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<h1 class="title">Training Stability &amp; Debugging</h1>
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Guide to monitoring, debugging, and stabilizing training runs in axolotl
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<p>This guide covers practical techniques for monitoring training health, diagnosing instability, and resolving common failures in both supervised fine-tuning (SFT) and reinforcement learning (GRPO/EBFT) workflows.</p>
<section id="monitoring-training" class="level2">
<h2 class="anchored" data-anchor-id="monitoring-training">Monitoring Training</h2>
<section id="key-metrics-for-sft" class="level3">
<h3 class="anchored" data-anchor-id="key-metrics-for-sft">Key Metrics for SFT</h3>
<p>Every SFT run should be monitored through at least these four metrics:</p>
<table class="caption-top table">
<colgroup>
<col style="width: 19%">
<col style="width: 45%">
<col style="width: 35%">
</colgroup>
<thead>
<tr class="header">
<th>Metric</th>
<th>What It Tells You</th>
<th>Healthy Range</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td><code>train/loss</code></td>
<td>How well the model fits training data</td>
<td>Decreasing; typically 0.5–2.0 for chat fine-tuning</td>
</tr>
<tr class="even">
<td><code>eval/loss</code></td>
<td>Generalization performance</td>
<td>Tracks train loss with small gap; divergence signals overfitting</td>
</tr>
<tr class="odd">
<td><code>grad_norm</code></td>
<td>Gradient magnitude</td>
<td>0.1–10.0; spikes above 100 indicate instability</td>
</tr>
<tr class="even">
<td><code>learning_rate</code></td>
<td>Current LR from scheduler</td>
<td>Should follow expected schedule (warmup then decay)</td>
</tr>
</tbody>
</table>
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<i class="callout-icon"></i>
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<div class="callout-title-container flex-fill">
<span class="screen-reader-only">Tip</span>Set Up Logging Early
</div>
</div>
<div class="callout-body-container callout-body">
<p>Enable W&amp;B or TensorBoard from the start. Debugging a failed run without metrics is guesswork.</p>
<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb1"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">wandb_project</span><span class="kw">:</span><span class="at"> my-project</span></span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="fu">wandb_run_id</span><span class="kw">:</span><span class="co"> # optional, for resuming</span></span>
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="fu">logging_steps</span><span class="kw">:</span><span class="at"> </span><span class="dv">1</span></span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
</div>
</div>
</section>
<section id="key-metrics-for-rl-grpo" class="level3">
<h3 class="anchored" data-anchor-id="key-metrics-for-rl-grpo">Key Metrics for RL (GRPO)</h3>
<p>GRPO training logs a richer set of metrics. These are the critical ones:</p>
<table class="caption-top table">
<colgroup>
<col style="width: 24%">
<col style="width: 45%">
<col style="width: 30%">
</colgroup>
<thead>
<tr class="header">
<th>Metric</th>
<th>Healthy Range</th>
<th>Red Flag</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td><code>rewards/&lt;name&gt;/mean</code></td>
<td>&gt; 0.15 within 20 steps</td>
<td>Stays at 0 – reward function is broken or task is too hard</td>
</tr>
<tr class="even">
<td><code>reward_std</code></td>
<td>&gt; 0 on most steps</td>
<td>Always 0 – no learning signal (all completions get the same reward)</td>
</tr>
<tr class="odd">
<td><code>frac_reward_zero_std</code></td>
<td>&lt; 0.8</td>
<td>1.0 on every step – zero-advantage skip fires constantly, no gradient updates</td>
</tr>
<tr class="even">
<td><code>grad_norm</code></td>
<td>0.001–1.0</td>
<td>0.0 is acceptable occasionally (zero-adv skip); &gt; 10.0 is unstable</td>
</tr>
<tr class="odd">
<td><code>entropy</code></td>
<td>0.05–0.5</td>
<td>&lt; 0.01 suggests mode collapse; &gt; 1.0 suggests the model is not converging</td>
</tr>
<tr class="even">
<td><code>kl</code></td>
<td>0.0–0.5</td>
<td>&gt; 2.0 suggests policy has diverged too far from reference</td>
</tr>
<tr class="odd">
<td><code>sampling/sampling_logp_difference/mean</code></td>
<td>&lt; 0.1</td>
<td>&gt; 1.0 means policy has diverged far from vLLM server weights</td>
</tr>
<tr class="even">
