lars
de5db25e3f
Pulled forward from the not-yet-mergeable condor-gpu-train-rollout branch: `new-run` resolves CLI hyperparameter overrides into a full config.toml and run dir (reusing the existing default_out_dir_name collision-avoidance and a newly factored-out router-override helper shared with `train`), so a run can be prepared and reviewed before `giant train` actually kicks off. Also brings README up to date with the model/CLI as it actually stands (physical/embedding conditioning, WGAN/MoE-router modes, giant analyze, W&B, setup-stage caching), which had drifted back to describing the Phase-1 proof-of-concept. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
151 lines
14 KiB
Markdown
151 lines
14 KiB
Markdown
# giant
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**G**eant4 **I**nference via **A**utoregressive **N**eural s**T**ep surrogate — a play on *Geant4* and the step function being the computationally heaviest part of the simulation.
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Conditional generative surrogate for the Geant4 step function. Given a pre-step particle state, the model samples a physically plausible post-step outcome — the primary's continuation plus the variable-length list of secondary particles it produces — replacing the stochastic Geant4 physics engine with a trained generative model. A trained checkpoint autoregressively rolls out full showers, stepping each primary and pushing secondaries as new tracks.
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Training is driven entirely from parquet files of the miniCaloSim steps tree. No Geant4 runtime dependency.
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## Architecture
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A **two-stage model**, both stages checkpointed together, with a choice of generative mode per stage (`--mode`):
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- **`flow`** (default) — conditional flow matching (Lipman et al. 2022): an MLP learns a vector field mapping noise → step outcomes, sampled via ODE integration in ~10 steps.
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- **`ddpm`** — a standard denoising diffusion baseline for comparison (`giant/model/schedule.py:CosineSchedule`).
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- **`wgan`** — a single-pass Wasserstein-GAN-GP generator/critic (`giant/model/wgan.py`), trading iterative sampling for one forward pass; implemented, not yet validated against the flow-matching baseline.
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**Stage 1 — primary (9D, `giant/constants.py:LOCAL_TARGET_NAMES`):**
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| Index | Variable | Encoding |
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|-------|----------|----------|
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| 0 | `step_length` [mm] | log |
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| 1–2 | `edep_logit`, `sec_logit` | ALR coords of the deposit/secondary/post-energy simplex |
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| 3–5 | `post_dir` in local frame | unit vector |
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| 6–8 | `travel_dir` (`post_pos − pre_pos`) in local frame | unit vector |
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The two energy logits decode via softmax over `[edep_logit, sec_logit, 0]` × `pre_E`, so `edep + e_sec + post_E == pre_E` exactly — **energy conservation is built into the parametrization**, not left to the loss (`energy_simplex_decode`). Stage 1 also has a classifier head (`predict_n_sec`) predicting the number of secondaries `n_sec ∈ {0..K_MAX}` (`K_MAX = 15`) from the conditioning alone, no diffusion noise involved.
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Both `post_dir` and `travel_dir` are expressed in the coordinate frame where `pre_dir = ẑ`, making the scattering distribution nearly azimuthally symmetric. `post_pos` itself is not a raw target — it's reconstructed at inference as `pre_pos + step_length * world_frame(travel_dir)`, so the two stay consistent by construction instead of being learned (and potentially diverging) independently.
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**Stage 2 — secondaries (`SecondaryDecoder`):** conditioned on the pre-step state *and* the Stage-1 outcome, a second net generates all `K_MAX` secondary slots at once — `(stick-breaking energy logit, local-frame direction, log-mass, charge)` per slot, ordered by descending energy; slots beyond the predicted `n_sec` are masked. Secondary energies are a stick-breaking partition of the `e_sec` budget from Stage 1, so the whole chain conserves energy. A secondary's mass/charge are regressed directly against its ground-truth PDG code's physical values (`giant.particles.particle_mass_charge`) and used as-is at inference — including for its own conditioning if it takes further steps in a rollout. No snapping to a known PDG code happens in the model path; `giant.particles.nearest_known_pdg` is a reporting-only lookup used to populate a nominal `pdg` label on output rows.
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**Conditioning (`--conditioning`, per-checkpoint):** pre-step position, log(pre-energy), pre-step direction, layer ID, plus particle/material physical properties — mass/charge (`giant/particles.py`) and Z_eff/A_eff/density/X0/λ_int (`giant/materials.py`). Two mutually exclusive modes:
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- **`physical`** (default) — the physical-property columns are routed through small MLPs, computable for any PDG code / material, letting the surrogate generalize to species/materials outside the training menu.
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- **`embedding`** — the original design: a learned `nn.Embedding` per PDG code / material, kept as a generalization-comparison baseline (memorizes the training menu).
