The Launch Script: Deploying a DDTree-Optimized DFlash Training Run

In the lifecycle of a machine learning research project, there is a moment that separates planning from reality: the writing of the launch script. Message <msg id=9272> captures precisely this transition. After dozens of edits, three parallel research investigations, a full day of debugging, and a carefully crafted git commit, the assistant writes a single bash command that will determine the next week of compute time on an 8-GPU Proxmox LXC container. The message is unassuming—a tee command piped through SSH—but it encodes the entire strategic pivot of the DFlash drafter training project, from architecture optimization to a DDTree-centric paradigm.

The Strategic Context: Why This Message Exists

To understand why <msg id=9272> was written, one must trace the chain of events that led to it. The story begins with a user directive in <msg id=9235>: "commit and checkout to new 'experiment-ddtree', then do: Gamma=10, increase max anchors, definitely do SWA, match noise (uniform), blend in maybe 15% soft kl… also attempt 2c llada aux loss." This was not a casual suggestion—it was a response to a deep investigation that had uncovered three fundamental bugs in the v5 training run (noise corrupting target logits, the fc shortcut including the target layer, and a loss function mismatch), followed by three additional bugs found by comparing the codebase line-by-line against the official vllm-project/speculators repository (wrong number of target layers in the fc projection, missing refinement layers for target logits, and an incorrect gamma default).

The experiment-ddtree branch represented a bet: that the DFlash drafter could be made dramatically more effective by tailoring it specifically for DDTree speculative decoding, rather than treating tree construction as an afterthought. The assistant had spent messages <msg id=9237> through <msg id=9268> implementing this vision: sliding window attention on the first four layers (matching the z-lab reference architecture), uniform noise to match the official speculators code, a 15% soft KL blend to teach probability ordering for top-K coverage, the CAP auxiliary confidence loss from LLaDA 2.0 to sharpen predictions, increased block size from 16 to 24, and gamma raised from 4.0 to 10.0 to weight later DDTree positions more heavily.

By <msg id=9268>, all code changes were committed. By <msg id=9269>, the assistant had checked the v6 baseline's final metrics (step 1225: accuracy 0.20, streak 1.7—a strong trajectory). By <msg id=9270>, v6 was stopped and its checkpoints archived. By <msg id=9271>, the updated Python files had been copied to the remote machine. Everything was in place for the launch. The only missing piece was the shell script that would bind all these decisions into a single executable command. That is the purpose of <msg id=9272>.

Anatomy of a Launch Script

The message writes the following script to the remote machine:

#!/bin/bash
set -e
export PATH=/root/.local/bin:$PATH
source /root/venv/bin/activate

exec python3 /root/train_dflash_pipeline.py \
    --target-model /dev/shm/Qwen3.6-27B \
    --data-dir /workspace/tokenized_completions \
    --output-dir /workspace/checkpoints \
    --target-gpus 0,1,2,3,4,5 --drafter-gpus 7 \
    --epochs 6 \
    --lr 6e-4 --warmup-ratio 0.04 --weight-decay 0.01 \
    --grad-accum 4 --grad-clip 1.0 \
    --token-budget 49152 --max-seq-len 8192 --max-batch-size 64 \
    --block-size 24 --max-anchors 1024 --num-draft-layers 5 \
    --gamma 10.0 \
    --noise-start 0.05 --noise-end 0.01 --noise-type uniform \
    --use-soft-labels --kl-weight 0.15 --kl-temperature 2.0 \
    --cap-lambda 0.1 \
    --save-interval 2000 \
    --wandb-project dflash-qwen36-27b \
    --wandb-run-name "exp-ddtree-g10-bs24-a1024-swa-kl15-cap01"

Every flag tells a story. The --target-gpus 0,1,2,3,4,5 and --drafter-gpus 7 assignment reveals the GPU topology: six GPUs dedicated to running the target model forward pass (Qwen3.6-27B, a 27-billion-parameter model), and a single GPU for the drafter. This is notable because the earlier plan and commit message had discussed using two drafter GPUs (GPUs 6 and 7), but the actual launch script uses only GPU 7. Whether this was a deliberate simplification to avoid complexity or an oversight is unclear from the message alone—but it represents a real design decision embedded in the command line.

The --block-size 24 and --max-anchors 1024 flags reflect the DDTree-specific scaling. Larger blocks mean each anchor sees more context, which is particularly valuable for DDTree's deeper tree structures. The --gamma 10.0 is the most consequential hyperparameter change: in the official speculators code, gamma controls how much later positions in the draft tree are weighted relative to earlier ones. At gamma=4.0 (the official default), the loss roughly follows a geometric distribution over tree positions. At gamma=10.0, later positions receive dramatically more weight, forcing the drafter to invest capacity in the deep, high-rejection-risk branches of the tree where correct predictions yield the largest speedup.