<td><code>sampling/importance_sampling_ratio/min</code></td>
<td>&gt; 0.1</td>
<td>Near 0 indicates stale off-policy data; increase <code>vllm_sync_interval</code></td>
</tr>
<tr class="odd">
<td><code>clip_ratio/region_mean</code></td>
<td>&lt; 0.1</td>
<td>&gt; 0.3 means PPO clipping is too aggressive</td>
</tr>
<tr class="even">
<td><code>completions/mean_length</code></td>
<td>Task-dependent</td>
<td>Monotonically increasing to max length suggests reward hacking</td>
</tr>
<tr class="odd">
<td><code>completions/clipped_ratio</code></td>
<td>&lt; 0.3</td>
<td>&gt; 0.8 means most completions hit <code>max_completion_length</code> – increase it</td>
</tr>
</tbody>
</table>
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<div class="callout-header d-flex align-content-center">
<div class="callout-icon-container">
<i class="callout-icon"></i>
</div>
<div class="callout-title-container flex-fill">
<span class="screen-reader-only">Note</span>EBFT-Specific Metrics
</div>
</div>
<div class="callout-body-container callout-body">
<p>For EBFT training, also monitor <code>ebft/alignment</code> (should trend upward, healthy 0.3–0.9), <code>ebft/diversity</code> (healthy 0.01–0.1; &gt; 1.0 indicates mode collapse), and <code>ebft/cfm_loss</code> (should trend downward, &lt; 10).</p>
</div>
</div>
</section>
</section>
<section id="sft-stability" class="level2">
<h2 class="anchored" data-anchor-id="sft-stability">SFT Stability</h2>
<section id="loss-plateau" class="level3">
<h3 class="anchored" data-anchor-id="loss-plateau">Loss Plateau</h3>
<p><strong>Symptom</strong>: Loss stops decreasing early in training, well above expected values.</p>
<p><strong>Causes and fixes</strong>:</p>
<ul>
<li><strong>Learning rate too low</strong>: Increase by 2–5x. Typical ranges: full fine-tune 1e-5 to 5e-5, LoRA 1e-4 to 3e-4.</li>
<li><strong>Insufficient warmup</strong>: Set <code>warmup_steps</code> to 5–10% of total steps. Too-aggressive learning at the start can push the model into a flat region.</li>
<li><strong>Data quality</strong>: Check that labels are correctly masked. Use <code>axolotl preprocess</code> and inspect tokenized samples to confirm only the target tokens are trainable.</li>
<li><strong>Weight decay too high</strong>: Default 0.01 is usually fine. Values above 0.1 can suppress learning in LoRA.</li>
</ul>
</section>
<section id="loss-spikes" class="level3">
<h3 class="anchored" data-anchor-id="loss-spikes">Loss Spikes</h3>
<p><strong>Symptom</strong>: Loss suddenly jumps by 2–10x then (possibly) recovers.</p>
<p><strong>Causes and fixes</strong>:</p>
<ul>
<li><strong>Bad data samples</strong>: A single malformed or extremely long example can cause a spike. Enable <code>sample_packing: false</code> temporarily and check if spikes correlate with specific batches.</li>
<li><strong>Learning rate too high</strong>: Reduce by 2–5x, or increase warmup.</li>
<li><strong>Gradient accumulation mismatch</strong>: Effective batch size = <code>micro_batch_size * gradient_accumulation_steps * num_gpus</code>. Very large effective batch sizes amplify gradient noise.</li>
<li><strong>Mixed precision issues</strong>: With <code>bf16: true</code>, some operations can lose precision. If spikes are severe, try <code>fp32</code> for diagnosis.</li>
</ul>
</section>
<section id="overfitting" class="level3">
<h3 class="anchored" data-anchor-id="overfitting">Overfitting</h3>
<p><strong>Symptom</strong>: Train loss keeps decreasing but eval loss starts increasing.</p>
<p><strong>Fixes</strong>:</p>
<ul>
<li>Increase <code>val_set_size</code> (e.g., 0.05) and monitor <code>eval/loss</code>.</li>
<li>Reduce <code>num_epochs</code> or <code>max_steps</code>.</li>
<li>Increase <code>weight_decay</code> (try 0.01–0.1).</li>
<li>Use a smaller LoRA rank (<code>lora_r</code>). Typical values: 8–32.</li>
<li>Increase dropout: <code>lora_dropout: 0.05</code>.</li>
</ul>
</section>
</section>
<section id="rlgrpo-stability" class="level2">
<h2 class="anchored" data-anchor-id="rlgrpo-stability">RL/GRPO Stability</h2>
<section id="reward-never-increases" class="level3">
<h3 class="anchored" data-anchor-id="reward-never-increases">Reward Never Increases</h3>
<p>If <code>rewards/*/mean</code> stays at 0 for more than 20 steps:</p>
<ol type="1">
<li><p><strong>Test reward function standalone</strong>: Run it outside training with known inputs to verify it returns nonzero values.</p>