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`n_sec` and `e_sec` are model outputs, not conditioning inputs — a rollout is self-contained and never injects ground truth.
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**Mixture-of-experts routing (`--router`, opt-in):** `giant/model/network.py` also implements a pluggable `Router` contract (`ROUTER_REGISTRY`: `energy`, `pdg`, `process`, plus a `composed` router combining several axes) that splits `DenoisingMLP`/`SecondaryDecoder` into per-expert trunks, soft-gated in training and top-1 dispatched at eval. Implemented; first rollout benchmark needs a retrain with a load-balancing loss and better-seeded router centers (see Roadmap). See `--router-type`/`--n-experts`/`--router-axis` on `giant train`/`giant new-run`.
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## Roadmap
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**Phase 1 (done):** `n_sec` and total secondary energy `e_sec` were conditioning inputs; the model predicted only the 9D primary post-step (energy-conservation PoC).
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**Phase 2 (implemented — baseline):** the two-stage model above predicts `n_sec` and each secondary's energy, direction, and species jointly with the primary, so a shower rollout is fully self-contained.
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**Physical-property conditioning (implemented):** replaces learned PDG/material embeddings with physical-property MLPs (see above); Stage 2 predicts a secondary's mass/charge directly instead of a snapped species embedding. Not yet done: the held-out-material/species generalization comparison against the `embedding` baseline — the natural dataset for that is the 34GB multi-material dataset at the repo root (6 materials, 237 PDG codes).
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**Faster-eval architectures (implemented, validation in progress):** both target a ~10× native-Geant4 eval budget. WGAN-GP (`--mode wgan`) has no rollout-vs-reference analysis run against it yet. The MoE router (`--router`) had its first rollout benchmark diverge from Geant4 despite matching bulk deposited energy — the experts weren't specializing (near-uniform gating), traced to a missing load-balance loss and a center-init that didn't match the real energy distribution; both are now fixable via `lambda_balance > 0` and quantile-seeded router centers, but a re-run to confirm hasn't happened yet.
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A multi-material sampling-calorimeter dataset is a planned future direction, not yet built.
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## Data
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Input: parquet files produced by [miniCaloSim](https://gitlab.etp.kit.edu/lbogner/minicalosim), or converted from a ROOT file via `dwarf convert`. Each row is one Geant4 step. Train/val split is by `event_id` (not row shuffle, and `--seed`-controlled) to avoid leaking correlated steps from the same shower.
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## Project structure
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```
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giant/
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├── giant/
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│ ├── data/
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│ │ ├── loader.py # parquet → numpy arrays (incl. streaming/chunked reads)
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│ │ ├── transforms.py # log transforms, local-frame rotation, energy simplex, secondary encode/decode
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│ │ └── dataset.py # StepsDataset / StreamingStepsDataset (PyTorch)
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│ ├── model/
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│ │ ├── network.py # ConditionEncoder, DenoisingMLP, SecondaryDecoder, Router/MoE, WGAN generator/critic
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│ │ ├── schedule.py # CosineSchedule (DDPM) and flow matching utilities
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│ │ └── wgan.py # WGAN-GP gradient penalty / critic / generator losses
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│ ├── constants.py # output/conditioning dims, K_MAX, secondary slot layout, schema keys
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│ ├── particles.py # PDG → (mass, charge) decode, incl. nuclear/ion codes; nearest-known-PDG lookup
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│ ├── materials.py # material name → (Z_eff, A_eff, density, X0, λ_int)
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│ ├── config.py # default hyperparameters, TOML config merging, device autodetect
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│ ├── pipeline.py # builds datasets/normalizers and kicks off a training run (with setup-stage caching)
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│ ├── train.py # two-stage training loop, checkpointing, graceful shutdown, W&B logging
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│ ├── sample.py # DDPM / DDIM / flow matching / WGAN samplers + secondary sampling
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│ ├── geometry.py # GeometryOracle: position → (material, layer_id, escaped) for rollout
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│ ├── rollout.py # autoregressive shower rollout driver
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│ ├── validate.py # step-level marginal + KL-divergence validation
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│ ├── analysis/ # rollout-vs-reference analysis pipeline (see `giant analyze` below)
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│ │ ├── sources.py # canonical LazyFrames + secondary view
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│ │ ├── reduce.py # streaming reduction primitives (hist1d, per-event scalars, profiles, ...)
