The Clean Slate: A Single Bash Command That Unblocks EAGLE-3 Training

In the middle of a complex multi-day effort to train an EAGLE-3 speculative decoding drafter for the Kimi-K2.5 language model, the assistant issues a deceptively simple command:

[bash] ssh root@10.1.230.174 "rm -rf /dev/shm/sglang_hs/* 2>/dev/null"

This single line — a remote shell execution that recursively deletes the contents of a temporary directory — is the quiet pivot point between a flawed approach and a corrected one. To understand why this cleanup matters, we must trace the reasoning that led to it, the problem it solves, and the critical pipeline it unblocks.

The EAGLE-3 Training Pipeline

The broader context is the training of an EAGLE-3 speculative decoding drafter for Kimi-K2.5, a large language model deployed across 8 NVIDIA RTX PRO 6000 Blackwell GPUs (SM120 architecture). EAGLE-3 is a technique that trains a lightweight "drafter" model to predict multiple future tokens in parallel, accelerating inference by accepting or rejecting draft tokens against the base model. The drafter requires access to the base model's internal hidden states at specific layers — in this case, layers 3, 31, and 59 — to learn the conditional distribution of future tokens.

The assistant had developed a server-side patch for SGLang (the inference engine) that captures these hidden states during the prefill (EXTEND) forward pass and saves them as binary .pt files to /dev/shm/sglang_hs/. Each request produces a directory like req_0/, req_1/, etc., containing aux_0.pt, aux_1.pt, aux_2.pt, and final.pt — tensors of shape [num_tokens, 7168] in bfloat16. This extraction pipeline is the critical data-generation step before the actual EAGLE-3 training can begin.

The Radix Cache Problem

During testing of the extraction pipeline, the assistant discovered a subtle but critical bug. SGLang uses a radix cache (also known as prefix caching) to avoid recomputing KV cache entries for repeated prompt prefixes. When a request shares a prefix with a previously processed request, the engine skips the forward pass for the cached tokens and only processes the new, uncached portion. The hidden state capture patch, however, only fires during the EXTEND forward pass — meaning it only sees the new tokens, not the full sequence.

The assistant confirmed this experimentally. In message [msg 3379], a request with 10 prompt tokens produced a dump showing only 1 token. A follow-up test with a completely unique prompt (including a random number to avoid any cache hits) in message [msg 3380] correctly produced 13 tokens matching the input. The discrepancy was definitively traced to radix cache interference: "The previous 1-token result was because of radix cache hitting on a previously sent prompt."

This is a fatal issue for EAGLE-3 training. The drafter needs hidden states for every token position to learn the full conditional distribution. Missing even a single position would corrupt the training data. As the assistant reasoned in message [msg 3381]: "For correct extraction, we MUST have all tokens go through the forward pass."

The Decision to Restart

The assistant considered two approaches: (1) work around the cache by ensuring each prompt is unique (which the training data naturally is, being different conversations), or (2) disable radix cache entirely. The first approach seemed viable until the assistant recognized a deeper problem: even with unique prompts, if two sequences share a common prefix (like a system prompt), the cached prefix would cause the hidden state dump to miss those tokens. The only safe approach was to restart the server with --disable-radix-cache.

Message [msg 3382] shows the server shutdown sequence: killing the SGLang process with pkill, waiting for cleanup, forcefully terminating any remaining Python processes, releasing GPU memory with fuser -k /dev/nvidia*, and verifying all 8 GPUs show 0 MiB memory used. Message [msg 3383] confirms the GPUs are clean.

The Cleanup Command

Now we arrive at message [msg 3384]. The old server is dead. The GPUs are free. Before launching the new server with the corrected configuration, the assistant cleans the old dump directory. This is not cosmetic — it is a correctness guarantee.

The command rm -rf /dev/shm/sglang_hs/* removes all files and subdirectories inside /dev/shm/sglang_hs/ without deleting the directory itself. The 2>/dev/null suppresses any error messages (e.g., if the directory doesn't exist or is already empty). The path /dev/shm/ is a tmpfs (RAM-based filesystem) on Linux, meaning these deletions free actual physical memory — important on a system with limited RAM and 8 GPUs consuming most of it.

Why is this cleanup necessary? Three reasons:

  1. Counter confusion: The server-side dump counter is monotonically increasing. If old dumps (req_0 through req_5) remain, the new server's counter might collide with them, or the extraction script might confuse old incomplete dumps with new correct ones.
  2. Data integrity: The old dumps were captured with radix cache enabled, meaning some of them may be incomplete (missing cached token positions). Using them in training would silently corrupt the EAGLE-3 drafter.
  3. Space management: The hidden state tensors are enormous — each token produces 4 layers × 7168 dimensions × 2 bytes (bf16) = 57 KB per token. With 10,000 samples averaging 2,103 tokens each, the total is over 1.2 TB. Every byte of stale data wastes precious tmpfs space.

What Follows

Immediately after the cleanup, message [msg 3385] launches the new server with the corrected configuration:

nohup bash -c "SGLANG_HS_DUMP_DIR=/dev/shm/sglang_hs NCCL_PROTO=LL ... \
  /root/ml-env/bin/python3 -m sglang.launch_server \
  --model-path /shared/kimi-k2.5-int4 \
  --trust-remote-code --tp-size 8 --mem-fraction-static 0.85 \
  --host 0.0.0.0 --port 8000 \
  --disable-cuda-graph --disable-custom-all-reduce \
  --disable-radix-cache --log-level info" \
  > /data/eagle3/synth_10k/sglang_hs_dump_v3.log 2>&1 &

The critical flag --disable-radix-cache ensures every token in every request goes through the full forward pass, producing complete hidden state dumps. The NCCL environment variables carry over the performance tuning from earlier work (achieving 90 tok/s single-stream throughput). The server logs are redirected to a new file (v3), keeping the history clean.

Message [msg 3386] then copies the updated extraction script to the remote machine, and message [msg 3387] inspects the tokenized training data — 10,000 samples totaling 21 million tokens, with an estimated hidden state storage requirement of 1,206 GB.

Assumptions and Reasoning

The cleanup command embodies several assumptions worth examining:

The Thinking Process

The reasoning visible in the surrounding messages shows a methodical, hypothesis-driven debugging approach. When the extraction produced only 1 token instead of 10, the assistant didn't jump to conclusions. It formulated two hypotheses (radix cache vs. chunked prefill), designed a controlled experiment (using a unique random prefix to guarantee zero cache hits), and interpreted the results to pinpoint the root cause. The decision to disable radix cache rather than work around it reflects a preference for correctness over convenience — a critical mindset for ML pipeline engineering where silent data corruption can waste hours of training time.

The cleanup step itself, while trivial in execution, represents the culmination of this debugging process. It is the moment when the assistant transitions from investigation to remediation, clearing the way for the corrected pipeline to run. In software engineering terms, it is the git clean before the rebuild — a small ritual that prevents subtle bugs from persisting across iterations.

Conclusion

Message [msg 3384] is a single bash command that deletes a directory. But in the context of the EAGLE-3 training pipeline, it is the final preparatory step before launching a corrected server that will produce 1.2 TB of clean hidden state data — data that will train a speculative decoding drafter capable of accelerating Kimi-K2.5 inference. The command is a testament to the importance of data integrity in machine learning pipelines: sometimes the most important thing you can do is start fresh with a clean slate.