<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb2"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="bu">cd</span> experiments <span class="kw">&amp;&amp;</span> <span class="ex">python</span> <span class="at">-c</span> <span class="st">"import my_rewards; print(my_rewards.accuracy_reward(...))"</span></span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div></li>
<li><p><strong>Check dataset columns</strong>: The reward function receives <code>**kwargs</code> containing dataset columns. Verify the columns it needs (e.g., <code>answer</code>) are not removed by the dataset transform.</p></li>
<li><p><strong>Check completion content</strong>: Enable <code>log_completions: true</code> in the <code>trl:</code> config and inspect logged completions in W&amp;B. If completions are empty or incoherent, the model may be too weak for the task.</p></li>
<li><p><strong>Verify vLLM is serving the right model</strong>: Hit the vLLM health endpoint and confirm the model name matches your config.</p></li>
</ol>
</section>
<section id="entropy-collapse-mode-collapse" class="level3">
<h3 class="anchored" data-anchor-id="entropy-collapse-mode-collapse">Entropy Collapse (Mode Collapse)</h3>
<p><strong>Symptom</strong>: <code>entropy</code> drops below 0.01; all completions become nearly identical.</p>
<p><strong>Fixes</strong>:</p>
<ul>
<li>Increase <code>temperature</code> in generation kwargs (try 0.8–1.0).</li>
<li>Reduce learning rate.</li>
<li>Add a KL penalty term (<code>beta</code> parameter in GRPO config).</li>
<li>Check that <code>num_generations</code> is sufficient (16+ gives better advantage estimates).</li>
</ul>
</section>
<section id="is-ratio-divergence" class="level3">
<h3 class="anchored" data-anchor-id="is-ratio-divergence">IS Ratio Divergence</h3>
<p><strong>Symptom</strong>: <code>sampling/importance_sampling_ratio/min</code> drops near 0, or <code>sampling/sampling_logp_difference/mean</code> exceeds 1.0.</p>
<p>This means the policy has diverged significantly from the weights used by vLLM for generation. The importance sampling correction becomes unreliable.</p>
<p><strong>Fixes</strong>:</p>
<ul>
<li>Decrease <code>vllm_sync_interval</code> (sync weights more often).</li>
<li>Enable <code>off_policy_mask_threshold</code> (e.g., 0.5) to mask stale off-policy samples.</li>
<li>Use <code>importance_sampling_level: token</code> for finer-grained correction.</li>
</ul>
</section>
<section id="gradient-norm-instability" class="level3">
<h3 class="anchored" data-anchor-id="gradient-norm-instability">Gradient Norm Instability</h3>
<p><strong>Symptom</strong>: <code>grad_norm</code> oscillates wildly or exceeds 10.0 regularly.</p>
<p><strong>Fixes</strong>:</p>
<ul>
<li>Enable gradient clipping: <code>max_grad_norm: 1.0</code> (default in most configs).</li>
<li>Reduce learning rate.</li>
<li>Increase <code>gradient_accumulation_steps</code> to smooth out noisy batches.</li>
<li>Check for NaN issues (see next section).</li>
</ul>
</section>
</section>
<section id="nan-and-inf-handling" class="level2">
<h2 class="anchored" data-anchor-id="nan-and-inf-handling">NaN and Inf Handling</h2>
<section id="common-causes" class="level3">
<h3 class="anchored" data-anchor-id="common-causes">Common Causes</h3>
<table class="caption-top table">
<colgroup>
<col style="width: 18%">
<col style="width: 51%">
<col style="width: 29%">
</colgroup>
<thead>
<tr class="header">
<th>Cause</th>
<th>Where It Manifests</th>
<th>Detection</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>FP8 zero-scale division</td>
<td>Forward pass logits</td>
<td><code>grad_norm: nan</code>, loss becomes NaN immediately</td>
</tr>
<tr class="even">
<td>Gradient explosion</td>
<td>Backward pass</td>
<td><code>grad_norm</code> spikes to inf, then loss goes NaN</td>
</tr>
<tr class="odd">
<td>Bad data (empty sequences)</td>
<td>Logprob computation</td>
<td>NaN in specific batches only</td>
</tr>
<tr class="even">
<td>Numerical overflow in log-softmax</td>
<td>Loss computation</td>
<td>Large negative logprobs cause exp() overflow</td>
</tr>
</tbody>
</table>
</section>
<section id="fp8-specific-nan-issues" class="level3">
<h3 class="anchored" data-anchor-id="fp8-specific-nan-issues">FP8-Specific NaN Issues</h3>
