Backfill CHANGELOG.md for v0.2.0-v0.3.2

The automated changelog (gitea #50) deliberately started fresh with no backfill; this reverses that call now that it's wanted. v0.2.0-v0.3.2 are generated from tag history via git-cliff/cliff.toml, matching the format of existing entries. v0.3.3 was bumped but never tagged, so its commits stay folded into the existing v0.3.4 entry. The v0.2.0 range (198 uncurated pre-automation commits) is hand-curated to drop duplicate commits and dev-log noise (WIP markers, incomplete-validation runs, repeated "Apply ruff format").
2026-08-24 15:30:14 +02:00
1 changed files with 476 additions and 1 deletions
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 - Document CI_TOKEN's write:repository scope requirement [gitea #50](https://git.larsbogner.de/lars/giant/issues/50)
-# Changelog
+## [0.3.2] - 2026-08-17
 ### Added
 - Add configs/baseline.toml as the kept reference model
 ### Fixed
 - Clamp analysis histogram bins before the i32 cast, not after [gitea #61](https://git.larsbogner.de/lars/giant/issues/61)
 - Clip raw predicted log_mass in decode_secondaries [gitea #54](https://git.larsbogner.de/lars/giant/issues/54)
 ### Changed
 - Let dwarf warm-cache take --config so it can't under-warm a config's cache keys [gitea #59](https://git.larsbogner.de/lars/giant/issues/59)
 - Implement n_sec.mode = "stop_token" for the AR secondary decoder [gitea #40](https://git.larsbogner.de/lars/giant/issues/40)
 - Bump patch version to 0.3.2
 ## [0.3.1] - 2026-08-14
 ### Added
 - Add an Objective registry for the flow/ddpm/wgan generator choice [gitea #32](https://git.larsbogner.de/lars/giant/issues/32)
 ### Changed
 - Make trunk architecture selectable via a registry [gitea #33](https://git.larsbogner.de/lars/giant/issues/33)
 - Make ResBlock's conditioning-injection mechanism selectable [gitea #34](https://git.larsbogner.de/lars/giant/issues/34)
 - Make HistoryEncoder a pluggable registry, like Router/Objective [gitea #35](https://git.larsbogner.de/lars/giant/issues/35)
 - Deduplicate n_sec_head/type_head MLPs into build_mlp_head [gitea #36](https://git.larsbogner.de/lars/giant/issues/36)
 - Give the cond_cat/cond_cont column layout one owner [gitea #37](https://git.larsbogner.de/lars/giant/issues/37)
 - Give Stage1Model/Stage2OneShot/Stage2Autoregressive a shared StageModel base [gitea #39](https://git.larsbogner.de/lars/giant/issues/39)
 - Pass ConditioningAxisConfig/ParticleTypeConfig themselves instead of raw dicts [gitea #38](https://git.larsbogner.de/lars/giant/issues/38)
 - Bump patch version to 0.3.1
 ## [0.3.0] - 2026-08-13
 ### Added
 - Add v0.3.0 design doc: Stage-2 autoregressive redesign
 - Add pytest-cov to dev deps and run coverage in CI
 - Add coverage for router-center seeding, geometry batch reader, material topN cache, and setup-cache corruption paths
 - Add render.py coverage: figure params, router diagnostics plots, gallery/condor glue
 - Add unknown-key validation to config.toml merge (issues.md Issue 2)
 - Add consumed-keys audit test (issues.md Issue 5)
 ### Fixed
 - Fix test_render_all_run_gallery_invokes_subprocess clobbering LaTeX's own subprocess.run
 ### Removed
 - Remove issues.md
 ### Changed
 - Refine v0.3.0 design: defaults, deferred scope, open questions
 - Document the differentiability position and its validation obligation
 - V0.3.0 step 1: new nested config schema, v0.2 migration shim
 - V0.3.0 step 2: network.py refactor to composable stage models
 - V0.3.0 step 3: per-stage train.py trainers + pipeline.py/cli.py rewrite
 - V0.3.0 step 4: type map + particle_type.target = "onehot"/"embedding"
 - V0.3.0 step 5: Stage2Autoregressive (history=markov) + §11.4 grad instrumentation
 - V0.3.0 step 6: sample.py/rollout.py AR generation + class->PDG decode
 - V0.3.0 step 7: AttentionHistory (KV-cached) + scheduled/never teacher forcing
 - V0.3.0 post-implementation audit: resolve all 9 tracked discrepancies
 - Refactor train.py into giant/training/ around a metrics collector