The noise parameters (--noise-start 0.05 --noise-end 0.01 --noise-type uniform) encode a lesson learned from the v5 regression analysis. The official speculators code uses AddUniformNoise, not Gaussian noise. The Gaussian noise used in earlier versions was identified as a root cause of the performance gap. The higher noise start (0.05 vs the previous 0.01) and higher noise end (0.01 vs 0.001) reflect the different scaling properties of uniform noise.

The loss configuration is the most architecturally interesting part. The --use-soft-labels flag activates a 15% soft KL divergence blend with the standard cross-entropy loss (--kl-weight 0.15). The idea is that soft labels preserve probability ordering information—the drafter learns not just which token is correct, but which tokens are plausible alternatives. For DDTree, this is critical because the tree's acceptance mechanism depends on the drafter's ability to assign reasonable probabilities to multiple candidates at each position. The --kl-temperature 2.0 softens the target distribution further, spreading probability mass across more tokens.

The --cap-lambda 0.1 flag activates the CAP (Confidence-Aware Prediction) auxiliary loss, borrowed from LLaDA 2.0. This loss minimizes the entropy of the drafter's predictions at positions where the target distribution is already confident. The effect is to "sharpen" the drafter's predictions, reducing the probability of low-ranked tokens that would cause the DDTree verifier to reject the draft. At lambda=0.1, it is a regularizer rather than the primary objective.

The Assumptions Embedded in the Command

Every launch script encodes assumptions about the environment it will run in. This one assumes that the Python virtual environment at /root/venv/bin/activate contains all required dependencies (PyTorch with flash-attn, the flex-attention kernels, the W&B client). It assumes the target model has been loaded into /dev/shm/Qwen3.6-27B—a RAM disk location that suggests the model was pre-cached to avoid disk I/O bottlenecks during training. It assumes the tokenized training data resides at /workspace/tokenized_completions and that the checkpoint directory at /workspace/checkpoints exists and is writable.

More subtly, the script assumes that the two Python files copied in <msg id=9271> are the only files that changed. The commit in <msg id=9268> modified 16 files with 2,858 insertions, but only train_dflash_pipeline.py and dflash_model.py were deployed to the remote machine. The other files—documentation, utility scripts, launch helpers—were committed to the git repository but not copied. This is a reasonable assumption for a codebase where the core training logic lives in two files, but it means the remote environment is not fully reproducible from the script alone.

A Potential Discrepancy

One notable detail deserves scrutiny. The commit message in <msg id=9268> explicitly states: "The final configuration runs on all 8 GPUs (5 targets + 3 drafters) at ~17.5 Ktok/s." Yet the launch script in <msg id=9272> specifies --target-gpus 0,1,2,3,4,5 (6 GPUs) and --drafter-gpus 7 (1 GPU), for a total of 7 GPUs. This is a significant mismatch. The chunk 0 summary of the current segment also describes a 5-target, 3-drafter configuration. It is possible that the launch script represents an intermediate configuration that was later revised, or that the assistant made an error in constructing the command. Either way, it illustrates the gap between the planned architecture and the deployed reality—a gap that would only be discovered when the training run began and the GPU utilization metrics told their own story.

The Broader Significance

Message <msg id=9272> is, on its surface, a simple file write operation. But it represents the culmination of an intense research and engineering cycle: from identifying bugs in the v5 training run, through line-by-line comparison with the official reference implementation, through three parallel research investigations into diffusion LM training, distillation for drafters, and DDTree tree construction, through the implementation of six major architectural changes, through the debugging of gradient checkpointing conflicts and GPU load imbalances, to finally writing the command that would bind it all together.

The message also marks a strategic inflection point. The experiment-ddtree branch was not just another hyperparameter sweep—it was a bet that the DFlash drafter could be fundamentally improved by aligning its training objective with the DDTree verification procedure. The soft KL loss teaches probability ordering, which DDTree needs for its top-K acceptance mechanism. The CAP loss sharpens predictions at positions where the target is confident, reducing rejection probability. The sliding window attention matches the z-lab reference architecture that had demonstrated superior performance. The increased gamma weights later tree positions, where correct predictions yield the largest speedup.

Whether this bet would pay off would not be known for days. The next message (<msg id=9273>) would start the training in a tmux session, and the assistant would monitor its progress over the following hours. But the launch script itself—this single message—is where the plan became action, where all the analysis, debugging, and implementation crystallized into a command that would consume 8 GPUs for a week. In the lifecycle of a machine learning project, there is no more consequential moment.