<p>FP8 quantization (<code>fp8: true</code>) can produce NaN when the activation quantization kernel divides by <code>max(abs(x)) / 448</code>. If the input tensor is all zeros (e.g., padding positions), the scale becomes 0, causing division by zero.</p>
<p><strong>Fixes applied in axolotl</strong>:</p>
<ul>
<li>The <code>act_quant_kernel</code> has a zero-guard: <code>s = tl.where(s == 0, 1.0, s)</code>.</li>
<li>A safety net <code>nan_to_num(logits, nan=0.0)</code> is applied in <code>_get_per_token_logps_and_entropies</code>.</li>
<li>Embedding padding is zero-padded for FP8 compatibility.</li>
</ul>
<div class="callout callout-style-default callout-important callout-titled">
<div class="callout-header d-flex align-content-center">
<div class="callout-icon-container">
<i class="callout-icon"></i>
</div>
<div class="callout-title-container flex-fill">
<span class="screen-reader-only">Important</span>After Modifying Triton Kernels
</div>
</div>
<div class="callout-body-container callout-body">
<p>If you patch any Triton JIT kernel (e.g., the FP8 quantization kernels in transformers), you must clear the Triton cache for changes to take effect:</p>
<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb3"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="fu">rm</span> <span class="at">-rf</span> ~/.triton/cache</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
</div>
</div>
</section>
<section id="general-nan-debugging-steps" class="level3">
<h3 class="anchored" data-anchor-id="general-nan-debugging-steps">General NaN Debugging Steps</h3>
<ol type="1">
<li><p><strong>Enable anomaly detection</strong> (slow, but pinpoints the source):</p>
<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>torch.autograd.set_detect_anomaly(<span class="va">True</span>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div></li>
<li><p><strong>Check grad_norm</strong>: If it goes to NaN, the backward pass is the problem. If loss is NaN but grad_norm was fine on the previous step, the forward pass is the problem.</p></li>
<li><p><strong>Reduce to single GPU, single batch</strong>: Eliminate distributed training variables.</p></li>
<li><p><strong>Inspect data</strong>: Print the batch that triggers NaN. Look for empty sequences, extreme token IDs, or unexpected padding patterns.</p></li>
</ol>
</section>
</section>
<section id="oom-debugging" class="level2">
<h2 class="anchored" data-anchor-id="oom-debugging">OOM Debugging</h2>
<p>Out-of-memory errors are the most common training failure. Use this systematic approach, from least to most disruptive:</p>
<section id="step-1-reduce-batch-size" class="level3">
<h3 class="anchored" data-anchor-id="step-1-reduce-batch-size">Step 1: Reduce Batch Size</h3>
<p>The single highest-impact change. VRAM scales roughly linearly with batch size.</p>
<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb5"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="fu">micro_batch_size</span><span class="kw">:</span><span class="at"> </span><span class="dv">1</span><span class="co"> # Start here</span></span>
<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a><span class="fu">gradient_accumulation_steps</span><span class="kw">:</span><span class="at"> </span><span class="dv">16</span><span class="co"> # Increase to maintain effective batch size</span></span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<p>For GRPO specifically, the logits tensor for policy logprob computation can be very large. <code>batch_size * num_generations * seq_len * vocab_size</code> in bf16. For example, with <code>num_generations: 16</code> and <code>micro_batch_size: 8</code>, the logits tensor alone is:</p>
<pre><code>8 * 16 * 2048 * 151936 * 2 bytes = ~75 GB (way too large)</code></pre>
<p>Reduce <code>micro_batch_size</code> to 2–4 for GRPO.</p>
</section>
<section id="step-2-enable-gradient-checkpointing" class="level3">
<h3 class="anchored" data-anchor-id="step-2-enable-gradient-checkpointing">Step 2: Enable Gradient Checkpointing</h3>
<p>Trades compute for memory by recomputing activations during the backward pass instead of storing them.</p>
<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb7"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="fu">gradient_checkpointing</span><span class="kw">:</span><span class="at"> </span><span class="ch">true</span></span>
<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a><span class="fu">gradient_checkpointing_kwargs</span><span class="kw">:</span></span>