 - Silence the fork-safety warning from num_workers>0 pipeline tests
 - Deduplicate giant/training/trainers.py shared per-stage logic
 - Rewrite README for v0.3.0 architecture, quick start, and data columns
 - Bump version to 0.3.0
 - Delete docs/v0.3.0-design.md and strip all references to it
 - Apply ruff format
 - Downgrade coverage-report upload to actions/upload-artifact@v3
 - Bump ruff line-length to 120 and reformat
 - Make config dataclasses the single source of truth for DEFAULT_CONFIG
 - Extract giant train/new-run's CLI override mapping into a table-driven function (issues.md Issues 3 & 4)
 - Mark issues.md Issues 3 & 4 as fixed
 - Extract predict/rollout's duplicated inference bootstrap into giant.checkpoint_io (issues.md Issue 5)
 - Mark issues.md Issue 5 as fixed
 - Unify the two v0.2->v0.3 migration surfaces (issues.md Issue 6)
 - Type the data/model/training batch contracts with NamedTuples (issues.md Issue 7)
 - Split giant/model/network.py into giant/model/ (issues.md Issue 8)
 - Move scripts/ to giant/tools/ (issues.md Issue 9)
 - Reject stage2_model.stage1_context = 'sampled' as unimplemented (issues.md Issue 1)
 - Honour wgan.critic_hidden_dim/critic_n_res_blocks in build_critics [gitea #28](https://git.larsbogner.de/lars/giant/issues/28)
 - Validate stage2_model.autoregressive.order in validate_config [gitea #30](https://git.larsbogner.de/lars/giant/issues/30)
 - Decouple secondary-species vocabulary from conditioning.particle.emb_dim [gitea #29](https://git.larsbogner.de/lars/giant/issues/29)
 - Skip router auxiliary loss compute when their lambda is 0 [gitea #31](https://git.larsbogner.de/lars/giant/issues/31)
 ## [0.2.0] - 2026-08-04
 ### Added
 - Add CLAUDE.md with architecture overview and dev commands
 - Add streaming data pipeline and giant CLI entry point
 - Add giant predict command
 - Add ROOT-to-parquet conversion script with convert dependency group
 - Add post_pos as a model target via travel_dir decomposition
 - Add --coord local mode to predict for raw-space prediction debugging
 - Add KL divergence to marginal validation and hook it into the training loop
 - Add graceful shutdown on SIGINT/SIGTERM
 - Add configurable dropout to ResBlocks
 - Add giant.analysis module for notebook-based model quality diagnostics
 - Add lazy polars I/O and duplicate KL/constraint checks for giant.analysis
 - Add ruff and ty as dev dependencies, fix lint/type findings
 - Add linear warmup before cosine LR decay
 - Add --batch-size auto to estimate batch size from free GPU memory
 - Add hyperparameter scan
 - Add --batch-size auto to predict, matching train
 - Add tqdm progress bar to predict
 - Add KL bar plots and sample_frac to load_predicted_local; ignore root parquet scratch files
 - Add event-level shower observables to giant.analysis
 - Add total length traveled per event to event observables
 - Add pdg energy/length contribution pie plots
 - Add export script for Tier 4 event-level/pdg-share plots
 - Add mean/median deposited energy and step length plots per event
 - Add export script for ETP group-update presentation plots
 - Add photon edep export scripts and per-step presentation plots
 - Add tooling for a versioned geant_steps dataset layout
 - Add --copy mode to migrate_geant_steps.py
 - Add update-manifest and create-manifest subcommands to bump_dataset_version
 - Add --to flag for bump-gen/bump-schema and --gen flag for update-manifest
 - Add disk usage summary to dwarf status
 - Add file counts and reference tracking to dwarf status
 - Add --comment option to predict, recorded in YAML sidecar
 - Add energy-conservation PoC ODE-step comparison scripts
 - Add autoregressive shower rollout driver
 - Add fast slab lookup for the GeometryOracle, replacing knn as the default
 - Add load_rollout_vs_truth to compare rollouts against held-out truth data
 - Add mixture-of-experts routing prototype for Stage 1 and Stage 2
 - Add ProcessRouter for physics-process-based expert gating
