After the Crash: Assessing Hidden State Integrity on a Migrated Disk

Introduction

In the high-stakes world of large-scale ML training, infrastructure failures are not a matter of if but when. Message 4201 captures a critical moment in an EAGLE-3 training pipeline for the Kimi-K2.5 model: the aftermath of a catastrophic VM crash triggered by a Ceph cluster running out of disk space. The message is deceptively brief — a single bash command and its output — but it represents the pivot point between disaster and recovery. This article unpacks the reasoning, context, and implications of that one message.

The Message

Here is the message in full:

/data is on the new 12T NVMe now. Model is fine on /shared. Let me check the hidden states integrity: [bash] ssh -o ConnectTimeout=10 root@10.1.230.174 'ls /data/eagle3/synth_100k/hidden_states/ 2>/dev/null; echo "==="; du -sh /data/eagle3/synth_100k/hidden_states/ 2>/dev/null; echo "==="; cat /data/eagle3/synth_100k/merged/stats.json 2>/dev/null | head -5' rows_0-2000 rows_10000-12000 rows_12000-14000 rows_14000-16000 rows_16000-18000 rows_18000-20000 rows_2000-4000 rows_4000-6000 rows_6000-8000 rows_8000-10000 === 2.3T /data/eagle3/synth_100k/hidden_states/ === { "total_records": 37312, "total_tokens": 87793206, "max_seq_len": 8192, "seed": 42,

WHY This Message Was Written: The Context of Catastrophic Failure

To understand why this message exists, we must trace back through the preceding conversation. The EAGLE-3 training pipeline had been running for days. The team had completed data generation via the OpenRouter API, merged datasets, and launched hidden state extraction — a process that sends 37,312 prompts through the Kimi-K2.5 model on 8 GPUs, captures the internal hidden states at every layer, and dumps them to disk for training a speculative decoding drafter. This extraction had reached approximately 49% completion (18,420 out of 37,312 samples) when disaster struck.

The user reported that the Ceph cluster underlying the VM had "ran out of space quite catastrophically." This is a severe failure mode: when a distributed storage backend runs out of capacity, writes can hang indefinitely, processes enter uninterruptible sleep (state Dl), and the entire system becomes unresponsive. The user was forced to kill the VM, attach a new 15TB NVMe disk directly to the host (bypassing Ceph entirely), and migrate the /data volume to this new physical drive.

Upon container restart, the assistant discovered that a vLLM inference server had auto-started via a systemd service, consuming all 8 GPUs with the Kimi-K2.5 model. This was a leftover from a previous deployment, now interfering with the extraction pipeline. The assistant had to kill the vLLM process, stop and disable the systemd service, and verify that GPUs were free (showing 0 MiB memory usage across all 8 cards).

Only after clearing these obstacles could the assistant address the fundamental question: what survived the crash? The hidden state extraction had been writing to a Ceph-backed volume that ran out of space. Some files might be corrupt. Some might be missing. The process had been stuck in uninterruptible sleep for hours. The data might have been partially written, partially lost, or partially corrupted during the forced shutdown and disk migration.

This is the context that drives message 4201. The assistant must determine whether the existing extraction output is salvageable, whether it needs to be restarted from scratch, or whether — ideally — it survived intact and can simply be resumed.

HOW Decisions Were Made: The Integrity Check Strategy

The assistant's decision to run this specific set of commands reveals a careful, layered assessment strategy. Rather than jumping into complex validation, the assistant starts with three lightweight probes that together paint a high-level picture of data health.

First probe: directory listing. The command ls /data/eagle3/synth_100k/hidden_states/ lists the top-level subdirectories. The output shows 10 directories named rows_X-Y, each covering a 2,000-sample range from 0 to 20,000. This immediately tells the assistant that the data is organized in a structured, sharded format — not a single monolithic file that could be wholly corrupt. The presence of all directories up to rows_18000-20000 confirms that the extraction had progressed past sample 18,000 before the crash, consistent with the pre-crash log showing ~18,420 samples completed.

Second probe: total size. The du -sh command reports 2.3 TB for the hidden states directory. This is a massive dataset — 2.3 terabytes of floating-point tensors representing the internal representations of the Kimi-K2.5 model across millions of tokens. The fact that this number is reported at all (without disk errors) is a strong signal that the filesystem migration succeeded and the data is accessible.

Third probe: metadata. The stats.json file in the merged directory contains the ground truth: 37,312 total records, 87,793,206 tokens, max sequence length 8192. This tells the assistant the expected scope of the full dataset. Comparing the 2.3 TB on disk against what would be expected for 18,420 samples (roughly half of 37,312) provides a sanity check: 2.3 TB is approximately half of what the full 4.6 TB dataset would occupy, which is consistent with ~49% completion.

The assistant's choice to use head -5 on the JSON file is also deliberate — it reads only the first few lines rather than the entire file, avoiding unnecessary I/O on a system that just recovered from a storage failure.

Assumptions Made

This message rests on several implicit assumptions, some well-founded and some worth examining critically.

Assumption 1: Directory presence implies data integrity. The assistant assumes that if a rows_X-Y directory exists and contains files, those files are valid. In reality, a directory could exist with partially written or zero-byte files — especially given the crash occurred during active writes. The earlier investigation (msg 4184-4185) had checked file counts within rows_18000-20000 and found 424 .pt files, suggesting data was present, but not that each file was uncorrupted.

Assumption 2: The stats.json is authoritative and uncorrupted. The assistant trusts that stats.json accurately reflects the dataset that was being extracted. If this file was written before the crash (which it likely was, since it describes the merged input data, not the extraction progress), it should be reliable. But if the crash corrupted the JSON file, the metadata could be misleading.