<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">use_reentrant</span><span class="kw">:</span><span class="at"> </span><span class="ch">false</span><span class="co"> # Recommended default</span></span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="callout callout-style-default callout-warning callout-titled">
<div class="callout-header d-flex align-content-center">
<div class="callout-icon-container">
<i class="callout-icon"></i>
</div>
<div class="callout-title-container flex-fill">
<span class="screen-reader-only">Warning</span>Reentrant Checkpointing Exceptions
</div>
</div>
<div class="callout-body-container callout-body">
<p>Some configurations require <code>use_reentrant: true</code>:</p>
<ul>
<li>DeepSpeed ZeRO-3 (non-reentrant causes <code>CheckpointError</code>)</li>
<li>EBFT strided mode with flex_attention</li>
</ul>
</div>
</div>
</section>
<section id="step-3-use-quantization" class="level3">
<h3 class="anchored" data-anchor-id="step-3-use-quantization">Step 3: Use Quantization</h3>
<p>Load the base model in reduced precision:</p>
<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb8"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="co"># 4-bit QLoRA</span></span>
<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a><span class="fu">adapter</span><span class="kw">:</span><span class="at"> qlora</span></span>
<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a><span class="fu">load_in_4bit</span><span class="kw">:</span><span class="at"> </span><span class="ch">true</span></span>
<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a><span class="co"># 8-bit</span></span>
<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a><span class="fu">load_in_8bit</span><span class="kw">:</span><span class="at"> </span><span class="ch">true</span></span>
<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a><span class="co"># FP8 (saves ~50% model VRAM, same compute speed as bf16)</span></span>
<span id="cb8-9"><a href="#cb8-9" aria-hidden="true" tabindex="-1"></a><span class="fu">fp8</span><span class="kw">:</span><span class="at"> </span><span class="ch">true</span></span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
</section>
<section id="step-4-reduce-sequence-length" class="level3">
<h3 class="anchored" data-anchor-id="step-4-reduce-sequence-length">Step 4: Reduce Sequence Length</h3>
<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb9"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="fu">sequence_len</span><span class="kw">:</span><span class="at"> </span><span class="dv">1024</span><span class="co"> # Down from 2048 or 4096</span></span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<p>For GRPO, also reduce <code>max_completion_length</code>. Memory scales quadratically with sequence length when using standard attention.</p>
</section>
<section id="step-5-use-flash-attention" class="level3">
<h3 class="anchored" data-anchor-id="step-5-use-flash-attention">Step 5: Use Flash Attention</h3>
<p>Reduces attention memory from O(n^2) to O(n):</p>
<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb10"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="fu">flash_attention</span><span class="kw">:</span><span class="at"> </span><span class="ch">true</span></span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
</section>
<section id="step-6-offload-with-deepspeed" class="level3">
<h3 class="anchored" data-anchor-id="step-6-offload-with-deepspeed">Step 6: Offload with DeepSpeed</h3>
<p>For extreme cases, offload optimizer states or parameters to CPU:</p>
<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb11"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="fu">deepspeed</span><span class="kw">:</span><span class="at"> deepspeed_configs/zero3_bf16.json</span></span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
</section>
<section id="diagnosing-the-specific-culprit" class="level3">
<h3 class="anchored" data-anchor-id="diagnosing-the-specific-culprit">Diagnosing the Specific Culprit</h3>
<p>Use the <code>profiler_steps</code> config option to capture GPU memory snapshots:</p>