 - Add PdgRouter for particle-type-based expert gating
 - Add ComposedRouter for multi-axis MoE gating
 - Add EMA weights, weight decay, step-based LR schedule, and grad-norm logging to training
 - Add WGAN-GP mode as a throwaway fast-eval experiment
 - Add router gating diagnostic for MoE checkpoints
 - Add Gitea Actions CI pipeline
 - Add configs for router energy (embedding/physical) and WGAN baseline runs
 - Add opt-in Weights & Biases logging for the training loop
 - Add test coverage for resolve_expert_dims
 - Add regression coverage for vocab/process index-map builders
 - Add dwarf warm-cache to precompute the setup-stage sidecar
 - Add giant new-run to scaffold a config.toml + run dir ahead of training
 - Add learnable per-expert width and shared temperature to EnergyRouter
 - Add opt-in straight-through Gumbel-softmax combine weights to MoE router
 - Add gumbel router configs sweeping learnable-knob combinations
 - Add gumbel/learn_centers/learn_width/learn_temperature to out-dir naming
 - Add bigger WGAN config (hidden_dim=512, n_blocks=6)
 - Add data-integrity guards against silent NaN/Inf propagation and races
 ### Fixed
 - Fix column names to match actual parquet schema
 - Fix installed torch version to be compatible with cuda drivers
 - Fix miniCaloSim link in README
 - Fix giant.analysis import after Phase 2 dataset API changes
 - Fix silent failure modes surfaced by extensive code review
 - Fix ruff, ty, and pytest failures; apply ruff format
 - Clamp n_sec classification label to K_MAX
 - Fix rollout edep mismatch and add truth overlay to Tier 4 observables
 - Fix crashes in physical-property conditioning edge cases
 - Fix router experts silently ignoring --hidden-dim/--n-blocks
 - Fix conditioning="physical" so it can actually generalize past training vocab
 - Fix training-loop checkpoint/resume and WGAN bugs
 - Fix stale-partial reuse and n_chunks mismatch in analysis condor pipeline
 - Fix CLI/tooling robustness gaps and dedupe the Conditioning enum
 - Fix test_write_submit_requires_synced_venv for active-venv resolution
 ### Removed
 - Remove scripts/train.py in favor of the giant train CLI
 - Drop orphaned child tracks instead of nulling secondary targets
 ### Changed
 - Initial commit: giant surrogate model with two-phase roadmap in README
 - Implement Phase 1: full data pipeline, model, training, and config support
 - Handle material column as string type
 - Rename pre_energy/post_energy columns to pre_E/post_E
 - Rename direction columns from pre_dir_x/y/z to pre_dx/dy/dz
 - Batch StreamingStepsDataset internally instead of per-row collate
 - Dedup training pipeline, add seeding/resume and per-epoch metrics logging
 - Split torch into cpu/cuda extras and pin dependency version bounds
 - Apply ruff format and document lint/type tooling in CLAUDE.md
 - Update README to match current architecture and tooling
 - Make sampler step count configurable for validation
 - Calibrate auto batch size separately for inference vs training
 - Skip rows with unknown PDG codes during predict
 - Buffer predict rows across row-group boundaries before inference
 - Export plots for knowledge base
 - Rework validation notebook with markdown sections and Tier 4 plots
 - Allow steps_to_parquet.py to accept multiple ROOT input files
 - Encode edep/secondary/post energy as a conservation-constrained simplex
 - Expose dataset/conversion scripts as uv entry points
 - Restrict holdout overlap check to holdout vs dev/full only
 - Route predict output to UUID-named parquet with YAML reference sidecar
 - Implement Phase 2: secondary particle prediction
 - Unify dataset/tooling scripts into a single `dwarf` Typer CLI
 - Fold --to/--gen dataset-versioning flags into the dwarf CLI
 - Prefix default train output dir with current date
 - Color-code dwarf status output by tree level
 - Show VERSIONS.md reason extracts in dwarf status
 - Wire up predict CLI to load and run the Stage-2 sec_decoder
