||
if(timer) clearTimeout(timer); // cancel #2
|
||
timer = setTimeout(() => {
|
||
fn.apply(this, args);
|
||
timer = throttle = false;
|
||
}, ms);
|
||
}
|
||
};
|
||
}
|
||
// Attach click handler to the DT
|
||
const annoteDls = window.document.querySelectorAll('dt[data-target-cell]');
|
||
for (const annoteDlNode of annoteDls) {
|
||
annoteDlNode.addEventListener('click', (event) => {
|
||
const clickedEl = event.target;
|
||
if (clickedEl !== selectedAnnoteEl) {
|
||
unselectCodeLines();
|
||
const activeEl = window.document.querySelector('dt[data-target-cell].code-annotation-active');
|
||
if (activeEl) {
|
||
activeEl.classList.remove('code-annotation-active');
|
||
}
|
||
selectCodeLines(clickedEl);
|
||
clickedEl.classList.add('code-annotation-active');
|
||
} else {
|
||
// Unselect the line
|
||
unselectCodeLines();
|
||
clickedEl.classList.remove('code-annotation-active');
|
||
}
|
||
});
|
||
}
|
||
const findCites = (el) => {
|
||
const parentEl = el.parentElement;
|
||
if (parentEl) {
|
||
const cites = parentEl.dataset.cites;
|
||
if (cites) {
|
||
return {
|
||
el,
|
||
cites: cites.split(' ')
|
||
};
|
||
} else {
|
||
return findCites(el.parentElement)
|
||
}
|
||
} else {
|
||
return undefined;
|
||
}
|
||
};
|
||
var bibliorefs = window.document.querySelectorAll('a[role="doc-biblioref"]');
|
||
for (var i=0; i<bibliorefs.length; i++) {
|
||
const ref = bibliorefs[i];
|
||
const citeInfo = findCites(ref);
|
||
if (citeInfo) {
|
||
tippyHover(citeInfo.el, function() {
|
||
var popup = window.document.createElement('div');
|
||
citeInfo.cites.forEach(function(cite) {
|
||
var citeDiv = window.document.createElement('div');
|
||
citeDiv.classList.add('hanging-indent');
|
||
citeDiv.classList.add('csl-entry');
|
||
var biblioDiv = window.document.getElementById('ref-' + cite);
|
||
if (biblioDiv) {
|
||
citeDiv.innerHTML = biblioDiv.innerHTML;
|
||
}
|
||
popup.appendChild(citeDiv);
|
||
});
|
||
return popup.innerHTML;
|
||
});
|
||
}
|
||
}
|
||
});
|
||
</script>
|
||
</div> <!-- /content -->
|
||
|
||
|
||
|
||
|
||
</body></html> |