<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb12"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="fu">profiler_steps</span><span class="kw">:</span><span class="at"> </span><span class="kw">[</span><span class="dv">1</span><span class="kw">,</span><span class="at"> </span><span class="dv">2</span><span class="kw">]</span></span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<p>This generates PyTorch profiler traces you can inspect to see exactly which tensor allocation caused the OOM.</p>
</section>
</section>
<section id="common-errors" class="level2">
<h2 class="anchored" data-anchor-id="common-errors">Common Errors</h2>
<table class="caption-top table">
<thead>
<tr class="header">
<th>Error Message</th>
<th>Likely Cause</th>
<th>Fix</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td><code>exitcode: -9</code></td>
<td>System RAM exhaustion</td>
<td>Reduce dataset size, <code>dataset_num_proc</code>, or number of data workers</td>
</tr>
<tr class="even">
<td><code>exitcode: -7</code> (DeepSpeed)</td>
<td>DeepSpeed version issue</td>
<td><code>pip install -U deepspeed</code></td>
</tr>
<tr class="odd">
<td><code>CUDA out of memory</code></td>
<td>GPU VRAM exhaustion</td>
<td>Follow OOM debugging steps above</td>
</tr>
<tr class="even">
<td><code>RuntimeError: NCCL communicator was aborted</code></td>
<td>GPU communication failure</td>
<td>See <a href="../docs/nccl.html">NCCL docs</a>; check <code>NCCL_DEBUG=INFO</code> output</td>
</tr>
<tr class="odd">
<td><code>ValueError: Asking to pad but the tokenizer does not have a padding token</code></td>
<td>Missing pad token</td>
<td>Add <code>special_tokens: { pad_token: "&lt;\|endoftext\|&gt;" }</code> to config</td>
</tr>
<tr class="even">
<td><code>'DummyOptim' object has no attribute 'step'</code></td>
<td>DeepSpeed on single GPU</td>
<td>Remove <code>deepspeed:</code> section from config</td>
</tr>
<tr class="odd">
<td><code>unable to load strategy X</code> then <code>None is not callable</code></td>
<td>Reward module not importable</td>
<td>Run <code>cd experiments &amp;&amp; python -c "import my_rewards"</code> to check</td>
</tr>
<tr class="even">
<td><code>generation_batch_size not divisible by num_generations</code></td>
<td>micro_batch_size too small</td>
<td>Set <code>micro_batch_size &gt;= num_generations</code> and make it divisible</td>
</tr>
<tr class="odd">
<td><code>'weight' must be 2-D</code></td>
<td>FSDP1 flattened parameters</td>
<td>Use <code>fsdp_version: 2</code> or skip <code>unwrap_model</code> when FSDP is enabled</td>
</tr>
<tr class="even">
<td><code>CheckpointError</code> (tensor count mismatch)</td>
<td>Non-reentrant checkpointing + ZeRO-3 or flex_attention</td>
<td>Set <code>use_reentrant: true</code> in <code>gradient_checkpointing_kwargs</code></td>
</tr>
<tr class="odd">
<td><code>BFloat16</code> TypeError during weight sync</td>
<td>NumPy does not support bf16</td>
<td>Fixed in axolotl’s <code>weight_serde.py</code> (auto bf16 to fp16 conversion)</td>
</tr>
<tr class="even">
<td><code>Content end boundary is before start boundary</code></td>
<td>Chat template parsing issue</td>
<td>Check <code>eos_token</code> matches template; file a GitHub issue if persistent</td>
</tr>
<tr class="odd">
<td><code>CAS service error</code> during data processing</td>
<td>HuggingFace XET issue</td>
<td>Set <code>export HF_HUB_DISABLE_XET=1</code></td>
</tr>
<tr class="even">
<td>Training hangs (multi-GPU)</td>
<td>FSDP + async prefetch deadlock</td>
<td>Set <code>async_prefetch: false</code> with FSDP</td>
</tr>
</tbody>
</table>
</section>
<section id="profiling" class="level2">
<h2 class="anchored" data-anchor-id="profiling">Profiling</h2>
<section id="pytorch-profiler" class="level3">
<h3 class="anchored" data-anchor-id="pytorch-profiler">PyTorch Profiler</h3>
<p>Axolotl supports PyTorch profiler integration via the config:</p>
<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb13"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="fu">profiler_steps</span><span class="kw">:</span><span class="at"> </span><span class="kw">[</span><span class="dv">1</span><span class="kw">,</span><span class="at"> </span><span class="dv">2</span><span class="kw">,</span><span class="at"> </span><span class="dv">3</span><span class="kw">]</span></span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<p>This captures profiler traces for the specified steps. View them in TensorBoard:</p>