 - Wire up n_sec/species/energy-fraction validation for Stage 2
 - Detach Stage-2 type-embedding target to stop self-referential collapse
 - Weight Stage-2 secondary loss equally between direction and type-embedding dims
 - Recalibrate batch-size estimate for the post-Phase-2 model size
 - Update CLAUDE.md and README for the implemented Phase 2 model
 - Error on missing secondary lists instead of silently zeroing Stage-2 targets
 - Derive a unique per-job seed for minicalosim shard generation
 - Rescale secondary energies to exactly consume the e_sec budget
 - Support --energy-gev in dwarf make-root for the new minicalosim energy arg
 - Stream giant rollout output instead of buffering the whole run
 - Scale auto batch-size estimate by MoE expert count during training
 - Rewrite analysis module as a lean, fully-streaming pipeline
 - Reimplement rollout-vs-truth comparison on the streaming analysis module
 - Condition on material/particle physical properties instead of learned embeddings
 - Ignore the scratchpad working directory
 - Quote the on: key in the CI workflow
 - Split CI lint stage into parallel jobs
 - Rewrite analysis as streaming rollout-vs-reference plotting pipeline
 - Analyze: drive prep/submit from the rollout YAML sidecar
 - Analyze: show model/training params on rendered figures
 - Deps: install plotstyle from git.larsbogner.de package index
 - Analyze: drop stale ty:ignore on plotstyle import
 - Test: replace prep(**_CTX) splat with a typed _prep helper
 - Analyze: add MoE router gating/share diagnostic plots
 - Chore: remove stray CUDA sanity script and stale Phase 2 planning doc
 - Docs: document compute environment, WGAN/MoE status, and condor-gpu-train-rollout
 - Analyze: normalize pdg dtype in open_side to fix rollout/reference concat
 - Analyze: chunk per-plot aggregation across HTCondor jobs
 - Analyze: expose bin/pdg options on `analyze submit`
 - Analyze: estimate per-job HTCondor walltime from chunk row count
 - Analyze: run condor compute jobs via .venv/bin/giant, not uv run
 - Analyze: default condor docker image to alma9-gridjob
 - Analyze: raise default condor job memory request to 8192 MB
 - Analyze: recalibrate condor walltime model from real cluster timings
 - Transforms: pad legacy cond normalizers for pre-physical-conditioning checkpoints
 - Analyze: default run directory to <repo>/analysis_runs, gitignored
 - Docs: record first MoE router rollout benchmark result in the roadmap
 - Router: seed EnergyRouter centers from data quantiles instead of a fixed linspace
 - Docs: note the EnergyRouter centers_init fix in the roadmap
 - Analyze: thread full model/training/rollout/dataset params to plots
 - Ci: share one uv sync across jobs, gate tests on lint+type-check, sync tag/version on release tags
 - Ci: replace unsupported artifact sharing with a bind-mounted uv cache
 - Ci: stop setup-uv from overriding UV_CACHE_DIR
 - Ci: re-pin UV_CACHE_DIR after setup-uv, which exports its own value regardless of enable-cache
 - Ci: set UV_LINK_MODE=copy to silence the cross-filesystem hardlink warning
 - Log batch-level metrics to W&B, not just per-epoch summaries
 - Log router health, WGAN grad-norm split, n_sec accuracy, GPU/throughput to W&B
 - Persist global_step across --resume so W&B step stays monotonic
 - Timestamp default checkpoint dir to avoid W&B run-id collisions
 - Skip empty-slice mean/std in sec phys validation print
 - Speed up giant train's setup stage
 - Speed up _WelfordAccumulator's per-chunk update
 - Make default checkpoint out_dir name reflect only non-default hyperparams
 - Cache giant train's setup stage in a sidecar file
 - Pass --seed through to the train/val event split
 - Offset event_id per file to avoid cross-file collisions
 - Store a quantile grid instead of a raw reservoir sample in the setup cache
 - Scope wandb run config to only-active hyperparameters
 - Resolve giant condor wrapper from the active venv, not a hardcoded path
 - Bump version to 0.2.0