speculative-decoding

# Speculative Decoding Draft-Model Training Everything needed to take a target model from "no draft head" to "validated acceptance rate" lives in this directory. Work through the stages below in order for a new model; jump straight to a stage when you already know which one you need. ## Two axes: stage and algorithm The pipeline is the same shape for every draft-model algorithm — synthesize data, dump base-model hidden states, train the draft, benchmark acceptance rate. What changes between algorithms is which training script and recipe run, which knobs matter, and which failures are typical. So this skill is split along those two axes, and **you almost always read one file from each**: | Axis | Directory | What it holds | | --- | --- | --- | | Stage | `references/stages/` | The procedure — algorithm-independent | | Algorithm | `references/algorithms/` | The data sheet — scripts, recipe, knobs, thresholds, known failures | Read the stage file for *what to do*, and the algorithm sheet for *the values to plug in*. When a stage file says "see the algorithm sheet", it means the section of `references/algorithms/<algorithm>.md` with the matching heading. ## Where the code lives The launcher scripts in `tools/launcher/common/{eagle3,specdec}/` are thin wrappers around `examples/speculative_decoding/` — `main.py` (training), `collect_hidden_states/` (dumps), `scripts/{export_hf_checkpoint,ar_validate,quantize_drafter}.py`. Read a wrapper for the flags it passes, that directory for what they do. Its `README.md` is also the non-launcher path: training invoked directly, without Slurm. ## Stages | Stage | Reference | Use when | | --- | --- | --- | | 1. Configure | `references/stages/configure.md` | Adding a model that has no pipeline YAML yet | | 2. Review logs | `references/stages/review-logs.md` | A run finished (or died) and you want a pass/fail summary | | 3. Triage | `references/stages/triage.md` | A task failed and you need root cause plus a fix | | 4. Validate | `references/stages/validate.md` | All tasks passed and you need to confirm the acceptance rate gate | Review-logs and triage overlap by design: review-logs is the fast sweep across all tasks, triage is the deep dive into one failing task. Start with review-logs unless the user already knows which task broke. ## Algorithms All recipes live in `modelopt_recipes/general/speculative_decoding/<algorithm>.yaml`. | Algorithm | Sheet | Family | | --- | --- | --- | | EAGLE3 | `references/algorithms/eagle3.md` | Autoregressive draft head | | DFlash | `references/algorithms/dflash.md` | Block diffusion | | DSpark | `references/algorithms/dspark.md` | DFlash backbone + Markov head + optional confidence head | | Domino | `references/algorithms/domino.md` | DFlash backbone + GRU causal correction head | | DFlash2 | `references/algorithms/dflash2.md` | DFlash backbone + sublayer convolutions + candidate selector | | LiLiCorr | `references/algorithms/lilicorr.md` | DFlash backbone + candidate-lattice reranker (covers `lilicorr_conv.yaml`) | Everything below EAGLE3 is a **DFlash variant**, not a separate pipeline: same `recipe_type: speculative_dflash`, same training script, same `dflash.*` config namespace, selected by `dflash_architecture_config.projector_type`. Read `references/algorithms/dflash.md` first, then the variant's sheet for the delta. LiLiCorr ships two recipes — `lilicorr.yaml` and `lilicorr_conv.yaml`, the latter adding DFlash2's sublayer convolutions — but one sheet covers both. If the user's algorithm has no sheet yet, the stage procedures still apply — derive the missing values from an existing launcher example for that algorithm (`tools/launcher/examples/*/*/hf_*_<algorithm>.yaml`) and its recipe, then write the sheet as you go. `references/algorithms/README.md` defines what a sheet must contain. ## End-to-end: a new model 1. Confirm the algorithm and find the closest existing launcher example. 2. **Configure** — write the pipeline YAML (`references/stages/configure.md`). 3. Preview with `--dryrun`, then submit: ```bash cd tools/launcher uv run launch.py --yaml examples/<Org>/<Model>/<config>.yaml --yes ``` 4. Register the job and set up monitoring per the **monitor skill**. 5. **Review logs** when it finishes (`references/stages/review-logs.md`). 6. **Triage** anything that failed (`references/stages/triage.md`), fix, re-run only the failed tasks onward via `pipeline.task_N.skip=true`. 7. **Validate** once all tasks pass (`references/stages/validate.md`). Model-support gaps that need code changes land in `modelopt/torch/speculative/` and require a separate ModelOpt PR — the pipeline YAML alone cannot fix an unrecognized architecture.