<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb14"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="ex">tensorboard</span> <span class="at">--logdir</span> output_dir/runs</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<p>Or open the <code>.json</code> trace file in <code>chrome://tracing</code>.</p>
</section>
<section id="cuda-memory-snapshots" class="level3">
<h3 class="anchored" data-anchor-id="cuda-memory-snapshots">CUDA Memory Snapshots</h3>
<p>For detailed memory analysis, use PyTorch’s memory snapshot API. Add this to your training script or use it interactively:</p>
<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb15"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> torch</span>
<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a><span class="co"># Enable memory history tracking</span></span>
<span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a>torch.cuda.memory._record_memory_history()</span>
<span id="cb15-5"><a href="#cb15-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb15-6"><a href="#cb15-6" aria-hidden="true" tabindex="-1"></a><span class="co"># ... run your training step ...</span></span>
<span id="cb15-7"><a href="#cb15-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb15-8"><a href="#cb15-8" aria-hidden="true" tabindex="-1"></a><span class="co"># Save snapshot</span></span>
<span id="cb15-9"><a href="#cb15-9" aria-hidden="true" tabindex="-1"></a>torch.cuda.memory._dump_snapshot(<span class="st">"memory_snapshot.pickle"</span>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<p>Visualize with PyTorch’s memory visualizer:</p>
<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb16"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="ex">python</span> <span class="at">-m</span> torch.cuda.memory._viz memory_snapshot.pickle</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
</section>
<section id="quick-gpu-memory-check" class="level3">
<h3 class="anchored" data-anchor-id="quick-gpu-memory-check">Quick GPU Memory Check</h3>
<p>During training, monitor GPU utilization in a separate terminal:</p>
<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb17"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="ex">watch</span> <span class="at">-n</span> 1 nvidia-smi</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<p>For programmatic access within axolotl, the logged metrics <code>memory/max_alloc</code> and <code>memory/max_reserved</code> come from <code>torch.cuda.max_memory_allocated()</code> and <code>torch.cuda.max_memory_reserved()</code>. Note these report PyTorch’s view of memory, which may differ from <code>nvidia-smi</code> (see <a href="../docs/faq.html">FAQ</a>).</p>
</section>
</section>
<section id="wb-and-logging" class="level2">
<h2 class="anchored" data-anchor-id="wb-and-logging">W&amp;B and Logging</h2>
<section id="enabling-logging" class="level3">
<h3 class="anchored" data-anchor-id="enabling-logging">Enabling Logging</h3>
<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb18"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a><span class="fu">wandb_project</span><span class="kw">:</span><span class="at"> my-project</span></span>
<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a><span class="fu">wandb_entity</span><span class="kw">:</span><span class="at"> my-team</span><span class="co"> # optional</span></span>
<span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a><span class="fu">wandb_run_id</span><span class="kw">:</span><span class="at"> run-123</span><span class="co"> # optional, for resuming</span></span>
<span id="cb18-4"><a href="#cb18-4" aria-hidden="true" tabindex="-1"></a><span class="fu">wandb_name</span><span class="kw">:</span><span class="at"> experiment-name</span><span class="co"> # optional</span></span>
<span id="cb18-5"><a href="#cb18-5" aria-hidden="true" tabindex="-1"></a><span class="fu">logging_steps</span><span class="kw">:</span><span class="at"> </span><span class="dv">1</span><span class="co"> # log every step (recommended for RL)</span></span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
</section>
<section id="debug-logging" class="level3">
<h3 class="anchored" data-anchor-id="debug-logging">Debug Logging</h3>
<p>For detailed axolotl-internal debug output:</p>
