Segment 53

This sub-session began with a deep investigation into why the v5 training run regressed despite three prior bug fixes. A line-by-line comparison against the official vllm-project/speculators repository uncovered three additional fundamental bugs: the fully connected layer used only 4 of 5 target layers instead of all 5, target logits were computed from the wrong layer (61 instead of 63), and the gamma default was 7.0 instead of 4.0. Fixing these in v6 produced dramatically better convergence. The user then pivoted to DDTree-specific optimizations, creating the experiment-ddtree branch with gamma=10, sliding window attention on layers 0-3, uniform noise, 15% soft KL blended with CE, CAP auxiliary confidence loss, block_size=32, max_anchors=1024, and a fused gradient-checkpointed loss function to avoid OOM. The pipeline was scaled to use 2 drafter GPUs with weight averaging, running on all 8 GPUs at ~17.5 Ktok/s. Subsequent debugging stabilized the pipeline by fixing a torch.compile conflict with gradient checkpointing, a GPU load imbalance via shared queue assignment, and an OOM during weight averaging by moving it to CPU, ultimately achieving 21.5 Ktok/s. With the pipeline stable, the user analyzed training data composition, discovering a heavy 77% coding skew, and authored a comprehensive data expansion plan (DATA_EXPANSION.md) to diversify the mix. The session concluded by halting the current training run to prioritize generating a larger, more diverse dataset, marking a strategic pivot from architecture/optimization tuning to data-centric improvements.

Diagnose v5 regression and fix bugs vs official speculatorsBuild DDTree-optimized DFlash training pipelineImplement sliding window attention and CAP lossDebug and stabilize multi-GPU training pipelineInvestigate data composition and author expansion planHalt training to prioritize data generation

The Full Arc of Segment 53: From Bug Diagnosis to Data Pivot in DFlash Drafter Training 4443 words

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