Assumption 3: The new NVMe disk is reliable. The disk migration was described as "new 15TB Nvme disk attached directly to the host." The assistant implicitly trusts that the data was copied correctly and that the new disk has no filesystem errors. No fsck or data validation was run.

Assumption 4: The extraction process was making progress before the crash, not stuck earlier. The assistant had previously observed the extraction log stalling at sample ~18,422 while the process was in state Dl (uninterruptible sleep). The assumption is that this was caused by the Ceph outage, not by a bug in the extraction script itself. If the extraction had been stuck for a different reason (e.g., a server-side issue), simply restarting it might hit the same bug.

Mistakes or Incorrect Assumptions

The most significant potential mistake is the lack of per-file validation. The assistant checks that directories exist and reports a total size, but does not verify that individual .pt (PyTorch tensor) files can be loaded without error. A corrupt tensor file would not be discovered until training time, potentially causing cryptic errors hours into a multi-GPU training run. A more thorough approach might have included loading a random sample of files from each shard and checking their shapes and dtypes match expectations.

A second concern is the absence of a checksum or comparison against expected counts. The assistant knows from earlier checks (msg 4185) that rows_18000-20000 contains 424 files, but doesn't verify whether this matches the expected number of samples in that range. If each sample produces one .pt file, and the range covers 2,000 samples, then 424 files suggests only ~21% of that shard was completed — which is plausible if the crash happened mid-shard. But the assistant doesn't explicitly compute this or flag it.

Third, the assistant does not check the extraction log for error messages. Earlier in the conversation (msg 4176), the log showed "errors: 3" — three extraction errors out of 18,420 samples. After the crash, the assistant does not re-examine the log to see if those errors are documented, which samples they affected, or whether the error rate increased near the crash point. This information would be valuable for deciding whether to restart from scratch or resume.

Input Knowledge Required

To fully understand this message, a reader needs knowledge spanning several domains:

Storage and infrastructure: Understanding that Ceph is a distributed storage system, that running out of space can cause processes to hang in uninterruptible sleep (state Dl), and that migrating a volume from Ceph to a local NVMe involves significant data copying and filesystem remounting.

ML training pipelines: Knowledge of the EAGLE-3 speculative decoding architecture, where a small "drafter" model is trained to predict the outputs of a large target model using its internal hidden states. Understanding that hidden state extraction involves running every training prompt through the full model and saving per-layer activations — a process that generates terabytes of data.

PyTorch serialization: Recognizing that .pt files are PyTorch tensor serializations, typically containing floating-point arrays of shape (seq_len, hidden_size) for each layer. Understanding that these files are binary and can be silently corrupted if a write is interrupted.

The specific project state: Knowing that the dataset has 37,312 records with ~88 million tokens, that the model has a hidden size of 7168 with 4 auxiliary layers, and that the extraction was targeting 18,000+ samples before the crash.

Output Knowledge Created

This message produces several critical pieces of knowledge that drive subsequent decisions:

  1. Data survived the crash. The 2.3 TB of hidden states are present on the new NVMe disk, organized in the expected directory structure. This is the single most important finding — it means the extraction does not need to be restarted from scratch.
  2. The extraction progress was preserved. The directory listing shows data up to rows_18000-20000, confirming that ~18,000+ samples were successfully extracted before the failure. Combined with the pre-crash log showing ~18,420 samples, the assistant can estimate that roughly 49% of the dataset is complete.
  3. The model weights survived independently. The model on /shared (a separate ZFS volume) was unaffected by the Ceph failure, as confirmed by the presence of config.json. This means the extraction server can be restarted without re-downloading or re-quantizing the model.
  4. The merged dataset metadata is intact. The stats.json file provides the authoritative record of what the full dataset should contain, enabling the assistant to plan the remaining extraction work.
  5. The new storage is faster and more reliable. The NVMe disk is local to the host, eliminating the Ceph network and distributed storage overhead that likely contributed to the original crash. Future extraction will benefit from lower latency and higher throughput.

The Thinking Process

The assistant's reasoning in this message is visible in the sequence of actions across the broader conversation, but the thinking behind message 4201 specifically can be inferred from the structure of the command itself.

The assistant leads with a declarative summary: "/data is on the new 12T NVMe now. Model is fine on /shared." This is not just reporting facts — it's establishing a mental model. The assistant is confirming that the two critical resources (training data storage and model weights) are on independent, healthy storage systems. The Ceph failure affected only /data, and that has been migrated. The model on /shared was never at risk.

Then the assistant says: "Let me check the hidden states integrity." The word "integrity" is carefully chosen. The assistant is not just checking existence — it's checking whether the data is usable. The three-part command (directory listing, size check, metadata check) forms a minimal but effective integrity probe. Each part answers a different question:

Conclusion

Message 4201 is a masterclass in post-failure assessment. In a single bash command, the assistant answers the most pressing question after a catastrophic storage failure: did the data survive? The answer — a qualified yes — enables the pipeline to move forward with a resume strategy rather than a full restart. The 2.3 TB of hidden states on the new NVMe disk represent weeks of data generation, API costs, and compute time. Salvaging them is the difference between a minor setback and a complete loss of progress.

The message also reveals the assistant's operational philosophy: verify the minimum needed to make a decision, trust but verify with lightweight probes, and never let perfect validation delay good-enough recovery. In the world of large-scale ML training, where a single GPU hour costs dollars and a full dataset generation can run tens of thousands of dollars in API fees, this pragmatic approach to data integrity is not just sensible — it's essential.