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│ │ ├── grouping.py # fixed bin edges + energy/pdg/material group sets
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│ │ ├── context.py # resolves grouping into `shared.json` once per run
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│ │ ├── catalog.py # declarative PlotSpec registry
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│ │ ├── condor.py # prep / compute-one / submit-description plumbing
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│ │ └── render.py # PDFs + HTML gallery (only module importing plotstyle/LaTeX)
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│ └── cli.py # `giant train` / `new-run` / `predict` / `rollout` / `analyze` Typer app
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├── scripts/ # dataset/tooling logic, unified under the `dwarf` CLI (`dwarf --help`)
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│ ├── dwarf.py # Typer app: convert, migrate, bump-gen, bump-schema, status,
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│ │ # update-manifest, create-manifest, make-root,
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│ │ # build-geometry-oracle, warm-cache, hparam-scan
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│ ├── steps_to_parquet.py # ROOT → parquet conversion (uproot/awkward/polars) — `dwarf convert`
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│ ├── steps_to_parquet_parallel.py # fan out conversion over several ROOT files — `dwarf convert --jobs N`
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│ ├── migrate_geant_steps.py # one-time move into the raw/processed/pools/derived layout — `dwarf migrate`
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│ ├── bump_dataset_version.py # cut a new raw gen or parquet schema, with a logged reason —
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│ │ # `dwarf bump-gen` / `bump-schema` / `status` / `update-manifest` / `create-manifest`
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│ ├── create_root_files.py # generate new ROOT shards via a minicalosim executable — `dwarf make-root`
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│ ├── geometry_oracle.py # fit a position → (material, layer_id) oracle — `dwarf build-geometry-oracle`
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│ ├── warm_setup_cache.py # precompute `giant train`'s setup-stage sidecar — `dwarf warm-cache`
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│ ├── hparam_scan.py # hyperparameter grid scan over `giant train` runs — `dwarf hparam-scan`
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│ └── profile_analysis_costs.py # profiling helper for the `giant analyze` reduction pipeline
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└── tests/
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```
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## Setup
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```bash
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uv sync --extra cpu # CPU-only torch (use --extra cuda for CUDA 11.8 instead)
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uv sync --extra cpu --extra dev # add dev tools (pytest, ruff, ty)
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uv sync --extra cpu --extra geometry # add scikit-learn, for `dwarf build-geometry-oracle` / rollout
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```
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`cpu` and `cuda` are mutually exclusive extras selecting the torch build (pinned to 2.3.x); plain `uv sync` installs no torch at all. See `CLAUDE.md` for details.
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## Training, prediction, and rollout
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```bash
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giant new-run --hidden-dim 512 --lr 3e-4 --comment "..." # scaffold a config.toml + run dir for a new run
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giant train path/to/steps.parquet --mode flow # train (flow matching; also --mode ddpm / wgan)
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giant predict path/to/steps.parquet --checkpoint checkpoints/.../best.pt
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# Full-shower rollout needs a geometry oracle (position → material/layer_id):
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dwarf build-geometry-oracle path/to/steps.parquet --out oracle.pkl
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giant rollout path/to/steps.parquet --checkpoint checkpoints/.../best.pt --geometry oracle.pkl
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```
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`train`/`predict` accept a TOML config file (`--config`) and CLI overrides for hyperparameters; see `--help` on any command for the full option list. `giant train --wandb` logs per-epoch metrics (the same ones written to `metrics.csv`) to Weights & Biases; requires `uv sync --extra wandb`. A repeat `giant train` against the same dataset (e.g. a hyperparameter sweep) reuses a cached setup-stage sidecar (vocab maps, event split, normalizer stats) unless `--no-cache-setup`/`--rebuild-setup-cache`; `dwarf warm-cache` precomputes it ahead of time. `giant rollout` seeds showers from the highest-energy entry step per event, then autoregressively steps the two-stage model to completion — pushing secondaries as new tracks and looking up `material`/`layer_id` from the oracle at each step. Tracks terminate on energy cutoff, per-track max steps, detector escape, or natural end; energy is deposited locally on every stop except escape (leakage), so showers conserve energy by construction.
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## Validation and analysis
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`giant.validate.validate_marginals` runs step-level marginal and KL-divergence checks during training (`--validate-every`).
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For deeper rollout-vs-reference diagnostics — marginals stratified by energy/pdg/material, per-event totals, shower profiles, species share, leakage, and secondaries — `giant analyze` runs a streaming compute/render pipeline against a `giant rollout` YAML sidecar:
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```bash
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giant analyze submit rollout.yaml --accounting-group cms # prep + one HTCondor job per plot (compute only)
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giant analyze render <run_dir> --gallery # local: styled PDFs + HTML gallery (needs LaTeX)
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```
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`<run_dir>` is derived next to the rollout parquet (`analyze prep`/`submit` print it). Compute jobs are polars/numpy only; only `render` imports plotstyle/LaTeX, so it always runs locally.
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## Development
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```bash
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uv run pytest # run tests
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uv run ruff check . # lint
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uv run ruff format . # format
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uv run ty check . # type check
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```
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