<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb19"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a><span class="va">AXOLOTL_LOG_LEVEL</span><span class="op">=</span>DEBUG <span class="ex">axolotl</span> train config.yaml <span class="dv">2</span><span class="op">&gt;&amp;</span><span class="dv">1</span> <span class="kw">|</span> <span class="fu">tee</span> /tmp/training.log</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="callout callout-style-default callout-tip callout-titled">
<div class="callout-header d-flex align-content-center">
<div class="callout-icon-container">
<i class="callout-icon"></i>
</div>
<div class="callout-title-container flex-fill">
<span class="screen-reader-only">Tip</span>Always Log to a File
</div>
</div>
<div class="callout-body-container callout-body">
<p>Pipe training output to a log file so you can inspect it after the run:</p>
<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb20"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a><span class="ex">axolotl</span> train config.yaml <span class="dv">2</span><span class="op">&gt;&amp;</span><span class="dv">1</span> <span class="kw">|</span> <span class="fu">tee</span> /tmp/my_run.log</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
</div>
</div>
</section>
<section id="what-axolotl-logs" class="level3">
<h3 class="anchored" data-anchor-id="what-axolotl-logs">What Axolotl Logs</h3>
<p><strong>SFT metrics</strong> (logged every <code>logging_steps</code>):</p>
<ul>
<li><code>train/loss</code>, <code>eval/loss</code> – training and validation loss</li>
<li><code>train/grad_norm</code> – gradient L2 norm (before clipping)</li>
<li><code>train/learning_rate</code> – current learning rate</li>
<li><code>memory/max_alloc</code>, <code>memory/max_reserved</code> – peak GPU memory</li>
</ul>
<p><strong>GRPO/RL metrics</strong> (logged every step):</p>
<ul>
<li><code>rewards/&lt;name&gt;/mean</code>, <code>rewards/&lt;name&gt;/std</code> – per-reward-function statistics</li>
<li><code>reward</code>, <code>reward_std</code> – aggregated reward across all reward functions</li>
<li><code>frac_reward_zero_std</code> – fraction of prompt groups where all completions got the same reward</li>
<li><code>completions/mean_length</code>, <code>completions/min_length</code>, <code>completions/max_length</code> – completion token lengths</li>
<li><code>completions/clipped_ratio</code> – fraction of completions that hit the max length</li>
<li><code>completions/mean_terminated_length</code>, <code>completions/min_terminated_length</code>, <code>completions/max_terminated_length</code> – lengths of naturally terminated completions</li>
<li><code>kl</code> – KL divergence between policy and reference</li>
<li><code>entropy</code> – policy entropy (measure of output diversity)</li>
<li><code>clip_ratio/region_mean</code>, <code>clip_ratio/low_mean</code>, <code>clip_ratio/high_mean</code> – PPO clipping statistics</li>
<li><code>sampling/sampling_logp_difference/mean</code>, <code>sampling/sampling_logp_difference/max</code> – log-probability difference between policy and sampling distribution</li>
<li><code>sampling/importance_sampling_ratio/min</code>, <code>sampling/importance_sampling_ratio/mean</code>, <code>sampling/importance_sampling_ratio/max</code> – IS ratio statistics for off-policy correction</li>
<li><code>num_tokens</code> – total tokens processed</li>
</ul>
</section>
<section id="reading-wb-charts" class="level3">
<h3 class="anchored" data-anchor-id="reading-wb-charts">Reading W&amp;B Charts</h3>
<p>For a healthy GRPO run, expect to see:</p>
<ol type="1">
<li><strong><code>reward/mean</code></strong>: Gradual upward trend. May start near 0 and reach 0.3–0.8 depending on task difficulty. Not monotonic – fluctuations are normal.</li>
<li><strong><code>entropy</code></strong>: Gradual decrease from initial values (often 0.3–0.6) as the model becomes more confident. Should not collapse to near-zero.</li>
<li><strong><code>grad_norm</code></strong>: Mostly in the 0.001–1.0 range. Occasional 0.0 values are fine (zero-advantage skip). Persistent values above 10.0 need investigation.</li>
<li><strong><code>kl</code></strong>: Starts near 0 and grows slowly. If it shoots up rapidly, the policy is diverging from the reference.</li>
<li><strong><code>completions/mean_length</code></strong>: Should reflect the task’s natural answer length. If it steadily increases to <code>max_completion_length</code>, the model may be reward-hacking by generating longer outputs.</li>
</ol>
</section>
</section>
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